Dedicated to My Grandfather (Feroze Khan) & My Father-In-Law (Muhammad Fazal)
ACKNOWLEDGMENT
for His magnanimous and chivalrous (ﺳــــﺒﺤﺎﻧہو ٰﺗﻌﺎﻟﯽ ) All praise to Almighty Allah blessing that enabled me to perceive and pursue my ambitions, and affectionate love to Holy
.for being a constant source of guidance for humanity (ﷺ) Prophet Muhammad
I owe my cordial gratitude and sincere thanks to my respected and esteemed supervisor Prof. Dr. Eatzaz Ahmad for his scholastic and sympathetic attitude, and for remarkable and unprecedented guidance throughout my research work. Beside my supervisor, I would like to thank the thesis evaluation committee members, namely Prof. Dr. Christopher J. Flinn (foreign reviewer), Prof. Dr. Jere R. Behrman (foreign reviewer), Dr. Fazal Husain (external examiner) and Dr. Rashid Aziz (external examiner) for their perceptive and insightful comments for the improvement of this thesis.
I am immensely grateful to Dr. Muhammad Idrees, Director School of Economics for his courteous, propitious and encouraging attitude. I express my deepest gratitude to all faculty members of the School of Economics for their frequent help and generous cooperation. I am also grateful to all the technical and administrative staff as they induced a conductive and congenial atmosphere for research and study in the School of Economics.
I pay my homage and most heartfelt gratitude from the core of my heart to my parents, siblings and my kids (Noors) for their prayers and holy wishes for my success. I am highly obliged to my gracious husband, Dr. Muhammad Tariq Javed (late) for supporting and up keeping my morale for accomplishing this study. My acknowledgement never complete without thanking my all family members, friends and well-wishers whose love, affection and prayers have been a source of constant encouragement for me throughout the years of study.
Samina Farooq (November 27, 2018)
TABLE OF CONTENTS
Page Plagiarism Undertaking iii Author’s Declaration iv Certificate of Approval v Dedication vi Acknowledgement vii Table of Contents viii
List of Figures xi List of Tables xii Abstract xiv
Chapter Page 1. INTRODUCTION 1 1.1. Background and Motivation 1 1.2. Objectives 5 1.3. Organization of the Study 11
2. HUMAN CAPITAL FLOWS FROM PAKISTAN: HISTORICAL, 12 OCCUPATIONAL AND SPATIAL TRENDS 2.1. Introduction 12 2.2. Human Capital Flows to and From Pakistan : Historical 12 Perspectives 2.3. Skill-wise Composition of Human Capital Flows from Pakistan 17 2.4. Occupational Composition of Human Capital Flows from Pakistan 21 2.5. Spatial Dimensions of Human Capital Flows from Pakistan 29
3. THEORIES OF HUMAN CAPITAL MOBILITY 34 3.1. Introduction 34 3.2. Theories of Migration: An Interdisciplinary Analysis 34 3.2.1. Primitive Theories of Migration: An Interdisciplinary 35 Approach 3.2.2. Assimilation, Social Networks and Migration: An 37 Interdisciplinary Approach 3.2.3. Dual or Segmented Labor Markets and Migration: An 38 Interdisciplinary Approach 3.2.4. Neo-Classical Theory of International Migration: An 41 Interdisciplinary Approach 3.2.5. International Migration and Transnationalism: An 44 Interdisciplinary Approach 3.2.6. World System Theory of Migration: An Interdisciplinary 47 Approach 3.3. Conclusion 49
4. REVIEW OF EMPIRICAL LITERATURE ON MIGRATION 50 4.1. Introduction 50
4.2. Empirics on Causes of Human Capital Mobility 51 4.2.1. International Evidence 51 4.2.2. Evidence from Pakistan 56
5. METHODOLOGY 59 5.1. Introduction 59 5.2. An Augmented Gravity Model of Human Capital Mobility 59 5.2.1. Theoretical Foundation 59 5.2.2. GROUP 1: Economic and Financial Drivers of Human 66 Capital Mobility 5.2.3. GROUP 2: Socio-Economic Drivers of Human Capital 73 Mobility 5.2.4. GROUP 3: Demographic and Labor Market Drivers of 80 Human Capital Mobility 5.2.5. GROUP 4: Fiscal Policy Variables of Human Capital 84 Mobility 5.2.6. Econometrics Specification of the Model 87
6. DATA SOURCES AND ANALYTICAL FRAMEWORK 92 6.1. Introduction 92 6.2. Data Sources 92 6.3. Multivariate Techniques to Construct Indices 93 6.3.1 Performing Principal Component Analysis 96 6.3.2. Performing Principal Factor Analysis 98 6.4. Estimation Procedure 100 6.4.1 Step 1: Test for Stationarity: Panel Unit Root Tests 100 6.4.2 Step 2: Panel Granger Causality Test 103 6.4.3 Step 3: Estimating Panel Co-integration Regressions: 105 Dynamic OLS
7. CONSTRUCTION OF INDICES FROM DRIVERS OF HUMAN 108 CAPITAL MOBILITY 7.1. Construction of Indices 108 7.1.1. Economic and Financial Drivers of Human Capital Mobility 109 7.1.2. Socio-Economic Drivers of Human Capital Mobility 117 7.1.3. Demographic and Labor Market Instruments of Human 125
Capital Mobility 7.1.4. Government Policy Variables that Ensure Human Capital 131 Mobility 7.2. Drivers of Human Capital Mobility: An Overview 135
8. EMPIRICAL ANALYSIS FOR DRIVERS OF HUMAN CAPITAL 140 MOBILTY FROM PAKISTAN 8.1. Tests for Stationarity: Panel Unit Root Tests 140 8.2. Pairwise Panel Granger Causality Test 148 8.3. Co-integration Regression Results 157
9. CONCLUSION 171 9.1. Summary 171 9.2. Conclusions and Policy Recommendations 176
Appendix-A: Summary of Literature on Migration Theories 179
Appendix-B: Empirics on Causes of Human Capital Mobility: 189 International Evidence Appendix-C: Empirics on Causes of Human Capital Mobility: Evidence 196 from Pakistan
Appendix-D: Mathematics of Principal Component Analysis 199
REFERENCES 200
LIST OF FIGURES
Figure 2.1 - Skill-Wise Total Human Capital OutFlows and its 19 percentage
Figure 2.2 - Percentage Share of Highly-Qualified and Highly-Skilled 21 Migrants in Net Brain Drain
Figure 2.3 - Number of Registered Migrants in Various Occupational- 24 Categories
Figure 2.4 - Profession-Wise Human Capital Flight (Brain Drain) 28 from Pakistan (number of persons)
Figure 2.5 - Total Migrants (Cumulative) Residing in Different 30 Regions of the World
Figure 2.6 - Highly-Qualified and Highly-Skilled Migrants Residing 31 in Different Regions of the World (1981-2016)
Figure 2.7 - Region-Wise Human Capital Flows from Pakistan 32
Figure 7.1 - Indices Constructed from Drivers of Human Capital 137 Mobility
LIST OF TABLES
Table 2.1 - Skill-Wise Total Human Capital Out Flows from 19 Pakistan
Table 2.2 - Occupational-Category-Wise Human Capital Flows 22 (number of migrants) from Pakistan
Table 2.3 - Profession-Wise Human Capital Flight (Brain-Drain) 26 from Pakistan
Table 6.1 - Variables, Definitions and Data Sources 94-95
Table 7.1 - Economic and Financial Drivers of Human Capital Mobility 111
Table 7.2 - Economic and Financial Indices 114
Table 7.3 - Socio-Economic Drivers of Human Capital Mobility 119
Table 7.4 - Socio-Economic Indices 121
Table 7.5 - Demographic and Labor Market Instruments of Human Capital Mobility 127
Table 7.6 - Demographic and Labor Market Indices 128
Table 7.7 - Government Policy Variables that Ensures Human Capital Mobility 132
Table 7.8 - Government Policy Indices 134
Table 8.1 - Results of Panel Unit Root Tests (Full Panel) 142
Table 8.2 - Results of Panel Unit Root Tests (Middle East and 143 Africa)
Table 8.3 - Results of Panel Unit Root Tests (East Asia and 145 Pacific)
Table 8.4 - Results of Panel Unit Root Tests (Europe and Central 146 Asia)
Table 8.5 - Results of Panel Unit Root Tests (North America) 147
Table 8.6 - Pairwise Granger Causality Test (for human capital 150-151 Flows) with lag length two
Table 8.7 - Pairwise Granger Causality Test (for human capital 154-155 flight i.e., brain drain)
Table 8.8 - Co-integration Regression Results (for net human 161 capital flows)
Table 8.9 - Co-integration Regression Results (for net human 162 capital flight i.e., brain drain)
ABSTRACT
The thesis is a systematic study of the causes of emigration, particularly of human capital flows and human capital flight (i.e., brain drain) from Pakistan to 27 destination countries over the past 36 years. The study reviews relevant Pakistani migration history, summarizes and compares models of migration, review empirical studies from multiple disciplines, develops a bi-polar specification of gravity model based on push and pull factors and augmented by a neo-classical utilitarian approach of migration, locates relevant data from Pakistan and 27 major destination countries, construct indices from drivers of human capital mobility using principal component and principal factor analysis, presents dynamic analyses of the drivers of human capital mobility from Pakistan with panel unit root tests, pairwise panel Granger causality test and dynamic ordinary least squares co-integration regressions, and interprets the results with particular attention to differences by regional destinations and to policy implications. Over all the empirical findings support the underlying theories of migration, and helps to conclude that in an over-populated country like Pakistan, unplanned brain-drain need to be re-oriented: first, to take the form of planned brain-export to improve the national balance sheet through foreign earnings in form of foreign direct investment and remittances from overseas Pakistanis and
secondly, through the return of experienced Pakistani diaspora and through the realization of professional and technical education in case of brain circulation.
Chapter 1
INTRODUCTION
1.1. BACKGROUND AND MOTIVATION
Globalization of world’s economies has brought about increasing flows of capital, both financial and intellectual. Talented, trained, and professional persons in any country are considered human capital of that country. Human capital fIight, also known as brain-drain, is one-way permanent flow of qualified, well-educated, and highly-skilled young individuals from their home country to another attractive destination country in search of better opportunities in term of favorable geographic, social, economic and professional environment. Therefore, such type of large-scale migration of talented manpower from economically less-advanced countries to the rich and developed countries of the world is indicative of loss of vital human resources for the countries of origin without formal compensation (Grubel and Scott,
1966a, 1966b, 1967, 1977; Bhagwati and Hamada, 1974; Bhagwati, 1979; Devan and
Tewari, 2001 and Iravani, 2011).
The traditional literature on human capital flight (Bhagwati and Hamada, 1974;
Bhagwati, 1979; Miyagiwa, 1991 and Monteleone and Torrisi, 2010) while exploring the impact of human capital flight on the economy of the source country considered it as a curse for developing countries as investment in higher education and training lost, when a highly-educated and trained individuals leave the motherland and do not return back. Thus the human capital flight benefits destination country with the
amount of talented and trained manpower for which recipient country has not invested. At same time the human capital flight reduces relative supply of highly- educated and highly-skilled manpower in the source developing countries and consequently public expenditures on higher education in the source countries are not utilized for their own development and long-run economic growth. According to
Schaeffer (2005) and Doghri et al. (2006) this situation is even threatening the industrialized Western European nations for losing their best-educated and most talented citizens to the green pastures of the United States of America and the United
Kingdom.
The most attractive centers of destination for international migrants e.g., the
United States, the United kingdom, Germany, Canada and Australia are characterized with high wages and salaries, expensive education, low population growth rate and mass consumption stage of development with high absorptive capacity for manpower.
Therefore, Sargent (2013) in projections for the United States science and engineering workforce highlighted that jobs in science and engineering are expected to grow with a compound annual growth rate of 1.3 percent between the years 2012 and 2022, which is faster than the overall 1.0 percent growth rate of workforce for the same period. The study forecasts a 2.3 million net increase in demand for scientists and engineers between the years 2012 and 2022, including 1.2 million computer experts and 0.55 million engineers.
In such circumstances the study by Sargent (2013) recommended that it’s more profitable for the United States of America to import talented manpower from India,
Pakistan, Bangladesh, Philippine and Sri Lanka than to build local manpower. For this purpose developed countries, predominantly the United States, Germany, the United
Kingdom, Canada, Australia and New Zealand give preferential treatment to highly- qualified and highly-skilled immigrants. They offer fellowships and well-paid assignments and relax the immigration rules to attract foreign students. Programs that channelize human capital flows of the best and brightest to these developed countries include the Green Card Scheme and H-1B Visa Program in the United States, Tier-4
Student Visa and Highly Skilled Migrant Programs by the United Kingdom,
Provincial Nominee Program for permanent settlement in Canada and Student and
Family Visa Programs by Australia for selection of skilled, professionally trained and qualified immigrants on the basis of labor market and social needs of destination countries (Hawthorne, 2005; Duleep, 2013 and Akbari and MacDonald, 2014). In the year 2000 the United States congress discussed BRAIN ACT (Bringing Resources from Academia for the Industry of Nation Act) to attract highly qualified foreign students. Historically, the United States had the most successful brain-gain immigration policy. Further, Joyce and Woodhouse (2013) asserted that government of New Zealand has announced measures to attract international students by making it easier for them to find employment during their studies and by passing legislative amendments for the protection of these international students.
The highly talented Pakistanis settled abroad, exceling in almost every field of life, are capable to benefit their embracing countries. According to Bureau of
Emigration and Overseas Employment, Government of Pakistan, in the past 47 years especially after 1971, economic and financial constraints in Pakistan have compelled the qualified and skilled manpower to migrate abroad. Further, it is stated in
Economic Survey of Pakistan (2016-17) that Pakistan is an attractive labor market for the manpower importing countries and it has a great capacity to export human
resources including highly-qualified, highly-skilled, semi-skilled and unskilled workers as well as professionals and experts in different fields particularly to the
Middle East and the Gulf regions. In essence, government of Pakistan is gauging excellent opportunities of manpower export during Expo 2020 in Dubai where demand for skilled and qualified manpower upsurges. Moreover, it is also expected that the huge new construction plans in Saudi Arabia may offer work opportunities for
Pakistani manpower.
Like other Gulf States, Qatar is prominent in offering incentives to Pakistan for its strategic position in South Asia. Qatar is going to host the FIFA 2022 World Cup and has allocated a huge budget to meet extensive infrastructural development. Qatar requires two million foreign manpower for online transmission of the mega event and for infrastructure development beside provision of medical and marketing facilities. In this context export of skilled and qualified human capital can reduces the issue of unemployment in Pakistan as well as bequeaths remittances to the homeland
(Economic Survey of Pakistan, 2016-17).
It is also reported in Economic Survey 2016-17 that the current demographic trends in both developed and developing countries are pointing toward significant potential economic gains from emigration of highly-skilled manpower from Pakistan to well-developed world economies. Labor force in many developed countries, including China and Japan peaked around the year 2010 and is expected to decline by
5 percent in the following two decades, accompanied by a rapid increase in dependency ratio (due to single child policy in China). On the other hand, labor force in many developing countries is rapidly expanding (e.g., in 2017 the Pakistani youth, i.e., population below the age of 30 years as a proportion of total population was 64
percent) that reduces dependency ratio. Such demographic imbalance creates strong demand for manpower, especially highly-skilled and highly-qualified manpower, in developed countries’ labor markets, especially in services sectors. In short, overview of recent statistical trends, especially supply demand gaps in international labor market highlights the importance of studying human capital flows from Pakistan. If human capital flight from Pakistan is treated as brain export and if it is facilitated and utilized properly it may reinvigorate economic activity (through attracting foreign direct investment and remittances) in developing source countries like Pakistan.
1.2. OBJECTIVES
The present thesis is an attempt to respond to a key challenge facing Pakistan that is a continuous outflow of well-educated, highly-qualified and highly-skilled young
Pakistanis to make their livings abroad. The human capital flows across international boundaries has economic, social, cultural and political effects in the source and destination countries. Therefore, it is equal responsibility of policymakers, economists, statisticians and politicians to pay their attention to deal with the issues raised by the increasing mobility of talented, trained scientific and technical personals. The present research is, therefore, framed to highlight these economic, social, cultural and political drivers of human capital mobility and to provide empirically the measures that aim to answer the questions that are at the top of international migration research and policy agenda. Particularly the study has the following specific objectives.
1.2.1. OBJECTIVE 1: To Trace Historical, Occupational and Spatial Trends of Human Capital Mobility from Pakistan
The socio-economic trends that the highly-educated and highly-qualified people leave the country and delink from their motherland after one or two generations, imply a great sacrifice from the country of origin. Therefore, the first objective of the study is to analyze the overall trends and the occupational and profession-wise composition of human capital flows from Pakistan along with recent dimensions of human capital flows to 27 major human capital recipient countries namely, Australia,
Bahrain, Canada, China, Cyprus, France, Germany, Greece, Indonesia, Italy, Japan,
Kuwait, Libya, Malaysia, Oman, Qatar, Russia, Saudi Arabia, Singapore, South
Africa, Spain, Switzerland, Thailand, Turkey, the United Arab Emirates, the United
Kingdom and the United States from the year 1981 through 2016. The selection of 27 destination countries for empirical analysis is entirely based on the intensity of knowledge-based demand of migrants by these destination countries as is affirmed by
United Nations’ Trends in International Migrant Stock: the 2017 Revision and by the
World Bank’s Migration and Remittances Fact book 2017.
Our study is different from early studies conducted on the topic for Pakistan, as we use rich and reliable secondary panel data on human capital flows from Pakistan from the year 1981 through 2016 to a cross-section of 27 destination countries. The early studies conducted for Pakistan (Altaf and Obaidullah, 1992 and Arif and Irfan,
1997) were based on primary survey data, directly collected from the correspondents, which did not represent the whole population of Pakistan. Our analysis not only considers the occupational, professional and skill-wise composition of human capital flows but it also explains the spatial dimensions of these flows.
1.2.2. OBJECTIVE 2: To Investigate the Drivers of Human Capital Mobility from Pakistan
The statistics provided by Ministry of Finance in Economic Survey (2016-17) show that the share of tertiary educated manpower in Pakistan is distressingly 9.14 percent of total labor force, which is very low compared to high-income developed countries. Second, in Pakistan the meager stock of human capital has been created from scarce financial resource. For example, in the fiscal year 2016-17 government expenditure on education was only 2.2 percent of gross domestic product. Third, common social trend is that a very small percentage of people are enrolled in institutions of higher learning e.g., in fiscal year 2016-17 the enrollment was 937132 students in degree colleges and 1355649 students in universities of Pakistan. Fourth, there is a shortage of institutions providing higher education e.g., in fiscal year 2016-
17 total degree colleges in Pakistan were 1418 and total universities were 163
(Economic Survey, 2016-17). Thus, the drainage of human capital is in real terms a big loss to Pakistan that entails sacrifices from the economy and society as a whole.
In essence the second objective of present study is to identify the main attractions towards the centers of destination and to explore the factors contributing to the emigration of qualified manpower and trained scientific and technical persons across the Pakistani border. Novelty in achievement of the second objective lies in the selection of methodology to access the drivers of human capital flows. Though limited empirical literature (Ahmad and Hasan, 1971 and Ahmad et al., 2008) is also available on the identification of the macroeconomic determinants of emigration from
Pakistan, yet the choice of the determinants in these studies is based on the discretion of researchers without deep theoretical base. In the present study, determinants are not randomly selected but have strong underpinnings from the theories of migration.
The model selected for present empirical analysis is a bipolar specification of augmented gravity model, which is hybrid of pull-push factors of migration models
(Lee, 1966 and Rogers, 1967), gravity models of migration (Flowerdew and Salt,
1979) and neo-classical microeconomic migration decisions based on individual’s welfare, self-selection, comparative advantage and earnings of migrants (Borjas,
1987, 1989 and 1999). According to the theories of migration the pull factors are the positive characteristics at the centers of destination that induce immigration and according to existing literature they are the high economic growth rate, technological advancements, better living standard, presence of compatriot community in the destination country and relaxation in the immigration laws in the migration policies of the developed countries. The push factors are responsible for the negative depressing characteristics operating at the center of origin, mentioned in existing literature are poor economic growth rate, high unemployment rate, high inflation rate, deteriorating situation of law and order, terrorism in the home country and negligible share of development expenditure to the gross domestic product.
Above all, instead of regressing net human capital flows on individual determinant which limits the scope of study, in present study we construct and use indices by grouping variables belonging to various categories through the techniques of principal component analysis and principal factor analysis as was used by Monteleone and
Torrisi (2010). These techniques are used to bundle up the number of variables into indices, without severely compromising the information available in data because in principal component analysis and in principal factor analysis we retain uncorrelated principal components as well as principal factors with maximum information in data on a set of variables grouped in a single index. This exercise increases the scope and
prospects of present research by considering a large number of drivers of human capital mobility that are combined into limited number of indices without any fear of degree of freedom.
1.2.3. OBJECTIVE 3: To Scrutinize Region-Wise Comparison of Human Capital Mobility from Pakistan
To scrutinize the magnitude as well as dimensions of intellectual migration from
Pakistan, we group the destination countries into four regions namely, Middle East and Africa (including Bahrain, Kuwait, Libya, Oman, Qatar, Saudi Arabia, South
Africa and the United Arab Emirates), East Asia and Pacific (consisting of Australia,
China, Indonesia, Japan, Malaysia, Singapore and Thailand), Europe and Central Asia
(containing Cyprus, France, Germany, Greece, Italy, Russia, Span, Switzerland,
Turkey and the United Kingdom) and North America (Canada and the United States of America).
The region-wise grouping of destination countries is based on the findings of
Khadria (2001) and Meyer and Brown (1999) that the developed countries like, North
America has knowledge-based demand, biased toward highly-qualified migrants.
These developed countries target student immigration, which not only is a source of fee income but also adds to the pool of knowledgeable workforce in these countries
(Akbari and MacDonald, 2014). Middle East and Africa are placed in a single group as in these regions according to Ahmad and Hasan (1971) there is a great demand for highly-skilled migrants, especially engineers, geologists, geophysicists and earth- scientists etc., to supervise the extraction and processing of oil. In this region labor force is also required for infrastructure development. In short, in our study for region-
wise comparative analysis, countries are grouped on the basis of nature of demand by these destination countries.
1.2.4. OBJECTIVE 4: To Propose Policies for Better Management of Human Capital Mobility from Pakistan
Human capital mobility is a cause of many economic, political, and cultural concerns that affect human resource development through education, training and employment. Human capital flight also affects economic growth, poverty, social development and welfare. Because of spillover effects of human capital flight, the international mobility of skilled and qualified manpower is decisive for policy making in the source and destination countries. Therefore, final objective of this study is to propose the policies designed on sound empirical grounds derived from the empirical and comparative analysis.
The relationship between present research and state policies can be depicted by pairwise Granger causality tests. First, fiscal policy can affect human capital flows via investment on education. Second, monetary policy can affect standards of living via inflation rate affects and, hence, influence human capital flows. Third, Human capital flight increases the value of education and, hence, can shift educational goals and expectations to be pursued by educational policy of a country. Fourth, labor policy may be employed for human capital formation and alternatively unemployment problems can be solved by free human capital mobility. Finally, the tool of immigration policy can be used to deal with the issue of human capital flight.
In short, though the debate on the human capital mobility and hence for the drivers of these flows is an old one, yet it has been based on theoretical and descriptive analysis based on primary data. The present study provides comprehensive
measures that enable researchers and policy makers to improve their analysis of the human capital flows, to obtain valuable insights into its socio-economic drivers and to adopt the institutional reforms through state policies to deal with the issues of human capital flight.
1.3. ORGANIZATION OF THE STUDY
To achieve the above mentioned objectives the thesis is divided into nine chapters.
Chapter 2 reviews the Pakistan’s history from the perspective of human capital mobility of talented and skilled manpower to and from Pakistan. Quantitative assessment of the historical evolution, occupational and profession-wise composition and spatial trends of human capital mobility including human capital flows and human capital flight are also discussed in the same chapter. In Chapter 3, a comparison of various theories of migration from multiple disciplines of social sciences is presented. The existing empirical literature at international level and for
Pakistan is reviewed in Chapter 4. In Chapter 5, an augmented gravity model of migration, based on broad theoretical and empirical literature review, is developed to determine the drivers of human capital mobility from Pakistan. In Chapter 6 data sources and analytical frame work for construction of indices and estimation procedure to be used in empirical analysis are explained. In Chapter 7, construction of indices from drivers of human capital mobility on the basis of principal component analysis and principal factor analysis is presented. In Chapter 8, we present empirical analyses for the drivers of human capital mobility from Pakistan with panel unit root tests, pairwise panel Granger causality tests and co-integration regressions, namely dynamic ordinary least square. Finally, Chapter 9 provides conclusions and lists major
policy implications through institutional reforms with particular attention to differences by regional destinations.
Chapter 2
HUMAN CAPITAL FLOWS FROM PAKISTAN: HISTORICAL, OCCUPATIONAL AND SPATIAL TRENDS
2.1. INTRODUCTION
The statistics provided by the United Nation’s dataset, in International Migration
Report 2017: Highlights, show that the number of international migrants rose from
220 million in the year 2010 to 258 million in the year 2017 (i.e., 3.4 percent of overall world population). The statistics also show that 106 million out of 258 million of international migrants were born in Asia. In overall ranking India has the largest diaspora (17 million) in the World followed by Mexico (13 million), Russia (11 million), China (10 million), Bangladesh (7 million), Syria (7 million) and Pakistan and Ukraine (6 million each). According to these highlights in the year 2017 the top migrant’s destination country is the United States. The Kingdom of Saudi Arabia,
Germany and Russia hosted the second, third and fourth largest numbers of migrants worldwide followed by the United Kingdom and Northern Ireland. Relevant to the present study is the observation that the United Nation’s report asserts the presence of
Pakistani diaspora all over the World and Pakistan is ranked third in South Asia (after
India and Bangladesh) and seventh in the World (after India, Mexico, Russia, China,
Syria and Bangladesh) for human capital mobility. Thus present chapter provides detailed descriptive and statistical analysis of human capital mobility from Pakistan.
2.2. HUMAN CAPITAL FLOWS TO AND FROM PAKISTAN: HISTORICAL PERSPECTIVES
The review of different studies on the topic of human capital flows to and from
Pakistan (Ahmad and Hasan, 1971; Jeffery, 1976; Ballard, 1987; Altaf and Obaidullah
1992 and Arif and Irfan, 1997) provides the following seven categories for chronological classification of the population mobility across Pakistani border. First, refugee movements between India and Pakistan at the time of its independence from
British rule in 1947. Second, emigration of Pakistani for permanent settlement for seeking employment in the United Kingdom in the 1950s. Third, emigration of qualified professionals and highly educated workers, termed as brain-drain to Britain,
Europe, America and Canada during 1960s and 1970s. Fourth, temporary emigration of semi-skilled manpower to the Middle East during the last three decades of 20th century. Fifth, influx of Afghan refugees to Pakistan in the 1980s. Sixth, illegal immigration of Bangladeshis and Burmese during 1990s, and Seventh, the more recent one, migratory stream of educated Pakistanis entitled as brain-drain to the
United States of America, Canada and Australia.
Arif and Irfan (1997) recognized that the withdrawal of colonial powers from the
Indian Subcontinent in 1947 was followed by a two-way exodus of population across borders of the newly independent states, India and Pakistan. Approximately 10 to 12 million people crossed the boundaries between India and Pakistan in the year 1947.
The pace of this large scale movement slowed down in 1951 when these countries imposed restriction on crossing of their borders.
In the late 1950s the colonial links facilitated migration of Pakistanis towards the
United Kingdom in response to labor shortage there. According to Ballard (1987) in early 19th century Kashmiris, known as seamen, began to work on merchant ships sailing out of Bombay and are the pioneers of the human capital mobility from
Pakistan to Britain. During the 2nd World war Britain’s heavy industries needed labor which attracted seamen to leave their ships and to take industrial jobs on shore.
Through a process of chain migration these seamen further brought the fellow villagers to help them in these industrial jobs. Ballard (1987) also specified that before independence of Pakistan the movement of unskilled and semi-skilled persons to
Britain was common that turned into job seekers, young educated migrants after 1947.
Jeffery (1976) stated that according to British census 1961, 32000 Pakistanis were residing Britain and majority of these early migrants were single males born in
Pakistan. Though these migrants were unskilled yet their immigration to Britain had no adverse effect on the British economy. On the other hand, the remittances sent back by these migrants had positive effect on Pakistan’s economy. The inflow of remittances brought about an immense building boom experienced in Azad Kashmir, particularly in Mirpur during the late 1960s and the early 1970s. The building boom affected the construction linked industries such as electrical appliances, sanitary wares and metal fixtures. Unluckily, since the 1960s Britain declared entry visas mandatory for persons from its ex-colonial states including Pakistan.
During the 1960s and the 1970s emigration of qualified professionals and highly educated workers (i.e., brain-drain) to Britain, Europe, America, Canada and Middle
East took place. With respect to the 1960s and the 1970s brain-drain, Ballard (1987) estimated that 13261 highly qualified Pakistanis went abroad from 1961 till 1966.
Three-quarters of the professionals were employed in Saudi Arabia and Libya.
According to Ballard (1987) the United Kingdom and Saudi Arabia were the main destinations for medical professionals, while more than half of engineers were employed in Libya. According to Ballard (1987) more than one thousand Pakistani
professionals migrated to Canada between 1967 and 1973. During this period
Canada’s point-based entry system of allotting immigration encouraged the immigration of skilled workers from South Asia. Thus, South Asian professionals in the United States rose to 51,000 in the year 1970. At the same time the South Asian diaspora from India, Pakistan and Sri Lanka to Australia increases in the year 1973.
After oil price rise of 1973, development projects started in the Middle East that created employment opportunities for Asian workers. Because of its geographic proximity and religious affinity with Arabs, Pakistan emerged as one of the leading exporters and the major manpower suppliers to Saudi Arabia and the United Arab
Emirates. Ballard (1987) argued that in 1973 world oil boom in Arab region opened an opportunity for semi-skilled Pakistanis professionals to work temporarily in the
Middle East on short-term contracts of up to three years. Temporary migrants to the
Middle East were usually admitted by the receiving countries for specific purposes and time periods and were subject to limitations on both stay and work. The annual placement of Pakistanis fluctuated substantially, peaking in 1977 and 1981. It declined dramatically in 1982 to 1986, and then during the 1987 to 1992, settlements increased steadily after the Gulf War, but after 1993 it again declined.
According to Arif and Irfan (1997) the decline in poverty level in Pakistan in the late 1970s and 1980s is attributed to the large-scale emigration of workers to the
Middle East and the resulting remittances from the workers to their families, which increased per capita income in Pakistan. Remittances from Middle East have affected consumption expenditure and hence the sectorial pattern of investment. Increased demand for consumer durables led to growth in local production of plastic and engineering industries in producing appliances such as washing machines, coolers and
the like. In the decade of the 1980s, professional workers migrated to the Middle East.
Pakistan Economic Survey: 2001-02 reported that the migration of Pakistani worker to the Middle East was exceptional in many ways. First, the primary migrants, being single young males, sent larger portion of their earnings to their families in Pakistan.
Second, the remittances received by the low-income households of migrant enabled them to set up small scale business. They acquired real estates and made improvements in their standards of livings. However, the construction boom in the
Middle East was slowed down in the early 1990s, which resulted in a decline in employment opportunities and earning of overseas migrants.
In the 1980s Pakistan received about three million refugees from Afghanistan. It was reported by New York Times in November 1988 that about one million Afghan refugees were living in the city of Peshawar alone, while more than two million were staying in Khyber Pakhtunkhwa. Indian Muslims who arrived in Pakistan between the years 1947 and 1951 had a right to become citizens of Pakistan due to mutual agreement between Pakistan and India. In other words, they were not subject to return to India. This right, however, was not granted to Afghan refugees who crossed the
Pakistani border after 1979 when Russia invaded Afghanistan. The 60 percent of the
Afghan refugees were ordinary Afghans’ that largely depend on Pakistan and international efforts for their sustenance.
According to Arif and Irfan (1997) in 1990s Intra-Asian labor migration has grown, particularly, from less-developed countries with massive labor surpluses. In the 1990s Pakistan also emerged as a destination of illegal immigrants from the surrounding countries, Bangladesh, Burma, India, Sri Lanka, Iran, Iraq and Vietnam.
The Iraq and Vietnam wars caused large-scale refugee movements. Pakistani returned to their country of origin after the Iraqi invasion of Kuwait and the 1990-91 Gulf war.
The human capital mobility in terms of brain-drain from Pakistan picked up again in the 1990s when educated and skilled Pakistanis started moving to Canada, the
United States and Australia. According to Shah (1994 and 1995) to welcome this diaspora from Asia, the Unites States, the United Kingdom, Canada, Australia and
New Zealand had even changed their immigration policies to admit persons with proven skills and achievements from these developing countries for permanent settlement. Educated and skilled Asians, including Pakistanis, who met the criteria set by these five countries, opted for permanent settlement in these countries.
The population movements discussed above and their temporal and spatial dimensions are numerous and complex. To the extent that Pakistan is caught up with phenomena of human capital flight in term of brain-drain may apparently entail sacrifices from the economy and society yet these flows rooted in the history have ambiguous effects on the society and economy. Therefore in the next subsections the composition and dimension of this exodus of Pakistani human capital are explored.
2.3. SKILL-WISE COMPOSITION OF HUMAN CAPITAL FLOWS FROM PAKISTAN
According to statistics provided by Bureau of Emigration and Overseas
Employment as well as reported in Economic Survey of Pakistan: 2015-16, the
Pakistani emigrant stock has increased from one million in the year 1971 to 8.77 million in December 2015. A comparison of occupational skill-wise migrants from
Pakistan for the period 1981 through 2016 is shown in Table 2.1 and Figure 2.1. The
Bureau of Emigration and Overseas Employment has grouped total migrants from
Pakistan into five categories namely, highly-qualified, highly-skilled, skilled, semi- skilled and un-skilled. This classification of skill is proposed, defined and used by
Hall et al. (2016) and Doghri et al. (2006). According to these studies the highly- qualified employees are very competent, they work efficiently and are capable to supervise skilled employees. Secondly, skilled-employees work efficiently, exercise independent judgments, perform their duties with responsibility and have a thorough and comprehensive knowledge of the task assigned to them. The third category, the semi-skilled workers perform somewhat skilled work but do not make important decisions on their own. The tasks assigned to semi-skilled workers are limited to the performance of routine operations of limited scope. Finally, un-skilled work demand physical exertion and performance of simple duties. It does not require independent judgments instead only needs familiarity with the occupational environment.
Table 2.1 and Figure 2.1 show that throughout the period of analysis skilled labor force has major share in total migrants (ranging from 40.13 percent in the years 2005-
08 to 51.18 percent in the years 1993-96) followed by unskilled labor force (37.19 percent in the years 2013-16 to 47.71 percent in the years 2005-08) in total migrants. Both skilled and unskilled labor forces show a decreasing trend in their shares in total migrants. The percentage share of unskilled labor force decreased from 47.03 percent in the years 1981-84 to 37.19 percent in the years 2013-16. The share of skilled labor force also decreased from 43.89 percent in the years 1981-84 to 40.61 percent in the years 2013-16.
The decline in unskilled migrants show that the host countries do not encourage immigration of un-skilled labor force, as according to Dadush (2014) unskilled migrants are imperfect substitutes for native labor force of destination countries.
Therefore Dadush (2014) stated that developed countries channelize the international mobility of labor force by adopting the visa policies (e.g., O-1 visas for migrants with
Table 2.1: Skill-Wise Total Human Capital Outflows from Pakistan Years Highly Highly Skilled Semi-Skilled Un-Skilled Qualified Skilled 1981-84 8207 25433 221298 12115 237134 (1.628 %) (5.044 %) (43.892 %) (2.403 %) (47.033 %) 1985-88 3224 16343 126039 9065 133395 (1.119 %) (5.673 %) (43.754 %) (3.147 %) (46.307 %) 1989-92 5641 32334 258388 16356 231249 (1.037 %) (5.944 %) (47.501 %) (3.007 %) (42.512 %) 1993-96 6322 34870 257010 15693 188247 (1.259 %) (6.944 %) (51.183 %) (3.125 %) (37.489 %) 1997-2000 9391 41674 212509 8784 163203 (2.156 %) (9.568 %) (48.790 %) (2.017 %) (37.470 %) 2001-04 11783 63333 317812 14445 255841 (1.777 %) (9.549 %) (47.920 %) (2.178 %) (38.576 %) 2005-08 27336 85947 418420 13502 497468 (2.622 %) (8.243 %) (40.130 %) (1.295 %) (47.711 %) 2009-12 28307 42130 781586 185133 824756 (1.520 %) (2.263 %) (41.978 %) (9.943 %) (44.296 %) 2013-16 134123 40462 1283775 527038 1175706 (4.243 %) (1.280 %) (40.612 %) (16.673 %) (37.193 %) Source: Bureau of Emigration and Overseas Employment, Government of Pakistan Note: in parenthesis are percentages for the row totals
Figure 2.1: Skill-Wise Total Human Capital Outflows and its Percentage (a). Number of Migrants (b). Percentages of Migrants
Source: Bureau of Emigration and Overseas Employment, Government of Pakistan
extraordinary ability in science, arts, education, business or athletics or H-1B visas for professional) that best fit to their demand of highly-qualified and highly-skilled labor force. The share of semi-skilled labor force in total migrants has increased from 2.4 percent in the years 1981-84 to 16.67 percent in the years 2013-16. We enumerate human capital flight or brain drain as an aggregate of highly-qualified and highly- skilled migrants as is defined by Doghri et al. (2006) definition. Table 2.1 shows that the share of brain drain was 6.67 percent of the total migrants in the years 1981-84.
Table 2.1 also ratify the findings of Doghri et al. (2006) and Adams (2003) that in general the share of brain-drain (defined as only highly-skilled persons) was around
10 percent of total migrants in the year 2004 in their studies. An important difference of present study with these analyses is that recently the brain-drain from Pakistan has become biased toward highly-qualified migrant, as justified by Khadria (2001) developed countries have targeted the student immigration that is not only a source of fee income but also adds to the pool of knowledgeable workforce in these countries.
We also explain the percentage share of highly-qualified and highly-skilled migrants in total human capital flight (i.e., brain-drain) for the period 1981 through 2016 in
Figure 2.2 below.
A shift from highly-skilled migrants (from the year 1981 through the year 2008) to highly-qualified migrants (2009 onward) is indeed clear in this figure. This shift in international intellectual mobility from highly-skilled to highly qualified personals in the Figure 2.2 is consistent with the immigration policies of rich and developed
countries, which are in favour of highly-qualified intellectuals. After East Asian Financial crises of 1997-98, event of 9/11 (September 11, 2001 pentagon attacks) and
Figure 2.2: Percentage Share of Highly-Qualified and Highly-Skilled Migrants in Net Brain Drain
Source: Bureau of Emigration and Overseas Employment, Government of Pakistan
Global financial crises of 2007-08 developed countries are particularly screening migrants and only allow human capital flows of intellectuals in term of highly- qualified persons.
2.4. OCCUPATIONAL COMPOSITION OF HUMAN CAPITAL FLOWS FROM PAKISTAN
Bureau of Emigration and Overseas Employment, Government of Pakistan have decked seven main occupational categories for total migrants from Pakistan. The first category is called Professional, Technical and Related Workers, further subdivided into groups containing accountants, artists, designers, doctors, draftsmen, engineers, nurses, pharmacists, photographers, programmers, surveyors, teachers and technicians. The second category, Administrative and Managerial Workers contains
managers. The third category named as Clerical and Related Workers contains clerks, typists, stenographers and storekeepers. The fourth category of Sales Workers represents salesmen. The fifth category called Services Workers includes cooks, waiters and bearers. The sixth category representing agriculturists is named as
Agricultural, Animal Husbandry and Forestry Workers, Fishermen and Hunters.
Finally, the seventh category of Production and Related Workers, Transport
Equipment Operators and Laborers is subdivided into subgroups containing blacksmith, cable jointers, carpenters, denters, drivers, electricians, fitters, foreman, supervisors, goldsmith, masons, mechanics, operators, painters, plumbers, riggers, steel fixers, tailors, welders and laborers. As micro level decomposition of occupational categories is not of our concern so we only list aggregate of the sub- categories into seven main categories in Table 2.2.
Table 2.2: Occupational-Category-Wise Human Capital Flows (number of migrants) from Pakistan Category -1 Category - 2 Category - 3 Category- 4 Category- 5 Category - 6 Category - 7
Professional, Administrative Clerical and Sales Services Agriculture, Production Years Technical and Managerial Related Workers Workers Animal Related, and Related Workers Workers Husbandry, Transport Workers Forestry, Equipment Fisherman Operator and Hunters and Laborer 1981-84 17596 1373 11147 3262 13467 20435 436025 (3.496%) (0.273%) (2.215%) (0.648%) (2.676%) (4.060%) (86.632%) 1985-88 12760 642 7543 6090 9059 19044 239018 (4.338%) (0.218%) (2.564%) (2.070%) (3.079%) (6.474%) (81.255%) 1989-92 29336 1024 18846 11481 16354 13947 464461 (5.282%) (0.184%) (3.393%) (2.067%) (2.944%) (2.511%) (83.619%) 1993-96 27868 794 2782 16748 13611 16823 419269 (5.597%) (0.159%) (0.559%) (3.364%) (2.734%) (3.379%) (84.208%) 1997- 39077 1260 2604 12028 8784 13893 357906 2000 (8.972%) (0.289%) (0.598%) (2.762%) (2.017%) (3.189%) (82.173%) 2001-04 55115 3266 4813 13741 14445 28073 543761 (8.310%) (0.493%) (0.726%) (2.072%) (2.178%) (4.233%) (81.988%) 2005-08 68885 9820 10250 18632 13502 48901 872683 (6.607%) (0.942%) (0.983%) (1.787%) (1.295%) (4.690%) (83.697%) 2009-12 119173 9150 17205 26005 26860 48817 1614702 (6.401%) (0.491%) (0.924%) (1.397%) (1.443%) (2.622%) (86.723%) 2013-16 241049 21996 48247 62606 62024 100498 2624684 (7.626%) (0.696%) (1.526%) (1.981%) (1.962%) (3.179%) (83.031%) Source: Categorization of Migrants by Bureau of Emigration and Overseas Employment
Note: in parenthesis are percentages for the row totals
All categories show a decline in migrants from Pakistan in the period 1985-88.
Similar decline is also observed in years 1997-2000 except for category-1 and category-2. This overall decline in human capital flows in years 1997-2000 is perhaps due to East Asian Financial Crises of 1997-98, but category-1 and category-2 having composition of highly-qualified and highly-skilled emigrants from Pakistan did not show declining trend. Although category-1 and category-2 show increasing trend over the period of analysis (i.e., from the year 1981 through 2016) yet their percentage share in total migrants is low. In the years 2013-16 the aggregate share of category-1 and category-2 was 8.32 percent (=7.626+0.696) in total human capital flows. The overall trends of all categories shows that net human capital flows from Pakistan is not much affected by the event of 9/11 in 2001 and Global financial crises of the years
2007-08. In short, human capital flow from Pakistan has dramatically increased in the years 2013-16. Table 2.2 also shows that throughout the period of analysis the percentage share of total migrant was high for category-7 that includes unskilled workers and its share in total migrants was 83.03 percent in the years 2013-16.
A combine graph of registered migrants in various occupational categories is presented in Figure 2.3. All categories show increasing number of registered migrants during the period of analysis with different level of ebbs and flows. These seven categories contain mixture of highly-qualified, highly-skilled, skilled, semi-skilled and unskilled labor force as share of total human capital flows. For category-1 the minimum number of registered migrants was in the year 1986, while maximum number of migrants registered with Bureau of Emigration and Overseas Employment in category-1 was in the year 2015. Category-2, including administrative and
managerial workers, shows over all increasing trend in the number of migrants in the period of analysis except for the years of Global financial crises (2007-2008) when a sharp decline of migrants registered with Bureau of Emigration and Overseas
Figure 2.3: Number of Registered Migrants in Various Occupational-Categories
Category-1: Professional, Technical and Category-2: Administrative and Managerial Related Workers Workers
Category-3: Clerical and Related Workers Category-4: Sales Workers
Category-5: Services Workers Category-6: Agricultural, Animal Husbandry, Forestry, Fishermen and Hunters
Category-7: Production Related Workers, Transport Equipment Operators and Laborers
Employment in this category is observable in Figure 2.3. Category-3 contains clerical and related workers and is vulnerable to period of international economic, financial and political instability. Particularly, number of migrants registered with Bureau of
Emigration and Overseas Employment in this category sharply declined in the period of Asian financial crises (1997-98).
In general, the occupational-categories of human capital flows are sensitive to
Global events namely, Iraqi invasion of Kuwait and the Gulf war (1990-91), East
Asian financial crises (1997-98), 9/11 Pentagon attack (September 11, 2001) and to
Global financial crises (2007-08).
From these registered migrants in various occupational categories enumerated in
Table 2.2 and shown in Figure 2.3, highly-qualified and highly-skilled manpower are aggregated to measure brain drain or more specifically human capital flight from
Pakistan. In Table 2.3 and Figure 2.4 we have profession-wise disaggregated data for human capital flight or brain drain. Following Doghri et al. (2006) and Gibson and
Mckenzie (2011a and 2011b) we measure brain drain by aggregation of following seven professions namely, engineers, doctors, teachers, accountants, managers,
computer programmers and geologists. The profession-wise distribution of migrants along with their percentage share in human capital flight is presented in Table 2.3.
Definitively, in absolute terms the number of migrants in all the professions shows an increase yet their percentage shares vary across period of analysis. For instance the number of migrants in the profession of engineers first increased from 32 percent in the years 1981-84 to 44 percent in the years 1997-2000 but again decline to 24 percent in the years 2013-16. In the categories of accountants, managers and geologists a decline in the absolute numbers of registered migrants with Bureau of
Emigration and Overseas Employment exist in the years 1985-1988 and in the years
1993-1996. Computer programmers only started their registration with Bureau of
Emigration and Overseas Employment from 1993 onward. Till early 1970’s computer technology was not much recognized and hence had negligible demand. Their share in human capital flows increased from zero in the year 1994 to 10 percent in the year
2016. Table 2.3 shows that in the year 2016 the share of engineers in total brain-drain was 24 percent followed by managers 21 percent, geologists 17 percent, accountants
16 percent, computer programmers 10 percent, doctors 8 percent and teachers 4 percent. The statistics provided in Table 2.3 also ratifies the international labor force demand in STEM (Science, technology, engineering and mathematics) disciplines based on great opportunities for STEM-related careers in developed countries.
Table 2.3: Profession-Wise Human Capital Flight (Brain-Drain) from Pakistan Years Engineers Doctors Teachers Accountants Managers Computer Geologists Programmer 1981-84 3335 257 455 2787 1373 0 2097 (32%) (3%) (5%) (27%) (13%) (0%) (20%) 1985-88 1075 250 401 856 642 0 711 (27%) (7%) (10%) (22%) (16%) (0%) (18%) 1989-92 2131 287 478 1721 1024 0 1029 (32%) (4%) (7%) (26%) (15%) (0%) (16%) 1993-96 2409 606 389 1782 794 221 480
(36%) (9%) (6%) (27%) (12%) (3%) (7%) 1997-2000 4949 1181 603 1398 1260 1159 741 (44%) (11%) (5%) (12%) (11%) (10%) (7%) 2001-04 3789 1955 1004 1769 3266 1712 768 (27%) (14%) (7%) (12%) (23%) (12%) (5%) 2005-08 7772 2022 1558 5218 9820 2989 1814 (25%) (6%) (5%) (17%) (31%) (10%) (6%) 2009-12 11188 3929 2566 6824 8950 3685 4022 (27%) (9%) (6%) (17%) (22%) (9%) (10%) 2013-16 24207 8260 4217 16512 21996 10661 17642 (24%) (8%) (4%) (16%) (21%) (10%) (17%) Source: Bureau of Emigration and Overseas Employment, Government of Pakistan Note: In parenthesis are the percentage shares for the rows total.
In Figure 2.4 we present the occupation-wise composition of highly-qualified and highly-skilled migrants (i.e., brain drain) from Pakistan. The minimum number of engineers in 1987 was due to Iran-Iraq war. After war, construction increases in the
Middle East and demand for Pakistani engineers also increased in the years following war. Over all doctors as migrants show an increasing trend in the period of analysis
(from the year 1981 through 2016). In fiscal year 2016 the doctors who registered with Bureau of Emigration and Overseas Employment was maximum showing a massive demand for Pakistani doctors in the international labor market in that year.
Teachers play important role in shaping the lives of young generation. In
Canadian survey study Gibson and Mckenzie (2011a and 2011b) show that scientists, teachers and professors are the most respectable occupations in Canada, accounting for 45 percent teachers with master’s degree or above and 65 percent of those with
PhD degrees. Pakistani teachers are proudly serving internationally. The teachers emigrated from Pakistan have diversified trend with minimum registration of teachers with Bureau of Emigration and Overseas Employment in the year 1991 and maximum registration of teachers with Bureau of Emigration and Overseas Employment in the year 2016.
With the advent of 21st century the accounting profession has improved rapidly.
Firms are investing heavily in their best and brightest in order to be competitive and to develop successively. It is easier for developed countries to attract experienced and skilled accountants from developing countries. A sharp decline in the number of accountants registered with Bureau of Emigration and Overseas Employment in the years following Iran-Iraq war, Asian financial crises, event of 9/11 and Global financial crises is observable in Figure 2.4.
Figure 2.4: Profession-Wise Human Capital Flight (Brain-Drain) from Pakistan (number of persons)
Engineers Doctors
Teachers Accountants
Managers Computer Programmers
Geologists
Manager is a leader who paves the path to successful business and hence desire knowledge of latest technology. Except for the years following Global financial crises the managers registered with Bureau of Emigration and Overseas Employment show increasing trend. Computer programmers or computer analysts play an essential role in all spheres of life including services industry. Computer programmers only start their registration with Bureau of Emigration and Overseas Employment in the year
1993 onward as is apparent in Figure 2.4. Like other professions, migration of computer programmers also has a strong set back by the event of 9/11 as well as by
Global financial crises (2007-08).
A geologist is a scientist who studies the composition of the earth and explores the natural processes that shape the earth. Pakistani geologists are demanded all over the
world for their expertise in the mining and in search for metals, oil and other buried resources. The number of Pakistani migrants as geologist dropped to minimum in the year 1989 due to Iran-Iraq war that adversely affected their demand in the Middle
East and Gulf countries. The number of registration of geologists with the Bureau of
Emigration and Overseas Employment sharply increases from year 2014 onward.
2.5. SPATIAL DIMENSIONS OF HUMAN CAPITAL FLOWS FROM PAKISTAN
According to Ministry of Overseas Pakistani and Human Resources, Government of Pakistan, till December 2016, around 8.4 million Pakistanis proceeded to Middle
East (54.80 percent), Europe (26.81 percent) and North America (11.90 percent). To scrutinize the region-wise composition of human capital flows from Pakistan to sampled destination countries, we group 27 destination countries into four regions namely, Middle East and Africa (Bahrain, Kuwait, Libya, Oman, Qatar, Saudi Arabia,
South Africa and the United Arab Emirates), East Asia and Pacific (Australia, China,
Indonesia, Japan, Malaysia, Singapore and Thailand), Europe and Central Asia
(Cyprus, France, Germany, Greece, Italy, Russia, Span, Switzerland, Turkey and the
United Kingdom) and North America (Canada and the United States of America). The total migrants (cumulative) residing in different regions of the World during the period of analysis (1981 through 2016) are shown in Figure 2.5.
Figure 2.5: Total Migrants (Cumulative) Residing in Different Regions of the World
Source: Bureau of Emigration and Overseas Employment, Government of Pakistan
Due to religious affiliation Saudi Arabia attracts the lion’s share of migrants from
Pakistan. According to dataset provided by Ministry of Overseas Pakistani and
Human Resources, Government of Pakistan, maximum number of Pakistani migrants in 2016, approximately 5 million (≅ 4.877 million) are residing in Middle East and
Africa. Followed by selected sample of Europe and Central Asia (0.859 million), selected countries of North America (0.568 million) and lastly to the countries of East
Asia and Pacific (0.259 million). Except for Middle East and Africa other three regions show an increasing number of immigrants throughout the period of analysis
(1981 through 2016). After a decline in human capital flows from Pakistan to Middle
East and Africa in the years 2004-06 a sharp increase exists in this flow.
Unfortunately, number of deported Pakistanis from Middle East and GCC (Gulf
Corporation Council) countries are also very high.
We present the total human capital flight (brain drain) that is aggregate of highly- qualified, highly-skilled migrants in Figure 2.6. The maximum number of highly-
qualified and highly-skilled migrants is residing in North America and this number has sharply increased after globalization and liberalization of world economies. The
World Bank’s dataset in Migration and Remittances fact book 2016 also stated that
North America has hosted 19 percent of international migrants in the year 2016.
Figure 2.6: Highly-Qualified and Highly-Skilled Migrants Residing in Different Regions of the World (1981-2016)
Source: Bureau of Emigration and Overseas Employment, Government of Pakistan.
In short, the brain drain in term of migration of highly-qualified and highly-skilled persons from Pakistan is biased toward North America. Europe and Central Asia are second attractive destinations for human capital from Pakistan in the years 1993 onward. Middle East and Africa stood third in attracting Pakistani intellectuals in the form of engineers, doctors, teachers, accountants, managers, computer programmers and geologists.
Figure 2.7: Region-Wise Human Capital Flows from Pakistan Year 1981-84 Year 2013-2016
Total Migrants
Highly Qualifie d and Highly Skilled Migrants
Source: Bureau of Emigration and Overseas Employment, Government of Pakistan.
A comparative analysis for statistics presented in Figure 2.5 and Figure 2.6 is also presented in Figure 2.7, where a significant shift in the distribution of highly-qualified and highly-skilled migrants from Pakistan to these destination regions is prominent during the period 1981-84 and 2013-16. The share of migrants residing in Middle East and Africa decreased from 79 percent in the years 1981-83 to 71 percent in the years2013-16. The share of Pakistani migrants to North America increased from 5 percent to 10 percent (i.e., it doubled) and the share of Pakistani migrants to East Asia and Pacific also increased from 2 percent to 4 percent (i.e., it doubled). The share of brain drain (i.e., human capital flight) from Pakistan to North America has increased from 46 percent in the years 1981-83 to 49 percent in the years 2013-16. Europe and
Central Asia hosted 29 percent of the highly-qualified and highly-skilled migrants from Pakistan in the years 2013-2016, as compare to 20 percent in the years 1981-83.
Similarly, there is an increase in brain drain from Pakistan to East Asia and
Pacific from 4 percent in the years 1981-83 to 7 percent in the years 2013-16. The difference in the composition of human capital flight, more specifically brain drain to selected regions is based on the difference in supply and demand for human capital in these regions. According to Ahmad and Hasan (1971) there is skill-based demand for migrants, especially engineers, geologists, geophysicists and earth-scientists, etc., to supervise the extraction of oil, in Middle East and Africa. Contrary to this, according to Khadria (2001) and Meyer and Brown (1999) in North America, Europe and
Central Asia and in East Asia and Pacific, there is Knowledge based demand biased toward highly-qualified migrants such as PhD’s and researchers. Thus region wise analysis presented in Figure 2.7 ratifies the findings of these earlier studies as well.
Chapter 3
THEORIES OF HUMAN CAPITAL MOBILITY
3.1. INTRODUCTION The process of human migration started with the advent of human history on the planet Earth. According to Brettell and Hollifield (2015) human migration entails human capital mobility over long distance to settle in the new location. The study asserts that early human migration was due to climate change, natural disasters, health hazards and famines. Nowadays, the process of migration is amplified due to the modern means of transportation, communication and knowledge exchange, as well as by the process of globalization. Thus knowledge of demographic, ethnical, educational and social structure of the migrants, as well as their geographical trajectory, temporal dynamics and spatial movement across territories, countries and continents is very important for formation of national and international policies. In
short, the process of migration is an intellectual pillar of population studies and act as a prism to view disciplinary shifts in the population literature. Therefore, in the present chapter we compare various theories of migration with reference to different disciplines of social science and then in next chapter we review the empirical literature available on the topic.
3.2. THEORIES OF MIGRATION AN INTERDISCIPLINARY ANALYSIS
According to Castles (1993) and Brettell and Hollifield (2015) human migration having roots in cultural studies, demography, economics, geography, law, political science, psychology and psychology is inherently interdisciplinary and globalist in nature. Therefore, instead of a single theory of human capital mobility, a segmented set of migration theories fragmented by disciplinary boundaries exists. The fact that each discipline of social sciences has its preferred and acceptable list of determinants makes it difficult to find hypotheses that are simultaneously multidisciplinary.
Therefore, before starting literature review, it is desirable to have a comparison of the theories of migration with reference to different disciplines of social science, where the major distinction drawn between theories is based on the causes (determinants) and consequences of the migration. Therefore, the following sub-sections are devoted to cross-disciplinary comparison of different aspects of the theories of migration.
3.2.1. PRIMITIVE THEORIES OF MIGRATION:AN INTERDISCIPLINARY APPROACH The primitive concept of human migration dates back to 1885, when Earnest
Georg Ravenstein defined the laws of migration (1985 and 1989). Since that time various explanations has been offered to explain causes of international human capital mobility in terms of cultural, economic, political and social factors. In present
subsections we therefore document some primitive ways namely, population projections, forecasting and mapping to theorize human migration.
3.2.1(a). Rationalist Population Projections, Forecasting and Mapping
At the onset, demographers formally documented the pattern and direction of migration flows and the characteristics of migrants (age, sex, education and occupation etc.) for an aggregate data and executed the population projections. These projections were based on natural change in rate of mortality and fertility for a certain period of time. Latter on Coale (1972), Keely and Kraly (1978) and Espenshade et al.
(1982) incorporated the assumptions of human capital mobility in the population projection technique and explored the relationship between net migrants and population size with different age structures. Social demographers Brown and Bean
(2012) formally used predictive models and used data on number of births, deaths, age and gender to forecast the future population.
In a pioneered work Hägerstrand (1957) recognized migration as a time-space process and developed innovative methods of visualizing time-space paths through
Geographic Information System (GIS) technique and dataset (Kwan, 1998). Tobler
(1970) formed maps to portrait the Currents of Migration to channelize the migrant’s flows. Later on, historians paid attention to population statistics e.g., in the institutionalization of national censuses and national registrations to track the births, deaths and marriages (Anderson, 1990) as well as nationality, citizenship and border- crossing (i.e., international migration).
3.2.1(b). Demographic Transformation Theory and Human Mobility
In the early 1970s Zelinsky (1971) generalized migration patterns and developed a mobility transition theory. Zelinsky developed synthesis of spatial and temporal perspective in the demographic transition and linked it with changes in human mobility and fertility. Accordingly, the study attached a mobility transition state to five stages of the demographic change namely, pre-modern society, early transitional society, late transitional society, advanced society and future super advance society.
The replacement effect accelerated by the retirement and gender discrimination are important determinants of the settlement geography by Wright and Ellis (1996 and
1997). The study also elucidates the replacement effect in migration theory through aging and emigration that creates opportunities for young workers from abroad.
3.2.1(c). Gravity Theory of Migration
Ravenstein (1885 and 1889) was the first gravity modeler of a theoretical approach to spatial interaction and his gravity model is the foundation of recent trade theory, transportation planning and migration research. Ravenstein observed that beside distance decay, migration is also affected by the size (i.e., native population) of the origin and destination countries. Latter on Stewart (1942) and Zipf (1946) measured the effect of distance, population growth and city size on human migration.
Flowerdew and Salt (1979) analyzed migration flows by gravity model and identified the residuals from this model as economic explanatory variables other than distance and population.
3.2.2. ASSIMILATION, SOCIAL NETWORKS AND MIGRATION: AN INTERDISCIPLINARY APPROACH
An important paradigm of assimilation theory in human migration is associated with Robert E. Park (1930) and the Chicago School (Park and Burgess, 1921 and
Gordon, 1964). Assimilation is a process of integration and cooperation that create resemblance in the characteristics of immigrant’s and host societies. Therefore, assimilation theories act as barometers for socioeconomic and political environments that influence the perception and reception of migrants. Gordon (1964) avowed structural assimilation as a source of close social relations (i.e., social networks) with the host society followed by ethnic identification and large-scale intermarriage in the host society that further facilitates migration.
Zangwill (1908) in a stage play, Melting-pot, portrayed interracial marriage of immigrants having diverse cultural backgrounds, different skin pigmentations and various ancestral costumes that walked through a symbolic melting pot upon arrival in the United States and reappeared on the other end as member of homogeneous culture presenting an assimilation theory of migration. Referring to this stage play Glazer and
Moynihan (1963) argued that ethnicity and racial characteristics are the main cause of human mobility.
Barnes (1954 and 1969) and Mitchell (1966 and 1974) pointed out the importance of network of family and friends that affect migration through kith and kin based ties.
Ladinsky (1967a and 1967b) and Barff and Ellis (1991) investigated the interaction between occupation and migration through the prism of location specific ties particularly, location specific occupations and social network. In the 1970s and 1980s the migration theory was developed on ethnicity beside class and gender based assimilation (Conzen et al. 1992 and Barken 1995). Harzig (1997) and Iacovetta et al.
(1998) acknowledged that gender discrimination within families and status of females in households and labor markets described feminist migration in the 1980s.
3.2.3. DUAL OR SEGMENTED LABOR MARKETS AND MIGRATION: AN INTERDISCIPLINARY APPROACH
In the second half of the twentieth century, the pioneers of dual or two parallel labor markets in migration theory were Doeringer and Piore (1971) and Piore (1979) whereas for segmentation of several labor markets was Reich et al. (1975). The dual and segmented labor markets theory suggests that immigrants have unequal access to job opportunities and hence exploitation of immigrants exist in destination countries.
The dividing line for dual and segmented labor markets can be cultural, rural-urban, gender (male-female) based, occupational (skilled-unskilled), demand-supply of labor or pull-push factors based migration.
3.2.3(a). Cultural Segmentation, Dual Labor Market and Migration
Cultural segmentation was developed in 1960s that was the period of Civil Rights
Movement and immigration policy reforms. Watson (1977) examined social structure and culture of migrants in Britain and acknowledged, admired and respected the cultural identity of immigrant’s traditions instead of forcing them to assimilate with the culture of destination country. That cultural diversity and strengthening voice of immigrants and minorities had triggered political and social revolution in the Britain.
3.2.3(b). Rural-Urban Segmentation, Dual Labor Market and Migration
Meillassoux’s study of migration (1981) in colonial West Africa identified and characterized various capitalist modes of production in hunter-gatherer societies to exploit commodity and labor market through export of commodities and human capital mobility. Fan (2002) acknowledged the role of state institution-based- opportunity-structure of socialist and transitional economies in labor market segmentation and hence on human capital mobility. In this study, state-sponsored
distribution of services and job opportunities to permanent migrants were linked with household registration system. Fan’s findings indicated that permanent migrants were the most privileged and successful persons, followed by non-migrant natives and temporary migrants were at the bottom in access to state-sponsored resources and for entry in labor market.
3.2.3(c). Industrialization, Labor Market Segmentation and Migration
Anderson (1993 and 1999) acknowledged the golden age of industrialization for privileged distribution of economic and social opportunities for migrants. In systematized and well organized labor markets of Western societies Anderson characterized the period of industrialization by full employment and social harmony.
Thus in a segmented labor market of insiders (locals) and outsiders (migrants), social- structural dividing line by Anderson is unequal distribution of employment opportunities with different economic, social and political conditions.
Emmenegger et al. (2009) distinguished between three types of labor market segmentation in industrialized economies. First, a simple labor market dualism referred to structural disadvantages to migrants (defined as labor market outsiders) in job opportunities, earnings and in job training. Second, in social protection dualism migrants were structurally disadvantaged in social rights coverage and in provision of welfare benefits. Third, in political integration dualism migrants had no political representative and hence were isolated from democratic decision making. According to Emmenegger et al. opportunities available in various types of labor market segmentation had influenced the migration decision.
3.2.3(d). Pull and Push Factors Based Segmentation for Migration
Lee (1966) summarized various reasons for migration as pull and push factors.
The lack of economic opportunities, religious and political discrimination and hazardous environmental conditions are classified as push factors that trigger emigration from the center of origin. Contrary to it the pull factors at the center of destination are the availability of job opportunities, religious and political freedom and the expectation for a favorable economic, political and natural environment. In short, Lee determined the size of migration by the social and economic opportunities associated with two places.
According to Hollifield (2004) necessary conditions for immigration to industrial democracies are sociological (kinship networks) and economic forces (demand- supply) beside favorable legal and political conditions for acceptance of citizenship rights of immigrants. Martin (2009 and 2014) and Brown and Bean (2012) linked human capital flows from developing to developed countries with rapid population growth and hence increase in labor force of developing source countries in contrast with stagnating or shrinking labor force in host developed countries (e.g., China).
These studies affirm that beside population differential in over populated and under populated countries, migration flows are also affected by the differentials in income, employment opportunities, rewards to education and political freedom in two regions.
3.2.3(e). Gender Segmentation in Employment and Theory of Distress Migrants
According to Morokvasic (1984) gendered relations and discrimination within families and in labor market play a decisive role for female migration. The study asserted that the increase in women’s formal education and job skills, increase in female labor force participation rate and gender wise employment segmentation help them to undertake long-distance, across border, migration decisions to find jobs that
match their labor market expectations. Roy (2002), Dyer et al. (2009) and Kofman
(2012) defined distress migrants in international movers as poor women to fill service jobs in informal sector as home-care workers, nannies and nurses. Tyner (2007) recognized the substantial representation of Filipinos in international flows as care worker.
3.2.4. NEO-CLASSICAL THEORY OF INTERNATIONAL MIGRATION: AN INTERDISCIPLINARY APPROACH
In economics, theories of migration are based on predictive models that rely on rationalist human behavior and are dealing with scarcity and choice (Martin, 2009 and
2014). From microeconomic perspective, economists sketch migrants as utility maximizers who evaluate different opportunities in term of cost-benefit analysis. Thus the neo-classical approach to international human capital flows is based on wage differentials between receiving and sending countries, as well as on the migrant’s prospects for higher earnings in destination countries (Todaro, 1969 and Harris and
Todaro, 1970).
3.2.4(a). Utilitarian Approach Led Migration
Economists, besides theorizing the wage and employment opportunity differentials as channels affecting flows of population, have also conducted the cost- benefit analysis for utilitarian (e.g., income maximization) decisions making about migration (Lewis, 1952; Todaro, 1969; Borjas, 1989 and Brettell and Hollifield,
2015). Wolpert (1965) and Brown and Moore (1970) stated that decision to stay or move depend on the utility of current location relative to that of alternative destinations. Infact, these studies consider migration as an adjustment process to
increase place utility whereby one residential location is substituted for another in order to better satisfy the desires and needs of intended migrants.
3.2.4(b). Amenities Led Migration
The American anthropologists, Redfield (1941 and 1953) advocated the idea of folk urban continuum and bright light theory of migration. Redfield argued that the migration from countryside to cities was of liberal type based on prospects for development, modernization and for breakdown of traditions. Further, bright light migration theory highlights the excitement and charm of urban life that draws attention of young and enthusiastic migrants.
Lowry (1966) in metropolitan-growth migration theory elucidated the direction of migration flows from places with low wages and high unemployment to places with higher wages and lower unemployment. Clark and Ballard (1981) linked human capital theory with the place-specific conditions of migration (i.e., high wage, low unemployment, age, education and socio-demographic variables). In Amenities-led migration theories Graves (1979) and Nelson and Nelson (2011) stated that people move to places where economic opportunities are relatively abundant than other potential destinations. Florida (2002) and Fielding (1992) acknowledged amenities abundant location with superior opportunities as escalator region and declared it a first choice for young talented persons.
3.2.4(c). Cost-Benefit Analysis for Decision to Migrate
Sjaastad (1962), instead of relating migration to income differential, classified migration in resource allocation framework and developed the costs and returns to investment in migration both at private and public level. DeJong and Fawcett (1981)
used value-expectancy approach and presented a framework integrating the value expectancy model with individual level, household level, and societal level determinants of migration. According to the study realization of comfort, status, prestige, power, wealth, autonomy, social connections and morality are the values and goals to be attained through migration.
3.2.4(d). Business Cycles and Human Migration
Hägerstrand (1957) in modeling migration to and from Swedish settlements recognized the effect of information and innovation diffusion on migration. Ballard and Clark (1981) and Clark and Ballard (1981), based on regional economic restructuring, explained outmigration of workers from depressed regions and the immigration of workers to potential destinations with efficient labor markets.
Bluestone and Harrison (1982) explained that recessions based Rustbelt deindustrialization lead many people to lose their work and compelled them to emigrate from the industrial centers of America.
3.2.5 INTERNATIONAL MIGRATION AND TRANSNATIONALISM: AN INTERDISCIPLINARY APPROACH
Massey (1990) emphasized that people (i.e., migrants, former migrants and non- migrants) living in country of origin and destination are bonded together via kin and friendship networks. Massey recognized that kin and friendship ties reduce risks associated with emigration and hence encourage circular migration based on theory of cumulative causation that is migration sustains itself by creating more migration.
Mountz and Wright (1996) and Conway and Cohen (1998) identified births, marriages, festivities, divorces, deaths and bereavement as social characteristics of kin and friendship networks that determine international migration.
3.2.5(a). Globalization, Homogenization and Transnationalism
Eades (1987) reviewed literature on theories of globalization and homogenization and gave consideration to political economy of migration, including the effect of
Marxism ideology. In response to global processes, Gardner (2006) applied impact of transnational marriages, kinship and caring in the global network to the microeconomic and macroeconomic approaches of migration. Particularly, the study addressed the reunion of families at single place, described the household and caring work performed by wives, and analyzed the role of wives in maintaining transnational links. Kymlicka (1995) differentiated between two types of multicultural groups. First immigrant group was ethnic group with different cultures and languages but without defined territory or claim of citizenship. Second was the national group having initial resident of poly-ethnic state that accommodate migrants. The first group migrated to poly-ethnic state by choice and finally assimilated with larger national group after several generations.
Barken (2004) suggested that trans-localism (i.e., transnationalism) better captures the networks of nineteenth-century migration, as in 1970s and 1980s the theoretical concerns for migration were developed on ethnicity beside class and gender of assimilation theory of migration. In interdisciplinary culture of globalized world
Harzig and Hoerder (2009) explained migration through the concept of social network and gender and they provided an interaction of migrants as economists, historians and scholars. Commitments by Hoerder and kaur (2013) and their persistent interest in gender and class as social structures (explorers, merchants, marriage-seekers, colonizers and refugees) further enriched migration analysis by combining quantitative and qualitative methods of international migration.
3.2.5(b). Globalization, Diaspora Community and Transnationalism
Clifford (1994) and Rouse (1995) explained different types of diaspora communities namely, identity and hybridity. Their results were highly supportive to hybridity (i.e., transnationalism) for African and South Asian diaspora that were less inclined toward root (country of origin) instead were oriented toward routes (countries of destination) through which a transnational culture and identity was formed.
Heikkila and Yeoh (2010) documented that the process of migration to form families is gender biased, as contract of marriage demands from women to leave her parents and relatives to join men in particular locations through local and international moves.
Thus Heikkila and Yeoh concluded that marriages and love for kith and kin may motivate a person to emigrate from their motherland.
3.2.5(c). Industrialization, Globalization and Transnationalism
Stolarik (1976) asserted that in the last decade of 1800s, mass production stage of goods in American industrialized cities offered new job opportunities. Therefore, millions of people from all over the world poured into American cities between the year 1866 and 1920 to fill the labor demand. Kim and Cohen (2010) argued that industrialization is centered toward cities of emerging industrialized economies that creates excessive labor demand in factories and hence attracts rural and international immigrants. Kim and Cohen also quantified the demographic, geographic, social, historical and economic determinants of international migratory flows to and from
Western industrialized countries.
3.2.5(d). Dual and Multiple Citizenship and Transnationalism
In across border international migration three issues are addressed. First is the role of the state in controlling migration flows. Second is the impact of migration on establishment of sovereignty and citizenship and third is related with corporation by homeland to deal with behavioral, normative and legal issues of citizens. Therefore,
Schuck (1998) simultaneously considered cultural, demographic, economic, legal and political frameworks to determine migration flows to the United States of America.
FitzGerald (2008) suggested government of Mexico to establish social contracts with their citizens abroad (e.g., in the United States of America) to control outflows by voluntary ties and dual nationality. The study claimed from political scientists to maintain the home ties among migrants through policy to discourage permanent settlement and to establish dual nationality to sustain presence abroad as well as attachment to homeland. Sadiq (2009) emphasized a state-mandated procedure by acquiring documentary citizenship practiced in India, Malaysia and Pakistan that provide a right to vote and travel under newly acquired documented nationality.
3.2.5(e). Revolution in Travel and Communication and International Mobility
Button and Vega (2008) explored the effects of air transportation on labor mobility in international labor markets. The study acknowledged that innovations in air transportation facilitates long-distance migration possible and helps migrants to contact with their home country. Cairncross (1997) explained the ways through which advancements and revolutions in telecommunication (telephone, television, internet and wireless) transform the business decisions in human society. Cresswell (2011) focused on high-tech hyper human capital mobility offered by air travel, the internet connectivity and by new personal communication devices. Cresswell also acknowledged that in geography transport, tourism and mobility are important
components of human capital migration research. King (2012) attributed decline in human migration in the United States to telecommunication revolution through which people may stay in touch with their families without physical mobility.
3.2.6. WORLD SYSTEM THEORY OF HUMAN MIGRATION: AN INTERDISCIPLINARY APPROACH
World system theory is a multidisciplinary, macro-scale approach to world history and social changes that emphasizes on the world system as a primary unit with a single division of labor and multiple cultures in social analysis (Wallerstein, 1974,
2004a and 2004b). Wallerstein stated that the constraints imposed on human capital mobility in capitalist system are key determinants to human capital flows. Kraly and
Gnanasekaran (1987) defined migration as a change in residence across political boundaries in the presence of national and international migration laws and regulations. Political scientists give emphasis to immigration policy (rules of entry) rather than emigration policy (rules of exit). They split human migration theories into two categories. Freeman (1995 and 1998) stressed on self-interest-based microeconomic rational choice approach to the study of human migration while
Hollifield (1992), Zolberg (1981 and 2006), Koslowski (2011) and Klotz (2013) explained the impacts of state and public policies on human mobility, migrant’s corporation and harmony, immigrant’s identity and citizenship.
Hollifield and Wilson (2011) analyzed the effects of state laws on the migration flows. Following the Marshallian trilogy of rights (i.e., civil, political and social rights) FitzGerald (2008) examined the right-based immigration laws and policies of
Europe and the United States. Hollifield (2004) stated that political and social rights regulate migration for state sovereignty and for economic well-being of citizens in
both Europe and North America. Therefore, Carens (2000) and Hollifield (2004) argued for rules of entry and exit formulated by state to regulate migration flows beside potential effect of human migration on international relations through international legal system for citizenship and state sovereignty. Thus human migration analysis was transformed from individual and state to international migration system
(Hollifield, 2004) based on normative concepts of morality and justice (Carens, 2000).
Brown and Bean (2012) theorized the rate of intermarriages, social capital and civil society, and explored the nature of international system and its effects on population dynamics. Parrado (2012) measured the efficiency of immigration administration policies in solving the issues of the undocumented immigrants and in relocating the employment opportunities back to native Mexican workers.
3.3. CONCLUSION
This chapter on the review of interdisciplinary theories of migration offers an acute analysis of key determinants of human capital mobility across disciplines. In this study contemporary analysis of migration process through classic theories of migration not only reveals the emergence and development of different scholarly field of migration over time and across cultures but also explores the ways through which historians started narration of the story of human life on the planet earth. Structural summary of interdisciplinary analysis of different theories of migration is presented in
Appendix A.
Chapter 4
REVIEW OF EMPIRICAL LITERATURE ON MIGRATION
4.1. INTRODUCTION
Before starting review of empirical literature, it is desirable to mention a sequential development on the topic of human capital flows. Roughly, three generations of economic research on the human capital flows exist. The first wave of papers contains welfare analyses in standard migration theory frameworks and dates back to the late 1960s (Grubel and Scott, 1966a, 1966b; Johnson, 1967 and Berry and
Soligo, 1969). In these papers, intellectual migration termed as brain drain is treated as an international public good and is determined by wage or earning differential between two regions. These early studies conclude that the impact of human capital flight specifically, brain drain on the source countries is neutral and hence these studies emphasize on the benefits of free migration to the world economy.
In the 1970s the second wave of empirical literature, produced under the leadership of Jagdish Bhagwati, developed a series of models of migration. The papers following Bhagwati (1979) analyzed the factors determining highly skilled migrants in terms of brain drain and recommended state policies to combat the adverse effects of such type of migration on the country of origin. At policy level these studies suggest the international community to implement a mechanism of international transfers to compensate the countries of origin for the losses due to human capital flight or brain drain for example destination countries should redistribute income tax-on-brain in the country of origin.
Finally in the late 1990s the main contributors to the third wave of theoretical and empirical literature on migration particularly Mountford (1995 and 1997), Stark et al.
(1997 and 1998), Vidal (1998), Docquier and Rapoport (2003), Beine et al. (2001,
2007, 2008a, 2008b and 2011) and Stark and Wang (2002) established that under
certain circumstances the brain drain or human capital flight is beneficial to the source country. A strategic and leading empirical literature for the forces ascertaining to intellectual migration of highly skilled and highly qualified personals is captivated by a series of rationally oriented papers of Frédéric Docquier (Docquier and Rapoport,
2003, 2012; Beine et al., 2001, 2007, 2008a, 2008b; Docquier and Marfouk, 2005,
2006 and Docquier et al., 2007, 2009). In these scholarly papers, the brain gain effect in terms of expected return to education dominates the brain drain effect of human capital flight.
4.2. EMPIRICS ON CAUSES OF HUMAN CAPITAL MOBILITY
This section is confined to a review of empirical literature for a better understanding of the topic, more specifically, to identify the drivers of human capital mobility (i.e., human capital flows as well as human capital flight) in international labor market.
4.2.1. INTERNATIONAL EVIDENCE
In present subsection we only highlight the empirical studies to be reviewed without going in the details about the period of analysis, the country of analysis, data sources, estimation technique and empirical results of these studies. In order to avoid repetition this information is only summarized in the Appendix B.
We start the review of empirical literature with the noteworthy contributions of
Brettell and Hollifield (2015) in an interdisciplinary analysis of international migration in social sciences across anthropology, demography, economics, geography, political science and sociology. They asserted that early humans’ migration was mainly because of climate change, natural disasters and famines
whereas nowadays modern means of transportation and communication beside globalization amplifies the process of human mobility.
In highly distinguished and scholarly work Docquier and Rapoport (2012) quantitatively assessed the magnitude, intensity, spatial distribution and determinants of human capital flight, brain drain, to developing and developed countries. Grogger and Hanson (2011) used a simple model of income maximization that accounted for two dominant types of international labor movements. First is positive-selection by individuals for migration and second is positive-sorting of migrants across destination countries. Montelone and Torrisi (2010) using micro data from 350 respondents, including professors, assistant professors and doctoral students, witnessed permanent migration from Italy in the year 2010. Bhagwati (2009) discovered that skilled migrants create less assimilation problems and were more appropriate in knowledge based developed economies than unskilled migrants. Therefore, developed coountries have shifted their labor force migration demand towards skilled migrants.
Docquier et al. (2009) in comparative analysis quantified and characterized the gender based international migration. The study distinguished among three groups of migrants on the basis of three educational levels i.e., medium skilled migrants, low skilled migrants and the high skilled migrants. This educational level based comparative analysis was conducted for the source countries grouped according to their demographic size, the geographical region and average per capita income.
Dustmann et al. (2009) studied that a person moves to a country where skills grow rapidly and efficiently and then sell these skills in a country where these skills command high prices.
Akkas (2008) provided a comparative analysis to determine the size and structure of human capital flight from Muslim countries. The study perceived brain drain or human capital flight as an emigration of trained and talented individuals motivated by strong pull factors at destination countries. Akkas declared international mobility of skilled workers as a critical issue for middle income, low income and Muslim countries, as their share of tertiary educated persons comparative to high income countries is very low. Bhargava and Docquier (2008) inspected for the factors determining migration of physicians from Sub-Saharan countries to seventeen OECD countries from period 1990 through 2004. Individual level survey in six African countries showed that more than half of the physicians wanted to move to developed countries for better working conditions and for peaceful and comfortable life.
Cattaneo (2008) while exploring the impact of international migration and brain drain on poverty also explored the channels through which magnitude of international migration was determined. Beine et al. (2008a and 2008b) besides examining the impact of migration on human capital formation in developing countries also determine the factors ascertaining to brain drain from small states. The study examined the relationship between human capital flows (measured as total migrants as well as brain drain) and the size and location of a country. The study concluded that the small sized countries were more efficient in molding skilled labor yet were less successful in retaining them. According to Beine et al. these small sized countries were the chief losers of their skilled labor force that strongly resisted for the unpleasant push factors prevailing in small sized home country.
Tritah (2008) used census data to perceive the migration of European engineers, scientists, researchers and academic personnel to the United States from the year 1980
through 2006. Infact, the study was motivated by emerging global competition for talented skilled persons between Europe and the United States. The study perceived that the European emigrants specialized in occupations that were most demanded in knowledge based economies despite the lack of demand for highly skilled labor in
Europe. Therefore the excess supply of highly skilled labor in Europe was the main cause of brain drain from Europe to the United States. Mayda (2007) empirically analyzed the determinants of migration flows to fourteen OECD destination countries from the year 1980 through 1995. The study concluded that average income differential between two countries, cultural, demographic and geographical factors and immigration policies of destination countries had significant effect on human capital flows to OECD countries.
Dreher and Poutvaara (2006) by using unbalanced panel data for 78 countries of origin discovered a close link between student flows and migration flows to the
United States. Mishra (2006) quantified the nature and magnitude of highly skilled labor migration from the Caribbean countries to OECD member countries and to the
United States from the period 1965 through 2000. Based on labor demand and labor supply model the study noted that during the period of analysis Caribbean countries had the highest emigration rate in the world. Solimano (2006) asserted that human talent having great economic value is highly mobile due to globalization, spread of information technology and low transportation cost. Solimano argued that well educated talented people were internationally more mobile than unskilled workers and the determinants of international mobility of these two categories of skill were different from each other. Solimano argued that the economic value of talented labor
stems from skill that can be sources of production, wealth creation, knowledge or social service.
The research by Schaeffer (2005) was motivated by the permanent migration of students and scientists from industrialized Western European countries to the United
States of America in 1990’s. These migrants to the United States were inspired by job opportunities for scientists and engineers in the United States (e.g., in Silicon valley) and by the government policies of the United States that preferentially treated highly skilled migrants. Clark et al. (2002) stated that the United States of America experienced a prodigious rise in migration level due to Amendment to the
Immigration Act for family reunion in 1965.
Borjas (2001) explored an association between interstate wage differential in the
United States for particular skill and the geographical sorting of migrants and native workers. The study concluded that interstate dispersion of economic opportunities within the United States had significantly affected the location decisions of immigrant and native workers. Straubhaar (2000) in comparative study determined that why and how the United States of America attracts more brain from the world as compared to
European Union. According to Straubhaar, America attract highly skilled people from all over the world because of a number of natural and man-made social and political factors. The study also asserted that contrary to the United States of America the
European Union lacked the magnetic power to attract highly skilled scientists.
Saxenian (1999) had stressed on the an important role of highly skilled engineers and scientists in Silicon valley, a center of technological innovation and the leading export region in California that served as a model for migration trends in the United
States of America. The study concluded that a quarter of Silicon valley’s high technology firms were headed by Chinese and Indian migrants, who came to the
United States for their studies and then stayed there for their bright future.
Harris and Todaro (1970) used two-sector expected utility maximization trade model and found that rural to urban migration exist as long as the expected urban real income per worker exceeded real average agriculture product per worker. Lee (1966) summarized the drivers of human capital flows in terms of pull and push factors of migration. Wolpert (1965) emphasized that the decision to move depend on the relative utility of current location relative to alternative destinations. Therefore people move to that alternative destination when utility in an alternative destination exceeds that in the current location by a critical threshold. The American anthropologist,
Redfield (1941) explained the change from relational and social networks to acculturation, homogenization and migration and suggested for the notion of a folk- urban continuum and bright-light theory of migration. Summary of the above mentioned International empirical literature for the drivers of human capital mobility are presented in Appendix B.
4.2.2. EVIDENCE FROM PAKISTAN
The causes of human capital mobility are very complex and they vary with time, across countries and across area of specialization. Therefore, to conduct and to streamline our empirical analysis, literature from Pakistan is also reviewed.
Afridi and Baloach (2015) empirically identified the factors determining outflows of Pakistani skilled workers. The research was based on pre migration survey data from 152 teachers of eighteen public sector universities of Khyber Pakhtunkhwa. A
push and pull factors based migration model was used to determine the emigration of physicians and health care workers from Khyber Pakhtunkhwa. Altaf et al. (2015) empirically studied the influence of unemployment, political instability and foreign remittances on brain drain of highly skilled individuals specifically emigration of scientists, doctors and information technology experts from Pakistan.
Arouri et al. (2014) contributed to the literature by determining macroeconomics drivers of human capital flight from Pakistan over the year 1972 through 2012 by using an autoregressive distributed lag (ARDL) approach to co-integration. Hashmi et al. (2012) used survey data to conduct a research for investigation of factors responsible for brain drain from Pakistan in three main professions specifically medicine, engineering and information technology. Irfan (2010) empirically showed that cross border as well as internal migratory streams in Pakistan depend upon the permanence (stability and durability), settlement process and condition of the economy which in term was determined by economic, social and demographic forces.
Ahmad et al. (2008) empirically established that the fluctuation pattern of international migration in Pakistan was mainly attributed to the economic conditions of home as well as the host countries as the income inequalities and poverty are the main causes of emigration from Pakistan. Doghri et al. (2006) used labor demand and labor supply model to determine the economic, social and political determinants of brain drain (i.e., human capital flight) from Pakistan. The study asserted that Pakistan had benefited from these human capital flows through reception of remittances and utilization of its outstanding emigrant professionals. Haque (2005) established a skill incentive parity equation to unveil the reasons for the emigration of skilled professionals from Pakistan.
Arif and Irfan (1997) described the cross border population mobility experienced by Pakistan from the year 1947 through 1997. The study explored the sources and magnitude of population flows to and from Pakistan. Beside regional peace and economic stability of neighboring countries the study found that economic, political and religious factors were responsible for these human capital flows from Pakistan.
The dichotomy of population mobility in terms of inward and outward flows showed that the former was traced by the political factors (partition of subcontinent and the
Afghan war) while the latter presents a job oriented move. Altaf and Obaidullah
(1992) stated that since the early 1970s Pakistan had witnessed excessive labor mobility to Middle East specially, after the oil boom. The study also determined the socio economic and political factors responsible for the present pattern of human capital flows from Pakistan. Above quoted literature review along with period of analysis of these studies, the country of analysis, data sources, estimation technique and empirical results are summarized in Appendix C.
Chapter 5
METHODOLOGY
5.1. INTRODUCTION
On the basis of intrinsically interdisciplinary nature of human capital flows having roots in in anthropology, cultural studies, demography, economics, geography, law, political science, psychology and sociology, the existing literature does not provide any single comprehensive theory, adequate for the understanding and explanation of the composition and the spatial dimensions of human capital flows (Brettell and
Hollifield, 2015). Therefore, in developing an econometric model for the study of human capital flight, it is practical to access it through a multi-disciplinary apparatus.
The main objective of the present chapter is to provide a general econometric framework for determining the human capital flows from Pakistan. In the following section 5.2 we consider bi-polar specification of augmented gravity model of push and pull factors by keeping in view the other approaches specifically utilitarian approach of migration.
5.2. AN AUGMENTED GRAVITY MODEL OF HUMAN CAPITAL MOBILITY
5.2.1. Theoretical Foundation
According to Turnbull (1960) Newton’s law of universal gravitation, originally presented in 1687 states that “the two bodies attract each other with a force that is proportional to the product of their masses and inversely proportional to the squares of the distance between them.” Lowry (1966) and Lee (1966), at first, applied
Newton’s law to migration research. Thus the econometric form of classical gravity model, as a migration flows model for Pakistan is given as;
lnM it k lnPit lnPPt lnDPit it ….. (5.1)
where lnM it is natural logarithms of the migration flows from Pakistan to country i in period t. lnP and lnP are the natural logarithms of populations at origin and at Pt it destination countries in period t. lnDPit is the physical distance between countries i and homeland. , and are elasticities, k is the gravitational constant, and is a it random error term. Following Newton (1687) we considered that 1 and
2 in the equation (5.1). Aleshkovski and Vladimir (2005) while evaluating the mathematical models of migration have highlighted that the relative simplicity in the development of model and availability of statistical data for analysis (interstate and
interregional migration, etc.) are actually advantages of gravity model of migration.
According to Aleshkovski and Vladimir, the unrealistic migration flows symmetry
assumption (M Pi M iP ) is the major disadvantage of the gravity model. The consideration of only three migration factors also results into a low goodness of fit and low predictive power of the gravity models (Aleshkovski and Vladimir, 2005).
Therefore, to deal with the above-mentioned shortcomings in gravity model of migration, Lowry (1966) framed a bipolar specification of augmented gravity model, that is hybrid of classical model of migration (Lee, 1966; Flowerdew and Salt, 1979;
Karemera et al.,2000; Rose, 2002 and Kim and Cohen, 2010) and pull-push factors based migration model (Lowry, 1966; Rogers, 1967; Datta, 2002 and Mayda, 2007,
2010). Following Lowry (1966) we present the gravity model of migration in the following econometrics form;
lnM it 0 1lnPit 2lnPPt 3lnX it 4lnX Pt 5 DPit it ….. (5.2) or
27 ….. (5.3) lnM it 0 1 ln(Yit YPt ) it i1
where in equation (5.2) X it and X Pt are explanatory variables other than population and distance in 27-destination countries and homeland (i.e., Pakistan) respectively. By following Lowry (1966) we construct pull and push factors based migration model in equation (5.3). We symbolize Y as explanatory variables that identify positive it characteristics and attracting features (including X , P and D ) at the center of it it Pit destination to induce immigration through pull forces. Similarly, Y in equation (5.3) Pt denotes explanatory variables describing negative depressing characteristics and poor
economic and social conditions (including X , P and D ) in homeland that trigger Pt Pt Pit emigration through push forces.
In the present study simultaneous use of pull and push factors of migration in single equation (5.3) infact depicts the interregional disparities at macro level variables e.g., differences in employment opportunities between the source and destination countries and resulting differences in standards of living. Thus for estimation of equation (5.3) the pull and push factors are combined by considering the percentage difference in the corresponding variables in destination country i relative
to Pakistan, that is, [100 Yit YPt YPt ]. In case the variable is already unit free i.e.,
in percentage form then we consider the simple difference as (Yit YPt ).
Before formal presentation of empirically testable econometrics form of our migration model, we prefer to provide a micro-economic touch to our equation (5.3).
For this purpose, we consider praiseworthy work of Borjas (1987, 1989, 1996, 1999,
2000 and 2001) on international migration based on individual’s welfare, self- selection, comparative advantage and earnings. The basic idea is that an individual has two choices i.e., either to stay at home country or move to countries i. In an influential work entitled as The Theory of Wages, Hicks (1932) said that “differences in net economic advantages, chiefly differences in wages, are the main causes of migration.” Thus a purely economic based migration decision of an individual dictates that an individual prefers to stay at home if wage rate is high in source country otherwise an individual will prefer to move to other country where forceful pull of high wage rate dominates. In present analysis of human capital flight and hence brain drain at macro level, per capita income is more appropriate than simple
wage rate. Practically, one of the economic disincentive for individuals in Pakistan that act as a push factor is low per capita income given as;
____ ….. (5.4) PCIPt P PCI pt Pt
____ where PCI Pt is per capita income at home land namely, Pakistan. PCI pt is mean per capita income in Pakistan. is the random error term measuring deviations from Pt mean per capita income and is normally distributed with mean zero and constant
2 variance Pt . The corresponding pull factor for economic incentive is high per capita income in the destination country faced by actual migrant is;
____ ….. (5.5) PCIit i PCIit it
____ where PCIit is per capita income at destination country. PCI it is mean per capita income in destination country. is the random error term measuring deviations from it mean per capita income and is normally distributed with mean zero and variance
2 it . Thus equation (5.4) and (5.5) completely describe the earnings opportunities available to an individual in Pakistan and in i destination countries. The migration decision of an individual to leave Pakistan for country i, determined by a comparison of earnings opportunities across countries net of migration costs, is defined as the
index Iit .
______( PCI it C ) ( PCI Pt ) i it it P Pt or
______….. (5.6) I ( PCI it PCI Pt C ) ( ) 0 it i P it it Pt
where C is a bilateral migration cost between source and destination countries. it
According to Borjas (1987) and Grogger and Hanson (2011) C is normally it distributed with mean and 2 variance. Further C is independent of PCI as c c it it
Cov(PCI ,C ) 0. In equation (5.6) and have a bivariate normal distribution it it Pt it with zero mean and covariance matrix containing the terms Pt , it and (Borjas,
1987). The and are standard deviations in country of origin and in the Pt it destination countries, and is the correlation in disturbances. The value of shows that though per capita income differs in country of origin and destination countries yet per capita incomes in destination countries are correlated with per capita income in home country. In short, the consistent estimates of and (above) are based on the P i selection of unobservable and . Pt it
Pakistani migrating to country i do not experience per capita income in Pakistan.
Similarly, Pakistani staying in homeland do not directly experience per capita income in country i. Therefore the individual perceive that P i , then the mobility decision of an individual becomes.
______I [(PCI it PCI Pt ) C ] 0 it it it or I (Z C ) 0 ….. (5.7) it it it it
where it Pt it is a univariate normally distributed random error term.
______
Advantageously, the term (Zit PCI it PCI Pt ) in equation (5.7) presenting relative inequality of economic resources in term of per capita income at source and
destination countries ratify further modification of equation (5.3). Thus to apply self- selection procedure of equation (5.7) to our bipolar specification of augmented gravity model of pull and push forces, we compare equation (5.7) with equation (5.3).
Comparison of these two equations show that (Yit YPt ) in equation (5.3) is just equal
______to the [(PCI it PCI Pt ) C Z C ] in equation (5.7). Where equation (5.7) show it it it that if pull effect of e.g., economic incentive is stronger (Zit 0) then emigration from Pakistan will exist. Contrary to this, if bilateral migration cost between source
and destination countries is zero (Cit 0) then emigration will not take place. This bilateral migration cost is in differential form and it measures net gain from migration.
The migration incentive is based on the fact that [Cit 0], otherwise migration will not exist. In short, for existence of human capital flows in two-country model, i.e., from Pakistan to destination country i we need (I 0). it
Since and C vary across individuals for any given Z . Some individuals i it it choose to migrate while other choose not to migrate depending upon
Iit (Zit Cit it ) ⋛ 0. As the value of Zit increases the number of persons choosing to migrate also increase, in other words at macro level the number of
Mit persons that choose to migrate is an increasing function of Z i.e., ( Z ) 0 and a it it
Mit decreasing function of C i.e., ( C ) 0 in bilateral analysis of two country two it it
variable model, M it f {Zit ,Cit }. Where lnM it is natural logarithms of the migration flows from Pakistan to country i in period t. Hicks (1932) defined Z as net it economic advantages. According to Borjas (1999) C is the bilateral cost of it migration that includes direct costs (e.g., transportation cost of persons and household
goods), forgone earnings (e.g., the opportunity cost of post migration unemployment) and psychic and cognitive costs (e.g., the disutility associated with leaving behind family ties and social networks).
The equation (5.7) establishes relationship between human capital mobility and differential in per capita income at home and abroad. To make our task practicable we generalize equation (5.7) for other drivers of human capital mobility as well. Based on theories of human capital mobility (chapter 3) and review of empirical literature on migration (chapter 4) we can divide various drivers of human capital mobility into four groups. Lets Group -1 contains economic and financial variables in destination countries relative to Pakistan, Group - 2 contains socio-economic variables in destination countries relative to Pakistan, Group - 3 contains demographic and labor market variables in destination countries relative to Pakistan and finally Group - 4 is for fiscal policy variables of destination countries relative to Pakistan. The “summary of literature on migration theories” in Appendix A, “empirics on the causes of human capital mobility: International evidence” in Appendix B and “empirics on the causes of human capital mobility: Evidence from Pakistan” in Appendix C suggest a large number of determinants of human capital mobility to be quartered into these four groups. Thus migration flows in general functional form are written as following.
M it f {EFINVit ,SECOVit ,DEMOVit ,FISPVit } ….. (5.8)
where M it represents migration flows from Pakistan to the destination country i,
EFINVit contains economic and financial variables in the destination countries relative to Pakistan that represents Group -1, SECOVit contains socio-economic
variables in the destination countries relative to Pakistan that represents Group - 2,
DEMOVit contains demographic and labor market variables in the destination countries relative to Pakistan that represents Group - 3 and FISPVit is for fiscal policy variables of the destination countries relative to Pakistan that represents
Group - 4. For precision, we comprehend the analysis and subgroup the large number of explanatory variables of each group into a manageable number of headings explicitly named as indices. Thus in next subsections 5.2.2 through 5.2.5 we specify the composition of different indices used in the composition of these four groups in detail.
5.2.2. GROUP-1: Economic and Financial Drivers of Human Capital Mobility
The review of existing empirical literature on theories of migration (chapter 3 and chapter 4) elucidate that the relative economic incentives, relative financial stability
and relative financial independence best present Group-1 i.e., EFINVit at macro-level as following.
EFINVit f {ECONit , FINSit ,FININit } ….. (5.9)
where ECON represents the index of relative economic incentives (Hicks, 1932; it Todaro, 1969; Borjas, 1987; Docquier et al., 2009 and Grogger and Hanson, 2011),
FINS is the index of relative financial stability (Solimano, 2002 and Mitra et al., it
2011) and FININ is the index of relative financial independence (Monteleone and it
Torrisi, 2010 and Arouri et al., 2014). Each index in the above function (5.9) is in differential form of push and pull factors affecting human capital flows.
The next step is construction of above-mentioned economic and financial indices by considering the viewpoints of different migration analysts. Primarily the neoclassical theory to international migration was based on wage differential between receiving and sending countries beside migrant’s expectations for higher earnings in host countries (Todaro, 1969). According to Harris and Todaro (1970) migration decision depends on expected income differential between two regions rather than wage differentials. Though the neoclassical models of migration estimated a negative relationship between GDP per capita in the country of origin and the incentive to migrate, yet Lopez and Schiff (1998) and Rotte and Vogler (2000) indicated that economic growth of developing countries provided incentives to emigrate.
Rotte and Vogler explained an inverted-U shaped relationship between emigration and GDP per capita in the countries of origin, in a way, that at a low GDP per capita, increase in income provides incentive to migrate by alleviating liquidity constraints.
However, when per capita GDP increases in country of origin the income difference between both countries falls that discourage emigration. Borjas (2001) estimated that location-decisions of immigrants in the United States were very sensitive to interstate wage differentials. Thus, Borjas concluded that the income maximizing migrants select the destinations that offer them the high-income opportunities in term of highest wages. Solimano (2002) asserted that the expectation for higher per capita income abroad was the cause of emigration. According to Gaag et al. (2003) economic determinants of migration were GDP per capita, newly established business and wage differential.
Beside other determinants of human capital flows and human capital flight,
Mayda (2007 and 2010) considered the effect of average income differential between
the countries of origin and destination and concluded that the magnitude of migration was significantly affected by the pull factors at the destination countries like increase in income opportunities and GDP per worker. Monteleone and Torrisi (2010) acknowledged economic incentives as main cause of migration. Grogger and Hanson
(2011) suggested that high wage differential between labor-exporting and labor- importing countries has motivated the high skilled labor to emigrate for income gain.
Arouri et al. (2014) stated that real GDP growth at destination country had attracted the highly qualified and highly skilled professionals for industrialization that in turn generated employment opportunities both for skilled and unskilled labor through spillover effect and hence reduces further human capital flows and human capital flight from homeland. On these theoretical and empirical bases, we propose the
following index of relative economic incentives, ECONit .
ECON it 1 (PCI it PCI Pt ) 2 (GPCI it GPCI Pt ) …. (5.9.1) 3 (GCF it GCF Pt ) 4 ( ABS it ABS Pt )
where
PCI it : Per capita gross national income of destination country in period t
PCI Pt : Per capita gross national income of Pakistan in period t
GPCI it : Percentage annual growth rate of per capita GDP of destination country in period t
GPCI Pt : Percentage annual growth rate of per capita GDP of Pakistan in period t
GCF it : Gross capital formation as a percentage of GDP in destination country in period t
GCF Pt : Gross capital formation as a percentage of GDP in Pakistan in period t
ABS : Domestic absorption as a percentage of GDP in destination country in it period t
ABS Pt : Domestic absorption as a percentage of GDP in Pakistan in period t
In equation (5.9.1) per capita income differential indicates current relative economic condition while differential in growth and capital formation indicate relative future prospects. Similarly, domestic absorption as a percentage of GDP represents business cycle.
Beside other factors, Solimano (2002) determined the direction and magnitude of international migration flows by economic prospects and the state of business cycle in both sending and receiving countries. Cattaneo (2008) declared that the financial market development reduces the credit constraints and facilitated people to emigrate.
Mitra et al. (2011) explored the impact of financial liberalization (measured through robustness of financial markets and their freedom) on the migration of highly skilled labor force to OECD countries. According to Arouri et al. (2014) the multinationals via foreign direct investment hire skilled and qualified professionals through attractive salaries, family health insurance and air ticket facilities for whole family, and hence effect migration decision. In short, based on the above explanation of theories of
migration the index of relative financial stability, FINSit , is constructed as follows.
FINS it 1 (REER it REER Pt ) 2 (GEQTY it GEQTY Pt )
3 (RES it RES Pt ) 4 (FDI it FDI Pt ) .…. (5.9.2)
5 (TURN it TURN Pt )
where
REER it : Real effective exchange rate index (2010=100) for destination country in period t
REER Pt : Real effective exchange rate index (2010=100) of Pakistan in period t
GEQTY it : Annual percentage change in S&P global equity index for destination country in period t
GEQTY Pt : Annual percentage change in S&P global equity index for Pakistan in period t
RES it : Total reserves including gold as a percentage of GDP for destination country in period t
RES Pt : Total reserves including gold as a percentage of GDP for Pakistan in period t
FDI it : Net inflows of foreign direct investment as a percentage of GDP for destination country in period t
FDI Pt : Net inflows of foreign direct investment as a percentage of GDP for Pakistan in period t
TURN it : Percentage of stocks traded, turnover ratio for destination country in period t
TURN Pt : Percentage of stock traded, turnover ratio for Pakistan in period t
To construct an index of relative financial independence we follow findings of
Anjomani (2002) that income tax and welfare benefits affect regional migration in the
United States through their effect on regional income. Clemens (2009) acknowledged professional up gradation and better working environment in destination countries as pull factors for health workers from Africa. Monteleone and Torrisi (2010) measured
justification-factors for migration through supremacy, respect in host institutions and better employment opportunities. In this study, the migration-satisfaction-level in the host country is measured by career prospects, freedom to pursue research, availability of scientific equipment, state of bureaucracy and workplace safety.
Grogger and Hanson (2011) stated that large post-tax wage differences for skilled labor in the United States and Canada helped to attract highly educated migrants as compare to OECD countries. Arouri et al. (2014) asserted that a strong financial sector at country of origin provided financial resources through savings and used these resources for investment purpose and to setup new business. Keeping in view the existing literature on migration, we propose the index of relative financial
independence, FININit as following;
FININ it 1 (SEMPit SEMPPt ) 2 (TAX it TAX Pt )
3 (MCAPit MCAPPt ) 4 (CREDITit CREDITPt ) ..…(5.9.3)
5 (INTSit INTS Pt )
where : Total self-employed workers as a percentage of total employed workers in SEMPit destination country in period t
SEMPPt : Total self-employed workers as a percentage of total employed workers in Pakistan in period t
TAX it : Taxes on income, profits and capital gains as a percentage of total revenue in destination country in period t
TAX Pt : Taxes on income, profits and capital gains as a percentage of total revenue in Pakistan in period t
: Market capitalization of listed companies, per hundred thousand of MCAP it population in destination country in period t
MCAP Pt : Market capitalization of listed companies, per hundred thousand of population in Pakistan in period t
: Domestic credit to private sector by banks as a percentage of GDP in CREDIT it destination country in period t
CREDIT Pt : Domestic credit to private sector by banks as a percentage of GDP in Pakistan in period t
: Percentage interest rate spread (lending rate minus deposit rate) in INTS it destination country in period t
INTS Pt : Percentage interest rate spread (lending rate minus deposit rate) in Pakistan in period t
5.2.3. GROUP-2: Socio-Economic Drivers of Human Capital Mobility
In this subsection, we construct indices present in Group- 2 titled as socio- economic drivers, SECOV , of human capital mobility. Based on empirical literature it and the theories of migration reviewed in chapter 3 and chapter 4, SECOV contains it the index of relative standard of living LIVINit , the index for the role of compatriot
community COMUit and the index of relative social openness OPENit as following;
SECOVit f {LIVINit ,COMUit ,OPENit } ….. (5.10)
The next step is to construct the indices used in function (5.10) by considering the viewpoints of various migration analysts. At first, we highlight the significance of these indices in migration theory and then provide their structure based on the
empirical literature and the theories of migration reviewed in chapter 3 and chapter 4.
To construct the index of relative standards of living LIVINit , we consider
Ravenstein’s (1885 and 1889) testimony that the utility of migrant at current location relative to alternative destinations provide relative difference in standards of living at these destinations and hence affect the decision to migrate. Redfield (1941 and 1953) based on folk-urban continuum and bright light theory of migration, also provided foundation for the role of urban agglomeration in human capital flows.
Wolpert (1965) and Brown and Moore (1970) regarded migration process whereby migrants substitute one location for another to fulfill their requirements and needs and hence to improve their standards of living. According to Straubhaar (2000) highly skilled persons give more weight to better quality of life and hence move to location with pleasant weather, fresh environment, social safety, freedom of choice, flexible working condition, secured property rights and friendly surroundings where they can up bring their children. Parikh and Leuvensteijn (2002) recognized wage differential, differential in hospital and hotel beds per inhabitant, differential in per capita rental of houses, differential in transportation charges per km, distance between main cities and differential in cost of living as the drivers of inter-regional migration.
Beine et al. (2008b) used life expectancy at time of birth as a proxy for living conditions and hence determine its impact on migration flows. According to Gibson and McKenzie (2011a and 2011b) and Clemens (2009), safety to family and lifestyle expressed standards of living and hence determined migration flows. Arouri et al.,
(2014) stated that inflation at country of origin has increased cost of production and unemployment and hence forced the highly skilled and highly qualified people to move to international labor market in search of job. From above review of empirical
literature on human capital flows, we propose the index of relative standards of living
LIVINit as follows.
LIVIN it 1 (URPOP it URPOP Pt ) 2 (GPCC it GPCC Pt )
3 (HLTH it HLTH Pt ) 4 (LE it LE Pt )
5 (ENER it ENER Pt ) 6 (WATER it WATER Pt ) ….. (5.10.1)
7 (SANI it SANI Pt ) 8 (INF it INF Pt )
9 (DOCS it DOCS Pt )
where
URPOPit : Urban population (urban agglomeration of more than 1-million population) as a percentage of total population in destination country in period t
URPOPPt : Urban population (urban agglomeration of more than 1-million population) as a percentage of total population in Pakistan in period t
: Annual growth rate of household final consumption expenditure per GPCCit capita in the destination country in period t
GPCCPt : Annual growth rate of household final consumption expenditure per capita in Pakistan in period t
: Total health expenditure as a percentage of GDP in destination country in HLTH it period t
HLTH Pt : Total health expenditure as a percentage of GDP in Pakistan in period t
LE it : Life expectancy at birth in destination country in period t
LEPt : Life expectancy at birth in Pakistan in period t
: Energy use (kg of oil equivalent per capita) in destination country in ENER it period t
ENER Pt : Energy use (kg of oil equivalent per capita) in Pakistan in period t
: Percentage of population with access to improved water sources in WATER it destination country in period t
WATER Pt : Percentage of population with access to improved water sources in Pakistan in period t
: Percentage of population with access to improved sanitation facilities in SANI it destination country in period t
SANI Pt : Percentage of population with access to improved sanitation facilities in Pakistan in period t
: Inflation rate (annual percentage increase in consumer price index) in INF it destination country in period t
INFPt : Inflation rate (annual percentage increase in consumer price index) in Pakistan in period t
: Number of physicians per hundred thousand people in destination country DOCSit in period t
DOCS Pt : Number of physicians per hundred thousand people in Pakistan in period t
Barnes (1954 and 1969) and Mitchell (1966 and 1974) suggested for the importance of networks of family and friends in new settlements in destination countries and hence acknowledged that migration depend on assistance of compatriot community settled abroad. Tilly and Brown (1967) and Lomnitz (1977) emphasized that the kin and friendship networks were important in shaping and facilitating
migration by reducing migration risks and hence encouraging circular migration flows. Massey (1990) acknowledged that migration created more migration through the theory of cumulative causation. The empirical literature by Clark et al., (2002),
Hatton (2003), Mitchell and Pain (2003) and Pedersen et al., (2004) provided extensive literature on the importance of existing social networks for migrants and asserted that the expatriate community by providing their social services had helped the newcomers in finding jobs.
Anjomani (2002) explained that the relatives and friends, through provision of accommodation and information about jobs at destination countries, had facilitated the migration flows. According to Hollifield (2004) immigration to the industrial democracies was a function of market forces and kinship networks that had reduced the transaction costs for moving from one place to another. Beine et al. (2008b) used the size of already migrated networks to capture the effect of family and friends on the migration flows. The study also explained the role of worker’s remittances as a share of GDP remitted by already existing migrants that had relaxed the credit constraints on human capital investment and hence for human capital mobility.
According to Cattaneo (2008) the established social network by pioneer migrants through provision of information, guidance and social assistance to low income individuals had reduced the migration risk and hence migration costs. Cattaneo studied the spillover effect of emigrants on non-migrants at homeland by dampening poverty through remittances and gifts and hence had accelerated the process of further migration. The foreign remittances sent home by emigrants had affected the domestic economy via the consumption multiplier that had indirectly affected migrant as well
as non-migrant households. Based on above analysis we propose the index for the role
of compatriot community COMUit in the equation (5.10.2) below.
COMU (MIGRANT MIGRANT ) (TRSER TRSER ) it 1 it Pt 2 it Pt ….. (5.10.2) (REMIT REMIT ) 3 it Pt
where
MIGRANTit : Net immigrants (i.e., international migrants stock as a percentage of total population) to destination country in period t
MIGRANTPt : Net immigrants (i.e., international migrants stock as a percentage of total population) in Pakistan in period t
TRSERit : Trade in services as a percentage of GDP in destination country in period t
TRSERPt : Trade in services as a percentage of GDP in Pakistan in period t
REMITit : Personal remittances received as a percentage of GDP in destination country in period t
REMITPt : Personal remittances received as a percentage of GDP in Pakistan in period t
Cairncross (1997) acknowledged advancements in telecommunication (i.e., telephone, television, internet and wireless) as important economic forces to affect human mobility. Straubhaar (2000) stated that the economy of the United States was socially open (i.e., openness of the social system, to innovation and knowledge, to foreigners for entering and leaving the country without restrictions) enough and had flexible labor markets to welcome the international citizens. Solimano (2006) asserted that human talent with great economic value was highly mobile due to globalization,
low transportation cost and with spread of information and technology in industrialized countries. Button and Vega (2008) searched for the effects of air transportation system on labor markets and on labor migration that had increased the feasibility of long-distance migration with frequent visits to home country.
Cresswell (2011) considered transport system, tourism studies and migration research as important parts of the human capital mobility. Gibson and McKenzie
(2011b) acknowledged that improvement in information technology and hence reduction in the air travel cost had helped high skilled migrants to keep in contact with their relatives in their home countries. Chichian (2012) recognized that expansion in media and internet connections had facilitated globalization and hence brain drain from Iran to the United States of America. King (2012) asserted that revolution in telecommunication had reduced migration flows in the United States, as people may stay in touch with their family without mobility in physical term. Arouri et al. (2014) declared that openness helped the skilled and highly skilled professionals to judge their demand in international labor market beside wage differential between different countries that motivate brain drain (i.e., human capital flight) for their future.
Based on above literature, the index of relative social openness, OPEN it is proposed as following.
OPENit 1(AIRit AIRPt ) 2 (TOURit TOURPt ) ….. (5.10.3) 3 (INTit INTPt ) 4 (CELLit CELLPt )
where
: Air transport registered carrier departures worldwide from destination AIRit country in period t
AIRPt : Air transport registered carrier departures worldwide from Pakistan in period t
: International tourism (number of arrivals) to destination country in TOUR it period t
TOURPt : International tourism (number of arrivals) to Pakistan in period t
INTit : Internet users per hundred thousand persons in destination country in period t
INTPt : Internet users per hundred thousand persons in Pakistan in period t
CELLit : Mobile cellular subscriptions per hundred thousand persons in destination country in period t
CELLPt : Mobile cellular subscriptions per hundred thousand persons in Pakistan in period t
5.2.4. GROUP-3: Demographic and Labor Market Drivers of Human Capital Mobility
In present subsection we construct two indices namely, the index of relative
demographic characteristics DEMOit , and the index of relative labor market structure
LMKTit , that constitute Group-3 derived from theories of migration (chapter 3) and
empirical literature (chapter 4). The functional form for Group - 3, DEMOVit containing demographic and labor market variables in destination countries relative to
Pakistan is given as;
DEMOVit f {DEMOit , LMKTit } ….. (5.11)
Coale (1972), Keely and Kraly (1978) and Espenshade et al. (1982) explored the relationship between net migrants and demographic characteristics namely, population of a country with different age groups. Borjas (1987), Stark (2004) and
Schaeffer (2005) stated that the individuals with more human capital investment had high tendency to migrate. Borjas (2000) further elaborated that the young people have more incentives to migrate as they have a longer period over which they can benefit from the returns to investment on human capital formation, secondly, highly educated people were keen to migrate based on their high efficiencies in assessing employment opportunities in international labor markets.
Anjomani (2002) declared population density, population size and its growth rate, migrants’ age and educational level as important demographic factors affecting human capital flows. According to Gaag et al. (2003) climate conditions, degree of urbanization, entertainment and leisure activities, population density and social behavior of local inhabitants were the main demographic drivers of migration flows.
Stark (2004) and Schaeffer (2005) indicated that desire to emigrate provide an incentive to achieve higher qualification and higher education. Schaeffer (2005) further compared pre-migration and post-migration human capital investment and concluded that the possibility of emigration encourage individuals to invest in human capital formation that ensure permanent residency in the desired destination country.
Mayda (2007) captured the impact of cultural, demographic and geographic factors of destination countries on migration flows. In this study, demographic determinants measured as a share young population at country of origin had supply side effect and acted as a push factor at center of destination. Clemens (2009) explored that the rate of human capital flight depended upon the desire of skilled
workers to agglomerate in highly populated developed regions. Martin (2009 and
2014) credited migration flows from developing to developed countries with rapid population growth and fast growing labor force in source developing countries besides stagnating population growth in developed host countries. According to
Grogger and Hanson (2011) majority of migrants departed countries of origin after acquiring at least post-secondary education. Therefore, general form of the index of
relative demographic characteristics, DEMOit is given as;
DEMOit 1 (GPOPit GPOPPt ) 2 (DENSit DENSPt ) (URBAN URBAN ) (YOUNG YOUNG ) .…. (5.11.1) 3 it Pt 4 it Pt 5 (EDU it EDU Pt )
where
GPOPit : Annual percentage growth rate of population in destination country in period t
GPOPPt : Annual percentage growth rate of population in Pakistan in period t
DENSit : Population density (number of persons per square kilometer of land area) in destination country in period t
DENSPt : Population density (number of persons per square kilometer of land area) in Pakistan in period t
URBANit : Urban population as a percentage of total population in destination country in period t
URBANPt : Urban population as a percentage of total population in Pakistan in period t
YOUNGit : Population ages 15-64 years as a percentage of total population in destination country in period t
YOUNGPt : Population ages 15-64 years as a percentage of total population in Pakistan in period t
EDUit : Labor force with tertiary education as a percentage of total labor force in destination country in period t
EDUPt : Labor force with tertiary education as a percentage of total labor force in Pakistan in period t
To construct the index of relative labor market structure, we follow viewpoint of
Harris and Todaro (1970) that in presence of high urban unemployment accompanied by high expected urban income, a rational migration flow exist from rural to urban areas. Walsh (1974) observed that Irish net migration flows were sensitive to relative labor market conditions in Ireland and Britain. Borjas (1987 and 1996) noted a high emigration rate among tertiary educated individuals compare to less educated individuals. According to Gaag et al. (2003) differential in unemployment rate and in working conditions were main drivers of human capital flows.
Dreher and Poutvaara (2006) showed a close relationship between student flows and migration flows to the United States and found that weak political participation rate and high unemployment rate are factors responsible for emigration. Cattaneo
(2008) stated that emigration of surplus labor resources from labor abundant countries infer low opportunity cost that imposes negligible loss in the size of labor force and had insignificant impact at production level of the country of origin. Etzo (2008a and
2008b) determined interregional migration in Italy by relative difference in the unemployment rate and crime levels at origin and destination regions. Based on the
theories of migration the index of relative labor market structure, LMKTit is proposed to be as follows.
LMKT it 1 (LFPR it LFPR Pt ) 2 (UEMPR it UEMPR Pt ) ..… (5.11.2) 3 (DEPit DEPPt )
where
LFPRit : Labor force participation rate (i.e., total labor force as a percentage of total population) in destination country in period t
LFPRPt : Labor force participation rate (i.e., total labor force as a percentage of total population) in Pakistan in period t
UEMPRit : Unemployment rate (i.e., unemployed labor force as a percentage of total labor force) in destination country in period t
UEMPRPt : Unemployment rate (i.e., unemployed labor force as a percentage of total labor force) in Pakistan in period t
DEPit : Age dependency ratio (i.e., age dependent population as a percentage of working age population) in destination country in period t
DEPPt : Age dependency ratio (i.e., age dependent population as a percentage of working age population) in Pakistan in period t
5.2.5. GROUP-4: Fiscal Policy Variables of Human Capital Mobility
In present subsection, we construct two indices namely the index of relative
provision of social safety nets SNETit and the index for research and development
facilities for human capital grooming RADIit , that constitute Group - 4, FISPVit as following.
….. (5.12) FISPV it f {SNET it , RADI it }
where FISPV represents the group of fiscal policy variables of human capital it mobility. The index of relative provision of social safety nets SNETit , and the index
for research and development facilities for human capital grooming RADIit in function (5.12) are in differential form of push and pull forces that channelize the human capital flows. Gaag et al. (2003) considered local taxes, defense spending, educational expenditure, government subsidies and urban area plan as fiscal policy variables to determine human capital flows. The studies by Kapur and McHale (2005) and by Gibson and McKenzie (2011b and 2012) acknowledged social protection, social safety nets and welfare benefits as fiscal policy variables that attract best and brightest to the destinations where these facilities were available. Akkas (2008) specified low salary, less respect of health workers, poor career prospects, miserable management by health system and negligible savings for retirement age as factors contributing to the flight of trained medical personnel from Ghana.
According to Montelone and Torrisi (2010) beside economic factors, other reasons for leaving Italy included less satisfaction at work place, poor career prospects, intolerable working hours, biased relationships with superiors and colleagues, unavailability of scientific equipment, discrimination in working groups, level of bureaucracy, lack of information and workplace risks. Based on above
discussion the index of relative provision of social safety nets, SNET it is proposed as follows.
….. (5.12.1) SNETit 1 (ODAit ODAPt ) 2 (COMPit COMPPt )
3 (GRANTS it GRANTS Pt ) 4 (SUBit SUB)
5 (INSit INS Pt )
where : Per capita net official development assistance in destination country in ODAit period t
ODAPt : Per capita net official development assistance in Pakistan country in period t
: Compensation of employees as a percentage of government expenditure COMPit in destination country in period t
COMPPt : Compensation of employees as a percentage of government expenditure in Pakistan in period t
: Grants and other revenues as a percentage of gross national income in GRANTS it destination country in period t
GRANTSPt :Grants and other revenues as a percentage of gross national income in Pakistan in period t
: Subsidies and other transfers as a percentage of government expenditure SUBit in destination country in period t
SUBPt : Subsidies and other transfers as a percentage of government expenditure in Pakistan in period t
INSit : Insurance and financial services as a percentage of service imports in destination country in period t
INSPt : Insurance and financial services as a percentage of services imports in Pakistan in period t
Saxenian (1999) highlighted the importance of highly skilled migrants, scientists and engineers in the Silicon valley, a center of technological innovation as well as a main export region in California, United States. The study showed that quarter of
Silicon valley’s high technology firms were led by Chines and Indian immigrants, who came to the United States for higher studies and permanently stayed there for their bright future. According to Straubhaar (2000) high standard educational system and the export of educational services to foreign students were money machines for the United States. The study showed close connection between research centers
(universities) and industrial development in the United States and concluded that the highly skilled and highly qualified individuals moved to United States because of high wage, an innovative environment and career prospects.
Dustmann et al. (2009) acknowledge that movement of individuals across national border was to acquire skills from more efficient sources and to sell these skills at high return destinations. Monteleone and Torrisi (2010) highlighted the reasons for leaving
Italy and stressed on the skill development, freedom to pursue research, easy access to information and career prospects. Therefore, the index for research and development
facilities for human capital grooming, RADIit is proposed as follows.
……. (5.12.2) RADI it 1 (GEXPit GEXPPt ) 2 (RADit RADPt )
where
GEXPit : Total government expenditure on education as a percentage of GDP in destination country in period t
GEXPPt : Total government expenditure on education as a percentage of GDP in Pakistan in period t
RADit : Research and Development expenditure as a percentage of GDP in destination country in period t
RADPt : Research and Development expenditure as a percentage of GDP in Pakistan in period t
5.2.6. Econometrics Specification of the Model
A migration function for bi-polar specification of gravity model based on push and pull factors and augmented by neo-classical utilitarian approach of migration, obtained by substituting functions (5.9) through (5.12) into equation (5.8) is given as;
M it f {ECONit ,FINSit ,FININit ,LIVINit ,COMUit ,OPENit , ….. (5.13)
DEMOit ,LMKTit ,SNETit ,RADIit }
where M it represents net migration flows from Pakistan to the destination country i.
ECONit , FINSit and FININit are the indices that constitutes Group -1 titled as
economic and financial drivers of human capital mobility (EFINVit ). The indices of
LIVINit , COMUit and OPENit are obtained from the socio-economic drivers of
human capital mobility representing Group - 2, SECOVit . The demographic and
labor market drivers of human capital mobility, DEMOVit in Group - 3 are divided
into two indices titled as DEMOit and LMKTit . Finally, the fiscal policy variables of
human capital mobility, FISPVit are replaced by the indices represented as SNETit
and RADIit that structure Group - 4. To have the empirically testable econometrics form of equation (5.13) we transform it into a double log equation as following.
ln M it cit it ln ECONit it ln FINSit it ln FININit ….. (5.14)
it ln LIVINit it lnCOMUit it lnOPENit
it ln DEMOit it ln LMKTit it lnSNETit
it ln RADIit it
The above equation (5.14) contains 10 parameters to be estimated and the number of years 36 (as period of analysis is from the year 1981 through 2016), therefore estimation of separate equations may be subject to serious degree of freedom problem. However, the equation (5.14) can be estimated for four regions namely,
Middle East and Africa, East Asia and Pacific, Europe and Central Asia and North
America, each containing at least two countries. The division of these four regions is based on the nature of demand by destination countries, where North America has knowledge-based demand biased toward highly qualified migrants while Middle East and Africa, pooled in a single group has highly skill-based demand for engineers, geologists and geophysicists. Secondly, based on geography as a metric of closeness of destination countries, region-wise empirical analysis is also considered.
Similarly, the test for consistency of coefficient vector overtime is difficult in present empirical analysis as for each period the number of observation will be only
27 (cross section of 27 destination countries) to estimate 10 parameters (i.e., for indices in equation 5.14). Therefore, instead of considering inconstancy of the coefficient vector across countries as well as overtime in model specification we prefer to have robust HAC (Heteroscadasticity and autocorrelation consistent) estimators through empirical estimation procedure that can best fit in the above circumstances. In short, in present empirical analysis we assume that the coefficient vector is same across all countries as well as overtime in equation (5.14).
After imposing homogeneity across cross sections and over time, the final econometrics specification of the augmented gravity model of human capital flows
(i.e., net out flows of migrants) as function of differential between push and pull factors is presented in equation (5.15) below. Also the econometric specification of the augmented gravity model of human capital flight (i.e., net out flows of highly qualified and highly skilled migrants) as a function of differential between push and pull factors is given in the next equation (5.16) below.
HCF HCF HCF HCF NETHCFit ck k ln ECONit k ln FINSit k ln FININit HCF ln LIVIN HCF ln COMU HCF ln OPEN k it k it k it HCF HCF HCF k ln DEMOit k ln LMKTit k ln SNETit ….. (5.15) HCF HCF k ln RADIit it
BD BD BD BD NETBDit ck k ln ECONit k ln FINSit k ln FININit BD ln LIVIN BD lnCOMU BD lnOPEN k it k it k it ….. (5.16) BD ln DEMO BD ln LMKT BD ln SNET k it k it k it BD BD k ln RADIit it
where k in the coefficient vector presents variation across regions namely, Middle
East and Africa, East Asia and Pacific, Europe and Central Asia and North America.
The variables’ notations in equation (5.15) and (5.16) are explained as follows.
NETHCFit : Net migrants (human capital flows) per hundred thousand populations from Pakistan to the destination country i in the year t
NETBDit : Net brain drain (human capital flight of highly qualified and highly skilled migrants) per hundred thousand populations from Pakistan to the destination country i in the year t
ECON it : Index of relative economic incentives
FINSit : Index of relative financial stability
FININit : Index of relative financial independence
LIVINit : Index of relative standards of living
DEMOit : Index of relative demographic characteristics
LMKTit : Index of relative labor market structure
COMUit : Index for the role of compatriot community
SNETit : Index for relative provision of social safety nets
RADIit : Index for relative research and development facilities
OPEN it : Index of relative social openness
it : Random error term with zero mean and constant variance
In present chapter, the proposed composition of above mentioned indices purely depends upon the previous literature on the subject yet the final construction of these indices in chapter 7 is on the basis of outcomes of the techniques of principal component analysis and principal factor analysis. These indices, constructed in chapter 7, are then utilized in the empirical analysis for the drivers of human capital mobility (i.e., human capital flows as well as human capital flight) in chapter 8.
Chapter 6
DATA SOURCES AND ANALYTICAL FRAMEWORK
6.1. INTRODUCTION
To conduct an empirical research on human capital flows and on human capital flight from Pakistan secondary panel data from the year 1981 through 2016 are used
for a cross-section of 27 destination countries. In this chapter we explain the construction of variables used in different indices, along with data sources and estimation procedure. In section 6.2 we provide data sources, in section 6.3 we present the procedure used for construction of indices (to be used in chapter 7) and in section 6.4 we outline the estimation procedure required to determine the drivers of human capital flows and human capital flight (to be used in the following chapters 8).
6.2. DATA SOURCES
The empirical analysis considers migration of Pakistanis to 27-major recipient countries (i 1,2,3,...... ,27) over the past 36 years, 1981 through 2016. The variables
measuring net Pakistani migrants are: First, NETHCFit i.e., net migrants per hundred thousand population from Pakistan to the destination country i in the year t in equation (5.15); and the second variable is NETBD i.e., net brain-drain (human it capital flight of highly qualified and highly skilled migrants) per hundred thousand population from Pakistan to destination country i in the year t in equation (5.16).
Country-wise, skill-wise, category-wise and profession-wise data on total migrants particularly, data required for highly qualified, highly skilled, skilled, semi-skilled and unskilled migrants are obtained from Bureau of Emigration and Overseas
Employment (BEOE), Government of Pakistan. The different issues of Trends in
International Migrant Stock: Migrants by Destination and Origin by United Nations
Population Division are also considered for confirmation of data obtained from
Bureau of Emigration and Overseas Employment.
The study uses extensive data on a large number of variables including socioeconomic, financial and demographic variables. All the variables used in the
construction of indices are defined and constructed according to definitions provided by World Bank for World Development Indicators (WDI, 2017). Summary of data required for construction of different indices is provided in the following Table 6.1.
6.3. MULTIVARIATE TECHNIQUES TO CONSTRUCT INDICES
The indices presented in equation (5.9.1) through (5.12.2) in chapter 5, are constructed on theoretical grounds, to depict the net effects of push and pull factors of migration on an augmented gravity models of equations (5.15) and (5.16). These indices covering economic, social and demographic drivers of human capital mobility are constructed by using techniques of principal component analysis (PCA) and principal factor analysis (PFA).
The non-parametric technique of PCA is introduced by Pearson (1901) and endorsed by Hotelling (1933 and 1936) is appropriate when we are confronted with the utilization of a large number of similar variables in regression analysis and want to group them up into a manageable number of headings, explicitly known as indices.
Actually, in large dataset, different variables can be correlated with each other and explain same phenomenon where in case of several interrelated variables, PCA is used to reduce the data into useful number of components that cater the maximum possible variation in the data set.
Table 6.1: Variables, Definitions and Data Sources DEPENDENT SYMBOLS DEFINITIONS DATA SOURCES VARIABLES
Net Brain Drain NETBD it Net brain-drain (human capital flight of highly qualified and highly skilled migrants) Bureau of Emigration and per hundred thousand population from Pakistan to destination country i in the year t Overseas Employment, Net Human Capital Flows NETHCF Net migrants (human capital flows) per hundred thousand population from Pakistan Government of Pakistan it to the destination country i in the year t for all data series (Year 2017) INDICES SYMBOLS DEFINITIONS DATA SOURCES Index for the Role of Weighted average of relative international migrant stock as a percentage of total COMUit Compatriot Community population, relative trade in services as a percentage of GDP, and personal remittances received as a percentage of GDP in the destination countries relative to Pakistan
Index of Relative DEMO it Weighted average of relative annual percentage growth rate of population, relative Demographic population density (number of persons per square kilometer of land area), relative Characteristics urban population as a percentage of total population, relative population ages 15 to 64 years as a percentage of total population and labor force with tertiary education as a percentage of total labor force in the destination countries relative to Pakistan World Development
Index of Relative ECON it Weighted average of relative per capita GNI, percentage annual growth rate of per Indicators (WDI) of Economic Incentives capita GDP of destination countries relative to Pakistan, gross capital formation as a World Bank for all data percentage of GDP for destination countries relative to Pakistan and domestic series (Year 2017) absorption as a percentage of GDP for destination countries relative to Pakistan
Index of Relative FININ it Weighted average of total self-employed workers as a percentage of total employed Financial Independence workers in the destination countries relative to Pakistan, taxes on income, profits and capital gains as a percentage of total revenue in the destination countries relative to Pakistan, market capitalization of listed companies per hundred thousand of population in the destination countries relative to Pakistan, relative domestic credit to private sector by banks as a percentage of GDP and percentage interest rate spread in the destination countries relative to Pakistan
Table 6.1(Continue): Variables, Definitions and Data Sources INDICES SYMBOLS DEFINITION DATA SOURCES
Index of Relative Financial FINS it Weighted average of real effective exchange rate index (2010=100) of destination Stability countries relative to Pakistan, annual percentage change in S&P global equity index of destination countries relative to Pakistan, relative total reserves (including gold) as a percentage of GDP, relative foreign direct investment as a percentage of GDP, and percentage of stocks traded, turnover ratio of destination countries relative to Pakistan
Index of Relative Standards of LIVIN it Weighted average of relative urban population (urban agglomeration of more than Living one-million population) as a percentage of total population, relative annual growth rate of household consumption expenditure per capita, relative total health expenditure as a percentage of GDP, relative total years of life expectancy at time of birth, relative energy use (kg of oil equivalent per capita), relative percentage of population having access to improved water sources and improved sanitation facilities, relative inflation rate and number of physicians per hundred thousand of World Development population in the destination countries relative to Pakistan Indicators (WDI) of Index of Relative Labor Market LMKT Weighted average of relative labor force participation rate, relative unemployment it World Bank for all data Structure rate and of age-dependency ratio in the destination countries relative to Pakistan series (Year 2017) Index of Relative Social OPEN it Weighted average of relative air transport (i.e., registered carrier departures) Openness worldwide, relative international tourism (number of arrivals), relative internet users and mobile cellular subscriptions per hundred thousand of population in the destination countries relative to Pakistan Index for Relative Research Weighted average of relative government expenditure on education as a percentage RADIit and Development Facilities of GDP and R&D expenditure as a percentage of GDP in the destination countries relative to Pakistan
Index for Relative Provision of SNET it Weighted average of relative per capita net official development assistance, Social Safety Nets relative compensation of employees as a percentage of government expenditure, relative grants and other revenues as a percentage of GNI, relative subsidies and other transfers as a percentage of government expenditure and insurance and financial services as a percentage of service imports in the destination countries relative to Pakistan
Technically, PCA is a procedure that uses an orthogonal transformation to convert a set of possibly correlated variables into indices of linearly uncorrelated variables called principal components (PCs). Thus each PC is a linear combination of optimally weighted observed variables where linear combination coefficients (weights) or loadings, obtained through PFA, are used in interpreting the newly constructed components (indices). The mathematics and properties of principal components are presented in Appendix D. These newly constructed (indices) are then used in subsequent panel data regression analysis namely, dynamic ordinary least squares
(DOLS) analysis in the following chapter 8.
6.3.1. PERFORMING PRINCIPAL COMPONENT ANALYSIS
For the construction of economic, social and demographic indices we carry out the following two steps procedure in the following chapter 7.
Step1: Feasibility Test for Principal Component Analysis
According to Kaiser (1970) and Kaiser and Rice (1974) the suitability of data for principal component analysis (PCA) can be tested on the basis of Kaiser Meyer Olkin
Measure of Sampling Adequacy (MSA) that quantifies the degree of inter correlation among the variables and appropriateness of PCA. The Kaiser’s MSA index ranges from 0 to 1 and indicates the degree to which data are suitable for PCA. According to
Kaiser (1960) the MSA values close to zero indicate that the sum of partial correlations are large compared to the sum of correlations, showing that the correlations are widespread and so are not clustering among few variables, indicating a problem for the construction of PCA.
According to Kaiser and Rice (1974) the values of Kaiser’s MSA above 0.90 are considered marvelous, the values in the 0.80s are meritorious, the values in the 0.70s are middling, the values in the 0.60s are mediocre, the values in the 0.50s are miserable and all others are unacceptable. The small values of Kaiser’s MSA indicate that PCA of variables is not good idea. A high value between 0.50 and 1.0 indicates that PCA is appropriate technique to be used. Thus, the measure of sampling adequacy that justify PCA should be overall 0.50 or higher, whereas variables with low MSA value must be removed and analysis be repeated.
Step 2: Selection Procedure for Appropriate Number of Principal Components
According to Preacher and MacCallum (2003), Fabrigar et al. (1999), Jackson
(1993) and Zwick and Velicer (1986) the selection of number of components is the most important decision in principal component analysis. Accordingly, in present thesis two main criteria are used to determine the retained number of principal components. Primarily, in chapter 5 (Methodology) we have used a prior criterion for the theoretical underpinning of variables to be used in the construction of various PCs
(indices).
For next chapter 7, we prefer to use the most commonly known method of Kaiser
Guttman unit eigenvalue or minimum eigenvalue criterion, in which the PCs with eigenvalue greater than one are preferred to be retained in column eigen vectors. The rationale behind this rule is that each variable in the data set contributes fraction of one unit to the total variance of the n-variables (i.e., total variance for n-variables is
1 x n). Algebraically, linearly transformed eigen vector equation is written as;
T(V) V ….. (6.1) where T(V) is a linear transformation of an eigen vector V. It shows that a linear transformation of an eigen vector scales the eigen vector by the scalar value λ that is known as an eigenvalue (Herstein, 1964 and Nering, 1970). Geometrically, an eigen vector corresponding to a real nonzero eigenvalue direct to a path extended by the transformation and the eigenvalue specify the magnitude by which it is extended. In case of negative eigenvalue the direction is reversed (Burden and Faires, 1993). Thus in unit eigenvalue criterion, a component with an eigenvalue greater than one captures greater variance than is accounted by a single variable in that index. In short, eigenvalue equal to or greater than one shows the importance of any component, therefore such PCs are worthy of being retained.
Jackson (1993) tested the accuracy of the unit eigenvalue criterion and preferred to retain the components with sum of proportional variance more than threshold fraction of the total variance. As a rule of thumb, the cumulative percentage of variance of eigenvalue extracted from successive components that exceeded 60 percent of total variance is good enough for selection of number of these PCs.
6.3.2. PERFORMING PRINCIPAL FACTOR ANALYSIS
We follow Gorsuch (1983), Grice (2001), McDonald (1981) and Green (1969) for the loading and construction of indices mentioned in equations (5.9.1) through
(5.12.2). Let principal factors (INDEX it ) are the linear combination of normalized relative economic, financial, demographic or social variable as following.
INDEX it W(Z it Z Pt ) ….. (6.2)
where W are factor score coefficients derived from the estimates of the principal factor model and are arrived on through unit eigenvalue criterion. We have
Z ( X ) and Z ( X ), where X and X are economic, financial, it it xi Pt Pt xP it Pt demographic or social variable for i destination countries and Pakistan, respectively.
and are mean values of X and X variables, respectively. xi xP it Pt
In equation (6.2) to estimate the score coefficients (W ) we use exact scoring of the coefficients through un-rotated Bartlett weighted least squares (WLS) regression method for the original loadings of factor coefficients (Bartlett, 1937). The method minimizes the estimated errors with respect to W. Then following Grice (2001) we use the unique recode method where the variable with the highest absolute value in a row is recoded to a non-zero value. Also the multiple correlation coefficients (i.e., multiple R) is used to measure the legitimacy of coefficients in each index.
The R-squared values for different indices show the percentage of variation in
Bartlett WLS regression explained by the selected components. Another goodness of fit indicator i.e., root mean squares residual (RMSR) represents the square root of the average or mean of the covariance residuals, i.e., the difference between corresponding elements of the observed and predicted covariance matrix. Zero RMSR represents a perfect fit. According to Browne and Cudeck (1989) RMSR should be less than 0.08 and ideally less than 0.05 (Steiger, 1990).
Then we use different criteria to illustrate the goodness of fit of estimated regression models that are used to construct different indices. Bentler-Bonnet Normed
Fit index (NFI) and Relative Fit index (RFI) are used to compare the target model with original (null) model. A value of Bentler-Bonnet NFI varies from 0 to 1, where value of 1 is ideal. The Bentler-Bonnet NFI equals the difference between the chi- square statistics of null and of target model, divided by the chi-square statistics of the null model. If the Bentler-Bonnet NFI exceeds 0.90 (Byrne, 1994) or 0.95
(Schumacker and Lomax, 2004) then regression model is acceptable. A Bentler-
Bonnet NFI of 0.90, for example indicates that the concerned model fit by 90 percent relative to null or independent model.
6.4. ESTIMATION PROCEDURE
The earlier estimation techniques used in panel data were pooled regression, fixed effect model, random effect model and generalized methods of moments. These estimation techniques have their own worth and are helpful in estimation with a panel of large number of cross-sections and for small time period. But our present data is biased toward large time-series with 27 cross-section units (i.e., destination countries) and 36 years.
In this section we explain panel co-integration regression technique beside prerequisites for panel regression analysis namely, panel unit root tests and panel
Granger causality test. In the following subsection three main steps to apply panel co- integration regression analysis namely, Dynamic OLS (Stock and Watson, 1993), are explained in detail that are used in the estimation of equation (5.15) for net human capital flows and equation (5.16) for net brain drain (i.e., human capital flight) in chapter 8.
6.4.1. Step 1: Tests for Stationarity: Panel Unit Root Tests
The stationarity test is important for any series before using it in a panel regression. The formal method to test the stationarity of multiple series is the panel unit root test. Five panel unit root tests namely, Levin, Lin and Chu (2002) test,
Breitung (2000) test, Im, Pesaran and Shin (2003) test, Fischer type tests (Maddala and Wu, 1999 and Choi, 2001) and Hadri (2000) test are used to determine the stationarity of a panel data. Let us consider an autoregressive process of order one
AR(1) for panel data;
INDEX it ρi INDEX it1 X it δi εit ….. (6.3)
where i 1,2,3,.....,27 are cross-section units observed over periods t 1,2,3,.....,36.
The X represent the exogenous variables in the model including fixed effects or it individual trends, the autoregressive coefficients , and random error terms . If i it
ρ 1, INDEX is said to be weakly stationary. On the other hand, if ρ 1 then i it i
INDEX contains unit root. To test the unit roots, we have two options. The first it
option assume that the parameters are common across cross-sections such that i for all i (Levin, Lin and Chu test, Breitung test and Hadri test). The second option is to allow to vary across cross-sections (Im, Pesaran and Shin test, Fischer-ADF test i and Fischer-PP test). In present study to consider both options of unit root tests we apply at least one test from each type. Levin, Lin and Chu (LLC) test consider a common unit root process across cross-sections through following Augmented Dicky
Fuller (ADF) specification.
di ΔINDEX it α INDEX it1 βij ΔINDEX it j X it δi εit ….. (6.4) j1
where we assume a common α ρ 1 and permit the lag order for the difference terms (i.e., d ) to vary across cross-sections. The null and alternative hypotheses for i the LLC test are then written as;
H : α 0 i.e., panel data series has unit root (non-stationary series) 0
H : α 0 i.e., panel data series has no unit root (stationary series) 1
The estimate of the coefficient α is obtained from the pooled proxy equation. Under the null hypothesis a modified t-statistic for the resulting α is computed that is asymptotically normally distributed. Im, Pesaran and Shin (IPS) test for individual unit root process allow to vary across cross-section units. Im et al. (2003) specify i a separate Augmented Dickey Fuller (ADF) regression for each cross section. The functional form based on ADF regression equation (6.4) and hypotheses of the IPS test are given below.
pi ΔINDEX it γi θt δit αi INDEX it 1 βij ΔINDEX it j uit ….. (6.5) j1
where above regression equation contains cross section effect , and the time effect i
along with time trend t. The coefficients are used to test stationarity. The t i i null hypothesis is written as;
H 0 : α i 0, for all i
while the alternative hypothesis is written as;
for i 1,2,3,...... , N α i 0 H 1 : α 0 i for i N 1, N 2, N 3,...... , N implies that a non-zero fraction of the individual process is stationary. After estimation of separate ADF regression the mean of the t-statistics for from the individual ADF regression is computed. In case of LLC test the stationarity decision is based on the probability of t-statistics and in case of IPS test the stationarity
decision is based on the probability of Kendall’s W-statistics of each i calculated from the ADF test. The probability values for both statistics ranges from zero to one and are computed assuming asymptotic normality. Over all the IPS test of individual unit root process is more flexible and reliable than LLC test of common unit root process.
6.4.2. Step 2: Panel Granger Causality Test
The Granger (1969) approach enables us to decide about the causality between the variables that may be bi-directional, unidirectional or no causality. Let us consider equation (5.16) of chapter 5. For the test of Granger causality the bivariate regression of net brain drain and each of its drivers (taken one at a time) takes the form as;
NETBD NETBD ..... NETBD it 0i 1i it1 ki itk …..(6.6) 1i INDEX it1 ..... ki INDEX itk it
INDEX INDEX ..... INDEX it 0i 1i it1 ki itk …..(6.7) 1i NETBDit1 ..... ki NETBDitk it
We treat the panel data as a large stacked set of data and then perform Granger causality test, with the exception of not letting data from one cross-section to enter as lagged values of data from the next cross-section. This method assumes that all the coefficients are same across all cross-sections, i.e., for equation (6.6) we have;
α 0 i α 0 j ,α1i α1 j ,...... , α ki α kj ij
β β ,...... , β β ij 1i 1 j ki kj
Similarly for equation (6.7) we have,
γ0 i γ0 j ,γ1i γ1 j ,...... , γ ki γkj ij
δ δ ,...... , δ δ ij 1i 1 j ki kj where for all possible pairs of NETBD and INDEX series in the group, the it it reported F-statistics are the Wald statistics for the joint hypothesis.
H A : β ..... β 0, i (INDEX ↛ NETBD ) 0 1i ki it it for at least one i or one H A : β 0 (INDEX → NETBD ) 1 ji it it j H B : δ ..... δ 0, i (NETBD ↛ INDEX ) 0 1i ki it it for at least one i or one H B : δ 0 (NETBD → INDEX ) 1 ji it it j
In order to give decision about the Granger causality, the probability of F-statistics is used. The value of F-statistics is computed as;
(RSS RSS )/REST F new old ….. (6.8) RSS old /d.f
where RSSold is the residual sum of squares of the original equations (6.6) and (6.7).
The RSSnew is the residual sum of squares calculated from the restricted model following the restrictions in the null hypothesis. The REST is number of restrictions in the null hypothesis and d. f is the degree of freedom in the unrestricted equations.
6.4.3. Step 3: Estimating Panel Co-integration Regressions: Dynamic OLS
In this subsection dynamic ordinary least squares (DOLS) regression proposed by
Saikkonen (1992), Stock and Watson (1993) and Phillips and Moon (1999) is explained for estimation of single equation co-integrating relationship. DOLS is a non-stationary estimation setting to deal with the problem of stationarity in panel data analysis and to construct an asymptotically efficient estimator. Let us simple co- integrating equation is given as;
NETBDit INDEX it β Dit γi uit ….. (6.9)
where D includes deterministic regressors and the INDEX consists of stochastic it it regressors. In present case when the series are co-integrated, simple OLS estimators of the co-integrating coefficient vector in equation (6.9) are though consistent and converges at a faster rate than standard rate (Hamilton, 1994) yet these estimates
exhibit non-Gaussian asymptotic distribution with asymptotic bias, asymmetry and are function of non-scalar nuisance parameters. In short in case of co-integrating vector, conventional estimation procedures (e.g., simple OLS method) are not valid and if we apply simple OLS method without modification, problematic asymptotic distribution of OLS estimates exists.
In such circumstances for estimating a single co-integrating vector we use extension of DOLS estimation techniques proposed by Kao and Chiang (2000), Mark and Sul (1999 and 2003) and Pedroni (2001) for panel data settings. The panel DOLS augments the above panel co-integrating regression equation (6.9) with cross-section
specific lags and leads of INDEX it to eliminate the asymptotic endogeneity and serial correlation. Following Kao and Chiang (2000) we use dynamic OLS to estimate the following augmented co-integrating regression equation.
~ ~ ri ~ ~ NETBDit INDEX it Dit i INDEX it j i vit ….. (6.10) jqi
where we have q lags and r leads of the differenced regressors. In present study we use an automatic Akaike information criterion (AIC) to specify observation-based maximum lag and lead orders in each cross-section to remove long run dependence across cross sections. According to main objective of panel DOLS, the dependent ~ ~ variable NETBD and the regressors INDEX represent the data without individual it it deterministic trends in equation (6.10). The short-run dynamics coefficients are i ~ ~ cross-section specific. Let Zit are regressors formed by interacting the INDEX it j
~ ~ ~ terms with cross-section dummy variables and let Wit (INDEX it , Zit ), then the pooled DOLS estimators used in present study are;
1 N T ~ ~ N 27 T 36 ~ ~ WitWit Wit NETBDit ….. (6.11) i1 t1 i1 t1
We follow Mark and Sul (2003) and use sandwich, heterogeneous coefficient covariance matrix beside individual long run variances as weights (estimated from DOLS residuals) for diagnostic test of co-integrating coefficient vector and to have HAC (heteroscadasticity and autocorrelation consistent) estimators for . We use
Bartlett Kernal options with the Newey-West-auto bandwidth method to compute individual long run variance estimates. According to Moon (1999), Pedroni (2000 and
2001), Kao and Chiang (2000) and Mark and Sul (1999 and 2003) these HAC coefficient estimates for estimated through above procedure are asymptotically unbiased and normally distributed.
In present thesis we obtain DOLS estimators of equation (6.11) by applying dynamic OLS to our equation of net human capital flows (i.e., equation 5.15) and to the equation of human capital flight (i.e., equation 5.16) in the following chapter 8.
Chapter 7
CONSTRUCTION OF INDICES FROM DRIVERS OF HUMAN CAPITAL MOBILITY
In chapter 5 equations (5.9.1) through (5.12.2) are constructed on the basis of theories of human capital flows (Chapter 3) and their empirical evidences (Chapter 4).
In these equations 46 variables affecting human capital flows as well as human capital flight are grouped into 10 indices. In this chapter we empirically construct these indices by using techniques of principal component analysis and principal factor scoring (loadings). The indices constructed here are then utilized in empirical analysis of Chapter 8.
7.1. CONSTRUCTION OF INDICES
To make our task practicable we divide the drivers of human capital flows as well as human capital flight into four groups. Group-1contains economic and financial variables, Group-2 contains socio-economic variables, Group-3 contains demographic and labor market instruments and Group-4 consists of government’s fiscal policy variables that ensure human capital mobility. In the panel setting various indices can be computed either for each destination country separately or for all the destination countries collectively. For each particular index, we prefer to compute one series by using single, same set of weights for all countries obtained from principal factor loading. In this way the variations in an index across countries indicate variations in the underlying factors rather than variations in the weights. Since all decisions belongs to single host country namely Pakistan, it makes sense that we assign equal weights to any particular factor irrespective of the country of destination to be chosen such that the migrants’ decisions reflect the choice based on underlying socio- economic and demographic conditions prevailing in various countries relative to
Pakistan. Thus based on this logic the construction of indices from the pooled data is presented in the following subsections 7.1.1 through 7.1.4.
7.1.1. ECONOMIC AND FINANCIAL DRIVERS OF HUMAN CAPITAL MOBILITY
The economic and financial variables affecting human capital mobility more
specifically, human capital flows (NETHCFit ) as well as human capital flight
(NETBDit ) are obtained from the theories of migration and are mentioned in chapter 5 in equations (5.9.1) through (5.9.3). By following the procedure, for construction of indices presented in chapter 6, we can practically construct the indices on the basis of economic and financial drivers of human capital mobility. First we test the suitability of data for principal component analysis (PCA) using Kaiser Meyer Olkin (Kaiser,
1970; Kaiser and Rice, 1974 and Dziuban and Shirkey, 1974) measure of sampling adequacy (MSA). The Kaiser’s MSA is constructed from partial correlation coefficients for individual variable and for overall aggregate data and is presented in
Table 7.1 below. The aggregate value of MSA index (0.6133) falls in the mediocre
(i.e., 60 percent and above) category by Kaiser and Rice (1974) that is a feasible range for construction of indices for that data.
After passing the feasibility test for construction of PCA we are able to construct indices. According to Preacher and MacCallum (2003), Fabriger et al. (1999),
Jackson (1993) and Zwick and Velicer (1986) choice of the number of components is an important decision in PCA. Table 7.1 also provides information about the number of principal components (PCs) retained based on unit eigenvalue criterion. On the bases of this criterion only three PCs out of 14 PCs are selected. The cumulative eigenvalue for first three selected PCs is equal to 9.0114 out of 14 (i.e., sum of eigenvalues of 14 variables).
The economic and financial drivers of human capital mobility can be divided into three PCs on the bases of unit eigenvalue criterion. Only PCs having eigenvalues
greater than unity are retained and their linear combination coefficients are presented in columns 3 through 5 of the Table 7.1. For loading we consider the variables having more than 50 percent variation in absolute term. The first principal component
(labeled PC1) is reasonably loaded by variables like relative foreign direct investment
(net inflows) as a percentage of GDP (FDI FDI ), relative annual percentage it Pt change in S&P global equity index (GEQTY GEQTY ), relative real effective it Pt exchange rate index (REER REER ), relative total reserves including gold as a it Pt percentage of GDP (RES RES ) and relative percentage of stocks traded it Pt turnover ratio (TURNit TURNPt ). So PC1 is basically labeled as an index of relative
financial stability (FINS it ).
The eigenvector loading of PC2 shows that it is denominated by relative domestic credit to private sector by banks as a percentage of GDP (CREDIT CREDIT ) it Pt relative percentage interest rate spread measured as lending rate minus deposit rate
(INTSit INTSPt ), relative market capitalization of listed companies per hundred thousand of population (MCAP MCAP ), relative self-employed workers as a it Pt percentage of total employed workers (SEMPit SEMPPt ) and relative taxes on income, profit and capital gains as a percentage of total revenue (TAX TAX ). it Pt
Table 7.1: Economic and Financial Drivers of Human Capital Mobility Eigenvector Loadings Variables MSA PC1 PC2 PC3
(ABSit ABSPt ) 0.5630 0.3831 0.2072 -0.5604
(CREDIT it CREDIT Pt ) 0.6581 0.4191 0.5434 -0.304
(FDI it FDI Pt ) 0.7729 -0.5973 0.3739 0.3024
(GCFit GCFPt ) 0.5916 -0.2217 0.1675 0.5605
(GEQTY it GEQTY Pt ) 0.6424 0.7787 0.1007 -0.3572
(GPCIit GPCI Pt ) 0.5478 -0.2991 0.281 0.5704
(INTS it INTS Pt ) 0.5019 0.3753 -0.6015 0.2654
(MCAPit MCAPPt ) 0.7113 0.3132 0.5839 0.3566
(PCIit PCI Pt ) 0.7162 0.1391 -0.2957 0.5438
(REER it REER Pt ) 0.6196 -0.5824 0.3483 0.2389
(RES it RES Pt ) 0.6058 -0.5831 0.3446 0.3304
(SEMPit SEMPPt ) 0.5422 0.3815 0.5881 -0.2638 (TAX TAX ) it Pt 0.5771 0.4531 -0.5899 0.2107 (TURN TURN ) 0.5361 -0.0775 -0.021 it Pt 0.5415 Feasibility Test 0.6133 ------(Kaiser’s MSA)
Eigenvalue --- 4.3450 3.1404 1.5260 (Average = 1) Cumulative Eigenvalue --- 4.3450 7.4854 9.0114 (Sum = 14) Explained Proportion --- 0.3104 0.2243 0.1090 (Percentage of Variance) Cumulative Proportion --- 0.3104 0.5347 0.6437 (Percentage of Variance)
Therefore, PC2 effectively presents the index of relative financial independence
(FININit ). Finally, eigenvector loading of PC3 shows that it is measured by relative domestic absorption as a percentage of GDP (ABS ABS ), relative gross capital it Pt formation as a percentage of GDP (GCF GCF ), relative percentage growth rate of it Pt per capita GDP (GPCI GPCI ) and of relative per capita gross national income it Pt
(PCI PCI ). Thus PC3 truly presents an index of relative economic incentives it Pt
(ECON it ).
Based on correlation matrix the sum of the scaled variances for the fourteen variables is 14, where first PC for full panel (i.e., PC1) accounts for 31.04 percent of the total variance (4.3450/14 = 0.3104). The second and the third PCs account for
22.43 percent and 10.90 percent of total variation, respectively. In other words these three retained PCs account for overall 64.37 percent of the total variation. The unobserved PCs selected in the above Table 7.1 are based on the covariance structure of the observed data (economic and financial variables). Therefore, after selection of the components of each index instead of using weight (loadings) from the eigenvector matrix, these unobservable PCs can be estimated through the principal factor analysis
(Gorsuch, 1983; Grice, 2001; McDonald, 1981 and Green, 1969) where we use un- rotated Bartlett weighted least squares (WLS) regression method for the original loadings of factor coefficients (Bartlett, 1937). A summary, of the factor score coefficients obtained through Bartlett weighted least squares regression estimates, is presented in Table 7.2 below.
The unique-recode method proposed by Grice (2001) is used to determine simplified weights by the elements of an exact coefficient weight matrix on the basis of their magnitudes. In this method the element with the highest absolute value in a row is recoded to a non-zero value, such that each variable loads on a single factor and maintains its sign. Grice’s unique-recode method helps us to provide a complete picture of constructed indices, namely the indices of relative financial stability
(FINS ), relative financial independence (FININ ) and relative economic incentives it it
(ECON ). In Table 7.2 the index of relative financial stability (FINS ) is constructed it it as a weighted linear combination of data for (FDIit FDIPt ), (GEQTYit GEQTYPt ),
(REER REER ), (RES RES ) and(TURN TURN ) with weights given in the it Pt it Pt it Pt second column of exact scoring coefficients based on Bartlett WLS regression
(0.6149, 0.1789, -0.1466, 0.9498 and 0.0962 respectively).
The R-squared values for these three indices FINS , FININ and ECON are it it it
0.8528, 0.7062 and 0.6076 respectively, which show the variation in the Bartlett WLS regressions explained by these selected components. The multiple-R is used to measure the validity of coefficients in each index. The multiple correlation coefficients (i.e., multiple R) for the first index (principal factor) is 0.9217 whereas the multiple correlation coefficients of the second and third principal factors are
0.8404 and 0.8079 respectively, showing that the validity of coefficients for these factors are approximately equal to or in excess of 0.80, the benchmark recommended by Gorsuch (1983).
The factors score correlation coefficients (off-diagonal elements) are equal to zero based on the unique-recode method proposed by Grice (2001) where each variable loads on a single factor (index) and maintain its sign, that’s why across factors correlation coefficients are zero. Goodness-of-fit of these indices is measured by
Bollen relative fit index (RFI) by Bollen (1986) and Bentler Bonnet normed fit index
(NFI) proposed and used by Byrne (1994) and Schumacker and Lomax (2004). The
RFI improves the values of these fitted indices by 88.93 percent, whereas NFI improves the fitted indices by 96.23 percent relative to independent (no factor) model.
The root mean square residuals (RMSR) as an incremental fit index is used to compare the fit of the estimated model against the independence model (Hu and
Table 7.2: Economic and Financial Indices Exact Scoring Coefficients Variables (on the basis of Bartlett WLS) FINS FININ ECON
(ABSit ABSPt ) ------0.4517
(CREDIT it CREDIT Pt ) --- 0.8405 ---
(FDI it FDI Pt ) 0.6149 ------
(GCFit GCFPt ) ------0.2511
(GEQTY it GEQTY Pt ) 0.1789 ------
(GPCIit GPCIPt ) ------0.7934
(INTS it INTS Pt ) --- 0.2613 ---
(MCAPit MCAPPt ) --- 0.4949 ---
(PCI it PCI Pt ) ------0.2835
(REER it REER Pt ) -0.1466 ------
(RES it RES Pt ) 0.9498 ------
(SEMPit SEMPPt ) --- 0.2370 ---
(TAX it TAX Pt ) --- -0.4739 ---
(TURN it TURN Pt ) 0.0962 ------Validity of Coefficients 0.9217 0.8404 0.8079 (Multiple-R) R-squared 0.8528 0.7062 0.6076 Estimated Scores Correlation FINS FININ ECON Coefficients FINS 1.0000 FININ 0.0271 1.0000 ECON -0.0239 0.0200 1.0000
Estimated Factor Correlation FINS FININ ECON Coefficients FINS 1.0000 FININ 0.0000 1.0000 ECON 0.0000 0.0000 1.0000 Goodness of Fit Summary: Root Mean Square Residuals RMSR---0.0415 (for fitted model) (RMSR) RMSR---0.2136 (for independent model) Bollen Relative Fit Index (RFI) RFI------0.8893 (for fitted model)
Normed Fit Index (NFI) NFI------0.9623 (for fitted model)
Bentler, 1999). The value of the RMSR for fitted model is 0.0415 (i.e., 4.15 percent) which is less than RMSR of independent model (i.e., 0.2136 = 21.36 percent). Zero
RMSR represents a perfect fit. According to Browne and Cudeck (1989) RMSR should be less than 0.08 and ideally less than 0.05 (Steiger, 1990). Thus 0.0415 value of RMSR is a token for good fit of estimated models (indices).
On the basis of explanation provided in the Table 7.2, the indices of relative economic incentives ECON , relative financial stability FINS and relative financial it it
independence FININ , arrived at through the principal component analysis and it principal factor analysis, take the following form.
ECON 0.2835 (PCI PCI ) 0.7934 (GPCI GPCI ) it it Pt it Pt ….. (7.1) 0.2511(GCF it GCF Pt ) 0.4517 (ABS it ABS Pt )
FINSit 0.1466(REERit REERPt ) 0.1789(GEQTYit GEQTYPt )
0.9498(RESit RESPt ) 0.6149(FDIit FDI Pt ) ….. (7.2)
0.0962(TURNit TURN Pt )
FININit 0.2370(SEMPit SEMPPt ) 0.4739(TAX it TAX Pt )
0.4949(MCAPit MCAPPt ) 0.8405(CREDITit CREDITPt ) ….. (7.3)
0.2613(INTSit INTS Pt )
In the index of relative economic incentives (ECONit ) positive loadings for relative per capita gross national income (PCI PCI ), relative percentage annual it Pt growth rate of per capita GDP (GPCI GPCI ) and relative gross capital formation it Pt as a percentage of GDP (GCFit GCFPt ) all provides monetary incentives to rational expected utility maximizers. The per capita gross national income differential in
ECONit in the equation (7.1) indicates the relative economic condition in destination countries relative to Pakistan. It shows that an increase in relative per capita gross national income at the centers of destination relative to Pakistan (PCI PCI ) loaded it Pt to ECONit is 0.2835 (i.e., 28.35 percent).
The negative loading for the relative domestic absorption as a percentage of GDP
(ABS ABS ) in ECON shows that when Pakistanis are able to afford the it Pt it expenditure in term of cost of migration, they may be financially more inclined to
move abroad through the channel of economic incentives (ECONit ). Also domestic absorption relative to GDP is an indication of business cycle. The negative sign of
loading is when treated with negative sign of (ABSit ABSPt ) the domestic push factor dominates the destination country’s pull factor. The differential in gross capital
formation as a percentage of GDP in the index of ECONit provides relative future prospects.
In equation (7.2) the positive loading for relative annual percentage change in
S&P global equity index (GEQTYit GEQTYPt ), relative total reserves including gold
as a percentage of GDP (RESit RESPt ), relative foreign direct investment (net inflows) as a percentage of GDP (FDI FDI ) and relative percentage of stock it Pt traded, turnover ratio (TURNit TURNPt ) show the increased stability in financial markets of destination countries relative to home country. However, the negative loading for relative real effective exchange rate index (REER REER ) shows that it Pt the appreciation in Pakistani real effective exchange rate empower Pakistanis to afford their stay (cost of migration) in destination countries in the transition settlement period. At the same time relative decline in real effective exchange rate of the destination counties also increases the financial stability of destination country through increase in exports and hence attract the human capital.
A strong financial sector generates financial resources by raising savings and distributes these resources to running investment and to setup new business that creates employment opportunities and absorbs both skilled and unskilled labor.
Therefore, in equation (7.3) except for relative taxes on income, profits and capital
gains as a percentage of total revenue, all other variables of the index of relative
financial independence (FININit ) have positive loadings. The reason for this negative loading is that increase in the taxes on income, profits and capital gains as a
percentage of total revenue decreases the financial independence (FININit ).
7.1.2. SOCIO-ECONOMIC DRIVERS OF HUMAN CAPITAL MOBILITY
In this subsection we construct the indices on the basis of relative socio-economic variables specified in the Chapters 5 and 6 that are affecting human capital mobility.
First we test the suitability of data for principal component analysis (PCA) using
Kaiser Meyer Olkin measure of sampling adequacy (MSA). The Kaiser’s MSA for individual variable and for aggregate data is presented in the Table 7.3. The aggregate value of MSA index (0.7341) falls in the middling (i.e., 70 percent and above) category of Kaiser and Rice (1974) that is feasible range for construction of indices for that data.
After passing the feasibility test for construction of PCA we are able to construct indices, where Table 7.3 provides information about the number of principal component (PCs) retained based on unit eigenvalue criterion. On the bases of unit eigenvalue criterion only four PCs out of 16 PCs are selected and the cumulative eigenvalue for these four selected PCs is equal to 12.9763 out of 16 (i.e., sum of eigenvalues for 16 variables).
The socio-economic drivers of human capital mobility can be grouped into four
PCs (indices) on the bases of unit eigenvalue criterion and PCs having eigenvalues greater than unity are retained. The column 3 through 6 describe the linear
combination coefficients for these retained PCs. We consider the variables having more than 50 percent variation in absolute term in the Table 7.3 for loading. The first principal component (PC1) is rationally loaded by variables like relative number of
physicians per hundred thousand people (DOCSit DOCSPt ), relative health expenditure as a percentage of GDP (HLTH HLTH ), relative life expectancy at it Pt the time of birth (LEit LEPt ), relative percentage of population with access to improved sanitation facilities (SANI SANI ) and to improved water sources it Pt
(WATERit WATERPt ). So PC1 is fundamentally labeled as the index of relative
standards of living based on health related facilities (LIVIN 1it ).
The eigenvector loading of PC2 shows that it is denominated by relative energy
use per capita (ENERit ENERPt ), relative annual growth rate of household final consumption expenditure (GPCC GPCC ), relative inflation rate measured by it Pt annual percentage increase in consumer price index (INFit INFPt ) and relative urban population in an agglomeration of more than one million people as a percentage of
total population (URPOPit URPOPPt ). Therefore, PC2 effectively presents the index
of relative standards of living other than health facilities (LIVIN 2it ).
Table 7.3: Socio-Economic Drivers of Human Capital Mobility Eigenvector Loadings Variables MSA PC1 PC2 PC3 PC4
(AIRit AIRPt ) 0.8161 -0.1564 -0.1201 0.7835 0.383
(CELLit CELLPt ) 0.7229 0.3781 0.2876 -0.5999 0.1978
(DOCSit DOCSPt ) 0.7490 0.6131 0.0446 0.077 -0.3716
(ENERit ENERPt ) 0.8334 0.3781 0.6351 -0.0939 0.2082
(GPCCit GPCCPt ) 0.8223 0.2575 -0.6503 0.2114 0.2135
(HLTHit HLTH Pt ) 0.8148 0.7773 -0.2292 -0.2254 -0.0907
(INFit INFPt ) 0.5259 0.2174 -0.6124 0.1704 0.2399
(INTit INTPt ) 0.7111 0.4436 0.2167 -0.6008 0.0349
(LEit LEPt ) 0.7161 0.7156 -0.0289 0.3106 -0.1217
(MIGRANTit MIGRANTPt ) 0.8136 0.2313 0.0027 -0.1821 0.8153
(REMITit REMITPt ) 0.5463 -0.0955 -0.2991 0.1596 0.5435
(SANIit SANI Pt) 0.5961 0.5478 -0.1657 0.0771 0.0576 (TOUR TOUR ) it Pt 0.8655 0.1359 0.3653 0.5708 -0.3026 (TRSER TRSER ) it Pt 0.6315 0.2689 0.2728 0.1692 0.9058 (URPOP URPOP ) it Pt 0.8084 0.3207 0.6195 -0.0411 0.1804 (WATER WATER ) it Pt 0.7734 0.8175 -0.0795 0.3224 -0.1284 Feasibility Test 0.7341 ------(Kaiser’s MSA)
Eigenvalue --- 6.2036 3.0020 2.5599 1.2108 (Average = 1) Cumulative Eigenvalue --- 6.2036 9.2056 11.7655 12.9763 (Sum = 16) Explained Proportion --- 0.3877 0.1876 0.1599 0.0757 (Percentage of Variance) Cumulative Proportion --- 0.3877 0.5753 0.7352 0.8109 (Percentage of Variance)
The eigenvector loading of PC3 shows that it is composed of relative air transport registered carrier departures worldwide (AIR AIR ), relative mobile cellular it Pt subscriptions per hundred thousand persons (CELLit CELLPt ), relative internet users
per hundred thousand persons (INTit INTPt ) and by relative international tourism
specifically number of arrivals (TOURit TOURPt ). Thus PC3 truly represents the
index of relative social openness (OPENit ). Finally, eigenvector loading of PC4 shows that it is dominated by relative international migrant stock (i.e., net immigrants) as a percentage of total population (MIGRANT MIGRANT ) relative it Pt personal remittances received as a percentage of GDP (REMITit REMITPt ) and
relative trade in services as a percentage of GDP (TRSERit TRSER Pt ). Thus PC4 presents the index for the role of compatriot community (COMU ). it
The sum of the scaled variances for the sixteen variables is equal to 16, where first
PC for full panel data (i.e., PC1) accounts for 38.77 percent of the total variance
(6.2036/16 = 0.3877). The second, third and fourth PCs account for 18.76 percent,
15.99 percent and 7.57 percent of the total variation, respectively. In other words these four retained principal components account for overall 81.09 percent of the total variation.
After selection of socio-economic variables constructing each index, we estimate them through the technique of principal factor analysis. A summary of the factor score coefficients through Bartlett WLS regression estimates is presented in the Table 7.4.
The unique-recode method is used in which the element with the highest absolute value in a row is recoded to a non-zero value, such that each variable loads on a single factor and maintains its sign. The unique-recode method helps us to provide a complete picture of constructed indices, namely the index of relative standards of living based on health related facilities (LIVIN1 ), the index of relative standards of it living other than health facilities (LIVIN 2 ), the index of relative social openness it
(OPENit ) and the index for the role of compatriot community (COMUit ).
Table 7.4: Socio-Economic Indices Exact Scoring Coefficients Variables (on the basis of Bartlett WLS) LIVIN-1 LIVIN-2 OPEN COMU
(AIRit AIRPt ) ------0.3158 ---
(CELLit CELLPt ) ------0.1596 ---
(DOCSit DOCSPt ) 0.5205 ------
(ENERit ENERPt ) --- 0.5021 ------
(GPCCit GPCCPt ) --- 0.2331 ------
(HLTHit HLTH Pt ) 0.3106 ------
(INFit INFPt ) --- -0.1349 ------
(INTit INTPt ) ------0.2722 ---
(LEit LEPt ) 0.1202 ------
(MIGRANTit MIGRANTPt ) ------0.4589
(REMITit REMITPt ) ------0.0169
(SANIit SANI Pt) 0.3298 ------(TOUR TOUR ) it Pt ------0.4444 --- (TRSER TRSER ) it Pt ------0.6527 (URPOP URPOP ) it Pt --- 0.3782 ------(WATER WATER ) 0.1860 ------it Pt Validity of Coefficients 0.9823 0.9514 0.9489 0.8676 (Multiple-R) R-squared 0.9649 0.9053 0.9003 0.7528
Estimated Scores Correlation LIVIN-1 LIVIN-2 OPEN COMU Coefficients LIVIN-1 1.0000 LIVIN-2 -0.0108 1.0000 OPEN -0.0041 -0.0256 1.0000 COMU -0.0079 -0.0049 -0.0063 1.0000
Estimated Factor Correlation LIVIN-1 LIVIN-2 OPEN COMU Coefficients LIVIN-1 1.0000 LIVIN-2 0.0000 1.0000 OPEN 0.0000 0.0000 1.0000 COMU 0.0000 0.0000 0.0000 1.0000 Goodness of Fit Summary: Root Mean Square Residuals RMSR--- 0.0426 (for fitted model) (RMSR) RMSR--- 0.3489 (for independent model) Bollen Relative Fit Index (RFI) RFI------0.9636 (for fitted model)
Normed Fit Index (NFI) NFI------0.9812 (for fitted model)
In the Table 7.4 the index of relative standards of living based on health related facilities (LIVIN1 ) is constructed as a linear combination of the data for it
(DOCS DOCS ), (HLTH HLTH ), (LE LE ), (SANI SANI ) and it Pt it Pt it Pt it Pt
(WATER WATER ) with weights given in the second column of exact scoring it Pt
coefficients based on Bartlett WLS regression (0.5205, 0.3106, 0.1202, 03298 and
0.1860 respectively).
The R-squared values for these four indices LIVIN1 , LIVIN 2 , OPEN and it it it
COMU are 0.9649, 0.9053, 0.9003 and 0.7528 respectively. The multiple correlation it coefficient (i.e., multiple R) for the first index (principal factor) is 0.9823, while the multiple correlation of the second, third and fourth principal factors (indices) are
0.9514, 0.9489 and 0.8676 respectively that show the validity of coefficients for these principal factors in excess of the benchmark (0.80) recommended by Gorsuch (1983).
In the Table 7.4, across factors correlation coefficients are zero based on the unique-recode method, where each variable loads on a single factor (index) and maintain its sign. Goodness-of-fit of these indices is measured by Bollen relative fit index (RFI) and Bentler Bonnet normed fit index (NFI). The values of these indices improve the values of fitted indices by 96.36 percent by RFI and 98.12 percent by
NFI relative to the independence (no factor) model. The value of the RMSR for fitted model is 0.0426 (i.e., 4.26 percent) that is less than RMSR of independent model (i.e.,
0.3489) and is an indication for good fit of estimated models (indices).
On the basis of explanation provided in the Table 7.4, the indices of relative
standards of living based on health related facilities (LIVIN1it ), relative standards of livings other than health facilities (LIVIN 2 ), relative social openness (OPEN ) and it it relative role of compatriot community (COMU ) arrived at through the principal it component analysis and principal factor analysis, take the following form.
LIVIN 1it 0.3106(HLTH it HLTH Pt ) 0.1202(LE it LE Pt )
0.1860(WATER it WATER Pt ) 0.3298(SANI it SANI Pt ) ….. (7.4)
0.5205(DOCS it DOCS Pt )
LIVIN 2 0.3782(URPOP URPOP ) 0.2331(GPCC GPCC ) it it Pt it Pt ….. (7.5) 0.5021(ENERit ENERPt ) 0.1349(INFit INFPt )
COMU it 0.4589 (MIGRANT it MIGRANT Pt )
0.4276 (TRSER it TRSER Pt ) ….. (7.6)
0.1069 (REMIT it REMIT Pt )
OPEN 0.3158(AIR AIR ) 0.4444(TOUR TOUR ) it it Pt it Pt ….. (7.7) 0.2722(INTit INTPt ) 0.1596(CELL it CELL Pt )
The positive weights to all the variables in LIVIN1it show that positive characteristics at the centers of destination are directly or indirectly linked with health facilities and improve the standards of living of people. The relative life expectancy at the time of birth as a proxy for living conditions in equation (7.4) is better representative for that index LIVIN1 as a person with batter health facilities may it expect healthy and longer life. The quality of life is also very important for people who want to live at the place with nice weather and clean environment beside availability of health related facilities. Better environment is measured by access of relative percentage of population to improved sanitation facilities and to the improved water sources in equation (7.4).
The negative weight for relative inflation rate, measured as an annual percentage increase in the consumer price index, shows that high inflation rate negatively affects
the relative standards of living of people in LIVIN 2it through two channels. First, high inflation reduces the purchasing power of individuals. Second, inflation raises cost of production that leads to unemployment and hence worsen the standards of
living of people. The components other than inflation rate have positive loading for
the index of relative standards of living (LIVIN 2it ) in equation (7.5). For instance, relative increase in urban population as a percentage of total population
(URPOPit URPOPit ) is a sign of urbanization and industrialization in the destination countries that provide employment opportunities and hence improves standards of living of people.
The equation (7.6) sketch the composition of the index for the role of compatriot
community (COMUit ) based on social networks and the social ties of migrants in the destination countries. Just as Alumni of students, migrants residing in the destination countries have community based spillover effect on the already existing migrants as well as on non-migrant community. The positive loading for relative net immigrants as a percentage of total population (MIGRANT MIGRANT ), relative trade in it Pt services as a percentage of GDP (TRSER TRSER ) and relative personal it Pt remittances received as a percentage of GDP (REMITit REMITPt ) describe the role of compatriot community and kinship networks in construction of the index for the
role of compatriot community (COMUit ).
The positive loading of personal remittances received as a percentage of GDP
(REMITit REMITPt ) sent by already existing migrants relaxes credit constraints from investment on human capital (in term of enrollment in higher education) and hence for human capital mobility (by stimulating cost of migration). The flows of remittances sent home by emigrants affects the economy through the consumption multiplier that produces indirect effects on the migrant’s families (compatriot community) as well as on the non-migrant’s households.
All components of the index of relative social openness (OPEN ) has positive it loading (weights) showing that relative air transport registered carrier departures worldwide (AIR AIR ), relative international tourism (TOUR TOUR ), relative it Pt it Pt internet users per hundred thousand persons (INTit INTPt ) and relative mobile
cellular subscriptions per hundred thousand persons (CELLit CELLPt ) improve and
enhance social openness (OPENit ) and hence integrate the world through human capital mobility.
7.1.3. DEMOGRAPHIC AND LABOR MARKET INSTRUMENTS OF HUMAN CAPITAL MOBILITY
In this subsection we construct the indices on the basis of demographic and labor market instruments ascertaining to human capital flows. First of all we test the suitability of data for principal component analysis (PCA) using Kaiser Meyer Olkin measure of sampling adequacy (MSA). The Kaiser’s MSA for individual variable and overall MSA index for aggregate data is presented in Table 7.5 below. The aggregate value of MSA index (0.6678) falls in the mediocre (i.e., 60 percent and above) category of Kaiser and Rice (1974) that is feasible range for construction of indices for that data.
After passing the feasibility test for construction of PCA, next step is to construct indices. In Table 7.5, on the bases of unit eigenvalue criterion only two PCs out of 8
PCs are selected. The cumulative eigenvalue for these two principal components is equal to 6.5592 out of 8 (i.e., sum of eigenvalues for 8 variables). The demographic and labor market instruments of human capital mobility are divided into two PCs on
the bases of unit eigenvalue criterion and column 3 through 4 describe the linear combination coefficients for these retained PCs.
We consider the variables having more than 50 percent variation in absolute term to load the indices. The first principal component (PC1) is loaded by variables like
relative age dependency ratio (DEPit DEPPt ), relative labor force participation rate
(LFPRit LFPRPt ) and relative unemployment rate (UEMPRit UEMPRPt ). Thus
PC1 is labeled as the index of relative labor market structure (LMKT ). it
The eigenvector loading of PC2 shows that it is denominated by relative population density measured by the number of persons per square kilometer of land
area (DENSit DENSPt ), relative labor force with tertiary education as a percentage of total labor force (EDU EDU ), relative annual percentage growth rate of it Pt population (GPOPit GPOPPt ), relative urban population as a percentage of total population (URBAN URBAN ) and relative population having age between 15 to it Pt
64 years as a percentage of total population (YOUNGit YOUNGPt ). Therefore, PC2 truly presents the index of relative demographic characteristics (DEMO ). it
The Table 7.5 shows that the PC1 for aggregate data accounts for 51.71 percent of the total variance (4.1371/8 = 0.5171) and the second PC account for 30.28 percent of the total variations. In other words these two retained PCs account for overall 81.99 percent of the total variation.
Table 7.5: Demographic and Labor Market Instruments of Human Capital Mobility Eigenvector Loadings Variables MSA PC1 PC2
(DENSit DENSPt ) 0.7360 0.3159 -0.6295
(DEPit DEPPt ) 0.6678 -0.8318 0.0156
(EDU it EDU Pt ) 0.5285 0.1685 0.6866
(GPOPit GPOPPt ) 0.5346 0.2672 -0.5322
(LFPRit LFPRPt ) 0.8106 0.7607 0.2656
(UEMPRit UEMPRPt ) 0.8005 -0.5132 -0.0112
(URBANit URBAN Pt ) 0.6225 0.3891 0.7298
(YOUNGit YOUNGPt ) 0.6423 -0.1478 0.6361 Feasibility Test 0.6678 ------(Kaiser’s MSA)
Eigenvalue --- 4.1371 2.4221 (Average = 1) Cumulative Eigenvalue --- 4.1371 6.5592 (Sum = 08) Explained Proportion --- 0.5171 0.3028 (Percentage of Variance) Cumulative Proportion --- 0.5171 0.8199 (Percentage of Variance)
We use un-rotated Bartlett weighted least square (WLS) regression method for the loadings of factor coefficients. A summary of the factor score coefficients through
Bartlett WLS regression estimates are presented in the Table 7.6. The unique-recode method helps us to provide a complete picture of constructed indices, namely the
index of relative labor market structure (LMKTit ) and the index of relative demographic characteristics (DEMO ). In the Table 7.6 the index of relative labor it market structure (LMKT ) is constructed as a linear combination of the it
(DEP DEP ), (LFPR LFPR ) and (UEMPR UEMPR ) with weights, it Pt it Pt it Pt negative 0.4921, 0.2746 and negative 0.5471 respectively, presented in the second column of exact scoring coefficients based on Bartlett WLS regression.
Table 7.6: Demographic and Labor Market Indices Exact Scoring Coefficients Variables (on the basis of Bartlett WLS)
LMKT DEMO
(DENSit DENSPt ) --- 0.4799
(DEPit DEPPt ) -0.4921 ---
(EDU it EDU Pt ) --- 0.6174
(GPOPit GPOPPt ) --- -0.3307
(LFPRit LFPRPt ) 0.2746 ---
(UEMPRit UEMPRPt ) -0.5471 ---
(URBANit URBAN Pt ) --- 0.2983
(YOUNGit YOUNGPt ) --- 0.3329 Validity of Coefficients 0.9820 0.8497 (Multiple-R) R-squared 0.9644 0.7219
Estimated Scores Correlation LMKT DEMO Coefficients LMKT 1.0000 DEMO 0.0310 1.0000 Estimated Factor Correlation LMKT DEMO Coefficients LMKT 1.0000 DEMO 0.0000 1.0000 Goodness of Fit Summary: Root Mean Square Residuals RMSR--- 0.0402 (for fitted model) (RMSR) RMSR--- 0.3466 (for independent model) Bollen Relative Fit Index (RFI) RFI------0.8541 (for fitted model)
Normed Fit Index (NFI) NFI------0.9323 (for fitted model)
The R-squared value for the index of relative demographic characteristics
(DEMO ) is 0.7219 and for the index of relative labor market structure (LMKT ) is it it
0.9644. The multiple-R is used to measure the validity of coefficients in each index.
The multiple correlation coefficients (i.e., multiple R) for the first index is 0.9820 and
0.8497 for the second principal factor showing that the validity of coefficients for these principal factors are in excess of 0.80, a benchmark recommended by Gorsuch
(1983). In the Table 7.6 the factor score correlation coefficients are zero based on the unique-recode method indicating that each variable loads on a single factor (index) and maintain its sign.
The goodness-of-fit of these two indices is also measured by Bollen relative fit index (RFI) and Bentler Bonnet normed fit index (NFI). The values of these indices improve by 85.41 percent by RFI and 93.23 percent by NFI relative to independence
(no factor) model. The value of RMSR for the fitted model is 0.0402 (i.e., 4.02 percent) that is less than RMSR of independent model (i.e., 0.3466) such that the fitted model provides a token for a good fit of indices.
On the basis of explanation provided in Table 7.6, the index of relative labor
market structure (LMKTit ) and the index of relative demographic characteristics
(DEMO ), arrived at through the principal component analysis and principal factor it analysis take the following form.
DEMOit 0.3307(GPOPit GPOPPt ) 0.4799(DENS it DENS Pt ) 0.2983(URBAN URBAN ) 0.3329(YOUNG YOUNG ) it Pt it Pt ….. (7.8) 0.6174(EDU it EDU Pt )
LMKTit 0.2746(LFPRit LFPRPt ) 0.5471(UEMPRit UEMPRPt )
0.4921(DEPit DEPPt ) ….. (7.9)
The index of relative demographic characteristics (equation 7.8) has negative loading for (GPOP GPOP ) showing that annual percentage growth rate of it Pt population in Pakistan act as a supply dominated push factor in homeland, which is stronger than demand oriented pull factors of population in centers of destination. The positive weights (loadings) to relative population density (DENS DENS ) it Pt measured as a relative number of persons per square kilometer of land area in equation (7.8) indicate that civilized development centers at centers of destination attract more people. The positive loading for relative urban population as a percentage
of total population (URBANit URBANPt ) indicates urbanization at centers of destination that attract youth via bright light theory of migration. The positive loading for relative population having age between 15 to 64 years as a percentage of total
population (YOUNGit YOUNGPt ) and positive loading for relative labor force with
tertiary education as a percentage of total labor force (EDUit EDU Pt ) in equation
(7.8) are indicative of demographic characteristics that are most desirable in international labor markets.
The equation (7.9) shows that the index of relative labor market structure
(LMKT ) is constructed by positive weight (i.e., 0.2746) for relative labor force it participation rate (LFPRit LFPRPt ), negative weight (i.e., -0.5471) for relative unemployment rate (UEMPR UEMPR ) and negative weight (i.e., -0.4921) for it Pt age dependency ratio (DEPit DEPPt ). The magnitude and signs of loading (weights) are supportive to the simple labor market structure in equation (7.9), as high unemployment rate and high dependency ratio in Pakistan are the main causes of emigration from homeland.
7.1.4. GOVERNMENT POLICY VARIABLES THAT ENSURE HUMAN CAPITAL MOBILITY
In this subsection we construct the indices on the basis of Government policy variables that ensure human capital mobility. First we test the suitability of data for principal component analysis (PCA) using Kaiser Meyer Olkin measure of sampling adequacy (MSA). The Kaiser’s MSA for individual variable and overall MSA index for aggregate data is presented in the Table 7.7. The aggregate value of MSA index
(0.6875) falls in the mediocre (i.e., 60 percent and above) category of Kaiser and Rice
(1974) that is feasible range for construction of indices for that data.
Table 7.7 provides information about the number of component retained based on unit eigenvalue criterion. On the bases of unit eigenvalue criterion only two PCs out of seven PCs are selected. The cumulative eigenvalue for these two selected PCs is equal to 4.6114 out of 7 (i.e., sum of eigenvalues for 7 variables). The Government policy variables that ensure human capital mobility are divided into two principal components (PCs) on the bases of unit eigenvalue criterion. The column 3 and 4 presents the linear combination of coefficients for these retained PCs.
We consider the variables having more than 50 percent variation in absolute term to load the indices. The first principal component (PC1) is loaded by variables namely, relative compensation of employees as a percentage of government expenditure (COMP COMP ), relative grants and other revenues as a percentage it Pt of gross national income (GRANTSit GRANTSPt ), relative insurance and financial
services as a percentage of service imports (INSit INSPt ), relative per capita net official development assistance (ODA ODA ) and relative subsidies and other it Pt transfers as a percentage of government expenditure (SUBit SUBPt ). Thus PC1 can
be surely labeled as the index of relative provision of social safety nets (SNETit ).
Table 7.7: Government Policy Variables that Ensure Human Capital Mobility Eigenvector Loadings Variables MSA PC1 PC2
(COMPit COMPPt ) 0.7445 0.5542 0.3566
(GEXPit GEXPPt ) 0.5535 -0.3549 0.5766
(GRANTSit GRANTSPt ) 0.7589 0.5099 0.2587
(INSit INSPt ) 0.7882 0.5971 0.2717
(ODAit ODAPt ) 0.7275 0.5934 0.0434
(RADit RADPt ) 0.5496 -0.1098 0.6423
(SUBit SUB) 0.6903 0.5216 0.3231 Feasibility Test 0.6875 ------(Kaiser’s MSA)
Eigenvalue --- 2.7139 1.8975 (Average = 1) Cumulative Eigenvalue --- 2.7139 4.6114 (Sum = 07) Explained Proportion --- 0.3877 0.2711 (Percentage of Variance) Cumulative Proportion --- 0.3877 0.6588 (Percentage of Variance)
The eigenvector loading of PC2 shows that it is denominated by relative total
government expenditure on education as a percentage of GDP (GEXPit GEXPPt ) and relative Research and Development expenditure as a percentage of GDP
(RAD RAD ). Thus, PC2 truly presents the index for research and development it Pt facilities for human capital grooming (RADI ). The Table 7.7 shows that the first PC it for aggregate data (i.e., PC1) accounts for 38.77 percent of the total variance
(2.7139/7 = 0.3877) and the second PC accounts for 27.11 percent of the total variations. In other words these two retained PCs account for overall 65.88 percent of the total variation.
After deciding upon the composition of each index, we use un-rotated Bartlett weighted least squares (WLS) regression method for the loading of factor coefficients.
A summary of the factor score coefficients through Bartlett WLS regression estimates is presented in the Table 7.8. The unique-recode method helps us to provide a complete picture of constructed indices, namely the index for research and
development facilities for human capital grooming (RAD it ) and for the relative
provision of social safety nets (SNET it ).
In the Table 7.8 the index for relative provision of social safety nets (SNET ) is it constructed as a linear combination of the relative amount of (COMP COMP ), it Pt
(GRANTS GRANTS ), (INS INS ), (ODA ODA ) and (SUB SUB ) it Pt it Pt it Pt it Pt with weights 0.3659, 0.1754, 0.2663, 0.3292 and 0.1554 listed in the second column of exact scoring coefficients that is based on Bartlett WLS regression.
The R-squared values for the index for research and development facilities for
human capital grooming (RADit ) and for the index of relative provision of social safety nets (SNET ), are 0.7832 and 0.8580 respectively. The multiple-R is used to it measure the efficiency of coefficients in each index. The multiple correlation coefficients (i.e., multiple R) for the first principal factor is 0.8236 and the multiple correlation coefficients for the second principal factor is 0.7969. In the Table 7.8 the factor score correlation coefficients are zero based on the unique-recode method indicating that each variable loads on a single factor (index) and maintain its sign.
The goodness-of-fit of these indices is measured by Bollen relative fit index (RFI) and Bentler Bonnet normed fit index (NFI). The fitted values of these indices improve by 87.32 percent by RFI and 95.17 percent by NFI relative to the independence (no factor) model.
Table 7.8: Government Policy Indices Exact Scoring Coefficients Variables (on the basis of Bartlett WLS) SNET RADI
(COMPit COMPPt ) 0.3659 ---
(GEXPit GEXPPt ) --- 0.6136
(GRANTSit GRANTSPt ) 0.1754 ---
(INSit INSPt ) 0.2663 ---
(ODAit ODAPt ) 0.3292 ---
(RADit RADPt ) --- 0.3831
(SUBit SUB) 0.1554 --- Validity of Coefficients 0.8236 0.7969 (Multiple-R) R-squared 0.8580 0.7832 Estimated Scores Correlation SNET RADI Coefficients SNET 1.0000 RADI 0.0045 1.0000 Estimated Factor Correlation SNET RADI Coefficients SNET 1.0000 RADI 0.0000 1.0000 Goodness of Fit Summary: Root Mean Square Residuals RMSR--- 0.0425 (for fitted model) (RMSR) RMSR--- 0.3976 (for independent model) Bollen Relative Fit Index (RFI) RFI------0.8732 (for fitted model)
Normed Fit Index (NFI) NFI------0.9517 (for fitted model)
The value of the RMSR for fitted model is 0.0425 (i.e., 4.25 percent) which is less than RMSR of independent model (i.e., 0.3976) and is an indication for good fit of estimated model (indices). Finally, on the basis of explanation provided in Table 7.8, the indices of relative provision of social safety nets (SNET ) and research and it development facilities for human capital grooming (RAD it ) arrived at through the principal component analysis and principal factor analysis take the following form.
SNET it 0.3292 (ODA it ODA Pt ) 0.3659 (COMP it COMP Pt )
0.1754 (GRANTS it GRANTS Pt ) 0.1554 (SUB it SUB ) ….. (7.10)
0.2663 (INS it INS Pt )
RADI it 0.6136(GEXPit GEXPPt ) 0.3831(RAD it RAD Pt ) ….. (7.11)
Social safety net is a tool through which government or other social welfare institutions provide services in form of cash or kind to the labor force (beside other segments of society) to protect them from social and economic issues. The relative
compensation of employees as a percentage of government expenditure (COMPit
COMP ), relative grants and other revenues as a percentage of gross national Pt income (GRANTS GRANTS ), relative insurance and financial services as a it Pt percentage of service imports (INS INS ), relative per capita net official it Pt development assistance (ODA ODA ) and relative amount of subsidies and other it Pt transfers as a percentage of government expenditure (SUBit SUB) positively
contribute in the construction of the index of SNET it in equation (7.10).
The relative total government expenditure on education as a percentage of GDP
(GEXP GEXP ) and Research and Development expenditure as a percentage of it Pt
GDP (RADit RADPt ) both contributes positively in the construction of the index for research and development facilities (RAD ) for human capital grooming in equation it
(7.11) that help to polish the intellectual asset of a country and hence facilitate human capital mobility.
7.2. DRIVERS OF HUMAN CAPITAL MOBILITY: AN OVERVIEW
In this section we present an overview of the indices constructed from drivers of human capital mobility, where each index is constructed by the loading of different drivers of human capital mobility in relative form. The negative or positive trend or shape of the curve depicts the strength of economic, financial, demographic and fiscal policy variables in foreign countries relative to Pakistan. The magnitude of relative
indices is measured along the vertical axis in their respective diagram and they are unit free (their normalized values lie between zero and one hundred). On the horizontal axis we consider time period from the year 1991 through 2016 for balanced panel duration.
The composition of the index of relative economic incentive (ECON ) is it depicted from the equation (7.1) where negative loading (-0.4517) is for the relative
domestic absorption as a percentage of GDP (ABSit ABSPt ). The consumption or expenditure oriented behavior of people is responsible for the present shape of
ECON in the Figure 7.1(a). it
The index of financial stability (FINSit ) takes the shape from the equation (7.2) and its shape in Figure 7.1(b) is depending upon relative optimistic condition of total reserves as a percentage of GDP in destination countries relative to Pakistan
(RESit RESPt ) with weightage of 0.9498 and relative foreign direct investment as a
percentage of GDP (FDIit FDI Pt ) with weightage of 0.6149.
The index of relative financial independence (FININit ) is presented by the equation (7.3) and in the Figure 7.1 (c) where the healthy effect of positive weightage
(0.8405) of domestic credit to private sector by banks as GDP dominates the negative effect of loading (-0.4739) for relative taxes on income, profit and capital gains as a
Figure 7.1: Indices Constructed from Drivers of Human Capital Mobility
7.1(a). Index of Relative Economic Incentives 7.1(b). Index of Relative Financial Stability
7.1 (c). Index of Relative Financial 7.1(d). Index of Relative Standard of Living Independence based on Health related Facilities
7.1(e). Index of Relative Standard of Living 7.1(f). Index for the Role of other than Health Facilities Compatriot Community
7.1(g). Index of Relative Demographic 7.1(h). Index of Relative Labor Market Characteristics Structure
7.1(i). Index for Relative Research 7.1(j). Index for Relative Provision and Development Facilities of Social Safety Nets
7.1(k). Index of Relative Social Openness
percentage of GDP. A strong positive net effect gives the positive slope to the index
of relative financial independence FININit in destination countries relative to
Pakistan.
The Figure 7.1(d) shows that though overall health related facilities grow throughout the period of analysis yet due to increase in population these facilities namely, relative number of physicians per hundred thousand people, relative health expenditure as a percentage of GDP, relative percentage of population with access to improved sanitation and water sources are inadequate per hundred thousand of
population as is indicated by the shape of LIVIN1it curve in Figure 7.1(d).
The index of relative standards of living (LIVIN2it ) other than health facilities is positively loaded by relative energy use (0.5021), relative annual growth rate of household final consumption expenditure (0.2331) and relative increase in urban population as a percentage of total population (0.3782) which indicate urbanization and industrialization in the destination countries by improving standards of living of people in destination countries relative to Pakistan as is depicted in Figure 7.1(e).
The index for the role of compatriot community (COMUit ) is constructed as a weighted average of relative international migrant stock as a percentage of total population, relative personal remittances received as a percentage of GDP and relative
trade in services as a percentage of GDP. Overall COMUit curve is showing positive trend but after the event of 9/11 due to decline in international migrant stock in
developed destination countries a sharp decline in the COMUit curve is observable in
Figure 7.1(f).
A negative slope of the index of relative demographic characteristics (DEMO ) is it due to negative loading of relative annual percentage growth rate of population, which is indication of qualitative deterioration of relative demographic characteristics due to increase in population. That’s why developed destination countries are now deporting unproductive Pakistani labor force. The positive slope of the index of relative labor
market structure (LMKTit ) in Figure 7.1(h) is based on the high labor force participation rate and low unemployment rate in destination countries relative to
Pakistan.
In Figure 7.1(i) we have the index for research and development facilities
(RADI ) for human capital grooming that is weighted average of relative total it government expenditure on education as a percentage of GDP and Research and
Development expenditure as a percentage of GDP. The shapes of the index for research and development facilities (RADI ) in the Figure 7.1(i) and the index for it provision of social safety nets (SNET ) in the Figure 7.1(j) are affected by the it behavior of government-based expenditure policies in the destination countries relative to Pakistan.
The positive slope of the index of relative social openness (OPENit ) in the Figure
7.1(k) is due to positive loading for air transport registered (0.3158), international tourism (0.4444) and for internet and mobile cellular subscriptions (0.2722 and
0.1596, respectively) per hundred thousand persons in destination countries relative to
Pakistan. These out comes are the result of globalization of the world economies and are depicted by the index of relative social openness constructed in the equation (7.7).
Chapter 8
EMPIRICAL ANALYSIS FOR DRIVERS OF HUMAN CAPITAL MOBILITY FROM PAKISTAN
In present chapter region-wise and full panel empirical findings of panel unit root tests, panel Granger causality test and panel co-integration regression results more specifically regression results of dynamic ordinary least squares (DOLS) regression are presented.
8.1. TESTS FOR STATIONARITY: PANEL UNIT ROOT TESTS
We use two unit root tests namely Levin, Lin and Chu (LLC) test and Im, Pesaran and Shin (IPS) test to distinguish between trend and difference stationary data series.
The LLC test assumes that there is a common unit root process so ρ is identical i across cross-sections whereas IPS test allows individual unit root processes such that
ρi vary across cross-sections. The results of these tests for full panel as well as region-wise are reported in the Tables 8.1 through 8.5. As both tests are sensitive to the trend and to the selected lag lengths therefore we apply panel unit root tests in presence and absence of trend as well as at lag lengths of zero and one. We use
Bartlett Kernal options for spectral estimation and the Newey-West automatic bandwidth selection method. In case of LLC test the stationarity decision is based on the probability of t-statistics and in case of IPS test the stationarity decision is based on the probability of Kendall’s W-statistics.
The Table 8.1 presents the results of panel unit root tests for full panel. The index
of relative economic incentives (ECONit ), the index of relative financial stability
(FINS ) and the index of relative standards of living other than health facilities it
(LIVIN2 ) are stationary at considered lags (zero and one) with and without trend it and at level and consequently are stationary at first difference. These indices
(ECON , FINS and LIVIN 2 ) show consistent and same results for both LLC and it it it
IPS tests. The indices of relative financial independence (FININit ), relative role of compatriot community (COMU ) and relative social openness (OPEN ) are non- it it stationary for both LLC and IPS tests at level but these data series become stationary at first difference with and without trend at both lag lengths in both tests.
All other data series namely, the index of relative standards of living based on
health facilities (LIVIN1it ), the index of relative labor market structure (LMKTit ), the index of relative provision of social safety nets (SNET ) and the index for relative it research and development facilities (RADI ) show mixed results and LLC and IPS it tests do not reach the same conclusion for these data series. For example, in case of
IPS test, the index for relative demographic characteristics (DEMOit ) is non- stationary at level with different lag lengths as well as with and without trend, but is stationary at first difference of the same combinations. In case of LLC test beside
above stationary cases DEMOit is also stationary at level without trend situation.
In the Table 8.2 we list the results of panel unit root tests for Middle East and
African countries including Bahrain, Kuwait, Libya, Oman, Qatar, Saudi Arabia,
South Africa and the United Arab Emirates. Only ECON it is stationary at considered lags (zero and one) with and without trend and at level as well as at first difference and hence shows consistent results for both LLC and IPS tests.
Table 8.1: Results of Panel Unit Root Tests (Full Panel)
H0: Non-Stationary Series (i.e., Panel Data Series has Common Unit Root Process) Levin, Lin and Chu (2002) Test by using Prob(t-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ECONit 0.0000 0.0000 0.0000 0.0000 0.0580 0.0000 0.1265 0.0000 FINSit 0.3113 0.0000 0.5243 0.0000 0.3770 0.0000 0.7591 0.0000 FININit
LIVIN 1it 0.0131 0.0000 0.9993 0.0000 0.0009 0.0000 0.9492 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 1.0000 0.0000 0.9844 0.0000 0.2451 0.0000 0.0000 0.0000 DEMOit 0.9996 0.0000 0.0000 0.0000 0.8697 0.0021 0.7678 0.0407 LMKTit 1.0000 0.0000 0.8463 0.0000 1.0000 0.0000 0.9921 0.0000 COMUit 0.0000 0.0000 0.0002 0.0000 0.4331 0.0000 0.9689 0.0416 SNETit 0.8228 0.0000 1.0000 0.0000 0.0156 0.0095 0.7418 0.0467 RADIit 0.9764 0.0000 1.0000 0.0000 0.0197 0.0022 0.7278 0.0458 OPENit
H0: Non-Stationary Series (i.e., Panel Data Series has Individual Unit Root Process) Im, Pesaran and Shin (2003) Test by using Prob(W-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ECONit 0.0000 0.0000 0.0000 0.0000 0.0068 0.0000 0.0055 0.0000 FINSit 0.9786 0.0000 0.6084 0.0000 0.9935 0.0000 0.6462 0.0000 FININit
LIVIN 1it 0.9987 0.0000 0.9996 0.0000 0.9623 0.0000 0.9720 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 1.0000 0.0000 1.0000 0.0000 0.9962 0.0000 0.4670 0.0000 DEMOit 1.0000 0.0000 0.0973 0.0000 1.0000 0.0000 0.9995 0.0000 LMKTit 1.0000 0.0000 0.9355 0.0000 1.0000 0.0000 0.9999 0.0000 COMUit 0.0000 0.0000 0.0000 0.0000 0.0315 0.0000 0.5812 0.0000 SNETit 0.3053 0.0000 0.9682 0.0000 0.0001 0.0000 0.0028 0.0003 RADIit 1.0000 0.0000 1.0000 0.0000 0.9997 0.0000 0.9944 0.0034 OPENit Note: values are the probabilities computed assuming asymptotic normality.
Table 8.2: Results of Panel Unit Root Tests (Middle East and Africa)
H0: Non-Stationary Series (i.e., Panel Data Series has Common Unit Root Process) Levin, Lin and Chu (2002) Test by using Prob(t-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ECONit 0.0078 0.0000 0.0000 0.0000 0.2725 0.0000 0.0451 0.0118 FINSit 0.9408 0.0000 0.8219 0.0000 0.9743 0.0000 0.9773 0.0014 FININit 0.1010 0.0000 0.9988 0.0000 0.0208 0.0117 0.9458 0.0306 LIVIN1it
LIVIN 2 it 0.0141 0.0000 0.0000 0.0000 0.4792 0.0000 0.1913 0.0000 0.0119 0.0103 0.9998 0.0366 0.0000 0.0001 0.0000 0.0029 DEMOit 0.0000 0.0000 0.0022 0.0000 0.4615 0.0194 0.3679 0.0497 LMKTit 1.0000 0.0000 0.9640 0.0000 1.0000 0.0000 0.9177 0.0000 COMU it 0.0452 0.0000 0.0038 0.0000 0.1469 0.0002 0.0925 0.0205 SNETit 0.9526 0.0000 0.9325 0.0000 0.7340 0.0011 0.7786 0.0353 RADIit 0.9994 0.0000 0.5737 0.0000 0.6312 0.0055 0.1895 0.0166 OPENit
H0: Non-Stationary Series (i.e., Panel Data Series has Individual Unit Root Process) Im, Pesaran and Shin (2003) Test by using Prob(W-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 ECONit 0.0003 0.0000 0.0000 0.0000 0.0404 0.0000 0.0021 0.0000 FINSit 0.9705 0.0000 0.6913 0.0000 0.9920 0.0000 0.9102 0.0000 FININit 0.9640 0.0000 0.9996 0.0000 0.8208 0.0001 0.9901 0.0043 LIVIN1it 0.0001 0.0000 0.0000 0.0000 0.0151 0.0000 0.0090 0.0000 LIVIN2it 0.6646 0.0045 1.0000 0.0397 0.0006 0.0000 0.0000 0.0000 DEMOit 1.0031 0.0000 0.0635 0.0000 0.6757 0.0001 0.6367 0.0185 LMKTit 1.0000 0.0000 0.9520 0.0000 1.0000 0.0000 0.9801 0.0000 COMU it 0.0312 0.0000 0.0059 0.0000 0.1401 0.0000 0.1162 0.0000 SNETit 0.5680 0.0000 0.7008 0.0000 0.1153 0.0000 0.1294 0.0001 RADIit 1.0000 0.0000 0.8570 0.0000 0.9999 0.0000 0.4229 0.0399 OPENit Note: values are the probabilities computed assuming asymptotic normality.
The indices FININ , COMU , RADI and OPEN are non-stationary in both it it it it
LLC and IPS tests at level but these data series are stationary at first difference with and without trend at both lag lengths in both tests (LLC and IPS). All other data series namely, FINS , LIVIN1 , LIVIN 2 , DEMO , LMKT and SNET show mixed it it it it it it results thus LLC and IPS tests do not reach the same conclusion for these data series.
In the Table 8.3 we present the results of panel unit root tests for East Asia and
Pacific region consisting of Australia, China, Indonesia, Japan, Malaysia, Singapore and Thailand. The data series namely ECON , FINS and LIVIN 2 are stationary it it it at considered lags (zero and one) with and without trend and at level and hence shows consistent results for both LLC and IPS tests. The indices of FININ , LIVIN1 , it it
COMU and OPEN are non-stationary in both LLC and IPS tests at level but these it it data series are stationary at first difference with and without trend at both lag lengths for both tests. All other data series like DEMO , LMKT , SNET and RADI show it it it it mixed results. For example, LMKT is non-stationary at level without trend at zero it lag length but is stationary with all other test options and show consistent results in both LLC and IPS tests.
In the Table 8.4 we present the results of panel unit root tests for European and
Central Asian countries consisting of Cyprus, France, Germany, Greece, Italy, Russia,
Span, Switzerland, Turkey and the United Kingdom. Here ECON and LIVIN 2 are it it stationary at considered lags (zero and one) with and without trend and at level and hence shows consistent results for both LLC and IPS tests.
Table 8.3: Results of Panel Unit Root Tests (East Asia and Pacific)
H0: Non-Stationary Series (i.e., Panel Data Series has Common Unit Root Process) Levin, Lin and Chu (2002) Test by using Prob(t-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0026 0.0000 0.0531 0.0000 ECONit 0.0046 0.0000 0.0001 0.0000 0.0469 0.0000 0.0488 0.0000 FINSit 0.4461 0.0000 0.3577 0.0000 0.6700 0.0000 0.7289 0.0002 FININit 0.0042 0.0000 0.9013 0.0000 0.0011 0.0005 0.7061 0.0197 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 0.0000 0.0000 0.4245 0.0000 0.0255 0.0419 0.4516 0.0274 DEMOit 0.9979 0.0000 0.0002 0.0000 0.6037 0.0078 0.8231 0.0404 LMKTit 0.9901 0.0000 0.3819 0.0000 0.9946 0.0000 0.9227 0.0003 COMU it 0.0143 0.0000 0.0041 0.0000 0.6419 0.0025 0.7475 0.0348 SNETit 0.6527 0.0000 0.9857 0.0016 0.0551 0.0150 0.5556 0.0496 RADIit 0.9968 0.0000 0.9705 0.0000 0.6913 0.0301 0.6640 0.0193 OPENit
H0: Non-Stationary Series (i.e., Panel Data Series has Individual Unit Root Process) Im, Pesaran and Shin (2003) Test by using Prob(W-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0004 0.0000 0.0032 0.0000 0.0134 0.0000 ECONit 0.0146 0.0000 0.0002 0.0000 0.0118 0.0000 0.0214 0.0000 FINSit 0.7907 0.0000 0.2746 0.0000 0.9072 0.0000 0.4802 0.0000 FININit 0.6565 0.0000 0.8826 0.0000 0.3282 0.0000 0.6747 0.0000 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 0.0410 0.0000 0.9964 0.0000 0.4785 0.0002 0.9939 0.0000 DEMOit 1.0000 0.0000 0.0030 0.0000 0.9941 0.0000 0.9432 0.0001 LMKTit 1.0000 0.0000 0.4540 0.0000 1.0000 0.0000 0.8817 0.0000 COMUit 0.0004 0.0000 0.0013 0.0000 0.1430 0.0000 0.3884 0.0000 SNETit 0.4869 0.0000 0.8095 0.0001 0.0319 0.0018 0.0816 0.0207 RADIit 1.0000 0.0000 0.9994 0.0000 0.9983 0.0016 0.9596 0.0204 OPENit Note: values are the probabilities computed assuming asymptotic normality.
Table 8.4: Results of Panel Unit Root Tests (Europe and Central Asia)
H0: Non-Stationary Series (i.e., Panel Data Series has Common Unit Root Process) Levin, Lin and Chu (2002) Test by using Prob(t-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0060 0.0000 0.0042 0.0000 ECONit 0.0231 0.0000 0.0058 0.0000 0.4892 0.0000 0.9061 0.0000 FINSit 0.3257 0.0000 0.4225 0.0000 0.1564 0.0000 0.2357 0.0000 FININit 0.2986 0.0000 0.7694 0.0000 0.2330 0.0000 0.7449 0.0000 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 1.0000 0.0000 0.3801 0.0000 0.9999 0.0145 0.0141 0.0018 DEMOit 1.0000 0.0000 0.2503 0.0000 0.8479 0.0284 0.8368 0.0272 LMKTit 1.0000 0.0000 0.3037 0.0000 1.0000 0.0000 0.9902 0.0000 COMUit 0.0014 0.0000 0.0655 0.0000 0.5319 0.0011 0.9980 0.0398 SNETit 0.3565 0.0000 1.0000 0.0061 0.0750 0.0378 0.6040 0.0305 RADIit 0.0800 0.0126 1.0000 0.0202 0.0100 0.0302 0.8511 0.0343 OPENit
H0: Non-Stationary Series (i.e., Panel Data Series has Individual Unit Root Process) Im, Pesaran and Shin (2003) Test by using Prob(W-Statistics) Lag Length zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0000 0.0000 0.0000 0.0000 0.0021 0.0000 0.0113 0.0000 ECONit 0.0029 0.0000 0.0021 0.0000 0.2040 0.0000 0.6407 0.0000 FINSit 0.9559 0.0000 0.8221 0.0000 0.9132 0.0000 0.4783 0.0000 FININit 0.9886 0.0000 0.9538 0.0000 0.9740 0.0000 0.9075 0.0000 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 LIVIN2it 1.0000 0.0001 1.0000 0.0000 1.0000 0.0541 0.9822 0.0000 DEMOit 1.0000 0.0000 0.8917 0.0000 0.9998 0.0000 0.9995 0.0000 LMKTit 1.0000 0.0000 0.8852 0.0000 1.0000 0.0000 0.9996 0.0000 COMU it 0.0001 0.0000 0.0015 0.0000 0.1333 0.0000 0.7242 0.0000 SNETit 0.1853 0.0000 0.9614 0.0000 0.0005 0.0001 0.0112 0.0567 RADIit 0.9370 0.0014 1.0000 0.0497 0.5758 0.0088 0.9870 0.0300 OPENit Note: values are the probabilities computed assuming asymptotic normality.
Table 8.5: Results of Panel Unit Root Tests (North America)
H0: Non-Stationary Series (i.e., Panel Data Series has Common Unit Root Process) Levin, Lin and Chu (2002) Test by using Prob(t-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0036 0.0000 0.0050 0.0000 0.0026 0.0000 0.0046 0.0000 ECONit 0.3994 0.0000 0.6686 0.0000 0.2727 0.0002 0.5471 0.0020 FINSit 0.2470 0.0000 0.2855 0.0000 0.2375 0.0000 0.2260 0.0000 FININit 0.2727 0.0000 0.6877 0.0000 0.1475 0.0000 0.4044 0.0002 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0009 0.0000 LIVIN 2it
1.0000 0.0035 0.9303 0.0001 0.9998 0.0161 0.5556 0.0261 DEMOit 0.9955 0.0000 0.2085 0.0000 0.9145 0.0419 0.5137 0.0104 LMKTit 0.9984 0.0000 0.2221 0.0000 0.9979 0.0004 0.6872 0.0003 COMU it 0.0850 0.0000 0.3993 0.0000 0.5402 0.0238 0.9855 0.0457 SNETit 0.6656 0.0023 0.9106 0.0367 0.2736 0.0494 0.7053 0.0329 RADIit 0.0864 0.0000 0.9241 0.0000 0.0445 0.0118 0.8329 0.0027 OPENit
H0: Non-Stationary Series (i.e., Panel Data Series has Individual Unit Root Process) Im, Pesaran and Shin (2003) Test by using Prob(W-Statistics) Lag Length Zero Lag Length One Without Trend With Trend Without Trend With Trend INDICES Level First Level First Level First Level First Difference Difference Difference Difference 0.0560 0.0000 0.2159 0.0000 0.0476 0.0000 0.2108 0.0000 ECONit 0.2356 0.0000 0.5528 0.0001 0.1518 0.0020 0.3698 0.0232 FINSit 0.6395 0.0000 0.4752 0.0000 0.6311 0.0000 0.3493 0.0004 FININit 0.8564 0.0000 0.5835 0.0003 0.7330 0.0008 0.2328 0.0104 LIVIN1it 0.0000 0.0000 0.0000 0.0000 0.0047 0.0000 0.0402 0.0000 LIVIN2it 1.0000 0.0120 1.0000 0.0002 1.0000 0.0326 0.9965 0.0390 DEMOit 0.9999 0.0001 0.7529 0.0022 0.9934 0.0274 0.9226 0.0183 LMKTit 0.9996 0.0000 0.4528 0.0000 0.9987 0.0052 0.8059 0.0052 COMU it 0.0461 0.0000 0.2941 0.0000 0.3405 0.0027 0.9336 0.0152 SNETit 0.3247 0.0000 0.5301 0.0015 0.0978 0.0498 0.2267 0.0274 RADIit 0.5259 0.0000 0.9887 0.0000 0.4172 0.0081 0.9723 0.0114 OPENit Note: values are the probabilities computed assuming asymptotic normality.
In the Table 8.4 the indices of FININ , LIVIN1 , LMKT and COMU are non- it it it it stationary in both LLC and IPS tests at level but these series are stationary at first
difference with and without trend at both lag lengths for both tests. All other data series FINS , DEMO , SNET , RADI and OPEN show mixed results. it it it it it
In the Table 8.5 we present the results of panel unit root tests for the North
American countries, more specifically the United States of America and Canada. In
this Table only LIVIN2it is stationary at considered lags (zero and one) with and without trend and at level and hence shows consistent results for both LLC and IPS tests. Here the indices FINS , FININ , LIVIN1 , DEMO , LMKT and COMU it it it it it it are non-stationary in LLC test as well as in IPS test at level but these data series become stationary at first difference with and without trend at both lag lengths in both the tests. Remaining data series ECON , SNET , RADI and OPEN show mixed it it it it results. For example, ECON is stationary at considered lags (zero and one) with and it without trend and at level and hence shows same results for LLC test but in IPS test it is non-stationary at level with trend at both lag lengths.
To sum up the results of unit root tests for full panel and for four regions, no contradiction exist between LLC test and IPS test as all series are unambiguously stationary at first difference with and without trend at both lag lengths (zero and one).
The results of unit root stationarity test definitely pave way to our non-stationary panel co-integration regression analysis.
8.2. PAIRWISE PANEL GRANGER CAUSALITY TEST
Before estimation of panel co-integration regressions, as preliminary analysis we
perform a pair-wise bivariate panel Granger causality test with lag length two of human capital flows (NETHCF ) as well as for human capital flight i.e., brain drain it
(NETBD ) with respect to each of their drivers (taken one at a time). To have an it overview of causality structure we use full panel as well as region-wise data where all decisions belong to single host country, Pakistan. The results of this test are presented in Tables 8.6 and 8.7 where decision about the Granger causality test is based on the probability of F-statistics.
The Table 8.6 provides pair-wise bivariate panel Granger causality test for net human capital flows (NETHCF ) with respect to each of its drivers. In almost all the it cases the drivers of human capital mobility Granger cause the NETHCF from it
Pakistan but reverse may exist in few cases. For example, in case of East Asia and
Pacific region, the index of relative financial stability (FINSit ), the index of relative
standards of living other than health facilities (LIVIN2it ), the index of relative
demographic characteristics (DEMOit ) and the index of relative social openness
(OPEN ) are bilaterally affected by NETHCF form Pakistan viz-a-viz East Asia it it and Pacific region.
In case of North America in Table 8.6 below the NETHCFit from Pakistan
Granger causes and hence affect the relative financial independence (FININit ), the
relative labor market structure (LMKTit ), the relative role of compatriot community
(COMU ) and the relative social openness (OPEN ) in the destination countries it it namely, Canada and the United States of America viz-a-viz Pakistan. Most
Table 8.6: Pairwise Granger Causality Test (for human capital flows) with lag length two Middle East East Asia and Europe and North Full Panel Null Hypothesis (H0) and Africa Pacific Central Asia America Conclusion Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) ECON does not Granger cause NETHCF 0.0008 0.0012 0.0093 0.0016 0.0002 Reject H0 it it does not Granger cause 0.5419 0.8475 0.9116 0.8489 0.2925 Do not Reject H0 NETHCFit ECONit FINS does not Granger cause NETHCF 0.0015 0.0015 0.0035 0.0013 0.0089 Reject H0 it it 0.7289 0.0067 0.5344 0.7676 0.1627 Do not Reject H0 NETHCFit does not Granger cause FINS it except for East Asia and Pacific FININ does not Granger cause NETHCF 0.0000 0.0006 0.0057 0.0010 0.0025 Reject H0 it it 0.1834 0.0989 0.3570 0.0014 0.8897 Do not Reject H0 NETHCFit does not Granger cause FININ it except for North America LIVIN1 does not Granger cause NETHCF 0.0044 0.0017 0.0069 0.0005 0.0039 Reject H0 it it 0.7639 0.0857 0.0086 0.4260 0.1203 Do not Reject H0 NETHCFit does not Granger cause LIVIN1it except for Europe and Central Asia LIVIN 2 does not Granger cause NETHCF 0.0000 0.0001 0.0089 0.0002 0.0000 Reject H0 it it 1.0000 0.0020 0.8848 0.3238 0.7587 Do not Reject H0 NETHCFit does not Granger cause LIVIN 2it except for East Asia and Pacific DEMO does not Granger cause NETHCF 0.0027 0.0000 0.0034 0.0000 0.0004 Reject H0 it it 1.0000 0.0002 0.8341 0.2731 0.0026 Do not Reject H0 NETHCFit does not Granger cause DEMO it except for East Asia and Pacific and for full panel
Table 8.6 (Continue): Pairwise Granger Causality Test (for human capital flows) with lag length two Middle East East Asia and Europe and North Full Panel Null Hypothesis (H ) and Africa Pacific Central Asia America Conclusion 0 Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) LMKT does not Granger cause NETHCF 0.0029 0.0014 0.0057 0.0016 0.0025 Reject H0 it it 0.9960 0.2062 0.5947 0.0013 0.0092 Do not Reject H0 NETHCFit does not Granger cause LMKT it except for North America and for full panel COMU does not Granger cause NETHCF 0.0000 0.0021 0.0023 0.0010 0.0000 Reject H0 it it 0.0004 0.0025 0.8136 0.0047 0.0013 Do not Reject H0 NETHCFit does not Granger cause COMU it except for Middle East and Africa, East Asia and Pacific, North America and for full panel SNET does not Granger cause NETHCF 0.0069 0.0005 0.0042 0.0019 0.0007 Reject H0 it it does not Granger cause 0.9895 0.8480 0.9902 0.4558 0.5330 Do not Reject H0 NETHCFit SNETit RADI does not Granger cause NETHCF 0.0077 0.0048 0.0058 0.0044 0.0037 Reject H0 it it 0.7917 0.8717 0.0083 0.1427 0.9710 Do not Reject H0 NETHCFit does not Granger cause RADIit except for Europe and Central Asia OPEN does not Granger cause NETHCF 0.0026 0.0000 0.0034 0.0018 0.0020 Reject H0 it it 0.8717 0.0005 0.9878 0.0023 0.0061 Do not Reject H0 NETHCFit does not Granger cause OPENit except for East Asia and Pacific, North America and for full panel
surprisingly, NETHCFit from Pakistan Granger causes the relative role of compatriot
community (COMUit ) in all regions and in full panel except for Europe and Central
Asia. A main reason behind this result is that in these countries namely, Cyprus,
France, Germany, Greece, Italy, Russia, Span, Switzerland, Turkey and the United
Kingdom, the assimilation theory of migration is weakly applicable to Pakistani diaspora due to hardships in proving their identity in these countries where they are treated as minority.
The region-wise analysis for North America show that the index for labor market
structure of the destination countries relative to Pakistan (LMKTit ) is affected by
NETHCF from Pakistan. These findings are in accordance to North American it positive visa policy to attract only best and brightest human capital from Pakistan to have positive and healthy effect on North America’s labor market.
The region-wise results for East Asia and Pacific in the Table 8.6 also show that the composition of the index of demographic characteristics (DEMO ) is affected by it the qualification and skills of NETHCF from Pakistan to these destination countries it namely, Australia, China, Indonesia, Japan, Malaysia, Singapore and Thailand. Where the index of relative demographic characteristics (DEMO ) is a weighted average of it relative annual percentage growth rate of population (GPOPit GPOPPt ), relative
population density (DENSit DENSPt ), relative urban population as a percentage of
total population (URBANit URBAN Pt ), relative population ages between 15 years to
64 years as a percentage of total population (YOUNGit YOUNGPt ) and labor force with tertiary education as a percentage of total labor force in the destination countries
relative to Pakistan (EDU EDU ). Thus results show that the NETHCF from it Pt it
Pakistan to East Asia and Pacific and to full panel positively affects GPOP , DENS , it it
URBAN , YOUNG and EDU through the channel of the index of demographic it it it characteristics (DEMO ) of these destination countries. it
The Table 8.7 provides pairwise bivariate panel Granger causality tests for human capital flight, more specifically brain drain (NETBD ) with respect to each of its it drivers. In almost all the cases, the drivers of human capital mobility Granger cause human capital flight but reverse may exist in only few cases. For example, in case of
North America the index of relative financial independence (FININit ), the index of
relative labor market structure (LMKTit ) and the index for relative research and development facilities (RADI ) are affected by human capital flight i.e., brain drain it
(NETBD ) from Pakistan to these destination countries. it
The destination countries of Middle East and Africa namely, Bahrain, Kuwait,
Libya, Oman, Qatar, Saudi Arabia, South Africa and the United Arab Emirates, the
relative role of compatriot community (COMU it ) as well as the relative standards of living based on health related facilities (LIVIN1 ) are affected by NETBD from it it
Pakistan. In short, the Granger causality from LIVIN1it to NETBDit from Pakistan shows that the index for the standards of living based on health related facilities
(LIVIN1 ) ensures the emigration of healthy people, enjoying good health based on it these facilities. The reverse of this channel is the Granger causality from NETBDit
from Pakistan to the index of LIVIN 1it that is responsible for increase in health
Table 8.7: Pairwise Granger Causality Test (for human capital flight i.e., brain drain) with lag length two Middle East East Asia and Europe and North Full Panel Null Hypothesis (H0) and Africa Pacific Central Asia America Conclusion Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) ECON does not Granger cause NETBD 0.0012 0.0006 0.0018 0.0015 0.0079 Reject H0 it it NETBD does not Granger cause ECON 0.9993 0.8551 0.8020 0.7871 0.9935 Do not Reject H0 it it FINS does not Granger cause NETBD 0.0016 0.0000 0.0086 0.0006 0.0000 Reject H0 it it NETBD does not Granger cause FINS 0.9573 0.0829 0.6096 0.9619 0.1829 Do not Reject H0 it it FININ does not Granger cause NETBD 0.0006 0.0011 0.0093 0.0007 0.0090 Reject H0 it it 0.2608 0.0008 0.6732 0.0021 0.2488 Do not Reject H0 NETBDit does not Granger cause FININ it except for East Asia and Pacific and North America LIVIN1 does not Granger cause NETBD 0.0007 0.0005 0.0027 0.0022 0.0006 Reject H0 it it 0.0001 0.0040 0.1280 0.3974 0.0005 Do not Reject H0 NETBDit does not Granger cause LIVIN1it except for Middle East and Africa, East Asia and Pacific and for full panel LIVIN 2 does not Granger cause NETBD 0.0001 0.0003 0.0092 0.0000 0.0030 Reject H0 it it 0.9761 0.3039 0.9786 0.5007 0.9947 Do not Reject H0 NETBDit does not Granger cause LIVIN 2it DEMO does not Granger cause NETBD 0.0061 0.0000 0.0051 0.0009 0.0084 Reject H0 it it 0.9995 0.0002 0.9896 0.7395 0.0000 Do not Reject H0 NETBDit does not Granger cause DEMO it except for East Asia and Pacific and for full panel
Table 8.7 (Continue): Pairwise Granger Causality Test (for human capital flight i.e., brain drain) with lag length two Middle East East Asia and Europe and North Full Panel Null Hypothesis (H ) and Africa Pacific Central Asia America Conclusion 0 Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) Prob. (F-Stats) LMKT does not Granger cause NETBD 0.0026 0.0071 0.0038 0.0008 0.0015 Reject H0 it it 0.8076 0.0025 0.8509 0.0006 0.0017 Do not Reject H0 NETBDit does not Granger cause LMKT it except for East Asia and Pacific, North America and for full panel COMU does not Granger cause NETBD 0.0054 0.0034 0.0052 0.0012 0.0045 Reject H0 it it 0.0004 0.0961 1.0000 0.6270 1.0000 Do not Reject H0 NETBDit does not Granger cause COMU it except for Middle East and Africa SNET does not Granger cause NETBD 0.0000 0.0000 0.0097 0.0068 0.0000 Reject H0 it it does not Granger cause 0.5616 0.8820 0.9999 0.5054 0.8184 Do not Reject H0 NETBDit SNETit RADI does not Granger cause NETBD 0.0009 0.0041 0.0046 0.0042 0.0022 Reject H0 it it 0.7295 0.9620 0.0006 0.0003 0.8860 Do not Reject H0 NETBDit does not Granger cause RADIit except for Europe and Central Asia and for North America OPEN does not Granger cause NETBD 0.0009 0.0000 0.0054 0.0024 0.0009 Reject H0 it it 0.1329 0.0007 0.9870 0.7733 0.0008 Do not Reject H0 NETBDit does not Granger cause OPENit except for East Asia and Pacific and for full Panel
related facilities in the destination countries due to the extensive emigration of experienced and qualified doctors and health workers to Middle East and Africa.
The pairwise Granger causality tests show that the East Asia and Pacific countries are the most affected regions by NETBD from Pakistan. As NETBD from Pakistan it it to these regions Granger causes the index of relative financial independence
(FININit ), the index of relative standards of living based on health related facilities
(LIVIN1 ), the index of relative demographic characteristics (DEMO ), the index of it it relative labor market structure (LMKT ) and the index of relative social openness it
(OPEN ) in these regions. it
The above explanation of results on the basis of pair-wise Granger causality tests are necessary to be highlighted but the main conclusion of these tests is that human capital mobility, more specifically human capital flows (NETHCF ) as well as it human capital flight in form of net brain drain (NEBDit ) are unambiguously driven
by indices namely, the relative economic incentives (ECONit ), the relative financial stability (FINS ), the relative financial independence (FININ ), the relative it it standards of living based on health related facilities (LIVIN1it ), the relative standards
of living other than health related facilities (LIVIN2it ), the relative demographic
characteristics (DEMOit ), the relative labor market structure (LMKTit ), the role of
compatriot community (COMUit ), the relative provision of social safety nets
(SNETit ), the relative research and development facilities (RADIit ) and the relative
social openness (OPENit ) in all selected regions and for the full panel.
8.3. COINTEGRATION REGRESSION RESULTS
The results of section 8.1 show that though some of the variables are non- stationary at level yet all the variables become stationary at first difference, which is an indication of their possible co-integration or long-run association. With long-run association between the variables it is suitable to apply panel co-integration regressions. We use pooled co-integrating dynamic ordinary least squares (DOLS) estimation technique to construct asymptotically efficient estimates. To handle the fixed effect we use a constant as the cross-section specific trend regressor. To augment the co-integrating regression equations (5.15) and (5.16) with lags and leads of regressors, we use automatic Akaike information criterion (AIC) for selection of maximum lags and leads length to remove long-run dependence. We employ sandwich-style HAC (heteroscadasticity and autocorrelation consistent), Bartlett
Kernel with Newey-West automatic band-width method for long-run variance and robust coefficient covariance estimates. The top portion of the Tables 8.8 and 8.9 summarizes the above mentioned settings used in estimation, show the deterministic trend assumptions, the lags and leads specification and the method for computing the long-run variances used in forming the coefficients covariances.
The estimation results of panel dynamic ordinary least squares (DOLS) for bipolar specification of augmented gravity model of equations (5.15) and (5.16) are presented in the Tables 8.8 and 8.9 below. The estimated long-run coefficients for drivers of human capital mobility along with their t-statistics are presented in these tables for full panel and for region-wise comparison. The majority of the parameters have predicted signs compatible with the theories of migration. We have not displayed the results for the short-run coefficients for lags and leads of the differenced co-
integrating dynamic regressors. Though these short-term dynamic parameters are used to construct the goodness of fit statistics and to compute the residuals. The high values of R2 and adjusted R2 show that the fitted models are good. The Wald test is used as coefficient diagnostic test where the values and the probabilities of F-statistics and Chi-square statistics are used to check the null hypothesis for joint significance of the co-integrating regression coefficients.
It is worthwhile to interpret the meaning of various parameter estimates of DOLS model in detail. As equations (5.15) and (5.16) are in double-log-form, so the coefficients of the explanatory variables (indices) are representative of the long-run elasticities of net human capital flows (NETHCF ) as well as human capital flight it
(NEBD ) with respect to the relevant variables. The indices are constructed by it considering the percentage difference in the corresponding variable in destination countries relative to Pakistan or for unit free variables by considering the simple difference in destination countries relative to Pakistan. Also each index is a combination of more than single variable. Therefore interpretation of the sign of each coefficient is a little bit technical.
In case of full panel the coefficient of lnECONit is 0.2137 that is a long-run
elasticity of NETHCFit with respect to the index of ECONit . The index of relative economic incentive (ECON ) is weighted average of relative per capita gross it national income (PCIit PCI Pt ), relative percentage annual growth rate of per capita
GDP (GPCI it GPCI Pt ), relative gross capital formation as a percentage of GDP
(GCF GCF ) and relative domestic absorption as a percentage of GDP it Pt
(ABSit ABSPt ) with weights 0.2835, 0.7934, 0.2511 and -0.4517 respectively.
These weights obtained in chapter 7 are combined with the coefficients of the index of
ECON , which is significantly positive in almost all the cases of human capital it mobility from Pakistan. In case of full panel, one percent increase in the per capita gross national income in the destination countries relative to Pakistan increase net human capital flow by 6.06 percent (0.2835*0.2137≅0.0606).
The region-wise comparative analysis shows that the long-run elasticity of
NETHCFit with respect to the index of ECONit is highest for North American region
(i.e., 0.5619) which shows that one percent change in (PCI it PCI Pt ) affect
NETHCFit by 15.93 percent (0.2835*0.5619≅0.1593) followed by East Asia and
Pacific 9.89 percent (0.2835*0.3490≅0.0989), Middle East and Africa 7.92 percent
(0.2835*0.2795≅0.0792) and then for Europe and Central Asia 4.01 percent
(0.2835*0.1414≅0.0401). The results of Table 8.9 show that the long-run elasticity of
NETBDit with respect to the index of ECONit is highest for North American region
(i.e., 0.4472) which shows that one percent change in (PCI it PCI Pt ) affect the
NETBDit by 12.68 percent (0.2835*0.4472≅0.1268) followed by East Asia and
Pacific 10.72 percent (0.2835*0.3783≅0.1072), Middle East and Africa 9.10 percent
(0.2835*0.3211≅0.0910) and then for Europe and Central Asia 7.62 percent
(0.2835*0.2689≅0.0762).
Next is the index of relative financial stability (FINS ). The co-integration it regression results of DOLS show that the long-run elasticities of both NETHCF and it
NETBD with respect to the index of FINS are negative in all regions including full it it
panel. Since each index is constructed by considering the percentage difference in the corresponding variable in destination countries relative to Pakistan, therefore if the relative financial stability at center of destination has negative affect on net human capital mobility from Pakistan, then alternatively financial stability in Pakistan has positive affect on the mobility from Pakistan. It shows that against the theory of migration, the factors constructing the index of FINS at the centers of destination it are weaker than the factor at center of origin i.e., Pakistan. Thus instead of financial stability of destination countries we interpret the results on the basis of the financial stability of Pakistan via its components namely, real effective exchange rate index
(REER ), annual percentage change in S&P global equity index (GEQTY ), total Pt Pt reserves as a percentage of GDP (RES ), net inflows of foreign direct investment as Pt a percentage of GDP (FDI ) and a percentage of stock traded, turnover ratio Pt
(TURN Pt ).
In chapter 7, in the construction of the index of FINS highest weight goes to it relative total reserves as a percentage of GDP (0.9498) and then to relative foreign direct investment as a percentage of GDP (0.6149) that encourage Pakistanis to move abroad via two channels. First, financial stability in Pakistan create good image to the destination countries which in return welcome Pakistani diaspora. Secondly, net inflows of foreign direct investment to Pakistan beside indication of its financial stability via formation of international franchise in Pakistan motivate Pakistani to move abroad.
The region-wise comparative analysis shows that the elasticity of net human
capital flows (NETHCFit ) with respect to the index of relative financial stability
Table 8.8: Co-integration Regression Results (for Net Human Capital Flows)
Dependent Variable: ln NETHCFit (i.e., natural logarithm of net human capital flows from Pakistan) Method: Panel Dynamic Least Squares (DOLS) where Panel Method: Pooled Estimation Sample Adjusted: 1991-2016 where Total Panel Balanced Observations depend upon Region-wise or Full panel data Co-integrating Equation Deterministics: C Automatic Leads and Lags Specification (based on AIC Criterion, max=*) Coefficient Covariance computed using Sandwich (Heterogeneous Variances) Method Long-run Variances (Bartlett Kernal, Newey-West Automatic Bandwidth) used for Coefficient Covariance Independent Middle East and Africa East Asia and Pacific Europe and Central Asia North America Full Panel Variables Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics ln ECONit 0.2795 (2.4412**) 0.3490 (2.5406**) 0.1414 (2.4567**) 0.5619 (2.4693**) 0.2137 (2.6040**) ln FINSit -0.2437 (-2.3853**) -0.3132 (-2.0917**) -0.2735 (-3.0508*) -0.3528 (-2.4793**) -0.2442 (-3.2349*) ln FININit 0.0972 (3.4199*) 0.2537 (3.6284*) 0.1267 (3.0872*) 0.5731 (2.1247**) 0.5114 (2.4108**) ln LIVIN1it -0.1540 (-2.5601**) 0.1969 (1.2991) -0.3615 (-1.7533) -0.3814 (-2.2565**) -0.1272 (-3.7810*) ln LIVIN2it 0.4308 (2.1041**) 0.3941 (2.1349) -0.3808 -(2.9469*) 0.2857 (2.1697**) 0.1049 (2.2741**) ln DEMOit -0.6826 (4.6129*) -0.4358 (-3.2333*) 0.3491 (-6.6304*) -0.1513 (-3.5856*) -0.1788 (-2.9971*) ln LMKTit -0.6052 (3.1929*) -0.4809 (-3.7467*) 0.4534 (-3.2175*) -0.1206 (2.4109**) -0.3208 (-3.0029*) lnCOMU it -0.0491 (-2.6658**) 0.2661 (2.6623**) 0.2152 (2.6858**) 0.2751 (2.7419**) 0.2101 (2.0525**) ln SNETit 0.0988 (1.4393) 0.2425 (1.9932***) 0.1419 (2.0221**) 0.2874 (2.3919**) 0.1992 (2.4981**) ln RADIit 0.1643 (1.7364) 0.1624 (1.4889) 0.3012 (2.2354**) 0.3859 (2.9957*) 0.1359 (2.0289**) lnOPENit 0.1301 (2.6991**) 0.2922 (2.8144*) 0.2312 (3.4378*) 0.4764 (2.3876**) 0.2199 (4.7816*) R-Squared 0.8745 0.9039 0.9455 0.9628 0.8956 Adjusted R-Squared 0.8547 0.8983 0.9302 0.9533 0.8837 Long-run Variance 0.0228 0.0417 0.0216 0.0044 0.0584 Coefficient Diagnostic test: Wald Test H0:C(1)=C(2)=…...=C(11)=1 F-Statistics 338.106 (0.0000) 457.447 (0.0000) 449.281 (0.0000) 179.178 (0.0000) 519.762 (0.0000) Chi-Square Statistics 3719.168 (0.0000) 5031.924 (0.0000) 4942.086 (0.0000) 1970.961 (0.0000) 5717.389 (0.0000) In parenthesis are the t-values. The t-statistics significant at 1%, 5% and 10% are indicated by *, ** and *** respectively.
Table 8.9: Co-integration Regression Results (for Net Human Capital Flight i.e., Brain Drain)
Dependent Variable: ln NETBDit (i.e., natural logarithm of net human capital flight i.e., brain drain from Pakistan) Method: Panel Dynamic Least Squares (DOLS) where Panel Method: Pooled Estimation Sample Adjusted: 1991-2016 where Total Panel Balanced Observations depend upon Region-wise or Full panel data Co-integrating Equation Deterministics: C Automatic Leads and Lags Specification (based on AIC Criterion, max=*) Coefficient Covariance computed using Sandwich (Heterogeneous Variances) Method Long-run Variances (Bartlett Kernal, Newey-West Automatic Bandwidth) used for Coefficient Covariance Independent Middle East and Africa East Asia and Pacific Europe and Central Asia North America Full Panel Variables Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics Coefficients t-Statistics ln ECONit 0.3211 (2.6667**) 0.3783 (2.7802*) 0.2689 (3.4486*) 0.4472 (2.7611*) 0.3636 (3.5649*) ln FINSit -0.2226 (-3.7523*) -0.2571 (-3.8974*) -0.2909 (-3.2935*) -0.3512 (-0.9248) -0.1553 (-3.3157*) ln FININit 0.0349 (2.7387*) 0.2663 (2.3136**) 0.1751 (2.0513**) 0.3785 (2.2739**) 0.3443 (2.8844**) ln LIVIN1it -0.1398 (-3.2788*) 0.3041 (2.7217*) 0.2281 (3.1987*) 0.2721 (5.0539*) 0.2689 (3.0329*) ln LIVIN2it 0.4819 (1.9768***) 0.2195 (2.9942*) 0.2855 (1.536) 0.4993 (2.3619**) 0.3623 (2.0807**) (- ln DEMOit -0.5147 (-3.6723*) -0.6531 (4.9380*) -0.4516 (-2.1205**) -0.3698 (-2.5406*) -0.2926 2.2016**) ln LMKTit -0.5305 (-2.4339**) 0.2405 (3.1219**) -0.5665 (-2.2998**) -0.3316 (-3.7289*) -0.2373 (1.8352) lnCOMU it -0.1318 (-1.2874) 0.2023 (2.0264**) 0.2741 (2.3307**) 0.2932 (3.3893*) 0.2811 (3.3184*) ln SNETit 0.1676 (1.0719) 0.1779 (2.5565*) 0.1868 (1.0767) 0.2951 (2.2989**) 0.1971 (3.4501*) ln RADIit 0.0995 (0.5211) 0.2279 (2.2819**) 0.3743 (2.0706**) 0.4385 (4.1279*) 0.2526 (2.8062**) lnOPENit 0.2193 (2.6547**) 0.2611 (3.1441*) 0.3372 (2.3957*) 0.4193 (2.3705**) 0.2481 (4.8799*) R-Squared 0.9382 0.9513 0.9294 0.9641 0.9301 Adjusted R-Squared 0.8995 0.9339 0.8938 0.9554 0.9259 Long-run Variance 0.0315 0.0362 0.0497 0.0026 0.0668 Coefficient Diagnostic test: Wald Test H0:C(1)=C(2)=…...=C(11)=1 F-Statistics 290.785 (0.0000) 584.237 (0.0000) 215.168 (0.0000) 213.947 (0.0000) 775.399 (0.0000) Chi-Square Statistics 3198.641 (0.0000) 6426.615 (0.0000) 2366.848 (0.0000) 2353.426 (0.0000) 8529.391 (0.0000) In parenthesis are the t-values. The t-statistics significant at 1%, 5% and 10% are indicated by *, ** and *** respectively.
(FINSit ) is highest for North American region (-0.3528) followed by East Asia and
Pacific (-0.3132), Europe and Central Asia (-0.2735) and Middle East and Africa
(-0.2437). The elasticity of net human capital flight (brain drain) from Pakistan
(NETBDit ) with respect to the index of FINSit is also highest for North American region (-0.3512) followed by Europe and Central Asia (-0.2909) and East Asia and
Pacific (-0.2571). The Middle East and Africa less bother the economic stability of
Pakistani migrants as compare to other regions which is indicated by the magnitude of the elasticity (-0.2226) for NETBD with respect to the index of FINS . it it
The further decomposition of the effect of the index of FINSit on human capital
mobility shows that in case of full panel the elasticity of NETHCFit with respect to
the relative foreign direct investment as a percentage of GDP (FDIit FDI Pt ) is
negative 15.02 percent (-0.2442*0.6149≅-0.1502) while the elasticity of NETBDit
with respect to (RESit RESPt ) is negative 9.55 percent (-0.1553*0.6149≅-0.0955) for full panel.
The elasticities of human capital mobility both for NETHCF and NETBD with it it respect to the index of relative financial independence (FININit ) are positive in all the cases. The index of FININ constructed in chapter 7 is dominated by relative it domestic credit to private sector by banks as a percentage of GDP (CREDITit
CREDITPt ), relative self-employed workers as a percentage of total employed workers (SEMP SEMP ) and the relative taxes on income, profit and capital gains it Pt as a percentage of total revenue (TAX TAX ) with weights 0.8405, 0.2370 and it Pt
- 0.4739 respectively. Thus the index of FININ is constructed from the variables it which provide better financial opportunities, successful employment environment and
facilitates the doing business prospects. The elasticity of both NETHCFit and
NETBD with respect to the index of FININ are low for Middle East and Africa it it
(0.0972 and 0.0349 respectively) and highest for North America (0.5731 and 0.3785 respectively). The reason for this difference lies in the fact that in Middle East non-
Arab are not allowed to establish private business that restricts the financial independence.
The financial Independence in term of opportunities to work is also low in Europe and Central Asia indicated by the elasticity of NETHCF w.r.t FININ (i.e., 0.1267) it it and of NETBDit w.r.t FININit (i.e., 0.1751) as compare to North America (0.5731
and 0.3785 respectively). The indirectly computed elasticity of NETHCFit with respect to the relative domestic credit to the private sector by banks as a percentage of
GDP (CREDIT CREDIT ) in case of full panel is 0.4298 (0.5114*0.8405≅0.4298) it Pt whereas long-run elasticity of the NETBDit with respect to (CREDITit CREDITPt ) is 0.2894 (0.3443*0.8405≅0.2894). Similarly for full panel the long run elasticity of
NETHCF with respect to relative taxes on income, profit and capital gains as a it percentage of total revenue (TAXit TAXPt ) is negative 24.24 percent (0.5114*-
0.4739≅0.2424) whereas long-run elasticity of the NETBDit with respect to
(TAX TAX ) is negative 16.32 percent (0.3443*-0.4739≅-0.1632). it Pt
The elasticities of NETHCF and NETBD with respect to the index of relative it it standards of living based on health related facilities (LIVIN1 ) have mixed signs. it
According to the theory of migration the positive sign of the elasticity of NETBD it with respect to the index of LIVIN1it shows that highly qualified and highly skilled migrants move to the destination countries where these facilities are available for instance its values ranges from 0.3041 for East Asia and Pacific region to 0.2281 for
Europe and Central Asia. On the other hand the explanation for the negative sign of the elasticity of NETBD with respect to the index of LIVIN1 (-0.1398) for Middle it it
East and Africa deviates from the theory of migration and shows that healthy
Pakistani with better health facilities are welcomed in the Middle East and Africa to perform labor intensive work. The weight allocated to relative health expenditure as a
percentage of GDP (HLTHit HLTHPt ) in construction of the index of LIVIN1it is
0.3106, therefore the elasticity of NETBD with respect to LIVIN1 is 8.35 percent it it
(0.3106*0.2689≅0.0835) for full panel.
The elasticity of NETHCFit with respect to the index of relative standards of living other than health related facilities (LIVIN 2 ) has positive sign for all regions it except for Europe and Central Asia (-0.3808). From chapter 7 it is perceived that
LIVIN 2it is a weighted average of relative energy used per capita (ENERit
ENERPt ), relative annual growth rate of household final consumption expenditure
(GPCC GPCC ), relative inflation (INF INF ) and relative urban population as it Pt it Pt a percentage of total population (URPOPit URPOPPt ) with weights 0.5021, 0.2331,
negative 0.1349 and 0.3782 respectively. The positive elasticity of NETHCFit with
respect to the index of LIVIN2it for full panel (i.e., 0.1049) confirms the theory of migration that destination countries providing basic necessities of life like energy and
high growth rate of household final consumption expenditure along with urban agglomeration ensure the financial wellbeing of mankind and hence attracts human capital from Pakistan. Whereas negative elasticity in case of Europe and Central Asia
(i.e., -0.3808) contradicts the concept of migration based on the negative loading of
inflation (-0.1349) in construction of the index of LIVIN2it.
The negative elasticities of NETHCF and NETBD with respect to the index of it it relative demographic characteristics (DEMOit ) show that the labor supply based factors at center of origin (Pakistan) encourage emigration from Pakistan. The index of DEMO is weighted average of relative population density (DENS DENS ), it it Pt relative labor force with tertiary education as a percentage of total labor force
(EDU EDU ), relative annual percentage growth rate of population (GPOP it Pt it
GPOPPt ), relative urban population as a percentage of total population (URBANit
URBAN ) and the relative population ages between 15 to 64 years as a percentage Pt of total population (YOUNGit YOUNGPt ) with weights 0.4799, 0.6174, -0.3307,
0.2983 and 0.3329 respectively. As emigration of surplus labor force has no opportunity cost for the country of origin, thus promote human capital flows as well
as human capital flight as is directed by the negative elasticity of NETHCFit with
respect to the index of DEMOit (-0.1788) and for NETBDit with respect to the index
DEMOit (-0.2926) in case of full panel. The long run elasticity of human capital flight (NETBD ) with respect to (EDU EDU ) for full panel is negative 18.07 it it Pt percent (0.6174*-0.2926≅-0.1807) and the long run elasticity of human capital flight
(NETBD ) with respect to (GPOP GPOP ) for full panel is 9.68 percent it it Pt
(-0.3307*-0.2926≅0.0968).
The negative elasticities of NETHCF and NETBD with respect to the index of it it relative labor market structure (LMKTit ) are -0.3208 and -0.2373 for full panel,
which show that the relative age dependency ratio (DEPit DEPPt ) and the relative unemployment rate (UEMPR UEMPR ) induce the people to leave the country it Pt of origin for livelihood and sustenance. The long run elasticity of human capital flight with respect to (DEPit DEPPt ) is 11.68 percent (-0.2373*-0.4921≅0.1168) for full panel. Also the long run elasticity of net brain drain (NETBD ) with respect to it
(UEMPR it UEMPR Pt ) for full panel is 12.98 percent (-0.2373*-0.5471≅0.1298).
The elasticities of NETHCF and NETBD with respect to the index for the role it it of compatriot community (COMUit ) show mixed signs. The net migrants
(MIGRANT MIGRANT ), relative trade in services as a percentage of GDP it Pt
(TRSER TRSER ) and the relative personal remittances received as a percentage it Pt of GDP (REMITit REMITPt ) describe the role of compatriot community and kinship
networks in construction of COMUit index. For example, the positive elasticity of
NETBD with respect to (MIGRANT MIGRANT ) for full panel is 0.1289 it it Pt
(0.4589*0.2811≅0.1289), which shows that one percent increase in compatriot community already residing in destination countries acts as a pull factor for kinships in the homeland by 12.89 percent. On the other hand the negative elasticity of
NETBD with respect to (REMIT REMIT ) received in country of origin provide it it Pt
incentive to emigrate as is in case of the Middle East and Africa where one percent increase in (REMIT REMIT ) received in Pakistan leads to 1.41 percent (0.1069* it Pt
-0.1318≅-0.0141) increase in emigrants from Pakistan. Actually remittances received from destination country are indication for promising economic conditions in the destination countries that augment the remittance in country of origin and people further move abroad as they are now able to bear the cost of migration.
The positive elasticities of NETHCF and NETBD with respect to the index of it it relative provision of social safety nets (SNET ) is based on the social and financial it wellbeing of residents at the centers of destination. The index of SNET is a weighted it average of relative compensation of employees as a percentage of government expenditure (COMP COMP ), relative grants and other revenues as a percentage it Pt of gross national income (GRANTSit GRANTSPt ), relative insurance and financial
services as a percentage of service imports (INSit INS Pt ), relative per capita net official development assistance (ODA ODA ) and relative subsidies and other it Pt transfers as a percentage of government expenditure (SUBit SUB) with weights
0.3659, 0.1754, 0.2663, 0.3292 and 0.1554 respectively. The elasticity of NETBD it with respect to the index of SNETit is highest for North America (0.2951) and lowest for Middle East and Africa (0.1676). The reason for this difference in magnitude of elasticities is that labor unions are strong in North America to protect the rights of labor force as compare to the Middle East and Africa.
The positive elasticities of NETHCF and NETBD with respect to the index for it it research and development facilities (RADIit ) is indicating that the human capital
mobility especially in the form of human capital flight (brain drain) is toward the destinations where the relative total government expenditure on education as a
percentage of GDP (GEXPit GEXPPt ) and the relative Research and Development expenditure as a percentage of GDP (RAD RAD ) for human capital grooming it Pt are abundant. The magnitude of the elasticity of NETBDit with respect to
(GEXP GEXP ) is highest for North America (0.4385*0.6136≅0.2691) followed it Pt by Europe and Central Asia (0.3743*0.6136≅0.2297) whereas it is lowest for Middle
East and Africa (0.0995*0.6136≅0.0611).
Finally, the elasticities of NETHCF and NETBD with respect to the index of it it relative social openness (OPENit ) is positive indicating that the relative air transport registered carrier departures worldwide (AIR AIR ), relative mobile cellular it Pt subscriptions per hundred thousand persons (CELLit CELLPt ), relative internet users
per hundred thousand persons (INTit INTPt ) and relative international tourism
(TOURit TOURPt ) significantly affect international human capital mobility. The magnitude of the elasticity of NETBD with respect to the index of OPEN is it it highest for North America (0.4193) followed by Europe and Central Asia (0.3372),
East Asia and Pacific (0.2611) and the Middle East and Africa (0.2193), which indicates that the degree of social openness is highest for North American region where migration theory of melting pot by Zangwill (1908) is effective while in the
Middle East and Africa least social openness exist where structural assimilation through close social relations via large scale intermarriages and ethnic identification between Arabs and non-Arabs is impossible. The low elasticities NETBD with it
respect to the relative mobile cellular subscriptions per hundred thousand persons
(CELL CELL ) in Middle East and Africa (0.1596*0.2193≅0.0350) and of it Pt
NETBD with respect to the relative internet users per hundred thousand persons it
(INTit INTPt ) in Middle East and Africa (0.2722*0.2193≅0.0350) are due to gender discrimination in these regions that through channel of social openness affects the human capital flows toward that region. For North America the highest elasticities of
NETBD with respect to the relative air transport registered carrier departures it worldwide (0.4193*0.3158≅0.1324) and with respect to the relative international tourism (0.4193*0.4444≅0.1863) are based on Redfield’s (1941 and 1953) bright light theory of migration through which young migrants with progressive ideas and desire for development after break down of homeland’s traditions are inclined to travel toward developed countries. In short, majority of the estimated long-run coefficients for drivers of human capital mobility have predicted signs compatible with the regional strategic situations as well as with the theories of international migration.
Chapter 9
CONCLUSIONS AND POLICY IMPLICATIONS
“The phenomenon of capital flight is treated much like a barometer of economic and political stability and good housekeeping; increased capital flight is treated as an indicator of the need for policy correction.” Nadeen-ul-Haq (2005)
9.1. SUMMARY OF THE STUDY
This thesis has been an attempt to respond to a key challenge facing Pakistan, that is, human capital mobility from Pakistan, including net human capital flows and
human capital flight (i.e., brain drain). The empirical analysis considers migration of
Pakistanis to 27 major recipient countries namely, Australia, Bahrain, Canada, China,
Cyprus, France, Germany, Greece, Indonesia, Italy, Japan, Kuwait, Libya, Malaysia,
Oman, Qatar, Russia, Saudi Arabia, Singapore, South Africa, Spain, Switzerland,
Thailand, Turkey, the United Arab Emirates, the United Kingdom and the United
States of America over the past 36 years from 1981 through 2016.
The patterns of human capital flows from Pakistan traced out historically and occupationally over 27 cross sections and 36 years witness the presence of Pakistani diaspora all over the World, where in terms of human capital mobility Pakistan is ranked third in the South Asia (after India and Bangladesh) and seventh in the World
(after India, Mexico, Russia, China, Syria and Bangladesh). The following seven categories for chronological classification of the population mobility to and from
Pakistan are identified. The first category is refugee movements between India and
Pakistan at the time of its independence from British rule, in 1947. The second one is emigration of Pakistani citizens for permanent settlement in search of employment to the United Kingdom in the 1950s. The third category includes emigration of qualified professionals and highly educated workers to British, Europe, America and Canada during the 1960s and 1970s. The fourth category consists of temporary emigration of semi-skilled manpower to the Middle East during the last three decades of the 20th century. The fifth category consists of influx of Afghan refugees to Pakistan in the
1980s, while the sixth category includes illegal immigrants from Bangladesh and
Burma during the 1990s. Finally, the seventh category is migratory stream of educated Pakistanis to the United States of America, Canada and Australia.
The statistical analysis shows that over the period of study from the year 1981 through 2016 the percentage share of unskilled labor force in total migrants decreased from 47.03 percent in the years 1981-84 to 37.19 percent in the years 2013-16 which shows that the host countries do not encourage immigration of un-skilled labor force that’s why developed countries channelize international mobility of labor force by adopting specifically designed visa policies that best fit to their demand of highly- qualified and highly-skilled labor force.
An important recent statistical trend is that the human capital flows as well as brain-drain from Pakistan is biased toward highly-qualified emigrant. After East
Asian financial crises 1997-98, the event of 9/11 (September 11, 2001 World trade center and Pentagon attacks) and Global financial crises of 2007-08 developed countries are particularly screening migrants and only allow human capital flows of intellectuals in term of highly-qualified persons. The Semi-skilled labor force shows increasing trend in its share and it increased from 2.4 percent in 1981-84 to 16.67 percent in 2013-16. The profession-wise data statistics also ratifies the international labor force demand in STEM (Science, technology, engineering and mathematics) disciplines based on great opportunities for STEM-related career in developed countries.
Based on occupational classification of migrants by Bureau of Emigration and
Overseas Employment, Government of Pakistan, we have analyzed the statistical data for seven categories of migrants from Pakistan namely, professionals, technicians, administrative and managerial workers, clerks and sale-men, services related workers, agriculturalists, animal husbandry and forestry related workers, fishermen and hunters, transport equipment operators and laborers. The first two categories
mostly containing highly-qualified and highly-skilled emigrants though show increasing trend during the period of analysis yet their percentage share in total migrants was low.
To scrutinize the region-wise composition of human capital flows from Pakistan the destination countries are grouped into four regions namely, Middle East and
Africa, East Asia and Pacific, Europe and Central Asia and North America. The statistics show that based on religious affiliation Saudi Arabia attracts the lion’s share of migrants from Pakistan. Therefore, the maximum number of Pakistani migrants in term of net human capital flows (approximately 5 million) was residing in the Middle
East and South Africa in the year 2016, followed by Europe and Central Asia and then
North America and East Asia and Pacific. Contrary to the net human capital flows the net human capital flight (i.e., brain drain) from Pakistan is biased toward North
America that hosted 19 percent of international migrants in the year 2016.
At the next stage of our analysis various economic, financial, socio-economic, demographic, labor market and government policy related drivers of human capital mobility (i.e., net human capital flows and human capital flight) are constructed by grouping a large number of variables (45-variables) into small number of indices (11- indices). The indices arrived through the technique of principal component analysis and principal factor analysis includes relative economic incentives (ECON ), relative it financial stability (FINS ), relative financial independence (FININ ), relative it it standards of living based on health related facilities (LIVIN1 ), relative standards of it living other than health related facilities (LIVIN2 ), the role of compatriot it community (COMU ), relative social openness (OPEN ), relative labor market it it
structure (LMKT ), relative demographic characteristics (DEMO ), relative it it provision of social safety nets (SNET ) and research and development facilities it
(RADI ) in destination countries relative to Pakistan. it
The two unit root tests namely, Levin, Lin and Chu (LLC) test of common unit root process and Im, Pesaran and Shin (IPS) test of individual unit root process, are used to distinguish between trend and difference stationary data series. The unit root tests for full panel and four regions show that all series are unambiguously statationary at first difference of with and without trend at both lag lengths (zero and one). The main conclusions of pair-wise Granger causality test are that net human capital flows as well as human capital flight are unambiguously driven by the indices constructed from the drivers of human capital mobility. The two-way causality exists in some regions, for instance on one side, two way causality show that human capital mobility is a norm of highly skilled and highly qualified migrants whereas on the other hand it creates financial independence among the migrants.
The emigration of experienced and qualified doctors and medical staff increase health related facilities and hence improves the standards of living of people in the destination countries relative to Pakistan. The skill-wise composition and the level of migrant’s education affect the demographic composition of destination countries relative to Pakistan. The best and brightest labor force has positive and healthy effect on destination country’s labor market by increasing labor force participation rate and by decreasing the dependency ratio. Migrants through social relations and kinship effects determine the composition of compatriot community present in any country.
Migrants correspond with non-migrants through mobile phones, internet and air travel
and hence encourage the global social openness. Migrants through brain gain and brain exchange further motivate the research and development expenditure in destination countries and in home land.
Coming to the main objective of the research that is to identify the drivers of net human capital flows and human capital flight through a bipolar specification of augmented gravity model, a dynamic ordinary least squares (DOLS) technique for panel data regression is used. The signs and the magnitudes of the long-run co- integrating coefficients of different indices are compatible with the regional strategic situations as well as are supportive to the theory of international migration and hence provide a comprehensive and simultaneous analysis of push and pull factors to determine the main cause of human capital flows from Pakistan. For example, in the index of relative economic incentives a push factor is low per capita gross national income in the source country Pakistan, while the corresponding pull factor is high per capita gross national income in the destination countries.
The high values of R2 and adjusted R2 show that the fit of the models is good. The probabilities of F-statistics and of Chi-square statistics show that all coefficients have values different from zero and hence affect human capital mobility in term of human capital flows and human capital flight. Important empirical findings of dynamic ordinary least squares along with policy implications are presented in the section 9.2.
9.2. CONCLUSIONS AND POLICY RECOMMENDATIONS
To comprehend our study and to achieve more precision we follow the three guiding principles for policy responses to human capital flight i.e., brain-drain. Our first principal is related with global liberty (i.e., freedom of movement). Based on this
principal one of our policy suggestion is that for over-populated country like Pakistan, with high population growth rate (i.e., 2.4 percent in 2016-17), with high unemployment rate (i.e., 6.8 percent in 2016-17) and with high youth to population ratio (i.e., 64 percent in 2016-17) there is no need to curtail brain drain but it is wise to convert it into brain-export. In short, to convert brain drain into brain gain,
Government can play its role to improve access to the international job market during the events of Expo 2020 in Dubai and FIFA 2022 World Cup in Qatar.
Our second guiding principal for policy response to human capital flight is related to global efficiency (i.e., maximizing the size of the global pie of resources). The results of co-integration regression analysis show that the index of demographic characteristics is an important driver of human capital mobility, and one of the components of this index is labor force with tertiary education as a percentage of total labor force. Thus the perspective of highly skilled and highly qualified emigration increases the attractiveness of educational investment, with a positive knock-on-effect for both sending and receiving countries. The results of bivariate Granger causality also show that net human capital flight i.e., brain drain affects the index of relative financial independence. Therefore Government can channelizes the domestic credit to private sector and decreases taxes on income to achieve financial independence to provide better financial opportunities, successful employment environment that facilitates the doing business prospects. Policy recommendation in this respect is to ignore the emigration of skilled manpower and to replace the human capital flight from Pakistan with education and training of competent locals at a rate faster than their departure. Government can do well to produce the labor force with high demand
in international labor market e.g., STEM-fields (science, technology, engineering and mathematics).
Our third guiding principal for policy response to human capital flight is related to global equity (i.e., promoting equality or granting priority to improvements in the well-being of the less advantaged domestic country). The two-way causality between net human capital flight and the index for the role of compatriot community suggests that the national balance sheet of brain exporting country can be improved by foreign exchange earnings through personal remittances, trade in services as a percentage of
GDP and through inflow of foreign direct investment. Thus, human capital flight compensated by remittances from the destination countries is significantly contributing national income in home country. In present circumstances when current account deficit is mounted to 5.7 percent of GDP (FY 2018), capital-intensive machinery imports for the Chinese infrastructure development projects made imports to immensely outweigh the country’s exports, labor force export to the Kingdom of
Saudi Arabia has declined, foreign debt rising at $ one billion per month (condition in
September 2018), Pak rupee is experiencing a major devaluation, exports are becoming expense due to energy shortage, foreign exchange reserves are depleting at faster rate and high expected inflation, in such circumstances the tax free transfer of remittances can help us out of these economic crises. Government policies that promote trade in services and service out sourcing especially in health sector (via, doctors and health workers) can also help us to deal with the uprising economic crises in Pakistan. Pakistan has failed to attract significant international investment other than from China. Out of 190 globally competitive markets, Pakistan ranked 147th in
World Bank’s doing business report (2018). The Government can streamline the
saving schemes and tax incentives to attract the foreign direct investment from emigrants to promote domestic commerce in Pakistan. Especially foreign direct investment in research and development sector can lead to the development of new products and services driving growth, creating jobs, and improving national welfare.
Appendix A: Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis 3.2.1. Primitive Theories of Migration: An Interdisciplinary Analysis 3.2.1(a). Rationalist Population Projections, Forecasting and Mapping Demography Population Coale (1972), Keely Local and All types Micro Gender, Demographic characteristics, e.g., projection and and Kraly (1978), International (voluntary and and family population changes through birth forecasting theory Espenshade et al. involuntary Macro structure and rate, death rates, fertility rate, and (1982) and Brown migration ) population of epidemics etc., affect migration and Bean (2012) a country Supply side analysis of migrants Geography Mapping (currents Hägerstrand (1957), International All types Micro Individuals, Stimulated thinking about spatial of migration) Tobler (1970) and (voluntary and and groups and migration process Kwan (1998) involuntary Macro population migration) History Institutionalization Anderson (1990) Local and All types Micro Individuals, Population statistics (e.g., birth, (in nation-state- International and groups and death, marriage, nationality, building) Macro country citizenship, border crossing etc.) 3.2.1(b). Demographic Transformation Theory and Human Mobility Geography Demographic Zelinsky (1971) Local and All types Macro Countries Migration is determined by; transition theory International excluding Demographic transition model forced (changes in mortality and fertility) (refugees) 5-stages of demographic transition Geography Demographic Wright and Ellis Local and All type Micro Population Replacement effect based on aging, transformation (1996 and 1997) International and retirement theory and Macro Gender and racial discrimination replacement effect State policies 3.2.1(c). Gravity Theory of Migration Geography Gravity theory Ravenstein (1885 Local and All types Micro Population Migration flows between two and 1889) International and regions are affected by the distance Macro between them and size i.e., the number of natives of the origin and destination countries
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis Sociology Population at Stewart (1942) National and Student’s Micro Students Migration flow is inversely related distance International migration and living in with distance between collage and Macro different states different states Statistics P1 P2/D Zipf (1946) Local Human Micro Population of Migration flow is directly related with Hypothesis mobility and cities product of P1 and P2 and inversely Macro related with D Demography Gravity theory Flowerdew and Salt Local area Labor Macro Migration The effect of distance and city size on (1979) units flows migration flows are evaluated by gravity model 3.2.2. Assimilation, Social Networks and Migration: An interdisciplinary Approach Sociology Classical Park and Burgess International All types Micro Individual, Structural assimilation via close social assimilation (1921), Park (1930) and ethnic groups relations, intermarriages and ethnic theory and Gordon (1964) Macro and social identification with the host society classes Sociology Melting pot Zangwill (1908) and International All types Micro Individual, Racial, ethnic values (i.e., ethnicity) theory of Glazer and Moynihan and groups and are source for human mobility migration (1963) Macro population Anthropology Relational Barnes (1954 and Urbanization All types Micro Individuals, Social network family and friends in networks theory 1969) and Mitchell households newly emerging settlements (1966 and 1974) and groups People prefer to live in neighborhood of own ethnic group and with own community Geography/ Location specific Ladinsky (1967a and Local and Labor Micro Individuals Location-specific, human and social Sociology occupations and 1967b) and Barff and International and and population capital matters in settlement behavior human mobility Ellis (1991) Macro
History Social networks Cozen et al. (1992), Local and All types Micro Individual and Class, gender and ethnicity stimulates theory and Barken (1995) International and groups human mobility across regions Macro
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis History Feminist Harzig (1997) and Local and Labor Micro Women Discrimination in gendered relationship migration and Iacovetta et al. International and within families, labor market and in assimilation (1998) Macro other social institutions provide theory framework for human migration 3.2.3. Dual or Segmented Labor Markets and Migration: An Interdisciplinary Approach Economics Dual labor market Doeringer and Local and Labor Micro Groups and Mobility between two segments of the Piore (1971) and International and community market on basis of good job and bad job, Piore (1979) Macro informal or formal job etc. Economics Segmented labor Reich et al. (1975) Local and Labor Micro Groups and Mobility of labor force on basis of market International and population different working condition, different Macro promotional opportunities, different wage, and different market institutions in segmented labor markets 3.2.3(a). Cultural Segmentation, Dual Labor Markets and Migration Sociology Social structure Watson (1977) Local and Social Macro Group of Beside wage rate differential in different and culture International classes migrants segments of society and Segregation in term of social structure groups and culture affects migration decisions People prefer to live in neighborhood community with familiar culture 3.2.3(b). Rural-Urban Segmentation, Dual Labor Market and Migration Anthropology Meillassoux’s Meillassoux Domestic Labor Micro Small-scale Impact of global capitalism via different theory of (1981) economy societies modes of production in rural urban areas migration Geography Urban rural labor Fan (2002) Domestic Labor Micro 3-categories Permanent migrants, non-migrant natives market and (subpopulation) and temporary migrants are affected by segmentation Macro state sponsored resources and labor market entry
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis 3.2.3(c). Industrialization, Labor Market Segmentation and Migration Economics The golden age Anderson (1993 Local and Labor Micro and Labor force Employment opportunities in segmented of and 1999) International Macro labor market induces labor mobility industrialization Economics Stratification and Emmenegger et Local and Labor Micro and Labor force Labor mobility is affected by; mobility al. (2009) International Macro Simple labor market dualism Social protection dualism for segmented labor Political integration dualism for labor 3.2.3(d). Pull and Push Factor’s Segmentation for Migration Economics/ Push-Pull theory Lee (1966) Local and All types Micro and Individuals Push factors are lack of job opportunities, Demography/ International Macro religious and political discrimination and Sociology risky environmental conditions Pull factors are availability of jobs, religious and political freedom and prospects for better environment Political Science Market forces Hollifield (2004) International All types Micro and Population Demand-pull and supply-push market forces Macro and social networks beside states willingness to accept immigrants Demography Push and Pull Martin (2009 and International All types Macro Population Population dynamics via push factors demographic 2014), and Brown working in over populated societies and factors and Bean (2012) pull factors working in under populated societies Economics Push and Pull Martin (2009 and International All types Macro Population Differentials in income, rewards to economic factors 2014) education, employment opportunities, political freedom and in population growth rates act as pull/push factors
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis 3.2.3(e). Gender Segmentation in Employment and Theory of Distress Migrants Geography Gender Morokvasic (1984) Local and Labor Micro Women Increase in women’s formal education, segmentation in International and labor force job skills, labor force participation rate employment Macro and employment segmentation in labor market affects feminists mobility Geography/ Theory of distress Roy (2002), Tyner Local and Labor Micro Women Informal type of international migrants Sociology migrants (2007), Dyer et al. (2009) International and labor force are home care workers, nannies, nurses and Kofman (2012) Macro and domestics cooks that encourage feminists distress migrants 3.2.4. Neo-classical Theory of International Migration: An Interdisciplinary Approach Economics Neo-classical Todaro (1969) Local Labor Micro Individual Cost of migration and possibility of (rural-urban employment from rural to urban area migration) affect migration when wage differential exist Economics Neo-classical Harris and Todaro (1970) Local Labor Macro Community Expected income differential between (two sector and country rural and urban area rather than wage analysis) differential (income maximization) 3.2.4(a). Utilitarian Approach Led Migration Economics Utilitarian Lewis (1952), Borjas Local Labor Micro Individual Expected income maximizing decision approach (1989) and Brettell and Hollifield (2015) Demography/ Rationalist Martin (2009 and 2014) International Labor Macro Population Access opportunity in cost-benefit, and Economics theories of human move to the region where these behavior opportunities exist Behavioral Urbanization Wolpert (1965) Local and Labor Macro Population The decision of human capital mobility Science International rest on the relative utility of current location relative to alternative destinations
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis Geography Intra urban Brown and Moore Local (intra Residential Macro Household Utility measures an individual’s level of migration and (1970) urban) movement satisfaction with respect to given location place utility therefore individual’s immediate needs determine the residential place 3.2.4(b). Amenities led Migration Anthropology Folk urban Redfield (1941 and Urbanizatio Wage labor Micro Individuals Initiative for progressive ideas and desire for continuum 1953) n (country development after breakdown of traditions and Bright side to Excitement of urban life draw young light theory cities) migrants Economics/ Metropolitan Lowry (1966) and Local and Labor Micro Population Migration is directed by relative levels of Geography growth Clark and Ballard International and and its sub employment and wage conditions which in Theory (1981) Macro groups term depend on socio demographic markers e.g., differential response to education and age Geography Amenities led Graves (1979), Fielding Local and All types Micro Creative People migrate to destination where migration (1992), Florida (2002) International and class economic opportunities/ amenities are theory and Nelson and Nelson Macro talented abundant (2011) people 3.2.4(c). Cost-Benefit Analysis for Decision to Migrate Economics Cost-Benefit Sjaastad (1962) International All type Macro Massive Migration is determined by the return to theory movement investment in migration rather than income differentials Economics Value DeJong and Fawcett International All type Micro Individual, Different motives for migration e.g., expectancy (1981) and household, economic motives, social status motive, theory Macro and residential priorities motive, influence by societal family and friends and life style preferences Goals attained through migration are wealth, power, prestige, comfort, autonomy, social connections and morality
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis 3.2.4(d). Business Cycles Theory and Human Migration Geography Theory of Hägerstrand (1957) International All Macro Population Information field beside distance is used innovation types for theorizing innovation diffusion via diffusion migration Economics Business cycle Ballard and Clark (1981), Local and Labor Macro Population Regional economic restructuring and theory Clark and Ballard (1981) International business cycles (boom and recession) and Bluestone and affects human mobility Harrison (1982) 3.2.5. International Migration and Transnationalism: an Interdisciplinary Approach Sociology Kin and Massey (1990) Local rural All type Micro population Kinship affect migration & assimilation via; Friendship to urban and Social status of migrants networks Macro Financial aid by helping kin group Spillover effect (migration cause more migration) Anthropology Family networks Massey (1990) Local urban All type Micro Marginal Impact of family and kinship on societies and or poor migration process Macro people Economics/ Transnationalism Mountz and Wright International All Micro households Impact of transnational cultural impacts Geography (1996) and Conway and types and and groups via effect of birth, marriages, divorces Cohen (1998) Macro etc. 3.2.5(a). Globalization, Homogenization and Transnationalism Anthropology Theories of Eades (1987) International All Macro Country Effect of Marxism, ideology, homogenization types globalization, homogenization and and globalization modernization Anthropology Transnational Gardner (2006) International Families Micro Country Impact of transnational cultural via effect migration especiall and of marriages, kinship and family caring y wives Macro on global network Political Science Multicultural Kymlicka (1995) International All Micro Ethnic and Immigrant groups migrate by choice and citizenship types and immigrant they integrate into large new society of Macro groups destination country
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis History Social networks Barken (2004) Local and All types Micro Individual Class, gender and ethnicity stimulates theory International and and groups human mobility across regions and leads Macro to transnationalism History Decolonization Harzig and Hoerder Local and All types Micro Countries Emphasize globalization and and (2009) and Hoerder International excluding and or states industrialization as the important drivers industrialization and Kaur (2013) forced Macro of migration migration Culture, gender and class for social construction 3.2.5(b). Globalization, Diaspora Community and Transnationalism Anthropology Diaspora Clifford (1994) International All types Micro Individuals, Diasporas not retaining identity community and households Social and cultural contexts and leads to Macro and groups migration and hybridity Cultural Studies Transnationalism Rouse (1995) International All types Macro Country Social and cultural contexts and transnational networks determine mobility by adopting hybridity (i.e., hybrid nation) Geography Marriage Heikkila and Yeoh Local and All types Micro Family Marriage migration selectively demands migration (2010) International and from woman to leave her relatives and selectivity Macro join man’s family in particular location 3.2.5(c) Industrialization, Globalization and Transnationalism Economics From field to Stolarik (1976) International Labor Micro Population Industrialization provide better standard factory and of living and jobs which facilitates labor Macro mobility Economics Industrialization Kim and Cohen International All types Micro Inflow and Demographic, geographic, social and and Migration (2010) and outflow economic factors to determine inflow and Macro out flows to Western industrialized countries
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis 3.2.5(d). Dual and Multiple Citizenship and Transnationalism Political Science Citizenships and Schuck (1998) International All types Micro American Political and legal system: laws and rights and population rights as determinants of migration also Macro create incentive structures for migrants Economics Membership FitzGerald (2008) International All types Macro Mexican Bureaucratic effort to manage (decrease) and population emigration from Mexico Micro Sociology/ Paper trial to Sadiq (2009) International All types Micro Citizens State-mandated procedures by acquiring Economic citizenship and citizenship-indicating documents Macro 3.2.5(e). Revolution in Travel and Communication and International Mobility Economics Death of distance Cairncross (1997) Local and All types Micro Human Advances in telecommunication act as an International and society economic force to affect human society Macro Economics Modern air Button and Vega International All type Micro Countries Modern air transportation system transport system (2008) and stimulate human capital mobility by Macro affecting labor market Geography Mobility transition Cresswell (2011) Local and All types Micro Countries Migration is affected by transport and International and tourism Macro Geography Revolution in King (2012) Local and All types Micro Countries Revolutions in telecommunications telecommunication International and affect migration and globalization Macro High-tech hyper mobility (air travel and internet) affect migration 3.2.6. World System Theory of Migration: an Interdisciplinary Approach Sociology World system Wallerstein (1974, International Labor Macro Country Constraints imposed by capitalist system theory and 2004a and 2004b) determine flows of migration globalization
Appendix A (Continue): Summary of Literature on Migration Theories Discipline Theory Main Proponent/ Type of Migration Level of Unit of Determinants Time Period Analysis Analysis Political science Institutional Zolberg (1981 and 2006), International All types Macro Citizens in State policy design and shapes the approach Hollifield (1992), industrial nation Koslowski (2011) and democracies Klotz (2013) Law Jurist approach FitzGerald (2008) and International Labor and Macro Political and Institutionalize and rationalist Hollifield and Wilson skilled and legal system process and rights create incentive (2011) migrants Micro for migrants Through laws, state regulate immigration flows Political Science International Kraly and Gnanasekaran International Labor Macro Household National and international system theory (1987) behavior v/s migration laws and regulations international affect migration labor market Political Science Interest based Freeman (1995 and 1998) International All types Micro Individuals Rational behavior of individuals rational choice determine the migration flows approach Political Science Emerging Carens (2000) and International All type Micro Citizens Rights are the key to regulate migration state Hollifield (2004) and migration as movement of people Macro involve greater political risk Demography International Brown and Bean (2012) International Labor Macro Household Intermarriage rates, social capital, system theory behavior v/s civil society and international International system explain the population labor market dynamics Political Science Immigration Parrado ( 2012) Local and Labor Micro Maxican Employment prospects on basis of enforcement International and immigrants racial difference and economic policies Macro recession as outcome of immigration enforcement policies
Appendix B: Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Brettell and Human capital from beginning of human Different models for Determinants of human capital mobility in social sciences are; Hollifield mobility on history till now different disciplines Demography (birth rate, fertility rate, death rate and epidemics) (2015) planet earth Geography (racial and colonial links beside distance) Anthropology (kin and friendship in relational network followed by intermarriages and ethnic identification) Sociology (push factors are religious and political discrimination and uncertain environmental conditions while pull factors are religious and political freedom beside favorable environment) Economics (utility maximization, amenities led migration, industrialization and globalization) Political science (citizenship rights, international system and immigration policies) Docquier and Human capital 1990 and 2000 panel data OLS with White’s Poor economic performance and its correlates such as poverty, corrupt Rapoport flight to Defoort (2008) correction for institutions, discrimination and political distress induce emigration (2012) OECD WDI of World Bank hetroscadasticity Geographical factors such as native population at destination country, countries size of country, developmental stage, distance, land lock variable and from rest of religious fractionalization initiate immigration the World Grogger and 102 source Empirical analysis for the Income maximization Positive-selectivity (individual migrate if utility based on wage Hanson (2011) and 15 OECD year 2000 framework, differential is high at destination country) destination Data from WDI of World Roy’s model and Positive-sorting (high post-tax earning attract skilled migrants while countries Bank Simulation liberal, refugees and asylum policies support low skilled migrants) OECD data set Bhagwati Brain drain Comparative analysis Comparison of Brain drain is affected by; (2009) from from the year 1990 immigration policies Family unification developing to through 2000 Supply determined point system in Australia and Canada developed Employer sponsorship demand determined system of the United States countries Skilled to unskilled composition of immigrants flows Interference of social legislations and bureaucracies with the free
function of modern classes
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Monteleone Permanent brain Micro-data collected from Descriptive analysis: Reasons for leaving Italy are; and Torrisi drain from Italy 350 respondents pivot tables and Likert Pull factors (relationship between knowledge and career, high salary (2010) to 10 recipient scales beside possibility of career development and research abroad) countries Multivariate analysis Justification factors for migration (bureaucracy in Italy, interest in i.e., PCA, OLS and specific research, supremacy, prestige in host institutions, up GLM gradation of skill, availability of latest technology, employment opportunities and other economic concerns) Satisfaction levels in host country (freedom in workplace and organizational policies to support and conduct research, career prospects, tolerable working hours, relationship with superiors and colleagues i.e., affinities in working groups, level of bureaucracy, availability of scientific apparatus, access to information and technology and workplace safety) Docquier, Female Comparative analysis for Comparative analysis Supply side determinants of female emigration (poverty, education of Lowell and migration from the year 1990 relative to based on three female labor force and size of country) Marfouk 195 World the year 2000 categories of female Demand side determinants of female emigration (increased demand (2009) countries to DM06 data set migrants for women labor in health care and services sector, importance of OECD family reunion programs, and cultural and social attitude of females destination toward migration in the source countries) countries Dustmann, Migrant flows OECD dataset from the Dynamic two skills Individuals move across national borders where efficient acquisition Fadlon and from developed year 1999 through 2008 Roy model (skill varies of skill is possible Weiss to developing over time) Individuals also move to sell their skills where they experience (2009) countries (return highest return whether in host country or in homeland intentions) Akkas Human capital Cross section data from Index of skilled Brain drain or human capital flight exist due to;
(2008) flight or brain 174 countries in 1990 and migrants aged 25+ as a pull factors (wage differential, different quality of life, educational drain from 195 countries in 2000 share of total labor opportunities for children and job security in the destination countries) Islamic WDI of World Bank force push factors (lack of job opportunities, religious conflicts, adverse countries to rest International Migration political situation, health hazards and racial discrimination etc.) of the World reports by United Nations
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Beine, 127 developing Cross section data for OLS regression, and Causes of human migration; Docquier countries years 1990 and 2000 Instrumental variable Supply side determinants (positive self- selection technique to and respectively technique agglomerate where human capital is abundant) Rapoport CIA World fact book Demand side determinants (quality-selective migration policies to (2008) International human attract global intellectual talent) capital indicators WDI of World Bank Bhargava Emigration of OECD dataset Dynamic random effect Countries with little reward to doctors, more enrollment in secondary and physicians from WDI of World Bank model education and higher HIV incidence have greater rate of medical brain Docquier Sub-Saharan ILO dataset drain (2008) countries to 17 WHO dataset OECD countries Cattaneo Migrant flows Bi-lateral migrant flows Comparative analysis Community network determine the migration decisions (2008) from OECD in the years 1991 and of bottom and top Diaspora abroad act as a catalyst to decrease the costs and the risk of member States 2001, respectively quintile of population, moving abroad (developing and OECD International OLS with regional Network community increase the probability of migration for the poor transition Migration Statistics dummies, and people economies) to 23 Instrumental Variable Better financial performance guarantee better economic opportunities destination technique in future that reduces migration cost and hence facilitates emigration countries Developed financial markets get rid of credit market imperfections and hence of credit constrains for migration Beine, 45 small Panel data from the year OLS with fixed and GDP per capita, geographical distance between country of origin and Docquier countries of the 1975 through 2000 random effect destination, linguistic proximity with same educational system,
and Schiff World estimation, colonial links, religious fractionalization, political stability without (2008) Pooled least square violence and increase in population size of a country are determinants (PLS) and of migration General to specific In the country of origin an inverted U-shaped relationship between estimation approach number of migrants and GDP per capita exists
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Tritah European migrants to the Census data from the year Supply and demand An investment in Research and Development renders low (2008) United States 1980 through 2006 framework wages for scientists, unstable job opportunities and high load of administrative tasks that cause an emigration Mayda Bi-lateral migration flows Annual panel data set from Gravity model based Drivers of migration are; (2007) to 14 OECD countries the year 1980 through on pull and push Economic factors (GDP per worker at destination and center 1995 factors of migration of origin) International Migration and country fixed Geographical factors (distance and land border) data set for OECD effect model Cultural factors (colony and common language) countries Demographic factors (percentage share of young population WDI dataset of World at country of origin) Bank Dreher Student flows to the Unbalanced panel data Panel OLS regression Student flows determine the immigration to the United States and United States from 78 from the year 1971 with fixed effect and and 9 other OECD host countries Poutvaara countries of origin, also through 2001 random effect Economic factors (i.e., relative GDP growth, relative GDP per (2006) student flows to 9 OECD U.S. Institute of specifications and capita and relative unemployment rate) countries from 36 source International Education least median square Human capital variables (e.g., years of schooling, share of countries Network estimates to deal with English speakers, previous immigrants) U.S. Immigration and outliers Geographical variables (i.e., distance from source country, Naturalization Service land locked country of origin and access to sea by home land) (INS) Statistical Yearbook Political participation (democracy in source county) OECD International Migration Statistics OECD Education database
Mishra High skilled labor flows Period of analysis is from Demand and supply High skilled labor flows are based on; (2006) from Caribbean countries the year 1965 through model of labor Demand based pull factors (high wage at destination to OECD member 2000 countries) countries and the United Panel dataset compiled by Supply oriented push factors (limited job opportunities for States Docquier and Marfouk highly educated persons at small geographical area) (2005) U.S. census data
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Solimano Mobility of talent OECD Observer Push factors approach The outflow of talent depend upon; (2006) (information National Science reward structure to talent technology experts, Foundation of United linguistic compatibility engineers, Nations socio-cultural affinity and social network entrepreneurs, information revolution and technological change in industrialized scientists, nurses, countries physicians and artists) visa policy adopted by destination countries Schaeffer Permanent migration Data statistics by Backward recursive Human capital flows to the United States were motivated by fast (2005) from industrialized National Science approach growth of job opportunities for science and engineering skills and Western European Foundation for the years preferential treatment to highly skilled immigrants by the U.S. countries to the United 1996 and 1998 government States respectively Clark, Immigration to the Data from the year 1971 Panel data estimation Immigration to the United States depends on; Hatton and United States from 28 through 1998 technique with Economic fundamentals(GDP per capita and income inequality Williamson source countries Foreign born population counterfactual ratio) (2002) for census year 1970, simulations Demographic variables (schooling, population aged 15-29 and 1980 and 1990 English speaking population) Statistical year book of Geographical factors (distance from U.S. and land locked source
Immigration and countries) Naturalization Service Social factors (pull imposed by friends and neighbors on for the year 1998 migration flows) Selective immigration policy (immigrant stock, diversity quota and refugee quota) as a constraint on migration flows Borjas Interstate and From the year 1950 Income and utility Native interstate migrants (compare the interstate wage (2001) international human through 1990 maximization differential and migration cost) are less mobile capital mobility in U.S. census data approach International self-selected migrants (give more weightage to wage USA differential and income opportunities)
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ESTIMATION RESULTS YEAR ANALYSIS/ DATA TECHNIQUE USED SOURCES Straubhaar Student and high IMF and ILO dataset Comparative study Divers of migration are; (2000) skilled migration (United States attracts Natural benefits (pleasant weather and hygienic environment) to the United more students compare Political factors (political security, flexibility to work and move, safe States and the to European Union) and secured property rights and tolerable tax system) European Union Social factors (freedom of choice in life, in earnings and in spending, friendly and cooperative surroundings and good prospects for next generation) Economic incentives (high wage and career prospects) Social openness and flexibility for foreigners (easy residence registration and no red tape) Educational services (openness to innovation and knowledge, expected return to USA qualified is high and close connection between research centers and industries) Saxenian Immigration to U.S. census 1990 Descriptive statistical Attractions for high skilled workers (technological development, (1999) California a PUMS data analysis economic and social openness, globalization and diversity in business center of California economy)
the United States High-tech firms in H1-B visa lottery also facilitates the high skilled workers to immigrate Silicon valley (1980- to the United States 1998) by Dun and Bradstreet database Interview from engineers, entrepreneurs and capitalists etc. Harris and Rural urban From the year 1931 Two sector trade model Human capital mobility continues until the urban real income per capita Todaro migration through 1939 exceeds the real agriculture product (1970)
Appendix B (Continue): Empirics on Causes of Human Capital Mobility: International Evidence STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Todaro Rural urban 1932 (depression period) 2 stage behavioral Two stage process for migration decision from rural to urban (1969) migration of model of rural urban area depends on; American labor migration and First: real income differential unskilled labor demand and Second: probability of attaining a job workers supply model Lee (1966) Local and British census data Push and Pull factors Push factors at origin (lack of economic prospects, international model religious and political discrimination and insecure migration environment) Pull factors at destination (ease in getting jobs, religious and political freedom and perception for a prosperous environment) Wolpert United States From the year 1950 Migration differential The decision to migrate depends on different categories of (1965) through 1960 approach for utility occupations, income, race and age
maximization Redfield Cross Survey study Acculturation study Collectivistic society (i.e., mixture of native and migrants) (1941) community observed well-organized culture with economic lifecycle, analysis of four family affinity, bonded society, religious beliefs, communities in legendary festivals and medical and magical practices Yucatan Assimilation and homogenization theory of migration holds
Appendix C: Empirics on Causes of Human Capital Mobility: Evidence From Pakistan STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Afridi and Emigration From the year 1971 Descriptive statistics Push factors for migration (low salary, deprived economic Baloach of physicians through 2014 and regression conditions, miserable health sector with high risk of infection, (2015) and health BEOE, Government of analysis low personal security in conflict areas, political uncertainty care workers Pakistan and problems in social integration) from Survey of 152 respondent Pull factors for migration (high income abroad, job prospects, Pakistan from 18 public sector attractive living conditions for migrants and for their families, universities of Khyber public services such as schools and health care facilities, Pakhtunkhwa social security, social connections and political stability) Altaf, Brain drain Annual time series data Johanson Brain drain from Pakistan is affected by; Kalsoom from from the year 1980 cointegration Unemployment rate and Pakistan through 2013 technique Political conditions Husnain WDI of World Bank Utilization of remittances in Pakistan (2015) BEOE, Government of Pakistan Arouri, Brain drain Time series data from the ARDL approach to co- Factors retarding brain drain from Pakistan; Rashid, from year 1972 through 2012 integration Economic growth (real GDP growth) Shahbaz Pakistan Economic Survey of Financial development (real domestic credit to private sector) and Teulon Pakistan Factors motivating brain drain from Pakistan; (2014) BEOE, Government of Inflation Pakistan Unemployment Trade openness
Appendix C (Continue): Empirics on Causes of Human Capital Mobility: Evidence From Pakistan STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Hashmi Emigration 300 professionals were Self-structured research Factors responsible for emigration of professionals; Zeeshan, of doctors, interviewed from Lahore instrument Demographic variables (age, gender, income, marital status and Mehmood, engineers profession) Naqvi and and Economic factors (high salaries, expectation for personal economic Shaikh information uplift, job accessibility according to qualification and employment (2012) technology opportunities promising high standard of living) experts Social factors (providing financial support to family, research and development spillover, opportunity in relevant field, social security, personal development and equal rights) Political factors (respectable work conditions, easy access to justice, dominance of law and order, transparent rules and regulations, less political resistance in employment opportunities and stable political system) Irfan Inter-district Time series data from the Regression analysis Determinants of international migration (age, job opportunities and (2010) mobility and year 1957 through 2009 extra amenities) international PLM (population labor Determinants of inter district mobility (population density, rural mobility force and migration) fertility rate, literacy rate, land holding pattern, type of skill, level survey data of education, cost of migration, infrastructure development, urbanization and previous level of migration to measure social network) Ahmad, International Time series data from Co-integration and Main causes of international migration (income inequality and Hussain, migration 1973 till 2005 vector error correction poverty) Sial, from BEOE, Government of models Push factors for migration (inflation, unemployment and Hussain Pakistan Pakistan deteriorating wage rate) and Akram Pull factors of migration (utilization of remittances in Pakistan) (2008)
Appendix C (Continue): Empirics on Causes of Human Capital Mobility: Evidence From Pakistan STUDY/ COUNTRY PERIOD OF ANALYSIS/ ESTIMATION RESULTS YEAR DATA SOURCES TECHNIQUE USED Doghri, Brain drain From the year 1995 Pull and push factors Supply side push factors (monetary benefits, educational policy, Khalafalla, from IDB through 2004 analysis research and intellectual development infrastructure, conductive Diagne and member OECD database environment, investment climate, quality of life, prospects for Jam (2006) countries IDB Statistical Monograph better life of their children and political stability. (including BEOE, Government of Demand side pull factors (need for workers in labor importing Pakistan) to Pakistan countries) OECD countries Haque Human capital For the years 2001 and Skill Incentive Parity Reasons for human capital flight are; (2005) flight from 2002 respectively Equation Higher rate of return to human capital based on high wage and Pakistan SESTAT database of NSF low tax rate at destination country in the United States Stable macroeconomic and socio political environment in destination country Professional survival based on grooming of professional skill and qualification through training and research in destination country Lack of infrastructure opportunities for research and professional training at homeland Arif and Inward and From the year 1947 Comparison of inward Influx of population was traced by political factors (partition of Irfan (1997) outward through 1997 and outward mobility subcontinent and Afghan war) population Population census of 1951, of population Exodus of human capital had a job oriented move mobility from 1961, 1981 and 1998 Pakistan Occasional surveys by International Agencies Altaf and Labor inflow From the year 1972 District-wise High outmigration (less developed areas) Obaidullah and outflow through 1981 comparison of labor Low outmigration (more developed areas) (1992) from Pakistan Survey conducted by outmigration index Low human capital mobility from under developed districts/areas BEOE, Government of in Sindh and lower Punjab was due to structural characteristics Pakistan particularly land tenure system of these areas
APPENDIX D: MATHEMATICS OF PRINCIPAL COMPONENT ANALYSIS Abdi and Williams (2010) defined the techniques of principal component analysis
(PCA) as a simplest of the eigen vector based multivariate analysis and they constructed principal components (PCs) by eigenvalue decomposition of data covariance or correlation matrix after normalization through mean centering. Jolliffe
(2002) mathematically defined PCA as an orthogonal linear transformation that converts the data to a new coordinate system such that the 1st principal component
(P ) captures the largest possible variation in the original data having the constraint 1 that the sum of square of loadings is equal to unity. The 2nd principal component (P ) 2 is completely uncorrelated with P1 and explains the maximum variation in the data but these variations are small in comparison to those computed by the 1st component.
The 3rd principal component (P ) accounts for the maximum variations that the 3 P1
and the P2 do not account for and so on. Thus, the subsequent components are mutually uncorrelated and capture smaller but additional variation. In short, fewer components are needed if the correlation among the original data is high.
Let us consider a data matrix X on variables X , X , X ,...... , X with n 1 2 3 k observations. Mathematical transformation through PCA technique is defined by a set of n x k dimensional vectors of weights or loadings Wk (w1, w2 ,...... ,wn )k that plot each row vector X of X matrix to a new vector of PC scores P (P , P ,...... ,P ) , i i 1 2 k i such that the individual variable P considered over the data set successively receives the maximum possible variance from X , with a loading vector W constrained to be a unit vector. In mathematical form this transformation is written as;
Pki Wk .X i ……. (D.1) where P vector is newly constructed i th principal component of k x 1 dimension ki column vector, X is the ith row vector of data matrix X , and W (w ,w ,...... ,w ) i k 1 2 n k are the scalar weights (loadings) for original variables (X1,X 2 ,...... ,X k )i . The overall
PCs decomposition of X in matrix form is given as;
P W.X ……. (D.2) where W is a k x k matrix with columns as eigen vectors of X X . The loading is performed through PCA and PFA such that PCs possess the following properties.
Property 1: Principal components are orthogonal to each other i.e., they are not mutually correlated.
Cov(Pj , Pk ) 0 ……. (D.3)
Property 2: Principal components are ordered in a way that the first component captures the highest variance of the data and the subsequent component captures the remaining total variance in the decreasing order as;
Var(P1 ) Var(P2 ) Var(P3 ) ...... Var(Pk ) ……. (D.4)
Property 3: The sum of the variances of the principal components is equal to the total sample variance.
k Var(X ) Var(Pi ) ……. (D.5) i1
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