Human Capital and Regional Development

TL;DR: This paper investigated the determinants of regional development using a newly constructed database of 1569 sub-national regions from 110 countries covering 74 percent of the world surface and 97 percent of its GDP.

Abstract: We investigate the determinants of regional development using a newly constructed database of 1569 sub-national regions from 110 countries covering 74 percent of the world’s surface and 97 percent of its GDP. We combine the cross-regional analysis of geographic, institutional, cultural, and human capital determinants of regional development with an examination of productivity in several thousand establishments located in these regions. To organize the discussion, we present a new model of regional development that introduces into a standard migration framework elements of both the Lucas (1978) model of the allocation of talent between entrepreneurship and work, and the Lucas (1988) model of human capital externalities. The evidence points to the paramount importance of human capital in accounting for regional differences in development, but also suggests from model estimation and calibration that entrepreneurial inputs and possibly human capital externalities help understand the data.

Summary

HUMAN CAPITAL AND REGIONAL DEVELOPMENT*

• The authors combine the cross-regional analysis of geographic, institutional, cultural, and human capital determinants of regional development with an examination of productivity in several thousand establishments located in these regions.
• By decomposing human capital effects into those of worker education, entrepreneurial/managerial education, and externalities using a unified framework, the authors try to disentangle different mechanisms.
• Some institutions or culture may matter only at the national level, but then large income differences within countries call for explanations other than culture and institutions.

II.A. Production and Occupational Choice

• As in Lucas (1978), more skilled entrepreneurs run larger firms.
• Using equation (6), one can determine wages, profits, and capital rental rates as a function of regional factor supplies via the usual marginal product pricing.
• Equation (7) describes the allocation of labor within in a region from the total quantities of human and physical capital (Hi, Ki).

II.B. The Spatial Equilibrium: Consumption, Housing, and Mobility

• To compute the allocation of human capital, the authors must characterize labor mobility by computing the utility that laborers obtain from operating in different regions.
• A higher human capital stock has a negative effect on the wage because of diminishing returns, but once externalities are taken into account the net effect is ambiguous.
• The authors then prove the following .
• Because wages (and profits) are higher in the productive than in the unproductive regions, labor migrates to the former from the latter.
• Regional externalities moderate the adverse effect of fixed supplies of land and housing on mobility.

II.C. Empirical Predictions of the Model

• The return to schooling mj varies across individuals, potentially due to talent.
• This allows us to estimate different returns to schooling for workers and entrepreneurs.
• Card (1999) offers some evidence of heterogeneity in the returns to schooling.

II.D. Regional Income Differences

• The coefficient on regional schooling captures the product of the ‘‘technological’’ parameter [1þ 1 ð Þ] and the nationwide average of the regional Mincerian returns i.
• A similar interpretation holds with respect to the schooling coefficient [1þ 1 ð Þ].
• This creates a serious concern: because in their model some human capital migrates to more productive regions, any mismeasurement of regional productivity Ai may contaminate the coefficient of regional human capital.
• It allows us to rule out some of the most obvious determinants of productivity.
• Second, the authors compare these results to the coefficients obtained from firm-level regressions.

II.E. Firm-Level Productivity

• In equation (13), the output of a firm j operating in region i depends on the human capital hE,j of his entrepreneur (the authors assume there is only one entrepreneur and identify him with the top manager of the firm, as determined by his schooling SE,j and return to schooling E, j).
• The coefficient on regional schooling is the product of the externality parameter g and the population-wide average Mincerian return .4 4.
• First, their model literally implies that output per worker should be equalized across firms within a region.
• This is the variation the authors appeal to when estimating equation (17).

III. Data

• The authors analysis is based on measures of income, geography, institutions, infrastructure, and culture in up to 110 (out of 193 recognized sovereign) countries for which the authors found regional data on either income or education.
• The final data set has 1,569 regions in 110 countries: (a) 79 countries have regions at the first-level administrative division; and (b) 31 countries have regions at a more aggregated level than the first administrative level because one or several variables (often education) are unavailable at the first administrative 7.
• Critically, some of the Enterprise Surveys keep track of the highest educational attainment of the establishment’s top manager as well as of that of its average worker.
• The authors use three measures of geography and natural resources obtained from the WorldClim database, which are available for all regions of the world.
• The authors compute years of schooling at the country level by weighting the average years of schooling for each region by the fraction of the country’s population 15 and older in that region.

IV. Accounting for National and Regional Productivity

• The authors present cross-country and cross-region evidence on the determinants of productivity.
• Such specifications are loaded in favor of each variable seeming important because it does not compete with any other variable.
• The authors report both the within country and between countries R2 of these regressions.
• None come close to education in explaining within country variation in income per capita.
• The index of institutional quality explains 25% of cross-country variation, consistent with the empirical findings at the cross-country level such as King and Levine (1993) or Acemoglu, Johnson, and Robinson (2001), but the index explains 0% of within-country variation of per capita incomes.

UNIVARIATE REGRESSIONS FOR REGIONAL GDP PER CAPITA

• OLS regressions of (log) regional income per capita.
• Table III presents regressions of national per capita income on geography and education, in some instances controlling for population or employment, as suggested by their model.
• The final specification combines geography, education, institutions, and culture in one regression.
• The authors also find a small adverse effect of travel time but no role for other infrastructure variables, such as the density of power lines.
• The weakness of institutional variables may result in part from different data and in part from the fact that institutions may be important at the national, but not at the regional level (see Table III).

V. Establishment-Level Evidence

• In Table V, the authors turn to the micro evidence and estimate essentially equation (17).
• Robust standard errors are shown in parentheses.
• In the most parsimonious specification in the first column, the authors include proxies for geography and regional education; worker and manager schooling, log number of employees; log of property, plant, and equipment; and industry fixed effects (for 16 industries).
• The similarity in the magnitude of the management and worker schooling coefficients drives their calibration exercise.
• These results on geography should partially address the concern that regional schooling picks up the effect of omitted regional productivity.

OLS.

• The authors added additional controls to these regressions, and obtained similar results, including similar parameter estimates as those in Table V.
• To make the estimated coefficients comparable to those for years of education in Table IV, the authors multiply the shares of the population with college and high school degrees by 16 and 12, respectively (their weights in their standard measure of years of education).
• The authors could estimate OLS regressions with firm fixed effects.

VI. Calibration

• For their exercise, the authors focus on the value calibrated using national account statistics, and thus target a= .55 as their main benchmark.
• This is much higher than the 3% found in their firm-level data (in their model entrepreneurial income is a constant share of a firm’s output), implying gigantic Mincerian returns under an entrepreneurial share of .1. Acemoglu and Angrist (2000) estimate that a one-year increase in average schooling is associated with a 1%–3% increase in average wages.

VII. Conclusion

• Evidence from more than 1,500 subnational regions of the world suggests that regional education is a critical determinant of regional development, and the only such determinant that explains a substantial share of regional variation.
• Using data on several thousand firms located in these regions, the authors have also found that regional education influences regional development through education of workers, education of entrepreneurs, and perhaps regional externalities.
• A simple Cobb-Douglas production function specification used in development accounting would have difficulty accounting for all this evidence.
• The empirical findings the authors presented are consistent with the general predictions of this model and provide plausible values of the model’s parameters.
• The central message of the estimation/calibration exercise is that although private returns to worker education are modest and close to previous estimates, private returns to entrepreneurial education (in the form of profits), and possibly also social returns to education through external spillovers, are large.

Figures

TABLE II UNIVARIATE REGRESSIONS FOR REGIONAL GDP PER CAPITA

TABLE V GROSS VALUE ADDED

FIGURE II (Continued)

Full text

HUMAN CAPITAL AND REGIONAL DEVELOPMENT*
Nicola Gennaioli
Rafael La Porta
Florencio Lopez-de-Silanes
Andrei Shleifer
We investigate the determinants of regional development using a newly
constructed database of 1,569 subnational regions from 110 countries covering
74% of the world’s surface and 97% of its GDP. We combine the cross-regional
analysis of geographic, institutional, cultural, and human capital determinants
of regional development with an examination of productivity in several thou-
sand establishments located in these regions. To organize the discussion, we
present a new model of regional development that introduces into a standard
migration framework elements of both the Lucas (1978) model of the allocation
of talent between entrepreneurship and work, and the Lucas (1988) model of
human capital externalities. The evidence points to the paramount importance
of human capital in accounting for regional differences in development, but also
suggests from model estimation and calibration that entrepreneurial inputs and
possibly human capital externalities help understand the data. JEL Codes:
O110, R110, I250.
I. Introduction
We investigate the determinants of regional development
using a newly constructed database of 1,569 subnational regions
from 110 countries covering 74% of the world’s surface and 97% of
its gross domestic product (GDP). We explore the influences of
geography, natural resource endowments, institutions, human
capital, and culture by looking within countries. We combine
this analysis with an examination of productivity in several thou-
sand establishments covered by the World Bank Enterprise
Survey, for which we have both establishment-specific and
*We are grateful to Nicolas Ciarcia, Nicholas Coleman, Sonia Jaffe, Konstan-
tin Kosenko, Francisco Queiro, and Nicolas Santoni for dedicated research assist-
ance over the past five years. We thank Gary Becker, Nicholas Bloom, Vasco
Carvalho, Edward Glaeser, Gita Gopinath, Josh Gottlieb, Elhanan Helpman,
Chang-Tai Hsieh, Matthew Kahn, Pete Klenow, Robert Lucas, Casey Mulligan,
Elias Papaioannou, Jacopo Ponticelli, Giacomo Ponzetto, Jesse Shapiro, Chad
Syverson, David Weil, seminar participants at the UCLA Anderson School, Har-
vard University, University of Chicago, and NBER, as well as the editors and
referees of this journal for extremely helpful comments. Gennaioli thanks the
Barcelona Graduate School of Economics and the European Research Council
for financial support. Shleifer thanks the Kauffman Foundation for support.
! The Author(s) 2012. Published by Oxford University Press, on behalf of President and
Fellows of Harvard College. All rights reserved. For Permissions, please email: journals
.permissions@oup.com
The Quarterly Journal of Economics (2013), 105–164. doi:10.1093/qje/qjs050.
Advance Access publication on November 18, 2012.
105

regional data. In this analysis, human capital measured using
education emerges as the most consistently important determin-
ant of both regional income and productivity of regional
establishments. We then use the combination of regional and
establishment-level data to investigate some of the key chan-
nels through which human capital operates, including educa-
tion of workers, education of entrepreneurs/managers, and
externalities.
To organize this discussion, we present a new model describ-
ing the channels through which human capital influences prod-
uctivity, which combines three features. First, human capital of
workers enters as an input into the neoclassical production func-
tion, but human capital of the entrepreneur/manager influences
firm-level productivity independently. The distinction between
entrepreneurs/managers and workers has been shown empiric-
ally to be critical in accounting for productivity and size of firms
in developing countries (Bloom and Van Reenen 2007, 2010; La
Porta and Shleifer 2008; Syverson 2011). In the models of alloca-
tion of talent between work and entrepreneurship such as Lucas
(1978), Baumol (1990), and Murphy, Shleifer, and Vishny (1991),
returns to entrepreneurial schooling may appear as profits rather
than wages. By modeling this allocation, we trace these two sep-
arate contributions of human capital to productivity.
Second, our approach allows for human capital externalities,
emphasized in the regional context by Jacobs (1969), and in the
growth context by Lucas (1988, 2009) and Romer (1990). These
externalities result from people in a given location spontaneously
interacting with and learning from each other, so knowledge is
transmitted across people without being paid for. Because our
framework incorporates both the allocation of talent between
entrepreneurship and work as in Lucas (1978) and human capital
externalities as in Lucas (1988), we call it the Lucas-Lucas
model.
1
By decomposing human capital effects into those of
worker education, entrepreneurial/managerial education, and
externalities using a unified framework, we try to disentangle
different mechanisms.
1. We do not consider the role of human capital in shaping technology adoption
(Nelson and Phelps 1966). For recent models of these effects, see Benhabib and
Spiegel (1994), Klenow and Rodriguez-Clare (2005), and Caselli and Coleman
(2006). For evidence, see Coe and Helpman (1995), Ciccone and Papaioannou
(2009), and Wolff (2011).
QUARTERLY JOURNAL OF ECONOMICS
106

Third, we need to consider the mobility of firms, workers, and
entrepreneurs across regions, which is presumably less expensive
than that across countries. Our model follows the standard urban
economics approach (e.g., Roback 1982; Glaeser and Gottlieb
2009) of labor mobility across regions with land and housing lim-
iting universal migration into the most productive regions. This
formulation allows us to analyze the conditions under which the
regional equilibrium is stable and to consider jointly the educa-
tion coefficients in regional and establishment level regressions.
To begin, we examine the determinants of regional income in
a specification with country fixed effects. Our approach follows
development accounting, as in Hall and Jones (1999), Caselli
(2005), and Hsieh and Klenow (2010). Among the determinants
of regional productivity, we consider geography, as measured by
temperature (Dell, Jones, and Olken 2009), distance to the ocean
(Bloom and Sachs 1998), and natural resources endowments. We
also consider institutions, which have been found by King and
Levine (1993), De Long and Shleifer (1993), Hall and Jones
(1999), and Acemoglu, Johnson, and Robinson (2001) to be sig-
nificant determinants of development. We also look at culture,
measured by trust (Knack and Keefer 1997), and ethnic hetero-
geneity (Easterly and Levine 1997; Alesina et al. 2003). Last, we
look at average education in the region. A substantial cross-
country literature points to a large role of education. Barro
(1991) and Mankiw, Romer, and Weil (1992) are two early empir-
ical studies; de La Fuente and Domenech (2006), Breton (2012),
and Cohen and Soto (2007) are recent confirmations. Across coun-
tries, the effects of education and institutions are difficult to dis-
entangle: both variables are endogenous and the potential
instruments for them are correlated (Glaeser et al. 2004). By
using country fixed effects, we avoid identification problems
caused by unobserved country-specific factors.
We find that favorable geography, such as lower average
temperature and proximity to the ocean, as well as higher natural
resource endowments, are associated with higher per capita
income in regions within countries. We do not find that culture,
as measured by ethnic heterogeneity or trust, explains regional
differences. Nor do we find that institutions as measured by
survey assessments of the business environment in the
Enterprise Surveys help account for cross-regional differences
within a country. Some institutions or culture may matter only
at the national level, but then large income differences within
HUMAN CAPITAL AND REGIONAL DEVELOPMENT 107

countries call for explanations other than culture and institu-
tions. In contrast, differences in educational attainment account
for a large share of the regional income differences within a coun-
try. The within-country R
2
in the univariate regression of the log
of per capita income on the log of education is about 25%; this R
2
is not higher than 8% for any other variable.
Acemoglu and Dell (2010) examine subnational data from
North and South America to disentangle the roles of education
and institutions in accounting for development. The authors find
that about half of the within-country variation in levels of income
is accounted for by education. This is similar to the Mankiw,
Romer, and Weil (1992) estimate for a cross-section of countries.
We confirm a large role of education and try to go further in iden-
tifying the channels. Acemoglu and Dell also conjecture that in-
stitutions shape the remainder of the local income differences. We
have regional data on several aspects of institutional quality and
find that their ability to explain cross-regional differences is
minimal.
2
In regional regressions, human capital in a region may be
endogenous because of migration. To make progress, we examine
the determinants of firm-level productivity. We merge our data
with World Bank Enterprise Surveys, which provide
establishment-level information on sales, labor force, educational
level of management and employees, as well as energy and capital
use for several thousand establishments in the regions for which
we have data. We estimate the production function predicted by
our model using several methods, including Levinsohn and
Petrin’s (2003) panel approach. The micro data point to a large
role of managerial/entrepreneurial human capital in raising firm
productivity. We also find that regional education has a large
positive coefficient, consistent with sizable human capital extern-
alities. However, because regional education may be correlated
with unobserved region-specific productivity parameters, we do
not have perfect identification of externalities.
To assess the extent to which firm-level results can account
for the role of human capital across regions, we combine estima-
tion with calibration following Caselli (2005). We rely on previous
research regarding factor shares (e.g., Gollin 2002; Caselli and
2. Recent work argues that regions within countries that were treated par-
ticularly badly by colonizers have poor institutions and lower income today
(Banerjee and Iyer 2005; Dell 2010; Michalopoulos and Papaioannou 2011).
QUARTERLY JOURNAL OF ECONOMICS
108

Feyrer 2007; Valentinyi and Herrendorf 2008), but then combine
it with coefficient estimates from regional and firm-level regres-
sions. Our calibrations show that worker education, entrepre-
neurial education, and externalities all substantially contribute
to productivity. We find the role of workers’ human capital to be in
line with standard wage regressions, which are the benchmark
adopted by conventional calibration studies (e.g., Caselli 2005).
Crucially, however, our results indicate that focusing on worker
education alone substantially underestimates both private and
social returns to education. Private returns are very high but to
a substantial extent earned by entrepreneurs, and hence might
appear as profits rather than wages, consistent with Lucas
(1978). Although we have less confidence in the findings for
externalities, our best estimates suggest that those are also siz-
able. In sum, the evidence points to a large influence of entrepre-
neurial human capital, and perhaps of human capital
externalities, on productivity.
In Section II, we present a model of regional development
that organizes the evidence. In Section III, we describe our
data. Section IV examines the determinants of both national
and regional development. Section V presents firm-level evi-
dence, and Section VI calibrates the model to assess its ability
to explain income differences. Section VII concludes.
II. A Lucas-Lucas Spatial Model of Regional and
National Income
A country consists of a measure 1 of regions, a share p of
which has productivity
~
A
P
and a share 1  p of which has prod-
uctivity
~
A
U
<
~
A
P
. We refer to the former regions as ‘‘productive,’’
to the latter regions as ‘‘unproductive,’’ and denote them by
i = P, U. A measure 2 of agents is uniformly distributed across
regions. An agent j enjoys consumption and housing according
to the utility function:
uðc, aÞ¼c
1
j
a

j
,ð1Þ
where c and a denote consumption and housing, respectively.
Half the agents are ‘‘rentiers,’’ the remaining half are ‘‘laborers.’’
Each rentier owns 1 unit of housing, T units of land, K units of
physical capital (and no human capital). Each laborer is endowed
with h 2R
++
units of human capital. In region i = P, U the
HUMAN CAPITAL AND REGIONAL DEVELOPMENT 109

Frequently asked questions

Q1. What are the future works in "Human capital and regional development*" ?

The observed large benefits of education through the creation of a supply of entrepreneurs and through externalities offer an optimistic assessment of the possibilities of economic development through raising educational attainment. 

Q2. What contributions have the authors mentioned in the paper "Human capital and regional development*" ?

The authors investigate the determinants of regional development using a newly constructed database of 1,569 subnational regions from 110 countries covering 74 % of the world ’ s surface and 97 % of its GDP. To organize the discussion, the authors present a new model of regional development that introduces into a standard migration framework elements of both the Lucas ( 1978 ) model of the allocation of talent between entrepreneurship and work, and the Lucas ( 1988 ) model of human capital externalities. The evidence points to the paramount importance of human capital in accounting for regional differences in development, but also suggests from model estimation and calibration that entrepreneurial inputs and possibly human capital externalities help understand the data. 

Q3. Why do the authors not include countries with no administrative divisions in the sample?

Because the authors focus on regions, and typically run regressions with country fixed effects, the authors do not include countries with no administrative divisions in the sample. 

Q4. What is the effect of a higher human capital stock on the wage?

A higher human capital stock has a negative effect on the wage because of diminishing returns, but once externalities are taken into account the net effect is ambiguous. 

Q5. What is the important determinant of regional income and productivity?

In this analysis, human capital measured using education emerges as the most consistently important determinant of both regional income and productivity of regional establishments. 

Q6. Why is the cutoff rule in (1) intuitive?

The cutoff rule in (1) is intuitive: more skilled people have a greater incentive to pay the migration cost because the wage (or profit) gain they experience from doing so is higher. 

Q7. What is the effect of an extra year of schooling on the TFP?

Iranzo and Peri (2009) estimate that one extra year of college per worker increase the state’s TFP by a very significant and large 6%–9%, whereas the effect of an extra year of high school is closer to 0%–1%. 

Q8. What are the main factors that contribute to productivity?

Their calibrations show that worker education, entrepreneurial education, and externalities all substantially contribute to productivity. 

Q9. How did the authors compute regional averages for temperature and distance to coast?

The authors computed regional averages for temperature and distance to coast by first summing the (average) values of the relevant variable for all grid cells lying within a region and then dividing by the number of cells lying within a region. 

Q10. Why do the authors not interpret the large education coefficients as the causal impact of human capital on regional?

Due to potential migration of better educated workers to more productive regions, the authors cannot interpret the large education coefficients—which appear to come through with a similar magnitude across a range of specifications—as the causal impact of human capital on regional income.