rafmacalaba/datause-encoder-data huggingface dataset:
scorecard treatment (“true” announcement), and half of the groups assigned to control (“false” announcement). This provides a “placebo” where the FOs are told that scorecards might happen but in fact do not. Comparing announcement to no announcement within the scorecard treatment provides a test of the extent to which awareness of the scorecards can itself boost efforts of the FO in training these specific farmers groups, while additionally comparing true and false announcement tells us about FOs’ ability to observe the scorecards. While we control for logbook treatment assignment dummies in our analysis of the announcement effect, we are not powered to study their interactions.

3.1 Data We use two sources of data: administrative data from our partner NGO, OAF, and survey data administered on a random subset of our study sample. First, we exploit OAF’s administrative client database for two years, 2013 and 2014. The client database includes the names of all farmers who register for OAF membership in the season, the name of the group to which the farmer belongs, the OAF Field Officer and Field Manager responsible for the group, the total amount of credit requested by the client, the quantity requested of specific agricultural inputs,<sup>19</sup> the number of repayments, and the proportion of the loan repaid. We use the 2013 client database as our sample frame. We track whether these farmers continue with the service by merging with the 2014 client database. If a client in 2013 does not register for the service in 2014, she is considered a dropout. If a farmer was not on the client list in 2013A, but registers in 2014, we consider her to be a new member. Some clients registered in 2013 (in and outside our study sample) switch farmer group in 2014; we do not consider these farmers to be either dropouts or new members. Hence, we report changes in membership net of these switches. For instance, if two clients were registered in group X in

> 19 OAF offers DAP, NPK-17, NPK-22, and Urea.

12
main forms of violence against women in Peru. The authors also find that early-life exposure to alcohol-induced IPV is indeed associated with lower test scores in vocabulary. Our results suggest that maternal depression is a mechanism through which alcohol induced IPV leads to worse child vocabulary outcomes.

The remainder of the paper is organized as follows. In the next section, we describe our research design, including details on the data we are using for our analysis and some descriptive statistics. In Section 3, we describe and motivate the use of instrumental variables as an empirical strategy. In Section 4, we present the main results, including some robustness checks. Finally, in Section 5 we discuss to the policy implications and conclude.

# **2. Research design**

In this section, we briefly describe the data we use to assess the impact of maternal depression on early child vocabulary in Peru. Then, we present some descriptive statistics of the different rounds of data use in our analysis. Since we restrict our study to the sample that has information across all rounds, we also briefly describe the results from the sample attrition analysis.

# **2.1 Data**

# **_A. Description_**

To measure the effects of MMH on child development, we use the first three rounds of the Young Lives Peru Survey (YL), conducted by the University of Oxford with core-funding from the UK Department for International Development.<sup>7</sup> This is a rich longitudinal survey that includes a complete set of individual, parental, household and community characteristics, including early developmental, economic and demographic indicators, as well as information about social assistance programs in every community. The baseline sample of YL is cluster stratified, with 20 districts randomly selected across the country.<sup>8</sup> Within each of the selected districts, 100 households with at least one child born between 2001 and 2002 (index child) were chosen randomly to participate in the project. The baseline round was conducted in 2002, when the index

> 7 The YL survey is also being conducted in Vietnam, Ethiopia, and India (Andra Pradesh region). As of now, 5 rounds of data have been collected.

> 8 Because the YL project is particularly interested in children living in poorer households (Wilson et al 2006), the sampling frame excluded the top 5 percent of districts as measured by a district poverty ranking. Despite excluding the least poor, it has been documented that the data reflect the Peruvian population in a broad range of indicators (Young Lives-GRADE 2008).

6
<mark>Imbens, G.W., 2004. Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review. Review of Economics and Statistics 86(1): 4–29.</mark> Imbens, G.W., 2015. Matching Methods in Practice: Three Examples. Journal of Human Resources 50(2): 373419.

Imbens, G.W., Rubin, D.B., 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press.

<mark>International Rescue Committee (IRC). 2014. E</mark> mergency Economies: The Impact of Cash Assistance in Lebanon.

Jakiela, P., Ozier O., 2016. Does Africa Need a Rotten Kin Theorem? Experimental Evidence from Village Economies. Rev Econ Stud. 83(1): 231–68.

<mark>Langendorf C., Roederer T., de Pee S., Brown D., Doyon S., et al., 2014. Preventing Acute Malnutrition among Young Children in Crises: A Prospective Intervention Study in Niger. PLOS Medicine 11(9): e1001714.</mark> <u><mark>https://doi.org/10.1371/journal.pmed.1001714</mark></u> Lee, D.S., 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Rev Econ Stud. 76(3):1071–102.

Lee, D.S., Lemieux, T., 2010. Regression Discontinuity Designs in Economics. J Econ Lit. 48(2):281–355.

Lehmann, M. C., Masterson, D. T. R., 2020. Does Aid Reduce Anti-Refugee Violence? Evidence from Syrian Refugees in Lebanon. Am Polit Sci Rev. 2020/06/30. 2020;1–8.

Maxwell, D., Caldwell, R., 2008. The Coping Strategies Index Field Methods Manual, Second Edition.

Oxford Policy Management, 2018. Evaluation of the Directorate-General for European Civil Protection and Humanitarian Aid Operations-funded Emergency Social Safety Net (ESSN) in Turkey.

<u>Özler, B., 2016. Definitions in RCTs with interference. Development Impact Blog, June 6, 2016. https://blogs.worldbank.org/impactevaluations/definitions-rcts-interference.</u> Penglase J. Consumption Inequality Among Children: Evidence from Child Fostering in Malawi*. Econ J. 2020 Aug 20; Available from: https://doi.org/10.1093/ej/ueaa104 Refugees Association Turkey, 2020. Numbers of Syrians in Turkey June 2020.

Regional Refugee and Resilience Plan /3RP. 2020. 3rp Regional Refugee and Resilience Plan in Response to the Syria Crisis, Turkey Country Chapter 2019/2020. pp. 5-7.

36
in consumption is smaller and the increase in investment bigger under a subsidy than under a mandate and thus the increase in GDP turns out to be bigger for the subsidy than for the mandate (see Figure 8).

## **3.4 Impacts on GHG emissions**

A 10% penetration of wind power in Brazil’s electricity generation mix would reduce 1.2% and 1.4% of total energy sector GHG emissions in the country in 2030 if the penetration was caused by mandate and subsidy policies, respectively. The mandate causes lower mitigation of GHG compared to the subsidy, as the mandate causes more replacement of hydro and less replacement of natural gas based electricity generation compared to the subsidy policy.

**Figure 9:** Economy wide CO2 emissions reduction (percentage deviation from the baseline).

<!-- Start of picture text --> ‐0.7<br>‐0.8<br>Subsidy<br>‐0.9<br>Mandate<br>‐1.0<br>‐1.1<br>‐1.2<br>‐1.3<br>‐1.4<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>Change from BAU [%]<br><!-- End of picture text -->

# **4. Conclusions**

Fiscal policies (subsidies in particular) and regulatory policies (particularly mandates) are the main instruments introduced in both developing and developed countries to encourage a large-scale deployment of renewable energy, such as wind power. Which policy instrument would be attractive from the broader economic perspective, however, has not been clearly 14
As noted among others by Estache et al. (2002; see also Estache, 2004; Komives et al., 2005; and Estache and Wodon, forthcoming), the policies that need to be implemented in order to promote higher coverage rates are very different depending on the nature of the obstacles to increase coverage. If the main obstacle is a lack of demand due for example to a lack of affordability, utilities or governments may consider implementing special tariffs or subsidies for the poor, whether this is done for reducing the cost of the consumption of households once they are connected, or for reducing the cost of connecting itself. If the main problem is a lack of supply, the first line of answer lies in finding the necessary resources in order to expand the network to those who do not have access. Given that the policy options for dealing with demand as opposed to supply-side issues are fairly different, it is important to try to measure the contributions of both demand and supply-side obstacles to better coverage of infrastructure services. The aim of this paper is to show how this can be done empirically in a simple way using household survey data.

The importance of assessing the role of demand as opposed to supply-side issues has been recognized by Foster and Araujo (2004, hereafter F&A) in their study of the impact of infrastructure reforms on the poor in Guatemala. These authors proposed a nice and simple statistical method for assessing the contribution of pure demand-side problems, pure supply-side problems, and combined demand and supply-side problems to coverage deficits. If a household living in an area with access to piped water or electricity service was not connected, this was taken as a sign that the service was not affordable for the household (pure demand-side problem). In practice, the authors assessed whether households lived in an area with access simply by checking if any other household living in the same primary sampling unit of the survey had access. Indeed, household survey samples rely on geographically defined primary sampling units which tend to be well delimited areas, especially in an urban setting. To the extent that the primary sampling units in urban areas are small (about 15-20 households per primary sampling units who tend to live in specific neighborhoods), access by one household in the primary sampling unit could be considered as indicating potential access for all the households in that primary sampling unit.

F&A then defined the magnitude of supply-side problems as the part of the lack of coverage that was not due to the pure demand-side problem mentioned above. In addition, they decomposed supply-side problems into two components. The authors noted that even if there
# **TABLE VI. Heterogeneous effects of Maternal Mental Health on Children Vocabulary at Age 5 by educational level of the mother**

**_Dependent variable: Standardized PPVT_**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||IV w<br>_Effects on_<br>_with p_<br>|ith PDS-s<br>_children fro_<br>_rimary inco_<br>|elected varia<br>_m mothers_<br>_mplete_<br>|bles and full<br>_Effects_<br>_mothers w_<br>_educ_<br>|regressor<br>_on children_<br>_ith at least_<br>_ation comp_<br>|set<br>_from_<br>_primary_<br>_lete_<br>|
||MHI-1|MHI-2|MHI-3|MHI-1|MHI-2|MHI-3|
|Maternal Mental Health (-)|-0.799|-0.389|-0.419|-1.829|-0.889|-0.949|
||(0.513)|(0.249)|(0.268)|(1.848)|(0.885)|(0.937)|
|Constant|1.292***|1.299***|1.300***|0.739|0.736|0.739|
||(0.401)|(0.398)|(0.397)|(0.778)|(0.771)|(0.763)|
|Observations|719|719|719|376|376|376|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|

Table VI presents estimated effects of maternal mental health on child vocabulary at age 5, separated by whether the mother has completed at least primary education or not. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Robust standard errors in parentheses.

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<!-- Start of picture text --> °<br>5<br>ite)<br>a<br>}<br>58<br>2<br>><br>2<br>as<br>eq<br>®<br>=<br>n<br>So<br>ES<br>=e<br>3<br>£<br>‘<br>o | ||<br>1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 2017<br>Year<br><!-- End of picture text -->
We also examine returns by level of schooling for both sectors (Table 4). In 1998, the returns to schooling for each level – complete and incomplete – were higher in the private sector. By 2002, the returns to primary and secondary (incomplete and complete) schooling were higher in the public sector. The returns to tertiary incomplete actually decreased significantly in the public sector. The returns to tertiary complete increased in both sectors. However, there was no change in the returns to tertiary incomplete in the private sector. Thus, much of the increase in the returns to schooling overall is due to the increase in the returns to tertiary education. The rates of increases, however, were higher in the public sector. This provides little evidence of screening since the private sector seeks to maximize profits and hence recognizes the higher productivity of the more educated relative to the public sector that tends to reward on a pay-scale basis regardless of productivity. Therefore, again there is little evidence of credentialism driving the returns to schooling.<sup>3</sup>

||Table 3:Determinants o<br>Public<br>|f Earnings by Secto<br>|rof Employment<br>Private<br>||
|---|---|---|---|---|
|1998|Estimated coefficient|Standard error|Estimated coefficient|Standard error|
|Years of education|0.089|0.003|0.101|0.002|
|Experience|0.033|0.003|0.038|0.002|
|Experience-squared|-0.001|0.001|-0.001|0.000|
|Constant|-0.327|0.049|-0.777|0.029|
|R<sup>2</sup>|0.244||0.224||
|N|3,667||10,778||
|2002|Estimated coefficient|Standard error|Estimated coefficient|Standard error|
|Years of education|0.099|0.003|0.111|0.003|
|Experience|0.036|0.003|0.036|0.003|
|Experience-squared|-0.001|0.001|-0.001|0.001|
|Constant|-0.669|0.049|-1.061|0.044|
|R<sup>2</sup>|0.323||0.216||
|N|3,085||6,178||
|Source: EPH|||||

> 3 Excluding the _Jefes_ beneficiaries does not alter the conclusions but rather strengthens them.  Few _Jefes_ beneficiaries work in the private sector, so the estimated coefficients for the private sector remain the same.  But the returns in the public sector decrease. _Jefes_ beneficiaries are less educated and tend to increase the variation in wages, thus increasing the returns to schooling in the public sector.  Excluding them tends to reduce returns in the public sector (see Appendix A).

- 8 -
research is needed in order to assess whether the reform increases the overall lifetime welfare of women who were exposed to it.

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Policy Research Working Paper 6114

# **Abstract**

Relative deprivation, shame and social exclusion can matter to the welfare of people everywhere. The authors argue that such social effects on welfare call for a reconsideration of how we assess global poverty, but they do not support standard measures of relative poverty. The paper argues instead for using a weaklyrelative measure as the upper-bound complement to the lower-bound provided by a standard absolute measure. New estimates of global poverty are presented, drawing on 850 household surveys spanning 125 countries over 1981–2008. The absolute line is $1.25 a day at 2005 prices, while the relative line rises with the mean, at a gradient of 1:2 above $1.25 a day. The authors show that these parameter choices are consistent with cross-country data on national poverty lines. The results indicate that the incidence of both absolute and weakly-relative poverty in the developing world has been falling since the 1990s, but more slowly for the relative measure. While the number of absolutely poor has fallen, the number of relatively poor has changed little since the 1990s, and is higher in 2008 than 1981.

This paper is a product of the Director’s office, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at schen@worldbank.org and mravallion@worldbank.org.

_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
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