rafmacalaba/data-use-mentions-extended huggingface dataset:
development). Three, combating inequality through policies increasing access to markets, services and social protection is unlikely to be effective in their own: they need to be accompanied by strengthening social cohesion and ensuring fair and legitimate interactions between governments and citizens. Four, more developed and wealthier societies are not automatically more inclusive and resilient societies. Instead, social sustainability interactions are complex and contextual. Experience of conflict, fragility, authoritarian political regimes, and exposure to natural disasters, among other factors, all matter. Countries as diverse as Mongolia (social inclusion), Kyrgyzstan (resilience), Ghana (social cohesion) and Mauritius (process legitimacy) top our table of 130 countries and territories in each of these dimensions of social sustainability.

Finally, our analysis shows that while social sustainability is an elusive and intricate concept, it can be operationalized in a way that captures its complexity—and that this can be done in a relatively simple and parsimonious way, through a manageable set of meaningful indicators, flexibly used. Both our database and correlation analysis are first steps in that direction. They do not provide normative and precise policy designs but underscore that social sustainability not only matters in itself, but potentially as an instrument associated with poverty reduction.

# **References**

Ahman, E. 2013. Social sustainability - society at the intersection of development and maintenance. _Local Environment_ , 18(10), 1153-1166.

- APM Research Lab 2020. The color of coronavirus: COVID-19 deaths by race and ethnicity in the U.S. Available at: 2020https://www.apmresearchlab.org/covid/deaths-by-race

Alkire, S. & G. Robles. 2017. ‘Multidimensional Poverty Index 2017: brief methodological note

- and results’, OPHI MPI Methodological Notes 44, Oxford Poverty and Human Development Initiative, University of Oxford.

- Azevedo, J.P. 2020. Learning Poverty: Measures and Simulations. Policy Research Working Paper, No. 9446. Washington, DC: World Bank

- Balseca,
number of its employees (see Table 1.1). All these imply that a “one size fits all” approach is less useful in analyzing the informal sector.

3. This study focuses on the smallest informal firms, the household enterprises (HEs), as these entities are uniquely placed within the informal sector. HEs represent both conditions of informal employment and informal enterprise. For the most part, they are started by a single entrepreneur so they create employment for the owner and establish small businesses within the household and the economy.

4. Household enterprises, as defined in household and labor surveys, consist of own-account operators and unpaid family workers. From the standpoint of enterprise surveys, they are tiny firms consisting of a single entrepreneur, perhaps working with unpaid workers who are likely to be family members.<sup>4</sup> In practice, many people may participate in HEs as a secondary activity, including farmers, civil servants, and schoolchildren. Although this complicates the statistical picture, it means that HEs may be even more important for coping with and exiting poverty than would be suggested by data based only on primary occupations (Steel and Snodgrass, 2008).

> 3 See ILO (2002), Fox and Gaal (2008), and Heintz (2004).

> 4 The term “ _nano_ enterprises” may also be applied to indicate that these are the lowest rung on the ladder of enterprise sizes, with no wage workers.

1
those who have only a farm — 0.72 ha.

> 5 The main occupation for non-farm wage workers with secondary education is as primary school teacher (61.5 percent of rural adults with secondary or further education who hold a non-farm occupation for their main activity are primary school teachers). The small proportion of individuals with further education who hold an occupation are primary and secondary school teachers, or high level civil servants.

> 6 Quintiles are based on data regarding consumption. These variables were derived by the Ministry of Finance in their poverty profile (2002). The description is in Annex 2 of the poverty profile. In short, “The lowest quintile contains the poorest 20 percent of people in Rwanda, ranked according to the consumption standard of living measure of their households, the second quintile the next poorest 20 percent and so on until the top quintile which contains the richest 20 percent.” (page XI, Ministry of Finance, 2002).

5
concern with this approach is that the phone sample will differ in systematic ways from the original 2019 sample, not least because ownership of mobile phones is correlated with wealth in this population (see Table 1). We account for this in the following way. Our 2019 survey contained a rich set of observable characteristics that we can use to predict the probability of response to our phone survey (see Table 1) using a logit model (see Table A1). We then

> 3 Poverty status was determined using a subjective poverty measure in which households were asked to rank themselves on a seven rung poverty ladder. The first rung represented the very poorest households in the village and the seventh rung, the very richest households in the village. Our previous work in Ethiopia shows that this poverty ladder is well correlated with other (more objective) welfare measures: durable asset levels, livestock holdings and self-reported food security. Non-PSNP households were chosen from the bottom four rungs of the ranking. Please see Berhane et al. (2020) for detailed discussion about this selection process.

> 4 This is an attrition of 2.9 percent largely related to two reasons: absence of households at home in the rainy season and some areas were inaccessible because of ongoing civil unrest.

> 5 The March 2019 sample considered 0-24 months old children. In June 2020, our children sample became 13 months older. In addition, a new child has been born to 15 percent of the mothers in our sample. Given the interest in younger children, whenever possible, we have replaced the old index child by the young ones. Thus, our current child sample is composed of 0-36 months old.

> 6 The median interview time was 32 minutes.

5
al. (2009) analyze five rounds of household surveys carried out for 10 years. However, this approach is costly and often impossible. In the transport sector, there are few data that are regularly collected.

6. In the literature, in addition, potential benefits from transport connectivity are often defined in a dichotomous fashion, depending on proximity (e.g., a distance of 2 km) to road infrastructure (e.g., Dercon et al., 2009; Khandker et al., 2009; IMF, 2020). This binary definition of connectivity is often not realistic particularly when panel data with large _T_ is considered. The potential impacts could be distributed more widely, depending on where people
- **Krauser, Mario** , “In the Eye of the Storm: Rebel Taxation of Artisanal Mines and Strategies of Violence,” _Journal of Conflict Resolution_ , 2020, _64_ (10), 1968–1993.

- **la Sierra, Raul Sanchez De** , “On the Origins of the State: Stationary Bandits and Taxation in Eastern Congo,” _Journal of Political Economy_ , January 2020, _128_ (1), 32–74.

- **Leysen, Jan, Ken Matthysen, Ntakobajira Zacharie Bulakali, and Thomas Muller** , “Analysis of the interactive map of artisanal mining areas in eastern Democratic Republic of Congo (2023 update),” Research Report, International Peace Information Service (IPIS), Antwerp 2023.

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- **Moreno, Laura, Jorge Gallego, and Vargas Juan** , “More roads, more conflict? The effect of rural roads on armed conflict and illegal economies in Colombia,” _Universidad de Rosario Working Paper_ , 2019, _248_ , 1–49.

- **Mueller, Hannes and
# **1. Introduction**

Does one need connections to get ahead in life? Three-quarters of adults, according to the data from the latest round of the Life in Transition Survey that queries adults across all transition economies and several Western European countries on their attitudes toward many aspects of life, report that connections are at least moderately important (more than half think them very important or essential) to get a good job in the government sector. More than two-thirds think connections are at least moderately important to get a good job in the private sector (EBRD, 2017). If socio-economic mobility is a universal human aspiration, of parents hoping for a better life for their children, as argued in the recent World Bank report _Fair Progress? Economic Mobility Across Generations Around the World_ (Narayan, Van der Weide et al., 2018), then these subjective assessments, based on nationally representative data covering a region with a population of about 500 million adults, present a very bleak picture of the perceived equality of opportunity for success in life.

A related, but different question is whether the perceived need for (and availability of) connections to access opportunities affects one’s expectations of future mobility? In other words, if chances for success are not equal, then the extent of one’s future mobility may be a function of access to connections or other factors that facilitate mobility. However, perceptions of inequality may influence not only where one ends up in the future, but also one’s own belief of where he or she will end up in the future, that is, one’s expectations of (or aspirations for) future mobility. The importance of the link between inequality (and inequality of opportunity) on one hand and mobility on the other, is illustrated prominently by the _The Great Gatsby Curve_ (Corak, 2017) which shows high levels of inequality to be associated with low levels of experienced mobility. The link between inequality and expectations of mobility is important in its own right
# Trends and Determinants of Female Labor Force Participation in Morocco: An Initial Exploratory Analysis

Gladys Lopez-Acevedo, Florencia Devoto, Matías Morales, and Jaime Roche Rodriguez

## **JEL classification** : J16, J21, O54

**Keywords:** Morocco, Female, labor force participation.

This is a background paper for the World Bank Jobs Diagnostic of Morocco (2020). We thank Uche Eseosa Ekhator-Mobayode, Gordon Betcherman, Vasco Molini, and Federica Alfani for useful discussions and suggestions in the crafting of this paper. All remaining errors are the responsibility of the authors. To contact the authors please write to gacevedo@worldbank.org. Gladys Lopez-Acevedo<sup>1</sup> is a Lead Economist and Global Lead at the World Bank in the Poverty and Equity Global Practice Group (PEGP). Florencia Devoto, Matias Morales, and Jaime Alfonso Roche Rodriguez are consultants at the World Bank in the Poverty and Equity Global Practice Group (PEGP). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Postal address: 2232 40<sup>th</sup> Pl NW, Washington DC, 20007, United States of America.
**<sup>**10**</sup>|210|5|18|

Source: EPZ FDI author’s compilation from individual country EPZ authorities; national FDI data from UNCTAD How does this investment translate into actual firms operating on the ground in the EPZs? Figure 2 gives a perspective on the number of active firms operating in the zone programs in each country under study, distinguishing between those firms operating in spatially‐defined zones (or “enclaves”) and firms which operate as “single factory” units. Again, what is clearly apparent from Figure 3 is the gap in scale between the African and non‐African zones in the study. The zone program in the Dominican Republic supports more than 550 firms, in Honduras there are nearly 350 firms, and in Bangladesh nearly 300. Vietnam (whose data are not presented in the graph for scale reasons) supports 3,500 firms in its export processing and industrial zones. In contrast, excluding the single factory units, the African zones in the study have, on average, little more than 35 firms operating in them. Such small scale operations not only have financial implications on program outcomes but are also likely to restrict the potential for their host countries to leverage dynamic benefits through the programs.

Both Senegal and Ghana (with 300 and 180 firms respectively<sup>11</sup> ) have a large number of firms operating through “single unit” free zone programs. But even taking these into account, the scale of the African

> 9 Source: compiled from data provided by national zone authorities. Note that data on investment in most EPZs: 1) does not differentiate FDI from domestic investment and 2) reports only the cumulative value of annual investments and does not provide actual FDI flow data. As a result, we have determined annual “flows” by taking the difference in cumulative FDI from one year to the next. We have then compared this to FDI stocks data from UNCTAD and again taken
|Table 11. Over|view of scorecard results<br>||
|---|---|---|
||Target fullyachieved|Performs above the median|
|Cost<br>recovery|Burkina Faso, Cape Verde, Chad, Côte<br>d’Ivoire, Kenya, Namibia, Senegal, Uganda|Ghana, Madagascar, Mozambique, Rwanda,<br>South Africa,|
|Efficiency|Chad, Kenya, Uganda, Ghana, Mozambique,<br>Rwanda, Botswana, Cameroon, Lesotho,<br>Malawi,Congo,Rep. of|Namibia, Congo, Dem. Rep. of|
|Affordability|Senegal, Cameroon, Cape Verde, South Africa,<br>Congo, Dem. Rep. of|Kenya, Tanzania, Zambia, Côte d’Ivoire|
|Equity|None|Cameroon, Cape Verde, South Africa, Côte<br>d’Ivoire, Congo, Dem. Rep. of|

_Source:_ Derived from Africa Infrastructure Country Diagnostic Power Tariff Database.

38
respondents grossly overestimated the number of deaths from both nuclear accidents. More than 75 percent of respondents think the number of deaths caused by the Chernobyl nuclear plant accident exceeded 10,000, with almost 17 percent believing that the accident caused a million or more deaths, but the actual estimates range from 50 deaths directly related to the accident to 4,000 total deaths from the radiation exposure over the lifetime.<sup>5</sup> About 92 percent of respondents think that the Fukushima accident resulted in fatalities much exceeding the actual estimates of no radiation-related deaths. Fifteen percent of respondents estimate the total number of fatalities in millions and almost 30 percent in hundreds of thousands.

We estimate a model that relates respondents’ perceptions of climate change and their willingness to pay for climate-change-mitigating policies with the aggregate index of their knowledge about fatalities in four global catastrophes. We use the instrumental variable (IV) approach to mitigate the effect of systematic non-response bias. Sensitivity analysis confirms the stability of our results to a wide range of alternative model specifications, aggregation methods, and variable definitions.

This paper presents evidence that challenges the assumption that more knowledgeable citizens invariably make better political decisions, particularly regarding climate change. It is widely believed that policies rooted in ignorance may lead to unjustified risks; our findings suggest that the opposite can be true in certain contexts. We show that individuals with _less accurate_ perceptions of catastrophe-related deaths tend to hold _more accurate_ beliefs about the societal risks of climate change and that people with more accurate beliefs about fatalities from catastrophes tend not to take climate change as seriously as scientists believe they should.

These findings challenge the conventional wisdom that better-informed citizens will make decisions that are in society’s best interests. In some cases, an inflated sense of risk from catastrophes may be more effective at instilling the urgency needed to address critical global challenges. Our findings suggest that a more nuanced understanding of the relationship between

> 5 These numbers correspond surprisingly well with the result of the 2012 All Russia Omnibus survey, which asked
The estimated coefficient of nominal wages in levels is negative, less than one and is statistically significant and different from zero. The solved long-run equation with zero autonomous growth, (Aw = Aut = Apt = AGDPt = 0), for manufacturing wages is: w = -4.1 + 4.5 u - 0.6 p + 1.4 GDP which describes a significant error-correction mechanism between wages, employment, GDP and prices. However, unemployment and prices seem to have no role to play in the error correction mechanism and, hence, on manufacturing wages in the longer term. The steady state solution of the model presented in Table 6 consistent with the growth of wages implied by the long-run equation (7) takes the form: One implication for wages is that the equilibrium value of nominal wages is larger _ceteris paribus_ the larger the steady state growth rate of GDP. Furthermore, the equilibrium value of nominal manufacturing wages is associated negatively with increases in the unemployment growth rate.

Different wage models are now considered, starting out analyzing whether manufacturing data fit a real wage specification. The results imply that real wages do respond to the strength of demand measured by GDP. Further, the real wage hypothesis seems to hold as the restrictions on the model, w-p=O, and the sum of t]he coefficients of w, u, GDP and p equal to zero cannot be rejected (F(1,20) = 0.65 [0.43]; F(1,20) = 2.05 [0.17])." The final model specification explains 72 percent of the variation in manufacturing wages.

> '" We rejec **t** he hypotheses that wages are determined by: (1) a first order error correction model with wages adjusting towards a static equilibrium that depends on prices; (2) a first order partial adjustmnent model with target value of wages depending on unemployment only.

30
here, the variation is due to both redistribution gains and efficiency costs.<sup>41</sup> There are two main take-aways from Figure 7. First, consistent with our model predictions, we find that over development, equity considerations tend to decrease the rate on food relative to non-food goods (thus increasing rate differentiation) but efficiency considerations tend to increase it. Equity effects dominate, so that optimal relative rates on food fall with development when both effects are taken into account. Second, comparing panels B and D, we see that once we take into account the fact that only formal varieties can be taxed, the optimal policy no longer subsidizes food relative to non-food in some poor countries (the ratio of food to non-food rates is higher than 1). Once the impossibility of taxing informal varieties is accounted for, taxing food less than nonfood goods cannot by justified on equity or on efficiency grounds in these countries. Appendix Figure A14 shows that both of these findings are robust to changing values of the cross-variety price elasticity.<sup>42</sup>

# **6.2 Effect of Consumption Tax Policy on Inequality**

Figure 8 presents the effects of different consumption tax scenarios on income inequality in the average country in our sample. In Panel A we use the calibrated optimal tax rates for each scenario (allowing efficiency costs to vary with development) and use these rates to calculate the net of tax income of each household in our data. Our redistribution metric is the percent change in Gini from the pre-tax income distribution to the netof-tax distribution. To benchmark our results against estimates in the literature, Panel B reports the inequality impacts of actual tax policies in place in a comparable sample of developing countries obtained by Lustig (2018). To calculate these effects, we use their Commitment to Equity (CEQ) database which contains information on income and estimates of taxes paid (consumption taxes, direct taxes and social security) for each income decile in 25 developing countries.<sup>
*|0.789|-0.021*|-0.022**|0.789|
|Improved sanitation|-0.007|-0.001|0.7581|-0.014|-0.009|0.7581|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

# **Table A14: Identifying the $3.20 poor based on covariates of poverty**

|<br>|<br>|<br>_Tajikistan_<br>|<br>_2015_<br>|<br>|<br>||
|---|---|---|---|---|---|---|
|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Square root**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Square**<br>**root poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|
|Asset index|-0.024***|-0.027***|0.6774|-0.146***|-0.149***|0.6774|
|Access to electricity|0.031|-0.025
|Yes|Yes|
|Birth fixed effects (Age Cohort)|Yes|Yes|Yes|Yes|
|Household controls|No|Yes|No|Yes|
|Constant|8.355***|4.677***|8.477***|4.651***|
|Observations|16,345|16,017|16,345|16,017|
|R squared|0.235|0.423|0.241|0.426|

Notes: The household level controls include log per capita consumption expenditure, gender, gender of household head, average years of education in the household, ethnic groups and religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

The estimated coefficients of the causal effect of war on education show an expected sign. However, underlying assumptions of the difference-in-difference model in the present context deserve more careful consideration. First, using department of birth as an identification strategy may not reveal the heterogeneous impact of war victimization on education for children from different socio-economic groups. In other words, there exists a possibility of selection into victimization across individuals which could be largely hidden by the total number of conflict events in a department. Second, the proximity to a war zone dummy variable may fail to identify the true impact of war on education because the intensity of war measured as the count of war events varies significantly across departments. Third, due to a large number of internally displaced people, it is often hard to track their movements between 2004 and 2008. If many 13
,_ 37:475-93.

- Caddell, J. 1996. “Evidence for Magnesium Deficiency in the Pathogenesis of Bronchopulmonary Dysplasia (BPD).” _Magnesium Research,_ 9:205-16.

- Chen, Y, Parvez, F, Gamble, M, Islam, T, Ahmed, A, Argos, M, et al. 2009. “Arsenic Exposure at Low-to Moderate Levels and Skin Lesions, Arsenic Metabolism, Neurological Functions, and Biomarkers for Respiratory and Cardiovascular Diseases: Review of Recent Findings from the Health Effects of Arsenic Longitudinal Study (HEALS) in Bangladesh.” _Toxicology and Applied Pharmacology,_ 239:184-92.

- Chong, Y.J., Khan, A., Scheelbeek, P., Butler, A., Bowers, D. and Vineis, P. 2014. “Climate Change and Salinity in Drinking Water as a Global Problem: Using Remote-Sensing Methods to Monitor Surface Water Salinity.” _International Journal of Remote Sensing,_ 35:1585-1599.

14