Questionsz Do real income converge across Philippine

Human Capital, Migration, andRegional Income Convergence inthe Philippines

regions?
z Why or Why not?

e.g. technological transfer
capital accumulation
capital movement
migration
human capital movement

Toya Hideki
Kaoru Hosono
Tatsuji Makino

Focus

Data

z Human capital and its accumulationz Per capita real GRP (Gross Regional Products)z Migration of population and human capital

13 regions
every 5 years over the 1980-2000 period.
z ASY (Average Schooling Years)

across regions

6ASY = ∑ Duration i * PopSharei
i =1

PopShare(i): Fraction of population for which theith level of schooling is the highest attained.

Dataz

Absolute Convergence

Net Migration Rate: 1875-80, 1985-90
Net Migration i =∑ Migration − ∑ Migrationj ≠ii, ji≠ jz Do all the regions tend to move towards thesame steady state?

j ,i

Pop i

 q i ,t 
 = α + β ln (qi ,t −1 ) + ∑ δ t DUM t + u i ,tlnt qi ,t −1 Migration(i,j): Number of people who move from region jto region i.
z Net Inflow of Human Capital: 1975-80, 1985-90
Net Inflow ofHuman Capital i =∑ (Migration × ASY ) − (∑ Migration ) ×ASYj ≠ii, jji≠ jj ,iz β=-0.008 (-1.492)(Table2, Column 1)i

Pop i * ASYi

1

Convergence Conditional on
Human Capital

Absolute Convergence (Figure 2)
Absolute convergence in per capita GRP growth

Do human capital and its accumulation affect the steadystate income level?
z Do regional incomes converge when human capital levels
are controlled for?
z-0.02R9-0.04

R10R2R1

R7R12GRP growth

R5 R1
R1R5R10R3R4
R6 R10 R3 R3
R6 R10R9R7R12R7R11 R4R4R12R2R6R3R2R2R8R8R5R1R9 q i ,t 
 = α + β ln (q i ,t −1 ) + X i ,t −1λ + ∑ δ t DUM t + u i ,tlnt
 q i ,t −1 

R7R11R12R9-0.08

NCR

R4R11R6R8R8R5-0.06

NCR

Table 2, Column 2λ(Human capital)=0.057(2.230)
z λ(Growth in human capital)=1.613(3.483)
z β(Initial income)=-0.011(-1.772):
z Speed of convergence=1.1% per annumzzNCR
NCR

R11-0.1-0.128.25

8.75

9.25

9.75

10.25

log of initial per capita GRP

Figure 3Figure 4

GRP growth and human capital

GRP growth and human capital accumulation
0.08

0.16
NCR
R10R9NCR
0.06

0.14

R10R2GRP growth

R90.12

R1

R8R50.1
R9R10
R3R2

R12R8R4NCR

R4R5R1

R110.04
NCR
NCR
0.02

R3NCR

R6R9R120.08

R110R2

R6R5R8R8 R4R1
R9R11R1
R10 R5
R6R1
R10R7 R4 R7R3R3
R9R10R3R6R2
R4 R8 R2 R4R12R11R5R5R11R8R12R11R1R3R10 R12R6

R4R6R7 R11
R5 R9R2R7R7R3R10R8R12NCR

R1

GRP growth

R6R5 R4
R7R11R2

R7R7R12R9R12R8

R6 R2
R1
R3

NCR

0.06
1.55

-0.02

1.65

1.75

1.85

1.95

2.05

2.15

2.25

Conditonal convergence in per capita GRP growth-0.14

GRP growth

0.01

0.02

0.03

0.04

z Table 2, Column 4-0.12

z λ(Equipment Investment)=0.034(0.466)

R2
R9R5 R8R1R8R5R1

R1R8z β(Initial income)=-0.006(-0.762)

R7R6R9R11R4R10R7R11 R10R5R5 R8-0.18

0Convergence Conditional on
Physical Capital Investment

Figure 5-0.16

2.35

Growth in total average schooling year

log of total average schooling year

R2
R2R9R1-0.2

R3 R3
R12 R12R6 R10R6 R10R9R2 R12R3R11R7R7R12R3R11R6NCR
R4R4NCR

R4NCR

-0.22
NCR

-0.24
8.25

8.75

9.25

9.75

10.25

log of initial per capita GRP

2

Migration and Human Capital

Determinants of Migration

z If human capital is the same across regions,

NetMigrati oni ,t = α + β ln( q i ,t ) + γDensity i ,t + δCrimeRate i ,t + ηDUM i ,t + ε i ,tz β=0.031 (10.19)(Table 3, Column 1)

Net migration and GRP per capita
0.18
NCR

R10NCR0.16
R3R12

R4 R4R10Net migration

migration from poor to rich regions tends toshrink regional disparities in income.
z If migrants are highly educated and if
human capital has positive external effects,
migration may widen regional disparities inincome.

R11R11R30.14
R2R12R7R6R8R1R50.12R2R9R1 R9R6R7R5R80.1
3.544.555.5

log of initial p er capita GRP

Direct effect of migration ongrowth q i ,t  = α + β ln (qi ,t −1 ) + X i ,t −1λ + ∑ δ t DUM t + u i ,tlnt qi ,t −1 z Table 4, Column 1z λ (net migration)=0.167 (0.807)

Migration and Human Capital∆ASYi ,t = α + β NetMigrati oni ,t −1 + γ ln( ASYi ,t −1 ) + δDUM t + ε i ,tz Table 5β=0.074(1.318)

z Total schooling years:z Primary schooling years:β=-0.304(2.840)
z Secondary schooling years:

β=-0.263(-2.315)
z Higher schooling years:β=0.204(2.356)

Effects of migration on higher
schooling years

Migration and Human Capital

Growth in higher schooling year and Net migration
0.02

R10

Growth in higher schooling year

Interpretation(1)Educational levels of migrants are higher thanthose 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 ishigh.
→Given that net migration variable is the 5-year
lagged value, (1) is most likely.

NCR

R120.01

R12R9R7R4R4NCR
R100R8R1R5R7R8R6R9R3R2R11R11R1

R3-0.01

-0.02
-0.04

R5R6 R200.04

Net migration

3

Effects of migration on higher
schooling years
Growth in higher schooling year and Inflow of human capital

We have found…
z No absolute convergence

0.015
R10z Convergence conditional on human capital

R120.01

R12Growth in higher schooling year

R90z Positive effects of human capital and its

NCR

R70.005

R4 R4R9NCR R2 R3R5R1R11R7R8R6-0.005

accumulation on growthz Migration from poor to rich regionsz Migration of highly educated people

R10R8R1

R11R3-0.01-0.015-0.03

R5R2
R6-0.01

0.01

0.03

Inflow of human capital

Conclusionz Migration of highly educated people frompoor to rich regions enhances the humancapital level of the host regions, which may
result in persistent income disparities acrossregions due to the externality of humancapital.

4