rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 60<br>47.9<br>50 46.7<br>39.0 39.8 38.5<br>40<br>35.3 34.9<br>33.7 33.3<br>31.4<br>30<br>20 17.4 15.8 17.9 15.6<br>13.0 13.3<br>10 5.6 6.8 6.6 7.7<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Figure A.4. Population shares of individuals (%) <!-- Start of picture text --> 50<br>46.3<br>44.9<br>45 43.1<br>40.4<br>40 37.8<br>33.7 33.5 33.9<br>35 32.6 31.8<br>30<br>25<br>20 15.217.8 16.5 17.9 16.1<br>13.3<br>15<br>10 6.3 7.0 5.8 6.2<br>5<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017)
in society. According to the most recent census, Kenya has over 40 ethnic groups, and the largest group (the Kikuyu) accounts for only about 17 percent of the population (KNBS 2019). Economic inequalities and political allegiance are distributed across regions and ethnicity, with political parties and coalitions created along clear ethnic lines (Asingo et al. 2018, Friedrich-Ebert-Stiftung 2012). In this context of diffuse ethnic groups and ethnic-based politics, ethnicity is a highly salient topic. Second, certain ethnic groups are underrepresented in the judiciary (see below), and there is a high degree of gender inequality across a number of dimensions, including representation in the judiciary and a variety of socioeconomic outcomes (IDLO 2020; UNDP 2020).<sup>2</sup> If in-group bias is widespread, it may disproportionately harm these underrepresented groups. Third, there is an ongoing debate regarding the extent to which co-ethnic bias affects decision-making in the context of Africa generally and Kenya specifically (Berge et al. 2015).

We employ several data sources to examine the extent and determinants of judicial bias in Kenya. Our main data source is the Kenyan Judiciary’s publicly available database for Superior Courts cases over the period 1976-2020.<sup>3</sup> Our analysis focuses on cases in which there is both a human defendant and human litigant (i.e., cases for which neither litigant is an organization or representative of the state). As such, since the state is always the prosecutor in criminal cases, criminal cases are not included in our dataset. Most of the cases we examine are civil, land environment, and succession cases. By scraping the metadata associated with each case, we determined key variables, such as case type and names of judges and litigants. We also used additional data sources to determine the gender and ethnicity of participants. Furthermore, we used machine learning techniques to extract other key variables, such as the outcome of the case and the degree to which judges,
Figure 6: Test for discontinuity of selected covariates: environmental data <!-- Start of picture text --> Percent baseline forest Ln(elevation in m)<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Ln(km to road) Ln(km city of 5000)<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Ln(km major city) Mean defor risk: INE version 2<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Percent agriculture (NFI 2000) Marginality index 2000<br>-10 -5 0 5 10 -10 -5 0 5 10<br>1 5<br>.5 0<br>0 -5<br>de-meaned value -.5 de-meaned value -10<br>2 1<br>0<br>0<br>-2<br>-1<br>-4<br>-6 -2<br>de-meaned value -8 de-meaned value -3<br>2 .6<br>1 .4<br>0<br>.2<br>-1<br>-2 0<br>de-meaned value -3 de-meaned value -.2<br>1 3<br>2<br>.5 1<br>0<br>0<br>-1<br>de-meaned value -.5 de-meaned value -2<br><!-- End of picture text -->

> 1 Figures plot the variables of interest from which we subtracted their state-level means, against the re-centered applicant point scores.

30
SPI exercise. Its purpose is to provide, for all relevant sectors (4-digit categories or classes—see below for definitions), expenditures according to the GFSM 2001 functional and economic classification. It should be filled in for the central government to cover the most recent 5 years of available data for:

- Budget estimates

- Budget releases

- Actual expenditures (audited expenditures if available)

Thus, ideally, 15 occurrences of this template (estimates, releases, and actuals for 5 years) should be completed for central government expenditures. The collection of separate data on estimates, releases and actuals allows for the subsequent calculation of various budget execution ratios and provides a much richer understanding of expenditure patterns and processes. However, in some cases it may not always be possible to do so.

Template 10 should also be completed for local governments following the same approach: budget estimates, releases, and actuals for the most recent 5 years of data available. Depending on the country, local government data may be available in an aggregate form provided by the central government. Where this is not the case, the data collection exercise should aim to cover the most important local authorities in spending terms, collecting data individually from each local authority and then aggregating it into a first order estimate of local government expenditure.

41
, i.e., the district-level COVID-19 death rate. These regressions are based on a modified version of specification (1) where we replace the district fixed effects with the following district-level controls from the 2011 Census of India: population density, share of population that lives in urban areas, share of population that belongs to a Scheduled caste, share of population that is female, share of population that lives in slums, and total district population. We also control for the all-cause mortality rate in 2019 at the district level from 8
<mark>The association of female principal applicants with higher rates of poverty before assistance when households have been matched suggests that there are gender‐specific barriers faced by female principal applicant households. Our results are consistent with women’s limited access to labor markets and economic opportunities being foremost among such barriers. With more income in the form of assistance or more adult men who can earn income in the household, the association between female principal applicants and a higher likelihood of poverty disappears.</mark>

# 5. Conclusion

<mark>Numerous reports about refugees say that women and children are especially vulnerable. By combining UNHCR’s ProGres data with household survey data, we have been able to shed more light on one aspect of this vulnerability—its association with income poverty for Syrian refugee households in Jordan whose</mark> principal applicant is a woman.

<mark>The combination of ProGres and JD‐HV data provides a rich source of information from which evidence on gender‐specific vulnerability to poverty can be distilled. The microdata stored in the ProGres database identifies a principal applicant and allows us to trace the relationship of people in each household to the principal applicant. By asking respondents to identify themselves as principal applicants as opposed to head of the household and by linking applicant status to receipt of benefits, the questionnaire for the ProGres database circumvents the social norms that make women reluctant to identify themselves as</mark> household heads or limit their ability to think of themselves in that way.

<mark>Our findings support the view that many women are especially vulnerable to poverty and shed some light on some of the drivers of the vulnerability of Syrian refugees in Jordan by establishing links between household poverty, female headship, and the demographic characteristics of households. One‐third of registered Syrian refugee households in Jordan have female principal applicants. Analysis of the demographic characteristics of male and female principal applicant households show that they are very different. Over half of all married female principal applicants are living without their spouse
# **2. Methodological Issues in the Modeling of Poverty**

**In line with** most recent work on poverty, the analysis in this paper is based on a money-metric measure of utility and welfare. Total household expenditure is used as measure of household welfare and as a basis to rank households and to define a poverty line. Expenditure is preferred to income because it is usually better reported in household budget surveys.' Furthermore, there is the important theoretical consideration that expenditure reflects better permanent income. This argument is particularly relevant in transition economies where the volatility of current income is still quite high, due to the lack of steady private sector employment and the resulting high rates of unemployment. Arrears on the payment of wages and pensions, especially in FSU countries, further adds to the unreliability of current income as a measure of welfare.

The analysis below takes into account differences in needs due to different household size and composition and therefore uses household expenditure per equivalent adult as the welfare measure. There is a wide choice of adult equivalency scales, and different scales are used in different countries. Our comparative analysis objectives require the use of a single scale, and we have opted for the OECD scale, because of its simplicity of use and wide familiarity. This scale is expressed as follows

> This is only recently the case in East European countries. Prior to transition, income was usually better reported, because most income sources were under the direct control of the state, and data collection agencies could verify reported income at the source. This is why most pre-transition analysis of poverty has used income-based measures. After transition, the emnergence of private sector income (especially self-employment income) has led to a decline in reliability of reported income data, in line **with** the experience of western countries (see, for example, Revesz, 1994 for the case of Hungarian income and expenditure data).

3
youth were also more likely to stop working and lose wage employment in the initial phase of the crisis, relative to adults, though bias due to the nature of the phone survey makes age differentials more difficult to measure. There is currently little systematic evidence on the distributional impacts of COVID-19 in Sub-Saharan Africa.

# **Furthermore, little is known about the medium-term impacts as the pandemic disrupted**

**economies for more than a year.** Kugler et al. (2021) finds a partial recovery in employment over the first six months of the crisis (April and August) was stronger for groups hit hardest by the crisis, such as women, less educated workers, and youth. But detailed information from SubSaharan Africa is difficult to obtain due to data limitations. The pandemic disrupted traditional data collection systems in many countries and alternative data sources such as administrative data are rarely available. While high-frequency phone survey data has compensated for such disruptions in several countries, these data are used to measure short-term dimensions, such as immediate shocks and short-term recovery measures.

**Understanding how the COVID-19 crisis affected urban informal household enterprises and how the owners responded sheds light on the type of social policy interventions that can support this largely unserved population** . This paper leverages High-Frequency Phone Surveys supported by the World Bank to estimate how COVID-19 affected the urban informal sector in 13 Sub-Saharan African countries in the short term and in 27 countries in the medium term. Specifically, we explore which types of urban household enterprises experienced a decline in farm or nonfarm income, which types of households utilized different coping strategies, the nature of the relationship between the use of coping strategies and income declines, and how these trends differed by gender and age of the household head as well as poverty status of the household. We consider these effects both at the beginning of the pandemic, as well as how patterns shifted throughout the first year of the pandemic.

**There are six main findings regarding households with enterprises in the 13 sampled countries
emergency or appeal for international assistance – arguably a sensible set of criteria for a disaster inventory. Not all events recorded in the micro-data meet these requirements. Importantly, the events recorded in the micro-data have substantial impacts on the livelihoods of farmers and the economies of the study countries.

At the same time, micro-data has drawbacks and limitations. First, it is rare that microdata in low- and middle-income countries are available annually, with surveys typically implemented every few years. Shock coverage and detail depend on the survey design, which typically differs from country to country. Finally, microdata does not provide the same cross-country coverage as disaster repositories. With these limitations, the microdata naturally also provides an incomplete picture (see discussion in _Appendix A_ ).

8
for the period 2000-2014. A general decline in infant and maternal mortality is noted even as the number of live births increased indicating overall declines in both infant and maternal mortality rates. Since our key objective is to evaluate the association between groundwater salinity and neonatal or infant deaths, we restricted the sample to the subpopulation of children from households which reported tubewell as their primary source of drinking water in every BDHS survey. The distribution of groundwater EC is skewed right (Figure 2). Since the EC data contained outliers, we winsorized the data at 95<sup>th</sup> percentile distribution when modeling EC as continuous exposure.

Non-Parametric Regression (NPR) was utilized to diagnose the functional form for the relationship between health outcomes and salinity using Stata 15. NPRs have the advantage of estimating mean outcomes for a given set of covariates without assuming a functional form, thus avoiding errors due to misspecification. Figures 3 and 4 present mean functions with 95% confidence interval (CI) predicted by NPR using EC as covariate variable over 1 mS/cm interval with 100 times simulation of bootstrap standard errors. The mean function of probability of infant and neonatal death show similar non-linearity patterns over different salinity levels. Groundwater salinity is modeled as a continuous variable, categorical variable and restricted cubic splines. In continuous exposure models, Ordinary Least Square (OLS) model using EC 8
# **2. The Impact of the Great Recession on Cities**

A large number of publications from international organizations, NGOs, and associations of municipalities published surveys of the impact (or likely impact) that the crisis would have on cities at the onset of the Great Recession (Clark 2009, European Commission 2011, United Cities and Local Governments 2009, Paulais 2009, United States Conference of Mayors 2010, United Nations-Habitat 2010, URBACT 2010, Lee, Morris and Jones 2009, Wial and Shearer 2010).

City officials around the world have reported that the Great Recession has had a major impact of on economic activity—for instance 80% of the 131 cities from the 25 European Union member countries surveyed by URBACT in 2009 reported such an impact. These effects range from the closing and bankruptcy of many companies, to the slowdown in economic activity in all its branches, decrease in investment, restriction of loans, reticence of companies to take risks in times of uncertainty, decrease in the number of new startups, and decline in exports.

The most serious impact is the sharp increase in unemployment (and underemployment) in cities, small or large. The issue is more serious in developing countries where a large number of persons are underemployed or work in the informal economy. For example, in Mexico, in September 2010, open unemployment in the 32 most important urban areas reached 6.6% of the labor force but the underemployment rate was 8.5%.

Urban poverty rates have also increased dramatically. In the US principal cities, for example, the poverty rate has shot up from 16.5% before the crisis to 20% in 2011 (Perlo 2011). Below, these trends are illustrated using recent census data.

**United States, 2005 to 2011. Poverty by Residence (in thousands)**

|**Year**|**Inside met**<br>**statistica**<br>|**ropolitan**<br
definition. Merrick’s study is of the urban labor market of Belo Horizonte in central eastern Brazil. For employed workers informality is defined by the lack of membership of a social security institute. Subsequent work which has adopted the criterion of no social protection or non-payment of social security taxes for distinguishing informal employees includes Portes et al. (1986), Marcouiller et al. (1997), Maloney (1999) and Saavedra and Chong (1999). Absence of social protection is regarded as an important indicator of informality, focusing attention on the potential costs of informality to employees in the event of adverse economic circumstances. The consequences of informality so defined may depend on the economic position of the individual within the household: that is whether they are the main or secondary earner. It may also depend on or the social protection status of other household members. This definition also focuses attention on the inefficiency of social protection systems as a possible driver of the growth in informality. However typical household or individual surveys used to investigate informality may not provide information on the social security status of the self-employed or small employer. So studies often rely on a “hybrid” definition of informality in which employees are classified according to social security status. Own-account workers are classified as entirely informal and employers classified according to either or both the size of their business (number of employees) or the activity of the business.

A small number of previous studies have investigated the sensitivity of measured rates of informality to choice of definition. Marcouiller et al. (1997) compare the size of the informal sectors in El Salvador, Peru and Mexico using definitions based on firm-size<sup>3</sup> and on nonpayment of social security taxes. The latter definition produces a considerably higher estimate of the size of the informal sector (43.2% compared to 30.8% of workers in the case of Mexico in 10
by the International Monetary Fund (IMF), the World Bank and the African Development Bank.

The crisis and the initial response to it led to a severe recession and increased poverty (World Bank 1995). It is reported that by 1990, real GDP stood 20 percent below its 1985 level. Furthermore, per capita income fell by about 50 percent between 1986 and 1993. The loss of competitiveness also led to the loss of export markets for agricultural products and made it hard for domestic food crops and industrial products to compete with imports. This squeeze implied a decrease of demand for labor both for tradable and non-tradable goods with adverse effects on living standards for both rural and urban areas. Also, reduced economic activity combined with a slackening of tax collection crippled the ability of the state to provide services, thus worsening the impoverishment.

In 1994, the Central African Economic and Monetary Community<sup>4</sup> of which Cameroon is a member devalued the CFA franc by about 50 percent in nominal terms (30 percent real), and implemented additional trade and fiscal reforms. This presented Cameroon with an opportunity to reverse the socioeconomic downturn. The country did experience some positive growth after the devaluation, but it was only in mid 1996, after some failed stabilization and adjustment efforts, that the government showed strong commitment to meaningful policy reforms. The successful implementation of these reforms led to macroeconomic stability and an average growth rate of real GDP in the neighborhood of 5 percent between 1997 and 2000. On the basis of the 1996 and 2001 household surveys, it is estimated that the incidence of poverty fell by 13 percentage points from about 53 percent to about 40 percent. However, income inequality remained high with the Gini index of inequality decreasing only by 3 percentage points, from 44 to 41 percent. Furthermore, other social indicators have not shown such an improvement.

A shift in borrowing strategy around 1986 combined with the severity of the socioeconomic crisis left the country saddled with an unsustainable debt burden. The stock of
# **2. Enterprise Restructuring and Its Determinants**

The empirical literature on enterprise behavior during transition utilizes a wide array of restructuring measures, based either on accounting or qualitative information. There is no prevalent methodology, as the concept of restructuring differs across studies. For the purposes of this paper, we define restructuring as changes in operations, interactions, and motivation towards success in a changing market environment. This definition captures the essence of restructuring changes, some which will be forced by the behavior of other market players, while others will be pursued by management and owners. Restructuring in this context can lead to two different outcomes. Viable firms would increase their productivity and market share, attract more resources, and upgrade their production process. At the opposite, non-viable firms would shrink to a viable core or close down altogether. Their assets (both human and capital) could be used for alternative production. Thus the decline in business activity of an enterprise need not be associated with a lack of restructuring. It is part of the general movement of resources to more productive uses.

Following on this definition, we use three complementary measures of restructuring: increases in labor productivity (sales per worker growth), assets sales, and renovations at the factory to improve working conditions. The first measure is based on accounting data and is most often used in previous empirical work (Frydman et al, 1997; Pohl et al., 1997; Estrin and Rosevear, 1998) as it depends on both "defensive" or "passive" restructuring (reduction in excess employment), and on "active" restructuring (increase in sales volume).' The measure may, however, be misleading since substantial improvements in labor productivity need not arise from "pro-active" restructuring, particularly when it is accompanied by a substantial drop in output. Instead, labor productivity growth may simply account for a low initial level of efficiency, i.e., for the elimination of waste.

I A better measure, and one which
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Ports

## **Achievements**

Port security plans are being put in place in the Port of Cotonou. The steps taken to date have been fairly basic, for example, constructing higher walls around the port, implementing strict controls at port gates, introducing floodlighting at the quayside, requiring all port workers to carry identity cards, and having a port security officer on guard 24 hours a day. These measures enabled Cotonou to earn the ISPS (International Ship and Port Facility Security) code, following a visit by the U.S. Coast Guard.

**Table 5. Port indicators for the Port of Cotonou and selected other ports**

|Port|Unit|Lomé,<br>Togo|Cotonou,<br>Benin|Dakar,<br>Senegal|Abidjan,<br>Côte d'Ivoire|Tema,<br>Ghana|Apapa,<br>Nigeria|Harcourt,<br>Nigeria|
|---|---|---|---|---|---|---|---|---|
|Container cargo - total<br>handled|TEU,annual|460,000|158,201|331,191|500,119|420,000|336,308|7,900|
|Container dwell time|Days|13|12|7|12|25|42|30|
|Truck turn-round time|Hours|4|6|5|2.5|8|6|24|
|General cargo vessel<br>pre-berth waitingtime|Hours||48|24|2.9|9.6|36|38.4|
|General cargo vessel<br>turnaround time|Hours||48|60|2.2|48|40.8|45.6|
|Craneproductivity|Containers<br>per