Three Essays in Environmental, Labor, and Education Economics

Ghadir Asadi

Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Economics

Nicolaus Tideman, Chair Klaus Moeltner Sudipta Sarangi Wen You

May 8, 2020 Blacksburg, Virginia

Keywords: Learning, Underground Water, Precipitation Shocks, Migration, Investment in the Quality of Education Copyright 2020, Ghadir Asadi Three Essays in Environmental, Labor, and Education Economics

Ghadir Asadi

ABSTRACT

Learning plays an important role in adopting new technology. While the role of learning in the decision to adopt is widely investigated in the literature, its role in knowing how to best use technology and the speed of learning is not. For instance, when farmers adopt ground- water extraction technology, they need to learn their private marginal cost and marginal benefit of extracting water. Comparing the extraction behavior of the owners ofnewwells with old wells, we explore the role of experience in shaping farmers’ decisions. We use three identification strategies to test the hypothesis that owners of new wells extract more water than owners of old wells. Employing panel data at the district level in a fixed-effects model, we find that groundwater extraction rises as the growth rate in new wells increases. Our second strategy uses the exogenous variation in precipitation shocks in a double-difference approach. Employing census data at the well level, we show that more water is extracted from new wells than older wells and that the difference in extraction increases in areas that experience negative precipitation shocks. The third strategy is the nearest-neighbor match- ing method which confirms the above findings and indicates that old wells are more efficient in maintaining their inter-temporal extraction. We also provide evidence regarding the speed of learning about using technology. Our findings have important implications for discussions of common pool regulation. Firms are often considered entities with complete private infor- mation about their true abatement costs. Our findings imply that quantity instruments for regulating groundwater extraction fail to guarantee productive efficiency when farmers face uncertainty about their marginal abatement cost. This paper also provides new insights for optimizing climate change scenarios, in light of the importance of the learning lag in using new technologies.

In chapter two, we discuss the effects of precipitation shocks on rural labor market and migration. The welfare of both agrarian and non-agrarian workers in rural areas is highly affected by agricultural output volatility, caused in part by weather shocks. This paper examines the impact of precipitation shocks on labor supply and out-migration in rural Iran. We use individual-level panel data combined with station-based precipitation data at the rural-agglomeration level to study the intensive and extensive margins of employment. Our results indicate different types of responses to positive and negative shocks. Using afixed effects panel data model, we find that workers in agriculture and industry sectors increase their hours of work in response to positive shocks. At the extensive margin, we find that negative shocks reduce the labor market participation of women. We observe heterogeneity in responses based on the sector of employment, gender, and the roles of individuals in the household. We also show that positive shocks affect the division of labor at the household level. Our estimates for the probability of migration indicate that negative shocks raise the probability of migration for young men. We show that the labor-migration of the same group is also affected by negative shocks, but the impact could be explained by the local unemployment rate, implying the labor market is a channel through which precipitation shocks affect migratory decisions.

In the final chapter, we investigate parents’ investment in the quality of their children. While school enrollment at the primary level has been rising in developing countries to almost complete national coverage, international measures of education quality, especially in basic knowledge of reading and math, do not exhibit a parallel improvement. Since parents’ expenditure is an important determinant of children’s school performance, we investigate parents’ investments in the quality of their children’s education, measured by their spending on books and other school materials. We develop an overlapping generations model, in which we consider families’ expenditure as an input to their children’s human capital. Moreover, in our model, parents use the current status of their children’s human capital as a screening measure for adjusting their investment, instead of the standard tradition of simply balancing the trade-off between future income and the current stream of direct and indirect school costs. From our theoretical analysis, our main hypothesis is that families consider better school performance to be a reliable predictor of future return, and this incentivizes them to invest more in children who are academically promising, considering other determinants of children’s schooling output, such as school quality. Empirically, we use an instrumental variables approach to test our main hypothesis, and it is accepted using data for Ghanaian primary school students in rural areas. Three Essays in Environmental, Labor, and Education Economics

Ghadir Asadi

GENERAL AUDIENCE ABSTRACT

Adopting new technology need learning, either in the form of knowledge or by working with the adopted technology. While the role of learning in the decision to adopt is widely investigated in the literature, its role in knowing how to best use technology and the speed of learning is not. In the first chapter, we investigate the adoption of the use of groundwater technology. When farmers adopt groundwater extraction technology, they need to learn about how to best use the technology to maximize their profit in the short and long run.We use five sets of data from Iran to show the existence of learning in the use of groundwater technology. Our findings improve the discussion on the regulation of firms in usingcommon resources. This paper also provides new insights for optimizing climate change scenarios, in light of the importance of the learning lag in using new technologies. In chapter two, we discuss the effects of precipitation shocks on the rural labor market and migration. Weather shocks affect the welfare of workers in rural areas. This paper examines the impact of precipitation shocks on labor supply and out-migration in rural Iran. We use individual-level and station-based precipitation data at the rural-agglomeration level to study the effects of precipitation shocks on employment. Our results indicate different types of responses to positive and negative shocks. We find that workers in agriculture and industry sectors increase their hours of work in response to positive shocks and negative shocks reduce the labor market participation of women. We also show that the labor market is a channel through which precipitation shocks affect migratory decisions. In the final chapter, we investigate parents’ investment in the quality of their children. While school enrollment at the primary level has been rising in developing countries, international measures of education quality, especially in basic knowledge of reading and math, do not exhibit a parallel improvement. Since parents’ expenditure is an important determinant of children’s school performance, we investigate parents’ investments in the quality of their children’s education, measured by their spending on books and other school materials. We develop a model, in which we consider families’ expenditure as an input to their children’s human capital. We hypothesize that families consider better school performance to be a reliable predictor of future return. Empirically, we test our hypothesis, and it is accepted using data for Ghanaian primary school students in rural areas. Dedication

To Nicolaus Tideman A beautiful mind with a big heart

v Acknowledgments

I have benefited from many advises by members of the committee during these years.This dissertation was not here without the valuable contributions of the members. I sincerely thank Dr. Mohammad Mostafavi-Dehzooei, who co-authored the first two chapters of this dissertation, for his dedication to work and patience with me. I need to thank Dr. Siddhart Hari for his wonderful discussion and help with the second chapter. Many thanks to Prof. Djavad Salehi-Isfahani for the inspiration and encouragement during these years. I also want to thank all participants in Applied Microeconomics Reading Group at the Department of Economics at Virginia Tech (2018 and 2019), Middle East Economics Association (2020), Agricultural and Applied Economics Association (2019), Virginia Association of Economists (2019), Midwest Economics Association (2018), and EGSC at Washington University in St. Louis (2017), for their valuable comments and suggestions. With humility, I thank everyone who contributed to this dissertation and I forgot to mention them here. Not to mention that the order in this acknowledgments does not represent the magnitude of contribution.

vi Contents

1 Groundwater Extraction and Adaption to Technology1

1.1 Introduction...... 1

1.2 Theoretical framework...... 6

1.3 Data...... 9

1.3.1 Panel...... 9

1.3.2 Wells census...... 11

1.3.3 Piezometric data and aggregate level water consumption...... 13

1.3.4 Weather...... 15

1.4 Identification strategy...... 17

1.4.1 Panel data fixed-effects...... 17

1.4.2 Double-difference...... 18

1.4.3 Nearest-neighbor matching...... 19

1.5 Results...... 20

1.5.1 Panel...... 20

1.5.2 Well level estimations...... 23

1.5.3 Matching...... 28

1.6 Conclusions...... 33

1.7 Graphs...... 36

vii 1.8 Tables...... 43

2 The Effects of Precipitation Shocks on Rural Labor Markets and Migra- tion 55

2.1 Introduction...... 55

2.2 Theoretical framework...... 60

2.3 Data...... 63

2.3.1 Labor Force Survey...... 63

2.3.2 Weather data...... 66

2.3.3 Household Expenditure and Income Survey...... 68

2.4 Empirical strategy...... 69

2.5 Econometric results...... 72

2.5.1 The intensive margin...... 73

2.5.2 The extensive margin...... 77

2.5.3 Migration...... 81

2.6 Conclusions...... 86

2.7 Graphs...... 88

2.8 Tables...... 91

3 Parents’ Investments in the Quality of Education, The Case of Ghana 106

3.1 Introduction...... 106

3.2 Background...... 110

3.3 Theoretical background...... 114

3.3.1 Model...... 118

3.3.2 Substitutability...... 123

3.3.3 Private regime...... 126

viii 3.4 Data...... 126

3.4.1 Household survey...... 127

3.4.2 Schooling data...... 129

3.5 Empirical specification...... 130

3.5.1 Choosing instruments...... 132

3.6 Estimation results...... 140

3.6.1 Parents’ investment...... 140

3.6.2 Child labor...... 144

3.7 Discussion...... 145

3.8 Conclusion and policy remarks...... 146

3.9 Tables...... 148

A App for Chapter 1 171

A.1 Robustness check...... 171

A.2 Extraction from new wells and depth to water...... 172

B App for Chapter 2 185

B.1 Attrition...... 185

B.2 Shocks happened in previous years...... 187

B.3 Estimation using the Probit model...... 188

C App for Chapter 3 197

C.1 Model solution and proofs...... 197

C.2 Further discussion on IV...... 204

ix List of Figures

1.1 Structure of a well...... 36

1.2 Mean yearly precipitation in Iran...... 36

1.3 Percent of Iran’s area affected by shocks...... 37

1.4 Depth to water in Iran...... 37

1.5 Geographic dispersion of shocks in Iran...... 38

1.6 Change in piezometric water level in selected periods...... 39

1.7 Distribution of extraction per well and its log form at the district level... 39

1.8 Distribution of extraction from wells and its transformations in the census of wells...... 40

1.9 The distribution of new wells and proximity to old wells...... 40

1.10 Depth and distance to the nearest well for new vs. old wells for each province 41

1.10 Extraction of new and old wells in Spring and Summer...... 41

1.11 Planted area of new and old wells by season...... 42

2.1 Percent of Iran’s area affected by shocks...... 88

2.2 Labor force participation rate in rural areas by gender...... 88

2.3 Unemployment rate in Iran...... 89

2.4 Sector of employment in rural areas by gender...... 89

2.5 Rural to urban migration rate...... 89

x 2.6 Precipitation shocks at the rural-agglomerations, 2009-2012...... 90

3.1 Perfect complement inputs...... 125

3.2 General response...... 125

C.1 Corner solution for 푆푡 ...... 200

C.2 One-dimensional, first-order, nonlinear system unique, globally stable, steady- state equilibrium...... 201

xi List of Tables

1.1 Summary statistics of the agricultural censuses...... 43

1.2 Summary statistics of the wells’ census...... 44

1.3 Extraction, extraction per well and per unit of surface...... 45

1.4 Change in agricultural activities and precipitation shocks...... 45

1.5 Extraction and the effect of new wells...... 46

1.6 Extraction, the effect of new wells, and precipitation shocks...... 47

1.7 Extraction of the new wells...... 48

1.8 The effect of wells’ age on extraction...... 49

1.9 The effect of age difference on the extraction...... 50

1.10 Probability of digging a new well...... 51

1.11 Distribution of old and new wells...... 52

1.12 nearest-neighbor matching estimates of the difference in extraction..... 52

1.13 Summary statistics of match quality...... 53

1.14 Matching results for the second set up...... 53

1.15 Summary statistics of match quality for the second set up...... 53

1.16 Matching results and the diffusion of knowledge...... 54

2.1 Summary statistics of the balanced panel...... 91

2.2 Summary statistics for all years...... 92

xii 2.3 Impact of precipitation shocks on hours of work of agrarians by gender... 92

2.4 Impact of precipitation shocks on hours of work in industry and service sectors 93

2.5 Change in weekly hours of work by the number of workers in the household 93

2.6 Impact of precipitation shocks on hours of work of head and spouse in agriculture 93

2.7 Impact of precipitation shocks on hours of work of head and spouse in industry and service...... 94

2.8 Impact of precipitation shocks on hours of work of children by sector.... 94

2.9 Impact of precipitation shocks on hours of work by age...... 95

2.10 Impact of shocks on the probability of quitting employment and labor force. 96

2.11 Impact of precipitation shocks on the probability of quitting employment by sector...... 96

2.12 Impact of shocks on the probability of entering employment and labor force. 97

2.13 Impact of precipitation shocks on the probability of quitting employment and labor force for head and spouse...... 97

2.14 Impact of precipitation shocks on the probability of entering employment and labor force for head and spouse...... 98

2.15 Impact of precipitation shocks on the probability of quitting employment and labor force for children...... 98

2.16 Impact of precipitation shocks on the probability of entering employment and labor force for children...... 99

2.17 Impact of precipitation shocks on migration...... 100

2.18 Motivation for migration...... 101

2.19 Impact of precipitation shocks on labor-migration...... 101

2.20 Economic conditions and the impact of precipitation shocks on migration.. 102

2.21 Economic conditions and the impact of precipitation shocks on labor-migration103

2.22 Impact of precipitation shocks on migration by role in household...... 104

xiii 2.23 Impact of precipitation shocks on labor migration by role in household... 105

3.1 Summary statistics...... 148

3.2 Different school characteristics by school type in rural areas...... 149

3.3 Basic national profile of primary schools in the 2009/2010 academic year.. 149

3.4 Schooling types in the sample(%)...... 150

3.5 Simple estimates of the investment in the quality of children criteria..... 151

3.6 Estimation using a local school-level set of instruments...... 152

3.7 Estimation using a district-level set of instruments...... 153

3.8 Model estimates for child labor...... 154

3.9 Model estimates for male students...... 155

3.10 Model estimates for female students ...... 155

A1 Some transformations to deal with the skewness of the extraction...... 176

A2 The effect of precipitation on the extraction...... 177

A3 The effect of standardized precipitation on the extraction...... 178

A4 Extraction, extraction per well and per unit of surface and depth to water. 179

A5 Extraction, the effect of new wells, and depth to water...... 180

A6 Depth to water and the effect of new wells...... 181

A7 Probability of digging a new well...... 182

A8 Extraction of the new wells...... 182

A9 Extraction of semi-deep new wells...... 183

A10 Extraction of deep new wells...... 184

B.1 Yearly attrition rate at household and individual level...... 189

B.2 Probability of attrition from the panel...... 190

xiv B.3 Impact of current and past precipitation shocks on migration...... 191

B.4 Impact of current and past precipitation shocks on labor-migration..... 192

B.5 Impact of shocks on the probability of quitting employment and labor force, probit model...... 192

B.6 Impact of shocks on the probability of entering employment and labor force, probit model...... 193

B.7 Impact of precipitation shocks on migration, probit model...... 193

B.8 Impact of precipitation shocks on labor-migration, probit model...... 194

B.9 Economic conditions and the impact of precipitation shocks on migration, probit model...... 195

B.10 Economic conditions and the impact of precipitation shocks on labor-migration, probit model...... 196

C.1 Oster(2019) bounds analysis for the base OLS result in Table 3.5...... 205

C.2 Standard tests and statistics from the first stage...... 206

xv Chapter 1

Groundwater Extraction and Adaption to Technology

1.1 Introduction

Groundwater is a renewable natural resource that is subject to the common pool problem. Achieving optimal extraction for a common pool requires market or non-market tools that are either imposed by a government or agreed through private coordination (Hunt 2007; Madani and Dinar 2012a; Madani and Dinar 2012b; Sekhri et al. 2013; Erdlenbruch et al. (2014); Bertone Oehninger et al. (2016); Tsur and Zemel 2018; Sayre and Taraz 2019). Price interventions, taxes, and quotas are more popular instruments for government intervention, each having benefits that depend on the nature of the problem and objective of the regulator. There are also some less popular methods such as public provision. Sekhri (2011) shows that public provision reduced over-extraction in rural India, where the fixed cost of extraction is high.

A conventional assumption in modeling government regulation of a common pool is that the regulator does not know the true abatement cost (the cost of reducing usage), but the firm

1 Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 2 has complete information about its cost structure (Weitzman 1974; Kwerel 1977; Lewis and Sappington 1995; Duggan and Roberts 2002; Montero 2008; Requate 2013). This assumption may not hold in some practical cases, at least in the short run. In the case of groundwater, the true abatement cost may not be initially known by firms. The incomplete information problem is even more profound when a new technology is adopted by firms, for two reasons. The firm has to learn the true costs and benefits of the new technology, and partofthis information is firm or technology-specific so they need to learn it by experience. Secondly, even if the firm knows the abatement cost of the new technology, it may not possessthe know-how to use the new technology efficiently.

The new technology that is discussed in this paper is pumping groundwater. In this case, a farmer knows the information on the label of the pump with regards to energy usage, however, all these ostensible values are related to specific assumptions about operating conditions. The actual energy consumption depends on many factors that are specific to the well it is operating in, such as the dynamics of the depth to water. In addition to this, farmers must learn how to manage their inter-temporal extraction, to maintain their production from planting to harvest. The new technology requires farmers to identify the combination of amount of water extraction, type of crop, and size of plot that maximizes their profit. Farmers have to learn the marginal benefit and marginal cost of the new technology through experience, so that they can make well-informed decisions. The costs may also be affected by other factors as well. For example, over-extraction raises the cost of maintenance by increasing the likelihood of clogging.1 Through experience, owners gain knowledge about the limits of the pumping capacity of their wells.

In this paper, we study the effect of experience in working with new technology on the behav-

1 A common reason for a drop in capacity of a well is the plugging of holes on the well screens. The amount of water available in a well drops if holes of the screens are clogged. For further information see Van Beek et al. (2009). Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 3 ior of owners of wells. We assume that more experience in using wells enhances knowledge about how to best employ wells, both in static (extracting water to have minimum operation cost) and in dynamic (optimizing extraction path over time) terms. The owners of newly established wells have limited information about best practices in using their technology. Over time, by using different ways of acquiring information, including learning by doing, the owners of new wells learn the actual marginal benefits and costs of groundwater extraction. In the wake of a negative shock to rainfall, surface water becomes scarce. This provides an increased incentive to extract groundwater. Owners of older wells, with greater experience, have a better understanding of the costs of over-extraction and therefore restrict their ex- traction, to sustain the future stream of water. We hypothesize that extraction behavior is different between the owners of new and old wells and that the former extract moreaggres- sively. We expect the difference in extraction to diminish as owners of new wells gainmore experience over time. The overly aggressive extraction hurts the firm itself since it keeps the firm from maintaining its stream of water.2 This behavior will also generate negative externalities to others, exacerbating the common pool problem.

We use three identification methods to find out how extraction behavior differs between newly established and old wells. Our first method employs a fixed-effects model, usinga panel of districts in Iran. Our panel data includes information regarding extraction levels, depth to water, station-based precipitation, and a host of other explanatory variables, all aggregated at the district level. We show that groundwater extraction rises as the growth rate in new wells in a district increases. We also show that a negative shock to precipitation increases the extraction per well and reduces the proportion of planted areas in districts. Positive shocks, on the other hand, raise the number of farms. These results indicate that farmers adjust their planting and extraction decisions based on the availability of surface

2 In the case of Iran, the extraction from wells is weakly monitored. Less than 2 percent of wells have a water meter. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 4 and groundwater.

We then employ data from Iran’s Census of Wells to study differences in behavior at the well level. These data are combined with other sources, such as depth to water and station base precipitation. Our second identification method is a double-difference model. We define a new well as a well that was dug since the start of the last calendar year. We compare the difference in response of new and old wells in locations with no shocks, tothesame difference in locations that experienced a positive (or negative) precipitation shock. This double-difference method allows us to remove potential bias due to the fact that precipitation shocks affect the probability of digging new wells, which can bias OLS estimates. Moreover, extraction from new wells can be larger than from older wells due to factors other than experience, i.e. more efficient devices, and depth. Using this model, we find thatnew wells extract more than older wells and the difference in extraction increases in areas that experience a negative shock to precipitation.

We employ two strategies to find the speed of acquiring knowledge about using new technol- ogy. First, we compare wells that are 2, 3, 4, and 5 years old with their older counterparts. This shows that the results we explained above hold only for very new wells, one year old or less. Foster and Rosenzweig (1995) show that Indian farmers fully adopt a new technology in less than five years. Our results indicate that learning how to use takes less timethan the decision to adopt for a new technology. Second, we compare new wells with ages less than or equal to 2, 5, 8, 10, and 30 years. We observe that there is a significant difference between new wells and the wells in all of these categories. Moreover, as we increase the age of the comparison group, the difference in extraction between new and older wells increases.

Our third identification strategy is the nearest-neighbor matching method. Using this method, we find matches for each new well based on geographical location and water flowof wells, with a handful of other characteristics used as independent variables to measure the Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 5 distance. We use the nearest-neighbor matching in several specifications. In our preferred specification, we use a weighting scheme in the distance formula to make our matched wells very similar in terms of geographical location and flow of water. We increase the weight for geographical location to make sure that groundwater is used under similar weather, soil, and other location-specific conditions. The weight for flow of water is raised to match anewwell with older wells that have a similar capacity of providing water regardless of the location and equipment of the wells. All of our specifications confirm our findings from the above methods that new wells extract more groundwater than old wells. Moreover, we provide evidence that owners of older wells manage their inter-temporal need for water more efficiently compared to owners of new wells. We find no evidence for the diffusion of knowledge between owners of old and new wells.

Our results can guide policy in several ways. The literature shows that quota-based sys- tems are more effective than price systems for prolonging the common pool resource’s life (Madani and Dinar 2013). Our findings suggest that the firms that do not know their true marginal abatement cost, increase the extraction costs for other firms operating on the same common pool in the short term. In the long term, these firms pose a higher risk to the life of aquifer by extracting more aggressively. Incomplete information about private marginal benefit and marginal cost result in equilibrium extraction that is higher than the Nashequi- librium under the common pool problem. Assuming the existence of a quantity instrument for regulating groundwater extraction, the presence of firms with uncertainty about their marginal abatement cost leads to productive inefficiency. In this case, a policymaker needs to employ additional instruments to increase the speed of learning. Policy instruments such as education outreach programs, mandatory training as part of the licensing process, and en- hancing the diffusion of knowledge by improving networking among farmers that are shown to be effective in acquiring new technologies (Carter and Batte 1994, Feder et al. 2004, Wu Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 6 and Zhang 2013), can also be effective for reducing the time of adjustment.

Our results also have important implications for studies regarding climate change adaptation. Improving agricultural technology is an important strategy for mitigating the impact of climate change (Lobell and Asner 2003, Howden et al. 2007, Olmstead and Rhode 2011, Endfield 2012, Parent et al. 2018, van Etten et al. 2019). A strand of literature studies the adoption of new technologies in agriculture with a focus on factors that affect the decision to adopt and speed of adoption of new technologies. The adoption rate is also considered in models that are used to predict the impacts of new technologies on climate change. Our paper contributes to this literature by emphasizing the role of experience and learning in the ability of farmers to employ new technologies and thus should be considered in climate change scenarios.

The paper is organized as follows: The next section provides the framework of groundwater and technology adoption. Data sources are introduced in Section 1.3, and Section 1.4 dis- cusses our identification strategies. Section 1.5 presents results together with a discussion of them, and Section 1.6 concludes.

1.2 Theoretical framework

One of the challenges for sustainable development in the face of climate change is to find strategies to cope with the increased variability in weather variables such as precipitation and temperature. Agriculture is one of the first sectors to suffer from variable rainfall. In India, for example, Mousinram near Cherrapunji experiences a shortage of water, even though it receives one of the highest rainfalls in the world (Kumar et al. 2005). Variable rainfall increases the dependency of agriculture on groundwater. In the case of Iran, the northern part of the country, which has the highest level and lowest variation in rainfall, Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 7 has developed the largest density of wells in the country. The management of groundwater resources is therefore of vital importance in the climate change era (Vaux 2011; Foster et al. 2015).

Technology plays an important role in the ability of agriculture to adapt to the changing climate. New technology in the form of resistant crops, irrigation and plantation methods, and use of groundwater enables farmers to maintain production when local temperature and rainfall patterns change. There is often a significant interval between the time a new tech- nology is developed and available in the market and the time it is used widely by producers (Sunding, Zilberman, et al. 2001). Diffusion of new technologies depends on factors such as knowledge and availability of information about the new technology (Shiferaw and Holden 1998, Tambo and Abdoulaye 2012, and Khataza et al. 2018), perception of farmers about the performance of new technology (Adesina and Baidu-Forson 1995), schooling, access to credit, contact with extension agent (Abdulai and Huffman 2005), farmers’ networks (Conley and Christopher 2001) and even gender of farmers (Doss and Morris 2000). Still, even after adopting a new technology, farmers need to learn how to employ it in practice. Studies on learning, from this point of view, are scant in the literature. Foster and Rosenzweig (1995), for example, show that as farmers’ experience with new technology increases, their profit rises as well.

Farmers will find out about the true costs and benefits of using new technology through learning by doing. A related notion of learning by doing, or learning by experience, has been the subject of many studies in the theory of consumer choice. An experience good is a new product for which consumers do not have perfect knowledge of true costs and benefits, and they have to learn about the new product they are using (Shapiro 1983; Cremer 1984; Milgrom and Roberts 1986; Farrell 1986; Bergemann and Välimäki 2006). This view of learning is close to what this paper is trying to provide evidence for. Farmers who adopt Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 8 a new technology, like groundwater use, need to learn about the short-term and long-term costs of production, how to maintain their crops and other related questions about the new technology. In this paper, we show that the true costs of using groundwater, in terms of the ability to maintain extraction even in the short-term, are typically not known to a farmer who has recently adopted the new technology (groundwater use). A study close to our paper is Hintermann and Lange (2013), which uses the experience good framework to show how taxes and/or subsidies can be used to help consumers learn the true costs of replacing a more polluting technology. The main objective of Hintermann and Lange (2013) is to develop a mechanism to increase the adoption of a new technology. By contrast, our goal is to show that learning can make farmers more efficient in the use of an already adopted technology.

There are several costs associated with the over-extraction of groundwater that farmers have to learn about to maintain their production and best use the newly acquired technology, i.e. wells. First, by extracting more water in one season, say Spring, there will be less water available locally to extract in the coming season (Summer). Therefore, for each farm, there exists an optimum path of extraction that is needed from planting to harvest. Second, over-extraction increases the depth to water for the well locally (The height for the cone of extraction increases, see 1.1.) An increase in depth to water increases the cost of extraction by raising the cost of fuel (electricity) in two ways: water needs to be pumped for larger height and longer hours. Third, if the depth to water approaches the well’s depth, over-extraction would increase the probability of clogging the well and hence, the cost of maintenance of the well.

Although part of the information that the owner of a well will need to acquire is available publicly or through networks, there remains a lot for the farmer to learn by doing. The information that one needs is well-specific. Two wells that are close to each other may have different depths, purposes, pumps, structures, etc. Therefore, the optimal (private) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 9 extraction level of one well may not be optimal for its neighbor. Even if two wells were similar in all aspects, their owners may not trust the information they receive from more experienced neighbors (Foster and Rosenzweig 1995). In that sense, farmers may even systematically ignore some information that helps them to adapt to the new technology. Farmers must gain at least part of this information through experience and learning by doing.

1.3 Data

We use five sources of data in this paper. The first set of data is a panel data constructed from two consecutive agricultural censuses eleven years apart: 2003 and 2014. The next set of data is a census of all wells in Iran, which was collected between 2003 and 2013. We also use piezometric data to estimate the change in the groundwater level and a time series of groundwater extraction, which we then match with our panel data. Finally, we use precipitation and temperature data from local weather stations in Iran. In the following subsections, we explain all parts of our data in more detail.

1.3.1 Panel

The first set of data we use is a panel constructed from the agricultural census collected by the Statistical Center of Iran (SCI). The agricultural census is a country-wide census which includes not only agricultural activities, but also information from all villages, in terms of water resources, availability of agricultural machinery and services from it, and livestock. Like any other census, agricultural censuses are costly and normally conducted about every ten years in Iran. SCI conducted this census in 1971, 1988, 1993, 2003, and 2014. Although SCI did collect the census data almost every decade, for consistency of variables Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 10 and availability reasons, we only use the last two rounds.

Since most of the collected variables have been reported at the district level, we set the cross-section dimension of the panel at the district level. The census is based on the official administrative divisions in each particular year. After ten years, we have one more province (Iran had 30 provinces in 2003 and 31 provinces in 2014) and 113 more districts (districts increased from 315 to 428). We merged all new divisions with their previous components and computed all variables for the resulting balanced panel. In our regression analysis, we use only 298 districts, due to a lack of other variables, mainly groundwater extraction data, as explained in subsection 1.3.3 below. Table 1.1 summarizes the main variables in our regression analysis. As one can see from the table, in 2014, more districts experienced a negative shock and fewer districts experienced a positive shock. We also defined two variables to summarize past positive or negative shocks. The number of positive (negative) precipitation shocks in the past three years counts the number of positive (negative) shocks for those districts that did not experience negative (positive) shocks in the same time frame. The number of shocks is defined based on the three past years, so it does not overlap with the positive (negative) precipitation shock indicator.

As can be seen in Table 1.1, in 2014, a smaller share of land was assigned to agricultural activities, and a bigger share of the assigned land was actually under cultivation. Moreover, from 2003 to 2014, more land was assigned to gardening and slightly more land was irrigated as opposed to rain-fed. While the area under wheat production stayed the same, a higher percentage of wheat land was rain-fed in 2014. The share of the rural population decreased, and the number of wells, as well as the density of wells per 푘푚2 increased between 2003 and 2014. Since total extraction stayed almost fixed, extraction per well decreased and extraction per irrigated hectare increased.

To provide more context for agriculture in Iran, the cultivated area has remained fairly Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 11 constant over a long period, based on the agricultural census. The total planted area of the country has declined from 13,073 thousand Hectares (Ha) to 12,744 thousand Ha between the 2003 and 2014 rounds of the Iranian Agricultural Census. The irrigated area in the same period has risen from 6,360 thousand Ha to 6,426 thousand Ha. Both these measures show almost no change, but they do show a slight substitution toward more irrigation. In the 2003-14 period, agricultural output grew at a rate of 1.74 percent per year on average3.

1.3.2 Wells census

We use the census of wells collected by the Iran Water Resource Management Company (IWRMC)4 which provides information about all wells in the country. IWRMC has collected these data from 2003. In collecting these data, IWRMC surveyed two to five provinces each year and collected information about all wells in those provinces and then moved to other provinces. Thus, we have a census that is spread between the years 2003 and 2013.5 Since the data for wells located near the borders of the country are not publicly available, and the released data only cover years up to 2013, the total number of wells in our data is 729,220, of which only 526,917 (72.26%) are operational. The rest are either being drilled (0.3%), not equipped (5.15%), have a technical problem in well structure (0.32%), a technical problem with equipment (1.1%), were shutdown at the time of the visit (4.84%), temporarily shutdown (4.48%), had no water (4.76%), were abandoned (4.67%), or they had some other non-working status (2.13%).

The main use of the extracted water is for an agriculture purpose, which is either farming

3 National Accounts, the Central Bank of Iran. 4 IWRMC is a public entity in the ministry of Energy which is responsible for the management of water resources. (Link:http://www.wrm.ir/) 5 IWRMC has updated these data by adding the information of new wells established beyond 2013, however, the publicly available micro data ends in 2013. As of 2019, the data includes about 788,514 wells in Iran. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 12

(84.95%), greenhouse planting (0.27%), or growing herbs (0.08%). The rest of the wells are used for drinking water (5.20%), livestock, fisheries and landscaping (4.73%), or in industry (2.58%) and the service sector (2.19%).6 Since our objective is to model extraction behavior in the agricultural sector, we only analyze the wells that are used (at least partially) for agricultural activities (farming, greenhouses, and herb production) and have a non-missing extraction value. Considering only this group of wells, the number of wells in our sample is reduced to 407,824. A well’s age is another important variable for us that we are unable to compute for 6,908 wells (either the survey year or the well’s establishment year is missing). Finally, some wells lack some other explanatory variables included in our regression analysis (mostly the well’s depth). Thus, most of our regressions that use the well census have 395,306 observations.

Table 1.2 summarizes the main variables we use in our regression analysis in part 1.5.2. Here, we briefly explain the most important statistics. The variable slope shows that most of the wells are in flat areas. This is consistent with the fact that most of the wells in Iranareused for flooding irrigation systems.7 Very few wells are in urban areas; the majority are in rural areas or farms which are located outside of towns. Areas of the country are heterogeneous in terms of well density as well; some wells have more than 1,500 other wells in their 10 km radius neighborhood, and some others have many fewer than this number. Although we will talk extensively about precipitation shocks, it is obvious at a glance that many of the wells experienced a negative precipitation shock, both in the survey year and in the three years before the interview date. Moreover, very few farmers use wells for irrigation in only one season of the year.

One variable that is important to mention here is “Extraction for agriculture purposes.” As

6 The listed categories are those IWRMC listed as the usage of the wells. Since we work only with the wells that are at least partially used for agriculture purposes, we do not explore the other mentioned categories. 7 Flood or surface irrigation is a practice in which an entire field is covered with water. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 13 mentioned above, extracted water has different usages, and we model only the part that is used for agriculture. There are some cases where the well has mixed usage. For example, one might own a well that is used for drinking in a rural area and use the remaining water for farming or a greenhouse. Or, a well could be used for a combination of industry, service, and farming. Fortunately, for almost all wells, the percentage of water that is devoted to each activity is specified. In our regressions, we use both total extraction and “Extraction for agriculture purposes,” which is the share of the total extraction used for agricultural activities.

1.3.3 Piezometric data and aggregate level water consumption

Water extraction depends on the availability of water under the ground. IWRMC manages and reports the piezometric data (monitoring wells) and has been recording these data since 1964 (When there were only 10 piezometric wells in the country). More recently, these data have been gathered for more than 13,000 wells, to monitor the groundwater levels. As mentioned in subsection 1.3.2, IWRMC does not report any data (neither well records nor piezometric data) in areas close to the borders of the country. That is why we only have 12,912 piezometric wells available in our data, all of them marked as non-border in the data from IWRMC.

The monitoring system needs to be explained. IWRMC divides the country into 6 major water basins, 30 smaller aquifers (second degree), 367 zones, and 11,138 localities. As one can imagine, each locality is a small area. The data are available for multiple wells for each locality, and 90% of the time the depth to water8 in the specific piezometric well is measurable (the well works). There are multiple reasons why a piezometric well might not

8 Depth to water in these data is the distance from the land surface to the water in the well when not pumping. This definition is close to static water level for a well. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 14 work. The most important reason is that the well dries out (40%). Anytime a well does not work, IWRMC digs another well in the same locality. In this paper, we connect each well to the nearest piezometric well which has at least four consecutive years of data. More than half of the wells in our census are connected to a piezometric well that is less than 30 km away from them.

Another source of data provided by IWRMC is the total amount of water extraction and consumption from underground and surface water. These data are collected using a sam- ple of wells and natural springs in each area, information from all dams and rivers, and finally, precipitation and evaporation estimates. Researchers have studied extensively the relationship between precipitation and groundwater (Sangrey et al. 1984; Park and Parker 2008), the recharge rate of groundwater (Cherkauer and Ansari 2005; Lorenz and Delin 2007; Nolan et al. 2007), and groundwater flow (Zhou and Li 2011). In a nutshell, usually, a wa- ter accounting framework is used with the help of hydrological equations to compute the change in the reservoir of water available in each area. For water accounting, precipitation, surface and underground flow coming into the reservoir, and water transferred to thearea are the sources of water. Evaporation, outgoing flows from surface and groundwater, and transferred water out of the area are the main ways water leaves an area. IWRMC uses the same approach to estimate groundwater consumption by considering groundwater flow to the area, precipitation, incoming flow from surface waters, irrigation, drinking water wastes from rural and urban areas, water waste from the industrial sector, the outgoing flow of the groundwater from the area, evaporation, and extraction from wells, qanats, and natural springs (IWRMC 1991; IWRMC 2015).

We compute the amount of groundwater extracted for each district based on the extraction reported for zones. When boundaries of zones (defined by IWRMC) do not coincide with the boundaries of districts (defined by SCI), we distribute the extraction for a zone between two Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 15 neighboring districts based on their shares of the zones’ combined area. Another important modification is to drop some districts because IWRMC does not report the extraction for areas close to the borders of the country.

The dependency of agriculture on groundwater, together with higher demand for it due to the growing population, has resulted in depletion of groundwater resources. Figure 1.4(a) shows the average depth to water for the whole country and six major water basins (Figure 1.4(b)). The depth to water has steadily increased in the 2002-14 period, and the rate of depletion has increased since 2008. During 2002-14, depth to water has increased from 27.6 meters to 33.6 meters with a yearly increase of 1.7 percent on average. Figure 1.4(b) shows that the depth to water has increased faster in the East, Central plateau and Karakum basins.

1.3.4 Weather

In this study, we use station-based precipitation and temperature data. Available data starts in 1952, with 17 weather stations for precipitation and temperature. Currently, Iran Meteorological Organization (IRIMO) publishes the weather data from 362 stations on an hourly basis. The monthly precipitation and temperature data are also available with a short lag. For our panel data models, to have a consistent long-term mean of precipitation, we take the average precipitation and temperature in a district from the stations that have continuous data between 1990 and 2014. After computing the precipitation, we define the positive and negative shocks for each district. In a particular year, the negative shock indicator takes value of one when the rainfall is more than one standard deviation below the average rainfall in the 1990-2014 period. Similarly, if the rainfall is higher than one standard deviation above the mean, the variable for positive precipitation shock takes a value of one. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 16

For studying the effects of precipitation shocks on the behavior of extraction from wells (well level data), we connect each well to the closest weather station and use the precipitation and temperature data from that station. Again, we use only the stations that continuously report data between 1990 and 2014. Tables 1.1 and 1.2 include the percentage of districts and wells that experienced a positive shock and the percentage that experienced a negative shock.

Precipitation dropped from the 1980s to the 2010s. Yearly precipitation that averaged about 400 mm in the 1980-84 period declined to 320 mm in 2010-14 (see Figure 1.2). During most of the focus period in this study, 2003-2014, rainfall was below its long run average, with relatively higher levels during 2004-08. The weather has become more volatile over the same period. Figure 1.3 shows the five-year moving average of the percentage of the country’s area that observed a positive or negative shock. Shocks to precipitation, especially negative shocks, have become more frequent in recent years. In the 1980s, the percent of the area of the country that was affected by negative shocks was well below 20 percent. This number increased in recent years and passed 25 percent in the 2000-02 and 2011-12 periods. Predictions suggest that weather variability and shocks are going to increase even more in the foreseeable future (World Water Assessment Programme 2012).

Located in an arid region of the world, Iran has a rainfall that is lower than the world average. Low rainfall has made agriculture in Iran very dependent on irrigation. Nearly half of the planted area in the country was irrigated in 2014, and more than 91% of rural agglomerations used wells for irrigation. In recent decades, wells have been used more intensively in response to the increased demand for water. The number of wells in the country has increased from 468,049 in 2003 to 788, 514 in 2014 (Iran Water Resource Management Company, 2019). Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 17

1.4 Identification strategy

In this paper, we test the hypothesis that, under the same conditions, owners of new wells extract more groundwater than presumably more experienced owners of old wells. We employ the data aggregated at the district level as well as well level data. Our aggregated data allow us to form a panel data and employ a fixed-effects model. The well level data allow usto study the behavior of individual extractors in regression-based and matching estimators.

1.4.1 Panel data fixed-effects

We first estimate the relationship between growth of new wells and extraction at the district level. We employ the panel data fixed-effects model:

′ 푌푖푡 = 훼0 + 훼1푊 푒푙푙퐺푟표푤푡ℎ푖푡 + 훽 푥푖푡 + 훾푡 + 휃푖 + 휖푖푡, (1.1) where 푌푖푡 is the outcome of interest for district i in year t. The outcomes are measures for groundwater extraction and planted area. The six measures that we use are as follows: Total extraction, extraction per well and extraction per unit of surface (푘푚2) are measures for extraction, and total planted area, the ratio of planted area and number of planted farms are our measures for the planted area. 푊 푒푙푙퐺푟표푤푡ℎ푖푡 is the growth rate in the number of wells in district i in year t. 푥푖푡 is a vector of district characteristics such as indicators for positive and negative shocks (defined in Section 1.3), temperature and its lags, groundwater level and its change in the past periods, percentage of irrigated land, total agricultural land and ratio of agricultural land to district area, the share of gardens in agricultural land, percentage of area under wheat production, population and share of rural population at the district level, as well as province-level characteristics such as share of the population in each education group, and share of farms in different planted area levels. 훾푡 and 휃푖 are the year and district fixed effects, respectively. This model allows us to find the relationship between Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 18 the growth of wells, or the rate by which new wells are dug, and extraction in districts.

1.4.2 Double-difference

Our district level estimates, as explained in Section 1.5 below, show that the districts with higher shares of newly established wells also have larger groundwater extraction. In this paper, we are interested in finding whether this is associated with the extraction behavior of the new wells rather than just a positive correlation between digging wells and high extraction in areas with ample groundwater. To establish the relationship between new wells and extraction, we use several methods using the data at well level. First, we use the disaggregated well data in the following least-squares model:

′ 푌푖푡 = 훼0 + 훼1푃 표푠푆ℎ표푐푘푖푡 + 훼2푁푒푔푆ℎ표푐푘푖푡 + 훼3푁푒푤푊 푒푙푙푖푡 + 훽 푥푖푡 + 훾푡 + 휖푖푡. (1.2)

Here 푌푖푡 is the yearly extraction (total extraction or extraction for agricultural purposes) of well i in year t. 푁푒푤푊 푒푙푙푖푡 is an indicator which is equal to 1 if the well was dug in the past twelve months. 푥푖푡 is a vector of characteristics of the well such as depth, area irrigated by the well, distance to the nearest city, number of wells in 10km radius, and indicators for irrigation method and type of crop, as well as yearly temperature and district fixed effects.

In order to find more reliable estimates for the difference in extraction from newwellsand old wells, we compare this difference in locations with no shock to the same difference for locations with positive (or negative) precipitation shock. This double-difference method uses the exogenous variation in shocks to identify the difference in the extraction from new wells compared to that of old wells when they are exposed to precipitation shocks. This model can be shown with the equation:

푌푖푡 = 훼0 + 훼1푃 표푠푆ℎ표푐푘푖푡 + 훼2푁푒푔푆ℎ표푐푘푖푡 + 훼3푁푒푤푊 푒푙푙푖푡+ (1.3) ′ 훼4푁푒푤푊 푒푙푙푖푡 × 푃 표푠푆ℎ표푐푘푖푡 + 훼5푁푒푤푊 푒푙푙푖푡 × 푁푒푔푆ℎ표푐푘푖푡 + 훽 푥푖푡 + 훾푡 + 휖푖푡. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 19

In this model, the coefficients of interest are the interaction terms of new well and shocks.

1.4.3 Nearest-neighbor matching

The regression analysis defined above compares the average extraction of new wells with older ones controlling for observable characteristics and unobservable time effects. The iden- tification in this model comes from the exogenous variation in shock exposure whichallows us to identify the difference in the behavior of new wells. A matching estimator allows usto calibrate our counterfactual selection for new wells. We employ a nearest-neighbor matching method to find the average treatment effect on treated (ATT) in which our treatment vari- able is the indicator for a new well. Using a distance function to measure proximity, we find the old wells (comparison) that are closest to a given new well based on well specific variables (such as depth, age, ...), farm characteristics (such as planted area, crop type, ...), weather variables (such as average temperature, positive and negative precipitation shocks, ...) and province-level characteristics (such as total and share of agriculture’s value-added).9 An advantage of using the nearest-neighbor matching method over the regression-based models is that we can find comparison wells that are very similar to new wells in terms of location- related attributes such as weather and soil characteristics by using a weighting scheme that puts a larger weight on geographical location in the distance function. Likewise, we raise the weight for the water flow of wells to control for unobserved heterogeneity that mayexist due to equipment and hydrological variations and remove potential bias. In all of the spec- ifications in Section 1.5.3 we use the bias-adjusted matching estimator proposed by Abadie and Imbens (2011).

9 For a complete list of variables see Section 1.5.3. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 20

1.5 Results

We present our results in two sections. First, in section 1.5.1, we describe our findings for estimation of Equation 1.1 with the panel of agricultural censuses and then, in section 1.5.2, we present the results from estimating the census of wells, using Equations 1.2 and 1.3. Then, in section 1.5.3 the results for the matching estimator are discussed.

1.5.1 Panel

In this section, we present the results from estimating a fixed-effects model with the agri- cultural censuses that were collected by SCI in 2003 and 2014. We use Equation 1.1 as our regression setup, and then we use total extraction from wells, extraction per well and per unit of surface (푘푚2) and their logarithmic (Ln) forms as the dependent variables. Moreover, we use six sets of control variables for all models. The first set is weather-related control variables, including precipitation shocks (negative and positive) in this year, the number of positive and negative shocks in the past three years (for those that did not experience mixed shocks in the past three years), and yearly temperature with three lags. The sec- ond set of control variables is province-level variables, including share of agricultural and industrial sectors’ value-added in total value added of the province, ratios of different land sizes to the total area under cultivation (land sizes are: less than one hectare, 1-5ha, 5-20ha, 20-50ha, 50-2000ha, 2000ha and more) and ratios of farmers in different education levels (illiterate, unofficial and primary school, less than high-school, tertiary in agricultural and non-agricultural studies).

The third set of control variables includes variables that present district-level agricultural activities, including share and the total amount of agricultural land, ratio of gardens to agricultural land, ratio of irrigated land, the share of land used for wheat production and Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 21

share of irrigated wheat production, the total population, and the ratio of rural to total population. The fourth set of control variables includes the ratio of irrigated land, and share of land used for wheat production. The next set of variables we use is the relative change in the depth to water in each district and its lags. And finally, we control for the number of wells per 푘푚2, the growth rate of wells (new wells/total number of wells), and a dummy variable that indicates whether, during the panel years, this district split into two or more districts or not.

Table 1.3 provides the estimation results of a fixed-effects model with dependent variables total extraction, extraction per well and per 푘푚2. In columns 4-6, we use the same dependent variables in the log form. Total extraction is the total amount of extraction from all wells in each district of Iran. None of the reported variables in the table are statistically significant for total extraction (column 1). The distribution for total extraction is highly skewed, so is the distribution for the other two dependent variables in this table.10. We, therefore, use the log form of dependent variables in our models (columns 4-6). Variables that relate to precipitation shocks are statistically significant only for extraction per well.

Our main variable of interest is the rate of growth in the number of wells. As one can see, the growth rate in the number of wells is positive and statistically significant in all models. For the log of total extraction, this significance is a trivial result. If we have more wells, they collectively extract more water. But for extraction per well and extraction per unit of the area this positive relationship raises three main possibilities. First, new wells systematically extract more water than the old wells, and that is why a positive change in the number of wells increases extraction per well. Second, new wells are dug in areas with larger water availability. Third, establishing new wells raises the competition among all wells in the region and all of them start extracting more water. In subsections 1.5.2 and 1.5.3 we provide

10The kernel density estimation of the extraction per well’s distribution presented in Figure 1.7(a) and 1.7(b) in the appendix. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 22

support that the first possibility is a more plausible one.

Another interesting finding is the reaction of farmers in terms of agricultural activities tothe weather shocks at the district level. Although Hornbeck and Keskin (2014) claim that using groundwater makes farmers more vulnerable to weather shocks, in an arid area like Iran, it is more plausible that farmers shield their business by groundwater. Table 1.4 presents three variables that indicate levels of agricultural activities. The ratio of planted is the ratio of farm land (farm or garden) to the amount of arable land (column 1).11 In other words, it separates the amount of fallow from planted land. Our results show that negative precipitation shocks reduce the ratio of planted land. Considering that a large share of agriculture in Iran is rain-fed, it is obvious that a negative precipitation shock decreases the amount of land that farmers cultivate. The total planted area (farm land), the dependent variable for models in column 2, is the numerator of the previous index. The result of this model is consistent with the previous model (column 1). The number of planted farms is another useful index. Small land ownership is a common phenomenon in Iran, and column 3 shows that more positive precipitation shocks increase the number of farm under cultivation. Bertone Oehninger et al. (2016) find an extensive version of our result by finding the effect of climate variableson demand for water, crop selection, shares of different crops, and adapting technology using a variety of model specifications and climate variables. We use the results of this tablewhen we explain our findings in the next section.

11There is a small discrepancy between the formal definition of agricultural land and arable land with the one we use here (Look at the definition from OECD, for example). The formal definition of both terms includes pastures, but our data excludes the information for pastures. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 23

1.5.2 Well level estimations

In this section, we answer some questions that arose in the previous section and provide more evidence for our hypothesis. We use Equation 1.2 to estimate different models. We have two main dependent variables, total extraction from each well in each year and the amount of extracted water that is used for agricultural purposes. We also estimate the same models with these dependent variables in log form. Before explaining the results, we briefly explain the regression setup and the dependent variables. We estimate the models using the data for wells that were active during the survey years, and at least partially, were used for agricultural activities. Moreover, since our hypothesis is about the age of wells, we exclude the wells for which either the survey year or the establishment year is missing. Finally, we report district-level clustered standard errors for all estimations.

In all regressions, we have four sets of control variables. First, weather-related variables which include precipitation, positive and negative shocks (defined based on precipitation), and temperature. We control for temperature in all regressions, but we include either precip- itation or its shocks in each regression. We also define a new variable, which is the number of positive (negative) precipitation shocks in the past three years for those who did not experience a negative (positive) shock in the same period. Second, we control for well/farm- specific variables such as irrigation method, availability of a water meter, the existence of water tank, depth, elevation, slope, distance to the nearest city, number of wells in a 10 km radius, area of the farm, and type of crop. With respect to the output of farms, we use an indicator for each crop, including wheat, rice, vegetable, oilseeds, saffron, fruit, nuts, other cereal, tea, and cotton. Third, we control for the number of seasons that the well works, share of agriculture, and industry in the total province value added in the specific survey year, and year and district fixed effects. Finally, we have interaction terms for the aquifer indicator and type of output (wheat, rice, vegetable), the log of elevation, wells’ age and Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 24

depth. These four categories of control variables are present in all of our models, but to preserve space, we present only a shorter version of the tables, containing our variables of interest.

Table 1.5 shows that, compared to the old wells, the owners of new wells extract more water. This holds at both the level and log form of the dependent variable, and for total extraction and extraction for agricultural purposes. In terms of magnitude, in the models in columns 1 and 2, the extraction from a newly established well is 11 and 12 percent more than an old well for total extraction and extraction for agriculture purposes, respectively. (The mean extraction for total extraction is 88,036푚3 and for extraction for agriculture purposes is 87,061푚3.) Considering the models with the dependent variables in the log form (columns 3 and 4), the extraction is 17.3 and 18.8 percent more for new wells. Previous studies have found that farmers change their crops when they use groundwater in a way that increases their water consumption (Hornbeck and Keskin 2014; Ji et al. 2018; Ji and Cobourn 2018). In our models, we have controlled for type of crops to capture the effect of crop selection.

A positive shock to precipitation increases extraction, but there is no statistically significant impact of a negative shock. Conversely, the number of positive shocks in the past three years does not have a statistically significant effect on extraction, while the number of negative shocks decreases extraction from the wells. In light of the results in Table 1.4, positive precipitation shocks encourage farmers to plant larger areas of land and negative precipitation shocks induce them to plant smaller areas. As farmers plant less land, they extract less water from wells.

We found that newly established wells extract more water than old wells. We test this hypothesis more rigorously using the model in equation 1.3, the results of which are presented in Table 1.6. Table 1.6 shows that the owners of new wells extract even more when they experience a negative precipitation shock. The first four columns of this table are the same Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 25 as Table 1.5, except that they do not have the dummy variable for a new well. Columns 5 to 8 are also the same as Table 1.5, except that, other than the indicator for new well, they include interaction terms between the new well and precipitation shock indicators. Looking at columns 5 and 6, we find that the extraction from newly established wells is more than older wells in general, but this difference becomes smaller when there is a positive precipitation shock. Moreover, columns 7 and 8 show that in the log form, the extraction from new wells is again more than old wells, and the owners of new wells extract even more when there is a negative precipitation shock. When we use extraction for agricultural purposes as the dependent variable (columns 2, 4, 6, and 8) the coefficients become generally larger.

One might raise the issue that, in terms of statistical significance, the results of Table 1.6 in columns 5 and 6, do not mimic the results of the log forms on the columns 7 and 8. This happens because the distribution of extraction is highly skewed.12 In Table A1 of the Appendix, we present four robustness checks for our results in Table 1.6 and show that if we tame the skewness of the distribution with functions other than the log, our results in columns 7 and 8 still hold.13 We have done further robustness checks in which we change the definition of new wells to wells with age less than two and then less than threeyears. The results of these checks are consistent with our findings presented here.14

The double-difference framework also helps us identify the difference in the extraction from new and older wells. Assuming that the unobserved heterogeneity between new and old wells (due to factors such as better equipment, more efficient depth, etc.) is fixed over time, the first difference captures the non-behavioral difference in well yields. We do notexpect that the difference in the delivery from new and old wells due to these unobserved factors

12The kernel density estimation of the extraction per well presented in Figure 1.8(a) in the Appendix. 13Figure 1.8(b) presents the kernel density estimation of two transformations of extraction per well. 14We do not present these results here, but they are available upon readers’ requests. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 26 to change as other determinants, such as weather shocks, vary. However, we observe that negative shocks cause the difference in extraction to increase. This increase in the difference (second difference) is mainly due to the different behavior of the owners of thewells.

Now that we know that owners of new wells extract more than owners of old ones, based on the analysis using precipitation shocks and depth to water,15 we expect to see that both the shocks (especially negative shocks) to have a significant effect on extraction from new wells when the regressions are restricted to the new wells only. Table 1.7 shows that both of the mentioned variables have statistically significant impacts when the log of extraction is used as the dependent variable.16

One important question that we investigate is how long it takes for owners of wells to adapt to the new technology. In other words, we are interested to see whether our results change if we compare the extraction from wells of different ages. For example, would owners ofa two-year-old well extract more than their older counterparts? Or, would a three-year-old well extract more than older wells? We test this question by estimating Equation 1.2, and instead of all other wells, we compare wells of a specific age to their older counterparts. Table 1.8 shows that none of the owners of 2, 3, 4, or 5 years old wells extract significantly more water than wells with greater age. We include the models with the log forms of the dependent variable in this table as well. However, using the levels of the dependent variables do not change the general picture. One implication of these findings is that the learning happens quickly, resulting in the coefficients of 2, 3, 4, 5 years old variables, become statistically insignificant.

15With a similar setup, we use depth to water to compare the behavior of new and older wells. We again confirm that extraction from new wells is greater than from old wells. Since the depth of a wellisfactor that affects extraction, we divide our sample to deep (deeper than50푚) and semi-deep (less than 50푚 deep) wells. For semi-deep wells, an increase in depth to water leads to an increase in extraction only for new wells. However, extraction from new and old deep wells do not differ. These results are presented in detail in Section A.2. 16In the Appendix, we present the pooled result, and then separate semi-deep (Table A9) and deep wells (Table A10), and the results are almost the same. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 27

We also test whether learning still takes place after the first year of adoption. The coefficients we found so far are the differences between the behavior of new wells and the average behavior of wells of different ages. The question is whether the results stay the same if we compare new wells with wells of a certain age; We do this comparison in Table 1.9, by re-estimating Equation 1.2 and restricting the wells’ age. The dependent variable for the first five columns is the log of total extraction and the dependent variable for the next five columns is the log form of extraction for agricultural purposes. In each column, we keep the indicator for new well and we restrict the wells’ age to 2, 5, 8, 10, and 30 years old. Based on the results presented in table 1.9, as we increase the age limit, the coefficient of interest (new well indicator) gets larger and remains statistically significant. We can compare our original results in Table 1.5 with this table. As we increase the age limit in Table 1.9, the coefficients for new well indicator approaches the ones in Table 1.5.

One might wonder whether new wells have an advantage in location compared to their old counterparts. In other words, if new wells appear only in locations with better access to groundwater, our results might be driven by a bias toward access to the water sources, or location superiority. In Graph 1.9(a), we map all new wells in Iran. Since the number of wells is very large, this distribution is visually the same as mapping all wells in Iran. Numerically, we present the percentage of new wells in each province in Table 1.11. The ratio is small and below seven percent with only two exceptions, Khuzestan and Lorestan provinces, which have high ratios of new wells. Both of these provinces have small numbers of wells (5,958 and 2,642, respectively) and a few new wells create a big jump in the ratio of new wells. We also check the distribution of new wells inside each district (or province). Graph 1.9(b) is a closer look at a district in the north of Iran. As the graph shows, new and old wells are distributed very close to each other. The same condition is true for almost all new wells.

Another way to assess the response to weather-related events is to estimate the relationship Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 28 between the probability of digging a new well and precipitation shocks. We use a linear probability model with the dependent variable being an indicator that is equal to one for wells established in the past twelve months. As explanatory variables, we include the same variables as in equation 1.2. Table 1.10 tests the relationship between weather shocks and the probability of digging new wells. A negative shock in this year and the number of positive shocks in the past three years both have statistically significant effects on the probability of digging a new well. In light of our results in Table 1.4, a negative shock decreases the planted area and a positive shock increases it. As we saw in the extraction discussion (see Table 1.5, for example), less land under cultivation means less need for water, which we can translate into less extraction and a lower probability of digging a new well. The opposite is true for a positive precipitation shock.

1.5.3 Matching

A concern in the double-difference method above is that, after controlling for observables, the average extraction of new wells is compared to that of old wells, some of them may be different, or subject to different location-specific characteristics. A more rigorous wayto control for location-specific factors is to match new wells, with old wells that have exactly the same, or tantamount, characteristics. For this purpose, we explore nearest-neighbor matching between new and old wells. As we previously discussed, new wells do not have visible advantages to old wells in terms of location and availability of water. In Table 1.11, we summarize the distribution of old and new wells in each province of Iran. In graphs 1.10(a) and 1.10(b), one can see the new and old wells are distributed evenly in almost all provinces of Iran. In the nearest-neighbor matching method, we are interested in the ATT, with the treatment group consisted of new wells. For each member of the treatment group, we find (at least) four nearest neighbors from the old wells (control group). Based on the discussion Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 29 in this paragraph, we are assured that each new well can be matched to a reasonable number of old wells with the same attributes.

For nearest-neighbor matching, we follow Abadie and Imbens (2011)17, by setting positive and negative precipitation shocks, number of positive and negative precipitation shocks in past 3 years, number of working seasons, as criteria for an exact match, and using cultivated area, well age, elevation, slope, distance to the nearest city (km), number of wells in 10 km radius, distance to closest well, the share of industry value-added from the province’s total value-added, the share of agriculture’s value-added, average temperature and its 3 lags, positive and negative precipitation shocks, number of positive and negative precipitation shocks in past 3 years and indicators for water meter, reservoir, irrigation method, prohibited drilling zones and type of crop as proximity variables. As the exact matching and proximity variables indicate, here we are using similar variables to the ones used in the regression analysis. We set the minimum number of matches to be 4 old wells for each new well but naturally, there is no maximum. Employing these criteria, for 13,388 out of 13,390 of the new wells, we find at least four matches among the control group.

One interesting feature of our matching is to match on the flow of water. In our regression- based models, some important variables are not possible to control for. For example, geolog- ical attributes of the wells, power of the pump, and the effects of wells’ age on the extraction capacity of the wells. By including the flow of water, we summarize all of these variables since if two wells can provide the same flow, they should have a combination of the attributes above that guarantees an exact yield for the well. Since the flow of water is an important variable to capture unobserved characteristics, in the distance function we raise the weight for this variable by a factor of one thousand. If, after matching on water flow, a new well is extracting more volume of water than their matched old wells, with more confidence we can

17See also Abadie and Imbens (2006) and Abadie et al. (2004). Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 30

claim that more extraction is due to the difference in the behavior of the wells’ owners. We also raise the weight for longitude and latitude to have matches that are geographically close to each other. This will guarantee that location-specific characteristics such as weather and soil quality are very similar. Well’s depth is also an important variable in determining the extraction from wells, therefore we raise the weight for this variable in the distance function by the same amount as the other two variables. We follow Abadie and Imbens (2011) to adjust our matching estimates for potential bias. The bias adjustment regressions include all variables used in the distance function except those in the exact match i.e. positive and negative precipitation shocks, number of positive and negative precipitation shocks in the past 3 years, and number of working seasons.

Table 1.12 presents the results of matching new wells with old wells using the nearest-neighbor matching method and the criteria described above. Column (1) shows the difference between the total extraction of new and old wells. On average, new wells extract 7,921 푚3 more than their older counterparts, which is 10.13 percent more. These numbers are 8,301푚3 and 10.73 percent, respectively, for the extraction for agricultural purposes, presented in column (2). Column (3) shows even a better picture of the extraction behavior of the owners of new and old wells. This column presents the average difference in the extraction of new and old wells between Summer and Spring. Figure 1.10 shows the amount of extraction in each season by type of well. New wells extract more in both seasons but old wells have the ability to significantly increase the extraction for Summertime compared to the newwells. New and old wells increase their extraction from Spring to Summer by 29.05 and 35.46 percent, respectively. This shows that owners of older wells are extracting less in Spring but in Summer they have more capacity to raise their extraction, presumably when they need more water due to the heat. Figure 1.11 gives us an even more clear picture of the effect of experience on managing water resources. One can see that old wells are plantinga Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 31 relatively stable amount of land in different seasons (around 10-11 hectare in each season), but new wells plant around 9 hectares in Spring but their ability to maintain their planted land decrease sharply. One can claim with more experience, owners of old wells distribute their water over seasons but new wells fail to do so.

The matching method described above produces very close matches for new and old wells in our sample. Table 1.13 presents variables to summarize the quality of matching. There is no discrepancy in the year, and the number of seasons worked, all of the wells have the same status in terms of weather shocks, and the mean distance between the peers is 40 km. The difference between water flow is very small; 12% of matched wells have the exact samewater flow as the new wells they are matched to, and the average difference is just0.188 푚3/푠. The same is true for depth; for 9% of the matches the difference is zero and the average difference in depth is less than four meters. We also check the robustness of our results by changing the weights in the distance function. After reducing the weights for water flow, longitude, latitude, and depth to 200, the results are quite the same as Table 1.12. For instance, the Summer-Spring difference is equal to 24.54 with the new weight.

Another way to have matched wells that are very similar with respect to water flow, loca- tion, and depth is to include these variables for an exact match. Since these variables are continuous, we chose the tolerance to be 0.5 and caliper to be 0.51.18 This small number guarantees that a matched well is found and is very similar to the new well. We also include the shock variables in the matching, but not an exact match, and raise the weight for them to a thousand. Employing these criteria, for 13,075 out of 13,390 treatment wells we find at least four matches among the control group. In Table 1.14 we present the results of this model. The results presented in columns (1) and (2) are very close to what we find in our regression analysis presented in Table 1.5, 10,508 and 10,706, respectively. Column (3) shows

18Caliper is the maximum value of the distance function to consider two observations as potential neighbors, and tolerance is the maximum distance for which two observations are considered equal for an exact match. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 32 that the difference between extraction in Summer and Spring is very close to what we present in Table 1.12. The statistics for quality of match for this model are presented in Table 1.15. The matched pairs are more similar in their flow of water, distance, and depth, and slightly less similar in the shock variables. To conclude, even when new wells are matched to old wells that are close to them and have similar water flow, new wells are more extensively used for extraction.

One final possibility to explore is to assess the existence of diffusion of knowledge between farmers. We present two types of evidence to explore the knowledge transfer or learning from others in using the groundwater extraction technology. First, in Tables 1.5 and 1.6, we present the estimates for the coefficient of the number of wells푘푚 in10 radius. Naturally, it is safe to assume that with more wells in the neighborhood, there is a higher chance for the farmer to learn about the technology from their more experienced counterparts. Thus, a negative sign for the number of wells in close proximity could be potentially a sign of knowledge diffusion among farmers. Another channel for a negative sign is that asthe number of wells in close proximity and hence competition for water increases, the local availability of water declines. Our matching method allows us to identify which channel is in effect.

We modify our matching method to address the problem of availability and identify the pure effect of knowledge diffusion. In our first setup for matching, we increase theweight from 103 to 106, for the variable presenting the ability of extraction from a well, i.e. flow of water. To assess the possibility of knowledge transfer, we divide new wells into five groups, in terms of the number of wells within the 10푘푚 radius, less than 20 wells, between 20 and 50 wells, between 50 and 100 wells, between 100 and 150 wells, between 150 and 200 wells, and more than 200 wells. The first group contains 29.5 percent of the matches, the other groups include 25, 25, 10.5, and 10 percent of the matches, respectively. The results of this Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 33 exercise are presented in Table 1.16. As one can see, there is a discrepancy between the extraction from old and new wells in different groups. We use a multivariate test forthe equality of the means of samples to see the statistical significance of the difference between groups. The test reveals that there is no statistical difference between these five groups.19 Thus, there is no evidence to support the diffusion of knowledge between farmers.

1.6 Conclusions

In this paper, we study the role of learning in adopting new technology. We explore how the decision to extract groundwater is affected as farmers learn how to use this technology. We hypothesize that in the initial stage of adopting technology, farmers do not know the true marginal cost and marginal benefit of extraction, so they cannot make efficient decisions regarding the amount and dynamics of extraction. Hence, owners of new wells have different extraction behavior from that of older, more experienced owners of wells.

We use different strategies to study our hypothesis. Our fixed-effects model employs panel data at the district level and Iran’s census of wells, combined with precipitation and depth to water. We find that extraction per well rises with an increase in new wells in adistrict. Moreover, farmers adjust their planted areas based on rainfall and a negative shock to precipitation causes farmers to reduce their cultivated area.

We then use well level data and show that owners of new wells systematically extract more water than their older counterparts. We also exploit the exogenous variation in precipitation shocks to study the different extraction behavior of owners of new and old wells in adouble- difference model. This approach allows us to control for unobserved heterogeneity, helping

19Stata provides four different tests for the test for equality of mean between multiple groups. Theyare Wilks’ lambda, Pillai’s trace, Lawley-Hotelling trace, and Roy’s largest root. All of these tests fail to reject the null that the means for groups are equal. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 34 us to estimate the effect of learning. We show that owners of newer wells intensify their extraction when there is a negative rainfall shock, and they do the opposite when there is a positive shock. We show that the difference in extraction persists when new wells are compared to wells at different ages and that the older the mean age in the comparison group, the larger the difference in extraction.

The nearest-neighbor matching estimator is the last method we use. We find exact matches for new wells based on geographical location and flow of water of wells. This allows usto control for unobservable variables such as pumping equipment and hydrological character- istics. Our estimates show that extraction from new wells is more than older wells. This result is persistent as we change the weights of variables in our distance function. Moreover, we show that experienced owners change their extraction and planted areas less than the new owners.

Our paper emphasizes a new aspect of adopting technology by focusing on the learning process that is required to use technology more efficiently. The role of knowledge and learning has extensively been studied in the literature on technology adoption, however, we are among the first to provide evidence for learning in adapting to new technology. In light of our results, the efficiency of the market and non-market instruments is not guaranteed. Uninformed inexperienced farmers undermine the efficiency of market-based instruments, such as price and quantity instruments, in managing groundwater extraction. Our results emphasize the role of policies such as information campaigns, training farmers, and promoting farmers’ networks in achieving private and socially optimum outcomes.

Our findings have important implications for climate change studies. Mitigating the adverse effects of climate change in agriculture requires farmers to adapt to new technologies. The adaptation takes time, and the success of adaptation strategies depends on the speed of learning. Our paper contributes to the literature on climate change by suggesting that the Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 35 behavior of farmers should be taken into account when predicting the effects of climate change. Our findings indicate that when water becomes more scarce, e.g. during adrought year, experienced farmers tend to extract relatively less. This is a favorable behavior since it permits a more sustainable stream of water. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 36

1.7 Graphs

Figure 1.1: Structure of a well

Note: Pumping water level is the distance from the land surface to the water in the well while pumping. Static water level is the distance from the land surface to the water in the well when not pumping. Source: groundwater Information Center, Montana Bureau of Mines and Geology Data Center

Figure 1.2: Mean yearly precipitation in Iran

Note: Five years moving average. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 37

Figure 1.3: Percent of Iran’s area affected by shocks

Note: Five years moving average. Positive (negative) shock is when precipitation in the last year is one standard deviation more (less) than its 45-year average

Figure 1.4: Depth to water in Iran

(a) Whole country (b) Major water basins Source: Iran Water Resource Management Company (IWRMC), 2017. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 38

Figure 1.5: Geographic dispersion of shocks in Iran

Note: Panel A shows the distribution of precipitation shocks in 2002, and Panel B is for 2006, Panel C is for 2010 and Panel D is for 2014. Source: Iran Meteorological Organization (IRIMO) local station base precipitation and authors’ calculations. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 39

Figure 1.6: Change in piezometric water level in selected periods

Note: Panel A is change in water level from 2002 to 2006, Panel B for 2006-10 and panel C for 2010-14. Source: Iran Water Resource Management Company (IWRMC) and authors’ calculations.

Figure 1.7: Distribution of extraction per well and its log form at the district level

(a) Extraction per well (b) Log of extraction per well and Normal density Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 40

Figure 1.8: Distribution of extraction from wells and its transformations in the census of wells

(a) Extraction (b) Log and 4푡ℎ root of extraction

Figure 1.9: The distribution of new wells and proximity to old wells

(a) (b) Notes: Panel (a) is the distribution of new wells which mimic the distribution of all wells. Triangles in the panel (b) are new wells. It is a place in the north of Iran that confirms new and old wells exist side by side. Source: Iran Meteorological Organization (IRIMO) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 41

Figure 1.10: Depth and distance to the nearest well for new vs. old wells for each province

(a) Depth (b) Distance to the nearest well Source: Iran Water Resource Management Company (IWRMC), 2017.

Figure 1.10: Extraction of new and old wells in Spring and Summer

Source: Iran Water Resource Management Company (IWRMC), 2017. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 42

Figure 1.11: Planted area of new and old wells by season

Source: Iran Water Resource Management Company (IWRMC), 2017. Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 43

1.8 Tables

Table 1.1: Summary statistics of the agricultural censuses Variable 2003 2014 Agricultural land /District area 0.19 0.17 (0.14) (0.14) Total planted/Agricultural land 0.72 0.77 (0.18) (0.17) Gardening/Farming 1.30 2.02 (12.10) (26.34) Total irrigated/Agricultural land 0.58 0.59 (0.31) (0.32) Wheat/Total farming 0.36 0.36 (0.20) (0.21) Irrigated wheat/Total wheat 0.48 0.46 (0.36) (0.37) Rural pop/Total pop 0.47 0.41 (0.20) (0.19) Growth in # of wells 21.73 22.23 (79.64) (81.59) # of wells per 푘푚2 0.65 1.21 (1.04) (1.95) Positive precipitation shock 0.23 0.02 (0.42) (0.14) Negative precipitation shock 0.03 0.17 (0.18) (0.37) # of positive precipitation shock in the past 3 years 0.05 0.20 (0.23) (0.46) # of negative precipitation shock in the past 3 years 0.70 0.44 (0.71) (0.62) Extraction per well (thousand 푚3) 130.12 89.97 (125.71) (95.92) Total extraction per irrigated land (thousand 푚3/푘푚2) 641,717 716,716 (869,598) (917,644) Observations 315 315 Source: Statistical Center of Iran (SCI)- Agricultural census (2003-2013). Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 44

Table 1.2: Summary statistics of the wells’ census Variable Mean Std. Dev. Min. Max. Depth 40.72 44.67 4 450 Water tank 0.18 0.39 0 1 Elevation 914 737 -28.68 3,179 Slope 0.89 1.3 0 25.70 Distance to the nearest city (푘푚) 8.56 9.83 0 99.85 # of wells in 10푘푚 radius 96 115 0 1,582 well’s age 15.72 10.67 0 107 Newly established well 0.04 0.2 0 1 Positive precipitation shock 0.13 0.34 0 1 Negative precipitation shock 0.27 0.44 0 1 # of positive precipitation shocks in the past 3 years 0.18 0.40 0 2 # of negative precipitation shocks in the past 3 years 0.45 0.53 0 2 Extraction (푚3) 87,120 172,633 0 2,838,240 Extraction for ag purposes (푚3) 86,166 171,473 0 2,838,240 Irrigation method Flooding 0.89 Furrow 0.05 Pressurized 0.06 # of seasons works one 0.04 two 0.31 three 0.33 four 0.32 Observations 400,916 Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 45

Table 1.3: Extraction, extraction per well and per unit of surface Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) Total Per well Per surface Total Per well Per surface Growth in # of wells -36.4 0.227** 16.75** 0.005*** 0.004*** 0.006*** (89.26) (0.088) (6.91) (0.002) (0.001) (0.002)

Positive precipitation shock 2239 -8.235 296 0.101 0.056 0.070 (11727) (11.611) (908) (0.205) (0.079) (0.204)

# of positive shocks in past 3 years 4803 -18.30* -5.182 -0.098 -0.015 -0.048 (10458) (10.35) (809) (0.183) (0.070) (0.182)

Negative precipitation shock -12885 23.52** -1417 -0.329 0.147* -0.329 (11787) (11.67) (912) (0.206) (0.079) (0.205)

# of negative shocks in past 3 years 3995 -4.614 530 0.103 -0.094*** 0.109 (4748) (4.70) (368) (0.083) (0.032) (0.083) 푅2 0.226 0.488 0.245 0.173 0.719 0.226 Observations 598 598 598 598 598 598 Notes: Dependent variables are extraction, extraction per well and per unit of surface, and their log forms. Standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran and Iran Water Resource Management Company (2003-2013).

Table 1.4: Change in agricultural activities and precipitation shocks (1) (2) (3) Ratio planted Total planted area (Ha) # of planted farms Positive precipitation shock 0.030* 1264.872 -35.044 (0.018) (2289.771) (322.917)

# of positive shocks in past 3 years -0.004 2813.541 742.406** (0.016) (2098.231) (295.905)

Negative precipitation shock -0.044** -5359.267** -359.854 (0.017) (2255.655) (318.106)

# of negative shocks in past 3 years 0.000 918.113 38.016 (0.007) (954.712) (134.639)

Growth in # of wells 0.000* 7.618 -0.283 (0.000) (18.117) (2.555) 푅2 0.464 0.240 0.350 Observations 598 598 598 Notes: Dependent variables are ratios of planted area, total planted area, and number of planted farms. Standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran and Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 46

Table 1.5: Extraction and the effect of new wells Extraction Ln of Extraction (1) (2) (3) (4) Total Agriculture Total Agriculture Newly established well 10387.538*** 10682.280*** 0.175*** 0.190*** (3163.371) (3154.700) (0.041) (0.044)

Positive precipitation shock=1 3716.939 4090.312 0.199** 0.215** (6746.880) (6668.527) (0.093) (0.093)

Negative precipitation shock=1 -3244.579 -3398.184 -0.089 -0.112* (4844.949) (4912.398) (0.061) (0.066)

# of positive precipitation shock in past 3 years -110.523 76.443 0.088 0.114 (7132.759) (7007.482) (0.073) (0.075)

# of negative precipitation shock in past 3 years -10182.740** -10443.820** -0.118** -0.150*** (4894.279) (4770.546) (0.048) (0.053)

# of wells in 10 km radius at the survey time 21.112 22.826 -0.001*** -0.001*** (17.235) (17.254) (0.000) (0.000) Adjusted 푅2 0.633 0.632 0.798 0.794 Observations 395321 395321 395321 395321 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table 1.6: Extraction, the effect of new wells, and precipitation shocks Extraction Ln of Extraction Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Positive precipitation shock=1 3618.269 3988.842 0.198** 0.214** 3939.217 4312.022 0.198** 0.215** (6754.634) (6675.841) (0.094) (0.094) (6723.834) (6645.486) (0.093) (0.094)

Negative precipitation shock=1 -3468.693 -3628.656 -0.093 -0.116* -3200.224 -3348.468 -0.104* -0.127* (4865.706) (4933.663) (0.062) (0.067) (4838.101) (4902.996) (0.062) (0.067)

# of positive precipitation shock in past 3 years 32.556 223.582 0.090 0.117 -20.413 165.944 0.089 0.115 (7126.926) (7002.180) (0.073) (0.076) (7127.687) (7002.639) (0.073) (0.075)

# of negative precipitation shock in past 3 years -10254.275** -10517.385** -0.119** -0.151*** -10173.026** -10433.871** -0.119** -0.151*** (4910.202) (4787.509) (0.048) (0.053) (4892.221) (4768.432) (0.047) (0.053)

# of wells in 10 km radius at the survey time 21.581 23.308 -0.001*** -0.001*** 21.173 22.886 -0.001*** -0.001*** (17.339) (17.363) (0.000) (0.000) (17.241) (17.262) (0.000) (0.000)

Newly established well=1 12278.425*** 12598.095*** 0.087** 0.099** (3258.155) (3268.395) (0.040) (0.040)

Newly established well=1×Negative precipitation shock=1 433.837 304.660 0.340*** 0.361*** (6424.036) (6423.738) (0.108) (0.116)

Newly established well=1×Positive precipitation shock=1 -14469.872*** -14405.415*** -0.006 -0.029 (3285.965) (3272.598) (0.050) (0.053) Adjusted 푅2 0.633 0.632 0.798 0.794 0.633 0.632 0.798 0.794 Observations 395321 395321 395321 395321 395321 395321 395321 395321 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table 1.7: Extraction of the new wells Extraction Ln of Extraction (1) (2) (3) (4) Total Agriculture Total Agriculture Positive precipitation shock=1 -3315.160 -3666.366 0.026 0.022 (7618.078) (7458.113) (0.136) (0.139)

Negative precipitation shock=1 -3879.446 -4467.492 0.239** 0.246** (5320.653) (5536.032) (0.116) (0.121)

# of positive precipitation shock in past 3 years 4260.051 3341.178 0.008 -0.003 (7808.428) (7724.615) (0.121) (0.121)

# of negative precipitation shock in past 3 years -3450.901 -3761.926 -0.024 -0.044 (5629.821) (5588.036) (0.069) (0.077) Adjusted 푅2 0.660 0.657 0.828 0.825 Observations 15833 15833 15833 15833

Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table 1.8: The effect of wells’ age on extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) Total Total Total Total Agriculture Agriculture Agriculture Agriculture Positive precipitation shock 0.195** 0.182* 0.169* 0.164* 0.210** 0.195** 0.182* 0.176* (0.095) (0.096) (0.095) (0.096) (0.096) (0.096) (0.096) (0.097)

Negative precipitation shock -0.106 -0.100 -0.090 -0.090 -0.130* -0.120* -0.110* -0.109 (0.065) (0.064) (0.064) (0.064) (0.070) (0.068) (0.067) (0.067)

# of positive shocks in past 3 years 0.084 0.070 0.054 0.057 0.110 0.090 0.075 0.077 (0.074) (0.073) (0.072) (0.072) (0.076) (0.074) (0.072) (0.071)

# of negative shocks in past 3 years -0.122** -0.126** -0.134*** -0.135*** -0.154*** -0.157*** -0.164*** -0.165*** (0.049) (0.049) (0.050) (0.050) (0.054) (0.055) (0.056) (0.056)

2-year old 0.047 0.045 (0.034) (0.038)

3-year well -0.025 -0.024 (0.026) (0.025)

4-year well 0.008 0.009 (0.023) (0.023)

5-year well -0.003 -0.002 (0.015) (0.015) Adjusted 푅2 0.797 0.798 0.798 0.798 0.794 0.795 0.795 0.795 Observations 379475 368756 355880 342485 379475 368756 355880 342485 Notes: Dependent variables are log forms of total extraction and extraction for agricultural purposes. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table 1.9: The effect of age difference on the extraction Ln of Total Extraction Ln of Extraction for Ag Purposes (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Only includes wells less than 2 years 5 years 8 years 10 years 30 years 2 years 5 years 8 years 10 years 30 years Newly established well 0.067** 0.096** 0.121*** 0.138*** 0.149*** 0.077** 0.105** 0.129*** 0.149*** 0.165*** (0.033) (0.044) (0.037) (0.034) (0.038) (0.034) (0.045) (0.038) (0.035) (0.041)

Positive precipitation shock 0.267** 0.334*** 0.285** 0.289*** 0.198** 0.295** 0.358*** 0.306*** 0.310*** 0.215** (0.129) (0.119) (0.116) (0.110) (0.090) (0.132) (0.122) (0.117) (0.112) (0.091)

Negative precipitation shock 0.086 -0.009 -0.024 -0.037 -0.100 0.065 -0.038 -0.054 -0.061 -0.122* (0.077) (0.069) (0.066) (0.064) (0.063) (0.083) (0.078) (0.075) (0.071) (0.068)

# of positive shocks in past 3 years 0.146 0.120 0.157 0.153 0.100 0.202* 0.156 0.184 0.179 0.127 (0.103) (0.105) (0.110) (0.107) (0.075) (0.117) (0.112) (0.114) (0.109) (0.077)

# of negative shocks in past 3 years -0.044 -0.090 -0.084 -0.105* -0.124** -0.071 -0.129* -0.118* -0.140** -0.158*** (0.055) (0.057) (0.054) (0.057) (0.050) (0.062) (0.066) (0.062) (0.064) (0.055) Adjusted 푅2 0.812 0.798 0.803 0.803 0.801 0.809 0.795 0.800 0.801 0.798 Observations 26552 68320 117688 157966 357649 26552 68320 117688 157966 357649 Notes: Dependent variables are log forms of total extraction, and extraction for agricultural purposes. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 51

Table 1.10: Probability of digging a new well Semi-deep Deep (1) (2) Newly established well Newly established well Positive precipitation shock=1 -0.008 -0.002 (0.009) (0.002)

Negative precipitation shock=1 -0.022*** -0.003 (0.008) (0.002)

# of positive precipitation shock in past 3 years 0.012** 0.003 (0.005) (0.002)

# of negative precipitation shock in past 3 years -0.007 -0.001 (0.005) (0.001) Adjusted 푅2 0.358 0.219 Observations 391583 383226 Notes: Dependent variable is the dummy variable for new well. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 52

Table 1.11: Distribution of old and new wells Province Name Old wells New wells Depth Nearest Well (m) # of Wells in 10km Depth Nearest Well (m) # of Wells in 10km New Well (%) Alborz 50.94 102.29 76.31 52.24 122.75 91.30 2.28 Ardebil 8.92 366.01 3.47 8.40 513.18 4.70 4.44 Bakhtiari 58.76 315.17 5.77 50.07 304.81 8.44 0.91 Bushehr 29.78 186.47 21.58 32.05 219.64 23.30 1.20 E.Azarbaijan 28.93 170.71 51.75 39.43 176.95 64.94 2.61 Fars 60.39 220.53 16.95 81.17 185.14 41.05 4.38 Gilan 11.52 101.17 55.47 14.71 110.86 67.38 7.91 Golestan 37.90 163.47 33.42 60.39 130.69 61.83 1.99 Hamadan 56.58 354.51 8.44 54.80 285.48 19.86 3.08 Hormozgan 35.69 206.50 19.71 45.52 253.56 21.48 1.08 Ilam 52.28 469.86 3.50 36.30 520.68 4.40 4.90 Isfahan 53.18 179.78 59.90 67.88 196.31 105.81 2.68 Kerman 68.13 351.06 14.59 95.36 551.39 14.05 1.69 Kermanshah 43.65 250.46 14.07 46.08 227.53 25.32 4.98 Khorasan Razavi 72.88 467.63 15.74 21.75 128.17 113.06 6.16 Khuzestan 41.19 288.60 12.81 16.78 180.28 35.07 15.11 Kohkiloyeh 32.60 206.05 12.58 28.56 342.04 11.69 5.59 Kurdestan 33.27 322.40 8.84 28.45 274.18 16.92 5.11 Lorestan 51.91 358.12 4.14 36.89 277.72 8.44 10.19 Markazi 61.65 368.45 6.14 56.02 317.97 13.96 1.68 Mazandaran 16.61 75.65 90.33 19.70 88.27 128.13 3.31 N. Khorasan 54.68 488.27 7.27 31.69 374.96 12.38 1.01 Qazvin 75.49 383.58 8.69 61.86 299.21 24.58 6.61 Qom 47.26 322.24 17.05 57.28 374.95 25.52 3.19 S. Khorasan 88.33 869.94 0.79 80.57 776.62 1.63 0.53 Semnan 105.18 488.73 5.89 77.80 467.04 5.75 0.91 Sistan and Baluchestan 26.78 347.97 8.22 27.14 377.32 10.00 0.92 Tehran 50.82 111.43 88.89 41.97 101.85 168.06 2.53 W.Azarbaijan 20.32 108.51 50.57 24.16 110.15 70.72 6.44 Yazd 102.55 691.61 1.50 125.86 737.42 2.33 3.06 Zanjan 31.44 211.30 18.46 29.96 196.83 32.24 4.46 Source: Iran Water Resource Management Company (2003-2013)

Table 1.12: nearest-neighbor matching estimates of the difference in extraction Total Extraction Extraction for Agricultural Summer-Spring Difference (1) (2) (3) Mean Extraction New Wells 86085.82 85653.45 172.46 Mean Extraction Old Wells 78164.66 77351.67 197.85 ATT (1 vs 0) Newly established well 7921.16*** 8301.77*** -25.39*** (922.64) (922.36) (2.9) Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 53

Table 1.13: Summary statistics of match quality Difference in Mean Std. Dev. Min. Max. % with zero Water flow 0.188 7.89 -72.90 88.5 12.29 Depth -3.607 34.06 -320 390 9.05 Age 12.12 9.79 1 89 Distance 0.36 1.20 0 14.05 Latitude (X) -0.036 0.92 -13.25 11.84 Longitude (Y) 0.056 0.85 -8.72 8.68 Year 0 0 0 0 100 Positive shock 0 0 0 0 100 Negative shock 0 0 0 0 100 # of positive shock in past 3 years 0 0 0 0 100 # of negative shock in past 3 years 0 0 0 0 100 # of season worked 0 0 0 0 100 N 53552

Table 1.14: Matching results for the second set up Total Extraction Extraction for Agricultural Summer-Spring Difference (1) (2) (3) Mean Extraction New Wells 84896.41 84344.73 170.3 Mean Extraction Old Wells 74388.69 73638.28 196.81 ATT (1 vs 0) Newly established well 10507.72 *** 10706.45 *** -26.51 *** (721.33) (721.59) ( 2.99) Source: Iran Water Resource Management Company (2003-2013)

Table 1.15: Summary statistics of match quality for the second set up Difference in Mean Std. Dev. Min. Max. % with zero Water flow -0.039 2.56 -31.9 28 19.8 Depth -1.523 12.55 -110 100 13.93 Age 11.459 9.61 1 89 Distance 0.115 0.13 0 0.5 Latitude (X) 0.052 0.13 -0.48 0.50 Longitude (Y) -0.006 0.10 -0.50 0.50 Year 0 0 0 0 100 Positive shock 0 0.067 -1 1 99.56 Negative shock 0.016 0.18 -1 1 96.72 # of positive shock in past 3 years 0 0.113 -1 2 98.77 # of negative shock in past 3 years 0.03 0.274 -2 2 93.54 # of season worked 0.012 0.48 -2 2 81.84 N 83326 Ghadir Asadi Chapter 1. Groundwater Extraction and Adaption to Technology 54

Table 1.16: Matching results and the diffusion of knowledge Total Extraction Extraction for Agricultural (1) (2) Neighbours 0-20 3986.69 4226.09 20-50 4606.36 4607.29 5-100 5667.10 5500.34 100-200 5155.95 4898.05 200-1281 3037.35 2888.21 ATT (1 vs 0) Newly established well 4685.88 *** 4668.95*** (657.26) (659.07)

Source: Iran Water Resource Management Company (2003-2013) Chapter 2

The Effects of Precipitation Shocks on Rural Labor Markets and Migration

2.1 Introduction

Climate change is observed to have raised extreme weather conditions around the globe (Wuebbles et al. 2017; Stocker et al. 2013). The lives of rural households, especially in developing countries, are affected by fluctuations in weather. Periods of droughts, floods, and shocks in precipitation are all shown to affect productivity and income of agrarians in developing countries (Nkedianye et al. 2011, Gray and Mueller 2012b, Gray and Mueller 2012a, and Call et al. 2019). Workers in other sectors are also vulnerable to weather shocks, directly and indirectly, through local markets for outputs and inputs.

The labor market has been shown to be affected by shocks to weather. Studying rural areas in India, Rose (2001) finds that an unusually high rainfall increases agricultural production and reduces participation of households in the labor market. In addition to this, Chaurey (2015)

55 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 56 shows that wages increase when positive shocks occur. The effect of shocks is not limited to workers in agriculture. Some studies find an increase in off-farm labor. Adhvaryu et al. (2013), for example, show that when agriculture exists alongside industry, as in rural India, industrial employment increases when positive rainfall shocks occur and Porter (2012) finds that severe rainfall failure reduces both farm and off-farm income of agrarians. Mathenge and Tschirley (2015), however, find no major adjustments in off-farm labor in the short-run.

Another strand of literature studies the relationship of precipitation shocks on migration. Economic theory predicts that people migrate out of negatively affected regions, toward regions with higher productivity levels (Moretti 2011). Empirically, Badiani and Safir (2010) show that households’ temporary migration decreases when rainfall increases in rural India. Dillon et al. (2011) find that households in Nigeria usually send males, but not females, to migrate in response to weather risks. Other studies show that both internal migration (Henry et al. 2004, McLeman and Smit 2006, Lewin et al. 2012, Mueller et al. 2014, Mastrorillo et al. 2016) and international immigration (Bazzi 2017) are affected by precipitation shocks. This strand of literature establishes a relationship between migration and rainfall. However, as Lilleør and Van den Broeck (2011) argues, there is very limited evidence as to what drives it.

In this paper, we study the impact of precipitation shocks on local labor markets and on rural out-migration. We further investigate labor markets as a channel through which these shocks affect migration decisions. We use individual-level panel data and match themwith station-based weather data at the rural-agglomeration level.1 Our rich panel data combi- nation consists of 655,202 individuals in 1218 rural-agglomerations over the 2009-12 period. Station precipitation data is used to define positive and negative precipitation shocks sepa-

1 Rural-agglomeration, or Dehestan, in Iran is an administrative division lower than the district and above village. Villages in each district are grouped to a few rural-agglomerations. Rural-agglomerations do not contain a city or town. Based on the 2011 census, there are 2497 rural-agglomerations in Iran with 25 villages in each on average. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 57 rately. We define a positive (negative) shock as a precipitation level more than one standard deviation above (below) the average precipitation in the 1995-2014 period in each agglomer- ation. A common approach in studying the impact of weather events is to define shocks in geographically wide areas (province or district) due to data limitations. Our paper improves on this approach by identifying the location of households with a precision well beyond that of available precipitation data. In Iran, rural-agglomerations cover a small area, 681 푘푚2 with 25 villages in each, on average. We are, therefore, more confident that households in our sample are affected by the defined shocks.

We employ a fixed effects model to find the impact of positive and negative shocks onhoursof work. Benefiting from the exogenous variation in precipitation, we show that at the intensive margin of employment, hours of work of men in the agriculture sector increases in response to a positive shock. We observe a large heterogeneity, based on the type of shock (positive or negative), sector, gender, and role of individuals in households. We find that a positive shock increases hours of work of men in industry and reduces hours of work of women in the service sector. We show that this heterogeneity in response is due to the household level decision on labor allocation. We find no significant impact on children’s intensive marginof employment. At the extensive margin, we explore the probability of quitting and entering the labor force and employment. We observe that the probability of entering the labor force for daughters declines with to positive shocks. We also find that negative shocks raise the probability of quitting employment by spouses.

Precipitation shocks affect not only labor but also the migration decisions of households. Building on our results for the labor market, we provide evidence that this market is one channel through which precipitation shocks affect migration decisions. Employing a linear probability model, we find that negative shocks increase the probability of migration for Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 58

men. We also show that positive shocks have a similar impact on labor-migration2 of the same group. We further investigate the role of labor markets in migration and show that the majority of men, especially those affected by negative shocks, in our sample report that they migrated in search of jobs. More importantly, we show that after controlling for the unemployment rate and share of agriculture in value-added at the initial locale, rainfall shocks have no significant impact on migration.

Our findings have important implications for the impacts of global climate change onhouse- holds. Climate change is expected to lead to more frequent extreme weather events, and thus, the vulnerability of people is likely to increase over time (Nordhaus 2007; Stern and Stern 2007; Stocker et al. 2013; Wuebbles et al. 2017), especially for resource-dependent agrarians (Lilleør and Van den Broeck 2011 and Mueller et al. 2014). The effects of climate change in Iran are very pronounced given that this country is located in an arid region, with more than half of its rural households relying on agriculture as their primary source of income. Iran experienced more frequent and variable negative shocks after 2000 (see Figure 2.1). The percentage of land with negative shocks has been as high as 32% in 2002 and as low as 5% in 2007, while before 2000 the maximum was just 18%. These shocks have affected the lives of rural Iranians who largely depend on agriculture. Salami et al. (2009) show that the value-added of the agriculture sector dropped by 25 percent in the severe 1999-2000 drought in Iran.

Many other countries in the Middle East and North Africa (MENA) region have the same situation as Iran. MENA is the most water-stressed region of the world, with 60% of its population living in highly water-stressed areas, compared to the world average of 35% (World Bank 2018). The region is highly vulnerable to the potential impacts of climate change, which, based on climate models, include longer droughts and more variable and

2 We consider a person to be a labor-migrant if she/he migrated during the preceding year and is in the labor market in the current year. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 59 intense rainfall in the coming decades (Alpert et al. 2008; Evans 2009; Sowers et al. 2011; World Water Assessment Programme 2012; Blanco et al. 2014; Waha et al. 2017; Masson- Delmotte et al. 2018). Studies focusing on the impacts of climate in the region have found that droughts have resulted in large-scale migration, for example in Syria and Jordan in the early 2000s (Weinthal et al. 2015). Droughts are also shown to have increased poverty (Olsson et al. 2014) and resulted in conflicts in Syria (Feitelson and Tubi 2017; Mathbout et al. 2018). Our study contributes to the debate on the impacts of severe weather conditions in this region.

The response of rural households depends heavily on their ability to cope with weather shocks. Household wealth, infrastructure and access to credit market are among the impor- tant factors that increase the ability of the households to mitigate the impacts of shocks (Rosenzweig 1988; Paxson 1992; Rosenzweig and Binswanger 1992; Udry 1994). Access to these items are limited in rural areas of developing countries. In the case of Iran, however, rural areas have benefited from many rural development programs. These programs extended access to electricity, roads, schools, health facilities and safe water for many rural Iranians (Salehi-Isfahani 2009a) and were effective in reducing rural poverty (Salehi-Isfahani 2009b). As a result, in 2011, 99% of rural households had access to electricity, 89% to piped water and 71% of villages had access to a dependable road and 65% hosted a school. Despite these investments in rural development, rural Iranians migrated to cities in record numbers. The rural population, which amounted to 46% of the total population in 1986, shrank to less than 30% in 2016. Further research is required to show which type of infrastructure is more effective to mitigate the impacts of weather shocks.

This paper is organized as follows: The next section provides information on the labor market and migration in Iran. The theoretical framework is presented in Section 2.2. Section 2.3 describes data and Section 2.4 discusses our identification strategy. Section 2.5 presents Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 60 results together with their discussion, and Section 2.6 concludes.

2.2 Theoretical framework

Studying migration has a long history, and many review papers have been published explain- ing the state of knowledge at different points in time (Lucas 1997 and Molho 2013). Even in 1977, the idea of writing a review seemed old (Simmons et al. 1977), and our aim is not to do so again after forty years. Here, we present a very brief review of the main theories and try to place our paper in the literature.

An important economic explanation for migration is based on comparing the stream of earnings before and after migration. This model, known as the neoclassical or Harris-Todaro model of migration, has been around for a long time (Lewis 1954; Hicks 1963; Harris and Todaro 1970). Many versions of this model exist, some of which consider proximity to urban areas and the probability of finding a job at the destination as important factors (Todaro 1969). While the credibility of this theory has been questioned on the theoretical grounds and on how well it fits with the reality (Arango 2000), the neoclassical model is still the workhorse in migration studies. Following the seminal work of Sjaastad (1962) on migration, researchers have modeled the migration decision as an investment in human capital, which enables the models to include individual characteristics such as age, gender, marital status, labor market status, education level, and skills. Other than the mentioned monetary aspects of migration, new models have included psychic costs and factors like expectations and preferences. For instance, the relative deprivation hypothesis proposes that migrants are not responding to an economic incentive but are improving their socioeconomic status relative to a reference group in their place of origin (Stark 1984; Stark and Taylor 1989; Stark and Stark 1991).

Like other neoclassical models, the migration model assumes perfect information and indi- Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 61 vidualistic maximization. Migration requires collecting information and considering many alternatives, and not all of this information is easy to acquire. Based on a lack of information, Wolpert (1966) and Speare (1974) consider stress as a basis for migration decisions. Lippman and McCall (1976) explain migration behavior by modeling the costly acts of searching for a job and collecting information on job opportunities. Other studies consider factors other than information. For instance, Stark and Stark (1991) consider family interdependence in migration decisions and Massey et al. (1993) connect the decision to income volatility and labor, credit, and/or insurance market failures in the household’s place of residence. Finally, modeling the existence of networks explores the facts that rival theories are not able to ex- plain (Boyd 1989; Massey et al. 1990). For example, networks can explain why people still move when a wage difference no longer exists, a circumstance that violates the neoclassical theory of migration. A network among residents and arriving migrants can decrease the cost and risk of moving, provide help for financing the migration and facilitate the search fora job.

Another related strand of literature studies agglomeration economies. The basic question in agglomeration economies is why there are dense populations in cities and what factors explain the distribution and flow of population among geographical locations. The literature inthis context is mainly developed around the Rosen (1979) and Roback (1982) models. This strand finds three main reasons: sharing, matching, and learning, for dense cities with higher wages and productivity (Duranton and Puga 2004). The difference in productivity growth among geographical locations is an important factor that speeds agglomeration through migration. Agglomeration economies have not been used directly for explaining migration. However, this idea is closely related to the neoclassical theory of migration and can be used as a basis for explaining the incentives and flows of migrants.

Our paper is guided by such a view of migration and the shock to productivity in one region. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 62

The difference between this paper and the literature summarized above is that our focusis on the role of productivity in causing migration out of a region, out of rural areas to be more specific. Since agriculture is the largest sector in rural areas of developing countries like Iran, productivity in these areas is highly affected by events in the agricultural sector. Agricultural productivity is volatile for many reasons, including crop loss, weather and price shocks. Shocks to precipitation are one type of these important events, which have gotten more attention in the literature, since climate change is raising their frequency and severity. Adhvaryu et al. (2013) show that rainfall shocks increase production in agriculture, and Rose (2001) finds that these shocks increase agricultural profits aswell. Kaur (2014) emphasized that a positive rainfall shock increases wage in rural India, and Chaurey (2015) show that the increase in wages occurs in industry sector too. We, therefore, expect to observe an employment response positively related to precipitation shocks.

While the above relationship holds in general in the labor market, the response of individuals can be different, based on decisions that are made at the household level. For example, agrarian households smooth their income by adjusting their participation in market work (Kochar 1999). Economic theory does not provide definitive predictions for many of these intra-household allocations, since these decisions are affected by many factors. Therefore, we study these decisions empirically in this paper.

In this paper, we first explore how the labor market adjusts in response to rainfall shocks. We expect positive precipitation shocks to increase and negative shocks to decrease the supply of labor, since most rural workers have low levels of income and the substitution effect is larger for them than the income effect. Then, guided by our theoretical framework, we extend our empirical analysis to the effects of shocks on migration. We expect that when productivity is reduced in rural areas due to negative precipitation shocks, migration of people out of these affected regions increases. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 63

2.3 Data

We use two data sources in this study, the Iran Labor Force Survey (LFS) and station-based weather data from Iran Meteorological Organization. We describe them in detail below, along with key variables that we construct based on these data.

2.3.1 Labor Force Survey

The first data set is the Labor Force Survey (LFS), collected by the Statistical Centerof Iran (SCI). We use four rounds of these data, from 2009 to 2012.3 LFS has been collected since March 2005 as a quarterly survey, and it has a rotating panel structure. Each family in the LFS sample is interviewed four times in a period of six consecutive quarters, having two quarters of rest in the middle. Thus, we observe each family in the same two seasons of two consecutive years. The summary statistics for the balanced panel for the year 2010 and the pooled panel of 2009-2012 are provided in Table 2.1.

The full sample consists of 150,611 individuals in 38,396 households in the spring season of 2010, from which 140,102 individuals were designated to be re-interviewed in the same season the following year. In practice, only 120,875 individuals were re-interviewed in the spring season of 2011. The pool of balanced panels in this study consists of three, two- year panels, 2009-2010, 2010-2011, and 2011-2012. Our final data set of all three panels includes 327,858 individuals in 88,266 households, comprising 655,716 observations. For the regression analysis, we restrict the sample to individuals in working age (15-64 years old), giving us 430,932 observations. The attrition rate for our panel is 9.75% for households and 15.69% for individuals, which is not high compared to other designated panel data in

3 In this paper, we use Gregorian years, while the actual survey is based on Iranian years, starting from March 21 and ending on March 20. For example, the year 2009 refers to the survey period between 21 March 2009 to 20 March 2010. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 64 developing countries. Mueller et al. (2020), for example, find an attrition rate of 15% for the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data for years 2009 -2014. The attrition rate of our data is also less than the attrition rate of other rotating panel data such as the Current Population Survey (CPS) (Madrian and Lefgren 2000). In our empirical models, we follow Fitzgerald et al. (1998) and use inverse probability weights to account for selective attrition. In Appendix section B.1, we provide a detailed discussion of this methodology. Table 2.2 presents a comparison between the full sample and the balanced panel. The statistics are very close especially in rural areas, which are the focus of this study.

The labor force participation rate for the population 16 years and older has consistently been more than 40% in the past decade. Participation in rural areas has been more than in urban areas, for example, 46% and 41% respectively in 2010. Figure 2.2 shows the rural participation rate by gender. An important fact depicted in this figure is the large gender gap in participation. In 2010, for example, the participation rate for rural men was 75%, while for women it was only 19%. This is a common phenomenon in the Middle East (Salehi-Isfahani and Mostafavi-Dehzooei 2018). Metcalfe (2008), and Verme (2014) show that female participation increased in this region in the past three decades, following a surge in educational attainment. However, they note that it is still lower than countries with similar levels of economic development.4 Several studies suggest that in addition to factors such as educational attainment, fertility, and economic transformation away from the agricultural sector, one should consider the role of cultural and social norms to explain this peculiarity (Youssef 1972, Ross 2008, Verme 2014, and Majbouri 2016). Thus, in Iran, for cultural or economic reasons, men are usually responsible for their household’s of income. Therefore,

4 This is also a common attribute among people originated from the Middle East who reside in industrialized countries. Antecol (2000), for example, finds that among the immigrants in the US, those with originsin the Middle East tend to have lower women participation rates. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 65 their attachment to the labor force is higher than that of women, and their response to productivity shocks may differ from that of women.

The unemployment rate has been lower in rural areas than in urban areas in the past decade (Figure 2.3, left panel). The rural unemployment rate declined from 8.5% in 2010 to 6.8% in 2013 and then increased until 2016. The right panel of Figure 2.3 presents the rural unemployment rate by gender. The unemployment rate for women has increased steadily in this period and became larger than that for men starting in 2011. The overall rural unemployment rate follows that of men more closely because the number of men in the labor market is much larger than the number of women.

Although jobs are more abundant in rural Iran, they are mostly in the agriculture sector, with more than half of individuals employed in agriculture. Figure 2.4 shows the share of rural workers in the agriculture, services and industry sectors by gender. The share of agrarian men has declined over the past decade. During the same period, the share of men in the service sector has increased and come close to the share of agrarians. For women, the shares of sectors are different. There are relatively more women in agriculture compared to men. In the 2005-2015 period, on average 65% of women and 51% of men worked in agriculture. Moreover, services and industry employment for women are much lower. Less than 30% of women are employed in industry and about 10% in services. Based on these differences in demographics, heterogeneous responses to shocks for men and women would not be surprising.

The life of rural households in Iran is highly dependent on agriculture, with more than half of them having at least one member working in this sector. Agricultural jobs are subject to high risks, especially due to weather conditions (Jayachandran 2006, Adhvaryu et al. 2013, and Kaur 2014). In 2010, for example, 26% of Iran’s rural population experienced a Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 66

positive and 9% negative precipitation shock.5 The existence of such shocks, especially when access to credit and insurance markets is limited in a developing country like Iran, affects the migration decisions of rural households who want to smooth their consumption stream over time (Nkedianye et al. 2011, Gray and Mueller 2012b, and Gray and Mueller 2012a).

2.3.2 Weather data

The second data set is the station-based weather data publicly available from Iran Meteoro- logical Organization (IMO). These data are based on 362 weather stations, from which we use the data of 157 stations for which precipitation and temperature readings are available starting from 1980. Since reanalysis models are unlikely to generate estimates of extreme events (Auffhammer et al. )2013 and the focus of this study is on the impact of precipitation shocks, station-based data is more relevant for our purpose. In the case of Iran, stations are well distributed over the country. The average distance from villages to their nearest station is 46 km, and only 4% of the villages are matched with a station that is more than 100km away. We are therefore confident about the precision of the weather variables of interest. Since we can follow the locations of households up to the rural-agglomeration level, for all regressions involving the intensive and extensive margins, we match the weather data to LFS at this level. Since rural-agglomerations in Iran are geographically small areas, we can safely assume that all households in each agglomeration experienced the same weather. There are three steps to merge the weather data with LFS. First, we connect all villages in Iran to the nearest weather station. Then we take the simple average of the precipitation and temper- ature of all villages inside a rural-agglomeration to find the rural-agglomeration’s weather variables. Finally, we match the households in LFS to the rural-agglomeration weather data

5 For discussion on how we define shocks in this study see Section 2.3.2 and for the distribution of shocks at the rural-agglomeration level in 2009-2012 see Figure 2.6 (in Appendix). Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 67 based on their agglomeration of residence.

We define precipitation shocks for each region based on water years. For example, theshock for 2010 is computed based on the rainfall in the fall and winter of 2009 and spring and summer of 20106. We study positive and negative shocks separately. A common approach in the literature is to consider only positive or negative shocks (Shah and Steinberg 2017), or to assume that higher and lower rainfall affect households in the same direction (Jensen 2000), or to consider different directions but include parametric restrictions on shocks (Rose 2001, Jayachandran 2006, Adhvaryu et al. 2013, Kaur 2014, and Chaurey 2015). Our approach allows us to show that individual responses, both in the labor market and for migration, are not homogeneous. We define the negative shock as a rainfall of more than one standard deviation below the average rainfall in the 1995-2014 period. Similarly, any rainfall higher than one standard deviation above the mean is a positive precipitation shock. The distribution of the shocks is depicted in Figure 2.6. As the figure shows, 2009 and 2012 had the most negative shocks, which occurred mostly in the western and northern parts of the country. Positive shocks are distributed in different parts of the country in different years. Since it is shown in the literature that higher (lower) rainfall is associated with higher (lower) crop yields and the probability of flooding in Iran is very low,7 we interpret negative shocks as productivity-decreasing and positive shocks as productivity-increasing (Adhvaryu et al. 2013, Kaur 2014, and Chaurey 2015).

For the study of the impact of shocks on migration, we use the full cross-section of the LFS data, since we can recover the past location of all individuals in terms of the province of residence and whether it is rural or urban. This way, the migration estimates are not affected by attrition. Since we can recover the past location at the province level, we modify our shock variable using province-level rainfall measurements. The logic is the same as in labor

6 This period is equal to October 1, 2009, until September 30, 2010 7 Only 8.3% of Iran’s arable land is at risk of flooding (Mashayekhi 2001) Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 68 market models; rainfall is measured as the average for all villages in the province. Another difference in the definition of shocks for migration models is related to the fact that migration decisions are made through time and migration itself takes time to happen. Our measure of precipitation shocks is, therefore, the number of shocks a household experienced in the past three years. To prevent positive shocks from eroding the impact of negative shocks, we count the number of negative shocks only for regions that have not experienced a positive shock in the preceding three years. The same criterion is applied to positive shocks.

The rural share of the population in Iran has declined over the past thirty years. Based on Census data, the rural population declined from 46% of the population in 1986 to 29% in 2016. This change in demographics is caused, among other things, by migration, usually from rural to urban areas. Figure 2.5 shows rural to urban migration rates for 2005-15 period. Although migration was volatile, it has declined in this period. The migration rate is higher in the first two years (.50% and .49%) than in the last two (.28% and .30%), with hikes in 2010 and 2012. Migration rates of men and women followed similar paths in this period. However, men migrated slightly more in 2006, 2008 and 2012, and slightly less in 2015. In addition to weather shocks and uncertainty in agricultural income, migration is affected by higher poverty in rural areas of Iran(Atamanov et al. 2016; Mostafavi-Dehzooei and Salehi-Isfahani 2017; Atamanov et al. 2020).

2.3.3 Household Expenditure and Income Survey

Another source of data used in this study are the Household Expenditure and Income Survey (HEIS) collected yearly by the SCI. HEIS data reports income information for households. We, therefore, use the information on the 2008 to 2012 rounds of these data to estimate the wage level. Based on Economic theory, the wage rate is an important factor that explains Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 69 rural-urban migration. We estimate province-level wage rates for low educated labor and merge it at province-level with the rest of the variables in Section 2.5.3. We define low educated as individuals with no college education. We assume the wage rate for the low educated is the most relevant factor to consider since most rural workers have low education levels and when they migrate to urban areas they tend to be hired for jobs with low education and skill requirements.

2.4 Empirical strategy

The goal of this study is to understand how shocks to precipitation affect the labor supply and migratory decisions of households. We first explore the impact of shocks on the labor supply of individuals at the extensive and intensive margins of employment. Then, we study how shocks affect migration out of rural areas. Building on the results for labor, weshow that the labor market effects of shocks are important in forming migration decisions ofthe household.

At the intensive margin of employment, we use a fixed effects model to estimate the following equation:

′ ℎ푝푤푖푗푡 = 훼1 * 푃 표푠.푆ℎ표푐푘푗푡 + 훼2 * 푁푒푔.푆ℎ표푐푘푗푡 + 훽 푥푖푡 + 훾푡 + 휃푖 + 휖푖푗푡, (2.1)

where ℎ푝푤푖푗푡 is the hours of work per week for the person i, in rural-agglomeration j, in year t. 푃 표푠.푆ℎ표푐푘푗푡 and 푁푒푔.푆ℎ표푐푘푗푡 are positive and negative shock indicators as defined in Section 2.3. There is ample evidence in the literature that more rainfall is associated with higher agricultural yields and productivity (Jayachandran 2006, Adhvaryu et al. 2013 and Kaur 2014). We apply a similar logic to these studies but use a less restrictive specification, Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 70 by considering positive and negative shock variables separately. Separating these variables allows us to capture the asymmetric impact of shocks, as shown in section 2.5. 푥푖푡 is a vector of individual and household characteristics and includes household size, indicators for the level of education and marital status, number of working members and children under the age of 5 in the household, and province unemployment rate. 훾푡 and 휃푖 are the year and individual fixed effects, respectively.

The identification in this specification is therefore based on the variation in shockexpo- sure in different geographical areas that cannot be explained by household and individual observables, as well as unobservable individual fixed effects. Since the source of variation is the rainfall at the rural-agglomeration level, we cluster standard errors at this level. As a robustness check, we clustered standard errors at the province level and found that the results are qualitatively similar (results not reported but available upon request).

At the intensive margin of employment, we consider the probability of quitting (or entering) the labor force. We employ the linear probability model as follows for estimation:

′ 푦푖푗푡 = 훼0 + 훼1 * 푃 표푠.푆ℎ표푐푘푗푡 + 훼2 * 푁푒푔.푆ℎ표푐푘푗푡 + 훽 푥푖푡 + 훾푡 + 휂푗 + 휖푖푗푡, (2.2)

where 푦푖푗푡 is an indicator for individuals who quit (or enter) the labor force. In this model, we add the coefficient of variation of rainfall at rural-agglomeration to 푥푖푡. We also add agglomeration fixed effects휂 ( 푗) in our model to account for possible unobserved variables that may vary over space.

The interdependence of employment responses of household members has important implica- tions for the response to shocks. We do not explicitly model the household decision-making process. However, we estimate responses separately for men and women and based on the role of members of the household (father, mother, and children). In Iran, and especially in Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 71

rural areas, fathers are mainly responsible for market work and most mothers are homemak- ers. In 2010, for example, the participation rates of fathers and mothers were 82% and 19%, respectively; and 76% of mothers were homemakers. Moreover, 42% of children aged 16-29 are in the labor force and 27% are in school. Based on these statistics, we expect to observe a different response to shocks for mothers, fathers, and children.

We also employ precipitation shocks as an exogenous source of variation to study migration from rural to urban areas. The equation we estimate is of the following form:

′ 푦푖푗푘푡 = 훼0 + 훼1 * 푁푃 표푠.푆ℎ표푐푘푘푡 + 훼2 * 푁푁푒푔.푆ℎ표푐푘푘푡 + 훽 푥푖푡 + 훾푡 + 휈푘 + 휖푖푗푘푡, (2.3)

where 푦푖푗푘푡 is an indicator that is equal to one for individual 푖 who in year 푡 was in an urban area of province 푗 and in year 푡 − 1 was in a rural area in province 푘. This indicator is zero for individuals who were in the rural area at province 푘 in both periods 푡 and 푡 − 1.

휈푘 represents province fixed effects. The shock variables in this model, 푁푃 표푠.푆ℎ표푐푘푘푡 and

푁푁푒푔.푆ℎ표푐푘푘푡, are different from labor specifications (Equations 2.1 and 2.2). Migration is a process that takes time, and it is not very likely that a one-time shock to income will cause many households to migrate to a new location. To capture the persistence of shocks in regions over time, we use the number of shocks in the preceding three years. As described in section 2.3, data limitations allow us to find the previous location of individuals onlyat the province level by urban and rural, so the shock variable at this model is defined at the province level. The benefit of this method is that we can use the full cross-section ofthe data for estimates of migration, and therefore the results are not subject to attrition bias.

One factor that affects rural migration is labor market conditions. More than half ofrural individuals in Iran work in the agricultural sector. In the agricultural sector in developing countries, productivity growth, and therefore wages are less than in industry and services Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 72

(Martin and Warr 1993, Bernard and Jones 1996, Reardon et al. 2001, Taylor et al. 2003, and Foster and Rosenzweig 2004). Moreover, agricultural job availability and income are subject to high shocks caused by weather fluctuations. We, therefore, test how labor market conditions explain rural out-migration in Section 2.5.3.

2.5 Econometric results

Most agricultural activity is in family-run businesses (Lowder et al. 2016; Graeub et al. 2016), and we expect that shocks to the productivity of the agricultural sector affect members of agrarian households engaged in agriculture and also those members who work in non- agriculture. Moreover, we expect that different members of the household might respond to the shocks differently since their duties vary in their family. Fathers are usually incharge of financing the household; thus even during bad times, their labor supply tends toremain stable. There can also be inter-sectoral transmission of shocks. We present our estimation results in this section and in three categories. First, we discuss the intensive margin, where we describe the effects of rainfall shocks on hours of work for those who are already inthe market. We first explore the impact of shocks on workers in the agriculture sector. Then,we investigate the impact on industry and service sectors. In the intensive margin subsection, we study individual responses to precipitation shocks in general and by the role of individuals in the household (parents, children). In subsection 2.5.2, we discuss the extensive margin of employment, which involves assessing the effects of shocks on the probability of quitting and entering employment and the labor force. Finally, in section 2.5.3 we discuss the effect of shocks on the migration decisions of rural households. In this section, we discuss how conditions in the labor market affect migration decisions. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 73

2.5.1 The intensive margin

Table 2.3 summarizes the effects of shocks on the hours of work for agrarian individuals. As is clear from the table, positive shocks have a positive and statistically significant impact on hours of work (column (1) of the table). The effect is larger when the model is restricted to men, being exposed to shocks increases weekly hours of work of men by 1.6 hours. The impact of positive shocks on women is insignificant (column (3)). Negative shocks have no significant impact on agriculture workers in general, however, the point estimates of the impact for women and all workers are negative. This result hints that the labor supply responds to an increase in productivity in the agriculture sector. We then restrict the sample to self-employed individuals for two reasons. First, hours of work are more flexible for this group as the wage workers may have pre-determined contracts for hours of work. Second, the decision on the amount of work for the self-employed is more related to the workers themselves, compared to the wage workers whose work is affected by the labor demand from employers as well. The results for self-employed agrarians are presented in columns (4)-(6). The response to positive shocks is larger for both men and women in this group. Men increase their hours of work by two hours and women by 2.9 hours, though the result for women is not statistically significant. Women’s response to negative shocks also becomes economically and statistically significant. Self-employed women reduce their hours of work by four hours when a negative shocks occurs.

Studies addressing the effects of weather shocks on hours of work have used different setups than this paper. Most studies combine the positive and negative shocks together, assuming a homogeneous effect of positive and negative shocks on the dependent variable. For example, Jayachandran (2006) combines positive and negative shocks together and defines the negative and positive shocks to be the 1st and 5th quantiles of the rainfall. She finds the elasticity of the labor supply to be between 0.238 and 0.62 for hours of work per day, depending on land Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 74 ownership: the more land you have, the greater the elasticity of your labor supply in response to a productivity shock. This result is in line with our findings here that the self-employed have a larger response to positive shocks.

We also explore the impact of shocks on non-agriculture sectors. Table 2.4 shows the response of workers in industry and service sectors to shocks. Positive shocks have a positive and statistically significant impact on workers in the industry. Column (2) shows that this impact is due to the response by men, which increase their labor supply by 2.7 hours per week if they observe a positive shock. There are three channels through which workers in the non-agricultural sectors receive and respond to the spill-over effects of a change in agricultural productivity. First, considering that most of the industries in the rural areas of Iran are the ones in the supply chain of agricultural products, an increase in agricultural production due to positive shocks raises the demand for labor in these industries. Second, an increase in productivity in the agriculture sector raises household income and thus the overall demand in the product markets and this can increase the local demand for labor (Johnson 2000, Lanjouw and Lanjouw 2001). Another possibility that increases working hours in non-farm jobs are income diversification (Reardon 1997) or the so-called “push/pull factor”. Since the impact of positive shocks on the service sector is not positive, the first and third effects above can be the most likely ones in the case ofIran.

An important finding of this section is for the service sector, where the impact of positive shocks on women (column (6)) is negative. This result is not directly related to the three channels discussed above. However, these results imply that there might be some intra- household reallocation of labor that is caused by precipitation shocks. As the opportunities in the industry and agriculture grow for men by positive shocks, households respond by cutting the labor supply of women in the sector that is less affected by these shocks, service sector. To show that this effect is due to the reallocation of labor in households, we calculate Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 75 the change in hours of work for households with single and multiple workers separately and present the results in Table 2.5. As is evident in the table, women working in the service sector cut their hours of work when a positive shock is observed only if they live in a household which has other members working as well. For single worker households, hours of work of women in the service sector increases when facing a positive shock. In section 2.5.2, using the results at the extensive margin, we discuss again that this result is a household decision and it is not due to market demand for labor.

We also find indications that negative shocks increase hours of work by women in Industry (Table 2.4, column (3)). While this result is significant only at the 10% level, it is possibly due to the push factors that force households to send members to other sectors when agricultural jobs are scant. Beegle, Dehejia, and Gatti (2006) find that crop loss (negative productivity shock) has a positive effect on child labor (between six and nine more hours of workper week). The setup of shocks in their paper is different than ours, and child labor has alot more explaining factors than working hours of adults (those over the age of 15 in our data), though their results are comparable to ours in terms of direction.

To relate our findings to the literature, we can mention studies that look for farmers’ incen- tives to supply labor to non-farm market jobs. Again, the setup of those studies does not exactly match ours, but the results are comparable. Kochar (1999) finds that experiencing positive shocks significantly increase market hours of work by 27.6 percent. He does notfind a significant effect on hours of work for women. Mathenge and Tschirley (2015) find that previous main-season rainfall deviation does not have significant effects on the probability of households engaging in off-farm activities, except for decreasing the chance of receiving a remittance. With the same setup, Ito and Kurosaki (2009) find that deviation of rain- fall from historical mean has a negative effect, decreasing the share of households engaging in self-employed agricultural activity by 2.5 percent and increasing those who are in the Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 76

non-agricultural wage work by 3.54 percent. All these studies assume positive and negative shocks homogeneously affect off-farm activity.

Impact by role in household

In this section, we present the results for the impact of precipitation shocks on hours of work by the role of members in the household. We look at the head, spouse and children, all between 15 and 64 years old, separately to see if there exists any differential response by these roles. Household heads are usually men in Iran. 97.9 percent of heads that participate in the labor market are men in our sample. Spouses are usually women in Iran and 99.78 percent of spouses who are in the labor force are women. To keep these groups coherent by gender, we include female heads in the spouse category. Therefore, in all the discussion that follows, all observation marked as heads are men and all spouses are women. We also present the results for children by gender to continue exploring gender differences in the labor supply. We first present the impact of shocks on couples. Table 2.6 presents the impact of shocks on men and women who work in the agriculture sector. The estimates in this table mimic the results of Table 2.3, positive precipitation shocks raise hours of work by all agrarian men, and self-employed men; and have no statistically significant impact on women, and negative shocks reduce hours of work of spouses. The impacts on heads and spouses who work in Industry and Service, presented in Table 2.7, are also very similar to the impacts on all workers in the same sectors shown in Table 2.4. The estimates of the impact of a positive shock for men in Industry and women in Service sectors are similar in sign. However, the effects are larger in absolute terms when models are restricted to heads and spouses, andthe response of spouses in service sector is now statistically significant at the 5 percent level.

Switching to the impacts on children, we look at the effects of precipitation shocks in agricul- ture, industry and service sectors separately in Table 2.8. Neither shock has a statistically Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 77 significant impact on children in any of the sectors. Our results presented in this andthe last paragraph show that the response differs by the role of individuals in the household. The overall picture shown in Tables 2.3 and 2.4 more closely follows the pattern for heads and spouses possibly because there are more of them in the workforce than children (72% of workers in our sample are couples).

Impact by age

In previous sections, we observed heterogeneity in response based on gender, role, and sector. In this section, we further explore the aspects on which there exists heterogeneity in response by looking at the impacts of shocks by age. We focus on three age groups, 20-29, 30-39, and 40-55. These age groups are chosen based on mobility which suits the discussion on migration and is adopted here for coherency. Table 2.9 presents the results for these age groups. The results shown in Panel (a) of the table indicate that positive shocks raise hours of work of 20-29 years old men in the service sector by 2.6 hours per week. For the 30-39 years old group, we observe a negative impact on the hours of work of women in the service sector when a positive shock happens. This result is in line with what we found earlier that spouses reduce hours of work when there is a positive precipitation shock. This effect only exists for this age group. Positive shocks also affect men in agriculture and exposure to one more shock increases hours of work of 40-55 years old men by 2.26 hours. This finding is also consistent with prior results.

2.5.2 The extensive margin

In this subsection, we discuss the movement at the extensive margin of employment. We explore employment and participation in the labor market as the extensive margins of em- Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 78 ployment. While the observed employment responses can be the consequence of both demand and supply side of the labor market, labor force participation is more heavily affected by the supply side decisions since the demand affects participation only indirectly, for example through discouraging effects. Therefore, presenting both employment and participation isof importance when looking at the extensive margin.

Table 2.10 shows the impact of precipitation shocks on the probability of quitting employ- ment and the labor force. Negative shocks increase the probability of quitting employment for women. There is no such impact on the labor force participation of women, indicating that the increase in quitting employment should be due to the less demand for labor in the market. Negative precipitation shocks reduce productivity and as a result, the demand for labor is reduced as well. To better understand the effect of shocks, we explore the impact on quitting employment for workers in agriculture, industry and service sectors separately (see Table 2.11). Our estimates show that negative shocks increase the probability of quitting employment for women in the agriculture sector. A negative shock raises the probability of quitting for women in agriculture by 10 percentage points. There is also an impact on em- ployment of women in the service sector. One more positive shock increases the probability of quitting employment for women by 23 percentage points. We observe that the responses in the extensive margin of employment are by women. This is not surprising in the case of Iran where men, usually fathers, are responsible for household finances. Therefore their attachment to the labor market is very high and their decision on quitting the employment and labor market is not derived by shocks. We also present the impact of shocks on entering the labor force and employment in Table 2.12. We observe indications that positive shocks affect the probability of entering the labor force and negative shocks affect probabilityof entering employment and labor force for women. Since these effects are significant only at the 10 percent level, we explore these findings further in the next section. There we provide Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 79 more explanation on how the responses are. We have also checked the robustness of our results by finding the impacts of shocks using probit models. The estimations using probit models are provided in Tables B.5 and B.6 in the Appendix. The results are similar to our findings in Tables 2.10 and 2.12, respectively.

Impact by role in household

In this section, we explore whether there is heterogeneity in response based on the role of individuals in the household at the extensive margin of employment. We first present the impact of shocks in the probability of quitting employment and labor force of head and spouse in Table 2.13. We observe that negative shocks increase the probability of quitting employment (column (3)) and labor force (column (6)) for the spouse. Since both employ- ment and labor force participation are affected, this result can be due to the effects onthe demand or supply side of the market. The demand for labor declines when negative shocks reduce productivity. The supply of labor may also be reduced because of the discouragement effects. There is no impact on employment and participation of heads by either shock.We observe a consistent response to the probability of entering employment presented in Table 2.14. Probability of entering employment, and the labor force, for spouse declines with a negative shock. Both of these probabilities are cut by three percentage points.

The results mentioned above, indicate that there is no response to negative shocks by heads. There are two reasons why heads of households do not quit the labor force with a negative productivity shock. First, household heads (men) are the primary worker and they need to financially support their family. Because of this, participation in the labor market ishighest among them (88%) compared to spouse (19%), and children (39%). Second, precipitation shocks more heavily affect the agriculture sector. In section 2.5.1, we found a larger response to positive shocks in the agriculture sector compared to industry and service. More than Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 80

65% of women were employed in agriculture while only less than half of men worked in this sector in our study period. Therefore, the effect of negative shocks is more significant on women than men.

We also explore the impact on employment and participation of children. There is no sta- tistically significant impact on the probability of quitting employment or quitting thelabor force for children as shown in Table 2.15. There is, however, a significant impact on the probability of entering the labor force for children as presented in Table 2.16. Dividing the sample to sons and daughters, we find that this response is due to the daughter’s behav- ior. A positive shock reduces the probability of entering the labor force for daughters by 5 percentage points. There are two main channels for this response by daughters. First, as positive shocks increase productivity and income for agrarian households, there is less finan- cial pressure on households to send their daughters to work. As a result, the labor supply for this group declines. This channel is similar to the response of the spouse to positive shocks as observed in Section 2.5.1 (Table 2.4). Second, young Iranians have the option to acquire almost free education at every level. This can push them out of labor market participation, especially when households can afford to do so. Prime age children in Iran frequently use education as a substitute for joining the labor market. In our sample, 11% of daughters who quit the job market go back to school. The result of the reduction in the probability of participation is in line with our findings in subsection 2.5.1, where we showed when positive shocks increase employment opportunities for men, the labor supply of women is replaced by men in households with multiple workers. Our results here confirm this previous finding and provide more evidence that this is a supply-side decision at the household level since it is actually participation, and not employment, of women that is affected by positive precipi- tation shocks. These findings are in line with what Rose (2001) found. She found a negative effect of rainfall shock on participating in the labor market (12% decrease in the probability) Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 81

but a positive effect of weather-induced bad crop yield on participation.

2.5.3 Migration

In this section, we present the results of the impact of precipitation shocks on rural out- migration. The estimated model in this section is based on equation 2.3, where the dependent variable is an indicator that takes value 1 for individuals who lived in rural areas at baseline and resided in an urban area the next year and takes zero otherwise. As explained in section 2.3, the data for migration estimations are based on the full cross-section of the years 2009- 12. We use the information in the data to find the location of individuals one year before the interview date (baseline). We can identify the baseline location in terms of the province of residence and whether it is rural or urban. Since migration estimates are based on the full cross-section, the results of this section are not subject to attrition.

Migration decisions are formed over time. A one-time shock may not trigger much migration, but if the shocks keep occurring consecutively, migration becomes more likely. More impor- tantly, some factors may prevent households from migrating soon and may force households to remain in their current location, waiting for an opportunity to migrate.8 Therefore, in the migration specifications of this section, the “Number of positive (negative) shocks” variable is the number of years with a positive or a negative shock in the past three years. As a robustness check, we used the number of shocks in the previous five years (not reported) as the precipitation shock measure, the results of which are qualitatively similar to the ones presented in this section. As mentioned in section 2.3, we define shocks using precipitation at the province level. We consider migration for three age cohorts based on their mobility: 20-29, 30-39 and 40-55 years old.

8 To verify this claim, we check whether migration depends on the lag of shocks. Results (presented in Appendix Table B.3 and B.4) show that a the second lag of the precipitation shock affect migration. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 82

Table 2.17 presents the results for the impact of precipitation shocks on the probability of migration of men and women. Column 2 shows that negative shocks increase the probability of migration of young men (30-39 years old) and positive shocks do not have a significant impact on any age group/gender. The impact of negative shocks is sizable, observing one more shock in the past three years almost doubles the probability of migration for 30-39 years old men (raises migration by 0.55 percentage points from a baseline 0.60 percent). There is no statistically significant impact by negative shocks on the migration of other age groups. Our estimates also indicate that the larger household size the smaller probability of migration of men and women in their 20s and 30s. This estimate indirectly implies that there are financial barriers to migration since on average larger households are poorer inIran and their migration is more costly as well.

There are several channels through which precipitation shocks can affect migration. One of the most important channels is the labor market. In Section 2.5.1, we showed that positive shocks raise hours of work for members of agrarian households and there are spillover effects in other sectors as well. If shocks change labor market conditions, that can well be a factor that pushes people toward migrating out of rural areas when conditions get worse and make migration less often when conditions improve. We, therefore, take a closer look at migration due to labor market conditions. The importance of employment opportunities can be seen in the self-reported motivations for migration. Among men in our sample who migrated out of rural areas, 61.17% reported they did so for job-related reasons (see Table 2.18). This response varies with shock exposure in their residence. In places where at least one negative shock is observed in the past three years, more men (63.41%) indicated they have migrated for jobs compared to places with no shock (60.73%). For women the situation is different. Only 5.53% of women considered employment as their migration motive. However, the majority of women (83.57%) followed their households in their migration decision. Based Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 83

on this and the fact that rainfall shocks affect the productivity of labor in rural areas, the stronger response from men compared to women in Table 2.17 comes as no surprise.

Since the labor market condition is an important motive for migration, we also test the impact of shocks on labor-migration. The model we use here is similar to equation 2.3, with only one difference: the dependent variable used here is an indicator that takes value 1 for thosewho both have migrated and are in the labor force at the destination, and takes zero otherwise. The results of this model are shown in Table 2.19. Since, as explained above, employment is not an important motive for migration of most women, our focus in labor-migration is only on men. Based on our estimates, negative shocks affect the labor-migration of 30-39 years old men (column 2). One additional negative shock more than doubles the probability of migration for this group (0.6 percentage points increase on an average migration rate of 0.57 for this age group). There is no statistically significant impact on women and men in other age groups, nor there is a statistically significant impact caused by positive shocks. As shown in Section 2.3.1, Iranian women’s participation in the labor market is low. Moreover, labor- migration is much smaller in magnitude among women (0.07%) compared to men (0.26%) in the study period. Low attachment to the labor market and low labor-migration explain why the coefficients in Table 2.19 are not significant for women.

We now focus on how important labor market conditions and economic activity are for migratory responses to precipitation shocks. To examine that, we add other explanatory variables to our model in equation 2.3 that control for the labor market (rural unemployment rate in the province) and general economic conditions (share of agriculture and industry value added from total). Table 2.20 reports the results for the impact on migration. In all columns of the table, the wage rate at origin is negatively and wage rate at destination is positively associated with migration. This comes as no surprise based in economic theory, individuals migrate from locations with lower wage to places with high wage. Moreover, migration Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 84 is larger out of rural areas of provinces with a larger share of agriculture in value-added. Migration is also lower toward provinces where agriculture share is larger. We also observe that shock coefficients become insignificant, indicating that even moderate control forthe labor market and economic conditions captures the effect of precipitation shocks.

A similar phenomena exists for labor-migration. Table 2.21 shows the estimates of the impact of precipitation shocks on labor-migration when the labor market and general economic conditions are controlled for in the models. The wage rate at origin is negatively related to migration and the wage rate at destination is positively related to migration. After controlling for the economic and labor market conditions, the effect of negative shocks on labor-migration of 30-39 years old men is no longer statistically significant (compare with Table 2.19, column (2)).

Our findings in this section support the idea that labor market conditions are important factors in migration decisions. A strand of the literature has focused on agriculture as a channel through which precipitation affects individuals. Mueller et al. (2014), for example, find that a positive rainfall increases farm and non-farm income in rural Pakistan. Mastrorillo et al. (2016) also suggest that agriculture may be a transmission channel for precipitation anomalies. On the other hand, Gray and Mueller (2012a) show that men’s labor-migration increases with drought. Our paper adds to this literature by showing that precipitation affects migration through the market for labor.

Migration and role in household

Migratory response to precipitation shocks, as we learned above, is subject to heterogeneity based on age and gender. In section 2.5.1, where we discussed labor markets, we observed heterogeneity based on role in the household. This section addresses heterogeneity in the Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 85 impact on migration based on role. We first explore the response of migration to precipitation shocks using the model in equation 2.3 and a set up close to what we had for tables 2.17 and 2.20. We divide the sample into two broad groups of couples and children. The results for the impact on couples are reported in Table 2.22 columns (1)-(4), and for children in columns (5)-(8). Our estimates indicate that as couples age, their probability of migration out of rural areas declines. We find that negative shocks increase the probability of migration for spouse and head (women and men, respectively) when the labor market and economic conditions are not controlled for (columns (1) and (2)). This effect does not exist for children (columns (5) and (6)). These results show that the migration response we find above is due to the response of parents and not their children. We also add unemployment, wage and share value-added in agriculture to the models. As expected, the probability of migration out of areas with larger agriculture shares is larger. Also, the higher the wage at origin, the migration out of that region is lower. Like above, when these controls are added, the estimates for the impact of shocks become statistically insignificant.

The role of individuals in the household is also important for their labor migration decisions. Table 2.23 presents the results of the estimation of the impact on labor migration. Based on our estimates, labor migration of head declines as their age increases. This result is different for sons. Sons’ probability of labor migration rises as their age increase. This hints to a quadratic age effect. When age is low, as it is for sons, the older the person isthemore their labor migration rises, possibly because they become more mature and more able to find jobs in the host societies. On the other hand, when age is high, as it isforfathers, the older the person gets the more his mobility declines. Our important result in this table is that one more negative precipitation shock increases the probability of labor migration of men by 0.31 percentage points. There is, however, no statistically significant impact on the labor migration of women and children. Here again, a moderate control for economic Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 86 and labor market conditions makes the estimate of the effect of negative shocks statistically insignificant. This indicates that the impact of shocks on (labor) migration is entirely due to economic factors.

2.6 Conclusions

The primary source of income for rural households in developing countries is agriculture. Many rural workers are employed in the agricultural sector, and many others are indirectly affected by agricultural shocks that influence local labor markets and markets forgoods and services. Climate change is expected to raise income uncertainty for rural households through weather variability. In this paper, we exploit the presumably random occurrence of precipitation shocks to estimate their impact on labor markets and out-migration in rural areas of Iran. We match station weather data with the Iran Labor Force survey, at the rural-agglomeration level.

We find a great deal of heterogeneity in responses by gender, the role of individuals in households, sector of employment and type of shock. We find that at the intensive margin, positive precipitation shocks increase farm hours of work for household heads. We further show that shocks affect hours of work in other sectors. Heads’ hours of work in industry rise and spouses’ hours of work in the service sector decline as a result of positive shocks. Three mechanisms explain how off-farm hours are affected. First, a shock can affect other sectorsin the production chain of agricultural products. Second, agricultural shocks affect household income and therefore affect local demand for non-tradeable goods and services. Third, the division of labor in household changes as a result of shocks. We believe that the observed increase in labor in industry is due to the first mechanism. We also show that the reduction in spouses’ hours of work in service and reduction in daughters’ labor force contributions is Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 87 due to the third mechanism.

We believe that labor markets are one channel through which precipitation shocks cause mi- gration responses. We observe that out-migration almost doubles for young men, in response to negative shocks. This is because negative shocks hinder employment opportunities. For the same group, we show that migration for employment is affected in the same direction and magnitude. We show the importance of labor markets in forming migratory decisions for young men. The majority of men in our sample report that they migrated for labor-related reasons, either in search of a job or for job relocation. The percentage of men who report they migrated for employment also increases when their former locale observe a negative shock. Moreover, controlling for the labor market and economic conditions at origin captures the effect of shocks on migration and labor-migration. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 88

2.7 Graphs

Figure 2.1: Percent of Iran’s area affected by shocks

Notes: 5-year moving average. Positive (negative) shock is when precipitation in the last year is one standard deviation more (less) than its 40-year average. Source: Iran Meteorological Organization and authors’ calculation.

Figure 2.2: Labor force participation rate in rural areas by gender

Note: Individuals 16 years old and above. Source: Labor force survey, 2005-15, SCI. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 89

Figure 2.3: Unemployment rate in Iran

Note: Left panel is by residence, right by gender, in rural areas. Source: Labor force survey, 2005-15, SCI.

Figure 2.4: Sector of employment in rural areas by gender

Note: Left panel is for men and the right panel is for women. Source: Labor force survey, 2005-15, SCI.

Figure 2.5: Rural to urban migration rate

Source: Labor force survey, 2005-15, Statistical Center of Iran (SCI). Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 90

Figure 2.6: Precipitation shocks at the rural-agglomerations, 2009-2012

Source: Iran Meteorological Organization and authors’ calculation. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 91

2.8 Tables

Table 2.1: Summary statistics of the balanced panel 2009 2010 2011 2012 All Years Female (percent) 0.48 0.49 0.49 0.49 0.49 (0.50) (0.50) (0.50) (0.50) (0.50) HH Size 4.77 4.67 4.50 4.39 4.59 (2.06) (1.99) (1.87) (1.82) (1.94) Number ofr Working Members in HH 1.56 1.51 1.43 1.42 1.48 (1.28) (1.24) (1.18) (1.16) (1.22) Number of Children Less Than 5 Years Old 0.36 0.34 0.31 0.28 0.32 (0.65) (0.63) (0.59) (0.55) (0.61) Years of Education 4.97 5.16 5.32 5.39 5.21 (4.30) (4.38) (4.45) (4.53) (4.42) Agricultural Workers (Percent) 0.25 0.23 0.22 0.22 0.23 (0.43) (0.42) (0.42) (0.42) (0.42) Labor Force Participation Rate 0.47 0.46 0.45 0.45 0.46 (0.50) (0.50) (0.50) (0.50) (0.50) Unemployment Rate 0.08 0.08 0.08 0.07 0.08 (0.26) (0.27) (0.27) (0.25) (0.26) Hours of Work (Last Week) 41.26 40.80 41.61 42.51 41.41 (22.75) (22.01) (21.29) (20.93) (21.76) Observations 83,911 168,046 154,144 72,286 478,387 Notes: Includes all adults 15 years or older. Source: Statistical Center of Iran, LFS 2009-2012. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 92

Table 2.2: Summary statistics for all years Full Sample Balanced Panel Urban Rural Urban Rural Female (percent) 0.50 0.49 0.50 0.49 (0.50) (0.50) (0.50) (0.50) HH Size 4.17 4.59 4.19 4.59 (1.63) (1.95) (1.58) (1.94) Number of Working Members in HH 1.13 1.49 1.10 1.48 (0.91) (1.24) (0.88) (1.22) Number of Children Less Than 5 Years Old 0.24 0.33 0.22 0.32 (0.50) (0.61) (0.47) (0.61) Years of Education 8.81 5.45 8.60 5.21 (4.97) (4.48) (5.02) (4.42) Agricultural Workers (Percent) 0.02 0.22 0.02 0.23 (0.14) (0.42) (0.14) (0.42) Labor Force Participation Rate 0.41 0.46 0.39 0.46 (0.49) (0.50) (0.49) (0.50) Unemployment Rate 0.14 0.08 0.14 0.08 (0.34) (0.28) (0.34) (0.26) Hours of Work (Last Week) 46.48 41.72 45.80 41.41 (20.18) (21.78) (20.08) (21.76) Observations 950,471 782,289 527,289 478,387 Notes: Includes all adults 15 years or older. Source: Statistical Center of Iran, LFS 2009-2012

Table 2.3: Impact of precipitation shocks on hours of work of agrarians by gender All agriculture Self-employed agriculture (1) (2) (3) (4) (5) (6) All Men Women All Men women Positive Precipitation Shock=1 1.44** 1.57* 0.81 2.12*** 2.01** 2.89 (0.70) (0.81) (1.05) (0.79) (0.79) (3.66)

Negative Precipitation Shock=1 0.09 0.20 -0.63 0.06 0.29 -4.37** (0.71) (0.76) (1.28) (0.80) (0.81) (2.02) Adjusted 푅2 0.054 0.062 0.034 0.077 0.079 0.312 Observations 99824 70340 29484 51443 47262 4181 Notes: Fixed effects model with dependent variable hours of work per week. Standard errors are clusteredat the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012. Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 93

Table 2.4: Impact of precipitation shocks on hours of work in industry and service sectors Industry Service (1) (2) (3) (4) (5) (6) All Men Women All Men Women Positive Precipitation Shock=1 2.55** 2.68* 2.11 0.35 0.54 -4.49* (1.23) (1.43) (1.80) (0.68) (0.69) (2.32)

Negative Precipitation Shock=1 0.47 -0.17 3.03* -0.73 -0.73 -1.25 (0.82) (1.00) (1.65) (0.58) (0.59) (1.41) Adjusted 푅2 0.074 0.095 0.102 0.062 0.064 0.192 Observations 20694 12262 8432 61996 58176 3820 Notes: Fixed effects model with dependent variable hours of work per week. Standard errors are clusteredat the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012.

Table 2.5: Change in weekly hours of work by the number of workers in the household Service Industry Negative shock No shock Positive shock Negative shock No shock Positive shock

Women Single Worker 2.0 -2.8 5.7 -5.6 0.5 8.3 Multiple worker -1.9 -0.3 -3.6 3.7 -0.4 -0.6

Men Single worker -0.4 0.0 -0.4 -1.4 -0.6 0.3 Multiple worker -2.3 -0.1 -0.6 1.4 -1.3 2.0 Notes: Individuals 15-64 years old at baseline. Source: Statistical Center of Iran, LFS 2009-12.

Table 2.6: Impact of precipitation shocks on hours of work of head and spouse in agriculture All agriculture Self-employed agriculture (1) (2) (3) (4) (5) (6) All Head Spouse All Head Spouse Positive Precipitation Shock=1 1.57** 1.60* 1.15 2.25*** 2.12*** 2.57 (0.77) (0.91) (1.04) (0.82) (0.82) (3.60)

Negative Precipitation Shock=1 -0.23 -0.10 -0.93 -0.00 0.25 -4.16** (0.77) (0.83) (1.27) (0.84) (0.84) (1.98) Adjusted 푅2 0.060 0.069 0.036 0.079 0.080 0.328 Observations 74680 51895 22785 47067 43231 3836 Notes: Fixed effects model with dependent variable hours of work per week. Heads are male and spousesare female. Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 94

Table 2.7: Impact of precipitation shocks on hours of work of head and spouse in industry and service Industry Service (1) (2) (3) (4) (5) (6) All Head Spouse All Head Spouse Positive Precipitation Shock=1 3.71** 3.22** 3.99 -0.19 -0.01 -6.92** (1.48) (1.56) (3.04) (0.78) (0.76) (3.11)

Negative Precipitation Shock=1 -0.16 -0.40 1.65 -0.54 -0.50 -1.88 (1.06) (1.21) (2.66) (0.66) (0.68) (1.92) Adjusted 푅2 0.095 0.119 0.110 0.063 0.066 0.203 Observations 12689 8466 4223 43836 41376 2460 Notes: Fixed effects model with dependent variable hours of work per week. Standard errors are clusteredat the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012

Table 2.8: Impact of precipitation shocks on hours of work of children by sector Agriculture Industry Service (1) (2) (3) (4) (5) (6) (7) (8) (9) All Son Daughter All Son Daughter All Son Daughter Positive Precipitation Shock=1 1.58 1.63 0.38 1.11 0.76 3.00 2.37* 2.44* -0.08 (1.17) (1.32) (3.22) (2.16) (3.44) (1.99) (1.36) (1.45) (4.27)

Negative Precipitation Shock=1 1.57 1.79 0.61 2.59* 1.58 3.37 -1.43 -1.54 -0.90 (1.42) (1.57) (2.70) (1.47) (1.78) (2.13) (0.97) (1.01) (3.58) Adjusted 푅2 0.056 0.063 0.128 0.090 0.135 0.175 0.088 0.094 0.389 Observations 24229 18012 6217 7773 3713 4060 17735 16434 1301 Notes: Fixed effects model with dependent variable hours of work per week. Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 95

Table 2.9: Impact of precipitation shocks on hours of work by age Agriculture Industry Service (1) (2) (3) (4) (5) (6) (7) (8) (9) All Men Women All Men Women All Men Women

Panel (a): 20-29 years old Positive Precipitation Shock=1 -0.08 1.11 -3.92 2.90 0.65 4.68 2.57** 2.60** 1.03 (1.38) (1.61) (2.66) (2.34) (2.94) (3.56) (1.19) (1.22) (4.76)

Negative Precipitation Shock=1 1.65 1.40 2.82 1.50 1.69 0.50 -1.61 -1.41 -5.93 (1.43) (1.59) (2.97) (1.36) (1.70) (2.07) (1.03) (1.08) (4.87) Adjusted 푅2 0.069 0.075 0.175 0.083 0.110 0.184 0.078 0.079 0.406 Observations 19149 14286 4863 6829 3703 3126 17270 16074 1196

Panel (b): 30-39 years old Positive Precipitation Shock=1 1.60 1.29 2.33 1.91 1.19 2.43 -1.78 -0.84 -10.97*** (1.11) (1.32) (1.79) (1.65) (2.00) (3.21) (1.31) (1.37) (3.52)

Negative Precipitation Shock=1 0.15 0.42 -0.60 1.13 0.56 3.59 -0.75 -0.95 -0.71 (1.23) (1.33) (2.26) (1.30) (1.69) (2.19) (0.97) (0.99) (2.10) Adjusted 푅2 0.095 0.113 0.101 0.132 0.163 0.178 0.073 0.077 0.289 Observations 21731 14954 6777 6437 3905 2532 20181 18665 1516

Panel (c): 40-55 years old Positive Precipitation Shock=1 2.19** 2.26* 1.51 4.76** 3.81 12.06 1.27 1.00 -4.23 (1.00) (1.16) (1.74) (2.38) (2.62) (7.64) (1.28) (1.30) (6.24)

Negative Precipitation Shock=1 -0.28 0.17 -2.01 -0.62 -1.33 6.86 -0.52 -0.40 -4.88 (0.93) (1.04) (1.56) (1.50) (1.60) (7.22) (0.96) (0.98) (3.87) Adjusted 푅2 0.064 0.074 0.056 0.162 0.177 0.289 0.080 0.081 0.537 Observations 37129 25261 11868 4910 3458 1452 18668 17802 866 Notes: Fixed effects model with dependent variable hours of work per week. Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 96

Table 2.10: Impact of shocks on the probability of quitting employment and labor force Employment Labor force (1) (2) (3) (4) (5) (6) All Men Women All Men Women Positive Precipitation Shock=1 -0.00 0.00 -0.02 -0.00 0.00 -0.02 (0.01) (0.01) (0.03) (0.01) (0.01) (0.03)

Negative Precipitation Shock=1 0.01 0.01 0.06* 0.01 0.01 0.05 (0.01) (0.01) (0.03) (0.01) (0.01) (0.03) Adjusted 푅2 0.095 0.100 0.143 0.094 0.093 0.139 Observations 90696 69378 21318 98295 75507 22788 Notes: Dependent variable is an indicator for quitting the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012

Table 2.11: Impact of precipitation shocks on the probability of quitting employment by sector Agriculture Industry Service (1) (2) (3) (4) (5) (6) (7) (8) (9) All Men Women All Men Women All Men Women Positive Precipitation Shock=1 0.01 0.02 -0.02 -0.01 -0.01 -0.02 -0.00 -0.01 0.23** (0.02) (0.02) (0.04) (0.03) (0.03) (0.07) (0.02) (0.02) (0.11)

Negative Precipitation Shock=1 0.03 -0.00 0.10** -0.00 0.02 0.01 0.01 0.01 -0.07 (0.02) (0.02) (0.04) (0.04) (0.03) (0.07) (0.01) (0.01) (0.12) Adjusted 푅2 0.098 0.126 0.149 0.192 0.108 0.208 0.097 0.106 0.285 Observations 50854 35632 15222 10475 6156 4319 30993 29049 1944 Notes: Dependent variable is an indicator for quitting employment. Columns (1)-(3) are restricted to individuals who worked in the agriculture sector at baseline, Columns (4)-(6) are restricted to those in industry, and Columns (7)- (9) to service. Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 97

Table 2.12: Impact of shocks on the probability of entering employment and labor force Employment Labor force (1) (2) (3) (4) (5) (6) All Men Women All Men Women Positive Precipitation Shock=1 -0.02 -0.04 -0.02 -0.03* -0.04 -0.02 (0.01) (0.03) (0.01) (0.01) (0.03) (0.01)

Negative Precipitation Shock=1 -0.01 0.01 -0.02* -0.01 0.01 -0.02* (0.01) (0.02) (0.01) (0.01) (0.02) (0.01) Adjusted 푅2 0.068 0.184 0.101 0.067 0.161 0.092 Observations 122473 29700 92773 114874 23571 91303 Notes: Dependent variable is an indicator for entering labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012

Table 2.13: Impact of precipitation shocks on the probability of quitting employment and labor force for head and spouse Employment Labor force (1) (2) (3) (4) (5) (6) All Head Spouse All Head Spouse Positive Precipitation Shock=1 -0.00 0.00 -0.02 0.00 0.01 -0.01 (0.01) (0.01) (0.04) (0.01) (0.01) (0.04)

Negative Precipitation Shock=1 0.02 0.01 0.09** 0.02* 0.01 0.07* (0.01) (0.01) (0.03) (0.01) (0.01) (0.04) Adjusted 푅2 0.084 0.060 0.157 0.093 0.072 0.156 Observations 65929 50696 15233 68315 52850 15465 Notes: Dependent variable is an indicator for quitting the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 98

Table 2.14: Impact of precipitation shocks on the probability of entering employment and labor force for head and spouse Employment Labor force (1) (2) (3) (4) (5) (6) All Head Spouse All Head Spouse Positive Precipitation Shock=1 -0.02 0.04 -0.01 -0.02 0.09 -0.01 (0.02) (0.05) (0.02) (0.02) (0.07) (0.02) Notes: Negative Precipitation Shock=1 -0.02* -0.00 -0.03** -0.02 0.02 -0.03** (0.01) (0.05) (0.01) (0.01) (0.06) (0.01) Adjusted 푅2 0.093 0.169 0.120 0.093 0.190 0.116 Observations 67465 8581 58884 65079 6427 58652 Dependent variable is an indicator for entering the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012

Table 2.15: Impact of precipitation shocks on the probability of quitting employment and labor force for children Employment Labor force (1) (2) (3) (4) (5) (6) All Son Daughter All Son Daughter Positive Precipitation Shock=1 0.00 0.01 -0.06 -0.01 -0.01 -0.08 (0.03) (0.03) (0.07) (0.02) (0.02) (0.06)

Negative Precipitation Shock=1 0.00 0.02 -0.04 -0.01 0.01 -0.02 (0.02) (0.03) (0.06) (0.02) (0.02) (0.06) Adjusted 푅2 0.119 0.146 0.193 0.126 0.155 0.187 Observations 24029 18286 5743 29121 22180 6941 Notes: Dependent variable is an indicator for quitting the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 99

Table 2.16: Impact of precipitation shocks on the probability of entering employment and labor force for children Employment Labor force (1) (2) (3) (4) (5) (6) All Son Daughter All Son Daugheter Positive Precipitation Shock=1 -0.03 -0.04 -0.02 -0.03* -0.04 -0.04** (0.02) (0.04) (0.02) (0.02) (0.04) (0.02)

Negative Precipitation Shock=1 0.00 0.00 -0.01 0.01 -0.00 -0.00 (0.01) (0.02) (0.01) (0.01) (0.03) (0.01) Adjusted 푅2 0.086 0.212 0.124 0.080 0.224 0.117 Observations 52933 20715 32218 47841 16821 31020 Notes: Dependent variable is an indicator for entering the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 100

Table 2.17: Impact of precipitation shocks on migration Men Women (1) (2) (3) (4) (5) (6) 20-29 30-39 40-55 20-29 30-39 40-55 Number of positive shocks 0.18 -0.66 0.15 0.24 -0.62 0.12 (0.25) (0.55) (0.19) (0.34) (0.55) (0.19)

Number of negative shocks 0.36 0.55** 0.03 0.65 0.54 0.24 (0.31) (0.25) (0.13) (0.74) (0.37) (0.21)

Age 0.22 -0.38 -0.18 0.56 -0.63 -0.06 (0.32) (0.59) (0.39) (0.94) (1.08) (0.50)

Read/Wrtie -0.58** -0.35 -0.38** 0.04 -0.05 -0.13 (0.27) (0.26) (0.17) (0.34) (0.22) (0.26)

Primary 0.30 -0.06 -0.29 0.01 0.10 -0.39** (0.26) (0.19) (0.22) (0.21) (0.10) (0.19)

Lower sec. 0.38 -0.07 -0.21 0.00 0.37 -0.37 (0.39) (0.16) (0.23) (0.20) (0.29) (0.26)

Upper sec. 0.12 0.10 0.18 0.51 0.09 0.40 (0.21) (0.17) (0.27) (0.43) (0.20) (0.49)

Post-sec 0.45 2.25 -0.37* 0.15 4.42 -0.58** (0.26) (2.40) (0.20) (0.23) (3.89) (0.27)

Tertiary 0.20 0.60 0.03 2.42 0.37 -0.43*** (0.24) (0.60) (0.26) (1.84) (0.30) (0.14)

Household size -0.12* -0.21*** 0.05 -0.10** -0.18*** 0.11 (0.06) (0.05) (0.05) (0.04) (0.06) (0.07) Adjusted 푅2 0.024 0.069 0.005 0.058 0.056 0.041 Observations 82250 61086 72194 88520 72797 81504 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and takes zero if an individual stayed in the rural area. Province clustered standard errors are in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 101

Table 2.18: Motivation for migration Men Women Type of shock: All Negative None Positive All Negative None Positive Job 61.17 63.41 60.73 56.89 6.91 5.79 8.03 5.53 Education 5.03 6.47 4.25 4.17 1.25 1.08 1.41 1.07 Military service 0.37 0.37 0.38 0.30 0.00 0.00 0.00 0.00 Following household 3.85 2.64 4.09 6.12 79.58 80.35 78.09 83.57 Other 29.59 27.11 30.54 32.52 12.26 12.78 12.47 9.82 Total 100 100 100 100 100 100 100 100 Notes: All numbers are in percent. Statistics are for head and spouse. Source: Statistical Center of Iran, LFS 2009-2012

Table 2.19: Impact of precipitation shocks on labor- migration (1) (2) (3) 20-29 30-39 40-55 Number of positive shocks 0.15 -0.56 0.02 (0.26) (0.50) (0.16)

Number of negative shocks 0.09 0.60** 0.04 (0.16) (0.24) (0.12) Adjusted 푅2 0.024 0.071 0.005 Observations 82250 61086 72194 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and is in the labor force, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 102

Table 2.20: Economic conditions and the impact of precipita- tion shocks on migration (1) (2) (3) 20-29 30-39 40-55 Number of positive shocks -2.86 -2.06 -0.98 (2.32) (1.09) (0.86)

Number of negative shocks 0.69 0.40 0.08 (1.07) (0.49) (0.42)

Age 0.08 -0.14 -0.23 (0.27) (0.51) (0.34)

Household size -0.11** -0.19*** 0.03 (0.05) (0.04) (0.04)

Unemployment at origin -3.71 -1.58 0.52 (4.83) (5.88) (8.18)

Unemployment at destination 3.28 1.42 -0.65 (4.72) (5.85) (8.14)

Share of agriculture VA at origin 4.95*** 4.45*** 6.69*** (1.12) (0.45) (0.96)

Share of agriculture VA at destination -5.25*** -4.61*** -6.85*** (1.06) (0.39) (0.94)

Wage at origin -0.19* -0.07** -0.08* (0.11) (0.03) (0.04)

Wage at destination 0.32** 0.17*** 0.13** (0.14) (0.04) (0.06) Adjusted 푅2 0.136 0.223 0.077 Observations 82243 61085 72183 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and takes zero if an individual stayed in the rural area. Province clustered standard errors in parentheses. † VA stands for value-added. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 103

Table 2.21: Economic conditions and the impact of precipita- tion shocks on labor-migration (1) (2) (3) 20-29 30-39 40-55 Number of positive shocks -1.34 -1.80 -1.02 (0.88) (0.98) (0.83)

Number of negative shocks 0.28 0.42 0.09 (0.43) (0.44) (0.40)

Age 0.12 -0.24 -0.30 (0.24) (0.50) (0.33)

Household size -0.16*** -0.19*** 0.02 (0.03) (0.04) (0.04)

Unemployment at origin -1.30 -1.51 1.08 (4.05) (5.75) (7.95)

Unemployment at destination 1.17 1.35 -1.21 (4.02) (5.73) (7.91)

Share of agriculture VA at origin 4.65*** 4.50*** 4.39** (1.11) (0.44) (1.67)

Share of agriculture VA at destination -4.83*** -4.62*** -4.54** (1.13) (0.39) (1.65)

Wage at origin -0.08** -0.06** -0.08* (0.03) (0.03) (0.04)

Wage at destination 0.15*** 0.16*** 0.12** (0.05) (0.04) (0.06) Adjusted 푅2 0.099 0.229 0.050 Observations 82243 61085 72183 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if an individual migrated from rural to the urban area and is in the labor force, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 104

Table 2.22: Impact of precipitation shocks on migration by role in household Couples Children (1) (2) (3) (4) (5) (6) (7) (8) Head Spouse Head Spouse Son Daughter Son Daughter Number of positive shocks -0.18 -0.21 -1.98 -2.35 0.19 0.24 -1.20 -2.99 (0.25) (0.25) (1.19) (1.54) (0.27) (0.32) (1.35) (3.37)

Number of negative shocks 0.29* 0.36** 0.30 0.46 0.11 0.54 0.31 0.99 (0.16) (0.17) (0.54) (0.66) (0.21) (0.55) (0.70) (1.69)

Age -0.15*** -0.14*** -0.18*** -0.16*** -0.01 0.01 0.05 0.01 (0.03) (0.03) (0.04) (0.04) (0.05) (0.02) (0.09) (0.01)

Household size -0.04 -0.03 -0.02 -0.01 0.07 0.02 0.04 0.02 (0.03) (0.06) (0.03) (0.06) (0.06) (0.03) (0.03) (0.02)

Unemployment at origin -0.60 -1.14 -5.70 -8.68 (6.23) (6.09) (5.27) (6.26)

Unemployment at destination 0.43 0.88 5.41 7.85 (6.18) (6.03) (5.22) (6.07)

Share of agriculture VA at origin 4.94*** 4.69*** 4.10** 6.31*** (0.62) (0.66) (1.75) (1.07)

Share of agriculture VA at destination -5.18*** -4.98*** -4.22** -6.70*** (0.59) (0.58) (1.72) (0.85)

Wage at origin -0.10** -0.14** -0.11 -0.29 (0.04) (0.07) (0.08) (0.20)

Wage at destination 0.19*** 0.23*** 0.18 0.47 (0.06) (0.08) (0.11) (0.30) Adjusted 푅2 0.016 0.033 0.150 0.149 0.011 0.031 0.092 0.239 Observations 202017 229279 162726 200859 168911 158209 125526 116983 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and takes zero if an individual stayed in the rural area. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 2. Precipitation Shock, Labor Market, and Migration 105

Table 2.23: Impact of precipitation shocks on labor migration by role in household Couples Children (1) (2) (3) (4) (5) (6) (7) (8) Head Spouse Head Spouse Son Daughter Son Daughter Number of positive shocks -0.19 0.03 -1.87 -0.92 0.23 0.13 -0.12 -0.58 (0.23) (0.10) (1.13) (0.84) (0.21) (0.10) (0.26) (0.80)

Number of negative shocks 0.31** 0.10 0.31 0.16 -0.05 0.11 0.05 0.21 (0.15) (0.07) (0.50) (0.33) (0.08) (0.13) (0.17) (0.40)

Age -0.14*** -0.02 -0.16*** -0.00 0.02** 0.01 -0.02 0.03 (0.03) (0.01) (0.04) (0.02) (0.01) (0.01) (0.03) (0.03)

Household size -0.06* 0.02 -0.04 0.01 0.03 -0.00 0.01 0.00 (0.03) (0.04) (0.03) (0.05) (0.04) (0.01) (0.03) (0.01)

Unemployment at origin -0.18 -0.75 -1.35 -2.09 (6.02) (0.81) (3.09) (1.34)

Unemployment at destination 0.01 0.59 1.30 1.89 (5.97) (0.76) (3.08) (1.30)

Share of agriculture VA at origin 4.83*** -0.10 0.88 -0.12 (0.56) (0.14) (2.30) (0.19)

Share of agriculture VA at destination -5.04*** -0.02 -0.94 0.03 (0.52) (0.07) (2.32) (0.14)

Wage at origin -0.09** -0.07 -0.03 -0.07 (0.04) (0.05) (0.02) (0.05)

Wage at destination 0.17*** 0.10 0.05*** 0.11 (0.06) (0.06) (0.02) (0.08) Adjusted 푅2 0.016 0.038 0.149 0.071 0.012 0.007 0.022 0.056 Observations 202017 229279 162726 200859 168911 158209 125526 116983 Notes: The dependent variable takes value one if individual migrated from rural to the urban area and is in labor force, and takes zero otherwise. Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012. Chapter 3

Parents’ Investments in the Quality of Education, The Case of Ghana

3.1 Introduction

While most economic development has been on the shoulders of governments, the undeniable role of families in the development of countries, especially for investment in education, has been established in prominent research by Becker (1962) and Lucas et al. (2002). Two important dimensions of parents’ decisions about children’s schooling are already highlighted in the literature. The first one is parents’ authority in enrolling their children in school (Glomm 1997), which naturally leads to paying the school costs. The second is determining the extent to which children devote their time to school-related activities (Basu and Van 1998). Although the problem of low enrollment rates seems to be solved in most developing countries and child labor is illegal in almost all countries, poor quality of education rises as a giant challenge. Based on the international reports on schooling outcomes, 21.1% of Ghanaian 6th-grade students have not learned to read, and 43.1% have not learned math.

106 Ghadir Asadi Chapter 3. Investment in the Quality of Education 107

These numbers for Namibia are 13.6% and 47.7%. For South Africa, they are 27.2% and 40.2%, for Congo 37.9% and 31.9%, and for Ethiopia, 54.2% and 56.3% (Brookings 2020).1 Since governments in the developing world are unable to spend more money to substantially improve school quality (Glewwe and Kremer 2006), an investigation of families’ incentives for more investment in the quality of education is warranted.

In this paper, we investigate families’ investment in their children’s human capital by mea- suring their expenditure on schooling necessities and supplemental materials. Our basic idea is: Parents understand the impact of their children’s education quality on their expected future wages, which incentivizes them to improve their children’s education quality by in- vesting in educational materials. For instance, a low quality of education blocks all other channels for increases in human capital. This lower quality of attained education has two main channels by which it decreases future expected returns. First, low education quality reduces the probability that children stay in school and attain any educational qualification (Castelló-Climent and Hidalgo-Cabrillana 2012). Second, low education quality decreases the future return of any qualifications attained (Card and Krueger 1992).

Modeling parents’ decisions on the quality of educational attainment by their children has been pursued with two different approaches. Parallel to earlier research, which treated the “quality” of children as a substitute for their quantity (Becker and Lewis 1974; Becker and Tomes 1976), research on returns to education compares the investment in the quality of children with the stream of future earnings (Mincer 1958). The literature on the quality- quantity trade-off assumes that child quality can be bought in a market with a determined price and that parents can choose the number and quality of their children easily. While this approach did improve our understanding of families’ decisions about household size,

1 They gather African literacy and numeracy achievement data from three sources. (1) SACMEQ (available for 14 countries). (2) PASEC (available for 11 countries). (3) National examinations for Ethiopia, Ghana, and Nigeria. Ghadir Asadi Chapter 3. Investment in the Quality of Education 108 the assumption about the purchasability of quality is very strong, and it is not entirely compatible with the reality of human capital accumulation. On the other side, most of the research on returns to education has considered years of education as their variable of interest and has employed strong perfect foresight assumptions in modeling, while compromising on uncertainty about future returns and the length of a working life, costs of schooling (direct and indirect), and taxes (Heckman et al. 2003).

Studies of returns to education have found a positive rate of return, especially for the primary level of education. However, from the small amount of families’ investments in education, scholars have concluded that families cannot finance the stream of investment costs in sucha way that they would be able to pay it back with the stream of future returns. These findings have led to the development of a rich body of literature on credit constraints (Caucutt and Lochner 2012). Despite the existing literature, which regards expenditure on schooling as the cost of schooling, we treat this expenditure as an important input in a child’s human capital formation. While other researchers have treated families’ decisions about monetary investment as an important input in their human capital formation (Heath 2017), to the best of our knowledge, ours is the first paper to formalize this investment in a model and provide empirical evidence for it.

We develop an overlapping generations model like that of Glewwe (2002), in which parents decide on the level of investment in the child’s human capital. Each individual lives for three generations. First as a child, which is the time for accumulating human capital in the school system. Then as a young adult whose earnings are proportional to their accumulated human capital in childhood. This generation needs to make four important decisions. They need to decide how much to invest in their children’s schooling. This investment is either in the shape of buying school necessities or devoting a higher share of their children’s time to school-related activities. They also need to decide how much they want to save for their Ghadir Asadi Chapter 3. Investment in the Quality of Education 109 future and how much to support their parents. Finally, the third and oldest generation has two sources of income, saving and support from children, and nothing to do.

Based on our model’s prediction, if the available schooling is better, and if parents expect more altruistic behavior from their children, then parents tend to spend more on their chil- dren’s schooling. Parents also tend to decrease their child’s work activity in response to an increase in schooling quality. A higher interest rate has a negative effect on parents’ monetary investment in schooling and increases children’s work activity.

In the empirical part of our research, we use our theoretical model to introduce a set of instruments that help us identify the effects of children’s schooling output on parents’ in- vestment. We used the Ghana Socioeconomic Panel Survey (2009-2010), since Ghana is a good example of a country experiencing significant increases in enrollment rates in primary school but still finding the quality of schooling lagging behind. Despite the significant im- provement in Ghanaian primary education in terms of accessibility and quality of education supplied, there is a significant gap between the available quality and a minimum acceptable quality of education, especially in rural areas. On the bright side, net enrollment in primary education increased from 62% in 1999 to 91% in 2015. Over the same period, the primary school completion rate went from 65% to almost 100%, repeaters decreased from 4.2% to 1.9%, and the share of trained teachers increased from 29.2% to 45.5%. On the other hand, in 2015, 55% of teachers were not trained, 16% of students in primary schools were over- aged, and 8.1% of children of primary school age were out of school. Our estimation results show that there is a strong relationship between children’s schooling output and parents’ investment and that parents adjust their investment in education in response to changes in their children’s cognitive achievements. Ghadir Asadi Chapter 3. Investment in the Quality of Education 110

3.2 Background

Since the pioneering works of Becker and Lewis (1974) and Becker and Tomes (1976), poli- cies driven by the child quality-quantity trade-off have been at the core of every proposed development program, especially those for African countries. There is universal agreement that education should be among the top priorities of any development program (UNICEF et al. 1991; United Nations 1994). Emphasis on girls’ education is also often at the forefront of development priorities, because it substantially decreases the fertility rate and enormously benefits the next generation (USAID 1995). Moreover, basic literacy and numeracy have ben- efits not only for the children themselves but also for their parents, other family members, and society at large.

Effects of educational attainment on macroeconomics have been extensively discussed in Barro (1991), Benhabib and Spiegel (1994), Barro and Sala-i Martin (1995) and many other papers. This literature is summarized in Bils and Klenow (2000), who showed that schooling has an impact on economic growth, but the effect is not as strong as previous literature claimed. They found that schooling could explain less than one-third of cross-country dif- ferences in growth. On a micro level, the literature on returns to education has two main messages. First, the return to education is positive (Heckman et al. 2003); and second, most of the return is associated with real skills and the quality of education, rather than with years of education (Ingram and Neumann 2006; Hanushek and Zhang 2006). Weisbrod (1962) in- troduced non-wage returns to schooling, from both personal and societal perspectives, and Rosenzweig (1995) introduced three channels for positive private returns to schooling. The first is improving access to information sources such as books, websites, newspapers and instruction manuals; the second is improving the ability to discover new information from external sources or learning; and the third is developing abilities to learn. Ghadir Asadi Chapter 3. Investment in the Quality of Education 111

All this research has led to a series of policies and foreign aid initiatives for developing countries to increase basic literacy, measured by the enrollment rate and average years of schooling. This financial support has substantially helped the recipient countries. Forin- stance, the net enrollment rate for primary education for Niger increased from 23% in 1990 to 62% in 2014. During the same period in the Gambia, the enrollment rate increased from 47% to 70%. In the Mozambique, it increased from 43% to 87%, and in Guinea from 26% to 75%. Other developing countries have experienced similar increases in primary education enrollment rates. However, statistics on completion of primary school are less satisfying.

During the period when enrollment rates sharply increased, research on the quality of edu- cation has been conducted. The literature on the definition of school quality started in the late 80’s and focused on either input, process, or output of the education system, to seek to identify a proper definition for the quality of education (Cheng and Tam 1997).2 Re- searchers have tried to find the most significant determinants of school output. Before 1990, they identified curriculum, learning materials, instructional time, classroom teaching, and students’ learning capacity as the main factors affecting educational quality (World Bank 1990). Finally, improvement in school quality results from developing new educational in- frastructure, growing culturally-contextualized curricula, training and monitoring teachers, and privatizing school systems. Recently, the role of school regulations has been high-lighted in the literature (Welfare et al. 2020; Lawson et al. 2020).

However, providing high-quality schooling for all students can be extremely challenging.

2 Following this literature, researcher and institutions established global measures of education quality, (Vedder 1994; Barro and Lee 1996), Southern and Eastern Africa Consortium on Monitoring Educa- tion Quality (SACMEQ), Programme d’Analyse des Systemes Educatifs de la CONFEMEN (PASEC), Third International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), projects administered by the International Association for the Evaluation of Educational Achievement (IAEEA), the Programme for International Student Assessment (PISA) project managed by the OECD, Latin American Program for the Assessment of Quality in Education (Laboratorio), and a group of 40 African, Arab, Asian, European, and Latin American countries in Monitoring Learning Achievement (MLA) are among them. Ghadir Asadi Chapter 3. Investment in the Quality of Education 112

Mehrotra and Buckland (2001) assessed the challenge of providing high-quality teachers and developed a long list of potential restrictions and difficulties, showing how hard it can beto simply provid the teachers for an existing school. Providing all of the factors leading to high- quality education is more difficult for developing countries. Rapid increases in enrollment rates strain financial and human resources, and policymakers are forced to choose between providing national-level access to schooling and providing high-quality education, but only to some (Eisemon et al. 1993; Hanushek 1995; Jimenez et al. 1991). The final result is that education quality did increase, but unfortunately not at the same pace as enrollment rates. Recent reports show that, for several countries, increases in the enrollment rate in primary education in schools of the available quality did not result in developing even basic skills. To elaborate on how poor this crude enrollment rate was as a measure of human capital, we offer a quote from Word Bank reports (World Bank 2006):

In Ghana, Niger, Peru, and Yemen, no more than 19 percent of sixth graders reached mastery levels in language; and no more than 11 percent do so in mathematics. In Ghana, where average test scores increased over 15 years, fewer than 10% of students have reached the mastery level in maths, fewer than 5% in English.

In India, nearly 50 percent of 7-10 years old could not read fluently in their local language at the first grade level. Mastery in French and maths among grade 6 students in 1999 in Niger was 13 and 11%, respectively; in Yemen, grade 6 students’ mastery of Arabic and maths were 19 and 9%, and in Peru, they were 8% for

Spanish and 7% for maths. In Vietnam, only 51% of grade 5 students were found to perform as independent readers.

As our very early understanding of educational opportunity emphasizes, especially in the initial stages of schooling, family characteristics are more important determinants of edu- cational achievement than other factors like school quality or teacher experience (Campbell et al. 1966). Having said that, we know that the rate of return to primary school investment might be much lower or even negative from a family’s point of view, even when it is high from Ghadir Asadi Chapter 3. Investment in the Quality of Education 113 society’s point of view. This is because a family receives only a portion of the total benefits from schooling but pays a significant share of the costs (Lloyd and Blanc 1996). Moreover, a family might be better off pursuing its goal of upward mobility without investing inits children’s education. In other words, for most of the developing world, if a family is going to improve its socio-economic status through the upward mobility of at least one child, it may be more likely to achieve its goal by conceiving as many children as the parents can afford than by investing in a smaller number of “higher-quality” children (Eloundou-Enyegue 1994).3

Comparing expenditure per student in low-income developing countries with that in high- income countries reveals that households bear much of the cost of education in developing countries. For instance, average public expenditure per student in low-income developing countries in PPP dollars was $202 in 1996. It was $833 for middle-income countries and $3,059 for high-income countries (Glewwe and Kremer 2006). The resource constraint is a universal obstacle in the developing world. While some countries, like Senegal, spend a considerable fraction of their budgets on education, most of the expenditure goes to staff compensation (20.7% of the budget and 76% on staff in Senegal in 2011). In 2010, such rates for Benin are 26% and 79.6%, for Ethiopia are 24% and 80%, for Guinea are 12% and 78%, and for Namibia are 23% and 89%. Thus, governments in the developing world are spending relatively small amounts of money, and a big portion of that money goes to staff compensation, leaving only a small portion for schools, books, and the other materials students need. That is why parents are usually responsible for providing textbooks, food, uniforms, transportation, and even chairs, and schools pass these costs on through official or unofficial fees.

Ghana is not very different from other developing countries. While the government spends

3 Kabeer (2000) has an extensive discussion about the investment in children schooling from their parents’ view and the difference between public and private return to education. Ghadir Asadi Chapter 3. Investment in the Quality of Education 114

21.2% of its budget on education, 87.5% of that goes to staff compensation. This low level of investment in the quality of education leads to the poor performance of Ghana in the various measures of school quality, input, process, and output. For instance, in the 2009- 2010 school year, only 52% of public primary schools had a functional toilet, only 59% had access to water, and only 48% of the male teachers were trained. From our survey, only 15% of primary level students in rural areas have access to all textbooks they need, and 95% of them have only one teacher for all courses. Since parents are responsible for providing most school materials, it is important to know how families decide how much to invest in the quality of their children, in terms of buying these materials.

We focus on the quality of education to highlight the difference between years of schooling and actual learning or growth in human capital. We follow Johnson and Stafford (1973), who were the first authors to define expenditure on the quality of education as expenditure per pupil per year and measured it by per pupil expenditure of governments across states in the U.S. We use the same definition, but from the perspective of families. We also use quality of schooling, cognitive achievement, and quality of children to refer to the quality of received education, measured by standard achievement tests in our survey.

3.3 Theoretical background

Becker and Lewis (1974) and Becker and Tomes (1976) formalized the choice between quality and quantity of children and argued that these two are closer substitutes for each other than any other goods in families’ consumption baskets.4 Becker and Barro (1988) showed that in

4 There is not a substantial agreement on the existence of such a trade-off. Using different methods in finding causality, people tried to examine the existence of such a trade-off. Black, Devereux, and Salvanes (2005) find no adverse effects of more siblings on school attainment with controlling for birthrank. Conley and Glauber (2006) found that children in a bigger family are less likely to attend private school. Despite Li, Zhang, and Zhu (2008) and Rosenzweig and Zhang (2009), who find a negative effect of family size Ghadir Asadi Chapter 3. Investment in the Quality of Education 115 open economies, fertility depends positively on the long-term real interest rate, the degree of parents’ altruism, and the growth of child-survival probabilities. Fertility depends negatively on the rate of technical progress and the growth rate of social security. Becker et al. (2014) develop a model based on the idea that parents have an old-age security incentive to invest in the quality of children, and that if selfish parents have altruistic children, they invest in child quality.

Other researchers have used variations of these models and have gotten almost the same result as Becker did. The above models have one strong assumption in common: quality is an easily accessible and attainable good, which can be purchased on a spot market for a specified price. This is far from reality! Human capital has a very complex, long-term and risky production process. As Ben-Porath (1967) stated in his pioneering work, quality cannot be regarded as a purchasable good, and a child needs to get engaged in the process of creating human capital, using his own innate or acquired abilities, in the context of the constraints and opportunities offered by institutional arrangements.

Parallel research on the returns to education has sought to explain the earning incentives of investment in education. Starting with Mincer (1958), researchers have tried to estimate the rate of return to education and establish a well-defined investment strategy for people through the stream returns. As Heckman et al. (2003) argued, this research neglects major determinants of actual returns, such as the costs of schooling (direct and indirect), taxes, the length of working life, and uncertainty about future returns at the time schooling decisions are made. However, following this literature, other scholars have developed models for selecting optimal years of education (Bedi and Marshall 2002; Glick and Sahn 2006; Orazem and King 2007).

on children’s schooling attainment, Angrist, Lavy, and Schlosser (2010) did not find any negative effects. Liu (2014) used China’s new family planning policy and children’s height and educational attainment as a measure of quality. He found a significant trade-off between quality and quantity when he used height but no meaningful trade-off for educational achievement. Ghadir Asadi Chapter 3. Investment in the Quality of Education 116

Another branch of literature has shown that the quality of education and real skills are better predictors of real earnings than years of education (Card and Krueger 1992; Ingram and Neumann 2006; Hanushek and Zhang 2006). Models of choosing years of schooling with the inclusion of a human capital formation function have incorporated the effects of time spent in school, ability and school quality (Orazem and King 2007), and family investment in their child’s schooling by providing schooling material (Glewwe and Kremer 2006). In the same context, other researchers have modeled investment in human capital instead of years of schooling, and they include an implicit or explicit function for human capital formation (Glomm 1997; Glewwe 2002; Galor et al. 2005; Galor 2011).

Our approach explains how families use the production function for children’s human capital to decide how much to invest in their children. We use an overlapping generations model like that of Glewwe (2002). Our research contributes to the existing literature by providing a new framework, one that explains parents’ investments in education in terms of two factors: a variable factor, namely parents’ expenditure on human capital formation and a locally fixed factor, namely school quality, which represents an important constraint for familiesin the developing world, especially in rural areas. We consider family expenditure on schooling materials as an input in the human capital formation function, and it appears in both the budget constraint and the return to investment in human capital. If we consider the expenditure on schooling simply as the price of schooling, as previous research has done, then the variations in these expenditures by families remain unexplained and overly simplified. But from our observations, we know that variation in schooling costs is much more than a price for a normal good.

Moreover, expenditure on child schooling is more discretionary than predetermined. School fees are usually exogenous to families and fixed, but expenditure on quality of education has many more dimensions that are discretionary. Our paper treats discretionary spending on Ghadir Asadi Chapter 3. Investment in the Quality of Education 117 quality of education as a special investment, with the family deciding how much to invest based on perceived return. That is where school quality comes into the model. It helps us understand investment behavior, and it has potential as an instrument for human capital.

Two things make our approach different from prior research on returns to education. First, while other researchers use years of education as a variable of interest, we use schooling output or student learning as our focus variable. We changed the variable of interest because parents do not understand the return to education as we calculate it for deriving optimal years of education, or, as Heckman et al. (2003) asserted, they are uncertain about the future return at the moment of decision, and they therefore may not be able to invest optimally. However, parents have a very good measure and understanding of their child’s learning. They see the child’s ability to read and to calculate simple math. This difference is highlighted in our model as a human capital formation function, and we used it to construct our identification strategy.

The second important difference is in the treatment of parents’ expenditure on schooling. Prior research has considered expenditure as a price for schooling, and it only appears in the budget constraint. Studies of credit constraints, families’ access to the loan market, and the implications of this access for investment in the education have emerged from this approach to school cost (Lochner and Monge-Naranjo 2012; Caucutt and Lochner 2012). But we are considering school cost as an investment and an important input for constructing students’ schooling outcomes. This highlights the role of parents’ expenditure in children’s performance in school and the family’s need to balance the trade-off between rewards from investment in children’s schooling and the burdening pressure of their budget constraints.

Using data from the developing world, we encountered a low level of available schooling quality and its discouraging effects. The discouraging effects of low school quality have been mentioned in earlier studies. As USAID (2007) reported, the most frequently given reasons Ghadir Asadi Chapter 3. Investment in the Quality of Education 118 for children aged 6-11 not to be in school were either that it was too expensive or that school was uninteresting or useless. The more frequent response among rural children that schooling is unnecessary or irrelevant is suggestive of the poor quality of education in rural schools and the possible limited relevance of the school curriculum to the needs of the rural population (Pryor and Ampiah 2003).

3.3.1 Model

Assume an overlapping generations model, with three periods and identical individuals with no population growth. In the first period, an individual is a child, and parents makethe child’s life decisions. After that, when individuals are young adults and have their own children, they need to decide how much to save for their old age, how much to invest in their children’s schooling quality, and to what extent to send their children to work. The young adults generation maximizes their utility as a function of their consumption in the

1 two coming periods. Their utility function has a CRRA form, where 휎 is the elasticity of inter-temporal substitution and 훽 is the rate of time preference.5

1 1 푈 푂 = [푐1−휎 + 푐1−휎] (3.1) 푡 1 − 휎 푡 1 + 훽 푡+1

Each generation has a length of one. Children split their time between work (푛푡) and

education (1 − 푛푡) but their parents decide on the distribution. The young generation works full time, and the older generation does not work at all. Assuming the young generation’s

5 We developed a model with selfish parents that maximize their own inter-temporal consumption. Wecan change our assumption and consider altruistic parents and our general results still hold. For a discussion on different shapes of parents’ altruistic transfer to their children, one can lookat Laitner (1997). Ghadir Asadi Chapter 3. Investment in the Quality of Education 119 wage to be equal to their human capital, we will have:

푂 푦푡 = ℎ푡 (3.2)

When the young generation’s human capital is the same as their income, what we call their human capital is proportional to their income. We normalized children’s wage to be one, implying their income is:

푗 푦푡 = 푛푡 (3.3)

In our model, the young generation makes all decisions. They need to decide how much of their income to save for their old age, how much to invest in their children’s human capital, and how much of their child’s time devote to school activities. But, as we explained, human capital is not a purchasable good. Both parents and children need to participate in a cooperative process to create the child generation’s human capital. Researchers have considered different inputs for human capital. Glewwe (2002) considered school inputs and years of schooling, Galor et al. (2005) and Galor (2011) used parental time investment in education and the rate of technological progress, Glomm (1997) used school input, parents’ education and the amount of time children spend on schooling, and Glewwe and Kremer (2006) considered years of schooling, school and teacher characteristics, child characteristics (including innate ability), and household characteristics. We assume human capital has the following form.

푎1 푎2 푎3 ℎ푡+1 = 휃 푒푡 푔푡 (1 − 푛푡) (3.4)

where 푔푡 is the amount that parents invest in their children’s schooling materials and com- plementary educational inputs such as textbooks, exercise books, notebooks, clothing, and

so on. We assume that parents assign a share of their income (휏) to this investment, and 푒푡

is the school quality. We can translate 1 − 푛푡 and 푔푡 as the family’s input into the children’s Ghadir Asadi Chapter 3. Investment in the Quality of Education 120

human capital formation, and 푒푡 as school input. In our model, parents make two types of investment in their children: first, by directly investing in school material and devoting a portion of their income to children’s human capital, and second, by decreasing the required work activity of children, which translates to an increase in the amount of available time for school. Moreover, the parents’ human capital does not help the formation of the children’s human capital, unless through 휏. One could interpret parents’ time devoted to helping their children in their schooling as an equivalent wage built into the direct investment, as if par- ents are paid by the family. The important point to consider is that families cannot clearly observe the school input and human capital production functions. Instead, families observe their children’s human capital accumulation through their skills like mathematics, reading, and writing, or simply their school grades. The family’s investment decision is a response to their children’s human capital accumulation or cognitive achievement. This fact has an important practical implication that we explain in the next section.

The young adult generation has two ways of saving for their old age. First, they can save money and receive interest in the next period. Alternatively, they can invest in their chil- dren’s schooling and receive a share of their children’s income in the next period, which

푒 depends on their expectation of their children’s altruism factor, 푘푡+1. The young genera- tion’s consumption in each period is:

퐶푡 = ℎ푡 − 푔푡 + 푛푡 − 푆푡 − 푘푡ℎ푡 (3.5) 푒 퐶푡+1 = (1 + 푟)푆푡 + 푘푡+1ℎ푡+1

where 푘푡ℎ푡 is the amount of money that they give to their parents. The young adult genera- tion maximizes their utility subject to budget constraints and institutions. We consider only a public school regime, in which the quality of the school or school input is an exogenous variable for each generation, and we assume a constant return to scale technology for the Ghadir Asadi Chapter 3. Investment in the Quality of Education 121

human capital production function, namely 푎1 + 푎2 + 푎3 = 1. The young generation solves the following maximization problem:

1 1−휎 1 1−휎 Maximize [푐푡 + 푐푡+1 ] 푆푡,푛푡,휏 1 − 휎 1 + 훽

푂 푗 푠.푡. 퐶푡 = 푦푡 − 푔푡 + 푦푡 − 푆푡 − 푘푡ℎ푡 푒 (3.6) 퐶푡+1 = (1 + 푟)푆푡 + 푘푡+1ℎ푡+1

푎1 푎2 푎3 ℎ푡+1 = 휃 푒푡 푔푡 (1 − 푛푡)

푂 푗 푂 푦푡 = ℎ푡 , 푦푡 = 푛푡 , 푔푡 = 휏 푦푡

After substitutions, we have a function of 푆푡, 휏, and 푛푡 that represents the young adult generation’s inter-temporal utility function. Solving the derived first-order conditions iden- tifies their investment decisions. In Equation C.2 in Appendix C.1, we derive the first-order conditions, and in the interior solution, we have:6

푒 푎1 1−푎3 푎3 1 푘푡+1 휃 푒푡 푎2 푎3 1 휏 = ( ) 1−푎2−푎3 (3.7) ℎ푡 1 + 푟

Our model’s basic hypothesis can be derived from Equation 3.7. School quality and overall productivity of the school system are two major determinants of investment in children’s schooling. Moreover, parents invest a higher share of their income in their children’s schooling when they think that their child will give a higher share of their future income to the parents. In other words, if parents expect a higher altruistic attitude from their children, they invest more in their children. And finally, a higher interest rate can negatively affect parents’

6 By replacing equations 3.7 and 3.8 in any of the first-order conditions, we obtain an explicit function of 푆푡. You can find the relation for 푆푡 and other details about the relations in Appendix C.1. Here we discuss the interior solution, but we explain the possibility of a corner solution in Appendix C.1, as well. Ghadir Asadi Chapter 3. Investment in the Quality of Education 122 investment decision, as it increases the return to the alternative to schooling, namely saving.

푒 푎1 1−푎3 푎3 푎 푘 휃 푒 푎 푎 1 3 푡+1 푡 2 3 1−푎 −푎 1 − 푛푡 = ( ) 2 3 (3.8) 푎2 1 + 푟

It is evident from Equation 3.8 that the amount of time that children have for their schooling is an increasing function of school output and of the gain of families from their children’s future income. In other words, anything that increases 휏 in Equation 3.7 will increase the

available time for schooling. The interesting fact about 푛푡 is that it is not related to family

income, and the model’s structural parameter can lead to a corner solution for 푛푡. While this violates a continuous version of the luxury axiom (Basu and Van 1998), other researchers

have previously violated that axiom (Bhalotra and Heady 2003). Since we can define 푛푡 as family-related non-school activities, our finding is not the exact opposite of the findings of research on child labor.

We can slightly modify our model to explain other findings in the literature. For example, we can add a minimum level of consumption for each family. In that case, there is a situation where families have credit constraints on financing the optimal level of investment (Lochner and Monge-Naranjo 2012). Our model explains some interesting facts in the data as well. For example, about three to five percent of families whose children do not have any needed books have at least one of the following: an air conditioner, a freezer, a satellite receiver, a dishwasher, and a car (or a combination thereof). This fact can be explained by our model. Since parents’ investments in their children don’t depend on their income, but on the school system characteristics, families’ might have the resources, but they do not invest in their children as a result of poor school infrastructure or an expectation that the children will not be altruistic. Ghadir Asadi Chapter 3. Investment in the Quality of Education 123

3.3.2 Substitutability

We can consider a more general production function for human capital formation. Our production function makes the important assumption that school quality and parents’ in- vestment in schooling material have a unit elasticity of substitution. We can relax this assumption by changing the production function to a nested CES function,7 and check par- ents’ behavior under different substitutability assumptions.

−훼 −훼1 −훼1 훼/훼1 −1/훼 ℎ푡+1 = 휃 (푎1푒 + 푎2 (훿1 푔 + (1 − 훿1)(1 − 푛푡) ) ) (3.9)

1 We know that 휂 = 1+훼 is the elasticity of substitution between two blocks of inputs, which 1 here are school inputs and family inputs, and 1+훼1 is the elasticity of substitution between family inputs. As 훼 → 0, our function approaches a Cobb-Douglas function, and the elastic- ity of substitution approaches one. As 훼 → ∞, our function approaches a Leontief production function, where the elasticity of substitution is zero.

Without going into a detailed derivation, we present a family’s investment response to a change in school quality in Figures 3.1 and 3.2.8 Here we consider the case where the elasticity of substitution is zero. We can interpret the school input as all the inputs that families are unable to provide with any amount of money, teaching the course materials, solving children’s problems, connecting different course materials, and evaluating child progress, for instance. In most rural areas, the understanding is that parents spend most of their money on food and clothing, and they generally cannot help their children with their school assignments or provide special educational materials like extra classes or private tutoring. Our survey

7 We intend to use a nested CES developed by Sato (1967) because with usual CES and more than two inputs, we are imposing strong pairwise elasticity of substitution assumption and the power of production 1 function can be translated into an elasticity form (휂 = 1+훼 ), only if the pairwise elasticity of substitution assumption is correct (Uzawa 1962). 8 We explain the derivations in Appendix C.1. Ghadir Asadi Chapter 3. Investment in the Quality of Education 124 confirms this understanding. While 88% of families spend nothing on extra classes, only24% spend nothing on clothes, and 42% spend nothing on student’s food. Although there is strong evidence for a positive effect of food on student performance at school (Jyoti et al. 2005; Mahoney et al. 2005), food cannot be considered a substitute for school quality. Therefore, we expect that elasticity of substitution between school input and families’ input is relatively low in rural areas.

Assume that families are investing 휏ℎ¯ 푡 and school input is fixed at 푒¯푡 (Point 퐴 in Figure 3.1). From microeconomics, we know that nothing can encourage people to invest more in their children, because that investment does not increase the children’s human capital while the school input is fixed. For instance, assume that the interest rate goes down. We expect that families find schooling to be a reliable alternative for investment. That is not the casehere, where the school input is fixed at 푒¯푡. It is not surprising that point 퐸 is a possible equilibrium for families, where they invest 휏ℎ˜ 푡, not investing enough to produce the maximum possible amount of human capital.

The general investment response of families to changes in the elasticity of substitution is depicted in Figure 3.2. As we expected, when the school input is fixed, a positive change

푒 in 푘푡+1, 휃 or a negative change in 푟 leads to a positive change in 휏. But as the elasticity of substitution becomes smaller, the positive effect of the mentioned changes quickly approaches zero. In the extreme case, when 훼 → ∞ and the elasticity of substitution approaches zero, families’ investment behavior approach what we explained based on Figure 3.1.

From our theoretical analysis, we conclude that, as school quality increases, parents will be encouraged to invest more in their child’s schooling and supplementary material and give them more time to spend in school. Other factors like families’ other opportunities for investment (푟) can change families’ investment decisions, but their effect is limited when school input and families’ input are complements of each other in producing schooling output Ghadir Asadi Chapter 3. Investment in the Quality of Education 125

푒푡

1 1−푎1 − 푒 = 휏ℎ푡( ) 훼+1 푎1

2 ℎ푡+1 퐶

퐸 ¯ 푒¯푡 ℎ푡+1 퐴 퐷

1 ℎ푡+1 퐵

휏ℎ푡 휏ℎ˜ 푡 휏ℎ¯ 푡 Figure 3.1: Perfect complement inputs

휕휏 푒 휕푟,푘푡+1,휃

휕휏 휕푘푒 ,휃 푡+1

훼

휕휏 휕푟

Figure 3.2: General response for children. As we explained, the implications of the model are both in agreement with some, and contrary to other aspects of previous research. The theoretical analysis also explains some facts in the data and indicates that some of our model’s predictions need to be tested with new data. Ghadir Asadi Chapter 3. Investment in the Quality of Education 126

3.3.3 Private regime

One possible extension of our theoretical approach is to consider the more general case of the possibility of choice over school quality. In the previous section, we assume school quality is fixed. Instead, we can modify our assumption to assume parents can buy different school inputs by paying more money to the school system. In that case, parents need to devote another fraction of their income to cover this additional investment. Considering a private school regime produces a result that looks like a standard quality-quantity trade-off model with one important difference. In the quality-quantity trade-off approach, parents buy amounts of quality with a specified price, but in our model and under the private school regime, they need to buy different types of inputs and mix them in the human capital production function to create the amount of human capital they desire for their children. Logically, they adjust their investment according to the return to that investment, which is determined by the human capital formation function. Since we are not going to explore this possibility in our empirical investigation, we do not extend the model to cover choice over school quality.

3.4 Data

To investigate families’ investments in child quality, driven by their incentives generated by the schooling quality available, we need a household income and expenditure survey that contains information about the schooling cost, along with measures of child quality. We also need school quality data that can be matched with the survey. We found data having these two requirements for Ghanaian families and schools, making them a good candidate for investigating investment in the quality of children. Ghadir Asadi Chapter 3. Investment in the Quality of Education 127

3.4.1 Household survey

We use a data set from the World Bank Living Standards Measurement Study (LSMS)9 micro data for Ghana. Ghana Socioeconomic Panel Survey, 2009-2010, is a joint work between the Institute of Statistical, Social and Economic Research at the University of Ghana (ISSER) and Economic Growth Center at Yale University.10 This is a planned panel survey whose first round of data we use. This survey is regionally representative data forthe ten regions of Ghana, collected between November 2009 and April 2010. It contains data for 5010 households from 334 Enumeration Areas (EAs). The 334 clusters were selected from a master sampling frame which is the 2000 Ghana Population and Housing Census. The clusters were selected from the list of EAs in each region, using a simple random sampling technique. Household sample weights have been computed by the surveyor and applied for the estimation of the survey results.

The survey has the following twelve parts. It starts by collecting household background information and then information on non-resident relatives and spouses, household assets - farm assets and financial assets, agricultural production - land information, crop sales and storage, non-farm household enterprises (types, assets, finance, labor, revenue, and expenses), household health including insurance, anthropometry and immunization, women’s health, men’s health, a children’s module (health, Digit Span test, Raven’s Pattern Cognitive Assessment), psychology and social networking, household consumption and expenditure, and finally housing characteristics.

We are particularly interested in three sections of the survey. The section on household background information asked about the educational records of family members. We are using household background information in three ways. It provides our dependent variables,

9 This data set could be downloaded from http://microdata.worldbank.org 10Data, further description, and help is also available in http://egcenter.economics.yale.edu/ Ghadir Asadi Chapter 3. Investment in the Quality of Education 128 which are parents’ investment in child quality and book provision. The household information section also enables us to divide the school cost into sub-categories like school fees, the contribution of family to the PTA, uniform and sports clothes, books and school supplies, transportation, food, extra classes, and other such expenses. Second, it provides a set of variables that can be used as a measure of school quality. These variables are absenteeism records of teachers, whether there is one teacher for all classes, the amount of fees that the school is receiving, the amount of the PTA membership fee, whether there is a free feeding program at school, book provision by the school, distance to school, and school type. Third, we can find parents’ educational records in the household background information partof the survey as well.

The second relevant section is the survey section for the children’s module, in which there are five sets of questions for all children age 5-15 in the household. These categories are a digit span test, forward and backward, Raven’s pattern cognitive assessment, math and English questions. These are standard tests designed to assess the cognitive achievement of children, and they are the same for all members of the survey. We are using these test results as a measure of children’s cognitive ability. We provide summary statistics of households in the sample in Table 3.1, which contains all variables used in the paper, with a description of how they have been created.

Finally, we use families’ production information to derive the amount of child labor they use. We do not use child labor in its standard sense (ILO 2018), but as the amount of labor families’ are extracting from their child. In other words, here child labor means all family- related works that child is providing for the family, which might have some interference with the child’s school time, the time the child can devout to school assignments, or simply rest. As we explained in the theoretical section, child labor will be zero if and only if the model’s parameters satisfy certain conditions. The production part of the survey has such Ghadir Asadi Chapter 3. Investment in the Quality of Education 129 information on family-related activities of the children. Families do not have production activities in all months of the year. Thus, we only compute an average number of working hours for children in the household for those months when school time overlaps with the family production season. For example, there are two agricultural seasons in the south of Ghana, one from April to July and a shorter one in September and November. In the north of Ghana, the agricultural season is in August and September. Considering the school calendar in Ghana, the agricultural season in the south of Ghana overlaps an almost completely with school time, while in the north, there is barely any overlap. We have a variety of different periods for individual non-agricultural home production as well.

3.4.2 Schooling data

We use publicly available data from the Ministry of Education in Ghana.11 They have school census data that are available at the district level for the 2009-2010 academic year.12 Data are presented for public and private schools separately, covering kindergarten, primary, junior high-school, and Creche/Nursery schools. There are ten measures of school quality at the district level, which are the percent of schools having a functional toilet, percent of schools having drinking water, percent of classrooms needing major repair, percent of teachers who are trained (Male/Female), per pupil core textbooks in school,13 per pupil other textbooks, per pupil seating places, per pupil writing places, teacher/pupil ratio, and percent of students who repeat a grade. We have presented a summary of school quality data at the national level in Table 3.3. Ghana’s school system has both public and private schools, which both can be either religious or non-religious. We summarize our final sample schooling characteristics

11http://www.moe.gov.gh/site/statistics 12Like many countries, the academic year in Ghana starts in August and ends in May of the next calendar year. 13Core subjects are Mathematics, English, and Integrated/General Science Ghadir Asadi Chapter 3. Investment in the Quality of Education 130 in Table 3.4.

3.5 Empirical specification

To the best of our knowledge, there are only two papers that examine parental investment in their children by looking at expenditure on supplementary educational materials. Brown (2006) investigated Chinese households and found that high cognitive ability in children has a weakly positive effect on educational investments by their parents. He uses non-required expenditure, book and desk provision, parents’ help in reading and homework, and parents’ discussion of child’s schooling with a teacher as dependent variables. He uses teacher rank and village fixed effect to control for school quality. However, Brown did not explorethe possibility of endogeneity weakening his conclusion. Lee (2008) investigates Korean families, where he measures investments by the total educational expenditure of the family. He uses an instrumental variables approach to solve the endogeneity problem with respect to the number of children, and he finds a statistically significant quality-quantity trade-off.

In this paper, we are interested in estimating Equation 3.10. Our dependent variable is the amount parents invest in their children. We measure monetary investment by summing all parental expenditure on a child’s school activity, other than the fees paid to the school. We also treat expenditure on books as a separate dependent variable, as it is an indication of how much parents care about the quality of their child’s education. In Table 3.6.2, we use the amount of child labor as a dependent variable using the same regression setup.

′ ′ ′ 퐸푋푄푖 = 훽0 + 훽1푄푖 + 훽2푋푖 + 훽3푍푖 + 훽4퐶푖 + 훿 + 휖푖 (3.10)

In Equation 3.10, 푄 represents a child’s quality or cognitive achievement, 훽1 is the coefficient Ghadir Asadi Chapter 3. Investment in the Quality of Education 131

14 of interest and 훿 is a vector of dummies for the enumeration area. If 훽1 is statistically significant, it indicates that a child’s schooling quality can affect how much parents investin their child. Our explanatory variables fall into three groups. The first group of explanatory variables involves family structure and family member characteristics (푋), and includes household income, wealth, religion, a cultural index, maternal education, household size, the number of children under five, and the sex and age of the household head. The second group is child characteristics (푍) and includes child sex, grade, birth rank, whether the child repeated any grade, and other characteristics of the child. The last group of explanatory variables contains information about the locale and cultural where the family resides (퐶) including such information as the enumeration area and average child enrollment rate in different grades.

Table 3.5 presents the linear regression estimation of Equation 3.10. We use OLS to estimate the effect of child’s quality on families’ expenditure, the Probit model to find theeffectsof child’s quality on the probability of buying the book, and the Tobit model to find the effect of child’s quality on the child labor. As the results show, there exists a statistically significant effect of math score on expenditure. A one percent increase in a students’ math score increases parents’ expenditure by 0.32 percent. There are also statistically significant effects of both average and math scores on child labor. A one percent increase inaverage and math scores decrease the monthly average child labor by 0.25 percent and 0.08 percent, respectively.

While we observe statistically significant effects of average and math scores on both parents’ expenditure and child labor, using simple linear regression leads to an identification prob- lem.15 The problem comes from two sources. First, there is an endogeneity problem because

14It would be nice to have an indicator for each village but the survey only provides the location up to an enumeration area. For all regressions, we control for enumeration area. 15For a discussion on the Oster (2019) bound analysis, please look at Table C.2. Ghadir Asadi Chapter 3. Investment in the Quality of Education 132 a child’s cognitive achievement depends on parents’ investment in their child. Second, we have omitted variable bias because we cannot control for the child’s innate ability and moti- vation, and the parents’ willingness and capacity to help their children with their schoolwork (World Bank 2004).16 We need an identification strategy the permits us to identify the pure effects of child cognitive achievement on parents’ investment. To find these effects,weuse an Instrumental Variable (IV) approach, discussed below.

3.5.1 Choosing instruments

We need to find an instrument for children’s cognitive achievement that has two features. First, it must have a high correlation with the quality of students’ education (relevance). Second, it must have no correlation with the regression error term (exogeneity). Or, except through the endogenous variable, the instruments should not have a direct relationship with the dependent variable. We suggest measures of school quality as valid instruments that satisfy both conditions and discuss both relevance and exogeneity of the instruments in the following two parts.

First, we need to establish what we mean by school quality and how it is related to student achievement and human capital accumulation. Most researchers define school quality based on either inputs, the process of learning, or both. The prominent definition presented in Fuller (1986). He defined school quality as “(a) the level of material inputs allocated per pupil (resource concentration), and (b) the level of efficiency with which fixed amounts of material inputs are organized and managed to raise pupil achievement.” Since we are using school quality as an input for human capital formation, we use the level of material input

16In addition, we are measuring the quality of the child’s schooling with a standard test that still may not measure the true human capital of the child without error. Although we are not solving the problem of measurement error specifically, our IV approach can improve the estimation result. Ghadir Asadi Chapter 3. Investment in the Quality of Education 133 as the measure of school quality. Material inputs determining school quality include teacher characteristics, the availability of clean water and a functional toilet, features of classrooms, existence and quality of seating, and the quality of a school’s library. 17

Our use of instruments follows prior research, which used exogenous variation in the supply side to identify demand-side determinants. For example, Card (1993) used proximity to a college to estimate the return to education, and McClellan et al. (1994) used proximity to a cardiac care center to identify the effect of more intensive treatments on mortality in elderly patients. Other researchers have used exogenous variation in schooling availability and quality as an instrument for identifying demand for schooling and the effect of school quality on wages. Duflo (2001) used the effects of massive school construction in Indonesia on the labor market outcome of the affected cohort. Other researchers like Card and Krueger (1992) have used district-level measures of school quality to estimate the effects of education on earnings.

Validity of the instruments: relevance

We need to assess the validity of school quality as an instrument for students’ cognitive achievement. It worth mentioning that the first stage regression in our setup is an estimation of the human capital formation function presented in Equation 3.4. Researchers have tried to estimate this relationship, to find the most influential determinants of human capital formation, from children’s ability and family-related factors to schooling characteristics and course materials. They have been particularly interested in finding the most relevant aspect of schooling for children’s learning output. It seems trivial that as school quality increases, we expect a student’s cognitive capacity and achievement to increase. The existence of a strong correlation between the different aspects of school quality and children’s schooling

17A very detailed study of school quality can be found in Benavot and Gad (2004). Ghadir Asadi Chapter 3. Investment in the Quality of Education 134 performance has been studied in recent research, such as Newhouse and Beegle (2006) and Aturupane et al. (2013).

We have studies on the relation of education quality, years of schooling, and returns to educa- tion. For instance, Behrman and Birdsall (1983) showed that return to years of education is half of what could be estimated for Brazilian young men when school quality was included.18 They also showed that the quality of the school has a much higher social return than the quantity of education, and school quality explains differences between the rate of return to schooling in different regions and between urban and rural areas. Card and Krueger (1992) showed that school quality has a substantial impact on the return to education. They found that men who were educated in states with higher quality schools had a higher return to each additional year of education. They used pupil-teacher ratio, duration of the school term, relative wage, sex, and average education of teachers as criteria for school quality. Glewwe and Jacoby (1994) found that school quality has a statistically significant effect on children’s early enrollment and test scores in math and English reading and that it has an indirect effect on how many years students stay in school. Bedi (1997) found that school quality has a statistically significant positive effect on the future earnings of students. He used teacher training, school infrastructure, and school crowding as measures of school quality. Hanushek et al. (2008) found that even in a developing country like Egypt, students respond to dif- ferences in school quality. For example, a student is much less likely to remain in school if attending a low-quality school rather than a high-quality school. These research results are consistent with Ingram and Neumann (2006), who showed that the return to years of education was constant during 1970-2000, while other aspects of skills exhibit heterogeneous returns among people with the same years of schooling.

18Other studies like Morgan and Sirageldin (1968), Welch (1969), Johnson and Stafford (1973), Wachtel (1976) also found the same result before. Some other studies found that going to a more qualified school decrease the black/white wage gap, among them, are Welch (1966, 1967, 1973a, 1973b), Freeman (1973), Smith and Welch (1989). Ghadir Asadi Chapter 3. Investment in the Quality of Education 135

To sum up, the above research results provide a theoretical background that develops the strong direct relationship between school quality and a student’s human capital accumula- tion, with the latter explaining their future earnings, the thing parents care about it the most. We can also monitor this relevance through three statistics, the joint significance of the instruments, the adjusted 푅2 of the first stage regression, and the Shea’s 푅2 (Shea 1997). When each of the latest two statistics is higher, we can infer that our set of instruments is more relevant to the endogenous variable. We present the first stage statistics in Table C.2 in Appendix C.2.

Validity of the instruments: exogeneity

Now we need to discuss the second condition, namely exogeneity. First, we present evidence for a more evident way to approach this condition. As Cameron and Trivedi (2005, p. 96-97) explain, exogeneity is satisfied if instruments do not have a direct relationship with the dependent variable unless the relationship is through the endogenous variable. In other words, a set of instruments should not be candidates for being in the set of regressors as independent variables. In our case, we need to show that parental investment in child education does not have a direct relationship with school quality, especially for the measures we are using. Before going into the detailed discussion, it intuitive to believe that parents do not observe measures of school quality and their more straight forward way to asses school quality is to observe their child’s quality in terms of reading, doing math, and developing other school induced skills. Even if parents observe some measures of school quality such as school amenities and teacher presence, it is very hard for parents to find the contribution of each measure to students’ education quality, just as it is for the researchers (Glewwe 2002).

We discuss the relationship between parents’ investment and school quality by looking at the Ghanaian official national-level statistics and our survey data. As is evident inTable 3.2, in Ghadir Asadi Chapter 3. Investment in the Quality of Education 136 general, public schools provide more books and food than private schools. This is consistent with the finding of Oduro (2000). He found that the cost of schooling is the most important reason of non-attendance for Ghanaian children, and among the costs, providing food and clothing, school fees, and registration costs are the three largest items of expenditure. This fact found support in our data too. Our survey shows that the share of food expenditure out of the total family’s expenditure on children’s schooling is 45%. The share for clothing (uniforms and sports clothes) is 15.8%, that for books and other school supplies is 15%, and the shares of extra classes and transportation costs are 5% and 2%, respectively. Public schools try to use these facts to attract more students to school by providing books and food.

The most important difference between private and public schools is the amount offees they ask of households. Public schools on average receive 1.75 GH19 per year, but private school fees are 48.2 GH per year. We present the fee ratio, which is the share of school fees in the family’s total expenditure on the quality of a child’s education (school fee plus expenditure on child quality) for each child. Since parents who send their children to private schools spend more on their children and pay much more in school fees, their fee ratio is high. Parents who send their children to a private school on average are paying 1.5 times more than those who send their children to public schools.

Based on a report by the Ghanaian ministry of education (Ministry of Education 2013), a family’s expenditure on fees has an insignificant effect on school quality. The average expense of a student in primary school was 224 GH in 2010, but the family expenditure survey shows that, in rural areas, the average fee parents paid was 1.8 GH in public religious schools and

1.6 GH in public non-religious schools. Comparing the average cost in public schools with

what parents usually pay for private schools shows that the family’s contribution to the

19GH is Ghanaian currency, Cedis, equal to 0.17 US dollars on April 15, 2020. Ghadir Asadi Chapter 3. Investment in the Quality of Education 137 school system covers only a very small fraction of the cost of education at private schools. Moreover, private schools try to add greater flexibility and special discounts to make the school affordable and increase the number of potential students. For instance, households can be allowed to pay the fees in installments during the term or year, and some low-fee private schools can give a fee discount for an additional child enrolled and allow a fourth child to be enrolled for no additional fee. Some private schools have preschools that enroll children between ages three and five for free, because that ensures that they have astock of children ready to enter the fee-paying stream. Sometimes parents who make prompt payments receive fee discounts of 10 to 15% (Akaguri and Akyeamapong 2010). Some school in the poorest areas allow a daily fee to be paid so that poor people can send their children to school on the days they have funds (Tooley 2005).

The same comparison works for contributions to PTAs. PTA membership in Ghana is not as voluntary as in other countries, like the U.S. In Ghana, all parents who enroll their child in the school are automatic PTA members. Usually, they receive an invitation to a PTA meeting, which they can choose to attend. Also, schools and PTA representatives usually decide how much money parents need to pay, and parents can choose to pay or not. Parents make more contributions in private schools, and on average 50-60% of parents are paying zero to the PTA. But this does not mean that they are poor or that they do not want to spend money on their children. As is presented in Table 3.2, the maximum spending on child quality for those who have zero contribution to PTA is rather large. Comparing the average amount of PTA and maximum ExQ |PTA=0, it becomes clear that families’ expenditure on the quality of children’s education has a more important role in children’s schooling outcomes than spending on PTAs.

Having said all these things, it worth noting that the statistics on the national average cost reflect just recurrent costs, and between 80 and 90 percent of spending is on teachers’ wages. Ghadir Asadi Chapter 3. Investment in the Quality of Education 138

Since what families are paying for public schools barely covers 0.8 percent of the current expenses, it is hard to believe that, for the overall quality of schools including buildings and teachers’ quality, parents’ expenditure has a consequential effect.

Now consider the correlation of instruments with the regression error term. Following (Glewwe 2002), the error term is not related to the instruments as long as it is exoge- nous of parents’ choice of school quality. There are different ways that school choice can be endogenous. First, there can be access to different schools with presumably different qual- ity. However, most of the rural areas investigated have access to only one school, and it is generally at a considerable distance from the student’s residence. In our sample, 40 percent of students spend less than 20 minutes traveling to their school, 29 percent spend between 20 minutes and 59 minutes, 21 percent of students spend more than 60 minutes, with the last 10 percent spending more than 100 minutes. Therefore, considering the fact that 90 percent of students walk to their school, we can be sure that access to school is limited for our focus group in rural areas. And this is why we exclude urban families: because they presumably have access to both public and private schools, and they could choose between them or choose a high-quality school over a low-quality one of the same type.20 In this paper, we focus on Ghanaian rural areas, where there is a limited choice over school quality, because we do not have access to schools’ and families’ exact locations.

Family migration and the sending of students to live with relatives is a second way that a family can choose school quality. This usually happens when a family does not have access to any school and has the opportunity to send their child to stay with relatives, especially for secondary school (Glewwe and Jacoby 1994). We include those students who live with their grandparents or other relatives, but the total number of persons who migrate for educational

20To identify the relationship between school quality and parental investment in an urban area, one needs to model the first stage choice of school type and then model the relationship between child cognitive achievement and parents’ investment. Ghadir Asadi Chapter 3. Investment in the Quality of Education 139 reasons is very small (12 people migrated directly and 8 people accompanied their parents, barely 1% of our sample). Moreover, 80% of students live with their immediate families, where the household head is their own father or mother, and 15% of the sample live with their grandparents. Because families have a broader definition in the developing world, and many young rural people live with their family even after marriage, it can happen that children reporting as living with their grandparents might also be living with their parents.

A third way that parents can influence the quality of schooling is to divert public resources toward their location. There is considerable dispersion in the distribution of resources be- tween urban and rural Ghana, and between different rural areas as well. For our study, the results are not affected by the unequal distribution of resources, as long as parents donot deliberately affect the distribution to change the school quality. As the investigate of Anli- machie (2016) in the problem of inequality in basic education in Ghana indicates, low level of monitoring and evaluation, lack of adequate and sustainable funding, weak financial and public administration systems, lack of adequate capacity by stakeholders, lack of adequate professional and experienced teachers, and irregular grants distribution are among the top cause of inequality in basic education. Although authorities in the four basic educational institution can divert resources in favor of some localities, based on the structure of the schooling system, there is a limited opportunity for parents to meddle in the distribution of schooling resources.21

The last possibility for families to have an impact on school quality is through school fees and contributions to parents-teacher associations (PTAs). As we extensively discussed the schooling cost and the amount of fees and PTA contributions parents are paying, it is evi- dent that the amount of money paid by a family provides almost no contribution to school quality. Discussing all possibilities of correlation between parents’ choice of school quality

21The four basic educational institution includes Minister of education, Ghana Education Service, Basic Education Circuit, and Ghana Inspectorate Board. Ghadir Asadi Chapter 3. Investment in the Quality of Education 140 and their expenditure and finding no direct relation between them in our setup assure us that exogeniety of the instruments we introduced.

3.6 Estimation results

We use two sets of school quality measures as instruments. First, we use our school-level quality measures derived from the household survey and then estimate Equation 3.10. We also use two measures of cognitive achievement, student average score, and student math score. Second, we use district-level school quality measures as an instrument. Finally, we use a measure of child labor and our sets of instruments. We present the estimation results in Tables 3.6, 3.7, and 3.8. In all regressions, we control for parents’ education, household head’s age, sex, and occupation, student’s relationship with the household, household religious group, family’s cultural background. We also controlled for the enumeration area using dummies. We use a logarithmic form of our continuous variable, which gives our estimated coefficient an elasticity interpretation.

3.6.1 Parents’ investment

In Table 3.6, we present the estimation results for the case where we use the students’ overall average and math scores as endogenous variables and local school-level quality measures as instruments.22 Both the overall average and math scores have positive and statistically significant effects on spending and book provision. Moreover, the effect of the overall average

22In all regression presented in this paper, standard errors presented in parentheses. All robust standard errors are clustered at the enumeration area level. We controlled for parents’ education, HH’s age, sex and occupation, student’s relation to the household, household religious group, family’s cultural background. We also controlled for enumeration area dummies. First stage regression’s statistics reported in Table C.2 in Appendix C.2. Ghadir Asadi Chapter 3. Investment in the Quality of Education 141 score is greater in magnitude, which indicates a stronger effect of overall achievement than just learning math on the family’s decision. From the table, a one percent increase in the average score increases parents’ expenditure by 2.32 percent. This value for math score is 2.15 percent for each percent change in the math score. To compare the magnitude of our preferred method of estimation with the coefficients estimated by the OLS, note thatthe IV estimates are almost eight times the OLS estimates (0.3 vs. 2.31 and 0.31 vs. 2.15 for average score and math score, respectively).23

Household size does not have a statistically significant effect on investment. Being agirl has a negative and statistically insignificant effect on investment, but not on book provision. Students in higher grades receive less investment, and the probability of receiving books is also lower, and this effect is statistically significant. While food cost does not change much with students’ grades (in fact, it slightly increases), the negative relationship between families’ investments and grade shows that families are adjusting their investment based on children’s performance over the years, and this partially explains the fact that the middle school enrollment rate is so low in Ghana ( about 50% in 2009). If we had a panel data set, we would be able to identify this grade effect and families’ investment adjustments with more confidence.

The well-being of families, as measured by expenditure per capita, does not significantly change families’ investment in children. This is in agreement with our theoretical model. As we can see in Equations 3.7 and C.20, families’ total investment (휏ℎ푡) is not significantly related to their income level. In practice, as we are using cross-sectional data, we expect a positive relationship between families’ well-being and their spending on children’s schooling, but here we find a positive but statistically insignificant relationship. However, paying rent for their house has a negative and statistically significant effect on parents’ investment.

23As Becker (2016) mentioned, in the presence of omitted variable and measurement error, 2SLS estimates might be larger than the OLS estimates. Ghadir Asadi Chapter 3. Investment in the Quality of Education 142

Finally, the school fee ratio has a negative and statistically significant effect on investment in book provision using the average score. This relation does not exist on parents’ investment and book provision using math score.

In Table 3.7, we use the same endogenous variable but we replace the local-level instruments with the district-level instruments. The average score in the regression for expenditure and math score for the estimation of book provision are statistically significant. In terms of magnitude, the effect of the average score on expenditure is smaller than our previous estimate (2.063 versus 2.321) but the effect of math score on the probability of book provision results in a larger estimate (2.851 versus 1.615).

In addition to the variables presented so far, there are other explanations of families’ ex- penditure behavior that have been discussed in the literature. Some research has found that parents behave differently with their first child or that birth order determines whatis provided for children. For instance, Parish and Willis (1993) argue that being a first child and particularly a female could be a disadvantage in developing countries. A first child may need to sacrifice for their younger siblings or the family’s economy. They found that having an older female sibling has positive effects on younger children’s educational performance. But if the first child is older or the family is richer, the effect of the first child andthatof the child’s gender is weaker. Thomas (1994) finds significant differences in the allocation of household resources, depending on the gender of the child, and the effect is heterogeneous for the sex of the parents. Using our data set, we examine the first-child effect by using a dummy for being the first child, a dummy for being the first child and a girl, and avariable containing birth rank. Results show that the first two dummies have different signs and al- ways are statistically insignificant, but birth rank has a persistent positive sign in all models. This means that families are trying to help their younger children more.

Moreover, we model human capital investment as a future-looking investment. But receiving Ghadir Asadi Chapter 3. Investment in the Quality of Education 143 the returns to education is not the only thing that is going to happen in the future. The family has some other forward-looking decisions that are closely related to investment in human capital. One such variable is the desired or expected number of children in the future. While most studies use only the number of children in the household as a measure of family size, we need to consider the family’s desired number of children as well. We include the expected number of children as an explanatory variable. It has no statistically significant effect in any of the model estimations, so we can conclude that decisions about investment in existing children are independent of families’ plans for future children.

Both regression tables of families’ expenditures omit a number of variables because of space limitations. Households with female heads spend less on educational quality and have a lower probability of buying books for their children. Households where the head’s age is higher spend more on their children. This could be an effect that comes from more income of families with older heads. Parents’ education has a positive but statistically insignificant effect on investment in the quality of children’s education. Students who live in their grandparents’ families or with other relatives usually receive less educational investment. Being from a Muslim or traditional family has a negative but statistically insignificant effect on both direct investment and book provision, and the different occupation groups have mixed signs, but most of them are statistically insignificant. we also used the district average score as the dependent variable and the district set of instruments. The results for expenditure are almost the same, except that they did not yield a strong statistically significant result for book provision. We do not present them here because of space limitations.

Two other variables could be suitable dependent variables in our model. The first one is the probability of dropping out. As Hanushek et al. (2008) observed, the probability of staying in school is higher when the quality of that school is higher. Thus we expect the probability of dropping-out to be higher when the quality of education is lower. Fortunately, 97.5% of Ghadir Asadi Chapter 3. Investment in the Quality of Education 144 the children between 6 and 12 years old in our sample are in school. So for assessing the effect of school quality on the drop-out decision, we need a much bigger sample and possibly a new method. The other variable of interest is child labor, which is the topic of next section.

3.6.2 Child labor

One of the interesting implications of our theoretical model concerns child labor. Based on Equation 3.8, we expect that child labor does not have any statistically significant relation- ship with income, but that it does have a significant negative relationship with schooling output. Using Equation 3.10 and children’s average non-school family-related activities as a dependent variable, we create Table 3.8. As the table shows, while average and math scores have negative and statistically significant effects on child labor using the local school-level quality measures as the instrument, using district-level instruments leads to a negative but insignificant effect. In terms of magnitude, a one percent increase in the average scorede- creases child labor by 13.88 percent, which is 3.5 hours per month on average. Using the math score, these values are 20.9 percent and 5.6 hours per month.

Finally, we produce the same set of results for male and female students, presented in Tables 3.9 and 3.10, respectively. Naturally, our sample for each gender got smaller. The results for the effects of average and math scores using both local school-level and district-level instruments stay statistically significant, though smaller for males and larger for female students. Moreover, the effects of average and math score for child labor only existsfor male students and for average score using the local school-level instruments. In terms of magnitude, a one percent increase in the average score decreases the child labor of male students by 21.79 percent, which is 7.2 hours per month. Ghadir Asadi Chapter 3. Investment in the Quality of Education 145

3.7 Discussion

In our investigation of families’ investments in child’s education quality, we do not mention some aspects of investment and some important factors that could potentially change our results. One such factor is the possibility of more investment for less observed quality, when a family invests in the quality of a less able child, to improve their success in school. Although we cannot observe the special health conditions or poor performance of students except via our cognitive achievement measure, from our estimation results we can still infer that this pattern is not strong. If it does exist, it would increase the statistical significance of our estimate.

Demanding schools is another important aspect of families’ investments. One counter- argument to our main hypothesis about families’ incentives to invest in child quality is a hypothesis about demanding schools. This hypothesis states that schools with higher qual- ity demand a greater investment from families by obliging them to pay for certain types of school-related materials. Although it is hard to test this hypothesis without detailed data on school functioning, it should be observable in families’ cost structures for items other than food and transportation. To test this hypothesis, we run a model with our usual definition of investment, minus the cost of food and transportation, leaving only the other school-related materials. Almost all of the school quality measures are negative insignificant, showing that more qualified schools are not demanding more investment.

Finally, teachers’ characteristics are very important. Teachers, as the main source of knowl- edge for the students, especially in rural areas, are the cornerstones of schools. But as we noted before, providing high-quality teachers is not an easy task. Acute shortages of teach- ers, absenteeism, late arrival and leaving the school during working hours are among the most serious problem in the Ghanaian school system (USAID 2007). Absenteeism has sev- Ghadir Asadi Chapter 3. Investment in the Quality of Education 146 eral causes, including poor working conditions, low morale, and a high pupil-teacher ratio. While we include teacher absenteeism in our set of local instruments, if we had the relevant data about teachers we could analyze the effects of teacher education level, experience, fi- nancial well-being and other characteristics on school quality and, through that, on parents’ investment behavior.

We used two sets of variables to represent school quality. The hiring of newly trained teachers can work as an alternate instrument in our analysis in rural areas. School systems hire newly graduated teachers yearly. If we had information on exactly whom they replace, then, by comparing the quality of these two teachers, we would be able to build another instrument to test our main hypothesis. This new instrument would be another source of exogenous variation that is closely related to student achievement and is not related to the regression error term. Compared to our current setup, this new instrument could have been a better source of identification, as we would have been able to infer causality more reliably.

3.8 Conclusion and policy remarks

The level and quality of education, especially at the primary level, are among the main concerns of developing countries. There has been a continuous effort to find the basic de- terminants of schooling and its quality, both in theory and in applied economics. Most of the attention on the demand side has focused on family-level determinants, which has led to research on the quality-quantity trade-off and returns to education. Other scholars, mainly in the field of education, have focused on school structure and facilities, teacher quality and behavior, book quality, student innate ability, and peer effects. Although all of these are important aspects of human capital formation: none of them provide a complete story.

We use a model that enables us to connect two different areas of research and to build a Ghadir Asadi Chapter 3. Investment in the Quality of Education 147 complete story, supported by an empirical model and data. We hypothesize that an increase in school quality has a strong effect on human capital formation through an increase in families’ expenditures on the quality of children, by providing school supplementary material and supporting their schooling agendas. To test the validity of the hypothesis, we developed an overlapping generations model that shows that parents’ investment in child quality is a function of child quality, which depends on school input in a human capital formation function. The main contribution of our model is that, unlike prior research, which treated schooling cost as a price, we treat schooling cost as an investment in children’s human capital. We do that based on two sets of evidence. First, variation in schooling cost is much greater than variation in the price for a normal good, because schooling cost is rather more discretionary than predetermined. Second, households are responsible for purchasing almost all educational resources other than teacher salaries, and this opens an important window for the family to optimize the level of investment in children’s education.

We test our model’s implications using Ghanaian rural families with primary school students. To do that and to resolve the potential endogeneity of our estimate, we use two sets of school quality measures as instruments at the district and local school level. Estimation results indicate that families tend to spend more on children whose quality is higher. Our results introduce a channel of causation that could stimulate education and its impact on the quality of children by increasing families’ investments in supplementary school materials.

Our results have two main implications for policymakers. First, an increase in the quality of supplied schooling can increase the return to education for students, which can amplify the demand for years of education as well. Second, enhancing the quality of school en- courages families to invest more in their children, by increasing their direct investment in supplementary material and by decreasing the required work time of children. Ghadir Asadi Chapter 3. Investment in the Quality of Education 148

3.9 Tables

Table 3.1: Summary statistics Variable Description Mean Std. Dev. Min. Max. Expenditure on Quality Amount of expenditure on the school supplementary material in Ghanaian Cedi 68.554 73.468 0.4 554 Household Size Household size 6.326 2.621 2 20 Expected Household Size Household size plus expected number of children in the future 9.064 6.502 2 44 Age of Household Head Age of household head 48.457 13.812 19 100 HH’s Years of Education Household head’s years of education 4.348 4.564 0 16 Mother’s Years of Education Child’s mother’s years of education 2.9 3.94 0 16 Cultural Index of Family Cultural index of family using multiple and joint correspondence analysis (mcb) -0.154 1.004 -4.831 1.248 Birth Rank Rank of child in the family 2.595 1.427 1 12 Grade Students’ grade 1.781 1.6 0 10 Expenditure Per Capita (Ln) Expenditure per capita 7.309 0.589 4.594 9.704 Contribution to PTA Contribution to parents teacher association 1.69 4.613 0 105 PTA/Total School Cost Contribution to parents teacher association to total expenditure on the schooling 0.052 0.131 0 1 School Fee School fee 6.407 29.668 0 750 School Fee/Total School Cost School fee to total expenditure on the schooling 0.055 0.143 0 0.976 Travel Time to School 12-18 years (Ln) Amount of time spent to travel to the school for students between 12-18 years old 3.504 1.178 0 6.97 English Teacher Absent Number of days that student’s English teacher was absent in last month 0.62 0.852 0 5

District level data Teacher Trained (percent) Percent of teacher trained 0.45 0.225 0 0.949 Per Pupil Textbook Core Core textbook per pupil 1.5 0.349 0 2.1 Per Pupil Textbook Other Other textbook per pupil 1.011 0.591 0 2.5 Per Pupil Writing Place Writing place per pupil 0.697 0.199 0.1 1.6 Per pupil seating place Seating place per pupil 0.734 0.165 0.1 1.2 Percent of schools with toilet Percent of schools has toilet 0.542 0.178 0 0.940 Percent of schools with water Percent of schools has water 0.593 0.21 0 1 Percent of classrooms NMR Percent of classrooms needs major repair 0.248 0.103 0 0.830 Percent of student repeat grade Percent of students repeat grade 0.039 0.036 0 0.201 Pupil Teacher Ratio Pupil teacher ratio 31.953 8.993 64 Dummies Christian Student comes from a Christian family 0.619 0.486 Muslim Student comes from a Muslim family 0.222 0.415 Traditional Student comes from a family with Ghanaian Traditional religion 0.067 0.25 Sex of Household Head Sex of household head 0.241 0.428 Professional Household head is a professional worker 0.017 0.129 Technician Household head is a technician 0.022 0.148 Skilledagric Household head is a skilled agricultural worker 0.234 0.424 Armforce Household head is in arm force 0.002 0.045 Sex Student’s sex(zero if male) 0.476 0.5 First Child Student is his family’s first child 0.217 0.412 Family pays rent for house Family pays rent for house 0.05 0.225 Free Feeding Program School has free feeding program 0.133 0.34 Access to All Textbook Needed School has access to all textbook needed 0.152 0.364 N 2009 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 149 Max EXQ|PTA=0 Textbook (%) Core (PP) PTA Pay Zero to Grade Repeat Teacher PTA Female Tr. Average Cont. to Ratio Teacher Fee Male Tr. Ratio Pupil/Teacher EXQ Average NMR Writing Place(PP) Classrooms Fee Average (%) Feeding Seating Schools w/ Water Program Place(PP) (%) Table 3.2: Differentschool characteristics by school type rural in areas by School Schools Textbook Book Provision w/ Toilets Other(PP) Table 3.3: Basic national profile of primary schools in the 2009/2010 year academic Source: Ministry of Education in Ghana, 2009-2010. PublicPrivate 52% 81%Public 59%Private 77% 1.2 0.6 25% 9% 0.7 0.9 47.8% 10.8% 76.5% 0.7 0.9 12.7% 1.6 1.2 31 24 4% 1.5% Source: Ghana Socioeconomic Panel Survey: 2009-2010. Public ReligiousPublic Non-religiousPrivate ReligiousPrivate Non-religious 69.4 69 49.6 48.8 14.21 11.78 1.69 2.16 0 1.82 52.53 34.65 0.032 81.2 54.04 144.54 145.88 0.02 0.212 1.33 0.237 1.64 2.57 4.07 64 53.68 58.82 52.74 420 501 395 522 Ghadir Asadi Chapter 3. Investment in the Quality of Education 150

Table 3.4: Schooling types in the sample(%) Urban/Rural Public Private Total Religious Non-religious Religious Non-religious Urban 17.53 11.71 4.91 13.91 48.06 Rural 21.32 24.10 1.96 4.56 51.94 Total 38.85 35.80 6.87 18.47 100 Source: Ministry of Education in Ghana, 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 151

Table 3.5: Simple estimates of the investment in the quality of children criteria Expenditure Book Child labor (1) (2) (3) (4) (5) (6) OLS Probit Tobit Average Score (Ln) 0.300 -0.181 -0.255*** (0.229) (0.485) (0.009)

Math (Ln) 0.317** -0.393 -0.079*** (0.139) (0.349) (0.007)

Household Size 0.009 0.005 0.146* 0.148* 0.526*** 0.526*** (0.035) (0.034) (0.077) (0.076) (0.001) (0.001)

Sex -0.191* -0.220* 0.267 0.340 0.062*** 0.061*** (0.113) (0.112) (0.294) (0.302) (0.008) (0.008)

Grade 0.018 0.014 -0.143 -0.128 0.056*** 0.044*** (0.057) (0.048) (0.127) (0.119) (0.003) (0.003)

Family pays rent for House -0.392 -0.407 -0.181 -0.150 0.655*** 0.664*** (0.312) (0.306) (0.555) (0.577) (0.006) (0.006)

Expenditure Per Capita (Ln) 0.149 0.114 0.644** 0.693*** -0.425*** -0.419*** (0.125) (0.121) (0.264) (0.255) (0.002) (0.002)

School Fee to Total School Cost 0.207 0.272 -1.806 -1.819 4.443*** 4.369*** (0.772) (0.776) (1.458) (1.477) (0.094) (0.092) Adjusted 푅2 0.590 0.598 Pseudo 푅2 0.243 0.249 0.261 0.261 Observations 435 435 230 230 437 437 Notes: Dependent variables are expenditure, book provision, and child labor. Sampling weights applied and clustered standard errors at the enumeration areas in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 152

Table 3.6: Estimation using a local school-level set of instruments Expenditure Book (1) (2) (3) (4) Average Score (Ln) 2.321*** 3.586*** (0.837) (1.108)

Math (Ln) 2.158*** 1.615* (0.621) (0.856)

Household Size 0.017 -0.013 0.075 0.111 (0.033) (0.035) (0.114) (0.085)

Sex -0.291*** -0.476*** -0.223 -0.151 (0.113) (0.141) (0.301) (0.303)

Grade -0.162* -0.162** -0.365*** -0.250*** (0.086) (0.079) (0.078) (0.092)

Family pays rent for House -0.443* -0.538* 0.056 -0.259 (0.234) (0.295) (0.385) (0.425)

Expenditure Per Capita (Ln) 0.120 -0.114 0.294 0.309 (0.128) (0.177) (0.366) (0.293)

School Fee to Total School Cost -0.394 0.129 -2.135** -1.435 (0.599) (0.643) (1.062) (1.164) Adjusted 푅2 0.349 0.152 Observations 435 435 230 230 Notes: Dependent variables are expenditure and book provision. Sampling weights applied and clustered standard errors at the enumeration areas in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 153

Table 3.7: Estimation using a district-level set of instruments Expenditure Book (1) (2) (3) (4) Average Score (Ln) 2.063*** 3.504 (0.679) (3.411)

Math (Ln) 0.848 2.851*** (0.674) (0.249)

Household Size 0.016 -0.000 0.076 0.040 (0.032) (0.027) (0.177) (0.064)

Sex -0.279*** -0.294** -0.132 -0.545*** (0.107) (0.130) (0.700) (0.203)

Grade -0.139** -0.037 -0.361* -0.229*** (0.070) (0.065) (0.212) (0.056)

Family pays rent for House -0.436* -0.444** 0.108 -0.456 (0.234) (0.215) (0.482) (0.504)

Expenditure Per Capita (Ln) 0.124 0.048 0.411 -0.229 (0.121) (0.117) (0.961) (0.207)

School Fee to Total School Cost -0.317 0.231 -2.103** -0.043 (0.594) (0.620) (0.834) (0.825) Adjusted 푅2 0.407 0.561 Observations 435 435 230 230 Notes: Dependent variables are expenditure and book provision. Sampling weights applied and clustered standard errors at the enumeration areas in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 154

Table 3.8: Model estimates for child labor District Level Local Level (1) (2) (3) (4) Average Score (Ln) -11.041 -13.877*** (21.922) (5.231)

Math (Ln) -61.472 -20.898* (288.752) (11.688)

Household Size 0.368** 0.887 0.339** 0.545** (0.160) (2.308) (0.167) (0.277)

Sex -0.495 2.674 -0.468 0.638 (0.613) (15.104) (0.656) (1.172)

Grade 1.480 7.038 1.782*** 2.539* (2.463) (31.903) (0.659) (1.409)

Family pays rent for House 0.661 7.360 0.612 3.018 (1.111) (30.415) (1.097) (2.401)

Expenditure Per Capita (Ln) -1.181* 1.891 -1.117 -0.110 (0.673) (14.450) (0.719) (1.312)

School Fee to Total School Cost 7.029 6.393 7.101*** 4.211 (6.012) (12.519) (2.663) (3.604) Observations 437 437 437 437 Notes: Dependent variable is child labor. Sampling weights applied and clustered standard errors at the enumeration areas in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 155 -11.942 *** 3.896 -5.247 -0.731 1.753 -13.149 ** *** *** 1.118 01 01 . . 0 0 ** 푝 < 푝 < , *** , *** Model estimates for female students 05 05 . . 0 0 -0.426 -48.203 0.286 -0.595 31.453 1.690 -6.180 2.727 푝 < 푝 < , ** , ** Local Level District Level Local Level District Level 0.657 -21.795 Table 3.9: Model estimates for male students 6.812 -4.973 3.171 Table 3.10: 10 10 . . 0 0 ** ** 푝 < 푝 < (0.567) (2.209) (43.770) (0.378) (2.345) (62.717) (1.633) (1.073) (9.213) (1.262) (0.775) (35.364) *** *** (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) 0.311 0.395 0.486 0.557 0.253 -0.309 0.342 0.024 (0.731) (2.822) (10.103) (0.338) (6.399) (0.982) (1.037) (.) (12.216) (0.902) (19.922) (27.348) Expenditure Expenditure book1 book1 Child Labor Child Labor Expenditure Expenditure book1 book1 Child Labor Child Labor Expenditure Expenditure book1 book1 Child Labor Child Labor Expenditure Expenditure book1 book1 Child Labor Child Labor 2 2 푅 푅 Average Score (Ln) 1.935 Adjusted Observations 254 254 96 96 254 254 254 254 96 96 254 254 Math (Ln) 1.211 Average Score (Ln) 3.596 Adjusted Observations 181 181 66 66 183 183 181 181 66 66 183 183 Math (Ln) 3.295 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Source: Ghana Socioeconomic Panel Survey: 2009-2010. enumeration areas in parentheses. * enumeration areas in parentheses. * Notes: Dependent variables are expenditure, book provision, and child labor. Sampling weights applied and clustered standard errors at the Notes: Dependent variables are expenditure, book provision, and child labor. Sampling weights applied and clustered standard errors at the References

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Zhou, Y. and W. Li (2011). A review of regional groundwater flow modeling. Geoscience frontiers 2 (2), 205–214. Appendix A

App for Chapter 1

A.1 Robustness check

The issue that was raised in section 1.5.2 was a seemingly inconsistent result of table 1.6 in columns 5 and 6, which is the behavioral response with respect to level, with the results for the log forms in columns 7 and 8. In Table A1, we present four robustness checks for our results in Table 1.6. First, in columns 1-4, we restrict the value of the dependent variable to be less than the average of itself. The table shows that with small extractions, both for the level and the log forms, owners of new wells extract more water when experiencing a negative precipitation shock. In columns 5 and 6 we use the squire root of extraction, and the results stay the same as Table 1.6. This happened because extraction is so skewed that the squire root does not tame the tale of the distribution. Using the 4th root creates a result that mimics the results of the log forms in Table 1.6. Our final robustness check is to usean even more extreme transformation to tame the skewness of the distribution. We use a simple reciprocal function, which is a very strong transformation, with a drastic effect on the shape of the distribution. Again, when we remove the skewness of the distribution, the results

171 Ghadir Asadi Chapter 3. Investment in the Quality of Education 172 that we found in Table 1.6 appear. Remember that since we use a reciprocal transformation function, we expect the coefficient in column 9 and 10 to be negative.

Instead of negative or positive precipitation shocks, some researchers use yearly precipitation as explanatory variables (Bertone Oehninger et al. 2016; Gronseth 2019). We estimate Equation 1.3 (equivalent to the results in Table 1.6) using yearly precipitation and its lags. Other than our usual control variables, in this model, we include the square of precipitation and its lags as control variables. Precipitation and its lags are not statistically significant but our main results still hold. Owners of new wells extract more water than owners of older wells, and the response of new wells to more precipitation is different from that of old wells (columns 5 and 6). A reduction in precipitation results in greater extraction from new wells compared to old wells. This result is in line with our findings in Table 1.6. Our result is consistent with the findings of Gronseth (2019). One might suggest that the change in precipitation is a local variable, and the level of precipitation is irrelevant if not adjusted for the average precipitation in that region. We use the standardized precipitation and re-estimate the model presented in Table A3. Again, standardized precipitation is not significant, but the main results still hold.

A.2 Extraction from new wells and depth to water

We can test the robustness of the results (presented in section 1.5.1) using the relative change in the depth to water in each district, presented in Table A4. We know that the depth to water is not the same in all parts of the same district, but we use the average of all piezometric wells as the measure of the depth to water for each district. This will allow us to capture the between-district variation in depth to water. The relative change in the depth to water is positively related to extraction per well at the level form and to extraction per unit Ghadir Asadi Chapter 3. Investment in the Quality of Education 173 of surface in the log form. Again, growth in the number of wells increases the extraction per well and per unit of area, which is a sign that new wells systematically extract more water than their older counterparts.

In Table A5, we re-estimate Equation 1.2 and replace the shock variables with the relative change in the depth to water in the past three years. Here, we can safely assume that depth to water (water level, for example) is public information, and both owners of the wells and those who might dig a new well are informed about the depth to water. We choose depth to water in the winter since, naturally, it is expected to be more stable and is less affected by local and temporary changes in extraction.

Since the responses of semi-deep and deep wells to the relative change in the depth to water might be different, we present the results separately. Table A5 shows that the owners of new wells extract more than old wells. The relative change in the depth to water has no effect on the extraction of semi-deep wells and a positive and statistically significant effect on the level of extraction from deep wells. This disparity can be explained by two facts. First, the inter-connection between underground and surface water: If the depth to water increases, naturally there is less surface water and farmers depend more on groundwater. Secondly, compared to semi-deep wells, deep wells have a higher difference between pumping water level 1 and the wells’ depths. Shallower wells have smaller differences between pumping water level and the wells’ depths. Thus, they have a smaller margin of tolerance to the change in depth to water. Therefore, at the time of need, deep wells have better capability to deliver more water, while semi-deep wells face difficulties with extracting water. The change inthe depth to water in the past two years has no effect on the extraction of the wells inany category.

As with Table 1.6, we interact with the dummy variable for newly established wells and the

1 The pumping water level is the distance from the land surface to the water in the well while pumping. Ghadir Asadi Chapter 3. Investment in the Quality of Education 174 relative change in the depth to water. Table A6 shows that the owners of new wells extract more than that of the owners of old wells. Moreover, the relative change in the depth to water significantly increases the extraction from old deep wells (columns 5-8). Thenew finding from comparing columns 3 and 4 of Tables A5 and A6 is that new semi-deep wells significantly extract more water than their older counterparts, while the extraction fromnew deep wells does not differ from their older counterparts. These results are in line withthe explanation presented above, about the greater difference between pumping water level and a wells’ depth for deep well. When the water level goes down, both new and old deep wells have enough margin of tolerance that their response does not differ from each other. For semi-deep well, the story is reversed. New semi-deep wells have enough margin (they have just been dug and the depth to water level is optimal), while the owner of older wells might have a problem in extracting more water.

Table A7 shows the change in probability of digging a new well in response to the relative change in depth to water. As the table shows, the response for both semi-deep and deep wells are both significant, though the response from semi-deep is both stronger. Table A8 shows the amount of extraction from new wells in response to the relative change in depth to water. As one can see, new wells extract more in response to an increase in the depth to water. Assuming that a change in the depth to water has a direct effect on the availability of surface water, especially for semi-deep wells, an increase in the depth to water decreases surface water and increases the farmers’ dependency on groundwater. This is in line with the findings in the literature on groundwater recharge (Cherkauer and Ansari (2005), for example) and the studies in conjunctive use of surface and groundwater (Peralta et al. 1990).

We also separate extraction from semi-deep and deep wells from each other here. Table A9 shows that extraction from semi-deep wells reacts significantly to the change in the depth to water, and Table A10 shows that extraction from deep wells increases when there is a Ghadir Asadi Chapter 3. Investment in the Quality of Education 175 negative precipitation shock. Table A1: Some transformations to deal with the skewness of the extraction Small Extractions Squire Root of Extraction 4th Root of Extraction Reciprocal of Extraction Extraction Ln of Extraction Extraction Extraction Extraction (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Total Agriculture Newly established well 55 70 0.023 0.030 13.5*** 14*** 0.363*** 0.385*** -0.000 0.001 (102) (101) (0.031) (0.032) (3.7) (3.7) (0.117) (0.116) (0.000) (0.001)

Negative precipitation shock -333 -357* -0.074 -0.099 -5.38 -6 -0.236 -0.271 0.000 0.001 (213) (210) (0.058) (0.063) (6.028) (6.2) (0.19) (0.19) (0.00) (0.00)

Newly established well×Negative precipitation shock 1323*** 1403*** 0.516*** 0.574*** 8.679 8.8 0.589** 0.613** -0.000*** -0.002* (190) (183) (0.064) (0.062) (8.6) (8.8) (0.298) (0.311) (0.000) (0.001)

Positive precipitation shock 303 274 0.189*** 0.213*** 9.8 10.2 0.434* 0.451* -0.000 -0.004** (307) (306) (0.067) (0.069) (7.86) (7.76) (0.259) (0.257) (0.000) (0.002)

Newly established well×Positive precipitation shock 167 138 0.004 -0.014 -11.9*** -12.4*** -0.20 -0.232* 0.000 0.000 (131) (132) (0.036) (0.041) (4.004) (4.005) (0.134) (0.135) (0.000) (0.002) Adjusted 푅2 0.481 0.491 0.603 0.615 0.773 0.771 0.813 0.812 0.108 0.605 Observations 195029 195029 195029 195029 395321 395321 395321 395321 394828 395321 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and three different transformations. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table A2: The effect of precipitation on the extraction Extraction Ln of Extraction Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Precipitation 10.730 16.115 0.001 0.001* 11.504 16.912 0.001* 0.001* (33.989) (34.613) (0.000) (0.000) (33.752) (34.359) (0.000) (0.000)

L1 Precipitation -43.720 -41.652 0.000 0.000 -43.263 -41.181 0.000 0.000 (41.814) (41.933) (0.000) (0.000) (41.693) (41.803) (0.000) (0.000)

L2 Precipitation 31.776 34.776 0.000 0.000 31.042 34.020 0.000 0.000 (38.141) (37.734) (0.000) (0.000) (37.949) (37.534) (0.000) (0.000)

L3 Precipitation 25.907 22.900 0.000 0.000 25.859 22.851 0.000 0.000 (35.460) (35.107) (0.000) (0.000) (35.325) (34.968) (0.000) (0.000)

Newly established well=1 16245.899*** 16726.627*** 0.200*** 0.236*** (5190.460) (5160.102) (0.065) (0.071)

Newly established well=1×Precipitation -11.578** -11.921*** -0.000 -0.000 (4.482) (4.404) (0.000) (0.000) Adjusted 푅2 0.628 0.627 0.796 0.792 0.628 0.627 0.796 0.792 Observations 395306 395306 395306 395306 395306 395306 395306 395306 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company Table A3: The effect of standardized precipitation on the extraction Extraction Ln of Extraction Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Standardized Precipitation -1405 -1042 0.043 0.051 -1342 -978 0.045 0.054 (3097) (3015) (0.040) (0.041) (3077) (2994) (0.039) (0.041)

L1 Standardized Precipitation -2235.861 -2062 -0.000 0.010 -2278 -2105 -0.001 0.009 (3910) (3863) (0.042) (0.046) (3903) (3855) (0.042) (0.046)

L2 Standardized Precipitation -1504 -1106 -0.021 -0.011 -1550 -1154 -0.021 -0.012 (4798) (4696) (0.052) (0.055) (4773) (4669) (0.051) (0.054)

L3 Standardized Precipitation 4864 4808 0.039 0.051 4736 4677 0.036 0.048 (3361) (3335) (0.036) (0.039) (3348) (3321) (0.036) (0.039)

Newly established well 9504*** 9786*** 0.156*** 0.170*** (2850) (2842) (0.036) (0.038)

Newly established well×Standardized Precipitation -3858* -3915* -0.102*** -0.114*** (2111) (2102) (0.029) (0.031) Adjusted 푅2 0.628 0.627 0.795 0.792 0.628 0.627 0.796 0.792 Observations 395306 395306 395306 395306 395306 395306 395306 395306 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table A4: Extraction, extraction per well and per unit of surface and depth to water Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) Total Per well Per surface Total Per well Per surface Growth in # of wells -19.64 0.219** 21.16*** 0.005*** 0.004*** 0.007*** (88.41) (0.088) (6.834) (0.002) (0.001) (0.002)

Ratio of change in depth to water 1053 0.685 -50.62 -0.027 -0.008 -0.018 (2604) (2.58) (201) (0.046) (0.018) (0.045)

Ratio of change in L1 of depth to water 18400.908 102*** 4513 1.2* 0.489* 1.491** (39203) (38.81) (3030) (0.687) (0.266) (0.681)

Ratio of change in L2 of depth to water -4050 6.60 4567* 0.471 -0.085 0.650 (31661) (31.35) (2447) (0.555) (0.215) (0.550) 푅2 0.221 0.485 0.243 0.165 0.712 0.225 Observations 598 598 598 598 598 598 Notes: Dependent variables are extraction, extraction per well and per unit of surface, and their log forms. Standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran and Iran Water Resource Management Company (2003-2013) Table A5: Extraction, the effect of new wells, and depth to water Semi-deep Deep All Extraction Ln of Extraction Extraction Ln of Extraction Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Total Agriculture Total Agriculture Relative change in depth to water 649 663 -0.053 -0.055 17572** 16864* 0.107** 0.103** 4981 4880 -0.023 -0.026 (2261) (2252) (0.061) (0.061) (8821) (8574) (0.048) (0.047) (3324) (3294) (0.050) (0.050)

Relative change in L1 of depth to water 2065 2209 0.043 0.052 13417 13062 0.080 0.079 6320* 6465* 0.073* 0.080* (2196) (2155) (0.047) (0.047) (8373) (8219) (0.050) (0.051) (3436) (3395) (0.044) (0.044)

Relative change in L2 of depth to water 438.725 518 0.028 0.034 4411 3036 0.059 0.054 658 583 0.039 0.045 (995) (993) (0.034) (0.035) (11151) (10415) (0.041) (0.043) (2463) (2380) (0.032) (0.033)

Newly established well 3535*** 3588*** 0.150*** 0.163*** 16666*** 17356*** 0.099*** 0.113*** 10497*** 10796*** 0.177*** 0.194*** (1243) (1234) (0.035) (0.037) (5263) (5313) (0.028) (0.028) (3152) (3143) (0.041) (0.044) Adjusted 푅2 0.495 0.496 0.744 0.744 0.569 0.568 0.699 0.696 0.633 0.631 0.798 0.794 Observations 286892 286892 286892 286892 108375 108375 108375 108375 395267 395267 395267 395267 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Table A6: Depth to water and the effect of new wells Semi-deep Deep All Extraction Ln of Extraction Extraction Ln of Extraction Extraction Ln of Extraction (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Total Agriculture Total Agriculture Total Agriculture Total Agriculture Total Agriculture Total Agriculture Newly established well 2713** 2735*** 0.151*** 0.164*** 18173*** 18389*** 0.101*** 0.118*** 9969*** 9976*** 0.176*** 0.194*** (1065) (1049) (0.035) (0.037) (5477) (5504) (0.029) (0.029) (3116) (3063) (0.043) (0.047)

Relative change in depth to water -841 -403 -0.066 -0.068 17639** 16876** 0.105** 0.101** 4079 4010 -0.034 -0.037 (1864) (1773) (0.061) (0.062) (8749) (8427) (0.048) (0.047) (3159) (3001) (0.050) (0.050)

Newly established well×Relative change in depth to water 27218 27418 0.346*** 0.348*** -1914 -1555 0.044 0.038 22830 22656 0.311*** 0.307*** (20078) (20125) (0.067) (0.067) (28453) (28628) (0.081) (0.081) (17781) (17751) (0.076) (0.076)

Relative change in L1 of depth to water 1873 1574 0.038 0.048 13364 13018 0.083 0.083 5584* 5873* 0.072* 0.080* (1985) (1972) (0.047) (0.048) (8311) (8245) (0.051) (0.051) (3293) (3321) (0.043) (0.044)

Newly established well×Relative change in L1 of depth to water 11947 12151 0.078 0.072 -1551 542 -0.083 -0.129 12228 12265 -0.001 -0.008 (7594) (7591) (0.074) (0.075) (17049) (17370) (0.094) (0.111) (8142) (8044) (0.089) (0.089)

Relative change in L2 of depth to water 365 521 0.032 0.039 3170 3482 0.060 0.054 726 754 0.043 0.050 (1020) (1021) (0.034) (0.036) (11227) (10414) (0.040) (0.042) (2394) (2304) (0.032) (0.034)

Newly established well×Relative change in L2 of depth to water -7.9 125 -0.115* -0.124* -17661 -17577 -0.019 -0.006 -4558 -4266 -0.114 -0.122* (2339) (2345) (0.066) (0.070) (12405) (12350) (0.070) (0.072) (5609) (5601) (0.070) (0.072) Adjusted 푅2 0.495 0.497 0.744 0.744 0.568 0.568 0.699 0.696 0.633 0.631 0.798 0.794 Observations 287328 286892 286892 286892 108375 108375 108375 108375 395703 395267 395267 395267 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 3. Investment in the Quality of Education 182

Table A7: Probability of digging a new well Semi-deep Deep (1) (2) New well New well Ratio of change in depth to water (Winter) -0.002 0.000 (0.005) (0.002)

Ratio of change in L1 of depth to water (Winter) 0.012*** 0.003** (0.005) (0.001)

Ratio of change in L2 of depth to water (Winter) 0.001 -0.001 (0.003) (0.001) Adjusted 푅2 0.357 0.219 Observations 391530 383173 Notes: Dependent variable is the dummy variable for new well. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013)

Table A8: Extraction of the new wells Extraction Ln of Extraction (1) (2) (3) (4) Total Agriculture Total Agriculture Ratio of change in depth to water (Winter) 32690.573 32836.460 0.215*** 0.219*** (27147.774) (27185.798) (0.075) (0.075)

Ratio of change in L1 of depth to water (Winter) 18170.515 18486.897 0.205*** 0.204*** (11274.021) (11279.797) (0.068) (0.077)

Ratio of change in L2 of depth to water (Winter) -2148.793 -1789.507 -0.019 -0.009 (3794.610) (3753.247) (0.063) (0.062) Adjusted 푅2 0.661 0.658 0.828 0.825 Observations 15831 15831 15831 15831 Notes: Dependent variables are total extraction, extraction for agricultural purposes, and their log forms. District level clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Iran Water Resource Management Company (2003-2013) Ghadir Asadi Chapter 3. Investment in the Quality of Education 183 * ** 0.154 0.157 * * 01 . (3911) (3844) (0.065) (0.063) 0 (25845) (25887) (0.078) (0.078) (12993) (12973) (0.076) (0.080) 푝 < , *** 05 . 0 푝 < , ** 10 . 0 푝 < Extraction Ln of Extraction Extraction Ln of Extraction Table A9: Extraction of semi-deep new wells (1) (2) (3) (4) (5) (6) (7) (8) 0.499 0.498 0.778 0.777 0.508 0.508 0.778 0.777 Total Agriculture Total Agriculture Total Agriculture Total Agriculture (4928) (4935)(2879) (0.132) (3060) (0.131) (5350) (0.138) (5209) (0.147) (3806) (0.154) (3822) (0.152) (0.074) (0.084) 2 푅 Notes: Dependent variables areDistrict total level extraction, clustered extraction standard forSource: errors agricultural in Iran purposes, parentheses. Water and Resource * their Management Company log (2003-2013) forms. Relative change in L1 of depth to water 14141 14485 0.145 Positive precipitation shockNegative precipitation shock# 197 of positive shocks in past 3 years -20.74 -2119# of negative shocks in past -3481 0.024 3 -2401 yearsRelative change -4087 in depth 0.033 0.219 to -1921 water 0.026 0.229 -1968 0.027 -0.060Relative change in L2 of -0.079 depth to waterAdjusted Observations 12095 32836 12095 32975 12095 0.152 2072 12095 2302 12094 -0.046 12094 -0.039 12094 12094 Ghadir Asadi Chapter 3. Investment in the Quality of Education 184 0.183 0.115 * 34979 * 01 . (42752) (42825)(18495) (0.100) (18909) (0.102) (17574) (0.123) (17157) (0.136) (0.116) (0.116) 0 푝 < ** , *** 0.353 05 . 0 ** 푝 < , ** 10 . 0 푝 < Extraction Ln of Extraction Extraction Ln of Extraction Table A10: Extraction of deep new wells (1) (2) (3) (4) (5) (6) (7) (8) 0.588 0.585 0.720 0.714 0.588 0.585 0.719 0.713 Total Agriculture Total Agriculture Total Agriculture Total Agriculture (24285) (24447)(23253) (0.153) (23036) (0.159) (24609) (0.158) (24653) (0.156) (0.119) (0.121) (49034) (48674) (0.274) (0.293) 2 푅 Notes: Dependent variables areDistrict total level extraction, clustered extraction standard forSource: errors agricultural in Iran purposes, parentheses. Water and Resource * their Management Company log (2003-2013) forms. Relative change in L1 of depth to waterRelative change in L2 of depth to water 34713 -12582 -11507 0.083 0.120 # of positive shocks in past 3 years# of negative shocks in past 20215 3 yearsRelative change in 19639 depth to -23860 water -0.219 -23206 -0.234 -0.024 -0.037 32323 32468 0.092 0.095 Positive precipitation shockNegative precipitation shock -74440 -76787 17955 0.148 18425 0.141 0.322 Adjusted Observations 3738 3738 3738 3738 3737 3737 3737 3737 Appendix B

App for Chapter 2

B.1 Attrition

As mentioned in Section 2.3.1, the LFS panel data is subject to attrition. In this section we explore how attrition is affected by observable characteristics, and then we discuss howwe address it for our sample. From the rural individuals who were interviewed at base line and were supposed to be re-interviewed in the following years, 84.41 percent are in the sample in the subsequent year. From baseline households, 90.25 percent are present in the subsequent year. There are several reasons for this attrition including, household did not respond at the interview day, household refused to respond to the questions, household were not in the dwelling. We report household and individual attrition rates by servey year in Table B.1. The attrition rate for our panel is low compared to other rotating panel data. Madrian and Lefgren (2000), for example, find an attrition rate of 29% for the Current Population Survey (CPS). Also, attrition rate of our panel data is close to the panel data for other developing countries. Mueller et al. (2020), for example, find an attrition rate of 15% for the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)

185 Ghadir Asadi Chapter 3. Investment in the Quality of Education 186 data of Ethiopia, Malawi, Tanzania, and Uganda between 2009 -2014.

Attrition, has only a very limited effect on the variables of interest in this study. Table 2.2 compares the full sample with the balanced panel. Individual and household characteristics such as gender, household size, number of working people in the household, and years of education have very close means to each other in the two samples. Similarly, labor market variables such as labor force participation, unemployment, and hours of work per week, all have very close means and standard deviations in the two samples. We further study if probability of attrition can be explained by observables. In order to do so, we estimate the following probit model

′ 푃 푟[푦푖푗푡] = 휑(훼0 + 훼1 * 푃 표푠.푆ℎ표푐푘푗푡 + 훼2 * 푁푒푔.푆ℎ표푐푘푗푡 + 훽 푥푖푡 + 훾푡 + 휂푗), (B.1)

where 푦푖푗푡 is an indicator for individuals who attritted from the panel. 푥푖푡 includes gender, household size, education level, number of working members in the household, indicators for marital status, province level unemployment, positive and negative precipitation shocks, proportion of nonresident household members, and number of children less than 5 years old.

푥푖푡 also includes distance to nearest city and district level attrition rate. In our model, we also year fixed effects훾 ( 푡) and province fixed effects (휂푗) as well.

The estimation results of equation B.1 are presented in Column (1) of Table B.2. We find that members of larger households are more likely to attrit. Our estimates also sho that the higher the level of education, the larger probability of attrition in our sample. Also, there is a positive association between distance to nearest city and attrition. The most important result, however, is that none of the shock variables have a significant effect on the probability of attrition. Since the focus of this study is on the impact of precipitation shocks, our results in Sections 2.5.1 and 2.5.2, are not likely to be prone to attrition bias. Ghadir Asadi Chapter 3. Investment in the Quality of Education 187

We also estimate restricted version of this model which excludes proportion of nonresident household members, distance to nearest city and district level attrition rate. In order to remove the potential attrition bias, we follow the Fitzgerald et al. (1998) method. Based on their method, we find the ratio of predicted values from unrestricted and restricted models to calculate the inverse probability of attrition. We then apply these attrition weights along with sampling weights to all regressions and statistics in this paper. The regression results of this study are not changed much by removing the attrition weights.

B.2 Shocks happened in previous years

Migration is a decision that takes time to be taken and takes time to be implemented. If a one time shock does not trigger migration, it may still be that if shocks keep happening to a person, or family, their migration decision may eventually be affected. More importantly, some factors may prevent households from migrating soon after a shock. In the event of a negative shock, financial constraints may force households to remain in their current lo- cation, waiting for a time when it will be easier to migrate. To investigate this, we check whether migration depends on lags of shocks. Results show that the second lags of negative shocks affect migration, while the shock itself does not affect migration (See Tables B.3 and B.4). This is particularly observable for negative shocks, where financial constraints play an important role. The results of Tables B.3 and B.4 are similar to Tables 2.17 and 2.19 and show that the number of years with a positive or negative shock in the past three years is a plausible variable to use. Ghadir Asadi Chapter 3. Investment in the Quality of Education 188

B.3 Estimation using the Probit model

In section 2.5.3, we use linear probability model to estimate the effect of precipitation shock on the probability of migration and labor-migration. We re-estimate the model using the Probit model and present the resulting marginal effects in Tables B.7 to B.10. These tables are the equivalence estimation of the main results presented in Tables 2.17, 2.19, 2.20, and 2.21. As one can see, the results are virtually the same. Ghadir Asadi Chapter 3. Investment in the Quality of Education 189

Table B.1: Yearly attrition rate at household and individual level Attrition rate 2009-10 2010-11 2011-12 Household level 10.04 9.68 9.50 Individual level 16.17 14.37 13.83 Source: Statistical Center of Iran and authors’ calculations, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 190

Table B.2: Probability of attrition from the panel (1) (2) Unrestricted Restricted Negative precipitation shock 0.03 0.04 (0.02) (0.02) Positive precipitation shock -0.02 0.01 (0.02) (0.02) Distance to nearest city 0.00*** (0.00) District atrition 3.85*** (0.12) Non-resident proportion 0.03 (0.03) Household size 0.02*** 0.02*** (0.00) (0.00) Only read/write ability -0.06*** -0.06*** (0.02) (0.02) Primary 0.23*** 0.22*** (0.01) (0.01) Lower secondary 0.37*** 0.34*** (0.02) (0.02) Upper secondary 0.60*** 0.57*** (0.02) (0.02) Some post Secondary 0.68*** 0.66*** (0.03) (0.03) Tertiary 0.67*** 0.65*** (0.03) (0.03) Pseudo 푅2 0.063 0.035 Observations 325860 325860 Notes: Dependent variable is individual attrition for individuals aged 15-64. Col- umn 1 is not restricted and includes all control variables. Columns 2 is restricted and excludes proportion of nonresident household members, distance to near- est city and district level attrition rate. In addition to the variables provided above, both models also include: number of working members in the household, province level unemployment, number of children less than 5 years old, indicators for marital status and gender as well as time and province fixed effects. Rural- agglomeration clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012. Ghadir Asadi Chapter 3. Investment in the Quality of Education 191

Table B.3: Impact of current and past precipitation shocks on migration Men Women (1) (2) (3) (4) (5) (6) 20-29 30-39 40-55 20-29 30-39 40-55 Positive precipitation shock at current year 0.10 0.17 0.18 0.11 0.18 0.16 (0.18) (0.16) (0.17) (0.23) (0.14) (0.20)

Lag1 of positive precip shock 0.22 -0.12 0.09 0.10 0.06 0.01 (0.25) (0.17) (0.24) (0.30) (0.18) (0.24)

Lag2 of positive precip shock 0.31 -0.25 0.01 0.29 -0.04 -0.16 (0.19) (0.22) (0.23) (0.20) (0.21) (0.27)

Negative precipitation shock at current year 0.34 0.25 0.19 0.35* 0.19 0.47** (0.22) (0.21) (0.20) (0.19) (0.15) (0.22)

Lag1 of negative precip shock 0.42* 0.28 0.15 0.21 0.18 0.13 (0.25) (0.20) (0.20) (0.28) (0.25) (0.26)

Lag2 of negative precip shock 0.18 0.38*** -0.28 0.20 0.22 -0.06 (0.31) (0.13) (0.27) (0.34) (0.24) (0.27) Adjusted 푅2 Observations 93046 67419 80076 100443 82472 78336 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if an individual migrated from a rural to an urban area, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 192

Table B.4: Impact of current and past precipitation shocks on labor-migration Men Women (1) (2) (3) (4) (5) (6) 20-29 30-39 40-55 20-29 30-39 40-55 Positive precipitation shock at current year 0.34 0.21 0.07 -0.13 0.02 -0.04 (0.32) (0.44) (0.15) (0.18) (0.16) (0.13)

Lag1 of positive precip shock 0.32 0.21 -0.02 0.03 0.16 0.25 (0.45) (0.69) (0.26) (0.21) (0.30) (0.15)

Lag2 of positive precip shock 0.54 -0.26 -0.01 0.16 -0.17 0.12 (0.46) (0.38) (0.23) (0.13) (0.12) (0.09)

Negative precipitation shock at current year 0.27 0.43 0.01 0.03 0.05 0.06 (0.19) (0.28) (0.12) (0.15) (0.08) (0.10)

Lag1 of negative precip shock 0.35 0.54* 0.05 0.11 -0.06 0.19 (0.25) (0.29) (0.13) (0.14) (0.12) (0.16)

Lag2 of negative precip shock -0.08 0.67** -0.09 0.42 -0.00 0.45 (0.24) (0.29) (0.19) (0.41) (0.06) (0.37) Adjusted 푅2 0.026 0.072 0.004 0.013 0.016 0.075 Observations 93470 69353 81925 100443 82472 92421 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if an individual migrated from a rural to an urban area and is in the labor force, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012

Table B.5: Impact of shocks on the probability of quitting employment and labor force, probit model Employment Labor force (1) (2) (3) (4) (5) (6) All Men Women All Men Women Positive Precipitation Shock=1 -0.00 0.00 -0.02 -0.00 0.00 -0.02 (0.01) (0.01) (0.03) (0.01) (0.01) (0.03)

Negative Precipitation Shock=1 0.02 0.01 0.06* 0.01 0.01 0.05* (0.01) (0.01) (0.03) (0.01) (0.01) (0.03) Adjusted 푅2 Observations 90494 68248 20706 98015 72858 22212 Notes: Dependent variable is an indicator for quitting the labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 193

Table B.6: Impact of shocks on the probability of entering employment and labor force, probit model Employment Labor force (1) (2) (3) (4) (5) (6) All Men Women All Men Women main Positive Precipitation Shock=1 -0.10* -0.15 -0.07 -0.10* -0.15 -0.10 (0.06) (0.09) (0.08) (0.06) (0.09) (0.07)

Negative Precipitation Shock=1 -0.04 0.02 -0.11* -0.02 0.03 -0.10* (0.04) (0.07) (0.06) (0.04) (0.08) (0.06) Adjusted 푅2 Observations 121746 29302 86299 114400 23153 86881 Notes: Dependent variable is an indicator for entering labor force (employment). Standard errors are clustered at the rural-agglomeration level and presented in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012

Table B.7: Impact of precipitation shocks on migration, probit model Men Women (1) (2) (3) (4) (5) (6) 20-29 30-39 40-55 20-29 30-39 40-55 Number of positive shocks 0.00 -0.00 0.04 -0.00 -0.00 0.00 (0.00) (0.00) (0.23) (0.00) (0.00) (0.00)

Number of negative shocks 0.01 0.00** 0.11 0.01 0.00 0.00 (0.00) (0.00) (0.24) (0.01) (0.00) (0.00)

Age 0.00 0.00 -0.10 0.00 -0.00 -0.00 (0.00) (0.00) (0.44) (0.01) (0.01) (0.00)

Household size -0.00** -0.00*** 0.07 -0.00*** -0.00*** 0.00** (0.00) (0.00) (0.05) (0.00) (0.00) (0.00) Adjusted 푅2 Observations 81912 59220 67228 88545 72814 68237 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and takes zero if an individual stayed in the rural area. Province clustered standard errors are in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 194

Table B.8: Impact of precipitation shocks on labor- migration, probit model (1) (2) (3) 20-29 30-39 40-55 Number of positive shocks 0.00 -0.00 -0.00 (0.00) (0.00) (0.00)

Number of negative shocks 0.00 0.01** 0.00 (0.00) (0.00) (0.00) Adjusted 푅2 Observations 81912 59220 66075 Notes: All coefficients are converted to percentage points. The dependent variable takes value one if individual migrated from rural to the urban area and is in the labor force, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 195

Table B.9: Economic conditions and the impact of precipitation shocks on migration, probit model 20-29 30-39 40-55 (1) (2) (3) (4) (5) (6) Men Women Men Women Men Women Number of positive shocks -0.00 -0.00 -0.00 -0.00 -0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Number of negative shocks 0.00 -0.00 0.00 0.00 0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Age 0.00 0.00 0.00 0.00 -0.00 -0.00 (0.00) (0.01) (0.00) (0.00) (0.00) (0.00)

Household size -0.00** -0.00*** -0.00*** -0.00*** 0.00 0.00** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Share of service VA† -0.07** -0.09* -0.11*** -0.05* -0.03 0.07 (0.03) (0.05) (0.04) (0.03) (0.04) (0.05)

Share of agriculture VA† 0.25*** 0.43*** 0.34*** 0.25*** 0.14** 0.03 (0.09) (0.13) (0.09) (0.07) (0.06) (0.10)

Unemployment rate -0.10 -0.23** 0.21** 0.14 0.02 -0.08 (0.08) (0.11) (0.09) (0.11) (0.06) (0.07) Adjusted 푅2 Observations 81912 88545 59220 72807 67222 68237 Notes: All coefficients are converted to percentage points. The dependent variable takes value oneif individual migrated from rural to the urban area and takes zero if an individual stayed in the rural area. Province clustered standard errors in parentheses. † VA stands for value-added. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01 Source: Statistical Center of Iran, LFS 2009-2012 Ghadir Asadi Chapter 3. Investment in the Quality of Education 196

Table B.10: Economic conditions and the impact of precipitation shocks on labor-migration, probit model (1) (2) (3) 15-24 25-34 35-54 Number of positive shocks 0.00 -0.00 -0.00 (0.00) (0.00) (0.00)

Number of negative shocks 0.00 0.00 0.00 (0.00) (0.00) (0.00)

Age 0.00 0.00 -0.00 (0.00) (0.00) (0.00)

Household size -0.00*** -0.00*** 0.00 (0.00) (0.00) (0.00)

Share of service VA† -0.05** -0.11*** -0.03 (0.02) (0.03) (0.03)

Share of agriculture VA† 0.15** 0.34*** 0.12** (0.06) (0.08) (0.06)

Unemployment rate 0.05 0.16** 0.02 (0.06) (0.08) (0.05) Adjusted 푅2 Observations 81912 59220 66069 Notes: All coefficients are converted to percentage points. The depen- dent variable takes value one if an individual migrated from rural to the urban area and is in the labor force, and takes zero otherwise. Province clustered standard errors in parentheses. * 푝 < 0.10, ** 푝 < 0.05, *** 푝 < 0.01. Source: Statistical Center of Iran, LFS 2009-2012 Appendix C

App for Chapter 3

C.1 Model solution and proofs

We prove the results for an interior solution (all first order condition hold with equality), and then we discuss the corner solutions as well. We have the following problem to solve:

1 1−휎 1 1−휎 Maximize [푐푡 + 푐푡+1 ] 푆푡,푛푡,휏 1 − 휎 1 + 훽

푂 푗 푠.푡. 퐶푡 = 푦푡 − 푔푡 + 푦푡 − 푆푡 − 푘푡ℎ푡 푒 (C.1) 퐶푡+1 = (1 + 푟)푆푡 + 푘푡+1ℎ푡+1

푎1 푎2 푎3 ℎ푡+1 = 휃 푒푡 푔푡 (1 − 푛푡)

푂 푗 푂 푦푡 = ℎ푡 , 푦푡 = 푛푡 , 푔푡 = 휏 푦푡

197 Ghadir Asadi Chapter 3. Investment in the Quality of Education 198

And our first order condition will be:

휕푢푡 1 = 푎 ℎ 휃 푘푒 푒푎1 (1 − 푛)푎3 (ℎ 휏)푎2−1휈 − ℎ (ℎ (1 − 휏) + 푛 − 푆 )−휎0 휕휏 훽 + 1 2 푡 푡+1 푡 푡 푡 푡 휕푢 1 푡 푒 푎1 푎3−1 푎2 −휎 = − 푎3 휃 푘푡+1 푒 (1 − 푛) (ℎ푡휏) 휈 + (ℎ푡(1 − 휏) + 푛 − 푆푡) 0 (C.2) 휕푛푡 훽 + 1

휕푢푡 1 + 푟 −휎 = ( )휈 − (ℎ푡(1 − 휏) + 푛 − 푆푡) 0 휕푆푡 훽 + 1 푒 푎 푎3 푎2 −휎 where 휈 = (휃 푘푡+1 푒1 (1 − 푛) (ℎ푡휏) + (푟 + 1)푆푡) . By satisfying the above first-order condition with equality, we have equations C.3 and C.4, and by replacing them in any of the

above relations in Equation C.2, we have a parametric solution for 푆푡, presented in Equation C.5. 푒 푎1 1−푎3 푎3 1 푘푡+1 휃 푒푡 푎2 푎3 1 휏 = ( ) 1−푎2−푎3 (C.3) ℎ푡 1 + 푟

푒 푎1 1−푎3 푎3 푎 푘 휃 푒 푎 푎 1 3 푡+1 푡 2 3 1−푎 −푎 1 − 푛푡 = ( ) 2 3 (C.4) 푎2 1 + 푟

푒 푎1 1 푎2−1 푎3 푎3 −푎2 휃 푘 푒 ( ) ( ) + 푎2 푤 푧 (ℎ푡 + 1) − (푎2 + 푎3)푤 푡+1 푧 푎2푧 푆푡 = (C.5) 푎2 푧 (푟 + 푤 + 1)

푎 1−푎 3 푒 푎1 3 1 푎3 휃 푘푡+1푒 푎2 1+푟 푎2+푎3−1 1/휎 Where 푧 = ( 푟+1 ) and 푤 = ( 훽+1 ) . Now we want to to see whether

휕푆푡 휕푟 > 0 or not. One way to assess this is to derive an explicit relationship for this derivative. A simpler method of getting the result is to take a derivative from Equation 3.5.

휕퐶 휕(휏ℎ ) 휕푛 휕푆 푡 = − 푡 + 푡 − 푡 (C.6) 휕푟 휕푟 휕푟 휕푟

휕퐶푡 휕(휏ℎ푡) 휕푛푡 휕푆푡 We know that 휕푟 < 0, 휕푟 < 0 and 휕푟 > 0. Therefore 휕푟 is positive. Second-order condition: From Equation C.1, we can show that the utility function is concave since its Hessian matrix is a negative definite matrix.

⎡ ⎤ ⎡ ⎤ −휎−1 푈푐 푐 푈푐 푐 −휎푐 0 ⎢ 푡 푡 푡 푡+1 ⎥ ⎢ 푡 ⎥ 퐻푈 = ⎣ ⎦ = ⎣ ⎦ (C.7) 휎 −휎−1 푈푐푡+1푐푡 푈푐푡+1푐푡+1 0 − 1+훽 푐푡 Ghadir Asadi Chapter 3. Investment in the Quality of Education 199

Using Equation C.7, it is obvious that 퐻푈 is a negative definite matrix. Since for any ℎ푡 ∈ 푅, the constraints form a compact set, the interior solution is a strict local maximum. An alternative approach to what we present is to substitute the constraints in the utility function.

Thus, instead of 푈(퐶푡, 퐶푡+1), we have 푈(휏, 푛푡, 푆푡) and if we show that the corresponding Hessian matrix is negative definite, we end up with the same result we have provided above.

⎡ ⎤ 푈 푈 푈 ⎢ 휏,휏 휏,푛푡 휏,푆푡 ⎥ ⎢ ⎥ 퐻휏,푛 ,푆 = ⎢ ⎥ (C.8) 푡 푡 ⎢푈푛푡,휏 푈푛푡,푛푡 푈푛푡,푆푡 ⎥ ⎢ ⎥ ⎣ ⎦ 푈푆푡,휏 푈푆푡,푛푡 푈푆푡,푆푡

The possibility of corner solution: It is evident from equations 3.7 and 3.8 that we have

a corner solution for 푛푡 only if:

푒 푎1 1−푎3 푎3 푎3 푘푡+1 휃 푒푡 푎2 푎3 1 ( ) 1−푎2−푎3 > 1 (C.9) 푎2 1 + 푟

¯ For 푆푡, a corner solution is just a matter of income. There is an ℎ푡 such that the optimal ¯ 푆푡 will be zero and positive above that. We find the parametric relation for ℎ푡 in Equation C.10. 푒 푎1 1 푎2−1 푎3 푎3 푎2 휃 푘 푒 ( ) ( ) + 푤(푎2(1 + 푧) + 푎3) ¯ 푡+1 푧 푎2푧 ℎ푡 = (C.10) 푎2 푧 푤

where 푧 and 푤 are the same as defined above. As is evident in Equation C.1, saving starts from a level of income and the slope of the line is:

휕푆 ( 푟+1 )1/휎 푡 = 훽+1 푟+1 1/휎 (C.11) 휕ℎ푡 ( 훽+1 ) + 푟 + 1

Stability of OLG model: For our model, we have a one-dimensional, first-order, nonlinear system and to have a globally stable steady-state, we need to show that (Azariadis 1993, Ghadir Asadi Chapter 3. Investment in the Quality of Education 200

푆푡

푒푡 1

푒푡 2

ℎ푡 ¯ ¯ ℎ1 ℎ2

Figure C.1: Corner solution for 푆푡 Page 76): 퐺(ℎ푡) lim [ ] = 0 (C.12) ℎ푡→∞ ℎ푡

where ℎ푡+1 = 퐺(ℎ푡). For our model we have:

푎1 푎2 푎3 ℎ푡+1 = 휃 푒푡 푔푡 (1 − 푛푡) (C.13)

By replacing Equation 3.8 and 3.7 in Equation C.13, we have:

푒 푎1 1−푎3 푎3 퐺(ℎ ) 푘 휃 푒 푎 푎 1 푎 푡 푎1 푡+1 푡 2 3 1−푎 −푎 푎2+푎3 3 푎3 −1−푎2 lim = 휃 푒푡 (( ) 2 3 ) ( ) ℎ푡 (C.14) ℎ푡→∞ ℎ푡 1 + 푟 푎2

and Equation C.12 only holds if −1 − 푎2 < 0, which is automatically satisfied. And, we can find ℎ˜ in Equation C.2 as:

푒 푎1 1−푎3 푎3 푘 휃 푒 푎 푎 1 푎3 1 ˜ 푎1 푡+1 푡 2 3 1−푎 −푎 푎2+푎3 푎3 1+푎 ℎ = (휃 푒푡 (( ) 2 3 ) ( ) ) 2 (C.15) 1 + 푟 푎2 Ghadir Asadi Chapter 3. Investment in the Quality of Education 201

ℎ푡+1

45

ℎ푡 ℎ˜ Figure C.2: One-dimensional, first-order, nonlinear system unique, globally stable, steady-state equilibrium Changing the production function: As we explained in Section 3.3.3, in order to have a more general case, we can change our production function to a nested CES function. Our maximization problem will be Equation C.16.

1 1−휎 1 1−휎 Maximize [푐푡 + 푐푡+1 ] 푆푡,푛푡,휏 1 − 휎 1 − 훽

푂 푗 푠.푡. 퐶푡 = 푦푡 − 푔푡 + 푦푡 − 푆푡 − 푘푡ℎ푡 푒 (C.16) 퐶푡+1 = (1 + 푟)푆푡 + 푘푡+1ℎ푡+1

−훼 −훼1 −훼1 훼/훼1 −1/훼 ℎ푡+1 = 휃 (푎1푒 + 푎2 (훿1 푔 + (1 − 훿1)(1 − 푛푡) ) )

푂 푗 푂 푦푡 = ℎ푡 , 푦푡 = 푛푡 , 푔푡 = 휏 푦푡

The set of first-order conditions are Equation C.17. 휕푢 (1 − 푎 )훿 ℎ 휃 푘푒 (1 − 휎)(ℎ 휏)−훼1−1휙 휖 푡 = −ℎ 휑 + 1 1 푡 푡+1 푡 0 휕휏 푡 훽 + 1 휕푢 (1 − 푎 )(1 − 훿 )휃 푘푒 (1 − 휎)(1 − 푛)−훼1−1휙 휖 푡 = 휑 − 1 1 푡+1 0 (C.17) 휕푛푡 훽 + 1 휕푢 (푟 + 1)(1 − 휎)휖 푡 = −휑 + 0 휕푆푡 훽 + 1 Ghadir Asadi Chapter 3. Investment in the Quality of Education 202 where 휙, and 휖 are:

훼 1 −훼1 −훼1 훼 −1 −훼 −훼1 −훼1 훼/훼1 − −1 휙 = (훿1(ℎ푡휏) + (1 − 훿1)(1 − 푛) ) 1 (푎1푒 + (1 − 푎1)(훿1(ℎ푡휏) + (1 − 훿1)(1 − 푛) ) ) 훼

푒 −훼 −훼1 −훼1 훼/훼1 −1/훼 −휎 휖 = (휃 푘푡+1(푎1푒 + (1 − 푎1)(훿1(ℎ푡휏) + (1 − 훿1)(1 − 푛) ) ) + (푟 + 1)푠)

푒 −휎 휑 = (1 − 휎)(ℎ푡(1 − 휏) + 푘푡+1ℎ푡 + 푛 − 푠) (C.18) −1 훿1 1+훼1 By solving the above first-order conditions, we have 푛푡 = 1 − ( 1−훿1 ) 휏ℎ푡 and 휏 satisfies the following equation.

훼 푒 −훼 −1 훿1 1 −훼 훼 −1 (1 − 푎 )훿 휃 푘 (ℎ휏) 1 ((훿 + (1 − 훿 )( ) 훼1+1 )(ℎ휏) 1 ) 훼1 1 1 푡+1 1 1 1 − 훿 1 (C.19) 훿 훼1 1 −훼 1 훼 +1 −훼1 훼/훼1 − −1 (푎1푒 + (1 − 푎1)((훿1 + (1 − 훿1)( ) 1 )(ℎ휏) ) ) 훼 − 푟 − 1 = 0 1 − 훿1

As is evident from Equation C.19, we can not solve the system for an exact parametric

solution for 휏, and therefore we can not find the solution for 푛푡 and 푆푡 as well. Finally, we are interested to see whether a change in the elasticity of substitution between different inputs to the human capital formation function changes the investment behavior of families

or not. Let’s assume a simpler case, where we have a corner solution for 푛푡. Then we are back to the standard CES production function. In this case, we can rewrite Equation C.19 as:

1 푒 −훼−1 −훼 −훼 − 훼 −1 (1 − 푎1) 휃 푘푡+1(ℎ푡휏) (푎1푒푡 + (1 − 푎1)(ℎ푡휏) ) − (푟 + 1) = 0 (C.20)

We know that in equilibrium, any increase in school input has a positive effect on families’ investment, which keep Equation C.20 satisfied, simply 휏/푒, and this is true for any level of substitution. To analyze the behavior of families’ investment when the substitution between school input and families’ input changes, I analyze the investment behavior for a small change Ghadir Asadi Chapter 3. Investment in the Quality of Education 203

in 푟.

휕휏 1 = 1 (C.21) 휕푟 −훼 −훼−2 −훼 −훼 − 훼 −2 푒 푎1푒 (ℎ푡휏) (푎1푒 + (1 − 푎1)(ℎ푡휏) ) ((−훼 − 1)(1 − 푎1) ℎ푡 휃 푘푡+1)

From microeconomics, we know that in equilibrium the following relationship exists between two inputs:

1 − 푎1 − 1 푒 = ( ) 훼+1 ℎ푡휏 (C.22) 푎1

By replacing Equation C.22 in Equation C.21, we have:

휕휏 1 = 1 1 1 −훼−2 −훼 1−푎1 −훼 − −2 1−푎1 −훼 푒 휕푟 푎1(ℎ푡휏) ((1 − 푎1)(ℎ푡휏) + 푎1(ℎ푡휏( ) −훼−1 ) ) 훼 (ℎ푡휏( ) −훼−1 ) ((−훼 − 1)(1 − 푎1) ℎ푡 휃 푘 ) 푎1 푎1 푡+1 훼 훼 1 1−푎1 1−푎1 − +2 휏( ) −훼−1 (푎1( ) −훼−1 − 푎1 + 1) 훼 푎1 푎1 = 푒 (−훼 − 1)(1 − 푎1) 푎1 휃 푘푡+1 (C.23) 휕휏 It is obvious from Equation C.23 that lim훼→∞ 휕푟 = 0. Ghadir Asadi Chapter 3. Investment in the Quality of Education 204

C.2 Further discussion on IV

Here, we try to show that how much we can trust the ols results presented in Table 3.5, using the Oster (2019) bound analysis. Before we discuss the presented results in Table C.1, it worth mentioning that the Oster procedure does not apply to Probit and Tobit. Thus, we have use a linear probability model to compute the Oster analysis. Therefore, the results of rows (3)-(6) are not fully reliable. Moreover, the Oster analysis aimed to help us evaluate the magnitude of omitted variable bias. While our study has omitted variables such as innate ability, the main problem we seek to solve is the issue of endogeneity. Thus, we should interpret the result with caution.

We produce three sets of results from the Oster (2019) analysis. First, we use the default values for 푅푚푎푥, which is one, zero for 훽, and one for 훿. From the presented results under

푅푚푎푥 = 1, only the effect of math score on the parents’ expenditure path the criteria ofthe Oster bound. Other estimates are not robust to the omitted variables. On the other hand, we know that explaining all variation in the dependent variable is not a realistic assumption. We consider the maximum of 푅2 to be 1.2 times the value we estimate in Table 3.5. Under

2 푅푚푎푥 = 1.2 * 푅 , we present two sets of results. First, we repeated the standard procedure and got the same results as before. In the last two columns, we set the 훽 to be equal to the

훽푖푣 and then compute the associated 훿. As one can see, with a very small 훿 we can get the

훽푖푣, which makes our IV estimation a plausible way to estimate the effect of child quality on parents’ investment.

Finally, we present the first-stage statistics for the instrumental variable estimation, to assess the relevance of the instruments. We have a total of four different first-stage regressions. As one can see, the first-stage 푅2 is a reasonable value and the regression for all four regression is meaningful (Joint F-Test-All). Although the Shea’s 푅2 is not very large, the joint significant Ghadir Asadi Chapter 3. Investment in the Quality of Education 205 test of the instruments testifies that the instruments are significant, except for the district- level instrument on the first-stage regression of the average score. We can see fromthe performance of the instruments that the district-level instrument result in some statistically insignificant results in both parents’ investments and child labor.

We also have test for the over-identification restrictions. We present Sargan and Basmann 휒2 tests in Table C.2. We know that a statistically significant test statistic indicates that the instruments may not be valid. From the table, local school-level instruments are a valid instrument while the district-level instruments are not a valid instrument.

Table C.1: Oster(2019) bounds analysis for the base OLS result in Table 3.5 2 푅푚푎푥 = 1 푅푚푎푥 = 1.2 * 푅 훿 Identified Set 푅푚푎푥 훿 Identified Set 훽푖푣 훿 if 훽 = 훽푖푣 (1) 1.28 [0.111,0.300] 0.90 2.14 [0.211,0.300] 2.321 2.91 Expenditure (2) 13.16 [0.317,0.393] 0.91 20.65 [0.217,0.317] 2.158 2.80 (3) 0.24 [-0.007,0.084] 0.74 0.75 [-0.007,0.002] 3.586 1.13 Book (4) 1.87 [-0.06,-0.053] 0.75 5.56 [-0.06,-0.058] 1.615 1.43 (5) -0.16 [-5.811,-.082] 0.65 -0.74 [-0.248,-.082] -13.877 1.15 Child labor (6) -0.05 [-0.691,-.010] 0.65 -0.20 [-0.073,-.010] -20.898 1.05 Source: Ghana Socioeconomic Panel Survey: 2009-2010. Ghadir Asadi Chapter 3. Investment in the Quality of Education 206

Table C.2: Standard tests and statistics from the first stage Average Score Math local-level instruments First Stage First Stage 푅2 59.7 47.6 First Stage Adj 푅2 31.7 11.2 Shea’s 푅2 6.6 4.43 Joint F-Test-All (P-value) 0.000 0.000 Joint F-Test-IV (P-value) 0.064 0.001 Over-identification Sargan chi2 (P-value) 0.024 0.249 Basmann chi2 (P-value) 0.149 0.523 District-level instruments First Stage First Stage 푅2 59.6 48.4 First Stage Adj 푅2 30.1 10.8 Shea’s 푅2 6.36 5.91 Joint F-Test-All (P-value) 0.000 0.000 Joint F-Test-IV (P-value) 0.113 0.020 Over-identification Sargan chi2 (P-value) 0.039 0.000 Basmann chi2 (P-value) 0.302 0.011 Source: Ghana Socioeconomic Panel Survey: 2009-2010.