rafmacalaba/datause-encoder-data huggingface dataset:
The reminder of the paper is organized as follows. Section II describes the data. Section III describes the methodology and estimators that are evaluated. Section IV assesses results in terms of efficiency, accuracy, and coverage. Section V considers a variety of robustness checks of the main method. Section VI considers the results of model-based and design-based simulations, and section VII concludes.

# II. Description of the data

## A. Constructing measures of non-monetary welfare and poverty in the census

The analysis is conducted using measures of non-monetary poverty constructed from the Sri Lankan and Tanzanian census, which were each conducted in 2012. Non-monetary poverty, unlike monetary poverty, is directly observed in the census population and can therefore provide a benchmark for evaluation. For each country, we identified a set of household asset and demographic proxies for welfare. Principal component analysis, weighted according to household size, was used to derive a score for each proxy welfare indicator. The welfare proxies used in the index and their estimated scores, or loading factors, are listed in Table 1. Each household’s non-monetary welfare measure is obtained by summing the product of each loading factor and the household’s value of the associated variable. No subsequent adjustment for household size was made, reflecting the non-rival nature of the index variables within the household, although household size is included as a welfare proxy in the index for both countries. The results are broadly reasonable, as higher levels of education for the household head are associated with higher non-monetary welfare in both counties, assets and dwelling conditions are positively associated with welfare in Sri Lanka, and non-agricultural work is strongly associated with nonmonetary welfare in Tanzania.

This measure of non-monetary welfare can be compared with a poverty line threshold to classify each household as poor or non-poor. We set the threshold at the 4<sup>th</sup> percentile of non-monetary welfare in Sri Lanka and the 20<sup>th</sup> percentile in Tanzania, to reflect prevailing national monetary poverty rates. In other words, households were classified as non-monetarily poor if their non-monetary welfare, defined as the score of the first principal component, fell in the bottom four percentiles in Sri Lanka and the bottom quintile in Tanzania. Below in the robustness checks section, we also consider results when the poverty rates of the two countries are swapped.

_Table 1: Factor loadings for census-based welfare index by country_

|Sri Lanka||Tanzania||
|---|---|---|---|
|Variable|Scoring<br>Coefficients|Variable|Scoring<br>Coefficients|
|Household size|0.09|Head Literate|0.45|
|Dependency ratio||Head Ever attended<br>school|0.45|
|Children 0-14 and 65+|0.04|Head age|-0.20|
|Children 0-14 Only|-0.05|Household size|-0.22|
|Gender ratio|0.04|Dependency ratio|-0.41|
|Household education||Male head|0.12|

5
equal numbers becoming newly displaced and returning in the years that have followed (e.g., TFPM 2017; OCHA 2019; D’Souza et al. 2022; Favari et al. 2022; etc.). However, these stressors have aggravated a number of longstanding problems in the Republic of Yemen that are too sensitive to measure with traditional modalities of data collection, including reports of increased domestic and gender based violence in the country (e.g., Oxfam 2020; the Guardian 2021; etc.); and have also led to an increase in other illegal activities, including reports of diversion of humanitarian assistance (e.g., Salisbury 2017).

In the face of such a volatile environment, data collection on all issues has become sensitive in parts of the country. In particular, DFA authorities have begun to tightly control the collection of data in regions under its control.<sup>7</sup> This control over data collection has further made respondents in DFA-controlled regions hesitant to respond or fully express themselves in a wide variety of surveys (e.g., Alamoyad et al. 2020; etc.). Thus, even what would normally be non-sensitive issues in non-conflict settings can actually become sensitive.

The inability to perform surveys at all across the entire country due to the conflict, the inability of these surveys to address all issues that are critical to policy makers, and the inability of individuals to respond truthfully to those questions even if they were posed have all created significant information gaps for the humanitarian and development response. Aside from intermittent food security assessments that are performed approximately every two years and are painstakingly negotiated with DFA authorities and remote food security monitoring via mobile phone, there is very little householdlevel data collected in the country (e.g., IPC 2018; WFP 2019; IPC 2020: IPC 2022). This leaves significant gaps as to how households are able to cope with the humanitarian disaster, how the private sector has adapted to the decline in capacity and the many conflict-related macroeconomic shocks, all the ways in which the conflict intersects with

> 7Although the exact conditions placed on data collection vary from survey to survey and organization to organization, the Social Fund for Development tried to conduct a survey on the humanitarian situation in DFA-controlled regions, but was forced to abandon the survey due to a large number of restrictions. In these restrictions, the DFA would not permit any questions to be asked about the conflict itself or anything that might traditionally be deemed sensitive, there were a large number of restrictions that interfere with the sampling and could bias the results (intentionally or not), and the DFA-appointed individuals needed to accompany the survey teams. See Aghajanian and Ghorpade (2022) for details.

9
**Table 10: Africa-wide impacts from AOGCM climate scenarios**

|**Impacts**|**PCM**<br>**2020**|**PCM**<br>**2060**|**PCM**<br>**2100**|**CCSR**<br>**2020**|**CCSR**<br>**2060**|**CCSR**<br>**2100**|**CCC**<br>**2020**|**CCC**<br>**2060**|**CCC**<br>**2100**|
|---|---|---|---|---|---|---|---|---|---|
|**Dryland**||||||||||
|ΔNet Revenue|231.6|196.2|199.7|-12.8|-82.8|-128.9|-72.1|-92.1|-139.0|
|($ per ha)|_(73.3%)_|_(62.1%)_|_(63.2%)_|_(-4%)_|_(-26%)_|_(-40%)_|_(-22%)_|_(-29.2)_|_(-44%)_|
|ΔTotal Net||||||||||
|Revenue|72.4|61.4|62.5|-4.0|-25.9|-40.3|-22.5|-28.8|-43.5|
|(billions $)||||||||||
|**Irrigated**||||||||||
|ΔNet Revenue|468.9|506.5|586.8|76.6|142.3|-420.9|49.1|137.6|297.1|
|($ per ha)|_(40%)_|_(44%)_|_(51%)_|_(6.7%)_|_(12%)_|_(-36%)_|_(4.3%)_|_(12%)_|_(26%)_|
|ΔTotal Net||||||||||
|Revenue|6.1|6.6|7.6|.99|1.8|-5.5|0.6|1.78|3.9|
|(billions $)||||||||||
|**Total (Africa)**||||||||||
|ΔNet Revenue|277.8|268.2|296.8|38.7|-58.7|-82.7|-71.1|-72.6|-148.7|
|($ per ha)|_(63%)_|_(61.5%_|_(68%)_|_(9%)_|_(-13%)_|_(-19%)_|_(-16%)_|_(-17%)_|_(-34%)_|
|ΔTotal Net||||||||||
|Revenue|90.5|87.4|96.7|12.6|-19.1|-26.9|-23.2|-23.6|-48.4|
|(billions $)||||||||||

_Note:_ Using coefficients in Table 6 and AOGCM country specific climate scenarios. The numbers in brackets represent the percentage change in net revenue per hectare relative to the mean of the sample.

36
# Table 7a: District-industry-year regressions for misallocation in financial loans

|||<br>Pa|nel estimation|<br>with district-ind|<br>ustry,state-year|and industry-yea|<br>r FE||
|---|---|---|---|---|---|---|---|---|
|||Using district||Using state-|Adding||Misallocation|Misallocation|
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using<br>balanced panel|without industry<br>aggregation step|using OLS<br>TFP metric|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||A. Using l|and and labor mi|sallocation in o|rganized sector|||
|Land misallocation index|0.389+++|0.376+++|0.371+++|0.389+++|0.389+++|0.451+++|0.476+++|0.476+++|
||(0.049)|(0.036)|(0.049)|(0.051)|(0.049)|(0.048)|(0.053)|(0.051)|
|Labor misallocation index|0.266+++|0.265+++|0.288+++|0.266+++|0.266+++|0.229+++|0.298+++|0.304+++|
||(0.065)|(0.050)|(0.064)|(0.067)|(0.065)|(0.068)|(0.053)|(0.052)|
|Log employment in|||||-0.004||||
|district-industry-year|||||(0.014)||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|
|Adjusted R-squared|0.429|0.410|0.444|0.429|0.429|0.437|0.542|0.546|
|<br>|||B. Using land|and labor misallo|cation in total m|anufacturing sect|or||
|Land misallocation index|0.293+++|0.247+++|0.290+++|0.294+++|0.293+++|0.344+++|0.398+++|0.417+++|
||(0.064)|(0.044)|(0.064)|(0.065)|(0.064)|(0.068)|(0.074)|(0.063)|
|Labor misallocation index|-0.057|-0.055|-0.053|-0.060|-0.057|-0.050|0.126+|0.102|
||(0.059)|(0.039)|(0.059)|(0.061)|(0.059)|(0.064)|(0.069)|(0.062)|
|Log employment in|||||0.001||||
|district-industry-year|||||(0.015)||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|
|Adjusted R-squared|0.217|0.171|0.234|0.217|0.217|0.215|0.344|0.375|

Notes: See Table 6a. Misallocation variables are expressed in unit standard deviations.
Table 3: Prediction 3: Effects on Candidate Competence

|**Panel A**|**Candidate has**<br>**primary education**|**Candidate has**<br>**secondary education**|**Candidate has**<br>**a criminal case**|**Candidate**<br>**assets**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Treat_|0.014|0.150|0.038|29.428|
||(0.030)|(0.068)**|(0.051)|(18.712)|
||[0.478]|[0.108]|[0.437]|[0.211]|
|**Panel B**||**First Stage**|||
|_Cut-of_|-0.238**|-0.278***|-0.246**|-0.233*|
||(0.120)|(0.105)|(0.116)|(0.122)|
|Untreated _y_ Mean|0.942|0.806|0.150|16.302|
|Observations|566|705|597|530|
|State FE|x|x|x|x|
|Year FE|x|x|x|x|
|State*Year FE|x|x|x|x|
|Clusters|218|275|231|206|
|Bandwidth|11.042|13.911|11.448|10.501|

_Notes: y_ refers to the dependent variable. Robust standard errors clustered by the running variable in parentheses. Anderson (2008) q-values in square brackets. Bandwidth is reported in female literacy rate *** ** * percentage points around treatment cut-off of 39.3 percent. _p <_ 0 _._ 01 ; _p <_ 0 _._ 05 ; _p <_ 0 _._ 10.

to the increased favorability of women candidates and field them more often (Beaman et al., 2012; Bhalotra et al., 2018; O’Connell, 2018). This insight is consistent with Prediction 4 of our model, which states that the effect of the DPEP on women’s likelihood of being elected should be stronger in districts where women were elected in previous elections.

In Table 4, we investigate whether more female candidates contest following a woman’s victory in the previous election in treated districts relative to untreated districts.<sup>15</sup> Column (1) shows that the number of female contestants increases significantly by 0.26 candidates in DPEP districts (relative to non-DPEP districts) when the previous election was won by a female, but there is no such effect when a man won the previous election (column (2)).<sup>16</sup> This pattern of results is consistent with parties being more willing to field female candidates as their winning probability increases. Notably, columns (3) and (5) show that this demonstration effect from a female winner is present both for the incumbent party (who

> 15Interestingly, the benefit derived by female candidates, as voters increased the relative weight assigned to leader competence, was accrued irrespective of the gender of the leader who was in power when the DPEP was first implemented in a district. This finding provides support to the “demonstration effect" induced by the DPEP itself. A full set of estimates is reported in Table A.2.

> 16The first stage coefficients are statistically significant, with the eligibility criteria impacting the probability of treatment by 20.0 and 20.1 p.p. for bandwidths of 11.71 and 12.49 literacy points, respectively.

21
The Dutch disease operates through the spending effect. Aid-financed activity results in increased demand, some of which will fall upon non-tradables. The price of the non-tradable goods and services will therefore rise relative to that of tradables, whose price is assumed to be given (the small country assumption): that is, there is an appreciation of the real exchange rate. (If there is a floating exchange rate regime the real appreciation may also operate through the nominal rate).

The basic mechanism, outlined in the previous paragraph, is simple. The analysis of actual cases is more complex on account of the different channels through which aid reaches the economy. For example, Younger's (1992) analysis of the aid boom in Ghana suggests that the aid crowded out private investment since the government maintained a tight monetary policy to counteract the aid-financed increase in demand as part of an attempt to maintain the real exchange rate.

More complex stories also emerge from consideration of changes in the composition of expenditures - especially investment - resulting from Dutch disease effects, an analysis that has been taken furthest in the work of David Bevan, Paul Collier and Jan Willem Gunning (for example, Bevan _et aL_ 1990) and their analysis of construction booms. As indicated above, a windfall gain (including an aid boom) results in increased demand for non-tradables - non-tradable capital goods are identified as "construction" so that the analysis of the impact of aid on investment becomes a theory of construction booms.

(b) Aid and other inflows: the recipient may substitute away from non-concessional sources of finance if aid funds are available, hence the increment in forex availability (and therefore imports) is less than the value of the increase in aid. One critic of aid, Peter Bauer, has argued that this saving of debt service charges is the only benefit of aid to the recipient (1976: 106-110). On the other hand - aid may "crowd in" other inflows if they support a reform process which improves the country's credit-worthiness. The notion that adjustment lending would act as a catalyst for other inflows was common in the early 1980s. The most recent World Bank report on adjustment lending restates the view, but with a recognition that there will be lags: Reforms increase the ability of a country to use foreign savings productively. Eventually, good performance is recognized by international capital markets and bilateral lenders, but 33
cent of the population, and the province with the smallest is Corrientes (0.5 percent).

Figure 1: Percentage of Natives by Province <!-- Start of picture text --> (7.6,8.7]<br>(6.5,7.6]<br>(5.4,6.5] 7.9<br>(4.3,5.4]<br>(3.3,4.3]<br>(2.2,3.3] 6.6 6.1<br>(1.1,2.2]<br>[0.5,1.1]<br>No data 3.9<br>1.3 1.2<br>1.9<br>1.3<br>0.5<br>1.2<br>1.2 1.5<br>1.6 1.1<br>1.9<br>2.4 2.2<br>1.9<br>4.5<br>8.0<br>7.2<br>8.7<br>3.6<br>2.9<br><!-- End of picture text --> _Source:_ REDATAM INDEC Census Argentina (Instituto Nacional de Estad ́ıstica y Censos, 2010). _Disclaimer:_ Falkland Islands - Islas Malvinas: A dispute concerning sovereignty over the islands exists between Argentina who claims this sovereignty and the U.K. which administers the islands.

How does our measure of Native self-identification relate to native ancestry? To answer this, we study the correlation of our self-identified measure of Native prevalence with a measure of prevalence based on genetic studies (Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin et al., 2019) across regions of the country. As Figure 2 5
ability of the previous literature to identify why a petition is filed against some HS-10 products and not others is hampered by the aggregate level of the analysis. Some sense of the difference made in the level of aggregation is apparent in the contrasting results obtained by Knetter and Prusa (2003) and Crowley (2009). Furthermore, the reliance on annual data is an additional constraint when identifying shocks since a petition can be filed at any point in the year. If two cases in the same HS-4 category are filed in January and December of the same year, then the standard econometric approach would be to assume that the same aggregate data are responsible for both petitions being filed. This lack of precision in terms of the products covered and the time of filing undermines the confidence in the inferences drawn from the aggregate analysis.<sup>8</sup> An important recent paper that addresses these issues in a slightly different manner is Bown and Crowley (2013b). These authors focus on variation in the level of applied protection under AD. Following the theoretical framework proposed by Bagwell and Staiger (1990), terms of trade shocks can lead countries to defect from their cooperative tariffs. The theory makes cross-sectional predictions about the likelihood of a breakdown in cooperation, but in the empirical study of Bown and Crowley (2013b) the only predictive variable that varies over time is import growth at the product-country level. In principle this would allow the authors to exploit more disaggregated data, but the model also relies on export supply elasticities that are only available at a higher level of aggregation. As a result, and because they also wish to consider industry level controls in the robustness exercises, Bown and Crowley (2013b) estimate their model for 283 industries that correspond roughly to HS4 digit international trade categories. Their data are also aggregated to the annual level. Relative to our aims of describing import dynamics and separating the named products from nearby neighbors in product space these sector definitions are still quite aggregated. In principle they aggregate over products that do and do not see an investigation, and they also aggregate over time periods that may and may not be relevant in predicting a filing. The crosssectional focus of Bown and Crowley (2013b) also precludes an investigation of the role of cumulative impacts of imports from more than one source country.

We address these deficiencies by utilizing both the HS-10 data associated with a petition and the month in which a petition was filed. The starting point is the motivation underlying the theoretical

> 8A small literature uses detailed import data to document trade dynamics following the conclusion of an AD case. Prusa (1997) exploits product-level U.S. trade data, while Pierce (2011) adds a plant-level dimension. Konings and Vandenbussche (2013) investigate the impact of European AD cases using firm-level export data from France and product-level trade data from the European Union. Besedeˇs and Prusa (2013) use a survival model to estimate the breaking of trade links following an US AD case.

6
# **1 Introduction**

Intimate partner violence (IPV) is the most common form of violence experienced by women. The global lifetime prevalence of IPV among women who have ever been partnered is 27 percent (Sardinha et al. 2022). In developing countries, IPV prevalence is even higher. For instance, approximately 35 percent of ever-partnered women in South Asia report experiencing physical or sexual abuse by their partners. IPV exacts an enormous toll in terms of mental and physical health, economic capability, and child well-being (WHO 2013). The global cost of IPV is estimated to be USD 4.4 trillion or 5.2 percent of global GDP—higher than the combined costs of civil wars, terrorism, and homicides (Fearon and Hoeffler 2014). Consequently, the societal benefits from understanding the determinants of spousal violence and identifying policy interventions that can reduce and prevent IPV are potentially substantial.

We investigate whether exposure to female leaders influences IPV experienced by women. Recent studies demonstrate that female leaders can improve gender-specific outcomes along multiple dimensions through better provision of public goods and legislative changes that benefit women.<sup>1</sup> Similarly, female leaders could potentially reduce the prevalence of IPV by lowering public acceptance of IPV (Kuipers 2020) or by strengthening women’s bargaining power through better access to education (Beaman et al. 2012) and employment opportunities (Deininger et al. 2020). On the other hand, if exposure to female leaders empowers women, it could heighten intrahousehold conflict and trigger backlash effects from male partners (Bobonis et al. 2013; Ashraf et al. 2014).<sup>2</sup> Hence, the overall impact of exposure to female leaders on the prevalence of IPV against women is _a priori_ ambiguous.

More specifically, we examine the impact of female leaders elected to state legislatures in India on the risk of domestic violence experienced by women in the districts from which these leaders are elected. To address potential endogeneity in the gender of the elected leader, we exploit a widely used instrumental variable (IV) strategy based on close elections between male and female politicians. Following Bhalotra and ClotsFigueras (2014) and Clots-Figueras (2011), we instrument the share of seats occupied

> 1These studies include Chattopadhyay and Duflo (2004); Beaman et al. (2009); Bardhan et al. (2010); Clots-Figueras (2011); Ford and Pande (2011); Iyer et al. (2012); Beaman et al. (2012); Bhalotra and Clots-Figueras (2014).

> 2Using lab-in-the-field experiments, Gangadharan et al. (2016) find evidence of significant male backlash against female leaders due to transgression of social norms related to gender.

1
<!-- Start of picture text --> oO<br>Sacsrup @ Economics<br>- @ Cognition ® Social sciences<br>3 ® Economics of Education Review Sd Public health<br>><br>Pw ® Medicine<br>° @JASA<br>o @® Educational Researcher<br>E @Qe<br>oO @Am J Psychol @® Psychological Science<br>a<br>Os<br>o<br>a @AJs<br>w<br>n @ Econometrica<br>2 @aeER<br>3 'V- @APSR<br>a<br>= @NEIM @uama@AmJ Epidemiol= ~ @rancet @int<br>OPH J Epidemiol<br>QPBsuIetin of ne WHO .<br>oS<br>0 A 2? 3<br>Fraction of articles with either term<br><!-- End of picture text -->
Table IV-1 (coat'd)

|Country,<br>Title of Project|runier,Statue<br>of Activity|Projet<br>Sumary|Use Prlvate<br>Distributioan<br>System|ER )y-<br>Om it|Characteriatics<br>Contract<br>with Private<br>Providera|of Project<br>Trainin|Evaluation<br>Cqtonn|
|---|---|---|---|---|---|---|---|
|rUMB<br>_mLT_<br>nU|<br>Ii-Ui<br>SnWIMS|(cot'd)||||||
|Indonesia:<br>Fadily Planing<br>at P.T. Inritea|P.T. lebritam<br>and 31U,<br>1974|Faily<br>plamng<br>is Integrated<br>with the campuy baltb<br>facili-<br>_tsl_<br>at tbie cany<br>of 1.S73<br>worer.<br>_h<br>itei<br>b,<br>py t-<br>_cLa_m<br>tstiff iudeeatke<br>mativailm<br>octivities<br>tbroug<br>film,<br>Ieter,<br>_<br>brochs.<br>ImIp, PAUs, e_s<br>am steril-<br>isatL<br>anprov-<br>d<br>Tde e<br>_m<br>-<br>reqairse<br>regiaratiam<br>With tdo<br>fiwy<br>plinie<br>clinc<br>a<br>_<br>ad_nftU<br>uLWcom<br>Itf<br>they fail to nugister.<br>F_lly<br>slhines<br>an oly_provided_to<br>thl<br>chiMes..|_No_|Ya|Yea, 3tfalm<br>for fre<br>con-<br>traceptiem|No|s|
|kf<br>_r_iae_<br>ag<br>Servi<br>f<br>Industrial<br>1okr|Pth_flader 5_<br>1,77-INS|Fmily pimir.<br>clini<br>s<br>eateblAd<br>at tuD pleuat.<br>Eight<br>fild<br>waars<br>at e_c be<br>eapected tagts<br>am receved<br>referral<br>ft<br>for Me A sign-<br>latim.<br>Clinics provide<br>cornelie<br>ad<br>sece.|ND|e||No<br>U<br>|11|
|Impact of<br>Trained_rmla_<br>Labor Unlon Shop<br>Stewardese"<br>on<br>nOtivators<br>**in**<br>Family Planing<br>Services|UC,19Sl|Tmeffectivmesof<br>)mltb<br>edueator,<br>_tradm_<br>(volunter)<br>'ahop stowardeae<br>already<br>employed by the_fig,_<br>end a<br>**control**<br>group**war**compred<br>as<br>td tbhlr<br>impat<br>on contraceptive<br>useacros<br>educstlon<br>program;<br>no aigificant<br>differences<br>were<br>noted across<br>typs of or lack of<br>a ottvator.|lb|yes|No|Yo, of<br>otivatorc|Yes, of cd_<br>i contracep-<br>tives|
|Tenta Induatricl<br>Famly<br>Planning<br>Project|PathfLnder<br>Fugd<br>1983|Family planing<br>lnformaton.<br>educatien.<br>**counellng**<br>**and**<br>wthods<br>(bhrtier<br>methods,<br>pills<br>**lUlh)**prowided<br>to**4,0CC**<br>factory<br>workers<br>at a clinic<br>on-site<br>during<br>working hours.<br>Health**_and_**sterlzeation<br>**needa**<br>will<br>be referred.<br>KAP & CPS<br>be<br>the|Yes, for<br>sterilisation|Yes|No|Yea of<br>edLcal<br>andadministra-<br>tion staff|IA? & CPS at<br>project<br>end|