rafmacalaba/data-use-mentions-extended huggingface dataset:
playgroup was closed, we visited the village and interviewed former center administrators and teachers to collect data on the date of closure, key reasons for closure, as well as what happened to the building, toys, and teachers after the project playgroup closed. If the project playgroups were still open we conducted interviews to collect information on current operations, teachers, and funding modalities.

# **_Main analytic sample_**

However, the main analytic sample in this paper is the 245 project playgroups that were interviewed in 2013 and followed up in 2016. We restrict our analysis to these panel project playgroups primarily because we have detailed data on the characteristics of these project playgroups at _two_ time points - in 2010 and 2013 as well as the knowledge that they were still operating in 2016 - whereas detailed follow up was not completed for the remaining playgroups in 2013. As a result, we have granular data on sustainability for these 245 playgroups - for each, we not only know whether the it was open or closed in 2016 but we also know the number of teachers who received the teacher training offered as part of the Indonesia ECED Project and a measure of playgroup quality, key data which were collected in 2013. These additional data from 2013 allow us to predict key elements of sustainability that are not possible if we use the full sample of 432 project playgroups. In Appendix Table 1, we show that there are no significant differences in various baseline center characteristics between the 245 panel project playgroups in the main analytic sample and the full sample of 432 project playgroups. Thus, we consider the main analytic sample to be broadly representative of the 432 project playgroups in these villages.

> 9 For details of the design of the evaluation see Pradhan et al, 2013. For detailed results on children’s developmental outcomes see Jung and Hasan, 2016 and Brinkman et al. 2017.

> 10 That is not to say that they had higher total enrollment overall.

5
depending on their primary input data: (1) satellite images of land cover and of built‐up characteristics; (2) satellite images of areas lit up at night; and (3) gridded population data created by using satellite images and other auxiliary information to redistribute aggregated data from population censuses.

To many, these approaches hold the promise of cross‐country neutrality and comparability because they ensure global coverage, spatial granularity, and temporal consistency. But a consensus has yet to emerge on the most reliable method to identify urban areas. To date, then, the results have been heterogeneous. At the global level, the urban areas identified cover from less than 300,000 square kilometers to over 700,000 square kilometers, depending on the method (Potere and others 2009).

Some features of the input data and their interpretation may help explain the large variations in results when using these methods:

- Most analyses are conducted at the cell level, but a city is more than the sum of its cells. This remark goes back to the subjectivity of the concept of a city. The proper of an urban setting is the interlinking of cells in a broader spatial context. Assessing whether a cell is urban without referring to the surrounding cells is thus potentially misleading, and even more so when the urbanization process is messy (World Bank 2015).

- Analyses relying primarily on land cover classification algorithms can accurately identify built‐up areas and infrastructurethe signature characteristics of urban areas. But cities include other land cover types such as parks and water bodies. The continuum between rural and urban areas poses a challenge to these algorithms as well (Mertes and others 2015; Pesaresi, <mark>Ehrlich, a</mark> nd others 2016; Schneider 2012).

- Analyses relying primarily on nightlight data capture the concentration of human activities in an unprecedented manner. However, problems such as blooming lead to biased estimates of the urban extent (Elvidge and others 2007; Elvidge and others 2009; Li
dependency rate, and a dummy indicating if the household has income from remittances. Workers who are employed in Public Administration, Utilities, Health, or Extraterritorial Agencies are protected from facing any job losses or income changes.<sup>15</sup> For all workers that retain their jobs but are not employed in the protected sectors (Public Administration, Utilities, Health, and Extraterritorial Agencies), their labor incomes are adjusted up or down by a single scale factor equal to the percentage change in private consumption per capita of their specific country in 2020.

Next, projected changes in remittances by country are also included in the microsimulations. The percent change in remittances at the national level is applied as a direct percent change in the remittance income of any household that receives this income in the household survey. Changes in other sources of non-labor income different from remittances are not considered because data was not available for all countries. Once all three channels of transmission of the shock are incorporated, household per capita income is calculated by summing up the final income of each household member and dividing by the total number of household members.

We derive the distributional impacts of COVID-19 exploiting heterogeneities in household characteristics in the microdata. The first source of heterogeneity is the difference in the composition of income per-capita by income source. Different shares from each source of income will imply a different effect of the macroeconomic projections for each household. For example, households that do not receive remittances are not affected by the economic shock on remittances. Similarly, household incomes that are derived from pensions, capital income, and public transfers are not affected by the economic shock.

The second source of heterogeneity is the sociodemographic characteristics across households. Households differ by gender of the head of household, by education levels of members, by household composition, etc. Different characteristics will predict a different probability to suffer an unemployment shock, or for those who gain employment their different characteristics will predict different new incomes.

<mark>The third source of heterogeneity is the sector
<!-- Start of picture text --> Timing of data Experiment 1: Double grant Experiment 2: Information<br>collection 250 schools 180 schools<br>randomization<br>Year 0<br>Control Treatment<br>Grant Double Grant<br>($500-700) ($1000-1400)<br>& &<br> Information Information<br>Year 1 125 schools 125 schools<br>randomization<br>Year 2<br>Control Treatment<br>No Grant No Grant<br>& &<br>No Information Information<br>100 schools 80 schools<br>Year 3<br><!-- End of picture text -->

**Figure 2:** Experimental design of two parental involvement interventions

Note: The figure summarizes the experimental design of the two randomized controlled trials in this study. Baseline survey data for experiment 1 was collected in year 0 and baseline survey data for experiment 2 was collected in year 2.
Yet, despite the large increase in employment rates for young people in rural areas, there is no evidence that school enrollment rates have decreased—although the “double burden” of work and school can lower the amount of learning. Overall enrollment rates among people aged 15-20 did not change from 2007 to 2012, including for groups where the employment expansion was most significant: both men and women aged 15-20 years-old in rural areas maintained stable enrollment rates in the period. Furthermore, family workers are much more similar to the not employed than to those in paid jobs when it comes to school enrollment: 71 percent of young family workers in rural areas are enrolled in school vs. 76 percent for people not employed and only 31 percent of those employed in paid professions (Figure A.1). This suggests that young people are not dropping out of school to enter the labor market, but are helping in their family business while in school. Of course, this “double burden” can affect the quality of education, such as reducing time devoted to studying or the amount actually learnt while in class (Heady, 2000, Emerson, Ponczeck, and Portela Souza, 2013, Bezerra, Kassoufm and Arends-Kuenning, 2009). There is some evidence that people engaged in helping their families are less committed to school. In 2012, for example, only 23 percent of family aids aged 15-20 in rural areas reported spending any time studying vs. 45 percent of those not employed. In a more general way, a much smaller share of people aged 15-20 in rural areas report having spent time studying in 2012 as compared to 2007, be they working as family aids or not. On the upside, conditional on spending any time studying, young people in rural areas actually reported a higher number of hours in 2012 compared to 2007.

# C. Unemployment Rates

National unemployment rates have declined steadily since 2001, falling from 43 percent to 27 percent in 2012, but remain higher than the LAC average (Table A.1
.8 25.7<br>40.9<br>40% 40%<br>31.4 14.4 13.0 38.2<br>30% 57.4 30% 20.1<br>20% 19.2 20% 27.6 4.1<br>29.1 30.6<br>10% 18.0 12.6 23.5 10% 17.3 10.8 22.7 21.4<br>0% 0%<br>Senegal Argentina Turkey Algeria Mali Senegal Argentina Turkey Algeria Mali<br>Age Education Gender Income<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS The decomposition of differences in the Equity Adjusted Coverage Rate (EACR) between countries summarizes if differences are driven by composition, equalization, or scale effects (Figure 13). For example, the EACR in Mali is 2.9 compared to 2.8 in Turkey. The net difference between the EACR values is only 0.1, but Figure 13 displays that different three different factors have individually larger impacts that neutralize each other. The higher EACR in Mali is driven by the scale effect, but diminished by equalization and composition effects. Savings has a higher coverage overall in Mali, however the equity of coverage among individuals in Mali contributes to a lower EACR. Compared to Turkey, Argentina has a lower EACR (2.8 vs 2.0 respectively). This lower EACR is explained by composition and equalization effects. In Argentina, equity of coverage in formal savings is poorer than in Turkey and the composition of
-103-

- C.L. Gilbert (1982), "Short-Cut Methods of Estimating Per Capita GDP and Tentative Results on the Distribution and Growth of Real GDP", Oxford Economic Par,ers, November.

- M. Gilbert and W. Beckerman (1961), "International Comparison of Real Product and Productivity by Final Expenditures and by Industry", in NBER, Output, Input and Productivity Measurement, New York.

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- M. Gilbert and Associates (.958), Comparative National Products and Price Levels, OEEC, Paris.

- F.M. Gollop and D.W. Jorgenson (1980), "US Productivity Growth by Industry 1947-1973" in J.W. Kendrick and B.N. Vaccara, eds. New Developments in Productivity Measurement and Analysis, University of Chicago Press.

- Gosplan (1965), Sopostavlennie Urovnei Ekonomicheskovo Razvitia Sotsialisticheskikh Stran, Moscow.

- P. Guinchard (1984), "Productivite et Competitivit6 des Grands Pays Industriels", Economie et Statistique, January.

- M.A. de Gusmao Veloso (1987), "Brazilian National Accounts 1947-1985", IBGE, Rio, August.

- B.D. Haig (1966), Real Product, Income and Relative Prices in Australia and the United Kingdom, ANU, Canberra.

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IBGE (1983), Censo
FMP _i_ in month _m −_ 1 and year _t_ . We control both for the possibility that migrants are not planning to leave Libya using _Remaini,m−_ 1 _,t_ , a dummy taking value one if Libya is one of the top destination countries for migrants in FMP _i_ in month _m_ , and for the possibility that migrants are planning to return home using _Returni,m−_ 1 _,t_ , a dummy taking value one if one of the countries of origin is also one of the top destination countries for migrants in FMP _i_ in month _m_ . _θi_ is the FMP fixed effects and _φm,t_ is the month-year fixed effects. _γo_ is the countries of origin fixed effect and _μd_ is the destination countries fixed effect. By including country-of-origin fixed effects, we compare - for a given FMP, the effect of the tone of the news on the migrants’ decision to move between two months of the same year when the FMP’s composition of migrants is the same. By including country-of-destination fixed effects instead, we compare - for a given FMP, the effect of the tone of the news on the migrants’ decision to move during two months of the same year when the FMP’s preferred country-of-destination is the same. As a result, by adding both sets of fixed effects, we sort out from our estimates the

> 24The list of parties part of each government coalition and the number of seats obtained by them in each election is retrieved from the ParlGov project ( `www.parlgov.org` ). Data from the Manifesto and the ParlGov project are matched using information from PartyFacts ( `https://partyfacts.herokuapp.com/` ).

> 25Note that the IOM-DTM data does not distinguish departing migrants from a FMP by their length of stay at the FMP. It follows that using the number of departing migrants from the FMP as an outcome would not be informative of the effect of changes in the tone of the news on the pace of the migrants
the probability of shadow employment for individual workers in LFS, using the previous estimated coefficients of the probabilistic models in the CASE_2007 database. This methodology is based on the idea of Propensity Score Matching; however in this case, different data sets are used for model estimation and for calculating the ―propensity scores‖.

The first step of the analysis was to select the groups of shadow workers to be used for the analysis, i.e., the groups of shadow workers to be later ―matched‖ by observations from the LFS data-set. For the reasons of analytical clarity, it was decided to use only those working without any written contract and those working with some form of written contract but not paying any taxes or social security contributions (later called shadow employees) and the same group extended to self-employed (or employers) with unregistered firms (latter called shadow workers). In either case, only those who were employed in the shadow sector in their main job have been taken into account. Further in text both of these groups are referred to as shadow workers according to basic definitions.

The next step was to identify the set of explanatory variables to be used in the ―matching models‖. The important limitation was that all the explanatory variables used in the model specification had to be transformable so as to achieve common definitions and categories for CASE_2007 database and for each and every LFS database used in the further course of the analysis (for years 2003-2007).
7 Henan, Hubei, Hunan, Jiangsu and Shandong.<sup>7</sup> The CHNS income data are already adjusted for cost-of-living differences across provinces (and within provinces between rural and urban areas) through a price index constructed by the CHNS project team. The team’s information was used to deflate the 1997 income data to 1989 prices, distinguishing between different provinces, and within provinces between rural and urban areas. The deflated income data for the panel provinces imply an annual real rate of growth of per capita household income of 6.0%, somewhat lower than the 6.7% figure for China as a whole for this period reported by Ravallion and Chen (2004). The per capita household income Gini for the CHNS panel rises from 0.3954 in 1989 to 0.4191 in 1997, giving a value of _∆ G_ of 0.0237 (Table 1).<sup>8</sup> The levels of the Gini in both years are a good deal higher the national figures reported by Ravallion and Chen, though the annual percentage change in the Gini in the CHNS data (0.7%) is somewhat smaller than the annual percentage change over the period 1989-97 reported by Ravallion and Chen for China as a whole (1.2%).

_Table 1: Per capita income growth and inequality changes_<sup>9</sup>

||China 1989-97|Vietnam 1993-98|
|---|---|---|
|Growth of per capita income/consumption p.a.|6.0%|6.6%|
|Initial Gini coefficient:_G_0|0.3954|0.3223|
|Final Gini coefficient:_G_1|0.4191|0.3346|
|Change in Gini coefficient:_∆G _|0.0237|0.0123|

Note: Computed from panel data from 1989 and 1997 waves of China Health and Nutrition Survey (CHNS) and
For example, lack of sufficiently standardized units of measurement or dosage information can render unit price comparisons flawed. While the analyzed products are highly standardized across the region, important quality differences may remain in the products themselves or in the delivery schedules (e.g. delivering earlier or later can have large price implications for suppliers). A crucial aspect of quality differences, especially impacting on equipment, is that the level of the hierarchical product classification used in the analysis may average over a range of important quality differences. Furthermore, missing information on payments and contract implementation raises questions about the validity of findings given that payments may well deviate substantially from contracted values.

On a methodological level, even though our models have performed exceptionally well in predicting prices on unseen data, we cannot claim to have identified causal relationships. Without more careful identification of causal effects, by for example using quasi experimental methods, the methodology can only capture correlations and has to rely on theory and case study methodology for further support.

From a policy perspective, while savings scenarios and predictors are actionable and directly relate to policy decisions made by procurement agencies, ministries of finance or individual 33
# **5.  Predictive Performance**

We assess the empirical implications of our results by predicting air pollution measures for the monitoring sources and comparing their performance. In the following tables, we present summary statistics for CAMS-3 measures and predicted values, along with sample sizes for each case.

Our exercise in matching, estimation and prediction has produced widely-varying sample sizes. To cite one example, Table 6 reports statistics for the period 1 PM - 2 PM that include daily observations for Sentinel-5P and hourly observations for CAMS-3 and Google Traffic. For daily matching, we have computed means for CAMS-3 and Google Traffic observations at 1 and 2 PM. The result of this necessary matching exercise is great disparity in available daily observations. For the Sentinel-5P regression, there are 362 CAMS-3 observations, along with predictions from 434 and 406 observations for the S5P aerosol index and tropospheric NO2 measure. For the Google Traffic (GT) regressions, which use GT data for two periods in 2019, CAMS-3 provides observations from 1 PM, 2 PM or both for 34 days. Predictions are produced from 93 days during which GT data are available for 1 PM, 2 PM or both.

**Table 6:  CAMS-3 PM2.5 vs predictions from Sentinel-5P and Google Traffic**

Units: ug/m<sup>3</sup> Collection period (Dhaka time): 1 PM - 2 PM

|||Sentinel-5P||Google|Traffic|
|---|---|---|---|---|---|
||CAMS-3|Aerosol<br>Prediction|NO2<br>Prediction|CAMS-3|Prediction|
|N|362|434|406|34|93|
|min|11|13|14|14|21|
|p10|22|25|25|23|23|
|p25|35|31|31|31|26|
|p50|55|43|44|84|36|
|
Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People‘s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations _Trends in International Migrant Stock_ details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People‘s Democratic Republic of Korea.

# _Recording and Recoding_

There is little standardization in the recording and dissemination practices for censuses across destination countries.<sup>14</sup> The level of detail with which destination countries record and disseminate migration data depends on the design of the original questionnaire. Some census questionnaires ask for a specific country of birth and others simply ask for a general geographic region, such as Africa. Even if the original questionnaire asked detailed questions, some countries disseminate data only on how many residents were born abroad or have foreign citizenship. In general, three types of migrant origin are observed in the disseminated census data:

- _Specific geographic regions:_ Some of these correspond to exactly one of the 226 countries and territories in the master list. Others pertain to localities that tend to be obscure territories, islands, or regions, such as the Isle of Man or Ceuta.

- _Aggregate geographic regions:_ These correspond
23

**Figure 12:  Belief that privatization has been beneficial in their country (%)**

<!-- Start of picture text --> Argentina<br>Uruguay<br>Paraguay<br>Chile<br>Colombia<br>Bolivia<br>Mexico<br>Guatemala<br>Nicaragua<br>Panama<br>Peru<br>Costa Rica<br>Honduras<br>El Salvador<br>Brazil<br>Venezuela<br>Ecuador<br>0 10 20 30 40 50<br>Percent<br><!-- End of picture text --> Source: Latinobarómetro, 2002

- In Sub-Saharan Africa, a recent AfroBarometer survey of 15 major countries undertaking economic reforms between 1999 and 2001 found that less than 35 percent of the population preferred privatization to state ownership -- ranging from 30 percent or less in Ghana, Lesotho, Mali, Uganda and Zambia to 40 percent or more in Botswana, South Africa, and Tanzania (AfroBarometer 1999/2001). In Ghana, for example, 70 percent of respondents opposed the privatization of public enterprises, with 50 percent “strongly” opposed. In Zambia, focus group researchers found people to be “deeply attached to public ownership and the public provision of employment” (Slattery 2004);

- In Eastern and Central Europe, a survey of 16 countries (excluding Central Asian states) found that 52 percent of the population between 1998 and 2001 supported private ownership over state ownership, with support being the highest (over 80 percent) in the Baltic states (New Democracies Barometer 1998, 2000). Yet, in Russia, 70 percent of people surveyed felt they were worse off after privatization and preferred state ownership, while in countries such as Belarus, Moldova and Ukraine less than 40 percent of the population favored privatization;

- In East Asia, a survey of eight countries (excluding Vietnam, Laos and Cambodia) found that only a third of the population surveyed favored