rafmacalaba/data-use-mentions-extended huggingface dataset:
- Hallward-Driemeier, Mary, Bob Rijkers, and Andrew Waxmen, 2010. “Women in Crisis: Firm level Evidence on the Labor Market Impacts of the Indonesian Crisis.” Mimeo. World Bank.

International Labour Organization, 2010a, “Global Employment Trends: 2010”

- International Labour Organization, 2010b, “Women in Labour Markets: Measuring Progress and Identifying Challenges”

- International Monetary Fund and International Labour Organization, 2010, “The Challenges of Growth, Employment, and Social Cohesion”, Background discussion paper.

- Ha, Byung-jin, Caroline McInerney, Steven Tobin, and Raymond Torres: “Youth Employment in Crisis”, ILO discussion paper 201/2010

- Leung, Ron, Marco Marco Stampini and Désiré Vencatachellum. 2009. Does Human Capital Protect Workers against Exogenous Shocks? South Africa in the 2008-2009 Crisis. IZA working paper no. 4608.

- Lim, Joseph. 2000, “The Effects of the East Asian Crisis on the Employment of Women and Men: The Philippine Case.” _World Development._ 28(7), p. 1285-1306.

- Khanna, Gaurav, David Newhouse, and Pierella Paci, forthcoming, “Fewer jobs or smaller paychecks? Aggregate Impacts of the Crisis in Selected Middle-Income Countries”

- Lokshin, Michael and Martin Ravallion, “Welfare Impacts of the 1998 Financial Crisis in Russia and the Response of the Public Safety Net,” _Economics of Transition,_ vol. 8 no. 2.

- McKenzie, David, 2004, “Aggregate Shocks and Urban Labor Market Responses: Evidence from Argentina’s Financial Crisis”, _Economic Development and Cultural Change._ 52, p. 719–758

- McKenzie, David, 2003, “How do Households Cope with Aggregate Shocks: Evidence from the Mexican Peso Crisis”, _World Development_ , Vol. 31 no. 7.

- Sabarwal, Shwetlena, Nistha Sinha, and Mayra Buvinic, 2011. “How do Women Weather Economic Shocks: What We Know” Economic Premise no. 46. World Bank

- Scarpetta
Hindu and Muslim households live in rural areas. In the 2011 Indian Census, the Hindu rural share was 71% compared to the Muslim rural share of 60%. These demographics align with those in IHDS-sampled households, for which approximately 68% of Hindu households resided in rural areas compared to 56% of Muslim households. Figure 1 plots the share of Indians and Muslims living in urban areas over time, which shows longstanding differences in urbanization between Hindus and Muslims and the (predominantly Hindu) Indian population. In each decade, Muslims are more urbanized than the overall Indian population.<sup>12</sup> The differences between Hindus and Muslims in latrine ownership reported in the IHDS data (Table 1) are similar to the differences in latrine ownership reported in the DLHS3 data (Appendix Table 1) and differences in latrine use reported in the NFHS-3 data (Appendix Table 2). The analysis calculates large unconditional differences between Hindus and Muslims, between 18 and 23 percentage points, which are similar to the 25 percentage point difference reported by Geruso and Spears (2018) using NFHS data pooled across 199293, 1998-99, and 2005-06. This overall difference is similar to the rural difference between Hindus and Muslims, with smaller differences between Hindus and Muslims in urban areas.

Table 1 also includes other sanitation practices: whether household members wash their hands after defecating, and whether the surveyor did not observe fecal material near the home. Hindu households report slightly higher handwashing than Muslim households in urban areas (row 2, column 6) and slightly lower handwashing in rural areas (row 2, column 9). Surveyors are less likely to observe fecal material near the homes of Hindus, and this between Hindus and Muslims in urban areas and the difference between Hindus and Muslims in rural areas. The overall difference also reflects lower latrine ownership in rural areas than in urban areas, along with a greater share of Hindus living in rural areas than the share of Muslims living in rural areas. For example, there could be no
the possible endogeneity of the schooling measure and instrument it appropriately. This gives a higher return to female education, but almost no change for men. On average, in Russia, an additional year of education provides a relatively small – and declining – increase in wages.

In the next section we provide a brief overview of the literature with a focus on Russia. Section 3 describes and analyzes the RLMS data used in this study. Section 4 presents the empirical results and Section 5 offers some conclusions.

# **2. Literature Review**

In a worldwide perspective, the latest findings on returns to education can be condensed to the following (Psacharopoulos and Patrinos 2018): (1) overall, an increased share of workers with tertiary education in the labor market has not reduced the magnitude of returns on the investment due to “skill-biasedness” of technological progress boosting the demand for higher skills; (2) lowand middle-income regions are characterized by the largest returns (except for the Middle East and North Africa, with the lowest returns); (3) the private returns to education for women outstrip those for men by roughly two percentage points; (4) private sector employees receive greater returns than those working in the public sector; (5) social returns to education are negatively associated with a country’s level of economic development and education level; and (6) on average, there is a growing trend in returns to higher education.

A small corpus of the research on returns to education has focused on the Russian/USSR case. In the USSR, during the period before education reforms, the private rate of return to schooling was strikingly low: 2-3 percent for secondary and 5 percent for higher education levels (Graeser 1988). Low returns to human capital were in line with a planned economy offering free education, centralized allocation of labor, and the ideology of the dictatorship of the proletariat; a similar picture was observed in other contemporaneous socialist countries (see, for example, Münich, Svejnar
# **Tables**

**Table 1: Data and sources.**

|Variable|Definition|Source|
|---|---|---|
|Colonyij|Dummy variable equal to unity if countries i and j<br>were once in a colonial relationship.|CEPII.|
|Colonizerij|Dummy variable equal to unity if countries i and j<br>were colonized by the same power.|CEPII.|
|Contiguousij|Dummy variable equal to unity if countries i and j<br>share a common land border, else zero.|CEPII.|
|Distanceij<br>|Geodesic distance between the main cities of<br>country i and country j.|CEPII.|
|Exportsij<br>k|Exports from country i to country j in sector k.|UN Comtrade via WITS.|
|GDPi|GDP in country i.|World Development Indicators.|
|Languageij|Dummy variable equal to unity if countries i and j<br>share a common language, else zero.|CEPII.|
|LPIi|Logistics Performance Index score for country i.|World Bank LPI database.|
|Parts<sup>k</sup>|Dummy variable equal to unity for trade in parts and<br>components,else zero.|Classification based on Ando<br>and Kimura(2005).|

_Note: All variables are for 2007._ 24
|Index of fatalities from violence (normalized 0-1)**|205|
|---|---|
|Index of homicides (normalized 0-1)***|90|
|_Social cohesion index (0-1)_|_68_|
|**Process Legitimacy**||
|Rule of law (normalized 0-1)|209|
|Control of corruption (normalized 0-1)|209|
|Government powers are effectively limited by the judiciary (score 0-1)|139|
|Equal treatment and absence of discrimination (score 0-1)|139|
|People can access and afford civil justice (score 0-1)|139|
|Government regulations are applied and enforced without improper influence (score 0-1)|139|
|The personal rights to life and security are effectively guaranteed (score 0-1)|139|
|_Process Legitimacy Index (0-1)_|_139_|

Source: Social Sustainability Global Database 2022; Social Sustainability Global Database 2022 Codebook. Notes: (*) Average set of assets that include computer, cellphone, radio, television, washing machine, sewing machine, car, fridge, motorcycle that is owned by a household in the country, normalized between 0 to 1 (1=having all assets considered). (**) Index of fatalities due to violence in a country. The indicator is normalized across the whole country sample using varvarmin/varmax-varmin (***) Index of the number of homicides per 100,000 inhabitants in a country normalized across all the countries in the sample using var-varmin/varmax-varmin

# **4.2 Linking social sustainability, poverty, inequality and income levels**

We use country-level data to scatter plot the logarithm of per capita GDP in PPP (international dollars 2017) and each of the social sustainability dimension indices. Additionally, we also plot poverty headcount, using national poverty lines of each country’s latest year with available information, against each of the indices. A third set of scatterplots looks into the relationship between
Our measures of urban bias (ratio of domestic to world price of cereals; black market premium) or of shocks to agriculture are never significant determinants of either the level or the pace of urbanization. At any rate, they do not help explain why Africa is different.

One can question whether the particularly high rural-urban wage differentials in Africa in the post-colonial period is a symptom of urban bias. It is possible, but by no means certain. They may have reflected differences in productivity - the fact that they gradually decreased over time at about the same rhythm as in East and South Asia supports this hypothesis. Anyway, they are surely best understood as a symptom of elite - rather than urban-- bias.

So does Africa's urbanization process remain a puzzle? Africa, at the end of the colonial period, was underurbanized given its income and income structure due to the policies of the colonial powers. The 1960-80 period was characterized by very rapid urbanization, even more rapid than can be explained by a catch-up hypothesis, traditional urban-bias measures, agricultural shocks, or civil disturbances. It is largely explained, however, by rural-urban wage differentials. The slowdown in the pace of urbanization after 1980 is significantly greater than can be explained by our explanatory variables. However, given that Africa had urbanized "exceedingly rapidly" in the 1960-80 period, the slowdown that followed is not unexpected. In fact, if we let initial urbanization level take 1975 values for the 1980-90 observations (instead of 1960), the post-80 Africa dummy is no longer significant.

Alternative explanations could, of course, be poor data. One argument is that urbanization data in Africa are simple projections based on old census data and therefore the results presented here have limited meaning and interest. But if this were the case, we should find that Africa in the post-80 period continued to urbanize exceedingly rapidly. The bias would be in the other direction. Another point frequently made is that we may severely underestimate income and income growth in Africa, as much
# **3.3 Before-after approach with block fixed effects**

While the above design will provide robust evidence on the impact of land titling, effects of policy changes that affect treatment and control areas equally are not identified. To separately analyze these impacts and those of new systematic or sporadic titling on newly registered owners’ gender and the share of plots for which transfer in land or mortgage markets were registered, we use annual data at block level for 20002019 as described above. Block and year fixed effects are used to control for time invariant local conditions and time variant common shocks. If included as an independent variable, the lagged stock of sporadically registered/titled plots is instrumented by its second lag. Formally, for each block _i_ and year _t_ , we have the number of plots (denoted by i ) that entered the registry for the first time in _t_ , separately for systematic ( i ) and sporadic ( i ) modalities of registration/titling. We rr vv pp also have information on the stock of registered plots at rr _t_ , denoted by rr i overall and by i and i for vv pp systematic and sporadic modalities, respectively. For both stock and flow, we know if plots entering the RR RR RR registry at _t_ are in the name of females alone ( i _or_ i ), males alone ( i or i ) or males and females FF FF MM MM jointly ( i or i ). Letting _P_ be an indicator variable for the post-2010 regime when the Land Act was rr RR rr RR JJ JJ effective and LAA had been established, the basic equation to be estimated is rr RR i 1 i 2 i i (2) ii ii where i is either i i or YYi = ααi , i.e., the difference between the number of plots registered in the + ββ rr + ββ rr ∗PP+ λλ + εε FF MM JJ MM name of females individually or jointly and that of men orYY rr −rr rr −rr i i ,
**Figure 10:  Effect of Changing Weight on Health in the Human Capital Index**

<!-- Start of picture text --> Human Capital Index: Robustness to Weights<br>Corr=.998 Corr=.997<br>.2 .4 .6 .8 1 .2 .4 .6 .8 1<br>Baseline Weight on Health Baseline Weight on Health<br>Corr=.993<br>.2 .4 .6 .8 1<br>Baseline Weight on Health<br>Note: Dataset version 21 Sept 2018<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Low Weight on Health High Weight on Health<br>.2 .2<br>1<br>.8<br>.6<br>.4<br>.2<br>Equal Weight on Health and Education<br><!-- End of picture text --> Notes: This graph shows the effect of changing the weights on the health and education components of the HCI. In each panel the horizontal axis corresponds to the HCI with baseline weights. In the top‐left (top‐right) panel the vertical axis corresponds to the HCI assuming a low‐end (high‐end) estimate for the return to health from the empirical literature, as discussed in Appendix A3. The bottom‐left panel assumes a much larger value for the return to health that generates the same gap in productivity between best and worst performers as is observed between the best and worst performers in learning‐adjusted years of school. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.
ages, the relationship between participation in wage work and enrollment is negative. At age 6 almost all children are enrolled in school and the share remains virtually unchanged until age 11. Starting from age 11 there is an inverse relationship between participation in wage work and schooling, with an increase in labor market participation being accompanied by a simultaneous decrease in school enrollment (Figure 2). At the same time, working does not affect whether younger children, especially those in elementary school, are in the correct grade. Younger children who participate in household chores are able to keep up with school and be enrolled in the correct grade.

**Figure 1 Share of children working in domestic and wage work Figure 2 Probability of schooling and wage work by age, 2015**

<!-- Start of picture text --> by age, 2015<br>100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10<br>10<br>0<br>0<br>5 6 7 8 9 10 11 12 13 14 15 16 17<br>5 6 7 8 9 10 11 12 13 14 15 16 17<br>Age<br>Age<br>Wage work Household chores<br>Schooling Wage work<br>All type of work<br>Percent of children<br>Percent of children<br><!-- End of picture text --> _Source: authors’ estimates using LFS 2015_ _Source: authors’ estimates using LFS 2015_ Participation in any type of work decreases with wealth, from 38.3 percent for children in the lowest decile to 31.2 percent for children in highest decile (Figure 3). There is no clear trend in labor market and household chores participation across wealth deciles and no gender differences. Using imputed consumption, the
the Understanding Children’s Work (UCW) project<sup>6</sup> (in international partner organizations and academic institutions) to obtain information on further relevant impact evaluations. We make use of the UCW inventory and present the most relevant results of all peer-reviewed papers. Non-reviewed studies were considered if they apply a plausible and rigorous strategy to identify the impact of the program on child labor.<sup>7</sup> The papers we discuss include randomized trials as well as regression discontinuity designs, natural experiments, and propensity score matching studies.<sup>8</sup> Papers that do not explicitly evaluate the impacts of the intervention on child labor outcomes were excluded.

As mentioned in the previous section, the concepts used to categorize children’s work and child labor are at times inconsistent in published statistics and research reports. Similarly, there is substantial variation in the productive activities covered by the impact evaluations discussed in this review. To achieve consistency, our discussion primarily focuses on impact estimates for children’s participation in economic activities conducted for pay and/or for the household (i.e. excluding household chores) and discuss, when available, the program impacts for different categories of economic activities or household chores. We present the impact on the extensive margin of child labor (as this is the outcome that most studies examine) and on the intensive margin of child labor. We focus on children aged 6 – 14 years, the cut-off is set as the minimum working legal age set in most countries. We refer the reader to the extensive literature on youth labor programs, defined as the population aged 15 to 24 (see Behrman 1999, Card et al. 2015, Blattman and Ralston 2015, among others).

Our filters produce a final sample of 31 evaluations. It should be noted that impact evaluations around child labor tend to suffer from two limitations: (i) seldom is child labor the main outcome of interest of labor and social protections programs and (
effects that control for all possible country-year specific factors. We also estimate models in which the dependent variable takes value 1 if the firm invested in year t. The idea is that there could be positive externalities associated with increasing investment. We estimate Equation 1 (and all the models described below) using the survey weights reported in the World Bank Enterprise Survey rescaled to add up to one for the firms included in the regressions.

While Equation 1 cannot tell us if the possible relationship between firms and government owned banks is driven by demand (i.e., firms with specific characteristics prefer to borrow from government-owned banks) or supply considerations (i.e., government-owned banks seek firms with certain characteristics), this is not an issue for us. If firms with certain characteristics prefer government owned banks, it must be the case that these banks offer them better conditions (in terms of quantity or cost of credit) which are specific to these 10
1

# 1. Introdudtion

- Most developing covntries have social security **systems** publicly administered sets of programs which provide for people in the event of lose of income (due to retirement, disability, death of a breadwinner, illness, maternity, work-related injury, or unemployment) and often in the event of need for medical care or the expense of raising children. Usually - but not always - the programs are not as extensive as programs in industrial countries. They usually cover only a small part of the population (often well under ten percent) and benefits **are** typically less generous than those in the industrial countries.

Still, social security systems make up a large proportion of public sector revenues and expenditures .in a number of developing countries. This fact may be hidden, for in most countries, systems are managed by autonomous public bodies whose financial flows are only partially reported in central government statistics. Social security systems have major implications for public finance for two further reasons. First, systems in most developing countries have growing reserve funds which finance much of their governments' deficits and also, sometimes, provide a pool of investment capital which furthers economic development. Second, social security systems pose special financial dangers for central governments. Unless systems are managed effectively with a view to covering future liabilities, they can become insolvent. Already a number of systems have come to
hazards of floods and droughts and are vulnerable to changes in climate.

BioClim is a global gridded database at 30 arc-second (1km2) spatial resolution of bioclimatic variables that are derived from monthly temperature and rainfall values from cleaned weather station data and elevation. The bio-physical model interpolates these meteorological variables and provides derivative variables of seasonality over the base period of 1960 to 1991 (Hijmans et al. 2005)<sup>17</sup> . We extract these 1km2 pixels for annual mean temperature ( _bio1_ in degrees Celsius * 10), temperature seasonality ( _bio4_ in standard deviation * 100), annual precipitation ( _bio12_ in mm * 10), precipitation seasonality ( _bio15_ in coefficient of variation * 10), precipitation of wettest quarter ( _bio16_ in mm * 10) and precipitation in driest quarter ( _bio17_ in mm). We aggregate the data to obtain mean values for each administrative area. In Figure A2.4 (left panel), we see the effect of elevation in the higher temperature of the Great Rift Valley

> 16We modified negative values that represent pixels classified as urban, closed forest or irrigated to equal zero for the mean calculations by administrative area.

> 17For the interpolation of noisy multi-variate meteorological data, the authors use thin plate smoothing splines from the ANUSPLIN program with latitude, longitude, and elevation as independent variables.

44
the exact wording of the question; the age, gender, and status (i.e., household head vs another household member) of the survey respondent; the unit of analysis (individual vs household) and the data source’s geographical representation (administrative levels and urban-rural). Small differences in the wording of some questions from different sources were assessed and categorized accordingly. For example, the indicator that captures freedom of expression, namely “ _share of population that agrees they are free to express what they think_ ”, derives from three different sources, with the original questions referring to freedom to say what you think; the extent to which freedom to express opinions is guaranteed; and agreement with the statement that people are free to say what they think without fear. In other cases, questions asked in different sources were judged to be too different to be harmonized. This is the case for the internet use indicator, which derived from several different sources, one of which was not possible to merge with the others given that it referred to using the internet as a source of information. As a result, two indicators were generated: internet use and internet use for information purposes. A codebook is included in online Annex A that shows the various definitions of variables and their differences in wording. Missing values is another issue that required harmonization. In the global data set, we assume that missing values for up to 5 percent of the sample are either random or do not cause a significant bias. When missing values exceed that cutoff (typically for sensitive questions in perception surveys), the global database does not include that variable and looks for a close alternative in another data source.

The final step is the _consolidation in a single data set_ of harmonized social sustainability indicators and other indicators of interest for our analysis. The consolidation exercise consists first of selecting a reference period. In order to maximize the number of variables and countries, we refer to a “most recent” period comprising 2016 to 2020, instead