rafmacalaba/data-use-mentions-extended huggingface dataset:
measurement completed from August 2013 to January 2014. The study was conducted in two districts, one on Unguja and one on Pemba Island, Zanzibar. The sample consisted of 1,247 households, with 1,932 cassava plots measured for area. Partners in the study included the Ministry of Agriculture and Natural Resources, Zanzibar, the Office of the Chief Government Statistician, Zanzibar, and the World Bank. The handheld GPS Unit used in the study was a Garmin eTrex 30.

The second data set used in this paper comes from the Ethiopia Land and Soil Experimental Research (LASER) study. The LASER study involved methodological validation of plot area measurement, soil fertility testing, and measurement of maize production. Area measurement and soil fertility testing was conducted on up to two randomly selected plots per household (where applicable, one pure-stand maize plot was selected for crop-cutting). The questionnaires were administered using computer-assisted personal interviewing. Fieldwork was conducted in multiple waves. Post-planting activities were 12
poverty rates depend on adjustments for differences across households in size and composition.<sup>5</sup> Per capita measures give children the same weight as adults when assessing household needs, and do not allow for larger households to benefit from economies of scale. Relaxing each of these assumptions will lower poverty rates for children relative to adults; the former by mechanically assuming that children require less expenditure to meet their minimum needs, and the latter by reducing poverty rates in larger households, which tend to have more children. Previous research by Batana et al suggested that extreme poverty rates for children in 2000 would fall drastically, from 38 percent to between 3 and 5 percent if different equivalence scales were used in place of the per-capita assumption (Batana et al, 2008).

This apparent sensitivity of child poverty rates to the choice of equivalence scale, however, results from a misleading comparison. The $1.90 per day poverty line is a per capita line derived from the national poverty lines of 15 poor countries, which in turn are each developed using per capita consumption as the welfare measure (Ravallion, Chen, and Sangraula, 2009). When examining the sensitivity of child poverty rates to alternative household equivalence scales, welfare measured in terms of equivalent adults is more meaningfully compared with a poverty line adjusted using the same equivalence scale, based on the composition of a typical poor family (Ravallion, 2015). After adjusting the poverty line accordingly, poverty rates among children are only modestly sensitive to the choice of equivalence scale, and remain substantially higher than adult poverty rates for all reasonable “two parameter” equivalence scales.<sup>6</sup>

> 4 Poverty and extreme poverty are used interchangeably and both refer to welfare, measured in money-metric terms, falling short of the $1.90 line. While poverty is recognized to be multi-dimensional, the use of a money-metric poverty line avoids the severe challenges in aggregating different dimensions of wellbeing into a common index (Thorbecke, 2007).
been partly offset by the simultaneous increase in the oil price. As a result, poverty increased especially for extremely poor people. The reason, the author suggested, is that the increase of the price of the households’ consumption basket overcomes the income rise experienced by the poorest households.

By simulating the impact of the oil price reduction at both the macro and micro levels, this paper will allow us to map precisely the impact of a terms of trade shock – in this case the drop of the oil price in an oil exporter country – on income distribution and thus on poverty, inequality and shared prosperity.

# Methodology

Evaluating the distributive impact of the oil price shock requires the use of a simulation model for at least two reasons. First, being a recent phenomenon, there are still no micro data to compare the distributions of welfare across households before and after the reduction in oil price. Second, even if data were available, they might include other shocks<sup>4</sup> ; hence, to identify the distributive effect of the terms of trade loss, net of (possible) additional shocks, a counterfactual welfare distribution needs to be generated.<sup>5</sup> To obtain the counterfactual distribution the effect of the shock needs to be analyzed under two different perspectives: the macro‐economic perspective, which provides the impact of the terms of trade variation at the aggregate level; and the micro‐economic perspective, which is required to take into account the heterogeneous effect across households. The macro‐micro simulation model used here follows a top‐ down (from macro to micro‐level) approach, where the macro and micro part of the model are handled separately and then linked through changes in prices and quantity computed with the CGE (Lokshin and Ravallion, 2008; Chen and Ravallion, 2004). The CGE (macro) and the microsimulation modules are briefly described below.

## _The macro CGE model_

The CGE model used in this exercise is fairly standard and
# **Does the Gender Wage Gap Influence Intimate Partner Violence in Brazil? Evidence from Administrative Health Data**

# **Elizaveta Perova,**<sup>a</sup> **Sarah Reynolds,**<sup>b</sup> **Ian Schmutte**<sup>c</sup>

Keywords: Intimate Partner Violence, Gender Equality, Gender Wage Ratio, Mandatory Reporting JEL codes: J12, J31 ___________________________ a. The World Bank, Washington, D.C.

b. The University of California, Berkeley, Berkeley, CA. Corresponding author: sar48@berkeley.edu. c. The University of Georgia, Athens, GA.

Funding for this study was provided by the Umbrella Facility for Gender Equality (UFGE), a World Bank multi-donor trust fund. This work was IRB exempt due to usage of de-identified, secondary data. None of the authors report any conflicts of interest. All authors have contributed equally to this work in making research design decisions and interpreting the results. Elizaveta Perova conceived the project and provided expertise on gender violence theory. Sarah Reynolds expanded the scope of the project to include the mandatory reports, provided expertise on IPV measurement, and wrote most of the manuscript. Ian Schmutte adapted the wage variable methodology to Brazilian microdata and provided expertise on labor. We thank Marina Dias and Robert Dominy for excellent research support. Brazil’s Secretaria de Vigilância em Saúde provided data (SINAN (VIVA/SVS/MS). We appreciate feedback and suggestions from Pablo Acosta, Anna Aizer, Manuel Contreras Urbina, Jacobus Joost De Hoop, Eeshani Kandpal, the Fernald research group, and members of LAC Gender Innovation Lab (LACGIL) of the World Bank. The findings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries. All remaining errors and omissions are authors’ own.
**|-0.295***|
||(0.038)|(0.039)|-0.098|-0.097|

Notes: The full results are contained in Appendix Table 4. Specification [1] is the basic model, with no controls for cognitive or noncognitive skills variables. Specification [2] is the full model, with controls for age, age-square, schooling, cognitive skills (measured as plausible values of literacy assessments), and noncognitive skills. Specifications [3] and [4] present the mean "coefficients" gender earnings gap from Blinder-Oaxaca decomposition of regressions where the controls in [3] are the same as in [1] and the controls in [4] are the same as in [2].All estimates are corrected for selection bias, with marital status, number of children and other adults in the household as instruments for selection into the paid work. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01. Data sources: STEP data for 9 countries, 2012-13

> 65 The coefficients do not change significantly between the comparable simple OLS estimates and the selectivitycorrected specifications.

52
**_<u>________________________________________________________</u>_**

GDP forecasts are based on the latest forecasts of the EU statistical office (Eurostat) in its "Quarterly National Accounts ESA". In those countries where forecasts are not available, trend analysis and average growth of neighboring countries are used to make the forecasts. Registration forecasts are based on time series analyses, taking into account other factors such as the development of transport volumes and GDP. Transport volumes forecasts are made using NEAC Transport Simulation System which quantifies the relationship between production sectors and the transport they generate, using various economic scenarios, to estimate future transport volumes in and between 176 regions.

# _-_ _<u>Organization for Economic Co operation and Development (OECD)</u>_

A paper _Quantitative Assessment of the Benefits of Trade Facilitation_ by Walkenhorst and Yasui (2003) presented at the 2003 Bangkok APEC Capacity Building Workshop develops two indicators-border process quality and border waiting time- to measure customs efficiency for 102 and 80 countries worldwide, respectively. Indicator of border quality suggests that those countries with higher per capita income generally have superior border process quality than ones with lower levels of per capita income. The border process quality indicator combines four subindicators from three data sources: Global Competitiveness Report, World Competitiveness Yearbook and Compendium of Trade Facilitation Recommendations.

Based on the methodology applied by Wilson, Mann and Otsuki (2003), the survey includes information on the border process environment (customs efficiency, hidden import barriers, administrative integrity), and government commitment toward trade facilitation (based on implementation of nine trade facilitation instruments). The value of the indicator varies from 0.25 to 1.85 implying that the country with the worst indicator value received a score in the rankings that was 75% below average, while the one with the highest value scored 85 percent higher than the average. The Border Waiting Time indicator is constructed by combining Hummel’s (2001) estimate of willingness-to-pay for saving time for shipment and WBES on typical border
which types of cities are experiencing the challenge of fast grow and pollution, particularly in developing regions, and lays out a typology of cities bringing together the concepts of competitiveness and pollution.

# Description of the Data

For this analysis we combine three data sets. First, we use the same data set used by the Competitive Cities report (World Bank, 2016) which includes city‐level data from the Oxford Economics Dataset.<sup>3</sup> Oxford Economics Dataset is a global administrative data set that provides economic indicators and

> 3 We would like to thank the Competitive Cities Team for cleaning and preparing the Oxford Economics data set.

3
taken with caution. In general the coefficients are negative and large in proportion to the control mean (20 to 100%) across all types of crime.

If this decline were persisted for the year, it would translate to 26 fewer crimes per person each year. Given the $530 cost of the two interventions, this is roughly $21 per crime, ignoring any other benefits of the program.

# **E.5 Heterogeneity analysis**

Table E.6 reports impact heterogeneity from an OLS regression of the antisocial behaviors summary index on baseline level of antisocial behaviors, treatment indicators, and interactions between treatment and baseline antisocial behaviors, controlling for baseline covariates and block fixed effects.<sup>18</sup> Therapy decreased the incidence of antisocial behaviors for the average participant, but men exhibiting more antisocial behavior at baseline saw larger declines. For example, men with average

> 18Recall that our measure of antisocial behaviors is a standardized index with mean zero. Therefore, the coefficient on the treatment indicator represents the treatment effect for an individual with mean level of antisocial behavior at baseline, while the coefficient on the interaction term is the additional effect for individuals whose baseline level of antisocial behaviors was 1 standard deviation higher than average.

xliii
perfectly correlated with C might be limited in practical applications, a highly correlated matching variable might be sufficient to ensure that the relationship between C and I, given X, is close to its true underlying relationship (Donatiello et al., 2016b).

**The concept of statistical matching draws from the concept of imputations often applied to missing observations in survey data (Bacher and Prander, 2018).** There are three overarching approaches to imputing missing values of the added variable from the recipient data (C) in the donor data: parametric approaches, nonparametric approaches, and mixed methods (European Commission, 2014; Lewaa et al., 2021). Parametric approaches are regression imputations. They make use of the functional form between X and C in the donor dataset to estimate the missing values of C in the recipient dataset (Bacher and Prander, 2018). While they are more parsimonious than nonparametric methods, they rely on pure predictions and model specifications, so the accuracy of the underlying model. Nonparametric approaches rely on distance functions and identify similar observations to impute actual observed values from the donor to the recipient dataset (European Commission, 2014). They often involve the segmentation of observations by socio-economic characteristics or geography as well as choosing a suitable distance function (Bacher and Prander, 2018). Examples of nonparametric approaches are hot deck approaches. Mixed methods combine parametric with nonparametric approaches, such as the estimation of a stochastic regression imputation followed by a nearestneighbor hot deck.

**Table 1 describes the basic concept behind the application in this paper: imputing energy expenditure shares to the EU-SILC in Bulgaria** . The goal of this paper is to create a synthetic dataset that contains information on income-based indicators, households' access to social protection programs, other indicators of households' welfare and living conditions, and energy expenditure shares, as well as energy poverty. To this end, we follow the approach in Table 1, which is highlighted in blue. More concretely speaking, we first create our variable
# **3 Discussion**

Using twelve years of data from across the Midwestern and Eastern US, we show that exposure to low-density smoke plumes increases both corn and soybean yields relative to days without smoke plumes. Days with high-density smoke plumes, however, have a negative impact that is roughly double the positive impact estimated for low-density smoke days.

This difference in marginal effects is consistent with the expectation that low-density smoke plumes increase the fraction of light that is diffuse and slightly reduce the total available light, so that the gains in photosynthetic efficiency from the increase in diffuse light dominate. Highdensity plumes, on the other hand, absorb more light than they diffract, so the reduction in available light dominates the increase in diffuse light. We are unable to test this hypothesis directly (see SI Section SI1.4), but we find that high density plumes substantially increase AOD while low density plumes have only a mild impact (Figure SI2). Our effects do not appear to be driven by changes in ground-level pollutants induced by wildfire smoke plumes, but we cannot rule out that some of our measured effect may be due to these changes.

Our estimates suggest that, if current trends in the number of low-density and high-density smoke days continue, the present positive impacts on yield will be substantially reduced by 2050. Projections of the number of low- and high-density smoke days by mid-century suggest that, for soy, the average county will experience a positive impact on yields roughly a third of today’s impact. The impact on the average corn-growing county will be a substantial reduction in yield relative to that under today’s smoke conditions. If low-density smoke days grow 1.5–2 _×_ faster than high-density smoke days, however, the effects will be reversed, and smoke will have an even higher yield-boosting effect.

The reduction in yields due to future smoke exposure may impose significant costs on farmers. Using 2020 production and price data from the USDA, combined with our estimated yield losses, we can conduct
# **III. Issues in Selected Sectors**

# _A. Manufacturing_

India has an unambiguous interest in actively seeking negotiations to bring down tariffs on industrial products.<sup>7</sup> The interest is fourfold. First, India's tariffs today, even after significant reform, remain exceptionally high and almost the highest in the world (Chart 2). Further liberalization is desirable but difficult for internal reasons and this domestic situation could yield negotiating coinage in the next round.

**Chart 2:** Cross-Country Comparison of Average Tariff Rates (1998)

<!-- Start of picture text --> (%)40_ ___. ____ _________________<br>35<br>30 - -<br>2 5-<br>2 0<br>1 5<br>1 5 - _ _ _ _.__.<br>5-<br>0<br>1-<br><!-- End of picture text --> _Sourrce:_ World Bank (forthcoming) Second, even if the pace of further liberalization is divorced from external bargaining imperatives, India has a strong interest in reducing the level of its Uruguay Round bound tariffs. The wedge between the applied and bound remains high and creates uncertainty and lack of predictabilty about trade policy (Table 2). According to a recent survey domestic investors appear not to have confidence in the stability of government policies (Table 3). Bringing bound levels closer to current and future applied levels can engender confidence in the predictability of policies. Recent tariff policy increases - the special additional duty of 4 percentage points imposed in 1998-99 and the surcharge equivalent to 10 percent of the basic customs duty levied in 1999-2000 - illustrate the costs of not having tighter external discipline on trade policies. Had such discipline existed in the form of bindings that "bit", the government would have been forced to raise revenues through alterative, trade-neutral and hence less costly measures.

> 7 See
national accounts; _employment_ through labor force statistics on the primary sector of activity of those employed; _agglomeration_ through population or firm censuses; and _demographic_ through demographic surveys (or population censuses). Each of these suffers from its own measurement problems, but those associated with the first two are most severe in SSA. This is primarily because of the absence of well-structured and monetized goods and labor markets, and also because of weak statistical capacity in low income countries. In the case of the measurement of _output_ by sector, it is well known that national accounts data underestimate production (especially agriculture) for home use, and the value added of unincorporated microenterprises and selfemployment (often called the ―informal sector‖ or ―informal enterprise activity‖). These omissions can be substantial. For example, attempts to more accurately account for this type of production in Ghana resulted in a 60 percent increase in GDP for 2006 and subsequent years.<sup>7</sup> They will also bias estimates of agricultural production downward.

The measurement of _employment_ by sector is even more difficult, owing to the fact that most employment in low income SSA takes place through economic activities done outside of a firm (and a labor market), does not take place in the same location every day, and likewise may not occupy a set schedule and may not even involve the same output sector every day or week or hour. For example, at one point in time a member of the household might be involved in running the household RNFE, or selling labor on a casual basis in the construction sector, and at another point harvesting the household crops for sale or consumption. This reality means that the conceptual variable used to measure the employment transformation - ―sector of employment‖ - is

> 7 See World Development Indicators, 2010, for discussion.

10
completely disagree’, ‘somewhat disagree’, ‘neutral/no opinion’, ‘somewhat agree’ and ‘completely agree” (Lazarus 2021). Others relied on a 4-point Likert scale to the question such as “y</mark> es, definitely,” “yes, probably,’ “no, probably not,” and “no, definitely not” (Africa CDC, Facebook 2021, de Figueiredo and Larson 2021, Wouters et al. 2021) <mark>, whereas the HFPS mostly relied on three answer options (yes, not sure, no). It is possible that “probably not” is not the same as “unsure,” although it is not known how this might bias the respondents’ answers and cross-study comparisons.</mark> Finally, variations in survey modality may have created biases. Among the published studies included for comparison, many studies were based on data from commercial online sample providers, often using quotas sampling to ensure an appropriate distribution in terms of gender, age, and region (Africa CDC, de Figueiredo and Larson, 2021, Facebook 2021, Gallup 2021, Lazarus et al. 2021, and Wouters et. A. 2021), while others were based on convenience samples (Anjorin et al. 2021, Asadi Faezi et al 2021, Bono 2021, Wong 2021), or a mix of methods (Solis-Arce et al. 2021 and Sallam 2021). There was also wide variety in the survey mode, including online, computer-assisted telephone, and face-to-face surveys.

<mark>It is not possible to know the extent to which each of the above-mentioned potential sources of bias affect the comparisons of the findings from the HFPS with the others. For example, it was possible to match the Facebook survey results for the month and year of the HFPS for 20 countries, thus taking away any differences in the timing of the survey. Nevertheless, the HFPS estimates were still lower than those reported by Facebook (Figure 9, Panel
% of children under-5 who are stunted <!-- Start of picture text --> 50%<br>40 am - a e e T v e dsv 1 ai 6———~" e e e<br>1 | 9<br>30 f .<br>20 J<br>Girls<br>0<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br><!-- End of picture text -->

Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Notes: Point estimates and 95% confidence intervals are based on groups of children defined by complete age in months. This chart uses 2 month intervals where, for example, 1 represents children with between 0 and 2 completed months of age. The line represents a non-linear prediction of the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from the gam smoothed line is shown as a grey area.
measurement (where feasible) complemented by farmer self-reported estimated area (for all plots).

An ancillary story emerging from the data concerns self-reported data. While confirming all the known issues with measurement error in self-reported land data, the analysis presented here provides at least one suggestion for limiting the scope of this error in the future. When land area is collected using nonstandard units, as opposed to forcing respondents or enumerators to perform a conversion to standard units at the time of the interview, data from self-report appears to be a better approximation of the benchmark preferred measure.

Furthermore, our analysis casts some shadows on the benchmark compass and rope measurement. It appears that a good deal of what we labeled for simplicity as GPS measurement error, may in fact be 27