rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III.1.b Gender**

As discussed in a recent survey on IGM in developing countries (see Torche, 2019), gender gaps in education have been disappearing or even moving in favor of women. I examine whether these patterns hold in this novel data set by estimating IGM for males and females separately and documenting how the gap between these populations has evolved across birth cohorts. I estimate upward and downward mobility<sup>17</sup> by country, birth decade of the “children” and gender. I do not find systematic differences by gender for older birth cohorts but it appears that there is a trend towards higher upward mobility for women as they have 3 percentage points higher upward mobility in the 1980s birth cohort (see Figure 8) while the gap in downward mobility move around similar values (the gap in favor of women is approximately 3 percentage points for 1980s birth cohort) with a flatter trend. Figure A16 and Figure A17 in the Appendix show estimates by sub-population rather than the gap between them for countries with data for at least 4 decades, suggesting that the gap has been increasing in favor of women because of an increase (decrease) more than proportional for them in upward (downward) mobility.

# **III.1.c Evolution over time**

As mentioned in the data section, the coverage over time is unbalanced with some countries spanning more years than others. This limits the analysis of trends over time and the conclusions that can be derived from comparisons between them at given points in time or for a given cohort. Nevertheless, I document estimates of mobility by country for the different birth cohorts that are available. Figure 9 reports these estimates. It is clear how the level of upward mobility has been increasing at the same time that downward mobility has been falling. This is not surprising given the fact that educational attainment has increased in 20
14 We calculate the food composition for all the foods available in the markets using the recently compiled Malawi Food Composition Table (MAFOODS 2019) supplemented by the USDA National Nutrient Database for Standard Reference where necessary (USDA 2018). Specific information regarding food item composition matching records available in the replication data files and we also point readers to the MAFOODS data tables (MAFOODS 2019). USDA records were used for edible portions. Where the item is not contained in the Malawi tables, USDA data used minimally and only where the item-nutrient was deemed unlikely to be affected by location-specific factors. All items are converted to kilograms using conversion factors provided by the NSO.<sup>3</sup> To perform seasonality analysis at the food group level, we also classify foods by food groups using a combination of food groups used for household, child, and women’s dietary diversity indicators (WHO 2008; FAO and FHI 360 2016; Kennedy, Ballard and Dop 2010; Ministry of Health (MOH) [Malawi] 2017).

Supplementary Tables B and C present the food item sources of each nutrient and the food items within each food group. As these tables illustrate, there are multiple food sources for all essential nutrients, and all food items contain multiple nutrients. This means that there are many food combinations that could meet all the minimum requirements. However, there might not be any solution to the linear optimization if there is no combination of foods that could meet the minimum requirements while also staying under all the upper limits and including the exact amount of energy required. This is the intuition behind the lack of solution to the least-cost diet problem. How the nutrient requirements drive the results is especially evident when the foods available in one market and month can meet the individual requirements (where the ranges 3 Provided to the research team directly, available upon request.
br>denote the mea<br>red. All sampl<br>near trends, an<br>; _p_-values are s|
|Hus<br>Husband’s<br>Years of<br>Skilled /<br>Schooling<br>Professional<br>(3)<br>(4)<br>1.116<br>0.026<br>(0.372)<br>(0.028)<br>[0.003]<br>[0.350]|2.28<br>0.083<br>7.10<br>6.40<br>19,784<br>19,814<br>mographic and Health Survey (in y<br>le is described at the top of each o<br> shown in column 1. The dependen<br>al, clerical, and skilled manual job <br>ed level of schooling, instrumented<br>easures _I_MTI-T<br>_zy_<br>and _I_MTI<br>_zy_<br>, which <br> in which two interventions occur<br>d zone fxed efects, zone-specifc li<br>ne level and shown in parentheses|
|Husband’s<br>Age<br>(2)<br>1.009<br>(0.884)<br>[0.254]|49.48<br>6.35<br>18,174<br>the Ethiopian De<br> dependent variab<br>married women is <br> include profession<br>g_izy_ is the predict<br> (MTI) intensity m<br>actions for regions<br>ude birth year an<br>clustered at the zo|
|Wife’s Years<br>of Schooling<br>[ First Stage<br>- Married Only ]<br>(1
still twice as large as the coefficient on own family background. This means that raising the average background of the peers has twice the effect on a student’s test score as increasing his own socioeconomic background.

School fixed effects, however, cannot address all sources of bias. Apparent peer effects may still be driven by differences in the quality of instruction or school management across classes, whenever these differences are correlated with the peer variables. This concern would arise if, for instance, richer parents are able to exert pressure on the school for a better teacher or if the school tracks children according to their performance. In other words, there is the concern of _selection of students into classes_ . In terms of the previous equation, the problem stems from the correlation between _bcs_ and peer characteristics _Ycs_ ( _−i_ ).

To address these concerns, I exploit random variation in peer-groups between sixth-grade classrooms within schools. Two-thirds of the pupils in Argentina attend schools that have more than one class in each grade. The assignation of pupils to a class is determined at the beginning of primary school (at six to seven years of age) and remains unchanged throughout the whole seven years of education. Also, unlike the United States or the United Kingdom, in Argentina children are not streamed on the basis of their performance year by year. Instead, the initial allocation of classes is done randomly and it remains fixed for the entirety of primary education. In other words, _bcs_ and peer characteristics are not systematically related. Combined with school fixed effects, the effect of the peers on a student’s performance is identified by the existence of variations in class composition across classes within a given school.

The strategy of combining school fixed effects when there is a random assignment of students across classes has been used in a number of papers. Ammermueller and Pischke (2009) follow a similar approach for estimating peer effects using PIRLS data of primary school children across six European countries. McEwan (2003) also bases his estimates of peer
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking ICT indicators**

||Unit|Low-income countries|**DRC**|Fragile states|
|---|---|---|---|---|
|GSM coverage|% population|42.42|**65.00**|62.55|
|International bandwidth|Mbps/capita|3.01|**0.19**|0.88|
|Internet|subscribers/100 people|0.13|**0.03**|0.07|
|Landline|subscribers/100 people|7.47|**3.90**|8.99|
|Mobile phone|subscribers/100 people|6.44|**3.88**|8.01|
|US$||**DRC**|Without submarine cable|Other developing regions|
|Price of monthly mobile ba|sket|**11.0**|11.12|9.9|
|Price of monthly fixed-line|basket|**28.17**|13.58|—|
|Price of monthly 20-hour In|ternet package|**74.00**|67.95|11.0|
|Price of international call to|U.S., per minute|**0.33**|0.86|0.67|
|Price of inter-Africa calls p|er minute, mean|**0.52**|0.72|—|

_Source:_ Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ Mbps = megabits per second; — = data not available.

# **Challenges**

Completing the expansion of
considering, for example, economic performance of a country. We controlled for two simple measures of economic performance, income level and income growth in the five preceding years. The results suggest that these two factors explain some of the difference between aggregate perception-based indicators and indicators generated from firm-level surveys. (Of course, those who see corruption as a major source of poor performance would expect corrupt countries to be poor and grow more slowly, so this might be considered over-controlling.) Adding further controls about development of a country such as urbanization did not change our finding. Figure 1 below presents the results graphically. The left panel shows the scatter diagram of correlations between CPI and ES rankings. The right panel adjusts the CPI rankings through regressions on income levels and growth (2005-2009). In the graphic the fitted CPI rankings are “compressed” as, for example, the top ranked countries are dragged down by the control for income levels. The results are inconclusive.

What we are left with is a significant variation of the apparent incidence of corruption among sectors, types of permits and other “targets” of corruption. Data on the size and frequency of bribe payments for a contract in the construction industry alone suggests considerable withincountry variation on the reported frequency and scale of bribe payments even within this limited sphere (Kenny, 2006). Indeed, the significant in-country variation between measures of different types of corruption casts some doubt on the utility of one overall measure such as the CPI as a guide to the extent and nature of the corruption problem within an economy.

Comparing individual country rankings, we observe some intriguing differences between the ranking in the CPI and our aggregate firm-based indicator. We consider the respective rankings of 107 developing countries. While most countries are ranked broadly similarly, China and India are clearly not. They rank at 35 and 40 respectively on the CPI. On the aggregate ranking derived from enterprise surveys they rank 69 and 100 – just two countries, but over 35 percent of humanity. Two other intriguing cases are
103***|0.103***|
||(0.009)|(0.012)|(0.014)|(0.009)|(0.013)|(0.014)|
|Observations|23,752|11,975|11,777|23,752|11,975|11,777|

Note: Robust standard errors in parentheses. Regressions are done using probit displaying marginal effects but no constant. Enrollment: child is enrolled in current school year. Attendance: child attends school at least three days (out of five) a week.

Wage work and household chores: children not engaged in these activities recorded as doing 0 hours of work. HH edu at least secondary: at least one household member has completed secondary education or higher. High income hh: households in the 4th or 5th income distribution quintiles. *** p<0.01; ** p<0.05; * p<0.1

# 6. Conclusion

This study finds that child work to differing degrees, depending on the type and intensity, negatively affects school enrollment and attendance, regardless of gender. In 2015, there is a clear trade-off between child work and schooling,<sup>12</sup> especially after the age of 11. The results suggest that children participating in wage work are less likely to be enrolled in school, regardless of the number of hours spent working and gender. However, wage work could reduce in a greater magnitude the likelihood of being enrolled and attending school for boys than for girls, whereas household chores affects girls disproportionately more. Hours spent working has different outcomes for both enrollment and attendance depending on the type of work and gender: for both girls and boys wage work intensity does not seem to matter, working less than or more than 40 hours significantly negatively affects enrollment and attendance. Intensity of household chores matters more for girls than for boys when looking at
series that are known to be cointegrated. We then ensure that signs are consistent with theoretical predictions.

The set of fundamentals found to be driving the long-run exchange rate for Argentina includes: the terms of trade, productivity differential, trade openness, and foreign exchange reserves. The indicators are defined as follows:

- **REER:** data for the REER ( _reer_ ) is taken from the BIS (1994-2015) and appended using data from the IMF (1980-1993), where both series are based on a trade-weighted nominal effective exchange rate (foreign currency units per unit of domestic currency) and relative CPI price indices. For the period December 2007-October 2015, we adjust the REER ( _reer_adj_ ) to correct the domestic price level for cumulative inflation discrepancies between (lower) official statistics and (higher) inflation figures produced by PriceStats.

- **Terms of trade:** in the monthly analysis, IFS data on the world price of soybeans deflated by CPI in advanced economies ( _psoy_ )<sup>14</sup> is used as a proxy for terms of trade, stipulating a ‘commodity currency’ property of the peso based on soybean exports. In our annual analysis, we use a terms of trade index ( _tot_ ) that measures the ratio of export-to-import prices obtained from the WDI database.<sup>15</sup>

- **Productivity differential:** this variable captures the Balassa-Samuelson effect based on the divergence of productivity levels in the country’s nontradable and tradable goods sectors. In the monthly analysis we two simplistic measures: the ratio of consumer-to-producer prices relative to main trade partners<sup>16</sup> ( _cpi/ppi_ , potentially underestimated) and industrial production relative to Brazil ( _ip_rel_ , potentially overestimated), Argentina’s main trade partner.<sup>17</sup> To correct for potential mismeasurement, we also make adjustments that increase CPI/PPI (increasing the ratio by 30% over 2008-2015)<sup>18<
<!-- Start of picture text --> Figure 6. Concentration of Social Protection Programs by market income quintiles<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Contributory pensions Non-contributory benefits<br>Poorest 2 3 4 Richest<br>Old-age pension Other pensions Pre-retirement pension Family pension Disability pension Maternity benefit Unemployment benefit Structural (farmer) pension Housing benefits Child birth grant Nursing Benefit (zasiłek) Nursing Allowance Social assistance Family beneifts Social pension<br><!-- End of picture text --> <u>Source: World Bank estimates based on HBS 2014.</u> Figure <u>7. Concentration of Education and Health by market income quintiles</u> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Poorest 2 3 4 Richest<br><!-- End of picture text --> <u>Source: World Bank estimates based on HBS 2014.</u> 25
to a 1 percent increase in unemployment. Thus wage elasticity of -.3 implies that an increase of 1 percent (not a percentage point!) in unemployment is associated with a .3 percent decrease in wages. The advantage of elasticity-based measures is that they are unit-free, and thus readily comparable across samples and countries.

While for most of the established market economies the wage elasticity with respect to local unemployment rates is around -0.1 after controlling for worker characteristics, evidence from transition countries is more diverse. Higher than flexibility than in Western countries was found in some former Communist countries, while in others no wage curve was discovered at all. OECD (2003a, Tables A3.1A3.2), using year 2000 LFS data, finds statistically significant wage curves in Estonia and Latvia but not in Lithuania; Hazans et al (2002) compare 1999 and 2000 results. The estimated elasticities in Estonia were –0.24 in 1999 and –0.15 in 2000, and in Latvia were –0.11 in 1999 and –0.05 in 2000. Thus, during the 1999 crisis the impact of unemployment on wages was twice as strong as in 2000. Men’s wages were much more sensitive to local unemployment rates than women's.

Results presented in Appendix Tables 24 and 25 confirm a very significant effect of local unemployment on wages across the 33 NUTS 4 regions<sup>14</sup> of Latvia. We find an elasticity of wages with respect to unemployment of -0.19 on average. Again there are substantial differences between the sexes. For men it is -0.24, while for women just -0.13. Private sector wages are more responsive to local labor market conditions than those in the public sector, with respective elasticities of -0.26 and -0.15 respectively. Finally, earnings of employees with higher education are significantly less sensitive to local unemployment rates than are earnings of less educated individuals. Estimated elasticities are
time series fluctuations. But NO2 also contains important crosssectional information. While NO2 has broader applications than just the COVID-19 pandemic, this is a nonetheless an example of a particularly stark reduction in economic activity where we may see geographic heterogeneity clearly.

**China.** Figure 3 depicts the change in NO2 in China, provinces outlined, between the first quarter of 2019 and 2020. Within the area of most significant downturns (blue) is Hubei province, containing Wuhan. Amongst all world countries, China is notable for how uniform the reduction in NO2 was in its populated regions reflective of the efficacy of lockdowns in spurring collective action. For reference, China had some of the highest global concentrations of NO2 before the crisis. World maps of NO2 in 2019 previous to the pandemic, and lockdown effects in 2020, are depicted in Online Appendix A.3.

**Europe.** Figure 4 depicts the change in NO2 in Europe between the second quarter of 2019 and

> 9NO2 was already below trend before the pandemic due in part to the 2019 manufacturing recession, which is more clearly seen in U.S.-specific data in Online Appendix A.2.

8
population. Approximately 15,038 household members in the baseline were re-interviewed in the wave of 2001, and 12,100 of these were surveyed in 2006. These figures imply an approximately 20 percent attrition rate between 2001 and 2006, excluding all new households that branched out from the 2001 original ones. For Mexico, the data is taken from the _Mexican Family Life Survey_ (MxFLS) for the first (2002) and second (2005) rounds. The survey is representative at the national, regional, urban and rural levels. The first wave includes approximately 8,440 households distributed in 150 localities, and the second wave includes 7,572 of the original households for an attrition rate of only 10 percent. As before, we only consider those households interviewed in both 2002 and 2005, excluding all new households that branched out.

Finally, for Peru we use the panel of the _National Household Survey_ (ENAHO Panel) collected by the Institute of Statistics (INEI), providing a five-wave unbalanced panel for the period 2002-2006. The size of the sample (6,257 in 2002; 4,217 in 2003; 6,490 in 2004; 6,778 in 2005; and 6,593 in 2006) is representative at the national, urban, rural, and regional levels (Coast, _Sierra_ and _Selva_ regions). The unbalanced nature of the panel results in gaps in the observations, since there is no information for all five waves for every household. Therefore, in this paper we use the information of those households interviewed in both 2002 and 2006 (5,092 households), which imply an attrition rate of approximately 20 percent similar to that found in the case of Chile, and comparable to longitudinal surveys in other regions of the world.

Then, the definition of the middle class with the vulnerability-to-poverty approach is applied to obtain the lower PPP income threshold to cross-sectional data in order to analyze the evolution of the middle class over a longer period of time. For Chile (1992-2009),
visits; data from the unannounced visit were used except in 29 cases where the school was closed at the unannounced visit but open at the announced visit. The results are robust if restricted to the unannounced sample only.

> 33 The Sindhi test has a large number of open-ended questions. Only multiple-choice items were used to fit the 2plIRT model. Results of the multiple-choice section of Sindhi were largely consistent with the English and Math tests and are omitted for brevity.

30
). Labor force participation rates increased slightly more for women (1.5%) than men (1.4%) (see also Cameron, 2000).<sup>8</sup> Thus, the impact of the crisis on aggregate employment was muted (Fallon and Lucas, 2002).<sup>9</sup> Thomas et al. (2002) conclude that ―the evidence suggests that short-run labor supply functions are fairly inelastic in Indonesia‖ (p. 177). If this is true, then a reduced form

> 8 By contrast, their estimates based on the Indonesian Family Life Survey, a rotating panel of households, suggest that women‘s employment rates went up slightly, primarily because many women started working as unpaid family workers.

> 9 The aggregate figures masks reallocation of employment across sectors, including a reversal of rural-urban migration patterns and an increase in informality. Both the industrial distribution of jobs shifted and patterns of job creation altered substantially: while manufacturing, construction and retail sectors had been growing rapidly in the lead-up to the crisis, that trend was reversed once it had begun.

7
on estimates derived from the statistical model.

The report is structured as follows. The remainder of this section reviews the definitions of the key indicators to be estimated. Section II reviews the data used in the study (relying on both survey microdata, and complementary satellite/administrative data). The approach used for the maps described in this report is discussed in more detail in section III. Section IV includes the district-level maps for Central Asia. Section V briefly discusses the results and gives two examples of uses for the maps (comparing migration rates to poverty rates and locating program locations on maps). The annexes include detailed tables of the results at the level of districts (rayons) throughout Central Asia, validation and robustness exercises, and the regression models used.

# **I.I – Definitions of Poverty in Central Asia**

The World Bank regularly produces internationally comparable estimates of poverty as part of its mandate. In 2017, the World Bank updated all such estimates to a new set of poverty lines, and at the same time, began applying up-to-date purchasing power parity (PPP) conversion factors for each country (based on estimates of price differences in 2011). The objective of calculating the World Bank’s internationally comparable poverty estimates is to estimate the prevalence of poverty in terms of a single global standard. Measures created on this basis are in turn used to monitor progress towards development goals set by the World Bank, the member states of the United Nations, and other partners.

The World Bank poverty estimates contrast with national official poverty estimates in Central Asia in several ways. The most common official poverty approaches in the region consider local patterns of 8