rafmacalaba/data-use-mentions-extended huggingface dataset:
population), as shown in Figure 2b. Other major changes include cost-of-living adjustments (COLA) in 2007 as well as the expiration of the ARRA in 2013. Figure 4a plots the changes in SNAP benefits per population across these four events. The Farm Bill and the ARRA contribute large and dispersed changes across states, whereas the 2007 COLA and 2013 ARRA expiration contribute limited and negligible variation.

We also use the SNAP Quality Control (QC) Survey, which contains detailed demographic, economic, and program-eligibility information for a nationally representative sample of approximately 50,000 SNAP households. This data set is collected by the USDA FNS and generated from monthly quality-control reviews of SNAP cases that are conducted by state SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for the state’s SNAP caseload. We get the formula from the FNS to impute synthetic benefit amounts. That is, we analyze the algorithms used by the FNS to audit benefit amounts and construct the QC data, and use the same algorithms to build the simulated instrument. The formula we use generates identical benefits to those in the QC data for 98% of households.<sup>14</sup>

# **2.2 The SNAP Formula and Instrumentation Strategy**

The federal SNAP formula is designed to ensure a floor of spending power on what the government considers sufficient nutrition for households in poverty. Every year, the USDA prepares a monthly dietary budget called the “Thrifty Food Plan” for a typical four-person household adjusting for changes in the Consumer Price Index (CPI). In October, the USDA updates the SNAP-benefit parameters based on the size of this budget, which becomes the maximum level of benefit a four-person household can receive from SNAP. The annual formula adjusts maximum levels for households with different numbers of family members based on a sliding scale. SNAP may update these parameters in months other than October at the discretion of the Congress. For example, the 2009 ARRA included a provision to add 13.6% to the Thrifty
difficult one. In general, however, the following rules of thumb apply. Large capital projects and feasibility studies are not included. However, specific training components attached to large capital projects are TA. On the other hand, small capital projects associated with a free-standing TA, such as renovating a building or supplying computers, are generally included in TA. Unfortunately, some agencies do not make any statistical distinction between TA and capital assistance and others just refer to the financing of personnel (or "experts") as TA and anything else as capital assistance.<sup>4</sup> Nevertheless, our analysis in section III will focus on projects for which the above distinctions are made.

# I(b): Trends in Technical Assistance

While we have some data on technical assistance provided by the World Bank, we only have detailed data on technical assistance in the form of grants. Total current dollar TA grants 2 Berg (1993: 42-47) goes into considerable detail on the definitions of TA and TC used by different agencies.

3 A differentiation is often made between "soft" and "hard" TA, where the latter is for specific investment needs and the former for more general purposes, including institutional development. Most free-standing TA is of the soft variety, while TA attached to projects may be of either sort.

4 Berg (1993: 48-52) discusses the distinction between capital assistance and technical assistance in detail.

2
participation in both sectors. The inclusion of different crop acreage in the Ghana regressions helps explain the linkages between agriculture and non-farm participation.

The econometric analysis shows that in both countries, female heads of households are significantly more likely to work in the non-farm sector and significantly less likely to work in agriculture. In Ghana, non-farm work is especially important for all women. In Uganda, the strongly significant result for female heads of household suggests that women who are on their own have made significant inroads into the non-farm sector. We have to interpret this with some caution however, because rather than being a choice, participation in non-farm work. may be necessary if the female household head does not have access to land. Given the very inequitable access to land by men and women in Uganda, this may be a real barrier. Nevertheless, the association of lower poverty rates for those female headed households in non-farm work shows that non-farm work is linked with higher standards of living at least at the lower income deciles. Another important aspect of the gender differences in Uganda compared to Ghana, however, is that Ugandan men are much more likely to participate in the non-farm sector than are Ghanaian men. The overall impression given by the Uganda data, from all of the angles of analysis on poverty, income, and participation, is that both men and women have gained from non-farm work in Uganda, with men gaining more on balance.

Further research is needed to better understand the distributional implications of non-farm work by gender. In upcoming work, we examine the role of non-farm income in income inequality and changes over time.

33
have no significant impact on consumption for other households. Interestingly, land does not mitigate the impact of the drought, though the safety-net appears to (noting that drought was not so severe in 2011 when the PSNP was in operation). In a moderate drought crop-losses are about 30 percent suggesting that a moderate drought causes a drop in consumption of 9 percent for many households in drought prone areas.

To investigate the impact on poverty of an extreme weather shock we estimate the poverty rate were the weather conditions experienced in 2002–the worst drought recorded in the LEAP database–to be experienced again. In this simulation the headcount poverty rate would increase from 30% to 36%, almost reversing all of the gains in poverty reduction recorded from 2005 to 2011.

Each 1 percentage point increase in the monthly inflation rate resulted in a 2 percent reduction in consumption for rural households, a 2.5 percent reduction for urban households with an educated household head and a 3.5-3.8 percent reduction in consumption for urban households with uneducated heads (female and male respectively). The impact of the food price shock in 2011 was therefore substantial, reducing consumption by 14 percentage points among households with uneducated heads given that the average monthly inflation was 3.87 percent from July 2010 to June 2011 (the start and end month of the survey).<sup>14</sup>

> 14In robustness checks we defines food price shock using the dummy self-report. The shock was associated with reduction in consumption by 10-13% for those affected in households with an uneducated female household head in an urban area. Educated households in urban areas, and rural households

13
# **2. Setting and Intervention**

Pakistan has a population of over 162 million, with over 60% living in rural areas. Although the agricultural sector continues to be important for overall growth, 45 percent of the rural poor rely on non-farm activities as important sources of income.<sup>2</sup> Pakistan’s financial system has grown significantly in the past few years due in part to the successful implementation of various financial sector reforms, including the granting of banking licenses to a number of new private banks in the early 1990s, the modernization of the governance and regulatory framework of the banking sector in the late 1990s, and the privatization of major public sector banks since the early to mid2000s.

Despite these recent achievements, access to financial services is still quite limited, especially in rural areas. According to the Access to Finance dataset (Nenova et al. 2009), only 14 percent of households interviewed reported using a financial product or service (including savings, credit, insurance, payments, and remittance services) from a formal financial institution.<sup>3</sup> When informal financial access is taken into account, however, this figure rises to just over 50 percent.

Overall, rural firms and households account for about 7 percent of total credit disbursement (about Rs 130.7 billion) and the bulk of this is for agricultural finance (Rs 108.7 billion), including both farm and nonfarm credit (see Akhtar, 2008). While microcredit volumes are skewed towards rural areas, microcredit currently accounts for only 17 percent of total rural credit and serves some 1.7 million clients. Comparative

> 2 Source: World Development Indicators database, April 2009 and World Development Report 2008. 3 In comparison, 32 percent of the population has access to the formal financial system in Bangladesh, and this figure amounts to 48 percent in India and 59 percent in Sri Lanka (World Bank, 2008).

4
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the respondent is presented with a long or a short list of consumption items can influence the quality of the responses.<sup>3</sup>

- _Diary versus recall surveys_ . The consumption diary is the main alternative to the recall approach to consumption measurement. It is generally expected that diaries suffer less from recall or telescoping errors because the consumption is intended to be recorded either simultaneously or soon after it occurs. Of course, this presumed accuracy is only achieved if the diary is used as intended. The extent to which diaries are supervised to ensure they are regularly filled is thus a key design feature. Unsupervised diaries may effectively become self-administered recall modules with endogenous recall periods if some types of respondents do not fill them in every day and, hence, render them subject to varying degrees of recall, telescoping, and rule of thumb reporting. Diaries administered among individuals should also prove better at capturing individual consumption outside the household (i.e. reduced personal leave out error), leading to a higher level of measured household total consumption (Grootaert 1986).

As a net result of these various types of reporting error, consumption estimates based on different methods of data capture (diary versus recall questionnaires), levels of respondent (individual versus household), recall periods, or degree of commodity detail may not be comparable. We have designed the survey experiment used here in part to assess the extent to which variations across these dimensions affects itemspecific and summary consumption measures in relation to the benchmark measure of the daily-supervised individual diary. We chose this diary design, described in more detail in the next section, to minimize the influence of recall, telescoping, personal leave out, and rule of thumb errors.

# **III. The Tanzania survey experiment**

The Tanzania survey experiment, conducted to shed light on the implications of survey design variations in food consumption measurement, systematically contrasts various design features. We strategically selected eight survey designs to reflect the most common methods utilized in low-income countries and that are typical
|Population estimates|Household census, global dataset,<br>global raster|2015 Zambia census, Open<br>Street Map , Nightime light<br>brightness|
|---|---|---|
|Household size|Household surveys||
|Night-time light brightness and<br>Digital Elevation Model|Global dataset Resolution|NASA (2015)‘ASTER Level 1<br>Precision Terrain<br>Corrected<br>Registered At-<br>Sensor Radiance<br>Version 3’. Sioux<br>Falls, South Dakota:<br>NASA EOSDIS Land<br>Processes DAAC,<br>USGS Earth<br>Resources<br>Observation and<br>Science (EROS)<br>Center.|
|Groundwater vulnerability to|Raster|KfW|
|contaminants|||
|Covariate grids were generated<br>features|based on GIS data describing relevant W|ASH infrastructure or natural|
|Distance from water network|Shapefile|LWSC|
|Distance from sewer network|Shapefile|LWSC|
|Distance from stream/river|Raster file (based on hydrological<br>analysis of Digital Elevation Model<br>data)|NASA(2015) – as per Digital<br>Elevation Model data source|
|Distance from cemetery/burial|Global Raster file|Open Street Map|
|ground<br>WASH covariate grids were gene<br>household or other survey data|rated by implementing a Bayesian geos<br>describing water and sanitation facilities|tatistical model applied to<br>, water quality, and flood risk|
|% HHs with soap in toilet|GPS point data from household|World Bank Lusaka|
|% HHs not treatingwater<br>% households with<br>unimproved sanitation<br>Density of population with<br>unimproved sanitation|surveys|Sanitation Assessment<br>survey (2015) and the<br>Lusaka Sanitation Program
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One notable omission from this list is the district’s profile in terms of scheduled tribe and caste populations. Iyer et al. (2011) show that castes and tribes vary in their overall rates of entrepreneurship, and castes and tribes also differ in their social norms on the appropriate role of women. Unreported tests analyze whether including these profiles influenced the gender results presented below. These inclusions are not important in our context when also controlling for intermediate variables like the female literacy rate, and so we adopt the more parsimonious specification that is also more easily comparable in the future to other contexts outside of India. We believe, nonetheless, that the role of these social norms is important for future research with respect to female participation in the Indian economy.

Beyond these basic demographics, we consider threeadditional local traits that may affect female entrepreneurship: quality of local physical infrastructure, travel time to one of India’s ten biggest cities, and stringency of a state’s labor laws. While these traits do not constitute an exhaustive list of local conditions, they are motivated by the literatures on entrepreneurship and India’s development.

Basic infrastructure services like electricity are essential for all businesses, but new entrants and the informal sector can be particularly dependent upon local infrastructure (e.g., established firms are better able to provision their own electricity if need be). Aghion et al. (2011) provide a theoretical model of this dependency, and Ghani et al. (2011c) and Mukim (2011) find that infrastructure strongly links to overall entrepreneurship levels in India. Lall (2007) links infrastructure investments to regional growth in India. The population census provides figures on the number of villages in a district which have telecommunications access, electricity access, paved roads, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access) to four (full
proxy._ A proxy for total per capita consumption is estimated using a subset of food items consumed in the last week, and non-food expenditures in the past month or twelve months, depending on the item.<sup>14</sup> We followed a two-step estimation to select the subset of items that best predicted consumption using baseline and midline data. We use district market prices from the market surveys to estimate the value of food consumption. We report the consumption proxy as an index of real values, where per capita consumption for control UP at endline equals 1. This allows for comparison across

> 12The average interval between phone survey rounds one and two was 50 days, while the average interval between survey rounds three and four was about 20 days.

> 13If there was no primary man in the household, the primary woman would complete both portions of the survey, and vice versa.

> 14Our consumption proxy explains 75% and 59% of the variability in consumption including UP and non-UP at baseline and midline, respectively (see Table A2 in Online Appendix Text 2 for further details).

13
_Table 3 - The unequal effect of shocks on the labor market. The sample includes individuals in LFS data. The binary dependent variables are estimated using Logit. Shock is measured as the year-on-year difference in log(USD to IRR rate) in each month. Control variables include being householder, gender, marital status, age, household size, job experience, and education fixed effects. Job fixed effects include occupation, job status, having insurance, and firm size dummies. Year and season fixed effects are in all regressions._

**Panel A: Regression with all LFS individuals**

||**Work**|**ing hours pe**|**r week**|**Unemp**|**loyment**|**La**|**y off**|**Unemploy**|**ment length**|
|---|---|---|---|---|---|---|---|---|---|
|||OLS||Lo|git|Lo|git|O|LS|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Afghan dummy**|1.078***|1.003***|2.588***|-0.888***|-0.839***|-0.354**|0.168|-12.123***|-3.217***|
||(0.199)|(0.190)|(0.177)|(0.038)|(0.040)|(0.163)|(0.