rafmacalaba/data-use-mentions-extended huggingface dataset:
tablets using ODK-X software (Brunette et al., 2017). Pre-piloting of the instruments subsequently took place in Egypt in two governorates, ten schools, ten classrooms, with ten teachers and 30 children. Training of the master trainers, a mix of MOETE officials, supervisors, and Egyptian academic experts, by the international experts took place in January 2020. Training of enumerators took place over 10 days starting in late February 2020, including piloting in schools. Enumerators were required to reach scores of at least 80% on activities and quizzes during training, to ensure adequate inter-rater reliability. Enumerators were graduates of faculties of kindergarten education or child psychology, or kindergarten teachers or supervisors. Data collection was initially scheduled to take place in mid-March 2020. On the date data collection was supposed to begin, schools were closed due to COVID-19.

In Fall 2021, public schools reopened on October 9. After schools opened, a repeat of training was held for enumerators. Data collection in schools took place from November 6, 2021, to December 8, 2021. Parents were interviewed over the phone through December 15, 2021.

# **_3.3 Sample_**

The study sample was designed to be nationally representative of Egyptian KGs and their students. Egypt’s Education Management Information System (EMIS) database from 2018-19 was the sample frame. The sample was stratified by type (public versus private), region,<sup>13</sup> and community

> 13 Regions were divided into: Urban Governorates, Lower Egypt, and Upper Egypt. Frontier Governorates will not be included.

8
- Google. Google covid-19 community mobility reports. _https://www.google.com/covid19/mobility/_ , 2020. (accessed 2020-Aug-07).

- J. Granja and S. Moreira. Product innovation and credit market disruptions. _Available at SSRN 3477726_ , 2019.

- T. Hale, N. Angrist, E. Cameron-Blake, L. Hallas, B. Kira, S. Majumdar, A. Petherick, T. Phillips, H. Tatlow, and S. Webster. Oxford covid-19 government response tracker. Technical report, Blavatnik School of Government, 2020.

- R. E. Hall. Employment efficiency and sticky wages: Evidence from flows in the labor market. _The Review of Economics and Statistics_ , 87(3):397–407, 2005.

- M. Hallward-Driemeier and B. Rijkers. Do crises catalyze creative destruction? firm-level evidence from indonesia. _The Review of Economics and Statistics_ , pages 1788–1810, 2013.

- T. A. Hassan, S. Hollander, L. van Lent, and A. Tahoun. Firm-level exposure to epidemic diseases: Covid-19, sars, and h1n1. Working Paper 26971, National Bureau of Economic Research, 2020.

- J. E. Humphries, C. Neilson, and G. Ulyssea. The evolving impacts of covid-19 on small businesses since the cares act. 2020a.

- J. E. Humphries, C. A. Neilson, and G. Ulyssea. Information frictions and access to the paycheck protection program. _Journal of public economics_ , 190:104244, 2020b.

- S. C. Ludvigson, S. Ma, and S. Ng. Covid-19 and the macroeconomic effects of costly disasters. Working Paper 26987, National Bureau of Economic Research, 2020.

- NYFED. 2020.

- OECD. Supporting businesses in financial distress to avoid insolvency during the covid-19 crisis. Technical report, OECD Policy Responses to Coronavirus (COVID-19), OECD, Paris, 2020.

- F. Schivardi and G
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At the subnational level, the SER for ages 19-24 is largely influenced by the availability of universities in the respective areas. For example, compared with the districts of Bangkalan and Sampang, Banjar Baru and Banjarmasin have more universities, and thus the SER for ages 19-24 is higher. The figures are even higher than the national average (24.40), which may be a result of some in-migration for these tertiary services. The SERs for Banjarmasin and Banjar Baru in 2018 are 39.07 and 37.11, respectively. In contrast, the SERs in Bangkalan and Sampang are much lower, at 16.15 and 16.19, respectively. Except for Sampang, the GPIs for SER in selected districts and provinces are above 1, which means that the SER for this age group is higher for girls than boys.

9
with mechanizing the sewing process.<sup>8</sup> At least for now, the ready-made garment industry appears to offer some opportunity for some nations in Africa.

# _The Business Environment_

Much has been made of the poor business environment in Africa and business environment does matter, of course. But as nations across Asia have shown, where there are profits to be made, businesses find a way to work around business environment problems. Similarly, despite the business environment in Africa, formal manufacturing firms have performed well in terms of productivity growth (Diao et al. 2021).

Indeed, measuring the business environment by the World Bank Doing Business index, many countries of Africa compare favorably to countries of Asia that have experienced rapid growth. In 2013, for example, Ghana ranked 27 countries ahead of Vietnam in the Doing Business indicators. According to these indicators, it was considerably easier to get credit in in Ghana than in Vietnam, paying taxes was less of a hassle, insolvency was much more quickly resolved, and access to electricity was less problematic. In terms of how well investors are protected, there was a 40-point difference between the two countries in favor of Ghana (McMillan et al. 2017). A comparison between the rankings of countries in Africa and those of countries in Asia with established bases in manufacturing for the year 2019 offers several similar examples. Rwanda ranks 40 points ahead of Vietnam at 29, Mauritius and Kenya are also ranked ahead of Vietnam at 21 and 61 respectively. 17 African countries rank ahead of Cambodia. Bangladesh has 5 million garment workers (ILO 2020) but out of 48 countries in Africa only eight countries are ranked below Bangladesh and seven of these countries are at war. Nigeria is ranked 30 points ahead of Bangladesh.

An important aspect of doing business not covered by the World Bank’s index is the ease and cost of international travel. For example, Campante and Yanagizawa-Drott (2018) find that air links increase business links and that the movement of people
# **Appendix: Robustness Checks**

We include three robustness checks on our main estimates of African inequality. First, we restrict the sample to those countries that are present in both 1993 and 2008. Second, we use the raw data set of decile groups without the imputation of a parametric Lorenz curve. Third, we check for the robustness to using the older set of PPP exchange rates for 2005. Table A1 summarizes the robustness checks on the inequality results which were already discussed in the main text. Below, we will discuss the results with the 2005 PPPs in more detail.

|||<br>**Table A1**|**: Africa**|<br>**n inequal**|**ity**||||
|---|---|---|---|---|---|---|---|---|
||**1993**|**change**<br>**(%, pp)**|**1998**|**change**<br>**(%, pp)**|**2003**|**change**<br>**(%, pp)**|**2008**|**1993‐2008**<br>**change (%, pp)**|
|**i. Regional inequality (from Tab**|**le 3)**||||||||
|**Gini index (%)**|51.68|0.92|52.16|3.78|54.13|3.69|56.12|8.59|
|**GE(0) (Theil‐L)**|0.47|0.16|
- - 900<br>10 - 3 -00<br>2--n<br>0- 2~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I- -02<br>0- - 60<br>5- -1 - -<br>.-2 -so<br>4 - .3~~~~~~~~~~~~~~~~~ vaevobly of -084<br>unphe dotblt component - 02<br>moao 1000 10092 1004 1000 . 1000 1905<br>-domestic deposit rates<br>differentsia between domestic deposit Wats and US L11SOR<br>(adjusted fur ex post exchange rate movements)<br><!-- End of picture text --> The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate on a constant and a time trend. Nominal exchange rate index 1990=100.

33
Another of our contributions is to highlight the importance of reducing inequality. SDG 10 urges policy makers to accelerate progress towards lowering inequality within countries. Although inequality in Viet Nam is lower compared to many other developing countries, we found a rising share of inequality within provinces. Greater inequality levels were negatively correlated with economic growth and positively correlated with poverty levels.

The recent Covid-19 pandemic can offer an illustrative example for both the importance of reducing inequality and poverty segregation. Analyzing Labor Force Surveys data spanning the pandemic, Dang, Nguyen, and Carletto (2023) find that the pandemic had far stronger effects on low-wage workers; specifically, it increased the proportion of below-minimum wage workers by 32% and worsened various wage equality indexes. This study also finds that the pandemic effects were smaller in provinces with greater openness to the global economy (as measured by the share of exports and imports in provinces’ GDP). Reviewing the literature that studied the pandemic’s effects on vulnerable population groups in Viet Nam, Dang and Do (2023) also find that poor households, informal workers, people in poor health, ethnic minority groups, and other disadvantaged groups have been most affected by Covid-19. These results further highlight that inequality in the country can be deepened during times of crisis, affecting the more vulnerable groups that are most in need of assistance.

Indeed, geographical disadvantages could explain most of the non-farm participation gap in disadvantaged communities (World Bank, 2019). Furthermore, while national target programs that specially support poorer communes have sustained high commune level investments, a smaller share went to the poorest communes (Pimhidzai and Niu, 2021). As such, place-based poverty interventions can help effectively target and mitigate the disadvantages of fewer economic activities in lagging areas, which typically have less population density. Investment in both digital 20
members<br>admitted that they are not clear<br>on what is required by a<br>“bottleneck analysis”.|**Implementation**<br>**has**<br>**not**<br>**advanced**<br>**sufficiently**<br>**to**<br>**confirm any problems yet**<br>**fixed.**<br>According to existing DHIS<br>data,<br>which<br>were<br>acknowledged<br>to<br>be<br>unreliable and incomplete,<br>most indicators appear to be<br>regressing as do reporting<br>rates. State officials explain<br>the regression as being the<br>result<br>of<br>implementing<br>partners existing.<br>Implementation that is taking<br>place<br>is<br>driven<br>by<br>implementing partners. For<br>example, improvements in<br>coverage of Penta 3, appear to<br>be driven completely by IPs.<br>The PMU has dedicated 0% of<br>funds to that area so far, and<br>yet it is the area where there is<br>best reporting and signs of<br>increased coverage.|**Regressed.**Enugu had a<br>sum total of ‐12.1%<br>reduction<br>from<br>its<br>baseline according to the<br>2016 MICS survey. We<br>are not privy to the<br>breakdown.|

32
a year—fell from a third of total unemployment in 1994 to one tenth in 2009.</mark> <mark>Despite liberal firing and hiring practices, employment has not fluctuated too much in response to output variability. But the spending on active labor programs is sizeable: in 2010, it was almost €10 billion. Denmark spent 4.5 of GDP just for all labor market programs in 2008 (a good year). So the main drawback is the high fiscal burden, which can become enormous in a protracted slowdown. The Danes have flexicurity because of their history, and can afford it in part due to high participation rates of 81 percent. Those wishing to learn from the Danes should note this too.</mark> <mark>Although flexicurity has been praised for keeping unemployment low in Denmark, some reservations need to be mentioned. Andersen and Svarer (2007) point out that this model is not automatically leading to low unemployment. On the contrary, changes in the labor market may cause unemployment, and the transfers offered by the welfare state may become an absorbing state. Essentially, this is a variant of the well-known finding that if there is a passive player in a tripartite relation, the passive player ends up carrying all the costs. This was the case in Denmark from the mid of the 1970s to the mid of the 1990s with high and persistent unemployment and a growing ‘‘transfer’’ obligation on the part of the welfare state. Further, it is difficult to assess how much flexicurity contributed to the improved performance of the labor market, since other factors like macroeconomic performance count as well. Bredgaard and Larsen (2007) underline that competitive labor markets may exclude certain groups of employees, which fail to meet expectations of employers. Finally, spending a lot on active labor market policies become less effective if there are not enough jobs to go around—a likely problem in a protracted recession.</mark> <mark>Source: World Bank, forthcoming
21

**S3 Table. Intervention effects on all secondary outcomes, including index subcomponents**

|||Control<br>Prevalence/|||Intervention<br>Prevalence/|<br>||CI|95%|
|---|---|---|---|---|---|---|---|---|---|
|Outcomes|n|<br>Mean|SD|N|<br>Mean|<br>SD|ITT|Lower|Upper|
|Improved water source|1845|43%||1437|74%||0.24|0.12|0.36|
|Improved sanitation|1843|14%||1437|34%||0.18|0.10|0.25|
|Water governance perception index|1845|0.00|1.00|1438|0.22|0.88|0.19|0.04|0.34|
|Committee selected fairly|600|1.76|0.81|1119|1.82|0.76|0.06|-0.04|0.17|
|Committee treats community fairly|621|4.15|1.31|1158|4.07|1.31|-0.09|-0.27|0.09|
|Committee manages money well|635|3.25|1.12|721|3.31|1.14|0.05|-0.13|0.23|
|Comm responds breakdown well|972|3.69|1.18|1227|3.67|1.23|0.01|-0.13|0.15|
|Confidence committee will solve reported issue|1009|3.10|1
<!-- Start of picture text --> Model Data 95% CI Model Data 95% CI<br>5 6<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1<br>1<br>0 0<br>2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012<br>SD(Size) SD(Conditional Size)<br>(a) Size of attacked ships (Std Dev) (b) Size of hijacked ships (Std Dev)<br>Model Data 95% CI Model Data 95% CI<br>3 0.8<br>0.7<br>2.5<br>0.6<br>2<br>0.5<br>1.5 0.4<br>0.3<br>1<br>0.2<br>0.5<br>0.1<br>0 0<br>2006 2007 2008 2009 2010 2006 2007 2008 2009 2010<br>SD(Ransom) SD(Delay)<br>(c) Ransoms (Std Dev) (d) Length of captivity (Std Dev)<br><!-- End of picture text --> Figure 7: Model fit of second moments Note: The figures plot the second moments by year of the size of attacked and hijacked ships, ransoms, and length of captivity in the data v.s. the model simulated at the benchmark parameters listed in Table 1. The confidence intervals are 1-standard-deviation bands. Data sources: IMB (2012), UNODC-WB, and authors’ calculations.

by 2009 and 2010. In 2011, the simulated success rate was 10.2 percent. However by 2012, simulated pirates that were still in the business hijacked smaller ships as a response of private maritime security, increasing
example, we gain insights into whether poverty is more of a chronic or transient nature, and how this changes over time and differs between regions. Moreover, we are able to estimate conditional probabilities, such as chances of escaping poverty. Nevertheless, the caveat remains that these results are not based on true panel data and depend on our willingness to assume that actual income correlation coefficients from other countries can, as a group, proxy for the correlation of Malaysian incomes over time.

Considering poverty dynamics, we observe a large decrease in chronic poverty over the study period, to between 2-3 percent of the population over the interval 2019-2022. This decline is evident across all three poverty lines applied. It also means that those living in poverty now have much better chances of escaping poverty. In addition, poverty entry figures have continued to decline. These poverty dynamics went hand-in-hand with larger transfers to low-income households in the wake of the Covid-19 crisis. This is important to bear in mind when interpreting these results.

In contrast to the Department of Statistics Malaysia, we do not observe an increase in poverty from 2019 to 2022. This is likely due to a significant increase in the poverty line employed by DOSM from 2019 to 2022. If we pick either of these lines and keep its real value constant over time, we observe a decrease in poverty.

Poverty conditions are worst in rural East Malaysia: around 15 percent of the population here lives in chronic poverty, and over each interval in our study, around 10 percent falls into poverty. Those in poverty here have the lowest chance of all Malaysians of escaping poverty. Moreover, those out of poverty face the highest chances of falling into poverty.

Looking at three-way income mobility, an important conclusion is that results depend on which income thresholds are used to define the three groups. If we use relatively lower poverty and vulnerability lines, a bit over 50 percent of the population is persistently secure, and downward mobility has declined.

However, if we employ higher income thresholds, the
Figure 2. Distribution of PLE Scores <!-- Start of picture text --> Distribution of PLE Scores - S1/S2 2012<br>0 10 20 30 40<br>Aggregate Score<br>Control Treatment<br>Note. Data from Check 3. Ranksum test p<0.001<br>.08<br>.06<br>.04<br>Density<br>.02<br>0<br><!-- End of picture text --> Note. Kernel density of aggregate Primary Leaving Exam for non-repeating Senior 1 and 2 cohorts across treatment and control schools in February 2012.

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Table 11 presents the ATT estimates from comparison of matched Jalanidhi and KWA built and managed schemes for selected indicators.<sup>4</sup> The ATT estimates indicate that although for most of the constituent indicators of availability and reliability index Jalanidhi schemes perform better than the KWA schemes, the differences between them are not significant except for adequate water pressure in the network, no colored water, and no seasonal shortage. In the matched sample, 96 percent of the Jalanidhi schemes reported no colored water compared to 35 percent of the KWA schemes. Similarly, 49 percent of Jalanidhi schemes reported no seasonal shortage and 64 percent of Jalanidhi schemes reported adequate pressure in the network. The respective proportions for KWA schemes are only 4 percent and 15 percent. Jalanidhi schemes also have been rated consistently and significantly higher for all dimensions of household satisfaction. However, for satisfaction with water quality rating, the difference is only significant at the 10 percent level. There are no significant differences between Jalanidhi and KWA schemes in terms of household opinion regarding connection charges and water tariffs. Similarly, there are also no significant differences between these two types of schemes in terms of longest breakdown of the system or the percentages of schemes facing no water shortage anytime last year. However, Jalanidhi schemes experienced significantly lower number of breakdowns than the KWA schemes, whereas significantly higher proportion of KWA schemes carried out yearly testing of water quality compared to Jalanidhi schemes.

**Table 11: PSM Comparison of Jalanidhi with KWA Built & Managed Schemes: Selected** **<u>Indicators</u>**

|**Variable**|**Jalanidhi**|**KWA-**<br>**BM**|**ATT**|**SE**|**P-value**|
|---|---|---|---|---|---|
|**Availability & Reliability Index**|||
urban and state/region levels. The survey was administered to approximately 23,500 households. Data on child work was gathered on individuals aged 5-17.

In the analysis we use a broad definition of child labor, embracing children engaged in wage work, unpaid family work, farming and self-employment. We do not focus on the distinction between working children and child labor as we are interested in understanding if working affects schooling outcomes regardless of whether or not the work is classified as child labor. We also consider a child as working if he or she is engaged in any one or more than one of the following household chores: cooking, shopping, cleaning, washing clothes, household repairs, childcare, elderly care and other household tasks.

For a robust analysis, this study uses two categories of outcomes: those related to work and those related to education. The first covers participation in wage labor, household chores, and any type of work (either wage labor or household chores), and the time spent doing these activities. The second covers school enrollment and attendance, the latter being a binary variable on attendance during the past week, equal to 1 if the child attended at least three days of school in the past week and 0 otherwise. We use a bivariate probit regression to estimate the joint probability of a child’s participation in wage work, household chores, or any type of work while simultaneously deciding to be enrolled. The bivariate probit model allows us to focus on the trade-off between current income and higher future income that is the return from education. Implicitly, we have a two-period model. In the first period, parents choose either to invest in children’s education or to send them out to work and gain from their earnings. In the second period, parents become inactive and rely on the economic support of their children, whose incomes depend on educational level. Given gender bias in the labor market, this model is often used to explain lower school enrollment for girls. Low expectation of