rafmacalaba/data-use-mentions-extended huggingface dataset:
The data we have on the Reduced Consumption Coping Strategies Index (rCSI) perhaps provide some clues on this puzzle. The index is significantly improved at the 6-month follow-up in the treatment group by 0.15-0.25 SD. However, consistent with the impacts on consumption and the FCS index, the effect is small and statistically insignificant at the 12-month follow-up, while turning negative and statistically significant at the 18month follow-up. Looking at the components of this index (Appendix Table 11), we can see that while the treatment group was less likely to reduce number of meals per day, portion sizes, and consumption among adults so that children can eat at the 6-month follow-up, they were equally or even more likely to use these coping strategies by the 18-month period. The reversal of the program impacts for the treatment group over time is likely explained by three, likely correlated, changes in the control group: (a) the strong trend showing a reduction in these coping strategies, (b) a smaller average household size, and (c) increased eligibility for ESSN. We also have information on the Livelihoods Coping Strategies Index (LSCI). Consistent with the evidence presented so far, refugee households in the treatment group are initially less likely to resort to livelihoods-based coping strategies, but this effect disappears by the 12-month follow-up. At 0.16-0.25 SD, the standardized treatment effect on this index at the 6-month follow-up is very similar to that for the rCSI. Examining the components of the index (Appendix Table 12), we see that, at the 6-month follow-up, the treatment group is less likely to sell HH assets, dip into savings, borrow money from non-relatives, buy food on credit or reduce expenditures on food, or to have moved or returned to country of origin during the past 30 days.<sup>30</sup> However, by the 18-month follow-up, not only have all of these effects disappeared, but treatment households report being more likely to reduce food expenditures and consume
We find the initial impacts of training appear to be higher for those owners with more personal initiative, but even this subgroup does not have lasting impacts. Second, we use the generic machine learning approach of Chernozhukov et al. (2020) to test whether there is predictable heterogeneity in treatment effect based on a set of baseline covariates, and cannot reject that there is no predictable heterogeneity. The drop-off in treatment effect therefore seems widespread.

Figure 2 graphically shows this reversal by showing the distribution of changes in business practices, sales, and profits by treatment status between the baseline and 2-month survey, and then between the 2-month survey and 6-month survey. Three results are apparent. First, not only is there a lot of volatility in sales and profits, but we also show that there is considerable churn in business practices. This is not something that has been documented in previous literature. Even in the control group, many firms are starting and stopping practices between survey rounds. Second, between the baseline and two months, we see relatively more treated firms adding business practices and fewer ones dropping them, and relatively more treated firms growing profits and sales than the control group. Third, in contrast, between 2 and 6 months we see relatively more of the control group adopting new practices, whereas more of the treatment group are dropping business practices, and more of the treated experience a drop in sales and profits. This figure also helps show that the difference in 2- and 6-month results is not being driven by a few observations, but is instead visually apparent in the distributions.

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where school districts control the day-to-day running of public schools. The other education example relates to national education systems and makes use of data from the OECD‟s Program for International Student Assessment (PISA) study; while undertaken by the OECD, the geographic coverage of the study now extends well beyond the OECD countries. Our first health example also uses national data and tries to get at the efficiency of health systems. Looking at patients treated misses the quality of care, and in any case one could argue that if a health system is successful at preventing illness and injuries it ought to be reducing the number of patients requiring treatment. Measures of population health tend to be too broad-brush to be compelling measures of the outcome of a health system: many are affected by factors beyond the health system, and many causes of death are not amenable to medical care at all or only
Moss (2011) observes that, in Ghana, Public Expenditure Tracking Surveys (PETS) found that between 50% and 80% of public spending was leaked through the system; by comparison, one million fraudulent recipients of direct transfers would represent the equivalent of only a 4% leakage. In addition, these

> 1 Reinikka and Svensson (2004) famously found that 87% of capitation grants for primary schools in Uganda did not reach the intended end-users.

6
Finally, to complement the evidence on the self‐reported incidence of engagement in anti‐social behavior and/or criminal activity, we examine the reported incidence of being a crime victim. The respondent was asked if he/she was a victim of stealing, assault, alcohol‐related crimes, or trespassing during the last 6 months. The only statistically significant result is for trespassing, in which the program is estimated to increase the probability of being a victim of trespassing, by about 7 percentage points. While this result is somewhat difficult to explain, it could result from participants moving to areas where trespassing is more likely to occur. With regard to the other three crimes, the estimated difference in difference effects are all negative but not statistically insignificant. However, for alcohol and assault, the 95 percent confidence intervals range from approximately a 16 percentage point reduction to no reduction, meaning that we cannot rule out substantial effects.

Overall, in contrast to the estimated effects on anti‐social behavior, there is little robust evidence that the program reduced participants’ in or exposure to crime. Besides the estimated effect on vandalism reported above, in no case was the estimated effect on crime negative and statistically significant at the 5 percent level. Furthermore, in many cases, the point estimates tend to be close to zero. However, the estimates are sufficiently imprecise that we cannot rule out the possibility that the program had substantial effects on assault or stealing.

# 5.2.4 Effects on Employment and Savings

To better understand these results, it is useful to examine the potential intermediary role of labor market outcomes. In the Follow‐up Survey, the treatment group was asked if they had a wage employment after the program participation. We find that 43.6% of program participants report having a job after the program participation, with 49% and 39.2% for OJT (internship) participants and YJC (public works) participants, respectively. Unfortunately, the same question cannot be asked for the
for women. The survival probabilities can thus be estimated as <sup></sup> = 1 − 0014 = 0986 for men and <sup></sup> = 1 − 0013 = 0987 for women. This yields, given that κ = 0501, The family savings rate for Brazil,  , is set equal to the private saving rate, which reached an average of 17 percent during the period 2003-10. Solving (30) backward for the preference parameter  yields, using the intergenerational discount factor 097<sup>25</sup> , which, using (44), yields According to 2010 IBGE data, average expenditure of a Brazilian family, in proportion of average income, is R$2626.3/R$2763.4 = 0942 percent, whereas the number of children per family, in proportion of the number of people per family is equal to 1931 = 0613. Assuming that family income is spent evenly between adults and children would suggest that the share of spending on children, in proportion of total income, is 0942 · 0613 = 058 percent. However, to account for scale economies in raising children, we reduce this share to 045 percent. In terms of the model, this can be taken as an approximation of the share of total family income devoted to child rearing, which corresponds to <sup></sup> . The gross fertility rate (number of births per woman) for Brazil, , is equal to 21 over the period 2000-09; given that value, the share of family spending on each child can therefore be estimated at <sup></sup> = 04521 = 0214. We assume that male spouses prefer to spend a smaller fraction, <
Health services are provided through the National Health Fund (NFZ), which is financed from health contributions. NFZ contracts health service providers and reimburses the cost of services provided to insured clients. The Ministry of Health directly funds a few highly specialized services along with pre-hospital emergency services. Contributions to the public system are mandatory for all classes of workers except those on pay-for-performance civil contracts, so most workers are covered, either directly or through family since anyone with uninsured family members can insure them. Persons receiving contributory benefits like pensions or unemployment benefits are also covered. In addition, an uninsured person without insured family members may have the right to health services financed from public money if they are: (a) below the age of 18, (b) pregnant or in the period of confinement, or (c) have income below the legal poverty threshold. In practice, only non-poor individuals who do not pay voluntary health insurance contributions are uninsured and would have to bear the cost of health services. Private insurance is available and is usually provided as a top-up to public health insurance, with people opting-out from publically provided services for primary or dental care, as well as some in-patient curative services.

# **III. Data sources, method, and assumptions**

## **Data Sources**

Data for 2014 were used to conduct this incidence analysis study in line with the availability of survey data. Specifically, we used the 2014 Poland Household Budget Survey (PHBS) by the Central Statistical Office of Poland (GUS). In contrast to the EU SILC, the PHBS contains both income and expenditure data, along with demographic and household characteristics, thus enabling the identification of direct and indirect taxes and benefits across the distribution. Following standard practice, the collected survey is corrected for nonresponse through sample grossing-up weights. However, these weights take into account and correct only for the original data sample design probabilities and do not reflect the additional bias in survey participation given the characteristics of participating households. For example,
|At least one elderly|0.7|
|Couple|No child|No elderly|20.1|
|Couple|No child|At least one elderly|6.7|
|Couple|At least one child|No elderly|43.4|
|Couple|At least one child|At least one elderly|1.5|

Source: authors’ estimations based on PNAD-C 2019. Family composition is determined by whether there is at least one household member that declared to be the spouse of the household head, while the presence of children and elderly is determined by the presence of individuals aged 0-15 (who are neither the household head nor the spouse) and 60 or more, respectively.

Figure 13 shows the poverty rates considering both the pre- and post-fiscal income aggregates for the twelve household categories of interest. It also presents the change in poverty rates. The first six bars represent households with at least one child, while the last six represent those with no child. Among the former, the presence of elderly is concentrated in the second, third, fourth bar, while among the latter it is concentrated in the seventh, eighth, and nineth bar. There seems to exist four distinct groups in terms of the change in poverty: (i) households with elderly but no children (orange bars), (ii) households with both elderly and children (green bars), (iii) households with children but no elderly (grey bars), and (iv) households with no children and no elderly (blue bars). The presence of elderly – regardless of headship structure – makes the household better off after fiscal policies and transfers, while the presence of children seems to have the opposite effect. Figure 14 shows that results that create population groups based on a _proxy_ for economic agency rather than headship structure, considering the highest-earning individual as the household main breadwinner, are qualitatively similar.

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Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|
|Poverty headcount (observed)|19.96%|39.95%|59.89%|19.96%|39.95%|59.89%|
|Householdpovertyrate(observed)|19.36%|37.59%|54.92%|19.36%|37.59%|54.92%|
|Household poverty rate (predicted)|12.71%|38.76%|58.73%|12.81%|38.76%|59.60%|
|Total accuracy|85.32%|79.63%|80.40%|83.01%|77.89%|79.26%|
|Poverty accuracy|44.91%|74.47%|85.63%|39.21%|72.15%|85.38%|
|Undercoverage|55.09%|25.53%|14.37%|60.79%|27.85%|14.62%|
|Leakage|4.98%|17.26%|25.96%|6.47%|18.65%|28.19%|

_Notes_ : This model includes all variables in Table 3, as well as location indicators for states and rural and urban areas where noted. The results are based on out-of-sample prediction on a random sample of 25% of households.

Table 10 presents the results of the Sudan national PMT with the inclusion of employment information in the form of the proportion of household members that have ever worked for income to total household members. Although this information is strongly and statistically significantly associated with per capita household consumption. Including this
## **Figure 5: Sector of employment prior to arrival in Chad of newly registered refugees**

<!-- Start of picture text --> Other 2<br>Self-employed 8<br>Informally employed 1<br>Formally employed 1<br>Agriculture/Livestock/Fishing 87<br>0 25 50 75 100<br>Percent<br><!-- End of picture text -->

## Source: UNHCR December 2023.

In view of the likeness of recently arrived refugees and those who have stayed in Chad longer, the remainder of this paper extends the description of Sudanese refugees staying in Chad as captured by the ECOSIT IV survey, with a particular focus on differences between better and worse off refugee households.

# 4. Poverty, refugee income and assistance

When analyzing the potential fiscal burden refugees might present, poverty measurement takes on particular relevance, for at least three reasons.

First, the poverty line, the yardstick against which consumption is compared to determine whether a person is poor, presents an objective standard for the minimum level of consumption needed to assure a dignified life. In a global context, the International Poverty Line is used to cost Global Basic Needs (World Bank and UNHCR 2024). As this note focuses on Chad, the national poverty line of FCFA 684 per refugee per day is used as benchmark.<sup>7</sup> Second, a straightforward way to estimate income earned by poor refugees is by deducting the (preassistance) poverty gap<sup>8</sup> from the poverty line. This difference reflects the average amount poor households consume net of humanitarian assistance and is a good proxy for the income earned by poor

> 7 Typically, poverty lines consist of two parts. The first part covers a food allowance needed for adequate nutrition based on a minimum caloric intake (2,400 calories in Chad) using a locally relevant basket of goods. The second part covers a non-food allowance reflecting the
# **4 Appendix G: Social Integration Results**

Table (G1) Impacts of the Program on Primary Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Social attitudes & perceptions (SD)|-0.33**|(0.14)|[0.08]|-0.00|(1.00)|1,102|
|Economic attitudes & perceptions (SD)|0.02|(0.16)|[0.56]|0.02|(1.02)|1,102|
|Policy preferences (SD)|0.17|(0.15)|[0.37]|-0.02|(1.00)|1,102|
|Altruism to Syrians|0.21|(0.18)|[0.37]|0.88|(1.18)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Social attitudes and perceptions is an index of the following outcomes: of the 3 people the respondent socializes most with, how many are Syrian; of the 3 people the respondent most often shares advice with, how many are Syrian; do children in the household have Syrian friends (binary); do children in the household share recreational space with Syrian children (binary); what is the
||(0.032)|(0.010)|(0.032)|
|---|---|---|---|
|Aged 11-14|0.829***|0.856***|-0.028|
||(0.031)|(0.011)|(0.030)|
|Aged 15-17|0.569***|0.743***|-0.174***|
||(0.039)|(0.016)|(0.037)|

Note: Y1 is the observed schooling rate of children whose parents have disabilities. Y0 is the counterfactual schooling rate, that is their enrollment if their parent had not been disabled. ATT=Y1–Y0 is the effect of parental disability on children’s schooling rate.

* significant at 10%; ** significant at 5%; *** significant at 1%

Figures in parentheses are standard errors. Standard errors are corrected for sampling weights and estimated using bootstrap (non-parametric) with 500 replications.

Source: Estimation from the 2006 VHLSS.

Table A.5 in the Appendix presents the ordered probit regressions of academic records. We tried both ordered logit with and without instruments for parental disability. Estimates of the parental variables are very small and not statistically significant in all the regressions. This means that once attending school, children with disabled parents do not have worse educational performance than children with non-disabled parents given that the estimation procedure controls for other observed characteristics.

Finally, the regressions of school enrollment and academic records show some interesting findings on other factors associated with education. Girls have higher school enrollment as well as better educational performance than boys even after other observed variables are controlled. Gender equality in children‟s education is very important, since it can contribute to gender equality in wage and other social economic activities when the children become adults. Other
Mongolia is a small, transitional country with a population of slightly above three million living in a landlocked area located between Russia and China. Ethnic Mongols account for about 95 percent of the population. Mongolia is one of the least densely populated countries in the world, with almost half its population living in the city of Ulaanbaatar, the country’s capital. Following the dismantling of the USSR in the early 1990s, Mongolia’s economy changed dramatically from a centrally planned system to a market-based one, and Soviet assistance, which had accounted for almost one-third of Mongolia’s GDP, disappeared almost overnight. Mongolia is resource-rich, with large deposits of copper, gold, coal, and uranium. Its mining sector accounts for almost one-fifth of GDP and 40 percent of exports, although it accounts for only 4.2 percent of employment. Mining has transformed Mongolia in a few decades from a traditional agricultural- and herding-based economy to a resource-based economy (World Bank Enterprise Survey, 2013). Today, Mongolia has a per capita GDP of USD $13,000 (PPP), which makes its economy comparable to that of South Africa or Sri Lanka. Agriculture nonetheless remains the largest employer in the country and still absorbs 30 percent of the labor force (down from 46 percent in 2003), followed by services (17 percent), public administration (15 percent), and manufacturing (10 percent) (World Bank Enterprise Survey, 2013).

Structural economic changes have been accompanied by two important demographic shifts that have imposed additional pressure on labor markets. Mongolia’s population is relatively young: the median age is 26 years, while the youth share of the working-age population is close to 55 percent. Likewise, the clearly declining trend of employment in herding and agricultural is mirrored by strong internal migration from provinces to the city of Ulaanbaatar. Indeed, since the late 1990s, population growth in Ulaanbaatar has averaged 5.8 percent, twice the country’s population growth rate. In 2013, 20 percent
||(0.0824)|(0.3525)|(0.2387)|(0.3254)|(0.2912)|(0.2996)|(0.2578)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|
|The mean value of the dependent<br>variables before the program|9.026|3.651|1.712|8.149|3.912|0.754|1.324|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

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, various measures of market size) move only slowly over time. However, they do move (otherwise there would be no development, i.e., poverty reduction), so that the time dimension of the data is essential for identification. Given the slow movement of most variables and the fact that our focus is on _sustained_ poverty reduction, and not on transitory changes, we do not utilize annual data, but employ 5-year windows instead.

Second, we do not measure increasing returns to scale by estimating production functions.<sup>6</sup> Our

> 4In a different context, Keane and Prasad (2002) provide cross-country evidence from the transition economies in the 1990s showing that domestic redistribution that reduced inequality promoted growth.

> 5Given that the focus is on the forces that set off the process of development, and not on the final outcome, we exclude countries that are already “developed” during our sample period. Developed countries may look today very different from when they started to develop, and one would need to go back in history and analyze historical data in order to understand their paths.

> 6Returns to scale have been empirically elusive (some of the reasons why empirical work may fail to find evidence for them even when they exist go back to the Klette and Griliches (1996) critique of revenue-based production function estimation). However, a recent literature has found indirect evidence for scale economies in several settings based on

9