Influences of transition in age-education structure and

Influences of transition

in age-education structure

and internal migration

on the labor market in Brazil

Ernesto Friedrich de Lima Amaral

Political Science Department (DCP)

Federal University of Minas Gerais (UFMG)

Funded by the Brazilian Institute of Applied Economics Research (IPEA)

1

Demographic transition

and economic development

Part of a larger project to look at the relationship between

changes in the age distribution and economic development

at the local level in both Brazil and Mexico (PI: Professor

Joseph Potter, UT).

Motivated by results for Asia and their relevance to Latin

America (Bloom, Canning, Williamson, Mason and others).

Awareness that the heterogeneity that prevails in Brazil and

Mexico could work to our advantage.

Figuring out how to take advantage of this heterogeneity led

us to look at studies that had been done on another major

demographic shock... the “baby boom” in the US.

2

“Baby Boom” and US Labor Market

Large literature on age-education shifts in the US (Freeman

1979; Welch 1979; Berger 1985; Triest, Sapozhnikov e Sass

2006).

Exceptionally large cohorts born during the “baby boom”

entered the American labor market in the 1970s with higher

levels of education.

Studies suggest that large cohorts depressed earnings.

Negative effects increase with education.

“Baby boomers” will still affect income structure after their

retirement.

3

The case of Brazil

Might such compositional changes have influenced earnings

in a large Latin American country such as Brazil?

As in other developing countries, age-education

transitions in Brazil provide a lot of variation in

demographic structure:

Fertility decline varied in timing and speed across states

and municipalities.

Educational enrollment increased substantially from very

low levels, but with much regional variation.

Our idea was to use this regional variation to analyze who

gains and loses from these compositional shifts, with a

cross-section time series approach.

4

Data

Microdata from the 1970-2000 Brazilian Censuses.

Census long forms are available for 25% (1970 and 1980)

and 10% or 20% (1991 and 2000) of households.

Long forms contain information on age, sex, education,

income, occupation, and migration.

We aggregate municipalities to the micro-region level,

yielding 502 comparable areas across the four censuses.

5

Categories

Time (census years): 1970, 1980, 1991, and 2000.

Age is categorized in four groups:

Youth population (15-24).

Young adults (25-34).

Adults (35-49).

Mature adults (50-64).

Educational attainment was classified in three groups

according to years of schooling completed:

No further than the first phase of elementary school (0-4).

Second phase of elementary school (5-8).

At least some secondary school (9+).

Earnings in main occupation: converted to January 2002.

6

0.0

3.0

6.0

9.0

12.0

15.0

18.0

21.0

24.0

27.0

30.0

15–24

years,

0–4

educ

15–24

years,

5–8

educ

15–24

years,

9+

educ

25–34

years,

0–4

educ

25–34

years,

5–8

educ

25–34

years,

9+

educ

35–49

years,

0–4

educ

35–49

years,

5–8

educ

35–49

years,

9+

educ

50–64

years,

0–4

educ

50–64

years,

5–8

educ

50–64

years,

9+

educ

Age-education Group

Perc

en

t

1970 1980 1991 2000

Source: 1970–2000 Brazilian Censuses.

Male population (15–64) by year and

age-education group, 1970–2000 (%)

7

Male population (15–64) with 9+ years of

schooling by micro-region and year

8

9 Male population (15–64) with 9+ years of

schooling by micro-region and year

0.00

250.00

500.00

750.00

1,000.00

1,250.00

1,500.00

1,750.00

2,000.00

2,250.00

2,500.00

15–24

years,

0–4

educ

15–24

years,

5–8

educ

15–24

years,

9+ educ

25–34

years,

0–4

educ

25–34

years,

5–8

educ

25–34

years,

9+ educ

35–49

years,

0–4

educ

35–49

years,

5–8

educ

35–49

years,

9+ educ

50–64

years,

0–4

educ

50–64

years,

5–8

educ

50–64

years,

9+ educ

Age-education Group

Ea

rnin

gs

Mean real monthly earnings in main

occupation of male population (15–64)

by age-education group, 2000

Source: 2000 Brazilian Census.

Obs.: Nominal income was converted to base 1 in January 2002, taking into account

changes in currency and inflation.

10

Estimation of models

Fixed-effects models allow the estimation of coefficients

that reflect relationships within 502 micro-regions between

12 age-education groups over time on labor outcomes.

Regressions only include males.

Areas with less than 25 people receiving income were not

included in the regression.

Dependent variable: the logarithm of the mean real income

in main occupation in a group.

Independent variables: age-education indicators (G) and

distribution of male population in 12 age-education groups

(X) interacted with time (θ); and area-time fixed effects (α):

log(Ygit) = β0 + (β1G12 + ... + β11G43)*θt + (γ1X11 + ... + γ12X43)*θt + αit + εgit

11

Data setup

Micro-

region

Age-

education

group

Census

year

Prop. of

people X11 X12 X13 ... X43

Log of

real

mean

income

Num.

of

obs.

110006

15-24

years &

0-4 educ.

1970 0.291 0.291 0 0 ... 0 5.82 1616

110006

15-24

years &

5-8 educ.

1970 0.041 0 0.041 0 ... 0 6.21 207

110006

15-24

years &

9+ educ.

1970 0.008 0 0 0.008 ... 0 6.75 39

... ... ... ... ... ... ... ... ... ... ...

110006

50-64

years &

9+ educ.

1970 0.003 0 0 0 ... 0.003 7.73 21

... ... ... ... ... ... ... ... ... ... ...

12

1.0

1.8

2.7

4.9

8.8

2.3

5.5

9.4

2.3

3.4

1.5

6.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0-4

educ

5-8

educ

9+

educ

0-4

educ

5-8

educ

9+

educ

0-4

educ

5-8

educ

9+

educ

0-4

educ

5-8

educ

9+

educ

Exp

on

en

tial

of

co

eff

icie

nt

15-24 years 25-34 years 35-49 years 50-64 years

13 Effects of age-education indicators

(G11-G43) on earnings, 1970–2000

Source: 1970–2000 Brazilian Censuses.

1000

1375

1750

2125

2500

Earn

ings

0 .05 .1 .15Proportion

Predicted 1970 Predicted 2000

400

650

900

1150

1400

Earn

ings

0 .05 .1Proportion


300

340

380

420

460

Earn

ings

.05 .1 .15 .2 .25 .3Proportion


500

875

1250

1625

2000

Earn

ings



400

550

700

850

1000

Earn

ings



250

275

300

325

350

Earn

ings



25–34 years

5–8 education 0–4 education 9+ education

35–49 years

5–8 education 0–4 education 9+ education

14 Effects of male proportions in 502 micro-

regions (X11-X43) on earnings, 1970 and 2000

New considerations

Objective is to develop methodological procedures to

include information of internal migration in the previous

models.

If there was no migration flows, the sending areas (which

already have lower relative earnings) would have even

lower earnings, and the receiving areas (which already have

higher relative earnings) would experience raises on

earnings.

By not controlling for migration in the models, results are

underestimating the negative effect of group size

(cohorts) on earnings.

The hypothesis is that, by controlling for migration flows,

the negative impacts of age-education-group proportions will

be even more negative than previous estimates.

15

Methodological procedures

Migration flows cannot just be introduced as independent

(exogenous) variables in the models.

Since internal population flows in Brazil are influenced by

availability of jobs and levels of income in sending and

receiving areas, migration is an endogenous variable.

As a strategy to correct for endogeneity problems, a

methodology was developed by congregating the estimation

of migration level (Stillwell 2005) and the modeling of

migration schedules (Rogers and Castro 1981).

These procedures were developed in collaboration with

Professor Eduardo Rios-Neto (CEDEPLAR/UFMG).

16

Estimation of migration level

Gravity models take into account distances among areas,

and are used to control for migration flows (Stillwell 2005).

Poisson regression uses migrants between region i and

region j (Mij); pop. at the beginning of the period (Pi); pop. at

the end of the period (Pj); and distance among regions (dij):

Mij = exp(b0 + b1log Pi + b2log Pj + b3log dij) + εij

Since flows between areas (502*501=251,502) have low

number of migrants, it was selected the 20-24 age group to

estimate the level of migration.

A model was estimated for each year (1991 and 2000) and

education group, using information on municipality of

residence five years before the census.

Result: populations at the beginning and end of the period

have positive effects; and distance has a negative impact.

17

Estimation of migration schedule

In order to estimate migration schedules, it is necessary to

estimate migration rates by age groups.

The estimation of migration rates for combinations of micro-

regions and year would generate low results.

The solution is to estimate rates for the flows among the

major-regions (North, Northeast, Southeast, South and

Central-West) in each year (1991 and 2000): 5*5*2=50.

Information on municipality of residence five years

before the census was used.

Age-specific immigration rates (ASIRx,ij) by age group

were estimated, considering populations (K) in regions of

origin (i) and destination (j):

ASIRx,ij = ∑(Kx,ij) / t*∑{[(Kx,j. + Kx,jj) + (Kx,j)]/2}

18

Modeling migration schedules

After the estimation of immigration rates by age group, the

mathematical models proposed by Rogers and Castro

(1981) were implemented to these rates.

Rogers and Jordan (2004) indicate that migration flows are

usually modeled with the following equation:

S(x) = a1*exp(–α1x) + a2*exp{–α2(x–µ2)–exp[–λ2(x–µ2)]} + c

This equation has a negative exponential curve in the first

age groups, followed by a parabola on labor ages, and a

constant term on post-labor ages.

For this exercise, rates were modeled only for those

between 15 and 64 years of age.

19

The model migration schedule

Source: Rogers and Castro (1981, p.6).

20

21

.05

.07

.09

.11

.13

.15

Pro

port

ional A

SIR

15 20 25 30 35 40 45 50 55 60Age Group

Observed, 1991 Observed, 2000

Estimated, 1991 Estimated, 2000

Observed and estimated proportional ASIR,

North to Southeast, 1991 and 2000

Source: 1991 and 2000 Brazilian Censuses.

22

0

.06

.12

.18

.24

.3

Pro

port

ional A

SIR

15 20 25 30 35 40 45 50 55 60Age Group





Northeast to Southeast, 1991 and 2000

23

.06

.075

.09

.105

.12

.135

Pro

port

ional A

SIR

15 20 25 30 35 40 45 50 55 60Age Group





Southeast to Southeast, 1991 and 2000

24

.03

.06

.09

.12

.15

.18

Pro

port

ional A

SIR

15 20 25 30 35 40 45 50 55 60Age Group





South to Southeast, 1991 and 2000

25

.06

.075

.09

.105

.12

.135

Pro

port

ional A

SIR

15 20 25 30 35 40 45 50 55 60Age Group





Central-West to Southeast, 1991 and 2000

Integrating level and schedule of migration

The level of migration for men aged 20-24 years between

the 502 micro-regions by education group (0-4, 5-8, and 9+)

and year (1991 and 2000) was estimated.

The pattern of migration was estimated with the modeling

of age-specific immigration rates (ASIRx,ij) for each

population flow among the five major-regions by year.

Then, the ratio between the level of migration and the ASIR

for the 20-24 age group was calculated.

The ratio was then multiplied by each ASIRx,ij of the other

age groups, considering the education group, area and year.

Finally, a measure of force of migration was estimated for

each micro-region, age-education group and year.

26

New regression models

Original model (1970-2000 & 1991-2000): age-

education*year; proportion by age-education*year.

Migration model 1 (1970-2000 & 1991-2000): original

model; state of birth; state of birth*year.

Migration model 2 (1970-2000 & 1991-2000): original

model; time of residence; time of residence*year.

Migration model 3 (1991-2000): original model; residence

five years before the census; residence five years before the

census*year.

Migration model 4 (1991-2000): original model; adjusted

migration; adjusted migration*year.

The elasticities for the 1991-2000 models...

27

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

1991

Original model State of birth State of birth * Year

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

2000

Original model State of birth State of birth * Year

28

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

1991

Original model Time of residence Time of residence * Year

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

2000

Original model Time of residence Time of residence * Year

STATE OF BIRTH

TIME OF RESIDENCE (LESS THAN 5 YEARS IN THE MUNICIPALITY)

Estimated elasticities of proportions

in age-education groups, 1991 and 2000

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

1991

Original model Residence five years before census Residence five years before census * Year

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

2000

Original model Residence five years before census Residence five years before census * Year

29

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

1991

Original model Estimated migration Estimated migration * Year

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

15–24; 0–4

15–24; 5–8

15–24; 9+

25–34; 0–4

25–34; 5–8

25–34; 9+

35–49; 0–4

35–49; 5–8

35–49; 9+

50–64; 0–4

50–64; 5–8

50–64; 9+

Ela

sti

cit

iy

Age-education group

2000

Original model Estimated migration Estimated migration * Year

RESIDENCE FIVE YEARS BEFORE THE CENSUS

ADJUSTED MIGRATION

Estimated elasticities of proportions

in age-education groups, 1991 and 2000

Final considerations

Findings follow the initial hypothesis, which addressed

that, by controlling for migration flows, negative impacts of

cohort size on earnings are even more negative than

estimates that did not take into account population flows.

The inclusion of internal migration has consistent results

only with the adjusted level and pattern of flows.

These strategies were designed in such a way that they can

be used in further studies, when new date become

available, as well as in the context of other countries with

the availability of migration data.

30

Documents

Influences of transition in age-education structure and