rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. INTRODUCTION**

Morocco has extensive untapped human resources. According to the most recent official data, more than half of the country’s working age population (15-64) is currently not participating in the country’s economic activity, making Morocco one of the countries with the lowest employment rates worldwide and in the Middle East and North Africa (MENA) region (World Bank, 2012).<sup>2</sup> Beyond low participation rates, the unemployment rate in the country remains globally high, especially among youth and women.<sup>3</sup> At the same time, the labor market in Morocco remains largely informal, with 80 percent of all workers not contributing to social security in 2014 according to official estimates.

Available research (Agénor and El Aynaoui, 2003; Gatti et al., 2013) indicates that labor markets in Morocco are quite segmented. There is a formal labor market, mainly composed of public servants and wage earners with access to social security. There is also a large residual informal labor market composed of agricultural workers, wage earners that do not contribute to social security, and self-employed. The informal labor market in Morocco is characterized by the prevalence of jobs that are low-pay/low-productive and largely unprotected against risks such as aging, income loss, and health (Gatti et al., 2013; Agénor and El Aynaoui, 2003). Moreover, previous studies indicate that the mobility between the informal and the formal sectors is quite limited and not competitive, especially for youth and for women (Agénor and El Aynaoui, 2003, Verme et al. 2013, and World Bank, 2012). Such low mobility of human capital is a definite symptom of labor market inefficiency – as human capital cannot be allocated where it could yield the highest returns (Gatti et al., 2013 and World Bank, 2012).

Previous comparative analysis of labor regulation in the MENA region (see Angel-Urdinola and Kuddo, 2010 and World Bank,
14 For example, one could try to estimate the impact of past inequality (at time t- 1) on how the poor may benefit from growth (at time t)? As noted by Ravallion (1997), one of the reasons why inequality may reduce the prospects of the poor of escaping poverty through growth is that the higher the initial inequality, the lower the share of the poor in the benefits of growth. Imagine that a single person has all the resources in an area. Then, whatever the growth, poverty will never be reduced through growth. More generally, the higher the inequality, the less elastic poverty will be to economic growth for a large class of poverty measures (but in a recession, the poor will be less affected if the level of inequality is higher). To test this argument, Ravallion proposed the restricted form r = P (l-IG)g where r is the rate of poverty reduction for the area from t- 1 to t, IG is the initial Gini index at time t- 1, g is the rate of growth between t- 1 and t, and f3 is a parameter to be estimated. Using a panel for 23 countries with 41 spells, Ravallion tested this specification against an ad hoc encompassing model, and accepted the restricted form. We replicated this estimation with our regional panel. The results proved highly sensitive to the specification of the encompassing model, and in most cases, we had to reject the Ravallion's hypothesis that what matters for poverty reduction is the rate of growth corrected for the extent of initial inequality.

Another reason why initial inequality (or poverty) may matter for future poverty reduction is known as the induced-growth argument, according to which higher initial inequality (or poverty) may result in lower subsequent growth, and thereby in a smaller rate of poverty reduction. The negative impact of inequality (or poverty) on growth may result from various factors (Persson and Tabellini, 1994; Alesina and Rodrik, 1994, Clarke
# **1. Introduction**

**This paper aims to understand gender inequality in the labor market and time use among marginalized Roma communities in the Western Balkans, using a household search model of the labor market with labor and fertility shocks and idiosyncratic spouses’ home production preferences.** The model contributes to our understanding of Roma spouses’ strategies when facing utility maximization over leisure, home production, and labor income.

**Although there are no recent reliable estimates of the Roma population in the Western Balkans, available estimates suggest that this ethnic minority may represent between 700,000 and 1,360,000 individuals in the region, approximately.** Census data allow for ethnic self-identification, likely leading to significant undercounting. According to the Council of Europe (CoE) estimates, the Roma population ranges from 20,000 in Montenegro to as high as 600,000 in Serbia. Proportionally, the Roma population represents from 1.7 percent of the total population of Kosovo, to as high as 9.6 percent of the population in North Macedonia (Robayo-Abril and Millan 2019).

**Roma women are one of the most deprived groups in Europe, as they suffer a double layer of exclusion: as women, and as members of Europe ́s largest ethnic minority.** According to the 2017 UNDP-WB-EC Regional Roma Survey<sup>2</sup> (hereafter RRS), Roma face higher unemployment rates than non-Roma populations living in close proximity, with females being particularly affected. On average, 53 percent of marginalized Roma females aged 15–64 in the Western Balkans are unemployed compared with 41 percent among Roma males. The labor force participation rate among Roma women is extremely low: in 2017, just 21.3 percent of Roma women participated in the labor market, compared to 49 percent of Roma males.<sup>3</sup> Limited access to jobs and socioeconomic rights keep Roma people trapped in a vicious cycle of poverty and social exclusion, where
607|
|**Panel F: Other effects**|||||||
|Total number of school days attended per child|-0.43*|(0.24)|[0.04]|2.33|(2.49)|1,031|
|Child alertness (s.d. units)|-0.73***|(0.20)|[0.00]|0.00|(1.00)|975|
|Diffculty with self-care (z-score)|0.38***|(0.14)|[0.08]|-0.00|(1.00)|1,610|
|Number of learning activities|0.19**|(0.09)|[0.08]|0.22|(0.56)|1,385|

_Notes:_ The table shows the regression results on statistically significant results from the midline survey. Each row is its own dependent variable. **Panel A** outcomes are Clean Water, defined as an indicator for households having treated drinking water (such as by a filter). Rooms and rooms occupied by family members are reported by the respondents. **Panel B** outcomes are food consumption, Respondent (and adult) food insecurity equals 1 if the respondent went to bed hungry on at least one day in the last week and 0 otherwise. **Panel C** outcomes are COVID symptoms, which is the sum of indicators that increase the likelihood of COVID contraction by the respondent including leaving the house, attending social gatherings, not keeping distance from others, going to mosque or other religious institutions, going to grocery store or market, and leaving village/neighborhood. The other outcomes are as explained in the labels. **Panel D** The periods of the loans referred to are before lockdown: (January
# **3.2 Variable**

We begin by examining whether workers stopped working after the pandemic set in. Workers are classified as having stopped work if they answered yes to the question “Was the respondent working before the pandemic?” (working pre-pandemic) and no to the question “Did the respondent work in the last week?” (currently working). However, surveys in most countries did not ask respondents if they were working before the pandemic if they reported that they are currently working. Hence, it is not possible to estimate the number of workers prior to the crisis. This in turn means that it is not possible to construct the share of workers who were working prior to the pandemic that stopped working, because the denominator is unknown. We therefore approximate the share of workers who stopped working by dividing the number of people who stopped working by the sum of the number of people who stopped working and those that were currently employed at the time of the survey. In the LAC countries, however, the questionnaires asked respondents about their past work and whether they were currently working or not. Across the 12 LAC countries where both measures can be constructed, the two measures have a correlation of 0.99, suggesting that this approximation is highly accurate.

In addition to stopping work, we consider several other variables from the HFPS surveys to measure economic impacts. These include _partial or no payment for work performed among wage workers_ (“For the work that you did in the last week, will you be paid/were you paid?”), _changing jobs_ (“Has the respondent changed jobs since the beginning of the pandemic?”), as well as _income change_ (“Has your household income changed since the pandemic started?”). For income change, we consider four income sources: (i) total; (ii) family farming, livestock, or fishing; (iii) non-farm family business; and (iv) wage employment. Table 4 summarizes
PSUs (11 states have established this), and (c) several technical committees and working groups on topical issues of cash and debt management.<sup>38</sup>

# **Debt Swap and Securitization: Move toward Market-based Financing**

The fiscal correction was given an impetus with the introduction of a ―debt swap scheme‖ during 2003–04 to lower the existing interest burden and increase market access. Loans from the center amounting Rs 1,000 billion (US$23 billion<sup>39</sup> ) with interest rates in excess of 13 percent were substituted with new market loans and small savings proceeds at lower rates of interest; the outstanding debt remained unchanged. The market conditions prevailing were fortituous and the rates were significantly lower, at 7.5 percent (RBI State Finances Study 2004–05, 24), enabling an interest savings for states of Rs 310 billion (US$7.1 billion<sup>40</sup> ) and 0.75 percent per year in revenue (Twelfth FC). This direction toward the market was reaffirmed by the Twelfth FC in conjunction with state debt relief, where it stated that, ―As regarding the future lending policy, the central government should not act as an intermediary and allow the states to approach the market directly‖ (Twelfth FC, p. 236).

States issued ―power bonds‖ to securitize the fiscal risks emanating from the losses of electricity utilities arising from the gap between the cost of producing power and the tariff charged. This gap between the cost and tariff had resulted in signifcant losses and an accumulation of arrears. With the securitization, arrears and accrued interest at about 1.5 percent of GDP were cleared by states through the issuance of 15-year tax exempt ―power bonds‖.<sup>41</sup> Cognizant of the moral hazard issue, this was clearly announced as a one-time settlement measure and was supplemented with reforms to ensure discipline going forward. Participating
**Figure 2: GDP per Capita vs.  Regulation Indices**

<!-- Start of picture text --> Overall Regulation Product Market Regulation<br>Correlation: -0.81*** Correlation: -0.87***<br>USA CHE DNKNLDISL FINSWENORAUT JPNBEL FRA NLDUSADNKFINSWECHEBELISLNORAUTJPN FRA<br>GBR CANAUSIRL ESPISRITA GBRCANAUSIRL ESPITA ISR<br>KOR PRTGRC PRT GRCKOR<br>ARG ARG<br>URY URY<br>MYSCHLJAMBWATURZAFTHATTOZMBLKAKENCOLSLVPERPNGGTMIDNGHACRIBGDDOMPHLPAKINDPANHNDMEXJORTUNMARZWEBOLSENNICPRYCIVEGYBRAECUGMBTGOVENIRN HTISYR COG CHLTURMYSJAMZAFBWATHAZMBCOLLKAPERTTOSLVKENPANMEXCRIIDNTUNJORGTMPAKGHAINDMARPHLEGYBRABGDPNGZWENICSENHNDDOMCIVBOLPRYVENIRNGMBECUTGOSYR COG HTI<br>NGA NERMDG BFA SLE NGA NERMDGBFA SLE<br>MWI MWI<br>.2 .3 .4 .5 .6 .7 0 .2 .4 .6 .8<br>Overall Regulation Index Product Market Regulation Index<br>Labor Regulation Fiscal Burden<br>Correlation: -0.12 Correlation: 0.49***<br>USA AUTJPNCHEDNK FRANOR NLDBELSWE FIN CHE ISLUSA NORFIN SWEAUTJPNDNKNLDFRA BEL<br>GBR CANISRAUS IRL ITAESP IRL GBR AUSESP CAN ISR ITA<br>KOR GRC PRT KOR GRCPRT<br>ARG ARG<br>URY URY<br>PNGMYSZWEKENBWATTOJAMGHAZAF SYRLKAZMBBGDIRNINDDOMMARCHLSENCIV TURECUIDNPAKCOLTUNHNDTHAPHLEGYCRIGTMNIC BOLTGOSLVJORHTIPRYCOGPERVENPANMEXBRA HTI PRYSLVDOMBOLGTMECUCHLBGDHNDGMBJAMTHAPHLPERTGOCRIIDNMYSVEN COLBWANICPNGMEXLKAZMBPANTTOJORPAKKENBRAGHACIVSENIND COGTURMARIRNZAFTUNZWEEGY SYR<br>NGA NER BFA MDG SLE MDGNGASLENER BFA<br>MWI MWI<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Labor Regulation Index Fiscal Burden Index<br>12 12<br>10 10<br>8 8<br>Log of GDP per capita Log of GDP per capita<br>6 6<br>4 4<br>12 12<br>10 10<br>8 8<br>Log of GDP per capita Log of GDP per capita<br>6 6<br>4 4<br><!-- End of picture text --> _Note: *, **, and *** denote significance at the 10%, 5%, and
of individuals being classified as migrants without ever having moved across an international border. As mentioned, this is generally possible only in the case of people born in one country but who are citizens only of another country. A similar situation arises with dependencies and former colonies. Residents of Martinique, a French dependency, are automatically granted French citizenship. The statistics for Martinique show all the domestic population as French, possibly leading one to think that Martinique is part of metropolitan France or that most of the population moved to France. In such cases, having data categorized by both foreign born and foreign nationality would enable differentiating between the number of locally born inhabitants of Martinique who are French (referred to as Martiniquais), those born in metropolitan France who moved to Martinique, and people from other countries.

5 Of course the country of birth may be redefined, as elaborated in the next section.

6 This article deals exclusively with migrant stocks. Nothing can be gleaned therefore about when a migration took place, save for inferences that can be made by comparing differences in stocks over time. Nor is anything known about the circumstances (such as visa type) under which an individual entered a particular destination country.

6
<!-- Start of picture text --> Water Private<br>Connection<br>382 N<br>Electricity Private Water Shared<br>Connection Connection<br>497 N 46N<br>660 electricity No Water<br>STEP 1 customers Connection<br>pulled from Electricity 69N<br>Utility Database<br>erat (Electricity coverage<br>TOR in Addis Ababa 98%)<br>NUMBER OF Electricity Shared<br>OBSERVATIONS Connection:<br>IN OUR SAMPE:<br>. _.<br>987N=# Water Private<br>Primary electricity Cconnection.<br>customer 17 N<br>163 N<br><!-- End of picture text --> <!-- Start of picture text --> Water Shared<br>Connection<br>Electricity Shared 327N<br>Connection:<br>STEP 2<br>All other households<br>sharing with primary No Water<br>customer Connection<br>327N 142 N<br><!-- End of picture text -->
<mark>experience with prior phone surveys in Nigeria, a response rate of 60% was assumed, implying that the required number of households to contact in order to reach the interview target was 3,000. These households were selected from the frame of 4,934 GHS-Panel households with at least one phone number for a household member or a reference individual. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted</mark><sup>_35_</sup> <mark>. This balanced sampling approach enables selection of a random sample that still retains the properties of the pre-COVID sampling frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance</mark><sup>_9_</sup> <mark>. The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.</mark>

**Reduced form estimation:** The reduced form econometric methods applied here describe behavior of the various outcomes of interest in the data (e.g. food security, education, income loss). This setup allows us to make inference and conduct more complex statistical tests with multiple controls for differences across populations than simple univariate tests for differences in means<sup>_10_</sup> . For a given outcome, y, define the conditional expectation function (CEF)<sup>_36_</sup> as:

Here the CEF is the expectation, or sample mean, of individual outcomes yi, given a k× 1 vector of covariates Xi, which is held fixed. The conditional density function of yi is fy(t|Xi = x
76%|69%|16%|37%|-|-|
|Female single earner|12%|52%|23%|37%|35%|70%|24%|55%|
|Male single earner|8%|23%|4%|14%|24%|50%|8%|39%|
|Majority female earners|3%|57%|9%|28%|9%|28%|15%|45%|
|Equal contribution|10%|24%|17%|16%|23%|63%|8%|41%|
|Majoritymale earners|5%|16%|15%|19%|20%|52%|9%|39%|

Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. According to the number and gender of the household income contributors, we find that refugee and IDP households are the most vulnerable regardless of the family classification. Among displaced populations, households dependent on a unique earner or on female earners are disproportionally more likely to fall into poverty, compared to other households with at least one earner. These findings indicate that differentiation by gender of the head, displacement status and subgroups of headship have important implications for policy and targeting.

# **7. MPI and monetary poverty**

We now compare the results using monetary and multidimensional poverty measures, as both can provide us with different insights into the short-term and long-term hardships that households face in displacement situations. Monetary poverty is calculated using the international poverty
# 2. Methodological framework

Stress testing is a useful (off-site) tool to analyze the resilience of banking systems to adverse events and can complement other supervisory information, tools, and actions. A reverse stress test is a targeted exercise that quantifies how much current viability conditions should change for a bank to hit a pre-determined adverse outcome (Basel Committee on Banking Supervision, 2009). Reverse stress tests can be particularly useful in countries where supervisory capacity and enforcement are weak, resulting in a lack of complete and reliable data which prevents the application of more sophisticated analyses, or when the effects of an ongoing crisis, such as COVID, obscure data reliability.

In this paper, we extend the reverse stress test framework by computing a banking system resilience measure from reverse stress test results for individual banks. Our approach hypothesizes an increase in the nonperforming loans (NPLs) ratio for each bank to identify the point at which it “breaks”, the Break Point (BP). Specifically, because an increase in NPLs is accompanied by new provisions for loan losses, the BP is the NPL level at which the new provisions deplete the bank’s buffers above minimum capital requirements. For each bank, we then compute the “Distance from Break Point” (DBP) as the difference between the BP and the actual NPL ratio.

Next, we aggregate this bank-level information at the country level by sorting banks according to their DBP to identify the group of most fragile banks (i.e., the lowest DBP) that collectively represent at least 20 percent of total banking system assets. For banks with the same DBP value, we select the largest banks first. The 20 percent threshold is commonly used to identify a systemic banking crisis (Laeven & Valencia, 2013).<sup>2</sup> We define this set of vulnerable banks as “Banks at Risk” which could consist of a few large institutions or several smaller ones. In a final step, we consolidate the balance sheets of the “
29

# **Appendix A:  Checking the Robustness of Findings Using Observed Expenditure**

# **Data**

The purpose of this appendix is to see if the poverty and inequality findings of Tables 4 and 5 of this paper are robust for different ways of defining household income (expenditure). Therefore, in this appendix all of the calculations are based on observed – rather than predicted – data. In other words, all calculations in this section are based on observed per capita household expenditures, and the income contribution of the migrant in the excluding remittances situation is assumed to be zero.

Using the observed data on per capita household expenditures, Appendix Table 1 replicates Table 4 in the text. All of the main results are as before. For example, the three poverty measures in Appendix Table 1 show that the inclusion of remittances – either internal or international – in household expenditure reduces the level, depth and severity of poverty in Guatemala. However, just as in Table 4, the size of the poverty reduction is greater when measured by indicators focusing on the depth and severity of poverty, such as the squared poverty gap. For instance, while the poverty headcount shows that the inclusion of international remittances in observed household expenditure reduces the level of poverty by only 2.3 percent, the squared poverty gap shows that the inclusion of international remittances reduces the severity of poverty by 13.6 percent. Including remittances – either internal or international -- in observed household expenditure has a greater impact on reducing the severity of poverty in Guatemala than it does on reducing the proportion of people living in poverty.

Appendix Table 1 also reveals that the inclusion of internal or international remittances in observed household expenditure has little impact on income inequality, as measured by the Gini
<mark>Ghana’s total public debt back to a sustainable path [.......] with PV of public debt declining to around 40 percent of GDP by the end of the projection period”. That meant reattaining sustainability by 2035.</mark> <mark>For economists acquainted with public debt crises in EMs, this leisurely time frame for reattaining public debt sustainability without a crisis would have been considered unrealistically long given the level of public debt, its dynamics and market sentiment. Alarm bells for drastic fiscal consolidation should have been ringing loudly in early 2016.</mark> As it turns out, the debt data shown in the public sector DSA in IMF (2021) contain a surprise. Public debt was estimated at 78% for 2015 in the 2016 DSA and shown in Table 2 above. Yet the DSA in IMF (2021) shows the public debt-to-GDP ratio (d) for 2018 at just 58%, a decline of 20 percentage points of GDP in three years.<sup>8</sup> Given the adverse public debt dynamics that prevailed in 2016, two possible explanations for the sizable drop in d are first, a commendable fiscal effort; and second, large revisions. As it turns out, Ghana was “bailed out” by another rebasing of GDP in 2017 that increased GDP by 26%. Without this boost to GDP, d for 2018 would have been a significantly higher 73%.<sup>9</sup> In addition to the GDP rebasing, two other idiosyncrasies are worth mentioning.

1. Ghana’s GDP level has been periodically boosted by new oil discoveries. These raise GDP significantly in the year when they become operational, and therefore lower d, but without a sustained increase in the growth rate. Box 1 in the DSA in IMF (2021) notes the key role of oil discoveries in supporting growth, containing the current account deficit and boosting FDI.

2. Oil discoveries would also raise primary