Human Capital, Migration, and Regional Income Convergence...
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Human Capital, Migration, and Human Capital, Migration, and Regional Income Convergence in Regional Income Convergence in the Philippines the Philippines Toya Hideki Kaoru Hosono Tatsuji Makino Questions Questions Do real income converge across Philippine regions? Why or Why not? e.g. technological transfer capital accumulation capital movement migration human capital movement Focus Focus Human capital and its accumulation Migration of population and human capital across regions Data Data Per capita real GRP (Gross Regional Products) 13 regions every 5 years over the 1980-2000 period. ASY (Average Schooling Years) PopShare(i): Fraction of population for which the ith level of schooling is the highest attained. ∑ = = 6 1 * i i i PopShare Duration ASY Data Data Net Migration Rate: 1875-80, 1985-90 Migration(i,j): Number of people who move from region j to region i. Net Inflow of Human Capital: 1975-80, 1985-90 i i j j i i j j i i Pop Migration Migration Migration Net ∑ ∑ ≠ ≠ − = , , i i i j i j i i j j j i i ASY Pop ASY Migration ASY Migration Capital ofHuman Inflow Net * ) ( ) ( , , ∑ ∑ ≠ ≠ × − × = Absolute Convergence Absolute Convergence Do all the regions tend to move towards the same steady state? β=-0.008 (-1.492) (Table2, Column 1) ( ) t i t t t t i t i t i u DUM q q q , 1 , 1 , , ln ln + + + = ∑ − − δ β α
Transcript of Human Capital, Migration, and Regional Income Convergence...
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Human Capital, Migration, and Human Capital, Migration, and Regional Income Convergence in Regional Income Convergence in
the Philippinesthe PhilippinesToya Hideki
Kaoru HosonoTatsuji Makino
QuestionsQuestionsDo real income converge across Philippine regions?Why or Why not?e.g. technological transfer
capital accumulationcapital movement migrationhuman capital movement
FocusFocus
Human capital and its accumulationMigration of population and human capital across regions
DataData
Per capita real GRP (Gross Regional Products) 13 regionsevery 5 years over the 1980-2000 period.ASY (Average Schooling Years)
PopShare(i): Fraction of population for which the ith level of schooling is the highest attained.
∑=
=6
1*
iii PopShareDurationASY
DataDataNet Migration Rate: 1875-80, 1985-90
Migration(i,j): Number of people who move from region j to region i.Net Inflow of Human Capital: 1975-80, 1985-90
i
ij jiijji
i Pop
MigrationMigrationMigrationNet
∑ ∑≠ ≠
−=
,,
ii
iji
jiijjji
i ASYPop
ASYMigrationASYMigrationCapitalofHumanInflowNet
*
)()( ,,∑ ∑≠ ≠
×−×=
Absolute ConvergenceAbsolute Convergence
Do all the regions tend to move towards the same steady state?
β=-0.008 (-1.492) (Table2, Column 1)
( ) titt
ttiti
ti uDUMqqq
,1,1,
, lnln +++=
∑−
−
δβα
2
Absolute Convergence (Figure 2)Absolute Convergence (Figure 2)
8.25 8.75 9.25 9.75 10.25-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
NCR
NCR
NCR
NCR
R1
R1
R1
R1
R2
R2R2
R2
R3
R3
R3
R3
R4
R4
R4R4
R5
R5
R5
R5
R6
R6
R6
R6
R7
R7
R7
R7R8
R8
R8
R8
R9
R9
R9
R9
R10
R10R10
R10
R11
R11
R11
R11
R12
R12
R12
R12
log of initial per capita GRP
GR
P gr
owth
Absolute convergence in per capita GRP growth
Convergence Conditional on Convergence Conditional on Human Capital Human Capital
Do human capital and its accumulation affect the steady state income level?Do regional incomes converge when human capital levels are controlled for?
Table 2, Column 2 λ(Human capital)=0.057(2.230)λ(Growth in human capital)=1.613(3.483)β(Initial income)=-0.011(-1.772):Speed of convergence=1.1% per annum
( ) titt
ttititi
ti uDUMXqqq
,1,1,1,
, lnln ++++=
∑−−
−
δλβα
Figure 3Figure 3
1.55 1.65 1.75 1.85 1.95 2.05 2.15 2.25 2.350.06
0.08
0.1
0.12
0.14
0.16
NCR
NCR
NCR
NCR
R1
R1
R1
R1
R2
R2
R2
R2
R3
R3
R3
R3
R4
R4
R4
R4
R5
R5
R5
R5
R6
R6
R6
R6
R7
R7
R7
R7
R8
R8
R8
R8
R9
R9
R9
R9
R10
R10R10
R10
R11
R11
R11
R11
R12
R12
R12
R12
log of total average schooling year
GR
P gr
owth
GRP growth and human capital
Figure 4Figure 4
0 0.01 0.02 0.03 0.04-0.02
0
0.02
0.04
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0.08
NCR
NCR
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NCR
R1
R1
R1
R1
R2
R2R2
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R6R7
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R8
R9
R9
R9
R9R10
R10
R10
R10
R11
R11
R11
R11 R12
R12
R12
R12
Growth in total average schooling year
GR
P gr
owth
GRP growth and human capital accumulation
Figure 5Figure 5
8.25 8.75 9.25 9.75 10.25-0.24
-0.22
-0.2
-0.18
-0.16
-0.14
-0.12
NCR
NCR
NCR
NCR
R1
R1
R1
R1
R2
R2R2
R2
R3
R3R3
R3
R4
R4
R4
R4
R5
R5
R5
R5
R6
R6
R6
R6
R7
R7
R7
R7
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R8
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R8
R9
R9
R9
R9
R10
R10R10
R10R11
R11
R11
R11
R12
R12
R12
R12
log of initial per capita GRP
GR
P gr
owth
Conditonal convergence in per capita GRP growth
Convergence Conditional on Convergence Conditional on Physical Capital InvestmentPhysical Capital Investment
Table 2, Column 4 λ(Equipment Investment)=0.034(0.466)β(Initial income)=-0.006(-0.762)
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Migration and Human CapitalMigration and Human Capital
If human capital is the same across regions, migration from poor to rich regions tends to shrink regional disparities in income.If migrants are highly educated and if human capital has positive external effects, migration may widen regional disparities in income.
Determinants of MigrationDeterminants of Migration
β=0.031 (10.19) (Table 3, Column 1)titititititi DUMCrimeRateDensityqonNetMigrati ,,,,,, )ln( εηδγβα +++++=
3.5 4 4.5 5 5.50.1
0.12
0.14
0.16
0.18
R8
R5R5
R8R1 R7R6
R6R7R2R1 R12
R9 R9
R2
R3
R11
R10 R11R4 R4
R3R12
NCR R10
NCR
log of initial per capita GRP
Net
mig
ratio
n
Net migration and GRP per capita
Direct effect of migration on Direct effect of migration on growthgrowth
Table 4, Column 1 λ (net migration)=0.167 (0.807)
( ) titt
ttititi
ti uDUMXqqq
,1,1,1,
, lnln ++++=
∑−−
−
δλβα
Migration and Human CapitalMigration and Human Capital
Table 5Total schooling years: β=0.074(1.318)Primary schooling years:β=-0.304(2.840)Secondary schooling years:
β=-0.263(-2.315)Higher schooling years:β=0.204(2.356)
tittititi DUMASYonNetMigratiASY ,1,1,, )ln( εδγβα ++++=∆ −−
Migration and Human CapitalMigration and Human CapitalInterpretation
(1)Educational levels of migrants are higher than those of original inhabitants.
(2)Original inhabitants who live with more migrants tend to attain a higher level of education.
(3)Migrants are likely to go to the regions where growth in human capital and hence in income is high.
→Given that net migration variable is the 5-year lagged value, (1) is most likely.
Effects of migration on higher Effects of migration on higher schooling yearsschooling years
-0.04 0 0.04-0.02
-0.01
0
0.01
0.02
R8
R5
R5
R8
R1
R7R6
R6
R7
R2
R1
R12
R9
R9 R2R3
R11
R10
R11
R4R4
R3
R12
NCR
R10
NCR
Net migration
Gro
wth
in h
ighe
r sch
oolin
g ye
ar
Growth in higher schooling year and Net migration
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Effects of migration on higher Effects of migration on higher schooling yearsschooling years
-0.03 -0.01 0.01 0.03-0.015
-0.01
-0.005
0
0.005
0.01
0.015
R8
R5
R1R5
R7R6
R8
R6R2
R1
R7
R12
R9
NCR R9
R2
R11
R3
R10
NCR
R11
R3
R10
R4 R4
R12
Inflow of human capital
Gro
wth
in h
ighe
r sch
oolin
g ye
ar
Growth in higher schooling year and Inflow of human capital
We have foundWe have found……
No absolute convergenceConvergence conditional on human capitalPositive effects of human capital and its accumulation on growthMigration from poor to rich regionsMigration of highly educated people
ConclusionConclusion
Migration of highly educated people from poor to rich regions enhances the human capital level of the host regions, which may result in persistent income disparities across regions due to the externality of human capital.
