rafmacalaba/data-use-mentions-extended huggingface dataset:
typically restrict the recall period for past migration episodes.<sup>5</sup> In addition, nationallyrepresentative surveys lack detail on the past migration experience which would allow to link migration patterns to labor market outcomes after return. Those nationally representative surveys can be complemented by surveys targeted to return migrants, which remain very rare globally and for South Asia, with the only exception being the Bangladesh Return Migrant Survey (BRMS) 2018/2019 recently collected by the World Bank (Bossavie, Gorlach, et al. 2021). The three nationally-representative household surveys from Bangladesh, Nepal and Pakistan used in this paper collect detailed information on current labor market outcomes of individuals. They also ask a few questions about past migration overseas. All surveys also collect information on how long returnees have been back home, which enables to shed light on the relationship between duration of stay back in the home country and labor market outcomes, as reintegration back into home labor markets may take time. These surveys also allow to carry out a profiling of return migrants and to compare their characteristics to those of nonmigrants, shedding light on selection in return migration.

The extent of information collected on past migration differs in the three surveys. The 2017/2018 Labor Force Survey (LFS) for Nepal collects the most comprehensive data on past migration. It includes a dedicated module on past migration overseas by current household members, which asks about country of destination, occupation at destination, wages abroad, date of departure and date of return. It also includes a module on current absentees overseas which allows to compare the characteristics of current migrants and returnees. For Bangladesh, the analysis uses the 2016/2017 Household Income and Expenditure Survey (HIES) where information on past migration overseas is much more parsimonious. The survey asks current household members whether they have returned from abroad in the past five years, but does not ask further questions about the past migration experience.<sup>6</sup> We are however able to complement the HIES data with
bank instability measure accounts for increases in credit risk. Other important sources of risk in EMDEs could be the sovereign-bank nexus (Feyen & Zuccardi, 2019), foreign exchange rate risk (Demirguck-Kunt & Detragiache, 1998), a sudden stop of capital inflows and sharp depreciation (Reinhart & Rogoff, 2013), and volatility in commodity prices (Eberhardt & Presbitero, 2021; Kinda et al., 2018).

- **Likelihood of the increase in NPLs.** The approach says little about the likelihood of occurrence of the potential increase in non-performing loans due to the credit shock (or recognition of the true performance of the actual portfolio). The reverse stress test mechanically computes the NPL breaking point without attaching an estimate of the likelihood for a bank of such occurrence. It is also silent on the time horizon over which the increase in NPLs materializes.

- **Quality of NPL data.** NPL data are backward-looking and come with a lag. NPL data may be underreported, particularly in countries with weaker supervisory practices.

- **Sample coverage.** The bank sample may be incomplete, especially in some countries and for specific bank types such as unlisted banks (including mutual banks or state-owned banks), smaller banks, rural credit institutions, and other credit institutions.

# 3. Application to the COVID-19 pandemic

The macro-financial shock caused by the COVID-19 pandemic precipitated a global economic recession in 2020 and put severe pressure on financial markets and institutions around the world. After the initial shock, many economies have (partially) recovered but the COVID-19 health crisis may produce long-lasting negative effects on banking systems, especially in EMDEs. Uncertainty in the development of the health crisis and its negative spillovers are likely to weigh heavily on banking sectors and are expected to lead to 7
|13.0|
|**0.1**|0.2|0.3|0.8|1.0|1.4|2.2|3.7|5.6|8.7|12.5|

**Panel B. Headcount poverty in households without children (percent)**

|**Child scale**<br>||||**A**|**dult scal**|**e parame**|**ter e**||||
|---|---|---|---|---|---|---|---|---|---|---|
|**parameter**|||||||||||
|<br>**b**|**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|**1**|
|**0.01**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.02**|2.8|3.2|3.5|3.9|4.2|4.7|5.4|6.5|8.6|11.3|
|**0.03**|2.8|3.2|3.5|3
As DeShazo and Fermo (2002) suggest, when conjoint analysis is applied, several design issues and options such as service definition and payment method need to be considered at the outset (see Pattanayak et al., 2001 and 2004 for a full discussion of design choices including survey and elicitation methods). This includes: (a) defining the commodity primarily involved (b) determining the type of WSS offered and how it differs substantively from current options, and (c) characterizing the institutional setting that is providing the service. Usually a careful review of existing levels of service and alternatives can identify and define the appropriate commodity for the study. In general, preparatory activities and focus groups can uncover important features of the selected service option(s) such as metering, hours of supply, and water quality. These can then be presented in detail in the descriptive/informational parts of the conjoint survey.

The scenario, or institutional setting, must also be adapted to local conditions. In general, if certain elements of the scenario are expected to substantively affect household demand for improved services, these elements become design features that vary across the surveyed households. Surveys with these different versions would be administered and econometric modeling could be used to detect whether the private versus public institutional context affects household demand and choices.

Payment methods, in other words, how individuals are asked to pay for the services, is also an important consideration. In the water and sanitation sector, the main approach is to ask the valuation question(s) from an _ex ante_ perspective such as increments to current bills or new bills. For households without a connection, the payment method may be a connection charge plus an increment to monthly bills. The ranges of the connection charge and monthly bill should be sufficiently wide to estimate demand and to capture relevant policy alternatives (e.g., cross-subsidization). The relative advantages of different payment method alternatives depend importantly on the target population and on the policy context. If cash income is a binding
fdt = bo+ pfd,, +b1lr, + b2lrt, + c11re, + c21re,1 + d1yt + d2yt1 + yrd, + Pci, + vda, + gt (2) The variables and their data sources are described in Appendix 1.

# **(b) Specification of the Relationship Between Land Registration and Economic Growth**

We specify the land registration/economic growth relationship on the strength of the conceptual framework in which land registration is linked to economic growth through investment and efficiency of resource use. The specification of the economic growth function follows the endogenous growth theory and the related empirical work (Crafts, 1996). It is the same approach used by Torstensson (1994) in his cross-sectional study of the effects of property rights on economic growth (Section I above). In looking for control variables, we face a dilemma in that as many as 50 variables have been used in different cross-sectional economic growth studies, and many of these variables do not hold when subjected to the type of rigorous analysis that Levine and Renelt (1992) undertook. In our study, we will build on three explanatory variables that they found to be robust, namely, investment rate, growth in investment in human capital and population growth. As we are dealing with a single country case study using time series data (as opposed to the bulk of the endogenous growth studies that are cross-sectional), some of the key variables used in the endogenous growth empirical literature have been left out, namely, the initial level of real GDP per capita and the initial human investment stock. Long run economic policy variables such as inflation, real exchange rate, and government expenditures were left out also because they were stable for Thailand during the period covered by the study since macroeconomic stability was the corner stone of economic growth policy (Christensen et al., 1993).

The specification of the economic growth equation is presented in equation 3 as a long-run or equilibrium relationship.

Yt = Po + P LRT, 1 +
POLICY RESEARCH WORK[NC, PAPER 1 715

# Summary findings

Canagarajah, Ngwafon, and Thomas present a poverty profile of Nigeria for the years 1985 and 1992 and show how poverty changed between those years. They show the nature of poverty in both years for different deciles of the population, different levels of education, different ages for household heads, and different parts of the country, including urban and rural areas There is no official poverty line in Nigeria, so they selected one based on two-thirds of average per capita spending in 1985 (N 395 a year per capita in 1985 prices). N198 (one third of mean per capita household spending) is used to indicate extreme poverty. The bottom 17 to 18 percent of income distribution had a lower standard of living in 1992 than in 1985 by any measure, but all other income groups had a higher standard of living.

An increase in mean per capita household spending reduced the proportion of the population in poverty from 43 percent to 34 percent.

But the benefits of growth were not shared equally by different parts of the country. Growth of household spending was faster in the southern and middle parts of the country and slower in the north. Poverty in Nigeria is overwhelmingly rural and regional, but is also greatly influenced by age, education, and the nature of employment. Most of the poor, especially the extremely poor, are uneducated.

The 8.9 percent decline in poverty was the net result of a + 13.6 percent growth factor and a -4.7 percent income distribution factor.

Improving the quality of data collection and analysis and establishing systems for monitoring poverty are important for policymaking. Canagarajah, Ngwafon, and Thomas recommend an integrated living standards survey to provide baseline data and to permit analysis of household behavior.

This paper - a joint product of Human Development 3 and Institutional and Social Policy, Africa Technical Families - is part of a larger effort in the region to analyze welfare and poverty issues. Copies of the paper are available free from
Information on employer demand is most often collected through subjective surveys. Cunningham and Villasenor (2016) reviewed 27 studies from various countries that attempted to identify the skills demanded by employers. They separated skills into four categories—socio-emotional, high-order cognitive, basic cognitive and technical—and found that employers tended to place greater emphasis on socio-emotional skills and higher-order cognitive skills. While employer surveys can help sharpen the focus of education and skills training, the subjective and qualitative nature of employers’ responses can make it difficult to assess the relative importance of different skills and skill combinations (Rutkowski, 2010).

Big data from online job portals can help quantify employer demand through text analysis. Researchers use text analysis to investigate large amounts of textual information and systematically identify its properties. They analyze the frequency of keyword usage, identify communication content and categorize the structure of the text. Using text analysis to examine labor market dynamics offers a number of advantages over more traditional approaches. Online job postings may reflect employers’ desired qualifications and skills more accurately than survey responses. Unlike printed advertisements, employers posting job offers on websites do not have to pay by the word and thus can provide more detailed information on the knowledge and skills they require (Gallivan et al. 2004). These descriptions, in turn, can help highlight the skills demanded by different occupations and in different locations, as well as the wage discrepancies associated with various skill sets. Traditional sample-based labor force surveys cannot capture this type of information.

Beblavý, Fabo and Lenaerts (2016a) examined two million job advertisements published on a US-based online job portal called Burning Glass. They grouped the skills and qualifications required in the postings into several categories, such as education and formal qualifications, cognitive skills, non-cognitive skills and experience, and then broke these categories down into sub-categories. They found that while employers placed a high value on both cognitive and non-cognitive skills, the emphasis on each in job postings varied by employee skill level. While 25 percent of all job advertisements highlighted computer skills,
gates<br>compulsory work • Community organizations – women’s groups, • Build eco stove for a neighbor<br>performed for cooperatives<br>others without pay;<br><!-- End of picture text --> Note: Table does not include responses of men and women from the cooperatives.

**What explains the discrepancy between answers to the household survey and performed activities by women in rural communities?**

The results of the study suggest that there are two explanations behind the observed discrepancy between responses to the survey questions regarding women’s employment and the activities in which women in rural communities engage. The first and immediate explanation behind that discrepancy is related to women’s and men’s perceived identities: Women and men each identify with specific spheres of life. Women commonly identify with the private sphere – and hence, the domestic sphere – while men identify with the public sphere – which in a rural context implies the agricultural sector. This is in line with traditional divisions of gendered life spheres according to which women’s role belongs to the private sphere and men’s role belongs to the public sphere, including market work and political voice and representation (Adams 2011). While these identities are understood to be mutually exclusive, for the women in this study there is a significant overlap between activities undertaken in the private sphere and economic activities.

A second explanation this study suggests is that women identify ‘employment’ with a set of necessary characteristics. Those characteristics may not all apply to their own individual cases. They are as follows: 15
written program, data were then subjected to a series of range and consistency checks, and a list of all possible errors and inconsistencies das printed Cut. During the second round, the enumerators filled out the remainder of the questionnaire and verified the answers that had been marked as potentially in error on the print-out. The completed questionnaires were then returned to the regional offices for data entry of the second round and of the corrected information pertaining to the first round. In combination with other featurer, such as a high ratio of supervisors to enumeratorP d random re-interviews of households, this system permitted to maintain a high level of quality control on the field operations. The data derived from the CILSS are therefore generally of high quality.1/ 2/ More detail about the CILSS tield work and the data entry system can be found in Ainsworth & Munoz (1986).
most countries for which reliable data is available. It is also part of the key innovation that spurred much of this research which, as noted earlier, was the use of alternative data sources to supplement the inadequate information contained in most household surveys on the richest households.

There is plenty of evidence that the very rich are becoming richer, both in the Unites States and in many (but not all) other countries, both rich and poor. There is also evidence to suggest that as relatively small powerful groups come to control a disproportionate share of a country’s economic resources, fairness and competition suffer both in markets and, perhaps most insidiously, in the body politic. These mechanisms of growing concentration of power – market and political – may constitute real threats to the well-being of poor and middle-class people in many places. So, the focus on rich people and rich countries is understandable.

But attention spans are limited, and there is a real risk that that focus could detract from interest in poor people and poor countries and, most of all, in poor people in poor countries. These are the people who should matter the most to progressive multilateral institutions, and therein lies the challenge to the World Bank and the IMF. Those staff in the two institutions who would like to promote greater equity must embrace the great recent expansion in global attention to inequality, while at the same time working to keep the bright light of evidence shining on those most deprived and least able to protect themselves from challenges like climate change: the poor in poor countries.

# **3.  The future of inequality and multilateralism: New ideas needed**

Although intellectual fashions are hard to predict, the intense public and scholarly concern with inequality that marked Phase 5 above is unlikely to be a passing fad, at least in the short- to medium-run. Automation and digitalization have led to a process of occupational polarization in most advanced industrial 19
/NPLs (lhs)<br>In millions of denars<br><!-- End of picture text --> Source: NBRM What were the lessons learned during the implementation of the macroprudential measures? First of all, maybe the most valuable experience was related to the role of the NBRM. This central bank established monetary policy and carries out supervisory responsibilities. In addition, beginning from 2006, the NBRM has intensified its duties related to the monitoring of financial stability.<sup>38</sup> As a result of the fact that all these tasks are under one roof, the NBRM has at its disposal adequate monetary and supervisory data to detect the main vulnerabilities of the banking and the financial system and to act accordingly. The crisis has proven that the efficiency of the undertaken measures is largely dependent on the right data and the timely reaction.

Regardless of these sound preconditions, it was rather challenging to choose the most appropriate measures. First of all, the NBRM, as all other central banks in this period, had little

> 37 As a reminder, the analysis in the second section of this paper regarding the credit quality of the loan to households was performed with the exclusion of new credit exposures.

> 38 The NBRM has issued its first Financial Stability Report, which reflects the trends and movements in 2006. With the adoption of the new Law on the NBRM (December 2010), financial stability is identified as one of the main tasks of the NBRM.

27
those individuals who could not be interviewed in person during the second phase and had not been successfully interviewed by phone in phase 1.

We also attempted to obtain migration outcomes using WhatsApp location sharing. During the baseline (August 2019), respondents were shown how to share their current location using WhatsApp. Respondents were asked via audio messages to share their current locations and for each location share, were compensated with mobile credit equivalent to 50 Gambian dalasi. This exercise was repeated during the endline survey (in August 2020) and afterwards (in January and February 2021).

We employed the same survey firm used for the baseline, to maximize the trust in enumeration and the chance of relocating individuals. Surveys were done on tablets with multiple consistency checks, and geo-coded and time-stamped to verify where and when surveys were done. After the data cleaning exercise, a total of 2,761 youths, 370 household heads and 391 elders were successfully surveyed through face-to-face interviews. We ended up with a final panel of 3,640 youth which were either interviewed face-to-face (n=2,761) or through the phone (n=475), or for which we got information from their household head (n=370), the elder survey (n=28) or only their WhatsApp location (n=6). Only 1 individual, a teacher was lost because he could not be contacted by phone and the whole household had moved out.

# **4.3 Estimating treatment impacts using the follow-up data**

We use outcomes from the follow-up survey and run the following linear regression to estimate intention-to-treat effect. As before, let _Information_ , _Senegal_ and _Vocational Training_ be dummy variables that take value one if the individual is assigned to the information intervention, Senegal migration alternative, and offer of vocational training respectively. Then for outcome _y_ for individual _i_ in settlement _s_ we estimate: 1I I I I 2S 3 I I + ∑ii,ss98 1�s � (3) ss ss ss yy =
buys infrastructure bonds issued by some corporations. The BNDES is financed through a combination of retained earnings, foreign funding (including form bilateral and multilateral lenders) and government resources (Walsh, Park and Yu 2011).

54. Several developing countries have also started to tap into international financial markets to finance infrastructure projects.<sup>34</sup> International bond issuances are attractive for infrastructure investments, not only because they enable countries to augment domestic savings and broaden the investor based, but also because they have a back-loaded repayment profile. But they also entail significant risks, which include a reversal in the confidence of international investors, exchange rate exposure, high refinancing needs and potentially large costs-of-carry.

55. But the private sector’s role in infrastructure investments has not been limited to these types of financing. It often engages in infrastructure financing through public-private partnerships (PPPs), which are established through a long-term contract between a government and a private investor, bundling investment and service provision into a single long-term contract. The investor (usually a group of private investors) finances and manages the construction of the project, and maintains and operates it over the time of the contract (usually around 20 to 30 years), before transferring the assets to the government. During the operation, the investor receives a stream of payments (for example, through user fees or government payments) as a compensation (seem for example, Engel, Fischer and Galetovich 2010).

56. Governments in developing countries have been increasingly interested in attracting Private Public Partnerships (PPPs) for infrastructure investments. One source of information is the World Bank’s data base on Private Participation in Infrastructure (PPI)<sup>35</sup> which provides information on infrastructure investment commitments in which private parties assume operating risks. These commitments reached a record high of US$170 billion in 2010, but have been historically heavily concentrated in a few countries and in one sector, telecommunications. India has been the top recipient of private sector