Investment in Education in Costa Rica

Alejandra M. Obando-Hernández

2009

I dedicate this dissertation to my sons Eduardo, Sergio, Javier, and Rolando

INVESTMENT IN EDUCATION IN COSTA RICA:

A LADDER OUT OF POVERTY?

By Alejandra M. Obando-Hernández

ABSTRACT

The purpose of this dissertation is to answer the question of what has prevented many Costa

Ricans parents from investing in their children’s education to a level that will allow them to rise out of poverty. Research conducted here relies mainly on the 2005 Costa Rican official household survey. The first empirical analysis conducted, based on Lars Ljungqvist’s model of convexity in the monetary private returns to education when credit markets for education are missing, demonstrates that the convexity of returns plays a key role in unskilled Costa Rican parents’ low expectations regarding future benefits of their children’s education and therefore in their decision to not invest in their education. Returns to education are convex and increase sharply only after secondary education has been completed, for all sectors, for all regions and for men and women.

The second empirical analysis, of the relationship between demand for schooling and returns to education, shows that the convexity of returns influences the demand for schooling differently depending on whether or not parents are liquidity constrained, which in turn depends on whether they are either both unskilled or both skilled, respectively. Unskilled parents are not responsive to increases in the returns to skilled labor and when the returns to unskilled labor increase, i.e. the type of labor these parents themselves provide, they are less likely, compared to skilled parents, to increase the demand for schooling. Hence, these unskilled labor parents trade off their children’s investment in education, their future wealth, for the current consumption needs of their families. On the other hand, skilled parents, in contrast with those who are not, settle for a different and more successful livelihood strategy, responding to the returns to education, either

ii skilled or unskilled, by investing in the education of their children. Two types of citizens are being clearly created: those educated and those uneducated, a process that, if it continues like this, will be difficult to reverse. Policy recommendations include the establishment of an educational subsidy focused on secondary education, and increase the employment opportunities and earnings of the unskilled workers.

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ACKNOWLEDGEMENTS

I would like to convey to my committee, Dr. Paul Winters, Dr. Laura Langbein, and Dr.

María Floro, my appreciation for their guidance, the time they spent reviewing the drafts and their positive encouragement during this long process. I am thankful to Dr. Leonardo Garnier, Minister of Education of Costa Rica, for his generosity in granting me access to all the information I needed and to the personnel of his ministry, and for allowing me to use the draft of his forthcoming book. I am also thankful to those government officers and Costa Rican researchers, especially Professor Juan Diego Trejos, who gave me of their time and lent me their work. My thanks go to the Instituto Nacional de Estadística y Censos for allowing me to use the Costa

Rican Household Survey 2005, indispensable tool of my research. At a more personal level, I am grateful to my sisters Lorna, Lorena and Ileana Obando and several dear friends who encouraged me in different ways just at the right moments: Drs. Mario and Diane Montano, Dr. Phil Brenner,

Dr. Nicholas Onuf, Mrs. Genie Dutton, Dr. Dora Tobar and Ms. Janice Weber. This work could not have been done without the unconditional support and love of my parents, my husband and my children. My loving gratitude goes out to them. To my parents Arnoldo and Vera: it feels so good to know that they are always there for me no matter how far away, tired or occupied they are; to my children: it is such a blessing to know that they are my best fans, always patient and enthusiastic about my crazy ideas, such as embarking on this Ph. D., and to my husband, Dr.

Daniel Masís: the most inquisitive, insightful and challenging interlocutor, the most faithful friend and the best editor. I love you guys! Writing a dissertation is in the end a lonely journey. It was not for me, however, because God was holding me all the time.

TABLE OF CONTENTS

ABSTRACT……………………………………………………………………..... ii

ACKNOWLEDGEMENTS……………………………………………………..... iv

LIST OF TABLES………………………………………………………………... ix

LIST OF ILLUSTRATIONS……………………………………….…………….. xii

LIST OF APPENDICES………………………………………………………….. xiv

Chapter

1. INTRODUCTION………………………………………………………….. 1

1.1 Introduction…………………………………………………………… 1

1.2 The problem and the research question……………………...…....….. 2

1.3 Relevance of the study within the field’s literature………..…...…….. 7

1.3.1 Growth, education, and poverty……………………....………. 8

1.3.2 Household decision making and education investment …...... 17

1.3.3 Education and poverty traps…………………………...……... 29

1.4 Research components……………………………………………….... 35

1.5 Recent scholarly research on Costa Rica related to the research’s topic…..……………………………………………………………..... 37

1.6 Proposed contributions………………….……………….....………… 39

1.7 Research methodology…………………………………….………….. 42

2. EDUCATION POLICY AND THE EDUCATION SYSTEM…………….. 46

2.1 Introduction…………………………………………………….…….. 46

2.2 Costa Rica’s development process and its education policy…...... ….. 46

2.2.1 The emergence of education as a modernizing, democratic, and social mobility mechanism………………………….……. 46

2.2.2 Education: the anchor towards a knowledge based society...... 48

2.2.3 The present administration’s priorities in education……...…... 55

2.3 Organization of the Costa Rican education system………….…..…… 57

2.4 Financial commitment to education……………………….……..…… 62

2.4.1 Financial allocations to education and their distribution……... 62

2.4.2 School inputs: teachers and infrastructure………………..…... 71

2.4.3 The core programs to meet the challenges………….…..…….. 80

Redistribution programs………….…………………………... 80

Retention/inclusion programs……….……..…………………. 86

Technology-anchored and knowledge-based programs...... 91

The informatics program………………………………….. 91

The foreign language program………………………...…... 92

Labor market-pertinent education programs……...…….…….. 94

2.5 Conclusion……………………………………………………………. 96

3. ACCESS TO KNOWLEDGE: THE CURRENT CHALLENGE………….. 98

3.1 Introduction…………………………………………………………… 98

3.2 Coverage and efficiency of the educational system……………..……. 98

3.2.1 Education coverage……………………………….…………... 99

3.2.2 Desertion……………………………………………………… 103

3.3 Inequalities of access……………………………..…………………... 111

The educational gap…………………………………………... 111

3.4 Poverty: the trap for the uneducated………………………………….. 121

3.5 Conclusion……………………………………………………………. 130

4. THE INCREASING RETURNS TO EDUCATION……………………….. 133

4.1 Introduction…………………………………………………………… 133 . 4.2 Literature review……………………………………………………… 136

4.3 Conceptual framework……………………………………………….. 144

4.4 Empirical analysis…………………………………………………….. 147

4.4.1 Data description………………………………………………. 147

4.4.2 The sample characteristics……………………………………. 150

4.4.3 Empirical model………………………………………………. 153

4.4.4 Empirical issues………………………………………………. 160

4.4.5 Regression outputs……………………………………………. 172

Returns for the country: the total sample………………… 172

Returns to education for the stratified samples: rural, urban female and male…………………………………………… 174

Returns to education for the regions………………………. 179

Returns to education: workers who reside in the same county they were born in………………………………….. 187

4.5 Conclusion……………………………………………………………. 189

5. THE DEMAND FOR EDUCATION: RETURNS TO EDUCATION 191 MATTER……………………………………………………………………

5.1 Introduction…………………………………………………………… 191

5.2 Literature review……………………………………………………… 193

5.3 Conceptual framework……………………………………………….. 197

5.4 Empirical approach…………………………………………………… 203

5.4.1 Data description………………………………………………. 203

5.4.2 Sample characteristics………………………………………… 204

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5.4.3 Estimation strategy…………………………………………… 209

5.4.4 Empirical issues………………………………………………. 215

5.5 Empirical results……………………………………………………… 220

5.5.1 Calculation of the returns to education……………………….. 220

5.5.2 Construction of the wealth index…………………………… 227

5.5.3 Regression results…………………………………………… 231

5.6 Conclusion……………………………………………………………. 237

6. CONCLUSION: PURPOSE, CONTRIBUTIONS, CONCLUSION AND POLICY RECOMMENDATIONS………………………………………… 240

6.1 Purpose and contributions…………………………………………….. 240

6.2 Conclusion……………………………………………………………. 243

6.3 Policy recommendations……………………………………………… 247

APPENDICES…………………………………………………………………… 253 .

Appendix to Chapter 2……………………………………………………… 253

Appendix to Chapter 3……………………………………………………… 258

Appendix to Chapter 4……………………………………………………… 261

Appendix to Chapter 5……………………………………………………… 272

REFERENCES………………………………………………………………….. 290

Bibliography………………………………………………………………... 290

Websites…………………………………………………………………….. 308

Interviews…………………………………………………………………… 309

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LIST OF TABLES

Table 2.1 Costa Rica: Enrollment, number of schools, number of teachers and pupil-teacher ratio by level of education, 1971-2004………………. 73

Table 3.1 Costa Rica: Gross and net enrollment ratio in secondary education. Formal and non-formal modalities by cycle……………………….. 102

Table 3.2 Costa Rica: Retention and exclusion by year and level of education (2000, 2002)………………………………………………………… 104

Table 3.3 Costa Rica: MEP’s educational sectors with the highest intra-annual desertion, 2005……………………………………………... 107

Table 3.4 Costa Rica: Efficiency in 7th and 10th grades (%) in selected years... 109

Table 3.5 Costa Rica: Attendance to schools by income quintiles, 2001-2005.. 118

Table 3.6 Costa Rica: Attendance to schools by regions and age groups, 2005…………………………………………………………………. 118

Table 3.7 Costa Rica: Working status of individuals between 18 and 60 years old according to levels of education, 2005…………………………. 123

Table 3.8 Costa Rica: Poverty rate and GDP growth…………………………. 128

Table 4.1 Costa Rica 2005: Sample distribution by categories……………….. 149

Table 4.2 Costa Rica 2005: Profile of the observations: total sample……….... 151

Table 4.3 Costa Rica 2005: Profile of the observations: total sample, by gender and by area………………………………………………….. 152

Table 4.4 Costa Rica 2005: Profile of the observations by regions………….... 153

Table 4.5 Costa Rica 2005: Description of variables used in the regression analysis……………………………………………………………… 159

Table 4.6 Costa Rica 2005: F test of joint equality of coefficients for the 161 different regressions………………………………………………....

Table 4.7 Costa Rica 2005: Test of model fitness for all regressions…………. 163

Table 4.8 Costa Rica 2005: Linear and quadratic regression outputs. Total sample……………………………………………………………….. 164

Table 4.9 Costa Rica 2005: F test of equality of every two consecutive estimated coefficients for the regressions: total sample and restricted sample……………………………………………………………….. 165

Table 4.10 Costa Rica 2005: F test of equality of every two consecutive estimated coefficients for the regressions: female, male, rural, and urban samples………………………………………………………. 166

Table 4.11 Costa Rica 2005: F test of equality of every two consecutive regression coefficients. Regionally stratified samples…………….... 167

Table 4.12 Costa Rica 2005: Returns to education. Total country……………… 175

Table 4.13 Costa Rica 2005: Returns to education. Rural and urban…………... 177

Table 4.14 Costa Rica 2005: Returns to education. Female and male………….. 178

Table 4.15 Costa Rica 2005: Returns to education by regions: Central, Chorotega, and Pacífico…………………………………………….. 181

Table 4.16 Costa Rica 2005: Returns to education by regions: Brunca, HuetarA and HuetarN………………………………………………………… 182

Table 4.17 Costa Rica 2005: Returns to education. Individuals who remained in the same county………………………………………………….. 188

Table 5.1 Costa Rica 2005: profile of the unit of analysis…………………….. 207

Table 5.2 Costa Rica 2005: Difference in the means total sample: Enrolled and not enrolled in formal school…………………………………… 208

Table 5.3 Costa Rica 2005: Difference in the means for the primary school sample: Enrolled and not enrolled in formal school………………... 209

Table 5.4 Costa Rica 2005: Difference in the means for the secondary school sample: Enrolled and not enrolled in formal school……………….. 209

Table 5.5 Costa Rica 2005: Likelihood test ratio test results for total sample: Model with interactions compared to model without them………… 216

Table 5.6 Costa Rica 2005: Mean years of education completed and mean wages per hour: Skilled and unskilled labor by area and by region.... 221

Table 5.7 Costa Rica 2005: Returns to schooling: Central, Chorotega, and Pacífico regions…………………………………………………….. 225

Table 5.8 Costa Rica 2005: Returns to schooling: Brunca, Huetar Atlántica and Huetar Norte regions………………………………………….... 226

Table 5.9 Costa Rica 2005: First component: Factor scores assigned to each variable………………………………………………………………. 229

Table 5.10 Costa Rica 2005: Wealth index: Descriptive statistics…………….... 230

Table 5.11 Costa Rica 2005: Likelihood of enrollment: When parents are liquidity constrained and returns are convex………………………... 235

Table 5.12 Costa Rica 2005: Likelihood of enrollment: When parents are liquidity unconstrained and returns are convex……………………... 236

LIST OF ILLUSTRATIONS

Chart 2.1 Cycles of Costa Rican education…………………………………… 60

Graph 2.1 Costa Rica: Public education expenditure as percentage of GDP….. 63

Graph 2.2 Costa Rica: Public education expenditure as percentage of government expenditure……………………………………………. 64

Graph 2.3 Costa Rica: Distribution of public expenditure by education level... 66

Graph 2.4 Costa Rica: Expenditure per pupil by educational levels and options 67

Graph 2.5 Costa Rica: Infrastructure deficit in primary schools (2004)………. 75

Graph 2.6 Costa Rica: Infrastructure deficit in secondary schools (2004)……. 76

Graph 2.7 Costa Rica: School equity related programs……………………….. 84

Graph 3.1 Costa Rica: Age specific enrolment ratio (2005)………………….. 99

Graph 3.2 Costa Rica: Desertion in secondary level by teaching modality and selected grades……………………………………………………... 106

Graph 3.3 Costa Rica: Education gaps by income strata, region, and gender (1989-2005)……………………………………………………….... 113

Graph 3.4 Costa Rica: Relative share of quintiles by education category, population between 18 and 60 years old, 2005…………………….. 122

Chart 3.1 Costa Rica: Average income of employed population between 18 and 60 years old, by education completed, 2005…………………... 124

Graph 3.5 Costa Rica: Average income by region and education category, 2005…………………………………………………………………. 126

Graph 3.6 Costa Rica: Average income in the main occupation according to worker’s qualifications ………….………………………………….. 130

Graph 4.1 Costa Rica 2005: Augmented component-plus-residual plot. Natural logarithm of wage and years of schooling completed. Total sample………………………………………………………………. 161

Graph 4.2 Costa Rica 2005: Predicted mean wages: total country…………….. 176

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Graph 4.3 Costa Rica 2005: Predicted mean wages for the female, male, rural, and urban groups……………………………………………………. 179

Graph 4.4 Costa Rica 2005: Predicted mean wages by regions……………….. 183

Graph 4.5 Costa Rica 2005: Predicted mean wages for total sample and for those born in the same county………………………………………. 187

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LIST OF APPENDICES

APPENDIX TO CHAPTER 2

Figure A.2.1 Map of Costa Rica……………………………………………….. 253

Figure A.2.2 Map of MEP’s regional divisions………………………………... 254

Figure A.2.3 Map of Costa Rica’s regional divisions………………………….. 255

Table A.2.1 Costa Rica: Annual distribution of expenditure by budget and education category……………………………………………….. 256

Table A.2.2 Costa Rica: Percentage of trained teachers………………………. 256

Table A.2.3 Costa Rica: Budget and beneficiaries of education subsidies by type of program…………………………………………………... 257

APPENDIX TO CHAPTER 3

Table A.3.1 Costa Rica: Primary school level desertion rate, selected years…. 258

Table A.3.2 Costa Rica: Desertion in secondary level by grade (1990-2005). Day modality: academic and technical…………………………... 258

Table A.3.3 Costa Rica: Desertion by teaching modality (1990-2005)……..... 259

Table A.3.4 Costa Rica: Population that completes at least each cycle of traditional education, by income strata, zone and gender, 1989- 2005………………………………………………………………. 260

APPENDIX TO CHAPTER 4

Table A.4.1 Key literature on the functional forms of the income- schooling relationship: Non-linearity and econometric issues……………... 261

Table A.4.2 Costa Rica 2005. Gross monetary private returns to education: Total country. Conversion of estimated dummy coefficients to semi-elastic values……………………………………………….. 271

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APPENDIX TO CHAPTER 5

Table A.5.1 Costa Rica 2005. Description of variables used in the regression analysis……………………………………………………………. 272

Table A.5.2 Costa Rica 2005. Descriptive statistics: total sample…………….. 273

Table A.5.3 Costa Rica 2005. Descriptive statistics: primary school sample…. 274

Table A.5.4 Costa Rica 2005. Descriptive statistics: secondary school sample.. 275

Table A.5.5 Costa Rica 2005. Central region: returns to schooling…………… 276

Table A.5.6 Costa Rica 2005. Chorotega region: returns to schooling………... 277

Table A.5.7 Costa Rica 2005. Pacífico region: returns to schooling…………... 278

Table A.5.8 Costa Rica 2005. Brunca region: returns to schooling…………… 279

Table A.5.9 Costa Rica 2005. Huetar Atlántica region: returns to schooling…. 280

Table A.5.10 Costa Rica 2005. Huetar Norte region: returns to schooling……... 281

Table A.5.11 Costa Rica 2005. Description of the variables used to calculate the wealth index using Principal Components …………………… 282

Table A.5.12 Costa Rica 2005. Descriptive statistics of the asset variables used to calculate the wealth index……………………………………… 283

Table A.5.13 Costa Rica 2005. Principal components: Eigenvalues and explained variation………………………………………………... 284

Table A.5.14 Costa Rica 2005. Regression output, total sample. Dependent variable: probability of enrollment: when parents are liquidity constrained and returns are convex……………………...... 285

Table A.5.15 Costa Rica 2005. Regression output, primary school sample. Dependent variable: probability of enrolment: when parents are liquidity constrained and returns are convex……………………... 286

Table A.5.16 Costa Rica 2005. Regression output, primary school sample. Dependent variable: probability of enrolment: when parents are liquidity constrained and returns are convex……………………... 287

Table A.5.17 Costa Rica 2005. Regression output total sample. Dependent variable: probability of enrollment: when parents are not liquidity constrained and returns are convex……………………………….. 288

Table A.5.18 Costa Rica 2005. Regression output secondary school sample. Dependent variable: probability of enrollment: when parents are not liquidity constrained and returns are convex…………………. 289

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CHAPTER ONE

INTRODUCTION

1.1 Introduction

This chapter is divided into seven sections. The following section deals with the problem and research question that this research attempts to solve. The third section is devoted to a review of the relevant literature appeared to date with the overall purpose of highlighting the value of this study while presenting my conceptual framework. It is divided into three subsections; the first one intends to position and justify this research within the development microeconomics field and to situate the topic in the current macroeconomic debates. The second and third subsection deals with the theories and models within the development microeconomics field that are relevant to the analysis of my problem and therefore will provide the basis for my research. Specifically, subsection two studies the household decision making model as it applies to investment in education. Sub-section three examines the theory of poverty traps at the micro-economic level as it relates to education. Section four describes and justifies the components of this research. The fifth section describes the kind of investigations that have been carried out on Costa Rica that is in some way related to the topics of this research: convexity of returns to education and the effects of returns to education on school attendance. This section also intends to establish the uniqueness of the study to and its contribution to

Costa Rica. Section six summarizes the expected contribution of the research to the different fields dealt with in the literature review. Section seven briefly describes the methodology to be followed in my research.

1. 2 The problem and the research question

Costa Rica’ GDP growth rate since 19911 has averaged 5% every five years, except for the period 2001-2005 in which it declined to 3.7%. Moreover, the country’s

GDP per-capita growth rate since 1991 has been higher than the average for Latin

America during the same period2. Unfortunately, this economic progress for the nation did not spill over to the less fortunate nor has it translated into better opportunities for most of its citizens.

The percentage of Costa Ricans living in poverty, when defined as insufficient income, has remained practically the same since 19943; between ¼ and 1/5 of the population.4

One of the reasons pointed out for this persistent poverty is the slowdown of household income growth.5 Labor income is the main, if not the only, source of total

1 In fact, this has been the pattern of growth since 1961, except for the period 1981-85 when the growth rate went down to 0.1%. World Bank, Costa Rica Country Economic Memorandum: The Challenges for Sustained Growth (Washington, D.C.: World Bank, Poverty Reduction and Economic Management Sector Unit, Central America Country Management Unit, Latin America and the Caribbean Region. Report No. 36180-CR. September 20, 2006): 6.

2 The difference averages approximately 0.3 percentages points for the period. Ibid, 4.

3 Actually it is stagnant since 1985. The percentage of families below the poverty line in 1986 was 23.7% and by 1995 that percentage was 20.5% (Juan Diego Trejos and Nancy Montiel, “The Capital of the Poor in Costa Rica: Access, Utilization and Return,” in: Portrait of the Poor: An Assets Based Approach, eds. Drazio Attanasio and Miguel Székely [Washington, D.C.: Inter-American Development Bank, 2001]: 173). What happened was that poverty increased from 1987 to 1992 to an average rate of 29%; in 1993 it went down to 23% and from then on has been more or less in the range of 20 to 24%. In fact, according to Trejos and Montiel (172), studies on Costa Rican poverty show that the socio-demographic profile of the poor has remained almost unchanged since 1980. This outcome is unexpected since Costa Rica’s real GDP has been increasing since 1983 and GDP per-capita was more or less steady until 1991 and started increasing thereafter (Leonardo Garnier, “Costa Rica within the Economy: the Role of Education, Training and Innovation Systems,” unpublished manuscript, April 15, 2002: 5).

4 Trejos and Montiel, 172.

5 World Bank, Costa Rica Country Economic Memorandum, 2 and Programa Estado de la Nación en Desarrollo Humano Sostenible, Estado de la nación en desarrollo humano sostenible: undécimo informe 2004 (San José, Costa Rica: Programa Estado de la Nación, 2005): 91-92.

reported household income for most Costa Rican families; it represents 87% of total household income6, not different from the rest of Latin American countries as revealed by household survey analyses.7 While from 1989 to 1994 household per capita income increased by 5% a year, from 1994 to 2000 it only increased 1.5% a year, and from 2000 onward it has remained constant.8

The slowdown in income growth doubly punishes poor households since earnings do not accrue equally to all workers. Skilled labor, understood as that provided by the worker who has finished at least secondary school, has a higher premium in the labor market. On average for the period 1990-2004, the income accrued to skilled workers in

Costa Rica was 120.1% higher than the income accrued to their non-skilled counterparts.9

In fact, the average income of non-skilled workers, which had remained practically unchanged from 1994 to 2001, subsequently began to fall.10 According to recent studies, this earnings gap between skilled and non-skilled workers is explained by the mismatch between the supply of low skilled workers and the rising need for high skilled workers in

Costa Rica.11 The new forms of foreign investment propelled by the globalization

6 Programa Estado de la Nación en Desarrollo Humano Sostenible, ibid, 91.

7 David De Ferranti, et al. Inequality in Latin America and the Caribbean: Breaking with History? (Washington, D.C.: The World Bank, 2004).

8 World Bank, Costa Rica Country Economic Memorandum, 2.

9 Skilled workers are defined as those with at least complete secondary education and unskilled those with incomplete secondary or less. Programa Estado de la Nación (2005), 92.

10 Also, the average income of skilled workers has been dropping since 2003. Ibid, 103.

11 D. J. Robins, and T. H. Gindling, “Trade Liberalization and the Relative Wages of More Skilled Workers in Costa Rica,” Review of Development Economics, Vol. 3, No. 2 (1999): 140-154. World Bank, Costa Rica Country Economic Memorandum, 2.

process12 and the technology- and knowledge-driven development strategy that Costa

Rica fostered since the 1990s13 increased the demand for high-skilled workers and further undervalued the unskilled labor force.14

High-skilled workers are rewarded by the labor market because they are more productive. The more endowed people are with assets, such as education or human capital,15 the greater their productivity and hence the higher their reward in the labor market. Although Amartya Sen argues that poverty seen as mere low income and consumption stems from too narrow a perspective,16 he also points out that the possession of capabilities17 such as health and education not only increases the ability of people to

12 Globalization is the process by which economies are integrated through flows of information, technology, and management know-how and through the linkage of international markets of goods, capital, and labor. Machiko Nissanke and Erik Thorbecke, “Channels and Policy Debate in the Globalization Inequality Poverty Nexus,” World Development, Vol. 34, No. 8 (2006): 1338.

13 Garnier (2002), 46.

14 Although it is understandable why Costa Rica has followed this strategy, it has produced negative side effects, such as this one. Globalization provides Costa Rica an opportunity to profit from its investment in human capital and democracy and to acquire technology and knowledge as well as to give an alternative solution, although short-term, to its unemployment and limited savings.

15 The concept of human capital as an asset encompasses a cluster of factors: nutrition, health, formal education and on the job training (Pranab Bardhan and Christopher Udry, Development Microeconomics [New York: Oxford University Press, 1999]: 123). However, for the purpose of this research we will use it as a synonym of education.

16 Amartya Sen, Development as Freedom (New York: Random House, 1999): 86. Poverty, for Sen, is a “deprivation of basic capabilities” (87). Income and resources (public goods and social institutions) are means to enhance capabilities (90, 111-145).

17 For Sen, capabilities are the “substantive freedoms to choose a life one has reason to value” (ibid, 74). In that sense, they encompass not only the primary goods (nutrition, health, education, income) a person possess, but also his or her intrinsic characteristics (being handicapped; being old; ideological belief and social values; location; or belonging to an ethnic minority) that allow her to choose between different lifestyles or “functionings” (ibid, 74-75). The capability set reflects the opportunities a person have (ibid, 76). Sen does not refer to capabilities as assets since capability is a broader concept. Education and health are just two components of the capability set. I will refer to education and health as assets. Sen’s concept of capabilities, as he himself notes, helps to understand the nature and causes of poverty and how it applies differently depending on the intrinsic characteristics of the poor.

have a better chance to overcome poverty, 18 but also allows them to have the same opportunities the non-poor have. Indeed, the major effect that investing in education has on poverty reduction is through increasing labor productivity.

In the Costa Rican case, a large number of this country’s citizens are, in fact, denied the opportunity of accumulating the levels of education needed to avoid, overcome or escape a destitute life. In Costa Rica, of the poorest quartile only 13.4% finish secondary school while the richest quartile the percentage is 67.9%. The exclusion from the formal education system starts around the age of 12 (when children typically finish primary school) as the system’s coverage rate drops from 100% to 91%. Dropping out continues all through secondary school, with the major reduction at 7th grade, until the coverage rate reaches a low 49%.19 Two thirds of the children who start first grade do not finish secondary school.20 Moreover, the average years of schooling (8.5 years) stopped increasing with the cohort born in 1959.21 In addition, despite Costa Rica’s efforts towards bringing technological knowledge to schools (to be explained below) as of 2005, only 3.4% of the children from poor households, compared to 47% of rich households, use the Internet.22

18 Ibid, 90-92.

19 Programa Estado de la Nación (2005), 87 and World Bank, Costa Rica: Social Spending and the Poor (Washington, D.C.: World Bank, Human Development Sector Management Unit, Central America Country Management Unit, Latin America and the Caribbean Region, Report No. 24300-CR, 2003): 75.

20 Leonardo Garnier (Minister of Education of Costa Rica), “Un nuevo estilo de educación,” La Nación, August 27, 2006, at: http://www.nacion.com/ln_ee/2006/agosto/27/opinion6.html (retrieved on August 27, 2006).

21 Programa Estado de la Nación (2005), 86.

22 Ricardo Monge and John Hewitt, Los Costarricenses en la Economía Basada en el Conocimiento: Infrastructura, Destrezas, Uso y Acceso a las TICS (San José, Costa Rica: Comisión Asesora de Alta Tecnología [CAATEC], 2006): 25. 5

However, Costa Rica’s investment in education does not seem to explain the disappointing education outcomes. The Costa Rican constitution guarantees free access to primary and secondary education. By 2000, the public education system provided education to 80% of preschool children, 93% of the primary school children and 87% of high school youth.23 Public education expenditures (in U.S. dollar and in real terms as well) in general education (pre-school, primary, and secondary) as a proportion of GDP24 have shown an upward trend since 1995,25 increasing from 4% to 4.9%26 in 2004, with the largest share of education expenditures going to general education27. To give an idea how Costa Rica compares with other countries in its financial commitment to education; in 2002, while Costa Rica spent 5.1 % of its GDP in education, countries under the same classification (upper middle income economies)28 such as, Brazil, and Chile spent around

4.2% and high income countries such as South Korea and United States spent 4.2% and

5.7 % respectively.29 Moreover, in 1988, as part of its knowledge-based development strategy, Costa Rica launched a massive program of computer literacy throughout

23 Garnier (2002), 42.

24 Also real per-capita expenditures in general education have been increasing. World Bank, Costa Rica: Social Spending and the Poor, 83, 85.

25 After a relatively stable trend throughout the last two and half decades (ibid, 82-86)

26 World Bank, Summary of Education Profile Costa Rica. http://devdata.worldbank.org/edstats/SummaryEducationProfiles/CountryData/GetShowData.asp?sCtry=C RI,Costa%20Rica (retrieved March 15, 2007).

27 World Bank, Costa Rica: Social Spending and the Poor, 83, 85.

28 The World Bank classifies the countries according to gross national income (GNI) per-capita. Countries whose GNI per-capita is between $3,466 and $10,725 are considered upper middle income. Countries with GNI per-capita higher than $10,725 are higher income economies.

29 UNESCO database, http://stats.uis.unesco.org/TableViewer/dimView.aspx?ReportId=251 (retrieved April 18, 2007).

primary and secondary school30 which has been expanding since then. This program has been praised as very forward-looking and as a pioneer in Latin America31. Costa Rica also has several programs that provide educational subsidies to encourage school attendance and lower the opportunity costs of school for families. On the other hand,

Costa Rican labor law seems to run counter to that effort since it establishes 12 as the minimum working age.

What has prevented many Costa Rican parents from investing in their children’s education to a level that will allow them to rise out poverty?32 The purpose of this dissertation is to answer this question through a review of the literature and secondary data and as well as analysis of primary data.

1.3 Relevance of the study within the field’s literature

Overall this literature review puts forward the conceptual framework33 of my research. It follows a methodological process that has several objectives. It intends to position the question within the areas of development theory that deal with it. I wish to

30 Garnier (2002), 46-49.

31 Michael Potashnik and Douglas Atkins, “Cost Analysis of Information Technology Projects in Education: Experiences from Developing Countries” (Washington, D.C.: World Bank, Human Development Department, Education and Technology Team, Education and Technology Series Vol. 1 No. 3, 1996): 4, 12 and Andrés Rodríguez-Clare, “Costa Rica’s Development Strategy Based on Human Capital and Technology: How It Got There, the Impact of Intel, and Lessons for Other Countries” (document written for the Human Development Report of 2001 [UNDP, February 2001], at: http://hdr.undp.org/docs/publications/background_papers/Rodriguez-Clare.pdf [retrieved on April 15, 2007]): 4.

32 I am following Ethridge’s method of breaking up the elements of a research proposal in the economics field. He suggests addressing the research problem with a general research objective and a set of sub-objectives that will contribute to the achievements of the general purpose; I decided to break them into components instead. (Don Ethridge, Research Methodology in Applied Economics: Organizing, Planning and Conducting Economic Research (Ames, Iowa: Iowa State University Press, 1995): 110-113.

33 A conceptual framework provides an analysis of the theory that centers on the problem. As such it examines the sources of the problem and alternative solutions; identifies variables relevant to the analysis of the problem, conceptualizes relationships and possible hypothesis to be tested (ibid, 130-142). 7

show the topic’s relevance: demand for education within these areas and to describe how they study it, as well as extract their contribution towards answering the question or guiding me towards the answer. This process should lead me to the area whose theories and models are more useful in my quest. I critically but briefly review these theories and models and the empirical research related to my topic in order to demonstrate the state of knowledge in this area and to provide arguments for the methodology in pursuing the research needed to answer the question.

1.3.1 Growth, education, and poverty

Although the macroeconomic development literature on growth and education gives convincing evidence regarding the complex causal relationships between growth and education, it does not give an explanation as to why Costa Ricans – or the citizens of any other country, for that matter – have been failing to invest in their human capital stock.

Previous theories of growth and conventional wisdom led to believe that an educated population is a condition for growth; however, more recent research has shown that the causality may operate in a reverse way: the reason is that people invest in schooling when they know that their efforts are going to pay off in the future34. Bils and

Klenow studied the relationship between schooling and growth for 52 countries during the period 1960-1990 and found that the expectation of higher growth and consequently higher earnings decreases the opportunity cost of studying which in turns encourages

34 William Easterly, The Elusive Quest for Growth: Economists’ Adventures and Misadventures in the Tropics (Cambridge, Massachusetts: MIT Press, 2001): 70-84.

people to stay in school longer35. On the other hand, while growth drives the demand for education, education further spurs sustained growth as a result of the positive externalities of education, the creation and/or adoption of technology and the creation of knowledge36. Advanced technology and new knowledge in the hands of educated workers have a multiplying effect over the production process because they can increase their productivity in a greater proportion and this increases the rate of growth.

Macroeconomic development theory also informed for decades that a favorable economic growth rate would eventually reduce poverty – the “trickle down” approach.

According to the traditional paradigm the process of economic development takes place when an economy maximizes its rate of growth of output37; and it was implicitly assumed that “economic changes would benefit all members of a society”38 . In fact, between the

1950’s and the early 70s’s the theories offered by economists to explain the lack of the development of poor countries were more concerned with increasing growth and finding the missing ingredients that were dragging down an economy’s output growth39. It was

35 Mark Bils and Peter J. Klenow, “Does schooling cause growth or the other way around?,” (Cambridge Massachusetts: National Bureau of Economic Research, Working paper 6393, February 1998): 1-2.

36 Ibid, 3; Easterly, 48-54 and 143-151. Nelson and Phelps back in 1966 formulated an economic model by which they demonstrate that in the presence of technological progress, society should accumulate more human capital relative to tangible capital; as it happens, innovation shows the way to imitators (a positive externality), and educated people stimulate innovation further by being good innovators and by disseminating technology (a positive externality). Richard R. Nelson and Edmund S. Phelps, “Investment In Humans, Technological Diffusion and Economic Growth,” Papers and Proceedings of the 78th Annual Meeting of the American Economic Association, American Economic Review, Vol. 56, Nos. 1/2 (March- May 1966): 75.

37 James Weaver and Kenneth Jameson, Economic Development: Competing Paradigms, (Washington, D.C.: University Press of America, 1981): 9 and Gerald Meier, Biography of the Subject: An Evolution of Development Economics (New York: Oxford University Press, 2005): 4.

38 Weaver and Jameson, 10, 48.

39 Gerald Meier, Biography of a Subject: An Evolution of Development Economics (New York: Oxford University Press, 2005): 55. 9

not until the end of 1960 that attention was shifted from growth to the material deprivation and poverty suffered by specific segments of the population despite growth in their respective countries.40 These concerns originated the growth with equity model at the beginning of the 1970’s, which did not last41 due to the emergence of the neo- classical economics view that proclaimed that “trickle down” could still be trusted: growth would take care of poverty reduction42. By the mid-90s, the failure of the orthodox prescriptions43 and mounting economic literature showed that reality was more complex. An increase in the rate of growth per se is necessary, but whether it will benefit the poor in the same proportion as other sectors of society depends on the pattern a country’s economic growth and development has followed, the initial distribution of the country’s assets (physical and human), the nature of market imperfections and government redistribution policies44.

Research on Latin America45 has found that the initial inequalities in the

40 Weaver and Jameson, 45-46.

41 Hulya Dagdeviren, Rolph van der Hoeven and John Weeks, “Poverty Reduction with Growth and Redistribution,” Development and Change, Vol. 33, No. 3 (June 2002): 383-413; Weaver and Jameson, 69-72.

42 Dagdeviren, van der Hoeven and Weeks, 2, Weaver and Jameson, 71, and Meier, 83, 91-92.

43 Meier, 92; Dagdeviren, van der Hoeven and Weeks, 2.

44 Nissanke and Thorbecke, 1344-145. Bardhan and Udry, 134. Also see Meier, 81-160 and Timothy Besley and Robin Burgess, “Halving Global Poverty,” Journal of Economic Perspectives, Vol. 17, No. 3 (Summer 2003): 1-12.

45 N. Birdsall and J. L. Londoño, “Asset Inequality Does Matter: Lessons from Latin America,” Papers and Proceedings of the 104th Annual Meeting of the American Economic Association, American Economic Review, Vol. 87, No. 2, May 1997: 33. López, Thomas and Wang found that unequal distribution of education has a negative effect in per-capita income. See: Ramón López, Vinod Thomas and Yang Wang, “Addressing the Education Puzzle: The Distribution of Education and Economic Reform,” (Washington, D.C.: World Bank, Economic Development Institute, Office of the Director and Macroeconomic Management and Policy Division, Policy Research Working Paper, December 1998).

distribution of assets,46 in particular education, had prevented the poor from increasing their income in the presence of economic growth and this, in turn, had a negative effect on growth.47 The income earnings opportunities that growth brings can only be seized by the poor if they have the necessary assets, the market incentives and appropriate welfare policies.

Costa Rica seems to provide a good example of the modern theories of growth and poverty explained above. Its chosen model of growth and development based on liberalization and attraction of high technology investment,48 although intended to capture the advantages of globalization and the country’s aggregate assets, seems to be creating a poverty trap for poor Costa Ricans, given Costa Rica’s inequalities in education, which in turn may reflect inefficient redistribution policies in education. In fact, I have found that the current literature on the effects of openness and globalization on poverty and inequality49 explains the linkages between Costa Rica’s development strategy, labor income, education investment and poverty. Anderson,50 one of the

46 In terms of income inequality Costa Rica was considered one of the most equal countries in the region, even though its Gini coefficient at that time (2000) was 44.6 (De Ferranti et al, [2003], 2-13).

47 A study of the determinants of Costa Rica’s growth experienced during the 90s shows that the increase in per-capita growth is mostly explained by changes in structural policies (openness, reduction in government consumption, infrastructure); education plays a small role, contributing with only 17% . (World Bank, Costa Rica Economic Memorandum, 12).

48 Deborah Spar, “Attracting High Technology Investment: Intel’s Costa Rica Plant” (Washington, D.C.: International Finance Corporation and the World Bank, Foreign Investment Advisory Service, Occasional Paper 11, 1998: 12-20. In addition, see: Felipe Larraín B., Luis F. López-Calva and Andrés Rodríguez-Clare, “Intel: A Case of Foreign Investment in Central America”, in: Economic Development in Central America. Volume I: Growth and Internationalization, edited by Felipe Larraín B. (Cambridge, Massachusetts: Harvard University Press [Published for the John F. Kennedy School of Government, Harvard University] 2001): 167-169. Also see Rodríguez-Clare, 1-12.

49 See: World Development Vol. 34, No. 8 (2006), Special issue: “The Impact of Globalization on the World’s Poor” (Guest Editors: Machiko Nissanke and Erick Thorbecke): 1333–1337.

50 Edward Anderson also provides an extensive review of the research in this area. This paragraph summarizes briefly the channels or hypotheses. Edward Anderson, “Openness and Inequality in 11

scholars in this field, posits four possible hypotheses regarding openness and poverty.

The first hypothesis contends that the lifting of barriers to trade and foreign investment accompanied by greater access to technology increases the demand for skilled labor51 relative to unskilled labor52 either by biasing production towards skill-intensive sectors or by increasing the utilization of foreign skill intensive technologies53. This is related to the positive externalities of education, as I mentioned before, and the complementarity between skilled workers and between skilled workers and technology and knowledge. As it happens, the productivity of a skilled worker increases, the more skilled their coworkers; not only because they transfer knowledge to each other but because it constitutes a self-reinforcing incentive.54 Also, technology requires skilled workers to reach their potential and they feel motivated working with technology because their productivity increases55. This entire synergetic process56 leaves out workers with

Developing Countries: A Review of Theory and Evidence,” World Development, Vol. 33, No. 7 (2005): 1045-1059.

51 Anderson does not attribute the demand for skilled labor to bursts of new technology but rather to changes in the relative shares in the factors of production either an increases in demand or supply due to openness. However, he concedes that availability of more advanced technology is a byproduct of openness and its adoption increases the relative demand for skill labor (ibid, 1046-1048). According to Franco Peracchi, the hypothesis that increases in the relative demand of skills are produced by rapid increases of technology, such as the computer revolution is called the “skill biased technical change hypothesis” and it can provide an explanation only for certain periods in the economic history. Franco Peracchi, “Educational Wage Premia and the Distribution of Earnings and International Perspective,” in: Handbook of the Economics of Education, Volume 1, Chapter 5, edited by Erik Hanushek and Finis Welch (Amsterdam, The Netherlands and Oxford, United Kingdom: North-Holland Publications, 2006): 215.

52 This is the result of the changes in the relative shares of factors of production. Anderson (2005): 1047-1048.

53 They argue that the latter is the case of Costa Rica. Ibid, 1057.

54 Easterly, 150-153, 156, and 158.

55 Ibid, 190.

56 In fact, this synergetic process in the presence of new technology and knowledge produces increasing returns to scale which reduces the production costs. The supply of labor is not fixed anymore 12

low levels of education: the unskilled workers. Another by-product of openness is that there is a high degree of substitutability between unskilled labor and capital which further expands the wage disparity.57 Costa Rica provides evidence for this first hypothesis.58

Robins and Gindling59 found that between 1985 and 1993, trade liberalization in Costa

Rica raised wage differentials (between more-skilled and unskilled)60 due to a rising demand for more-skilled labor and recommend compensatory policies to counteract this effect.

The second hypothesis asserts that if the effect of openness is real income reduction,61 credit access will be unlikely, and poor people are then likely to decrease their investment in assets (education for instance). In this sense, Birdsall62 found that in

because advanced technology will allow a “given amount of skilled labor to go further” increasing the output per worker. Easterly, 51 and 146.

57 Nissanke and Thorbecke, 1348.

58 Adrian Wood also found evidence of this hypothesis for many countries in Latin America. See his “Openness and Wage Inequality in Developing Countries: The Latin American Challenge to East Asian Conventional Wisdom,” The World Bank Economic Review, Vol. 11, No. 1 (January 1997): 53. De Ferranti and others arrived at the same conclusion in a thorough study they performed for Latin America. See David De Ferranti, et al., Closing the Gap in Education and Technology (Washington, D.C.: The World Bank, World Bank Latin American and Caribbean Studies, 2003): 49-73

59 Donald Robins and Thomas Gindling, “Trade Liberalization and the Relative Wages for More Skilled Workers in Costa Rica,” Review of Development Economics, Vol. 3, No. 2 (1999): 152.

60 Robins and Gindling had just two categories in their analysis, highly skilled and unskilled. University educated workers were clearly highly skilled and primary school graduates unskilled. They assigned the workers with secondary education to either group by weights they obtained regressing the wages of secondary workers on the wages of primary and university workers. On this basis, 82% of the workers with secondary education were assigned to the primary complete group and 18% to the university group (146-147). It is important to note that it is not clear if they were including in the university group only graduates or workers with some university education.

61 According to Anderson (2005), openness, instead, might increase the real income of poorer groups (1048). I believe this is might be the case when there are not many differences in the stock of human capital between the poor and well off groups, or when openness generates demand for unskilled labor in the domestic economy.

62 Nancy Birdsall, “Education: The People’s Asset,” Washington, D.C.: Brookings Institution, Center on Social and Economic Dynamics, Working Paper No. 5, September 1999, at: 13

Brazil credit constraints as a result of unskilled workers’ low income have significantly reduced families’ option to keep their children in school. Additionally, asset inequality might be exacerbated, if the higher returns received by the skilled workers encourage them to accumulate more human capital.63 Indeed, for some developing countries with low levels of education, openness has produced negative effects on enrollment rates in secondary and tertiary levels, as a response to low returns to education in those levels.64

The third hypothesis asserts that openness might enlarge the income inequality gaps

(gender, regions) already present in the countries. Evidence of this type of effect was found in South and East Asian countries65. Finally, according to Anderson, openness might limit the government’s ability to redistribute resources to reduce inequalities.66

This analysis of the relationship between growth, education investment, and poverty, I believe, shows that there is, prima facie, an urgent need for eliminating inequalities in educational levels for Costa Rica to maintain a pattern of sustained growth and reduce poverty, and hence to understand the existing structure of incentives for the country’s citizens to invest in education. In this connection, the study of the linkages between a country’s development model based on liberalization and openness; the country’s initial aggregate stock of human capital; the individuals’ intrinsic

http://www.brookings.edu/es/dynamics/papers/education-tpa/education-tpa.pdf (retrieved on December 12, 2006): 9-10.

63 The opposite could happen too: that higher returns on skilled labor do not encourage skilled workers to accumulate more education; if that is the case, the asset gap will be reduced (Anderson [2005], 1048).

64 Ibid, 1057.

65 Ibid, 1058.

66 This is so because the owners of the factors of production highly benefiting (the capital owners either local or foreign and the highly skilled labor) from openness not only oppose increases in taxes; but also can threaten with mobilizing their factors of production. Ibid, 1049-1050. 14

characteristics such as credit constraints and level of education; and labor income might explain why the stable growth rates experienced by Costa Rica did not raise the expectations of poor Costa Rican parents, which would have encouraged them to keep their children in school longer. Indeed, it is likely that there were other factors that lowered those expectations.

I can put forward two possible, but not mutually exclusive, explanations for the low demand for education in Costa Rica: the first is that Costa Rican unskilled workers’ income reduction, in comparison with skilled workers’ earnings,67 increased their liquidity constraints that reduced their budget allocations for investing in their children’s education. The second is that parents’ expectations were driven by the low returns of the unskilled workers rather than the higher returns of the skilled labor. Although this does not seem to be prima facie a rational response, it could very well be rational because there might be constraints that preclude parents from taking advantage of opportunities.68

In this regard, nevertheless, Birdsall argues that research on low human capital accumulation in Latin America shows that, along with liquidity constraints, there has been another factor: parent’s low expectations, and the latter has been grounded in the low returns for un-skilled labor, labor market discrimination against certain minority groups and the declining quality of education,69 reflected in the low returns on education.

She indicates that the private returns to public primary education, in Latin America,

67 I am referring here to those with at least secondary education.

68 Becker refers to these constraints as non-observed costs, monetary and psychic, that preclude parents from taking advantage of profitable opportunities. Gary Becker, “The Economic Approach to Human Behavior” (1976), in: The Essence of Becker edited by Ramon Febrero and Pedro S. Schwartz (Stanford, California: Hoover Institution Press, 1995): 6. Also see by Becker, “Irrational Behavior and Economic Theory” (1962), ibid, 31.

69 Birdsall, 10-11.

average 10%, compared to 30% in other developing regions, and the private returns to secondary education are 11% lower than the ones in other regions70. Only higher education extracts high returns, but for the poor, she maintains, this is not an option, and that is why poor students drop out. Other evidence of the poor quality of education, she notes, is the fact that well-off families move their children to private schools and that some Latin American children obtain low scores in international tests, compared to their

Asian counterparts.71

These outcomes for Latin America seem to show that only highly skilled workers72 (with university studies or tertiary education) receive a high reward for their education. If Costa Rica is obtaining the same patterns of returns to education as other

Latin American countries, completing high school is just the same as being unskilled in terms of rewards for the investment; hence this must have exerted a negative effect on parents’ expectations. On the other hand, we already mentioned that in Costa Rica the average income for skilled workers (with at least secondary complete), has been increasing while the average income of unskilled workers remained unchanged for a number of years and has actually been falling in recent years73. Clearly, this should have given an incentive to parents to persuade their children to at least finish secondary school.

However, this also depends on the difference between the private returns on tertiary

70 Birdsall, 11.

71 Ibid.

72 This is so because workers are considered skilled if they have completed high school, unskilled if they do not finish primary school and semi-skilled if they have completed primary school (Juan Diego Trejos Solórzano and Miguel Del Cid, “Decent Work and the Informal Economy in Central America,” (Geneva: International Labor Office, Policy Integration Department, August 2003): 28.

73 See section 1 and the evidence on wage gaps for Costa Rica provided above in this section.

education (university) and secondary education and in the return differential between secondary complete and primary complete. The most recent estimates of the private returns to education for Costa Rica by gender divided by education levels seem to confirm Birdsall’s findings; however, these estimates show a large discrepancy with previous estimates.74 On the other hand, expectations do not depend only on private returns to education; there could be other factors that lowered family expectations or deterred families from accumulating human capital.

Overall, the analysis above illustrates the importance of studying the private returns to education for Costa Rica according to different education levels or other economic groups, since these returns might be influencing the expectations of parents from different societal groups differently. It also reveals the need to study, along with returns to education, other factors that have influenced Costa Rican parents’ low demand for education. This requires to examine the household decision making model as it relates to education investment.

1.3.2 Household decision making and education investment Schooling decisions and educational investment are taken at the household level.

To understand the factors that influence education decisions it is necessary to carefully consider educational investment at this level: this is formally done by economists using a household production model.

The analysis of household behavior uses the household production model. In this

74 The latest calculations are found in De Ferranti et al., Inequality and Latin America and the Caribbean, 429. However, they differ greatly from Psacharopoulos’ and Ng’s estimates. George Psacharopoulos and Ying Chu Ng, “Earnings and Education in Latin America: Assessing Priorities for Schooling Investments,” (Washington, D.C.: World Bank, Technical Department, Latin America and the Caribbean Region, Policy Research Working Papers, WPS 1056 [December 1992]). 17

model, developed by Gary Becker (1965), households take not only consumption but also production decisions. They produce basic commodities such as meals and cleaning that benefit all members equally, using time and the goods they purchase in the marketplace.75

In addition, households’ production decisions also involve investment decisions, e.g., regarding schooling and health, as it was made clear by Becker when he introduced his theory of human capital in the household model.76 Human capital decisions are considered investment decisions because they involve using present resources to obtain resources in the future. The basic argument posits that households are concerned with the present and future welfare of their members, all their offspring included, and any decision regarding resource allocation to investment activities is pursued so as to maximize inter- temporally the household utility, subject to its budget constraint and expected earnings production function of each child.77 Parents will invest in education for their children if the present value of the future returns of that investment is equal to the costs of that investment (direct and indirect).78

The household model applied to the intra-household allocation of parents’ resources to children relies on the following assumptions79: 1) markets are complete, in

75 Gary Becker, “A Theory of the Allocation of Time” (1965), in: Febrero and Schwartz, eds., The Essence of Becker, 93.

76 Ramón Febrero and Pedro Schwartz. “The Essence of Becker: an Introduction,” ibid, xviii-xvix.

77 Jere Behrman, “Intra-household Distribution and the Family” in Handbook of Population and Family Economics, Vol. 1A, edited by M. R. Rosenzweig and O. Stark (Amsterdam, The Netherlands and New York: Elsevier Science B. V., 1997): 129.

78 These costs will be defined later in this section.

79 The first five assumptions are shared by the all household production models and agricultural household models. Inderjit Singh et al., Agricultural Household Models: Extensions, Applications, and Policy (Baltimore and London: The World Bank and The Johns Hopkins University Press, 1986): 86. The other assumptions are specific to household models that deal with parents transfers to children.

other words, there are no imperfect or missing markets. This ensures that production and investment decisions are separated from consumption decisions. Investment decisions such as investment in education affect consumption decisions, but the reverse does not occur. In other words, household production and investment decisions depend on wages and input prices and household characteristics, but not on preferences. 2) A household is represented by a single unit maximizing a single welfare function that represents the parents’ preferences. However, parents, in the absence of credit constraints, are assumed to be altruistic, that is, their utility is dependent on the levels and distribution of their offspring’s utility and income.80 3) Income is pooled regardless of who accrues it, and consequently, it is allocated according to collective preferences. 4) Prices are exogenous and there are no transaction costs. 5) Children’s endowments are exogenous. 6) Each child provides the same amount of utility to parents and hence parents will allocate resources equally to them.81 This assumption is known in the literature as “equal concern”.82 In Becker and Tomes’ model this assumption implies that parents will compensate their higher investment in their better endowed children with bequests to their less endowed children.83 Parents invest more in better endowed children because parents are also profit maximizers and children with higher endowments lower investment costs. An implication of this behavior is that parents will reinforce children

80 Behrman (1997), 128.

81 Gary S. Becker and Nigel Tomes, “Child Endowments and the Quantity and Quality of Children,” The Journal of Political Economy, Vol. 84, No. 4, Part 2: Essays in Labor Economics in Honor of Gregg Lewis (Aug 1976): S144.

82 Behrman (1997), 130.

83 Becker and Tomes (1976), S145.

endowment differentials.84 7) Investments in human capital are subject to diminishing returns.85 8) Rates of returns on investments are known to parents since their children’s endowments are revealed to them prior to their investment on children.86 9) Rates of return on assets are the same for all people and parents can borrow at the asset interest rate to finance their investment in their children. Also, this debt is accrued to children when they become adults.87

Given this model’s conceptual framework, households’ investment in education would be Pareto optimal and would benefit parents or their offspring. However, families take decisions whose outcomes are not socially efficient because markets are not perfect: there are externalities associated with education that are not internalized by households,88 such as the benefits for society as a whole in terms of increases in productivity, acquisition of democratic values and lawful behavior.89 In fact, positive externalities, along with other market failures such as the absence of capital markets for general education, and to some extent informational deficiencies as well as equity arguments, are the justification for the public financing and provision of education.

Household’s decisions regarding education might not be privately efficient either.

84 Ibid.

85 Behrman (1997), 130.

86 Gary Becker and Nigel Tomes, “Human Capital and the Rise and Fall of Families,” (1986), in Febrero and Schwartz, eds., The Essence of Becker, 349.

87 Becker and Tomes, ibid, 349.

88 T.N. Srinivasan, “Introduction to Part 3,”in Handbook of Development Economics, Vol. I, eds. Hollis Chenery and T.N Srinivasan (Amsterdam: Elsevier Science Publishers B.V., 1988): 470.

89 Mark Blaug, An Introduction to the Economics of Education (Hampshire, England: Gregg Revivals, 1991): 104-113; Joseph Stiglitz, Economics of the Public Sector, 3rd edition (New York: WW Norton and Company, 2000): 427.

Although the model is a good analytical tool to study family behavior, some of its assumptions do not represent the reality faced by parents. An analysis of how these assumptions can be violated follows. I will try to separate assumptions in the same order that were listed previously; however, linking them in my analysis in some instances is unavoidable, since assumptions are usually interdependent.

As I have just mentioned, markets are not perfect in the education sector for several reasons: credit markets to finance primary and secondary levels of education are missing or imperfect,90 and information is imperfect91; there are transaction costs, and there are cultural or institutional factors that influence an efficient allocation of resources.

The violation of this perfect market assumption causes that consumption decisions influence production decisions. In other words, families deciding on investing in education are forced not only to consider their budget constraint and children’s potential utility and income but also the prices of the consumption goods (as opposed to input prices) and household labor endowments as they can use the latter in their alternative use in the labor market. The implication, for instance, of the lack of credit availability when families have liquidity constraints is that investing in education has an opportunity cost for families, i.e., the value of consumption forgone as they invest part of their disposable income in education for their children. The case that education is publicly provided does not mean that it is completely free,92 since there are costs due to transportation, clothing

(in many countries, uniforms) and school materials that have to be financed by the

90 Bardhan and Udry, 126-127.

91 Blaug (1991), 20. Srinivasan (1988, 470) also reports this market inefficiency as affecting parent’s decisions regarding fertility; however, I believe it also affects education decisions.

92 Abhijit V. Banerjee, “Educational Policy and the Economics of the Family,” Journal of Development Economics, Vol. 74, No. 1 (June 2004): 3-4.

families.93 The presence of this market failure makes investment in education more costly

(due to the consumption forgone) for liquidity constrained parents and consequently they increase their discount rate94 (the rate that equates the present value of returns to the present value of costs). For Becker and Tomes liquidity constrained parents are those who do not have assets that can be used as collateral and whose earnings are low.95

Discount rates are lower for parents whose earnings are higher.96 According to Becker, the cost of funds is no longer the same for all families, and expenditures on children become also dependent on parent’s earnings,97 their generosity towards children and also on “uncertainty about the luck of children.”98 Hence, the absence of a credit cause the mixing of decisions on investment in education with consumption decisions, since families have to defer present consumption in order to invest and attain greater consumption in the future. This consumption depends on the level of income from households’ use of their assets, and consumption needs depend on household composition and characteristics. The presence of information failures, also distorts parent’s decisions

93 The statement that education is publicly provided implies two things: that there is a hidden private cost to households because it is financed with taxes and that there is a cost to society because the subsidy applied to education precludes the application of a subsidy to an alternative social investment.

94 Gary Becker and Nigel Tomes, “Human Capital and the Rise and Fall of Families,” (1986), in Febrero and Schwartz, eds., The Essence of Becker, 352-353.

95 Ramón Febrero and Pedro Schwartz. “The Essence of Becker: an Introduction” in The Essence of Becker, ibid, xxvi) and Gary Becker and Nigel Tomes, “Human Capital and the Rise and Fall of Families,” (1986), in Febrero and Schwartz, eds., The Essence of Becker, 352-353.

96 Another reason for low discount rates is that their children are poorly endowed (Gary Becker and Nigel Tomes, “Human Capital and the Rise and Fall of Families,” (1986), in Febrero and Schwartz, eds., The Essence of Becker, 353.

97 In addition to children’s endowments and public expenditures (Gary Becker and Nigel Tomes, “Human Capital and the Rise and Fall of Families,” (1986), in Febrero and Schwartz, eds., The Essence of Becker, 348 and 353.

98 Becker and Tomes, “Human Capital and the Rise and Fall of Families,” 352-353.

as they are not able to foresee their implications for the future of their children. The illiquidity of education as an investment an asset brings uncertainty as a factor in the decision making. According to Becker, uncertainty is inversely related to the knowledge or information factors that seem out of control of parents such as: the length of time needed to obtain the returns, length of life of parents and children, the ability of their children and any other unpredictable event that could affect the returns.99 The way parents respond to this uncertainty depends on the amount and nature of the investment and parent’s tastes and attitudes.100 When parents are educated this inefficiency might be mitigated. There is an extensive body of research that shows that children’s years of schooling and current school enrollment are positively correlated with the education of the parents.101 The reliance of parents on social networks as sources of information might be perverse in some instances, when they tend to perpetuate negative behavioral patterns; this is the case because social networks are stratified by class and race.102 Also, the presence of liquidity constraints and cultural or institutional factors for several households in developing countries might disrupt parents’ utility maximizing behavior regarding their children. This is the case of households where children work103 or

99 Gary Becker, “Investment in Human Capital” (1962), in Febrero and Schwartz, eds., The Essence of Becker, 61.

100 Ibid, 71.

101 John Strauss and Duncan Thomas, “Human Resources Empirical Modeling of Households and Family Decisions,” in: Handbook of Development Economics, Vol. 3A, edited by Jere Behrman and T. N. Srinivasan (Amsterdam, The Netherlands: Elsevier Science Publishers B. V., 1995): 1923.

102 Although Schneider when making this point was referring to the United States, the result is equally applicable to developing countries. See: Mark Schneider, “Education and Choice in Educational Privatization,” in: Privatizing Education: Can the Market Place Deliver Choice, Efficiency, Equity and Social Cohesion?, ed. Henry M. Levin (Boulder, Colorado: Westview Press, 2001): 77-78.

103 Strauss and Thomas, “Human Resources Empirical Modeling,” 1980.

undertake the household work and child care of their siblings (mostly assigned to daughters) so their mothers can work outside the home.104 In fact, there is an additional opportunity cost associated with investment in schooling; “the foregoing productive contribution their children would have made to the family income had they not attended school.”105 In this sense, Basu and Tzannatos argue that poverty is the main cause of child labor and can be encouraged or discouraged by social norms, the availability and quality of schools, and the presence of transaction costs.106 Given that child labor hinders the accumulation of human capital, it is argued that it might become what is known as a dynasty poverty trap.107 Regarding transaction costs Pollack108 argues that families are a governance structure and that the structure is able to provide incentives and monitoring and enforcing agreements to deal with adverse selection and moral hazard behavior within the household. This argument also shows us that there are transaction costs involved in intra-household allocations; to this respect I argue that as long as parents are altruistic and there are consensual preferences between parents and between parents and children, and there are no liquidity constraints, these transaction costs might not produce

104 Ibid, 1989.

105 Paul T. Schultz, “Education Investment and Returns,” in Handbook of Development Economics, Vol. I, eds. Hollis Chenery and T.N Srinivasan, 544.

106 Kaushik Basu and Zafiris Tzannatos, “The Global Child Labor Problem: What Do We Know and What Can We Do?,” The World Bank Economic Review, Vol. 17, No. 2 (2003): 148-152.

107 Ibid, 153-155. On the other hand Ravallion and Wodon argue that casual observations and statistics do not support this hypothesis. Their research in Bangladesh shows that enrollment subsidies do not reduce the incidence of child labor, in a large proportion. The reduction of child labor is one fourth (one eighth) of the reduction in the school enrollment rate for boys (girls). Martin Ravallion and Quentin Wodon Quentin, “Does Child Labor Displace Schooling? Evidence on Behavioral Responses to an Enrollment Subsidy,” Conference Paper, Economic Journal, Vol. 110, No. 402, (March 2000): C173.

108 Robert A. Pollack, “A Transaction Cost Approach to Families and Households,” Journal of Economic Literature, Vol. 3, No. 2 (June 1985): 585-591.

privately inefficient decisions. However, in the event of a divorce, or when children work, or when children do not share parental preferences, or there are liquidity constraints, these transaction costs increase to a point where the outcome does not benefit the welfare of the children and is inefficient.109

The preferences of households do not necessarily reflect the welfare of their offspring110 and, indeed, might not be commonly shared by household members (both parents and children) either. If parents are not altruistic they will not dedicate enough resources to the education of their children. Also, providing equally for the children depends on being sufficiently wealthy or sufficiently altruistic.111 How they allocate their resources in this situation will depend on the parents’ preferences. These preferences might not be consensual and might express gender specificities within the household.

Women’s utility is more oriented towards collective goods whereas men’s is towards personal goods.112 For example, when women in Thailand are in charge of income, the shares of the household budget allocated to education, health and housing increase.113

This example and several others114 like this one also might suggest that, in fact, income is

109 Ibid, 603-04.

110 T.N. Srinivasan, “Introduction to part 3”, in Handbook of Development Economics, Vol. 1, eds. Chenery and Srinivasan, 470. It should be clarified that Srinivasan raised this factor and incomplete information as problems that impede socially optimal decisions regarding fertility.

111 Jere Behrman, “Intra-household Distribution and the Family” in Handbook of Population and Family Economics, eds. M. R. Rosenzweig and O. Stark, 132.

112 Elizabeth Katz, “Breaking the Myth of Harmony: Theoretical and Methodological Guidelines to the Study of Rural Third World Household, Review of Radical Political Economics, Vol. 23, Nos. 3 &4 (1991): 42.

113 Strauss and Thomas. “ Human Resources Empirical Modeling of Household and Family Decisions,” 1998.

114 Behrman reports two empirical studies that arrived to the same finding. Behrman, “Intra- household Distribution and the Family,” 175.

not pooled as it is assumed.115 Also, children might bargain with their parents in their schooling decisions116 and inefficient decisions might prevail. In fact, the introduction of preferences117 in the household model turns education into a consumption good that yields direct utility.118 For instance, parents might spend on the education of their children if they can benefit from it through care during their old age119 or parents might derive more satisfaction from investing in their sons instead of their daughters.120 On the other hand, education can also be perceived both as an investment and consumption good. Parents may have aversion for their children’s inequality, but in the face of a crisis they will protect the investment in education of the older progeny at the expense of the younger siblings.121 This previous example also entails a violation of the equal concern assumption. I can find the rationale in Rosensweig’s list of the situations that bring about the violation of this assumption; these factors emerge in a crisis. Rosensweig lists the following situations: there are different market returns to human capital, differences in

115 However, as Behrman cautions, these findings are related to women allocating un-earned income; to discard the income pooled assumption, the same results should be obtained with earned income. Behrman, ibid, 175.

116 Paul Glewwe and Michael Kremer, “School, Teachers and Education Outcomes in Developing Countries” in Handbook of the Economics of Education, Vol. 2, eds. Eric Hanushek and Finish Welch (Amsterdam, The Netherlands: North Holland Publications, 2006): 967.

117 These tastes and preferences might reflect, as Strauss and Thomas note, cultural and social values.” Strauss and Thomas, “Human Resources Empirical Modeling of Household and Family Decisions,” 1985.

118 Schultz, 557.

119 Banerjee, 4.

120 Strauss and Thomas, “Human Resources Empirical Modeling of Household and Family Decisions,” 1985.

121 This was the response of rural and urban Indonesian parents to cope with the 1998 financial and economic crisis. Although the authors do not mention explicitly that the parents did not have a taste for discrimination, this seems to have been the case, since before the crisis all offspring were sent to school equally. Duncan Thomas et al., “Education in a crisis,” Journal of Development Economics Vol. 74, No. 1 (June 2004): 76-79.

endowment levels among children, differences in returns to human capital inputs and an increase in return in some family member’s time122 An additional example of the violation of the equal concern assumption is offered by Rosensweig: this is the case when a job creation program for adult women unintentionally provides an incentive for daughters to substitute their mother in home production.123 Finally, Thomas provides empirical evidence of the rejection of the common preference and pooled income assumptions in his studies of family health and nutrition in Brazil.124

Based on this model, parents’ decisions on investment in their children’s education are influenced by several factors, but two of them are very influential: the expected returns to education and the costs of education and as such require more explanation.

There are two types of returns to education.125 The monetary returns include expected income gains from increased productivity enhanced by education. The non- monetary private benefits of education include improvement in nutrition and health

122 Mark R. Rosenzweig, “Programme Interventions, Intra-household Allocation, and the Welfare of Individuals: An Economic Model of the Household,” in: Beatrice Lorge Rogers and Nina P. Schlossman, eds., Intra-household Resource allocation: Issues and Methods for Development Policy and Planning. Food and Nutrition Bulletin, Supplement No. 15. (Tokyo: United Nations University Press, 1990) http://www.unu.edu/unupress/unupbooks/80733e/80733E00.htm (retrieved on April 27, 2007).

123 Ibid, 35.

124 Duncan Thomas, “Intra-household Resource allocation: An Inferential Approach,” Journal of Human Resources, Vol. 25, No. 4 (Fall 1990): 646-647.

125 An extensive list of private benefits and social effects of education can be found in Barbara L. Wolfe and Robert H. Haveman: “Social and Non-market benefits from education in an advanced economy” in: Education in the 21st Century: Meeting the Challenges of a Changing World, edited by Yolanda K. Kodrzycki (Boston: Federal Reserve Bank of Boston, Conference Series 47 [Proceedings] June 2002), pages 104-106; at: http://www.bos.frb.org/economic/conf/conf47/conf47g.pdf, retrieved July 16, 2009.

status, and higher quality of life.126 The positive impact of own education on adult health and parental education on children’s nutrition and health has been exhaustively researched. For instance, Strauss et al. found in their research for four developing countries that schooling improves adult health127 and Ross and Wu found that adult health is improved directly and indirectly (through work and economic conditions) by high educational attainment.128 Thomas et al.’s findings in their research in Brazil evidence the significant positive effect of parental education on child survival and nutrition (height for age).129 Similarly, Wolfe and Behrman’s research on Nicaragua found a robust positive effect of mother’s education on child’s nutrition130. There are also non-monetary social effects of education or positive externalities that benefit society as a whole, among them one can mention: lawful behavior, self reliance and social cohesion. It is important to point out that in my research I will focus on the monetary returns to education as the main determinant of parent’s decisions.

The private131 costs to education that parents take into account are132 direct and

126 However, one can argue that not all parents consider all or some of the non-monetary returns to education. Certainly, poor or uneducated parents are at a disadvantage because they might not have all the information or they have not experienced themselves the whole range benefits obtained from education.

127 John Strauss, Paul J. Gertler, Omar Rahman, and Kristine Fox, “Gender and life cycle differentials in the patterns and determinants of adult health,” Journal of Human Resources, Vol. 28, No.4 (1993): 791- 837.

128 Katherine E. Ross and Chia-Ling Wu, “The links between education and health,” American Sociological Review, Vol. 60, No. 5 (1995): 738.

129 Duncan Thomas, John Strauss and Maria-Helena Henriques, “Child survival, height for age and household characteristics in Brazil,” Journal of Development Economics, Vol. 33, No. 2, (1990): 211-215.

130 Barbara L. Wolfe and Jere R. Behrman, “Women’s schooling and children’s health: Are the effects robust with adult sibling control for the women childhood background,” Journal of Health Economics, Vol. 6, No. 3 (1987): 252.

131 There are social costs to education because it is publicly subsidized.

indirect. Direct costs are pecuniary and include that expenditure that would not have been incurred if the child had not been in school such as tuition, fees, books and supplies, uniforms, and transportation. The indirect costs are the opportunity costs of education, such as the forgone earnings from work while the child is in school.

In sum, at the household level, the demand for education is therefore a function of the parents’ expected returns to their investment in schooling net of costs (direct and indirect), the household income, the family’s preferences and the parents’ information.

However, as I have mentioned, parents’ expectations on their investments also include the also the quality of education, labor market discrimination, and government policies.133

The household decision making model is very informative. Nevertheless, I believe that it is useful to introduce two complementary notions: the livelihood approach to household strategies and the poverty trap theory. Both will enrich my analysis by providing a more comprehensive and dynamic view of how households marshal their resources to face difficulties and by helping us identify the factors that have a bearing on poor families’ decisions to bypass opportunities that would help them escape further penury.

1.3.3 Education and Poverty Traps

The poverty trap theory provides an explanation for a self-reinforcing pattern of persistent poverty134 that can be found among the poor which keeps them in a vicious

132 Becker, Gary. “Investment in Human Capital: A Theoretical Analysis” (1962) in: The Essence of Becker, edited by Ramon Febrero and Pedro S. Schwartz (Stanford, California: Hoover Institution Press, 1995): 54-56.

133 Birdsall, “Education: The People’s Asset,” 10-11.

134 I use Barrett and Swallow’s conceptualization of persistent poverty. Poverty is persistent as opposed to transitory when the percentage of the population who is poor (based on head count ratio and 29

cycle of poverty that is handed down from one generation to the next. This theory departs from the standard theories of poverty and of theories that link growth with poverty.135

Along with the livelihood approach (to be explained below) on which it builds, the poverty trap theory at the household level complements the household decision making model and contributes, as I also demonstrate below, to my analysis of demand for schooling, suggesting factors and mechanisms which lead households to settle for livelihood strategies that block their opportunities to escape poverty. These factors might be affecting demand for schooling and education attainment.

The livelihood approach to household strategies asserts that the different strategies households develop to survive, or keep or improve their standard of leaving,136 when market failures or government failures, combined with unanticipated situations out of their control threaten their constant stream of income or consumption, depend on their holdings or access to a stock of assets: natural, physical, human, financial, and social,

measured in real currency) remains practically unchanged for a long period of time. This is a simplified construct; Carter and Barrett argue that when the head count ratio is used to measure persistent poverty, it is very difficult to assert that the households that are counted in one period are the same ones that are counted in other periods; hence even if the percentage remains equal, there is no way of knowing which ones suffer from chronic poverty unless an asset approach to measure poverty is used. Christopher B. Barrett, and Brent M. Swallow, “Fractal Poverty Traps,” World Development, Vol. 34, No. 1 (2006): 2. Michael R. Carter, and Christopher B. Barrett, “The Economics of Poverty Traps and Persistent Poverty: An Asset Based Approach,” Journal of Development Studies, Vol. 42, No. 2 (February 2006): 179-180.

135 The theory can be applied to countries, regions, households and individuals. See: Barrett and Swallow. Also see: Samuel Bowles, “Institutional Poverty Traps” in Poverty Traps, eds. Samuel Bowles, Steven N. Durlauf and Karla Hoff (Princeton, New Jersey: Princeton University Press): 117-137.

136Frank Ellis gives an excellent explanation of the livelihood diversification mechanisms and coping strategies of rural households in developing countries. Although he focuses on rural areas, he informs that this approach can be applied to urban areas and to any country. Frank Ellis, “Household Strategies and Rural Livelihood Diversification,” Journal of Development Studies, Vol. 35, No. 1 (October 1998): 3.

including family, community networks and public services.137 This view argues that assets, social infrastructure, connections and values are both sources of support as well as of constraints for family decisions.138

I believe that this approach contributes to the household decision making model by underlining the role of external environmental factors and asset holdings in the decision making process. In terms of this research, this approach allows me to justify the understanding of education as an asset whose accumulation or depletion expands or limits households’ possibilities of survival.139 This is essential, because for the majority of the population in Latin America (Costa Rica included), total wealth is comprised mostly of education and housing140 and education, as noted above, is the most productive asset. It also leads us to explore the role of social capital assets141 as a possible constraining factor in the Costa Rican households’ strategy towards schooling of their children. The power of social capital to support or limit household decisions becomes clear when preferences or incomplete information are situated within the context of reference groups and community networks or when the government fails in its role as reliable provider of public goods.

137 Ibid, 4-8. Also see Paul Winters, Benjamin Davis and Leonardo Corral, “Assets, Activities and Income Generation in Rural Mexico: Factoring in Social and Public Capital,” Agricultural Economics, Vol. 27, No. 2 (August 2002): 1, 2, 1 and 54.

138 Ellis, 4-8. Also: Winters, Davis and Corral, 4.

139 The livelihood approach focuses on assets rather than on income. Although the household model does not exclude the notion of financial, natural, human and physical assets when it refers to income or wealth, it does not emphasize them.

140 De Ferranti et al., Inequality in Latin America and the Caribbean, 6-4.

141 This approach underscores the role of social capital in strategies for survival. It also draws attention to the responsibility of the government as a reliable provider of public goods, since access to public goods is considered part of household social capital assets, in addition to its role as an intervening agent to correct the distortions of the market.

Poverty traps are critical thresholds that imply turning points between staying poor or moving along a path out of poverty.142 Crossing these thresholds entails pooling an extraordinary amount of resources and it requires choices among different livelihood strategies.143 These strategies are defined according to households’ or individuals’ initial asset holdings, their preferences, the expected returns from the chosen strategy, entry barriers (i.e., the amount of investment and sacrifice they will entail, lack of credit, information, access to public services, geographic location) to some strategies, and the presence of market and government failures. There are strategies with higher returns but that might not be accessible to some households because they require a different or more valuable stock of assets or a set of different incentives.144

Two types of circumstances cause poverty traps, according to Carter and

Barrett.145 One is the presence of increasing returns to scale in alternative investments.

This happens when the path out of poverty for a household means to engage in a productive process that might bring higher returns; however, for a transitional period, higher accumulation of capital (human or physical) is needed to obtain additional marginal returns.146 Lars Ljungqvist147 formulated a model to explain low demand for education when there is no access to education loans and returns to education are

142 Barrett and Swallow, “Fractal Poverty Traps,” 3-4.

143 Ibid, 5.

144 Ibid, 5-6.

145 Carter and Barrett, 187.

146 Ibid, 187-189.

147 Lars Ljungqvist, “Economic underdevelopment: the case of a missing market for human capital” Journal of Development Economics, Vol. 40 No. 2 (April 1993): 221. Bardhan and Udry (123-130) provide a very clear explanation of this model as well.

increasing in a strong form148 that not only provides an example of this type of poverty trap but also provides an explanation for the failure of Costa Rican parents to keep their children longer in school in spite of high returns to skilled labor. The model posits that if the returns to education are increasing in a strong form and there is no credit for education, unskilled workers do not have an incentive to invest in education despite the high returns skilled labor extracts. The cost of education becomes too high relative to their low earnings. The length of time to accumulate savings to become skilled is so long that it implies giving up consumption in order to invest in education. The unskilled worker has to settle for the low return strategy of not investing in education.

The other type of circumstances is the presence of adverse fundamental characteristics149 of households or individuals that keep them from achieving their desired level of asset accumulation and level of well-being. Among these inherent characteristics mentioned in the literature are: a) individuals’ initial asset holdings which are the product of previous decisions and circumstances, predispositions and discount rates,150 b) lasting human capital depletion as a result of sickness, childhood under- nutrition and lack of education.151 These circumstances underscore what I mentioned above about the disadvantage that initial inequality in assets entails regarding the possibility of taking advantage of macro-development policies; c) “neighborhood

148 Ljungqvist calls this type of returns to education, indivisibilities in education. Bardhan and Udry describe them as a strong type of increasing returns Bardhan and Udry (126,130). This is so because increasing returns in this model are such that only when a large stock of education is reached a person could obtain the expected returns to make the investment worthwhile. However, to acquire that stock entails a costly investment.

149 I considerably modified this category to include other circumstances that I think are also inherent to some households or individuals.

150 Carter and Barrett, 187.

151 Barrett and Swallow, 10.

effects,”152 which emerge when households or individual reference groups exert a negative influence (sometimes through role models) in their expectations, aspirations and preferences, thwarting their asset accumulation and pursuit of successful likelihood strategies.153 Neighborhood effects might cause an information asymmetry when individuals or households have as reference groups only the groups to which they belong.

This circumstance reinforces the aforementioned informational failures that cause inefficient allocation of resources at the household level; d) geographic determinants, such as communities or villages with deficient infrastructure (bad roads), or insufficient asset stock (land or education), and information at the village level, that hinder the ability of the village households or villagers to catch up with their counterparts and take advantage of economic opportunities. This condition again underlines the fact the initial disadvantages curtail the possibilities for poor people to seize the gains of growth and also shows how government can fail in making sure that all segments of society are on an equal footing; and e) the presence of dysfunctional institutions154 such as class or power structures that shape the economy and protect the interests of some groups at the expense of others. This set of circumstances helps us to elucidate determinants of low demand for education. For instance, the identification of specific groups that have been characterized

152 Neighborhood effects are considered a type of poverty trap on their own; this notion has been developed by Steven N. Durlauf in his “Groups, Social Influence and Inequality,” in Poverty Traps, eds. Bowles, Durlauf and Hoff, 141-175.

153 In general, Durlauf argues that the socioeconomic groups to which a person belongs influence his or her propensities, aspirations and outcomes. Durlauf, “Groups, Social Influence and Inequality,” 142. Also, Bénabou formulated a model to demonstrate that the accumulation of human capital depends on family background, community factors (neighborhood effects) and economic inputs (fiscal and technological spillovers). This model was developed to account for the relationship between ethnic stratification and the system of financing public education in the US. Roland Bénabou, “Heterogeneity, Stratification, and Growth: Macroeconomic Implications of Community Structure and School Finance,” American Economic Review, Vol. 86, No. 3 (June 1996): 584-609.

154 This is also considered a type of poverty trap on its own. Bowles, “Institutional Poverty Traps,” 117-137. 34

by poverty and low demand for education located in particular geographic zones might allow us to hypothesize about neighborhood effects or geographical determinants or dysfunctional institutions that are preventing individuals from investing in education.

In conclusion, both the household decision making model as it applies to human capital decisions, and the theory of poverty traps as it relates to investment in education, inform that parents’ decisions on schooling reflect their strategy conditioned by their initial asset holdings, including their own assets and access to public services; their expected private returns from keeping the children in school compared to other choices, such as maintaining or reducing their level of income and consumption; and their preferences, incentives and information affected by their social network, geographic location and institutions; and by market and government constraints. I find that taken together, both provide the general conceptual framework for this research. Moreover, I have not yet found an instance of this combination having been used before.

1.4 Research components

The analysis above has demonstrated the importance of studying those factors that affect parents’ expectations, particularly, the returns to education. It also reveals the need to study, along with returns to education, other factors that have influenced Costa Rican parents’ low demand for education such as policy of education and quality of education delivery. I believe that increasing returns to education could be key to explaining parents’ low demand for their children’s secondary education in Costa Rica and also, if quality of education is poor, this might be reinforcing parents’ low expectations regarding their children’s schooling. This study has the following components:

The first component is the subject of chapters 2 and 3. It studies the role of Costa

Rican education policy, resources allocated and education delivery. It also examines coverage, efficiency of the education delivery system and equality of access to education.

This research component has two purposes. The first one is to assess the quality of education and key policy programs: inclusion programs to reduce desertion; redistribution programs to increase access; and technology anchored/labor pertinent programs to increase relevance in accordance with Costa Rica’s strategy and labor market needs. The second one is to obtain a deeper knowledge of the nature of challenges, strengths and weaknesses of the education delivery system in CR; and of the education related inequalities.

The second component, dealt with in chapter 4, involves the testing of the convexity of the monetary private returns to education in Costa Rica as the main determinant of parent’s decisions. Ljungqvist’s notion provides a plausible answer to the research question regarding their children’s schooling and should be explored.

The objective of the third component is to further demonstrate Ljungqvist’s model at the household level and learn more about the relationship between parents’ choices on schooling for their children and convexity of monetary returns to education. A demand for education model is formulated that reproduces the assumptions of Ljungqvist’s model at the household level and also establishes a causal relationship, using regression analysis, between parents’ investment in schooling and convexity of monetary private returns to education under conditions of no access to credit for education and that parents are either liquidity constrained or not.

1. 5 Recent Scholarly Research on Costa Rica Related to the Research’s Topic

The purpose of this section is to report any research that has been carried out on

Costa Rica that deals with monetary private returns to education and the effect of private returns to education on the demand for children’s education. An additional purpose is to establish the uniqueness of this study and its contribution to Costa Rica.

Empirical research in Costa Rica, performed after 1990, and focused specifically on calculating the monetary private returns to education, is scarce. There has been research, also scant, geared to prove labor market segmentation.155 These studies156 intend to define a profile of workers per sector. Hence, the returns to education are obtained, but as one of the several individuals’ characteristics that affect wages in the sectors under investigation.

The research geared specifically towards analyzing the relationship between earnings and education, according to my knowledge, is the following: Ferranti et al. calculated the monetary private returns to education by gender divided by educational levels for several countries including Costa Rica for the years 1990, 1995 and 2000, as part of a comparative study. Psacharopoulos and Ng157 calculated the monetary private

155 The idea of labor market segmentation is to compare the determinants of wages and earnings to test whether there are barrier to mobility between sectors. J. R. Behrman, “Labor Markets in Developing Countries,” Ch. 43 of Handbook of Labor Economics, Volume 3C, eds. Orley Ashenfelter and David Card (Amsterdam and New York: Elsevier Science B. V., 1999).

156 Edward Funkhouser, “The Urban Informal Sector in Central America: Household Survey Evidence,” World Development, Vol. 24, No. 11 (November 1996): 1737-1751. T. H. Gindling, “Labor Market Segmentation and the Determination of Wages in the Public, Private-Formal and Informal Sectors in San José, Costa Rica” Economic Development and Cultural Change, Vol. 39, No. 3 (April 1991). José Itzigsohn, Developing Poverty: The State, Labor Market Deregulation, and the Informal Economy in Costa Rica and the Dominican Republic (University Park, Pennsylvania: The Pennsylvania University Press, 2000).

157 Psacharopoulos and Ng, “Earnings and Education in Latin America,” passim.

rates returns to education for Costa Rica as part of an extensive study whose purpose was to compare social and private rates of returns158 to education for several countries in Latin

America for the years 1981 and 1989. There is large discrepancy however between these two World Bank studies that is revealed by the large change in the returns between 1989 and 1990. However, they are not quite comparable because they used different specification in the calculation. Funkhouser estimated the monetary private returns to education on schooling in Costa Rica from 1976 to 1992.159 His study examined the trends in the returns to schooling by school level, gender and region of residency during the period as part of the main focus of the study which as to find the determinants of the changes in those returns. Trejos calculated the average monetary private returns to education for Costa Rican males and females using the household survey of 1995. He used these estimates to calculate the probability of poor households leaving poverty if they could utilize more their education. Although he examines the relationship between returns to education and poverty, he does examine the effects of the returns on investment in education.

The previous review underscores the fact that there is no recent estimation of the monetary private rates of return for education that accounts for their non-linearity160.

Moreover, the focus of the previous research has not been on the relationship between the

158 The only difference between the two is that the private cost of education (the foregone earnings of the person while in school) is increased by the true full cost of education which is paid by the state (Psacharopoulos and Ng, ibid, 2).

159 Edward Funkhouser, “Changes to the Returns to Education in Costa Rica,” Journal of Development Economics, Vol. 57, No. 2 (December 1998): 294.

160 Except for Psacharopoulos and Ng who calculated the average returns associated with levels of education (primary completed, secondary completed and tertiary completed) for Costa Rica in 1989: Psacharopoulos and Ng, “Earnings and Education in Latin America,” passim.

rewards for investment in education as it might affect parent’s expectations and consequently the demand for schooling.

Regarding the influence of monetary private returns to education on demand for education, to my knowledge, only one scholarly study has been done on demand for schooling in Costa Rica. Funkhouser161 performed a very interesting study in which he examined the effect of the macroeconomic recession experienced by this country during the period 1981-1983162 on household decisions on teenagers’ (12-17) school attendance.

One of the control variables was the potential labor market rewards of the teenagers themselves based on current salary estimates of child labor. He found that this variable had no impact on school attendance.

1.6 Proposed contributions

While addressing the broader policy-relevant issue of investment in education as a path out of poverty, this dissertation intends to offer a contribution to the literature of the economic development fields concerned with monetary private returns to education, and demand for schooling.

This research, in broader terms, validates existing knowledge because it applies known theories and methods to a specific country with a distinct approach and with data, to my knowledge, that have not been used before with this purpose in mind. The value of this contribution, I believe, is enhanced by the uniqueness of the case study which makes this research very interesting to scholars for several reasons. Costa Rica is cited in the

161 E. Funkhouser, “Cyclical Economic Conditions and School Attendance in Costa Rica”. Economics of Education Review, Vol. 18, No. 1 (February 1999): 31.

162 Although his pooled data covers the period 1981-1985 (ibid, 38). 39

scholarly literature as a country where there has been a government commitment for decades to the social welfare of its people and particularly education. Costa Rica is considered a pioneer in educational initiatives geared towards increasing equality and in making education and technological knowledge one of the main components of its educational policy. Costa Rica is also cited for successfully promoting a knowledge- based economy tied to foreign direct investment that has built on Costa Rica’s stock of human capital. Learning that a sizable portion of the Costa Rican population has been failing to invest in education for several decades and that this is the cause of its low productivity, and hence low rewards, and that the country’s poverty rate persists, is quite shocking. It is also puzzling that, one the one hand, Costa Rica’s growth rate is high and the rewards to high skilled labor in Costa Rica are considerable, which according to theory should encourage investment in education, and yet this is not the case in Costa

Rica.

I also believe that this research contributes to filling in the gaps of existing knowledge by looking at the low investment in education from a perspective that ties the theory of household decision making as it applies to investment in education with the theory of poverty traps. This perspective enriches the analysis of Costa Rica’s education policy and the interpretation of the empirical findings. Therefore, it is my hope that this study will provide a contribution to the field of development microeconomics, and particularly its policy area. I expect to offer additional evidence on the significance of education as a path out of income inequality and poverty and to shed light on the barriers that have kept poor families from accumulating human capital. I also expect that this research adds to the current debate on the role of expectations in educational decisions at

the household level, and to the emerging theory of poverty traps at the microeconomic level.

More specifically in reference to the research components, the contribution to the existing knowledge, I believe, is the following. I am validating existing knowledge by testing the hypothesis of the non-linearity of the returns to education in Costa Rica and by using the demand for school model to discern the influence of monetary returns to education in parent’s schooling decisions for their children. I am filling in the gaps of existing knowledge by providing an explanation of parent’s decision with respect to child schooling when they are informed of the monetary returns to education in the labor market and loan for education are not available.

Finally, this study hopes to provide an input to Costa Rican government officials and policy makers in their efforts to stop this trend of human capital depletion that not only will obstruct any reduction of poverty but will increase inequality and impede growth. For a country traditionally committed to the well-being of its people, a commitment ingrained in its institutions and culture, the realization that the currently insufficient accumulation of education by the poor is bound to produce a vicious circle of poverty and vulnerability is painful. Hopefully, however, the trend can be reversed.

Judging by the policy statements contained in Costa Rican official documents,163 and the recent studies of international financial institutions164 and local policy groups,165

163 Costa Rica, Ministerio de Planificación y Política Económica, Plan Nacional de Desarrollo 2006- 2010. (San José, Costa Rica: 2007) Richard Beck et al., “Estrategia Siglo XXI” (San José, Costa Rica: 2006). This strategy formulated by a group of Costa Rican experts commissioned by the government was attached to Costa Rica’s National Development Plan.

164 World Bank, Costa Rica Country Economic Memorandum, 2006. Inter-American Development Bank, IDB Strategy with Costa Rica (Washington, D.C.: IDB, May 2003).

165 Programa Estado de la Nación en Desarrollo Humano Sostenible, Estado de la nación en 41

it is a priority for the Costa Rican government and for the official development assistance donors to develop a knowledgeable labor force, improve the country’s competitive advantage, and thus avoid poverty-led growth. Increasing the years of schooling and providing Costa Rican children and youth with the skills they need to take advantage of the new employment opportunities and higher monetary private returns is a requirement for the sustainability of its development strategy and the welfare of its people. Thus, learning more about the relationship between monetary private returns to education, on the one hand, and demand for schooling, on the other, becomes necessary.

1.7 Research Methodology

In addition to extensive reliance on scholarly literature in the different areas of my research, I use two types of empirical methods: the descriptive or historical method to carry out the analyses of Costa Rican policy in education; and statistics and econometric tools to carry out the work for the other three components. Both require the use of secondary and primary data.

To carry out the analysis of the first component I use trend analysis. When data is available, I examine patterns of change for periods covering 35 years (1970-2005) or 15 years (1990-2005). The sources of information are documents from the government of

Costa Rica, particularly the Ministry of Education, studies from Costa Rican policy groups and international organization as well as research conducted by Costa Rican scholars. Other sources of information are interviews with the current Minister of

Education, Dr. Leonardo Garnier, and personnel of the Ministry of Education. I also use desarrollo humano sostenible (2005; 2006); Ricardo Monge and John Hewitt, Los Costarricenses en la Economía Basada en el Conocimiento: Infrastructura, Destrezas, Uso y Acceso a las TICS (San José, Costa Rica: Comisión Asesora de Alta Tecnología [CAATEC], 2006.) 42

the UNESCO and World Bank data bases as well as the Ministry of Education’s data base. I addition, I utilize the Costa Rican Household Survey for Multiple Purposes,

(Encuesta de Hogares de Propósitos Múltiples – EHPM), conducted in July of 2005 by the National Statistics and Census Institute (Instituto Nacional de Estadísticas y Censos –

INEC).

The survey is country wide and covers approximately one percent of the population living in private dwellings. The INEC has been performing household surveys since 1976; however, the EHPM in its present design has been carried out since

1987. The EHPM is conducted using a random sample obtained in two stages based on a stratified population. First, the population is divided into 12 strata, each of the country’s

6 economic regions divided by two types of zones: rural and urban; second, a random sample of survey segments is obtained within each stratum, and third, a random sample of dwellings is obtained within each survey segment. Specifically, the survey for 2005 covered a total of 11,480 households and 43,400 individuals. The survey is carried out every year and it is the only source of detailed information on earnings and personal and household characteristics of all workers. The survey has 4 modules: basic information, socio-demographic characteristics, economic activities, and housing. In 2005 a module on Internet use was included only for that year. With respect to the type of data needed for the analysis, the economic activities module provides information for household members aged 12 years and older about employment status, types of jobs, monthly income earnings in primary and secondary occupations, and hours worked per week. The socio-economic module focuses on educational attainment. The questions in the education module inquire of individuals older than 5 years of age their last year of

schooling passed, whether they are attending some form of educational institution and if they are of what type or level (primary, secondary, distance education, etc.) it is or, if not,, reasons for not attending, whether the school is private or public, the degree obtained, career, and languages spoken.

I rely on this survey to perform the empirical analysis required to test the convexity of the monetary private returns to education (chapter 4) and to test the demand for education model (chapter 5).

The empirical formulation to test the convexity of monetary returns to education is borrowed from the fields of private returns to education and was developed by

Hungerford & Solon in 1987. It is a step function that allows for flexibility in the relationship between the log of hourly wages and intervals of one year of schooling. This regression analysis was performed for the total sample, for the 6 country regions, for the two areas, urban and rural and for the two genders, male and female. The total sample used for this analysis includes12,535 individuals that were born in Costa Rica, between the ages of 20 and 60 and from all sectors of the economy who are receiving income from their work and whose work hours are known.

In order to investigate Costa Rican parents’ demand for education when returns are convex and parents are liquidity constrained or unconstrained a probit function was chosen. Only whether or not children were attending school could be observed. The regression analysis was performed for three samples: total sample, primary, and secondary students. The total sample used for this regression analysis covers 6,371 Costa

Rican children between 7 and 19 years old166 who are single and reported in the survey as sons or daughters in the household; their parents’ education is known and who are also reported as either attending formal school (either primary or secondary) or not attending school but who were supposed to be attending since they had not completed the grades for each specific level (primary or secondary) during 2005.

166 Children on the academic track finish secondary school at age 17, but the ones on the vocational track finish at 18. Moreover, there are a considerable number of students who at age 19 have not yet completed 11th or 12th grade because they are repeating grades. 45

CHAPTER 2

EDUCATION POLICY AND THE EDUCATION SYSTEM

2.1 Introduction

This chapter studies the role of Costa Rican education policy, priorities and resources allocated to education as they affect the nature, quality and quantity of education delivery and in turn the demand for education. It has five sections. The following section provides a brief review of Costa Rica’s educational policy and its relationship with the development path undertaken by this country. The third section describes the education system in Costa Rica. Section four provides an analysis of Costa

Rica’s fiscal and economic commitment to education, an assessment of the allocation of resources as well as an examination of the core programs geared to carry out Costa Rica’s equal opportunity mandate in education and its educational policy. The last section contains the conclusions of the chapter.

2. 2 Costa Rica’s development process and its education policy

2.2.1 The emergence of education as a modernizing, democratic, and social mobility mechanism

During the 19th century and the first four decades of the 20th century, education was the priority of government efforts. As a new independent state Costa Rica, in 1844, established education as a sacred right of all Costa Ricans and guaranteed by the state.

The Constitution of 1871167 established compulsory primary education for both

168 males and females, free of charge, and publicly provided. However, by 1914 it was clear that the provision of public education was a mean to serve the dominant economic and political class elite. By the 1930s, social and intellectual movements emerged in

Costa Rican as a reaction to several inequalities evident not only in the educational sector but also in the health and labor sectors.169 These movements culminated with the revolution in 1948. Education, after the revolution, was considered an instrument of equality and social mobility and intellectual development of a democracy.170 In fact, the

Costa Rican Constitution, enacted in 1949, establishes that education is mandatory from first to ninth grade171 and free of charge from preschool to twelfth grade. In fact, in its article 78, the Constitution provides that annual public expenditure allotted to education, from pre-school to university should not be lower than 6% of gross domestic product.172

The Constitution also guarantees that the pursuit of education should not be constrained by lack of food or clothing; the state will provide those needs to the indigent student.173

167 Costa Rica became independent from Spain on September 15, 1821.

168 Costa Rica, Ministerio de Educación Pública, “Informe para la Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura” (San José, Costa Rica: 1997), chapter 5, section 5.3.1. It should be clarified that this document incorrectly reports that the previous Costa Rican Constitution was enacted in 1869 when in fact it was enacted in 1871.

169 Leonardo Garnier, “Costa Rica within the New Economy: The Role of Education, Training and Innovation Systems” background paper for the World Bank’s Flagship Report on Latin America,” April 15, 2002: 2.

170 Juan Rafael Quesada. “Estado y Educación en Costa Rica: del agotamiento del liberalismo al inicio del Estado Interventor: 1914-1929”: 21. In the Constitution of 1871, there were only two articles that made reference to education; the Constitution of 1949 has a chapter dedicated to education and culture.

171 Actually the compulsoriness of general basic education until 9th grade was the result of a modification of the Constitution in 1973.

172 Constitución de la República de Costa Rica (1949), article 78.

173 Ibid, Title VII. 47

Public university financial sustainability is guaranteed by a constitutionally established fund for higher education. In fact, the Costa Rican state finances three colleges and four universities.174 On the other hand, the Constitution encourages private initiatives in education under government monitoring.175

Indeed, the 1950s witnessed a rapid expansion of primary, secondary and university education (the University of Costa Rica was created in 1941). The illiteracy rate went down from over 21% in 1950 to less than 10% in 1973 and education coverage during the 30-year period from 1940 to 1970, increased from less than 70% to more than

90% in primary and from 20% to 60 % in secondary.176 An indicator that summarizes the social policy accomplishments of this period is that by 1976, the father of 40% of Costa

Rican industry owners had not been owners themselves.177

2.2.2 Education: the anchor towards a knowledge based society

The remarkable strides in social welfare and economic growth during the three decades starting in 1950s seemed to come to a halt during the 80s. While GDP annual growth rates averaged 6% during the 70s, it fell to 0.75% in 1980 and to a lowest -7% in

1982.178 Social expenditure decreased from 19% of GDP during the late 1970s to 13.5%

174 Costa Rica, Ministerio de Educación Pública de Costa Rica, “Informe para la Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura,” chapter 3.

175 Constitución de la República de Costa Rica, 1949, Title VII.

176 Garnier and Blanco, Costa Rica, un país subdesarrollado casi exitoso (forthcoming book, June 2007 manuscript): 12.

177 Ibid, 13.

178 World Bank, World Development Indicators, at: http://devdata.worldbank.org.proxyau.wrlc.org/dataonline/old-default.htm, retrieved September 2007

of the already very low GDP in 1982,179 and expenditure in education fell between 1980 and 1982 from 6.2% to 4.2% of GDP. Moreover, and as it shall be seen below, during these years half of the population was below the poverty line. The crisis was triggered by

Costa Rica’s inability to pay its external debt but Costa Rica already was having difficulty to sustain its welfare goals with an economic sector that was not productive,180 inefficient, and in fact very expensive in terms of fiscal incentives and tax breaks. Costa

Rica’s protectionist import substitution model and very deficient tax system could not provide the resources for its social policy.

During the 1980s Costa Rica made some changes. It implemented a series of stabilization programs while only partially carried out the adjustment or reform programs.

It renegotiated its foreign debt and pursued an export-oriented strategy, further strengthened by the Caribbean Basin Initiative and the Export Processing Zone regime; the latter was designed to attract foreign direct investment (FDI). As a result Costa Rica’s economy turned around, and by 1984 GDP annual growth was 6.8%181 and the percentage of people living in poverty decreased to 20%. On the other hand, Costa Rica addressed its fiscal problems not by increasing its public income through reforming its tax system or privatizing the public utilities. Rather, Costa Rica decreased its public expenditure through voluntary layoffs and the reduction of public investment and infrastructure and the reform of the public service retirement system.182 The former two

179 Garnier and Blanco, 20.

180 Ibid, 18 and 30-32.

181 World Development Indicators.

182 Garnier and Blanco, 51-62.

measures, particularly, seriously impaired Costa Rican public institutions mainly in the

Central government and eroded their capacity to provide good quality services.183

Despite the institutional and financial constraints faced by the education sector,

Costa Rica between 1986 and 1990 undertook important education initiatives that demonstrated the country’s commitment to social equality and to improving the quality of education, as well as its awareness of the important role of education in supporting the country’s efforts towards developing an economic sector anchored in scientific and technological development. Costa Rica’s economic strategy since 1982 had been the promotion of exports and the attraction of foreign direct investment. The National

Development Plan for 1986-1990 framed this strategy, stating clearly that the country‘s policy was to achieve competitiveness based on high factor productivity and not on low wages as a result of low skilled labor.184 Hence, some of the measures and programs taken during this period were the re-initiation of the national baccalaureate or bachillerato exams at the end of high school to ensure the achievement of the expected basic knowledge at the end of secondary school,185 the creation of the public scientific high schools specializing in teaching students interested in and able to study scientific

183 Ibid, 55. This also has been recognized by the World Bank. See World Bank, World Development Report 1997 (Washington, D.C.: World Bank, 1997).

184 Garnier, “Costa Rica within the New Economy,” 13.

185 These are 5 comprehensive exams in Math, Spanish (writing, literature and grammar), Social Studies, a science (Chemistry, Biology or Physics), and a language (French or English). Although these exams were one of the requirements for high school graduation (the other being the passing of 11th grade, the last year of high school), they had been eliminated by a previous administration. 50

majors/careers,186 and the establishment of the informatics for education program which was one of the most forward looking undertakings in the Costa Rican education system.

In the early 1990s, Costa Rica started pursuing an aggressive policy of attracting foreign companies with a high technological component in their production.187 Although the share of non-traditional exports of total exports has increased from 35.2% to 58.5% between 1978 and 1990,188 60.6% of those non-traditional exports were either apparel maquiladoras (23.4%) or non-traditional agricultural and sea products (12.1%), both associated with the use of low level technology, unskilled labor, and therefore low salaries and low productivity. Costa Rica became aware that its competitiveness based on un-skilled labor was neither sustainable nor strategic. The stock of highly trained human capital after years of investment in education favored the attraction of skilled intensive high-tech foreign direct investment (FDI).189 Costa Rica focused its efforts in the electrical, electronic and telecommunications sector taking into account, among other factors, its considerable supply of professionals and technicians in the engineering field and the extensive knowledge of the English language. Public education was to become an essential component of this strategy.

186 República de Costa Rica, Ministerio de Educación Pública, “Informe para la Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura” (San José, Costa Rica: 1997), chapter 2, section 2.3.3.7.

187 Andrés Rodríguez-Clare, “Costa Rica’s Development Strategy Based on Human Capital and Technology: How It Got There, the Impact of Intel, and Lessons for other Countries” Written for the Human Development Report of 2001, United Nations Development Program (UNDP), February 2001: 7.

188 Garnier and Blanco, graph 71, page 71.

189 Rodríguez-Clare, 9. 51

In 1994, the country approved: “La Política Educativa hacia el Siglo XXI”

(“Education Policy Towards the 21st Century”).190 The Policy for the 21st century established the central role of public education in Costa Rica’s attempts to evolve into an economy anchored in technology, knowledge and human capital while pursuing social and economic equality and the respect for Costa Rican fundamental values. It was intended to frame future decisions and actions in education in Costa Rica’s education policy. This visionary policy that has been guiding all the initiatives in education since its inception, did a good job of identifying the educational bottlenecks to Costa Rica development and Costa Rica’s development strategy and also an excellent job in formulating innovative programs to address these gaps. However, these programs did not entail any extensive reforms to address the main concerns of deficient quality, inequality and low participation. For instance, some of the programs dealing with quality and efficiency in secondary schools were merely pilot projects; they did not require any changes in the contents of the subjects taught, in order to make them more updated towards the needs of the students, or any changes in the methods of teaching or evaluating students, in the procedures and criteria for recruiting and evaluating teachers, or in the materials or books used in class. None of the programs addressed the lack of investment in education that, as it was mentioned, started in 1980. The inability of this policy to solve these problems has been a characteristic of all education plans, including the ones existing before the development of this policy. There are three main reasons for this outcome. The first one is the short period that each Costa Rican administration stays in power, only four years, which only encourages the introduction of changes that

190 Soledad Chavarría and Francisco Tovar, La Política Educativa hacia el siglo XXI: sus bases conceptuales (San José, Costa Rica: Ministerio de Educación Pública, 1998): 1. 52

respond to the priorities of the political party in power. The second one is the teachers’ unions’ opposition to deep reforms. The third is the limited availability of financial resources for universal reforms experienced after 1980. As a matter of fact, this reason has also diminished the different administrations’ capacity to negotiate with the unions and perhaps the ability to recruit more qualified teachers. This is so because, as part of the adjustment program in 1995, the teachers’ retirement system was significantly modified to diminish public expenditure.

Efforts to attract high technology FDI were successful and had a tremendous impact on Costa Rica’s economy. By 1997, 75% of the 30 microelectronics manufactures operating in the country had initiated operations in 1994. By the same year, the manufactures of microelectronics, energy and telecommunication components contributed with one third of the employment and paid an average salary 74% higher than the rest of producers within the Export Processing Zone.191 Along with this electronic and telecommunications cluster further strengthened by Intel, Costa Rica has been developing a medical equipment cluster that comprised 17 companies by 2004.192 The establishment of Intel in Costa Rica in 1998 represented a culmination of the nation’s policy to attract FDI. Being Intel the main producer of computer processors in the world, the computer chip company’s decision constituted a vote of confidence for the country, duly registered in international markets, regarding Costa Rica’s advantages as a high tech manufacturing spot. In addition, Costa Rica is also exporting services that take advantage of the availability of Costa Ricans’ knowledge of English.

191 Garnier and Blanco, 72.

192 Ibid, 77. 53

Indeed, by 2004, the composition of exports and its relationship with low level technology, unskilled labor, and therefore low salaries and low productivity were no longer concerns. Of total exports, 86.4% were non-traditional and only 35% of those were associated with either agricultural and sea products (11.5%) or apparel maquiladoras (18.9%).193 The concerns had to do with Costa Rican capacity to provide the increasing supply of the skilled labor necessary to attract more FDI and to satisfy the needs of the high-tech and service companies already in operation, either national or local.194 The recent low supply of skilled labor was not a consequence of a flight of Costa

Rican highly skilled labor (brain drain) to more developed countries195 but rather the use of all the stock of human capital that Costa Rica had accumulated over years that has not been replenished with sufficient skilled labor. This is evidenced by the late difficulties faced by the high-tech and English-based service companies in recruiting high skilled personnel.196 In fact, it is perceived by government officials and workers that companies are competing for the skilled labor and in some cases lowering their requirements. Also, it has become hard for the education system to hire qualified English and computer science teachers.197

193 Ibid, graph 21, page 70.

194 Ibid, page 76

195 There is no research regarding brain drain in Costa Rica, which demonstrates that this has not become an issue.

196 Information obtained in interviews with the Minister of Education, Dr. Leonardo Garnier and other officials the Ministry of Education. For a full list of the persons interviewed, see the reference section.

197 Information obtained in interviews with Ministry of Public Education (MEP) and Fundación Omar Dengo official that deal with this reality in practice. For a full list of the persons interviewed, see the reference section.

2.2.3 The present administration’s priorities in education

The new administration starting in 2006 shared the same economic strategy applied by Costa Rica during the last 20 years of seeking to experience an accelerated growth and becoming able to compete globally through highly productive and well paid workers, high tech and knowledge based investment, and sustainable management of natural resources and the environment.198 Education plays a pivotal role in this strategy and as such has been recognized by the Arias administration.199

Despite the fact that the incoming Arias administration was prompt to recognize the limitations it was facing at the outset to make the profound reforms needed to change the education system and its indicators drastically,200 its electoral platform had already declared the education system in crisis and identified four main problems201: low coverage; low quality regarding teachers, inadequate and outdated educational program contents and curriculum; deficient and deteriorated infrastructure; outdated legislation and deficient organization and administrative methods.

To address these problems, this new government has defined several strategic areas.202 Among them: 1) providing the education system with adequate financing – 6% of GDP – provided that new fiscal income. 2) Guaranteeing the right to education with equity. This entails providing existing redistribution programs with proper budget and

198 Partido Liberación Nacional, “Hacia la Costa Rica desarrollada del Bicentenario: Programa de gobierno 2006-2010” (San José, Costa Rica: November 2005): 64-65.

199 Ibid, 12.

200 Ibid, 23.

201 Ibid.

202 Ministry of Public Education website: www.mep.co.cr, accessed on October 18, 2007 and interview with the Minister of Education, August 2007 55

addressing its targeting problems as well as diminishing the high desertion rate that prevails in students coming from economically stricken families. 3) Development of entrepreneurial and productive capacity of youths through the teaching of labor-oriented skills such as languages and information and communication technologies. This strategy is being carried out by strengthening the informatics for education program and extending its coverage to high schools; by supporting the foreign language program and vocational high schools. 4) Improvement of the quality of teachers and administrative personnel in the schools. This is currently being done through training and increase of the professional requirements. 5) The use of evaluation instruments to provide feedback regarding the quality of teaching and learning. 6) Improvement of equipment and infrastructure; this effort is being gradually implemented.

The above analysis demonstrates that Costa Rica is committed to the provision of equal access to education. Moreover, since 1986, it has been implementing innovative policies and programs intended to develop the human capital stock necessary to satisfy the demands of a more technological, competitive and knowledge-based society, capable of becoming integrated to the global economy. However, policy and programs need to be supported with resources; the amount of resources as well as the degree to which they are efficiently and effectively used will determine the quantity and quality of the supply of education.

2. 3 Organization of the Costa Rican education system

The Costa Rica education system is legally grounded in the Costa Rican

Constitution and the Fundamental Law on Education.203 The Costa Rican education system is directed by the Ministry of Education (MEP) and its administration has been organized using 20 educational sectors.204 MEP actually divided the country in 20 geographical sectors that responded to their organizational needs. Hence, these sectors do not correspond to the political division of Costa Rica in 7 provinces. They do no match, either, the country’s regional partition in 6 socio-economic regions.205 The map of the

MEP’s regional divisions and the country’s regional planning divisions as well as a map of Costa Rica are shown in the Appendix to this chapter, figures A.2.1, A.2.2 and A.2. 3.

Costa Rica’s education program is divided into four consecutive components,206 as it can be seen in chart 2.1: Preschool education, General Basic Education, Diversified

Education and Higher Education. It also includes two additional components: Education for Adults and Special Education (not shown). The General Basic Education component is compulsory and is divided into three stages or “cycles”. The first two, one from first to third grade and the second one from fourth to sixth grade, constitute what is called primary education. The third cycle is also compulsory and goes from seventh grade to

203 República de Costa Rica, Ministerio de Educación Pública, División de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas, “Informe nacional sobre el desarrollo de la educación en Costa Rica” (San José, Costa Rica: 2004): 4.

204 Ibid, 5.

205 Costa Rican regional division responds more to the socioeconomic and geographic similarities within the regions and was developed to facilitate development planning and administration. Ronulfo Alvarado S., “Regiones y cantones de Costa Rica (San José, Costa Rica, Instituto de Fomento y Asesoría Municipal [IFAM]): 3-8, at: http://www.ifam.go.cr/PaginaIFAM/docs/regiones-cantones.pdf

206 This paragraph is based on Ministerio de Educación Pública de Costa Rica, “Informe para la Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura,” 1997, chapter 4. 57

ninth grade and in UNESCO’s207 terms is also called lower secondary. This cycle offers mostly the academic option, but there are some schools that offer the possibility to take a pre-vocational option at this lower secondary level. Once the student concludes this level, she moves to the fourth cycle or diversified education (upper secondary in UNESCO’s terms) where she has three options: the academic path; the artistic path and the technical or vocational path. The first two paths are two years in duration; the third three years since it also includes the academic curriculum, although limited to selected core academic subjects. The vocational option has three modalities: industrial, which supports engineering areas such as auto mechanics, electronics, construction, refrigeration and air conditioning; agricultural, and commercial that provides certificates in subjects such as accounting, secretarial services, informatics, tourism, technical drawing.

The third and fourth cycles constitute what is known as secondary education.

Higher education, also called tertiary in UNESCO’s nomenclature, includes colleges and universities, both private and public. Both the general basic education and diversified education are offered during the day and as night school.

Costa Rican students are expected to study for 11 or 12 years, (not including preschool). Pre-school starts at the age of 4 years and 6 months. General basic education serves children between 6.5 years old and 12.5 years old. Diversified education serves children from 12.5 to 17.5 or 18.5 years of age, depending on the option the student chooses in this cycle. Schooling is compulsory, as I mentioned above, from preschool to

9th grade, that is, until students are around 15 years old. Along these lines, the Code for

207 The United Nations Educational, Scientific and Cultural Organization (UNESCO) was founded on 16 November 1945 and has 192 Member States and 6 Associate Members. It promotes education, science and culture. Its fields of action are Education, Natural Sciences, Social and Human Sciences, Culture, Communication and Information http://portal.unesco.org/en/ev.php-URL_ID=3328&URL_DO=DO_TOPIC&URL_SECTION=201.html 58

Childhood and Adolescence (Código de la niñez y la adolescencia) authorizes restricted child labor at the minimum age of 15. Among other restrictions, children cannot hold jobs that interfere with their regular attendance to school.208

In 2005, there were 4,946 educational institutions (public, private, private with public subsidy) distributed in the following way: 3.4% pre-schools; 81% primary schools

(50% of them are unidocentes and hold only 6.7% of the enrollment209), 12.6% secondary academic schools; 1.7% technical schools and the remainder special education schools.210

In higher education, the supply consisted of 4 public universities and 6 public colleges as well as 52 private post-secondary institutions. The number of private universities is not available, but it is considerable for a small country: around 20. Indeed, the quantity of higher education institutions is not the problem; the concern is the quality of the private institutions and their cost.

208 República de Costa Rica, Código de la Niñez y la Adolescencia, Ley N° 7739, 6 January 1998, available at: http://www.unhcr.org/refworld/docid/46d6b7c12.html [last accessed 16 September 2009], Article 78.

209 Unidocente schools (one teacher schools, similar to the one-room schoolhouse in the United States) are those in which only one teacher acts as both the instructor and the principal for all levels. The escuelas unidocentes are found in rural areas and constitute a modality for schools of less than 30 students; when student enrollment is higher, an assistant teacher is added.

210 República de Costa Rica, Ministerio de Educación Pública, División de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas, Evolución del Sistema Educativo Costarricense (San José, Costa Rica: MEP, 2005): 22. 59

Public university 71,344 Public college CYCLES OF COSTA RICAN Private university EDUCATION 4th Cycle 85,036

10 11 12

Vocational 22,174

Academic/ Artistic 62,862 3rd Cycle 206,674

7 8 9

1st Cycle 2nd Cycle 271,976 249,445

1 2 3 4 5 6

General Basic Education Diversified education

Preschool Primary Secondary Tertiary 112,632 521,421 292,710

Ages 4.5 6 7 8 9 10 11 12 13 14 15 16 17 18 18+

Chart 2.1: Cycles of Costa Rican Education

Source: Constructed using information from: Costa Rica, Ministerio de Educación Pública, “Informe para la Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura” (1997), Ch. 4, p. 4 (http://www.oei.es/quipu/costarica/cost04.pdf).

Note: The numbers correspond to the total enrollment in public, private, private subsidized schools in 2005. The 14,033 enrolled special education students are not included in the figures. Data comes from the Ministry of Education’s (MEP) database.

Educational services in Costa Rica, from kindergarten through twelfth grade (K-

12), are mostly public; only public institutions offer night schools. Of the total number of institutions in 2005, 90.1% were public, 8.96% private and 0.9% private with government subsidy. Of these institutions, 32.9% were located in urban areas and 67.1% in rural areas.211

The chart shows that, in 2005, the initial enrollment at public, private and government subsidized private schools amounted to 940,796 students (including special education students). Public schools (K-12) catch most of the enrollment, 90%, while private subsidized and private are 2% and 8% respectively. Of those students enrolled in

2005, 11.9% were enrolled in pre-school, 54.6 % registered in primary school and 22.1% were enrolled in the third cycle or lower secondary and 9% in the 4th cycle of diversified education or lower secondary. This distribution is representative of a trend over several years in which the number of students in secondary school is about half of those in primary school. Also, of the students enrolled in the third cycle, 17% are on the technical track (36,418), and of the ones enrolled in the diversified or fourth cycle, only 26% are enrolled in the technical option (22,174). This means that of all the students enrolled in secondary school (292,710) about 80% are taking the academic path (this figure includes

0 .3% enrolled students who are in the artistic track). Of the 20% (58,592/292,710) of students following the vocational track, about 75% are enrolled in the more traditional commercial (55.3%) and agricultural (19.5%), modalities and (25.2%) have chosen the industrial modality. This choice does not support the government efforts towards an economy anchored in technological knowledge.

211 Ibid. 61

2.4 Financial commitment to education

2.4.1 Financial allocations to education and their distribution

Education in Costa Rica has been a sector of macroeconomic priority. Graph 2.1 shows the expenditure in education as a percentage of GDP in current and real terms, telling us that during the three decades starting in 1950, when the country experienced an economic expansion, investment in education as a percentage of GDP in current terms grew more than three times, from 1% to 4.5% (current). On the other hand, the next 10 years were of contraction; from 1980 to 1990, the share of education within GDP returned close to the levels of 1970, that is, a 20 years’ loss: expenditure in education as percentage of GDP in 1990 was 3.25% and 2.93% (current) in 1970. However, by 1986 the country was already on the path of recovery with stable growth rates and education, as it was mentioned, was a key sector for Costa Rica’s development strategy; it can be observed that the sector recovered its importance within the economy by 2000, reaching, in 2004, 4.92% (current) as its share of GDP.

Regarding the importance of education within fiscal expenditure, as it can be seen in graph 2.2, for the Costa Rica government, this sector constituted a priority for three decades. In 1950, 15.34% of government expenditure was allotted to education; it progressively increased to reach figures close to 30% for the years 1975 through 1979.

Between 1980 and 1990, the effects of the economic crisis and the resulting stabilization measures mentioned above had a negative impact on Costa Rica’s public expenditure in general and especially in social sectors such as education. Public expenditure in education as a share of GDP, during that 11 years period, fell abruptly and by 1990 had returned to the levels of 1960.

Costa Rica: Public education expenditure as percentage of GDP

4.5

3.5

Percent

2.5

1.5

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2004

current real

Graph 2.1: Costa Rica: Public education expenditure as percentage of GDP Source: prepared by the author with data from Ulate et al., table A1, p. 61 for the period 1950-2004 and World Bank database for the year 2004.

Note: public expenditure in education refers to the expenditure made in education by the central government.

During the following decade, this indicator continued to fall and, by 2000, the country, in terms of expenditure in education as a percentage of total expenditure, was back to the levels prior to 1950 (less than 15%), i.e., contrary to the trend observed previously that showed the importance of education within GDP. Indeed, the economic reforms and aggressive policy of attracting foreign investment with a high technological component were producing more economic growth which allowed Costa Rica to allocate more of its real production to education. On the other hand, the fact that Costa Rica had been financing its fiscal deficit with debt since the 1980s implies that a large proportion of the

Costa Rica: Public education expenditure as percentage of government expenditure 30 29 28 27 26 25 24 23 22

Percent 21 20 19 18 17 16 15 14

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2004

Graph 2.2: Costa Rica: Public education expenditure as percentage of government expenditure Source: prepared by the author with data from Ulate et al., table A1, page 61, for the period 1950-2004 and World Bank database for the year 2004.

government budget has gone to service debt and less of the budget is allocated to education and indeed to other sectors within the public sector budget as well. Although by 2004, the share of public expenditure in education was close to 20%, showing some recovery, it seems unlikely that the country will return to figures higher than 25% in the medium term. There are now additional pressing priorities based on the country’s development path within the world economy (such as physical infrastructure and improved telecommunications) during the present period than there were in the 60s and

70s and the fiscal deficit is very high, increasing the debt service burden every year.

What is more likely is that more of the economic wealth will be dedicated to education as it is a constitutional mandate to attain the figure of 6% of GDP, which is more than 1% higher than the 4.92% achieved in 2004.

From another perspective however, these figures are remarkable compared to high income countries, although as it was pointed out before, they fall short of Costa Rica’s constitutional goal of devoting 6% of its GDP to education. For instance, public expenditure in education as a percentage of GDP and as a percentage of public expenditure represented respectively, 4.62% (2003) and 14.72% (2002) in the Republic of Korea and 5.86% and 17.14% in the US.212 Costa Rica’s expenditure in education is not only higher or comparable to higher income countries and to other upper middle income countries in the recent years but also this expenditure 30 decades ago was higher than that achieved by these countries in the recent years. On the other hand, how the educational budget is distributed is important because it might affect the quality of education unevenly at the pre-university levels and in turn the demand for education.

The distribution of education expenditure by level is shown in graph 2.3. It can be observed that the distribution has remained basically the same since 1999; however a closer look shows that while in 1999 expenditure in primary education was 2.7 times the expenditure in higher education, in 2004 it went down to 2.42 times. For the same year, expenditure in secondary education also went down from being 1.88 times the expenditure in higher education to 1.42 times. Overall it can be seen that the expenditure share in secondary education has been consistently reduced over this five-year period.

Indeed, primary education and secondary education get the greatest share because their population is larger

212 UNESCO data base. 65

Graph 2.3: Costa Rica: Distribution of public expenditure by education level (%) Source: prepared by the author using UNESCO data base

The real average expenditure per pupil at primary and secondary level, over the

35 year period from 1970 to 2005, observed in graph 2.4, shows the unfortunate effects of a fiscal policy that has not met the needs of the education sector; this argument is more compelling if it is considered that the figures regarding expenditure per pupil refer not only to public education but also to private education, which means that they are a little larger than they would be, had only public education been considered. It can be seen that the expenditure per pupil in primary education had a steady increase for the 10 year period from 1970 to 1980; however, the decrease during the next 15 years reached levels prior to those of 1970. In fact, it has taken 25 years to achieve a per pupil expenditure at levels just barely higher than those of 1980. Secondary education, as I already mentioned, was not the priority of the government; the behavior of real average expenditure per student during this period is disappointing. Although Costa Rica’s economic crisis and fiscal constraints started in 1980, per student expenditure in

Graph 2.4: Costa Rica: Expenditure per pupil by educational levels and options (Colones: 2000) Source: prepared by the author using data by J. Diego Trejos.

Note: The expenditure per pupil is obtained by dividing, the total expenditure in education, at each educational level or option, executed by schools that are public or private or subsidized private, by the enrollment at each educational level or option. The total expenditure includes central government and local governments’ expenditure and the autonomous institutions’ expenditure.

education had started to diminish since 1975. Indeed, per pupil expenditure in secondary education, the academic option, started falling as early as 1975 and has not been able to return to the 1970 levels. Real average expenditure per student in the secondary academic option in 1975 was 1.42 times higher than in 2005. Similarly, real average per pupil expenditure for the technical option started to drop since 1975 and by 2005 it was half as much as the 1975 levels. This clearly illustrates the investment depletion that secondary education has suffered. Technical education requires a high investment in technological equipment that should be kept up to date. It can be seen that expenditure per student in the technical option is much higher than the academic option in any given year. In fact,

between 1970 and 1975 it was, on average, 2.5 times higher and after 1975 this figure was, on average, 1.75 times higher. On the other hand, if investment has decreased so much for the technical option, one can easily surmise that investment in science laboratories, in the academic track, must also be low.

This analysis informs us that secondary education might not have been, at least in pecuniary terms, a priority for Costa Rica despite the fact, as I mentioned above, that the

21st Century Policy issued in 1994 and framing the education initiatives since then, establishes as a priority to improve the quality of secondary education and for that a higher budget is needed. It is important to point out that expenditure per pupil in higher education, although much higher, i.e., about 3 times that of secondary education also decreased substantially: about 30% between 1980 and 2001. Even though expenditure in higher education is important in order to build a knowledge-based economy, so is expenditure in secondary education. On the other hand, higher education is a privilege to which few have access: particularly the more accomplished students, who happen to be those well off to begin with. This might display a flaw in the redistribution policy of

Costa Rica. It has been estimated that the resource distribution of the education sector gives preference to the highest income groups at the university level and the lowest income groups at the primary level. Specifically, it has been asserted that 70% of all the resources allotted to the primary level benefit the poorest 50% while 70% of the resources allocated to the university level benefit the richest 30%.213 Indeed, 80% of the

213 World Bank, Costa Rica: Social Spending and the Poor (Washington, D.C.: World Bank, Human Development Sector Management Unit, Central America Country Management Unit, Latin America and the Caribbean Region, Report No. 24300-CR, 2003): 87.

students that go to public universities214 come from private schools. If it is considered that the three public universities accept only 20% of the applicants,215 this bias towards the well-off is a disincentive for any student from modest families considering that private universities are very expensive. The cost in a public university such as the

University of Costa Rica, is about $356216 per semester regardless of the number of credits the student takes whereas on average in a private university the registration cost is between $180 to $300 a year plus $80 per course, hence a student that takes 4 courses a year might expend between $500 and $620.217 These estimates do not include the cost of books, materials and room and board. There is governmental institution that provides loans for higher education and technical education.218 The size of the loan can go up to approximately $5000 a year for studies in Costa Rica, and its interest rate is 8% a year over the balance. These loans are available only to a selected group of individuals those who can put forward the loan’s minimum required collateral219: two guarantors that together earn monthly an amount that represent 20% of the loan and at least one of the them earns (using 2006 for example)220 more than 2 times the average household per-

214 Claudio Mora García, “UCR: educación para ricos”, La Nación newspaper, February 12, 2008. At: http://www.nacion.com/ln_ee/2008/febrero/12/opinion1420938.html.

215 Jairo Villegas, “Universidades públicas rechazan 80% de las solicitudes,” La Nación, January 14, 2006. At: http://www.nacion.com/ln_ee/2006/enero/14/pais0.html.

216 Exchange rate at 497.39 colones per US dollar. Banco Central de Costa Rica, http://indicadoreseconomicos.bccr.fi.cr/indicadoreseconomicos/Cuadros/frmVerCatCuadro.aspx?idioma=1 &CodCuadro=%20473 Retrieved January 2006.

217 Villegas, “Universidades públicas.” 218 This institution is called Comisión Nacional de Préstamos (CONAPE) or National Loan Commission, created in 1977 by Law No. 6041. See: http://www.conape.go.cr/.

219 CONAPE website: http://www.conape.go.cr/.

220 República de Costa Rica, Instituto Nacional de Estadística y Censos (National Institute for Statistics and Census), website: http://www.inec.go.cr/.

capita income ($190) or more than 4 times the average household per-capita income of an urban family belonging to the second quintile ($85). It is understandable them that eighty percent of the loans go to individuals studying in private universities.221

The budgetary distribution of financial resources also undermines Costa Rica education policy goals since practically none of the resources go to investment in capital goods. Most of the financial allocations are used in ordinary expenses222 because personnel salaries and ordinary transfers eat up most of the education budget. In 1990

(see table A.2.1. in the Appendix), 96.6% of education expenditure was ordinary; by

2000, the figure remained the same, in 2005 it had increased 2 percentage points. Most of the ordinary expenses (98.8%), in 2005, as it has been in previous years, were destined to personnel salaries (60%) and to ordinary transfers (38%).223 The ordinary transfers include the transfer to schools (K-12) for basic services and social programs and the transfers to higher education that constitute the government disbursements, through the

Ministry of Education budget, to finance public universities and post secondary institutions. Leaving out transfers to higher education, MEP personnel salaries represent

75% of the MEP budget and increase every year at 15.6% rate.224 Most of MEP personnel

221 Lorelle Espinosa and José L. Santos, “The impact of post- secondary privatization in Costa Rica”. Paper submitted to the Association for the Study of Higher Education, University of California, Los Angeles, November 2, 2006 (http://www.globalhighered.com/articles/espinosasantos_privatization.pdf).

222 Ordinary expenses include personnel salaries, purchase of goods and services, ordinary transfers, and debt service. Ordinary transfers consist of transfers to higher education and to the financing of social programs.

223 República de Costa Rica, Ministerio de Educación Pública, División de Planeamiento y Desarrollo Educativo. “Costa Rica, financiamiento del sistema educativo” (San José, Costa Rica: MEP, n.d.): 13.

224 Ibid, 15.

consists of teachers (from K-12) 83%, about 53,000 teachers225; however, they earn the lowest salaries of all professional groups (as opposed to technical groups); their monthly average income (2005) represents 51% of that earned by the other professionals.226

Given the above, the allocations in this two budget items, i.e., salaries and ordinary transfers are practically fixed; they cannot be reduced unless personnel is laid off: budget distribution becomes an inflexible and predictable exercise that can do very little to solve the problems of infrastructure and equipment affecting the school and high schools. Hence, the only way to increase capital expenditures is through an increase in the education budget while making sure this increase does not go to ordinary expenses.

2.4.2 School inputs: teachers and infrastructure

How the above mentioned budgetary limitations have affected quality in the delivery of the public education system can be explored with the aggregate data available by examining the relationship between enrollment, the number of teachers and the number of institutions during a specific time period. The data used is presented in table

2.1. The data for enrollment refers to public schools while the data available for the number of schools and teachers covers the total for public, private and publicly subsidized private schools. This situation will not diminish the value of the analysis because the number of private schools in Costa Rica has been historically very low given that they are very costly. For instance, during 2004, the lowest monthly tuition for a private secondary school was equal to 45% of the average household income, while the

225 Ibid, 14.

226 República de Costa Rica, Consejo Nacional de Rectores (CONARE), Estado de la Educación Costarricense (December 2005) (http://www.conare.ac.cr/, retrieved in April 2007): 45. Also available at: http://www.oei.es/quipu/costarica/estado_educ.html. 71

highest monthly tuition was is 3.2 times the average household income.227 In the same year, 6.8% of the primary schools and 12.6% of the secondary schools were private; these

228 percentages have not increased since the year 2000.

Given that the number of private schools is small, the share of teachers working in private schools is also small. On the other hand, the case of using somewhat inflated indicators of the number of teachers and schools in connection with the school enrollment indicator only strengthens the argument, as I demonstrate in the following analysis.

As can be seen in table 2.1, during a period of 34 years, from 1971 to 2004, the number of primary schools increased 54%, from 2,574 to 3,971.229 That figure seems low considering that public school enrollment rose 67% in that period and that the number of schools refers also to private schools. Regarding secondary education, there were 108 schools in 1971; by 2004, that number had increased about 5 times to 592 schools to meet the public schools enrollment needs which had risen by four times from 74,215 to

292,669.230 Again, the construction level is not sufficient given that the number of schools is inflated by the inclusion of private schools. Moreover, in analyzing these data it has to be kept in mind that the fact that some of the public schools buildings are used also as night schools both in primary and secondary and the enrollment in these other two modalities of education also increased means that they deteriorate faster.

227 Ibid, 24.

228 República de Costa Rica, Ministerio de Educación Pública, División de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Informe Nacional sobre el Desarrollo de la Educación en Costa Rica” (San José, Costa Rica: MEP, 2004): 23. Available at: http://www.oei.es/quipu/costarica/ibecostarica.pdf.

229 Ministry of Education (MEP) data base.

230 Ibid. 72

Table 2.1 Costa Rica: Enrollment, number of schools, number of teachers and pupil- teacher ratio by level of education, 1971-2004

Primary Secondary Enrollment Schools Teachers Ratio Enrollment Schools Teachers Ratio 1971 359,351 2,574 11,487 31 74,215 108 2,608 28 1975 370,480 2,772 10,677 35 125,935 186 4,295 29 1980 364,836 2,914 10,966 33 162,993 207 8,089 20 1981 366,367 2,944 10,481 35 161,408 207 8,213 20 1982 360,663 2,944 10,824 33 156,347 208 8,259 19 1983 364,296 2,993 10,767 34 145,428 208 8,112 18 1984 369,112 3,035 10,379 36 138,731 208 7,975 17 1985 385,997 3,091 11,526 33 131,401 208 7,727 17 1986 404,419 3,107 11,785 34 133,063 207 7,745 17 1987 416,203 3,170 12,490 33 133,415 210 7,518 18 1988 431,260 3,207 12,829 34 132,928 212 7,612 17 1989 444,639 3,238 13,073 34 136,790 214 7,640 18 1990 459,231 3,269 13,651 34 143,750 224 7,884 18 1991 480,759 3,317 14,078 34 152,982 226 8,212 19 1992 498,148 3,359 14,584 34 165,419 229 8,604 19 1993 512,200 3,442 14,949 34 173,425 247 9,222 19 1994 523,930 3,472 15,806 33 183,179 258 9,830 19 1995 537,098 3,544 16,565 32 192,650 286 10,324 19 1996 547,281 3,607 17,554 31 191,805 318 11,114 17 1997 555,586 3,671 18,358 30 202,550 353 11,904 17 1998 562,897 3,711 19,235 29 207,988 386 11,895 17 1999 569,037 3,768 20,185 28 215,324 415 12,831 17 2000 571,455 3,801 21,255 27 231,563 466 13,365 17 2004 584,333 3,971 24,975 23 292,669 592 17,759 16

Source: prepared by the author using data from J. D. Trejos’ database, MEP, CONARE.

An examination of the pattern of investment in school construction along those 34 years shows that the decade of the 1980s was the worst; this is no surprise given that that was the period of the economic crisis. Indeed, by 1980, 73% (2,914/3,971) and 35%

(207/592) of the primary and secondary schools, respectively, existing in 2004, had been already built. Between 1980 and 1990, the construction of new schools increased by 9% in primary and 3% in secondary schools. In other words, the bulk of the investment in school construction in primary education was done before 1980 and in secondary education after 1990. This investment pattern clearly shows that previously to the 80s the

emphasis in construction was given to primary schools and once the country overcame the crisis and more budgetary resources were available, the target was high schools; however, the figures are low. This low level of school construction must have had negative consequences on the demand for education. Research shows that school enrollment can be increased considerably with more construction of schools.231 There is no research to my knowledge that explores the relationship between the construction of new schools and the increase in the attendance rate or decrease in the dropping out rate.

However, it seems obvious that these outcomes might be expected. This low level of construction of new schools might not be the main problem, but the deterioration of the investment in existence and the inability to expand the existing facilities as well as provide the schools with the necessary equipment and materials.

A recent survey (2004) gave an account of the needs in school infrastructure as stated by primary and secondary public school principals. Graphs 2.5 and 2.6 display these deficits, in absolute terms, for selected categories. In primary schools, with respect to what is needed, the deficit in the number of library rooms, number of science labs, number of computer labs and number of computers is 53%, 96%, 51% and 56% respectively. Secondary schools have a deficit of 20% in the number of classrooms, 35% in the number of library rooms, and 61% in the number of science laboratories, 24% in the number of computer labs and 35% in the number of computers. The shortage of basic academic facilities such as number of classrooms, desks, chairs and blackboards for both primary and secondary runs around 20% and 30%. On the other hand, the current facilities are not in good condition; for example in secondary schools only 61% of the

231 Research performed by E. Duflo in Indonesia in 2001 reported in Glewwe and Kremer, “Schools, Teachers and Education Outcomes in Developing Countries,” 977. 74

libraries are in good shape, and this situation worsens in the rural areas. Moreover,

33.6% of the shops for technical education in the technical vocational high schools are in middling or bad shape.232

Costa Rica: Infrastructure Deficit in Primary Schools (2004)

1.0 2,356 48,024 47,774 7,705 0.8 576 549 9556 0.6 139 14,466 218,458 213,839

Percent 0.4 18,167 504 515 0.2 7661 15 0.0

Desks Chairs Libraries Classrooms Computers Science labs Comput labs Blackboards Available Deficit Number of units

Graph 2.5: Costa Rica: Infrastructure deficit in primary schools (2004) Source: prepared by the author with data from CONARE, Estado de la Educación Costarricense, December 2005, p. 43

Although the reliability of this survey can be questioned given that the design of the questionnaires233 might encourage principals to exaggerate their needs, the results of this survey have been confirmed by an investigation and audit performed by the Office of the

Comptroller General (Contraloría General de la República) which is the entity in charge of controlling public finances by constitutional mandate.234 The report found235 that, due to the lack of technical and legal capacity of the entities designated by the Ministry of

Education to contract the infrastructural work, and the lack of supervision by this

232 CONARE, Estado de la Educación, 2005, p. 42. 233 Ministerio de Educación Pública, División de Planeamiento y Desarrollo Educativo, Departamento de Estadística: questionnaire regarding infrastructure, which is a section of a larger questionnaire regarding registration, institution, and student information. 234 Article 184 of the Constitution of Costa Rica.

235 República de Costa Rica, Contraloría General de la República, “Informe DFOE-SOC-49- 2007,” at: http://www.cgr.go.cr/. 75

Costa Rica: Infrastructure Deficit in Secondary Schools (2004) 1.0 1,436 14,221 13,191 1,578 148 120 2,622 0.8 164 0.6

6,693 70,124 70,113 7,797 Percent 0.4 380 275 5,288 0.2 101

0.0

Desks Chairs Libraries Classrooms Computers Science labs Comput labs Blackboards Available Deficit Number of units

Graph 2.6: Costa Rica: Infrastructure deficit in secondary schools (2004) Source: prepared by the author with data from CONARE, Estado de la Educación Costarricense, December 2005, p. 43

Ministry and the Ministry of Public Works, a considerable amount of the infrastructural work in schools and high schools is not done or is delayed to the point that the funds are insufficient or when it is done, it turns out to be deficient, incomplete, of low quality and put the children at risk and in many cases force the students to receive their classes under trees, or tarpaulins, or crowded in small classrooms or in school kitchens or gyms.

Regarding the relationship between initial enrollment in public schools and number of teachers, table 2.1 tells us that in 1971, the number of teachers in primary education was 11,487 and by 2004 this number had practically doubled to 24,795. By the same token, in secondary education, the number of teachers during this period increased

581% from 2,608 to 17,759. These increases seem huge; however, if the ratio between number of initial pupils enrolled and number of teachers in a given year is calculated, the growth in the number of teachers was sufficient to maintain this ratio with some stability for about 13 years, starting in 1982, which guaranteed some minimum level of attention

to the students; after 1996 it started to improve. In fact, in primary education this ratio oscillated between 35 and 33 pupils per teacher between 1982 and 1995; in 1996 it started to fall gradually, reaching 23 enrolled students per teacher by 2004. In secondary education, this ratio remained between 18 and 19 pupils per teacher, during the same period, after which it fell to 17, reaching 16 students per teacher in 2004.236

Overall, these findings are consistent with the analysis of the budget allocation emphasis on personnel services. This emphasis has ensured the hiring of some minimum number of teachers in times of economic crisis and even a decline of this ratio after 1995 which coincides with the country’s policy of using education to anchor its development strategy. On the other hand, despite the fact that the most recent estimate of this ratio suggests that the access of students to teachers in primary and in secondary schools is satisfactory, the interpretation of this indicator can be misleading. In primary education, it does not consider multi-grade teaching (unidocentes). Moreover, the low student ratio in secondary school in the case of Costa Rica might not indicate the ability of the system to provide an adequate number of teachers to students but rather the inability to enroll students and to keep them in school. In this respect, in 1999, on average a teacher was attending 0.8 sections, a figure which is much lower than is considered the optimum by

UNESCO, 1.1 to 1.5.237 This might indicate that a considerable proportion of teachers are

236 These ratios are calculated using a number of teachers that includes public and private schools, while the enrollment refers only to public schools. Hence, for example, in public secondary schools, the number of students per teacher would be greater than 16 in 2004.

237 World Bank, Costa Rica: Social Spending and the Poor, 8.

not teaching classes, because they either are ill or they are performing administrative tasks.238

Another measure of school quality is the percentage of trained teachers in public schools (table A.2.2 in the Appendix shows data for the period 1995-2004). In 2004, it was 92% for primary level and 89% for secondary level which shows that there is not much of a difference between the two levels. These figures seem satisfactory but, again, could be misleading. A recent newspaper investigative report239 shows that about 19.6% of the teachers do not have a degree in education, but in business administration, physical therapy, psychology, or any other career; and that most of these teachers are teaching in the vocational/ technical modality of diversified education. Hence, the number of untrained teachers in secondary is not 11%, but could reach the 30% figure. This is not a very encouraging figure since the government has recognized secondary education as a priority in its educational policy; moreover, the quality of technical/vocational education is fundamental for its development strategy. On top of this, the investigation reports that there are teachers with degrees, for instance, in English, but who are teaching social studies or teachers graduated in computer science teaching special education. According to the Vice-minister of Education, these cases are found mostly in rural areas, this is not good either because it does not go along with Costa Rican policy of diminishing inequalities. In addition, it has been pointed out in different evaluations, regarding teachers who do have a degree in education, that their level of knowledge of the subject

238 Ibid.

239 Jairo Villegas, “Uno de cada 10 maestros enseña sin tener título”, La Nación newspaper, 19 November 2007, at: http://www.nacion.com/ln_ee/2007/noviembre/19/pais1320292.html.

they are teaching is questionable.240 The causes pointed out for this are: 1) the passing rate of the secondary school students in the national baccalaureate tests is very low; 2) most of the teachers graduate from private universities that do not have proper academic credentials. Public universities have traditionally trained teachers, but they are very selective; 3) the teachers that graduate from public universities do not have the knowledge required to meet the new demands of this century and of government policies; public universities have not changed their curricula to meet the new needs. Unfortunately,

Costa Rica does not require a certification issued by the MEP or any other entity that proves that the teacher knows the subject matter she is supposed to teach, the only requirement being to hold a degree.

On the other hand, the lack of availability of qualified teachers could be explained by low salaries, as I mentioned above. It can be seen that the system is trapped in a situation in which it cannot select the best teachers within a strong pool of candidates but select only from what is available, and what is available does not allow MEP to carry out its policies. As I mentioned before, the Arias administration is gradually training the teachers already hired and also preparing certification tests for all subjects.

In addition, MEP is trapped in a dependency path. There are old ways and old structures, teachers that for long have used the old methods, an educational system that is becoming fragmented and dysfunctional. In addition, MEP has to deal with the teacher unions that have done very little to improve the quality of education.

240 Consejo Nacional de Rectores (CONARE), Hacia un modelo educativo para elevar la calidad de la educación costarricense: una propuesta de políticas, estrategias y acciones (San José, Costa Rica, Editorial UNED, May 2006): 1-20, and interviews with personnel of the Ministry of Education. 79

2.4.3 The core programs to meet the challenges

By 2006, I can highlight four types of programs, already being implemented, that are responding to Costa Rican policy in education and addressing the main challenges faced by Costa Rica. The redistributive programs support Costa Rica’s values of social mobility and equality. The retention programs intend to increase enrollment, stop desertion and help students to move forward in the education system. Some of the programs in this category are supported by the redistributive programs since they serve a population of students that are for the most part poor. The innovative programs were designed in 1994 and back Costa Rica’s pursuit of an open, knowledge- and technology- based economy: the informatics and foreign language program. The labor market- pertinent education programs offer curriculum options that attempt to meet the needs of the productive sector with the purpose of making education services more relevant by increasing job opportunities for students.

Redistribution programs

Costa Rica is a pioneer in the formulation of social programs that pursue equal opportunity access and school attendance. These programs responded to the democratic value ingrained in the Constitution of guaranteeing the role of education as an instrument of equality and social mobility. Three programs have been implemented for several decades. The school nutrition program was originally an initiative financed by a tax imposed in 1950 on each fanega of coffee241 produced.242 As a program it was later re-

241 Fanega is a measure of volume corresponding to 1.6 bushels (51.2 dry quarts or 56.4 liters) or approximately 562 lbs. (or 255 kg) of coffee cherries, freshly harvested. Instituto Costarricense del Café (ICAFÉ), “National Coffee Stabilization Fund,” at: http://www.icafe.go.cr/icafe/Fonecafe_eng.htm, and Coffee Kids, “What’s New,” at: http://www.coffeekids.org/about/whats_new.htm, and the volume 80

created in 1974 and was provided permanent financing by law.243 The program provides breakfast and lunch or only lunch for a symbolic cost (about 10 to 25 U.S. cents)244 to students from preschool, primary and secondary. Since 1997, this program gives priority to all students attending urban marginal schools, single-teacher and the educational units located in counties whose populations belong to nutritionally deficient rural areas.245 By

2004, this program covered 4000 primary schools and had 508,534 beneficiaries

(approximately 61% of student population).246 On the other hand the program only benefits about 35% of secondary education enrollment. The school nutrition program is the most costly of all; in 1999 it represented 5% of the education sector’s budget.247

The school scholarship program has two modalities. The first is the bonus for education, instituted in 1993, that provides an annual scholarship of about US $30 to the poorest primary school students to cover their school-related expenses at the start of the year (school uniforms and supplies).248 This amount is not enough: in 2005, the average

converter at http://www.math.com/students/converters/source/volume.htm, all accessed on 26 November 2007.

242 República de Costa Rica, Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Evaluación de Impacto de los programas sociales” (San José, Costa Rica: MEP, 2007): 8.

243 Law 5662 (Ley 5662 de Desarrollo Social y Asignaciones Familiares) created the Fondo de Asignaciones Familiares that provides permanent funding to the program. The law can be found at: http://www.poder-judicial.go.cr/salatercera/leyes/leypenal/leyasignacionesfamiliares.htm.

244 Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Informe Nacional sobre el Desarrollo de la Educación en Costa Rica” (2004): 44-45.

245 Ibid, 47-48.

246 Ibid.

247 World Bank, Costa Rica: Social Spending and the Poor, 150.

248 Ibid.

cost of uniforms and school materials to send a student to primary school was approximately $87; most of this cost is accruable to uniforms (81%).249 For families with secondary school students this initial investment in 2005 amounted to approximately

$97.250 The second grant modality, instituted in 1997,251 the high-risk scholarship, confers scholarships to students at the secondary level, in high risk of desertion, in order to keep them in school252 and partially cover the opportunity cost associated with remaining in school and of foregone labor income.253 Up to 2004, this program benefited students that had scarce economic resources and good academic performance (in the sense of passing the grades). It also provides scholarship to teenage single mothers, the students that are also workers and students from indigenous populations. The financing of this program has been strained by a Costa Rican Supreme Court of Justice ruling ordering that these scholarships be granted to foreign students as well since by 2004 there were

45,000 foreign students in Costa Rica, 80% of them Nicaraguans who are very poor.254

249 Hárold Brenes, “Entrada a curso escolar cuesta 43,000 colones”, La Nación, January 7, 2008 http://www.nacion.com/ln_ee/2006/enero/07/pais2.html.

250 Exchange rate 459.64 colones per dollar in January 2005. Banco Central de Costa Rica, http://indicadoreseconomicos.bccr.fi.cr/indicadoreseconomicos/Cuadros/frmVerCatCuadro.aspx?idioma=1 &CodCuadro=%20473.

251 During the previous years (since 1965), this modality was only intended to help financially poor students from secondary schools.

252 World Bank, Costa Rica: Social Spending and the Poor, 150.

253 Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Evaluación de Impacto de los programas sociales” 2007, 12.

254 Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Informe Nacional sobre el Desarrollo de la Educación en Costa Rica”, 2004, 44-45.

Student transportation is the third program, and its objective is to encourage high school attendance from poor rural students by providing free transportation to school.255

The program has three modalities: the first one, giving a subsidy to the students to pay for public transport; provision of transportation to special needs students in the urban areas of the main provinces of the country, and contracting the routes with a private provider.

The latter started 1994 and is financed by MEP. In 1998, the program had 567 routes; by

2003 the number of routes was 702,256 that is, an increase of approximately 24%.

Graph 2.7 plots the annual budget allocations in millions of real Costa Rican colones (2000=100) and the number of annual beneficiaries in thousands. The analysis of the behavior of the three programs in broad terms indicates that: 1) for the lunch programs the budget allotments during the period do not match the number of beneficiaries. It seems that budget allocations act independently of the size of the target group. While the number of beneficiaries of this program has remained practically the same since 1997, the budget allocation in real terms has had a tendency to grow during the period examined, except for the drop during 2001 and 2003, but returning to 2002 levels in 2004. The scholarship program had an increase in the number of beneficiaries of 125 % in 2001 (not visible in the graph, see Appendix, table A.2.3) and thereafter the size of the target group has not changed much. The budget allocation in 2001 to meet this greater demand augmented by 188%; the possible explanation is that size of the grants and bonuses was increased at the same time. The transport program, on the other hand,

255 World Bank, Costa Rica: Social Spending and the Poor, 150.

256 Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Planes y Programas. “Evaluación de Impacto de los programas sociales” (2007): 16. 83

had its largest increase, 19%, in the number of beneficiaries in 2001; to match this

Graph 2.7: Costa Rica: School equity related programs Source: Number of beneficiaries and budget allocations on current prices (CONARE: Estado de la Educación 2006, p. 57). Price index for the period obtained from the IMF data base to calculate the budget in real terms.

increase the budget allocation augmented 39%. 2) The lunch program has been the largest of the programs in terms of beneficiaries and budget allocations. The second largest is the school transport program in terms of budget allocations. The scholarship program is the smallest in both budget and number of beneficiaries. 3) The number of beneficiaries over the period of both the scholarship program and the transport program are very similar.

A closer analysis of these patterns raises some concerns about program targeting.

Considering that enrollment is decreasing in general and that most of the reduction comes from low income families one has to conclude that either the records of the beneficiaries

have not been updated for a while or the programs are benefiting more students than reported. If the beneficiaries have remained constant or have decreased (which is possible) then the money is being used for feeding entire communities, in the case of the lunch program, or for other purposes. In fact, a study performed by the MEP in 2003 found that the school lunch program targets only 34% of the poorest 20%.257 In the case of the transportation program, it seems that in 2001, Costa Rica became more rural or the schools in rural areas moved farther away from the students.

Moreover, the reduction in enrollment and the high dropout rates in rural areas lead us to expect a reduction of beneficiaries for this program, unless the records are also unreliable.

Although the new administration is considering the elimination of 211 routes contracted privately of the 727 (existing in 2006) because there is public transportation available to the areas once considered remote,258 it plans to replace the routes with a scholarship/subsidy to the student to pay the public transport. Regarding the scholarship program, the MEP study mentioned above also showed that the education bonus reaches only 40% of the poorest families and the high risk scholarship is wrongly targeted practically in its entirety.259 In fact, one of Costa Rica’s most important national newspapers recently reported that the institution in charge of granting the scholarships hired 300 university students to find 65,000 potential beneficiaries with a list of addresses provided by this institution. The fact that university students were not very successful in

257 World Bank, Costa Rica: Social Spending and the Poor, 150.

258 Mercedes Agüero R., “MEP estudia eliminar 211 rutas de transporte gratuito para estudiantes,” La Nación newspaper, 24 August, 2007.

259 World Bank, Costa Rica: Social Spending and the Poor, 150.

this endeavor, given that the addresses were wrong or the potential grantees have moved or were “phantom grantees,” pictures the deficient targeting in these programs.260

The analysis in this section demonstrates that the government of Costa Rica has as its goal to include poor and vulnerable children and youth in the education system; this is shown by the resources dedicated and by the nature and scope of the programs. However, as of 2004, available data demonstrates that the programs are not reaching the target population.

Retention/inclusion programs

The program Avancemos (Let’s Press Forward) is the one with the higher profile and it is basically the high risk scholarship program with some differences. The size of the scholarship is bigger, primary education students are also included; the objectives are broader: to the previous goal of maintaining poor students within the education system, and decrease the opportunity cost associated with it, two more goals were added, that of stimulating a culture of savings and investment in the students and to provide additional income to families classified as poor.261 The target of the program is 130,000 students that should be attending school and who belong to 80,000 households in poverty.

For primary school students, the scholarship is about $13.5 monthly. The grant for secondary school students is provided in an escalated fashion starting in the 7th grade with approximately $28.9 monthly and with increases of $9.7 at each further grade level

260 Esteban Oviedo, “Difícil búsqueda de 65.000 jóvenes para darles becas,” La Nación, 11 January 2007.

261 Ministerio de la Vivienda y Asentamientos Humanos. Rectoría Sector Social y Lucha contra la Pobreza. Programa Interinstitucional Avancemos: “Construyamos el futuro compartiendo responsabilidades,” Folleto Informativo No. 1 (San José: MIVAH, May 2007).

until reaching $96.5 monthly at 12th grade. There is a limit of $154.4 per family.262 The size of the scholarships allows us to gauge the priority given to this program. To give an idea of this, the average household per-capita monthly income for the country in 2006 was approximately $190 and for the first and second bottom quintiles was $41 and $85, respectively.263 Moreover, the cost of the basic food basket to satisfy the food needs of average individual in the same year (October) was $43.7 for urban areas and $37.9 for rural areas.264 In other words, a rural student in 7th grade can bring home 70% of the average household per-capita monthly income of a household in the lower quintile or

78% of the cost of the basic basket in rural areas. The importance of this benefit remains even considering that in 2006 the initial cost of uniforms and school materials amounted to $86.7 and $98.5 for a primary and secondary school student, respectively.265

Furthermore, during the school year families have to incur in additional costs of materials and contributions to support the school needs. This means that sending at least two children to primary school or alternatively to secondary school, for households in 2006, represented 91% and 104 %, respectively, of the average household per-capita monthly income. Moreover, the initial cost of two children in secondary school amounted to approximately 4.5 times the cost of a food basket for an individual in the urban area. A

262 Exchange rate 518 colones per dollar at October 2006. Banco Central de Costa Rica. At: http://indicadoreseconomicos.bccr.fi.cr/indicadoreseconomicos/Cuadros/frmVerCatCuadro.aspx?idioma=1 &CodCuadro=%20473.

263 Instituto Nacional de Estadísticas y Censos (data on line) http://www.inec.go.cr/.

264 Ibid.

265 For primary level the cost was $86.7. Brenes, “Entrada a curso escolar cuesta 43,000 colones.” The exchange rate used was 497.39 colones per dollar for January 2006.

government document266 reports that after one year of implementation (July 2006 to July

2007), the program Avancemos had added 70,913 additional students to the scholarship program, that is, 54.5% of its target. Most of these students are in 7th (28%) and 8th (22%) and 9th (17.6%) and are more or less well distributed between urban (55.5%) and rural

(44.8%) areas.

It is too soon to evaluate this program. However, it seems that the program has two goals that although complementary could be competing and it all depends on which of these two goals prevails. One goal is to increase the demand for schooling and the other is to decrease the number of people under the poverty line in a very short period of time. Although the majority of the children at risk for desertion are poor, not all poor children abandon school and poverty might not be the only cause of desertion. Hence the program should be focusing on diminishing desertion, and for that it requires a screening and follow-up system that might not be in place given that the entity in charge of this program is the Ministry of Housing, that also deals with social development, in coordination with the Ministry of Education.

The recent results of the 2007 Household Survey regarding a substantial decrease in poverty might have been influenced by the generous disbursements of this program.

The number of people under the poverty line went down from 20.2% in 2006 to 16.7% in

2007, the lowest figure in 30 years.267 If in fact this is the case, this outcome would be fine as long as the desertion also decreased.

266 Ministerio de Vivienda y Asentamientos Humanos. “Programa Avancemos: Reporte de cobertura de la población incorporada al programa al 16 de julio del 2007,” July 2007.

267 Patricia Leitón, “Crecimiento económico bajó la pobreza a nivel histórico,” La Nación newspaper, 1 November 2007, at: http://www.nacion.com/ln_ee/2007/noviembre/01/economia1298816.html.

It is important to mention at this point that conditional cash transfer programs have been tried in other countries as an incentive to parents to send their children to school and participate in preventive health programs. An evaluation performed by

Schultz,268 in 2004, of the Progresa program in Mexico, whose participants were randomly selected, shows that the program had a positive impact. He found that enrollment increased by 3.4%, on average for all students in grades 1 through 8 and particularly during the transition between primary and secondary school. He also found that the program improved the educational attainment of the poor. Moreover, Alain de

Janvry, et al.269 found that this cash transfer (Progresa) in the presence of idiosyncratic or covariate shocks (unemployment or illness of the household head or natural disasters) largely prevented children from not enrolling in school but did not deter child work. On the other hand, Angrist’s evaluation of a school based randomization design that offered cash awards to low achieving students showed that there was a positive effect on the enrollment of girls.270

The other important retention program is the Program for New Opportunities

(PNO), created in 2000 with the purpose of incorporating into the education system those secondary school students excluded from it, either because the dropped out, or never participated in the formal system with other educational options besides the formal

268 Glewwe and Kremer, “Schools, Teachers and Education Outcomes in Developing Countries,” 979.

269 De Janvry, Alain et al., “Can conditional cash transfer programs serve as safety nets in keeping children at school and from working when exposed to shocks?,” Journal of Development Economics, Vol. 79, No. 2, April 2006): 372.

270 Joshua D. Angrist and Victor Lavy, “The Effect of High Stakes High School Achievement Awards: Evidence from a Group-Randomized Trial,” July 2006, http://www2.wiwi.hu- berlin.de/wpol/schumpeter/seminar/pdf/angrist2.pdf (retrieved November 5, 2009): 5, 30-31.

education system. The option offered to these students is a non-formal or nontraditional option, a fast track, and open option, similar to a distance education modality. It includes for each subject a combination of two weekly classes and complementary tutoring. The subjects offered are reduced to the basic ones and the textbooks are designed for distance education learning. The evaluation methods are the same as in regular school. The PNO started with 10,414 students and by 2006 the number of students remains practically the same. An evaluation of this program performed by the Department of Educational

Research of the MEP271 showed that between 2000 and 2002 the passing rates of these students are between 35% and 55 % and the desertion rates are between 25% and 40 %.

In fact, for some teachers this program is a trap because students are not able to finish the

11th grade. The reasons for this failure are due to the inadequacy of the teaching methods for secondary school students and mainly for dropouts, who are not able to learn in many ways on their own, the lack of training and the frequent absence of teachers, as well as the scarcity of books. It seems that this program might be precluding students from finishing high school. First, its fast track nature might encourage students to trade it for a long term school commitment and, second, its poor design and poor academic quality makes graduating even more difficult for students that already have failed in a more appropriate environment for learning.

271 Ministerio de Educación Pública, Division de Planeamiento y Desarrollo Educativo, Departamento de Investigación Educativa “Estudio sobre programa de Nuevas Oportunidades para jóvenes,” January 1997.

Technology-anchored and knowledge-based programs

The informatics program272

In 1988 Costa Rica initiated a massive introduction of computer technology in primary and later in lower secondary. The philosophy of the program is to generate creative and analytical thinking and technological fluency with emphasis on the pedagogical aspects rather than the instrumental aspects.273 The main software used is

Logo for Multimedia as well as other conventional computer software in word processing, spread sheets and encyclopedias. The program is administered by an NGO, the Omar Dengo Foundation (FDO), created for this purpose, which receives funds from the MEP but also from international and national donors.

This program was intended to give priority of place to the students of rural and marginal urban areas, even to one-teacher schools in remote areas of the country. It serves the 81 counties of the country.274 The informatics program also trains the teachers in the use of the LOGO software and has a permanent staff of tutors275 that advise the teachers that work in the computer labs of the schools benefiting from the program

At the end of 2005, the program was reaching 55.9% of the students in primary education, 74% of them from urban areas. By the same year, 72.4% of the students in the third cycle of secondary education were benefiting from the program and 75.7% were

272 This section does not include information on the use of computers in upper secondary because it was not available.

273 Garnier, “Costa Rica within the New Economy,” 46.

274 Fundación Omar Dengo – MEP, Programa Nacional de Informática Educativa, at: http://www.mep.go.cr/.

275 Interview with personnel of the Fundación Omar Dengo. Also: Garnier, “Costa Rica within the New Economy,” 46.

from urban areas.276 Although the program’s objective was to give priority to the rural areas, it did not work that way since its inception. On the other hand, 60% of the counties considered to have priority (given its concentration of economic and social problems) of the country are benefiting from the program in primary education and 58% in secondary education. The program offers Internet connection in some schools. The FDO reports that, as of 2007, 35% of the computer labs in primary schools are able to use the Internet and 50% of the computer labs in secondary schools do so as well.277

One of the criticisms made of this program has been its exclusive use of so many resources and computer labs (although information of the cost of this program is not available) only for teaching the children a software package that is not a tool that the student can use to advantage in other school subjects or eventually in the workplace.

Also, it seems to us that the low coverage of the Internet connection fosters a digital divide that could be added to the other existing gaps in the education system.

The foreign language program

English and French have been traditionally taught in secondary schools for decades. However, the students only receive 3 lessons weekly. In 1994, the foreign language program was created as part of the country’s 21st Century Policy with the purpose of aiding the integration of Costa Rica into the global economy, by giving primary school children the opportunity to access scientific and technological knowledge

276 Information provided by the Fundación Omar Dengo.

277 Fundación Omar Dengo, “Informe estadístico y de cobertura: Programa Nacional de Informática, San José, Costa Rica: MEP – FOD para preescolar, I , II y III ciclos”, June 25, 2007

in languages different from Spanish.278 With the support of the diplomatic, international and national community, MEP offered three options to the primary schools: English,

French and Italian. With this program these three languages (however, the chosen language is English in the majority of the schools) became part of the curriculum in some primary schools with 5 lessons a week and by 2006 it was offered by 44% of the primary schools benefiting 78% of enrolled students. One of the main problems of this program is the difficulty to recruit trained teachers and the availability of appropriate teaching materials.279

In my opinion the basic problem of this program is the target population. It should not have initiated English in primary school without having first targeted English in secondary schools. The quality of English teaching at the secondary level is very poor and the number of lessons insufficient. Hence, primary school English might compete for resources with secondary schools, and, since English is deficient in secondary school, whatever they learn in primary school might be lost; moreover, if English is not well taught in primary, students will lose interest in the language at an earlier age.

The number of English lessons in secondary school is insufficient and, and the teachers are not well trained; furthermore, qualified English teachers have other job options. In lower secondary, only three lessons are taught weekly. This is the same situation for upper secondary, except in the case of experimental bilingual high schools

(17 out of 547 high schools in 2007) where the lessons in English are 10 hours weekly and, in the case of vocational secondary schools, where the number of hours taught

278 Soledad Chavarría, Francisco Tovar and Sheila Quesada, La Política Educativa hacia el siglo XXI: propuestas y realizaciones (San José, Costa Rica: Ministerio de Educación Pública, 1998): 16.

279 Document prepared by Ana M. Bonilla, Directora de Lenguas Modernas (Director of Modern Languages) of the Ministry of Education. 93

weekly is five. However, I already mentioned that vocational secondary schools have the less qualified teachers.

In sum, programs are not well designed, i.e., well targeted. Indeed, the targeting problems evidenced in this section imply a waste of resources, a dead weight loss to society that only reinforces Costa Rican government failures in the implementation of its education policy. An evidence of the importance of addressing the problems of this program is the fact that in 2005 more than 50% of the individuals who know English belong to the richest quintile.280

The labor market-pertinent education programs

There are several small-scope initiatives that can fall within this category. One of the programs is under the “Comisión Inter-institucional para la Articulación del

Currículo” [Inter-institutional Commission for Curricular Articulation], a commission created in 1998 to link the technical-vocational secondary school curriculum with the curriculum of the para-university public institutions and to obtain recognition of secondary school studies towards some of the occupational options.281 This allows students to continue pursuing a higher degree in the same or in a similar occupational option. Another initiative is the establishment of the “Mesas empresariales” project, a forum of continuous dialogue between the productive sector and the MEP in order to obtain private sector feedback regarding actions that could improve the vocational

280 Costa Rica Household Survey 2005.

281 Costa Rica, Consejo Nacional de Rectores (CONARE), Estado de la Educación Costarricense, 34.

curriculum.282 Another initiative is the pilot project for dual education that combines teaching in vocational schools with training in the private sector.283

Yet another government program, not a MEP program consists of the work of an autonomous institution, the Instituto Nacional de Aprendizaje (INA) [National Training

Institute], created by law No. 3506 in 1965284 and later modified in 1983. Its mandate is to promote and develop training for workers in all the economic sectors in order to foster economic development and improve the workers’ standard of living.285 Indeed, INA might constitute a sort of an alternative or perhaps substitute for vocational training in the formal setting. INA targets individuals older than 13 years of age from lower income groups; some of them might have been excluded from the formal educational system.

Also, INA’s occupational careers are of short duration and attendance and evaluation requirements are much less stringent than those in the vocational schools. Indeed, vocational secondary schools are oriented towards producing middle level technicians while INA’s training is geared towards forming semi-qualified and qualified workers.

Also, INA focuses exclusively on teaching the occupation, while in the vocational schools, in addition to the technical curriculum, an academic curriculum has to be taught.

Moreover, most of INA’s students are working at the same time in occupations related to what they are studying, taking advantage of INA’s main modality of training: a combination of apprenticeship and technical training. On the other hand, INA offers a

282 Ibid.

283 Ibid.

284 ILO website: http://www.ilo.org/public/spanish/region/ampro/cinterfor/ifp/ina/index.htm.

285 República de Costa Rica, Ley N° 6868 del 6 de mayo de 1983: Ley Orgánica del Instituto Nacional de Aprendizaje, at: http://www.cinterfor.org.uy/public/spanish/region/ampro/cinterfor/dbase/legis/c_rica/vii_a.htm#ley6868.

wider and more diverse curriculum than vocational schools which makes it more attractive. INA offers 52 occupational options within 10 areas: agriculture and livestock, commerce and services, tourism, food industry, textile industry, auto-mechanics, fishing and navigation, electronics and computer hardware, and materials technology and handicraft processes.286 In 2004, INA trained 80,097 individuals between 15 and 24 years old287 while only 57,414 students were enrolled and only 4,178 graduated,288 from the vocational education option in secondary education in the same year.

It is possible to argue that there are two things that undermine Costa Rica’s efforts towards a technologically-anchored and knowledge-based society: 1) that the MEP labor market-pertinent programs are, for the most part, small-scope initiatives whose impact has not been measured, and 2) the only government program that is successful, at least in capturing students for vocational training, the INA, might be competing for the same students; in fact, it might be encouraging traditional school desertion.

2.5 Conclusion

Overall, in this chapter, I have analyzed Costa Rica’s policy in education, the supply of education and the core programs to meet Costa Rican challenges. I have shown that Costa Rica’s policies and programs show a commitment to equal access in education and to develop the human capital stock necessary to satisfy the demands of a more technological, competitive and knowledge-based society capable of becoming integrated

286 Instituto Nacional de Aprendizaje (INA) website: http://www.ina.ac.cr/oferta_servicios/index.html.

287 Costa Rica, Consejo Nacional de Rectores (CONARE), Estado de la Educación Costarricense, Diciembre 2005, p 34.

288 Ministry of Education (MEP) database. 96

into the global economy. Costa Rica has also provided the public education sector with financial resources and this allocation, although insufficient given the scope of the goals, does not seem to be the main problem faced by the supply of educational services. The main problem, Costa Rica’s inefficient use of its financial resources, however, is not easy to solve because it has to do with a fixed budget structure that favors current expenditures and transfers to expensive redistributive programs as well as transfers to higher education; path dependency problems and short presidential administrations.

The outcome of this situation is what interests us: low quality of educational services, that increases the marginal cost of learning for students and as well as the opportunity cost of studying.289 These deficiencies run counter Costa Rica’s efforts to build a stock of technologically and science-savvy future labor force. Although the focus of this research is not the supply side, but the demand for schooling, there is a need to recognize the impact of the supply of education on the demand for schooling. In the next chapter I will analyze some indicators of the low demand for education.

289 Information about the student’s direct cost of studying is not available. 97

CHAPTER 3

ACCESS TO KNOWLEDGE: THE CURRENT CHALLENGE

3.1 Introduction

This chapter examines the coverage and efficiency of the education delivery system as well as the equality of access to schooling. It is divided in five sections. The next section examines indicators of education coverage and desertion in Costa Rica.

Section three addresses the inequalities of access to education section focusing in income gender and regional gaps. Section four analyzes the relationship between inequalities of access to schooling and poverty. More specifically this section attempts to show the link between the educational gap and the labor income gap and the vicious cycle it entails.

The last section provides the conclusions of this chapter.

3.2 Coverage and efficiency of the educational system

Parents’ and students’ demand for educational services depends on the capacity of the educational system to provide access to good quality education to all students from different levels of income and backgrounds, so that students may accumulate years of schooling until they attain the level of education required to earn the expected payoff to their investment. A glance at graph 3.1 tells us that in Costa Rica parents’ or students’ expectations are not met by the educational system. Costa Rican students start abandoning the school system at considerable rate after they reach age 12. Graph 3.1 shows the percentage of population, by age, that is enrolled in the educational system,

irrespectively of the level of education. It evidences that the enrollment rate drops from

92.3% at 12 years of age to 75% and 47.6% at the ages of 15 and 17, respectively.

Costa Rica: Age Specific Enrolment Ratio (2005)

100

Percent

4 5 6 7 8 9 10 11 12 13 14 15 16 17 Age

Graph 3.1: Costa Rica: age Specific Enrolment Ratio (2005) Source: prepared by the author with data from Carlos Castro V., “Informe final: Educación” 2006

In fact, in Costa Rica, the average years of schooling for people older than 15 have increased only by 1.3 years (from 6.5 to 7.8) between 1988 and 2004, as it is shown below.290

A more detailed picture of the extent and nature of the demand for education in

Costa Rica is given below using indicators of coverage and efficiency of the educational system such as enrollment and desertion, among others.

3.2.1 Education coverage

The small increase in average years of schooling depicts the low demand for education that Costa Rica has been experiencing since 1980. Going back to table 2.1, in

290 Carlos Castro V., “Informe final: Educación,” for: Programa Estado de la Nación en Desarrollo Humano Sostenible, Estado de la nación en desarrollo humano sostenible: duodécimo informe 2005 (San José, Costa Rica: Programa Estado de la Nación, 2006): 44. 99

chapter 2 (section 2.2.3), it can be observed that, for instance, enrollment in secondary education grew more than 2 times from 1971 to 1980, at approximately an annual rate of

12%, whereas from 1981 to 1990, enrollment decreased by 11%, which means an average annual fall of 1.1%. In fact, enrollment recovered its 1980 levels in 1992; however, the average annual growth rate during the 23-year period between 1981 and by 2004 was

3.5% well below the 12% of the previous 10-year period. A reduction of the school-age population might not explain these results since change in the population structure was not drastic. According to available population statistics,291 although from 1971 to 1980 the population between the ages of 0 and 14 years old was on average 40% of the total, during the 20-year period from 1981-2000, this group constituted 35.5% of the population. Enrollment, however, does not portray the complete picture regarding demand for education, since does not reflect desertion.

Table 3.1 displays the gross and net enrollment ratio292 by education level and cycles between 1995 and 2005. As table 3.1 illustrates, net enrollment in primary education has been practically 100%, which reflects a high level of participation in primary education. On the other hand, table 1 below also shows that participation at the secondary level is low: gross enrollment rate and net enrollment rate were, respectively,

85.8% and 69.4% in 2005; this outcome is consistent with my analysis of the previous graph. On the other hand, the gross enrollment rate, in secondary education, rose about

15 percentage points from 2000 to 2005; the increase is associated with construction of

291 Centro Centroamericano de Población, Universidad de Costa Rica: http://ccp.ucr.ac.cr/observa/CRnacional/cuadros/cuadro3s.htm, retrieved on January 15, 2008.

292 Gross enrollment ratio comprises the total enrollment in a specific level or cycle or grade regardless of age, expressed as percentage of the population with age to be enrolled in that level or cycle or grade. The net enrollment ratio is the total enrollment of the official age group in a specific level or cycle or expressed as percentage of the population with age to be enrolled in that level or cycle or grade. 100

public high schools during this period, particularly between 2003 and 2005.293 Also, the enrollment rate in the non-formal or nontraditional modality rose between 2003 and 2004 as a result of the 13 distance education programs opened.294 Notwithstanding these patterns of increase, by 2005, 30% of the Costa Rican youth of the official age for secondary education were not enrolled (net enrollment rate was 69.4%). Notice that the difference between the gross enrollment rate and the net enrollment rate gives us a proxy of the percentage of overage children enrolled in secondary school; for instance, in secondary school, in 2005, approximately 15% (85.8%-69.4%) of the students were over the official age established for being in secondary school. Table 3.1 also tells us that the educational services or programs outside the formal or traditional secondary schools such as open education or the New Opportunities programs (PNO) offered by the MEP to retain or recover students have not been successful. The enrollment rate in non-formal education has barely been increased. The behavior of enrollment by cycle is also shown in table 3.1; it seems that the education system at the diversified cycle level has not been able to capture even 50% (40.1% in 2005) of the youth of the official age for that level.

293 Castro V., 11.

294 Ibid. 101

Table 3.1 Costa Rica: Gross and net enrollment ratio in secondary education. Formal and non-formal modalities by cycle (%)

Gross enrollment rate Net enrollment ratio

1995 2000 2001 2002 2003 2004 2005 1995 2000 2001 2002 2003 2004 2005

Primary (cycles) 104.8 105.3 105 104.9 104.8 103.7 103.9 99.8 99.0 99.2 99.2 99.0 98.5 98.8

First cycle 113.3 112.4 111.3 110.3 109.8 109.5 110.5 101.4 101.7 100.7 100.3 99.9 100.6 101.4

Second cycle 95.7 98.4 98.9 99.6 100 98.1 97.5 85.8 87.2 88.0 88.8 88.7 88.3 88.0

Secondary (types) 68.4 70.4 75.4 79.2 84.0 85.8 59.5 60.8 63.8 66.2 69.3 69.4

Formal 58.2 60.9 62.6 65.6 68.8 72.6 75.6 51.4 55.3 56.7 58.7 60.9 63.8 66.1

Non Formal ---- 7.5 7.7 9.7 10.4 11.5 10.3 --- 4.2 4.1 5.2 5.5 5.5 3.3 102

Secondary (cycles)

Third cycle --- 80.8 82.9 90.2 94.7 100.3 101.9 ----- 59.5 60.8 63.8 66.2 69.3 69.4

Formal 67.5 70.9 72.6 77.5 81.3 85.5 88.5 56.7 55.3 56.7 58.7 60.9 63.8 66.1

Non Formal ---- 9.9 10.3 12.7 13.5 14.8 13.1 --- 4.2 4.1 5.2 5.2 5.5 3.8

Diversified

Education ----- 48.0 50.8 53.3 56.0 59.8 61.9 --- 34.6 37.3 37.6 38.1 39.7 41.3

Formal 43.4 44.4 47.1 48.0 50.2 53.1 55.9 30.5 33.6 36.4 36.0 36.6 38.4 40.1

Non formal ---- 3.6 3.7 5.3 5.8 6.6 6.0 ----- 1.1 0.9 1.6 1.5 1.3 1.3

Source: prepared by the author with data from: Programa Estado de la Nación en Desarrollo Humano Sostenible, “Educación y conocimiento en Costa Rica: desafíos para avanzar hacia la política de estado”. (San José, Costa Rica: Programa Estado de la Nación 2004, Serie aporte al análisis del desarrollo humano sostenible número 8): 17 and MEP, Department of Statistics. Notes: 1. Enrollment refers to enrollment in public, private and private publicly subsidized educational institutions. 2. Enrollment in primary education includes only enrollment in formal education. The basic difference between formal or traditional education and non-formal is that in the former attendance is compulsory while in the latter it is not. 3. The ages to calculate enrollment are for primary 7-12 and for secondary 13-17.

3.2.2 Desertion

Analysis of the behavior of the enrollment rate through the different education cycles during a year shows that the transitions from cycle to cycle are critical moments of school abandonment. For instance, table 3.1, above, also informs that the transit from primary to secondary school (from the second cycle to the third cycle, 6th to 7th grade) reflects a considerable decrease in education coverage (see formal modality). Although this decrease in enrollment has been diminishing since 2002, it was still considerable in

2005. In fact, in 2005, education coverage in the formal education modality (measured as net enrollment rate) decreased 22.2 percentage points (from 88.3 to 66.1%) as students moved from the second cycle in 2004 to the third cycle in 2005. Similarly, in 2005, the transit from the third cycle to the fourth cycle of diversified education diminished the net and gross enrollment rate by 29.6 percentage points and 23.7 percentage points respectively. The reduction in coverage in this transition point has been increasing since

2002.

The annual dropout rate by grade completes the picture of abandonment. In primary education (cycles I and II) the average annual dropout rate was 4.5% between

1990 and 2000; by 2004 and 2005 this figure has decreased to 3.4% (see data in table

A.3.1 in Appendix to this chapter). The percentage of abandonment in the different grades (1-6) is very similar to the total average for primary education which implies that there are no differences between grades in terms of exclusion. Perhaps the only grade where the dropout rate is higher than the average is first grade.

In table 3.2 below, the results of cohort study can be seen. Of the students that enrolled in first grade in 1996 (2002-6), 27% quit primary school. This process of

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abandonment, as it was observed in graph 3.1 above, worsens as the student continues to higher levels in the education system. Of the pupils that initiated first grade in 1993

(2002-9), approximately 60% have left school before completing cycle III and of the pupils who started first grade in 1991, 73% drop out before finishing 11th grade. These figures of desertion have improved if compared with the ones in 2000, but they are still high: by 2002, only one fourth of the students that entered first grade remained in the system long enough to graduate.

Table 3.2 Costa Rica: Retention and exclusion by year and level of education (2000, 2002)

2000 2002

Primary Secondary Secondary Primary Secondary Secondary

I and II III cycle 11th grade III cycle 11th grade

cycles

Start year 104,128 106,860 94,066 105,314 103,442 106,860

End year 75,579 39,998 24,081 79,548 40,929 28,725

Retention 72.6 37.4 25.6 75.5 39.6 26.9

Exclusion 27.4 62.6 74.4 24.5 60.4 73.1

Source: R. Mora and P. Ramos, “Educación y conocimiento en Costa Rica: desafíos para avanzar hacia una política de Estado” (San José: Programa Estado de la Nación, ponencia al Noveno Informe Estado de la Nación, Serie de Aportes para el análisis del desarrollo humano sostenible, No. 8, 2004): 26.

Note: Start year in primary is final year minus 6; in cycle III it is the final year minus 9 and in the 11th grade it is the final year minus 11. The final year is 2000 or 2002.

The analysis above yields the conclusion that abandonment becomes a more severe problem at the end of primary school, when students need to move from the second cycle to the third cycle of secondary education and continues through secondary school.

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Graph 3.2 provides more insight into the desertion process at the secondary level, the focus of this research, since it is more severe than the one at primary level. It shows the percentage of students that drop out of regular (day modality) secondary school during a school year. It can be seen that of the total number of students who abandoned secondary school, the percentage of abandonment during the last 15 years has been somewhat higher in the technical modality than in academic one since 1997. Students drop out of school from all grade levels, but graph 2 depicts those grades where desertion intensifies. Exclusion is severe in 7th grade where around 19% of 7th graders dropped out of school, 8 percentage points higher than the total for secondary school. Indeed, this statistic has not improved much during the last 15 years. The next critical period is 10th grade, where pupils are initiating the diversified education cycle; they are either in the academic or technical track. Another grade that experiences high drop-out rates is 8th grade, when students who manage to survive 7th grade give up and do not continue to 9th grade to finish their general basic education. The exclusion rates are more serious in the night school modality where, in 2005, they were 2 times and 1.6 times higher than the academic and technical tracks, respectively, of the day school modality (see table A.3.3 in Appendix). This outcome tells us that once the students are excluded from the regular or formal system, it is more difficult to retain them through other educational options such as night school or open education programs.

Desertion in the III cycle and in diversified education is a problem that afflicts the whole country. However, in nine out of MEP’s twenty sectors, as it can be seen in table

3.3, it is at least 1.3 percentages points higher than the national average, these sectors are mostly located in rural areas. In broad terms, it can be argued that some of the country’s

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socio-economic regions are in their totality affected by desertion. For instance, together the sectors: Nicoya, Liberia, Cañas and Santa Cruz make up almost the entire (90%)

295 Chorotega region.

Costa Rica: Desertion in secondary level by teaching modality and selected grades

Academic

Technical

Seventh grade

Tenth grade

Eight grade

Percent

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Graph 3.2: Costa Rica: desertion in secondary level, by teaching modality and selected grades

Source: table A.3. 2 and A.3.3 of Appendix to this chapter.

Note: desertion refers to public schools, private and private publicly subsidized and it also refers only to the day modality. Tenth grade includes both the academic and the technical tracks.

The San Carlos and Upala sectors, in turn, comprise about 80% of the population of the

Huetar Norte region296. Limón by itself represents 80% of the population of the Huetar

295 This region is in the North Pacific, predominantly rural, with little land available for small plot agriculture since from the 1950s most of the land has been bought by big landowners and dedicated to livestock. Lately, this region has been developing the tourism industry mainly along the coast. Ronulfo Alvarado S., “Regiones y cantones de Costa Rica,” (San José, Costa Rica: Instituto de Fomento y Asesoría Municipal [IFAM], Serie de cantones de Costa Rica No.2, 2003): 26 .http://www.ifam.go.cr/PaginaIFAM/docs/regiones-cantones.pdf).

296 This region is also predominantly rural and agriculturally based.

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Atlántica region.297 Coto represents 60% of the Brunca region population.298 Aguirre, on the other hand, runs across two socioeconomic regions, the Central and Pacífico Central regions.

Table 3.3 Costa Rica: MEP’s educational sectors with the highest intra-annual desertion, 2005

III and IV cycle (%) 7th grade (%)

Total Male Female Total Male Female

Total country 12.5 14.4 10.6 20.7 23.4 17.7

Coto 21.5 23.8 19.1 28.4 31.2 25.3

Aguirre 19.2 24.1 14.6 28.7 32.9 23.9

Nicoya 18.7 20.7 16.7 25.4 29.5 21.0

Liberia 17.3 20.9 14.0 23.6 28.4 18.1

Cañas 16.5 19.5 13.4 28.7 35.6 20.6

Santa Cruz 15.4 18.8 11.9 23.3 27.7 17.7

Limón 15.2 16.0 14.5 22.7 25.3 19.6

San Carlos 13.9 16.4 11.5 24.6 27.5 21.5

Upala 13.8 16.1 11.5 22.3 25.5 18.8

Source: prepared by author with data from Carlos Castro V., “Educación: informe final” para Duodécimo Informe sobre el Estado de la Nación en Desarrollo Humano Sostenible (2006): 52.

Note: Includes day and night education delivery modalities and educational institutions which are public or private, or private but publicly subsidized.

Abandonment of the school system is probably perceived by several students as the end of many of his or her academic problems. Table 3.4 below informs us of these problems by showing the rates for repetition, approval, failing and delayed approval

297 Limón is on the Caribbean coast, is mostly rural, and is where the most important Costa Rican port is located.

298 The Coto sector was economically stricken when the main banana company left the region in the early 1980s, and continues to be an economically deprived area that is afflicted by unemployment.

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decision rates299 for 7th and 10th graders over the last 15 years. The delayed approval decision rates refer to the percentage of the students who, at the end of the school year, having failed in no more than three subjects, are given the opportunity to pass the failed subjects by doing further testing just before the beginning of the next school year. If the student does not pass all the subjects in question, she fails the grade.300 The statistics in the table demonstrate that indeed the marginal cost of students in the educational system is very high. For instance, on average in 2002, 14% of enrolled students in 7th grade at the beginning of the year are repeating the grade; these students are the ones that did not quit the previous year during their first or second try in 7th grade, yet they failed the grade. It can be seen that the repetition rate has remained around the same figure over a 15 years period. If the repetition rate of 14.5% is added to the desertion rate of 19%,301 it can be argued that the educational system was a disappointment to 3 out of 10 students that enrolled in 7th grade in 2002. This outcome is understandable if consider that of every of

20 7th graders enrolled at the end of the school year only 10 passed the grade during the regular school calendar, 6 (20*0.31) had to take the test again at the end of the summer and 4 (20*0.19) failed the grade. Tenth grade is also a challenge to the students: more than 10% of those enrolled at the beginning of the year are repeaters; the percentage of

299 The approval, failing and delayed approval decision rates are calculated based on the final enrollment, that is, they should add approximately 100%. The annual dropout rate is the difference between the enrollment at the beginning of the year and the one at the end of year divided by the enrollment at the beginning of the year. The repetition rate is calculated using the beginning of the year enrollment as reference.

300 This policy was changed in 2008 by the MEP. The new policy is that students will be able to move on to the next grade and repeat only the subjects that they failed during the previous grade. La Nación, “En pocas palabras: Leonardo Garnier, Ministro de Educación” November 12, 2008. http://www.nacion.com/ln_ee/2008/noviembre/12/pais1772652.html, retrieved November 12, 2008.

301 The percentages cannot be added because the bases are different. However, the idea is that in absolute numbers more than 1 student out of 10 was repeating at the beginning of the year and 2 out of 10 deserted during the year. 108

Table 3.4 Costa Rica: Efficiency in 7th and 10th grades (%) in selected years

1990 1992 1994 1996 1998 2000 2002

7th

Repeating 14.2 11.8 13.4 16.8 16.2 14.5 14.5

Approved 49.2 48.8 47.4 42.1 44.4 49.7 49.5

Failed 17.7 19.5 20.6 23.1 21 17.9 19.0

Approval delayed until students pass

tests in failing subjects 33.1 31.6 32 34.8 34.5 32.4 31.3

Desertion 17.5 19.3 20.4 20.3 19.8 18.6 19.1

10th

Repeating 12.7 11.6 10 11.6 11.2 8.6 13.6

Approved 46.4 50.6 46 44.4 47.7 50.7 46.2

Failed 14.1 13.5 15.4 14.8 13.1 12.4 15.7

Approval delayed until students pass

tests in failing subjects 37.3 34.0 36.0 39.0 39.5 35.8 37.6

Desertion 10.2 10.9 10.0 8.8 8.0 8.0 10.4

Source: Ángel Ruiz, “Universalización de la educación secundaria y reforma educativa, informe final para Undécimo Informe sobre el Estado de la Nación en Desarrollo Humano Sostenible,” (2005), 50-55.

Note: The rates are calculated for students enrolled at the beginning and at the end of the year in regular schools, that is, day modality: academic and technical tracks.

students that pass the grade at the end of the school year is lower than that of 7th graders:

10 of every 20 (about 50%: 13.6+37.6) students could not learn or were not taught well enough to pass the final tests to be promoted to the next grade; of the 10, 3 of them failed the grade, and 7 pupils will have to retake the tests. The table does not show the performance of secondary school students in the most difficult challenge in order to graduate from high school, that is, to pass the national baccalaureate exams to ensure the

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achievement of the expected basic knowledge at the end of secondary school.302 To finish secondary school, Costa Rican students need to pass 11th or 12th grade and the baccalaureate exams. In 2004, 27.5% of the students failed the mathematics test, and between 12% and 14.55 failed the sciences tests (biology, physics or chemistry). It has been argued by Costa Rican experts in education that these tests constitute such an impediment to the students that some of them quit school before trying. The case is that many students are not prepared to take them. For some households the solution is to find tutors, for most of the households this is not an option. Certainly, the quality of school is affecting household’s expectations regarding their children’s finishing secondary school.

In fact, cohort studies performed by the MEP have found that the average time students take to finish secondary school is 9.4 years,303 that is, almost the double of the expected duration of secondary education in the academic track (5 years) and 1.5 times what a student in the vocational track would take to finish secondary school (6 years).

Considering that, on average, during the last 10 years, enrollment in the technical option has remained around 20%, the 9.4 years figure refers mostly to the students in the academic option. The same cohort study showed that in 2002 only 61%304 of the students who graduated from high school did it in the expected time period to complete primary and secondary education, 11 (or 12, in the vocational track) years, that is, without repeating a grade. On the other hand, the average number of years students take to graduate in primary education is 7.4 years, 1.4 more years than expected. Although 1.4

302 These are 5 comprehensive exams on Math, Spanish (writing, literature and grammar), Social Studies, a science (Chemistry, Biology or Physics), a language (French or English) and Civics.

303 Costa Rica, Consejo Nacional de Rectores (CONARE), Estado de la Educación Costarricense, (San José, Costa Rica, December 2005, at: http://www.conare.ac.cr/, retrieved in April 2007): 28.

304 Ibid. 110

does not seem to be a high figure, it has to be considered that these students will be overage when they enter secondary school and this is constitutes a threat of desertion, mainly if they find secondary school very difficult or if they fail again. It is clear that going through secondary school entails a difficult decision for students; every ladder implies a higher marginal cost for the majority of students who remain in the system and, for the ones who are excluded, the cost in term of future forgone opportunities is also very high.

As it will be examined in the next section, those families or students who decide not to continue demanding school services belong to the more deprived areas or sectors of society precluding them from the only means to increase their standard of living.

3.3 Inequalities of access

The contribution to education to social integration and better opportunities to all members of society has to do with the nature of coverage and exclusion. In Costa Rica the low coverage and exclusion phenomenon described above is experienced unequally by different sectors of the society.

3.3.1 The educational gap

The following graph 3.3 displays the educational gaps by educational phases according to the categories of income, area (rural/urban) and gender over a 16 year period. The first sub-graph shows the percentage of the individual between 14 and 15 years old that finished primary education. The second shows those individuals between

17 and 18 that completed the general basic education stage (9th grade) and the last sub- graph includes individuals between 20 and 21 that finished secondary education. Notice that the scale on the y axis of the three sub-graphs varies to reflect, for instance, that for

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primary completion (the first sub-graph) the percentages of attainment overall and across categories are the highest and the gap between subcategories is the smallest compared to the other educational phases or stages represented in the other two sub-graphs; and that for secondary completion (third sub-graph), the overall attainment is the lowest and the gap between subcategories is the highest.

The analysis of the trend (1989 to 2005) on education coverage in Costa Rica, shown by the dashed lines (Costa Rica total) in each of the sub-graphs, shows that on the whole, it increased during the period. However, this increase varies by education phases.

The largest increase, 16.9 percentage points, was in secondary education completion shown in the third sub-graph, from 22.0 % in 1999 to 38.9% in 2005, for the whole country. The reader will note that this improvement was mostly influenced by the large increase in participation by the richest 25% (22.6 percentage points), depicted by the steeper slope. Also, secondary completion by students from rural areas increased 11.4 percentage points in this period. The increase in primary education coverage, shown in the first sub-graph, was modest (as it is displayed by the low incline of all the lines), compared to the increase in secondary education. In fact, the coverage of primary education for the country increased by 9.2 percentage points (from 80.2% in 1999 to

89.4% in 2005).

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Population that at least completed primary education(%) Population that at least completed general basic education(%)

100

1989 1994 1999 2005 1989 1994 1999 2005

Population that at least completed secondary education(%)

1989 1994 1999 2005

Graph 3.3 Costa Rica: Education gaps by income strata, region and gender (1989-2005)

Source: Table A.3.4 of Appendix to this chapter.

On the other hand, the poorest 25% and the rural group rose during this period considerably more than the increment in primary education coverage for the country, 16.1 and 11.4 percentage points, respectively. Regarding coverage in general basic education, displayed in the second sub-graph, the increase was very small for the most part: the lines show a very small change in slope (45.6% in 1999 to 49.3% in 2005 as country averages); however, three subgroups had larger increases than the average: the richest

25% (7 percentage points), the rural subgroup (8 percentage points) and women (5

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percentage points). In sum, analysis by educational stages informs us that the increases in coverage over this 16 year period were mostly in secondary education. It also tells us that the increases in coverage in all stages of education and specifically and more importantly in secondary education completion were driven primarily by the increase in participation of the 25% richest students and secondly by students from rural areas. Whether or not the increase in participation by students in rural areas refers to better off students from rural areas remains a question.

The analysis of income, region of origin and gender gaps tells the following:

First, the income gap (richest 25% and poorest 25%) are greater than the regional gap

(rural and urban) at all stages of education (comparing the distance between the two corresponding lines). For instance, in 2005 families (and this goes for previous years) and students from the richest 25% were 1.16, 2.14 and 5.06 times more likely than those from the lowest quintile to have completed primary, general basic education and secondary education respectively, while families and students from urban areas were 1.07, 1.45 and

1.64 times more likely than families from rural areas to finish primary, general basic education and secondary education, respectively. On the other hand, the gender gap in

Costa Rica, which favors females, is the smallest: by 2005, female students compared to male students were 1.05, 1.27 and 1.19 times more likely to finish primary, general basic education and secondary education. In fact, the higher gains in terms of inequities regarding education access, during this 16 years period across all stages of education, are in the reduction in the discrepancy between urban and rural areas. While the income gap between the top or richest 25% and lowest or poorest 25% students completing primary, general basic education, and secondary education decreased 8%, 32% and 39%,

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respectively during this 16 year period; the urban/rural gap decreased 12%, 41% and

51%, respectively during the same period. These findings are supported by the national survey of income and expenditures, performed in 1988 and 2004. According to this survey, Costa Ricans increased, on average, their years of schooling by 1.3 years during the 16 year period from 1988 to 2004. However, for the individuals belonging to the upper quintile, the increase in years of schooling more than doubled the total average increase while for the ones in the lower quintile, this increase was less than half of the average increase.305 On the other hand, the same survey showed that by region, the urban area, on average, had a larger increase in years of schooling than the rural area. This does not contradict the previous statement about the reduction between the urban/rural gap: what can be inferred from examining the sub-graphs is that, while for primary education and general basic education the rural/urban gap started to decrease since 1989, the discrepancy in secondary education completion between urban and rural areas started diminishing only after 1994; in other words, to effectively augment the indicator years of education completed, the coverage in secondary school completion has to increase.

Second, given any year the gaps (income and rural/urban and gender) increases with the educational level except in the case of gender for secondary completion. Indeed, girls’ participation in each stage of education is higher than boys,’ except in the last stage of secondary completion where the gender gap decreases, favoring male students. Finally, observing the trend of the discrepancies over the 16 year period in all education phases, it can be concluded that all types of gaps have been decreasing, except in the case of the income gap in secondary education completion, which increased from 1999 to 2005.

305 Castro V., 36 and 44. The data is originally from the Income and Expenditure surveys performed in 1988 and in 2004. 115

While in 1999 rich students (the richest 25%) were 4.4 times more likely than poor students to finish secondary education, in 2005, this figure changed to 5.1.

Summarizing, education coverage in Costa Rica rose over the period between

1989 and 2005. This increase varied by education phases and was influenced mostly by the expansion in coverage in secondary education. However, the increases in coverage in all education stages, and mainly in secondary education completion, were driven primarily by the increase in participation of the richest 25% students and secondly by students from rural areas. This raises the question of whether or not there is an intersection between these two groups: students from rural areas and the richest 25%.

On the other hand, observing the trend of the income, region, or gender gaps over the 16 year period in all education phases, it can be concluded that all types of gaps have been decreasing, except in the case of the income gap in secondary education completion.

However, the higher gains in terms of inequities regarding education access, during this

16-year period across all stages of education, are in the reduction in the discrepancy between urban and rural areas. Indeed, the income gaps are greater than the rural/urban gaps at all stages of education. The gender gap in Costa Rica, which favors females, is the smallest.

These discrepancies in education attainment between students belonging to different income groups, previously analyzed, are the result of the process of abandonment identified in the first section of this chapter. Table 3.5 allows us to examine this process in greater detail: dividing the attendance by income quintile for the period 2000-2005, using data from household surveys. It can be seen that for ages corresponding to primary school, attendance is similar among quintiles, but for ages

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between 13 and 17 corresponding to secondary school,306 school attendance decreases gradually as we move from the upper to the lowest quintiles. In 2005, the 13 to 17 year- olds belonging to the fifth quintile were 1.3 times more likely to attend school than the same age youth from the first quintile. The table also shows that during the period 2003 to 2005, the improvement in participation in secondary education was considerable higher for the lowest quintile which seems to contradict my previous analysis. However, as it was mentioned before, during this period, there was a rise in school construction that might have benefited the low income groups.

Table 3.6 shows that some socioeconomic regions are being more affected by low attendance rates and this varies by age groups. These regions affected are mostly rural; this finding complements the previous analysis of the desertion patterns by MEP’s sectors. Data from the 2005 household survey about attendance to school by children and youths within 4 age groups, 7 to 12, 13 to 14, 15 to 17 and 18 to 24 (this latter group is necessary to include considering that students from 18 to 24 who had repeated a grade can form part of the enrolled student population during the day or night secondary school modality) allows us to identify some patterns of attendance by age groups within socioeconomic regions by comparing with the national averages for attendance. First, the attendance rate within the age group 7 to 12 is very similar among regions and around the national average (99%). Second, the Huetar Norte region has the lowest attendance rates to school, 79.8%, 56.7% and 30.5%, from three different age groups, 13 to 14, 15 to 17 and 18 to 24, respectively. This might be interpreted as a region characterized by high

306 We are assuming here using the age as a parameter that children from 13 to 17 are in secondary which might not be true for all of them since some children of that age group could be still in primary school 117

Table 3.5. Costa Rica: Attendance to schools by income quintiles, 2001-2005

2001 2002 2003 2004 2005

7-12 years 97.2 97.6 98.3 98.6 99.0

Quintile 1 95.6 95.8 97.8 97.3 98.5

Quintile 2 97.4 97.1 97.5 98.5 99.2

Quintile 3 97.7 98.8 98.6 99.7 98.9

Quintile 4 99.3 99.9 99.9 99.6 99.7

Quintile 5 98.2 99.0 99.0 99.8 100

Quintile 5/1 1.03 1.03 1.02 1.03 1.02

13-17 71.7 74.8 75.7 79.4 80.3

Quintile 1 71.7 64.4 65.7 72.3 75.1

Quintile 2 61.0 74.1 71.6 75.9 76.3

Quintile 3 68.4 73.4 76.9 80.9 79.5

Quintile 4 69.1 84.7 85.0 84.2 85.7

Quintile 5 92.2 91.2 94.3 94.7 97.7

Quintile 5/1 1.51 1.42 1.44 1.31 1.30

Source: prepared by author from data from CONARE: Estado de la Educación, 2006, Statistical Annex, 114, and Castro V. Carlos “Informe final: Educación” para Duodécimo Informe sobre el Estado de la Nación en Desarrollo Humano Sostenible (2006), 43.

Table 3.6. Costa Rica: Attendance to schools by regions and age groups, 2005

Central Chorotega Pacífico Brunca Huetar Huetar Total Central Atlántica Norte 7 -12 99.3 98.5 98.5 99.1 98.7 98.5 99.0 13-14 91.7 86.2 86.1 88.2 83.2 79.8 88.9 15-17 80.0 73.4 65.6 70.9 66.8 56.7 75.5 18-24 44.1 38.6 29.6 36.1 30.6 30.5 40.5 Total 74.0 72.7 67.3 71.7 68.7 66.3 72.3

Source: Castro V. Carlos “Informe final: Educación” para Duodécimo Informe sobre el Estado de la Nación en Desarrollo Humano Sostenible (2006), 43.

desertion rate from late primary through secondary school. Second, in the Pacífico

Central Region and the Huetar Atlántica regions, youths whose ages range between 15 to 118

17 and 18 to 24 years old have the greatest incidence of abandonment, which means that they are more likely to drop out in secondary school. Finally, in the Chorotega and

Brunca regions, the characteristic is a low attendance rate from students within the age group 18 to 24; as mentioned before, these regions experience a high desertion rate.

When the age group of 18 to 24 displays a low incidence of attendance in some regions such as Huetar Atlántica, Chorotega and Brunca, it could mean that those regions might be characterized by overage students in secondary school and by small participation of students in tertiary education.

The household survey questionnaire, to frame 12 to 17 year-old youths’ responses, provides a list of several reasons to choose from for not attending school. I grouped these causes into three themes to facilitate the analysis of the reasons given by youths of these ages, in 2005. The ones more academically related (45.6%) consisted of lack of interest in formal learning (28.5%), learning difficulties (12.5%), and problems of access to school (4.6%). These figures did not differ much by gender, except for the first one which for males represented 31.7% and 25.2% for females of total number of causes.

The ones economically associated (30.3%) included: unable to pay the cost of school

(20.8%), the need to work (6.3%), and the need to help at home (3.2%). The percentages for males and females were very similar for the first and third reason; however, in the case of needing to work, for boys this reason represented 10% of the total while for the girls it represented 2.4%. Personally related reasons (12.9%) comprised a preference for working rather than studying (8.4%) and marriage or becoming parent (4.5%). In both cases, the response varied by gender. Whereas for males the two reasons, preference for working and marriage/ becoming parent represented 12.2% and 0%, respectively, for

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females the percentages were 4.6% and 9.1% respectively. Some conclusions can be drawn of this information and considering the analysis in the previous. First, the type and quality of the education that is being delivered is not good enough to stimulate the interest of the students or to help them to overcome their learning difficulties or to delay their preference or need for working. In fact, lack of interest in formal learning, learning difficulties and preference for working represent 50% of the causes for quitting school.

Second, the direct cost of education was the second most important cause for abandonment for both males and females. Even in a public system, education is not free and as I noted in the previous chapter, in 2005, the one-time cost of uniforms and school materials, that parents had to incur in sending their children to primary or secondary school amounted to $87 and $97, respectively. This problem is being addressed by the

Costa Rican government with the retention and inclusion programs, as it was also pointed out, in the same chapter; however, these programs will be successful only if two conditions are satisfied: the first one is that targeting improves significantly and the second one is that these programs remain in place once the present administration ends its term. Pregnancy or becoming a parent is also an important cause of dropping out for female students. However, in Costa Rica, since 1997, pregnant students are encouraged to continue attending school and once the baby is born, the MEP and other government institutions have the mandate to facilitate the inclusion of these mothers in the educational system so that they are able to finish their general basic education.307 Despite this law, however, remaining in school is a decision of the students and their families.

307 República de Costa Rica. Ley general de protección a la madre adolescente, No. 7735 del 17 de diciembre de 1997. 120

Overall, the inequities in the access to education analyzed so far lead us to conclude that the marginal cost of remaining in school in Costa Rica is considerably unequal regarding income and zone. It seems than only parents and students belonging to the 20% richest group fulfill their educational expectations. Although the higher gains in overcoming inequities regarding education access, during this 16-year period (1989-

2005) across all stages of education, are in the reduction in the discrepancy between urban and rural areas, the indicators of desertion and attendance for 2005 continue to show bias against students from rural areas. This might mean that the gains might be associated with well-off students in the rural areas.

3.4 Poverty: the trap for the uneducated

The higher the levels of education, the less likely it is to be poor. Costa Rica constitutes a perfect and regrettable example of how this relationship can be so perfectly opposite for individuals at ages at which they are expected to have finished high school and belong to the labor force. The comparison of individuals with no education, or incomplete primary education, (blue line) with individuals with tertiary education (gray line) in graph 3.4 depicts in a striking way this poverty trap. Both lines go in opposite directions. While the blue line decreases, the gray increases and both seem to be doing so at the same rate. As the levels of education increase, the share of the upper quintile increases and the share of the lowest quintile decreases, and vice-versa. We can see that individuals with no education or incomplete primary education are 11 times more likely to belong to the poorest 20%, while individuals with tertiary education are 15 times more likely to belong to the 20% richest category. While of the share of Costa Ricans that have completed primary education 25% are from the first quintile, only 8% are from the

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fifth quintile. On the other hand, whereas 25% of individuals who have completed secondary education belong to the fifth quintile, only 9% belong to the first quintile. The lines almost cross for individuals with general basic education where the share is similar, but following the same pattern, the share is 3 percentage points higher for the first quintile. It can also be observed that the other lines representing the relative participation of the other quintiles in the different levels of education also diminishes as years of schooling accumulate. In fact, quintiles two and three manage to have better distribution than the quintile one, with a higher share of individuals with general basic education and secondary education, but still the percentages start descending once primary education is completed. The fourth quintile line evidences how having completed secondary education guarantees higher levels of income but not enough to belong to the fifth quintile.

Costa Rica: Relative share of quintiles by education category Q5, tertiary population between 18 and 60 years old, 2005 education, Q1, none or 54.4 less than primary, 40.07 Q1, at least Q5, at least primary secondary complete, Q1, at leastcomplete, Q5, at least 25.18 general basicQ1,25.44 at least Q5, at leastgeneral basic educ, 17.49secondary Q5, none orprimary educ, 14.53 Q1, tertiary complete, complete, less than 9.7education, primary, 3.62 8.05 3.58

Q1 Q2 Q3

Graph 3.4: Costa Rica: Relative share of quintiles by education category; population between 18 and 60 years old, 2005

Source: prepared by author using the Costa Rica Household Survey 2005.

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One explanation for the previous findings is given by the relationship between education and the labor market not only in terms of participation but also in terms of rewards.

The likelihood of being employed increases as the level of education increases as it is shown in table 3.7. Indeed, individuals with completed secondary education and some tertiary education are, respectively, 1.5 times and almost twice as likely to be employed as individuals with no education or incomplete primary education. The likelihood of being excluded from the labor force also decreases with higher levels of education. It is interesting to note that although women hold higher levels of education, they are more likely to be out of the labor force than men, and at any level of education; however, once they are in the labor force, they are less likely to be unemployed. As well as their male counterparts, women increase their chances of being employed as their level of education increases.

Table 3.7. Costa Rica: Working status of individuals between 18 and 60 years old according to levels of education, 2005

Out of the Unknown Labor force labor force Employed Underemployed Unemployed None or less than primary education 34.54 4.17 22.88 36.45 1.95 Male 46.62 4.00 36.11 9.73 3.53 Female 23.42 4.33 10.69 61.06 0.50 At least primary completed 43.50 4.73 20.76 28.44 2.57 Male 59.27 4.78 27.94 3.96 4.05 Female 28.36 4.68 13.85 51.96 1.15 At least general basic education completed 46.63 6.0 14.85 30.24 2.28 Male 59.31 5.61 18.12 13.52 3.44 Female 34.46 6.37 11.71 46.30 1.16 At least secondary education completed 51.49 4.41 12.67 26.84 4.59 Male 65.35 4.14 14.86 8.95 6.70 Female 39.45 4.65 10.76 42.40 2.74 Tertiary education 61.79 3.44 8.11 22.73 4.02 Male 66.73 2.80 9.64 15.76 5.06 Female 57.30 4.03 6.71 28.88 3.08

Source: prepared by the author using the Costa Rica Household Survey 2005.

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The following chart 3.1 displays the average income of Costa Rican workers between 18 and 60 years of age by amount of education completed. Overall the labor market makes a sharp distinction between individuals with at last one year of higher education and the other education completion groups. In fact, the closest category, having secondary education completed, allows individuals to make, on average, 54%, about half, of what people with some tertiary education make, on average. Individuals with at least

9th grade completed and at least primary education completed make on average, 42% and

37.5%, respectively of what the higher education individuals earn on average. At the bottom of the scale, individuals with no education or incomplete primary school education earn, on average, a third of what an individual with at least one year of higher education earns.

At least one year of some para-university or university education

318,031.7

At least secondary complete

171,181.9

At least general basic education

133,886.5

At least primary completed

118,975.8

None or less than primary

105,858.5

Chart 3.1: Costa Rica: Average income of employed population between 18 and 60 years old, by education completed, 2005

Source: prepared by author using the Costa Rica Household Survey 2005.

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Note that an individual who finishes primary school, instead of receiving only 5 years or less of schooling only increments her average income by 12.4% and if she continues to study for at least 3 more years, to at least complete the third cycle of general basic education, her income only increases by an average of 12.5%. On the other hand, finishing high school, which implies 2 or 3 more years of schooling, means a greater jump in terms of average income, a 27.9% increase. However, the larger labor market gains are in the continuation of schooling towards higher education which is a level not only very costly but selective as it was mentioned in the previous chapter.

The average income gap is much greater in the rural than in the urban areas, as it is shown in graph 3.5. It is interesting to note that the differences become smaller as the amount of education increases until 9th grade, 21.5%, 14.5% and 8%, respectively. In other words, it pays for individuals from rural areas to continue studying (if they intend to stay and find a job in the rural area) until they at least complete the third cycle. However, after the completion of at least 9th grade, the labor market in the rural areas slows down its rewards for higher levels of education and the differences start increasing. Indeed, the average income in urban areas as compared to rural areas for an individual who has completed secondary education is 18.5% higher and 22.8% higher if she has some sort of tertiary education. The reason for this outcome lies in the fact that in the labor market in the urban areas, the rate of increase in average income remains constant, around 7.5%, until 9th grade, after that point it starts increasing sharply (32.8% from general basic education to complete secondary). In other words, in the urban areas, the accumulation of years of education is not recognized until secondary education is completed. This can be seen if we try to put together and next to each other all the urban bars and draw an

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imaginary line on the top of the bars. On the other hand, in the rural areas, the rate of increase, although it starts from a lower average income, is higher than in the urban areas and increases steadily until secondary education is completed (13.7%, 14.4%, 18.9%, respectively for the categories); after this point it increases sharply to recognize higher education; but at a lower rate than in the urban areas.

Costa Rica: Average income by region and education category 2005 population employed between 18 and 60 years old

400000

329396

300000 268155

200000 178396

150244 Colones Colones (current) 136925 126436 126393 118006 110432 97117.4 100000

0 rural urban rural urban rural urban rural urban rural urban less than primary primary complete at least 9th grade secondary complete tertiary education

Graph 3.5: Costa Rica: Average income by region and education category, 2005 Source: prepared by the author with data from the Costa Rica Household Survey 2005

Notes: less than primary includes also those who do have not gone to school. At least 9th grade refers to the completion of the third cycle or general basic education. Tertiary education includes all those who at least have a year in higher education, either para- university or university education.

It was demonstrated in section 3.2 that the low accumulation of schooling affects considerably more the low income families or families from rural or more deprived areas.

In this section it is also shown that low levels of education substantially decrease the participation in the labor market and the average income of Costa Ricans who participate

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in the labor force, mainly in rural areas. Considering that labor income represents 80% of

Costa Ricans’ total income, one could expect that low demand for education affects harmfully their welfare and keeps them in a vicious cycle of poverty.

Despite a GDP growth rate considered higher than the average for Latin America,

Costa Rica has been experiencing a threshold of poverty of around 20% since 1980, as table 3.8 below shows. In fact, the percentage of households living below the poverty line increased while the gross domestic product (GDP) and GDP per-capita based on purchasing power parity308 was also increasing. During the first half of the 1990s the trend in poverty incidence was more closely synchronized with economic growth but not for long. Since 1995 it has remained at around 20% and with a tendency to remain more or less constant, while GDP has continued increasing. This type of behavior of poverty for a continuous number of years is what Barret and Swallow call a pattern of self- reinforcing poverty or persistent poverty that could mean a poverty trap for all or the majority of households that are included in this statistic.309 Moreover, the Gini coefficient for Costa Rica increased from 37.4 in 1990310 to 40.6 in 2005.311 In 2000,

308 Purchasing power parity (PPP) is the most favored method to convert national economic indicators such as GDP and GDP per capita to a common currency. These indicators are converted to international dollars instead of US$. The conventional conversion uses the nominal official exchange rates to convert these indicators to US$. This conversion translates into very low figures for developing countries which do not reflect the purchasing power of those countries given that in those countries prices are generally lower. The PPP method converts these indicators to a common currency that reflects purchasing power parity. As a result the conversion using the conventional method will produce lower amounts than the conversion using PPP. Gerald Meier, Biography of the Subject: An Evolution of Development Economics (New York: Oxford University Press, 2005): 4, 207 (note 1). However, this method does not reflect the purchasing power of the country.

309 Barrett, Christopher B. and Brent M. Swallow, “Fractal Poverty Traps,” World Development, Vol. 34, No. 1 (2006): 1-3.

310 Programa Estado de la Nación en Desarrollo Humano Sostenible, Estado de la nación en desarrollo humano sostenible: undécimo informe (San José, Costa Rica: Programa Estado de la Nación, 2005): 98.

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while the richest 20% of the population earned 51% of total income, the poorest 20% earned only the 4.2%.312

Table 3.8.Costa Rica Poverty rate and GDP growth

Years Poverty rate GDP growth 1986 23.7 5.50 1990 27.1 3.60 1995 20.4 3.9 2000 21.1 1.80 2005 21.2 5.9

Sources: Gross domestic product based on purchasing-power-parity (PPP) per capita and GDP annual growth (IMF data base web site: IMF: http://www.imf.org/external/ns/cs.aspx?id=28 retrieved March 2007); Poverty Incidence: 1986 (Trejos, J.D. Portrait of the Poor, p 173); 1987-1991 (World Bank. Social Spending and the Poor, p 13; 1991-1993 (Costa Rica: Informe del Estado de la Nación, 2002, p. 340); 1994 -2005 (Costa Rica: Informe del Estado de la Nación, 2004 p. 406

This increasing inequality is given by a income fall in the only source of income of the poorest households, lowest 25%, their earnings from work and a substantial rise in the income sources of the richest households ( the top 20%), wages or earnings from their work, rents and profits.313 In addition to the intrinsic class inequities planted by the old Costa Rican institutions, going back to the Spanish colonization, the main factors that are affecting this earning differential are the income gap between qualified and non- qualified labor, the regional gap and gender gaps in terms of labor income and the prevalence of a chronic poverty mass that does not have access to the labor market.314

311 Ibid, 107.

312 David De Ferranti, et al., Inequality in Latin America and the Caribbean: Breaking with History? (Washington, D.C.: The World Bank, 2004), Statistical Appendix, tables A2 and A3.

313 Programa Estado de la Nación en Desarrollo Humano Sostenible, Estado de la nación en desarrollo humano sostenible: undécimo informe 2004, 106.

314 Ibid, 107 128

I have shown how the low demand for education has a perverse impact on Costa

Ricans’ standard of living. I have also shown that the labor market seems to reward higher education with a considerably difference in participation and in salary with respect to complete secondary education. It has been also shown that secondary education is a turning point for the labor market, most clearly established in the urban areas, in terms of the rate of increase of average income compared to the previous stages of accumulation of education. Hence, the question that should be raised is why students do not accumulate education if the rewards would be higher if they continued studying? In fact, the average income gap between qualified and non-qualified workers shown in graph 3.6, increased every year from 1990 to 2002 when a qualified worker was more likely to earn 2.47 times the average; in 2003 the gap started to decrease, but still the average for qualified labor is more than twice as much the average income of a non-qualified worker by 2005 (2.33 times). This outcome is magnified if it is considered that at least two thirds of the labor force in 2005 is unskilled labor and the size of unskilled labor is more than twice the size of skilled labor.315

315 These figures are obtained with a labor force between 20 years old and 60 years old. These statistics increase to 70% and 2.5 times, respectively, if individuals 18 years and older are included. (Source: my own calculations using the Costa Rican Household Survey 2005). 129

Costa Rica: Average income in the main occupation according to worker's qualifications (2006:0)

Non qualified Qualified Total

Graph 3.6: Costa Rica: Average income in the main occupation according to worker’s qualifications (2006: 0)

Source: prepared by author using data provided by Pablo Sauma.

Note: Qualified labor are those employed who have secondary complete or more. Non-qualified labor refers to those with less than complete secondary education

3.5 Conclusion

The analysis of the education statistics in this chapter demonstrates that school abandonment is very severe in Costa Rica. The critical moments are the transitions from cycle to cycle, starting at the end of primary school, when students make the transition from the second cycle to the third cycle of secondary education (6th to 7th), and during 7th and 10th grades. The high rates for repetition and failing seem to exacerbate this problem.

In fact, among the causes for dropping out pointed out by youths the academically related ones represent 45% of the total, being the economically associated ones 30% of the total. Desertion is a problem that does not afflict the whole country equally. It affects all income groups in an escalating way, except for the richest 25% and also the rural areas. Although the higher gains in terms of inequities regarding education access,

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during this 16-year period (1989-2005) across all stages of education, and particularly in secondary education, are in the reduction in the discrepancy between urban and rural areas, the discrepancy is still considerable and the indicators of desertion and school attendance (2005) show bias against students from rural areas. The country’s socioeconomic regions which are mostly rural are affected by desertion and low school attendance practically in their totality. It might also be the case that the increase in completion of secondary level in rural areas compared to urban areas, during the last 16 years, is also driven by well-off students in rural areas.

However, the increases in coverage in all education stages, and mainly in secondary education completion, were driven primarily by the increase in participation of the richest 25% students and secondly by students from rural areas. This raises the question of whether or not there is an intersection between these two groups: students from rural areas and the richest 25%.

Although it is common knowledge that the higher the levels of education, the less likely poverty is, in Costa Rica this relationship is perfectly opposite for the groups belonging to the lowest and upper quintiles. In fact, having completed secondary education guarantees higher levels of income but not enough to belong to the fifth quintile. The labor market makes a sharp distinction between individuals with at least one year of higher education and the other education completion groups: the larger labor market gains lie in the continuation of schooling towards higher education. The average income gap is much greater in the rural than in the urban areas. However, non-linearities vary also within zones: it seems that in rural areas, for it pays off for individuals to continue studying until they at least complete the third cycle, while in the urban areas, the

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accumulation of years of education is not recognized until secondary education is completed.

Therefore, there seem to be some non-linearities in the returns to education as well as academic, economic and personal factors that vary with region and income levels that are influencing decisions to drop out and when to do it. Further inquiry regarding these aspects will be the topic of the next chapters.

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CHAPTER 4

THE INCREASING RETURNS TO EDUCATION

4.1 Introduction

The objective of this chapter to test the convexity of the monetary private returns to education in Costa Rica. Convexity or increasing returns to education characterizes the relationship between earnings and years of schooling if only higher levels of education extract high returns.

I have analyzed in previous chapters some factors that in Costa Rica lower the expectations of parents and children regarding their returns to education and increase their marginal costs of schooling.316 One of these factors is that the quality of public education at primary and secondary level is deficient. This constitutes a considerable obstacle to pass the comprehensive tests to graduate from secondary school317 and is also an entry barrier to enter the public universities, which are very selective.318 It also

316 This is so because for parents and their children who are deciding whether or not to invest in education, their expectations regarding the returns of their investment are depicted in their demand for education (the discounted value of the marginal benefits from schooling). Their demand for education is a function of quantity and quality of schooling, innate ability and motivation and family background (Paul Glewwe and Michael Kremer, “Schools, Teachers and Education Outcomes in Developing Countries,” Chapter 16 of Handbook of the Economics of Education, Volume 2, edited by Eric Hanushek and Finis Welch [Amsterdam: North-Holland Publications, 2006], 965). How much quantity of schooling they will demand depends also on their marginal cost of schooling. This cost includes the pecuniary and non- pecuniary (opportunity costs) cost of attending school.

317 See Chapter 3, section on desertion.

318 See Chapter 2, section on school resources.

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increases students’ marginal cost of staying in school.319 Another factor is the non-free nature of public education. Although education is publicly provided, it is costly. As mentioned previously, the initial investment incurred in sending at least two children to primary school or alternatively to secondary school, represented around 100% of the average household per-capita monthly income.320 This cost does not include additional expenses such as transportation, school supplies, replacement costs of uniforms or shoes and contributions to PTAs that families confront during the school year.

In other words, schooling is extremely expensive even for families who are not considered the poorest of the poor. Moreover, and this is another factor, access to educational loans is limited and costly and as expected exists only for higher education321; it is not an option for poor or even for middle income families, which becomes a serious problem since most of the families in Costa Rica are liquidity constrained. This is so because in Costa Rica income distribution is very unequal322 and since labor is the main source of income for Costa Rican families, it does not seem that the situation will change for most of them. There is not only a large discrepancy between the wages of unskilled and skilled labor, but also the ratio between skilled and unskilled is low. In 2005, as mentioned before, the average earnings of a skilled worker are 2.33

319 See analysis in Chapter 3.

320 For households in 2006, this cost represented 91% and 104 %, respectively of the average household per-capita monthly income. Moreover, the initial cost of two children in secondary school amounted to approximately 4.5 times the cost of a food basket for an individual in the urban area and to more than 2 times the average per-capita income of a family in the second quintile (see Chapter 2, section on retention/inclusion programs).

321 See Chapter 2, section on the quantity of and financial allocations to education.

322 The Gini coefficient in 2005 was 40.6 in 2005. In 2000, while the richest 20% of the population earned 51% of total income, the poorest 20% earned only the 4.2%. (See Chapter 3, section on poverty as a trap for the uneducated).

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larger than the average earnings of an unskilled worker and the segment of unskilled labor is more than 2 times the size of the segment of skilled labor.323

This test of convexity of returns is necessary to ascertain the prediction about accumulation or lack of accumulation of human capital contained in the theoretical model developed by Lars Ljungqvist to explain the behavior of skilled and unskilled workers regarding investment in education. I adapted this macroeconomic model to make it more applicable to the micro-level decisions of parents regarding their children’s schooling.

The adapted model predicts that if two critical assumptions hold: there is a missing market for educational loans and returns to education are increasing in such a way that only a critical mass of education extracts high returns, then unskilled parents will have no incentive to invest in the education of their children because accumulating that stock of education requires to give up consumption for a long period of time.

The use of Ljungqvist’s theory for understanding the role of returns to education on parent’s decisions regarding their children’s education in Costa Rica is one of the main contributions of my research. Ljungqvist’s model was not devised to provide explanations at the household-level; moreover, parents and children were not the actors in his model, but skilled and unskilled workers. However, I adapted his model to be applicable to household decisions on children’s schooling.

The chapter is divided in five sections. Section one contains the introduction.

Section two includes the literature review. In section three the conceptual framework is laid out. The fourth section deals with the empirical analysis. It is divided in five subsections: data description, analysis of samples characteristics, empirical model, empirical

323 See Chapter 3, section on poverty as a trap for the uneducated. 135

issues and analyses of regression outputs. Finally, the fifth section renders the conclusions.

4.2 Literature review

I was unable to find any literature regarding the testing of Ljungqvist’s model in any specific country. To my knowledge, this is the first attempt to use the non-linearity test to show convexity in the returns to education to uphold Ljungqvist’s theory.

To support this claim and also given that most of the effort in this chapter regarding the testing of this model is to examine whether or not the monetary returns to education in Costa Rica are increasing, I review the main literature regarding the two empirical methods scholars have taken to calculate the relationship between the private rewards to education in the labor market and the time spent in schooling. A detailed historical review of the literature regarding the functional forms developed to calculate the monetary private average returns to time in schooling as well as the econometric issues surrounding them can be found in the appendix, in table A.4.1.

It is necessary before proceeding with the literature review, to briefly explain the two conventional approaches to calculate rates of return.324 The basic assumption in the theory of human capital investment is that individuals facing a given interest rate choose to invest to maximize their net present value of their life earnings net of cost (direct and

324 This section was written using the following sources: Mark Blaug, The Economics of Education and the Education of an Economist (Washington Square, New Jersey: New York University Press, 1987): 4-8; Psacharopoulos, George, “The value of investment in education: Theory, evidence and policy,” Journal of Education Finance, Vol. 32, No.2, Fall 2006: 113-136, and Behrman, J.R, “Labor Markets in Developing Countries,” Ch. 43 in Handbook of Labor Economics,” Volume 3C, edited by Orley Ashenfelter and David Card (Amsterdam and New York: North-Holland, and New York, N.Y.: Elsevier Science Pub. Co., 1999): 2910-2912.

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indirect).325 In other words, individuals try to optimize their investment in human capital by accumulating years of schooling until the point where the marginal return to schooling is equal to its marginal cost. The first approach calculates the internal rate of return or discount rate that equates the marginal benefit to the marginal cost. This return can be calculated from the individual perspective: the private internal rate of return, and from the social perspective: the social internal rate of return. The method to find the private internal rate of return is to obtain for a particular year, and for different individuals, according to their levels of education, the direct economic rewards after taxes and the private costs (indirect cost or forgone earnings while he or she studies but also direct cost such as tuition, books, materials and other private costs) associated with each particular level of schooling. The internal rate of return would be the discount rate that would equate the present value of the earnings at each level with the corresponding cost. The social rate of return is obtained by including earnings before taxes (instead of earnings after taxes) and adding the public cost of education (subsidies) to the private cost.

The alternative and most frequently approach used is the Mincerian framework326 that calculates the monetary average private rate of return to time spent in schooling. To obtain the average rate of return to education, the natural logarithm of individual earnings of a sample of individuals with different levels of education is regressed on the years of completed schooling controlling for a quadratic experience (post schooling) term.

Compared to the previous approach, the one that calculates the optimization private

325 Robert J. Willis, “Wage Determinants a Survey and Reinterpretation of Human Capital Earnings Functions,” in: Handbook of Labor Economics, Volume 1, eds. O. Ashenfelter and R. Layard (Amsterdam and New York: North-Holland and Elsevier Science B.V., 1986), 545.

326 Jacob Mincer, Schooling, experience and earnings (New York: National Bureau of Economic Research and New York and London: Columbia University Press 1974), passim. 137

discount rate, two important differences should be highlighted. Mincer’s method does not include the direct or pecuniary costs as part of the private cost, but only time spent in school as a proxy for forgone earnings.327 Hence, the monetary returns calculated in the

Mincer’s method are gross monetary returns. Also, the method does not calculate the investment optimization rate, but how much average earnings increase with schooling328.

The calculation of the gross monetary private rate of return to time spent in schooling which corresponds to the second approach is the focus of this chapter.

There are two empirical methods to calculate the gross monetary private rate of return to time spent in schooling. The most frequently used has been the one already mentioned and developed by Jacob Mincer (1974). It determines that the logarithm of earnings is a linear function of years of completed schooling.329 The schooling coefficient obtained by regressing the log of individual earnings on the years of completed schooling is interpreted as an estimate of the average rate of return to an additional year of education.330

The second method, which is the one used in this research, poses that the natural logarithm of earnings are a nonlinear function of years of completed schooling and therefore the schooling coefficient represents the gross monetary private rate of return

327 Mincer assumes “all investment costs are time costs” and “that each year of schooling reduces ones earning life by exactly one year.” Mincer, 7-8.

328 James J. Heckman, Lance Lochner and Petra E. Todd, “Earning functions, Rates of Return and the Treatment Effects: The Mincer Equation and Beyond,” Chapter 7 of Handbook of the Economics of Education, Vol. 1, eds. Eric Hanushek and Finis Welch (Amsterdam: North-Holland, 2006), 318.

329 In Mincer’s framework the logarithm of individual earnings is an additive function of a linear education term expressed in years of completed schooling plus a quadratic experience term: Ln y[S,X] = β0 2 + β1S+ β2 X+ β3X + u.

330 I am adopting Heckman, et al.’s interpretation of this coefficient since it serves our purpose to know how much parents expect an additional year of schooling will increase the income of their children on average. I will refer indistinctly to the estimate of the schooling coefficient as average rate of return or rate of return, following scholarly usage. See Heckman et al., 318, 327-329 138

associated with each year of schooling. This method formulated to capture sheepskin effects,331 i.e., wage premium or higher returns at certain points in the education process, was devised by Hungerford and Solon in 1987.332 The basic difference between the two methods is that while the non-linearity feature of this method assumes that the logarithm, earnings do not increase in the same proportion with each year of schooling, the linearity feature of the Mincerian framework assumes that each additional year of schooling, regardless of the level of education, affects earnings in the same proportion,333 i.e., the rates of return are equal whether the individual is in primary, secondary or tertiary education and that the rate of return to an additional year of schooling is equal within each level no matter if it is the first year or the last year of school. Most of the empirical research after the 1990s has assumed linearity in the earnings education relationship,334

331 This, also called screening, signaling or credentialism, identifies a phenomenon in which higher rates of return are obtained by individuals with diplomas as opposed to those without them (Richard Layard and George Psacharopoulos, “The Screening Hypothesis and the Returns to Education,” The Journal of Political Economy,” Vol. 82, No. 5 [September-October,1974], 986, 995). Arrow affirms that college graduates will get paid more than non-graduates because employers do not have enough information to select workers based on their marginal productivity, so they rely on education as a filter to identify those already more able (Kenneth Arrow, “Higher Education as a Filter,” Journal of Public Economics, Vol. 2, No. 3 [July 1973]: 193-216.) This sheepskin effect notion challenged, in its early stages, the human capital theory that considered schooling as a means to acquire or increase individuals’ skills improving their productivity on which the Mincerian model was built. However, by the end of the 1980s, it was extended in empirical research to represent the wage premium for completing the final years of each school level and credentialism was seen as an additional instrument to identify the more productive workers (Harry A. Patrinos, “Non-Linearities in the Returns to Education: Sheepskin Effects or the Threshold Levels of Human Capital?,” Applied Economic Letters, Vol. 3, No. 3 [March 1996]: 171); also see: John G. Riley,” Testing the educational screening hypothesis,” The Journal of Political Economy, Vol. 87, No. 5, Part 2 (October 1979): Education and Income distribution: S227-S252, and Hungerford and Solon.

332 Thomas Hungerford and Gary Solon, “Sheepskin effects in the returns to education,” The Review of Economics and Statistics, Vol. 69, No. 1 (Feb 1987), 176.

333 David Card, “The Causal Effect of Education on Earnings,” in: Handbook of Labor Economics, Vol. 3, eds. Orley Ashenfelter and David Card (Amsterdam and New York: North-Holland, and New York, N.Y.: Elsevier Science Pub. Co., 1999), 1806.

334 See for example David Card, ibid; David De Ferranti, et al., Inequality in Latin America and the Caribbean: Breaking with History? Washington, D.C.: The World Bank, 2004; George Psacharopoulos and Y.C. Ng, “Earnings and Education in Latin America: Assessing Priorities for Schooling Investments” (Washington, D.C.: World Bank, Technical Department, Latin America and the Caribbean Region, Policy 139

despite the supremacy, in my opinion, of the non-linearity premise. The assumption of non-linearity is superior to that of linearity because it permits to explore both a linear and a non-linear relationship by allowing flexibility in the functional relation between log earnings and schooling. Another advantage of assuming non-linearity is that its occurrence points to further research regarding to its possible causes. Indeed, non- linearities might result not only from individual differences in the accumulation process of education,335 but also from a labor market structure that favors skilled workers336 or requires particular diplomas for having access to some jobs337 or from the threshold effects triggered by the accumulation of a critical mass of high skilled individuals338 or

Research Working Papers, WPS 1056, December 1992); George Psacharopoulos and Eduardo Velez, “Schooling, Ability and Earnings in Colombia 1988,” Economic Development and Cultural Change, Vol. 40, No. 3 (April 1992): 629-643 and George Psacharopoulos, “Returns to Investment in Education: A Global Update,” World Development, Vol. 22, No. 9 (1994): 1325-1343.

335 James Heckman, Anne Layne-Farrar and Petra Todd, “Human Capital Pricing Equations with an Application to Estimating the Effect of Schooling Quality on Earnings,” The Review of Economics and Statistics, Vol. 78, No. 4 (November 1996), 568.

336 Either because it rewards differently across skill groups or because skilled labor is more tradable across labor markets than un-skilled labor (ibid, 568). Also in Heckman et al., “The Schooling Quality Earnings Relationship: Using economic theory to interpret functional forms consistent with the evidence” (Cambridge, Massachusetts: National Bureau of Economic Research, Working Paper 5288, October 1995), 15.

337 Norbert R. Schady, “What Education Pays? Non-Linear Returns to Schooling among Filipino Men,” Paper submitted to the Latin American and Caribbean Economic Association (or Asociación de Economía de América Latina y el Caribe) (LACEA), 8 September 2000.

338 At these thresholds the returns to labor take off at a higher scale as a result of externalities produced by this stock of know how. In their model, which assumed perfect credit markets, homogeneous households and equal ability among individuals, an individual, when she is young, will choose to acquire the high-fixed cost techniques if the higher relative return compensates her for longer preparation time. Indeed, their model reinforced the productivity role of human capital: externalities are triggered when a critical mass of overqualified people is developed. (Costas Azariadis and Allan Drazen, “Thresholds Externalities in Economic Development,” Quarterly Journal of Economics, Vol. 105, No. 2 (May 1990): 501-526. Patrinos tested their hypothesis in Guatemala and argued that thresholds effects explained the non-linearities of the relationship between earnings and education rather than sheepskin effects (Patrinos, 1996).

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from differences in quality of schooling across levels339 which in turn produces a process of self-selection. Moreover, and this is what this analysis is more concerned, the presence of non-linearities might lead to discrepancies in the acquisition of education by different economic groups which eventually causes the emergence of income inequalities, poverty traps and dual economies.

Unfortunately, this last focus has not been the objective of the scarce empirical research exploring non-linearity in the field of returns to education. Six articles can be mentioned where convexity of the relationship between wages and education was explored for a developing country during the last 10 years340: Savanti and Patrinos in

Argentina (2005)341, Arabsheibani and Manfor in Libya (2001)342, Blom et al. for Brazil

(2001)343, Schady for the Philippines (2000),344 Patrinos for Guatemala (1996)345 and

339 John Strauss and Duncan Thomas (voicing Behrman and Birdsall 1983), “Wages, schooling and background: investments in men and women in urban Brazil,” in: Opportunity forgone: Education in Brazil, edited by Nancy Birdsall and Richard H. Sabot (Washington, D.C.: Inter-American Development Bank, 1996), 161.

340 Other articles that have calculated the returns to education for Latin-American focus on reporting the changes through time. They use a simplified semi-parametric form to observe the education effects in which dummies represent each level of education. See for example Blom et al, “Education, Earnings, and Inequality in Brazil, 1982-1988: Implications for Education Policy, Peabody Journal of Education, Vol. 76 (3 & 4) (2001): 180-221, and Harry Anthony Patrinos and Chris Sakellariou, “Economic Volatility and Returns to Education in Venezuela: 1992-2002,” Applied Economics, Vol. 38, No. 17 (September 2006): 1991-2005.

341 Maria Paula Savanti and Harry A. Patrinos, “Rising Returns to Schooling in Argentina, 1992- 2002: Productivity or credentialism?” (Washington, D.C.: World Bank Policy Research Working Paper 3714, [September 2005]).

342 G. Reza Arabsheibani and Lamine Manfor, “Non-linearities in Returns to Education in Libya,” Education Economics, Vol. 9, No. 2 (2001): 139-144.

343 Blom, Andreas, Lauritz Holm-Nielsen and Dorte Verner, “Education, Earnings, and Inequality in Brazil, 1982-1988: Implications for education policy,” Peabody Journal of Education, Vol. 76, Nos. 3&4 (2001): 180-221.

344 He also touched on the relationship between increasing returns and poverty or inequality by mentioning differences in the relative demand for and supply of workers with different amounts of education and credit constraints in the Philippines as possible reasons for convexity. Norbert R. Schady, “What education pays? Non-linear returns to schooling among Filipino men,” Paper submitted to the Latin 141

Strauss and Thomas, also for Brazil (1996). Strauss and Thomas346 went beyond finding the discontinuities in the returns to education to disprove the linearity assumption. They elaborated on the possible causes of the convexity of the returns and expressed concern regarding the impact of convexity on the relationship between logarithm of earnings and schooling on income inequality.347 They posited two plausible but not conclusive causes348 of convexity either the presence of a positive correlation between the quantity and quality of human capital investments (because children who go on to post-primary levels may attend better quality and hence more expensive schools in comparison to the average primary schools) or the structure of labor demand. They argued that in either case, it might imply that as the level of education increases inequality will also increase.349 Moreover, for Strauss and Thomas, Brazilians’ choice of not staying in school was somewhat disconcerting since they were not taking advantage of the much higher returns to education at higher education levels.350

This phenomenon was also observed by Ljungqvist when comparing the patterns of growth of developed and underdeveloped countries and led to the formulation of his theory. He was puzzled by the fact that in underdeveloped countries, “characterized by

American and Caribbean Economic Association (or Asociación de Economía de América Latina y el Caribe) (LACEA), 8 September 2000: 12-14.

345 Patrinos (1996).

346 And perhaps to some extent Blom et al: 182, 185-194 and Schady: 12-14.

347 Strauss and Thomas (1996): 179.

348 They discarded credentialism and the selection of higher ability individuals into higher education as possible causes. Ibid, 161.

349 Ibid.

350 Ibid, 179.

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high ratio of unskilled workers in the labor force, a low gross national product, a small stock of physical capital and large wage differential between skilled and unskilled workers,”351 the presence of a large differential in returns to skilled labor and unskilled labor did not seem to encourage unskilled workers to become skilled. Was individual rationality consistent with the development of dual economies352 and eventually poverty traps for those already deprived? Ljungqvist developed a model in 1991 that provided an explanation to this enigma. In his theory, the absence of a financial market for human capital and the presence of a special case of increasing returns to education where only a critical stock of education produced high returns (the two critical assumptions of his model) prevented unskilled workers from demanding the education they needed to become skilled despite the high rewards to skilled workers.

Ljungqvist’s theory did not transfer into the field of returns to education because,

I believe, the interest of the scholars in the field was in either obtaining the linear average returns to education for a specific country, or in disputing the linearity of the returns by testing the non-linearity. Also, the relationship between returns to schooling and investment in education was not a preoccupation in the field of returns to education.

Ljungqvist was not particularly concerned by this linkage either; but he was troubled by the perverse effects of human capital accumulation (or lack thereof) on income inequality which led him to establish in his model a relationship between returns to education and demand for education. Finally, Ljungqvist’s research belongs to the fields of new economic growth theory and poverty traps, and this type of research does not seem to

351 Lars Ljungqvist, “Economic underdevelopment: the case of a missing market for human capital” Journal of Development Economics, Vol. 40, No. 2 (April 1993): 221.

352 Ibid, 220 Ljungqvist’s theory evidences that the unskilled workers’ response was indeed consistent with individual rationality. 143

transport easily into the field of returns to education.

The use of Ljungqvist’s theory in my research provides a sound explanation of the conundrum Costa Rica has been facing of a lack of accumulation of its most productive asset, education, despite the high returns to skilled labor and the country’s commitment to develop a stock of human capital. Indeed, Costa Rica fits the typology of an underdeveloped country in his model: a high ratio of unskilled workers in the labor force, a low gross national product, a small stock of physical capital, a large wage differential between skilled and unskilled workers, and a high rate of return on human capital. Also, one of his two critical premises: that future labor earnings cannot serve as collateral for a loan holds for the Costa Rican case; his other premise, that the functional form between logarithm of earnings and education is of such convexity that it shows indivisibilities in education, is the subject of my research in this chapter. To my knowledge, the linkage between returns to education and children’s demand for education has not been examined using Ljungqvist’s model.

4.3 Conceptual Framework

The purpose of this section is to model the relationship between gross monetary returns to education and parents’ decisions on the amount of schooling to be invested in their children by making a simple adaptation of Ljungqvist’s theory.

In Ljungqvist’s model,353 there are two types of agents, unskilled workers and skilled workers, who have the same preferences and abilities, and each type of workers represents a family dynasty. Unskilled workers can become skilled if trained by a skilled

353 My brief transcription of the model is based on Pranab Bardhan and Christopher Udry’s interpretation of it in their Development Microeconomics (New York: Oxford University Press, 1999)as well as Ljungqvist’s own explanation of his theoretical model.

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worker (educator). Workers have to be retrained each period and each period stands for a generation. There is a large wage difference between skilled and unskilled labor and there are no loans to finance workers’ education, which implies that workers have to accumulate savings to pay the cost of their education. Under these conditions, if the returns to the amount of education needed to become skilled are as high as the returns to capital, both types of workers will choose to invest in education; however, only skilled workers can pay for their education without forgoing consumption. Unskilled workers would have to reduce consumption for a long period of time in order to accumulate the savings needed to pay for their training. As more skilled workers are in demand, the wage differential becomes arbitrarily large, which causes the cost of education to become too high relative to the wage of unskilled workers; and the length of time to accumulate savings, for the amount of schooling required to obtain the expected returns, takes too long. An unskilled worker will have two available strategies: low current consumption in order to accumulate education rapidly, or higher current consumption with slower accumulation. Clearly, the unskilled worker would not be facing this decision if credit was available and the returns to investment in human capital were not increasing. The returns are increasing because, as Bardhan and Udry 354 put it, since a worker is either educated or not (meaning that he or she has or does not have the level of education required to get the high rewards), a small investment in education does not produce any returns; the high returns come only with a large amount of investment in education.

My main adaptation of the model is that I assume that parents of school age children are themselves unskilled and skilled labor who take decisions not about themselves, but about the education of their children, as they face the distribution of the gross monetary

354 Bardhan and Udry, ibid, 126. 145

returns in their labor market for either skilled or unskilled labor. The gross monetary returns in the labor market reflect what their rewards are or expect to be according to their (parents) skills and the expected returns for their children. I also assume that there is asymmetry of information in the labor market. Information on gross monetary returns is linked to the regions or areas (rural/urban) or communities or groups of reference. For example, an unskilled parent has a low level of education, obtains very low rewards for his work (or perhaps he is unemployed) and might live in a rural area in one of the country’s regions other than the Central region, characteristics that might limit the scope of information he receives and how he perceives the gross monetary returns to education.

Also some parents might not be informed of non-monetary private returns to education, which might be the case of unskilled parents. Although what parents observe is the gross monetary returns, they know the direct and indirect costs involved in sending their children to school; hence parents consider them in their decision. These costs vary for parents depending on whether they are unskilled or skilled and also where they live. If parent are unskilled and live in rural areas, direct costs constitute a large share of their income and also the opportunity costs might be higher. For instance, in some rural areas the schools might be more distant, mainly secondary schools, and this involves not only a higher opportunity cost (less time helping at home or on the farm) but also higher costs of transportation or higher expenditure in replacement of shoes.

I make additional assumptions as follows. Parents are altruistic. Children have to complete high school at least in order to become high skilled labor. Education is costly for low skilled parents in relation to their labor earnings. There is no education credit available. Parent’s decisions would determine whether or not their children would finish

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high school and continue to higher education.

In this model, poor unskilled parents face a difficult decision because the loss of utility in giving up consumption,355 to accumulate savings for their children’s education, is higher than the delayed gains of their children’s future consumption that would result from having more education.356 If only high levels of education obtain high monetary returns, this model predicts that unskilled parents choose not to invest in their children’s education.

4.4 Empirical Analysis

4.4.1 Data description

The survey used is the Costa Rican Household Survey for Multiple Purposes

(Encuesta de Hogares de Propósitos Múltiples – EHPM), conducted in July of 2005 by the National Statistics and Census Institute (Instituto Nacional de Estadísticas y Censos –

INEC). The survey is country wide and covers approximately one percent of the population living in private dwellings. The INEC has been performing household surveys since 1976; however, the EHPM in its present design has been carried out since

1987. The EHPM is conducted using a random sample obtained in two stages based on a stratified population. First, the population is divided in 12 strata, each of the country’s 6 economic regions divided by the 2 types of zones: rural and urban; second, a random sample of survey segments is obtained within each stratum and third, a random sample of

355 Assuming that “the marginal utility of consumption approaches infinity as consumption approaches zero.” Ibid, 129.

356 As mentioned in chapter 1, section 1.3.2, liquidity constrained parents (no access to credit and low earnings) have a higher discount rate than that of the non-liquidity constrained parents. Liquidity constrained parents not only might have higher direct costs but also those direct costs represent a large proportion of their income and therefore incurring in them implies giving up consumption. 147

dwellings is obtained within each survey segment. Specifically, the survey for 2005 covered a total of 11,480 households and 43,400 individuals. The survey is carried out every year and it is the only source of detailed information on earnings and personal and household characteristics of all workers. The survey has 4 modules: basic information, socio-demographic characteristics, economic activities, and housing. In 2005 a module on Internet use was included only for that year. With respect to the type of data needed for the analysis, the economic activities module provides information for household members aged 12 years and older about employment status, types of jobs, monthly income earnings in primary and secondary occupations, and hours worked per week. The socio-economic module focuses on educational attainment. The questions in the education module inquire of individuals older than 5 years of age their last year of schooling passed, whether they are attending some form of educational institution and of what type or level (primary, secondary, distance education, etc.), reasons for not attending, whether the school is private or public, the degree obtained, career, and languages spoken.

The unit of analysis is the employed individuals that were born in Costa Rica.

Employed are those that during the reference period, the week preceding the week of the interview, spent at least an hour in the production of goods and services or who held a job from which they were absent for circumstantial reasons. The sample is constituted by

12,535 individuals between the ages of 20 and 60 and from all sectors of the economy who are receiving income from their work and whose work hours are known. For analytical purposes the sample was divided into subsamples, shown in table 4.1.

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INEC has been collecting and analyzing data since 1987; hence the procedures have been standardized, which diminishes the possibility of inconsistency and variability in the collection and analysis of data. This consistency in data collection plus the non- abstract nature of the constructs, and the large number of observations reduce potential random and nonrandom errors in their measurement.

Table 4.1. Costa Rica 2005: Sample distribution by categories

Categories Sample size

Absolute %

Females 4279 34.14

Males 8256 65.86

Rural 6805 54.29

Urban 5730 45.71

Central 6551 52.26

Chorotega 1272 10.15

Pacífico 1070 8.54

Brunca 1523 12.15

Huetar Atlántica 1290 10.29

Huetar Norte 829 6.61

Total 12,535

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4.4.2 The sample characteristics

Tables 4.2, 4.3 and 4.4 show profiles of the total sample, and of the stratified subsamples: gender, areas (rural and urban) and regions (6). The stratification is necessary since the test of convexity is performed not only for the total sample but also by subgroups to be able to examine if the increasing returns to education is also typical of the different subgroups.

Table 4.2 shows that the majority of workers are unskilled, 67% of the total sample. Indeed unskilled workers more than double skilled workers. Also, the earnings per hour of unskilled workers are less than half of those of skilled workers. The educational gap between unskilled and skilled workers is remarkable. While the majority of unskilled workers (71%) have less than or completed primary education, the majority of skilled workers (61%) have completed more than secondary education. This explains the large gap in salary and also shows the amount of education an individual requires to be considered skilled in the market, despite the fact that, by definition, to be skilled is to have at least completed secondary education. Unskilled workers’ low level of education, their lack of knowledge of a foreign language and the fact that most of them are in rural areas explains their lower earnings. Unskilled workers are on average also older, come from rural areas, do not know a language different from their native language and have a greater concentration of males than the skilled workers.

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Table 4.2. Costa Rica 2005: Profile of the observations: total sample

Variable Total unskilled skilled

Mean wage per hour 934.6 690.60 1433.45

Mean log of wage 6.53 6.31 6.99

Average years of schooling 8.44 6.05 13.32

With less than primary complete (%) 14 22 –

With primary complete (%) 34 50 –

With less than secondary complete (%) 19 28 –

With secondary complete (%) 13 – 40

With more than secondary complete (%) 20 60

Average age 36.96 37.50 35.85

Only Spanish (%) 90 97 75

Spanish and English (%) 9 2 23

Female (%) 34 29 44

Rural (%) 54 64 35

No. of observations 12535 8417 4118

Tables 4.3 and 4.4 below show the profiles of the observations of the samples different from the total sample. The large concentration of unskilled workers in the labor force is more striking in the analysis by sub-groups. It can be observed that on average in all sub-groups unskilled workers represent between 70% to 80% of the labor force except for the sample of females and individuals from the Central region where they represent close to 60%. Unskilled workers average earnings in all subgroups are lower than the average for the country (690.6 colones) except for those that work in the Central and

HuetarN regions, or in urban areas and are males. Earnings however, for both unskilled

151

and skilled workers, are higher than the country average if the individuals work in the

Central region, in an urban area and are males. The overall pattern of larger concentration of unskilled workers with primary complete or less and of skilled workers with more than secondary complete remains for all subsamples.357 There are some differences in the size of the concentration for some subgroups. For instance, in the urban areas the percentage of unskilled workers who have less than or have completed primary education (44%) is smaller than the country’s. Also, the percentage of skilled workers who have more than secondary complete is the highest for the female subgroup

(67%) and smaller than the country’s for males (55%), rural (55%) and for the Pacífico,

Brunca, and HuetarA regions (around 55%). Another similarity among most of the samples is that the majority of observations come from rural areas.

Table 4.3. Costa Rica 2005: Profile of the observations: total sample, by gender and by area

Variable Female Male Rural Urban Unskilled workers (%) 57.2 72.3 78.8 53.3 Mean wage per hour 946.1 928.7 788.5 1108.2 Mean log of wage 6.52 6.54 6.38 6.71 Mean wage per hour unskilled 662.8 702 658.5 747 Mean wage per hour skilled 1324 1520 1272 1521 Average years of schooling 9.47 7.90 7.29 9.80 With less than primary complete (%) 10 17 20 8 With primary complete (%) 28 37 42 24 With less than secondary complete (%) 19 19 17 22 With secondary complete (%) 13 12 10 17 With more than secondary complete (%) 30 16 11 30 Average age 36.49 37.20 36.90 37.03 Only Spanish (%) 89 90 94 85 Spanish and English (%) 10 9 5 14 Female (%) ------29 40 Rural (%) 47 58 ------No. of observations 4279 8256 6805 5730

357 Not shown in the tables. 152

Table 4.4. Costa Rica 2005: Profile of the observations by regions.

Variable Central Chorotega Pacífico Brunca HuetarA HuetarN Unskilled workers (%) 59.7 68.6 76.9 75.9 82.6 71.4 Mean wage per hour 1043 842 887 768 731 906 Mean log of wage 6.65 6.43 6.44 6.35 6.32 6.50 Mean wage per hour unskilled 721.4 653 674.4 653.9 635.1 736.9 Mean wage per hour skilled 1518.4 1254.4 1596.3 1126.8 1185.6 1328 Average years of schooling 9.18 8.31 7.48 7.50 6.96 8.01 With less than primary complete (%) 9.6 14 18.5 17 20.5 15 With primary complete (%) 31 33 37 39 40.5 37 With less than secondary complete (%) 19.5 21 19 18 20.5 18 With secondary complete (%) 15 13 10 12 8 11.5 With more than secondary complete (%) 24.9 19 13.5 14 10 18.5 Average age 36.79 37.10 37.84 37.41 36.82 36.30 Only Spanish (%) 87 93 95 96 90 91 Spanish and English (%) 12 7 5 4 8 8 Female (%) 37 32 30 32 31 30 Rural (%) 43 63 62 69 73 61 No. of observations 6551 1272 1070 1523 1290 829

4.4.3 Empirical model

I wish to detect convexity in the relationship between earnings and schooling. In order to do so I will use one specification that allows for detection of non-linearities in the returns to education, particularly strong increasing returns. This formulation is a step function with intervals of one year of schooling that does not impose any restriction on the data in order to capture any discontinuity. As said before, with this specification one obtains the gross monetary rate of return associated with each year of schooling; in other words schooling costs are not included.

153

The formulation is the following:

∑ ∑

Where: i=1…n individuals; k=0…n years of schooling and j=1..n counties

The dependent variable for the wage equation is the logarithmic hourly wage varying over individuals i. Wages per hour is calculated using two variables: income accruable to principal job, and hours worked. In some empirical research, earnings have been measured using annual, monthly or hourly wages; however, more educated people might work less and yet earn the same as a less educated person who works more.358 The hourly wage is a more reliable and valid measure because it eliminates the distortion in work earnings due to differences in time dedicated to work; otherwise, the estimated coefficient will be upwardly biased.359 The logarithm of hourly wage is used for two reasons. It compresses the scale of the earnings which reduces the heteroscedasticity present in this data. The data is cross-sectional and it is affected by the variability of sizes of the individuals as income earners. Also, the logarithmic linear model is theoretically the correct functional form for the relationship between wages and education.

358 This issue was raised by Zvi Griliches, “Estimating the returns to schooling: some econometric problems,” Econometrica, Vol. 45, No. 1 (January 1977), 3; Paul T. Schultz, “Education Investment and Returns,” 592 and Card (1999), 1808-1809.

359 Card (1999), 1808, 1809 154

The main research variable is years of completed schooling Sik. It intends to measure human capital, and in this sense is limited, as Griliches360 has pointed out.

However, and following Card as he analyzed the validity of this indicator in the United

States,361 I believe this measure has substantial face validity: Costa Rica does not have multiple education streams and hence the number of years to complete primary and secondary education is the same for all Costa Rican students. It is measured in the specification by 20 school dummies that go from whether or not individuals have no schooling to whether or not they have completed the maximum number of years of schooling in Costa Rica, 20 years of education. This is basically a piecewise function with 19 intervals which allows flexibility in the functional relation between log earnings and schooling. In order to better explain this staircase function I also run another complementary regression that is the same piecewise function, only that it gives the differential coefficients between each school year and whether or not they are significant.

These differentials are the slopes between any two years of schooling and represent the marginal return to each corresponding year of education. Another advantage of this complementary regression is that it allows us to see whether or not the increments in returns between school years are significant. The coefficient βk, measures the rate of gross monetary returns associated with each year of completed schooling. It is expected that this coefficient becomes positive and significant with the completion of secondary school. After that point the coefficient becomes larger, positive and significant.

A variable that accounts for the knowledge of a foreign language, Li, is also included because it is expected that this variable influences earnings mainly given the

360 Griliches (1977), 3

361 Card (1999), 1806 155

size of the FDI sector in the labor market and the weight to highly skilled labor given by this labor market. Also, this variable is associated with schooling and it is likely that individuals that know a second language went to a school of better quality; hence its introduction will reduce the bias of the estimated schooling coefficient. I do not think this variable is endogenous because it is not a measure of current welfare; in other words, I believe that the knowledge of an additional language by a Costa Rican in addition to her native Spanish is an asset that has been acquired previously and depends on her households’ long run economic status.

2 The independent variables Ai and Ai denote age and age squared. It was decided not to use potential experience (E), measured as age of individual (A) minus age of entry to school (DE) minus years of schooling (S), chosen by Mincer as proxy for the quadratic relationship between earnings and real experience. The reasons for this are that the use of potential experience introduces a considerable measurement error (please see the annexed table on functional forms for scholarly discussion on the topic).362 One example is when individuals do not have continuous work histories such as the case of women.363 This according to Blinder will bias upwardly the schooling coefficient.364 Also, for individuals where DE+S is equal to their age, the potential experience will be zero. This

362 See particularly the controversy on the subject between Blinder and Rosenzweig. Also, for different opinions, see in the same table, George Psacharopoulos, “Schooling experience and earnings,” Journal of Development Economics, Vol. 4, No. 1 (March 1977), Card (1999) and Heckman (1996).

363 Some scholars still prefer the use of the proxy for potential experience, but in general when they do so, they do not include women in the sample. However, Strauss and Thomas (1996) used age instead of potential experience in their work in Brazil even with only males in the sample.

364 Alan Blinder, “On Dogmatism in Human Capital Theory,” The Journal of Human Resources, Vol. 11, No. 1 (Winter 1976), 13-15.This argument is also raised by John Strauss and Duncan Thomas, “Human Resources Empirical Modeling of Households and Family Decisions,” in: Handbook of Development Economics, Vol. 3A, edited by Jere Behrman and T. N. Srinivasan (Amsterdam, the Netherlands: Elsevier Science Publishers B. V., 1995): 1969. 156

might place on the same footing, in terms of experience, individuals who work while studying with individuals who have been repeating years in school. In addition, I obtained several cases with negative potential experience when using Mincer’s measurement for potential experience. Given the above and the fact that I am including women in our sample, I decided to use age instead of potential experience to lessen the measurement error. Age controls for the differences in earnings due to the age cycle which I believe might be more associated to schooling and affect earnings than potential experience.

A zone dummy, Ri, accounting for whether the individuals come from rural or urban areas is included. Observations in the sample that come from rural areas might have experienced a different labor market as compared to observations from urban areas; also, their years of schooling might have been influenced by different factors affecting observations from urban areas. For instance, the distance to school is probably larger and the quality of education and infrastructure available is more deficient. A gender dummy,

Gi, is also introduced because the previous analysis shows that this group is in general different from the other groups: it is more educated and more urban. There is something in this group that makes them higher achievers compared to the male group. The fixed effects dummies, C, varying over j counties (79) were included in the specification to control for unobserved differences between the units of analysis. These dummies also control for differences in access to information on labor markets. Further, the county dummies are also part of the formulation to account for quality of education given that only 58% of Costa Ricans in my sample remain in the same county they were born. A description of the variables is presented in table 4.5 below.

157

The wage function is estimated 12 times. It is calculated for the total sample at the country level, for each gender (2), for each zone or area (2) and for each region (6). The purpose of the 10 regressions stratified by subgroups is to test if the returns to education are convex by regions and sectors despite their heterogeneity; hence the purpose is not to show that the same type of convexity exists across the samples. Finding increasing returns in the different regions and areas might indicate that convexity characterizes the labor market across regions and sectors, which strengthens the robustness of the results.

Also, although Costa Rica is small, migration between regions is not generalized.365

Therefore, households decisions might be influenced by the particular rates of return to labor they are facing in their communities of reference. Finally, a wage function for a reduced/restricted country sample that includes only the individuals who reside in the same county they were born is estimated with the purpose of controlling in a more direct way for school quality. Individuals in the restricted sample who belong to same county attended primary and secondary schools located in that county. It is expected that these individuals were exposed to the same quality of education and educational policies.

365 There are two regions where migration has been detected, the Brunca region, from where people have emigrated to the United States and the Chorotega region, from where people move to other regions. 158

Table 4.5. Costa Rica 2005: Description of variables used in the regression analysis

Variables Description Dependent variables Log of wage Logarithm of hourly earnings Independent variables Education in years of education No school* Dummy =1 if no schooling; 0 otherwise One year Dummy = if 1 year of schooling; 0 otherwise Two years Dummy =1 if 2 years of schooling; 0 otherwise Three year Dummy =1 if 3 years of schooling; 0 otherwise Four year Dummy =1 if 4 years of schooling; 0 otherwise Five year Dummy =1 if 5 years of schooling; 0 otherwise Six year Dummy =1 if 6 years of schooling; 0 otherwise Seven year Dummy =1 if 7 years of schooling; 0 otherwise Eight year Dummy =1 if 8 years of schooling; 0 otherwise Nine year Dummy =1 if 9 years of schooling; 0 otherwise Ten year Dummy =1 if 10 years of schooling; 0 otherwise Eleven year Dummy =1 if 11 years of schooling; 0 otherwise Twelve year Dummy =1 if 12 years of schooling; 0 otherwise Thirteen years Dummy =1 if 13 years of schooling; 0 otherwise Fourteen year Dummy =1 if 14 years of schooling; 0 otherwise Fifteen year Dummy =1 if 15 years of schooling; 0 otherwise Sixteen years Dummy =1 if 16 years of schooling; 0 otherwise Seventeen years Dummy =1 if 17 years of schooling; 0 otherwise Eighteen years Dummy =1 if 18 years of schooling; 0 otherwise Nineteen years Dummy =1 if 19 years of schooling; 0 otherwise Age continuous Age squared The square of the individual’s age Only Spanish* Dummy =1 if only knowledge of Spanish; 0 otherwise Spanish & English Dummy =1 if knowledge of English (and Spanish); 0 otherwise Spanish & other language Dummy =1 if knowledge of any other foreign language; 0 otherwise Female Dummy =1 if female; 0 otherwise Rural Dummy =1 if from rural zone; 0 otherwise County dummies Dummies for 79 counties Default category A Costa Rican male from the urban area who has not gone to school.

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4.4.4 Empirical issues

The use of a step function to reflect the functional form between the natural logarithm of wage and years of completed schooling responds to the objective of the analysis pursued in this chapter and the theory supporting it. Certainly, only the step function permits any relationship between both variables to emerge without imposing any restrictions. However, statistical tests were run to demonstrate that the relationship between natural logarithm of wage and years of completed schooling is non-linear, that the piecewise specification chosen provides the best fit compared to the linear and quadratic models and also that the returns are convex or increasing sharply at a certain point.

Two tests of non-linearity were performed. Graph 4.1 displays the augmented component-plus-residual plot (acprplot) for the total sample which clearly shows a deviation from linearity.

The second test performed is an F test of joint equality of the estimated coefficients obtained after running the step regressions (both the standard step and the incremental one). The null hypothesis is that all the estimated coefficients are equal. This hypothesis was rejected; hence the notion that the association between logarithm of wage and years of schooling is linear is discarded. This is also a test of convexity since, as will be shown in the following section, the returns to education are increasing. This test was performed for all the regressions using all the stratified samples. The results of the F test for the incremental step regressions are presented in table 4.6 below.

160

-2

Augmented component plus residual plus component Augmented

-4 0 5 10 15 20 years of education

Graph 4.1. Costa Rica 2005: Augmented component- plus-residual plot. Natural logarithm of wage and years of schooling completed. Total sample.

Table 4.6. Costa Rica 2005: F test of joint equality of coefficients for the different regressions

F p-value n Total sample (at country level) (18, 1243)= 21.93 0 12535 Restricted sample: born in the same county (18, 6673) =11.21 0 6777 Female (18, 4178) = 11.42 0 4279 Male (18, 8155) = 14.38 0 8256 Rural ( 18, 6713) = 12.18 0 6805 Urban ( 18, 5641) = 11.37 0 5730 Central region ( 18, 6483)= 12.44 0 6551 Chorotega ( 18, 1235) = 10.83 0 1272 Pacífico ( 18, 1037) = 6.80 0 1070 Brunca ( 18, 1492) = 3.75 0 1523 HuetarA ( 18, 1258) = 2.99 0 1290 HuetarN ( 18, 798) = 3.29 0 829

Table 4.7 displays the results of the comparison of the step model with the

quadratic and linear model for all samples using two criteria recommended to select 161

between alternative models.366 Table 4.8 shows the regression output of the linear and quadratic model only for the total sample; the step regression output for the total sample can be found in the empirical results section. It can be seen in table 4.7 that both the adjusted R2 and the AIC (Akaike information criteria) agree on the choice of the step function for most of the regression outputs; the lower the AIC the better the fit. It should be born in mind that the AIC impose a penalty to the step specification for adding more regressors to the model as compared to the linear and the quadratic model.367 The fact that the quadratic specification provides a second best alternative supports the hypothesis of convexity of the returns. The quadratic formulation, unfortunately, does not fit the flats, falls and jumps of the relationship between earnings and schooling displayed in the regression outputs, as is shown in the next section.

366 Damodar N. Gujarati, Basic Econometrics (Boston: McGraw-Hill, 2003): 536-538.

367 Ibid, 537-538. 162

Table 4.7. Costa Rica 2005: Test of model fitness for all regressions

Step Quadratic Linear Total sample Adjusted R2 0.324 0.320 0.304 Akaike’s information criterion (AIC) 23601.56 24587.7 23948.2 Born in the same county Adjusted R2 0.297 0.295 0.279 Akaike’s information criterion (AIC) 12707.2 12713.6 12866.6 Female Adjusted R2 0.349 0.342 0.327 Akaike’s information criterion (AIC) 8523.9 8549.5 8645 Male Adjusted R2 0.294 0.30 0.293 Akaike’s information criterion (AIC) 15337.7 15123 15293.3 Rural Adjusted R2 0.259 0.255 0.239 Akaike’s information criterion (AIC) 12878.22 12892.44 13033.21 urban Adjusted R2 0.334 0.330 0.312 Akaike’s information criterion (AIC) 10692.03 10708.2 10858.8 Central Adjusted R2 0.347 0.342 0.327 Akaike’s information criterion (AIC) 11950 11980.3 12127 Chorotega Adjusted R2 0.315 0.293 0.278 Akaike’s information criterion (AIC) 2479.3 2504.8 2530.1 Pacífico Adjusted R2 0.288 0.283 0.237 Akaike’s information criterion (AIC) 2097.8 2088.6 2154.2 Brunca Adjusted R2 0.241 0.23 0.22 Akaike’s information criterion (AIC) 2965.1 2951 2987.2 HuetarA Adjusted R2 0.209 0.207 0.199 Akaike’s information criterion (AIC) 2580.0 2566.3 2579.5 HuetarN Adjusted R2 0.278 0.281 0.258 Akaike’s information criterion (AIC) 1526.6 1506.8 1530.6

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Table 4.8. Costa Rica 2005: Linear and quadratic regression outputs. Total sample

Variables Coefficient p-value coefficient p-value

Years of completed schooling 0.080*** 0 -0.013** 0.019

Years of completed schooling squared 0.005*** 0

Age 0.042*** 0 0.041*** 0

Age squared 0.000 0 0.000*** 0

Spanish and English 0.239*** 0 0.186*** 0

Spanish and other language 0.149*** 0.001 0.104* 0.023

Female -0.167*** 0 -0.173*** 0

Rural -0.124*** 0 -0.128*** 0

Constant 5.052*** 0 5.458*** 0

Fixed effects ( 79 counties) Yes Yes

No. observations 12,535 12,535

Adjusted R squared 0.304 0.320

Note: Dependent variable is log of wages per hour. Robust standard errors were required. ***; **,and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

The test of convexity that was applied was the test of the equality of every two consecutive estimated schooling coefficients obtained after running the regressions for each of the samples. This test was performed for the total sample, for the restricted total sample and the other stratified samples. Tables 4.9, 4.10, and 4.11, below, show the results. It can be observed that equality of the coefficients at higher levels of education is rejected which provides a test for the convexity of the returns to education.

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Table 4.9. Costa Rica 2005: F test of equality of every two consecutive estimated coefficients for the regressions: total sample and restricted sample

Total sample Restricted total sample Years of schooling F p- value F p- value One year = two years 0.88 0.349 0.04 0.845 Two years = three years 0.19 0.663 0.07 0.797 Three years = four years 2.06 0.151 0.57 0.451 Four years = five years 1.08 0.299 0.15 0.697 Five years = six years 7.2** 0.007 4.36* 0.037 Six year s= seven years 3.59~ 0.058 9.08** 0.003 Seven years = eight years 2 0.1578 0.01 0.924 Eight years = nine years 0 0.993 0.02 0.875 Nine years = ten years 2.24 0.1346 3.13~ 0.077 Ten years = eleven years 17.28*** 0 4.04* 0.044 Eleven years = twelve years 8.52** 0.003 9.07** 0.003 Twelve years = thirteen years 2.71~ 0.09 0.07 0.794 Thirteen years = fourteen years 3.06~ 0.08 11.13*** 0.001 Fourteen years = fifteen years 35.51*** 0 14.56*** 0.000 Fifteen years = sixteen years 42.55*** 0 15.08*** 0.000 Sixteen years = seventeen years 1.54 0.215 1.4 0.237 Seventeen years = eighteen years 0.03 0.859 0.02 0.891 Eighteen years = nineteen years 6.7** 0.009 1.51 0.219

Note: ***, **,* and ~ refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

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Table 4.10. Costa Rica 2005: F test of equality of every two consecutive estimated coefficients for the regressions: female, male, rural, and urban samples

Years of Female Male Rural Urban schooling F p- value F p- value F p- value F p- value

1 = 2 2.72 0.099 0.46 0.497 0.65 0.418 0.73 .393

2 = 3 0 0.975 0.8 0.372 0.29 0.593 0.13 0.718

3 = 4 0.11 0.741 2.16 0.142 1.24 0.265 1.19 0.276

4 = 5 1.89 0.170 0.3 0.587 0.22 0.641 2.39 0.122

5 = 6 8.22** 0.004 3.63~ 0.056 3.28~ 0.070 5.51* 0.019

6 = 7 0.02 0.893 1.37 0.242 3.27~ 0.071 0.36 0.549

7 = 8 4.53* 0.033 0.01 0.938 3.24~ 0.072 0.01 0.939

8 = 9 0.59 0.442 2.31 0.129 0.01 0.934 0.14 0.711

9 = 10 0.52 0.470 3.25~ 0.070 0.18 0.669 3.69~ 0.055

10 = 11 13.77*** 0.000 7.95** 0.004 2.26 0.132 15.45*** 0.000

11 = 12 9.63** 0.002 0.42 0.515 11.95** 0.001 2.69 0.090

12 = 13 0.01 0.929 4.5* 0.034 0.16 0.691 1.8 0.180

13 = 14 5.02* 0.025 0.86 0.354 0.87 0.350 2.45 0.100

14 = 15 35.25*** 0.000 9.62*** 0.000 15.46*** 0.000 21.3*** 0.000

15 = 16 18.59*** 0.000 28.17*** 0.000 14.97*** 0.000 27.31*** 0.000

16 = 17 0.86 0.355 0.94 0.331 0 0.969 1.9 0.168

17 = 18 0.09 0.761 0.04 0.843 0.74 0.390 0.65 0.420

18 = 19 2.65 0.104 4.67* 0.030 21.6*** 0.000 0.49 0.530

Note: ***, **,* and ~ refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

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Table 4.11. Costa Rica 2005: F test of equality of every two consecutive regression coefficients. Regionally stratified samples

Years Central Chorotega Pacífico Brunca HuetarA HuetarN of schooling F p F p F p F p F p F p 1 = 2 3.91* .048 0.17 .683 0.33 .567 1.32 .253 0.00 .961 0.64 .423 2 = 3 0.51 .476 6.32* .012 2.38~ .123 0.12 .729 0.81 .367 1.94 .164 3 = 4 0.71 .397 1.28 .258 2.59~ .107 0.70 .402 2.54 .115 4.00 .046 4 = 5 5.04* .024 0.60 .439 0.16 .693 0.26 .608 0.07 .793 0.28 .595 5 = 6 9.64** .002 0.04 .844 0.10 .752 1.59 .208 1.35 .245 0.20 .655 6 = 7 6.38 .011 10.18*** .002 2.57~ .100 1.20 .274 1.52 .218 5.36* .021 7 = 8 0.01 .916 2.97~ .085 0.25 .617 0.04 .841 5.89* .015 1.87 .172

167 8 = 9 0.01 .936 5.16* .023 0.38 .538 2.42 .12 0.40 .526 0.51 .475 9 = 10 0.13 .716 0.74 .389 1.55 .213 3.46~ .063 0.12 .728 1.62 .203 10 = 11 27.07*** 0 0.03 .870 1.28 .259 0.16 .669 0.51 .476 0.00 .973 11 = 12 0.54 .463 8.12* .004 0.00 .993 8.60** .003 11.61*** 0 11.19*** 0 12 = 13 5.08*** .024 3.01~ .083 3.86*** .049 2.46 .116 0.89 .345 0.02 .897 13 = 14 0.05 .832 9.97* .001 0.03 .868 3.16~ .075 1.64 .201 0.42 0.516 14 = 15 41.20*** 0 0.48 .490 0.48 .489 0.57 .450 1.18 .278 3.45~ .064 15 = 16 22.60*** 0 6.80* .009 7.05*** .008 3.75~ .053 5.25 .022 0.50 0.479 16 = 17 1.09 .295 0.40 .520 1.08 .289 0.13 .720 0.50 .482 3.18~ .075 17 = 18 0.62 .432 2.61 .106 1.20 .274 0.35 .550 1.04 .308 0.37 0.545 18 = 19 4.780*** .028 6.43* .011 2.36~ .124 0.00 .957 0.05 .815 2.47 0.116

Note: ***, **,* and ~ refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

As mentioned above, the purpose of the analysis by subgroups is not to show that the estimated rates of return associated with each year of completed schooling (the estimated schooling coefficients) are equal for all the sets of data. Therefore, no test is performed to sustain this claim of equality of the estimated school coefficients across data sets. Indeed, the objective is to examine if the expected convexity in the returns to schooling is shared by different sectors of the labor force. If this is the case the robustness of the results are strengthened.

Regarding the independent variables considered in the specification, measures of ability, family background and school quality indicators are not included due to lack of data, a limitation that is faced by most of the studies in the field. Overall these measures try to control for selectivity bias.368 It should be pointed out, however that their inclusion does not guarantee an unbiased estimator of the schooling coefficient. The ability measures have been questioned in their construct validity,369 mainly considering the strong downward effect they cause in the schooling coefficient370; on the other hand, their omission, according to Schultz, will produce a bias in the schooling coefficient not greater than 5% to 15%.371 Measures of quality of education372 also face problems of construct validity and the possibility of endogeneity when school quality is associated

368 Griliches (1977), 6-13, Willis, 535, Schultz, 587, Strauss and Thomas, (1995), 1969.

369 Griliches (1977), 6-12. Also, Strauss and Thomas contend that measures of ability such as numeracy and literacy test as well as the Raven test have been included in earnings functions in addition to years of schooling; however, they argue that these measures are crude indicators of productivity (who guarantees that good scores in this test predict higher productivity and hence higher earnings?) and particularly, the Raven test biases against girls. Strauss and Thomas (1995), 1967-1968.

370 Strauss and Thomas, ibid.

371 Schultz, 587

372 Measures of quality of education such as: test scores or teachers’ quality indicators have been included in some studies because the better the quality of the school, the more likely the student will stay longer since the investment in schooling will be more productive. Schultz, 590. 168

with better schools.373 However, their inclusion diminishes the estimated returns to schooling from 20% to 11%.374 With respect to the insertion of family background indicators,375 Carr notes that their inclusion might still lead to an upward bias in the schooling coefficient unless the family background measures can pick up all the unobserved ability elements (due to the fact that ability and schooling are correlated).376

Lam and Schoeni, using a step function, found in their research in Brazil that the estimate of the schooling coefficient diminishes between 13.6% and 17.6% when workers’

377 parents’ education are added in the specification. Similarly, Strauss and Thomas’ review of other studies in Latin America showed that while the inclusion of control for parents’ education in empirical studies using data from Latin America,378 had a positive effect on earnings, it lowered the subjects’ own education effects by 25%.379 The research of Strauss and Thomas, Lam and Schoeni in Brazil as well as the research of Schady in

373 In this case, it is recommended to treat the choice of school as an endogenous variable. Strauss and Thomas (1995), 1969.

374The quality measured used was average years of education of all teachers within the state. Ibid, 1972.

375 Parents can affect their children’s future productivity and earnings by providing better quality education and an environment that favors learning and by the genetic transmission of ability (Schultz, 588). In this sense, family background variables are included in the regression equations as a proxy for ability and for the investment in human capital (health, nutrition, education) that parents made before children start school. Strauss and Thomas (1995), 1969.

376 Card (1999), 1825.

377 David Lam and Robert F. Schoeni, “Effects of Family Background on Earnings and Returns to Schooling: Evidence from Brazil,” Journal of Political Economy Vol. 101, No. 4 (August 1993), 726-737. The decrease amounted to a third when additional controls for family background were added; however, Strauss and Thomas (1996) in their study of Brazil found that the inclusion of these additional controls amounted to a very small part of the returns to schooling.

378 Also, Card reports studies using data from developed countries in which the estimated returns to education are lowered by 5% to 10% (Card, 1842).

379 Strauss and Thomas also report that this outcome differs in studies performed in Asia where returns to schooling remain unaffected after controlling for years of schooling. Strauss and Thomas (1995), 1969.

169

the Philippines also produced a very important outcome: the inclusion of parents’ education in the regression does not change the non-linear shape of the returns to schooling. This means that the convexity of the profile or the flats or jumps remained whether or not this control variable is included.380On the other hand, Psacharopoulos and

Patrinos contend that, overall, the effects of all the omitted variables mentioned above do not exceed 10% of the estimate of the schooling coefficient.381

Finally related to this point, the influence of different discount rates382 has been raised by some scholars as a variable influencing the distribution of earnings. Mincer acknowledges the absence of information on this factor and decides to treat it as part of the residual variation.383 Lang considers that the absence of this rate in the specification produces what he calls the “discount rate bias” which might be offset by the presence of ability bias since each bias goes in different directions.384 Harmon et al., considers this problem an endogeneity bias.385 The discount rate variation arises because individuals have different access to funds or tastes for schooling and this varies their marginal returns

380 Lam and Schoeni, 727; Schady, 12. Similarly, Strauss and Thomas (1996), 149, 167, 172.

381 George Psacharopoulos and Harry Anthony Patrinos, “Returns on Investment in Education: A Further Update”. Policy Research Working Paper 2881, Latin America and the Caribbean Region Education Sector Unit, World Bank, September 2002: 1. In this discussion, Psacharopoulos and Patrinos consider the arguments of Card and Griliches. See: David Card, “Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems,” Econometrica, Vol. 69, No. 5 (September 2001): 1127-1160 and Zvi Griliches, “Notes on the Role of Education in Production Functions and Growth Accounting,” in Education, Income and Human Capital, ed. W. L. Hansen (New York: National Bureau of Economic Research, 1970).

382 See chapter 1, section Household Decision Making and Education Investment for an explanation

383 Jacob Mincer, Schooling, experience and earnings, 7-8.

384 Kevin Lang, “Ability Bias, Discount Rate Bias and the Return to Education’, Department of Economics, Boston University, March 12, 1993.

385 Colm Harmon et al., The Returns to Education: Microeconomics,” Journal of Economic Surveys, Vol. 17, No. 2 (2003), 119

170

and costs.386 Hence, individuals with a high discount rate are those with lower marginal returns or higher marginal costs, who choose low levels of schooling. The presence of different discount rates is identified as one cause of convexity in the returns.387

The estimation strategy selected for this analysis addresses the omitted variable bias in two ways. First, fixed effects were introduced for the 79 counties, zone dummy

(rural and urban) and gender dummy to control for unobserved differences between the units of analysis that make them not equitable. Secondly, the introduction of the FE county dummies and a measure of the knowledge of a foreign language (FL) helps to control for quality of education considering that only 58% of Costa Ricans in the sample remain in the same county they were born and that FL might capture school quality.

Thirdly, the data is stratified by regions, zones and gender not only to observe the relationship between earning and schooling across different subgroups, but also to pursue some homogeneity in the unit of analysis so that the omitted variable bias is lessened. As

Strauss and Thomas argue, regions and zones might not be homogeneous388 regarding their labor markets, their quality of education and any other non-observable characteristic and that is certainly what analysis, in previous chapters, shows. The gender stratification is justified on the same grounds. Finally, a regression that only includes the Costa Ricans who reside in the same county they were born, and therefore attended the same county’ schools, is also run; this would account for quality of education and will improve the validity of the results.

386 Ibid, 118-119.

387 Card, “The Causal Effect of Education on Earnings,” 1811.

388 Strauss and Thomas, “Wages, Schooling and Background,” 148. 171

Heteroscedasticity is tackled by requiring robust standard errors. Finally, the use of a large sample size, the stratification of the sample and the use of FE dummies decrease the random error which in turn increases the statistical validity of the model.389

4.4.5 Regression outputs

In this section the results of the twelve estimated wage functions: total sample at country level, for the female sample, for the male sample, for the rural area, for the urban area, for each country region: Central, Chorotega, Pacífico, Brunca, Huetar Atlántica and

Huetar Norte and for the restricted total sample (that includes only the individuals who reside in the same county they were born) are examined. The main focus is on the results of the regression that uses the country sample. As explained previously, the additional eleven regressions are estimated to improve the robustness and validity of the results.

Returns for the country: the total sample

Table 4.12 and graph 4.2 show the returns to education for the total of the country’s workers.390 The graph displays the predicted mean of the logarithm of wages by years of schooling for the country, based on the regression output.391

389 See: Laura Langbein with Claire L. Felbinger, Public program evaluation: a statistical guide (Armonk, New York: M.E. Sharpe, 2006): 156-157, 160.

390 It is important to clarify that to properly read the estimated coefficients of dummy variables, as percentages, in a semi-logarithmic regression, they should be transformed to semi-elastic values; however, I did not perform this conversion because it does not affect the analysis since this transformation does not change the relationship between the coefficients and hence the shape of the conditional log wage function. What the transformation does is to make more positive the positive coefficients and more negative the negative coefficients, but the pattern remains. Moreover, I believe that leaving the estimates unchanged facilitates the understanding by the reader of my analysis. However, for illustrative purposes, the conversion of the dummy coefficients to semi-elastic values for the regression output that includes the total sample was performed; this is presented in the annex, table A.4.2. The procedure was developed by Halvorsen and Palmquist and is fully explained by Gujarati. The procedure requires, firstly, obtaining the antilog of the dummy coefficient; secondly, to subtract 1 from it and then multiply the obtained value by 100. Damodar N. Gujarati, Basic Econometrics (Boston: McGraw Hill, 2003): 320-321. Also see: Robert 172

It can be observed that the returns do not increase with every level of education, during the first 10 years of schooling, as the graph shows, the curve is almost flat or with downward jumps: the estimated annual average returns increase at a very low rate, or do not increase at all or decrease. Indeed, it seems that to reap the benefits of having been in school since the 1st grade, students should complete the last year of high school, 11th grade. It can be seen that for the first 6 years of education the yearly average return is 4%

((0.26/6)*100), and it was mostly due to the estimated marginal return obtained after completing 6th grade (8.8%), holding other variables constant. It also appears that in

Costa Rica it does not pay off to continue into secondary school unless one is sure that one will finish the secondary school cycle. In fact, those students who move to 7th grade and quit at 10th grade, after 4 years of studying, will receive an estimated average annual return of 4%, just the same average obtained for each year of primary school. On the other hand, the average annual return of secondary school is 6% ((0.567-0.263)/5) and most of this return is attributed to the marginal return obtained just for the completion of the last year of high school, 13.8%. In fact, the estimated average annual return for 11 year period of primary and secondary school is 5% (0.567/11). More fortunate are the students who are able to enroll in tertiary education because their estimated average annual returns more than doubled, 15.7% the yearly average returns obtained during high school (6%) just with one year of higher level education. However, the years that yield the higher marginal returns are the 15th and 16th year associated with undergraduate and

Halvorsen and Raymond Palmquist, The interpretation of dummy variables in semilogarithmic equations, American Economic Review Vol. 70, No. 3, (June 1980): 474–475.

391 Although the slopes between any two years of schooling, in the predicted mean wage functions, represent the marginal return to each corresponding year of education, these marginal returns do not coincide with the incremental coefficients for the schooling variables. The reason is that the conditional wage functions are drawn based on all the coefficients of the equation. However, the shape of the function remains the same. 173

graduate degrees. Finally, table 4.12 shows that knowledge of English increases the logarithm of hourly wage rate in the order of 18%, holding other variables constant.

Returns to education for the stratified samples: rural, urban female and male

The returns of the rural, urban and gender groups are shown in tables 4.13 and 4.14 as well as in graph 4.3; they are also characterized by increasing returns once the level of education reaches the end of secondary school. These results are consistent with the ones at the country level: individuals from urban areas and from the two genders should invest in schooling at least 11 years in order to obtain the rewards of their effort; although the greater returns come with higher levels of schooling. The behavior of returns is different for individuals from rural areas who obtain statistically significant marginal returns at 12 years of schooling instead of 11 years of schooling. Moreover, the results might reveal that rural area individuals and males should make an extra effort and invest in additional years of schooling to obtain the same rewards individuals from urban areas and females obtain at completing 11 years of schooling. Although both males and females obtain significant marginal returns at year 11, the marginal return obtained by males at year 11 are much lower than the female group and it is only after two years of additional schooling that the accumulated returns are similar. In fact, this premium assigned to higher level education might explain why students in Costa Rica, whose parents realize that they are not going to be able to enroll in tertiary education, quit school after finishing

6th, 7th, 8th and 10th grades which, after all, are the schooling years that yield in some groups modest returns compared to no returns at all in previous school years.

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Table 4.12. Costa Rica 2005: Returns to education. Total country.

Variables Coefficient ∆ coefficient One 0.112 ------Two 0.180** 0.068 Three 0.156** -0.024 Four 0.223*** 0.067 Five 0.175*** -0.048 Six 0.263*** 0.088** Seven 0.320*** 0.058 Eight 0.374*** 0.054 Nine 0.374*** 0.000 Ten 0.429*** 0.054 Eleven 0.567*** 0.138*** Twelve 0.723*** 0.157** Thirteen 0.826*** 0.102 Fourteen 0.904*** 0.079 Fifteen 1.115*** 0.211*** Sixteen 1.354*** 0.239*** Seventeen 1.414*** 0.059 Eighteen 1.403*** -0.011 Nineteen 1.613*** 0.210** Spanish and English 0.180*** Spanish and other language 0.110* Age 0.039*** Age squared -0.000*** Female -0.176*** Rural -0.132*** Constant 5.352*** Fixed effects included No of observations 12535 Robust standard errors were required Adjusted R2 0.32

Note: Dependent variable is log of wages per hour. Returns to education refer to gross monetary private returns to education ***; **, and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively.

175

7.5

6.5

Predicted mean log wage log mean Predicted

5.5

6 11 13 15 17 19 Years of education

Graph 4.2. Costa Rica 2005: Predicted mean wages: total country

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Table 4.13. Costa Rica 2005: Returns to education. Rural and urban

Rural Urban Variables Coefficient ∆ coefficient Coefficient ∆ coefficient One 0.088 ------0.122 ------Two 0.157* 0.069 0.236 0.114 Three 0.123* -0.034 0.198 -0.039 Four 0.183** 0.06 0.298** 0.1 Five 0.158** -0.025 0.15 -0.147 Six 0.228*** 0.07 0.300*** 0.150* Seven 0.297*** 0.069 0.329*** 0.029 Eight 0.388*** 0.091 0.334*** 0.004 Nine 0.384*** -0.004 0.351*** 0.017 Ten 0.408*** 0.024 0.444*** 0.093 Eleven 0.489*** 0.081 0.607*** 0.164*** Twelve 0.762*** 0.273*** 0.723*** 0.115 Thirteen 0.801*** 0.039 0.829*** 0.106 Fourteen 0.873*** 0.073 0.916*** 0.086 Fifteen 1.105*** 0.232*** 1.119*** 0.203*** Sixteen 1.365*** 0.259*** 1.347*** 0.228*** Seventeen 1.361*** -0.004 1.424*** 0.077 Eighteen 1.268*** -0.093 1.484*** 0.06 Nineteen 1.877*** 0.609*** 1.542*** 0.058 Spanish and English 0.245*** ------0.146*** ------Spanish and other language 0.02 ------0.156* ------Age 0.045*** ------0.030*** ------Age squared -0.001*** ------0.000*** ------Female -0.207*** ------0.151*** ------Constant 5.057*** ------5.474*** ------Fixed effects included No. of observations 6805 6805 5730 5730 Robust standard erros required Adjusted R2 0.26 0.26 0.33 0.33

Note: Dependent variable is log of wages per hour. Returns to education refer to gross monetary private returns to education.

***; **, and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

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Table 4.14. Costa Rica 2005: Returns to education. Female and male.

Female Male Variables Coefficient ∆ coefficient Coefficient ∆ coefficient One 0.034 ------0.119 ------Two 0.333* 0.299 0.157* 0.038 Three 0.344** 0.012 0.109 -0.049 Four 0.324* -0.02 0.209*** 0.1 Five 0.182 -0.143 0.186** -0.023 Six 0.377*** 0.195** 0.244*** 0.059 Seven 0.387** 0.01 0.325*** 0.081* Eight 0.573*** 0.186* 0.308*** -0.017 Nine 0.521*** -0.052 0.344*** 0.036 Ten 0.503*** -0.017 0.454*** 0.110* Eleven 0.680*** 0.177** 0.552*** 0.098* Twelve 0.960*** 0.280*** 0.624*** 0.072 Thirteen 0.946*** -0.014 0.805*** 0.181* Fourteen 1.072*** 0.126* 0.836*** 0.032 Fifteen 1.332*** 0.260*** 0.997*** 0.160** Sixteen 1.513*** 0.181*** 1.299*** 0.303*** Seventeen 1.550*** 0.037 1.374*** 0.075 Eighteen 1.565*** 0.015 1.351*** -0.023 Nineteen 1.726*** 0.161 1.618*** 0.267* Spanish and English 0.176*** 0.183*** Spanish and other language 0.130* 0.091 Age 0.039*** 0.040*** Age squared -0.000*** -0.000*** Rural -0.144*** -0.126*** Constant 5.077*** 5.347*** Fixed efects included No. of observations 4279 8256 Robust standard errors requird Adjusted R2 0.35 0.35 0.31 0.31

Note: Dependent variable is log of wages per hour. Returns to education refer to gross monetary private returns to education.

***; **, and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

178

8 8 rural female urban male

7.5

6.5

5.5

6 11 13 15 17 19 6 11 13 15 17 19

Graph 4.3. Costa Rica 2005: Predicted mean wages for the female, male, rural and urban groups

Returns to education for the regions

The results by regions are no different from the ones for the country or the other groups.

They are, however, striking because the facts I have been demonstrating are more evident: see tables 4.15 and 4.16 and graph 4.4. First, the marginal returns to primary school, for all regions, are very low or zero and those additional years of secondary school add very little to wages. Indeed, it seems that investment in schooling in Costa

Rica is worth the effort only if the individual intends to go beyond the eleven years of secondary school. In four of the six regions, Chorotega, Brunca, Huetar Atlántica and

Huetar Norte, an additional year of schooling (tertiary education) is necessary. In these regions, it is the 12th year of schooling not the 11th year that has significant marginal returns. In the case of the Pacífico region, two additional years of tertiary education are required to extract the returns of having been in school for 11 years. Second, in most of

179

the regions sixteen years of schooling, which is equivalent to an undergraduate degree, is what seems to be highly rewarded by the labor market. This pattern is similar for the gender groups and area groups (rural and urban), although for these groups 15 years of schooling is also rewarded in the labor market.

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Table 4.15. Costa Rica 2005 Returns to education by regions: Central, Chorotega and Pacífico

Central Chorotega Pacífico Variables Coefficient ∆ coeff Coefficient ∆ coeff Coefficient ∆ coeff One 0.055 -0.04 0.36 Two 0.243** 0.188* 0.083 0.124 0.217 -0.143 rThree 0.186* -0.057 0.542** 0.459* -0.034 -0.25 Four 0.249** 0.064 0.354 -0.188 0.145 0.179 Five 0.084 -0.165* 0.472* 0.118 0.094 -0.051 Six 0.236*** 0.151** 0.455** -0.018 0.126 0.032 Seven 0.351*** 0.115* 0.251 -0.204** 0.326* 0.2 Eight 0.356*** 0.006 0.427* 0.176 0.256* -0.07 Nine 0.360*** 0.004 0.668*** 0.242* 0.188 -0.067 Ten 0.377*** 0.017 0.588** -0.081 0.333** 0.144 Eleven 0.601*** 0.223*** 0.574*** -0.013 0.445*** 0.113 Twelve 0.649*** 0.049 1.095*** 0.520** 0.445 0 Thirteen 0.824*** 0.175* 0.757*** -0.338 1.065*** 0.620* Fourteen 0.836*** 0.012 1.146*** 0.389** 1.025*** -0.04 Fifteen 1.103*** 0.267*** 1.227*** 0.082 1.137*** 0.112 Sixteen 1.312*** 0.208*** 1.564*** 0.336** 1.595*** 0.457** Seventeen 1.373*** 0.061 1.654*** 0.09 1.271*** -0.323 Eighteen 1.312*** -0.06 1.883*** 0.229 1.622*** 0.351 Nineteen 1.530*** 0.217* 2.170*** 0.287* 1.928*** 0.306 Spanish and English 0.221*** 0.264** 0.017 Spanish and other language 0.155* 0.117 -0.125 Age 0.034*** 0.030* 0.058*** Age squared -0.000*** -0.000* -0.001*** Female -0.185*** -0.179*** -0.139** Rural -0.084*** -0.241*** -0.081 Constant 5.424*** 5.318*** 5.252*** Fixed effects included No of observations 6551 1272 1070 Robust standard errors were required Adjusted R2 0.35 0.31 0.29

Note: Dependent variable is log of wages per hour. Returns to education refer to gross monetary private returns to education.

***; **,and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively.

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Table 4.16. Costa Rica 2005: Returns to education by regions: Brunca, HuetarA and HuetarN

Brunca Huetar Atlántica Huetar Norte Variables Coefficient ∆ coeff Coefficient ∆ coeff Coefficient ∆ coeffi one -0.177 0.266 0.273 two 0.111 0.288 0.26 -0.006 0.126 -0.147 three 0.044 -0.066 0.15 -0.111 -0.099 -0.226 four -0.052 -0.097 0.363* 0.214 0.195 0.294* five 0.007 0.059 0.330* -0.033 0.101 -0.093 six 0.109 0.103 0.439*** 0.109 0.165 0.064 seven 0.188 0.078 0.325* -0.114 0.379** 0.214* eight 0.205 0.018 0.658*** 0.333* 0.194 -0.185 nine 0.072 -0.133 0.577*** -0.081 0.280** 0.086 ten 0.322* 0.25 0.623*** 0.047 0.441** 0.161 eleven 0.270** -0.052 0.710*** 0.086 0.445*** 0.004 twelve 0.783*** 0.513** 1.164*** 0.454*** 0.785*** 0.340*** thirteen 0.481*** -0.302 1.002*** -0.162 0.803*** 0.018 fourteen 0.725*** 0.244 1.223*** 0.221 0.690*** -0.113 fifteen 0.816*** 0.09 1.372*** 0.149 1.014*** 0.324 sixteen 1.036*** 0.22 1.676*** 0.305* 1.116*** 0.102 seventeen 1.088*** 0.052 1.525*** -0.152 1.459*** 0.343 eighteen 1.236*** 0.148 1.809*** 0.284 1.331*** -0.128 nineteen 1.258*** 0.022 1.745*** -0.064 1.921*** 0.591 Spanish and English 0.08 0.104 0.036 Spanish and other language 0.073 0.09 -0.293* age 0.068*** 0.031* 0.031* Age squared -0.001*** -0.000* 0 female -0.166*** -0.165*** -0.203*** rural -0.301*** -0.118* -0.110* constant 4.914*** 5.570*** 5.354*** Fixed effects included No of observations 1523 1290 829 Robust standard errors were required Adjusted R2 0.24 0.21 0.28

Note: Dependent variable is log of wages per hour. Returns to education refer to gross monetary private returns to education. ***; **,and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively.

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Central Chorotega 8 Pacífico

7.5

6.5

5.5

6 11 13 15 17 19 6 11 13 15 17 19 6 11 13 15 17 19

8 Brunca Huetar Atlántica Huetar Norte

7.5

6.5

5.5

6 11 13 15 17 19 6 11 13 15 17 19 6 11 13 15 17 19

Graph 4.4. Costa Rica 2005: Predicted mean wages by regions.

Although convexity characterizes all the samples, the great disparities among regions and some subgroups regarding the schooling year where the marginal returns

(associated with each year) start increasing are noteworthy. The accumulation of the critical mass of education to extract the returns of the investments takes longer for several groups. Clearly, these patterns of the returns to education are associated with the type of convexity of returns in the different groups. Despite the fact that the testing the convexities of returns in different samples was not intended to show the equality or inequality in the estimated rates of return associated with year of schooling, it might be useful to attempt to provide a potential explanation for these differences.

The discrepancies can be summarized as follows. For individuals in the urban area, in the Central region, and belonging to the gender groups, reaching the 11th year of

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schooling is associated with statistically significant marginal returns. However, as mentioned before, for males the marginal returns associated with that year are modest compared to the other groups. On the other hand, for individuals in the rural area, and in the four regions: Chorotega, Brunca, Huetar Norte and Huetar Atlántica, it is the 12th year of schooling that is associated with statistically significant marginal returns. In the case of the Pacífico region, the key year is the 13th year of schooling.

There are some characteristics in common among the groups (the rural, male and

5 regions samples) that need to accumulate a larger stock of schooling to extract high returns: first, they are composed of individuals that are mostly unskilled (see section

4.4.2) which might mean that individuals belonging to these groups started working at an early age. The share of unskilled labor in these groups is between 68% and 80% while for the female, urban and Central region samples it is less than 61%. Also, a considerable proportion of the unskilled labor, as mentioned previously in this chapter, is at the lower end of the continuum (at most the completion of primary). Moreover, the skilled labor in these groups tends to concentrate on the upper end of the continuum (more than the completion of secondary school); those who finish high school continue to the next level.

Second, they are constituted by observations that are located in rural areas, between 58% and 73%, compared to less than 47% in the Central region and the female group (see section 4.4.2). Third,392 the rural areas of the five regions hold most of the areas of greater poverty in the country. There are few opportunities for employment and the

392 The points that follow were based on: Programa Estado de la Nación en Desarrollo Humano Sostenible, Undécimo Informe Estado de la Nación en Desarrollo Humano Sostenible Humano (San José, Costa Rica: Programa Estado de la Nación, 2005):104-115, and Ronulfo Alvarado S. “Regiones y Cantones de Costa Rica” Dirección de Gestión Municipal, Sección de Gestión y Desarrollo. Serie de Cantones de Costa Rica No. 2, 2003.

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public infrastructure is poor. In fact in two regions, Brunca and Chorotega, a pattern of migration of males has been identified. The population in the rural areas of these regions is characterized by dispersed, small concentrations of population. Except for the Pacífico region that is small in terms of population and territory, the other four concentrate a large part of the national territory. Fourth, these five regions have limited and narrow productive sectors compared to the more diversified economy of the Central region where most of the knowledge based industry is located. The five regions, in general, are characterized by the presence of large multinationals involved in the production of profitable non-traditional products coexisting with small producers and cooperatives involved in the production of low priced traditional agricultural products. This type of dual relationship also characterizes the touristic sector of these regions.

Given the above, I believe that there could be two possible causes or the combination of both, for these discrepancies and particularly, for the significance of the estimated marginal returns of the 12th year of schooling in five of the regions. The first cause is a polarization of the labor demand given the coexistence of two types of economies in these five regions. The larger and stagnant economy requires unskilled labor for agricultural work, self-employment and some small service industries such as tourism. For some of these activities, perhaps, it does not make much difference in terms of skills and earnings to have 7 or 10 years of schooling. The second economy is formed by those large foreign companies that have a demand for skilled labor, but with tertiary education. This labor market probably takes advantage of the shortage of skilled labor, and of the inequalities in the distribution of skilled labor. This polarization of the labor market is not the case of the Central region. Its labor market offers a broader range of

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opportunities and hence completion of secondary school is valued. In addition, the supply of skilled labor might be more proportionate, in other words, not accumulated at some points of the continuum. The second cause might be that the stagnation, poverty, low infrastructure and low quality of education in these regions increase the direct and opportunity costs of education of their inhabitants so much and lower their expectations that they just abandon school. Their time and expenditures in school had a more valuable alternative use for their parents and themselves given their poverty. Only those individuals whose opportunity costs of remaining in school were low finished high school and continued to tertiary education. Their households had the resources to compensate or circumvent all the problems of these regions. These individuals might have grown up in the urban centers of these regions and perhaps went to private schools. These might be the individuals who get the high paid skilled labor jobs in these regions. In fact, as I have showed in chapter 3, section 3.3.1, the increases in participation during the 16 year period, 1989-2005, in secondary school were driven by the richest 25% of students and secondly by students from rural areas. I raised the question of whether or not there is an intersection between these two groups: students from rural areas and the richest 25%.

This finding cannot be directly linked to the results of this chapter: the samples and periods of analysis are different. However, it supports the explanation. This second potential explanation points to the presence of higher discount rates for the unskilled households living in the rural areas of the regions different from the Central region.

Further research on these potential causes of the patterns of convexity across samples is needed.

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Returns to education: workers who reside in the same county they were born in

Table 4.17 presents the returns to education considering a smaller sample of those workers who still live in the same county they were born, graph 4.4 depicts the predicted mean log wages based on the regression output. The regression output and graph show increasing returns in this more restricted total sample that controls directly for quality of education, which reinforces the robustness of the results. Indeed, individuals in this sample that come from the same county experienced the same quality of education and educational policies. Moreover, it is remarkable how the two conditional wage functions

(the total sample displayed in graph 4.2 and the restricted sample) coincide and the returns to education for every year of schooling seem very similar, under a visual test.

8 total sample born in the same county

7.5

6.5

5.5

6 11 13 15 17 19

Graph 4.5. Costa Rica 2005: Predicted mean wages for total sample and for those born in the same county.

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Table 4.17. Costa Rica 2005: Returns to education. Individuals who remained in the same county

Variables Coefficient ∆ coefficient One 0.109 Two 0.127 0.018 Three 0.106 -0.021 Four 0.152* 0.046 Five 0.127 -0.025 Six 0.224*** 0.097* Seven 0.339*** 0.115** Eight 0.334*** -0.005 Nine 0.327*** -0.007 Ten 0.415*** 0.089 Eleven 0.505*** 0.090* Twelve 0.706*** 0.201** Thirteen 0.686*** -0.02 Fourteen 0.881*** 0.195*** Fifteen 1.065*** 0.183*** Sixteen 1.271*** 0.207*** Seventeen 1.351*** 0.08 Eighteen 1.340*** -0.011 Nineteen 1.508*** 0.168 Spanish and English 0.150*** Spanish and other language 0.123 Age 0.045*** Age squared -0.000*** Female -0.155*** Rural -0.149*** Constant 5.282*** Fixed effects included No of observations 6777 Robust standard errors were required Adjusted R2 0.3

Note: returns to education refer to gross monetary private returns to education

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4.5 Conclusion

I have demonstrated in this chapter that in Costa Rica the earning functions are convex and that the increasing returns to education are such that they increase sharply only after a critical stock of education is reached. The convexity of the relationship remains for all sectors and for all regions and for men and women. Moreover, the gross private monetary returns start increasing sharply after 11 years of education or 12 years of education until reaching 16 years where the marginal benefits of staying in school decrease. In fact, this premium to higher level education might explain why students in

Costa Rica, who realize that they are not going to be able to enroll in tertiary education, quit school after finishing 6th, 7th, 8th, and 10th grades which after all are the schooling years before tertiary education that guarantee some returns. In some regions, however, quitting school at any year before completing high school will not make any difference since the returns are practically zero at each corresponding year. Moreover, in most of the regions (Chorotega, Huetar Norte, Huetar Atlántica, Brunca and Pacífico) and rural areas, the critical mass of education is reached at 12 years of education. This might explain why desertion is very high in these regions. These findings clearly show the wage differential between skilled and non-skilled workers and demonstrate that in Costa Rica education is not divisible; a worker is considered educated if she or he has completed high school at least. Moreover, it is with tertiary education that Costa Ricans extract the benefits of their investment.

These results provide a plausible explanation for the low demand for education in

Costa Rica. This conclusion relies on the modified Ljungqvist’s model that predicts that convexity on the returns to schooling provides a disincentive to poor parents to invest in

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their children’s education. Parents’ and students’ expectations are low because only higher levels of education extract higher returns, there is no access to credit, the returns for un-skilled labor are low, and the quality of education is deficient; hence the choice they might have is to abandon schooling. Consequently, the prospects for the majority of families in Costa Rica are gloomy since the ladder towards breaking the cycle of poverty or achieving upward mobility is limited to few. Indeed, the relationship between human capital accumulation and income inequality (or lack thereof) is vicious and might create a poverty trap.

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CHAPTER 5

THE DEMAND FOR EDUCATION: RETURNS TO EDUCATION MATTER

5.1. Introduction

The previous chapter demonstrated that the convexity of the gross monetary private returns to education in Costa Rica is a dominant explanation of the pattern of non- investment in education. The analysis in this chapter seeks to determine the role of these returns in the education investment decisions of skilled and unskilled parents.

I have devised a model that reproduces the assumptions of Ljungqvist’s model at the household level and also establishes a causal relationship, using regression analysis, between households’ investment in schooling and convexity of gross monetary private returns to education in the labor market under conditions of household poverty; that is, when parents are unskilled and there is no access to credit for education. In doing this, while I maintain consistency with Ljungqvist’s model, I provide a contribution to the explanation of the parental decision with respect to child schooling, something that was not done by Ljungqvist. His model, as mentioned in chapter four, was developed to explain the decisions of unskilled workers when there are no loans for education and the returns are increasing. I also use the same model to observe the causal relationship between households’ investment in schooling and convexity of gross monetary private returns to education returns in the labor market when parents are skilled and there is no access to credit for education.

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One of the substantial contributions of my approach lies in its empirical method.

I plan to empirically test a modified version of Ljungqvist’s theory that takes into account the effects of returns to education in parent’s schooling choices when credit markets are incomplete. More specifically, I hope to demonstrate, by means of regression analysis, the convexity of returns and by association, the bifurcation of the returns to education between those who will be educated and those who will not, and in that way the existence of a vicious poverty trap for those who are forced to remain uneducated.

The chapter has six sections. Following this introduction, the second section provides an analysis of existing literature related to the topic. Section three lays out the conceptual framework. Section four renders the empirical approach. It has four subsections. Sub-section 5.4.1 contains a description of the data. Sub-section 5.4.2 includes an analysis of the descriptive statistics of the variables used in the econometric formulation and of the t test performed on the three samples to which the model is applied to. Sub-section 5.4.3 explains the estimation strategy used. Sub-section 5.44 discusses the econometric issues confronted, and how they were dealt with. In section five the empirical results of the regression analysis are dealt with. It also provides a description of the methods used for the two variables created separately for their introduction in the specification, returns to education and wealth index. Finally, the sixth section offers my conclusions.

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5.2. Literature review

Perhaps the notion that expected returns to education influence parents’ present decisions on investing in education appears to be so self-evident, that empirical research on this subject is, for the most part, sparse and recent.

There are two empirical approaches to the study of the impact of gross monetary private returns to education on child schooling. The first one centers on finding the process by which these returns to schooling influences household decisions on the education of children. Foster and Rosenzweig showed that in rural India, agricultural growth due to technical change affected the returns to education and these in turn affected schooling decisions in a positive way for children of landowners.393 Kochar demonstrated through his work in rural India, that high returns from growth (i.e. urban) areas positively impact schooling decisions in low growth (i.e. rural) areas, particularly for landless children.394 Anderson et al.’s research in Malaysia was focused on the effect of parents’ expectations on schooling choices. He found that the labor market rewards to fathers and mothers positively affect schooling demand for their children, but that their effect is small.395 Finally, Yamauchi was interested in the way in which people obtain information about returns to education and transfer that learning into schooling decisions.

393 Andrew D. Foster and Mark R. Rosenzweig, “Technical Change and Human-Capital Returns to Investments: Evidence from the Green Revolution,” American Economic Review, Vol. 86, No. 4, (September 1996): 931-953.

394 Anjini Kochar, “Urban Influences on rural schooling in India,” Journal of Development Economics, Vol. 74, No. 1 (June 2004): 113-136.

395 Kathryn H. Anderson, Elizabeth M. King and Yan Wang, “Market returns, transfers and demand for schooling in Malaysia, 1976-89,” The Journal of Development Studies, Vol. 39. No.3 (February 2003), 1-28.

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He found that households discern the returns to education by income differences between educated and uneducated households in their community.396

The second approach investigates the effect of gross monetary private returns to education on child labor-schooling choices in situations of liquidity constraints. It is based on Baland and Robinson’s model397 of the tradeoff between child labor and schooling in a situation of income and cash-flow constraints when capital markets are imperfect398. Gormly and Swinnerton,399 in their empirical work in South Africa, were the first ones to add to this model returns to schooling in the labor market as a determinant of the child labor-human capital accumulation trade-off if the child’s household was liquidity-constrained. They examined the effect on enrollment of the adult returns to education in a region where a child lives, and they captured income constraints stratifying the sample by income percentiles (using an instrumental variable for income).

They found that there is a positive and significant relationship between the rate of return

396 Futoshi Yamauchi , “Social Learning, neighborhood effects and investment in human capital: Evidence from Green-Revolution India,” Journal of Development Economics, Vol. 83, Iss. 1 (May 2007): 37-62.

397 Jean-Marie Baland and James A. Robinson, “Is child labor inefficient ?”, The Journal of Political Economy, Vol. 108, No. 4, (August 2000): 663-679.

398 Prior to Baland and Robinson’s work, the emphasis had been on the impact of poverty just on child labor. See for example Kaushik Basu and Pham Hoang Van, “The Economics of Child Labor,” The American Economic Review, Vol. 88 N0. 3 June 1988, 412-426 and later Priya Ranjan, “Credit constraints and the phenomenon of child labor”, Journal of Development Economics, Vol. 64, Iss. 1 (February 2001): 81-102. Alternatively, there has been a parallel strand of research that examines the impact of income constraints on schooling. See for example, G. Hanan Jacoby and Emmanuel Skoufias Risk, “Financial markets and human capital in a developing country,” Review of Economic Studies, Vol. 64, No. 3 (July 1997): 311-335; Daron Acemoglu and J.S. Pischke, “Changes in the wage structure, family income and children’s education,” European Economic Review, Vol. 45, Iss. 4-6 (May 2001): 890-904 and Philippe Belley, and Lance Lochner, “The changing role of family income and ability in determining in educational achievement”(Cambridge, Massachusetts: National Bureau of Economic Research, Working Paper 13527, October 2007).

399 Sarah Gormly and Kenneth A. Swinnerton “The effects of adult returns to schooling on children’s school enrollment : theory and evidence from South Africa” (Washington, D.C.: U.S. Department of Labor, Bureau of International Affairs, June 22, 2004).

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to education and enrollment for those children whose household income is below the

95% percentile and not statistically significant for those above on the upper 5% percentile400. Their claim that liquidity constraints are binding is grounded on the inverse relationship that a set of other variables different from returns to education have with enrollment401. Kingdon and Theopold402 used a very similar empirical model for their research in India403. Their results agreed with those of Gormly and Swinnerton in that they found a positive and significant association between the demand for education and returns to education; in their case, however, there is evidence that liquidity constraints matter given that the positive relationship holds only for girls and for non-poor boys404.

For the poorest boys (1st decile) income constraints are a barrier to their education because the effect of returns to education on their years of schooling is negative. A third effort of empirical research using this approach was undertaken by Chamarbagwala in

India.405 Her study differs from the other two in two aspects: her estimation strategy captured more directly the transaction between child schooling and child labor406 and

400 Ibid, 13-14, 16, 29, 30, and tables 5a and 5b, 29 and 30.

401 Ibid, 15,16, 29 and 30.

402 Geeta Gandhi Kingdon and Nicolas Theopold ,“Do returns to education matter to schooling participation? Evidence from India”, Education Economics Vol. 16, No. 4 (December 2008): 329-350.

403 They investigated the role of regional returns to education on years of education completed in India when households are either poor and non-poor by stratifying the sample by quartiles of per-capita expenditure and subdivided by gender.

404 These includes girls from the 1st to the 3rd quartile and boys from the 3rd to the 4th quartile. Table 8, p. 344.

405 Rubiana Chamarbagwala, “Regional Returns to education, child labor and schooling in India”, Journal of Development Studies, Vol. 44, No. 2 (February 2008): 233-257

406 She uses a bivariate probit that takes into account that schooling and work decisions are interdependent.

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isolated the income effect from the return effect produced407 when households respond to wage changes408. She did so by focusing on the responses of uneducated parents to adult wage differentials by education levels (a proxy for returns to education). Similarly to those studies she seized liquidity constraints by stratifying the sample by quintiles of per- capita expenditure. Chamarbagwala found that uneducated parents are driven by a substitution effect when the wages to individuals with a full primary education increase: their children’s participation in school is likely to rise, and child labor is likely to fall.

These parents are also indifferent (no income or substitution effect) to the changes to wages attached to higher levels of education409. When she performs the analysis by income quintiles, uneducated parents no longer respond to an increase in wages associated with full primary education.410

My research is similar to the second approach in that it is interested in liquidity- constrained parents’ decisions about their children’s education in response to gross monetary private returns to education when credit markets for education are missing.

However, it differs in several aspects. First, it is driven by a different theory, a theory that

407 According to the theory of school-labor choice, two effects take place when wages (returns to education) increase, the income effect and the substitution (or return effect). These effects differ whether or not households are liquidity constrained. If the household is income constrained, a current increase in lifetime earnings might induce parents to increase current consumption. Since they do not have access to loans, they will send their children to work or allow them to work more. The substitution effect on these households will be such that a rise in returns might make education more profitable and parents will send their children to school. On the other hand, for rich households the income or substitution effects will only increase demand for education See Baland and Robinson, 669-670 and also Gormly and Swinnerton, 7.

408 She isolated the two effects by observing how parents with no education responded to natural logarithm of wages of the different levels of education (from less than primary to college). The argument was that parents with no education will not experience an income effect when there are increases on the wages of the individuals holding different levels of education because their salary will remain the same; any reaction to the returns will be a return effect. 409 The coefficients associated with school participation and child labor are not significant. (see table 6, columns 2 and 4, p. 247).

410 The coefficients associated with school participation and child labor are not significant (see table 8 columns 2 and 4, 248). 196

focuses on the convexity of gross monetary private returns and how they influence unskilled and skilled parents in their schooling choices when financial markets are incomplete. Second, the focus is different. It is not particularly centered on the impact of returns on the tradeoff between child labor and school, but on the effect of convexity of returns to education on whether or not to go to school. This is so because children that do not go to school could be contributing to household income in two other ways, different from them becoming part of the labor force, i.e. staying at home so their parents can work, or helping in the family business. Third, it recognizes that parents’ demand for their children’s education might differ depending on whether children are potential consumers of primary school or of secondary school.

Two important points should be clarified about my approach in this research. The first point is that although parents take into account private monetary and non-monetary returns to schooling net of costs (direct and indirect) in their investment decisions on education, in this research I use gross monetary private returns as measured by hourly wages. These private returns do not account for direct costs and only account for the indirect cost of forgone earnings while the individual is in school. The second point is that liquidity constrained parents are defined as parents who are unskilled and liquidity unconstrained parents are defined as parents who are skilled. The rationale for this is explained in the next section.

5.3 Conceptual framework

The objective of the conceptual framework is to understand the process by which parents’ decisions on their children’s schooling (primary and secondary education) are

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influenced by the returns to education when these returns are convex and parents cannot borrow to finance their children’s education.

This model focuses on those households that are formed by two types of parents: both parents unskilled and both parents skilled. Unskilled parents are liquidity constrained while skilled parents are liquidity unconstrained. This assumes liquidity constrained parents are those with limited education, thus have low earnings and correspondingly do not have assets to serve as collateral for credit and therefore savings are not possible; the opposite is the case for liquidity unconstrained or skilled parents.411

Hence, it is assumed that parents’ level of schooling determines the household’s level of income and the possibilities to accumulate assets, in other words, it determines the short term and long term income.412 Also it is assumed that their level of education determines the social network or community they interact with.413

Unskilled parents have less than secondary schooling (Εu) and receive an income,

Yu. Skilled parents have completed at least secondary schooling (Es) and receive income

Ys. In other words, Yu= f(Eu) and Ys=f(Es). There is convexity in the monetary private returns to education because there is a large wage gap between skilled and unskilled earnings (Ys-Yu).

411 As mentioned in chapter 1, household income and assets become relevant in human capital investment decisions because credit markets are imperfect.

412 This assumption is supported by research. See Behrman J. R., “Mother’s schooling and child education: A survey,” Pier Working Paper 97-025, Penn Institute for Economic Research (May 1997): 6, 21, and 26. Also see: Wolfe, Barbara L. and Jere R. Behrman, “Who is schooled in developing countries? The roles of income, parental schooling, sex, residence and family size,” Economics of Education Review, Vol. 3, No. 3 (1984): 233.

413 This assumption is supported by research in livelihood strategies and poverty traps. See for example Durlauf, Steven N., “Groups’ Social Influence and Inequality,” in: Poverty Traps, edited by Samuel Bowles, Steven N. Durlauf and Karla Hoff (New York: Russell Sage Foundation and Princeton, New Jersey: Princeton University Press, 2006): 142.

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I assume that both types of parents are informed of the gross monetary private returns to education, prevalent in the region and area surrounding them, for both skilled

(θu) and unskilled labor (θs) and that they regard these returns as the expected rewards for their children’s education. Access to information on the monetary returns might be limited for some parents. The labor market is segmented between skilled and unskilled labor and between rural and urban areas with each segment constituting a different type and set of opportunities and constraints that influences how parents obtain information and process it. Communities are also segmented by level of income. Unskilled parents who live in rural areas might be surrounded by mostly their own kind and hence they do not obtain the same information on returns to education a skilled parent obtains in the more labor market- and community-diverse dynamic urban centers. In addition, information on the nonmonetary returns to education is asymmetric since unskilled parents might not perceive these nonmonetary returns. I also assume that parents’ education also defines the way they process and/or react to information related to expected prices such as future returns to schooling414 and other factors that might affect those expected returns.415 Hence, I assume that skilled parents differ from unskilled parents not only in the way they obtain information but also in how they react to it.

I also assume that whether or not to send children to school (primary and secondary) is a parental choice. The decision of parents whether to send their children to school or not, Є=1 or 0, depends on the expected gross monetary private rates of return to education present in their area and the costs of their children’s schooling (e) which

414 Behrman argues “that more schooled mothers assess better the returns to education than less schooled mothers”. See J. R. Behrman, “Mother’s schooling and child education: A survey,” 6.

415 This is also supported by research on the influence of community networks. 199

includes direct cost such as: transportation, supplies, food and clothes, and opportunity costs such as: the opportunity cost of household labor, then Є=g(θu, θs,e). The opportunity costs are 0 for skilled parents and higher than 0 for unskilled parents. Also the direct costs may vary for skilled and unskilled parents given that they have access to a different set of opportunities and constraints. The amount of expenditures in schooling increases with the quantity of education parents decide their children to acquire (Є), since more quantity of education implies more time. In this model, parents use their earnings on consumption and on expenditures on schooling.

I further assume that children’s consumption in adulthood depends on the returns to education they receive (θu, θs). I also assume these returns are determined exogenously. Also, the amount of education acquired defines the children’s rate of return on education.

The key idea in this model is that there is a difference between unskilled and skilled parents because their level of education defines the household capacity to consume and invest and also the way the expected rates of return to education influence their schooling choices for their children.

In this model households cannot acquire educational loans or borrow to smooth their consumption. I assume that parents are altruistic so their utility is derived from two sources: parent’s own consumption (С) and the future consumption of their children (Сf).

Hence parents maximize household utility: U (С, Сf) subject to the following constraints:

(1) s ≥0 credit constraint

f (2) С = θu + (θs - θu ) children’s expected consumption

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(3) С + e ≤ Yu + (Ys-Yu) budget constraint where Yu= f(Eu) and Ys=f(Es)

Given the credit market constraints, parents’ decisions regarding their children’s schooling depend on their level of education. If parents are unskilled, their choice of C,

С f and Є entails a tradeoff between investment in education and consumption smoothing. Hence, these parents might not be encouraged by the high returns to skilled labor (θs) to increase their investment in education (Є) for their children; as matter of fact, they might choose to decrease it. Parents are not only aware that an increase in the rewards for skilled labor increases the gap between their rewards and those of skilled individuals, but it also increases the magnitude of their struggle to keep afloat while investing in the education of their children. They know that those jobs and returns are not available to them and might not be available to their children as well. On the other hand, if the returns to unskilled labor (θu) increase, these parents might not send their children to school. They need to maintain or improve the family standard of living; therefore, these parents might marshal their family resources, including their children, towards keeping or increasing their household income. This livelihood strategy might lead generations of the same families into a poverty trap.416

Indeed, unskilled parents realize that the returns to investment in skilled labor are associated with very costly and selective post-secondary schooling. They also realize, based on their own experiences, that the low quality of education along the primary and secondary schooling and the low exposure to other sources of learning might affect their

416 Scholars have developed dynamic models to study this behavior, particularly farmers, when faced with covariant risk. See Frederick J. Zimmerman and Michael Carter, “Asset Smoothing, Consumption Smoothing and the Reproduction of Inequality Under Risk and Subsistence Constraints,” Journal of Development Economics, Vol. 71, Iss. 2 (August 2003): 233-260. Also, see Rosenzweig, Mark and Kenneth I. Wolpin, “Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low Income Countries: Investments in Bullocks in India,” The Journal of Political Economy, Vol. 101, No.2 (April 1993): 223-244. 201

children’s chances to aspire to those post-secondary options that have the higher payoffs.

However, they cannot compensate for the deficient education supplied to their children by themselves providing them with the additional help or the additional resources needed for their children to climb the school ladder successfully. Moreover, they might perceive that some of those highly rewarded skilled labor jobs are obtained through social networks they do not have access to. Education for these parents and their children might be more a post-experience good than an experience good because not only its quality, but also the assessment of its benefits will be apparent only until the whole package to obtain the high returns has been bought. Hence, increases in returns to skilled or unskilled labor might trigger a perverse effect of not providing the incentive for parents to demand education.

If parents are skilled, credit constraints do not matter because their level of income is high enough to pay for their children’s education and smooth their consumption. For skilled parents, high returns to skilled and even unskilled labor are an incentive to invest in education of their children.

Skilled parents and their children do not face the same dilemma; they will not have to trade the future consumption of their children for the present consumption of the family. Also, they do not confront information failures regarding the impact of education on their children. They will be responsive to returns to skilled labor because they have high expectations and they have different coping strategies than those of the unskilled parents. They can influence the outcomes, they can compensate for the low quality of education, and they are certain that they can afford the cost of post-secondary education.

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They respond positively to increases in returns to unskilled labor because these increases also might inform about larger increases in the returns to skilled labor.

Given the above model, my hypotheses are the following: first, unskilled parents are less likely to invest in schooling of their children (mainly secondary school) when the returns to skilled labor increase. Alternatively, unskilled parents are more likely to decrease their investment in education when the returns to unskilled labor increase.

Finally, skilled parents are more likely to demand more schooling when either the returns to skilled or unskilled labor increase.

5.4 Empirical Approach

5.4.1 Data description

The survey being used for this analysis is the Costa Rican Household Survey for

Multiple Purposes (EHPM) already described in chapter 4. For the purposes of this chapter, the information provided in the four modules of the survey is used for the construction of the variables related to education and household and individual characteristics, the wealth index and the returns to education.

The sample for this research is constituted by the number Costa Rican children between 7 and 19 years old417 who are reported as attending formal school (either primary or secondary) or not attending school but who were supposed to be attending since they had not completed the grades for each specific level (primary or secondary) during 2005 is 11,278 children. The unit of analysis for this research, however, is

417 Children on the academic track finish secondary school at age 17, but the ones on the vocational track finish at 18. Moreover, there is a considerable number of students who at age 19 have not yet completed 11th or 12th grade because they are repeating grades. 203

comprised of a subset of these children, 6,371 children who are also single and reported in the survey as sons or daughters in the household; their parents’ education is known.

The procedures to collect and analyze the data have been standardized by the

INEC since 1987. This diminishes the possibility of inconsistency and variability in the collection and analysis of data and ensures the overall measurement reliability of the data.

5.4.2 Sample characteristics

Table 5.1 below gives a profile of the characteristics of the observations in the three samples (total, primary school, and secondary school).

The total sample was stratified in two samples by level of education: the primary school sample with 3368 (53%) observations that is constituted by those children enrolled or supposed to be enrolled in primary school and the secondary school sample with 3003

(47%) observations formed by those children enrolled or supposed to be enrolled in secondary school. Of the total sample, 86% (5,491) are enrolled. Practically 100% of the primary school sample is enrolled whereas only 75% of the secondary school sample is enrolled. This sample pattern confirms what I said in chapter 3 about the low demand for secondary school education and the mandatory character of primary school enrollment.

Most of the children are from the rural area (64%) and children are almost equally distributed between males and females.

The average age is 12.5 for the total sample and this varies according to whether the children belong to the primary school group or to the secondary school group. If the average age in the primary and secondary groups is compared with the number of grades completed by the same groups, a pattern of delay that starts in primary school is revealed.

In fact, there are children who are 17 years old and have not completed primary school.

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Primary school completion takes much longer than expected and consequently children feeling much older than their peers or perhaps less academically prepared for secondary school decide not to continue to secondary school or drop out in the early grades of secondary school. In fact, it was pointed out in chapter 3 that a considerable amount of the desertion occurs between 6th grade and 7th grades, that is, in the transition from primary to secondary school, and also in 7th and 8th grades.

It is important to underline that more than two thirds of the children of either the total sample or the primary or secondary samples have both parents with less than complete secondary school, in other words unskilled. The figure is strikingly high for those children who are not attending school, more than 90%, in all three samples. It is essential at this point to report some characteristics of the households when both parents are unskilled or skilled.418 When both parents are unskilled, households have an average wealth index of -.72, 74% are located in rural areas, and concentrated in the regions

(more than 71%) other than the Central region (57%). Households of skilled parents have an average wealth index of 3.64, most of them are located in the urban areas (72%), and are scattered around the regions (less than 8%), except for the Central and the

Chorotega regions that concentrate 21% and 11% of them, respectively. The remarkable differences between the two types of households provide support to my assumption about unskilled parents being liquidity constrained and skilled parents being liquidity unconstrained. Unskilled parents have barely accumulated assets and most of them live in the most deprived and stagnant areas of the country where they are most likely to obtain

418 This information is not reported in the tables, it comes from an additional analysis of the data.

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low earnings. Skilled parents seem to have asset holdings and live in areas of greater economic opportunities.

Also, 448 or 51% (448/880) of the children in the total sample who are not enrolled are working, which might imply that opportunity costs of unskilled parents might increase as the children’s age increases. Of these children,419 81% are from the rural area and 90% have both parents classified as unskilled labor.

These relative figures remain similar for the other sub-samples, except that in absolute terms they are much smaller for the primary school group given that in this group most of the children are enrolled. These outcomes seem to imply that the higher rewards being received by skilled labor (gross monetary private returns on skilled labor),

14%, on average, are not motivating parents to send their children to school; after all, parents and children have to invest during a long period to see their children obtain them: more than 15 years of schooling on average as mentioned above and in my previous chapter420. On the other hand, the average gross monetary private returns associated with unskilled labor are such that an increase in one year of education increases returns by only 3%. However, teenagers might still be needed to improve family income, either by themselves being part of the labor force or by working with their parents if they have a small business or a little farm or by taking over some home chores so their parents can work (such as the case of girls helping mothers).

The secondary education group compared to the primary school group shows a higher average level of parents’ education and a smaller number of children in the

419 This data is not shown but comes from further analysis of the sample data.

420 The average time a youth takes to go through secondary school is 9.5 years (almost double of what it should take). Primary school is expected to take 6 years. The addition of the two figures amounts to 15.5 years. 206

households and a positive average wealth index. Then, a higher attendance rate in secondary school compared to primary school should be expected; however, this is not what it is happening. This apparent contradiction seems to respond to two reasons. The first one is that primary school attendance is mandatory and less costly than secondary school and the second one is that the figures for the average wealth index and the percentage of children with parents who have not completed high school diminishes for children of the secondary school group who are not enrolled. In fact, for this sub-group

(secondary school group who are not enrolled), the average wealth index is negative (-

.76), and the percentage of children with unskilled parents is very high (91%).

Table 5.1. Costa Rica 2005: Profile of the unit of analysis

Characteristics Total Primary Secondary Children enrolled 86% 99% 75% Children not enrolled 14% 1% 25% Average years of education completed 4.9 2.7 7.3 Average age of children 12.5 10 15.4 Children coming from rural area 64.0% 65.7% 62.9% Children that are male 52% 54% 50% Average wealth index (units) 0.14 -0.14 0.45 Number of children in HH 2.9 3.0 2.8 Average returns to unskilled labor 3.3% 3.4% 3.2% Average returns to skilled labor 14.1% 14.2% 14.0% Both parents are unskilled 69% 70% 67% Total sample 6371 3368 3003

The differences between enrolled and not enrolled are statistically significant for the majority of the variables for the three samples (see tables 5.2, 5.3 and 5.4). Overall for the three samples, children who are not enrolled in school are more likely to be boys, more rural, less well-off (for example, for the total sample the average wealth index is -

0.86 as opposed to 0.31) and 100 times less likely to have parents who both have at least completed high school, or at least one of the parents (for the total sample see Table 5.2)

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has completed high school, and are, on average, much older (as the age of the child increases, children are less likely to be enrolled in school)421. The differences in the means of the returns to education, either for skilled or unskilled labor, faced by children who attend school and do not attend school are not significant for the total sample. They become significant with p values of about 7% for the secondary school sample. For the primary school group only the differences in the means of the returns to skilled labor are significant (p value = 8%).

Table 5.2. Costa Rica 2005: Difference in the means total sample: Enrolled and not enrolled in formal school

Variable Non Enrolled Enrolled Difference p-value Returns to skilled labor 0.142 0.141 0.001 0.32 Returns to unskilled labor 0.034 0.033 0.000 0.40 Both parents unskilled 0.913 0.651 0.262 0 Both parents skilled 0.009 0.148 -0.139 0 Wealth index -0.879 0.308 -1.187 0 Male 0.610 0.511 0.099 0 Rural 0.802 0.618 0.184 0 Number of children 3.176 2.866 0.311 0 Observations 5491 880

421 The age specific ttest are not shown in the table; they are significant with p-values equal to 0. 208

Table 5.3. Costa Rica 2005: Difference in the means for the primary school sample: Enrolled and not enrolled in formal school

Not Variable Enrolled Enrolled Difference p-value Returns to skilled labor 0.147 0.143 0.005 0.079 Returns to unskilled labor 0.034 0.034 0.000 0.940 Both parents unskilled 0.921 0.695 0.226 0 Both parents skilled 0.007 0.124 -0.117 0 Wealth index -1.483 -0.079 -1.404 0 Male 0.712 0.534 0.179 0 Rural 0.799 0.651 0.147 0 Number children 3.777 2.975 0.802 0 Observations 3229 139

Table 5.4. Costa Rica 2005: Difference in the means for the secondary school sample: Enrolled and not enrolled in formal school

Not Variable Enrolled Enrolled Difference p-value Returns to skilled labor 0.141 0.139 0.002 0.0749 Returns to unskilled labor 0.034 0.033 0.001 0.0627 Both parents unskilled 0.911 0.587 0.324 0 One parent skilled 0.080 0.230 -0.151 0 Both parents skilled 0.009 0.183 -0.173 0 Wealth index -0.766 0.859 -1.625 0 Male 0.591 0.479 0.112 0 Rural 0.803 0.571 0.232 0 Number of children 3.063 2.710 0.354 0 Observations 2262 741

5.4.3 Estimation strategy

I intend to test my three hypotheses by researching the empirical relationship between demand for schooling and returns to schooling in two situations: when parents are unskilled or in a situation of liquidity constraints (model 1) and when parents are skilled and therefore do not face those constraints (model 2). In order perform the test a

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probit model was chosen because what is observed is only whether a child is in school or not. The first model intends to capture the first situation and hence tests the first two hypotheses:

( )

∑

where i=1…n individuals; j=0…n returns to schooling and m=1..n counties

The dependent variable, Ei, is a latent variable that proxies for the demand for education and accounts for whether the ith child is enrolled or not in the formal school system at the time of the survey.

The research variables: RSkji and RUSkji stand for estimated gross monetary private returns (hereafter returns to education) to skilled and unskilled labor, respectively.

These two variables are introduced in the specification to recreate convexity in the returns. RUSkji intends to capture the low returns obtained for those low levels of schooling before the critical mass of education is acumulated. RSkji attempts to seize the high returns obtained by the high levels of education once the critical stock is achieved.

Taken together, they both recreate the convexity of the returns to education in Costa Rica.

Hence, parents’ responses to these two different estimated expected prices could be observed. These estimated gross monetary rates of returns represent two sets of 12 element vectors of linear rates of gross monetary private returns calculated for skilled and unskilled labor for each of the six regions stratified by area (rural and urban). These estimated rates of return, explained below in detail, were calculated using wage equations

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whose dependent variable was natural logarithm of wage per hour and the independent variables were years of completed schooling, age, age squared, whether female or male, whether there is knowledge of foreign languages, and county fixed effects to account for quality of education and unobservables. Each set of 12 estimated rates of return were assigned to children according to their location. The calculation of these two variables is explained in the empirical results section.

The two variables POSki and PUSki are dummies that account for the education of parents. The first one takes the value of one if one of the parents is skilled and 0 otherwise. The second one takes the value of 1 if both parents are unskilled and 0 otherwise. The default dummy PSki takes the value of 1 if both parents are skilled and 0 otherwise for the first model. The relevant variables for the general analysis (both

422 models) are: PUSki, and PSki and are included to measure parents’ liquidity constraints or lack thereof as explained and justified in the conceptual framework. The first one, both parents unskilled, captures liquidity constrained parents and the second one, both parents skilled, liquidity unconstrained parents. These variables also account for the way households process and/or react to information related to expected prices such as returns to schooling as also mentioned in the conceptual framework423. In Model 1, the key variable is PUSki because it is interacted with the two returns to education variables as explained below.

422 POSki is included in the specification, but it is not interacted. It is when parents have a mixed skill level (one parent skilled, the other unskilled). The focus of the analysis is on the impact of returns on educational decisions if both parents are unskilled (liquidity constrained) and both parents are skilled (liquidity unconstrained).

423 Also, the variables regarding parents’ education might be capturing children’s ability and motivation for school and other un-observables.

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The two interactive terms PUSki* RSkji and PUSk* RUSkji are included in order to capture the effect of returns to education on the demand for schooling under a situation of liquidity constraints, that is when parents are unskilled. The reason for the inclusion of these interactions derives from the model explained in the conceptual framework and is the following: how the expected returns to schooling influence whether or not parents send their children to school depends on both parent’s earnings and the way parents process information about returns to education. In turn, these two factors are determined by parents’ level of education. Parent’s level of schooling classifies them in two labor market groups, unskilled and skilled. It is expected that the estimated coefficient, β5, will be small, negative and not significant and the estimated coefficient, β6 , will be large, significant and negative. These coefficients compare how returns influence schooling choice in households with both parents unskilled relative to all other households (including those with both skilled parents and one skilled parent).

The variable WIDi is a wealth index that proxies for the long run economic situation of the family.424 The decision to be enrolled is affected by both current income and long run economic welfare. There are, in addition, some independent variables that might be correlated with wealth such as parents’ education which also depend on individuals’ financial endowments, so a control for wealth is needed. Income measures available in the data could not be used because they are a measure of current welfare425 and consequently are endogenous. The index was constructed using the principal

424 Deon Filmer and Lant H. Pritchett, “Estimating Wealth Effects Without Expenditure Data – Or Tears: An Application to Educational Enrollments in States of India,” Demography, Vol. 38, No. 1 (February 2001): 116.

425 The wealth index not a proxy for households’ s current welfare or poverty. Ibid. 212

components method (PCA). The construction of this index is explained in the empirical results section.

The control variables Z include the following child characteristics: 1) age specific dummies to account for differences in enrollment associated with age of the child because as the child age increases the opportunity costs for unskilled parents increase, 2)

Whether the child is female or male to reflect parents’ preferences or any differences in demand for schooling due to gender characteristics. In Costa Rica, both boys and girls are sent to school by their parents and in this sense there are no preferences towards educating boys more than girls either in the urban or rural areas. However, my previous analysis (Chapter 3) shows that the gender gap in education favors girls. Although this could be explained by the need of poor boys, who are more likely to get jobs than girls, to start helping their families, there could be some girls’ characteristics that explain the gender gap, and 3) area (rural or urban) where the child lives, to capture any differences in school attendance due to location. School infrastructure and quality of teachers is more deficient in rural areas. Also, in rural areas compared to urban ones, the distances to, mainly, secondary schools are larger. Moreover, in this particular sample, there are more rural students who are non-enrolled than urban students who are non-enrolled. Also the majority of non-enrolled students who work are from the rural areas. The other variable included is the number of children in the household. It is included to seize the effect of family size on enrollment decisions and also because the number of children might be correlated with income related variables included in the specification.

Finally, fixed effects dummies were included for 79 counties. They are necessary to account for differences in school quality and other un-observed characteristics.

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Model 2 shown below, was devised in order to estimate the influence of convexity in the returns to education on the demand for schooling when households are well off and also to test the third hypothesis. The study of the behavior of well-off parents, when faced by the same decision, provides a rich contrast that will allow us to see the negative implications of household decisions when liquidity constraints are binding.

( )

∑

The variables included in this specification are the same as above except that

PUSki is the default dummy in this model. PSki takes the value of 1 if both parents are skilled and 0 otherwise and POSki, as in model 1, takes the value of one if one of the parents is skilled and 0 otherwise. The two interactive terms: PSki* RSkji and PSki*

RUSkji were included to observe the impact of returns to education on investment in education in a situation in which households do not have liquidity constraints. The rationale for these interactions is the same rationale for the interactions used in model 1.

The coefficient β5 and β6 are expected to be positive and significant. The increase in returns to unskilled labor is seen as a positive sign that comes along with an increase in returns to skilled labor.

The two models were applied to the total sample of children (primary plus secondary) that were enrolled or were supposed to be enrolled in the formal school system. Given that parents’ decision making might differ according to whether their children are candidates for primary or secondary school, the analysis was performed for

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two additional samples: the primary school sample that included children that were enrolled or were supposed to be enrolled en primary school (given that they have not completed primary school), and the secondary school sample that included children that were enrolled or were supposed to be enrolled in secondary school (given that they had not completed secondary school).

A description of the variables used in the regression analysis and the descriptive statistics is presented in tables A.5.1 and A.5.2 of the Appendix to this chapter.

5.4.4 Empirical issues

The reason why the two models formulated include two interactions is that they help to understand the process by which parents take into account returns to schooling in their choices about education for their children, i.e., there is a theoretical justification for these interactive terms. Moreover, previous research in this field has formulated models that include interactive terms between returns to education and other variables. Indeed without the interactive terms the model is miss-specified, yielding biased estimates of the coefficients.426 Despite this theoretical grounding, it was deemed necessary to perform a likelihood ratio test (for nested models) to compare the specification without interaction with the specification with interactions (the one of interest in this research) to demonstrate the joint significance of the interactive terms427 and the better prediction of

426 Laura Langbein with Claire L. Felbinger, Public Program Evaluation: A Statistical Guide (Armonk, New York: M.E. Sharpe, 2006), 172.

427 There is a controversy regarding the significance of estimated coefficients resulting from interactions. According to Friedrich, the estimated coefficients and t values of an interactive model are not meaningless or arbitrary. They differ from the ones obtained from an additive model because they are conditional: whether a variable has a significant effect on a dependent variable depends on the particular level of another variable. See Robert J. Friedrich, “In Defense of Multiplicative Terms in Multiple Regressions Equations,” American Journal of Political Science, Vol. 26, No. 4 (November 1982): 797-833.

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demand for schooling that the interactions-included models provide. Table 5.5 shows

that the interactive terms for model 1: PSk* RSkji and PSk* RUSkji are jointly significant

for the total sample and for the secondary sample. It also shows that the model with

interactions has better predictive ability than the model without interactions because the

AIC (Akaike information criterion) is smaller for the interactions-included specification.

The test for model 2 yields the same results. The secondary school sample, although with

a good AIC, shows a level of joint significance at the 10% level.

Table 5.5. Costa Rica 2005: Likelihood test ratio test results for total sample: Model with interactions compared to model without them

Total sample Secondary school AIC Joint significance AIC Joint significance Model 1 No interaction 3,046.96 2520.14 with interactions: 3,043.33 Ch2=7.61 2517.72 Ch2=6.42 β5(PSk* RSkji) and β6( PSk* RUSkji ) P-value=0.022 p-value=0.044 Model 2 No interaction 3,046.96 2520.14 with interactions : 3,044.46 Ch2=6.48 2519.58 Ch2=4.56 β5 PSki* RSkji and β6 PSki* RUSkji p-value=0.039 p-value=0.102

Measures of a child’s ability and motivation, parents’ willingness and parents’

capacity to assist their children with their school tasks and parental preferences as well as

teacher’s motivation and management skills of school principals428 are not included in the

regression equations because these variables are unobserved. In the case of the child’s

ability, some scholars have attempted to use some proxies for it but, as pointed out in the

previous chapter, these measures have been questioned regarding their construct validity.

Measures of school quality are also not included due to the lack of data, a

limitation that is faced by most of the studies in the field. Household surveys do not

428 Glewwe and Kremer, “Schools, Teachers and Education Outcomes in Developing Countries,” 968. 216

collect information on school quality; these indicators are collected by the ministries of education and usually reported at the macro level; hence their use is prone to measurement errors, not all of it random, hence, the gain in internal validity by including some of these variables is offset by the loss in the same validity due to the systematic error created.

The estimation strategy addresses the omitted variable bias in two ways. First, those unobserved variables correlated with parents’ schooling such as the child’s ability and some of the endowments, parents’ preferences and even school and community characteristics that are influenced or elected by the type of community residents are controlled for by the introduction in the specification of the variable parents’ education.

Behrman argues that it is not advisable to include in the specification measures of unobserved predetermined variables correlated with the mother’s schooling because they will bias downward the estimate of this variable.429 Second, quality of education is accounted for by the inclusion of fixed effects for the 79 counties, and a zone dummy

(rural and urban). This approach is justified by the fact that 83% of the children of the sample remain in the same county they were born, i.e., they have not moved to other counties, and by the fact that 93% of the children from the sample who are enrolled attend the public school system, 2% attend the publicly subsidized private system and 5% the private school system. It is very likely, following the analysis made in chapter 2, that the large majority of the children not attending school were previously enrolled in the public school system. Third, the fixed effect dummies, the zone dummy and the gender dummy also account for those unobservable differences between the units of analysis that

429 Behrman Jere R. “Mother’s Schooling and Child Education: A Survey,” (Philadelphia, Pennsylvania: Penn Institute for Economic Research, University of Pennsylvania, Pier Working Paper 97- 025), 8, 15. 217

could have affected their demand for schooling, such as cultural differences, natural disasters, and political or economic differences.

Endogeneity bias 430 is not a concern in this research. In general, Costa Rican parents do not send children to certain public schools based on their quality, but rather to the nearest school with space available. Although there are differences in the quality of public schools, with the ones located in the more remote rural areas at a disadvantage, parents stay where they have a job, even if in those places schools are deficient. If it is unlikely that parents are able to choose the school of their children, there is no possibility of affecting the quality of the school; hence sample selection bias is not a threat.

In order to avoid the measurement errors of income431 reported in the literature, I created an asset or wealth index that was created using the principal components, as explained above. The more relevant measurement error when income is used occurs because annual or monthly income only seizes a limited portion of the true long run household resource constraints associated with children’s education.432 Moreover, as happens with other surveys, there were a considerable number of the observations with no information on income, and in these cases the use of the wealth index prevented the reduction of the sample size and consequently increased the statistical and external validity of our claims. For these reasons433 the measure most preferred by scholars, but usually unavailable (and certainly in my data), to proxy for economic status is

430 Based on Glewwe and Kremer’s analysis of the situation in which these types of validity threats could occur. Glewwe and Kremer, 969-978.

431 The survey does not report consumption levels.

432 Behrman, 26.

433 See for example: Jere R. Behrman and James C. Knowles, “Household Income and Child Schooling in Vietnam,” World Bank Economic Review, Vol. 13, No. 2 (1999); Acemoglu and Pischke. 218

consumption expenditures. However, Filmer and Pritchett showed that this index is as reliable as the consumption expenditures in predicting enrollment434 and has the advantage over this measure, as well as over income, of eliminating the possibility of endogeneity bias that arises when income and consumption decisions are made simultaneously with schooling decisions, which is one of the assumptions of my model.

In fact, Filmer and Pritchett demonstrated the validity and reliability of this asset index for estimating the relationship between wealth and school enrollment across households in India and across 35 countries.435 It should be pointed out, however, that although the type of assets included in the calculation of the wealth index are numerous and diverse, information on land ownership, real estate and holdings such as livestock is not included because that type of data is not gathered in the survey. This might produce a smaller coefficient than that one that could have included more information on asset holdings, but the wealth index will still be a good predictor of enrollment.

Heteroscedasticity is addressed by using robust standard errors. The inclusion of interactive terms in the specification might introduce multicollinearity. This statistical problem is not a concern in the econometric literature and certainly not in the literature about interaction terms since the high correlation among the interactive terms and their constituent variables does not impair the interpretation of the results.436 In addition, the

434 See Filmer and Pritchett (2001), 115, 122-125.

435 In fact, they compared the measurement errors in OLS and instrumental variables estimates produced by the use of the asset index and by the use of the consumption expenditures measure as proxies for predicting school enrollment and the later measure had considerable more measurement errors (see Filmer and Pritchett, “The Effect of Household Wealth on Educational attainment: Evidence from 35 Countries,” Population and Development Review, Vol. 25, No. 1 [March 1999], 88). Also see Filmer and Pritchett (2001), 117-119.

436 Friedrich, 803.

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signs of multicollinearity are not present in the regression outputs.437 Cross-section or spatial autocorrelation as a result of omitted variable bias is not regarded as a serious threat to internal validity in this model since the relevant variables have been accounted for.

5.5 Empirical Results

I will begin this section by describing the calculation of the returns to education and creation of the asset index, since these two variables were created separately and included in the models. The analysis of the regression results follows afterwards.

5.5.1 Calculation of the returns to education

Two types of linear returns to education, one for skilled labor and one for unskilled labor, were calculated to recreate the convexity of the returns to education that characterizes the relationship between the logarithm of earnings and years of school completed in Costa Rica. These two types of returns were calculated for each of 12 subsamples or segments: each of the six regions and within them the two types of areas found in each region: rural and urban. Convexity of returns prevails by regions, as shown in chapter 4, and it is also the case that this relationship holds out for the rural and urban areas within each region. Besides, households get information about expected prices from the region and area where they are located.

The magnitude of the differences between skilled and unskilled workers, education and wage displayed in table 5.6, provides evidence of the convexity of the returns to education as well as the dilemma poor parents, given their low income, face

437 Signs such as the concurrence of a high adjusted R2 and most or all of the estimated coefficients being not significant (see Langbein, 178). 220

when deciding on the education of their children. It shows that in all segments, while the average number of years of school completed by skilled workers is 13, this figure is half or less for unskilled workers. Also, in all segments, skilled workers compared to their unskilled counterparts almost double the average hourly wage.

The total sample used to obtain the returns to education is based on 12,535 individuals438 between the ages of 20 and 60 from all sectors of the economy who were receiving income from their work and whose work hours were known in 2005.

Table 5.6. Costa Rica 2005. Mean years of education completed and mean wages per hour: Skilled and unskilled labor by area and by region

Skilled Unskilled Skilled/ unskilled Areas/ Years of Wage per Years of Wage Years of Wage per Regions education hour education per hour education hour Urban Central 13.5 1606.37 6.7 755.60 2.0 2.1 Chorotega 13.3 1318.09 6.7 741.66 2.0 1.8 Pacífico 13.0 1234.56 6.2 689.25 2.1 1.8 Brunca 13.6 1370.75 6.4 782.88 2.1 1.8 HuetarA 12.9 1238.77 6.4 734.28 2.0 1.7 HuetarN 13.3 1404.71 6.5 716.08 2.0 2.0 Rural Central 13.0 1304.85 6.0 691.22 2.2 1.9 Chorotega 13.2 1166.02 5.8 618.42 2.3 1.9 Pacífico 13.4 2046.77 5.5 667.01 2.4 3.1 Brunca 12.6 864.85 5.5 613.20 2.3 1.4 HuetarA 13.0 1137.34 5.5 605.76 2.4 1.9 HuetarN 13.5 1220.92 5.6 746.26 2.4 1.6

The two types of linear returns to schooling were calculated using the following wage equations:

438 The estimated population that this sample represents when the expansion factor is applied is 1,274,697. 221

∑ ∑

where i is the index for the individual and j is the index for each of the 12 segments and k stands for the 79 counties . The dependent variables for the first and second formulation, respectively, are the logarithmic hourly wage of skilled workers varying over individuals i, skilled workers, within segment j and the logarithmic hourly wage of unskilled workers varying over individuals i, unskilled workers, within segment j. Wages per hour is calculated using two variables439: income accruable to main job, and hours of work. The research variable S stands for years of education completed. The independent

2 variable Ai and Ai denote age and age squared. Age controls for the differences in earnings due to the age cycle. L is a set of three dummies where the first one takes the value of 1 if the individual knows only knows only Spanish, 0 otherwise; the second takes the value of 1 if the individual knows Spanish and English, 0 otherwise and the third one takes the value of 1 if the individual knows Spanish and another foreign

439 For a more thorough description of the variables used in this specification and the rationale for their inclusion in the specification please refer to chapter 4. Although the specification in chapter 4 was developed to capture the convexity of the returns to education by defining a step function between wages and schooling, and these two wage equations establish a linear relationship between wages and schooling, the variables used in that formulation (chapter 4) are the same ones included in this formulation and were included for the same reasons. 222

language, 0 otherwise. It is expected that knowledge of a foreign language influences earnings mainly given the size of the FDI sector in the labor market and the weight given by this labor market to highly skilled labor. G is a dummy that stands for gender. R is a dummy that determines whether the individual is located in a rural or urban area. C represents fixed effects dummies for each of the 79 counties.

The estimated schooling coefficient β1 is the coefficient of interest. It represents, for the first equation, an estimate of the average rate of return to an additional year of education of a skilled worker, holding other variables constant. The schooling coefficients estimated for each segment are shown in tables 4.7 and 4.8. The complete regression outputs are presented in the Appendix, tables A.5.5, A.5.6, A.5.7, A.5.8, A.5.9 and A.5.10. These estimated schooling coefficients were assigned to each child according to his or her region and area of location.

There is a considerable and consistent difference in the explanatory power of the two empirical models (see tables 5.7 and 5.8). The adjusted R squared for all the regression outputs that establish the relationship between earnings of unskilled labor and schooling is about one third of the one associated with the regression of earnings of skilled labor and schooling. Obviously, there are some unobservables common to the unskilled individuals not accounted for in the specification as compared to the skilled labor specification. The sample of individuals in this analysis is the same sample of the analysis of convexity in chapter 4, only that the sample is segmented differently; hence the characteristics of that sample are useful for the following analysis. Unskilled workers are mostly males, mostly located in rural areas and in the five regions other than the

Central region. Hence, they work in areas where extensive poverty, poor infrastructure,

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traditional agriculture and unemployment coexist with large multinationals involved in the production of profitable non-traditional products and tourism. The quality of education in these areas is deficient and going to school is more costly than in urban areas in terms of direct costs and, mainly, opportunity costs since they themselves come from poor, liquidity constrained households. The parents of those unskilled workers faced those stagnant economies and realized that the few well-paid jobs required tertiary education and their children did not have chance to continue to higher education: they probably could hardly pay some secondary school and education quality is deficient.

Given this rationale, it is possible that the discount rate is the variable that could add explanatory power to this specification. A high discount rate captures the weight of all those high direct and indirect costs when parents have to trade future consumption for present consumption. Unfortunately, the discount rate is an unobserved variable and data on costs is not available. Moreover, some opportunity costs might not be observed. The other possible missing variables are those related to family background and factors such as quality of education and community characteristics in which data is not available for this sample. These characteristics together might capture the struggle for survival of the poor people represented in this case by the unskilled workers. Further research is required in order to reach a more certain answer to this discrepancy in the explanatory power of the two models.

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Table 5.7. Costa Rica 2005. Returns to schooling: Central, Chorotega and Pacífico regions

Skilled Unskilled Regression model coefficient p-value coefficient p-value Central Urban Years of schooling 0.126*** 0.000 0.020** 0.007 Observations 1872 1831 Adjusted R2 0.289 0.040 Rural Years of schooling 0.108*** 0.000 0.040*** 0.000 Observations 771 2077 Adjusted R2 0.273 0.127 Chorotega Urban Years of schooling 0.153*** 0.000 0.068*** 0.000 Observations 232 245 Adjusted R2 0.243 0.134 Rural Years of schooling 0.196*** 0.000 0.039* 0.010 Observations 167.0 628.0 Adjusted R2 0.409 0.104 Pacífico Urban Years of schooling 0.173*** 0.000 0.011 0.198 Observations 137 274 Adjusted R2 0.260 0.004 Rural Years of schooling 0.184*** 0.000 0.030** 0.007 Observations 110 549 Adjusted R2 0.528 0.068 Fixed effects included Robust standard errors were required

Note: Dependent variable is log of wages per hour. Control variables included are age, age square, dummies for knowledge of foreign languages in addition to Spanish, dummy for gender, dummy for area of location and fixed effect county dummies. Returns to schooling refer to the gross monetary private returns to schooling.

***; **, and * refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

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Table 5.8. Costa Rica 2005. Returns to schooling: Brunca, Huetar Atlántica and Huetar Norte regions

Skilled Unskilled Regression model coefficient p-value coefficient p-value Brunca Urban Years of schooling 0.121*** 0.000 0.037~ 0.068 Observations 190 277 Adjusted R2 0.373 0.047 Rural Years of schooling 0.157*** 0.000 0.019~ 0.071 Observations 177 879 Adjusted R2 0.290 0.080 Huetar Atlántica Urban Years of schooling 0.158*** 0.000 0.068** 0.007 Observations 107 243 Adjusted R2 0.201 0.065 Rural Years of schooling 0.135*** 0.000 0.053*** 0.000 Observations 118 822 Adjusted R2 0.386 0.082 Huetar Norte Urban Years of schooling 0.103*** 0.000 0.030* 0.047 Observations 107 243 Adjusted R2 0.201 0.065 Rural Years of schooling 0.188*** 0.000 0.034* 0.016 Observations 118 822 Adjusted R2 0.386 0.082 Fixed effects included Robust standard errors were required

Note: Dependent variable is natural logarithm of wages per hour. Control variables included are: age , age square, dummies for knowledge of foreign languages in addition to Spanish, a dummy for gender, a dummy for area of location and fixed effect county dummies Returns to schooling refer to the gross monetary private returns to schooling. . ***; **, *, and ~ refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

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5.5.2. Construction of the wealth index

The wealth index was calculated for a sample of 11,278 observations constituted by Costa Rican children between 7 and 19 years of age. The number of observations is larger than the sample used for estimating the demand for education (6,371) because it had to be reduced to include only those children who are single, live with their parents, and their parents’ education is known. The richness of the data regarding information on the dwellings made possible the use of 33 characteristics for the calculation of the wealth index. The variables were divided into three groups: characteristics of the dwelling including indicators such as the quality of the dwelling materials (high, medium, low); household ownership of consumer durables as whether the household owns a washing machine; and household ownership status (whether the dwelling is owned, is rented or is a shanty). Overall, this information shows that on average most Costa Rican families have the basic public services and assets to have a modest living. On the other hand, there are other indicators that show the poverty level suffered by some Costa Ricans; for instance, 13 % of the households cook with biomass, the average number of rooms is

1.42 and 50% of Costa Ricans live in a low quality dwelling. The list of variables and descriptive statistics are shown in the Appendix, table A.5.11 and A.5.12, respectively.

The PCA allows the ranking of households by their economic status based on their assets. The PCA method summarizes all the information contained in the variables into a set of mutually uncorrelated components of the data440. In PCA, the first component is chosen to represent the overall index441 because it is the one that captures

440 Filmer and Pritchett (1999), p 88.

441 Although Filmer and Pritchett warned of the uncertainty of considering this component by itself as the main holder of the relevant information about household assets, they also point out the 227

the greater proportion of the co-movement among the asset variables442. In my case, the first principal component captures 21% 443of the common variation in the 33 asset variables and has an eigenvalue of 6.8. The eigen values and common variations of the first nine components are presented in the Appendix, table A.5.13. The analysis of the loadings of the factor scores or weights assigned to each variable of the first component show the right signs (see table 5.9). For instance, while an observation living in a high quality dwelling increases its index by 22 units, an individual living in a low quality dwelling decreases the index by 24 units. Based on these weights the wealth index was predicted for each observation according to his or her household asset holdings.

difficulty of interpreting the second component and by the same token the other components with less explanatory variance. Filmer and Pritchett (2001), 119.

442 Filmer and Pritchett tested the effect on the wealth index by including the first five components and they reported that the changes in the marginal wealth were very small. Ibid, 121.

443 The common variation as well as the factors loadings variation captured is not high. This is due to the considerable use of dichotomous variables (0 1). An alternative way to construct an index that circumvents this problem could be the use of Item Response Theory (IRT). IRT allows the establishment of a position of an observation (in my case households) as function of a latent trait or key characteristic (wealth in my case) based on a set of parameters (the variables). Ivailo Partchev, A Visual Guide to Item Response Theory (Friedrich-Schiller-Universität Jena, E-book last revised on February 6, 2004, at: http://www.metheval.uni-jena.de/irt/VisualIRT.pdf, accessed on 13 November 2009): 5.

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Table 5.9. Costa Rica 2005. First component: factor scores assigned to each variable

Variable Comp1 Unexplained No pipe_water -0.116 0.909 Pipe out_ water -0.041 0.989 Toilet 0.166 0.813 Electricity 0.109 0.920 Nowater -0.018 0.998 Well -0.112 0.915 River -0.080 0.957 Cook_other means -0.017 0.998 Cook_gas -0.004 1.000 Cook_ fuelbiomass -0.166 0.813 Room_number 0.227 0.651 Bath_number 0.227 0.650 Water tank 0.077 0.960 Hot_shower 0.218 0.678 Hotwater_tank 0.127 0.891 Internet 0.196 0.740 High quality dwell 0.223 0.665 Low quality dwell -0.242 0.603 Refrigerator 0.171 0.802 Washing machine 0.158 0.830 Microwave 0.248 0.585 Soundtrack_number 0.183 0.774 VHS 0.197 0.737 DVD 0.200 0.730 TV_number 0.268 0.514 CableTV 0.198 0.733 Home phone_number 0.230 0.643 Cellphone_number 0.255 0.558 Computer_number 0.251 0.573 Car_number 0.232 0.635 Oth_living arrangements -0.065 0.972 Rented -0.007 1.000 Slum -0.034 0.992

As expected in the principal components analysis, the wealth index is normalized and has a mean of 0 and a standard deviation of 2 (see table 5.10). It is important to

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highlight that this normalized mean and standard deviation holds for the 11,278 sample, but these statistics change when the sample is reduced to 6,371 children. This change does not affect the index at all since its eigenvalue and variance explained remain the same.

Table 5.10. Costa Rica 2005. Wealth index: descriptive statistics

mean 0.000 standard dev. 2.603 minimum -7.879 maximum 10.901 Observations 11278

The validity of the wealth index to summarize the information was tested by substituting in the two models, and for all samples, the wealth index by the variables with the higher loadings. Those variables were: number of rooms, high quality dwelling, low quality dwelling, whether the household has a microwave, number of computers, number of phones and number of cars. The coefficients varied very slightly and the level of significance remained the same in all cases. Also, the analysis of correlation of the estimated coefficient of the wealth index with the estimated coefficients of the main research variables shows that wealth index shares 13% and 2.5% of its variability with the variables returns to skilled labor and returns to unskilled labor. It also shares 29% and

26% of its variability with the variables: both unskilled and both skilled.

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5.5.3 Regression results

Table 5.11, below, shows the coefficients of the variables of interest, the wealth index, the interacted variables and the interactive terms for the total sample, the primary school sample and the secondary school sample, using Model 1. The complete outputs are in the Appendix (tables A.5.14, A.5.15 and A.5.16) and, as expected, all the control variables are significant and show the right signs according to the theory of the determinants of demand for education and the specific context of Costa Rica.

For the total sample, the chi square and its p-value show that the model as a whole is statistically significant. It can be observed that being poor decreases the probability of enrollment. If the wealth index decreases by 1 unit (which is equivalent to having a low quality dwelling, not having a washing machine, not having a refrigerator, not having a microwave and not being able to heat water for a shower), holding other variables constant, the probability of enrollment will decrease by 0.7 percentage points. Although the coefficient is small, it gives the direction of the decision in relationship to asset holdings.

Focusing on the main research coefficients, i.e., those of the interactive terms, we can observe in Table 5.11 (first column, fifth line) that, for the total sample, if the returns to skilled labor rise and parents are both unskilled, the probability of enrollment diminishes relative to those with at least one skilled parent. This coefficient is not significant; hence unskilled parents’ decisions on schooling seem not to be driven by high returns to skilled labor; on the other hand, its negative sign seems to inform us that these parents see limited benefits with the rise in the rewards to skilled labor. Moreover, for their children to obtain those returns it might imply giving up consumption during at least

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12 years of education. Alternatively, if the returns to unskilled labor increase, unskilled

(liquidity constrained) parents are less likely to invest in the education for their children when compared to parents among whom at least one is skilled. Notice also that this coefficient is not only significant but the magnitude is much larger than the coefficient associated with returns to skilled labor444. It seems that for poor parents the rise in the unskilled rewards might imply a possibility for higher household consumption and they might react to it by working more and for that they might need their children’s help in their business (if parents are self-employed) or to stay at home to allow parents to work outside the home or, also, if children are not doing well in school they might be encouraged to work. It is important to recall here some of the findings of section 5.4.2. Of the children of the total sample who are not enrolled, 51% work, 80% live in the rural areas, and live in households of a wealth index of -.88.

Table 5.11 also shows (in columns 3 and 4) the likelihood of enrollment in primary and secondary school when parents have liquidity constraints (unskilled).

The primary school regression is rather limited because 1577 observations were lost. Given that most of the children in this sample are enrolled, several counties were dropped because all observations in those counties were enrolled; for the same reason all the observations whose age was between 17 and 19 were dropped, i.e., there was not enough variability (there are very few students of this age that remain in primary school).

However, its results can still be interpreted and this comes across when it is analyzed in

444 There is a controversy regarding the interpretation of estimated coefficients resulting of interactions. Norton and others have developed a method to overcome, according to his opinion, the drawbacks of calculating marginal effects from interactive terms, and consequently making easier its interpretation; their method is useful just for very basic models and has considerable limitations. To circumvent the problem of interpreting marginal effects, I decided to use the estimated coefficients of the regular probit of the interactive terms for interpreting the results. (See Edward C. Norton, Hua Wang and Ai Chunrong, “Computing Interaction Effects and Standard Errors in Logit and Probit Models,” The Stata Journal, Vol. 4, No. 2 (2004): 154-167; also see Friedrich.) 232

comparison to the secondary school enrollment regression. Although the estimated coefficients of the interactive terms are not significant, these might indicate that parents of primary school children in the end are indifferent to the variable returns to education.

Here we need to remember that these outcomes make sense considering that primary schooling is compulsory and is less costly and that having completed primary education is the minimum labor market requirement.

In the case of the secondary school group,445 the coefficient when the returns to skilled labor rise and parents are both unskilled (liquidity constrained), as in the case of the total sample, is negative and not significant, signaling that unskilled parents relative to the default category (those with both skilled parents and one skilled parent) do not see those returns as an incentive to increase enrollment. On the other hand, the interactive coefficient of -19.4 shows that liquidity constrained parents, compared to those not liquidity constrained, are less likely to demand secondary school education for their children, holding other variables constant, when the returns to unskilled labor increase. As in the case of the total sample, it looks as if income constraints and consumption needs might be leading the schooling decisions of unskilled parents regarding their secondary school age offspring. It is reasonable to suppose that these children are at an age at which they are more able to help their families, which would in turn constitute a disincentive to remain in school. These

445 This group lost some of the observations due to lack of variability in some counties because all children in those counties were enrolled. I do not consider this loss of observations a loss of internal validity. In the context of propensity score matching, these cases would be dropped anyway because they are extremes (virtually certain to graduate from some grade), and make a poor comparison group. There is an efficiency loss, but not necessarily a loss of internal validity, and, arguably, a gain. See Guido M. Imbens and Jeffrey M. Wooldridge, “Recent Developments in the Econometrics of Evaluation,” Journal of Economic Literature, Vol. 47, No. 1 (March 2009), 43-46.

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assertions are supported by the facts reported in Section 5.4.2. In the secondary sample, the average of children is around 15 years old. Of the children who are not enrolled, 50% work, 80% live in the rural areas, 91% have both parents classified as unskilled and live in households with a wealth index of -.77. Having parents who are unskilled and living in rural areas makes an extraordinary difference for these children because their parents seem to be the most deprived of the poor. They do not have assets, their earnings are low and they live in areas mostly of traditional agriculture where the infrastructure is precarious and there are few opportunities of employment. I have not explored in this research the health status of these poor families but certainly it is very possible these parents’ health and nutrition are so low that they need to rely on their children considerably. These children might also have nutritional problems that make it very taxing for them to cover distances to go to school or even to just study. This is certainly a topic for further research.

An additional consideration is that they might have, along with their parents, a clearer picture of their chances of finishing their studies given the low quality of education they are receiving. This raises their opportunity costs even more.

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Table 5.11. Costa Rica 2005. Likelihood of enrollment: When parents are liquidity constrained and returns are convex

Total sample Primary School Secondary school Variables coefficient p-value coefficient p-value coefficient p-value Return skilled 0.154 0.452 0.000 0.862 0.537 0.553 Return unskilled 1.318* 0.003 0.001~ 0.008 3.825~ 0.056 Both unskilled -0.025 0.31 -0.001*** 0 -0.108 0.361 Unskilled parents* returns -0.982 0.731 2.706 0.64 -1.168 0.702 skilled Unskilled parents* returns -19.349** 0.004 -15.585 0.417 -19.384** 0.009 unskilled Wealth index 0.007*** 0 0.000* 0.02 0.031*** 0 Fixed effects included Robust standard errors required Number of observations 6218 1791 2913 Chi squared 22.09 0.000 ------686.2 0.000 Pseudo R squared 0.439 0.664 0.293

Note: Dependent variable is whether or not children are attending school. The coefficients shown in this table for the interactive terms are not mfx but those of the probit. ***; **, * and ~ refer to p-values less or equal to 0.001, 0.01, 0.05, and 0.10, respectively

Table 5.12, below, shows the results of the second model for the total and secondary school samples. The regression output for the primary school is not shown: the two interactive terms were dropped because there was only a set of parents who were both skilled for the non-enrolled group. The complete regression outputs are shown in the

Appendix, tables A.5.17, and A.5.18.

It is observed that when both parents are skilled and hence not liquidity constrained, if the returns to skilled labor increase, the probability of enrollment of their children is more likely to increase relative to those parents among whom at least one is unskilled. The probit estimated coefficients for both, the total sample and the secondary school sample, 41.07 and 39.94, respectively, are positive and significant at the 5% level.

Similarly, these skilled parents are more likely, compared to the default category (those with both unskilled parents and one skilled parent), to demand schooling for their children when the returns to unskilled labor rise. In this case a plausible explanation

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might be that increases in returns to unskilled labor are seen by these parents as positive labor market signals that inform them that the increases in the returns to skilled labor might be potentially larger.

Table 5.12. Costa Rica 2005: Likelihood of enrollment: When parents are liquidity unconstrained and returns are convex

Total sample Secondary school Variables coefficient p-value coefficient p-value Return skilled 0.066 0.617 0.138 0.825 Return unskilled 0.342 0.237 -0.037 0.979 Both skilled -0.994* 0.039 -0.955~ 0.091 Skilled parents * returns 41.073* 0.016 39.942* 0.045 skilled Skilled parents* returns 63.104* 0.04 56.291~ 0.084 unskilled Wealth index 0.007*** 0 0.030*** 0 Fixed effects included Robust standard errors required Number of observations 6218 2913 Chi squared 1147.61 0.000 690.78 0.00 Pseudo R squared 0.438 0.293

Taken together, the regression results of the two interactions in model 1 and the two interactions of model 2 show that when returns are convex, returns to education affect parents’ decisions regarding their children’s enrollment in school. When parents are unskilled this effect bifurcates according to the type of returns: to skilled or non- skilled labor, but overall it seems to deter children’s enrollment in school. The rise in the returns for skilled labor does not seem to provide an incentive to increase demand for education. On the other hand, the increase in returns to unskilled labor seems to provide an incentive to lower their demand for schooling for their children. Unskilled parents might interpret the rise in returns to unskilled labor as a signal to increase their household

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income, which decreases the likelihood of enrollment relative to parents not liquidity constrained

Unskilled parents’ liquidity constraints, costs (direct and opportunity costs) and length of education to extract the high returns might be the reasons for these outcomes.

The expected net returns to the education of their children do not compensate the loss in consumption for such a long period of time. For these parents, the use of their meager funds in schooling expenditures instead of their consumption needs, the use of children’s time in schooling or studying (which becomes more difficult if the quality of education is poor or the distances are long) instead of helping with the household income, or the use of parent’s time in tasks or jobs their children could perform might not be reasonable and hence their discount rate might be high. Data on these direct and opportunity costs is, unfortunately, unavailable, which prevents me from exploring further these possible explanations.

When parents are skilled, the impact is positive regardless the type of returns: skilled or unskilled. Moreover, the effect seems to be a direct response to the market information conveyed by the returns to education given the absence of liquidity constraints.

5.6 Conclusion

The results of the empirical analysis demonstrate that convexity of returns influences Costa Rican parents’ decision to invest in education, but this effect is led by their liquidity constraints or lack thereof. More specifically, it supports the claim that when credit markets are incomplete, unskilled parents are not responsive to returns to

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skilled labor and hence their investment in education would not increase. These parents are driven by the returns they receive, i.e., the returns to unskilled labor because they bring information about their own income; consequently, when the returns to unskilled labor increase they are less likely than the more well off parents to demand education for their children. These unskilled labor parents trade off their children’s investment in education, their future wealth, for the current consumption needs of their families.

The empirical results also support the argument that Costa Rican skilled parents settle for a different and more successful livelihood strategy from the one adopted by the unskilled parents. They respond to the returns to education, either skilled or unskilled, by investing in the education of their children.

The findings of this chapter go beyond the predictions of Ljungqvist’s theoretical model, because they also reveal that poor parents’ expectations about the future of their children are influenced by the expected prices of the type of labor they themselves provide. This is so because those returns affect their present well-being and frame their options. For unskilled parents, the rise in returns of skilled labor might mean that the target is moving higher and farther away. The increase in returns to skilled labor might further lower their expectations about their children being skilled: the quality of education is deficient and they do not have power to affect the outcomes in the presence of market and government failures. Alternatively, an increase in the returns they obtain for their work, the returns to unskilled labor, means that they have a safe chance to increase the household income (addressing their pressing consumption needs) which might mean that some of their offspring might need to quit school to stay at home or help with the family business or become a new member of the labor market. The most

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consequential inference that these parents might make is that the increase in the returns to unskilled labor bring the prospect of assured income and job possibilities for their children who, after all, are very unlikely to become skilled workers and that, on the other hand, the increase in the returns to skilled labor is a phenomenon that will not favor them.

As a result, poor parents will rationally not invest in schooling for their children. The expected gross monetary returns to the education of their children do not compensate the loss in consumption for the long period of time which the investment requires. It seems that they have a high discount rate for their children’s use of time and the use of funds.

The direct and opportunity costs of education are higher for them. Even though there are information inefficiencies that preclude these parents from foreseeing the consequences of their decisions, the liquidity constraints and what they entail are binding.

These outcomes show that a poverty trap is being created for those who cannot afford to accumulate their most productive asset, human capital. Two types of citizens are being clearly created: those educated and those uneducated, a process that, if it continues like this, will be difficult to reverse.

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CHAPTER 6

CONCLUSION

PURPOSE, CONTRIBUTIONS, CONCLUSION AND POLICY

RECOMMENDATIONS

6.1 Purpose and contributions

It has been the purpose of this dissertation to answer the following question: What has prevented many Costa Ricans parents from investing in their children’s education to a level that will allow them to rise out poverty?

I have argued that a large proportion of Costa Rican parents do not keep their children longer in school despite the high gross monetary private returns to skilled labor because their expectations regarding the future benefits of their children’s education are low. I argued that increasing returns to education could be central to explaining parents’ low demand for their children’s education in Costa Rica and also, if quality of education is poor, this might be reinforcing parents’ low expectations regarding their children’s schooling. Clearly, parents consider, in their investment decisions regarding schooling, not only the returns to education, but also the direct and opportunity costs involved. Low quality of education increases the costs of education. This is so because the demand for education is a function of the parents’ expected net returns to their investment in schooling, their household income, the family’s preferences and the parents’ information.

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The theoretical notions that have framed this research include Gary Becker’s household production model because schooling decisions and educational investment are taken at the household level. They also include the livelihood strategy approach and the poverty trap theory. The former contributes to the household decision making model by underscoring the role of external environmental factors and asset holdings in the decision making process. The latter complements the household decision making model and contributes to the analysis of demand for schooling, suggesting factors and mechanisms which lead poor households to settle for livelihood strategies that block their opportunities to escape poverty. Key to this research is Lars Ljungqvist’s model of convexity in the returns to education because it provides a plausible explanation of the failure of poor Costa Rican parents to keep their children longer in school in spite of high returns to skilled labor and for the parents’ choice of a strategy that only guarantees a dynasty of poverty. Finally, an evaluation of the role of Costa Rican education policy, priorities and resources allocated to education as well as the coverage, efficiency and equity of the education delivery system has been an important component of this research, since these factors affect the nature, quality and quantity of education delivered and in turn the demand for education.

This dissertation has attempted to offer a contribution to the literature in the economic development fields concerned with monetary private returns to education and demand for schooling while addressing the broader policy-relevant issue of investment in education as a path out of poverty. The combined use of the theory of household decision making as it applies to investment in education, the notion of livelihood strategies and the theory of poverty traps to explain the low investment in education introduces a new

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perspective to the existing knowledge on demand for education. In addition, the use of

Ljungqvist’s theory for understanding the role of returns to education on parents’ decisions regarding their children’s education in Costa Rica is one of the main contributions of my research. Ljungqvist’s model was not devised to provide explanations at the household level nor were parents and children the actors in his model, but skilled and unskilled workers. However, I have adapted his model to make it applicable to household decisions on children’s schooling. I believe that one important contribution of my research lies in the empirical testing of the convexity of monetary private returns to education in Costa Rica. The purpose of this test is to apply

Ljungqvist’s prediction. Also, a substantial contribution of this research is the formulation of a model that reproduces the assumptions of Ljungqvist’s model at the household level. It seeks to determine the role of the convexity of returns in the educational investment decisions of unskilled (liquidity constrained) and skilled (liquidity unconstrained) parents. In doing this, while I maintain consistency with Ljungqvist’s model, I provide a contribution to the explanation of the parental decision with respect to child schooling, something that was not done by Ljungqvist.

The value of my contribution, I believe, is enhanced by the uniqueness of the case study which makes this research very interesting to scholars for several reasons. Costa

Rica is cited in the scholarly literature as a country where there has been a government commitment for decades to the social welfare of its people and particularly to education.

Costa Rica is considered a pioneer in educational initiatives geared towards increasing equality and in making education and technological knowledge one of the main components of its educational policy. Costa Rica is also cited for successfully promoting

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a knowledge-based economy tied to foreign direct investment that has built on Costa

Rica’s stock of human capital.

Therefore, it is my hope that this study will provide a contribution to the field of development microeconomics, and particularly to its policy area. I also expect that this research will add to the current debate on the role of expectations in educational decisions at the household level, and to the emerging theory of poverty traps at the microeconomic level.

Finally, as also stated at the outset, this study hopes to provide an input to Costa

Rican government officials and policy makers in their efforts to stop this trend of human capital depletion that not only will obstruct any reduction of poverty but will increase inequality and impede growth.

6.2 Conclusion

The results of this research support my hypothesis that the convexity of gross monetary private returns play a key role in Costa Rica parents’ low expectations regarding future benefits of their children’s education and therefore on their decision to not invest in their children’s education. However, the effect of convexity of returns on the demand for education is led by parents’ liquidity constraints or lack thereof.

Most Costa Rican parents do not invest in schooling for their children, mostly in secondary school, because the returns to education are such that only higher levels of education extract high returns, direct and opportunity costs of education are high, there are no loans to finance education and these parents are liquidity constrained since they themselves constitute unskilled labor.

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The analysis of the earning functions shows that these functions are convex and that this convexity is such that the gross monetary private returns to education increase sharply only after a critical mass of education is reached. The convexity of the relationship between earnings and years of school completed remains for all sectors and for all regions and for men and women. Moreover, the returns start increasing sharply after 11 years of education are reached, and in the case of several regions and the rural area after 12 years of education. Indeed, this premium attached to higher level education might explain why students in Costa Rica, whose parents realize that they might not be able to enroll in tertiary education, quit school after finishing 6th, 7th, and 10th grades which, after all, are the schooling years before tertiary education that guarantee some returns in some regions, areas or gender groups. In fact, in some regions quitting school at any year before completing high school will not make any difference since the returns are practically zero at each corresponding year. These findings clearly show the wage differential between skilled and non-skilled workers and evidences that in Costa Rica education is not divisible; a worker is considered educated if she or he has completed at least high school.

The empirical analysis of the relationship between demand for schooling and returns to education shows that the convexity of returns influences the demand for schooling differently depending on whether or not parents are liquidity constrained, which in turn depends on whether they are either both unskilled or both skilled, respectively. Unskilled parents are not responsive, regarding their investment in education, to increases in the returns to skilled labor. This finding supports the predictions of Ljungqvist’s theoretical model (as modified). However, when the returns

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to unskilled labor increase, they are more likely to decrease the demand for schooling.

These parents respond to the returns they receive. Hence, these parents whose labor is unskilled trade off their children’s investment in education, their future wealth, for the current consumption needs of their families. It is important to underline that those poor parents’ decisions of not investing in education affects children at the secondary school level. Unskilled parents send their primary school level children to school despite their liquidity constraints, because the direct and indirect costs are much lower.

On the other hand, this analysis also shows that Costa Rican skilled parents settle for a different and more successful livelihood strategy from the one adopted by the poor unskilled parents. They respond to the returns to education, either skilled or unskilled, by investing in the education of their children.

It is important to note that the findings of this study go beyond the predictions of

Ljungqvist’s theoretical model, because they not only reveal that unskilled parents do not see increases in skilled labor rewards as an incentive to increase their demand for education, but also they reveal that unskilled parents’ expectations about the future of their children are influenced by the expected prices of the type of labor they themselves provide.

I believe that for unskilled parents, the rise in returns to skilled labor might mean that the target is moving higher and farther away. The investment for their children to become skilled is lengthy and costly in Costa Rica involving high opportunity costs for these parents mainly when the quality of education is poor. Deficient quality of education increases students’ marginal cost of staying in school and constitutes a considerable obstacle to pass the comprehensive national baccalaureate or bachillerato

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examinations to graduate from secondary school. This also constitutes an entry barrier to enter the more affordable yet very selective public universities. Also, they might perceive that some of those highly rewarded skilled labor jobs are obtained through social networks they do not have access to. These liquidity constrained parents do not have the financial resources to compensate for those inefficiencies. Alternatively, an increase in the returns to unskilled labor brings the prospect of higher household consumption and might mean job possibilities for some household members. Clearly, this circumstance might entail that some of their offspring quit school to stay at home or help with the family business or just work in the labor market. After all, these parents would have to give up household consumption for a long period of time for their children to become skilled which they might believe is a very unlikely outcome. As a result, liquidity constrained parents will rationally not invest in schooling for their children. The expected net returns to the education of their children do not compensate the loss in consumption for such the long period of time the investment entails. The direct and opportunity costs of education are higher for these parents which might be increasing their discount rate.

Even though there are information inefficiencies that preclude these parents from foreseeing the consequences of their decisions, the liquidity constraints and what they entail are binding. These parents might feel the need to take advantage of the increases in the returns of their labor while they last (they may perceive the increases as a short term relief); after all they might have been suffering from deprivation for a long time.

The bifurcation of the impact of convexity of returns to education between those children whose parents are both skilled and those, whose parents are both unskilled, generates a vicious poverty trap for those for those who cannot afford to accumulate their

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most productive asset, human capital. Two types of citizens are being clearly created: those educated and those uneducated, a process that, if it continues like this, will be difficult to reverse. Costa Rica’s goal of becoming a technology-anchored and knowledge-based society is tied to a rope, formed of that majority of individuals staying behind; that rope might still have some slack and hence not be entirely visible but, surely, once it has completely unwound, will pull any Costa Rican efforts back to a full stop.

6.3 Policy recommendations

Costa Rica faces a dilemma. Its chosen model of growth and development based on liberalization and attraction of high technology foreign investment to spur the production of nontraditional products, particularly information technology, although intended to increase employment opportunities and competiveness based on high factor productivity and not on low wages for unskilled labor, is causing such a wage disparity between skilled and unskilled labor and enlarging the inequality gaps (rural, regional, income, educational)446 already present in the country that unskilled parents are not investing in their children’s education. Clearly this strategy is not benefiting the majority of Costa Rican children whose parents happen to be unskilled. Not only is a dynasty of poverty currently being created but also the striking inequalities in education that are being generated defeat the objectives of this strategy.

The unequal distribution of endowments and assets among Costa Rican parents, the labor market inefficiencies created by this strategy, the absence of a credit market for education and the government failures in its redistribution and educational policies has

446 These effects of the technology anchored knowledge based strategies of development have been already documented by research. Please see chapter 1, section 1.3.1 for a brief review. 247

made investment in education a lengthy and costly (in both direct and opportunity costs) endeavor for unskilled parents.

The government of Costa Rica should revisit its development strategy but it should also examine its educational and redistribution policies. The objective should be to decrease the wage discrepancy between skilled and unskilled workers, and to decrease the costs of schooling for unskilled parents, in terms of both direct costs and opportunity costs.

Since the set of policies required to achieve these objectives is extensive and covers different sectors, its examination in depth goes beyond the scope of my research.

However, I am going to outline what seem to me the most important ones and I will elaborate on those that are more linked to the educational sector.

The first set of policies should be geared to increase the employment opportunities and earnings of the unskilled workers to benefit those unskilled workers who are unemployed or sub-employed and also to allow for labor mobility to jobs with greater marginal productivity. These policies should be applied not only in the urban area and Central region, but also in the rural area and in the regions where most of the unskilled workers are concentrated. Some of the policies I can suggest are the following:

1) Support the Costa Rican owned companies and foster their development. Costa Rican companies have more linkages with the rest of the economy and also more of them (as compared with foreign companies) can be found in rural areas. Some of the measures should be to provide access to credit and technical assistance to the small producers and cooperatives located in rural areas and help them to access international markets. 2)

Ensure that the non-traditional and technology anchored economic activities have

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extensive backward and forward linkages. The negotiation strategy with the foreign companies should include this requirement given the tax breaks and preferential treatment in provision of public infrastructure Costa Rica provides to these companies. 3)

Supply public infrastructure (roads, bridges, and internet communications) to the rural area and regions different from the Central to allow for greater development of productive activities and access to markets in those areas. 4) Offer financial support for education to the unskilled workers, not the technical low level training that keeps them at the lower end of the continuum of unskilled labor, but training that moves them towards becoming skilled, at least completing secondary school. This help should be extended to those unskilled workers who want to pursue tertiary education. Public universities and post-secondary institutions that receive subsidies from the government should become part of this effort. Also, credit should be provided at low cost for higher education training.

The second set of policies should be oriented towards decreasing the direct and opportunity costs of education to unskilled parents. Basically, the main policy I recommend to address this objective is to provide a cash transfer, to unskilled parents, which is equivalent to the value of foregone consumption as they invest part of their disposable income in the education of their children. This subsidy should also take into account the opportunity cost of foregone income had the children been working instead of being at school and the differences in these costs between rural and urban areas. As reported in Chapter 2, empirical research has shown that conditional cash transfers are effective in increasing enrollment and attainment. Additional policies that are advisable are those that decrease the marginal costs of education, basically those related to

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improvements in the quality of education. They lower the transaction costs, direct cost and opportunity costs of schooling. An improvement in the quality of education reduces repetition and hence the length of time in school and consequently the direct and indirect costs of education; therefore, the redoubling of the efforts currently being made by the

Costa Rican government to improve the quality of education is a must.

The Costa Rican government’s educational policies currently in place do not have the focus of the policies I am proposing. Although most of them point in the same direction in that they pursue equal access to education, are intended to decrease desertion and aimed at improving the quality of education, these purposes need to be reoriented in order to develop programs that can address the problems identified in this research.

Moreover, the current programs suffer of considerable deficiencies: they do not reach their target population, some of them overlap and in general the resources are not used efficiently. Regarding the policies to decrease the marginal cost of education, the Costa

Rican government seems to be implementing some of the right policies such as providing training to teachers, bringing the salary levels of public school teachers to match those of the private sector so better teachers are recruited, allocating funds for school infrastructure,447 technology and the teaching of foreign languages. The problem is the lack of resources to accomplish its goals and in this regard there is currently a bill before the National Legislative Assembly to amend the constitution, increasing the funding for education to 8% of GDP.

447 As mentioned in chapter 3, increases in enrollment in secondary in Costa Rica during the period 2003-2005 are associated with construction of new high schools. 250

The subsidy to liquidity constrained parents, I recommend, should be conditioned to their keeping their children in secondary school. This is not the target population of the several subsidies within the redistribution and retention programs currently in place in

Costa Rica: their target is students in primary and secondary school. I believe this should be re-examined. Unskilled parents send their primary school level children to school despite their liquidity constraints; the direct and indirect costs are much lower. I believe the proposed cash transfer should replace the program Avancemos or in other words,

Avancemos should be redesigned. Currently, the Avancemos subsidy does not distinguish between rural and urban areas and is geared to primary and secondary school students.

The nature of the cash transfer I am proposing will probably require revisiting the need of the other subsidies different from the lunch program. Well designed, this subsidy would accomplish the objectives of the several subsidies in place. A final word on this proposed subsidy is that an impact evaluation should be designed and a monitoring system should be established to control the use of funds and the targeting as well as being able to determine if the subsidy has a positive effect on increasing the demand for education.

In addition to the recommendation above, there are two other recommendations to the Costa Rican government that could help to increase the demand for education. The first one is regarding the labor market-pertinent education programs that are geared towards filling the needs of the country’s technology anchored knowledge based economic sector by providing the skilled labor they require. They should be re-examined because the target populations of these programs are the poor students in rural and urban marginal areas who, very likely, are the children of unskilled parents. This research shows that children with this profile are the ones who are not going to secondary school.

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This might mean that these programs might have a wrong focus. Moreover, one of the programs within this group might be even encouraging desertion. This is the vocational training offered mainly to school dropouts by the Instituto Nacional de Aprendizaje

(INA, the national institute for vocational training). INA’s training only guarantees a low return to education, that of an unskilled worker with little formal education. The role of the INA should be revisited. The second recommendation intends to reduce the information asymmetries of unskilled parents. Unskilled parents and their children aware of the open opportunities they will have if they finish high school.

Finally, it is important to keep in mind that the priority in the design of policies is to make sure that the children attend school by addressing the main problem, the liquidity constraints of their parents and the costs of education. This is the only way that the vicious cycle of poverty and the depletion of human capital can be stopped.

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APPENDIX TO CHAPTER 2

Figure A. 2.1. Map of Costa Rica

Source: U.S. Central Intelligence Agency, Costa Rica (Shaded Relief) 1987, University of Texas at Austin Map Collection, http://www.lib.utexas.edu/maps/americas/costa_rica.gif Retrieved on 24 November 2007

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Figure A.2.2. Map of MEP’s regional divisions

MEP’s regional divisions:

1. San José 2. Desamparados 3. Puriscal 4. Pérez Zeledón 5. Alajuela 6. San Ramón 7. San Carlos 8. Upala 9. Cartago 10. Turrialba 11. Heredia 12. Liberia 13. Nicoya 14. Santa Cruz 15. Cañas 16. Puntarenas 17. Coto 18. Aguirre 19. Limón 20. Guápiles

Source: http://www.dcc.mep.go.cr/d.r.mapa.html. Retrieved Jan 2008

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Figure A. 2.3. Map of Costa Rica’s regional divisions

Source: INEC: http://www.inec.go.cr/. Retrieved January 2008

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Table A.2.1. Costa Rica: Annual distribution of expenditure by budget and education category

Expenditure distribution (%) Budget and level of education category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2005 Total Ordinary Expenditures 96.6 97.4 97 96.5 95.3 96.4 95.1 92.8 92.4 95.6 96.7 98.3 Capital Expenditures 3.4 2.6 3 3.5 4.7 3.6 4.9 7.2 7.6 4.4 3.5 1.7 General Education Ordinary Expenditures 99.8 99.9 99 98 97.1 98.9 96.6 94.1 93.3 95.9 ------Capital Expenditures 0.2 0.1 1 2 2.9 1.1 3.4 5.9 6.7 4.1 ------Post high School Education 256 Ordinary Expenditures 91.8 93.8 94.2 93.8 92.2 92.5 92.4 90.2 90.6 94.7 ------

Capital Expenditures 8.2 6.2 5.8 6.2 7.8 7.5 7.6 9.8 9.4 5.3 ------

Source: prepared by author with data from: World Bank, Social Spending and the Poor, 84; MEP, “Financiamiento del sistema educativo,” 1.

Note: General Education includes primary and secondary; post high school includes colleges and universities.

Table A.2.2. Costa Rica: Percentage of trained teachers

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Primary % trained teachers 82.4 84 85.6 86 88.1 89.4 90.1 91.2 92.0 92.3 Secondary % trained teachers 74.7 76.9 76.7 80.8 80.4 82.3 84.2 86.3 88.6 89.4

Source of data: CONARE: Estado de la Educación 2005, Statistical annex, 46, 115.

Table A.2.3. Costa Rica: Budget and beneficiaries of education subsidies by type of program

Millions of constant colones and thousands of beneficiaries Free lunch budget Scholarship budget Transport budget Year lunch_bud Lunch_ben Sch_bud Sch_ben Trans_bud Trans_ben 1997 6287.4 492.7 343.7 11.8 …….. …….. 1998 6708.1 479.2 350.7 12.7 1611.8 32.9 1999 7147.1 468.6 529.9 15.1 2108.3 36.7 2000 7167.5 471.1 844.1 18.5 2070.0 39.1 2001 6456.0 470.6 2436.7 41.7 2876.1 46.5 2002 6766.5 417.4 2726.9 47.0 3461.4 50.4 2003 4844.7 470.1 2584.1 45.6 3491.7 58.2 2004 6373.5 550.3 2670.8 53.9 4008.8 61.6

Source: prepared by author with data from: a) CONARE Estado de la Educación 2005, 57: Number of beneficiaries and budget allocations on current prices, and b) IMF data base: the Price index for the period.

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APPENDIX TO CHAPTER 3

Table A.3.1. Costa Rica: Primary school level desertion rate, selected years

1990 1995 2000 2001 2002 2003 2004 2005 Total 4.7 5.0 4.1 4.1 4.0 3.9 3.3 3.4 I cycle 5.3 5.7 4.5 4.5 4.2 4.3 3.6 3.6 1st grade 6.8 7.2 5.9 5.9 5.3 5.1 4.1 4.5 2nd grade 4.9 5.0 4.2 4.2 3.8 3.8 3.5 3.3 3rd grade 4.0 4.5 3.5 3.5 3.4 3.9 3.1 3.0 II cycle 3.8 4.2 3.6 4.0 3.7 3.5 3.1 3.2 4th grade 3.9 4.6 4.0 4.3 3.8 3.6 3.4 3.3 5th grade 4.2 4.1 3.6 4.4 3.7 3.6 3.1 3.3 6th grade 3.1 3.6 3.3 3.2 3.5 3.4 2.8 2.9

Source: prepared by author with data from MEP, Department of Statistics

Note: includes public, private and private publicly subsidized schools

Table A.3.2. Costa Rica: Desertion in secondary level by grade (1990- 2005). Day modality: academic and technical

Year Total Secondary 7th 8th 9th 10th 11th 12th 1990 10.3 17.5 6.9 4.6 10.0 4.3 4.7 1991 9.6 17.0 6.1 4.1 9.5 4.0 3.0 1992 11.9 19.3 9.1 6.9 10.9 5.3 4.7 1993 11.1 19.2 7.7 5.5 8.8 4.7 5.2 1994 11.6 20.4 7.4 5.1 10.0 4.2 4.7 1995 12.7 21.8 8.8 5.7 11.6 4.9 1.4 1996 11.0 20.3 6.8 5.2 8.8 3.0 2.4 1997 10.8 19.9 7.5 5.2 7.0 3.1 1.3 1998 10.9 19.8 7.4 4.8 8.0 2.5 3.9 1999 9.2 17.1 6.0 4.0 6.9 6.9 2.3 2000 10.2 18.6 7.9 4.4 8.0 2.7 4.3 2001 11.3 19.5 9.1 4.7 10.4 3.1 3.8 2002 10.8 19.1 7.8 4.7 9.7 2.6 3.9 2003 9.4 16.6 6.8 4.1 8.7 3.0 5.3 2004 10.3 18.3 8.5 4.0 9.4 2.9 4.6 2005 11.0 19.2 9.4 5.0 10.2 2.9 1.8

Source: prepared by author with data from MEP, Department of Statistics.

Note: It includes public, private and private publicly subsidized educational institutions.

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Table A.3.3. Costa Rica: Desertion by teaching modality (1990-2005)

Day modality Night modality Total Year Secondary Academic Technical Academic Technical 1990 14.4 10.3 10.3 36.6 20.5 1991 13.5 9.3 10.9 34.8 19.3 1992 15.2 11.7 12.7 34.2 18.1 1993 14.1 10.9 11.8 32.6 19.8 1994 14.6 11.8 10.7 34.0 5.5 1995 16.1 12.3 14.1 37.6 22.3 1996 13.7 11.1 10.9 32.5 19.2 1997 13.7 10.6 11.5 36.2 25.6 1998 13.7 10.5 12.2 37.1 22.3 1999 11.3 9.1 9.9 31.2 18.1 2000 11.9 10.1 10.5 28.5 12.4 2001 12.4 11.2 11.5 23.6 21.1 2002 12.0 10.5 12.0 23.2 14.4 2003 10.0 9.1 10.7 20.5 17.3 2004 11.6 10.0 11.3 23.8 19.2 2005 12.5 10.9 11.7 24.0 18.8

. Source: prepared by author with data from MEP, Department of Statistics

Note: includes public, private and private publicly subsidized educational institutions

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Table A.3.4. Costa Rica: Population that completes at least each cycle of traditional education, by income strata, zone and gender 1989-2005 (%)

Educational level, strata, zone Primary education Basic Education Secondary education and gender

1989 1994 1999 2005 1989 1994 1999 2005 1989 1994 1999 2005 Total country 76.8 80.3 80.2 89.4 33.4 42.1 45.6 49.3 15.1 14.8 22.0 38.9 By income strata 25% poorer 70.4 69.7 67.3 83.2 20.0 26.5 28.8 34.3 3.6 3.0 10.2 13.4 25 richer 89.8 93.8 94.5 97.3 63.0 64.7 66.6 73.6 30.4 28.3 45.3 67.9 Gap rich/poor 127.6 134.6 140.3 116.9 315.7 244.0 231.3 214.3 837.2 946.4 442.5 505.0 By zone Urban 86.1 88.3 84.6 92.2 50.4 60.1 55.2 56.7 25.0 27.0 32.0 46.1

260 Rural 70.3 74.1 74.2 85.6 20.2 27.4 31.4 39.1 7.3 7.0 11.6 28.0

Gap Urban/rural 122.5 119.3 114.0 107.7 249.5 219.5 175.6 145.2 340.6 388.0 267.3 164.4 By gender Male 75.3 77.9 79.2 87.2 30.7 39.4 41.0 43.2 17.0 14.6 22.8 35.6 Female 80.4 82.9 81.2 91.7 37.9 44.7 49.8 55.0 14.5 17.7 25.1 42.4 Female/male gap 1.07 1.06 1.03 1.05 1.23 1.13 1.21 1.27 0.85 1.21 1.10 1.19

Source: Informe Estado de la Nación 2005, p 99

Note: Primary education refers to individuals between 14 and 15 years old that completed at least 6 years of general education Secondary education includes individuals between 17 and 18 years old that completed at least 9 years of general education Secondary education refers to individuals between 20 and 21 years old that completed at least secondary education Quartiles are based on households’ per-capita income There is one modification to the source table: the calculation of the female/male gap instead of the male/female gap

APPENDIX TO CHAPTER 4

Table A.4.1 Key literature on the functional forms of the income- schooling relationship: Non-linearity and econometric issues

Scholar/date/objective Proposed model or hypothesis Selected conclusions

2 Mincer 1974 Ln Y= β0 + β1Si + β2Ei+ β3Ei + ui Logarithm of earnings is a linear function of years of completed schooling which assumes that regardless of the level of education Objective: to propose a model in which Y=earnings any year of education adds to earnings the same percentage. The human capital is the explanatory variable quadratic functional form of the experience terms intends to for income inequality S= years of schooling completed capture the concavity of the lifetime earnings which start This model was an extension of his increasing at the beginning of the working life and beyond some doctoral dissertation in 1957 E= potential experience (a proxy for experience): point start decreasing. 261 age of individual- day of entry to school-years of Mincer also added the logarithm of weeks worked to the right

Data: 1960 US Census Sample of white schooling. hand side of the equation to account for differences in earnigs urban non student men , age 14-64 due to time worked. U= error term 2 Blinder 1973 Ln Y= β0 + βxSi + β2Ai+ β3Ai + β4Zi+ u He estimated two sets of equations for every socio-economic group to determine the differentials due to endowments and due Objective: to find out how much of the Y= hourly wage to discrimination. He found that white men earn more than black wage differential between white and black men and men earn more than women and the dominant factor males and between males and females are Si = captures 6 schooling dummies favoring these differences is the wage-age profile. due to their different endowments or to the employers’ perception of these A= age He introduced three variations to the Mincer equation: characteristics Z= set of family background variables 1. Logarithm of hourly wage is better instead of the alternative Data: Michigan survey Research Center’s using logarithm of earnings or that plus the addtion of logarithm Panel study of income dynamics 1967. of weeks worked in the right hand side. He claimed that weeks Sample of heads of household, either black worked was endogenous. or white, older than 25 years and who held a remunerated job. 2. There is a non-linear relationship between schooling and wages represented by a step function of 6 educational dummies

3. The use of age instead of experience which caused a controversy between Blinder and Rosensweig (see below).

Scholar/date/objective Proposed model or hypothesis Selected conclusions

2 Heckman & Polacheck, 1974 1. Ln Yi=β0 + β1Si+β2Ei+ β3Ei They tested these three specifications and concluded that:

Objective: to evaluate Mincer’s functional Y=earnings/wage rates 1. Mincer’s gave the best fit. form S= schooling 2. Weeks is preferred to log weeks. Data: Survey of Economic Opportunity (SEO) 1967 and US Census data 1960. E= experience: age of individual- day of entry to 3. The third one gives ambiguous results: it works for the Census; White nonfarm males age 14-65 school-years of schooling. however, not for the SEO data.

2 2. Ln Y= β0 + β1S+β2Ei+ β3Ei +Wi Wi= weeks worked

3. Ln Y= β0 + β1S+β2lnE

Kenneth Arrow 1973 Graduates will get paid more than non graduates This hypothesis questioned the underlying assumption in

262 (the dropouts) because their degree signals to their Mincer’s model that schooling increases productivity and this is

Non-linearity: sheepskin effects hypothesis potential employer higher initial ability and rewarded in the labor market. This hypothesis states that consequently higher marginal productivity diplomas signal ability and employers use them to screen workers.

Chiswick, Barry R. 1973 It is counterintuitive that firms would pay higher wages to college graduates regardless of their differences in productivity. Objective: to examine the implications of There are two possible reasons why dropouts (the ones who don’t the screening hypothesis and propose finish college) get lower returns: either schooling is not a alternative explanations. divisible investment (divisible meaning that “the productivity of 4 years of college might be 4 times greater than the productivity of the 1st year”) or the dropouts leave because the returns on schooling (pecuniary and non pecuniary) do not compensate their schooling costs (such as difficulty in leaning or low ability).

Scholar/date/objective Proposed model or hypothesis Selected conclusions

Layard Richard & G. Psacharopoulos The authors reviewed the arguments made by those scholars who 1974 tested empirically and did not reject the screening hypothesis. They raised inconsistencies in the theory. In fact in one of the Objective: to question the studies the private rates of returns of graduates are as high as the sheepskin/screening hypothesis. private rates of return of dropouts (non-graduates) which contradicts the theory. Their work was very influential since it practically removed the notion of sheepskin effects from research for the next 10 years.

2 Rosenzweig and Morgan 1976 Ln Y= β0 + β1S + β2E+ β3E +u The use of A instead of E creates a differential bias in the male- female schooling coefficiente that favors males. Objective: to contend against Blinder’s use of age instead of experience in the “Age is not a good proxy for work experience since people of the earnings equation to compare earnings same age who have spent a different number of years in school between males and females will also have different levels of labor force experience”

263 Rosenzweig and Morgan , page 4

Data: 1970 US Census

Blinder 1976 Mincer’s potential experience is a good proxy for experience for individuals who do have continuous work histories; otherwise Objective : to respond to Rosenzweig and measurement errors will be present, the schooling coefficient will Morgan’s 1976 critique of his choice of be upwardly biased and the experience estimate will be age instead of experience underestimated.

The use of age as opposed to potential experience is justified by its empirical usefulness. His schooling coefficient β1, he argues, measures the impact on log y of an additional year of schooling for people of the same age when S increases which means that those that have more schooling are also older.

Rosenzweig 1976 Blinder’s coefficient is biased because “an additional year of Objective: to show why potential work schooling holding age constant implies a year less of work experience is more relevant than age in the experience which would reduce earnings and partially offset the earnings equation. estimated returns to an additional year of schooling” p 24

The relevant aspect he contended (based on studies’ findings) is

Scholar/date/objective Proposed model or hypothesis Selected conclusions

that empirical evidence demonstrates that work history (E) is the determinant factor of earnings and not age (A).

However, he pointed out two problems (already raised by Mincer and Blinder)with the earnings specification:

1.Use of potential experience or age will always cause a specification error because they are proxies

2. The previous problem is magnified if the sample includes married women. He advises the use of samples made of groups with continuous work histories, be they males or single females and any case separate samples of males and unmarried females.

2 Psacharopoulos 1977 1.Ln Y=β0 + β1S +β3E+ β4E +u

264 1) Potential experience (E) should be used instead of age (A) in 2 2 Objective: to test Mincer’s specification in 2. Ln Y=β0 + β1S+ β2S +β3E+ β4E +u order to differentiate the human capital effects of time from the an LDC and also to test other formulations biological effects. 2 2 including a quadratic association between 3. Ln Y=β0 + β1S+ β2S +β3E+ β4E + β5(S*E) +u education and earnings 2) The Mincerian equation fits LDCs’ situation

Data: random sample of 1600 male Moroccan fulltime employees, 1970

Griliches 1977 The use of annual earnings will reflect not only labor market transactions but labor leisure choices and the effects of Objective: Explore the econometric issues unemployment. It is better to use wage rates per hour/week raised by the use of Mincer’s functional form The operationalization of the construct human capital through measures of school inputs or years of schooling is limited. Ability has been left out from the specification which implies that the estimated coefficient of schooling is upwardly biased. Ability also is measured with proxies that might not have anything to do with ability.

The exercise of excessive caution by including many variables to diminish the bias in the schooling estimate can significantly

Scholar/date/objective Proposed model or hypothesis Selected conclusions

increase the measurement error.

There is a self selection problem that is expressed in two ways: the first one is that individuals are more able and hence choose to educate themselves; this is solved by adding an ability measure. The second one is that individuals choose to invest in school because they want to optimize their rewards. However, he considers that the latter is not a significant problem because individuals’ optimizing behavior is attributable to them only in part, because it is also the result of many outside influences.

Neidert and Tienda 1984 They tried variations of the non-linear specification (left) and Ln Yi=β0 + β1Si+ β2Sxi +β2D8i+β3D12i+ β4D16i + concluded that all of them provided a better fit than the linear one 2 2 Objective: Show that Mincer’s functional β4D16i β5Ei+ β6Ei + β7Z i + ui (based on R ). form does not reflect the earnings- .

265 schooling relationship for Hispanics

D8, D12, D146= dummy variables for those who Data: (US) Survey of Income and have completed 8, 12 and 16 years of schooling: Education 1976. Sample of Hispanic men diploma years. aged 18-64 who were in the labor force in 1975, separated the sample by nation of Si= total number of years of schooling origin. A sample of non-Hispanic whites was also used to compare with Hispanics Sxi= number of years of schooling per each class: elementary (maximum 8), high school(maximum 4 years), and college (maximum 4 years)

Z= set of control variables including area wage rate

Hungerford & Solon 1987 Sheepskin effect function 1. This functional form has been the basis for testing nonlinearities since then. Objective: to test the sheepskin effects Ln Yi=β0 + β1Si+β2D8i+β3[(Si -8)* D8i ] +β4D12i+ hypothesis β5[(Si -12)* D12i ]+β6D16i + β7D17i +β8D18i + 2. Their test did not reject the hypothesis of sheepskin effects 2 β9Ei+ β10Ei + ui particularly at 16 years of education, which corresponds to Data: May 1978 current US Population college graduation. Survey. Sample of white male D8, D12, D16, D17, D18= dummy variables for nonagricultural ages 25-64. those who have completed at least 8, 12 and 16, 17, Their model also extended the sheepskin effects hypothesis to

Scholar/date/objective Proposed model or hypothesis Selected conclusions

18 years of schooling: diploma years. D8: S>=8 diplomas at middle school level. D12: S>=12, D16: S>=16, D17: S=17, D18: S=18

Β3[(Si -6) * D6i ]= interaction term

β1= mean rate of return to the first 7 years of education (S<8)

th β1+β2= rate of return to the 8 year of education (S=8)

β1 +β3= mean rate of return to the first three years of secondary schooling S>8 S<12

266 th β1 +β3+ β4=rate of return to the 12 (last )year of

secondary schooling

β1 +β3+ β5 mean rate of return to the first 3 years of university S>12 S<16

th β1 +β3+ β5+ β6= rate of return to the 16 bachelor degree S>=16

th β1 +β3+ β5+ β7= rate of return to the 17 S=17 th β1 +β3+ β5+ β8= rate of return to the 18 S=18

Step function

2 Ln Y= β0 + βxSi + β2Ei+ β3Ei + ui

Si: captures 18 schooling dummies

Scholar/date/objective Proposed model or hypothesis Selected conclusions

2 Psacharopoulos and Ng 1992 (a) 1. Ln Y= β0 + β1Si + β2Ei+ β3Ei + ui 1. Model 2 (non-linear) is inferior to model 1 (Mincer’s) because the latter assumes flat-earnings profiles for different levels of Objective: to present evidence of the education. earnings education relationship in Latin 2. Ln Yi= β0 + β1 PRIMi +β2SECi+ β3UNIVi+ β5 2 America (LA) Ei+ β6Ei +ui 2. The rates of return using the non-linear specification were decreasing with the rate of return on primary education higher Data: Household survey over the LA (18 PRIMi, SECi ,UNIVi are dummies that denote than the other two levels for the majority of the LA countries countries) region performed between 1989 whether or not the individual has completed except for 5 of the 18 countries, including Costa Rica. He and 1990. Different samples for males and primary, secondary and university education concludes that investment in primary education remains the most females ages 15-65for analytical and not profitable in LA. self selection reasons Psacharopoulos is a well known scholar in the field of private Also, and social returns to education. His publications on the subject are vast using his own empirical estimations or those of others Psacharopoulos 1992 (b), and with the purpose of reviewing and evaluating the functional 267 forms used when calculating the rates of return for LA. Also, he

Psacharopoulos 2002 does not recommend the use of control variables because “they steal the effects of education on wages” (2002), mainly variables such as occupation, except for ability that, according to him, upholds the positive impact of education on earnings (1992b).

Lam and Schoeni 1993 Step function The returns are increasing and evidence sheepskin effects.

2 Objective: To identify the magnitude of Ln Y= β0 + βxSi + β2Ei+ β3Ei + β4Fi +ui The estimate of the schooling coefficient diminishes between and the family background bias in estimates of 13.6% and 17.6% when workers’ parents’ education are added in returns to schooling Si: captures 17 schooling dummies the specification. If all controls for family background are added Data: 40,000 married Brazilian working to the specification then the decrease is about a third. However, if men aged 30-55 from the 1982 PNAD Fi: mother’s and father education as well as wife’s the measurement error in the schooling variable is considered, the survey. and in laws’ education bias amounts to about 10% to 15%.

Park 1994 Step function The schooling profile is closed to be log-linear; however, there are some deviations from this profile, particularly the returns at 2 Objective: To inspect the shape of the Ln Y= β0 + βxSi + β2Ai+ β3Ai + β4Fi +ui 15 years of schooling are the same as 14 years of schooling. The returns to schooling function to see if there use of quintile regression did not change this outcome. are departures from the log-linear profile. Spline functions If the profile is non-linear find its sources. Park examined several cause for the on-linearity on the returns:

Scholar/date/objective Proposed model or hypothesis Selected conclusions

Ln Yi=β0 + β1Si+β2D12i+ β3D16i+ β4DS15i + a) personal and employment characteristics are correlated with 2 Data: Current Population survey (CPS) β5DS16s i + β6Ei+ β7Ei + ui earnings, b) the imposition of the same experience profile in the from 1979 to 1991. Sample of white males specification, mismeasurement of schooling, c) the productivity aged 25 to 64 whose highest grade with 14 years of schooling does not increase with one year more completed is at least 9 of education, d) signaling and the sheepskin hypothesis. He Ln Yi= Ln Yi=β0 + β1Si+β2D12i+ β3[(Si -12)* D12i concluded that the plausible cause was the mismeasurement of ] + β4D16i+ β5[(Si -16)* D16i ] + β6DS15i + schooling although his work did not provide enough evidence on 2 β7DS16 i + β8Ei+ β9Ei + ui this. Regarding signaling and sheepskin hypothesis the answer was not conclusive. D12, D16, DS15, DS16= dummy variables for those who have completed at least 12 and 16,, that is, and DS15: S=15, DS16: S=16 to reflect the diploma years.

2 Heckman et. al 1996 Ln Yi=β0 + β1Si + β2Dsci+ β3 Dci + β4Ei+ β5Ei + β6 1. Linear models in schoolings are rejected by the data. Later in

268 Zi + ui 2006, Heckman et al. added’” that imposing linearity in

Objective: evaluate which functional forms schooling leads to upward biased estimates of the rate of return to are more consistent with the data and how Si= number of years of schooling completed grades that do not produce a degree, while it leads to downward sensitive are the specifications to the data biased estimates of the degree completion years” Dsci= 1 if the individual i completed some college Data: 1970, 1980, 1990 U.S. micro but less than 4 years 2. Non linear specifications show that the only effect of Census. Samples of white men born in US education quality on earnings is at the college level. between 1910-1959 Dsci= 1 if the individual i completed 4 or more years of college 3. Also, Heckman et al. (2006) contended that the assumption of Also, a quadratic relationship between potential experience and Heckman et. al 2006 Zi= vector of individual characteristics: marital earnings is not needed once the assumption of linearity between status earnings and schooling is lifted.

2 Patrinos 1996 Ln Y= β0 + βxSi + β2Ei+ β3Ei + ui Nonlinearities were found , but not sheepskin effects. These Objective: 1. To show non-linearities on discontinuities at 3, 9, 11, 14 and 16 years of schooling, he the returns to education to support either Si: captures 18 schooling dummies argued, are due to productivity and threshold effects Particularly, the sheepskin effects hypothesis or he identifies 3 to 4 years of schooling as a threshold level where threshold externalities. a minimum average education is achieved for education to start having an impact. He based this conclusion on Azariades and Data: 1989 Guatemalan National Drazen’s hypothesis of threshold externalities. Household survey. Sample of nonagricultural, non-public sector employed

Scholar/date/objective Proposed model or hypothesis Selected conclusions

males ages 15-64 with positive labor market earnings.

Strauss and Thomas 1996 1. The relationship for all sectors, regions, men and women is 2 Ln Yi=β0 + βXSi+β2Ai+ β3Ai + β4Ri + β5PEi+ β6SDi non-linear: characterized by steps Objective: 1. Find out why the wage +ui education relationship is convex. 2. Steps are also observed for the self-employed which lead to the 2. Find out whether the positive correlation Yi= wage per hour, per month rejection of the hypothesis of sheepskin effects. Schooling between wages & education means that enhances the productivity of the individual. either education is productive or that it is a Sx= dummy variable for each of 17 y of education signaling device. If the high returns persist 3. Plausible causes of convexity: a) The selection of higher in the self-employed sector, then the Ai= age ability individuals into higher education. However, this notion of education as a signaling possibility was controlled by Strauss and Thomas’s inclusion of mechanism is not conclusive. Ri= race parental education and additional family background characteristics, b) a positive correlation between the quantity and

269 Data: Household heads and spouses (given PE i= parent’s education quality of human capital investments by parents and their

that only for them information on parents’ children in the presence of credit constraints. Children who education is available), ages 25-60 to SD i=state dummies continue on to secondary and tertiary levels may go to better avoid sample selection issues that would quality and hence more expensive schools in comparison to the arise with young and old household heads. They also tried linear and polynomial functions average primary schools. In other words, some children are and controlled for self selection as well. unable to continue studying because they cannot afford to pay the next level higher quality schools, c) The structure of labor demand. However, for Strauss and Thomas these two last plausible causes did not provide a conclusive explanation to the convexity of the profile. Firstly, they included controls for current state of residence of respondent to account for school quality and structure of labor demand. Secondly, the profile of the returns was more convex for females relative to the males and for the Northeast relative to the South implying something not likely: that school quality and the nature of labor demand were gender specific and region specific. They suggested that some other unobserved characteristics could be playing a role. However, they warned that if convexity is the result of either one of the last two causes, “it suggests that as the level of education increases inequality will also increase”; “if in the other hand it is due to selectivity among those who stay at school, then as education opportunities expand, the shape of the function may

Scholar/date/objective Proposed model or hypothesis Selected conclusions

change and it does not necessarily increase in inequality “(p 161). 4. Convexity has serious implications for income inequality. Moreover, the fact that this is not an incentive for people to acquire more education requires more research.

Card 1999 1. Earnings and age relationship: Even the polynomials F(S, A) +u functional form has problems approximating the precise Objective: Interpret the evidence regarding curvature of the age profiles for different education groups. The the causal relationship between earnings S and A: can be measured by a complete set of effect is an underestimation of the rate of earnings for younger and schooling. dummy variables or can be estimated non- college-educated men and women relative to high school parametrically by using kernel density estimators graduates (for a US pooled sample of male and female separated in smaller datasets. by gender, using log hourly earnings, a linear education term, a cubic term of experience and a dummy for race) . A: age or potential experience for sample of only men 2. Non-linearity. Card informs that Park (1994) found, using US

270 data, that the linear model fits the data well. Indeed, he found that 2 3 Ln Yi=β0 + β1Si+β2Ai+ β3Ai + β3Ai + ui the apparent non-linearity at 16 years of education results from provides a significant improvement in fit, also the fact that there is an extremely low return at 15 years of 4 β4Ai schooling rather than a very high return at 16 years of schooling. in comparison to the linear formulation (see Card also contends, using his estimations for a US sample of men comment 1.) ages 40-55, that the linear functional forms provide good fit to the data if an estimate of the years of education is assigned to 2 Ln Y= β0 + β1Sxi + β2Ei+ β3Ei + ui each education group and a linear relationship is assumed.

Si= number of the years of completed education for 3. Adding family background as control may reduce the bias in β1 each reported education class/group but may still lead to an upwardly biased estimate unless all of the It fits well the data(US data)(see comment 2) unobserved ability components are absorbed by the family background controls.

4. Log of earnings. It is better to use log of hourly earnings. When log of annual earnings is used only two thirds of the estimated coefficient is attributable to log of earnings per hour. Also, given that more educated individuals tend to work more, their average return to schooling will be higher for weekly or annual earnings than to hourly earnings

Table A.4.2 Costa Rica 2005. Gross monetary private returns to education: Total country Conversion of estimated dummy coefficients to semi-elastic values

Variables Coefficient Antilog Antilog-1 ∆ coefficient Antilog Antilog-1 one 0.1118797 1.1183783 0.1183783 two 0.1797528 1.1969214 0.1969214 0.0678731 1.0702295 0.0702295 three 0.1560849 1.1689254 0.1689254 -0.0236679 0.97661 -0.02339 four 0.2231004 1.2499461 0.2499461 0.0670155 1.0693121 0.0693121 five 0.1748637 1.1910839 0.1910839 -0.0482368 0.9529081 -0.0470919 six 0.2626813 1.3004122 0.3004122 0.0878176 1.091789 0.091789 seven 0.3202821 1.3775163 0.3775163 0.0576008 1.059292 0.059292 eight 0.3741428 1.4537447 0.4537447 0.0538607 1.0553376 0.0553376 nine 0.3744168 1.4541431 0.4541431 0.0002741 1.0002741 0.0002741 ten 0.4286978 1.535257 0.535257 0.054281 1.0557812 0.0557812 elev 0.5665097 1.762106 0.762106 0.1378119 1.1477596 0.1477596 twelv 0.7232967 2.0612172 1.0612172 0.156787 1.1697464 0.1697464 thirte 0.8257792 2.2836595 1.2836595 0.1024825 1.1079179 0.1079179 fourte 0.90443 2.4705233 1.4705233 0.0786508 1.0818265 0.0818265 fifte 1.11502 3.0496292 2.0496292 0.2105896 1.2344057 0.2344057 sixte 1.354388 3.8743891 2.8743891 0.2393686 1.2704467 0.2704467 sevte 1.413614 4.110785 3.110785 0.0592255 1.0610145 0.0610145 eigte 1.402741 4.0663305 3.0663305 -0.0108731 0.9891858 -0.0108142 ninte 1.612667 5.0161715 4.0161715 0.2099261 1.2335869 0.2335869 English 0.1795459 1.1966738 0.1966738 ------o_lang 0.1096946 1.1159372 0.1159372 ------age 0.0391842 ------age2 -0.0004176 ------female -0.1761019 0.8385325 -0.1614675 ------rural -0.1320393 0.8763066 -0.1236934 ------Constant 5.352445 211.12387 210.12387 ------

Note: For example the semi-elastic estimate of 7 years of schooling is 5.92% after the conversion. Without the conversion it is approximately 5.76%

271

APPENDIX TO CHAPTER 5

Table A.5.1 Costa Rica 2005. Description of variables used in the regression analysis

Variables Description Dependent variables Enrolment Dummy 1 if enroled in formal school (primary or secondary); 0 otherwise Enrolment primary Dummy 1 if enroled in primary school; 0 otherwise Enrolment secondary Dummy 1 if enroled in secondary school; 0 otherwise Independent variables Returns skilled labor Gorss monetary private returns to education for the skilled labor force Returns unskilled labor Gross monetary private returns to education for the unskilled labor force Both parents unskilled Dummy = if 1 if parents are both unskilled; 0 otherwise One parent unskilled Dummy = if 1 if one parent is unskilled; 0 otherwise Both parents skilled Dummy = if 1 if parents are both skilled; 0 otherwise Interaction Interaction of the dummy when both parents are unskilled with returns to skilled labor Interaction Interaction of the dummy when both parents are unskilled with returns to unskilled labor Wealth index A wealth index Gender Dummy = 1 if male; 0 otherwise Area Dummy =1 if rural; 0 otherwise Number of children Number of children in the household Seven years Dummy =1 if 7 years old; 0 otherwise Eight years Dummy =1 if 8 years old; 0 otherwise Nine years Dummy =1 if 9 years old; 0 otherwise Ten years Dummy =1 if 10 years old; 0 otherwise Eleven years Dummy =1 if 11 years old; 0 otherwise Twelve years Dummy =1 if 12 years old; 0 otherwise Thirteen years Dummy =1 if 13 years old; 0 otherwise Fourteen years Dummy =1 if 14 years old; 0 otherwise Fifteen years Dummy =1 if 15 years old; 0 otherwise Sixteen years Dummy =1 if 16 years old; 0 otherwise Seventeen years Dummy =1 if 17 years old; 0 otherwise Eighteen years Dummy =1 if 18 years old; 0 otherwise Nineteen years Dummy =1 if 19 years old; 0 otherwise County dummies Dummies for 79 counties Default category A seven years old female from the urban area whose parents among who at least one is skilled (this changes to parents among who at least one is unskilled)

272

Table A.5.2. Costa Rica 2005. Descriptive statistics: total sample.

Standard Variables Mean deviation Minimum Maximum ret_sk 0.141 0.030 0.103 0.196 ret_unsk 0.033 0.014 0.011 0.068 both_unsk 0.687 0.464 0 1 one_sk 0.184 0.388 0 1 both_ski 0.129 0.335 0 1 unsk_rski 0.099 0.072 0 0.196 unsk_runsk 0.024 0.019 0 0.068 wealthind 0.144 2.568 -7.795 10.628 male 0.525 0.499 0 1 rural 0.644 0.479 0 1 nchild 2.908 1.366 1 10 seven 0.071 0.257 0 1 eight 0.085 0.279 0 1 nine 0.080 0.271 0 1 ten 0.086 0.280 0 1 eleven 0.089 0.284 0 1 twelve 0.088 0.283 0 1 thirteen 0.086 0.281 0 1 fourteen 0.089 0.285 0 1 fifteen 0.092 0.288 0 1 sixteen 0.084 0.278 0 1 seventeen 0.067 0.250 0 1 eighteen 0.048 0.214 0 1 nineteen 0.035 0.183 0 1 Observations 6371

273

Table A.5.3. Costa Rica 2005. Descriptive statistics: primary school sample

Standard Variables Mean deviation Minimum Maximum ret_sk 0.143 0.031 0.103 0.196 ret_unsk 0.034 0.014 0.011 0.068 both_unsk 0.705 0.456 0 1 one_sk 0.176 0.381 0 1 both_ski 0.119 0.324 0 1 unsk_rski 0.103 0.072 0 0.196 unsk_runsk 0.024 0.019 0 0.068 wealthind -0.137 2.543 -7.795 8.698 male 0.541 0.498 0 1 rural 0.657 0.475 0 1 nchild 3.008 1.412 1 10 seven 0.134 0.341 0 1 eight 0.161 0.368 0 1 nine 0.151 0.358 0 1 ten 0.162 0.369 0 1 eleven 0.167 0.373 0 1 twelve 0.126 0.331 0 1 thirteen 0.042 0.200 0 1 fourteen 0.016 0.126 0 1 fifteen 0.010 0.100 0 1 sixteen 0.008 0.091 0 1 seventeen 0.008 0.091 0 1 eighteen 0.004 0.067 0 1 nineteen 0.010 0.097 0 1 Observations 3368

274

Table A.5.4. Costa Rica 2005. Descriptive statistics: secondary school sample.

Standard Variables Mean deviation Minimum Maximum ret_sk 0.139 0.029 0.103 0.196 ret_unsk 0.033 0.014 0.011 0.068 both_unsk 0.667 0.471 0 1 one_sk 0.193 0.395 0 1 both_ski 0.140 0.347 0 1 unsk_rski 0.095 0.072 0 0.196 unsk_runsk 0.022 0.019 0 0.068 wealthind 0.458 2.561 -7.645 10.628 male 0.507 0.500 0 1 rural 0.628 0.483 0 1 nchild 2.797 1.305 1 9 seven 0 0 0 0 eight 0 0 0 0 nine 0 0 0 1 ten 0 0 0 0 eleven 0.000 0.018 0 1 twelve 0.046 0.209 0 1 thirteen 0.136 0.343 0 1 fourteen 0.172 0.377 0 1 fifteen 0.183 0.387 0 1 sixteen 0.169 0.375 0 1 seventeen 0.133 0.340 0 1 eighteen 0.097 0.296 0 1 nineteen 0.063 0.243 0 1 Observations 3003

275

Table A.5.5. Costa Rica 2005. Central region: returns to schooling

Skilled Unskilled Variables coefficient p-value coefficient p- value Urban edu 0.126 0.000 0.020 0.007 age 0.022 0.044 0.026 0.004 age2 0.000 0.308 0.000 0.033 English 0.204 0.000 0.319 0.002 oth_lang 0.196 0.018 0.178 0.330 female -0.130 0.000 -0.159 0.000 Observations 1872 1831 R2 adjusted 0.289 0.040 Rural edu 0.108 0.000 0.040 0.000 age 0.084 0.000 0.030 0.001 age2 -0.001 0.000 0.000 0.010 English 0.197 0.004 0.334 0.002 oth_lang -0.268 0.017 0.238 0.009 female -0.151 0.001 -0.257 0.000 Observations 771 2077 R2 adjusted 0.273 0.127 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

276

Table A.5.6. Costa Rica 2005. Chorotega region: returns to schooling.

Skilled Unskilled coefficient p- value coefficient p- value Urban edu 0.153 0.000 0.068 0.000 age 0.063 0.051 0.005 0.825 age2 -0.001 0.100 0.000 0.845 English 0.067 0.601 0.610 0.030 oth_lang 0.294 0.050 -0.303 0.351 female 0.002 0.977 -0.310 0.000 Observations 232 245 R2 adjusted 0.243 0.134 Rural edu 0.196 0.000 0.039 0.010 age 0.071 0.039 0.013 0.422 age2 -0.001 0.106 0.000 0.411 English 0.367 0.022 0.468 0.002 oth_lang 0.708 0.000 -0.032 0.726 female -0.198 0.053 -0.195 0.009 Observations 167.0 628.0 R2 adjusted 0.409 0.104 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

277

Table A.5.7. Costa Rica 2005. Pacífico region: returns to schooling

Skilled Unskilled coefficient p-value coefficient p-value Urban edu 0.173 0.000 0.011 0.198 age 0.053 0.323 0.041 0.167 age2 -0.001 0.394 0.000 0.264 English -0.197 0.245 0.128 0.546 oth_lang 0.206 0.260 (dropped) female -0.136 0.240 -0.184 0.052 Observations 137 274 R2 adjusted 0.260 0.004 Rural edu 0.184 0.000 0.030 0.007 age 0.053 0.273 0.056 0.002 age2 -0.001 0.378 -0.001 0.006 English -0.180 0.200 0.288 0.079 oth_lang -0.595 0.000 (dropped) female 0.071 0.536 -0.151 0.018 Observations 110 549 R2 adjusted 0.528 0.068 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

278

Table A.5.8. Costa Rica 2005. Brunca region: returns to schooling.

Skilled Unskilled coefficient p-value coefficient p-value Urban edu 0.121 0.000 0.037 0.068 age 0.070 0.016 0.044 0.092 age2 -0.001 0.056 0.000 0.146 English -0.361 0.002 0.264 0.448 oth_lang (dropped) (dropped) female -0.219 0.003 -0.132 0.113 Observations 190 277 R2 adjusted 0.373 0.047 Rural edu 0.157 0.000 0.019 0.071 age 0.096 0.013 0.066 0.000 age2 -0.001 0.025 -0.001 0.000 English 0.105 0.456 0.840 0.000 oth_lang (dropped) 0.015 0.965 female -0.015 0.870 -0.196 0.001 Obs 177 879 R2 adjusted 0.290 0.080 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

279

Table A.5.9. Costa Rica 2005. Huetar Atlántica region: returns to schooling.

Skilled Unskilled coefficient pvalue coefficient pvalue Urban edu 0.158 0.000 0.068 0.007 age -0.025 0.617 0.040 0.198 age2 0.000 0.490 0.000 0.278 English 0.129 0.429 -0.442 0.032 oth_lang 0.296 0.410 (dropped) female -0.177 0.190 -0.023 0.832 Observations 107 243 R2 adjusted 0.201 0.065 Rural edu 0.135 0.000 0.053 0.000 age 0.092 0.037 0.031 0.031 age2 -0.001 0.098 0.000 0.057 English 0.257 0.090 0.420 0.009 oth_lang (dropped) 0.095 0.278 female -0.030 0.750 -0.230 0.000 Observations 118 822 R2 adjusted 0.386 0.082 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

280

Table A.5.10. Costa Rica 2005. Huetar Norte region: returns to schooling.

Skilled Unskilled coefficient p- value coefficient P- value Urban edu 0.103 0.000 0.030 0.047 age -0.100 0.021 0.090 0.002 age2 0.002 0.004 -0.001 0.003 English 0.072 0.526 -0.346 0.017 oth_lang 0.088 0.512 -0.726 0.000 female -0.369 0.000 -0.142 0.106 Obs 138 184 R2 adjusted 0.357 0.149 Rural edu 0.188 0.000 0.034 0.016 age -0.012 0.797 0.023 0.217 age2 0.000 0.809 0.000 0.373 English 0.105 0.542 0.285 0.006 oth_lang (dropped) -0.205 0.001 female 0.084 0.521 -0.305 0.001 Obs 99 408 R2 adjusted 0.360 0.063 Fixed effects included with robust standard errors

Note: Dependent variable is natural logarithm of wages per hour. Returns to schooling are gross monetary private returns to schooling.

281

Table A.5.11. Costa Rica 2005. Description of the variables used to calculate the wealth index using Principal Components

Variables Description Characteristic of households’ dwelling 3 dummies to account for no pipe water in the dwell, water inside the Water supply dwell (default); and pipe outside the dwell. Toilet inside D = 1 if toilet inside;0 otherwise Whether electricity D=1 if there is electricity; 0 otherwise 4 dummies to account for well, river, public service( default) and Source of water other. Energy source for cooking 4 dummies to account for gas, electricity (default), biomass and other. No. of rooms No. of rooms on dwelling No. of bathrooms No. of bathrooms on dwelling Whether water heater tank D=1 if yes; 0 otherwise Whether hot shower D=1 if yes; 0 otherwise Whether water heater tank D=1 if yes; 0 otherwise Whether Internet at home Dummy= 1 if yes; 0 otherwise 3 dummies to account for high quality, medium quality (default) and Dwelling quality* low quality. HH ownership of consumer durables Own refrigerator Dummy= 1 if yes; 0 otherwise Own washing machine Dummy= 1 if yes; 0 otherwise Own microwave Dummy= 1 if yes; 0 otherwise No. of soundtracks No. of soundtracks in dwelling Own VHS Dummy= 1 if yes; 0 otherwise Own DVD Dummy= 1 if yes ; 0 otherwise No. of TV sets No. of TV sets in dwelling Own cable TV Dummy= 1 if yes; 0 otherwise Own residential telephone Dummy= 1 if yes; 0 otherwise No. of cellular telephones No. of cellular telephones by HH No. of computers No. of computers in dwelling No. of cars No. of cars in dwelling

Home Ownership 4 dummies to account for owned (default), rented, slum or other Whether dwelling is owned arrangement.

Note: * means quality of dwell based on materials of roofs, floors and walls.

282

Table A.5.12. Costa Rica 2005. Descriptive statistics of the asset variables used to calculate the wealth index.

Variable Mean Std. Dev. Min Max nopipe_water 0.023 0.148 0 1 pipein_water 0.951 0.215 0 1 pipeout_water 0.026 0.160 0 1 toilet 0.932 0.253 0 1 electric 0.987 0.114 0 1 nowater 0.003 0.051 0 1 publicwat 0.898 0.303 0 1 well 0.058 0.235 0 1 river 0.041 0.198 0 1 cook_oth 0.002 0.048 0 1 cook_elect 0.465 0.499 0 1 cook_gas 0.393 0.488 0 1 fuel_biomass 0.139 0.346 0 1 room_num 5.075 1.427 1 11 bath_num 1.139 0.442 0 4 watertank 0.113 0.316 0 1 hot_showe 0.332 0.471 0 1 hotwat_tank 0.025 0.156 0 1 internet 0.068 0.252 0 1 mediumq_ dwell 0.272 0.445 0 1 highq_dwell 0.226 0.418 0 1 lowq_dwell 0.502 0.500 0 1 refrig 0.905 0.293 0 1 wash_mach 0.900 0.300 0 1 microwave 0.478 0.500 0 1 soundtrck_number 0.592 0.562 0 3 vhs 0.249 0.432 0 1 dvd 0.228 0.419 0 1 tv_num 1.385 0.839 0 5 cabletv 0.160 0.367 0 1 hphone_num 0.632 0.529 0 3 cellph_num 0.677 0.950 0 5 comp_num 0.252 0.488 0 3 car_num 0.329 0.583 0 3 oth_arra 0.084 0.277 0 1 own 0.791 0.407 0 1 rented 0.114 0.318 0 1

(Table continues on the next page.)

283

Table A.5.12. Costa Rica 2005. Descriptive statistics of the asset variables used to calculate the wealth index (continued).

Variable Mean Std. Dev. Min Max slum 0.012 0.108 0 1 Observations 11278

Table A.5.13. Costa Rica 2005. Principal Components: Eigenvalues and explained variation.

Component Eigenvalue Difference Proportion Cumulative Comp1 6.78 4.27 0.21 0.21 Comp2 2.50 1.12 0.08 0.28 Comp3 1.38 0.12 0.04 0.32 Comp4 1.26 0.04 0.04 0.36 Comp5 1.22 0.08 0.04 0.40 Comp6 1.14 0.06 0.03 0.43 Comp7 1.08 0.02 0.03 0.47 Comp8 1.05 0.05 0.03 0.50 Comp9 1.00 0.02 0.03 0.53

284

Table A.5.14. Costa Rica 2005. Regression output total sample. Dependent variable: probability of enrollment: When parents are liquidity constrained and returns are convex.

dF/dx P value x-bar ret_sk 0.154 0.452 0.141 ret_unsk 1.318 0.003 0.033 both_unsk* -0.025 0.309 0.693 one_sk* -0.063 0.000 0.184 unsk_rski -0.058 0.731 0.100 unsk_runsk -1.152 0.004 0.024 wealthind 0.007 0.000 0.106 male* -0.013 0.000 0.526 rural* -0.024 0.000 0.654 nchild -0.005 0.000 2.921 eight* 0.025 0.058 0.086 nine* 0.013 0.344 0.079 ten* 0.020 0.139 0.087 eleven* 0.019 0.170 0.087 twelve* -0.035 0.050 0.089 thirteen* -0.130 0.000 0.087 fourteen* -0.237 0.000 0.088 fifteen* -0.350 0.000 0.092 sixteen* -0.435 0.000 0.084 seventeen* -0.519 0.000 0.067 eighteen* -0.695 0.000 0.049 nineteen* -0.841 0.000 0.036 FE included N 6218 Prob of chi 2 0.000 Chi squared 1142.1

285

Table A.5.15. Costa Rica 2005. Regression output primary school sample. Dependent variable: probability of enrollment: When parents are liquidity constrained and returns are convex.

dfx p value x- bar ret_sk 0.000 0.862 0.152 ret_unsk 0.001 0.008 0.032 both_unsk* -0.001 0.000 0.736 one_sk* -0.407 0.000 0.169 unsk_rski 0.000 0.640 0.115 unsk_runsk 0.000 0.417 0.024 wealthind 0.000 0.020 -0.618 male* 0.000 0.242 0.554 rural* 0.000 0.070 0.710 nchild 0.000 0.022 3.178 eight* 0.000 0.039 0.168 nine* 0.000 0.327 0.151 ten* 0.000 0.226 0.169 eleven* 0.000 0.041 0.154 twelve* 0.000 0.329 0.130 thirteen* 0.000 0.588 0.046 fourteen* -0.001 0.000 0.022 fifteen* -0.128 0.000 0.014 sixteen* -1 0.000 0.016 FE included N 1791 pseudo R2 0.664

286

Table A.5.16. Costa Rica 2005. Regression output secondary school sample. Dependent variable: probability of enrolment: When parents are liquidity constrained and returns are convex

Variables dF/dx p-value x-bar ret_sk 0.537 0.553 0.139 ret_unsk 3.825 0.056 0.033 both_unsk -0.108 0.361 0.676 one_sk* -0.193 0.001 0.195 unsk_rski -0.288 0.702 0.096 unsk_runsk -4.780 0.009 0.023 wealth~d 0.031 0.000 0.406 male* -0.064 0.000 0.507 rural* -0.097 0.000 0.639 nchild -0.023 0.000 2.807 twelve* 0.181 0.000 0.046 thirteen* 0.221 0.000 0.136 fourteen* 0.226 0.000 0.169 fifteen* 0.209 0.000 0.184 sixteen* 0.188 0.000 0.168 seventeen* 0.160 0.000 0.133 eighteen* 0.089 0.002 0.100 FE included N 2913 Prob of chi 2 000 Chi squared 686.82 Pseudo R2 0.293

287

Table A.5.17. Costa Rica 2005. Regression output total sample. Dependent variable: probability of enrolment: When parents are not liquidity constrained and returns are convex.

dF/dx p-value x-bar ret_sk 0.066 0.617 0.141 ret_unsk 0.342 0.237 0.033 both_ski* -0.994 0.039 0.123 one_sk* 0.021 0.000 0.184 bsk_rski 2.290 0.016 0.016 bsk_runsk 3.518 0.040 0.004 wealthind 0.007 0.000 0.106 male* -0.012 0.000 0.526 rural* -0.023 0.000 0.654 nchild -0.005 0.000 2.921 eight* 0.024 0.054 0.086 nine* 0.013 0.313 0.079 ten* 0.019 0.129 0.087 eleven* 0.018 0.153 0.087 twelve* -0.031 0.056 0.089 thirteen* -0.122 0.000 0.087 fourteen* -0.223 0.000 0.088 fifteen* -0.336 0.000 0.092 sixteen* -0.417 0.000 0.084 sevteen* -0.502 0.000 0.067 eighteen* -0.683 0.000 0.049 nineteen* -0.831 0.000 0.036 N 6218 Prob of chi 2 0.000 Chi squared 1147.61 Pseudo R2 0.438

288

Table A.5.18. Costa Rica 2005. Regression output secondary school sample. Dependent variable: probability of enrollment: When parents are not liquidity constrained and returns are convex

dF/dx p-value x-bar ret_sk 0.138 0.825 0.139 ret_unsk -0.037 0.979 0.033 both_ski* -0.955 0.091 0.129 one_sk* 0.107 0.000 0.195 bsk_rski 9.387 0.045 0.017 bsk_runsk 13.230 0.084 0.004 wealth~d 0.030 0.000 0.406 male* -0.061 0.000 0.507 rural* -0.094 0.000 0.639 nchild -0.021 0.000 2.807 thirteen* -0.022 0.621 0.136 fourteen* -0.064 0.157 0.169 fifteen* -0.140 0.003 0.184 sixteen* -0.193 0.000 0.168 seventeen* -0.268 0.000 0.133 eighteen* -0.465 0.000 0.100 nineteen* -0.621 0.000 0.064

289

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Interviews

Ministerio de Educación Pública (MEP)

Dr. Leonardo Garnier, Minister of Education, 20 August 2007

Mr. Carlos Badilla, Director, Planeamiento Presupuestario, 23 August 2007

Mr. Fernando Bogantes, Director, Educación Técnica, 3 September 2007

Ms. Anabelle Castillo, Director, Programas de Equidad, 7 September 2007

Lic. Aura Padilla Meléndez, Director, División de Planeamiento y Desarrollo Educativo, 23 August and 12 September 2007

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Ms. Rosa Bonilla, Departamento de Lenguas Extranjeras, 3 September 2007

Consejo Nacional de Rectores (CONARE)

Professor Ing. Alejandro Cruz, Consejo Nacional de Rectores (CONARE), 24 August 2007

Instituto Nacional de Estadística y Censos (INEC)

Mr. Jaime Vaglio, Director, INEC, 22 August 2007

Ms. Guiselle Arguello, Coordinator, Proceso de Muestreo, Área de Censos y Encuestas, 4 September 2007

Fundación Omar Dengo

Ms. Andrea Anfossi, Director, Programa de Informática Educativa, 12 September 2007

Programa Estado de la Nación

Lic.Pablo Sauma, Drofessor and Researcher, Programa Estado de la Nación, 24 August 2007

Lic. Juan Diego Trejos, Director, Instituto de Investigaciones Económicas, University of Costa Rica, and Researcher, Programa Estado de la Nación, 21 August 2007

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