Fertility,

the Demographic Dividend,

and Economic GrowthDavid E. Bloom,

David Canning

Günther Fink

Jocelyn E. Finlay

Harvard School of Public Health

Fourth Annual Research Conference on Population, Reproductive Health, and Economic Development

Cape Town, January 2010

Effects of Fertility

on Income per Capita

Investigate the effects of fertility on income

Control for endogeneity by using changes in

abortion law as an instrument. About 26% of

pregnancies end in abortion.

Look at the mechanisms through which fertility

operates

Income per worker

Labor force participation

Working age share of population

Not Population Growth!

Population growth is fertility rate – mortality

rate + net migration.

Fertility rate and mortality rate have very

different economic effects.

The “effect of population growth” is not well

defined unless we know the source of the

growth.

Caveats

Income per capita is not a welfare measure

Focus on average income not distribution and

poverty

Macro income per capita still interesting. Macro

can capture effects micro misses

Social norms in behavior

Thresholds and critical value effects

Social Spillovers Micro model

Difficult to estimate since endogenous and

common to everyone in the community

Macro model

y x y

y x y

y

1y x

Figure 4: Income per Capita

and Fertility in 2000

Albania

Algeria

Antigua and Barbuda

Argentina

Armenia

AustraliaAustria

Azerbaijan

Bahamas, TheBahrain

Bangladesh

Barbados

Belarus

Belgium

Belize

Benin

Bosnia and Herzegovina

BotswanaBrazil

Brunei

Bulgaria

Burkina Faso

Burundi

Cambodia

Cameroon

Canada

Cape Verde

Central African RepublicChad

Chile

China

Colombia

Comoros Congo, Rep.

Costa Rica

Cote d'Ivoire

Croatia

Cuba

Cyprus

Czech Republic

Denmark

Djibouti

Dominica

Dominican Republic

EcuadorEgypt, Arab Rep.El Salvador

Equatorial Guinea

Eritrea

Estonia

Ethiopia

Fiji

FinlandFrance

Gabon

Gambia, The

Georgia

Germany

Ghana

Greece

Guatemala

Guinea

Guinea-Bissau

HaitiHonduras

Hungary

Iceland

India

Indonesia

Iran, Islamic Rep.

IrelandIsraelItaly

Jamaica

Japan

Jordan

Kazakhstan

KenyaKiribatiKorea, Dem. Rep.

Korea, Rep.

Kuwait

Kyrgyz Republic

Lao PDR

Lebanon

Lesotho

Liberia

Lithuania

Luxembourg

Macedonia, FYR

MadagascarMalawi

Malaysia

Maldives

Mali

Malta

Mauritania

Mauritius

Mexico

Micronesia, Fed. Sts.

Moldova

Mongolia

Morocco

Mozambique

Namibia

Nepal

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Norway

Oman

Pakistan

Panama

Papua New GuineaPeruPhilippines

Poland

Portugal

Qatar

Romania

Russian Federation

Rwanda

Samoa

Sao Tome and Principe

Saudi Arabia

Senegal

Sierra Leone

Singapore

Slovak Republic

Slovenia

Solomon Islands

Somalia

South Africa

Spain

Sri Lanka

St. Kitts and Nevis

St. LuciaSt. Vincent and the Grenadines

Sudan

Suriname

Swaziland

SwedenSwitzerland

Syrian Arab RepublicTajikistan

Tanzania

Thailand

Togo

Tonga

TunisiaTurkey

Turkmenistan

Uganda

Ukraine

United Arab Emirates

United Kingdom

United States

Uruguay

UzbekistanVanuatu

Venezuela, RB

Vietnam

Yemen, Rep.

Zambia

Zimbabwe

67

89

1011

Log

Rea

l GD

P p

er C

apita

, 200

0

0 2 4 6 8TFR, 2000

Mechanisms

Most economic models focus on income per

worker effects – Malthus, Solow

We also have effects on workers per capita

Age structure – working age share

Female labor force participation

Workers per capita is bounded – cannot explain

long run growth but can vary a lot in the

“medium” term

Components of Income per Capita

Identity

t t t t

t t t t

Y Y L W

P L W P

log log log logt t t t

t t t t

Y Y L W

P L W P

Income per worker

Land per worker

Malthusian effect, number of workers

Capital per worker

Solow effect, growth rate of workforce

Savings rather than children for old age security

Human capital per worker

Investment in children, quality quantity tradeoff

Income per worker: Timing

Most effects only occur when fertility affects

growth in labor force. Time lag of 15-20 years

before children enter the labor force.

Large effects after long run adjustment to steady

state – income to investment - several

generations.

Working Age Share

Lower fertility always reduces the youth dependency rate.

Lower fertility lowers the number of worker age people in 20-60 years, increasing old age dependency

Turning point in overall effect is close to replacement fertility.

Youth dependency effect is immediate. Old age dependency effect is longer run.

Figure 1: Relationship between fertility and steady

state working age share

0%

10%

20%

30%

40%

50%

60%

70%

0 1 2 3 4 5 6 7 8 9

Total fertility rate

Wor

kin

g ag

e sh

are

Life expectancy 40 years (Zambia, 2005)

Life expectancy 60 years Yemen, 2005)

Life expectancy 80 years (France, 2005)

Figure 2: Total fertility rates and

working age shares in 2000

AlbaniaAlgeria

Angola

Argentina

Armenia

AustraliaAustria

Azerbaijan

Bahamas, The

Bahrain

Bangladesh

BarbadosBelarus

Belgium

Belize

Benin

Bolivia

Bosnia and Herzegovina

Botswana

BrazilBrunei

Bulgaria

Burkina FasoBurundi

Cambodia

Cameroon

Canada

Cape Verde Central African Republic

Chad

Chile

China

Colombia

Comoros

Congo, Rep.

Costa Rica

Cote d'Ivoire

Croatia

Cuba

Cyprus

Czech Republic

Denmark

Djibouti

Dominican RepublicEcuador

Egypt, Arab Rep.El Salvador

Equatorial GuineaEritrea

Estonia

Ethiopia

Fiji

Finland

France

Gabon

Gambia, The

Georgia

Germany

Ghana

Greece

GuatemalaGuinea

Guinea-Bissau

Guyana

HaitiHonduras

Hungary

Iceland

India

Indonesia

Iran, Islamic Rep.

Ireland

Israel

Italy

Jamaica

Japan

Jordan

Kazakhstan

Kenya

Korea, Dem. Rep.

Korea, Rep.Kuwait

Kyrgyz Republic

Lao PDR

Lebanon

Lesotho

Liberia

Libya

LithuaniaLuxembourgMacedonia, FYR

MadagascarMalawi

Malaysia

Maldives

Mali

Malta

Mauritania

Mauritius

Mexico

Micronesia, Fed. Sts.

Moldova

MongoliaMorocco

MozambiqueNamibia

Nepal

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Norway

Oman

Pakistan

Panama

Papua New GuineaParaguay

Peru

Philippines

PolandPortugal

Qatar

RomaniaRussian Federation

Rwanda

SamoaSao Tome and Principe

Saudi Arabia

Senegal

Sierra Leone

Singapore

Slovak RepublicSlovenia

Solomon Islands

Somalia

South Africa

SpainSri Lanka

St. LuciaSt. Vincent and the Grenadines

Sudan

Suriname

Swaziland

Sweden

Switzerland

Syrian Arab Republic

TajikistanTanzania

Thailand

Togo

Tonga

TunisiaTurkey

Turkmenistan

Uganda

Ukraine

United Arab Emirates

United KingdomUnited States

Uruguay

Uzbekistan

Vanuatu

Venezuela, RBVietnam

Yemen, Rep.Zambia

Zimbabwe

4050

6070

80

Wor

king

age

/Pop

ulat

ion

0 2 4 6 8Total fertility rate

Household Model

Utility

Time Constraint

Consumption

1 fl d bf

f f mc w l w e

0( , , , ) log log ( ) ( )e

U c d f e c c d f k N ff

Household Decisions

Female labor supply

Fertility

Investment in children

0( )11

(1 )

mf

f

e c wl bf

w

1 flf

b k

0

1f f me w l w c

Effects of Fertility

Female labor supply adjusted for investment in

children

Investment per child adjusted for labor supply

1f

f

l bfw

(1 ) /m ftf

t t

w wew b

f f

Female Labor Force Participation

High labor force participation in poor countries –

possible to work and care for children at the same

time.

Fertility to female labor supply effect may appear

when women have formal sector work where

child care and work time are separated.

Migration to urban areas may split extended

family links that provide childcare.

Income per Capita and Female Labor Force

Participation, 2000

Tanzania

Mozambique

Sudan

Egypt

Thailand

Iceland

US

0

10

20

30

40

50

60

70

80

90

100

6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11

Log (Real GDP per Capita 2000)

Fem

ale

Lab

or F

orce

Par

tici

pati

on R

ate

2000

Quality Quantity Tradeoff

Increased investment in children as fertility declines

May be household effect or effect via government spending

Influences income per worker in the long run

Short run effect on school enrollment

Enrollment may not reflect all investment educational quality, health

Figure 3: Change in the

Total Fertility Rate 1960-2000

Algeria

Australia

Barbados

Belgium Benin

Brazil

Burkina Faso

Cameroon

Canada

Cape Verde

Chad

Chile

China

Congo, Rep.

Denmark

Dominican Republic

Egypt, Arab Rep.El Salvador

Equatorial Guinea

Ethiopia

France

Gambia, The

Ghana

Greece

Guatemala

Guinea

Guinea-Bissau

HondurasIndia

Indonesia

Iran, Islamic Rep.

Ireland

IsraelItaly

Jamaica

Japan

Jordan

Kenya

Korea, Rep.

Lesotho

Luxembourg

Madagascar

Malawi

Malaysia

Mali

Mauritius

MexicoMorocco

Mozambique

Netherlands

New Zealand

Nicaragua

Niger

Nigeria

Pakistan

Panama

Peru

Philippines

Portugal

Romania

Rwanda

Senegal

Singapore

South Africa

Spain

Sri Lanka

SwedenSwitzerland

Syrian Arab Republic

Tanzania

Thailand

Togo

Turkey

Uganda

United Kingdom

United States

Uruguay

Venezuela, RB

Zimbabwe

-6-4

-20

2

Cha

nge

in T

FR

196

0-20

00

1 2 3 4 5 6 7 8 9 10Total fertility rate, 1960

Abortion Laws

Figure 5: Abortion Index:

Average 1960-2005

2

2.5

3

3.5

4

4.5

5

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Inde

x (s

ampl

e av

erag

e)

Are Abortion Laws Exogenous?

Timing of changes may be exogenous.

Contingent factors in many examples.

Extreme views and majority rule give sharp discontinuities in legal changes.

French and UK liberalization had spillover effects to former colonies. Laws often used as templates for changes to laws “inherited” at independence.

We use laws not enforcement.

Table 3 The effect of Fertility

on Income per Capita

(4) (5) (6)

Dependent variable: log GDP per capita

Total fertility rate -0.369*** -0.551*** -0.196**

(0.026) (0.160) (0.086)

Year dummies Yes Yes Yes

Country fixed effects No Yes Yes

Regional time trends No No Yes

Estimation method IV IV IV

Observations 1169 1169 1169

R-squared 0.541 0.897 0.957

Cragg-Donald F-stat 373.0 16.15 35.05

Table 4 First Stage: The effect of Abortion Laws

on Fertility

(1) (2) (3)

Dependent variable: Total fertility rate

Abortion index -0.410*** -0.072*** -0.096***

(0.020) (0.020) (0.018)

Year dummies Yes Yes Yes

Country fixed effects No Yes Yes

Regional time trends No No Yes

Observations 1169 1169 1169

R-squared 0.354 0.928 0.950

Note: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Table 5: The Effect of Fertility on the

Components of Income per Capita (1) (2) (3) (4)

Dependent variable: ln(GDP/P) ln(GDP/L) ln(L/W) ln(W/P)

Total fertility rate -0.196** -0.061 -0.071*** -0.068***

(0.089) (0.085) (0.019) (0.010)

Year dummies Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes

Regional time trends Yes Yes Yes Yes

Estimation method IV IV IV IV

Observations 1169 1105 1129 1145

R-squared 0.957 0.965 0.897 0.941

Cragg-Donald F-stat 35.05 32.38 32.75 34.49

Note: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

Table 6: The Effect of Fertility on Income per

Capita: Mechanisms

(1) (2) (3) (4)

Dependent variable: Log capital per

worker

Population

growth rate

Female labor

force

participation rate

Male labor

force

participation rate

Total fertility rate 0.021 0.645*** -9.947*** 0.495

(0.099) (0.292) (2.210) (0.695)

Year dummies Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes

Regional time trends Yes Yes Yes Yes

Estimation method IV IV IV IV

Observations 1105 999 1129 1129

R-squared 0.976 0.690 0.906 0.897

Cragg-Donald F-stat 32.38 14.87 32.75 32.75

Table 6: The Effect of Fertility on Income per

Capita: Mechanisms

(5) (6) (7)

Dependent variable: Working age share

Youth

dependency

rate

Old-age

dependency

rate

Total fertility rate -4.076*** 12.38*** -0.964**

(0.646) (1.780) (0 .416)

Year dummies Yes Yes Yes

Country fixed effects Yes Yes Yes

Regional time trends Yes Yes Yes

Estimation method IV IV IV

Observations 1145 1145 1145

R-squared 0.935 0.957 0.955

Cragg-Donald F-stat 34.49 34.49 34.49

Table 7: The Effect of

Fertility on Education

Why is the Macro Labor Force Effect

so Large?

Social Spillovers.

Work is contagious.

Life course decisions different with the possibility of fertility control.

Abortion laws affect women who are at he margin of working (local average treatment effect).

Effect mainly in middle and high income countries?

Future Directions

Add laws on access to contraceptives as well as

abortion.

Use micro data (DHS) at different levels of

aggregation.

Interaction with demand side

Unemployment, underemployment