rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5810

# **Abstract**

This paper uses household survey data collected in September–October 2009 on a nationally representative sample of 2,000 households in Bangladesh to examine the nature of shocks experienced by households over the preceding 12 months and the type of coping mechanisms that were adopted. The analysis finds that more than half the sample claimed to have faced a shock—economic, health, climatic, or asset related—over the previous year. Surprisingly, the non-poor face a larger share of these shocks compared with the poor. A closer look at this result shows that the non-poor report a significantly larger share of “asset-related” shocks, which is consistent with the fact that the poor have fewer assets to lose. Health-related shocks dominate and households appear to have coped with these shocks through savings and loans, help from friends, and depletion of assets. The results show that households, when faced with covariate shocks due to climatic reasons, are less able to cope. As would be expected, the poor are less able to cope with shocks compared with the non-poor; the poor are more likely to use coping mechanisms that could have negative welfare implications in the longer term, including the depletion of assets, reduction of essential consumption, and use of high-interest loans. Econometric analysis suggests that geographical location, socio-economic status, and access to microfinance all affect the ability to cope with shocks. Policy implications include the importance of developing safety nets that take into account the vulnerability to climate-related shocks and further developing the links between micro-finance and safety net programs.

This paper is a product of the Social Protection Unit, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at isharif@worldbank.org.

_The Policy Research Working Paper Series disseminates
For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. When _w = 0,_ our accounting de facto only includes schooling; for any value _w > 0_ , we include learning as well as schooling. We include 131 countries in this development accounting exercise.

_Source:_ Schooling data are from Barro-Lee (2013). GDP data are from PWT 9.0. Learning estimates are from our database. Literature estimates are derived from the referenced papers.

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36 Bank, and of several household survey collections. We also searched for household surveys online. Through this process, we have so far identified, tried to access and (where possible) vetted 1,752 household survey data sets from 178 countries – see Table 5. We are in the process of identifying, trying to access and vetting other data sets, some of which will be added to the HEFPI data set in due course. Of the 1,752 surveys, 299 are currently inaccessible and 465 lack key variables. The remaining 988 data sets were accessed, many through the World Bank Development Data Hub (DDH).<sup>34</sup> They were then vetted and those that had the necessary information were analyzed; after a series of quality checks (see below for details), 570 data sets were kept.

Most of the surveys are HIES or HBS surveys, or multipurpose household surveys like the LSMS. Very few come from a highly standardized multi-country survey program like the LSMS. However, many of the datapoints come from versions of the microdata that have been harmonized ex post, such as the Luxembourg Income Study (LIS) and various World Bank ex post harmonized data set ‘collections’ (e.g. ECAPOV). Such ex-post harmonization exercises consist of applying a common set of standards and guidelines to the construction of specific variables such as total income, or a consumption aggregate across different data sets. Table 6 summarizes the key details of these survey ‘collections’. Sometimes we have produced and compared results from both the original ‘master’ data set and the ex-post harmonized ‘adaptation’. Indeed, sometimes we have produced and compared estimates from different adaptations. In addition, both masters and adaptations can be updated, so we have recorded the version of each master and adaptation used. All datapoints in the data set are labeled with their data source in the referenceid variable – see annex for naming conventions.

> 34 <u>https://datacatalog.
. This variation across organizations is consistent with evidence from other management surveys of the public sector (Bloom et al., 2015; Rasul and Rogger, 2018; Rasul et al., 2018).

> 12To provide a sense of the holistic (rather than specific) nature of these questions, we go through one example: a question relating to management practices relating to monitoring was, “In what kind of ways does your Directorate track how well it is delivering services? Can you give me an example?” Enumerators could then score responses on a continuous 1-5 scale, where for indication the scoring grid described a score of one as corresponding to circumstances where the “Directorate does not track performance”; a score of three corresponded to, “Directorate tracks a number of performance indicators. These are seen and reviewed by senior management only”; and a score of five corresponded to, “Full set of indicators are tracked formally and continuously. Reviews are conducted regularly and involve representatives of all directorate staff groups. The results of the review are formally communicated to all staff.”

> 13These topics measure multiple aspects of management including staff involvement and incentives, all of which could be said to be means of aligning the preferences of agents to those of principals. For example, what are performance incentives but a mechanism for aligning agent action to the desires of principals?

> 14The ‘Monitoring’, and ‘Other’ indices are positively correlated with each other, with a coefficient of 0.62. Importantly, the partial correlations of these two dimensions of management can still be separately estimated from each other.

13
# **IV.2 Credit Allocation**

Portfolio composition data for privatized, provincial, and private banks in June, 1996 are presented in Table 9. While time series data on portfolio composition would show the evolution of privatized provincial bank credit allocation over time, the snapshot presented here is also telling.33 In general, regardless of the industry, the percent of total credits rated good was highest among private banks. The better established privatized provincial banks (those that privatized prior to April, 1996) eclipsed the private banks on one measure -- the percentage of good credits to the government services sector (99.9 versus 99.6%). The earliest privatizers approached the private bank percentages on two other measures -- family loans (77.2 versus 84.9% good credits) and construction (83.3 versus 91.8%). It is important to note that the private bank sample contains only the ten largest in Argentina, presumably among the best banks in the country.34 The percentage of good credit in other sectors (primary production, manufacturing, utilities, trade, and other services) was somewhat lower for the early privatizers than for private banks as of 1996.

In general early privatizers had higher good credit percentages than either public provincial banks, recent privatizers (since June, 1996), or those that had begun but not completed the privatization process ("beginners"). Public provincial banks had slightly higher good credit percentages than early privatizers in only two categories -- primary production and utilities. Further, there are two reasons why the good credit percentages for the public provincial banks are likely to be significantly overstated. First, several measures of performance (nominal assets, net worth, and NW/TL) of privatized banks appear to have declined quite remarkably during the

> We did also have credit allocation data for December, 1995. However, for the five categories of banks in Table 9 the qualitative differences between that data and the 1996 data were
stage of production.<sup>2</sup> From raw material to the finished product, the whole process of goods production has experienced a fragmentation that today allows each sub‐process to be conducted where the skills and raw materials are available at competitive costs. As a result, today almost two‐thirds of the world’s manufactured imports are intermediate goods, meaning primary goods, parts, components and semi‐finished goods. A key challenge for Morocco and other countries is how to insert themselves into these production chains.

Regarding the third dimension (uncertainty regarding the long‐term growth prospects in the European Union), Morocco’s geographical position and history are such that fluctuations in its economy remain closely linked to activity fluctuations in Europe. This dependence is reflected in the geographical distribution of its imports and exports, and also remittances from Moroccan workers abroad. However, the region continues to face a number of structural problems (OECD, 2014), related in particular to high ratios of public debt, long‐ term unemployment in some countries, low fertility, aging populations, and high inequality. The recent decision by the United Kingdom to exit from the European Union has added a new layer of uncertainty to the Union’s prospects. Except for a few countries, growth prospects for the next two decades are weak. Without continued effort to diversify its trade partners, Morocco may find itself in a situation where its ability to export is constrained by a lack of foreign demand.

Regarding the fourth dimension (continued move towards automation and digitalization of activity), the advances in artificial intelligence and robots’ growing abilities and adaptability are dramatically changing society and affecting the future of employment. According to Frey and Osborne (2013) for instance, who examined 702 detailed occupations in the United States, about 47 percent of those – including taxi drivers, accountants, paralegals and loan officers – could be replaced by robots in the next decade or two. These fears may be somewhat exaggerated; while automation may
# **Analysis by Importers’ Characteristics**

# **Main activity**

The analysis of average dwell time with regard to the shippers’ main activity indicates that manufacturers perform better than traders overall (Figure 12). Moreover, manufacturers have a significant lower average dwell time – one day lower – than the other importers (t(313.826) = 1.679; p = 0.047)<sup>7</sup> . They appear to be more efficient and should be the primary counterparts of Customs/Terminals in contractualization initiatives because they master their logistics (raw materials or intermediary products) and can reach top performance by implementing efficient processes.

# **Figure 12:** **<u>Cargo Dwell Time, by Shippers’ Main Activity</u>**

<!-- Start of picture text --> 20 18.60<br>18<br>16<br>14<br>12<br>10 8.45 8.06 9.59 8.12 8.91 9.17<br>8<br>6<br>4<br>2<br>0<br>Manufacturing Assembling Domestic Transit Small & Other Total<br>Wholesaler Wholesaler Medium<br>Retailer<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

Furthermore, small and medium retailers experience the longest dwell time. They have a significant longer dwell time than the other importers– around 10 days longer (t(9.056) = 1.7991; p = 0.05)). This is not surprising: small retailers generally do not have their own warehouse and probably use the port as a storage facility; they may also experience a slower clearance process than larger shippers<sup>8</sup> . This may be due to informal practices and possible ―negotiations‖ with regard to lowering
Policy Research Working Paper 10890

# **Abstract**

Poverty maps are a useful tool for targeting social programs on areas with high concentrations of poverty. However, a static focus on poverty ignores its temporal dimension. Thus, current nonpoor households still face substantial welfare volatility and are at risk of becoming poor in the face of shocks. This paper combines the methods of poverty mapping and vulnerability estimation to create highly disaggregated vulnerability maps. The maps include predictions of the share of chronically poor households (poverty-induced vulnerability)—the focus of traditional poverty maps—and the share of households showing a significant probability of falling into poverty (risk-induced vulnerability). As an application of the method, the paper estimates a vulnerability map for Senegal that provides quotas for the expansion of the social registry. Accounting for the poor and the population at risk of poverty implies, in practice, the expansion of coverage into urban and peri-urban areas that tend to experience lower poverty rates. The inclusion of nonpoor households also serves as a first step toward supporting a dynamic social registry.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at obarriga@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive
Figure 1: _Prevalence of intimate partner violence in the past 12 months, by type of violence_ <!-- Start of picture text --> Psychological<br>17%<br>Some type of<br>Physical domestic Sexual<br>violence<br>3% 4%<br>25%<br>Economic<br>11%<br><!-- End of picture text --> Source: Own elaboration based on data from the 2020 ENVIF-VCM.

Figure 2: _Distribution of women aged 15 to 65 years who were victims of some type of intimate partner violence in the past 12 months, according to the type of violence experienced_ <!-- Start of picture text --> Psychological<br>and economic<br>but not<br>physical<br>and/or sexual:<br>8,2%<br>Only Only<br>psychological: economic:<br>43.6% 24.5%<br>Physical and/or<br>sexual and<br>psychological<br>Physical and economic:<br>Physical<br>and/or sexual 9.2%<br>and and/or sexual<br>and economic,<br>psychological but not<br>, but not<br>economic psychological<br>Only 1.5%<br>7.6%<br>physical or<br>sexual:<br>Physical only: 1.3%<br>5.5%<br>Sexual only: 4.0%<br>Physical and sexual: 0.2%<br><!-- End of picture text --> Source: Own elaboration based on data from the 2020 ENVIF-VCM.

The prevalence rates of all forms of violence were analyzed according to individual characteristics of women, characteristics of partners or ex-partners, and attitudes towards inequitable gender norms.
# **3. Data and Methodology**

# _3.1. Data_

Our analysis relies on data from the Gallup World Poll for the period 2010-2020, unless stated otherwise. During this period, the Gallup World Poll surveyed annually between 1,000-3,000 randomly selected adult Brazilians (15 years and older). In total, our sample includes 13,911 observations. We have all the relevant information for 10,228 observations that constitute our common sample. Table 1 shows the socio-demographic composition of the sample, which is representative at the country level as the surveys are designed to cover entire countries except for unsafe or inaccessible regions. As shown in Appendix C, the Gallup World Poll in Brazil is reasonably spatially representative over the period 2010-2020, although some states (Espirito Santo, Rio de Janeiro) are slightly overrepresented, while other states (Minas Gerais, Pará) are slightly underrepresented. Sampling weights are used in all analysis to make the sample as representative as possible.

**Table 1. Descriptive statistics based on the Gallup World Poll data sample for Brazil, 2010-20**

|**Variable**|**N**|**Mean**|**SD**|**Min**|**Max**|
|---|---|---|---|---|---|
|SWB|10,228|6.65|2.35|0|10|
|Age|10,228|37.87|16.51|15|97|
|Female|10,228|0.51||0|1|
|Has a partner|10,228|0.53||0|1|
|Elementary education|10,228|0.39||0|1|
|Secondary education|10,228|0.54||0|1|
|Tertiary education|10,228
was predicated on the assumption that countries could accelerate their performance to that of the 80<sup>th</sup> percentile of countries in their respective regions (Figure 12). Even if we were to assume that countries could accelerate their performance to this level as they re-open schools and address the challenges created by COVID-19, it would mean that the world will not halve Learning Poverty by 2030 – it will at best reach the learning poverty target by 2034.

83 Rogers and Sabarwal (2020). 84 For additional details please see World Bank (2020) available online at <u>http://documents.worldbank.org/curated/en/531681585957264427/Guidance-Note-on-Remote-Learning-andCOVID-19.</u>

> 85 See for instance the policy response options described in Rogers and Sabarwal (2020).

> 86 Al Tuwaijri et al. (2020).

> 87 UNESCO-UNICEF-World Bank are working on a joint Survey on National education responses to COVID-19. The first round has already been collected, and the team is moving to the second round. For more information please see <u>http://uis.unesco.org/en/news/survey-national-education-responses-covid-19-school-closures-due-12-june-2020</u> 88 Building on past experiences, such as the Listening to LAC, Listening to Africa, and Listening to Tajikistan, the World Bank is scaling up efforts to collect high frequency phone surveys of households in over 100 countries across all developing world to supports countries’ policy responses to COVID-19 (for more information see <u>https://blogs.worldbank.org/opendata/high-frequency-monitoring-covid-19-impacts).</u>

89 In March 2020, the World Bank in partnership with DFID, launched a specific windows of the Strategic Impact Evaluation Fund (SIEF) intended to generate experimental and quasi-experimental evidence that would be immediately useful for countries’ education systems as they deal with the Covid-19 pandemic (for more information please see <u>https://www.worldbank.org/en/programs/sief-trust-fund/brief/
exchange buying auctions from October 2010 onward. Moreover, the bank introduced further measures to direct the capital inflows driven by global monetary expansion to longer-maturity investments.

It is widely accepted that a high current account deficit may threaten financial stability in case of a sudden stop or reversal of capital inflows. Turkey’s current account deficit has widened sharply on the back of continued revival in domestic demand and imports as well as higher energy prices as the recession was left behind and as the country resumed fast economic growth. The 12-month current account deficit had reached US$68.1 billion as of May 2011. It is worth noting that in addition to the role of credit growth in fuelling import demand, Turkey’s dependence on imported energy leaves it vulnerable to the volatility of global energy prices. A recent IMF report prepared for the G20 in July 2011 estimated that the current account deficit-toGDP ratio would reach 10.5 percent in Turkey at the end 2011.<sup>7</sup> Although the current account

> 7 Participants of the Expectations Survey (July 2011) of the CBRT expect the current account deficit to be US$68.1 billion as of the end of 2011 (8.7 percent of estimated GDP for 2011 in the Medium-Term Programme) and US$62.5

8
the tariff hikes in 2018. In contrast to official employment statistics, which capture both labor demand and supply-side factors and provide only total employment counts, online job adverts provide an almost real-time proxy for firms’ intentions to hire and include comprehensive and rich information on the characteristics of jobs on offer. This allows us to cleanly pin down the link between trade policy and labor markets.

We investigate three key ways in which the trade war could have affected online job postings in the US: (i) the impact of protection for US firms from import competition (‘output tariff exposure’), (ii) the impact of the higher cost of imported inputs for US producers (‘in-

> 1https://www.whitehouse.gov/briefings-statements/the-inaugural-address

> 2A detailed overview of the US Trade War timeline is provided by Bown and Kolb (2019), and Crowley, ed (2019) presents a discussion of key elements of the conflict, including its origins, costs, and the challenges it poses for the future.

2
# **3. FACILITATORS AND BARRIERS WOMEN EXPERIENCE IN ACCESSING FORMAL VAW SERVICES**

In this section, data from in-depth interviews with users from the four selected centers providing psychosocial services are analyzed. The purpose of these interviews was to understand the experiences of women living in situations of violence and factors that influence their decisions to reach out to formal support services. By identifying the barriers and facilitators of women’s help-seeking, women’s experiences can inform policies that improve the accessibility and effectiveness of services and, ultimately, provide better and more comprehensive protection and support to survivors.

## 3.1 **Profiles and context of interviewees**

Although the profile of the interviewees was heterogeneous, most of the women were white, with low levels of education and aged between 40-59 years. Most were not working at the time of their interview.<sup>12</sup> Almost all cases contained elements of psychological violence, such as controlling behavior, insults and threats. Most cases involved physical and sexual violence. Most women interviewed had suffered at the hands of more than one aggressor over the course of their lives. Interviewees notably described violent family contexts with more than one case of domestic violence in their households or in their original families, some of which involved other survivors such as small children or older family members.

Many women described how their arrival at the support center (CIAM) was triggered by a recent episode of violence and how, after a series of sessions with the social worker or psychologist, they recognized the extent of the abuse they were experiencing or that they had suffered several other episodes of abuse throughout their lives. This process of “discovery” shows the workings of ingrained naturalization of domestic violence, which must not be understood as a particular experience (Campbell et al. 1998, Campbell et al. 1999, Kirkwood 1992, Moss et al. 1997). As shown in other research, this is partly due to the fact that physical abuse is often so intertwined with