# 4. Results

## <mark>4.1 Sources of Bias</mark>

Before examining how to correct for bias, it is important to first understand the sources of bias that enter into the phone survey sample and how that bias affects the composition of the sample. Tables 4.1a to 4.3b present weighted means for a wide array of characteristics<sup>11</sup> from the four LSMS-ISA F2F surveys across several different subsamples ending ultimately with the sample of successfully interviewed households in the HFPS for each country. Comparing the means across these subsamples will indicate (1) at what stage any bias is entering into the samples and (2) across what set of characteristics the samples are biased in relation to the F2F surveys. Figure 4.1 also presents estimated bias (measured by deviation from LSMS-ISA mean) in graphical form for selected characteristics.

### <mark>4.1.1 Coverage bias</mark>

Table 4.1a presents results comparing characteristics for the full F2F sample (columns 1, 4, 7, and 10) as well as the portion of the F2F sample that has contacted information (columns 2, 5, 8, and 11) for the samples from all four countries. The portion of the sample with contact information serves as the frame of eligible households to be selected for the phone survey sample. Therefore, any differences between these two samples would indicate the presence of coverage bias brought on by exclusion of households who are ineligible (i.e. have no phone number to be contacted on). Columns 3, 6, 9, and 12 present differences between the two samples (with significant differences indicated with “*” and highlighted in red).

Looking down the variety of characteristics contained in Table 4.1a for Nigeria (column 9), it is evident that there are no substantial differences between the full sample and phone survey frame, suggesting coverage bias is not a major concern for Nigeria. This is not surprising however, since the share of households without contact information is very small (less than 1%) reflecting the relatively high penetration of mobile phones in the country.

However, the results presented in columns 3, 6 and 12 (Table 4.1a) provide a clear indication of coverage bias in the Ethiopia, Malawi and Uganda samples. Households with contact information are generally richer (as measured by per capita consumption expenditure), more likely to own key assets (TVs, refrigerators, and mobile phones), more likely to live in dwellings with improved features such as a modern roof and floor, improved water source and toilet facilities, and electricity, as well as more likely to have a financial account. In addition, the household heads in the eligible sample tend to be better educated and more likely to working in more formal wage employment (in Ethiopia and Malawi). However, when the Ethiopia sample is disaggregated into urban and rural areas (the two domains for that sample) in Table 4.1b, many of the indications of coverage bias are not observed, particularly for the urban sample. This suggests that coverage bias is not a serious concern in urban areas, but may be a more serious concern in rural areas where mobile phone penetration is much lower and as a result 38 percent of ESS rural households are ineligible (compared to just 7% of urban households).

> 11 In order to save space, a subset of characteristics analyzed are presented in the results that follow. The results for the full set of characteristics considered can be found in the online appendix.

11