Testing for Attrition

Panel surveys have traditionally suffered from sample attrition. If attrition is non- random and severe enough, it can render the sample non-representative and could invalidate the estimates. The reason for this is that respondents that drop out of the longitudinal survey may differ systematically from individuals that are re-interviewed. Therefore, results of studies that only incorporate continuing panel respondents may suffer from severe attrition bias. The problem of attrition is especially widespread in household surveys conducted in developing countries due to communication means being underdeveloped. It is not easy to track individuals that have moved from one survey to another. Tracking movers can implicate substantial investment in terms of time and money.

Using the first two waves of the Indonesia Family Life Survey, Thomas, Frankenberg and Smith (2001) showed that panels in developing countries are not all necessarily contaminated by high rates of attrition. Statistics from the IFLS show an optimistic picture. 94% of the households interviewed in 1993 were re-interviewed in 1997. This rate of re- contact tops even the best surveys in the United States.

Since the study uses the last two waves of the IFLS, I test for the presence attrition between the third wave (baseline) and fourth wave (post-treatment) of IFLS. I first examine descriptive statistics by comparing the group of attritors and non-attritors across multiple variables measured at the baseline. Then, I estimate a binary dependent variable model of attrition as a function of variables measured at the baseline in order to examine whether differences between attritors and non-attritors hold after controlling for a comprehensive set of socio-demographic characteristics. The model used includes

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demographic characteristics, health characteristics, labor force variables, household composition and resource variables, as well as spatial and community characteristics in 2000.

Based on the IFLS, 93.46% of individuals in the 2000 data are successfully re- interviewed in the 2007 wave. Therefore, the attrition rate between these two surveys is 6.54%, which is similar to the 94% that Thomas et al. (2001) estimates using the first and second wave. Table 12 presents the comparison of descriptive statistics and t-test between the group of attritors and group of non-attritors. It appears that for most variables the group of attritors are significantly different. The individual demographic characteristics point out to the fact that older married individuals with higher education level are more likely to be attritors. There is no clear difference in health status. The labor force participation variables indicate that attritors are more likely to work and work more hours and less likely to work in the informal sector. Household composition and resource variables appear to indicate that attritors are wealthier (very significant difference in proxy means tested score), live in smaller households, and have less children. Community characteristics appear to show that individuals that are attritors live in areas that are wealthier and have better overall infrastructure. Attritors are also more likely to live in urban areas. However, these results are only indicative at best, as they only provide a comparison of means individually. It is important to control for all other variables that may cause attrition.

Table 13 provides estimates of a binary dependent variable model of attrition as a

function of variables measured at the baseline wave. After estimating a full model, it appears that some differences remain between the group of attritors and non-attritors.

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Age, proxy-means tested score, hours worked per week and number of health centers show significant differences that are small in magnitude. Attritors are more likely to be married, more likely to have higher education, are less likely to have children, are less likely to work in the informal sector, are more likely to have piped water and are more likely to live in an urban area. However, the magnitude of these variables is small.

The presence of attrition (even though small) can bias our estimates. Thus, it is necessary to apply a method to correct the sample from attrition bias. IFLS provides sampling weights that allow to account for attrition in the survey. Strauss, Sikoki, Witoelar, and Watie (2009) describe the procedure in order to correct the attrition bias specific to the IFLS Survey. They compute weights specific to each survey to be used by researchers in order to obtain a representative sample. The inverse probability weights provided in the Indonesia Family Life Survey dataset correct for both sampling bias as well as attrition bias. The methodology to compute the longitudinal analysis individual weights is the following: In order to correct for in between-survey attrition, they first estimated a logistic model of the probability that an individual found in a baseline wave of IFLS was found in a subsequent wave, conditional on basic individual and household characteristics at the baseline. They then calculated the predicted probability that the individual was found. From that predicted probability, they computed the inverse-probability-of-attrition weights for each individual. I use the weights provided in the IFLS in order adjust for attrition. Conditional on these weights, attrition can be considered as ignorable and random.

Finally, because all respondents who were interviewed in the later waves but were not in the original household roster (IFLS1) are not assigned longitudinal weights, it is

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necessary to restrict the analysis to only the individuals that were present in the original survey (1993).