Contraception and Poverty

Why Access to Family Planning Remains a Challenge for Poor Families in Indonesia

Introduction: The Hidden Barrier of Poverty Behind Family Planning Access

Behind Indonesia's globally recognized Family Planning (KB) program lies a concerning inequality: poor families face the greatest difficulties in accessing contraception. Data from the 2007 Indonesia Demographic and Health Survey (SDKI) reveals that only 54% of poor women were active KB participants, significantly lower than more economically capable groups . The 1997 monetary crisis worsened this situation—contraception purchasing power plummeted, and recovery has been slow among marginalized groups.

Key Finding

Only 54% of poor women were active KB participants compared to higher economic groups

This article examines the social, economic, and policy factors that hinder contraception access for poor households and their implications for public policy.

Key Concepts: From Behavioral Theory to Health Economics

1. Behavioral Theory Framework (Health Belief Model)

Contraception decisions are influenced by individual perceptions:

  • Perceived Benefits: belief that KB improves maternal-child health
  • External Barriers: cost, distance to facilities, or social stigma

For poor families, "barriers" often outweigh "benefits" due to acute economic pressures .

2. Bronfenbrenner's Ecological Model

Factors divided into four layers:

  • Microsystem: low education, lack of husband support
  • Exosystem: limited access to clinics/KB workers
  • Macrosystem: non-inclusive government policies
  • Chronosystem: prolonged impact of 1997 economic crisis
3. Donabedian Accessibility Framework

Contraception access depends on:

  • Availability: distribution in remote areas
  • Affordability: subsidies/service costs
  • Acceptability: alignment with cultural/religious values

Key Experiment: Unpacking SDKI 2007 Data

Methodology: How Data Was Collected and Analyzed?

Research by Gadjah Mada University (2011) analyzed SDKI 2007 data with specific focus on poor households . Steps:

Sampling
  • Selected 12,000 households nationwide
  • Filtered samples by poverty criteria (expenditure < equivalent to 240kg rice/capita/year)
Data Collection
  • Structured questionnaire interviews about: KB history, contraception preferences, income, education, and healthcare access
  • Data analyzed with logistic regression to identify dominant factors
Key Variables
  • Dependent: Contraception Use Status (Active/Inactive)
  • Independent: education, income, location (urban/rural), husband support, distance to KB facilities

Key Findings and Significance

  • Husband support was the strongest predictor 7× more likely
  • Low education (≤elementary) reduced KB use 68% less likely
  • Geographic factors critical 40% lower in rural
Long-term Impact

The 1997 economic crisis had prolonged effects: contraception purchasing power remained low a decade later .

Data Visualization

Table 1: Respondent Profile by Socio-Economic Characteristics
Characteristic Category KB Users
Education No School 32%
Elementary 48%
Junior High+ 71%
Location Urban 65%
Rural 43%
Table 2: Most Used Contraception Methods
Type Percentage Dominant Reason
Injection 58% Practical, affordable
Pill 22% Easily accessible
IUD 9% High effectiveness
Table 3: Dominant Factors in KB Use (Logistic Regression Analysis)
Factor Odds Ratio (OR) Significance
Husband Support 7.2 p<0.001
Education ≥Junior High 5.1 p=0.003
Access ≤5 km to Health Facility 3.8 p=0.01

Research Tools: Key Instruments for Understanding the Problem

SDKI Questionnaire

Collects demographic-FP data for national household surveys

BPS Poverty Dataset

Identifies poor households based on expenditure

SPSS Software

Analyzes logistic regression to calculate social factor odds ratios

Health Facility Access Map

Visualizes geographic disparities in "KB deserts"

Policy Implications: Building Inclusive Solutions

Targeted Subsidies

Free contraception for documented poor families to remove cost barriers.

Inclusive Education

KB socialization involving husbands and religious leaders to address cultural barriers.

Mobile Services

Clinic outreach programs to reach remote "KB desert" areas.

Program Integration

Connecting KB with healthcare/SME credit services for holistic support.

Critical Note

The 2007 SDKI data is now 18 years old. Updated analysis is needed to capture pandemic/JKN impacts. However, these findings remain relevant as a warning: without specific interventions, KB access inequality will continue to widen .

References