rafmacalaba/data-use-mentions-extended huggingface dataset:
at marriage generally (Jain & Kurz, 2007). Minimum marriage age laws have also been shown to contribute to the reduction in child marriage, although laws alone are not sufficient to deter from the harmful practice (Maswikwa, Richter, Kaufman, & Nandi, 2015). High levels of wealth and exposure to media have also been correlated with lower levels of child marriage (Plesons et al., 2021; Rumble, Peterman, Irdiana, Triyana, & Minnick, 2018).

Interventions to reduce child marriage highlight the importance of factors at the national, community, family, and individual levels (Psaki et al., 2021). At the community level, it is important to include interventions that address social norms. Lee-Rife et al. (2012) undertook a systematic review of 23 evaluated child marriage interventions in developing countries and found that engaging communities and empowering girls can be effective in delaying marriage. At the family and individual level, a number of studies have examined interventions that can address drivers of child marriage. A recent review of interventions identified programs that support girls’ schooling through cash or in-kind assistance as the most effective approach to reducing child marriage (Malhotra & Elnakib, 2021). Cash transfer programming has been shown to delay very early child marriage (girls aged 10-14) in Ethiopia (Erulkar, Medhin, & Weissman, 2017), reduce early marriage for Syrian refugee girls aged 15-19 years in Lebanon (Moussa et al., 2021), and agricultural seed support reduced child marriage in the Syrian Arab Republic (Baliki, Brück, & Stojetz, 2018).

In a systematic review of quantitative country studies by Neal et al. (2016), the direct effects of conflict on child marriage were found to be mixed. Specific studies sometimes show conflict leads to an increase (Randall, 2005; Shemyakina, 2013; Valente, 2011), a decrease (Blanc, 2004; Saxena, Kulczycki, & Jurdi, 2004), or has no clear impact on child marriage (
23.93|23.23|
|75-100%|380|1.09|1.22<sup>***</sup>|1.14|-10.57|-6.30|
|Total|1,546|0.62|0.53|0.64|18.27|21.10|

**Notes:** GPS stands for area measurement using handheld Global Positioning Systems, while SR stand for self-reported plot size in acres. PP stands for post planting visit while PH stands for post-harvest visit. These are quartile-specific mean and relative biases as a percent of (mean) GPS-measured plot size.<sup>***</sup> Represents statistical test differences between GPS values and self-reported value in the post-planting survey.

Figure 1 shows that rounding is a major source of measurement error in self-reported plot area. Moreover, Figure 1 shows that heaping at focal points remained pervasive even after the true plot areas and the extent of measurement error were shared with treatment group farmers. About half of the self-reported plot sizes assume either of the four rounded values (0.5, 1.0, 1.5 or 2.0) in the post-planting and post-harvest interviews.<sup>11</sup> The slight differences in the distribution of plot sizes between the post-planting and post-harvest rounds appears to be only for those plots greater than 1 acre, where rounding at 1.5 and 2 acres slightly diminishes in the post-harvest survey. Appendix Table A2 reports Kolmogorov-Smirnov tests for differences between each pair of distributions in Figure 1. These non-parametric tests suggest that we cannot reject the null hypothesis of no significant differences between self-reported values across control and treatment groups, and between self-reported pre-treatment and post-treatment values within the treatment group. However, we can clearly detect statistically significant differences between self-reported

> 11 The corresponding share associated with GPS-measured values is less than 3 percent.

9
means “not applicable.” Group III, predominantly negative program impact, includes the proportion anemic and hemoglobin count. For these two indicators there are a fairly large number of significant coefficient estimates, and all indicate negative program impact. This is a bothersome result although these two are only 13 percent of the total indicators that we consider, and we do not have a good explanation as to why the program had this effect. Unless anemia were of much greater concern than other aspects of child development reflected by the indicators in Group I (which does not seem to be the case), these negative program effects should not be interpreted to dominate in the overall evaluation of the program. It would be desirable to learn why they occur, however, and to consider whether it is worth the costs of rectifying whatever causes these negative outcomes.

20
|---|---|---|
||Change (1 =|Yes)<br>Diference|
|Tone of the news at destination|0.0641<br>(0.0639)|0.2285<br>(0.1485)|
|Country of destination fxed efects|Yes|Yes|
|Year-month fxed efects|Yes|Yes|
|Number of Observations|1,165|1,165|

_Notes:_ OLS regressions results. Standard errors clustered at the FMP level in brackets. In column 1, the dependent variable is a dummy which takes value one if there is a difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1, and zero otherwise. In column 2, the dependent variable is equal to the difference in the number of returnee countries indicated among the preferred destinations in FMP _i_ between month _m_ and _m −_ 1.The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ . *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

34
# **2.1 GDP and Geographic Data**

Historic data on GDP per capita come from INEGI for 2000, 2005 and 2010. The geographic location of the municipality affects productivity, which in turn impacts poverty. Therefore we account for each municipality s latitude, longitude and elevation above sea level in the analysis. Distance from the municipality to the nearest federal and state road are also used as a proxy measure of connectivity.

# **2.2 Climate and Weather Data**

Historic weather data were imputed to municipalities using a Variable Infiltration Capacity Model (VIC) for Mexico, developed by Liang et al. (1994)<sup>6</sup> . The VIC model interpolates data using grids by: i) creating correlation matrices between existing rainfall stations and radar stations that indicate the presence of rain, but cannot capture its amount; ii) computing the corresponding means for both types of stations from the grids within the municipality; and iii) using those values multiplying the values of the core stations by the means ratio to scale them, through the reconstruction of a climatic model.

The climatic variables employed for the study are annual average precipitation (in millimeters) and temperature, both estimated from daily records.

# **2.3 Climate Change Scenarios**

Climate change modifies the mean and variance of rainfall and temperature. Different scenarios proposed by IPCC were tailored for different regions in Mexico to predict climate change. We assembled one global scenario of rainfall and temperature obtained for 22 climate models assembled by the World Bank Climate Change Knowledge Portal (2011)<sup>7</sup> . Global climate change

> 6VIC as a semi-distributed macroscale hydrological model, VIC balances both the water and surface energy within the grid cell; and its sub-grid variations are captured statistically. Distinguishing characteristics of the VIC model include: subgrid variability in land surface vegetation classes; subgrid variability in the soil moisture storage capacity; drainage from the lower soil moisture zone (base flow) as a nonlinear
taxes and trade costs). Surveys are administered to nationally representative samples of formal firms with at least five workers. We use the standardized version of the database which compiles only those surveys following the WBES Global methodology, yielding information that is comparable across countries and years. We restrict our analysis to manufacturing firms identified through ISIC codes 15 to 36.<sup>16</sup> Since in most low-income countries, only a very limited number of firms operate in some manufacturing sectors defined at the 2-digit ISIC level, we aggregate the 22 ISIC manufacturing divisions into five sectors of interest (Table A1 in the Appendix): (i) manufacture of food, beverage and tobacco products (ISIC codes 15-16); (ii) manufacture of textile and leather-related products (ISIC codes 17-19); (iii) manufacture of wood-related products (ISIC codes 20-22); (iv) manufacture of minerals, metals and chemicals (ISIC codes 23-28); and (v) manufacture of advanced products (ISIC codes 29-36).<sup>17</sup> We end up with a sample of 16,475 firms whose selected characteristics are displayed in Table 1.<sup>18</sup> We find that 30 percent of manufacturing firms are direct exporters and among these, 74 percent import the inputs that enter their production process. In contrast, only one-third of firms that exclusively sell in the domestic market are also direct importers, comforting us in our approach. In

> 14For example, in the orange or red channel the inspector assesses the appropriate value and taxes and duties to be paid.

> 15The WBES database was accessed in August 2017.

> 16The survey question allowing to identify input-importing firms applies to manufacturing firms only, naturally dictating our sample choice. The Enterprise Surveys classify firms based on the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 3.1.

> 17Recycling (code 37) is excluded from the analysis due to poor data coverage in the World Bank Enterprise Surveys
earnings (wage rate times employment) does not change above the BIG. This will hold if the wage elasticity of labor demand is -1. This seems to be a reasonable assumption for the average wage elasticity based on past research on labor demand in the U.S.<sup>12</sup> However, there is undoubtedly variation around this mean elasticity, so we can expect (positive and negative) effects on earnings for many “non-BIG” workers not captured by our analysis.

It is of obvious interest to see how the size of this market compares with the current count of undocumented migrants. However, this count is uncertain, with current estimates in the (wide) range 11-22 million. The lower bound is derived from the Census Bureau’s survey-based estimates of the total migrant population (foreign-born people who have not become naturalized citizens) after deducting the count of legal migrants based on administrative data.<sup>13</sup> There are naturally concerns that illegal migrants will be reticent to reveal this fact, and may instead report that they are citizens. (Some illegal migrants may also be outside the sample frame of the household surveys.) The upper bound of 22 million is based on <u><mark>Fazel-Zarandi et al. (2018)</mark></u> <mark>who used a time-series accounting of flows with demographic modelling. There is a continuing debate on the size of the undocumented migrant population in the U.S.</mark><sup>14</sup>

# **4. Results**

When the option of renting out one’s WP is confined to those currently in the U.S. workforce, we obtain the results in <u>Table 1, for various combinations of parameter values. (We</u> have chosen the succession in pairs of scenarios to help assess the partial effect of the parameters and policy choices.) Column 1 is for no costs
> h in private investment because of a change in government investment). Here, notice that the sign of the estimated value of is governed by as highlighted in our simple model in section 2. Hence, a positive (negative) value of kkpp,kk implies that public and private investment are gg αα FF Edgeworth complements (substitutes) and that public investment crowds-in (out) private h ααh investment. The parameters and capture country- and year-effect for different horizons, respectively. The term represents the error term, which capture all other ii ii sources of variation in private investment changes ββ δδ h periods into the future. Finally, notice tt+h that equation (3.1) nests several scenarios. Specifically, when εε h= 0 and , the specification in (3.1) reduces to our baseline model in equation (2.1), and captures the ll= 0 impact multiplier. αα

# 3.1 Data

We use the IMF’s Investment and Capital Stock dataset which provides investment data for the general government and private sector. Investment data is based on gross fixed capital formation. The data is available for 109 economies. In line with equation (3.1), we convert nominal public and private investment in local currency to real using the domestic real GDP deflator. We take the annual changes in public and private investments, and then divide them by lagged real GDP from the IMF’s World Economic Outlook (WEO). One limitation of this dataset is that when public investment data is not available, it is assumed to grow by the same rate as private investment. This affects observations further back in history where data is sparse. The instrumental variable should ameliorate any concerns about the correlation this assumption imposes.

7
- Br ̈uckner, M. and A. Ciccone (2011). Rain and the democratic window of opportunity. _Econometrica 79_ (3), 923–47.

- Burke, M., J. Dykema, D. B. Lobell, E. Miguel, and S. Satyanath (2015). Incorporating climate uncertainty into estimates of climate change impacts. _Review of Economics and Statistics 97_ (2), 461–71.

- Carletto, C., S. Gourlay, S. Murray, and A. Zezza (2017). Cheaper, faster, and more than good enough: Is GPS the new gold standard in land area measurement. _Survey Research Methods 11_ (3), 235–65.

- Central Statistics Agency of Ethiopia (CSA) (2014). Rural Socioeconomic Survey 2011-2012. Public Use Dataset. Ref: ETH ~~2~~ 011 ERSS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2053` on 6 September 2019.

- Central Statistics Agency of Ethiopia (CSA) (2015). Ethiopia Socioeconomic Survey 20132014. Public Use Dataset. Ref: ETH ~~2~~ 013 ~~E~~ SS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2053` on 6 September 2019.

- Central Statistics Agency of Ethiopia (CSA) (2017). Ethiopia Socioeconomic Survey, wave 3 (ESS3) 2015-2016. Public Use Dataset. Ref: ETH ~~2~~ 015 ~~E~~ SS ~~v~~ 02 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/2783` on 6 September 2019.

- Chen, J. J., V. Mueller, Y. Jia, and S
Focusing exclusively on them resulted in 561,624 places being considered for sampling.

The total population of these 561,624 polygons in 2011 was 1,174 million, or 3.0 percent less than reported by the 2011 Census of India for the country as a whole (ORGI 2011a). Almost a third of this population gapabout 11.3 million peopleis accounted for by the small states excluded from the analysis. Some of these small states are richer than the Indian average and some are poorer, which suggests that their exclusion from the analysis may not bias results much. The rest of the gap is associated with the villages excluded because their boundaries or population figures were unavailable in the official  sources. These villages are most likely rural in practice, and their population is small about 200 people at the medianwhich implies that they too are unlikely to be an important source of bias.

# _A two-stage stratification sampling_

We used a two-stage disproportionate stratification strategy to select the sample of administrative units for human judgment (figure 1). In the first stage, we divided the universe into seven strata by administrative categories, following the definitions of the 2011 Census of India. Four of these categories state capitals and municipal corporations, municipalities, industrial towns, and other townsare   statutorily designated as urban. Two of the categories census towns and outgrowths are statutorily rural but recognized by the 2011 Census as urban. The seventh category is villages, or areas that are rural according to both the Constitution and the 2011 Census of India (ORGI 2011a).

6
.ug/media/pressreleases/2020/03/30/more-guidelines-covid19-preventive-measures-need-shut-downpresident.</u> 23. Speech by President Mutharika on 21-day Covid-19 lockdown. (Lilongwe, 2020); <u>https://malawi.un.org/en/46797-speech-president-mutharika-21-day-covid-19-lockdown.</u> 24. Republic of Malawi in the High Court of Malawi, Lilongwe District Registry, Judicial Review Cause No. 22 of 2020, Before Honorable Justice Kenyatta Nyirenda, “Order granting permission to apply for judicial review and interlocutory order of injunction,” (Lilongwe, 2020); https://perma.cc/MML4-8FNU.

25. ILO, ILO Monitor: COVID-19 and the world of work. 6th edition. (ILO, Genève, 2020).

26. F. Ahmed, N. Ahmed, C. Pissarides, and J. Stiglitz, Why inequality could spread COVID-19. _Lancet Public Health_ **5** , e240 (2020).

27. D. Laborde, W. Martin, R. Vos, Poverty and food insecurity could grow dramatically as COVID-19 spreads (IFPRI, Washington, D.C., 2020); <u>https://www.ifpri.org/blog/poverty-andfood-insecurity-could-grow-dramatically-covid-19-spreads</u> 28. _The Millennium Development Goals Report 2015_ (United Nations, New York, 2015).

**Acknowledgments:** We gratefully acknowledge the research assistance provided by Ann Furbush and Joshua Brubaker. We also owe a great debt to the individuals involved in the design, implementation and dissemination of high-frequency phone surveys on COVID-19, specifically the World Bank Living Standards Measurement Study (LSMS) team, and the phone survey

10
we study the effect of the AIA on the different types of cloud included in the E-Commerce survey. These are cloud databases (column 6), storage (column 7), customer relations management (CRM) software (column 8), office software (column 9) and email (column 10).<sup>34</sup> We find that the AIA incentivized firms to invest only in the types of capital that were upon request.

> 34We report results for finance software and own software in Table A6, along with Eurostat (2018) definitions of low, medium and high-tech types of cloud technologies.

22
Although the Indian sugar market has been insulated from the world market by import and export controls, domestic prices have consistently been at or below the prevailing levels of domestic prices in the efficient exporting countries, and much lower than domestic prices in protected markets such as the US and the EU. Some inter-country comparisons of domestic prices for the years 1980, 1986 and 1992 are given in Data S2.

Figure 9: **Levy prices and free market prices for sugar, 1965 -94 (nominal Rs/quintal)** <!-- Start of picture text --> .1~~~~~~~~~~~~~~~~7 E : 1<br><!-- End of picture text -->

**Source :Annex 5.**

**Note : In 1972 and 1978 there was no levy price. All sugar was sold on the free market.**

# _Trade policies_

Before 1994 sugar imports were controlled by import licensing. Scheduled import duties were extremely high, but if imports were allowed, either by STC or by private importers to whom licenses were issued, special tariff exemptions reduced the import duties, in some cases to zero. In any case, as with nearly all other Indian products before the 1991 reforms, the binding controls were the quantitative controls, not the tariffs. No change was made to this basic policy in 1991, but in March 1994 sugar imports were delicensed and tariffs were cut to zero. If maintained, this new policy will have a major impact on the industry and potentially on the world sugar market.

Before 1991 exports of sugar were canalized by STC. Mainly in order to maintain a presence in quota markets, sugar was frequently exported at a loss .To cover these losses, the Sugar Export Promotion Act of 1958 gave STC the power to administer a scheme under which the losses were shared by the sugar mills in proportion to their production. Exports were decanalized i.e. STC'
effect would dominate child migration? Estimation results using the 2009 wave of the RUMiC survey show that the non-IV estimates (Table 7, row 1) are negative, and are between 60 percent and twice larger in absolute magnitude than those for 2008 (bottom of Table 2). Since the upward biased non-IV estimates

> 42 We recode the answers to the questions about depression such that higher scores imply a more intense state of depression. We define a person as depressed if the summation of his/her scores is greater than 22.

31
risk, but on the population-level effect of mass behavior change on the risk of an average member from the population. A cluster design allows us to estimate such population-level effects.

# **4.2 Stratified Recruitment**

We used Facebook advertising to recruit survey respondents from the previously mentioned circles in both treatment and control districts, incentivizing participation with mobile credit ranging from 2.6 to 3.6 USD. Respondents were asked to complete a series of surveys conducted via chatbot on Facebook Messenger and delivered by a page that we created called “Global Insights”.<sup>18</sup> The main benefit of employing chatbot surveys on Messenger lies in their seamless integration into the ad platform itself. Moreover, the chatbot streamlines follow-up with respondents, as inviting them to additional surveys can be as simple as sending a new chat message.

Before conducting the study, we carried out a formative research phase to guide our sampling design. Specifically, in July 2020, we started recruiting respondents asking them several demographic and socio-economic questions, as well as whether they or anyone in their household had had malaria in the past 5 years and in the previous two weeks. These surveys indicated that one of the original study states, Odisha, exhibited low malaria rates among our target population. Consequently, we opted to exclude it from our trial due to statistical power constraints.

Importantly, the analysis of our formative research data in the other three study states revealed that dwelling type was the most robust household-level predictor of past and current malaria incidence.<sup>19</sup> Its predictive capacity, both in terms of Pearson pairwise correlation and OLS regression coefficients, surpassed that of other factors such as proximity to health facilities, membership to a backward caste, family size, religion, and the educational background of the respondent (Annex Tables D1 and D2). Specifically, past and current malaria incidence rates in households living in solid dwellings (i.e., those with walls and roof made of bricks,