rafmacalaba/datause-encoder-data huggingface dataset:
opposite was true in the less conservative setting where women had higher overall status. In the less conservative site, individual measures of autonomy and participation in credit schemes had no impact on the risk of violence, rather, living in a community where more women participated in credit groups, and where women had a higher status overall had a protective effect. These findings suggest that the same condition (mobility or participating in a credit group) may have completely different effects on a woman’s risk of violence, according to whether the activity is seen as acceptable or not by community norms. These findings underscore the complexity of these issues and the dangers of applying knowledge gained from one site to another without understanding the broader cultural context.

# **Figure 1.4.  Risk factors often associated with violence against women: an ecological model**

**Individual level:** biological and personal history factors among both victims and perpetrators **Relationship level:** proximal social relationships, most importantly those between intimate partners and within families.

**Community level:** the community context in which social relationships are embedded, including peer groups, schools, workplaces and neighborhoods.

**Societal level:** larger societal factors that “create an acceptable climate for violence, reduce inhibitions against violence.”  (Adapted from Krug et al., 2002).

<!-- Start of picture text --> Societal Community<br>Relationship<br>Individual<br>• Traditional gender norms that • Weak community sanctions • Marital conflict • A history of violence in the<br>give men economic and against GBV • Family dysfunction perpetrator’s or victim’s family<br>decision-making power in the • Lack of shelters or other forms of • Male dominance in the family of origin (including intimate<br>household assistance/sanctuary • Economic stress partner violence and child abuse)<br>• Social norms that justify violence • Poverty • Early age at marriage • Male alcohol use<br>against women • Women’s traditional gender roles • Large number of children • Male personality disorders<br>• Women’s lack of legal rights in transition • Friction over women’s (particularly in low prevalence<br>(including access to divorce) • Normative use of violence to empowerment settings)<br>• Lack of criminal sanctions settle all types of disputes • Family honor considered more • Young age (both women and<br>against perpetrators of GBV • Social norms that restrict important than the health and men)<br>(impunity) women’s public visibility safety of the victim<br>• High levels of crime • The safety of public spaces<br>• Armed conflict<br><!-- End of picture text -->

**Source:** Adapted from Heise and Garcia Moreno, 2002; and Jewkes, Sen and Garcia Moreno, 2002.

15
tion have in multiple cases led to conserved forest<sup>1</sup> . Quasi-experimental evaluations of PES programs in Costa Rica, Mexico, Ecuador, and Brazil (Alix-Garcia et al. 2012, Arriagada et al. 2012, Robalino & Pfaff 2013, Robalino et al. 2014, Alix-Garcia et al. 2015, Jones & Lewis 2015, Sims & Alix-Garcia 2017, Simonet et al. 2018) have found avoided deforestation impacts as well as neutral or positive livelihood impacts. An RCT of a pilot program in Uganda also demonstrated high absolute additionality of PES (Jayachandran et al. 2017). However, many important questions remain about the opportunity costs of PES programs and practical approaches to maximize the environmental and social net benefits of such programs. Concern about high enrollment of nonadditional properties has been salient in the literature on PES from the very beginning (Jack et al. 2008, Alix-Garcia et al. 2008, W ̈unscher & Engel 2012). Strong program additionality at low cost depends on targeting payments to areas where the risk of deforestation is high yet WTA is low. Although risk of deforestation and WTA are often correlated, opportunities to improve targeting exist as long as these dimensions are not perfectly correlated and information can be collected about both.

We begin by estimating and examining the distribution of willingness to accept for conservation incentive payments among a representative population of common property applicants. Landowners in Mexico can apply voluntarily to the program on an annual basis, responding to published rules on eligible zones, available payments, and prioritization of applicants. The program currently accepts applications from within eligible areas of the country, then ranks these according to multiple criteria reflecting national environmental and social goals. Applicants with the highest scores within each state are offered 5-year contracts until the available budget is exhausted. Program payment levels are differentiated by ecosystem type and risk of land cover change and the majority of payments go to common property landowners, who own more than half of all land in Mexico (Fox 2007). We focus on payments to these landowners, who make decisions about program enrollment and land management through an assembly of members and an elected council.

The willingness to accept analysis is based on answers to a question within a survey we designed and supervised in 2016 of 862 community leaders and over 8,000 household heads across communal properties. We included a referendum-style stated preference question designed using best practices within the stated preference literature (Johnston et al. 2017). Specifically, respon1For full reviews of the literature, see Alix-Garcia & Wolff (2014), Miteva et al. (2015), B ̈orner et al. (2017).

3
**Table 6. Countries with higher transition (fossil fuel exports) risk and at least one elevated macro‐ financial risk**

|**EAP**|**ECA**|**LAC**||**MENA**|**SAR**|**SSA**|
|---|---|---|---|---|---|---|
|Vietnam<br>Myanmar<br>Papua New<br>Guinea|Azerbaijan<br>Bosnia and<br>Herzegovina<br>Kazakhstan<br>Russian<br>Federation<br>Ukraine<br>Greece<br>Croatia<br>Hungary|Bolivia<br>Venezuela,<br>RB<br>Jamaica<br>Ecuador|Bahrain<br>Tunisia<br>Qatar<br>Algeria<br>Egypt, Arab<br>Rep.<br>Libya|Oman<br>Iraq<br>Iran, Islamic<br>Rep.<br>Syrian Arab<br>Republic<br>United Arab<br>Emirates||Mozambique<br>Sudan<br>Nigeria<br>Angola<br>Ghana<br>Kenya<br>Congo, Rep.<br>South Africa|

# _Conclusions_

The analysis shows that a significant number of countries face a double jeopardy due to the simultaneous presence of climate related and macro‐financial risks. Even though elevated climate‐related risks indicate the need for adaptation and mitigation measures, these countries have limited macro‐financial capacity to act. Especially, high public sector and financial sector vulnerabilities limit these countries’ ability to mobilize necessary financing. As physical risks such as natural disasters materialize, high macro‐financial risks mean low macro‐financial resilience and a high risk of prolonged crisis and hardship.

The analysis also suggests that there is a systematic relationship between macro‐financial and climate related risks, with countries facing higher climate related risks typically also facing higher macro‐financial risks. This may to some extent reflect the role of climate related risks as a source of macro‐financial risks. However, further analysis will be necessary to understand this systematic relationship.

The presence of twin risks underlines the importance of reducing macro‐financial risks in order to enhance countries’ macro‐financial resilience to the physical impacts of climate change and their macro‐financial capacity for climate adaptation and mitigation. In the first instance, country specific reforms will be at the core of building macro financial resilience and capacity to deal with climate related risks. However, given that there appears to exist a systemic relationship between macro‐financial and climate‐related risks, there may also be need for the international community to consider strengthening the macro‐ financial resilience and capacity of the most exposed countries as an element of international efforts to tackle climate change.

29
in the two data sets. Next, we focus on the names of politically exposed persons. Specifically, we match the names of the people reported in the PEP data set with the names of shareholders and owners listed in Orbis. Bussolo, Commander, and Poupakis (2018) exploit the network dimensions of these connections and provide a detailed discussion of these matching strategies.

Our sample consists of about 1.7 million observations from more than 460,000 firms from six countries over the period 2008-2013, of these 460,000 firms about 2,000 are classified as politically connected. Table 1 reports the distribution of firms across the six countries covered in our sample and Table 2 reports the descriptive statistics for all the variables used in our analysis (the top panel reports data for all countries and the mid and bottom panels separate connected and unconnected firms).

There are large differences in the number of firms reported by Orbis and in the share of connected firms. While Orbis includes data form more than 160,000 firms in Hungary and Romania, we have fewer than 50,000 firms for Serbia and the Slovak Republic, and fewer than 30,000 firms for Bulgaria and the Russian Federation (Table 1). The share of connected firms ranges from about 0.15% in Bulgaria, Hungary and the Russian Federation, to nearly 1% in the Slovak Republic (the values for Romania and Serbia are 0.5% and 0.3%, respectively).

Comparing median and average values highlights that the underlying distributions are skewed for most variables. To minimize the influence of outliers, we winsorize the investment, cash flow, and sales growth variables at 1%. Extreme values in the left (right) tail are replaced with the value at the 0.5 (99.5) percentile. We observe a large support for winsorized sales growth which in the full sample ranges between -0.9% and 6.6%. Despite the winsorization, the support of cash flow remains large, with values ranging from 0% to 33,310%. Cash flow ranges between 0 and 1,100% in the subsample of connected firms. However, while the standard deviation of the cash flow of non-connected firms is larger than that of connected firms, there are no large differences in the mean value of cash flow of connected and non-connected firms. In fact, despite the large variability in the data, connected and non-connected firms look similar in terms of the level of investment or capitalization. Connected firms tend to be slightly older and are more likely to be large.<sup>4</sup>

> 4We cap firm age at 100 years (there are 180 firms, corresponding to 0.04% of the sample, that report being created more than 100 years ago). All the results discussed in subsequent sections are robust to neither winsorizing nor capping firm age.

5
weaknesses limit regulators’ ability to employ market-based instruments, which also work less effectively in countries where market failures are common and legal and institutional supports for formal market activities are weak.

Information-based instruments can be effective in developing countries where strong regulatory institutions and/or well-developed markets are absent, but where enough information can be reliably obtained to provide credible performance ratings. In practice, diverse information programs have served as complements to command-and-control and market-based instruments [21]. Information programs reduce the information asymmetry between polluters and environmental stakeholders (consumers, communities, NGOs, investors), empowering these stakeholders to pressure polluters for improved environmental performance [5] [17] [26]. When implemented correctly, information instruments promote better interaction and dialogue among firms, stakeholders and regulators [10].

Information instruments also leverage markets in significant ways. An extensive empirical literature suggests that disclosure of firms’ bad environmental performance reduces their stock prices both in developed countries [8] [14][19] [23], [24] and developing countries such as Argentina, Chile, Mexico, and the Philippines [7]. Jackson [16] and Boyle and Kiel [4] review the impacts of disclosure on housing prices in the US, which are found to be lower near Superfund sites [22] [29], hazardous waste sites [30], non-hazardous landfills [25], nuclear radiation sources [9], and polluting manufacturing plants [11]. Housing prices also respond to publicized environmental contamination incidents [19] [20].

Information instruments have diverse forms, including reports of measured pollution, environmental accident reports, and environmental performance ratings. In the US, for example, the Toxics Release Inventory (TRI) discloses toxic chemical releases and waste management
# **Table S1.1.** Continued.

|**Dependant variable:**||**A**|**gricultural production**|||
|---|---|---|---|---|---|
||**Caste**|**Caste & occup**|**Jati & occup**|**WHEAT**|**RICE**|
|**Village characteristics**||||||
|Village population (in log)|0.084<sup>*</sup>|0.090<sup>** </sup>|0.093<sup>** </sup>|0.11<sup>** </sup>|0.11<sup>*** </sup>|
||(0.044)|(0.044)|(0.044)|(0.051)|(0.036)|
|Village land|−0.000040<sup>*</sup>|−0.000039<sup>*</sup>|−0.000044<sup>*</sup>|−0.000081<sup>*</sup>|−0.000070|
||(0.000022)|(0.000022)|(0.000026)|(0.000042)|(0.000056)|
|Observations|4405|4405|3333|1165|1362|
|_R_<sup>2</sup>|0.819|0.819|0.829|0.846|0.848|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. This table provides the coefficients on demographic and farm characteristics estimated in tables 2 to 5, baseline specification. I do not report the gender of the head of household and the share of land owned, as they are not significant. For soil characteristics, I only report the variables that have a significant impact on productivity at the 5 percent level.

# **Table S1.2.** First Stages—Specification 2, Tables 2, 3, and 4

|**Dependant variable:**||**Educ (**|**survey)**||
|---|---|---|---|---|
|_Group: Caste_|||||
|Educ (listing)|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>***</sup>|
||(0.016)|(0.016)|(0.016)|(0.017)|
|_Group: Caste & Occup_|||||
|Educ (listing)|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>*** </sup>|0.72<sup>***</sup>|
||(0.017)|(0.017)|(0.017)|(0.017)|
|_Group: Jati & Occup_|||||
|Educ (listing)|0.71<sup>*** </sup>|0.71<sup>*** </sup>|0.71<sup>*** </sup>|0.71<sup>***</sup>|
||(0.020)|(0.021)|(0.021)|(0.021)|
|Household level controls|Yes|Yes|Yes|Yes|
|Agricultural controls|Yes|Yes|Yes|Yes|
|Group controls|No|Yes|Yes|Yes|
|Controls for other group|No|No|Yes|Yes|
|Additional group controls|No|No|No|Yes|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ :<sup>*</sup> _p <_ 0.1,<sup>**</sup> _p <_ 0.05,<sup>***</sup> _p <_ 0.01. Robust standard errors clustered at the village level are reported in parentheses. This table provides the first stages of specification 2 in tables 2, 3, and 4.
# Women's Changing Participation in the Labor Force:

# A World Perspective*

Women's economic activities are hard to compare precisely across countries and sometimes difficult even to compare within countries over time. Cultural variation in interpreting what is productive work compounded by differences in the statistical definition of who is in the labor force are responsible for much ambiguity in measurements of women's productive roles that straddle home and market economic activities. It is widely recognized that women's activities are changing rapidly in high income, technologically advanced, countries.' The diversity is even greater across low income countries in what women do, but there is little consensus on how these roles are changing today. This paper assesses patterns in women's labor force participation and the composition of this participation among wage earners, self-employed, and unpaid family workers.

The shift of the locus of employment ia contemporary societies from family to firm is attributed to technological economies of scale in production and finance, and the capacity of firms to minimize transaction costs, particularly in such major sectors as manufacturing, construction, commerce and services.2 For women, it is also hypothesized that obtaining a job for wages outside of the family contributes to women's control over the returns to their lator hence augments their relative power in the allocation of household economic resources.3 This economic status of women relative to men may in turn be associated with specific consumption patterns, investments in the health and education of children, and declines in fertility.

Despite the limitations of international census and survey data, about sixty countries report labor force data by sex, for at least two points in time,
Table 4: Multivariate Predictions of Conflict

||**Mod**|**el 1**||**Mod**|**el 2**|||**Mod**|**el 3**|
|---|---|---|---|---|---|---|---|---|---|
||PC<br>RF|LC<br>TC|PC|RF|LC|TC|PC|RF|LC<br>TC|
|**Predictive Performance**||||||||||
|Type 1 Error|0.174 0.087|0.116 0.130|0.188|0.246|0.101|0.101|0.174|0.290|0.101 0.072|
|Type 2 Error|0.429 0.630|0.464 0.408|0.431|0.396|0.462|0.398|0.405|0.334|0.460 0.310|
|Objective Function|0.301 0.359|0.290 0.269|0.310|0.321|0.282|0.250|0.290|0.312|0.281 0.191|
|**Explanatory Variables**||||||||||
|**_Latent Conflict_**||||||||||
|Ethnic Income Inequality|0.187 5.950|0.175 0.788|0.13|5.59|-0.04|0.98|0.101|4.635|0.125 0.632|
|Ethnic Fractionalization*|||0.02|4.66|-0.15|0.95|0.056|3.652|0.077 0.890|
|Religious Fractionalization*|||||||0.184|4.376|0.071 0.747|
|Spatial inequality*|||0.256|6.548|0.156|0.989|0.210|4.990|0.186 0.493|
|Natural Resource Rents|0.405 5.907|0.634 0.142|0.372|7.232|0.596|0.205|0.315|5.713|0.371 0.165|
|Male 15-29 Pop. Share*|||||||0.063|4.247|0.051 0.947|
|History of Conflict|||||||0.064|3.115|0.161 0.088|
|**_Institutions_**||||||||||
|CPIA*|||0.204|6.342|0.375|0.581|0.081|5.811|0.107 0.594|
|Freedom House|0.724 3.921|0.763 0.666|0.131|5.333|0.300|0.858|0.119|4.761|0.152 0.741|
|Political Terror Scale|0.373 6.400|0.317 0.532|0.467|4.378|0.629|0.661|0.387|4.007|0.415 0.508|
|Log GDP per capita*|||0.539|7.261|0.320|0.603|0.393|6.136|0.414 0.591|
|Log Population*|||||||0.110|5.556|0.082 0.786|
|**_Shocks_**||||||||||
|GDP per capita Growth*|0.373 6.400|0.317 0.532|||||0.079|5.536|0.097 0.539|
|TOT change|||||||0.002|4.378|-0.019 0.958|
|Natural Disaster|||||||0.041|1.840|0.076 0.191|
|Neighbors in Conflict|0.242 1.000|0.258 0.999|0.164|1.000|0.380|0.664|0.103|1.000|0.138 0.921|
|Number of Breaches||2||||2|||5|
|Notes: PC=Probit Classifier, RF=Rando<br>rates and the minimized value of the p<br>individual explanatory variables. Colu<br>predict conflict. Columns for TC repor<br>lower values imply more breaches and<br>purity, normalized to 1 for the lowest<br>thresholds that must be crossed to sig<br>correlated with conflict in our sample|m Forest, LC=Line<br>rediction loss fun<br>mns for PC and LC<br>t the percentile o<br>therefore a grea<br>improvement -- h<br>nal conflict. Varia<br>have been re-orie|ar Classifier, TC<br>ction. Remainin<br>report weights<br>f the distribution<br>ter role in predic<br>igher values imp<br>bles indicated w<br>nted to beposit|=Thresho<br>g rows r<br>on indic<br>of the i<br>ting con<br>ly greate<br>ith * hav<br>ivelycor|ld Clas<br>eport m<br>ated va<br>ndicate<br>flict. C<br>r impo<br>e orien<br>related|sifier.<br>easure<br>riables<br>d varia<br>olumns<br>rtance.<br>tation<br>with c|Top pane<br>s of imp<br>in linear<br>ble wher<br>for RF r<br>Numbe<br>reversed<br>onflict.|l reports<br>ortance i<br>combina<br>e the opt<br>eport me<br>r of brea<br>, i.e. var|Type 1<br>n predi<br>tion of<br>imal th<br>an impr<br>ches re<br>iables t|and Type 2 error<br>cting conflict for<br>variables used to<br>reshold is set --<br>ovement in node<br>fers to number of<br>hat are negatively|

39
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30
**Table A7.3: Impact on Other Measures of Management Practices**

||WMS|WMS|WMS|WMS|WMS|MOPS|
|---|---|---|---|---|---|---|
||Overall|Operations|Monitoring|Targets|People|Score|
|_All firms interviewed in 2016_|||||||
|**Panel A: No controls**|||||||
|Individual Treatment|0.040|0.100|0.152|‐0.045|‐0.003|‐0.008|
||(0.169)|(0.345)|(0.225)|(0.238)|(0.156)|(0.034)|
|Group Treatment|0.075|0.035|0.152|0.041|0.053|0.013|
||(0.170)|(0.298)|(0.209)|(0.230)|(0.153)|(0.031)|
|**Panel B: Baseline Controls**|||||||
|Individual Treatment|‐0.000|‐0.030|0.095|‐0.076|‐0.007|‐0.005|
||(0.166)|(0.307)|(0.235)|(0.243)|(0.152)|(0.032)|
|Group Treatment|0.061|0.009|0.094|0.094|0.025|0.018|
||(0.166)|(0.276)|(0.210)|(0.231)|(0.162)|(0.030)|
|Sample Size|70|70|70|70|70|95|
|Control Mean in 2016 of outcome|2.92|2.90|3.28|2.94|2.61|0.52|
|Control S.D. in 2016 of outcome|0.55|1.07|0.68|0.79|0.54|0.13|
|_50 firms interviewed in WMS in 2013 & 2016_|||||||
|**Panel C: No Controls**|||||||
|Individual Treatment|0.143|0.321|0.314|‐0.086|0.131|0.010|
||(0.218)|(0.423)|(0.256)|(0.311)|(0.199)|(0.051)|
|Group Treatment|0.283|0.357|0.312|0.225|0.284|0.064|
||(0.216)|(0.363)|(0.254)|(0.293)|(0.183)|(0.045)|
|**Panel D: Baseline Controls**|||||||
|Individual Treatment|0.029|0.123|0.153|‐0.188|0.074|‐0.011|
||(0.204)|(0.388)|(0.257)|(0.304)|(0.197)|(0.055)|
|Group Treatment|0.242|0.238|0.210|0.276|0.241|0.066|
||(0.203)|(0.350)|(0.266)|(0.286)|(0.175)|(0.049)|
|**Panel E: Baseline Controls + Ancova**|||||||
|Individual Treatment|0.072|0.233|0.168|‐0.160|0.133|‐0.009|
||(0.199)|(0.394)|(0.252)|(0.299)|(0.199)|(0.055)|
|Group Treatment|0.267|0.335|0.232|0.296|0.214|0.068|
||(0.214)|(0.372)|(0.276)|(0.302)|(0.163)|(0.048)|
|Sample Size|50|50|50|50|50|46|
|Control Mean in 2016 of outcome|2.88|2.89|3.24|2.96|2.51|0.53|
|Control S.D. in 2016 of outcome|0.65|1.13|0.76|0.90|0.56|0.14|

Notes: Each panel represents treatment impacts from a separate regression. 70 of the 159 firms were given the WMS survey in 2016, of which 50 had also received this survey in 2013. Panels A and C regress outcomes on treatment dummies only. Panels B and D add controls for dummies for the Cundinamarca and Valle regions, a dummy for having 10 to 50 workers at baseline, the number of employees in 2013, labor productivity in 2013, and the 2013 Anexo K management practice score. Panel E also controls for the baseline value of the outcome measure.

Robust standard errors in parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

68