Hi-Stat

Discussion Paper Series
No.18

Human Capital, Migration,
and Regional Income Convergencein the Philippines

Hideki Toya, Kaoru Hosono
and Tatsuji Makino
March 2004

Hitotsubashi University Research Unit
for Statistical Analysis in Social Sciences
A 21st-Century COE Program

Institute of Economic Research
Hitotsubashi University
Kunitachi, Tokyo, 186-8603 Japan
http://hi-stat.ier.hit-u.ac.jp/

February 28, 2004

Human Capital, Migration, and Regional Income Convergence in thePhilippinesψ

Hideki Toya*
Kaoru Hosono**
Tatsuji Makino**** Nagoya City University** Gakushuin University*** Chuo University

E-mail: toya@econ.nagoya-cu.ac.jp (H. Toya),kaoru.hosono@gakushin.ac.jp (K.Hosono)q8110003@crow.grad.tamacc.chuo-u.ac.jp (T. Makino)ψWe would like to thank Kazumi Asako, Kyoji Fukao, Hirohisa Kohama, Tsutomu
Miyagawa, Konosuke Odaka, Tetsuji Sonobe, Ken Togo and seminar participants at
Hitotsubashi University and Japan Economic Society and the members of the Project
for Economic Development and Industrial and Trade Structure in Asian Countries forhelpful comments and suggestions. Loida D. Cruz, Estrella V. Domingo, Isidoro P.
David, Raul V. Fabella, Yasuhiro Nimura and Shigeaki Fujisaki kindly provided us
with data and information. All remaining errors are ours. Financial supports from
J.S.P.S. Scientific Research Grand of Fundamental Reserach(C) (1) for "Economic
Development and Industrial and Trade Structure in Asian Countries: Empirical
Studies Based on Long-term Asian Statistics," The Japan Economic Research Institutefor "Economic Development and Industrial and Trade Structure in Asian Countries,”and Grand-in-Aid for Research in Nagoya City University are gratefully acknowledged.

Human Capital, Migration, and Regional Income Convergence in the Philippines

Abstract

We test the convergence of real income using the Philippine regional dataover the period of 1980-2000. Differences in real income across regions were large
and persistent. Though regional incomes did not converge towards a common level(absolute convergence), they did converge controlling for human capital measured
by average schooling years (conditional convergence). Human capital and its
accumulation contributed to economic growth. People with higher human capitalwere more likely to move across regions. In addition, people tended to move frompoor to rich regions. The absence in the absolute convergence may be due to thefact that higher human capital tended to move from poor to rich regions.

1Human Capital, Migration, and Regional Income Convergence in the Philippines

1. Introduction
Does regional dispersion in real income within a country tend to converge? If
so, how fast do they converge? If not, how can we restore the regional balance?
These questions have attracted many researchers’ and policymakers’ interests
recently. Most empirical studies have tested the regional convergence withindeveloped countries, e.g., the U.S., Japan, and Europe (e.g., Barro andSalai-i-Martin (1995), Sala-i-Martin (1996), Hofer and Wörgötter (1997), Funkeand Strulik (1999), Kawagoe (1999), Shioji (1999)), possibly due to the availabilityof regional statistics. Though some studies are emerging that treat the regional
convergence in developing countries (e.g., Koo et al. (1998) for Korea, Togo (2000)
for Malaysia, Lyons (1991), Chen and Fleisher (1996), Raiser (1998), and Wei(2000) for China), there is still scarce evidence on the convergence in developing
countries. Regional differences in developing countries are, however, generally by
far larger and are more important policy issues than those in developed countries.
In the Philippines, for example, the region with the highest per capita income
enjoys more than five times higher income than that with the lowest per capita
income. Though most of the studies on developed countries support theconvergence hypothesis, it still remains unanswered whether regional income
disparities converge in developing countries, taking into consideration the poor
domestic transport and communication, the concentration of industry in the central
region, etc. We aim at testing the convergence of regional income using thePhilippine regional data. Especially, we focus on the role of human capital and its
movement across regions in the differences of regional income across regions. The
role of human capital in growth is emphasized by many preceding cross-country
studies (e.g., Mankiw et al. (1992)).
We first test the absolute convergence, i.e., whether all the regions tend tomove towards the same steady state level of income. Using the pooled regional data2

of average growth rates over every 5 years from 1980 to 2000, we reject theabsolute convergence, on the contrary to many preceding regional studies in thedeveloped countries. We then test the conditional convergence, i.e., whether eachregion tends to move towards a level of income of each own steady state. Our
results support the convergence conditional on human capital. We find that humancapital measured by average schooling years contributed to growth, while physicalcapital investment ratios did not. We also examine whether migration acrossregions contributed to regional income convergence. If people move from poor torich regions, initially rich regions may see an increase in population and a decreasein per capital income as long as there is little difference in human capital acrossregions. On the other hand, if human capital varies across regions, whether
migration contributes to or hampers the income convergence depends on thequality of migrants. Our estimation results suggest that though rich regions
actually attracted more people, people with higher human capital were more likely
to move across regions. These facts concerning migration may account for theabsence in regional income convergence.
In section 2, we describe the data used in the following empirical studies. In
section 3, we test the absolute convergence. In section 4, we test the conditional
convergence. In section 5, we explore the determinants and effects of migration on
growth. Section 6 concludes.

2. Data
The main data that we use are per capita real GRP (gross regional
products) in the Philippines. GRP is available for 13 regions, i.e., NCR Metro
Manila and the other 12 regions over 1980-2000 from Philippine Statistical

Yearbook by NSCB. They are based on 1985 constant prices. Other data are fromthe Philippine Statistical Yearbook by NSCB, Philippine Yearbook, and Census ofPopulation and Housing. Details of the definitions and variables are described inAppendix A. Descriptive statistics are shown in Appendix B. Physical capital3

investment data is available only for the period of 1988 to 2000. Migration data isavailable only for the periods of 1975 to 1980 and 1985 to 1990.
We constructed the average schooling years based on Barro and Lee (1993).
Specifically, letting ASY denote the average schooling years, we define
6

ASY = ∑ Durationi * PopSharei

(1)

i =1

, where Durationi is the effective duration in years of the i th level of schoolingand PopSharei is the fraction of the population for which the i th level ofschooling is the highest attained. The levels of schooling consist of 1) elementary, 2)high school, 3) post secondary, 4) college undergraduate, 5) academic degree holder,and 6) post baccalaureate. Considering the presence of people who did not complete
schooling, we set the effective duration less than the complete duration: 4, 8, 9, 10,
13, 15, and 17 years for each level of schooling.1
As for the net movement of human capital, we constructed the following
measure.

Net Inflow ofHuman Capital i =∑ (Migration × ASY ) − (∑ Migration ) ×ASY
i, j

j ≠i

j

i≠ j

j ,i

Popi * ASYi

i(2)

, where Migrationi , j , ASYi , and POPi denote the number of people who moved
from region j to region i, average schooling years of region i, and population ofregion i, respectively. We implicitly assume that the schooling years of the migrants
from region i are on average equal to those of the residents of region i. If net inflow
of human capital is positive for region i, then those who migrates from the otherregions to region i is on average higher than those who migrates from region i tothe other regions.

3. The Absolute Convergence

The duration of compulsory education is 6 years, followed by 4-year second level
education (UNESCO, 95).

1

4

3.1 Stylized Facts
The average growth rates of real per capita GRP over the period of 1980-1985,
for example, vary from 0.17% in Ilocos Region to -4.63% in Western Visayas (Table
1).Such large differences across regions can be seen for the other periods. The
standard deviations are large and persistent.
We are interested in whether these large differences in growth rate tend toshrink the initial differences in the levels of regional income. Real per capita GRP
are plotted in Figure 1. We see that NCR Metro Manila is consistently higher thanany other regions. Little evidence seems to be found from Figure 1 that real per
capita GRP tends to converge to a common steady state level.

3.2 Statistical Tests
We first test whether all the regions tend to move towards the same steadystate. If such absolute convergence holds, the coefficient of the initial income, β , issignificantly negative in the following OLS estimation:

 q i ,t 
 = α + β ln (qi ,t −1 ) + ∑ δ t DUM t + u i ,t
ln

q
t
i
,
t
−
1



（3）

The dependent variable is the growth rate of GRP per capita for every five years over
1980-2000 for 13 regions. The explanatory variables are the logarithm of the initial real
per capita GRP and period dummies. The number of observations is 52. The estimation
result in the first column of Table 2 shows that the coefficient in the initial income isnegative but not statistically significant. Therefore, we cannot reject the null
hypothesis that regional income level did not converge to the same steady state level.
This is in contrast to many preceding studies that often found the absolute convergencein developed countries, i.e., in the U.S. states and Japanese prefectures (Barro andSala-i-Martin(1995)), and in the West German Länder (states) (Funke and Strulik(1999) )2. Figure 2 shows that the negative correlation between the initial real per

2

Hofer and Wörgötter (1997) found no significant absolute convergence in the Austrian5

capita GRP and the following growth rate is not clear.

4. Conditional Convergence: Human Capital and Private Investment
Despite the lack in the absolute convergence, each region may tend to movetowards an income level of its own steady state. We examine whether human capitaland physical capital affect the steady state income level of each region and whether
regional income disparities converge or not when these factors are controlled for.
We estimate the following equation using pooled regional data for every 5 years
over 1980-2000:

 q i ,t 
 = α + β ln (qi ,t −1 ) + X i ,t −1λ + ∑ δ t DUM t + u i ,t
ln

q
t
 i ,t −1 （4）, where X is a vector of the variables that may affect the steady state income level.
First, we control for the logarithm of the initial average schooling years and thegrowth rates of average schooling years. These are proxies of the average level ofhuman capital and the human capital accumulation, respectively. Column 2 of Table 2
shows that both of the human capital indices are significantly positive. These positive
correlations are illustrated by Figures 3 and 4. In addition, the initial per capita GRP
is significantly negative, implying that regional income disparities tended to converge
once the human capital level and its accumulation rate are controlled for. The
estimated speed of convergence, −1ln(1 + 5βˆ ) , is 1.1%.3 In Figure 5, we see a clear
5

negative partial relationship between the initial income level and the growth rate ofincome once human capital indices are controlled for. In column 3 of Table 2, we further
divide the average schooling years into the primary, secondary, and higher schoolingyears. Though all the human capital indices and their growth rates are positive, theregions during 1961-1989.
The estimated speed of conditional convergence, 1.1% per annum, is slower than thepreviously estimated speeds of absolute convergence. For example, Shioji (1999)
obtained an estimate of 8.6% for the Japanese prefectures. Islam (19995) obtained
estimates within a range from 4% to 10% for the cross-country evidence.
3

6

logarithm of initial higher schooling years and the growth rate of primary schoolingyears are significant at the 10 percent significance level.
Next, we control for the equipment investment as a proportion of GRP as ameasure of physical capital investment. Surprisingly, column 4 of Table 2 shows thatthe equipment investment ratio is not significant. In column 5 of Table 2, we include
the logarithm of the initial average schooling years, its growth rate, and equipment
investment ratio as explanatory variables. While human capital indices are significant,equipment investment ratio is not. Though we cannot further explore the reasons why
physical capital investment did not contribute to regional income growth, an a weakfinancial system might have lead to inefficient allocation of capital.

5. Migration and Human CapitalGiven large income differences across regions, people may move from a poor
to a rich region, which may contribute to the income convergence if human capitallevels are the same across regions. On the other hand, if human capital varies
across regions, whether migration contributes to or hampers the income
convergence depends on the quality of migrants. In this subsection, we estimate
the determinants of migration and its effect on regional income.

5.1 The determinants of migration
We explore the determinants of migration using the migration data over theperiods of 1975-80 and 1985-90. Specifically, we first estimate the following
equation following Barro and Sala-i-Martin (1995).

NetMigrationi ,t = α + β ln(qi ,t ) + γDensityi ,t + δCrimeRatei ,t + ηDUM i ,t + ε i ,t(5)

, where NetMigrationi ,t , Density i ,t , CrimeRatei ,t are the inflow of populationfrom the other regions to region i subtracted by the outflow of population fromregion i to the other regions as a proportion of total population in region i, thelogarithm of population density of region i, and crime rate of region i. We try to7

capture the degree of disamenity of region i by Density i ,t and CrimeRatei ,t .
Column 1 of Table 3 shows that the coefficient of the log of real per capita GRP issignificantly positive, suggesting that people tend to move from a poor to richregion. This can be clearly seen by Figure 6. Columns 2 and 3 show that thelogarithm of population density is significantly negative, as is expected, and thecrime rate is negative but insignificant.
Next, to account for the quality of labor that moves across regions, weestimate the determinants of net inflow of human capital. Columns 4 through 6 ofTable 3 show that the determinants of net inflow of human capital are similar to
those of net inflow of population except for the fact that the coefficient of the crime
rate is negative and marginally significant. Human capital, as well as population,
moves from a poor region to a rich one.

5.2 The Effects of Migration on Growth
Because population tends to move from a poor to a rich region, migration may
contribute to the convergence of regional income differences. On the other hand,because human capital also tends to move from a poor to a rich region, themovement of human capital may hamper the convergence of regional income
differences. In this subsection, we explore the effects of migration on regional
income.
In column 1 of Table 4, we see that net migration does not have a significant
effect on regional growth. This is robust to whether human capital indices areincluded (in column 2) or not (in column 1).In columns 3 and 4 of Table 4, we seethat net inflow of human capital does not have a significant effect on regional
growth, either. These results suggest that migration did not contribute to theconvergence of regional income.

5.3 The effects of Migration on Human Capital
Migration may affect the levels of regional human capital. If, for example, people8who attained higher education are more likely to migrate, the region that receives
migrants tend to attain a higher level of human capital. To explore this possibility, we
estimate the following equation:∆ASYi ,t = α + βNetMigrationi ,t −1 + γ ln( ASYi ,t −1 ) + δDUM t + ε i ,t(6)

, where ∆ASYi ,t is the growth in average schooling years. Column 1 of Table 5 showsthat net inflow of population have a positive but insignificant effect on the growth inhuman capital. In columns 2 to 4, we estimate the effects of migration on the growthrate of each level of education. The estimation results reveal that net inflow of
population have a significantly negative correlation with the growth in primary andsecondary schooling years, while it has a significantly positive correlation with thegrowth in higher schooling years. The latter relationship is illustrated by Figure 7.
These results suggest that people who attained higher education were more likely to
migrate than those who attained primary or secondary education. If this is the case,migration has two opposing effects on the growth of the region that receives migrants.
One is a growth-retarding effect through an increase in population growth. The other isa growth-enhancing effect through an increase in human capital. These two opposing
effects may account for the insignificant effect of migration on regional growth.
To investigate more closely the effect of migration on human capital, we estimate
the following equation:∆ASYi ,t = α + βNetInflowofHumanCapital i ,t −1 + γ ln( ASYi ,t −1 ) + δDUM t + ε i ,t(7)

Column 1 of Table 6 shows that net inflow of average schooling years has asignificantly positive effect on the growth in total schooling years. Estimating Eq.
(7) for each educational level (column 2 to 4), we see that net inflow of humancapital has a significantly positive effect on the growth in higher schooling years.
These results again suggest that people who attained higher education were more
likely to migrate than those who attained primary or secondary education.
6. Conclusion
Our results can be summarized as follows. First, there is no evidence that
regional income converges on a common steady state level from 1980 to 2000. Second,
there is statistical evidence that regional income converged conditional on humancapital. Third, while human capital and its accumulation tended to enhance real per

9capita income, physical capital investment did not. Fourth, while people tended tomove from a poor region to a rich one, migration did not have a significant andindependent effect on real per capital income. Finally, people who accumulated higher
human capital were more likely to migrate. The absence in income convergence may be
due to the fact that higher human capital tended to move from poor to rich regions.

10Reference

Barro, Robert J. and Xavier Sala-i-Martin (1995), Economic Growth, McGraw-Hill.
Chen, Jian and Belton M. Fleisher (1996), “Regional Income Inequality and Economic
Growth in China,” Journal of Comparative Economics 22, 141-164.
Funke, Michael, and Holger Strulik (1999), “Regional Growth in West Germany:
Convergence or Divergence?” Economic Modelling 16, 489-502.
Hofer, Helmut and Wörgötter, Andreas, “Regional Per Capita Income Convergence inAustraria,” Regional Studies, 31.1, 1-12.
Islam, Nazrul, “Growth Empirics: A Panel Data Approach,” Quarterly Journal of

Economics, 110, 1127-1170.
Kawagoe, Masaaki (1999), “Regional Dynamics in Japan: A Reexamination of Barro
Regressions,” Journal of the Japanese and International Economies, 13, 61-82.
Koo, Jaewoon, Young-Yong Kim, and Sangphil Kim (1998), “Regional Income
Convergence: Evidence from a Rapidly Growing Economy,” Journal of Economic

Development, 23(2), 191-203.
Lyons, Thomas P. (1991), “Interprovincial Disparities in China: Output andConsumption, 1952-1987,” Economic Development and Cultural Change.
Mankiw, N. Gregory, David Romer, and David N. Weil (1992), “A Contribution to the
Empirics of Ecnomic Growth,” Quarterly Journal of Economics, 107, 407-437.
Raiser, Martin(1998), “Subsidizing Inequality: Economic Reforms, Fiscal Transfers andConvergence across Chinese Provinces,” Journal of Development Studies 34(3),
1-26.
Sala-i-Martin, Xavier (1996), “Regional Cohesion: Evidence and theories of regional
growth and convergence,” European Economic Review 40, 1325-1352.
Shioji, Etsuro (1999), “Regional Income Convergence and Social Infrastructure in
Japan,” Yokohama National University Discussion Paper 99-J-1 (in Japanese).
Togo, Ken (2000), “Economic Growth and Regional Inequality,” Keizai Bunseki[Economic Analysis], 160, 92-119, Economic Research Institute, Economic

11Planning Agency, Tokyo, Japan.
Wei, Yehua Dennis (2000), Regional Development in China: States, Globalization, andInequality, Routledge.

12Appendix A: Definitions and Sources of Variables
Variables

Definition

PeriodSourceGRP per capita growth

Average annual growth rate ofreal per-capita Gross Regional
Products

1980-1985, 1985-1990,
1990-1995, and 1995-2000

PSY

Log of initial GRP per
capitaLogarithm of GRP per capita ineach initial period

1980, 1985, 1990, and 1995

PSY

Log of initial total
schooling year

Logarithm of average year of
total schooling in each initialperiod1980, 1985, 1990, and 1995

CP

Log of initial primaryschooling year

Logarithm of primary schoolingyear in each initial period

1980, 1985, 1990, and 1995

CP

Log of initial secondary
schooling year

Logarithm of secondary year ineach initial period

1980, 1985, 1990, and 1995

CP

Log of initial higher
schooling year

Logarithm of higher schoolingyear in each initial period

1980, 1985, 1990, and 1995

CP

Growth in total
schooling year

Growth in total schooling year
in each initial period

1980-1985, 1985-1990,
1990-1995, and 1995-2000

CP

Growth in primaryschooling year

Growth in primary schoolingyear in each initial period

1980-1985, 1985-1990,
1990-1995, and 1995-2000

CP

Growth in secondary
schooling year

Growth in secondary year ineach initial period

1980-1985, 1985-1990,
1990-1995, and 1995-2000

CP

Growth in higher
schooling year

Growth in higher schoolingyear in each initial period

1980-1985, 1985-1990,
1990-1995, and 1995-2000

CP

Equipment investment /GRP

Average gross domestic capitalformation in durable equipment/ GRP

1985-1990*, 1990-1995, and1995-2000

PSY

Population growth

Growth in total population

1980-1985, 1985-1990,
1990-1995, and 1995-2000

PSY

Net migration

Net inflow of population forthe previous period / total
population

1975-1980 and 1985-1990

CP

Net inflow of humancapital

Net inflow of total schoolingyear for the previous period /total schooling year

1975-1980 and 1985-1990

CP

Log of population
density

Logarithm of population
density1980 and 1995

PSY

Crime rate

Average crime rate for theprevious period

1975-1980 and 1985-1990

PY

Sources:

PSY: Philippines Statistical Yearbook
CP: Census of population
PY: Philippines Yearbook*We applied the data for 1988-1990 to the period of 1985-1990 due to the limited data
availability.

13Appendix B: Summary of Statistics of all Variables Used in the Analysis
Mean
GRP per capita growth
Log of initial GRP per capita
Log of initial total schooling year
Log of initial primary schooling year
Log of initial secondary schoolingyear
Log of initial higher schooling year
Growth in total schooling year
Growth in primary schooling year
Growth in secondary schooling year
Growth in higher schooling year
Equipment investment / GRP
Population growth
Net migration
Net inflow of human capitalLog of population density
Crime rate

0.001
9.104
1.918
0.718
0.825

Standard
deviation
0.023
0.447
0.168
0.246
0.309

Number of
Observations
52
52
52
52
52

0.832
0.016
-0.005
0.033
0.011
0.058
0.022
-0.002-0.002
5.409
0.003

0.294
0.006
0.021
0.012
0.022
0.053
0.007
0.016
0.012
1.221
0.001

52
52
52
52
52
39
52
26
26
26
26

14

Table 1: Average growth rates of real per capita GRP (%)
RegionMetro Manila
ILOCOS REGIONCAGAYAN VALLEY
CENTRAL LUZON
SOUTHERN TAGALOGBICOL REGIONWESTERN VISAYASCENTRAL VISAYASEASTERN VISAYASWSTERN MINDANAONORTHERN MINDANAOSOUTHERN MINDANAOCENTRAL MINDANAO
Mean
Std. Dev

1980-1985 1985-1990 1990-1995 1995-2000
NCR -4.67
3.43
-1.26
1.58
R1
0.17
2.99
-1.17
1.98
R2
-4.38
0.83
-0.69
4.25
R3
-2.96
1.57
-0.13
-0.97
R4
-2.69
3.17
-1.16
0.10
R5
-1.96
0.98
1.78
0.12
R6
-4.63
0.87
1.46
0.99
R7
-3.39
3.95
-0.87
2.57
R8
-1.53
0.41
1.24
0.68
R9
-2.44
0.03
3.81
0.65
R10
-2.31
0.95
-0.22
4.46
R11
-1.02
0.25
-2.86
2.37
R12
-2.26
0.54
2.50
-0.23
-2.62
1.54
0.19
1.43
1.37
1.30
1.78
1.59

15

Table 2: Growth convergence and the relationship between Human capital and Growth
Dependent variable: GRP per capita growth (1980-1985, 1985-1990, 1990-1995, and 1995-2000)

Constant
Log of initial GRP per capita

1
0.043(0.916)-0.008(-1.492)

Log of initial total schooling year2
-0.054(-1.034)-0.011(-1.772)
0.057(2.230)Log of initial primary schooling year

3
0.075
(1.014)-0.019(-2.183)

Log of initial higher schooling year
Growth in total schooling year

1.613
(3.483)Growth in primary schooling year

1.915
(3.384)0.919(1.701)0.387(1.255)0.529(1.330)Growth in secondary schooling year
Growth in higher schooling year
Equipment investment / GRP

Dummy for 1990-1995
Dummy for 1995-2000
Number of observations
Adjusted R2

5-0.060(-0.782)-0.010(-1.104)
0.071(2.170)0.016(0.778)0.012(0.605)0.065(1.681)

Log of initial secondary schooling year

Dummy for 1985-1990

4
0.071(0.977)-0.006(-0.762)0.041(7.096)0.028(4.692)0.040(6.838)
52
0.533

0.038
(6.091)0.016(2.234)0.023(2.748)
52
0.594

0.036
(3.907)0.013(0.974)0.007(0.181)
52
0.589

0.034
(0.466)0.028(0.407)-0.013(-2.022)-0.001(-0.206)
39
0.047-0.024(-3.679)-0.018(-2.562)39
0.221

Numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistentcovariance matrix.

16

Table 3: The determinants of inflow of population
Dependent variable

Net migration(1975-1980 and 1985-1990)
1
2
3-0.140-0.150-0.148(-9.980) (-10.742) (-8.682)
0.031
0.038
0.037
(10.19)(9.274)(8.307)-0.004-0.003(-2.205) (-1.522)-1.644(-1.036)-0.002-0.001-0.001(-0.570) (-0.308) (-0.369)
26
26
26
0.709
0.743
0.744

Constant
Log of initial GRP per
capitaLog of population density
Crime rate
Dummy for 1985-1990
Number of observations
Adjusted R2

Net inflow of human capital(1975-1980 and 1985-1990)
4
5
6-0.077-0.096-0.093(-3.070) (-8.177) (-6.494)
0.017
0.030
0.029
(3.008)(7.410)(6.822)-0.007-0.006(-5.304) (-4.082)-2.440(-1.792)-0.002-0.000-0.001(-0.571) (-0.131) (-0.251)
26
26
26
0.332
0.628
0.660

Numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistentcovariance matrix.
Table 4: Inflow of population and Growth
Dependent variable: GRP per capita growth (1980-85 and 1990-95)
Constant
Log of initial GRP per capita

1
0.185(2.349)-0.023(-2.698)

Log of initial total schooling year
Growth in total schooling year
Net migration

0.167(0.807)2
0.066(0.764)-0.020(-1.677)
0.037(1.265)1.446(2.967)0.052(0.210)Net inflow of human capital
Dummy for 1990-1995
Number of observations
Adjusted R2

0.027(4.862)
26
0.550

0.019
(2.685)
26
0.617

3
0.157
(3.014)-0.020(-3.556)

4
0.035(0.527)-0.016(-1.514)0.036(1.231)
1.513(2.990)0.105(0.594)0.027(4.830)26
0.546-0.095(-0.387)
0.019(2.712)26
0.619

Numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistentcovariance matrix.

17

Table 5: Inflow of population and human capital accumulation
Dependent variable

Growth in total
schooling year
1

Constant
Net migration
Log of initial total
schooling year

0.077
(5.795)0.074(1.318)-0.034(-4.676)

Log of initial primaryschooling year

Growth in
Growth in
Growth inprimary
secondaryhigherschooling year schooling year schooling year23
4-0.014(-1.146)-0.304(-2.840)0.048(9.333)-0.263(-2.135)0.046(14.69)0.204(2.356)-0.003(-0.246)

Log of initial secondary
schooling year

-0.008(-0.834)

Log of initial higher
schooling year
Dummy for 1990-1995

0.007(2.737)0.000(0.062)-0.010(-1.591)-0.034(-7.960)
0.009(3.143)

Number of observations
Adjusted R2

26
0.497

26
0.160

26
0.465

26
0.606

Numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistentcovariance matrix.

18

Table 6: Inflow of human capital and human capital accumulation
Dependent variable

Growth in total
schooling year
1

Constant
Net inflow of humancapitalLog of initial total
schooling year

0.072
(6.597)0.100(1.950)-0.032(-5.195)

Growth in
Growth in
Growth inprimary
secondaryhigherschooling year schooling year schooling year23
4-0.022(-1.903)-0.160(-1.043)

Log of initial primaryschooling year

0.054
(11.13)-0.056(-0.376)0.043(18.55)0.229(2.771)

0.007(0.544)Log of initial secondary
schooling year

-0.019(-2.006)

Log of initial higher
schooling year
Dummy for 1990-1995

0.007(2.753)0.002(0.570)-0.005(-0.728)-0.029(-7.977)
0.008(2.814)

Number of observations
Adjusted R2

26
0.509

26
-0.011

26
0.392

26
0.617

Numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistentcovariance matrix.

19

Figure 1: Gross regional product per capita from 1980 to 2000

log of GRP per capita

10.3

9.8

9.3

8.8

8.3
1980

1985
NCR

R1

1990
R2

R5
R10

R6
R11

R7
R12

20

1995
R3

2000
R4

R8

R9

Figure 2

Absolute convergence in per capita GRP growth
-0.02

R9
-0.04

R1

R10

R2
R7
R12

GRP growth

R5 R1
R8R8
R5

-0.06

R6
R1

-0.08

R8
R8
R5

R2
R2
R1R9

R7
R11

R12

R10
R3
R4
R6 R10 R3 R3
R6 R10R9R7R12R7R11 R4R4
R12
R2
R6
R3

R9
R5

NCR

NCR
NCR

R11

-0.1

-0.12
8.25

NCR

R4
R11

8.75

9.25

log of initial per capita GRP

21

9.75

10.25

Figure 3

GRP growth and human capital
0.16
NCR
R10

NCR

0.14
R2
R7

GRP growth

R11

R5

R9

0.12

R1

R8

R5

0.1
R9

R7

R10
R3
R12 R4
R2
R6
R11
R7
R5 R9
R2

R8

R8

R12
R9
0.08

0.06
1.55

1.65

NCR

R1
R11
R3

R10
R8

R6
R4
R7

R4

R1
R3
R10 R12
R6

NCR

R4
R5

R1

R3

R11

R6
R12

R2

1.75

1.85

1.95

2.05

log of total average schooling year

22

2.15

2.25

2.35

Figure 4

GRP growth and human capital accumulation
0.08
R9

0.06

R10

GRP growth

R11

0.04

R12

R2

R7

R7
R6R5R8
R8 R4

R1
R9

R11

R12

NCR
NCR
0.02

R1
R10 R5R6
R10R1
R7 R4 R7
R3
R3
R9
R10
R3
R6
R2
R4 R8 R2 R4
R12
R11
R5
R5

NCR
R11

0

R9
R12

R8

R6 R2
R1
R3

NCR

-0.02
0

0.01

0.02

Growth in total average schooling year

23

0.03

0.04

Figure 5

Conditonal convergence in per capita GRP growth
-0.12

-0.14

GRP growth

-0.16

R2
R5 R8
R1
R8
R5
R1

R9
R1

R5
R5 R8
-0.18

R8

R2
R2R9
R1

-0.2

R7
R6

R9

R11
R4

R10

R7
R11 R10R3 R3R12 R12
R6R10
R10
R6
R9
R2 R12

R3
R11

R7
R7
R12

R3
R11
R6

NCR
R4
R4

NCR

R4
NCR

-0.22
NCR

-0.24
8.25

8.75

9.25

log of initial per capita GRP

24

9.75

10.25

Figure 6

Net migration and GRP per capita
0.18
NCR

R10
NCR

0.16
R3

R12

R4 R4

Net migration

R10

R11

R11
R3
0.14
R2

R8
R1

R5

0.12

R2
R9
R1 R9

R12
R7
R6
R6
R7

R5
R8

0.1
3.5

4

4.5

5

log of initial per capita GRP

25

5.5

Figure 7

Growth in higher schooling year and Net migration
0.02

Growth in higher schooling year

R10
NCR

R12

0.01

R12

R9
R7

R4
R4
NCR
R10

0
R8

R1
R5
R7
R8R6

R9

R3

R2

R11
R11

R1

R3
-0.01

-0.02
-0.04

R5

R6 R2

0

Net migration

26

0.04

Figure 8

Growth in higher schooling year and Inflow of human capital
0.015
R10

R12

0.01

R12

Growth in higher schooling year

R9

0

NCR

R7

0.005

R4 R4

R10

R8

R9
NCR R2 R3

R5
R1
R7
R8
R6

-0.005

R11
R11

R1

R3
-0.01

-0.015
-0.03

R5

R2
R6

-0.01

0.01

Inflow of human capital

27

0.03