Dissertation Proposal
Human Capital and Regional Growth:
A Spatial Econometric Analysis of
Pakistan
Sofia Ahmed
[email protected]
Doctoral Program in International Studies
Universita degli Studi di Trento
February 2009
1
Human Capital and Regional Growth: A Spatial Econometric Analysis of Pakistan
ABSTRACT
From the industrial revolution to the emergence of the so-called knowledge economy,
history has shown that economic development has taken place unevenly across regions. A
region’s economy is a complex mix of varying types of geographical locations
comprising different kinds of economic structures, institutions, and infrastructure.
Policies that treat an economy as a homogenous unit across space neglect the role of
spatial dependencies within it. The operation of human capital and knowledge spillovers
play an important role in generating these regional dependencies and disparities. It has
been demonstrated that regions located in an economic periphery experience lower
returns to skill attainment and hence have reduced incentives for human capital
investments and agglomerations. As a result of emerging core-periphery boundaries,
human capital levels are increasingly diverging across regions. The concentration of
economic activity and human capital agglomeration is inevitable and desirable for
growth, but the spatial differences in welfare levels that accompany them can be reduced.
Policy assistance at regional levels can mitigate such inequalities in the creation and
distribution of human capital.
The over all aim is to identify, measure, and model the temporal relationship
between space and human capital across Pakistan. It will examine the links between
human capital dispersion and regional growth inequalities by taking into account regionspecific factors (such as geography and demography), sector-specific factors (such as
human and physical capital intensity), and the interactions among them. Specifically, by
using panel data at district level from 1970 to 2007, it will apply exploratory and spatial
econometric techniques to determine the temporal effect of distance and contiguity
among Pakistan’s 113 administrative districts on their human capital characteristics and
agglomeration. This technique will provide some of the first spatially explicit correlations
between Pakistan’s administrative districts and their human capital characteristics.
2
INTRODUCTION
Since independence in 1947, Pakistan’s economic growth trends have been paradoxical.
One of the reasons for this is that the periods of high growth have not resulted in the
improvement of its human development indicators. Today the country comprises
approximately 170 million people and in terms of population, it is the seventh largest
country in the world. The United Nations projects that by 2050, Pakistan’s population is
expected to double to about 350 million people making it the world’s third or fourth most
populous country. Currently the country is also experiencing a demographic transition
representing an increase in the population of the working age group 15 to 64 years only
(also known as demographic dividend). It has been claimed that up to 40 percent of the
success of the East Asian Economic miracle can be attributed to the fact that the region
took advantage of its demographic dividend (Bloom, Canning and Sevilla, 2002:45). In
the case of Pakistan, whether this opportunity of a demographic dividend can be
translated into the country becoming a regional or a global power crucially depends on
the characteristics of its population. By 2050, it is also estimated that the number of
potential workforce will be approximately 221 million and Pakistan will lose its
opportunity to take advantage of this demographic dividend if it is unable to invest in its
human capital and provide employment to this emerging workforce (Nayab, 2007;
Cohen, 2008).
At the same time, with the existing political insecurities, the country has become
unattractive for its existing human capital supplies. It suffers from a constant brain drain
and the large diaspora of highly educated Pakistanis throughout the world has reduced
incentives to contribute towards the growth and development of the country.
Consequently, human capital enhancement strategies matter more for Pakistan as
compared to any other time. These strategies are required not only to realize its potential
demographic dividend but also to reduce the disincentives for human capital migration,
ethnic polarization and the extent of elite domination in the education system. They are
also needed to achieve sustained increases in economic growth and development,
improve quality of governance, and to raise standards of civil society awareness in order
to combat extremist and violent elements currently influencing the population.
3
Moreover like most developing countries today, Pakistan faces the daunting
challenge of overcoming its unequal regional growth. Administratively, Pakistan is
divided into 4 provinces which contain 26 divisions comprising 113 districts, 420 subdistricts and 48,344 villages. The spatial concentration of economic activities
(particularly services sector activities) together with human capital abundance in Punjab,
have resulted in it achieving the status of the most privileged province of Pakistan in
terms of public investments in human capital. With increasing agglomeration of
manufacturing and research and development activities in the ‘core’ areas of Punjab and
some parts of Sindh, it can be expected that localized human capital investments can lead
to geographic stratification of the population and result in widening the existing regional
human development disparities—human capital inequalities being one of them1.
In the background of the above mentioned challenges, Pakistan’s potential labour
force struggles with unequal opportunities of human capital development and
employment across its regions. These inequalities become aggravated at a regional level
as institutional sources of education and employment are unequally dispersed throughout
the country. This calls for attention towards studying intra-country regional differences
not only in order to identify the most neglected subgroups of the population in terms of
literacy, innovation and employment but also to assist in the development of policies to
alleviate these issues of income and human development inequalities.
Economists have pointed to a number of causes of regional disparities such as
institutional ineffectiveness, governance issues, sluggish technological diffusion,
endowment disadvantages and the penalty of remoteness from core areas. This project
will focus on regional differences created by human capital inequalities. By utilizing
exploratory and spatial econometric data analysis techniques, this project aims to
investigate the link between human capital, spatial heterogeneity and spatial dependence
between Pakistani provinces to elaborate on its existing disparities.
1
Theoretically ‘human development indicator’ and ‘human capital’ have separate connotations, but for the
purpose of my argument they will be used interchangeably only in this proposal.
4
LITERATURE REVIEW
“Most of what we know we learn from other people. We pay tuition to a few of these
teachers…but most of it we get for free, and often in ways that are mutual—without distinction
between student and teacher…What can people be paying Manhattan or downtown Chicago rents
for, if not being near other people?”
(Robert E. Lucas, Jr., 1988: 38-39)
In the last 20 years the new endogenous growth theory has increasingly influenced the
economic growth literature. It emphasizes the role played by human capital accumulation
in boosting growth through stimulating technological creation and invention, eventually
leading to increased productivity. However, the empirical estimations of the impact of
human capital on economic growth have achieved mixed results.
One reason for this is that in most parts of the world the creation and emission of
human capital externalities are not evenly distributed because knowledge spillovers
usually tend to be localized (Acs and Varga, 2002). The role of geography has been
accepted as both positive and negative in determining the distribution of knowledge,
skills, and employment. On the one hand, human capital and technology agglomeration
has led to the creation of knowledge hubs and industrial centers across the world; on the
other hand, the increased mobility of factors of production and globalization has invited
skepticism towards economic geography (Cairncross, 1997; Dicken, 1998). Yet the
constant persistence of spatial disparities in terms of growth and human development
between countries and regions within countries across the world calls for increased
attention to the role of economic geography in determining human development and
growth outcomes.
Mainstream economics consists primarily of two levels of analysis— the ‘micro’ and the
‘macro’ level. Among the various reasons that have been put forward to encourage the
analysis to move beyond these two levels only and to consider the economic activities
that occur across space, is the importance of geography in the operation of human capital
and knowledge spillovers that have effected the long run evolution of regional disparities
(see Martin, 2003). While certain scholars argue that the unequal spatial evolution of
economic activities shows that localized human capital investments have led to the
5
geographical stratification of populations aggravating the already existing inequalities,
others have argued that this unequal evolution is inevitable and can be growth enhancing
(Krugman, 1991). The forthcoming World Development Report (2009) on ‘Spatial
Disparities and Development Policy’ also reflects the increasing concern of policymakers
over the causes and consequences of spatial disparities of growth, income and other
development variables with which many countries continue to grapple.
In the light of these issues this study will highlight the relation between human
capital externalities and New Economic Geography (NEG). It will discuss the two
literatures individually before proceeding to identify the distinctions between the two.
The micro-macro debate over human capital
The literature on human capital occupies a prominent place in both macro- and microeconomic growth literatures. The micro literature has produced several estimates of the
monetary returns to education. According to the Mincerian human capital model, a
change in a country’s average level of schooling is one of the key determinants of its
income growth. However this literature emphasizes private monetary returns to education
at the expense of social returns. This led to the development of macro growth literature
which has been motivated by the possible existence of positive social externalities of
education which may exceed or fall short of private returns.
There are two main debates in the macro growth literature: the traditional growth
models of Solow and Swan (1956), Cass (1965), and Koopmans (1965) and the new
endogenous growth theories. While the former disregards human capital, the later
incorporates it. In the traditional growth models physical capital drives growth. They are
based on the crucial assumption of diminishing marginal returns to capital, which leads
the growth process of an economy to eventually arrive at the steady state where the rate
of technological progress is exogenously given (Islam, 1995). Whereas in the new
endogenous growth models, human capital occupies a central role in spurring growth as
knowledge spillovers and human capital externalities aid in delaying the tendency for
diminishing returns to capital accumulation ( Barro and Sala-i-Martin, 2004).
6
Aghion and Howitt (1998) have observed that the modeling of the role of human
capital in new endogenous growth theories can be further divided into two approaches:
the Nelson-Phelps (NP) (1966) and the Lucas approach (1988). The NP approach argues
that the stock of human capital directly influences growth by enhancing the innovation
capability of the country and indirectly influences growth through its ability to facilitate
technology adoption and ‘catch up’ with other leading countries. In contrast, the Lucas
(1988) approach, which is also broadly consistent with the Mincerian earnings function
literature, assumes that growth is driven by the accumulation of human capital.
These approaches have different implications for human capital investments on
the long-run growth of a country. Under the NP approach, raising human capital levels
will affect the country’s level of output while the Lucas approach predicts that raising the
level of human capital will affect the country’s permanent growth rate. Therefore,
empirical results of testing the importance of human capital for economic growth are
mixed. Some studies have found no relation between human capital and growth under the
Lucas approach (see Benhabib and Spiegal, 1994). Some authors consider this as the
‘micro-macro paradox’ because the findings of the macro growth regressions and the
micro based Mincerian earnings function fail to match (Pritchett, 2001). Other studies
have challenged or reversed these results declaring them as ‘premature’ results because of
model misspecification issues (Keuger and Lindhall, 2001). These studies have further
attempted to reconcile micro econometric estimates of returns to education with macro
econometric results on the influence of human capital on growth2.
Inequality, human capital, and growth
This branch of literature highlights the role of human capital in explaining the relation
between inequality and economic growth. Various studies have demonstrated that in most
parts of the world the process of local growth is heterogeneous (see Usai and Paci, 2001
and Castella and Domanech, 2002 for case studies on Italy). Although not seen as crucial,
the role of human capital is important since the distribution of income is mainly driven by
the distribution of human capital across or within countries. Golmm and Ravikuman
(1992), Saint-Paul and Verdier (1993) or Galor and Tsiddon (1997) are among those few
2
See Engelbrecht (2003) for a debate over this issue
7
who present models in which the sources of inequality are driven by inequalities in
human capital distribution. There exists a dyadic relation between human capital
accumulation and inequality with both affecting each other. It has also shown that human
capital inequality negatively affects economic growth rates not only through inefficient
resource allocation but also through decreased investment rates (Bird-Sal and Londono,
1997; Lopez et. al., 1998).
Since policy makers have realized the importance of distribution of human capital
and its externalities, the creation and distribution of these benefits calls for increased
assistance at the regional level. Lucas (1988), Nijkamo and Poot (1998), Martin and
Sunley (1998) have emphasized the importance of human capital in fostering regional
growth. Human capital investment (expenditures) on education, training, health have all
been acknowledged as generating externalities that increase the quantity and quality of
regional labor force (Weisbrod, 1961). However, this empirical work on public
investments in human capital often overlooks spatial interaction among regions. Most
regions are highly interconnected through family networks, transport, trade, migration
and regional labor markets. They often exhibit a high degree of interaction due to which
unemployment and other human capital issues may be highly spatially correlated (Molho,
1995; Acemoglu, 2001)3.
It is also argued that human capital levels are diverging (Berry and Glaeser, 2005)
and its concentration is likely to continue to occur in certain regions only (Florida, 2002b,
Berry and Glaeser, 2005). Proximity and distance play an important role in spatial
inequality literature as well because it has been argued that countries with a lower market
access may also have lower education levels. As a result some authors have shown that
the already peripheral countries in the world may continue to become remote over time
(see Redding and Schott, 2003).These results indicate that policies aimed at increasing
growth should focus not only on education but also on region wise distribution of human
capital enhancing infrastructure and institutions that can cater to a larger section of the
population.
3
See Zhang et. al. (2006), Elhorst (2004) and Karlson and Haynes (2002) for a review of spatial
perspectives on regional labor markets.
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New Economic Geography, human capital, and growth
Although the new endogenous growth theory emphasizes the role of knowledge
spillovers in macroeconomic growth, it tends to neglect the regional dimension. Recent
evidence suggests that knowledge spillovers tend to be spatially bounded or localized. It
has been shown that regions are not isolated but are a part of a core-periphery system.
Disregarding of interactions between individual regions in the system can lead to an
adverse impact of regional policies (Baldwin et. al., 2003 and Midelfart, 2004)4.
Therefore, economists have turned their attention towards the so-called new economic
geography (Krugman, 1991; Fujita et al., 1999; Krugman and Venables, 1995; Ottaviano
and Puga, 1998; Puga and Venables 1997, 1999; Darlauf and Quah, 1999). The
acceptance of new economic geography and agglomeration is represented by the
application of mechanisms such as clusters, innovation networks, knowledge externalities
and other similar spillovers (Maskell & Malmberg, 1999; Moulaert and Sekia, 2003;
Henry and Pinch, 2006). According to certain authors this has stimulated the so-called
‘third wave’ of regional industrial policies (Benneworth and Hospers, 2006; Bradshaw
and Blakely, 1999; Larosse, 2004) which aims to promote knowledge based success and
regional technology and innovation policies (Lagendijk and Hassink, 2001; Hospers,
2006; Charles et al., 2003).
With this increased interest on regional development issues and enhancement of
spatial data sets, empirical analysis using spatial econometric techniques has also gained
popularity (Arbia 2006). Spatial externalities model social interaction that introduces
dependence among the agents of a social system (Anselin 2003a, Arbia 2006). Among
the various kinds of spatial externalities, international and regional level studies have
confirmed the existence of a positive spatial correlation between knowledge spillovers of
human capital, R&D and other types of training activities and economic growth (Coe and
Helpman 1995, Feldman 1994, Anselin et al., 1997). Further more from the findings of
Nelson and Phelps (1966) to Benhabib and Spiegal (1994), studies have shown that
4
For surveys on the effectiveness of regional policy see: Bijvoet and Koopmans (2004), Rodriguez-Pose
and Fratesi (2004) and Ederveen et. al. (2002)
9
economies located closer to a technology leader benefit more and grow faster5. Recent
results have also revealed that human capital has a positive and a significant effect on
total factor productivity growth when interacted with the distance to the technology
leader usually measured in terms of income per capita (Pede, Florax and L.F de Groot
2006). This is because technology is also spatially bounded, and is not completely
exogenous and freely available in a region (Jaffe et al., 1993, Lawson and Lorenz 1999).
Human capital and agglomeration
One of the strands of literature that is driven by the existence of spatial externalities
relates to the agglomeration of economic activities. This field consists of different types
of approaches each of which highlights a different kind of agglomeration process.
Agglomeration economies have played an important role in explaining regional growth
and development, and industrial locations. Some studies argue that it is increasing returns
and market interactions that cause geographic concentration of activities (Krugman,
1991). Others argue that it is the presence of factor endowments such as amenities
(Rosen, 1979 and Roback, 1982), existence of tolerance and openness to diversity
(Florida 2002a, b, c), urbanization or localization externalities (Jacobs, 1969, Marshall,
1890) or technological spillovers and human capital externalities (Lucas, 1988 and
Henderson et al., 1995) that contribute to geographical concentration of economic
activities and human capital (see Hanson, 2000).
However spatial externalities do not spread without limits (Darlauf and Quah,
1999) as a result of which closely related economies or regions tend to have similar kinds
of human capital externalities and technology levels as compared to the more distant ones
(see Quah, 1996; Mion, 2004). It has also been shown that regional economies that
comprise an integrated area experience more and faster technology diffusion as they
benefit from externalities such as a common pool of labour, markets, and access to capital
and human capital (Vaya et al 1998). This is in line with the findings of Glaeser et al
(1992) and Venables (1996) which show that pecuniary externalities can lead to the
concentration of large firms in the main regions of an economy thereby distributing the
5
Other studies that examine the links between human capital, development and growth include Bils and
Klenow (2000), Eicher and Garcia-Penalosa (2001), Galor and Mountford (2001) and Mankiw et. al.
(1992)
10
technological and human capital externalities from firm to the regional level. Rosenthal
and Strange (2005), Moretti (2004a), and Rauch (1993) also take a similar stance while
highlighting the benefits of spatial concentration in terms of higher wages for labor which
is driven by proximity to college educated workers (an example of human capital
externality).
Human capital in Pakistan
Different strands of work on human capital in Pakistan have emerged overtime. Most of
the studies (micro and macro) have found a positive association between levels of
education and earnings and an inverse relationship between the extent of income
inequality and educational attainment (see Hussain, 1995). The phenomenon of growth
without human capital development in Pakistan has been extensively discussed in
Easterly (2001), Hussain (1995), and Behrman (1995). Since the professional elite within
Pakistan are at a similar level to those in other advanced countries, these studies have
advanced political economy explanations for this phenomenon. This is in line with the
arguments presented in the international political economy literature on regional and
ethnic fractionalization and poor public service and institutional outcomes (Bourgignon
and Verdier, 1999; Acemoglu and Robinson, 1998; Alesina, Baqir and Easterly, 1999;
Goldin and Katz, 1999)6. Studies on the problems of education inequalities (Jamal and
Khan 2005), brain drain (Haque, 2005), low levels of domestic commerce (Haque, 2006),
and lack of genuine entrepreneurship (Haque, 2007) in Pakistan further highlight the
consequences of a human capital deficit in Pakistan. For this some scholars have
discussed the key issues related to education in Pakistan with particular emphasis on the
dilemma of higher education (Andrabi, Das and Khawaja 2002; Hoodhboy (ed) 1998;
Memon 2007). Others have argued that Pakistan needs to break out of its low level skill
trap (Amjad 2005; Kemal 2005), for which some studies have used regression analysis to
highlight the importance of public investments in human capital (Pasha, Hasan et. al
1996a, 1996b; Birdsall, Ross and Sabot 1993; Sawada 1997; Yousef and Sasaki 2001).
Other studies have called for enhancement of vocational training (Mustafa, Abbas and
6
These studies have analyzed the conditions under which the dominance of elite is unsupportive of human
capital investments. They also highlight the role of ethnic heterogeneities across regions as a plausible
cause of underinvestment in human capital.
11
Saeed, 2005) and have also raised the issue of regional inequalities in education and its
consequences for the country’s growth (Jones, 2000; Iqbal and Zahid, 1998; Hussain and
Qasim, 2005).
An often repeated exercise has been the calculation of returns to human capital
using the Mincerain equation for Pakistan. Some studies have carried out a gender
disaggregated analysis as well (Nazli and Nasir, 2000; Nasir, 2002; Abbas and ForemanPeck, 2007). Finally, Qaisar (2001) has estimated the role of human capital in economic
growth by using the standard endogenous growth equation and also compared the results
of Pakistan with those of India and Sri Lanka.
This review of literature has shown that existing studies in Pakistan have not
explicitly considered spatial analysis. Most of them have used dummy variables in
standard regressions or indexes to estimate the effect of space or location. The lowest
denomination of calculating regional effects has either been a provincial or a rural-urban
comparison. The few studies that do carry out district level analysis rely on descriptive
statistics or exploratory analysis for comparisons. Moreover, most of the literature either
estimates Mincer’s equation with new data or estimates the impact of human capital
(specifically raised levels of education) on economic growth using standard growth
models. There is therefore a need to re-examine previous findings in the light of recent
developments in the field of spatial econometrics which so far has not been used in
Pakistan. The spatial econometric techniques will allow us to analyze the spatial
evolution of regional human capital accumulation and human capital externalities, and
the consequences of varying concentrations of human capital in different regions. The
findings using spatially explicit methodologies will provide better insights on
understanding the effect of space and will have an important application in government
human development and rural/urban planning policies.
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RESEARCH PROBLEM
‘t is distance lends enchantment to the view…’
(Thomas Campbell)
The overall aim of this project is to identify, measure, and model space and its relation
with human capital across Pakistan. It will examine the relationship among spatial
dispersion of human capital, regional growth, and inequality issues by taking into account
region-specific factors (such as geography and demography), sector-specific factors (such
as human and physical capital intensity), and the interactions among them7.
More specifically, the project initially aims to extract from existing data sets,
information about the characteristics of human capital and the regions where it is located.
The data will be organized by applying spatial coordinates to the selected districts. This
technique will provide some of the first spatially explicit correlations between Pakistani
administrative districts and public human capital investments. The study will then
measure the effect of contiguity and distance between the administrative districts on
human capital accumulation. It will model contiguity between districts to analyze the
types of influence that is exerted on a district by the number, size, and human capital
characteristics of its neighboring districts. It will also estimate the effect of distance from
the nearest centers of human capital—provincial capital cities or other districts with
industrial zones—on the human capital attributes of the population of a district to
highlight whether they face a remoteness penalty. Another task is to determine how
quickly these externalities attenuate with distance over time. The estimation of distance
effect will demonstrate whether incentives to invest in human capital are lower for
districts depending upon their position in the core-periphery system of Pakistan and
whether they have changed with time.
This procedure will be followed by the measuring the evolution of human capital
over time in each district to find the extent to which human capital is diffused across
Pakistan. After achieving initial estimates, the project will carry out simulation exercises
7
Similar studies have been carried out in the advanced countries in Europe such as Germany ( Suedekum,
2006), Italy Usai and Paci, 2001; Dominicis, Arbia, L.F de Groot, 2007) , Poland (Brodzicki and Ciolek,
2006) and US cities by Glaeser, 1992, various sectors of US metropolitan areas (Henderson et al 1995;
Pede, Florax and L.F. de Groot, 2006)
13
and calculate the extent of spatial externalities by measuring the spatial impact of a shock
in one location—such as opening up of a new college or a factory—on the human capital
accumulation in other districts. This will allow us to investigate the link between
localized knowledge spillovers and the agglomeration/diffusion of human capital and
their effects on the economic activities of regions.
The project will employ panel spatial econometric techniques to analyze the
spatial dependencies and to detect whether clusters of economic activities are influenced
by human capital concentration across Pakistan. This methodology may also be
applicable to other countries experiencing regional disparities and could be used to
estimate cross border comparisons of human capital disparities between Pakistan and its
neighboring countries.
The concentration of economic activity and human capital agglomeration is
inevitable and desirable for growth but the spatial differences in welfare levels that
accompany them over time can be avoided. Therefore, the ultimate aim will be to use
spatially explicit data in order to introduce a new methodology to study human capital
externalities across space and time in Pakistan and propose development enhancing
policies for city planners and human development policymakers.
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RESEARCH DESIGN
“Everything is related to everything else, but near things are more related than distant things”.
(Tobler’s first law of geography (1979)
This section begins by elaborating three key concepts that are central to this project
namely; human capital, distance, and contiguity.
i) Human Capital
Human capital is a multifaceted concept which comprises various types of investments in
people. Its definition has evolved over years of research on it. Although, initially it was
simply considered as skills and knowledge acquired by people (Schultz 1961a), today it
has expanded to incorporate individual competencies, non-market activities, innate
abilities, and individual attributes that facilitate the creation of social, personal and
economic wellbeing as well (OECD, 2001:18; Laroche et al 1999; Le et al 2005).
However, human capital remains to be a complex concept and with the development of
‘knowledge economy’ literature, its definition continues to broaden. Since it is an
intangible concept and simultaneously a stock and flow variable, its estimates must be
constructed indirectly. The precise magnitude of the human capital externalities on the
growth and development can be understood only after accurate operationalization of the
concept of human capital. The choice of human capital proxy selection has generated a
controversial debate. Although, the search for suitable human capital proxies continues,
disagreements over the choice of proxies have not prevented substantive research on
human capital and improvement in the quality of labour related data.
The three most common approaches to human capital measurement include the costbased approach, the income-based approach, and the education-based approach8. Out of
these three approaches, the most commonly applied approach in the literature on human
capital and economic growth is the usage of education related proxies. Due to the
limitations of the cost- and income-based approaches to explain the affect of human
8
The ‘cost based’ approach measures the costs incurred in the acquisition of skills and competencies. The
‘income-based’ approach measures human capital by considering people’s income/earnings in the labor
market. The ‘education-based’ approach estimates human capital by looking at the level of educational
qualifications in the population (where qualifications are a proxy for skills)
15
capital in economic growth, education related measures are considered as closest proxies
if not direct measures of human capital. They include literacy rates, school enrollment
rates, years of schooling and public expenditures on education, aggregate expenditure on
labour (Harberger, 1998), investment in education (Mankiw et al., 1992), percentage of
the working age population having completed secondary school (Mankiw et al., 1992),
tertiary education (Romer, 1989; World Bank, 1994; Madden and Savage, 1998)
enrollment ratios (Benhabib and Spiegal, 1994). However each of these proxies has its
limitations in the form of presenting problems of endogeneity, bias or correlation and
hence poses empirical measurement problems.
This study will only focus on the key components of human capital—cognitive
and non-cognitive abilities of human capital—that can be further used to produce goods
and services and disseminate knowledge. A district’s human capital will be measured by
its educational stock and employment levels. This can be done by using enrollments data
at various educational levels. The Population Census provides information on age-wise
student population (5-9 years, 10-14 years and 15-24 years). These age groups represent
primary, secondary and tertiary educational levels. The tertiary sector can further be
divided into general (arts or science) and technical (engineering, medicine, commerce,
business administration, teaching, agriculture, law) enrollment ratios. Enrollments rates at
different levels together with data on vocational training and employment rates can
further be combined to represent district wise human capital characteristics (dependent
variable). This is possible through the procedure of Principal Component Analysis
(PCA). Instead of assigning equal weight to each indicator (enrollment ratios,
employment rates, vocational training), PCA can be used to generate weights. PCA
allows the representation of a set of human capital characteristics of large dimensions in
terms of a single or small number of representative components. This process has been
previously applied by Jamal and Khan (2005) to estimate District Education Index for
Pakistani districts.
ii) Distance
In this study the economic distance is a metric which limits the spillover effects of a
phenomenon (human capital agglomeration) and makes it a public good. Distance effects
16
between districts can be calculated in various ways. Midelfart –Knarvik et al. (2004) have
developed an Index of Spatial Separation that takes distances between locations into
account. Similarly, Lafourcade and Mion (2007) measure proximity by measuring
minimum road distances among pairs of locations. Some studies have also used railway
connections between spatial units or the minimum time-cost path on a network to
estimate the economic distance between them (Conley et al 2003). Another possible
method can be to use arc distances for geographical midpoints expressed in longitude and
latitude of the districts. This is calculated by using the spherical law of cosines as
previously used by Pede, Florax and de Groot (2006)9. However, Fotheringham et al
(2000) suggests that when spatial data is being used from small parts of the world, the
curvature of the earth’s surface can be ignored and it can be assumed to be a flat surface.
This assumption allows one to use the Cartesian co-ordinate system and distances
between two spatial units can be calculated by using the Pythagoras Theorem. The
question of which measure out the above mentioned will be used can only be answered
once I have analyzed the district level data.
iii)
Contiguity
It shows the neighboring regions (in geographical terms— regions that share a border
with each other—or also in terms of proximity) of a district/county which can be easily
be observed from an administrative map of a country. Another way of supposing
contiguity is in terms of distance. Nodes on a network (districts in our analysis) can be
treated as neighbours if they are within a given minimum distance or fall within the same
radius around a point or a spatial unit under consideration (e.g a location will be selected
if it is distance h from location i).
9
Spherical Law of Cosines :
d=R.arc cos[ sin (lat1) x sin(lat2) + cos (lat2) + cos(lat1) x cos(lat2) x cos (long2 – long 1) ]
where R=radius of Earth (3,959 miles) and longitude and latitude are expressed in radians
17
DATA
Three kinds of data will be used in this project: data on economic growth indicators,
human capital and demographic characteristics, and the geographical characteristics of
Pakistan. The two most important data sets for the purpose of this study will be the
Labour Force Survey of Pakistan (LFS) and the Population Census reports. Several
versions of the LFS will be appended in order to construct a pooled cross sectional data
base. By adhering to these two main reports, the estimation exercise will become less
disputable or debatable as far as data sources are concerned (see Appendix for data
description).
Potential data issues
Developing countries in general suffer from doubtful data quality. For example, in terms
of data on education, inflating statistics on enrollments, and by considering educational
enrollments rates only at the beginning of academic years (at the end of a year , students
may have actually dropped out) may result in upward biases in estimation. Some data sets
contain concepts and definitions that do not conform to international standards. As a
remedy the project will consider only the nationally administrative data sets with the
greater coverage and lesser missing values.
METHODOLOGY
This study focuses on spatial economics by observing geographically determined human
capital externalities in Pakistan. Recent contributions that take space explicitly into
account through dynamic externalities modeling (Glaeser et al 1992, Henderson 2003),
innovation-based spatial modeling (Jaffe et al 1993) and endogenous growth theory
(Aghion and Howitt 1998) emphasize the spatial role of knowledge possessed by
economic agents. They have emphasized on identifying knowledge spillovers between
agents as these are crucial factors leading to external economies of scale in production
(Romer 1986, Lucas 1988).
18
The spatial level of analysis in this project is intra-country (local) as compared to
the more commonly researched international spatial (global) analysis in social science
issues. Traditionally spatial models have been applied at a ‘global’ level implying that
one set of results is generated from the analysis and these results are assumed to apply
equally across the spatial unit of interest. However, if we examine relationships across
the spatial system under consideration, the results may have limited or no application at
all to certain units of the system. For example if we accept that Pakistan has a literacy
rate of 49 percent, definitely some parts of the country may not contribute to this figure at
all. This implies that local modelling is more useful for national policies as compared to
global modelling, since it dissects global statistics into their local components and tests
for local variations in relationships across space within the system.
Finally, in the first stage of this study, a static spatial econometric model (one
which ignores temporal dependence) will be estimated. It will be followed by introducing
time effects into the model in order to analyze the processes of human capital
agglomeration and diffusion across space and time.
Why a spatial econometric analysis
Spatial modelling typically aims to look for associations instead of trying to develop
explanations (Haining 2003:358). Classical statistical inference such as conventional
regressions are inadequate for an in-depth spatial analysis since they fail to take into
account problems of spatial data analysis such as spatial autocorrelation, identification of
spatial outliers, edge effects, modifiable areal unit problem, and lack of spatial
independence10. These reasons necessitate the use of a spatial econometric method that
will take spatial effects into account. The following is a brief description of spatial
econometric methodology and has been mainly derived from Fotheringham et al (2000),
Haning (2003), Anselin (1988), LeSage (1999) and Van Oort (2004).
10
Modifiable Areal Unit Problem: When attributes of a spatially homogenous phenomenon (e.g people) are
aggregated into districts, the resulting values (e.g totals, rates, ratios) are influenced by the choice of the
district boundaries just as much as by the underlying spatial patterns of the phenomenon.
19
Spatial effects
Spatial effects can be divided into two main kinds: spatial dependence and spatial
heterogeneity. If the Euclidean sense of space is extended to include general space
(consisting of policy space, inter-personal distance, social networks etc) it shows how
spatial dependence is a phenomenon with a wide range of application in social sciences.
Spatial dependence refers to the lack of independence which is often present in cross
sectional data sets. It can be considered as a functional relationship between what
happens at one point in space and what happens in another. Two factors can lead to this.
First, measurement errors may exist for observations in contiguous spatial units. The
second reason can be the use of wrong functional frameworks in the presence of different
spatial processes (such as diffusion, exchange and transfer, interaction and dispersal) as a
result of which what happens at one location might be determined by what happens
elsewhere in the system under analysis.
Spatial heterogeneity refers to the display of instability in the behavior of the
relationships under study. This implies that parameters and functional relationships vary
across space and are not homogenous throughout data sets.
Quantifying spatial effects — the spatial weight matrix
Spatial dependence is an inherent property of attributes situated in geographic space
(Haining 2003:74). Any data collected on them will inherit this property. Usually most
data is spatial in nature however by using standard econometric techniques, most
standard analysis ignores specific spatial effects. The phenomenon of spatial dependence
puts forward the need to determine which spatial units in a system are related, how spatial
dependence occurs between them, and what kind of influence they exercise on each
other. Formally these are answered by using the concepts of neighbourhood expressed in
terms of distance or contiguity.
Boundaries of spatial units can be used to determine contiguity or adjacency
which can be of several orders (e.g. first order contiguity or more). Contiguity can be
defined as linear contiguity (i.e. when counties which share a border with the county of
interest are immediately on its left or right), rook contiguity (i.e. counties that share a
common side with the county of interest), bishop contiguity (i.e. counties share a vertex
20
with the county of interest), double rook contiguity (i.e. two counties to the north, south,
east, west of the county of interest), and queen contiguity (i.e when counties share a
common side or a vertex with the county of interest) (LeSage 1999).
The most popular way of representing a type of contiguity is the usage of the
binary contiguity (Cliff and Ord 1973, 1981) expressed in a spatial weight matrix (W). In
spatial econometrics the spatial weights matrix provides the composition of the spatial
relationships among different points in space. It displays the properties of a spatial system
and can be used to gauge the prominence of a spatial unit within the system. The usual
expectation is that values at adjacent locations will be similar. It also enables us to relate
a variable at one point in space to the observations for that variable in other spatial units
of the system and is used as a variable while modelling spatial effects contained in the
data. Generally it is based on using distance or contiguity between spatial units.
If there are n areas, the spatial weight matrix is defined with as many rows and
columns as are the number of areas (n x n). Each area is given a particular row and a
column and usually the matrix W is symmetric about its diagonal. If two areas d and f are
defined to be linked through contiguity or a distance related measure then:
wd , f = w f , d = 1
otherwise any cell takes the value 0.
As an example consider below a typical spatial weight matrix for three units 1, 2, and 3:
The elements of matrix W, (w d , f ) can be defined by using inverse distance between d
and f or by using a minimum distance between the two areas in order to consider them as
neighbours. In such a case all the entries of the matrix are non zero (for all areas i and j,
the elements (w d , f ) ≥ 0 and with ( wd , d ) = 0) and tend to get smaller as the distance
between the two areas increases. Another way to is to consider (w d , f ) as the common
border length between areas d and f. In this case, the entries are non zero only when two
21
areas actually share a border and tend to decrease as the one area’s share of another area’s
border gets smaller and the farther away they get.
Finally, it is a standard procedure to ‘row standardize’ the W matrix so that the
sum of each row is equal to 1. For this each entry on the row is divided by the sum of the
row entries.
Explanatory spatial models
If the matrix W is multiplied by the dependent variable y i.e. Wy, it will represent a new
variable which is equal to the mean of the observations from contiguous units. This Wy is
used as an explanatory variable in the spatial econometric model to highlight the
variation in the dependent variable across observations in the spatial sample. In matrix
notation, this model displays the linear relationship between the dependent variable and
the spatial lag variable:
y = ρWy + u
(8)
Where ρ is the spatially autoregressive parameter suggesting spatial dependence in the
sample data and u is the disturbance term. It measures the influence of neighbouring
spatial units on the spatial unit being considered in vector y (LeSage 1999). A significant
ρ implies spatial dependence and employing OLS will produce biased and inconsistent
estimates. This spatial dependence can be overcome by using two other types of spatial
econometric models, spatial autoregressive models (SAR) or/and spatial error models
(SEM).
Using a spatial autoregressive model is an appropriate methodology when spatial
dependence occurs in the form of a spatially lagged dependent variable which is used as
an explanatory variable:
A SAR model in matrix notation can be written as:
y = Xβ + ρWy + u
(9)
where y is the vector of explained variable (dependent variable), X is the matrix of the
exogenous explanatory variables that change over time in regions and includes the
additional variable besides Wy that help explain variation in y, W is the spatial weights
matrix, ρ is the coefficient of spatial autocorrelation and u is the error term. Wy is the
22
spatially lagged dependent variable for the matrix W, and it can be considered as the
spatially weighted average of the dependent variable in neighbouring spatial units
After performing a sequence of matrix operations we can obtain:
y − ρWy = Xβ + u
y = ( I − ρW ) −1 Xβ + ( I − ρW ) −1 u
≡ ( I − ρW ) −1 Xβ + v
(10)
Here we have obtained a model with an error term v, which is a linear transformation of
the original, independent vector u . The model now shows a particular spatial correlation
structure in the errors as well as spatial effects associated with the explanatory
variables11, and is used to estimate how far the dependent variable varies across space.
An alternative specification to the spatial lag model is the spatial error
specification. This model is appropriate when spatial dependence occurs through the error
term. Spatial dependence is shown through a spatial autoregressive process caused by
misspecification of the error term (e.g. due to an omitted variable).
Consider a SEM in matrix notation:
y = Xβ + u
u = λWu + ε
(10)
where u represents spatial dependence in the off-diagonal elements of matrix W, λ is an
autoregressive parameter and ε is a spatially independent error term. Ignoring spatial
dependence in the error term can lead to the biased estimate of its variance hence leading
us to wrong conclusions. Both the SAR and SEM have correlated error terms v and ε but
it is through the SEM that we obtain linkages between locations through the error
generating process. In the end, a detailed data analysis before deciding between using the
SAR, SEM or both is more useful.
We can conclude this section by noting the three main advantages of using spatial
econometric techniques as opposed to standard econometric techniques while analyzing
spatial units: 1) externalities can be defined by means of a specification of the weights
The elements of matrix ( I − ρW ) show how a change in X in one location will affect the dependent
variable in other locations after considering all spatial effects.
11
−1
23
matrix; 2) the significance of regional externalities can be estimated; and 3) the intensity
of the across-region externalities can be consistently quantified (Vayá et al 2000).
Exploratory spatial data analysis
However before estimating the spatial econometric models, the presence of spatial
dependence has to be detected. This is done by using another popular technique in spatial
data analysis commonly known as exploratory spatial data analysis (ESDA). ESDA
describes and visualizes spatial distributions, identifies spatial outliers, detects
agglomerations and local spatial autocorrelations, and highlights the types of spatial
heterogeneities (Haining 1990; Bailey and Gatrell 1995; Anselin 1988; Le Gallo and
Ertur 2003; Oort 2004:107). One of the techniques that will be employed by this study
will include a Moran’s I statistic. The Global Moran’s I demonstrates the spatial
association of data collected from points in space and measures similarities and
dissimilarities in observations across space in the whole system (Anselin, 1995).
However in the presence of uneven spatial clustering the Local Indicators of Spatial
Association (LISA) is utilized. This is a local spatial indicator which measures the
contribution of individual spatial units to the global Moran’s I statistic (Anselin, 1995).
The project will also generate Moran’s scatter plots to demonstrate and estimate the
spatial characteristics of human capital across Pakistan.
The model
This project will follow the recent literature on human capital externalities which has
utilized exploratory and spatial econometric data analysis to: produce a spatially explicit
endogenous growth model (Pede, Florax and de Groot 2006), estimate the impact of
agglomeration and proximity to human capital on labor productivity and earnings
(Rosenthal and Strange 2005, Mion 2004), explore mechanisms by which human capital
externalities percolate at macro- and micro-geographic levels (Fu 2006, Usai and Paci
2001), test the strength of human capital externalities in explaining growth and economic
convergence across national borders (Vayá et al. 2000), identify sectoral location patterns
in industries and services (Dominicis, Arbia and L.F de Groot 2007, Midelfart-Knarvik et
24
al 2006), and to calculate the evolution of spatial distribution of human capital over time
(Suedekum 2006).
This study will use a spatial GMM estimation method to allow for correlated
errors across observations for which it will apply a standard panel model to a spatial
panel. It will append several cross section data sets while the spatial unit of analysis will
be the ‘districts’ of Pakistan. A lower level unit of analysis is not being used because of
two main reasons. Firstly, data on regional scales below the district level in Pakistan
suffers from reliability issues. The second issue is more technical. In order to give
information on 48,344 villages of Pakistan instead of 113 districts, the project will need a
matrix of distance with
48,344 × (48,344 + 1)
= 1,168,595,340 free elements to evaluate.
2
For this reason as explained by Mion (2004) it is better to use a smaller unit of
geographical detail to avoid the problems of collecting massive amounts of data and
issues of calculations.
Generally spatial econometric models are of two kinds: spatial linear regression
models for cross section data or spatial linear regressions for space-time data (Anselin
1988, p.36). This project will employ the latter type. Although, general spatial studies
explicitly adjust space while modeling, most of them fail to incorporate the ‘time’
dimension and combine it with the spatial dimension. For example most studies that
incorporate spatial autocorrelation do not incorporate temporal correlations (See Frazier
and Kockelman 2005; Case 1992; Coughlin et al 2003; Dublin 1991). This model will
use a time panel to incorporate the time dimension. Although this increases complexity in
estimation, it is definitely of more interest to us since it allows us to estimate the trends of
agglomeration over time.
The static version (as previously shown) of the specification of the spatial linear
auto regressive model for panel data generally takes the following form:
y = Xβ + ρWy + u
[1]
where y is the vector of explained variable (dependent variable), X is the matrix of
explanatory variables that change over time in regions (in this case district wise human
25
capital characteristics)12, W is the spatial weights matrix, ρ is the coefficient of spatial
autocorrelation and u is the error term. In this project the dependent variable y will be the
district wise human capital level which will be created using Principal Component
Analysis.
The ρWy term on the right hand side of equation [1] will measure the spatial effects or
spillovers. Externalities that arise due to the accumulation of human capital indicators
can flow across regions and can translate into dependence between them. Regarding W in
this project, the elements of the matrix will be based on contiguity as defined below (and
at a later stage of this project, the matrix will also be based on the distance of a district
from the capital of the province):
wdf = 1, when district d is a neighbour of district f. It means they have a common border
and 0 otherwise.
wdd = 0, the diagonal elements of the matrix. It implies that there are no self spillovers,
districts can have spillovers on each other only,
D = the total number of districts in the model.
This model focuses on spatial externalities embodied in human capital indicators across
space and time. Since the model combines the space and time dimensions, we now need
to generalize the static version over time by introducing the time subscripts. Intuitively
most explanatory variables—for example educational infrastructure per district—will not
change rapidly over time but only some variables will.
k
D
D
j =1
f =1
f =1
y d ,t = β 0 + ∑ β j x j , d ,t + ρ ∑ wd , f y f ,t + λyd ,t −1 + µ ∑ wd , f y f ,t −1 + η d + ud ,t
[2]
where λyd ,t −1 represents the temporal lagged effect of human capital characteristics. This
implies that the agglomeration of characteristics across space and time is governed by the
stock of human capital attributes that existed in earlier time periods.
12
For example, these may include the education enrolment statistics at primary, secondary, and tertiary
educational institutions; the number of vocational centers; the amount of manufacturing activity; and the
fraction of skilled workers in a particular district.
26
D
The term µ ∑ wd , f y f ,t −1 is the lagged effect which represents those human capital
f =1
characteristic spillovers that take time.
The term η d represents fixed effects to account for unobserved heterogeneity in the
model. In other words these are all the reasons for which the explanatory variables are
unable to explain the pattern and effects of the accumulation of human capital
characteristics.
The incorporation of time dimension in the model allows us to incorporate
unobserved heterogeneity as well. Clearly, we do not observe everything that we discuss
and model and therefore fixed effects allow us to represent it. In static models, statistical
procedures such as OLS fail to do so. As a remedy we apply the General Methods of
Moments (GMM) technique to the differenced equation [2]:
D
D
f =1
f =1
∆yd ,t = ∑ β j ∆x j , d ,t + ρ ∑ wd , f ∆y f ,t + λ∆yd ,t −1 + µ ∑ wd , f ∆y f ,t −1 + ∆ud ,t
[3]
Equation [3] shows that we have differenced equation [2] over time to eliminate all the
elements that we cannot observe. The use of GMM is a standard econometric technique
for estimating models in which explanatory variables cannot be taken as independent of
the error term and where the likelihood function is too complicated to permit Maximum
Likelihood Estimation. These are precisely the conditions that apply to dynamic panel
models such as the one being used in this project (Arellano 2003).
This modelling procedure will enable us to address some of the following issues to
facilitate regional development policy making:
i)
Analysis of the evolution of district wise agglomeration and diffusion of
human capital characteristics and highlight the extent of some characteristics
that make some districts more successful than the others in terms of regional
growth;
ii)
Determine the temporal extent of spatial spillovers of the agglomeration of
human capital characteristics.
iii)
Determine the kind of characteristics that lead to growth without human
development in certain districts only.
27
iv)
Distinguish the extent to which intra-district interactions are caused by
knowledge spillovers of human capital alone.
v)
The extent to which ‘neighbouring’ districts complement each other in terms
of accumulation and agglomeration of human capital characteristics?
vi)
Compare districts that neighbour provinces across the international borders of
Pakistan and highlight the difference between the effects of international
borders and the intra-country regional borders.
After estimating the model by using data on Pakistani districts, the project will carry out
simulation exercises to determine the effects of a shock (such as the construction of a
university) in one district on a contiguous district or those in proximity. This will be
complemented by an exploratory data analysis using the Geographic Information System
(GIS) software. It will produce cartograms and electronic maps of the spatial
characteristics of the districts, Moran’s scatter plots and also use indexes to measure
agglomerations of human capital and economic activities.
A possible extension to this work can be the application of spatial econometrics to
study political economy issues in Pakistani districts. At the international level, spatial
econometrics has been used to model the process of globalization, economic integration
in certain regions, and the role of spatial dependencies.
However, at the national level, the use of spatial econometrics has been more
useful for studying political economy characteristics of districts. Most political data that
report the processes and events at specific locations are actually spatial data. For issues
such as political communication, political conflict, elections, democratization and most
importantly policy diffusion across regions, scholars claim that proximate units behave
more similarly than spatially distant ones since they have shared political and economic
concerns. (Huckfeldt 1986, Vasquez 1995, Berry and Berry 1990). Spatial diffusion of
economic and political activities occurs because units’ behaviour is influenced by the
behavior of other neighbouring units’13. Proximate districts experience similar climatic
and topographic conditions, have similar infrastructure facilities, and usually face
13
The definition of neighbours is critical while modelling spatial dependencies. This project refers to
neighbours as contiguous regions although the definition is generalizable and does not necessarily imply
contiguity.
28
common institutional and political problems as well. This in turn breeds interaction,
competition, and political and economic interdependence among them. Similarly,
political manifestations in one district may encourage neighbouring districts to engage in
similar economic development activities, and election outcomes in one district may
depend on the outcomes in the other nearby districts. For these reasons, this project may
employ some political stability and institution variables to the model and measure the
impact of political stability and property rights on human capital diffusion across
Pakistan. Another possible extension to the estimation of the spatial econometric model
includes the analysis of the data using Spatial Computable General Equilibrium
Modelling framework.
29
BIBLIOGRAPHY
Abbas, Qaiser (2000). "The Role of Human Capital in Economic Growth: A Comparative Study
of India and Pakistan." The Pakistan Development Review 39: 4 Part II: 451-473.
Abbas, Qaiser (2001). "Endogenous Growth and Human Capital: A Comparative Study of
Pakistan and Sri Lanka." The Pakistan Development Review (40:4 part II): 987-1007.
Abreu, M., Henri. L. F de Groot, Raymond J.G.M. Florax (2004). "Space and Growth."
Tinbergen Institute Discussion Paper 129/3.
Acs, Zolton J., and Attila Varga (2002). "Geography, Endogenous Growth and Innovation."
International Regional Science Review 25(Special Issue on Regional Innovation
Systems): 132-148.
Aghion, Phillipe and Peter W. Howitt (1998). Endogenous Growth Theory. Cambridge, Mass,
MIT Press.
Hussain, Fazal and Qasim M. Ali (2005). "Inequality in the Literacy Levels in Pakistan:
Existence and Changes Overtime." South Asia Economic Journal 26: 251-264.
Amjad, Rashid (2005). "Can Pakistan Break out of the Low-Level Skills Trap?" Pakistan
Development Review 44(4 Part I).
Andrabi, Tahir, Jishnu Das, and Asim Ijaz Khwaja (2002). "The Rise of Private Schooling in
Pakistan: Catering to the urban Elite of Educating the Rural Poor."
http://www.economics.pomona.edu/Andrabi/Research/.
Anselin, Luc (1988b). Spatial Econometrics: Methods and Models. Dordrecht, Kluwer Academic
Press.
Anselin, Luc (2003a). "Spatial Externalities, Spatial Multipliers and Spatial Econometrics."
International Regional Science Review 26: 147-152.
Anselin, Luc., Varga, Attila, and. Acs, Zolton (1997). "Local Geographical Spillovers Between
University Research and High Technology Innovations." Journal of Urban Economics
42: 422-448.
Arbia, Giuseppe (2006). Spatial Econometrics. Statistical Foundations and Applications to
Regional Convergence. Berlin, Heidelberg, Springer-Verlag.
Arbia, Giuseppe., Marco, Bee, and Giuseppe Espa (2007). “Aggregation of Regional
Econometric Time Series with Different Spatial Correlation Structures.” University of
Trento, Department of Economics, Discussion Paper No. 20.
Arellano, M (2003) Panel Data Econometrics, Oxford, Oxford University Press.
30
Baltagi, Badi. H. (1995) Econometric Analysis of Panel Data, Chichester, John Wiley and Sons.
Barro, Robert. J. (1999). "Human Capital and Growth in Cross-Country Regressions." Swedish
Economic Policy Review 6(2): 237-277.
Barro, Robert. J. (2001). "Human Capital and Growth." American Economic Review 91(2): 1217.
Beck, Nathaniel (2006). “Space is more than Geography: Using Spatial Econometrics in the
Study of Political Economy.” International Studies Quarterly 50(1):22-44
Behrman, Jere (1995). "Pakistan: Human Resource Development and Economic Growth into the
Next Century." Background Paper for Pakistan 2010, World Bank.
Benhabib, Jess and Mark M. Spiegal (1994). "The Role of Human capital in Economic
Development: Evidence from Aggregate Cross-Country Data." Journal of Monetary
Economics 34: 143-173.
Benneworth , Paul S, and Gert-Jan Hospers (Forthcoming). "The New Economic Geography of
Old Industrial Regions: Universities and Global/Local Pipelines. Envoirnment and
Planning C: Government & Policy."
Birdsall, Nancy, David Ross, and Richard Sabot (1993). "Underinvestment in Education: How
Much Growth has Pakistan Foregone?" The Pakistan Development Review 32:4 Part I:
453-499.
Birdsall, Nancy and Juan L. Londono (1997). "Asset Inequality Matters: An Assessment of the
World Bank's Approach to Poverty Reduction." American Economic Review 87(2): 3237.
Bloom, David E, Canning David, and Sevilla Jaypee (2002). "The Demographic Dividend: A
New Perspective on the Economic Consequences of Population Change." Santa Monica:
RAND Corporation Report No. MR-1274.
Brawdshaw, Ted. K and Edward J. Blakely (1999). "What are 'Third Wave' State Economic
Development Efforts? From Incentives to Industrial Policy." Economic Development
Quarterly 13: 229-244.
Cairncross, Frances (1997). The Death of Distance:How the Communication Revolution will
Change Our Lives. Cambridge, Harvard Business School Press.
Case, A.C. (1992). "Neighbourhood Influence and Technological Change." Regional Science and
Urban Economics, 22: 491-508
Castella, Amparo and Rapfael Domanech (2002). "Human Capital Inequality and Economic
Growth: Some New Evidence." The Economic Journal 112(No. 478): 187-200.
31
Cass, David (1965). "Optimum Growth in an Aggregative Model of Capital Accumulation."
Review of Economic Studies 32: 233-240.
Charles, D. R., Beth Perry, and Paul Benneworth (2003). Regions and Science Policy. Seaford,
Regional Studies Association.
Ciccone, Antonio and Angel de la Fuente (2003). “Human Capital in a Global and KnowledgeBased Economy." Unitat de Fonaments de l' Analisi Economica (UAB) and Institut d'
Analisi Economica (CSIC) UFAE and IAE Working Paper 576.03.
Coe, David T and Elhanan Helpman (1995). "International R&D Spillovers." European
Economic Review, Elsevier 39(5): 859-887.
Conley, Timothy. G, Fredrick, Flyer, and Grace, R.Tsiang (2003). “ Spillovers from Local
Market Human Capital and the Spatial Distribution of Productivity in Malaysia.”
Advances in Economic Analysis & Policy, Vol 3(1).
Conley, Timothy Fredrick Flyer and Grace R. Tsiang (2003). “Spillovers from Local Market
Human Capital and the Spatial Distribution of Productivity in Malaysia.” Advances in
Economic Policy & Analysis 3(1) Art 5.
Cohen, Craig (2008). "Pakistan 2020: The Policy Imperatives of Pakistani Demographics."
NPEC Working Papers Series available at: http://www.npec-web.org/Essays/20080928Cohen-Pakistan2020.pdf.
Darlauf S.N and Dany Quah (1999). The Empirics of Economic Growth. North Holland, New
York and Oxford, Elviser Science.
Darmofal, David (2006). “Spatial Econometrics and Political Science” Department of Political
Science,
University
of
South
Carolina,
available
at:
polmeth.wustl.edu/retrieve.php?id=575 accessed on 30th November 2008.
Dicken, Peter (1998). Global Shift: Transforming the World Economy (3rd Edition), Paul
Chapman Publishing
Dubin, Robin A. (1992). “Spatial Auto Correlation and Neighborhood Quality”. Regional
Science and Urban Economics 22: 433-452.
Easterly, William (2001). "The Political Economy of Growth without Development: A Case
Study of Pakistan." Development Research Group, World Bank.
Elhorst, J. Paul (2003). “Specification and Estimation of Spatial Panel Data Models.”
International Regional Science Review 26: 244-268.
32
Engelbrecht, Hans-J. (1997). "International R&D Spillovers, Human Capital and Productivity n
OECD Economies: An Empirical Investigation." European Economic Review 41: 14791488.
Engelbrecht, Hans-J. (2003). "Human Capital and Economic Growth: Cross Section Evidence
for OECD Countries." The Economic Record 79(Special Issue): 40-51.
Feldman, Maryann P. (1994). The Geography of Innovation. Boston, Kluwer.
Florida, Richard (2002a). "Bohemia and Economic Geography." Journal of Economic
Geography 2: 55-71.
Fotheringham, A.Stewart, Chris, Brunsdon and Martin, Charlton (2000). Quantitative
Geography: Prespectives on Spatial Data Analysis. London, Sage Publications.
Foreman-Peck, James and Qaiser Abbas (2007). "The Mincer Human Capital Model in Pakistan:
Implications for Education Policy." Cardiff Economic Working Papers E2007/24.
Foreman-Peck, James and Qaiser Abbas (2007). "Human Capital and Economic Growth:
Pakistan, 1960-2003." Cardiff Economic Working Papers E2007/22.
Franzese, Robert J. and Jude C. Hays (2007). “Spatial Econometric Models of Cross-Sectional
Interdependence in Political Science Panel and Time-Series-Cross-Section Data.”
Political Analysis 15: 140-164
Fu, Shihe (2006). "Smart Cafe Cities: Testing Human Capital Externalities in Boston
Metropolitan Area." Journal of Urban Economics, Elsevier 61(1):86- 111.
Fujita, Masahisa, Paul Krugman, and Anthony J. Venables (1999). The Spatial Economy: Cities,
Regions and International Trade. New York, MIT Press.
Arif, G.M.,. M. F Kiani, and Khalid H Sheikh (2002). "Labour market Dynamics in Pakistan."
The Pakistan Development Review 41:4 part II: 701-720.
Galor, Oded. and Daniel Tsiddon (1997). "The Distribution of Human Capital and Economic
Growth." Journal of Economic Growth 2(1): 93-124.
Glaeser, Edward. L, Hedi D. Kallal, Jose A. Scheinkman and Andrei Shleifer (1992). "Growth
in Cities." Journal of Political Economy 100: 1126-1152.
Glaeser, Edward L., and David C. Mare (2001). "Cities and Skills." Journal of Labour
Economics 19(2): 316-342.
Glomm, Gerhard. and B Ravikumar (1992). "Public Versus Private Investment in Human capital:
Endogenous Growth and Income Inequality." Journal of Political Economy 100(4): 818834.
33
Goldin, Claudia and Katz Lawrence (1999). "The Shaping of Higher Education: The Formative
Years in the United States, 1890-1940." Journal of Economic Perspectives 13(1): 37-62.
Greene, William. H. (1993). Econometric Analysis, 2nd Edition. New York, Prentice Mac
Millan.
Griliches, Zvi (1998). Technology, Education and Productivity. Oxford, Basil Blackwell.
Hanson, Gordon (2000). "Scale Economies and the Geographic Concentration of Industry."
NBER Working Papers 8013.
Haining, Robert (2003). Spatial Data Analysis:Theory and Practice.Cambridge University Press
Haque, Nadeem-ul (2005). "Brain Drain or Human Capital Flight." PIDE, Lectures in
Development Economics No. 11.
Haque, Nadeem ul- (2006). “Awake the Sleeper Within: Releasing the Energy of Domestic
Commerce” PIDE- Working Papers 2006:11
Heckman, James, J and Pedro Carneiro (2003). "Human Capital Policy." IZA Discussion Paper
No. 821.
Heckman, James J, Lance J. Lochner, and Petra J. Todd (2003). "Fifty Years of Mincer earnings
Regressions." NBER Working Papers 9732.
Hoodbhoy, Parvez (Ed) (1998) Education and State: Fifty Years of Pakistan, Oxford University
Press.
Huckfeldt, Robert (1986). Politics in Context: Assimilation and Conflict in Urban
Neighborhoods. New York: Agathon Press:
Hussain, Ishrat (1999). The Economy of an Elitist State. Karachi, Oxford University Press.
Islam, Nazar-ul (1995). "Growth Empirics: A Panel Data Approach." The Quarterly Journal of
Economics 110(No.4): 1127-1170.
Jaffe, Adam. B, Manuel Trajtenberg, and R. Henderson (1993). "Geographic Localization of
Knowledge Spillovers as Evidenced by Patent Citations." Quarterly Journal of
Economics 108: 577-598.
Jamal, Haroon and Amir Jahan Khan (2005). "The Knowledge Divide: Education Inequality in
Pakistan." The Lahore Journal of Economics 10(No.1): 83-104.
Kemal, A. R. (2005). "Skill Development in Pakistan." The Pakistan Development Review 44:4
Part II: 349-357.
34
Keuger, Alan. B and Mikael Lindhal (2001). "Education for Growth: Why and for Whom?"
Journal of Economic Literature 6(2): 289-339.
Kockelman, Kara M and Chris Frazier (2005). "Spatial Econometric Models for Panel Data:
Incorporating Spatial and Temporal Data." Transportation Research Record No. 1902:
80-90
Koopmans, Tjalling. C. (1965).
Amsterdam:North-Holland.
On the Concept of Optimal Economic Growth.
Krugman, Paul (1999). "Increasing Returns and Economic Geography." The Journal of Political
Economy 99(3): 483-499.
Krugman, Paul and Anthony J. Venables (1995). "Globalization and Inequality of Nations."
Quarterly Journal of Economics 11: 857-880.
Lagendijk, Arnoud and Paivi Oinas (2005). Proximity, Distance and Diversity, Issues on
Economic Interaction and Local Development. London, Ashgate.
Dominicis, Laura de, Guiseppe G. Arbia, and Henri L.F. de Groot (2007). "The Spatial
Distribution of Economic Activities in Italy." Timbergen Institute Discussion Paper
094/3.
Le Gallo, Julie. and Cem Ertur (2003). An Exploratory Spatial Data Analysis of European
Regional Disparties, 1980-1995, in European Regional Growth (Advances in Spatial
Sciences) by Bernard Fingleton (Ed), Springer.
Le, Trinh, John Gibson, and Les Oxley (2005). "Measures of Human Capital: A Review of the
Literature." New Zealand Treasury Working Paper 05/10.
Le, Trinh, John Gibson, and Les Oxley (2004). A Forward Looking Measure of the Stock of
Human Capital in New Zealand, Manuscript, Canterbury, New Zealand.
Lopez, R., V. Thomas and Y. Wang (1998). "Addressing the Education Puzzle. The Distribution
of Education and Economic Reforms." World Bank Working Papers 2031.
Lucas, Robert. E. (1988). "On the Mechanics of Economic Development." Journal of Monetary
Economics 22: 3-42.
Madden, Gary, and Scott Savage (1998). "CEE Telecommunications, and Economic Growth."
Information Economics and Policy 10.
Mankiw, Gregory, David Romer, and David Weil (1992). "A Contribution to the Emperics of
Economic Growth." Quarterly Journal of Economics: 407-437.
Marshall, Alfred (1890). Principles of Economics. London, MacMillan.
35
Martin, Ron (2003). “Putting Economics in its Place: On Economics and Geography.” Paper
presented at the Cambridge Journal of Economics Conference, available at
http://www.econ.cam.ac.uk/cjeconf/delegates/martin.pdf (accessed on 30th Nov 2008)
Martin, Ron. and Peter Sunley (1998). "Slow Convergence? The New Endogenous Growth
Theory and Regional Development " Economic Geography 74(3): 2010-227.
Maskell, Peter and Anders Malmberg (1999). "Localised Learning and Industrial
Competitiveness." Cambridge Journal of Economics 23(2): 167-185.
Memon, Ghulam R. (2007). "Education in Pakistan: The Key Issues, Problems and the New
Challenges." Journal of Management and Social Sciences 3(1): 47-55.
Midelfart-Knarvik Karen H, Henry G., Overman, Scott G. Redding and Anthony G. Venables
(2004). The Location of European Industry, in: A. Dierx, F. Ilzkovitz and K. Sekkat
(eds), European Integration and the Functioning of Product Markets. Cheltenham,
Edward Edgar Publishing.
Mincer, Jacob (1974). Schooling, Experience and Earnings. New York, Columbia University
Press.
Mion, Giordano (2004). "Spatial Externalities and Emperical Analysis: The Case of Italy."
Journal of Urban Economics 56: 97-118.
Moretti, Enricco (2004a). "Estimating the Social Return to Higher Education: Evidence From
Longitudnal and Repeated Cross-Sectional Data." Journal of Econometrics 121(1-2):
175-212.
Nasir, Zafar. M. (2001). "Poverty and Labour Market Linkages in Pakistan." MIMAP Technical
Paper Series No. 7.
Nasir, Zafar. M. (2002). "Returns to Human Capital in Pakistan: A Gender Disaggregated
Analysis." The Pakistan Development Review 41(No.1): 1-28.
Nasir, Zafar. M. and Nazli, Hina (2000). "Education and Earnings in Pakistan." PIDE Research
Report No.177.
Nayab, Durr-e- (2006). "Demographic Dividend or Demographic Threat in Pakistan." PIDE
Working Papers 2006:10.
Nelson, Richard and Edmond Phelps (1966). "Investment in Humans, Technological Diffusion,
and Economic Growth." American Economic Review: Papers and Proceedings 51(2): 6975.
Nijkamp, Peter and Jacques Poot (1998). "Spatial Perspectives on New Theories of Economic
Growth." Annals of Regional Science 32(1): 7-37.
36
Ottaviano, Gianmarco I.P. and Diego Puga (1998). "Agglomeration in the Global Economy: A
Survey of the New Economic Geography." World Economy 21(6):707-731.
Paci, Rafael and Stafano Usai (2001). "Externalities and Local Economic Growth in
Manufacturing Industries." Centro Ricerche Economiche Nord Sud, Universita degli
Studi di Cagliari, Contributi di Ricerca 01/13.
Pasha, A. G., Hafiz Pasha, R. Ghaus, A.R. Khan, N. Ahmed, and R. Khan (1996). "Social
Development Ranking of Districts of Pakistan." Social Policy and Development Centre,
Karachi SPDC Research Report No. 10.
Pasha, H. A., S. Malik, and H. Jamal (1990). "The Changing Profile of Regional Development in
Pakistan." Pakistan Journal of Applied Economics 9.
Pede, Valerien O., Raymond. J. G. M. Florax and Henri L.F. de Groot (2006). "Technological
Leadership, Human Capital and Economic Growth: A Spatial Econometric Analysis for
U.S. Counties, 1969-2003." Purdue University, Department of Agricultural Economics,
Staff Paper No. 06-09.
Pritchett, Lant (2001). "Where Has All the Education Gone?" World Bank Economic Review
15(3): 367-391.
Puga Diego and Anthony J. Venables (1997). "Preferential Trading and National Location."
Journal of International Economics 43(3-4): 347-368.
Rauch, James (1993). "Does History Matter only When it Matters little?" Quarterly Journal of
Economics 108: 843-867.
Rey, Sergio J. and Mark V. Janikas (2005). "Regional Convergence, Inequality and Space."
Journal of Economic Geography 5: 155-176.
Saint-Paul, G. and Thierry Verdier (1993). "Education, Democracy and Growth." Journal of
Development Economics 42(2): 399-407.
Sala-i-Martin, Xavier and Robert J Barro (1992). "Convergence." The Journal of Political
Economy 100(2): 223-251.
Sawada, Yasuyuki (1997). "Human Capital Investments in Pakistan: Implications of Micro
Evidence from Rural Households." The Pakistan Development Review 36:4 Part II: 695712.
Shapiro, Jesse M (2006). "Smart Cities: Quality of Life, Productivity, and the Growth Effects of
Human Capital." Review of Economics and Statistics 88(2).
37
Solow, Robert M. (1956). "A Contribution to the Theory of Economic Growth." Quarterly
Journal of Economics 70: 65-94.
Rosenthal, Stuart S and William C. Strange (2008). "The Attenuation of Human Capital
Spillovers." Journal of Urban Economics 64(2): 373-389
Suedekum, Jens (Forthcoming) "Convergence of Human Capital Shares Across Cities." in
Regional ScienceUrban Economics http://www.uni-due.de/js/smartcity.pdfand
Temple, Jonathan (1999). "A Positive Effect of Human Capital on Growth." Economic Letters
65: 131-134.
Temple, Jonathan (2000). "Growth Effects of Education and Social Capital in the OECD
Countries." Economic Department Working Papers, OECD, Paris No. 263.
Temple, Jonathan (2001). "Growth Effects of education and Social Capital in the OECD
Countries." OECD Economic Studies 33: 57-101.
Usman Mustafa, Kalbe Abbas and Amara Saeed (2005). "Enhancing Vocational Training for
Economic Growth in Pakistan." The Pakistan Development Review 44:4 Part II: 567-584.
Van Oort, Frank. G. (2004). Urban Growth and Innovation. Spatially Bounded Externalities in
the Netherlands. Aldershot, Ashgate.
Vasquez, John A. 1995. “Why Do Neighbors Fight? Proximity, Interaction, or Territoriality.”
Journal of Peace Research 32(3): 277-293.
Vaya, Esther, Enrique Lopez-Bazo, Rosina Moreno, and Jordi Surinach (2000). "Growth and
Externalities Across Economies. An Emprical Analysis Using Spatial Econometrics."
Universitat de Barcelona. Espai de Recerca en Economia, Working Papers in Economics
No. 59.
WöΒmann, Ludger (2003). "Specifying Human Capital." Journal of Economic Surveys
17(No.3): 239-270.
Woessmann, Ludger (2000). "Specifying Human capital: A Review, Some Extentions, and
Development Effects." Kiel Working Paper No. 1007.
Wooldridge, Jeffrey M. (2002). Econometric Analysis of Cross Section and Panel Data.
Cambridge, MA, MIT Press.
38
APPENDIX:
The following table summarizes the possible data available for this project:
Data Set
Source
Time Period
Description
1972, 1981,
1998, (next
expected in
2009)
1998, 1999,
2000, 2001
Demographic characteristics
1.
Population Census
(National and
Provincial reports)
Pakistan
Census
Organization
2.
Pakistan
Demographic Survey
(PDS)
Pakistan Integrated
Household Survey
(PIHS)
Federal
Bureau of
Statistics
Federal
Bureau of
Statistics
4.
Household Income
and Expenditure
Survey (HIES)
Federal
Bureau of
Statistics
5.
Pakistan Social and
Living Standards
Measurement Survey
(PSLM) Provincial
and district reports
Labour Force Survey
(LFS)
Federal
Bureau of
Statistics
3.
6.
7.
8.
Federal
Bureau of
Statistics
Pakistan Rural
Household Survey
(PRHS)
PIDE/IFPRI
Agriculture Census
Agriculture
Census
Organization
1990/91,
1995/96,
1996/97
1998/99
2001/02
1992/93
1996/97
1998/99
2001/02
2004/05
2006/07
From
1963/67 to
2006/07
1986-1991
Households
Revisited in
2001
1960, 1972,
1980, 1990,
2000
(cross section data set)
Information on trainings received,
average individual earnings,
school starting age, years of
schooling, , cognitive ability,
consumption, employment
characteristics,
(cross section data set)
Socio economic indicators:
health, education, household
assets, employment etc.
(cross section data set)
District wise labor force and
employment characteristics.
Regional distribution of
employment, unemployment,
underemployment rates.
Information on percentage
distribution of population by:
labor market activity, education
and distribution of workers by
establishments.
(Longitudinal Panel Data set)
Rural households income and
expenditure reports
Specific population and
employment characteristics. Also
include information on land
39
9.
10.
11.
12.
13.
14.
15.
16.
17.
ownership.
Labour Market
Asian
(cross section data)
Survey
Development
2003
Detailed information on region
Bank Report
wise vocational training in
Pakistan
Economic survey of
Ministry of
Extensive coverage of sector and
Pakistan
Finance
1960-2008 industry wise employment and
expenditure activities, their
exports and imports, and budgets.
Annual Budget
Ministry of
Districts and Sector wise
Report
Finance
1950-2008 information on public sector
expenditure on education and
R&D.
Statistical Handbook
State Bank
No of teachers in educational
of Pakistan
of Pakistan
2005
institutions ( 1963-2005)
Enrollment rates (1947-2005)
Distribution of population
according to occupation ( 19632004)
Other available data sets for potential cross country analysis:
Penn World Tables
World development World Bank
Human development indicators
Reports
UNESCO
United
Yearbooks
Nations
Asia-Pacific Year
Books
International Labor International
Labor statistics
Organization Year
Labor
Book
Organization
The Labour Force Survey of Pakistan (LFS)
The Federal Bureau of Statistics (FBS) has been carrying out the LFS since 1963. The
survey collects comprehensive statistics on the various characteristics of the civilian
labour force in Pakistan. The data collection is spread over four quarters of the year in
order to capture any seasonal variations in activities. The universe of the LFS consists of
all the rural and urban areas of the four provinces of Pakistan as defined by the
Population Census of 1998 excluding Federally Administered Tribal Areas (FATA) and
military restricted areas. The excluded regions do not present a grave problem since the
population of the excluded areas constitutes about 2% of the total population. Each
40
administrative district in Punjab, Sindh and NWFP is considered as an independent
stratum whereas in Balochistan, each ex-administrative division constitutes a stratum14.
14
The questionnaire for the LFS can be obtained at the following web link:
http://www.statpak.gov.pk/depts/fbs/publications/lfs2006_07/questionnaire-2006-07.pdf
41