rafmacalaba/data-use-mentions-extended huggingface dataset:
weakening the effect of the regulation. In regulation, less complexity can often mean more in terms of results.<sup>14</sup> One problem with the recent regulatory reforms is that they have been leading to ever more complicated regulations. In the case of the United States, for example, numerous observers have pointed out the great complexity of the regulation introduced during the crisis.<sup>15</sup> Also in a broader sample of countries covered by the World Bank’s Banking Regulation and Supervision survey, it is correct to say that regulatory complexity has been on the rise, with even many small, low-income jurisdictions opting for relatively complex

> 13 Čihák, Demirgüç-Kunt, Martínez Pería, and Mohseni-Cheraghlou (2012) provide an update on regulatory developments in individual countries based on the World Bank’s Banking Regulation and Supervision survey. World Bank (2012) overviews the global regulatory response, focusing on Basel III and FSB reforms. 14 For a broader discussion on simplicity versus complexity, see for example Maeda (2009).

> 15 For example, The Economist (2012a) pointed out that at 848 pages, the Dodd-Frank law of 2010 is “longer, less intelligible, more incomplete than any other financial reform that was ever passed into law in the US”.
Validation efforts by Novella and Thiaw (2013) found that low reporting rates for some GTS stations degrades model performance in those regions. Other findings are a general tendency to underestimate rainfall, which is enhanced in areas of high relief or complex topography.

Data and technical documentation are available for download from `https://www.cpc.ncep. noaa.gov/products/international/data.shtml` .

# **A.1.2 Climate Hazards group InfraRed Precipitation with Station data (CHIRPS)**

Like ARC2, the CHIRPS rainfall data set builds on established techniques for merging gauge and remote sensing data. Produced by the Climate Hazards Group at University of California, Santa Barbara this data set is designed for monitoring of drought and environmental change at a global level. To minimize latency, there are two products, a preliminary version with two day lag, and final output available at three weeks. Outputs are available at time-steps from six hours to three months. As inputs, CHIRPS makes use of a monthly climatology CHPclim, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis version 7 (TMPA 3B42 v7) and global Thermal Infrared Cold Cloud Duration (TIR CCD) from two NOAA archives. The remote sensing data are then merged with gauge data from five public archives, including the Global Historical Climatology Network (GHCN) and GTS, several private sources, and meteorological agencies. While targeted gauge data collection efforts resulted in a greater number of input stations for years prior to 2010, the number of stations going forward is more limited, particularly in Sub-Saharan Africa. Detailed metadata by country is available and may be a useful reference to determine if coverage for a region of interest is sufficient for the analysis.

Validation for select countries found that the climatology input CHPclim outperformed other climatology data sets in data sparse regions and complex terrain (Funk et al., 2015). Furthermore, in an assessment of wet season statistics CHIRPS showed less bias than other rainfall sources and good correspondence with Global Precipitation Climatology Centre (
potential for a social crisis originating in a specific social and economic category (fishermen), and a public health crisis that can hardly be alleviated by cash transfers.

Using the 2015 Input-Output (IO)<sup>3</sup> table for Angola, we simulated the impact of the fuel subsidy removal on the cost structure, and in which sectors the impact will be concentrated. We simulate an increase in oil input prices using the IO table (see Annex 1 for the Methodology) with 124 sectors and simulate the past increases from June 2023 and April 2024 and the full impact of the subsidy removal starting from the first price increase in June 2023. We further provide an estimation of the size of the needed mitigation.

The June 2023 increase in gasoline prices by 87.5 percent generated a price increase of 0.6 percent in total, including 2.4 percent for fishing and 2.0 percent for transportation (Annex 2). Fully compensating for the increase in fishing would have absorbed 10.9 percent of the savings the government generates, and fully compensating for road transportation would have absorbed 16.5 percent. Hence, with 27.4 percent of the savings, the government could have fully compensated for the increase in fishing and road transportation costs.

April 2024 increase in diesel by 48 percent generates a price increase of 1.0 percent in total, including 3.5 percent for fishing and 3.9 percent for transportation (Annex 3). Fully compensating for the increase in fishing would absorb 10.3 percent of the savings the government generates, and fully compensating for road transportation would absorb 20.8 percent. Hence, with 31.1 percent of the savings, the government can fully compensate for the increase in fishing and road transportation costs.

The combined increases of June 2023 and April 2024 generate a price increase of 1.6 percent in total, including 5.9 percent for both fishing and transportation (Annex 4). Fully compensating for the increase in
- **3 Prevalence of stunting and access to adequate food security, child care, and health and environment**

In line with the common practice in the literature, the report adopts height‐for‐age as the measure of chronic malnutrition. Given that stunting, height‐for‐age more than two standard deviations below the median height‐for‐age for the particular age and gender, captures chronic malnutrition, it is chosen over wasting, weight‐for‐height more than two standard deviations below median weight‐for‐height, since the latter most frequently indicates a recent episode of severe weight loss associated with starvation and/or severe disease resulting in acute malnutrition.

# **3.1 Prevalence of stunting**

Although stunting remains high in most of the study countries, it has decreased significantly over time for both under two‐year olds as well as for under five‐year olds. Figure 2 depicts stunting in the under two‐year group in the eight countries and Figure 3 depicts stunting in the under five‐year group. It is important to bear in mind that for Bolivia, Cambodia, Ethiopia, Nepal, Peru, and Zimbabwe the stunting rates are based on stunting rates reported by the StatCompiler of DHS for the indicated years using all the children with height information. For Bangladesh the information is derived from the HK and IFPRI data sets and for Indonesia from the RKD data set. In both age groups the seven countries for which there are multiple years of data show no increases in stunting. In Ethiopia and Nepal the reductions have been the greatest. From 2001 to 2011, Nepal experienced a 17 percentage point reduction in the prevalence of stunting in Nepal in the under 5‐ year cohort and 15 point reduction in the under 2‐year cohort. In Ethiopia from 2000 to 2011, stunting reduced by 13 percentage points in both age cohorts. Ethiopia and Nepal were also the two countries with the highest baseline stunting rates. In addition, Peru achieved large reductions in
adequate for their needs, and whether income was improving over time. And we added questions to the UHSS on DB participation and income received from DB, for the purpose of this paper. However, this only includes the cash transfer from DB. It appears that some local governments also provide non-monetary benefits to DB participants, such as health-care and schooling entitlements, and sometimes a discount for the cost of utilities (notably in the north). We do not have data on these extra DB benefits. The UHSS was done during 2003 and 2004. The surveys in Beijing, Fujian, Hainan provinces and Kunming (the capital city of Yunnan province) were finished in 2003 while all others finished in 2004.

Another problem is that we do not have a municipal cost-of-living index. The DB lines may well reflect cost-of-living differences, but they will also reflect other variables, including local fiscal capacity. We will discuss the likely biases due to this problem.

# **3. Model of** **_Di Bao_ participation**

In using survey data to assess targeted transfer programs it is generally assumed that the income as measured in the survey is the same income measure used in implementing the program. This is a questionable assumption from three points of view. Firstly, there must be a strong presumption that income is measured with error in any survey; there are the usual reporting errors, but on top of this there are the likely extra errors in using a single income question, as well as the fact that the survey was done after the program was assigned, so the survey-based income net of DB receipts may differ from the income observed at the time the program was assigned (after the checks made by local authorities).

Secondly, potential participants face an incentive to misreport their incomes; possibly the survey-based incomes are more accurate. The DB program does not rely solely on self-reported incomes. Local authorities and neighborhood committees try to assure that recipients are 6
Owing to the complex data structure produced by the PIRLS survey design and the multilevel nature of the explanatory variables, the error term ε of the regression has a non-trivial structure. Although we include a considerable amount of school-related variables, we cannot be sure that there are no omitted variables at the school level. Given the possible interdependence of students within the same school, the use of class- and school-level variables and the fact that schools were the primary sampling unit (PSU) in PIRLS, there may be unobservable correlation among the error terms ε _is_ at the school level (cf. Moulton 1986). We correct for potential correlations of the error terms by imposing an adequate structure on the covariance matrix. Thus, we suppose the error term to have the following structure: where _ηs_ is the school-level element of the error term and _υi_ is the student-specific element.

We use clustering-robust linear regressions (CRLR) to estimate standard errors that recognize this clustering of the student-level data within schools. The CRLR method relaxes the standard independence assumption and requires only that the observations be independent across the PSUs, i.e. across schools. By allowing any given amount of correlation within the PSUs, CRLR estimates appropriate standard errors when many observations share the same value on some but not all independent variables (cf. Deaton 1997). To avoid inefficiency due to heteroscedasticity, CRLR imposes a clustered covariance structure on the covariance matrix, allowing within-school correlations of the error term. Thus, the form of the covariance matrix _V_ is with ∑ _i_ ( _i_ =1,..., _l_ ) representing the covariance matrices of the residuals of least-square regressions within each school cluster (PSU). Observations of two different PSUs are thus assumed to be independent and uncorrelated, leading to the block-diagonal matrix _V_ with PSUs as diagonal elements. This method yields consistent and efficient estimates, enabling valid statistical inferences of the obtained estimation results (cf. White 1984).

Finally, the stratification of the PIRLS sampling design within each country produces varying sampling
curate information allows expeditious updating of _ex ante_ probabilities as new information emerges. All these features of IT act to compress realized variation in the conditional forecast of the probability of non-compliance. This motivates our analysis of reduced ambiguity in the theoretical model.

# **3 Descriptive evidence**

In this section, we use data provided by the Serbian customs agency to support two arguments that are central to the paper: (1) ambiguity is a pervasive phenomenon in the oversight of import shipments, and (2) agencies with oversight responsibilities respond to ambiguity when allocating scarce inspection resources. We lack a direct measure of ambiguity, so our arguments are necessarily indirect. In our view, a primary source of ambiguity in the oversight of import shipments is the infrequency with which inspecting agencies observe many shipment types. We use the frequency with which shipment types are observed as an inverse proxy for ambiguity. We show that shipment types that are infrequently observed are numerous and that infrequently observed shipment types are inspected at higher rates. The negative relationship between inspection rates and shipment frequency remains, even after controlling for available indicators of risk.

The Serbian customs agency already operates under risk management protocols that exploit sophisticated IT. Since our thesis is that risk management involving IT reduces the salience of ambiguity in the inspection decision, the agency’s response to ambiguity is likely to be muted relative to what one would observe in a pre-reform environment. For our purposes, it would be preferable to use data from a pre-reform environment, but unfortunately agencies that have not adopted modern IT usually lack the high quality data necessary for our exercise. We use the Serbian customs data because it is high quality and because it is available to us.

The Serbian customs agency provided us with comprehensive data for the years 2006-16. The raw data are defined at the level of an import transaction (hereafter, a shipment). Each shipment record contains information about the shipment’s value, weight, origin country, associated duties and taxes paid, the importing firm’
liberalization results from the Global Trade Analysis Project (GTAP) model of global trade. They assume that households are currently maximizing utility subject to their budget constraint and this permits them to infer the change in household welfare associated with marginal price changes by multiplying these price changes by the households’ budget shares – taking into account net sales by the household as well (including sales of labor and capital services). Since this calculation is computationally simple, they do so for the entire household survey, and draw conclusions about the likely impacts of accession by individual household type and location.

One of the main findings in the work of Chen and Ravallion, as well as the subsequent work of Ravallion and Lokshin (2004) on Moroccan trade policy, is the heterogeneity of effects across different types of households with the same pre-reform level of welfare. They refer to this as “horizontal inequality.” In their Morocco paper, Ravallion and Lokshin (2004) find that, depending on the depth of cuts in cereals protection, the impact on aggregate inequality can be fully explained by the horizontal component. Specifically, they conclude that for small cuts in protection – on the order of 10% – there is an increase in inequality that is largely fueled by the benefits flowing disproportionately to the wealthy (vertical inequality). However, in the case of full liberalization in the grains markets, they find the opposite to be the case; horizontal inequality dominates. That is, the predominate impact of trade liberalization on inequality is through the differential impact on households with the same level of pre-reform income.

The potential for generating highly disaggregate policy impacts is a great advantage of the Ravallion _et al._ approach. However, like the cost-of-living approach, this abstracts from the secondorder impact that price changes might have on the households’ mix of consumption and earnings. While households are unlikely to radically change their consumption patterns in the wake of a modest change in relative prices, this is not necessarily the case with sales revenue. While a farm household might
with error, it is notable that the coefficients of interest are not discernibly changed with the inclusion of these controls (Table A.10). In column (2) of Table A.10, we control for a different (also imperfect) measure of preferences that reflects actual choices rather than stated preferences —the state-year gender school enrollment ratio at ages 6-11 and 11-14—and, again, the results are stable. The coefficients of these additional terms are interesting in their own right, and are consistent with our expectations. The coefficients of _Ideal fraction of sons * Female_ is positive and that of _Girl-boy enrollment rate 6-11 ∗ Female_ is negative, implying that EFM is higher in families with _*_ a stronger preference for sons. The coefficient on _Ideal number of children Female_ is negative, implying that, conditional on son preference, a declining trend in fertility leads to higher EFM, a finding that is consistent with Jayachandran (2016).

We also estimated equations in which son preference (measured as the mother’s reported ideal fraction of sons) is the dependent variable with a view to testing if it evolved differently between families with firstborn girls and firstborn boys; see Table A.11. This is not straightforward because we do not have longitudinal data on son preference. Instead, we have son preference recorded at the time of the survey in three cross-sections. The survey dates for the NFHS are 1991-92, 199899, and 2005-06, which are all post-ultrasound, but they span 15 years, allowing us to investigate preference decline during this period. For each wave, we keep women who had their first birth within a calendar year from their survey date, so that their preferences at the survey date reflect their preferences around the birth of their first child. We find a trend decline in son preference but no significant difference in the trend decline between firstborn-girl and firstborn-boy families.

As discussed earlier, Table A.3 suggests that there was no change in the composition of firstborngirl families post-ultrasound. To the extent
or otherwise, was rare: only 16 percent of people engaged in any paid agricultural work for others, and 11 percent performed paid nonagricultural work. A large share of people spent at least some time collecting firewood and water. About a quarter spent at least one day in school, and slightly less than half were sick for at least one day over the season.

Table 2, column 2 shows the average number of days spent in a given activity, as reported through the weekly visits, conditional on the performance of any reported labor activity that week. While important, family farm labor was perhaps less frequent than might be expected: people spent an average of 1.88 days a week working on their household farms, conditional on the performance of any reported work that week. We show below that this does not necessarily imply a regular weekly work pattern. There is considerable irregularity and cyclicality in agricultural work. As suggested in a number of studies of farm labor in Sub-Saharan Africa (see the discussion in Arthi and Fenske 2016), we find that the agricultural workday typically lasts four or five hours.<sup>13</sup> It is much shorter than the hours spent in nonagricultural and market activities (such as paid nonagricultural work, non-agricultural household business, fishing, livestock keeping, and schooling), conditional on the performance of such work.

> 11 Our experiment was initiated at the beginning of the maize cycle, in January 2014, and followed respondents to the completion of the harvest in August–September. 12 The time to commute to and from plots is not included in any of the working times reported in this study. Households were explicitly instructed to exclude commuting time in reporting the time worked in farming activities. 13 Although some of the discrepancy between the true and assumed agricultural workday is a function of recall bias, the work reported by recall-surveyed individuals suggests that even they experience shorter agricultural workdays and fewer agricultural workdays per week than are typically assumed.

10
Finally, we administered the Strengths and Difficulties Questionnaire (SDQ), which is a brief behavioral screening questionnaire. All items are answered using a 5-point answer format that ranges from 1 (“Disagree strongly”) to 5 (“Agree strongly”). Cronbach’s alphas for these different measures, before and after acquiescence bias corrections, are displayed in Table 2. Only the SDQ passes the 0.7 threshold. The Cronbach’s alphas for the other measures range from 0.04 to 0.58. Moreover, we randomly resurveyed 13% of randomly selected respondents within the same week, using a subset of items. Thus, we can compute the test-retest correlations for the Big-Five constructs. These are low, with an average of 0.3 (Table 2), reflecting a large amount of measurement error. Moreover, when conducting exploratory factor analysis, the skills factor structure is not reproduced. Only three factors are retained, and one single factor encompasses many items that aim to measure different concepts.

# _D.3.2. Pakistan pilot_

Given the results from Laajaj and Macours (2021) and those from the data collection in Cambodia, we decided to conduct a large pilot before our data collection in Pakistan. We conducted the pilot in the district of Okara, between February and March 2018. Interested participants were invited to a central location, resulting in a total sample size of 403 pilot respondents. Then, two weeks after the completion of the first round, we tracked and re-surveyed a random half of them (201 respondents).

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dramatically shorten the traditional season-long recall period, as an approach to large-scale data collection or as a means to create a consistent adjustment factor that can be applied to past and future recall surveys in the traditional vein. Of course, whether the latter is a reasonable approach to correcting systematic bias in reporting will depend on the specifics of the research context and the degree of variability in these specifics within a given survey group, for instance, the location by region, the crop, the degree of irregularity in farming, the degree of individual responsibility over plots, the prevalence of other types of economic activity, and the uses to which the resulting data will be put.

The second contribution of this study is to the debate on the agricultural productivity gap (Gollin, Lagakos, and Waugh 2014). Systematically overestimated measures of the amount of work people carry out on smallholder farms leads to underestimates of labor productivity in agriculture. Furthermore, it is likely that the effects are correlated with individual characteristics. For example, we find that more highly educated respondents produce less recall bias in reported family farm labor than do their less well educated counterparts (not reported). If people with greater levels of human capital are less likely to overestimate their labor in recall, perhaps because they are more well able to cope with the cognitive burdens of remembering and inferring irregular labor, then their higher labor productivity may not be entirely attributable to true differences in

> 23 Indeed, survey experiments that test the level—for example, person-plot, person, and so on—at which individuals provide the most accurate labor histories would be a promising area for future research.

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