inverse of the predicted response probability to construct the response weight variable for each IHPS adult household member who has been subject to an individual interview; (2) winsorize the response weights at the top 1 percent to account for potential outliers; and (3) set it equal to 1 for all adults in the IHS4 sample, which includes the most knowledgeable survey respondents and adult household members who were not interviewed by the IHS4. Henceforth, all statistics are weighted using the response weight.

# **2.3. Descriptive Statistics**

The scope of the socioeconomic data collection was near identical across the IHS4 and the IHPS. All sample households were administered a multi-topic household questionnaire that collected individual-disaggregated information on demographics, education, health, and wage employment, as well as data on housing, food consumption, food and non-food expenditures, food security, nonfarm enterprises, access to infrastructure and exposure to shocks, among other topics. Appendix Table A2 provides sample means for men and women on individual, household and geographic characteristics across the two surveys that we also control for in the empirical analysis. There are some statistically significant differences across the survey samples, although the magnitudes of these differences are typically not very large (often not more than 5-7 percentage points across the two survey approaches). The IHS4 does have a greater share of male and female household heads (73 percent of men respondents were household heads in the IHS4, compared to 65 percent in the IHPS; among women, these shares were 26 and 21 percent, respectively). The IHS4 was also more likely to represent the North region of the country. The IHPS households, while still mostly rural, do have lower share of rural residence vis-a-vis the IHS4 households, with somewhat greater nonfarm employment, ownership of mobile phones, and access to electricity. We control for these individual, household and regional characteristics in the regressions below.

On agricultural land ownership and rights, both surveys allow us to compute _individual-level_ indicators related to (i) reported ownership, (ii) economic ownership, (iii) right to bequeath, and (iv) right to sell, in a way that **aggregates** the information reported at the parcel-level. In other words, in the **IHPS** , a self-reporting adult household member is tagged as a reported owner if <u>he/she reported himself/herself as a reported owner for at least 1 agricultural parcel. In the case of</u> number of months living away from the household over the past year; and (iii) household covariates, including household size, dependency ratio, and wealth index. The latter is a factor analysis-based index that is composed of (i) a series of dichotomous variables that capture the ownership of mortar, bed, table, chair, fan, air conditioner, radio, radio with flash drive/micro CD, TV, VCR, sewing machine, kerosene/paraffin stove, electric/gas stove, refrigerator, washing machine, bicycle, motorcycle, car, minibus, lorry, beer-brewing drum, sofa, coffee table, cupboard/drawers, lantern, desk, clock, iron, computer, satellite dish, solar panel and generator, and (ii) a series of dwelling covariates, including number of dwelling rooms per capita and categorical variables that identify construction material (permanent; semi-permanent; traditional); roof type (grass; iron sheets; clay tiles/concrete/plastic sheeting/other); floor type (sand; smoothed mud; smooth cement/wood/tile/other); water source (piped/well; borehole; other), and toilet facility (flush/VIP toilet; traditional latrine; other/none).

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