rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 3: Share of each type of occupation by migration status Panel a. Natives Panel b. Migrants <!-- Start of picture text --> 5.1% 11.0% 5.1% 9.8%<br>Non-telework Non-telework<br>21.5% 21.6%<br>15.5% 22.5%<br>16.1% 18.0%<br>Non-essential Non-essential<br>11.3% F2F 8.5% F2F<br>13.8% 10.2%<br>5.7% 4.2%<br><!-- End of picture text --> C. Migrants from Western Europe D. Migrants from Eastern Europe E. Migrants outside Europe <!-- Start of picture text --> 7.4% 9.0% 3.6% 11.7% 5.0% 9.4%<br>Non-telework Non-telework Non-telework<br>17.7% 29.3% 20.3%<br>15.8% 23.3% 24.3%<br>17.5% 14.9% 19.2%<br>Non-essential Non-essential Non-essential<br>13.2% F2F 6.9% F2F 7.6% F2F<br>13.9% 7.4% 10.1%<br>5.5% 3.0% 4.2%<br><!-- End of picture text --> Source: Own elaboration based on data from the 2018 EU-LFS and combining Dingel and Neiman (2020), RioChanona et al (2020) and Avdiu and Nayyar (2020) methodologies.

36
J. Vecci. (2016). “Social identity and governance: The behavioral response to female leaders.” European Economic Review, 90, 302-325.</mark> <mark>Gulesci, S., E. Meyersson, and S. Trommlerová (2019). “The effect of compulsory schooling expansion on mothers’ attitudes towards domestic violence in Turkey.”</mark> _<mark>World Bank Economic Review</mark>_ <mark>. https://doi.org/10.1093/wber/lhy021</mark> <mark>Heise, L. L., and A. Kotsadam (2015). “Cross-national and multilevel correlates of partner violence: an analysis of data from population-based surveys.”</mark> _<mark>The Lancet Global Health</mark>_ <mark>, 3(6), e332-e340.</mark> 23
receive the subsidy. However, if the delay in subsidy disbursement is very long, households will face higher costs of investing in a toilet since they will need to pay higher interest charges since they cannot use the subsidy to repay the loan.<sup>36</sup> If subsidy disbursement delays become apparent after loan take-up, the household may have taken the sanitation loan, but may choose to make another investment in order to obtain funds to repay the loan. Thus, we hypothesise that both the extensive margin (i.e. whether a delay is expected or not)and the intensive margin (i.e. the length of the delay) will influence the loan uptake decision; but the length of the delay will also influence the sanitation loan to new toilet conversion decision if these are realised after loan take-up. In particular, if delays in subsidy disbursement turn out to be substantial, households will be less likely to translate these loans into new sanitation investments.

To examine this, we use variations in average experienced delays in subsidy disbursements across communities. We use reports by community SBM officials during the endline survey on delays in subsidy disbursements experienced by the average household living in their community over the study period. We focus on villages in which SBM officials reported either delays of 1-3 months (‘Small delay’) or delays of 3-6 months (‘Large delay’). Because of coding errors during data collection, we cannot distinguish

> 34Part of the subsidy could have been received before the completion of toilet construction under SBM. However, this observation of subsdiy receipt before toilet construction could be due to recall errors, or due to subsidy receipt for an older toilet that was later replaced.

> 35This information is missing for one GP.

> 36In addition, households might face a higher risk of default or repayment difficulties, risking the longer-term engagement with the MFI.

21
points; and in all cases, the 2016-2018 estimate of the share deprived is contained in the 95 percent confidence interval for the 2009 estimate.

Given the large sample sizes of each survey, the standard errors are precise and the estimates can rule out any large decline in deprivation at standard levels of significance. At the 5 percent confidence level, the estimates can rule out any poverty reduction greater than 2.4 percentage points, which would still be very low amounts of poverty reduction given the amount of resource-related growth that occurred relative to some of the other settings that have been analyzed (e.g., Savoia and Sen 2021). Furthermore, for access to basic services, the estimates can rule out declines in poor access to essential services greater than a threshold between 0.8 and 7.7 percentage points.

Further illustrating the extremely limited ability of economic growth to benefit the population in PNG, we show how poor this lack of improvement in well-being looks relative to comparison countries and the rest of the world. Although we cannot estimate standard errors given that we do not have access to the microdata in all countries, Figure 7 reports how the average of the share deprived changed for each comparison region. The figure reports the average number of the five indicators in comparison regions that improved by greater than 5 percent, which are likely to be statistically significant improvements given the large sample sizes of nationally representative surveys.

We compare PNG’s performance to several different comparison regions. Specifically, we compare PNG to countries with the most similar share of the population that was multidimensionally poor in the pre-period, capturing the fact that countries with the most deprivations in the world might be improving at a faster rate than other countries; and we also compare PNG to the rest of the countries aside from the those with a similar MPI profile captured in the World Bank’s global database.

Figures 7a-7b illustrate that there was broad improvement in each of these comparison regions across nearly all indicators, including in places that did not have
are known to be very important in 19. The most common approach in the literature is instrumental variable strategies exploiting exogenous aggregate factors at the origin or destination: past migration rates (McKenzie and <u>Rapoport 2010, Antman 2011, Zhang et al. 2014), financial infrastructure (Calero et al. 2009), and political unrest</u> (Bansak and Chezum <u>2009) at the origin-level; employment conditions (Antman 2011, Cortes 2015) and exchange</u> rate crises (Yang <u>2008) at the destination-level.</u> 20. Analytically: where _c_ is the Moldovan community; _j_  1,2,3,..., _J_ is the migration destination countries and _t_ = 2004, 2005, ..., 2010.

21. An advantage of our setting is that migration has been a relatively recent phenomenon in Moldova, and, thus, there is little scope for the non-migrant population to be influenced over time due to spillovers and long-term confounding developments that might have arisen over time. As a robustness check, we exclude for the analysis the migrant households which already had a migrant in 2004 or before as they might be included in the Census migration rates. The main results do not change qualitatively (available upon request).

10
# **Introduction**

This chapter illustrates some of the potential consequences of a Doha agreement and alternative reform scenarios on incomes and poverty globally. Use of global models can set the overall stage for the impacts of multilateral trade reforms, even though by design they are not particularly well suited for looking at poverty impacts because few global models contain systematic information at the household level. Global models can nonetheless provide some useful information for assessing potential poverty impacts of multilateral trade reform. In particular, they generate changes in domestic and international prices that have a direct impact on factor incomes and consumer prices. These may be used to construct an estimate of the change in real income at the poverty line. When accompanied by additional information about the elasticity of poverty with respect to income, this can provide an estimate of the change in poverty by country.

An overall benchmark for assessing the gains from partial trade reform that are likely to arise from a successful conclusion to the current multilateral trade negotiations is full merchandise trade reform. Using the World Bank’s LINKAGE model (see van der Mensbrugghe [2004b]), it is estimated that the global gains from full merchandise trade reform would amount to about US$290 billion in 2015. This is below the previous estimate (using the same model) of about US$400 billion.<sup>1</sup> These differences can be explained by noting three facts. First, a new GTAP dataset is used here, with a 2001 base year instead of the earlier 1997 benchmark. The tariff data reflect reforms between 1997 and 2001 and the incorporation of nonreciprocal tariff preferences, which were largely ignored in the previous version. Second, the new dataset reflects the changing structure of the global economy, particularly the rising prominence of Asia’s

1. See, for example, World Bank (2002, 2004).
implications, it is therefore surprising that there is little direct evidence on the relationship between foreign aid and population attitudes in the

> 2 A U.S. state department report documents 1,839 terrorist incidents in Pakistan in 2008 compared to 890 in 2007 (U.S. State Department, 2009).

> 3 See for instance in discussions leading to the passage of the Kerry-Lugar Bill signed this year to provide aid to Pakistan. A June 2010 poll in Pakistan by Pew shows that 68 percent of the population has an “unfavorable” view of the United States.

> 4 See for instance Howard LaFranchi in the _Christian Science Monitor_ (http://www.csmonitor.com/USA/Foreign- <u>Policy/2010/0803/Pakistan-flood-relief-Could-it-undercut-Taliban-influence), Huma Yusuf in</u> _Dawn_ (http://www.dawn.com/wps/wcm/connect/dawn-content-library/dawn/the-newspaper/columnists/19-huma-yusuf- <u>the-link-with-governance-180-hh-06) or the editors of CNN.com (http://afghanistan.blogs.cnn.com/2010/08/03/u-ssees-opportunity-in-pakistani-floods/)</u>

2
organization examined was primarily a lending organization not involved in lines of activity that seek to address empowerment or human development as many self-help groups in India now do. Finally, a discussion paper by Pandey et al (2019) provides evidence that arguably pulls in two directions: in their evaluation of the National Rural Livelihoods Mission in India, the authors find that more women transition into work compared with a retrospective control, that access to lower interest rates has been expanded, and that some but not all moments of the income distribution have risen. As Brody et al (2017) conclude, in their systematic review there is clearly a need for further research, given the relative scarcity of rigorous evaluation and sometimes contradictory results reported and so in this paper, we contribute to the literature in the following ways.

First, we evaluate a self-help program in terms of its impact on women’s quality of life using _15 capability indicators_ developed on the basis of self-reported survey results to provide a quality of life assessment corresponding to what Sen (1985) calls (dis-) advantage. Our use of capability indicators has some conceptual overlap with empowerment measures (particularly broader definitions which go beyond empowerment as decision-making ability) and this is the first time these capability indicators have been used in a low or middle income country program evaluation.<sup>4</sup> Second, we offer a quasi-experimental research design based on propensity score matching models (PSM) which we go on to combine with evidence of length of time in program and show that at least some of the PSM results can be accounted for by the amount of time a woman has been in the program. Related and finally, we discuss the

> 4 Greco et al (2018) have recently reported on the testing of psychometrics of a capability index in Malawi and were the first to do so. See also Kanbur (2016) on problems arising from the need for direct measures of potential and their confusion with other indicators.

4
The purpose of this paper is to examine how labor markets contribute to mitigating shocks due to conflict. First, we establish how households redistribute on-farm and offfarm work in responding to conflict shocks, and how this prompts a redistribution in a household’s use of time for the household head and the spouse. Second, we study if offfarm work is directed at formal labor markets or at subsistence activities that produce lower wages. Third, we explore whether responses are heterogeneous by gender. By examining the differentiated impact vis-à-vis gender, we establish whether the welfare losses generated by responses to shock—such as a reduction in leisure time or time dedicated to children—are borne differently by men and women. Lastly, we identify whether an increasing labor supply is an effective strategy for countervailing, or at least reducing, the impact of shocks.

We use data for Colombia, a country that has experienced a long-standing conflict for over 60 years. The data collected is the baseline of a longitudinal survey of 4,800 rural households, the first household survey applied in Colombia to households living in conflict regions, as until recently, most research has concentrated on forcefully displaced populations. Forced displacement, an extreme coping strategy utilized in times of warfare, is widespread in Colombia. Today, more than 3.3 million persons have fled their hometowns in order to save their lives. Research shows that forcefully displaced persons face large asset losses, as well as a severe disruption in risk-sharing mechanisms (Ibáñez and Moya 2010). Sharp drops in income push displaced women to increase their labor participation; consequently, their contributions to households’ earnings rise significantly, yet their bargaining power remains the same and domestic violence escalates (Calderón and Ibáñez 2010). Evidence regarding coping strategies adopted by households that decide to stay in conflict zones is scarce in Colombia. This paper contributes to better understanding how households cope with conflict shocks, and how labor markets become important mechanisms for preventing further losses stemming from violent conflict.

The paper also contributes to the economic literature
# **3 Scenarios**

## **3.1 Base-Data and Base-Year Adjustments**

The starting point for the model simulations is the GTAP dataset, Version 6.0, which is aggregated to 27 regions and 25 sectors (see Appendix A). The aggregated dataset is then adjusted to incorporate additional information on protection. In particular, an output subsidy of 36 percent is imposed on U.S. cotton (the plant-based fiber [pfb] sector),<sup>11</sup> and bilateral tariffs are lined up with the scenario file prepared by CEPII.<sup>12</sup> A process based on the GTAP _Altertax_<sup>13</sup> procedure is used to impose these changes while minimizing the distortions to the original GTAP dataset.

## **3.2 Baseline Scenario**

The starting point for the baseline scenario is the adjusted 2001 GTAP database. The model is solved in recursive dynamic fashion through to 2015, lining up with the World Bank’s most recent medium- and long-term forecast for the global economy.<sup>14</sup> The baseline scenario includes two changes to the base-year level of protection. First, the export tax equivalents on textiles and clothing are removed, emulating the removal of quotas. Second, tariffs are adjusted to match existing commitments—completion of Uruguay Round commitments, Chinese WTO accession, EU expansion, and so forth. Both of these changes are phased in between 2002 and 2004. From 2005 forward, there are no changes in the levels of protection.

> 11. This is an approximation of the effect of the various programs supporting cotton production in the U.S., see Sumner (2005).

> 12. The CEPII scenario file contains 10 Doha scenarios, the base tariffs, and the preshock tariffs. The base tariffs are consistent with the GTAP tariffs at the 87-region, 57-sector level, that is, they correspond exactly to the GTAP Version 6.0
Basel III.

This paper makes three contributions. First, to support policy formulation and evaluation by policy makers and researchers, the paper introduces a new global database and a simple policy classification framework. Containing over 3,100 individually classified measures, the database provides a comprehensive, though not exhaustive, overview of the financial sector policy response to the COVID-19 crisis, based on publicly available information. Second, the paper documents patterns of the measures taken around the world and introduces the _COVID-19 Financial Policy Response Activity Index_ , a simple country-level indicator that tracks the total number of measures taken. Third, as an initial step towards understanding the determinants of the policy response, the paper explores the association between relevant country characteristics (i.e., domestic exposure to COVID-19 and fiscal, economic, and financial fundamentals) and policy makers’ 2
% level for the more-volatile NUTS2 data, and at the 98% level for the country-level data. Reversing this in Online Appendix Table A4.2 so NUTS2 data are winsorized at 98%, and country-level data are winsorized at 90%, generates broadly similar results (few multipliers above one). While a more aggressive winsorizing level (lower percentage) often leads to marginally larger estimates with slightly smaller standard errors, results are still similar to our baseline, even winsorizing at the 80% level (Online Appendix Table A4.3).

**Alternative Controls.** Our default specification is parsimonious; it only includes lagged GDP growth as a control (in addition to time and region fixed effects). In this section we show that our results are robust to a range of controls used in the literature. First, we add the change in the institutional index (also used by Durand and Espinoza 2021).<sup>54</sup> Results are almost identical to those in Table 1 (not reported). A second possible control is population growth, as much of the literature seeks to explain per capita GDP growth rather than aggregate GDP growth (as we do in Equation 1). Adding population growth (sourced from Eurostat) has almost no effect on the results; significance at the 5% is the same as in the baseline results, and most estimates are almost identical (not reported).

> 54 To get the institutional index, we use data from the International Country Risk Guide (ICRG). In particular, we construct an index of institutional quality as an average of subindices of government effectiveness, the rule of law, regulatory quality, and control of corruption, where each subindex is first normalized to be between zero and one.

19
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<!-- Start of picture text --> 12% 11% 20% 18%<br>10% 18%<br>8% 16% 4% 13% 14%<br>14%<br>6% 12%<br>4% 10%<br>8%<br>2% 1% 1% 6%<br>0% 4%<br>2013 2014 2016 7 2%<br>-2%<br>0%<br>-4% “2 2013 2014 2015 2016 2017<br>==" Returns on Sales ——~ Linear (Returns on Sales) S88 Gross profit margin ——— Linear (Gross profit margin)<br><!-- End of picture text -->
Policy Research Working Paper 10335

# **Abstract**

This paper advances the understanding of the linkages between trust in government and citizen participation in Latin America and the Caribbean, using machine learning techniques and Latinobarómetro 2020 data. Proponents of the concept of stealth democracy argue that an inverse relationship exists between political trust and citizen participation, while deliberative democracy theorists claim the opposite. The paper estimates that trust in national governments or other governmental institutions plays neither a dominant nor consistent role in driving political participation. Instead, interest in politics, personal circumstances such as experience of crime and discrimination, and socioeconomic aspects appear to drive citizen participation much more strongly in the Latin America and the Caribbean region. This is true across models imposing simple linear trends (logit and lasso) and others allowing for nonlinear and complex relations (decision trees). The results vary across the type of participation—signing a petition, participation in demonstrations, or involvement in a community issue—which the paper attributes to increasing net costs associated with participation.

This paper is a product of the Social Sustainability and Inclusion 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 npecorari@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 Directors of the World Bank or
on the host population depends on their spatial proximity and their relative density. From the sampling viewpoint, the clustering of immigrants would help to implement a design that has proven to be successful in conducting similar surveys elsewhere. For instance, such as those addressed to immigrants from the rest of the continent in South Africa (Plaza, Navarrete and Ratha, 2011); Syrian refugees and their hosts in Lebanon, Jordan and Iraqi Kurdistan (World Bank, 2018, and Aguilera, Krishnan, Muñoz, Russo, Sharma and Vishwanath, 2020); and Myanmar Rohingya in Bangladesh (World Bank, 2020 b).

The paper is structured as follows: section II describes the innovative use of Big Data from Telefonica de Ecuador to come up with a strategy for generating representative samples of host and migrant households for the Human Mobility and Host Community Survey (EPEC acronym in Spanish). Section III presents the implementation strategy. Section IV concludes by drawing general lessons from our experience in sampling migrant populations.

> 7 The Human Mobility and Host Communities Survey in Ecuador (Encuesta de personas en Movilidad y Comunidades de Acogida – EPEC acronym in Spanish).

> 8 World Bank (2020 a).

3