A Dynamic Structural Model of Contraceptive Use and Employment Sector Choice for Women in Indonesia

- Uma Radhakrishnan

University of Virginia

Third Annual Research Conference on Population, Reproductive

Health, and Economic Development

2

Indonesian Context

Total Fertility Rate in Indonesia, 1965-70 to 2000-02

Notes: This figure is from Mize (2006).Source: Contraceptive Prevalence Survey (1987) and Indonesian Demographic and

Health Survey (1991, 1994, 1997, 2002-2003)

3

Indonesian Context Contraceptive Prevalence Rate in Indonesia, 1977-

2006

Notes: This figure is from Mize (2006).

4

Indonesian ContextLabor Force Participation Rates by Gender in Rural and Urban Areas: Indonesia in 1971, 1980, and 1990

1971 1980 1990

Male Urban Rural Total

61.270.468.7

59.171.268.5

64.074.471.1

Female Urban Rural Total

22.534.232.1

24.235.232.7

31.642.238.8

Both Sexes 49.9 50.2 54.7

Notes: This table is quoted from Manning (1998). Source: CBS, Population Censuses, 1980 and 1990.

5

Indonesian Family Planning Program

Introduced in late 1960s Family planning program was

introduced as part of five-year development plans

Initial geographic expansion Phase 1- 1970-74 (6 provinces including Java and

Bali) Phase 2- 1975-79 (10 provinces belonging to

Outer Islands 1) Phase 3- 1980-84 (remaining provinces)

6

Indonesian Family Planning Program: Geographic Expansion

Phase 1  

Phase 2  

Phase 3  

7

Indonesian Family Planning Program: Changing Nature

Initially followed a clinic-based approach Community Health Centers (Puskemas) Failed to reach a large group of target women

Community-based approach was first established in mid 1970s

Key idea was to use existing institutions to promote family planning Family Planning Distribution Points (PKKBD) Village Integrated Health Posts (Posyandu)

8

Women, Child Care, and Informal Sector

What remains unchanged is that women continue to hold primary child care responsibility

Large fractions of working women are employed in the informal sector characterized by:

Flexible timing Easy entry and exit Proximity to residence Compatibility between work and family responsibilities

(especially child care)

9

Research Motivation

Very little investigation of the impact of the family planning program on women’s labor force participation and wages

To understand the compatibility between work and family responsibility, especially child care provision as women make joint contraceptive method and employment sector choices

Structural model allows me to conduct policy experiments

10

Literature Review

Impact of family planning programs on fertility and socio-economic outcomes Goldin and Katz (2002); Miller (2005); Joshi and Schultz

(2007) Female labor force participation in developing

countries Jaffe and Azumi (1960); Tiefenthaler (1994)

Modeling contraceptive behavior Carro and Mira (2002)

Joint modeling of employment and fertility decisions Hotz and Miller (1988); Francesconi (2002)

11

Contribution of this Research

Distinguish between formal and informal sectors of employment

Allow joint contraception and employment choices to understand link the between family responsibility and employment

Endogenize wage rates so that sector-specific experience impacts wages, and this in turn affects cost of having a child

Allow uncertainty in fertility control Allow for unobserved preference heterogeneity,

unobserved ability, and unobserved natural fecundity level

12

Economic Model

I develop a finite horizon, discrete choice dynamic structural model in which married women in each period choose both method of contraception and sector of employment to maximize their expected discounted life-time utility function

13

More on the Model

Marriage and education are treated as exogenous

Choose a sector of employment, k=1, formal sector k=2,informal sector k=3, not working Choose a contraceptive method, m=1, modern method m=2, traditional method m=3 , not using contraception

o tk

m tm

14

Utility Function

Expected discounted life-time utility function:

ct - consumption (pecuniary component)

qkmt - nonpecuniary component

- choice-specific time shock

E c qt A

t A

T

t km t km t

F

[ ( )]

0

0

km t

15

Motivation to Control Fertility and Sector of Employment

Motivation to control fertility depends on compatibility between raising children and employment sector

Motivation to control fertility can be inferred by method of contraception used

While making contraceptive decisions, a woman considers the trade-off between costs of having a child and the benefits from having one

Employment Decisions

Endogenous wage rates implies work experience affects future wage rates and this in turn impacts the cost of having a child

Access to modern methods of contraception provides women better control over their fertility and thereby widens the employment choices

16

17

Data

Indonesia Family Life Survey 1(IFLS 1), 1993 Covers 13 provinces (321 Enumeration Areas)

and 83% of the population Retrospective panel Individual and family level data on

employment, income, education, migration, contraception use, and fertility

Community level data that can be linked to individual and household level data

18

IFLS 1 Provinces

19

Joint Choices and Identification

Model joint contraception and employment decisions

Unobserved heterogeneity may drive both decisions leading to biased estimates

Use exogenous variation in timing of introduction of 3 different types of family planning clinics and exogenous variation in minimum wages rates for identification

20

Estimation Outline

Solve the dynamic programming problem Model unobserved heterogeneity using Heckman and Singer

(1984) approach Use representative people to reduce computational cost (Brien,

Lillard, and Stern (2006))

Estimate the birth probability function and wage equation outside the structural model.

After solving the dynamic programming problem and estimating parameters of wage and birth function, using data on observed choices and state variables , estimate parameters of utility function and budget constraint using simulated maximum likelihood techniques.

21

Policy Simulations

Decrease cost of using contraceptives. Improvement in quality of family planning

services such as reduced wait times Reduction in price of contraceptives Reduction in distance to clinics

Decrease disutility experienced by working mothers Reduction in cost of child care Flexible timings in formal sector

employment Simulate sector-specific wage subsidies.

22

Conclusion Investigate the expansion of Indonesian family

planning program on employment and contraceptive choices of women, while recognizing the interdependency of these choices.

Although outcomes are observed at the individual level, it has implications for the economy as a whole: participation of women in labor force increases per capita income and this translates into economic growth.

Once the estimation of the structural model is complete, I can conduct policy experiments that are of interest to researchers.

23

Nonpecuniary utility function

q o X m X o m

o O m M t n b n N o

o n N o r o N b n b n o

b n N n r

km t km tk

t km tm

t tk k

tm m

tk

tk

tm

tm

t t t t tk

tk

t t tk

t tk

t t t t t tk

t t t t t oi

1 1 2 2 3 4

5 1 6 1 7 8

9 10 11 12 13 14

15 16 17

1 35

' '

( )

mi

24

Exogenous Variation in Minimum Wage Rates

Exogenous variation in minimum wage rates in the different provinces over time is used to identify parameters related to employment choices.

Minimum wage rate is set by Ministry of Manpower based on recommendation of governors in different provinces.

Internal and external pressures unrelated to local economic conditions in setting of minimum wage rates

Reasonable to assume variation in minimum wage rates does not impact contraceptive choices.

25

Real Minimum Wages in IFLS 1 provinces

Real Regional Minimum Wage in Indonesia, 1985-1994 (Rupiah/month)

-

500

1,000

1,500

2,000

2,500

3,000

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

Year

Min

imum

Wages North Sumatra

West SumatraSouth SumatraLampungJakartaWest Java Central JavaYogyakartaEast Java Bali South Sulawesi South KalimantanWest Nusa Tenggara

Notes: Real Wages are in 2000 Indonesian Rupiah.Source: Minimum Wage data was obtained from Arup Suryahadi and David Newhouse.

26

Solving the Dynamic Programming Problem

Solution involves obtaining value function for each person for each point in the state space for a given set of parameters.

Backward recursion. For t<T*, value function at each point in the

state space is the sum of current utility plus the discounted value of the expected best choice next period.

Backward recursion continues until t=0. Large state space makes it computationally

expensive to evaluate the value function at every point.

27

Reducing Computational Cost

Impose upper bounds on several state variables (age of youngest child, birth spacing, and number of births).

Interpolation Model unobserved heterogeneity using

Heckman and Singer (1984) approach

28

Representative People

Individuals differ by the following exogenous characteristics: religion location presence of 3 types of clinic 4 types of people with respect to unobserved

heterogeneity This results in 2*2*23*4=128

representative people Use interpolation method in Brien, Lillard,

and Stern(2006)

29

Birth Probability Function

Estimate this is as a Probit, where the dependent variable is 1 if a birth occurs and 0 otherwise.

Use parameters from Probit regression to obtain probability of birth in the structural model after conditioning on method used, duration of use, age of the women, and unobserved fecundity level.

30

Wage Equation

Estimate outside structural model to reduce computational costs.

Two-stage method in Heckman(1979) is used to correct for selection bias, as wages are observed only for working women.

31

Likelihood Contribution

P r( , , ( ) )exp [ ( | ( ( ) , , ) / ]

exp [ ( | ( ( ) , , ) / ]( ) ( )

{ , }{ , }

ikm t t i

lz t t il z

k m

d

t tt S tV d S t

V d S tdF dG

km t

1 2

P r( , , ( ) , )exp [ ( | ( ( ) , , ) / ]

exp [ ( | ( ( ) , , ) / ]( ) ( )

{ , }

{ , }

i tkm t t i

lz t t il z

k m

d

t t tt S t wV d S t

V d S tg w w dF

km t

2 2 2 1

Likelihood contribution of a woman who is not working in period t is:

Likelihood contribution of a woman working in the informal sector in period t is:

32

Why Structural Model?

Enables policy simulation According to Professor Steven Stern: “Adds discipline to modeling and

estimation, and makes it easier to talk about the model and the economics in it”

33

Geographic Expansion of Indonesian Family Planning Program

Phase 1 provinces - West Java, Jakarta, Central Java, East Java, Yogyakarta, and Bali

Phase 2 provinces - Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, North Sulawesi, South Sulawesi, South Kalimantan, West Kalimanatan, and West Nusa Tenggara

Phase 3 provinces-Riau, Jambi, Bengkulu, East Nusa Tenggara, Central Kalimantan, East Kalimantan, Central Sulawesi, South East Sulawesi, Maluku, Irian Jaya, and East Timor.

34

Descriptive Statistics: Education

Education Percentage

Primary 0.572

Junior Secondary 0.167

Senior Secondary 0.208

College 0.050

Source: IFLS 1

35

Estimates of Contraceptive Failure Rates in the United States

Method Failure Rate in 12 Months(Typical Use)

Implant 2.8

Injectable 3.2

IUD 3.7

Pill 6.9

Diaphram 8.1

Male Condom 8.7

Withdrawal 18.8

Periodic Abstinence 19.8

Other 32.0

Notes: Failure rate is the percentage of women who accidentally become pregnant as estimated in Tussell and Vaughn (1999) using 1995 National Survey of Family Growth in the United States.Source: Quoted from Tussell and Vaughn (1999).

36

Distribution of sample Women by Province,

1993 Province

Number of women

Percentage

North Sumatra

197 9.53

West Sumatra

99 4.79

South Sumatra

116 5.61

Lampung 87 4.21

DKI Jakarta

213 10.30

West Java

331 16.01

Central Java

200 9.68

DI Yogyakarta

101 4.89

East Java

288 13.93

Bali 128 6.19

West Nusa Tennegara

121 5.85

South Kalimantan

96 4.64

South Sulawesi

90 4.35

Total 2067 100

Source: IFLS 1

37

Descriptive StatisticsVariable Mean Standard Deviation

Sample of 2067 Women

Age at time of marriage* 19.67 3.97

Urban 0.51 0.50

Muslim 0.86 0.35

Sample of 20,707 woman-year observations

Age* 24.20 5.21

Number of children 2.44 1.27

Age of youngest child* 2.07 2.29

Gives birth 0.22 0.42

Duration in formal sector* 2.62 1.81

Duration in informal sector* 2.93 1.98

Duration not working* 10.79 8.69

Duration using modern methods* 2.37 1.61

Duration using traditional methods* 0.10 0.53

Duration not using contraceptives* 4.47 2.88

Notes: * denotes unit of measurement is Year. Source: IFLS 1

38

Number of Family Planning Clinics Introduced between 1980-93

010

20

30

40

Nu

mbe

r of P

osyan

du

1980 1985 1990 1995Year

Number of Posyandus' introduced between 1980-93

05

10

15

20

Nu

mbe

r of P

uskem

as

1980 1985 1990 1995Year

Number of Puskemas introduced between 1980-93

010

20

30

Nu

mbe

r of P

KK

BD

1980 1985 1990 1995 2000Year

Number of PKKBDs introduced between 1980-93

Notes: Posyandu is Village Integrated Health Posts. Puskemas is Community Health Center. PKKBD is Family Planning Distribution Points. Source: IFLS 1

39

Identification of the Wage Structure

Identification of the wage structure comes from covariation of wages and observables across the two sectors for similar occupations.

40

Identification of State Dependence

State dependence is separately identified from unobserved heterogeneity by variation in choices made by individuals with similar observable characteristics who have experienced a certain state relative to individuals who have not experienced that state.

41

Exogenous Variation in Timing of Introduction of Posyandu

02

46

Avera

ge

Fe

rtility

1940 1950 1960 1970 1980Year of Birth

Before After

Average Fertility by Birth Cohort and Timing of Introduction of Posyandu

Notes: Posyandu is Village Integrated Health Posts. “After” is for EAs where Posyandu was introduced after 1980 and “Before” is for EAs where Posyandu was introduced before 1980.Source: IFLS 1

42

Exogenous Variation in Timing of Introduction of PKKBD

12

34

56

Avera

ge

Fe

rtility

1940 1950 1960 1970 1980Year of Birth

Before After

Average Fertility by Birth Cohort and Timing of Introduction of PKKBD

Notes: PKKBD is Family Planning Distribution Points. “After” is for EAs where PKKBD was introduced after 1980 and “Before” is for EAs where PKKBD was introduced before 1980.Source: IFLS 1

43

Exogenous Variation in Timing of Introduction of Puskemas

12

34

56

Avera

ge

Fe

rtility

1940 1950 1960 1970 1980Year of BIrth

Before After

Average Fertility by Birth Cohort and Timing of Introduction of Puskemas

Notes: Puskemas is Community Health Center. “After” is for EAs where Puskemas was introduced after 1980 and “Before” is for EAs where Puskemas was introduced before 1980.Source: IFLS 1

44

Budget Constraint

- sharing rule parameter - wage earnings of the woman in sector

k - husband’s wage - unearned income of husband and wife - price of contraception used - average expenditure on a child

w t

k

w th

YtPmPn

c w w Y P m P Nt tk

th

t m tm

n t [ ]

45

Issues with Using Access to Family Planning Program as Instruments for Identification

Outcomes of interest may be biased by non-random nature of program expansion.

Correlation between timing of introduction and unobserved taste for fertility will lead to biased estimates

46

Identification (Utility Parameters)

Parameters of utility function are identified by

Data on choices and individual characteristics Variation in timing of introduction of different

types of fertility clinics within each enumeration area and variation across enumeration areas over time in access to contraceptives

Exogenous variation in local labor market conditions (real minimum wage rates) across provinces and over time

47

Identification (Wage equation)

Coefficients of the wage equation are identified by covariation of observable characteristics and wages across individuals within a sector

Variance of the wage error is identified by differences in wages across individuals in a sector in a given period conditional on observables

48

Identification of Unobserved Heterogeneity

Variance of the unobserved preference heterogeneity is identified by persistence in choices made by individuals over time relative to individuals with same observables.

Variance of unobserved ability is identified by persistent differences over time across individuals in wages conditional on observables.

Variance of unobserved natural fecundity level is identified by variation in fertility across women conditional on observables and choices made.

49

Nonpecuniary Utility

Number of births Age of youngest child State dependence Duration dependence Birth spacing Unobserved preference heterogeneity Birth in the previous period Interactions of choices with exogenous

characteristics such as age, religion, location, access to contraceptives

Several other interaction terms

50

More about the Model

Choose 1 of 9 alternatives; denote dkmt=1, if sector k and method m are chosen in period t

Decision making horizon is from A0 to T*, but women live until TF, TF>T*

51

Likelihood Equation

Solution to individual’s optimization problem provides the choice probabilities in the likelihood equation

Sample likelihood equation is the product across individuals, time, and choices of the contributing probability corresponding to each alternative

L t S t dHiti

i(. ) P r( , , ( ) ) ( )

52

Wage Equation

- education t - age - provincial minimum wage rates - experience in formal sector - experience in informal sector - wage error - unobserved ability

X tL

O t 11

O t 12

tk

G

wi

w w G t X O Otk

k k k k tL

k t k t tk

wi 0 1 2 3 4 1

15 1

2

53

Women as Decision Makers

The utility maximization problem can be considered as A two-stage benevolent dictator problem. Chiappori’s collective approach

54

Variables used in Empirical Analysis

Source: IFLS 1

N=2,067 Age at time of marriage Wages

Urban Unearned Income

Muslim Method of contraception

Age Sector of Employment

Education Duration in formal sector

Birth spacing Duration in informal sector

Number of children Duration not working

Age of youngest child Duration using modern methods

Gives birth Duration using traditional methods

Province Duration not using contraceptives

Enumeration Area

55

Descriptive StatisticsDistribution of Woman-Year Observations by Choices Made

Choice Percentage

Modern Method and Formal Sector 9.17

Modern Method and Informal Sector 7.44

Modern Method and Not Working 23.82

Traditional Method and Formal Sector 0.96

Traditional Method and Informal Sector 0.55

Traditional Method and Not Working 1.80

No Contraceptives and Formal Sector 10.14

No Contraceptives and Informal Sector 10.78

No Contraceptives and Not Working 35.33

Total 100

Source: IFLS 1

56

Nonstructural Estimation:Marginal Effects at Means for Select Independent Variables from Employment Sector Multinomial Probit

Variable Pr(Sector = Formal) =0.1858dP/dx

Pr(Sector = Informal) =0.1460dP/dx

Pr(Sector = Not

Working) =

0.6681dP/dx

Muslim -0.0225*(0.0117)

-0.0455*(0.0115)

0.0681*(0.0144)

Urban 0.0346*(0.0080)

-0.1672*(0.0078)

0.1325*(0.0101)

Gave Birth Last Period -0.0464*(0.0081)

-0.0126(0.0099)

0.0590*(0.0116)

Number of Children -0.0422*(0.0037)

0.0055(0.0033)

0.0366*(0.0045)

Age of Youngest Child -0.0005(0.0018)

0.0088*(0.0016)

-0.0083*(0.0023)

Age 0.0176*(0.0009)

0.0024*(0.0008)

-0.0201*(0.0011)

Choice Last Year -0.1641)*(0.0107)

-0.1294*(0.0100)

0.2935*(0.0114)Notes: N= 20,707. Standard Errors are in parenthesis. *implies statistical significance at 5%.

Source: IFLS 1

57

Nonstructural Estimation:Marginal Effects at Means for Select Independent Variables from Contraceptive Choice Multinomial Probit

Variable Pr(Method = Modern) =0.2730dP/dx

Pr(Method = Traditional)

=0.0059dP/dx

Pr(Method = No Contraceptive)= 0.7210

dP/dx

Gave Birth Last Period -0.0918*(0.0103)

-0.0036*(0.0011)

0.0954*(0.0104)

Number of Children 0.1137*(0.0056)

0.0012(0.0007)

-0.1150*(0.0057)

Age of Youngest Child -0.0384*(0.0029)

-0.0008*(0.0004)

0.0393*(0.0029)

Age -0.0003(0.0012)

-0.0000(0.0001)

0.0003(0.0012)

Posyandu 0.0532*(0.0129)

0.0023(0.0016)

-0.0556*(0.0129)

Puskemas 0.0109(0.0131)

0.0005(0.0016)

-0.0115(0.0132)

PKKBD 0.0159*(0.0015)

0.0005(0.0015)

-0.0165(0.0115)

Choice Last Year -0.2144*(0.0099)

-0.0129*(0.0021)

0.2273*(0.0099)

Notes: N=20,707. Standard Errors are in parenthesis. *implies statistical significance at 5%.Source: IFLS 1

58

State Space and Value Function

S t o m D r N b tt t t t t t t t( ) ( , , , , , , , , )

1 1 1 1 1

V V S t V S tt t i t i m ax[ ( ( ), ) , . . . . . . . . . , ( ( ), )], , , ,1 1 3 3 V S t U S t EV S t S t dk m t i km t i t i km t, , ( ( ), ) ( ( ), ) ( ( ), | ( ) , ) 1 1 1

V U S tk m Tt T

t T

T

km t i

F

, ,' *

''*

*

' ( ( ' ) , )

State space at time t is:

Value function at time t given state S(t) and unobserved heterogeneity is:

where

For A0 <= t < T*

For T* <= t’ < TF

59

Birth Probability Function

- age - method of contraceptive in

period t - duration for which the method

was used - unobserved fecundity

t

m tm

M tm

fi

F t m Mm t tm

tm

fi

, ( , , , ) 1

60

Research Outline

Develop a dynamic structural model to investigate the impact of the Indonesian family planning program on labor force participation and contraception choices of women

Estimate model using simulated maximum likelihood techniques with Indonesia Family Life Survey 1(IFLS 1) data

Use exogenous variation in timing of introduction of 3 types of family planning clinics for identification

61

Sources of Exogenous Variation

Community level data in IFLS 1 includes timing of introduction of 3 types of fertility clinics (access to contraceptives) in each enumeration area

Community Health Centers or Puskemas (33% introduced after 1980) Family Planning Distribution Points or PKKBD (58% introduced after 1980) Village Integrated Health Posts or Posyandus (77% introduced after 1980)

62

Classification of Choices

Sector of Employment Formal: self-employed with permanent workers,

government employees, private employees Informal: self-employed, self-employed with

temporary workers, family workers

Contraceptive Methods Modern: implants, IUD, condoms, pills, injections Traditional: rhythm, withdrawal, traditional herbs