RESEARCH METHOD Data Description

3. RESEARCH METHOD Data Description

This study used data from the National Socio Economic survey (Susenas) in Indonesia, which is conducted by Statistics Indonesia. The survey consists of core and module sections and is performed every year. Core questions remain predominantly the same every year; they discuss general information on the individual and household. Meanwhile, there are three types of module namely consumption, housing, and socio culture and education one. Since 2009, social capital questions are included in the last type of module. Within any single year, only one type of module is included to complement the core section, so each module shows up once every three years. However, in 2014, social capital questions were moved to a new type of module called social resilience, replacing the consumption module. Hence data on social capital is available for 2009, 2012 and 2014.

Table 3.1 Sample by Type of Area

From 2009 to 2014, the survey was conducted four times each year (once in every three months), covering all provinces. In 2009, module including social capital was asked in every trimester, but in 2012 and 2014 it was only asked in the third trimester. Hence, the number of samples is larger in 2009 than in 2012 and 2014 (Table 3.1). All provinces were covered except North Kalimantan, the new province in 2014.

The survey takes household level as its sample unit, which is randomly selected in urban and rural areas. However, questions regarding social capital are only asked to one respondent in the selected households — either the head of household or the spouse, depending on who is available at the time of interview. This is because the social capital data needs to represent the accumulation of capital belonged to all members of the household, including the head and spouse, who are usually the most socialized ones in the family.

The social capital data are then generated into a single index. Generally it is adapted from the index used in Grootaert and van Bastelaer‘s (2001) research. The first proxy involves question related to groups in the community which play a role as an information-sharing institution. The variables cover the number of groups, participation in groups, and the extent of the network. The second proxy uses questions regarding norms that involve the extent to which the household can give and receive help from the community. The final proxy for collective action, which discusses social cohesion, is represented through cooperation for the common purpose. The questions involve participation in collective actions for the community.

Methodology

The social capital index is calculated using weight produced by factor analysis. Factor analysis, also known as exploratory factor analysis, is a method that can identify structures underlying data and extract factors from them. The newly formed factors can be considered a representation of the correlated observed variables (Garret, 2006). These factors are also considerably independent of each other in constructing the social capital index.

Furthermore, as the data are only available at three points of time, which are not periodic, the most possible option to observe social capital‘s role in affecting welfare is to use traditional OLS. The dependent variable is the natural logarithm of household expenditures which have been adjusted for inflation. Meanwhile the explanatory variables consist of the household‘s social and human capital, demographic characteristics, and dummy variables of year. The social capital proxy is the index mentioned earlier, while human capital is approximated conventionally by years of education (Grootaert, 1999). The demographic characteristics involve the household‘s size, age, and dummy variables showing gender and whether agriculture is the industry of the head of household (Grootaert, 1999). The other two variables are dummy ones for years to differentiate the general economic and social conditions of the associated time.

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The next estimation relates the effect of human capital on social capital. According to Dinda (2006), human capital is considered important for predicting social capital. Education stimulates people to engage in dialogue and build networks so that a community is created. To identify this relationship, the next regression adopted from Dinda (2006) is performed.

As social capital is a broad concept that may also correlate with other unobservable variables, to address the endogeneity problem, the two stage least square (TSLS) method needs to be performed using instrument variables (IV). The instrument is required to be correlated with the endogenous variable (i.e., social capital), but cannot be correlated with the error term (Schmidheiny, 2012), meaning it cannot directly affect welfare (Grootaert, 1999). The literatures have suggested several instruments, but the availability of data limits this research to only certain candidates —namely, religious diversity in the community (Knack and Keefer, 1997), trust in the traditional figures as well as trust in government officials (Narayan and Pritchet, 1997), and period of living in the area (Grootaert et al., 1999). The TSLS results confirm the positive and significant effect of social capital on welfare. The TSLS technique can be summarized as follows:

There are at least a couple of tests that can be used to validate the TSLS result (Schmidheiny, 2012). The first one is testing the relevance of the instrument, which is related to the first requirement of the instrument. However, the second requirement, exogeneity of the instrument, can generally not be tested unless the number of instruments is more than the number of endogenous variables. The second test is to challenge the endogeneity of the problematic variable by using Hausmann test.