rafmacalaba/datause-encoder-data huggingface dataset:
Croke et al. (2016) classification system in the 2015 update; we retain the 2012 classification in this analysis.<sup>21</sup> See Appendix A for detailed information on each individual study in the full sample.

# **4 Hypothesis tests and estimation strategy**

In light of TMSDG's conclusion that there is “substantial evidence” of no impact of deworming MDA, we first report a test of the hypothesis that the true impact of multipledose deworming on weight is zero in all settings. This involves testing the null hypothesis that β=0 in a standard fixed effects meta-analysis estimate:<sup>�</sup> ıı EE where is the estimated effect in study i; YY = ββ+ σσ is the true deworming treatment effect, and is a random variable, representing measurement error, assumed to be distributed YY<sup>�</sup> ββ EE σσnormally, with mean zero and a standard deviation equal to the standard error of the estimated treatment effect in each study. Rejection of this null hypothesis implies that deworming affects child weight in at least some circumstances.

We then report the estimated average impact using a random effects model:

> 21 TMSDG state that “We changed the classification of Stephenson et al. (1989) and Stephenson et al. (1993). Previously these trials were in the ‘all children in an endemic area’ category, whereas now they are classified in the ‘children with infection.’ This decision was based on reviewing the trials with parasitologists and examining the prevalence and intensity of the infection where clearly the whole community was heavily infected” (Taylor-Robinson et al., 2015, p. 154). It is worth noting that although TMSDG exclude Stephenson et al. (1993), they include Watkins, Cruz and Pollitt (1996); the highest recorded worm baseline prevalence in Watkins, Cruz and Pollitt (1996) by STH species is 92% (for ascaris); the highest prevalence in Stephenson et al. (1993) is also 92% (for whipworm). Thus this reclassification does not appear to have been done systematically by worm prevalence. In our view, assessing the merits of the WHO policy by including studies in environments with prevalence below WHO thresholds while excluding MDA studies in areas with high prevalence may lead to risk of bias. Since our goal is to examine the effect of MDA, we retain the 2012 classification. Stephenson et al. (1989) is a single dose deworming trial so does not enter into the present meta-analysis but Stephenson et al. (1993) has one multi-dose treatment arm, which we incorporate. See Appendix A.3 for more detail.

20
Policy Research Working Paper 9336

# **Abstract**

As of 2019, more than 1.2 million Venezuelans passed through Ecuador and more than 400,000 settled (almost 3 percent of Ecuador’s population). This paper analyzes the location choices of Venezuelan migrants in Ecuador and the labor market consequences of these choices, using data from Ecuador’s labor force survey and mobile phone records on the geographic distribution of Venezuelan migrants. Around half of the migrants live in four cantons (of 221). Their location is primarily driven by local economic conditions, rather than point of entry. Overall, the regions with the largest inflows of Venezuelans have not seen any effects on labor market participation or employment, compared with regions with fewer inflows. However, our difference-in-difference estimates clearly indicate that young, low-educated Ecuadoran workers in high-inflow regions have been adversely affected. Specifically, the estimates that these workers have experienced reductions in employment quality, a 5 percentage-point increase in the rate of informality, and a 13 percentage-point reduction in earnings, relative to workers with similar characteristics living in areas with very low or non-existent inflows of Venezuelans.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at solivieri@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
immobility because of the negative consequences of such shocks on their liquidity. This should come as no surprise: since the determinants of migration, and especially climate-related migration, are complex (IPCC, 2014), it is expected that the sign of the relationship cannot be known _a priori_ , as the direction will depend on local circumstances and is ultimately an empirical question.

# _B. Direct versus indirect effects_

Climate change could exert both direct and indirect effects on migration. In the latter case, one must understand how climatic events affect other drivers of migration, via demographic, socio-economic and political channels. Among the main channels of the indirect effects of weather- and climate-related hazards on migration, the two most important ones are the economic and socio-political drivers. To date, however, there is only limited and partial evidence about these links. Feng, Krueger, and Oppenheimer (2010) provide empirical support about the links between extreme temperatures, crop yields, farmers’ income, and migration from Mexico to the United States. In an important study, Marchiori, Maystadt, and Schumacher (2012) leverage annual panel data from 1960-2010 and find that in Sub-Saharan Africa weather shocks boost rural-urban migration through a decrease in rural wages. In turn, this weatherdriven internal migration into cities brings about downward pressure on urban wages, causing urban workers to move internationally, a mechanism they label the ‘economic geography channel’. Similarly, Dallmann and Millock (2017) find that drought effects on inter-state migration in India are stronger in agricultural states. On top of these microeconomic, household-specific income channels, there is also a macro-level income driver: developing countries are particularly vulnerable to climate-related hazards, because they have a large share of their income in agriculture (the most weather-dependent sector), tend to be hotter and closer to biophysical limits, and lack adaptive capacity to cope with the negative impacts of climate change. For example, using bilateral migration data from 1980 to 2010, Cai et al. (2016) demonstrate that temperature shocks induce international migration only from agriculture-dependent countries and increase migration to OECD countries. Investigating the role of climatic factors in engendering international migration, Beine and Parsons (2015) find evidence of an indirect channel operating through wages. Niva et al. (2021) find that income is a key determinant in explaining both netnegative and net-positive migration, and conclude that it is the difference between income-levels of the origin and destination areas that matters, rather than income level _per s_ e. Other than affluence, the main socio-political factor investigated in the specialized literature is the well-known (and often prominent in the media) conflict channel. For example, Kelley et al. (2015) suggest that a prolonged and unprecedented drought in parts of Syria exacerbated the (pre-existing) vulnerability of the affected population, 7
as short-term, short to medium-term and medium to long-term indicators of children’s nutrition and health status.

We find no effects on anthropometric measurements at round 3, while significant improvements are measured at round 2. A possible interpretation of that contrast is that 2010 (round 3) was a better year than 2009 in terms of rainfall and harvest. For the main crops relevant for the Nahouri region and analyzed later in table 6 (millet, sorghum and peanuts), yields in 2009 are lower than yields in 2010. Moreover, yields in 2009 are between 0.23 and 0.79 standard deviations below average yields of the 10 previous years (1998-2008). In contrast, yields in 2010 are higher than the long run average yields, except peanuts with yield 0.05 standard deviations below the long run average.<sup>19</sup> If cash transfers act as a safety net, they should have a larger impact when times are difficult. At round 2, giving the cash transfers to fathers rather than mothers seems to be more effective in improving the nutritional status of children: CTF outperforms CTM for weight-forage and height-for-age and have an impact statistically greater than zero for arm circumferencefor-age and height-for-age. These findings might seem surprising at first, given the general perception that mothers are more directly involved in taking care of children’s health. In the context of Burkina Faso and West Africa more generally, it is important to note that culturally, fathers are seen as responsible for providing food on the family’s table (see Kazianga and Wahhaj 2013 and Duflo and Udry 2004). This social norm was confirmed in qualitative interviews and focus groups we conducted in the program villages after the intervention<sup>20</sup> . This strong cultural norm might lead them to devote additional resources such as cash transfers, in priority to more and better food, potentially explaining these findings.

Also at round 2, conditional cash transfers appear to outperform UCTs in improving anthropometric outcomes. They have a statistically stronger impact than UCTs on weight-for-age z-scores and they are improving arm circumference-for-age and height-for-age z-scores, while UCTs have no significant impacts on those measures.

> 19 The average yields in kg/ha over the 1998-2008 were 838 for peanuts, 795 for millet and 955 for sorghum. In 2009, they were 722, 774 and 920 respectively, while in 2010 they were 830, 843 and 1004, respectively. Food and Agriculture Organization of the United Nations. 2014, FAOSTAT database, available at <u>http://faostat3.fao.org/home/E (accessed on 10/30/2014).</u>

> 20 For the focus groups, we selected one village from each of the four treatment groups. In each village, we conducted a focus group discussion with 10 men from different recipient households and a separate focus group discussion with 10 women from different recipient households.

17
# CONCLUSION

In this paper, we investigated public attitudes toward foreign aid across 19 donor countries. We argued that both individual- and country-level factors contribute to support for foreign aid. Using data from two sources, the World Values Survey, and the Gallup International Voice of the People Survey, we tested theories using multilevel models. Using two surveys allowed us to combine their distinct strengths and provide tests of our individual- and national-level theories across disparate samples.

Like the sole previous study on this topic (Chong and Gradstein 2006), our paper demonstrates that individual-level factors matter to support for foreign aid. But this paper goes beyond a focus on demographic variables to test hypotheses related to a respondent’s religiosity, attitudes about poverty, attention to international affairs, and psychology. Models provide support for all four proposed theories. For example, in the WVS, individuals who attend religious services, watch TV, believe the poor can escape poverty, and trust others are more likely to support foreign aid. Of note in the GI survey, individual trust in the United Nations and the World Bank appears more important to support for foreign aid than trust in one’s own government.

Apart from these individual-level effects, the paper highlights the contextual embeddedness of attitudes about foreign aid. The country-level results suggest that wealth and existing development support matter for individual support of aid. For example, citizens in countries with high levels of existing aid express less support for increasing aid. Citizens from countries with a history of colonization also express more support for foreign aid on average.

In many ways the United States appears exceptional in these analyses. It is also the most important donor to understand, as the largest economy but with a relatively low level of aid as a share of national income. The United States spent about 0.16 percent of gross national income (GNI) in 2007, compared to the average of 0.45% among the 22 DAC members.<sup>11</sup> . If U.S. aid as a share of GNI had been equal to that of France - the median value among the other 21 DAC members – total DAC aid in 2007 would have been increased by nearly one-third, from $103.7 billion to about $136 billion. Increasing U.S. aid is challenging however in the face of low public support. Only 55.5% of

> 11 See DAC data on ODA for 2007 at http://www.oecd.org/dataoecd/27/55/40381862.pdf.

18
|Table 5<br>Estimated parametric adjustm<br>Baseline specification|ent functions<br>|||
|---|---|---|---|
||White collar<br>adjustment|Blue collar<br>adjustment|Capital<br>adjustment|
|Constant|0.074|0.166|0.060|
||[0.022]***|[0.023]***|[0.021]***|
|Pos Shortage|0.137|0.1406|0.026|
||[0.039]***|[0.037]***|[0.036]|
|(ShortageW)^2|0.189|0.029|0.006|
||[0.011]***|[0.012]**|[0.013]|
|(ShortageW)^2*Pos|-0.103<br>[0.017]***|||
|(ShortageB)^2|-0.009|0.099|-0.025|
||[0.015]|[0.012]***|[0.013]*|
|(ShortageB)^2*Pos||-0.071<br>[0.019]***||
|(ShortageK)^2|-0.012|0.021|0.179|
||[0.011]|[0.010]**|[0.007]***|
|(ShortageK)^2*Pos|||-0.137<br>[0.013]***|
|(ShortageW)*(ShortageB)|-0.042|0.0011|0.011|
||[0.014]***|[0.013]|[0.016]|
|(ShortageW)*(ShortageK)|-0.025|0.001|-0.001|
||[0.012]**|[0.014]|[0.012]|
|(ShortageB)*(ShortageK)|0.026|-0.058|-0.059|
||[0.016]|[0.0121]***|[0.011]***|
|Observations|4945|4945|4945|
|Number of id|627|627|627|
|R-squared|0.3|0.29|0.37|
|Note: Pos Shortage: Dummy=<br>ShortageW. ShortageB. Short<br>collar and capital. Standard e<br>*significant at10%;**signific|1 if Shortage is p<br>ageK are the sho<br>rrors in brackets<br>ant at 5%;***sig|ositive<br>rtages for whit<br>nificant at1%|e collar. blue|

22
products as whole account for an estimated 6.4 percent of total household expenditures; with the bottom quintile households spending 5 percent of their total expenditures on these products and the richest quintile spending 6.4 percent.

|**Table 3. Expenditure on subsidized petroleum products relative to t**<br>|**otal ex**<br>|**penditure**<br>|**s (%)**<br>||
|---|---|---|---|---|
|kerosene|LPG|gasoline|diesel|Total|
|Quintile 1<br>0.7|2.4|1.9|0.0|5.0|
|Quintile 2<br>0.6|2.0|3.4|0.0|6.0|
|Quintile 3<br>0.6|1.8|4.3|0.0|6.6|
|Quintile 4<br>0.4|1.6|4.8|0.1|6.9|
|Quintile 5<br>0.2|1.1|4.4|0.6|6.4|
|Total<br>0.4|1.5|4.1|0.3|6.4|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes:</u> Population quintiles <u>based on spatially adjusted consumption per capita before the reform.</u>

|**Figure 2. Expenditure on subsidized petroleum products relative to total expenditures (%)**|
|---|

<!-- Start of picture text --> .06<br>kerosene<br>LPG<br>gasoline<br>diesel<br>.04<br>.02<br>0<br>0 .2 .4 .6 .8 1<br>Percentiles (p)<br>The expenditure shares<br><!-- End of picture text --> Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes:</u> Population percentiles <u>based on spatially adjusted consumption per capita before the reform.</u> In terms of actual amounts spent on subsidized products, richer households far outspend poorer ones. For example, the poorest quintile was spending 7 times less on subsidized petroleum products than the richest quintile (6 percent of total national expenditures versus 42 percent as shown in figure 3). This quite directly indicates that wealthier households received higher per capita subsidies than poorer households. Table A1 shows that for all products, per capita subsidies are lower for poor households and this is especially pronounced for gasoline and diesel.

6
Furthermore, we do not include candidate variables that are applicable to only one segment of the population, such as farming assets and crop production, that only apply to household farmers. Although we do include categorical variables on the sources of livelihood (e.g., farming, aid, wages, etc.). Finally, we avoid variables that capture a temporary state of the household, such as a recent sickness or other shock to the household. However, a PMT could be designed for specific use cases or combined with additional information to target such households, e.g., households impacted by recent hazardous weather and that have a low PMT score.

A limitation of this analysis is that the 2014/15 household survey is outdated, particularly with the multiple shocks Sudan has experienced in recent years. With support from the World Bank, the Sudan Central Bureau of Statistics has started preparing a new household budget and poverty survey to update poverty and other socio-economic statistics. The survey was due to be fielded in 2022, but has been paused following the military takeover in October 2021. Survey preparations should resume once things return to normalcy. The PMT methodology presented in this paper can readily be applied to new household survey data when it becomes available.

# _Methodology_

In an effort to avoid overfitting on specific in-sample characteristics of the data, we split the household survey data into a training and a testing set using a stratified random sample based on the outcome variable of log per capita household consumption expenditure (Kuhn and Johnson 2019, Chapter 3).<sup>13</sup> All modeling decisions are based on the training data, before arriving at a small number of potential models that are applied to the testing data to explore model performance on unseen, out-of-sample information. Statistical analyses are performed using the statistical programming language R and the Tidymodels package (R Core Team 2022; Kuhn et al. 2020).

To select a subset of variables from all candidate variables, we use the Least Absolute Shrinkage and Selection Operator (LASSO) estimator (Tibshirani 1996). This is done to develop a parsimonious PMT formula that can be more easily implemented in the field. The LASSO estimator selects a subset of candidate variables by minimizing the sum of squared errors conditional on a tuning parameter that penalizes the number of parameters in the model. The tuning parameter is chosen to minimize the mean squared prediction error using split sample cross validation (CV) to reduce overfitting.<sup>14</sup> This is a primary advantage

> 13 We use a 75% and 25% split of household survey data for the training and testing set, respectively.

> 14 Julia Silge provides an excellent example of developing a LASSO model and tuning the penalty parameter using CV on her blog at <u>https://juliasilge.com/blog/lasso-the-office/</u>

9
# **Asset concentration: What happened to the wealth of nations?**

The process of resource extraction has a first-order effect of reducing a countries overall wealth, by depleting the stock of natural capital given subsoil assets’ finite non-renewable nature. However, countries may use the proceeds from this depletion to accumulate other forms of capital – such as building physical infrastructure, or investing in human capital, via improvements in education and healthcare of citizens. Meanwhile, the stock of natural capital can also increase due to rising prices applied to remaining resource base, or new discoveries – some occurring as part of the discovery-by-extracting process.

Although the boom offered a unique opportunity for resource-rich African countries to increase of their wealth, its total wealth per capita still grew at a similar average annual rate than its non-resource-rich counterparts and around two thirds what other resourcerich elsewhere in the world achieved. The lack of investment in other assets different from the resource sector during the boom, left resource-rich African countries even less prepared for the end of the commodity boom, when their total wealth, mainly deposited in nonrenewable natural resources, resulted in negative growth numbers (see Figure 21).

Figure 21. Average annual total wealth per capita growth by period <!-- Start of picture text --> 4 3.7<br>3<br>2.3 2.2<br>1.8<br>2<br>1.1<br>1 0.7<br>0<br>-0.1<br>-0.3 -0.3<br>-1<br>Pre-boom Boom Post-boom<br>RR nonRR RR RoW<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012). Source: Changing Wealth of Nations 2021.

This poor growth of per capita wealth in resource-rich Africa has widened the divide the amount of wealth between them, the rest of Africa and other resource-rich countries in the world. Other lower income resource-rich countries listed in IMF (2012) – such as – Mongolia managed to increase their total per capita wealth to almost double their value before the boom (Figure 22).

32
output, while the unorganized sector accounts for over 80% and 99% of Indian manufacturing employment and establishments, respectively (Ghani, Kerr, and O’Connell 2013a).

The organized sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Our data for the unorganized sector come from the National Sample Survey (NSS) Organization’s periodic “Survey of Unorganized Manufactures”. These surveys are used for many published reports on the state of Indian businesses and government agency monitoring of the Indian economy. With respect to establishment counts surveyed, the Indian sampling frame is about three times larger than that used with the Annual Survey of Manufacturing conducted in the United States.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The surveys provide sample weights to construct population-level estimates. We utilize the micro-level data directly when considering productivity estimations, and these estimations are weighted by these survey weights. We also undertake analyses at the district or district-industry level. For these estimations, we use the sample weights to first construct population-level estimates that can then be analyzed. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions. The average district size is around 5,500 square kilometers—roughly twice the size of a U.S. county—and there is substantial variability in district size (standard deviation of ~5,500 square kilometers). Indian districts can be effectively considered as self-contained labor markets and, to some degree, economic units.

Our core sample contains 415 districts that are located in 31 Indian states.<sup>3</sup> We set two criteria with respect to district size for inclusion: that the district has a population of at least one million in the 2001 census and has 10 or more establishments sampled. Finally, we drop from the sample any observations reporting zero or missing data in major input or output categories (total output/sales, total persons engaged, or total spending on intermediate inputs), or observations reporting an industrial classification outside of manufacturing. The exclusions are minor in terms of economic activity, and the resulting data account for over 95% of employment in the manufacturing sector.

3 The states of Arunachal Pradesh, Meghalaya and Sikkim and the Union territory of Lakshadweep are not present in our sample due to the ASI sampling frame not extending to these areas.

5