# IV.   Earnings and decision-making roles

## Complementing missing data on earnings

In surveys, earnings are measured through (a) individual wage/salary earnings; (b) profit from the farm (measured at the household or plot level); and (c) profit from the nonfarm enterprise (measured at the enterprise level, which if own-account could be assigned to an individual household member). The quality of rural earnings data can vary substantially across country surveys, often resulting in missing and outlier values for occupations where rural women also tend to be overrepresented. In wage employment, for example, this includes informal and seasonal jobs that can be paid out irregularly and/or in-kind; and in self-employment, in family farms/businesses where the value of household members’ labor is not recorded (if, as is common in rural areas, multiple family members are involved).

For individual wage/salary earnings, carefully collected community-level data on average wages or earnings for men and women, for formal/casual work across different sectors, could be useful as complementary data to understand expected labor market returns — particularly in contexts when many women are out of the labor force. For self-employment, within farming, several recent studies have discussed the use of individual-level data on productivity that is associated with farm income (see, for example, studies from Sub-Saharan Africa including Doss, 2018; Kilic et al, 2015). And as discussed in more detail below, for cases where income is pooled (across individual earnings, and farm/non-farm enterprises) the LSMS and other household surveys are exploring alternative questions about decision making over earnings to better understand individuals’ control over income. A joint pilot study in Sri Lanka by the World Bank and ILO under the WWEP, to coordinate survey questions across the LFS and LSMS, will also test questions on decision making in agriculture (Gaddis and Palacios-Lopez, 2018, Walsh, 2018).

## A closer look at decision making over earnings

Questions on decision-making over wage earnings, as well as income from self-employed farm/off-farm operations are receiving increasing attention in surveys, as well as recent rounds of the LSMS-ISA. A main issue with decision-making questions, however, is the choice of respondent, which as seen earlier with Ethiopia’s RCBP can lead to gender inequalities in program targeting. A growing number of within and crosscountry studies are also showing that men and women within the same household can respond very differently to the same sets of questions on the main decision maker (see Donald et al, 2017 for a crosscountry study from Sub-Saharan Africa; Anderson et al, 2017 from rural Tanzania). One approach to address this issue (albeit more costly) is to ask both the most knowledgeable adult man and adult woman in the household. The Demographic and Health Surveys ask both husbands and wives, for example, in decision making questions over their spouse’s earnings and large household purchases. Within farming, the WEAI survey module also asks both men and women about decision making roles in production and income (Malapit and Quisumbing, 2015). The Uganda GSARS pilot also had two respondents for the module on decision making – (a) the plot holder, and (b) the holder’s spouse/partner if they lived in the household and were also engaged in agricultural work on the holding (GSARS, 2017).

Similarly with enterprise income, cross-checking ownership and management of the enterprise across male/female respondents would be important, to understand whether gender norms affect reporting of these outcomes — including whether women entrepreneurs themselves underreport their decisionmaking roles in the business, as was found in an entrepreneurship training program in rural Pakistan (Giné and Mansuri, 2014). Using the 2016-17 Malawi survey, for example, Figure 7 compares men’s and women’s responses on whether they ran a business in the _household labor mo_ dule, with responses in the _non-farm_ 15