rafmacalaba/datause-encoder-data huggingface dataset:
Lewis and Lockheed development occurs, these communities inevitably come in contact with the larger society, which affords them less respect than the majority population. Traditional status hierarchies, such as caste rankings in India and Nepal, lead to exclusion of those lower in the hierarchy by those higher in the hierarchy. Loury (2000) defines the socially excluded as those who receive inadequate support from public institutions and whose opportunities remain constrained due to structural and cultural factors that serve to exclude them.

Explanations for the education gender-gap <u>within</u> marginalized groups can be found in the economics and social psychology literature, which discuss the consequences of being marginalized, discriminated against and subject to lower expectations. Economic theories tend to stress supply, demand and discrimination constraints, while social-psychological theories tend to focus more on expectations and subtle constraints observed in interpersonal interaction.

**_Economic perspectives._** Marginalization leads to both an inadequate and substandard public supply of schools and lower demand for education, often related to its costs and perceived (lack of) benefits. Overt discrimination by the majority against the minority may explain these constraints.

The quality of public programs directed at marginalized groups, even mainstream services such as education, tend to be inferior as compared to those aimed at majority populations (Alessina and others, 2003; Habyarimana and others 2007). Studies of schools serving rural communities, for example, have found them to be of poorer quality (schools with leaking roofs, no electricity and fewer classrooms than grades) than those serving urban communities in Egypt, Pakistan, Chile, India, Vietnam, Laos (Lloyd, Mensch and Clark 2000; Lloyd and others 2003; Lloyd, Mete and Grant 2007; King and van de Walle 2007; van de Walle and Gunewardena 2001; Lloyd ; World Bank 1997) Because marginalized groups remain outside the mainstream their members often do not participate in civic activities and have little if any knowledge about education; public leaders are not responsive to their needs And, although poor quality schools affect both girls and boys, girls’ school participation seems to be more sensitive to variations in the quality of schooling.

Children of marginalized groups suffer not only from poor quality schools, but also from discriminatory treatment when they reach schools. Their parents also have little say over the content of the curriculum (to ensure local relevance or provide input on the language of instruction), teaching methods or the selection or oversight of teachers. In some cases, parents 11
market conditions such as local unemployment rates at the time the worker joined the ...rm, but not by subsequent labor-market conditions. The reason is that the worker and ...rm will bargain for their optimal implicit contract at the beginning of their relationship, at which point the current unemployment rate will have an e¤ect on workers’bargaining power and hence on the wage agreed to; but if implicit contracts are strong and provide good insurance to the worker, for the remainder of the job, wages will simply follow the agreed-upon wage regardless of subsequent labor-market conditions. She ...nds that (i) a workers’current wage is a¤ected by initial conditions at the time of beginning of the job, independently of current labor-market conditions; and (ii) in industries hit by a rise in import penetration, the current wage is much more dependent on the current labor-market conditions compared with other industries. Together, these ...ndings suggest that (i) implicit contracts are important, and (ii) import competition indeed weakens them, as predicted by her model.

# 3.5 Labor Market Frictions.

Krishna and Senses (2009) o¤er an empirical study of the impact of openness to trade on domestic income. Whereas previous studies examine the impact on wage growth or wage premia, Kirshna and Senses examine the impact on wage volatility. Using longitudinal earnings data on workers from three panels (spanning 1993-2003) of the Survey of Income and Program Participation, they estimate the relationship between labor income risk (de...ned as the variance of unpredictable changes in earnings) and import penetration, a measure of industry exposure to international trade. They ...nd that a 10 percent increase in import penetration will increase the standard deviation in persistent (as opposed to transitory) income shocks by 20 to 25 percent for all workers. Their results are both statistically and economically signi...cant.

Krishna and Senses (2009) also estimate subsets of their data to identify the impact of openness to trade on particular industries and on workers who have changed employment from one industry to another; they ...nd higher income risk among workers who have switched from 41
4 International Monetary Fund (IMF) has, for some time, been analyzing pass-through coefficients, primarily for gasoline and diesel but sometimes including kerosene. Coady et al. (2010) reported medians between end-2003 and mid-2008 for eight groups of countries, and Arze del Granado, Coady, and Gillingham (2010) reported pass-through coefficients for gasoline, diesel, and kerosene for 21 countries between end-2003 and varying end periods, with the last end period being mid-2008. A recent IMF publication reported pass-through coefficients for Pakistan and six countries in the Middle East and North Africa between the fourth quarter of 2010 and the second quarter of 2011 (IMF 2011). Pass-through coefficients between 2008 and 2011 have been presented in meetings (Parry 2011) but they have not yet been published. This paper complements the work carried out by the IMF by adding LPG, citing publicly available data sources for retail price information where it is available, and providing detailed information on fuel quality and reference prices.

# **2. Methodology**

Throughout this paper, income category assignments are those of the World Bank as of July 2012. Regional assignments are also of the World Bank; country identification by region can be found under “Countries” on the World Bank’s Web site at worldbank.org.

# **2.1 Vulnerability levels and changes**

A rise in oil prices both increases the value to the economy of any domestic oil production and decreases GDP according to the consumption of oil products. Different measures of vulnerability and the mechanisms by which higher oil prices affect GDP are discussed in Bacon and Kojima (2008). As in the 2008 publication, this paper defines vulnerability as the ratio of the value of the net volume of traded crude oil and oil products to GDP. This measure of vulnerability does not show the distributional or fiscal effects of a change in oil prices.

Vulnerability, so defined, can be related to a number of factors through an accounting identity. Such a decomposition analysis allows a study of how much changes in individual factors contribute to the overall changes in vulnerability. Following Bacon and Kojima (2008), this paper uses a refined Laspeyres index—rather than a log mean Divisia index—to enable the incorporation of both production and consumption effects, thereby allowing the inclusion of countries that are net oil exporters. The change in vulnerability (V) is decomposed into several factors between two dates. One advantage of this approach is that the decomposed factors are additive and their relative contributions to the overall change in vulnerability can be easily ascertained.

The identity is expressed as the sum of two terms that separate the production of oil and consumption of oil products, each of which is further decomposed into several individual factors:
|EFTA Agreement|Goods|19/06/1997|01/12/1999|Iceland, Liechtenstein,<br>Norway, Switzerland,<br>Morocco|
|---|---|---|---|---|
|EU-Morocco|Goods|26/02/1996|01/03/2000|EU,Morocco|
|Morocco - United Arab<br>Emirates|Goods|25/05/2001|09/07/2003|Morocco, United Arab<br>Emirates|
|Türkiye-Morocco|Goods|07/04/2004|01/01/2006|Morocco,Türkiye|
|Unites States -<br>Morocco|Goods|15/06/2004|01/01/2006|Morocco, United States|
|Agadir Agreement|Goods|25/02/2004|27/03/2007|Arab Republic of Egypt,<br>Jordan,Morocco,Tunisia|
|United Kingdom -<br>Morocco|Goods|26/10/2019|01/01/2021|Morocco, United<br>Kingdom|

**Source:** World Trade Organization (https://www.wto.org/english/thewto_e/countries_e/morocco_e.htm)

Average tariff rates fell considerably and trade openness (measured as the sum of imports and exports as a percentage of GDP) increased significantly after these trade agreements went into effect. The mean tariff rate<sup>3</sup> in Morocco decreased from 45.4 percent in 1993 to 3.61 in 2020 (Figure 2.1). At the same time, trade openness increased from 52.10 percent in 1993 to 87.14 percent in 2019, followed a constant increasing trend except for the period after the 2008 crisis (Figure 2.2). After entry into force of the EU agreement in particular, trade as a percentage of Moroccan GDP increased from 59.1 percent (26.8 percent from exports and 32.3 from imports) in 2000 to 87.1 percent (39.1 percent from exports and 48 percent from imports) (WITS, 2022).

**Figure 2.1. Tariff rates considerably Figure 2.2. Trade openness almost declined doubled**

Moroccan tariff rate of all products in Total trade as percentage of GDP percentage <!-- Start of picture text --> 50 90<br>40 80<br>30 70<br>20 60<br>10<br>50<br>0<br>40<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

**Source:** Authors’ elaboration using data from Our World in Data

(https://ourworldindata.org/) 3 Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country.

3 | P a g e
# **4.  Estimation of Priority Indices**

# **4.1 Model Data**

Using the storage site-specific data, we construct variables for alternative forms of model (13) as follows: (1) Vulnerability-weighted population in the delegation (ΣΦiPi): We set φ=1 for less-vulnerable population groups. In the weighted-population version, we use weights of 2 and 1.5 for children younger than 5 and women aged 20-49, respectively. In the un-weighted version, we assign weights of 1 to all three groups.

(2) Average delegation inhabitant’s distance from the stockpile site (di): We base our distance proxy on simple geometry. Within a circular area around a stockpile, average distance from the stockpile is proportional to the radius. Accordingly, our average distance proxy is the square root of delegation area (this is a constant (π<sup>.5</sup> ) times the radius.<sup>10</sup> In our tests of model robustness we use three values of γ, the distance risk-decay parameter in equation (10): -1, -2 and 0.5.

(3) The proportion of pesticides that have container problems at the site (Si): We compute the share of total active-ingredient pesticides that are in broken or leaking containers, or have already leached into the soil.

(4) Hazard-weighted pesticide volume at the site (ΣρjQij): We use two radicallydifferent hazard ratings to test the robustness of our methodology. For the first set, we use exponential differences in hazard ratings: ρj = 100, 10, 1, 0.1 for WHO classes 1a, 1b, 2 and 3, respectively. For the second set, we set weights equal to 1 for all four hazard classes.

> 10 The square root of the area is the best approximation we can provide for average distance in non-circular cases as well.

10
cumulative density function of the wellbeing vector in PovcalNet, we identify the poverty line (z) for each country _i_ in year _t_ , such that zit = F<sup>-1</sup> (HCit).<sup>13</sup> We argue that this approach offers several advantages over other available data sets of national poverty thresholds, such as the one documented in RCS (2009), which are particularly relevant for analysis meant to compare values of national poverty lines across countries.

One advantage of the lines used in this paper is that they are all expressed in terms of per capita income or consumption. This is in contrast to RCS, which includes a mix of poverty lines, some expressed in terms of minimum-needs per capita, and others, per adult equivalent.<sup>14</sup> Typically in low-income countries, the same level of minimum needs is greater in value when expressed in terms of adult-equivalents than when expressed in per capita terms. This is largely a function of the age composition of the country – in poorer countries where the average person is an adolescent, per capita needs will be substantially lower than per adult needs.<sup>15</sup> In itself, the mix of per capita and per adult-equivalent lines creates an inherent issue of non-comparability, and this concern is exacerbated by the empirical tendency for average age to be correlated with level of economic development. By harmonizing all national poverty lines in per capita units, our analysis of the relationship between overall national wellbeing and the value of national poverty lines is not contaminated from the usage of differing units of analysis.<sup>16</sup>

> 13 The online appendix to Jolliffe and Prydz (2016) discusses the precision of this methodology and contrasts the estimation error from this approach with the error associated with approaches that build up a database of national poverty lines from national poverty assessment reports. Appendix available at: https://static-content.springer.com/esm/art%3A10.1007%2Fs10888-016-93275/MediaObjects/10888_2016_9327_MOESM1_ESM.pdf.

> 14 For a description of adult-equivalence adjustments, see James and Schofield (1990).

> 15 Van de Boom, Halsema, and Molini (2015) note that per-capita based food poverty lines are on average seven-tenths the value of the corresponding adult-equivalent version of this line.

16 Another advantage of the Jolliffe-Prydz lines for this analysis is due to the construction of these lines. Since they are derived from national poverty headcount rates, they correspond directly to the level of poverty as assessed nationally. This is a relevant issue for countries that do not have a national poverty 10
# **3.3 Marital Status and Female Labor Force Participation**

Marriage has been highlighted in the literature as a marker for one’s status as a labor market participant. Marital status affects FLFP in two key ways. First, after marriage, women typically take on the role of care-giver in the family, which significantly alters the allocation of their time. Second, marriage broadly changes a woman’s social position and status; a married women joins the labor force only when social norms and the stigma attached to paid labor conforms with family restrictions.

The distribution of working age women by marital status suggests that the share of currently married women is higher in rural areas (at 71.2 percent in 2011-12) than in urban areas (66.6 percent in the same year) (Figure 4). The share of currently married women was largely stable during the period between 1993-94 and 2011-12, with 71.2 to 73.5 percent of women in rural areas being married, and 65.3 to 66.6 percent of women being married in urban areas. Therefore, any change in FLFP by marital status can be considered as the direct effect of change in status rather than a relative change in composition.

**Figure 4: Marital Status of All Working Age Females**

<!-- Start of picture text --> 73.5<br>26.5<br><!-- End of picture text --> <!-- Start of picture text --> 71.5<br>28.6<br><!-- End of picture text --> <!-- Start of picture text --> 71.2<br>28.8<br><!-- End of picture text --> <!-- Start of picture text --> 66.6<br>33.4<br><!-- End of picture text --> <!-- Start of picture text --> 65.3<br>34.7<br><!-- End of picture text --> <!-- Start of picture text --> 66.6<br>33.4<br><!-- End of picture text --> Source: Authors’ own estimate based on NSSO data.

We find that the association between marital status and FLFP did vary across location. In rural areas, the FLFP rate of married women was distinctly above that of unmarried women in all time 18
Table 3. Direct and indirect impact of concessions: effects on dropout rates

||Direct impact on dropout rates|Indirect impact on|dropout rates||
|---|---|---|---|---|
|Probit estimates|||||
|Dependent variable|<br>Concession|<br>Schools near|<br>concession||
||Coefficient<br>Std. Err.|Coefficient<br>|Std. Err.||
|Total number of students|-0.008<br>0.001|*<br>-0.015|0.003|*|
|Dispersion in the test|-0.366<br>0.080|*<br>-0.708|0.178|*|
|Localization 4||13.472|8.162|*|
|Localization 5|0.024<br>0.168|9.129|8.556||
|Localization 7|0.212<br>0.138|13.929|8.089|*|
|Localization 8|-0.052<br>0.163|12.411|8.234||
|Localization 9|0.048<br>0.155|12.455|8.311||
|Schedule Night|-0.243<br>0.173|1.966|0.430|*|
|Ration teacher / stud.|-0.284<br>0.046|*<br>0.514|0.094|*|
|Average year of educ. teac.|-0.035<br>0.025|0.080|0.079||
|% of students repeating|-1.723<br>0.351|*<br>1.407|0.502|*|
|% of students in overage|3.327<br>0.719|*<br>5.040|1.709|*|
|Number of computers, 1999||0.027|0.020||
|Number of class rooms, 1999||0.053|0.038||
|Number of restrooms, 1999||-4.686|0.719|*|
|Number of sport facilities, 1999||-1.723|0.260|*|
|Number of teachers, 1999||-0.476|0.278|*|
|Number of administrative p. 1999||0.202|0.050|*|
|Number of psychologist, 1999||-0.069|0.187||
|Ration teacher / stud., 1999||0.059|0.038||
|% teachers with second., 1999||35.714|10.066|*|
|% teachers with college, 1999||33.968|9.841|*|
|% teachers with grad. st., 1999||25.912|8.898|*|
|Log likelihood|-529|-231|||
|PseudoR2|0.1391|0.5714|||
|Number of obs.|2334|1217|||
|Matching|Impact variable: Dropout rate|Impact variable|: Dropout rate||
|Variable: Dropout rate|||||
|Difference Treatment-Control|||||
|Unmatched|-0.0130|-0.0069|||
|ATT|-0.0173|-0.0082|||
|Bootstrap statistics|||||
|Repetitions|1000|1000|||
|Standard error|0.0039|0.0096317|||
|Bias-corrected conf. Inter.|-0.0244<br>-0.0112|-0.0341|0.0004||

23
# <u>Table A11. Reconciling mean and median regression results – second specification (continued)</u>

||**High dfb**<br>**villa**<br>|**eta - out**<br>**ge**<br>|**High dfbeta**<br>**o**<br>|**- school and**<br>**ut**<br>|**High dfbet**<br>**ou**<br>|**a - cog and**<br>**t**<br>|**High dfbeta**<br>**o**<br>|**- noncog and**<br>**ut**<br>|
|---|---|---|---|---|---|---|---|---|
||_Median_<br>Monthly|_Mean_<br>Monthly|_Median_<br>Monthly|_Mean_<br>Monthly|_Median_<br>Monthly|_Mean_<br>Monthly|_Median_<br>Monthly|_Mean_<br>Monthly|
||earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|earnings<br>(TC)<br>|
||(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
|Years of schooling (a1)|-0.22|0.33|0.12|0.69|-0.050|-0.23|0.19|-0.0051|
||(0.86)|(1.02)|(0.90)|(1.04)|(0.83)|(0.92)|(0.78)|(0.94)|
|Out Village (a4)|32.5**|8.01|28.5*|16.5|31.7*|8.27|34.8***|1.36|
||(15.7)|(15.0)|(16.1)|(15.5)|(16.7)|(15.4)|(11.0)|(16.4)|
|Cognitive skills (a2)|0.47|2.11|-0.38|2.53|-0.23|3.60|-1.19|3.30|
||(3.85)|(4.33)|(3.95)|(4.36)|(3.34)|(3.76)|(3.54)|(4.11)|
|SEM skills (a3)|14.2***|13.7***|14.4***|12.6***|14.7***|13.7***|14.5***|11.6***|
||(2.77)|(2.89)|(2.83)|(3.02)|(2.73)|(2.86)|(2.51)|(2.44)|
|Interaction YrsSchooling and Out Village<br>(b1)|4.86***|7.96***|4.87***|5.97***|4.71**|7.76***|4.47***|8.29***|
||(1.73)|(1.90)|(1.79)|(1.57)|(1.87)|(1.86)|(1.12)|(1.87)|
|Interaction Cog and Out Village (b2)|12.6*|-0.21|13.2*|3.85|**15.6***|**4.37**|16.7***|0.19|
||(6.87)|(6.90)|(7.22)|(7.52)|(8.21)|(6.48)|(3.92)|(7.82)|
|Interaction SEM and Out Village (b3)|-7.41*|-12.0*|-5.27|-8.80|-7.34|-13.2**|-7.08|-6.10|
||(4.01)|(7.19)|(5.25)|(6.35)|(4.75)|(6.37)|(5.03)|(4.54)|
|Constant|38.5|50.2***|35.2|47.7***|37.1|50.1***|34.7|41.7***|
||(37.9)|(10.0)|(56.2)|(9.46)|(41.0)|(8.61)|(60.7)|(8.25)|
|Observations|1894|1894|1892|1892|1896|1896|1888|1888|
|R-squared|0.11|0.13|0.11|0.11|0.11|0.13|0.11|0.14|

* p<.10  ** p<.05  *** p<.01

_Notes_ . Columns (1) and (2) show mean and median regressions that correspond to columns (4) in Table 7 and Table A9. Columns (3) and (4) drop observations with Cook's distance>4/N. Columns (5) and (6) drop observations with standardized residuals>2. Columns (7) and (8) drop observations with dfbeta>2/sqrt(N) for the cognitive skills coefficient. Columns (9) and (10) drop observations with dfbeta>2/sqrt(N) for the years of schooling coefficient. Columns (7) and (8) drop observations with dfbeta>2/sqrt(N) for the “out village” coefficient. Columns (9) and (10) drop observations with dfbeta>2/sqrt(N) for the “interaction yrsschooling and out village” coefficient. Columns (11) and (12) drop observations with dfbeta>2/sqrt(N) for the “interaction cog and out village coefficient.” Columns (13) and (14) drop observations with dfbeta>2/sqrt(N) for the interaction NonCog and Out Village coefficient.

92
In this paper, we provide the most comprehensive estimates of IOp in SSA to date. Our analysis spans 18 countries with data collected in 2012 or later.<sup>5</sup> We use consumption per capita as the outcome and we include the following circumstances: region of birth, urbanrural birthplace, parental education, parental industry, ethnicity, and religion. To estimate IOp, we use a recent methodological improvement based on machine-learning – conditional inference regression trees and forests – that effectively trades off positive and negative bias and has various other advantages, which are discussed further in Section 2. Our contribution relative to the existing literature on IOp in SSA is threefold. First, we expand the number of countries from 10 to 18, which increases the coverage of the SSA population from 39 to 59 percent. Second, we update estimates by a full decade by using and harmonizing the most recent available data. Third, we increase the circumstance variables included and their availability across countries, thus decreasing under-estimation and improving the comparability of IOp estimates across countries.

Taking a simple average across countries, we find that IOp explains 54 percent of consumption inequality. This is 15 percent higher than previous estimates in Brunori et al. (2019) and is shockingly high: it means that over half of inequality in SSA is due to IOp from inherited circumstances. We find that IOp is also 54 percent at the median and shows a wider range than previously thought. It reaches 74 percent in South Africa, followed by Niger (62 percent) and Burkina Faso (64 percent). By contrast, it is as low as 26 percent in Ethiopia, followed by Gabon (41 percent) and Malawi (45 percent). Put in context, our estimates suggest that IOp in SSA is broadly in the same range as IOp in South Asia and Latin America, and notably higher than in East Asia, Central Asia and high-income countries (World Bank, 2023; Bussolo et al., 2023).<sup>6</sup> A breakdown of the importance of each circumstance reveals that birthplace, parental education, and ethnicity are most important, though there is variation in the importance of each circumstance across countries. South Africa stands out as the country in which race plays an outsized role in explaining consumption.

Given the variety of contexts and surveys, not all circumstances are available in every country. Therefore, we conduct two separate analyses: (1) estimate IOp using all available circumstances in each country, which produces the best possible country-specific estimate of IOp, and (2) estimate IOp using the circumstances that are available in all countries, which include birth region and parental education – two of the largest drivers

> 5 Surveys in our study sample were collected between 2018-2019 for 11 countries and between 2012-2017 for the remaining 7 countries.

> 6 A caveat is that it is difficult to compare IOp in SSA with other regions due to differences in available data and methodologies. An analysis using methods comparable to this paper confirms that IOp is much higher in SSA than in Europe, where it never exceeds 15 percent (Brunori et al., 2023).

4