# _5.4 Impact of the INDH on Income, Consumption, and Wealth_

# 5.4.1 OLS and Quantile (Median) Regression Results

The estimates of OLS models for income, consumption, and wealth in level variables and then in logarithms are presented in Table 4. A binary variable for whether the household is in one of the INDH target communes is included in each of the models. The poverty rate in 2004 for the commune is also included in these regressions. These are essentially RDD models using the full sample and assuming a linear effect of community poverty rates as well as a uniform effect of INDH across the sample.

First, with regard to the income variable, the INDH effect was positive for the three periods surveyed. The INDH effect was DH 714 in 2008, but was not statistically significant. The effect was larger (DH 880) in 2011, though still not statistically significant. This figure was halved in 2013, amounting to only DH 443 and was statistically insignificant. The relationship between income and the 2004 rate of poverty variable showed a decrease in income as poverty rates increased for 2008 and 2011 but the opposite in 2013. However, the parameter associated with this variable is statistically insignificant for all three rounds of observations. One must remember that in view of the chosen identification and sampling strategy, the range of this variable was, by design, very limited (between 27 percent and 32 percent). When we consider the quantile regression results for income, we find the same overall trend. The INDH likewise has a positive effect on log income, 12.5 percent in 2008, 16.5 percent in 2011, and 18.4 percent in 2013, but these coefficients are not statistically significant.

Turning now to consumption, the INDH effect varied substantially across rounds. It was DH 584 in 2008, statistically significant at the 10 percent level. The same average gap almost doubled in 2011 (DH 932), and became significant at 5 percent. However, the coefficient in 2013 is only DH 75 and statistically insignificant. As expected, the relationship between consumption and the 2004 poverty rate is negative. The parameter associated with this same variable is, however, statistically insignificant for two of the three survey waves (2011 and 2013). In the quantile regressions, the effect of the INDH is positive and significant in 2008 (at 10 percent) and 2011 (at 10 percent) but not in 2013. Examining the impact on log consumption, the INDH effect is 12.5 percent in 2008, 20.7 percent in 2011, but 2.9 percent in 2013. These figures are statistically significant only for 2008 and 2011.

Almost all the parameters in the wealth regressions are not statistically significant. The sign of the effect of the INDH on this variable was positive for the three rounds. It amounted to an average of DH 5,215 for households in targeted rural communities in 2008, DH 2,962 in 2011, and DH 458 in 2013 but remains statistically zero. The relationship between the wealth level and the 2004 poverty rate is decreasing and significant only in 2008 (at 10 percent). It is increasing and statistically insignificant for the other two years. The same pattern of insignificant INDH effects is observed using the median, quantile regression, and log wealth. Overall, across the outcomes, we find, at most, weak economic impacts of Phase I that do not persist over time, as effects tend to dissipate by 2013 and are statistically insignificant.

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