rafmacalaba/data-use-mentions-extended huggingface dataset:
significant, but smaller, effects of temperature in the long run, which suggests that households likely adapt to permanent changes in weather conditions.

Our findings add to the ongoing discussion of richer countries’ responsibilities in mitigating the global effects of climate change. Over the last decades, poorer countries have been calling for compensation for the costs of climate change (also known as “climate reparations”) from wealthier nations, who are generally considered to be more responsible for global climate change. Our study contributes to this discussion by offering new global evidence that poorer regions are bearing the heaviest burden of global warming, and thus richer countries could provide further support in order to reduce the effects of climate change.

The availability of subnational poverty data opens other avenues for future research. While our study provides supportive evidence that agriculture is an important factor influencing climate-induced poverty and inequality, alternative channels, such as civil conflicts and labor productivity, could offer another direction. Further research on these topics would help provide policy inputs for more effective actions by different countries to address the challenge of global warming.

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Two aspects of these results are particularly noteworthy. First, the survey used to calibrate the model covers only 17% of the villages in Sri Lanka. The fact that the combination of satellite imagery with this type of small survey predicts population density as accurately as the estimates that use an entire census demonstrates the cost-effectiveness of this approach.<sup>5</sup> In fact, simulations indicate that combining survey data with satellite data generates sub-district estimates of population density that are as precise as a survey that samples 80 percent of the villages nationwide. Second, although our estimates are not as accurate as the most accurate top-down estimates, the model gives comparable or better performance than most others. However, because surveys are collected much more frequently than censuses, bottom-up methods that combine satellite imagery with surveys have the crucial advantage of remaining up to date even as the census ages.<sup>6</sup> This study fits into a rapidly growing literature on using satellite imagery to predict population counts and density in local areas. Wardrop et al. (2018) provide an overview of these studies. Top-down approaches to distribute population remain popular in the literature. For example, Stevens et al. (2015) use a dasymetric approach to redistribute population counts from the census using a Random Forest model-based weighting scheme in Cambodia, Vietnam, and Kenya. Using a combination of regression and tree-based methods, Anderson et al. (2014) predict population density for districts in Peru where no direct samples were available, using two coarse satellite-based covariates: Normalized Difference Vegetation Index (NDVI) and the daytime Land Surface Temperature (LST).<sup>7</sup> Our contribution to this literature is threefold. First, relatively few studies use “bottomup” techniques (Wardrop et al., 2018). Early studies using bottom-up style methods either use simple area measures and/or coarse satellite imagery, or mostly focus on developed countries where there are fewer data limitations (Sutton et al., 2001;
in response to a hike in the federal funds rate. Even though, one would expect a negative effect on exporters via financial constraints and appreciation, a potential explanation for this result is that the biggest exporters responsible for most of the aggregate exports are less financially constrained and with the contraction of the domestic demand might be more aggressive in selling their products abroad.

In sum, it seems that uncertainty matters in an international setting, particularly for imports. In the subsequent sections we look at whether the preliminary results provided by the VAR analysis conducted on US data carry through to an international setting. Specifically, we will be focusing on the impact on exports of importer uncertainty. This, based on the preliminary results, is expected to be more important for exporters than uncertainty experienced at home.

# **4 Importer uncertainty in bilateral trade data**

## **4.1 Methodology**

In accordance with the theoretical insights summarized above and with results from the VAR analysis, we focus on the effects of importer uncertainty on trade flows over the period 1990-2009. We examine the evolution of quarterly trade flows by means of a model of bilateral trade. The use of bilateral trade allows us to assess the effect of foreign uncertainty while controlling for domestic developments and additional

> 5To our knowledge, the only cross-country dataset reporting monthly (and quarterly) bilateral trade data is Trade Map of the International Trade Centre (ITC). Market Access Map, Market Analysis Tools, International Trade Centre, www.intracen.org/marketanalysis which provides data series starting in 2005 only.

11
# 1. Introduction

The format of this module is designed to elicit information that, after analysis, will enable researchers to assess the degree of empowerment of various individuals and groups within the three domains and eight sub-domains of the ME framework.<sup>35</sup> It will also allow analysis of agency and opportunity structure, the factors associated with empowerment.

The module combines an institutional mapping section and an individual questionnaire. The institutional mapping section is designed to capture data regarding opportunity structure. During analysis, researchers may also need to take into account asset indicators or opportunity structure indicators captured using pre-existing data sources and indices.

The module can also be used in combination with other survey instruments that already capture some of the data elicited by the module, such as the Social Capital Assessment Tool (SCAT), the Integrated Questionnaire for the Measurement of Social Capital (IQMSC), and the Living Standards Measurement Survey (LSMS). When questionnaire is used in conjunction with other instruments, it must be adapted to ensure that questions are not duplicated.

> 35 This module is from Holland Brook 2004.

80
al. (2013) reported a general trend towards low density growing for some of the largest LAC cities.

# _3.2 Use of Night‐time Lights Data for urban extents delineation_

The nighttime lights data (NTL) are based on nighttime imagery recorded by the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS), which reports the recorded intensity of Earth’s surface lights. Nighttime lights products have high correlation to human activities (Hsu et al, 2015), and have been previously used for regional and global analysis of urbanization (Cheng et al., 2016; Pandey et al., 2013; Sutton et al., 2006; Zhang and Seto, 2011; Zhou et al., 2015a; Zhou et al., 2015), population modeling (Anderson et al., 2010; Lo, 2001), and economic performance (Cao et al.,2016; Forbes, 2013).
likely than men to lose their jobs. Because the policy directly affects the active working-age population only, and females have low labor force participation rates, few women are likely to be affected by the policy.

2. The majority of potentially affected workers are unskilled workers. Given the higher wage elasticity for unskilled workers, job losses could be substantial for this group.

3. Young, skilled workers might experience significant increases in earnings without losing their jobs, given that many young minimum-wage workers are skilled.

4. The impact on poverty of an increase in the minimum wage is expected to be small. Poor individuals are overrepresented among potentially affected workers. However, the net effect on labor earnings might be negative because job losses are higher among the unskilled. Leakage is high: about 83.9 percent of workers who could be impacted live in non-poor households. Quantitative evidence from Shapley decompositions indicates that the impact on poverty can be negative but small.

> 23 The poor are defined as those living below the national poverty line.

> 24 Leakage could be even higher considering there is underreporting of income in the HBS.

20
at which_ data collection is undertaken, for example, interviews or other means of enquiry undertaken at the individual, household, group, community, town, or regional level. Further, both levels about and levels of data collection are distinct from pooling, or _levels at which data are analyzed._ For example, a household survey applied at the local level can ask about activities relating to the local, intermediary, or macro levels. The information and data resulting from that survey are usually pooled and analyzed at an intermediary or national level—such as in the case of an LSMS or national poverty monitoring exercise.

# 2.2 Examples of Application in Interventions

Feedback from project teams indicates that the application of the framework for structuring analysis and developing empowerment indicators is a manageable task. Lead researchers for each case study were either involved in the development of the framework or briefed on its content by the coordinating team. The lead researchers then independently instructed local researchers about its application.

The following examples from Ethiopia, Nepal, and Honduras are taken from the measuring empowerment studies in those countries.<sup>17</sup> In each case presented, methodologies have been developed but findings have not yet been fully analyzed. A

> 16 This is the case even when some tasks are undertaken simultaneously.

> 17 To date, the framework has been applied only to the evaluation of investment loans. Measuring empowerment in the context of adjustment/development policy loans has not yet been initiated but is scheduled to begin in two countries during FY04.

16
Italy exports a great deal of intermediate services embodied in goods (machinery, vehicles, chemicals and petrochemicals, etc), as reflected in the earlier discussion on Table 3.

Table 4: Italy: value added composition of the economy, 2007

||value<br>added<br>total<br>economy|gross exports<br>share of<br>total value|value added<br>in exports:<br>forward<br>linkages|value added<br>in exports:<br>backward<br>linkages|
|---|---|---|---|---|
||A|B|C|D|
|primary sectors|0.03|0.01|0.04|0.02|
|processed foods, beverages|0.03|0.05|0.03|0.05|
|textiles and clothing|0.02|0.07|0.04|0.07|
|chemicals, petrochemicals|0.03|0.14|0.08|0.11|
|motor vehicles|0.01|0.07|0.02|0.06|
|other machinery|0.07|0.28|0.15|0.28|
|other manufacturing|0.10|0.21|0.20|0.22|
|utilities, construction|0.10|0.01|0.05|0.01|
|commercial services|0.42|0.15|0.36|0.18|
|public services|0.21|0.01|0.02|0.01|
|total|1.00|1.00|1.00|1.00|

_source: own calculations, using GTAP8 data._ While Italy is similar to other high income economies,
do not find support in the panel setting.<sup>15</sup> Changes in the Chinitz metric yielded implausibly large coefficients values due to outliers, and we do not report them. Of our metrics, the Chinitz effect is the most sensitive due to how it embodies both the establishment size distribution and input-output exchanges. Its sensitivity is thus not very surprising.

Overall, the panel estimations strongly support our core evidence on the link between entrepreneurship and local industrial conditions in India. We think that India‘s industrial past, and the government-led spatial allocation of industrial activity that is rapidly becoming undone, provides a very interesting laboratory for testing many features of agglomeration and urban

> 15 One potential factor that may contribute to the greater sensitivity of the labor metric compared to inputoutput measures is that the labor metric builds upon worker flows in the United States which may differ substantially from India. The input-output metrics, by contrast, directly build upon India‘s input-output tables.

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consider countries that had a rate of extreme poverty in 1990 above 10 percent. For this simulation we are left with 38 countries (see Table 2).

# **Accuracy of the parametric approximation**

The core of our analysis is based on numerical simulations that apply changes to parameterized income distributions. The accuracy of such numerical approximations is therefore of central importance. Accordingly, in what follows, we compare the accuracy of each parametric approach using data for Latin America, where we can use the survey data to obtain direct estimates of the poverty rates.

**Figure 2: Actual vs. Predicted headcount ratios (Latin America, 1995)**

<!-- Start of picture text --> 0.7<br>Actual<br>0.6 Best fit of General Quadratic and Beta Lorenz<br>Lognormal<br>0.5 Nonparametric<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br><!-- End of picture text --> Figure 2 presents four different estimates of the headcount ratio (at a threshold of 2.5 dollars a day in PPP terms). The first is the actual poverty headcount ratio estimated directly from household surveys (our estimate of reference for the comparison). The second estimate represents the headcount ratio computed from the best fit between the General Quadratic and the Beta Lorenz approximations. The third estimate is the headcount ratio computed from an income distribution parameterized with a lognormal approximation. Finally, the last estimate is the headcount ratio computed from a fully nonparametric approximation of the shape of the income distribution. Among the three parameterizations, the one that deviates significantly from the actual poverty estimate is the lognormal 8
of health care delivery goes beyond telemedicine applications and utilization as it enhances customization of health services to match specific needs of each patient. For this to be put in motion, each patient needs to be identified and registered in a national social registry as it is the case with the Carte Vitale in France or Medicare Beneficiary Identifier in the US. In developing countries, the World Bank and development partners have been working with client governments to establish a universal access regime to health care which also requires the establishment of social registry identifying citizens eligible to the scheme. In short, the digitization of health care provision systems ultimately improves its ability to deliver services including for the poor. Regarding education, the digital transformation is well advanced through the deployment of digital platforms allowing pedagogical content to be accessible remotely in synchronous or asynchronous modes. The issue to be dealt with here is to ensure affordability and accessibility to such platforms. Deploying broadband internet across the country, ensuring that citizens, teachers and schooling facilities are well equipped, and enhancing digital literacy, e.g., through access to Personal Digital Assistant (PDA) are among the critical policy measures to be considered. Road connectivity ensures access to markets and is therefore indirectly impacted by digitization. Providing farmers with better market information on prices and other aspects of demand through access to digital platforms has an impact on the implementation of mobility solutions offered by the private sector and hence on the way road infrastructure is utilized and even maintained since collected traffic information can also bring along information on quality of the infrastructure. All in all, information digitization is affecting the way basic social services are delivered and how their delivery can be improved. Digitization also brings together greater interplay and convergence of basic social services. 8 For a list of the OECD countries, see the appendix.

> 9 Concerns about the quality of the SC in MENA have been raised both in institutional and academic circles. A top development journal has recently published a special issue on the framing of a new SC in the region.
vulnerability to climate shocks. We propose that indicators should be selected to capture two aspects of vulnerability: (i) a person’s physical propensity to experience severe income, asset or health loss, (ii) the ability of a person to cope with and recover from asset or income loss. In this paper we select indicators to cover both aspects using data that was readily available for multiple countries:

- For physical vulnerability we consider access to improved water and electricity. This is a very small set of infrastructure measures, driven in part by evidence in the literature, but also by what measures were readily available at the subnational level on a global scale. Other measures that could be considered are access to sanitation or irrigation, mobility and access to markets or services, investments in water drainage infrastructure, improved household dwelling structures, resilient farming practices, and access to early warning systems.

- For ability to cope and recover, we consider not having sufficient income to manage the impact of a shock; not having education to switch livelihoods or access information and resources to reduce income losses; not having access to public support; and not having access to financial services.

The selection of these variables is motivated further in a subsequent section, along with a description of the data used to measure them.

Transparency and coverage are important characteristics of a global measure. Transparency may require a certain simplicity, because a measure that becomes too complicated can be hard to interpret, and the drivers of change can be opaque. Coverage requires data being available for as many countries as possible and updated frequently over time. Although adding more dimensions of vulnerability is attractive given the multidimensional nature of vulnerability, this can often run counter to achieving simplicity and coverage. Survey data is the source of the indicators of vulnerability used here and survey data is unavailable for some countries on each indicator. Coverage sometimes declines when an additional indicator is added (World Bank 2018). It is thus important to ascertain that adding a new indicator adds value in that
Policy Research Working Paper 10495

# **Abstract**

This paper investigates the impacts of the Brazilian fiscal system on poverty and inequality, with a focus on the effects on vulnerable populations. Leveraging a broadly applied and accepted methodology and several household surveys and administrative data, the paper shows that Brazilian fiscal policies in 2019 were typically poverty- and inequality-reducing, but with a large heterogeneity in the effectiveness of fiscal tools. The poverty impacts of fiscal policies increased over time due to direct transfers. Income inequality reduction is among the highest in a comparable set of middle-income countries, yet the post-fiscal Gini is still high at 0.521. The results indicate that elderly people are the largest beneficiaries of the fiscal system and households with children experience a smaller decline in poverty from government transfers compared to those with no children. At the individual level, the findings also show that children and young adolescents (ages 0–15) were made poorer after taxes and transfers, which suggests that Brazilian fiscal policies in 2019 also increased poverty rates for some population groups. These findings contribute to provide a comprehensive overview of the fiscal system in Brazil and have wide-ranging consequences for the formulation of public policies.

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 glaraibarra@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
<!-- End of picture text --> Source: Bankscope, Bureau van Dijk. EUR data from European Central Bank (ECB) Consolidated Banking Data Note: ASIA: Asia. CEE: Central and Eastern Europe MENA: Middle East and North Africa. LATAM: Latin America. EUR: Euro Area. SSA: sub Sahara Africa. OTHER: Other developed economies.

**3. Against this background, further reduction in European bank leverage is necessary, particularly because leverage levels are still high** . Some degree of deleveraging is desirable if it is carried out in an orderly fashion and if it involves exiting activities of doubtful economic value, thereby enhancing the soundness of the banking system. Moreover, despite some
5 these predictions indicated a much less rapid decline in poverty during the 1990s than the official numbers and provided a qualitatively similar assessment of poverty decline as the Sen and Himanshu (2004) estimates. In this approach, the underlying relationship between consumption and its correlates is assumed to remain stable over time, ruling out possible changes in the ―returns‖ to factors such as education and labor.<sup>5</sup> This too is a controversial assumption, especially in fast growing economies such as India.

Going one step further, Tarozzi (2007) used both the 30-day consumption items and non-consumption variables such as educational status and land as predictors. He tested the validity of the stable parameter assumption on the 30-day consumption items and on the non-consumption variables, using the much smaller NSS rounds that are fielded during the intervals between the large, ―quinquennial‖ rounds that underpin the official poverty estimates. Tarozzi found indirect support for the assumption of parameter stability. In his datasets the large reduction in poverty implied by the official figures received some empirical validation. However, his analysis also remained disputed because the year-to-year poverty changes implied by his calculations were difficult to accept. Concerns were expressed as to how well suited the ―thin‖ rounds were to this kind of analysis. Despite, or perhaps because of all these efforts, the poverty trend in India during the 1990s remains a subject of debate.

In the absence of regularly fielded rounds of the same consumption surveys, researchers have also exploited the comparability and availability of data across time from alternative data sources. For example, Kenya had not conducted a national household budget survey since 1997, but conducted three Demographic and Health

> 5 Strictly speaking the approach requires that the relationship between consumption and its predictors is stable at the overall, equation, level.  Changes in returns to different factors can be accommodated as long as these are offsetting.