Transcript Contraceptive Prevalence Rate in Indonesia, 1977-2006

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
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)
2
Indonesian Context
Contraceptive Prevalence Rate in Indonesia, 1977-2006
Notes: This figure is from Mize (2006).
3
Indonesian Context
Labor Force Participation Rates by Gender in Rural
and Urban Areas: Indonesia in 1971, 1980, and 1990
1971
1980
1990
Male
Urban
Rural
Total
61.2
70.4
68.7
59.1
71.2
68.5
64.0
74.4
71.1
Female
Urban
Rural
Total
22.5
34.2
32.1
24.2
35.2
32.7
31.6
42.2
38.8
Both Sexes
49.9
50.2
54.7
Notes: This table is quoted from Manning (1998).
Source: CBS, Population Censuses, 1980 and 1990.
4
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)
5
Indonesian Family Planning Program:
Geographic Expansion
Phase 1
Phase 2
Phase 3
6
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)
7
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)
8
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
9
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)
10
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
11
fecundity level
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
12
More on the Model



Marriage and education are treated as exogenous
k
Choose a sector of employment, ot
k=1, formal sector
k=2,informal sector
k=3, not working
m
Choose a contraceptive method, mt
m=1, modern method
m=2, traditional method
m=3 , not using contraception
13
Utility Function
Expected discounted life-time utility function:
TF
E[  
t  A0
t  A0
(ct  q kmt  kmt )]
ct - consumption (pecuniary component)
qkmt - nonpecuniary component
kmt - choice-specific time shock
14
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
15
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
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
17
IFLS 1 Provinces
18
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
19
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.
20
Policy Simulations

Decrease cost of using contraceptives.




Decrease disutility experienced by working
mothers



Improvement in quality of family planning
services such as reduced wait times
Reduction in price of contraceptives
Reduction in distance to clinics
Reduction in cost of child care
Flexible timings in formal sector employment
Simulate sector-specific wage subsidies.
21
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.
22
Nonpecuniary utility function
q kmt   o X 1t   2 km m X 2t   o   4 m
k
1km t
m
t
k 'k
3 t
m 'm
t
 5otk Otk1   6 mtm M tm1   7 1(t  35)nt  8bt nt N t otk
  9 otk nt  10 N t otk  11rt otk  12 N t  13bt nt  14 bt nt otk
 15bt nt N t  16 nt  17 rt    
i
o
i
m
23
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.
24
Real Minimum Wages in IFLS 1 provinces
Real Regional Minimum Wage in Indonesia, 1985-1994
(Rupiah/month)
Minimum Wages
3,000
2,500
2,000
1,500
1,000
500
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
-
North Sumatra
West Sumatra
South Sumatra
Lampung
Jakarta
West Java
Central Java
Yogyakarta
East Java
Bali
South Sulawesi
South Kalimantan
West Nusa Tenggara
Year
Notes: Real Wages are in 2000 Indonesian Rupiah.
Source: Minimum Wage data was obtained from Arup Suryahadi and David Newhouse.
25
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.
26
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
27
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)
28
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.
29
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.
30
Likelihood Contribution
Likelihood contribution of a woman who is not working in period t is:


 exp[V (d kmt |( S (t ),  t , i ) /  ] 
Pr( i , t , S (t ))    

{k ,m}  exp[V ( d lzt |( S ( t ),  t , i ) /  ]
 {l ,z}



d kmt
dF ( t1 )dG ( t2 )
Likelihood contribution of a woman working in the informal sector in period
t is:


 exp[V (d kmt |( S (t ),  t , i ) /  ] 
2
Pr( i , t , S (t ), wt )    

{k ,m}  exp[V (d lzt |( S (t ),  t , i ) /  ]
 {l ,z}



d kmt
g ( wt2  wt2 )dF ( t1 )
31
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”
32
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.
33
Descriptive Statistics: Education
Education
Percentage
Primary
0.572
Junior Secondary
0.167
Senior Secondary
0.208
College
0.050
Source: IFLS 1
34
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.
35
Source: Quoted from Tussell and Vaughn (1999).
Distribution of sample Women by Province, 1993
Province
Number of women
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
Percentage
36
Descriptive Statistics
Variable
Mean
Standard Deviation
19.67
3.97
Urban
0.51
0.50
Muslim
0.86
0.35
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
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
Sample of 2067 Women
Age at time of marriage*
Sample of 20,707 woman-year observations
Age*
Duration not working*
Notes: * denotes unit of measurement is Year. Source: IFLS 1
37
Number of Family Planning Clinics
Introduced between 1980-93
Number of Puskemas introduced between 1980-93
15
0
0
10
5
10
Number of Puskemas
20
Number of Posyandu
30
40
20
Number of Posyandus' introduced between 1980-93
1980
1985
1990
1995
Year
1980
1985
1990
1995
Year
10
20
Notes: Posyandu is Village Integrated Health Posts.
Puskemas is Community Health Center.
PKKBD is Family Planning Distribution Points.
Source: IFLS 1
0
Number of PKKBD
30
Number of PKKBDs introduced between 1980-93
1980
1985
1990
Year
1995
2000
38
Identification of the Wage
Structure
 Identification of the wage structure
comes from covariation of wages and
observables across the two sectors for
similar occupations.
39
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.
40
Exogenous Variation in Timing of
Introduction of Posyandu
0
2
4
6
Average Fertility by Birth Cohort and Timing of Introduction of Posyandu
1940
1950
1960
Year of Birth
Before
1970
1980
After
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
41
Exogenous Variation in Timing of
Introduction of PKKBD
1
2
3
4
5
6
Average Fertility by Birth Cohort and Timing of Introduction of PKKBD
1940
1950
1960
Year of Birth
Before
1970
1980
After
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
42
Exogenous Variation in Timing of
Introduction of Puskemas
1
2
3
4
5
6
Average Fertility by Birth Cohort and Timing of Introduction of Puskemas
1940
1950
1960
Year of BIrth
Before
1970
1980
After
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
43
Budget Constraint
ct    [w  w  Yt  P m  Pn N t ]
k
t
h
t
m
m t
- sharing rule parameter
wtk - wage earnings of the woman in sector k
wth - husband’s wage
Yt - unearned income of husband and wife
Pm - price of contraception used
Pn - average expenditure on a child

44
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
45
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
46
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
47
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.
48
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
49
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*
50
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(.) 
   Pr( , t , S (t ))dH( )
i
i
i
t
51
Wage Equation
w  wk 0  k1G  k 2 t   X   O   O    
k
t
L
k3 t
1
k 4 t 1
2
k 5 t 1
k
t
i
w
G - education
t
- age
X tL - provincial minimum wage rates
O - experience in formal sector
O - experience in informal sector
 tk - wage error
 - unobserved ability
1
t 1
2
t 1
i
w
52
Women as Decision Makers
The utility maximization problem can be considered as
 A two-stage benevolent dictator problem.
 Chiappori’s collective approach
53
Variables used in Empirical Analysis
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
Source: IFLS 1
54
Descriptive Statistics
Distribution 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
55
Nonstructural Estimation:
Marginal Effects at Means for Select Independent Variables from
Employment Sector Multinomial Probit
Variable
Pr(Sector = Formal) =
0.1858
dP/dx
Pr(Sector =
Informal) =
0.1460
dP/dx
Pr(Sector = Not
Working) =
0.6681
dP/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
56
Nonstructural Estimation:
Marginal Effects at Means for Select Independent Variables from
Contraceptive Choice Multinomial Probit
Variable
Pr(Method =
Modern) =
0.2730
dP/dx
Pr(Method =
Traditional) =
0.0059
dP/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
57
State Space and Value Function
State space at time t is:
 
S(t )  (ot 1 , mt 1 , Dt 1 , rt 1 , Nt 1 ,bt , t ,t , t )
Value function at time t given state S(t) and unobserved
heterogeneity is:
Vt  max[V1,1,t (S (t ), i ),.........,V3,3,t (S (t ), i )]
where
Vk ,m,t (S (t ), i )  U kmt (S (t ), i )  EVt 1 (S (t  1), i | S (t ), d kmt  1)
TF
Vk , m , T 
*

t ' T
For A0 <= t < T*
U kmt ' ( S (t ' ), i ) For T* <= t’ < TF
t ' T*
'
*
58
Birth Probability Function
Fm,t 1   (t , m , M ,  )
m
t
m
t
i
f
t - age
mtm-
method of contraceptive in period t
M - duration for which the method was
used
 if - unobserved fecundity
m
t
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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
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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)
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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
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