line is set to Br 4,360 (in 2016 prices), which corresponded to the 40<sup>th</sup> percentile of consumption expenditure distribution in 2016.<sup>13</sup> Households that earn less than Br 4,360 per adult equivalent per year (in 2016 prices) are considered as poor households in each wave.

According to the recent three waves of the ESS, household consumption has been dropping in many regions. Among the untreated group of communities, the average per adult-equivalent annual consumption (expressed in 2016 prices) has steadily decreased from 6,347 birr in 2012 to 6,030 birr in 2014 and 5,383 birr in 2016 (Table 3). In tandem, the share of population in poverty, or poverty rates have been rising in those communities, reaching 43.7 percent in 2016. By contrast, the mean consumption level and poverty rates have been relatively stable in treated communities during the period.

This study employs an objective indicator of drought exposure. The 2015/16 drought is thought to be one of Ethiopia’s worst droughts in the past five decades. About 10.2 million people in Ethiopia needed assistance. The drought was a result of rain failure during both _Meher_ (the main harvest) and _Belg_ (spring harvest) seasons in 2015. In order to identify communities that were affected by the recent droughts, an objective measure is prepared.<sup>14</sup> The normalized difference vegetation index (NDVI) is a commonly used measure of vegetation, and its deviation in the period of the droughts in 2015/16 from the long-term average score—which is calculated as the z-score— indicates the effect of the drought for agricultural production: where X��� indicates the monthly value of NDVI in community _j_ in year _y_ ; X��� is the mean value over each month for the years 2000 to 2016; and SD�X��� is the standard deviation over the period.<sup>15</sup> Negative z-scores mean that the vegetation during the drought period was worse than the long-term average. Thus, households in the communities with z-scores below zero are identified as exposed to drought shock, accounting for 37.5 percent of households in the treated communities and 41.0 percent in the control group.

# **4. Empirical strategy**

The primary approach of the econometric analysis in this study is a DID matching approach (Smith and Todd 2005), which combines DID and propensity score matching. In the impact analysis of rural road development, endogeneity problems arise when initial conditions influence project placement and the subsequent growth paths and prospects of the communities. While either DID or household fixed-effects-panel regression models can statistically control for time-invariant factors,

> 13 Official poverty lines are developed for HICES, which is a different household expenditure survey from the ESS. Thus, poverty estimates in this paper are not comparable with the official poverty statistics. 14 In the ESS data, a lot of households reported that they had been affected by drought during the last 12 months. In the study area, 29 percent of the households were affected by drought in 2015/16. However, such measure of drought exposure reported by households—and even by communities—can be susceptible to measurement errors, and the errors can be correlated with the respondents’ income levels if lower-income households are more likely to overstate the drought impact and/or higher-income households tend to understate the impact.

> 15 NDVI was calculated based on the MODIS Terra satellite data.

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