rafmacalaba/datause-encoder-data huggingface dataset:
# **2 The Interventions**

We randomly assigned participants in our experiment to a treatment group (either the game or the video) or to a control group. Groups were randomized at registration, after which each individual received Zoom meeting details to join a call at a specific date and time. Once they joined the call, they received a treatment. We describe each treatment in detail below. Immediately after the treatment, we collected a survey to elicit prosocial attitudes and behaviors. The control group only answered the survey and did not receive any treatment.

# **2.1 Treatment arms**

# **2.1.1 Game treatment**

In this arm, participants undertook an activity called _En Otros Zapatos_ (EOZ), which means _In Someone Else’s Shoes_ . The Inter-American Development Bank created the EOZ platform to promote empathy towards marginalized groups and underrepresented minorities by inviting users to “step into” the shoes of these individuals. The website is open to the public and allows users to participate in several interactive stories.<sup>11</sup> For this experiment, we used a new story of a Venezuelan forced migrant who moves to Colombia. A writer created this fictional story based on interviews he conducted with Venezuelan forced migrants in Colombia. In the game, the user takes the role of “Mile Rodr ́ıguez,” a female Venezuelan migrant with a husband and two young children. Participants begin by reading her story, which describes the challenges Mile faces in Venezuela (e.g., lack of food and proper medical attention) that eventually prompt her family to emigrate to Colombia. With this information, the user must decide whether to migrate directly to Bogot ́a—the capital and her husband’s preferred option—or to Riohacha, a northern city recommended by a neighbor. Each decision leads the user to a different life path. Once at either destination, the user continues to read Mile’s story and to make decisions for her.

11 For more details, visit https://enotroszapatos.iadb.org/.

9
to behave in accordance with fraudsters’ scripts, and become victims of specific fraud schemes. Furthermore, the need for social network expansion also increases the amount of social interactions of credit-constrained people, which would increase the probability of encountering fraudsters.

Based on the analysis above, we propose our hypothesis. Households subject to credit constraints from formal credit institutions are more likely to suffer from fraud victimization. Credit constraints make people less patient and more susceptible to fraud schemes disguised with high returns in short periods. Credit constraints increase the needs of creditconstrained people for social network expansion, which in turn may increase their amount of social interactions and therefore the probability of encountering fraud, in addition to inducing them to behave more cooperatively to disguised fraud schemes, both of which increase the chance of fraud victimization.

# **3. Data**

We rely on data from the Chinese Household Finance Survey (CHFS), conducted by the Survey and Research Center for China Household Finance at the Southwestern University of Finance and Economics. This survey was started from 2011 with a sample of 8,438 households covering 25 provinces in both urban and rural China. The CHFS sample size is enlarged every other year with a new wave of the survey. The most recent wave was conducted in 2019 with 40,000 households. We use wave 3 (CHFS-III) in 2015 because this is the only wave with nationally representative information on fraud victimization.

CHFS adopts a three-level (county, community and residents) stratified random sampling method. In CHFS-III, firstly 350 counties were randomly selected nationwide, then four communities within each county were randomly chosen, and finally, 25-50 (20) households in each community in urban (rural) areas from each community were randomly selected. The final sample consists of 37,000 households in 1,394 communities in 350 counties, which are located in 29 provinces.

There are several major advantages of using CHFS-III for this research. First, apart from information on demographics, CHFS contains a wide range of information on income/expenditure, financial assets, real estate, asset structure, among others. Second, it has details on household financing behavior and historical debt. Third, the 2015 survey additionally included a module on fraud victimization. This is perhaps the first nationally 11
Figure A1: Specification Charts for Mean Daily Rainfall <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .4<br>.1 .2<br>0 0<br>* Weather Product * -.1 * Weather Product * -.2<br>TAMSAT -.2 TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>1 .2<br>.5 .1<br>0 0<br>* Weather Product TAMSAT* -.5-1 * Weather Product TAMSAT* -.1<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.1 .3<br>.05 .2<br>0 .1<br>-.05 0<br>* Weather Product * -.1 * Weather Product * -.1<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

49
compatible with fixed effects, which we include to tackle potential confounders. Thus, as a robustness check, we use a Poisson Pseudo-Maximum likelihood model (PPML), as proposed by Correia (2016) and Correia et al. (2019). In the results section, we present our baseline linear probability models. Figures in Appendix A.2 show that our estimates are robust to using linear probability models fixed-effects or PPML estimators.

We build on specification 1 to assess the effectiveness of individual adaptation actions in mitigating diarrheal diseases in section 4.2. Potential actions influencing the incidence of diarrhea for which we have data in the DHS are toilet and fridge ownership, urban/rural indicators, and education level. For all of them, we create a dummy variable equal to 1 when the household owns the asset, lives in an urban area, or has at least some education, respectively. Then, we interact the bins of significant weather variables in equation 1 with indicators of adaptation actions. The coefficient on the interaction term assesses the effectiveness of the considered adaptation action in decreasing the impact of weather on diarrhea incidence in children. It is important to keep in mind that we do not have exogenous variation in these characteristics when interpreting the results. As a result, households owning a specific asset or characteristic may disproportionately adopt other adaptation strategies that increase their resilience to climate-related risks.

In Appendix A.4, we examine the long-term consequences of in-utero environmental haz– ard exposure on child health. We achieve this by imputing environmental variables maximum temperature, average pollution levels, and average precipitation – for each trimester of pregnancy. Our findings reveal no substantial impacts on reported birthweight or measured indicators of stunting or underweight among our dataset’s children. However, these findings are subject to limitations from the relatively small number of observations available for these specific variables and the challenges in precisely assigning exposure levels. Indeed, the pollution exposure assignment during pregnancy is only accurate if households have not relocated too far since birth. Otherwise, the measurement error introduces a bias of the coefficient towards zero. In light of these considerations, we exercise caution as the DHS dataset may not be the optimal resource for examining the long-term effects of environmental hazards on child health. Nevertheless, we present these results transparently, aiming to offer insights and guidance for future researchers in this field.

data (Silva and Tenreyro, 2011).

9
intended to boost the productivity and earnings of self-employed women, women farmers and women wage workers in developing countries. Table 1 summarizes the principal evaluation studies grouped by intervention type. As shown in the table, this review draws on credible evaluations of the effectiveness of different interventions—evidence that is rigorous enough to ensure that the observed outcome was the result of the intervention rather than of selection effects or placement biases. The article updates and benefits from thematic reviews of this evidence commissioned by the Women’s Roadmap Project.<sup>1</sup> The main question guiding the review is “what works” to increase women’s productivity and earnings as proxies for the more complex notion of economic empowerment. This question is followed by the question of “for whom,” hypothesizing that the evidence would show differential outcomes for poor or very poor versus non-poor women (defined by income levels, type of occupation, or the nature and size of firms or farms) and for young (between the ages of 15 and 24, or older in post-conflict settings) versus adult women (aged 25 to 64). The gender literature’s emphasis on documenting gender differences has often underplayed the equally important documentation of heterogeneity among women.

# **Methodological Issues**

Interventions were assessed according to the strength of empirical evidence. They were judged as proven when the evidence was robust, with more than one credible evaluation yielding similar results across different settings or circumstances. Promising interventions were backed by positive findings from one credible study or its equivalent. Unproven interventions yielded no evidence of positive effects on productivity and earnings for the categories of women and settings evaluated.

3
higher than that of teacher salaries (1.2 times higher), however that for increasing just classroom size is between 1.7 and 4 times higher. The cost effectiveness of spending on instructional inputs is between 4 and 14 times higher than that of increasing teacher salaries.

Again the actual ratio between the marginal product per dollar of salary inputs and instructional materials is different from the level predicted by optimizing model by more than an order of magnitude, 14 to 1.

|**Table 2:**<br>Test**score gain per rupee for various inputs relative to teacher salar**<br>**across low-literacy districts in eight states of India, 1993**|**y (=1), average**|
|---|---|
|Instructional materials||
|_ Complete package of instructional materials and aids|14.0|
|Coverage under Operation Blackboard, equipment only|4.0|
|Physical facilities<br>||
|I<br>-<br>Full package of facilities_|1.2|
|One additional square foot per student||
|Cost based on school construction in Uttar Pradesh|4.3|
|,Cost based on DPEP average|1.7|
|Teacher Quality<br>||
|fIncreasing<br>average teacher salary by Rs. 100 per month per school|1.0|
|<br>Opportunity<br>to learn||
|Reducing the pupil teacher ratio by one|0.1|
|Adding one additional school day per academic year|1.0|
|Adding one additional teaching hourperacademicyear|0.6|
|Adding one additional hour of language instruction per academic week|29.0|
|Source: World Bank (1996a) Annex F||

What is somewhat puzzling is that class size reductions are even less effective than teacher salaries. However both of these results are consistent with recent estimates by Kingdon (1996) using sample survey data for students from 30 schools in urban Uttar Pradesh, who finds that if the cost effectiveness of teacher salary is normalized to 1, the cost 18
Policy Research Working Paper 5062

# **Abstract**

This paper analyzes complementarities between different Millennium Development Goals, focusing on child mortality and how it is influenced by progress in the other goals, in particular two goals related to the expansion of female education: universal primary education and gender equality in education. The authors provide evidence from eight Sub-Saharan African countries using two rounds of Demographic and Health Surveys per country and applying a consistent micro-econometric methodology. In contrast to the mixed findings of previous studies, for most countries the findings reveal strong complementarities between mothers’ educational achievement and child mortality. Mothers’ schooling lifts important demand-side constraints impeding the use of health services. Children of mothers with primary education are much more likely to receive vaccines, a crucial proximate determinant of child survival. In addition, better educated mothers tend to have longer birth intervals, which again increase the chances of child survival. For the variables related to the other goals, for example wealth proxies and access to safe drinking water, the analysis fails to detect significant effects on child mortality, a finding that may be related to data limitations. Finally, the study carries out a set of illustrative simulations to assess the prospects of achieving a reduction by two-thirds in the under-five mortality rate. The findings indicate that some countries, which have been successful in the past, seem to have used their policy space for fast progress in child mortality, for example by extending vaccination coverage. This is the main reason why future achievements will be more difficult and explains why the authors have a fairly pessimistic outlook.

This paper—a product of the Development Economics Prospects Group—is part of a larger effort in the group to do forward-looking analyses of development strategies, including strategies for achieving the Millennium Development Goals. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lay@giga-hamburg.de and robilliard@dial.prd.fr.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table (E20) Impacts of the Program on NGO Assistance

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Government, Last 12 Months (=1)|-0.03|(0.05)|[0.40]|0.10|(0.30)|1,422|
|UNHCR, Last 12 Months (=1)|0.14*|(0.08)|[0.33]|0.64|(0.48)|1,422|
|WFP, Last 12 Months (=1)|-0.06|(0.04)|[0.33]|0.94|(0.24)|1,422|
|IP Assistance, Last 12 Months (=1)|0.08**|(0.03)|[0.14]|0.04|(0.19)|1,422|
|Community-Groups, Last 12 Months (=1)|-0.00|(0.03)|[0.51]|0.04|(0.20)|1,422|
|Other, Last 12 Months (=1)|0.06*|(0.04)|[0.33]|0.06|(0.24)|1,422|
|Total Cash, Last 12 Months (USD PPP)|706.91|(586.39)|[0.40]|4,431.51|(4,004.01)|1,422|
|Total In-Kind, Last 12 Months (USD PPP)|92.67|(67.83)|[0.33]|127.02|(481.35)|1,422|
|Total, Last 12 Months (USD PPP)|788.26|(562.86)|[0.33]|4,578.48|(4,020.73)|1,422|
|Government, Last 12 Months (USD PPP)|111.04|(96.21)|[0.40]|169.37|(792.44)|1,422|
|UNHCR, Last 12 Months (USD PPP)|420.89|(305.03)|[0.33]|1,655.25|(2,226.52)|1,422|
|WFP, Last 12 Monthse (USD PPP)|74.02|(293.95)|[0.51]|2,618.38|(2,404.40)|1,422|
|IP Assistance, Last 12 Months (USD PPP)|128.25***|(46.50)|[0.10]|28.74|(198.87)|1,422|
|Community-Groups, Last 12 Months (USD PPP)|-2.58|(10.47)|[0.51]|10.17|(70.04)|1,422|
|Other, Last 12 Months (USD PPP)|60.20*|(36.76)|[0.33]|44.94|(231.61)|1,422|

**_Notes_** :

- The table shows the regression results on financial assistance using the 2021 in-person data. Each row is its own dependent variable. These outcomes were not pre-specified as they were added after the first-wave phone survey data were collected. UNHCR refers to the United Nations High Commissioner for Refugees; WFP refers to the World Food Programme; IP refers to the implementing partner.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- All values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to the outcomes in the table.

30
<u>Table 8. Changes in the schools: teachers and infraestructure</u>

||Chec|k 1|Chec|k 2|Che|ck 3|
|---|---|---|---|---|---|---|
||Control Mean<br>and SD<br>(1)|Treatment-<br>Control<br>(2)|Control Mean<br>and SD<br>(1)|Treatment-<br>Control<br>(2)|Control Mean<br>and SD<br>(1)|Treatment-<br>Control<br>(2)|
|A. Teachers|||||||
|Number of teachers|15.27|0.986|17.04|2.521|22.02|-0.689|
||(4.12)|(1.02)|(4.61)|(1.81)|(11.08)|(1.85)|
|Percent female|0.19|0.01|0.19|0.012|0.21|-0.026|
||(0.11)|(0.02)|(0.09)|(0.02)|(0.10)|(0.02)|
|Percent permanent|0.64|0.039|0.63|0.015|0.58|-0.029|
||(0.23)|(0.05)|(0.20)|(0.04)|(0.19)|(0.04)|
|Percent in class|0.22|0.069*|0.22|0.074**|0.23|0.031|
||(0.15)|(0.04)|(0.13)|(0.03)|(0.12)|(0.03)|
|Percent absent|0.29|-0.083|0.28|-0.004|0.31|-0.028|
||(0.26)|(0.05)|(0.20)|(0.04)|(0.20)|(0.04)|
|Percent with secondary or lower|||0.09|-0.033|0.06|0.085**|
||||(0.16)|(0.03)|(0.10)|(0.03)|
|B. Infraestructure|||||||
|Students per chair, grade 1|2.2|-0.033|2.36|-0.234|2.14|0.082|
||(1.45)|(0.33)|(2.51)|(0.44)|(0.92)|(0.21)|
|Students per chair, grade 2|2.07|-0.331|1.81|-0.043|2.26|-0.215|
||(1.27)|(0.24)|(0.88)|(0.19)|(1.05)|(0.24)|
|Index of class: condition, noice, clean|7.72|0.226|7.67|0.161|8.01|0.057|
||(1.46)|(0.31)|(1.39)|(0.30)|(1.44)|(0.29)|
|Library (yes or no)|||0.26|0.129|0.38|-0.064|
||||(0.44)|(0.10)|(0.49)|(0.11)|
|Number of working toilets|||7.2|1.277|7.15|-0.109|
||||(5.38)|(1.20)|(6.26)|(1.33)|
|Laboratory|||0.67|0.199**|0.57|0.215*|
||||(0.47)|(0.10)|(0.50)|(0.11)|
|Index of instruments (lab)|||6.46|0.323|8.44|0.089|
||||(3.48)|(0.72)|(1.42)|(0.33)|
|Max. Number of observations|48|96|46|93|47|95|
|Control (baseline and region fixed ef.)||Yes||Yes||Yes|

Note. Columns (1) include the mean and SD for each variable for the control group. Columns (2) present the coefficient of the regression of each variable against treatment status. All regression controls for baseline characteristics and fixed effects at the region level.

25
were to become evident before the implementation of contracts, they would want to address the increased risk of drought by raising the premium. When presented with additional information about ENSO-based forecasts, they expressed interest in exploring a range of options for incorporating that information. Assuming availability of capital and institutional capacity for design and implementation, a variety of approaches could be explored for integrating seasonal climate forecasts into the bundled credit-insurance Malawi scheme. Variables that could be controlled based on predictions include premium price, size of individual loans, kinds of inputs provided and total number of participating farmers. We are interested in exploring potential schemes that not only share the financial risk associated with droughts, but also actually reduce the vulnerability of subsistence farmers to droughts and climate change. One way to accomplish this goal may be to adjust the kinds and/or quantities of agricultural inputs given to farmers in accordance to expected rainfall conditions.

# **4 METHODS**

Building on the 2006 index insurance package for Kasungu, Malawi, we formulate a contract structure in which ENSO-based insurance pricing is used to adjust the size of the loan. The approach builds on a theoretical framework for the relationship between forecasts, production, decisions and insurance proposed by Carriquiry and Osgood (2006). For the sake of illustration, we considered a hypothetical farm with 3.14 ha of arable land, in which the farmer uses only the inputs provided by the proposed ENSO-adjusted scheme, using the planting density and fertilizer application recommended for the 2006 package. The insurance contact was designed to support a loan for inputs for 0.405 ha (1 acre) of hybrid maize production, using the prices, parameters, and constraints that stakeholders negotiated for the 2006-2007 season. Although the packages 11