Community Level Control Variables

I add several controls to account for community level characteristics. There are large disparities between rural and urban areas in Indonesia. In fact, even within urban areas and rural areas, communities may differ substantially. These characteristics might have an effect on both the likelihood of employment and the propensity of receiving Askeskin. The economy and the labor market are in general more dynamic in urban areas and jobs are easier to find. Moreover, the population in rural areas is likely to be low skilled and less educated. The supply of health services is lower in rural areas as there less health care facilities and professionals and access is more difficult. Infrastructure are more rudimentary in rural areas and the quality of health services lower. These factors would render the enrollment in Askeskin relatively less desirable. As a result, not accounting for this spatial heterogeneity might lead to omitted variable bias and endogeneity of my coefficient of interest. Adding infrastructure and supply of health care variables at the community level as controls should capture most of the community-level differences in propensity to receive targeting as well as differences in labor market decisions.

I include a dichotomous variable indicating whether the individual lives in an urban or rural community, a dichotomous variable for the presence of an asphalt road in the village, the percentage of households that have electricity in the community, the presence

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of a sewage system in the community and the presence of piped water in the community. These variables should account for the infrastructural differences between communities. Areas with better infrastructure should have better access to health care service due to better transportation routes. This would render the benefit more desirable. Additionally, I include the number of health centers present in the town and an indicator for the presence of a midwife30_{. The larger the quantity of health providers and the better the quality of}

health care provided, the more valuable the benefit to potential recipients. I also include a variable at the community’s subjective wealth. Considering the current conditions of the village population, this variable asks a village official to rank their village on a scale from one to six, one corresponding to the village where the population is poorest, and six representing the village where the population is richest.

Finally, since there is a large spatial disparity in the distribution of the population and important socioeconomic heterogeneity across provinces, I include dummies for all provinces where the survey has taken place.

30 _{A midwife is a person that is trained to assist women in childbirth. Due to high maternal and child mortality,} maternal care has been a priority for the government for decades. In 1989, the government implemented a program in which midwives were placed in birth facilities in most villages across the country. Due to low access to care in remote areas, the midwife sometimes provides basic health care when a physician is not present.

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Chapter Seven: Preliminary Regressions

This section provides preliminary regression results using different methodologies both cross-sectional and longitudinal. The purpose of this exercise is expositional in nature. Chapter 8 provides the main (“preferred”) results of this dissertation based on the propensity score matching with difference in differences discussed in earlier sections.

Tables 5 to 10 present cross sectional regression (with and without controls), fixed effects

and propensity score matching estimates. The propensity score estimates only use the 2007 values for the dependent variables and the pre-treatment values for the independent variables. The regressions are estimated using OLS (i.e. linear probability model for binary dependent variables). The propensity score matching estimates use the kernel matching method. The results are presented for the full sample, for subsamples by gender and by residence status (urban vs. rural) in order to investigate potential heterogeneous impacts. In fact, as commonly known, labor markets are seen as heterogeneous and segmented in developing countries since the labor market conditions faced by different groups (men vs. women, urban vs. rural, skilled vs. unskilled) may be different. Fields (2011) argues that the overall labor market in developing countries is a network of interconnected labor market segments that are connected by the potential mobility of firms and workers. The segments that make up the labor market differ from one another by the level of income and benefits and the employment arrangements. Labor mobility between the better segments (usually formal) and the less desirable segments is

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assumed to be limited (i.e. informal). In these less desirable segments, underemployed is ubiquitous. Certain groups within these segments are less privileged than others. In fact, women are usually disadvantaged in developing countries’ labor markets; they usually earn less, work more often in the informal market and are more likely to hold irregular positions. There are also spatial differences in labor markets. The nature of the industries available in the rural areas is more rudimentary. Individuals are more likely to be engaged in agricultural activities. In addition, informality is more prevalent in the rural world. The urban world is characterized by greater wage labor and greater formal employment. Therefore, it is important to investigate the impact on different samples.