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

-Uma Radhakrishnan

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

Cape Town, 2010

2

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 for the period 1979-93

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

3

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)

4

Indonesian Context

Contraceptive Prevalence Rate in Indonesia, 1977-2006

Notes: This figure is from Mize (2006).

5

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

1971 1980 1990

MaleUrbanRuralTotal

61.270.468.7

59.171.268.5

64.074.471.1

FemaleUrbanRuralTotal

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.

6

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)

7

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

8

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)

9

Indonesian Family Planning Program: Geographic Expansion

Phase 1

Phase 2

Phase 3

10

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)

11

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 countriesJaffe and Azumi (1960); Tiefenthaler (1994)

Modeling contraceptive behaviorCarro and Mira (2002)

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

12

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

13

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

14

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

ot

k

mt

m

15

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

16

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

17

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 kmt kmt

F

[ ( )]

0

0

kmt

18

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

wt

k

wt

h

Yt

Pm

Pn

c w w Y P m P Nt t

k

t

h

t m t

m

n t [ ]

19

Wage Equation

- education

t - age

- provincial minimum wage rates

- experience in formal sector

- experience in informal sector

- wage error

- unobserved ability

X t

L

Ot1

1

Ot1

2

t

k

G

w

i

w w G t X O Ot

k

k k k k t

L

k t k t t

k

w

i 0 1 2 3 4 1

1

5 1

2

20

Nonpecuniary Utility

Number of births

Age of youngest child

Duration dependence

Birth spacing

Birth in the previous period

Interactions of choices with exogenous characteristics such as age, religion, location, access to contraceptives

Several other interaction terms

21

Birth Probability Function

- age

- method of contraceptive in period t

- duration for which the method was used

- unobserved fecundity

t

mt

m

Mt

m

f

i

F t m Mm t t

m

t

m

f

i

, ( , , , ) 1

22

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

23

IFLS 1 Provinces

24

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

25

Descriptive Statistics

Variable 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

26

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

27

Estimation Outline

Solve the dynamic programming problem

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.

28

Birth Probability Function-Probit Model

Variables Estimates

Age of Woman -0.0109*(0.0021)

Lagged Modern Method -0.5655*(0.0574)

Lagged Traditional Method -0.3438*(0.1262)

Duration of Modern method * LaggedModern Method

-0.0885*(0.0182)

Duration of Traditional Method *Lagged Traditional Method

-0.087(0.0468)

Constant -0.3439(0.0508)

29

Main Results from Structural Estimation 1:Contraception

Covariates Modern Method Traditional Method

Age of Woman Positive Positive

Muslim Positive Negative

More than primary Education Positive Negative

Posyandu Positive Negative

Puskemas Positive Negative

PKKBD Positive Positive

Age of Youngest Child Negative Negative

Birth in Previous Period Negative Negative

Number of Births Positive Positive

Duration Positive Positive

Urban Negative Positive

30

Main Results from Structural Estimation 2: Employment

Covariates Formal Sector Informal Sector

Age of Woman Positive Positive

Muslim Negative Positive

Number of births Negative Positive

Birth in Previous Period Negative Positive

Age of Youngest Child Positive Positive

Duration Positive Negative

Urban Positive Negative

31

Main Results from Structural Estimation 3:Choice-Independent

Covariates(Not choice Dependent)

Change

Utility from birth when age>35 Negative

Utility from birth spacing Positive

Utility from birth Positive

Utility from number of births Positive

32

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

Increase utility experienced by working mothers Reduction in cost of child care Flexible timings in formal sector employment Better working condition in informal sector

33

Punch Line !

Estimates indicate that : Informal sector jobs offer greater compatibility between work

and child care. Women with more kids and young kids derive more utility

from working in the informal sector. Access to modern methods of contraception (presence of clinics)

reduces cost of using modern methods. Choice of contraception method and employment sector vary

by exogenous characteristics such as religion, urban or rural residence, Muslim, presence of clinics and age of woman.

Number of kids , birth spacing, and age of youngest child play an important role in impacting choices made by married women.

34

Conclusion

Investigate the expansion of Indonesian family planning program on employment and contraceptive choices of married women, while recognizing the interdependency of these choices.

Access to contraceptives increases the likelihood of participating in the formal sector, conditional on adequate job creation rates in formal sector.

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.

35

THANK YOU !

36

BACK UP SLIDES

37

Migration

Non-random selection in migration

Migration to obtain contraception will bias estimates

Modeling migration will not reduce bias unless it is exogenous

Assume that women lives in EA in all sample years as found in 1993, IFLS 1.

38

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

kmt km t

k

t km t

m

t t

k k

t

m m

t

k

t

k

t

m

t

m

t t t t t

k

t

k

t t t

k

t t

k

t t t t t t

k

t t t t t o

i

1 1 2 2 3 4

5 1 6 1 7 8

9 10 11 12 13 14

15 16 17

1 35

' '

( )

m

i

39

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.

40

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

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

Year

Min

imu

m W

ag

es

North Sumatra

West Sumatra

South Sumatra

Lampung

Jakarta

West Java

Central Java

Yogyakarta

East Java

Bali

South Sulawesi

South Kalimantan

West Nusa Tenggara

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

41

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.

42

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

43

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)

44

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.

45

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.

46

Likelihood Contribution

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

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

{ , }

{ , }

i

kmt t i

lzt t il z

k m

d

t tt S tV d S t

V d S tdF dG

kmt

1 2

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

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

{ , }

{ , }

i t

kmt t i

lzt t il z

k m

d

t t tt S t wV d S t

V d S tg w w dF

kmt

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:

47

Why Structural Model?

Enables policy simulation

According to Professor Stern:

“Adds discipline to modeling and estimation, and makes it easier to talk about the model and the economics in it”

48

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.

49

Descriptive Statistics: Education

Education Percentage

Primary 0.572

Junior Secondary 0.167

Senior Secondary 0.208

College 0.050

Source: IFLS 1

50

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).

51

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

52

Descriptive Statistics

Variable 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

53

Number of Family Planning Clinics Introduced between 1980-93

010

2030

40

Num

ber

of P

osya

ndu

1980 1985 1990 1995Year

Number of Posyandus' introduced between 1980-93

05

1015

20

Num

ber

of P

uske

mas

1980 1985 1990 1995Year

Number of Puskemas introduced between 1980-93

010

2030

Nu

mbe

r of

PK

KB

D

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

54

Identification of the Wage Structure

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

55

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.

56

Exogenous Variation in Timing of Introduction of Posyandu

02

46

Ave

rage

Fer

tility

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

57

Exogenous Variation in Timing of Introduction of PKKBD

12

34

56

Aver

age

Ferti

lity

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

58

Exogenous Variation in Timing of Introduction of Puskemas

12

34

56

Ave

rage

Fer

tility

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 introducedafter 1980 and “Before” is for EAs where Puskemas was introduced before 1980.

Source: IFLS 1

59

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

60

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

61

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

62

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.

63

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*

64

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(.) Pr( , , ( )) ( )

65

Women as Decision Makers

The utility maximization problem can be considered as

A two-stage benevolent dictator problem.

Chiappori’s collective approach

66

Descriptive Statistics: Education

Education Percentage

Primary 0.572

Junior Secondary 0.167

Senior Secondary 0.208

College 0.050

Source: IFLS 1

67

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 max[ ( ( ), ),........., ( ( ), )], , , ,1 1 3 3

V S t U S t EV S t S t dk m t i kmt i t i kmt, , ( ( ), ) ( ( ), ) ( ( ), | ( ), ) 1 1 1

V U S tk m T

t T

t T

T

kmt 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

68

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 = Not Using

Contraceptives)= 0.7210dP/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. Method 1 denotes Modern Method, Method 2 denotes Traditional Method, and Method 3 denotes Not Using Contraceptives. Standard Errors are in parenthesis. *implies statistical significance at 5%.Source: IFLS 1

69

Selection in Wage Equation

Estimated outside structural model

Wages are observed for women in sectors they actually work

To correct for selection bias, I model selection process as multinomial logit

Use Fournier and Girgand’s (2004) user written SELMLOG program

70

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. Sector 1 denotes Formal Sector, Sector 2 denotes Informal Sector, and Sector 3 denotes Not Working. Standard Errors are in parenthesis. *implies statistical significance at 5%.Source: IFLS 1

71

Imputation

Why Missing data is a problem?

-Missing data maybe correlated with person-specific characteristics

-May lead to biased estimates

Use Sequential Regression Multivariate Imputation (SRMI) algorithm.

Imputations are performed outside structural model

72

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

73

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)

74

Likelihood Optimization

Numerical optimization routine

Berndt, Hall, Hall, and Hausman(1974) algorithm

Start with an initial guess of the parametervector

Compute the likelihood function and itsderivatives

Update guess until likelihood function ismaximized