rafmacalaba/data-use-mentions-extended huggingface dataset:
rain, snow, earth movement (liquefaction, earthquake, landslides) and debris. The most common vulnerability and damages occurred from wind resulting from hurricanes, storms, winter weather or debris picked up by wind. All infrastructure in earthquake areas where liquefaction occur saw damages (Watson 2013). Wind was found in many case studies to topple transmission lines and poles (Thompson 2011; Sinclair Broadcast Group 2013; Hoffman and Bryan 2013; Sinclair Broadcast Group 2012; Dispatch 2015).

Wildfires present an interesting and unique vulnerability to transmission and distribution infrastructure. Various case studies illustrate that during high risk conditions (drought, high temperatures, high wind), curtailments were used to reduce the risk of transmission infrastructure causing a wildfire. The potential risk was illustrated in California (USA) in 2017 when transmission lines were identified as a likely cause of fire start. Here San Diego Gas & Electric was found liable for causing three fires that led to three deaths and the destruction of 1,300 homes. The utility ultimately paid out $2 billion in settlements (Daniels 2017). Recent wildfires have put Pacific Gas and Electric, a large utility in California, under scrutiny due to $10 billion in liabilities from fires in 2017 and unknown amounts from ongoing fires in 2018 (McNeely 2018). A key parameter in overall grid vulnerability was the geographic distance between generation facilities and end-use. Summary vulnerabilities identified in the case studies are detailed in Table 3, with detailed case study data presented in Appendix B and C.

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In addition, Figure A5 in the Online Appendix depicts the main result from these regressions. I plot the coefficients for the most restricted definition of workers’ informality. The main finding is that there is a negative relationship between informality rates and transit improvements when locations that experienced the shock are compared with census tracts close to lines that were planned in the 1980s. For instance, informality rates for workers decrease on average between 4.0 and 11.0 percentage points, which is a more significant effect than the one found previously. This effect corresponds to a decrease of approximately 15%, using the mean of the control group before the shock. In most of the specifications, I also find parallel trends, suggesting that after the shock treated locations experienced changes in informality.

# **3.2.4 Households’ Composition and Lower Bound Effects**

A potential concern regarding the identification strategy from the previous section is that locations close to the new subway line might experience a change in the composition of households due to worker sorting.<sup>25</sup> For example, high-skilled workers that would prefer to work in the formal sector might migrate to these census tracts and, as a result, there would be a decrease in informality rates that could explain my findings. Ideally, I would deal with this issue by using a multi-year panel of workers before and after the shock. Unfortunately, no such panel is available.

I deal with this concern by comparing household characteristics before and after the shock. The goal is to show that at least in terms of observable covariates, there was no change in households’ composition. For that purpose, I run the same specification in equation 3.2 on household characteristics, such as the high-skilled share of workers on the left-hand side.

Table 3 reports the results, including all the set of controls. On average, I find that household characteristics in locations close to line B were not affected by the shock relative to other areas in Mexico City.
reasonably accurate at the regional level, but the loss of mangroves may be much larger in some countries. Furthermore, the mangrove database shows the extent and shape of the mangrove area, but does not indicate the status of the mangroves; for example, patchiness, health, size of trees, etc. Studies have shown that specific characteristics of mangroves are important for protection from storm surge. For example, if stands are not dense enough, they provide insufficient resistance to wave energy, but if the stand is too dense, waves may simply pass over.

Conversely, factors that may underestimate coastal protection and resources at risk include: geographic limitations of the data, elevation measurement error, a lack of GDP and population estimates to 2100 and the conservative estimates from direct exposure. With regards to geographic limitation, some small‐island nations in Africa, Asia and the Pacific, and Latin America are not included in our analysis due to lack of data. The elevation data (SRTM) has measurement error due to signal interference from surface features such as dense canopy (or high forest cover percent see Shortridge and Messina 2011) and built‐up up environments, which would under estimate risk by overestimating height. For exposure estimates, we used 2005 data for population and GDP in absence of reliable country‐specific projections of coastal population and GDP out to 2100; and we did not consider potential growth in the coastal economies over time. Direct exposure estimates for calculating vulnerable population and GDP are also conservative estimates and do not consider the losses from proximity or network effects due to mangroves at risk (e.g. the economic loss generated from a to the transportation network).

Finally, we do not know if there is any positive or negative bias introduced by the following: rounding of elevation data, spatial allocation methods and the functional form of the distance decay function. The unit of measurement for the SRTM data is meters and the rounding may introduce a positive or negative bias. Spatial allocation methods used for estimating population and
land or livestock disputes to community leaders other than the village head for resolution, and the training of a civil society leader had little additional effect. The bottom row shows that household respondents were slightly less likely to contact civil society leaders other than the village head for assistance in villages where a civil society leader was trained. Thus, civil society leaders do not appear to have improved local governance by competing with the village head to provide services.

The remaining two mechanisms – capacity upgrading and monitoring – are distinguished by the fact that the first involves only changing capacity to implement reforms the village head already favors and the second also involves changing their incentives to implement reform. Both of these mechanisms share two observable implications: the trained civil society leaders should have more information about village-level

> 23In our survey of civil society leaders, we also sampled dare members. Because they were not eligible to be the “civil society leader” included in the training, we exclude them from analyses in this section. 24We estimate there were about 5 eligible civil society leaders per village.

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. Uzbekistan never had a population census since the last census conducted by the Soviet Union in 1989 which implies that any figure drawn from sample surveys should be treated with caution. Moreover, macroeconomic official statistical figures have been questions by the ADB, EBRD, IMF and World Bank in several occasions, including GDP figures and particularly during the period 1998-2002. There are several indications that GDP has been overestimated due to a consistent underestimation of inflation and the deflators used to deflate GDP.

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Policy Research Working Paper 9878

# **Abstract**

This study applies the cost-of-basic-needs approach to estimate food and total poverty lines for the Brazilian case. Using detailed data on expenditures from a 2017/18 household budget survey and caloric information from the Brazilian Table of Food Composition, calorie intake is assigned to more than 1,400 items to estimate the cost per calorie for a representative group of the population. The preferred results estimate the value of the food poverty line at R$258 (in 2018 urban Southeast prices), and the lower total poverty line (covering also nonfood necessities) - at R$455. Robustness checks show that varying the assump tions leads to qualitatively similar results. The findings are also close in value to lines found in earlier studies and the societal poverty line. Finally, this work provides a datadriven validation of the income threshold used to determine eligibility for Brazil’s 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 glaraibarra@worldbank.org or apaffhausen@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 the governments they represent._ Produced by the Research Support Team
opposite results. For example, using a CGE for Mozambique, Arndt et al. (2008) find that biofuels would have favorable effects on growth and income distribution. They show that the welfare and distributional effects of higher production of sugar cane to feed into ethanol industries are positive; the benefits would be larger if the expansion sugarcane is materialized through smallholders than through large plantations as the former employs more unskilled labor, provides higher rents to their land and also provide higher wage. Similarly, applying a CGE model, Arndt et al. (2011) show that cultivation of jatropha feedstock reduces poverty and increases GDP in Mozambique. A similar finding is reported by Hertel (2009) where developing countries with significant agricultural self-employed poverty population benefit from higher factor returns following increased production of biofuels. However, the positive impacts of biofuels on poverty reduction depend on how biofuel policies are designed. For example, HabibMintz (2010) finds that jatropha-based biofuel development in Tanzania would not help reduce poverty and food insecurity unless strong regulatory frameworks for land, investment management, and rural development are in place.

This paper aims to analyze the effects of large-scale expansion of biofuels on the global income distribution, and poverty. The model used in our study explicitly represents both biofuels and land-use changes, which are critical to fully assess overall impacts of biofuels. Our global computable general equilibrium (CGE) model includes four types of biofuels: sugar ethanol, corn ethanol, grains ethanol and biodiesel. The land use module we developed uses a multi-level constant elasticity of transformation (CET) nest of various uses of land: forestry, pasture, agricultural crops (disaggregated further to grains, oilseeds, sugar, rice, wheat and other crops). With these developments, we are able to capture the fuel vs. food demand for agricultural commodities, land reallocation, and land rent. To assess the distributional and poverty impacts, we feed the CGE results to a Global Income Distribution Dynamics (GIDD) model, a model that is based on detailed household surveys
<!-- Start of picture text --> 100%<br>14%<br>90% 22% 26% 25% 26%<br>31% 31% 33%<br>80% 40% 37%<br>6%<br>Services 70% 5% 4% 6%<br>8% 6% 7%<br>Public administration 60% 9% 8%<br>13% 11%<br>5% 8% 7% 7% 25%<br>Construction 50%<br>12% 12%<br>Utilities 40% 21% 7%<br>69%<br>4%<br>30% 60%<br>57%<br>Manufacturing 51% 50% 52%<br>20% 37% 38%<br>Extractive industries 28% 29%<br>10%<br>Agriculture, forestry<br>and fishing 0%<br>Oriental<br>Khénifra<br>Béni Mellal- Drâa-Tafilalet Fès-Meknès Settat Souss-Massa Hoceima<br>Grand Casablanca- Marrakech-Safi Rabat-Salé-Kenitra Southern Regions Tanger-Tetouan-Al<br><!-- End of picture text -->

**Source:** Estimates based on the Enquête Nationale sur l’Emploi (ENE; Labor Force Survey)

The regions with the lowest female labor participation rates are also those with the highest unemployment (Oriental and Southern regions). The informality rate also varies across regions, with the southern regions having the lowest driven by high employment share in public administration. It is not surprising that the informality rate in Drâa-Tafilalet is highest since most jobs there are in agriculture.

# **3. Gravity Analysis**

The gravity model of international trade is the tool economists use to estimate the contribution of various factors to bilateral international trade flows. Tinbergen (1962) proposed this model to illustrate the asymmetry of global trade flows. Trade flows between two
counterparts.

Figure 6. Service Access of Children and Adolescents <!-- Start of picture text --> (a) School Enrollment (b) Sisb ́en Enrollment<br>0.96 0.94 0.96 0.74 0.73<br>0.89<br>0.74 0.73<br>0.30<br>0.28<br>Colombian Venezuelan Colombian Venezuelan 5-10 yrs 11-17 yrs<br>5-10 yrs 11-17 yrs Colombian Venezuelan<br>pre-migration enrollment 2022 enrollment<br>(c) Health Insurance Enrollment (d) Regularization Program Take-up<br>0.92 0.89 0.70<br>0.64<br>0.58 0.57<br>0.39 0.39<br>Colombian Venezuelan Colombian Venezuelan<br>5-10 yrs 11-17 yrs<br>5-10 yrs 11-17 yrs<br>pre-migration insurance 2022 insurance<br>1 .8<br>.8<br>.6<br>.6<br>.4<br>.4<br>Share of children enrolled<br>Share of children enrolled .2<br>.2<br>0 0<br>1 .8<br>.8<br>.6<br>.6<br>.4<br>.4 Share enrolled<br>Share of children insured .2<br>.2<br>0 0<br><!-- End of picture text --> _Notes:_ Panel (a) depicts the likelihood of school enrollment for children in each nationality-age-group enrolled in school, Panel (b) depicts the likelihood of Sisb ́en enrollment in each nationality-age-group (the proxy means test used to target social programs in Colombia), Panel (c) depicts the likelihood of health insurance in each nationality-age-group, and Panel (d) depicts the take-up rates of the
tail (revealing the presence of few very unequal countries in the sample). Regarding changes in the (log) Gini, the average annual change between two five-year periods would be .1 percent, indicating that on average inequality changes little over time. However, there is considerable dispersion: the 2 standard deviation interval would range from about –6.8 percent to 6.8 percent. The changes in the Gini coefficient also present a skewed distribution but in this case it is toward the lower tail. As for the income data, average annual growth rates between two five-year periods would be about 2 percent, but dispersion is also high (very similar to that found in the changes in the Gini coefficient). The variables that aim to represent the various aspects of economic development tend to be skewed toward the lower tail, something that would reveal the presence of a few very underdeveloped countries in the sample. The inflation rate also presents a skewed distribution but in this case is likely to reflect a few instances of extreme macroeconomic mismanagement.

# **V. Empirical results**

We start this section by presenting results for equations (10) and (11) under the restriction that β=ω=0. Thus equation (11) is basically reduced to a test of convergence for inequality considering the potential impact of income levels, whereas in equation (10) we explore, in addition to the speed of convergence for per capita income levels, the potential impact of inequality on growth. We estimate the equations with four different methods: OLS, fixed effects, 2SLS<sup>14</sup> , and the Arellano and Bond (1991) GMM estimator. In turn, we present the results of the Arellano and Bond estimator treating the nonlagged-dependent-variable regressor both as an exogenous variable and as a predetermined variable.

Inspection of table 3 suggests convergence in inequality levels, with the estimators that take into account the possibility of fixed effects resulting in a faster speed of convergence. For example, while the
system. This paper will thus also examine whether _improvements_ in a country’s DB rankings enhance economic performance.

# **3. Data and Methods**

Variable selection for the analysis below generally follows Djankov _et al._ (2006), yet the data are drawn for a more recent time period, from 2003 to 2009, and a number of additional variables will be used. The dependent variable to be used in the analysis is annual per capita growth in real Gross Domestic Product (GDP), averaged over 5 and 15 years. These averages will be calculated over the previous years (i.e. time _t_ to _t-5_ and _t_ to _t-10_ ). To estimate the effect of DBI reform on growth, averages will be calculated for the subsequent four years (i.e. _t_ to _t+4_ ). Only four years can be included in the average due to data availability. GDP data were obtained from the World Bank’s _World Development Indicators_ .

The key independent variables are all based on the DBI rankings, with data obtained from the _Doing Business_ website. The following variables will be used (c/f table 1): the annual aggregate DBI ranking based on the nine individual DB rankings from ‘Starting a Business’ to ‘Closing a Business’; the individual indicator rankings themselves; and annual changes in the subcomponents of the individual rankings (including procedure, time, cost, and legal strengths). All rankings are based on the percentile positions of each individual country. In accordance with the _Doing Business_ methodology all rankings will be normalized to vary between zero and one, using the formula _[(Ri-Rmin/Rmax-Rmin)],_ for ranking _R_ and country _i_ .

Where individual indicators are aggregated, simple averages will be used, thus replicating the DB rankings (however, the analysis departs from the published rankings by consistently excluding the ‘Employing Workers’ indicator).

Annual changes in the subcomponents of the individual indicators are calculated in accordance with the _Doing Business_
confidence in the police and the courts, and the likelihood of crime and violence. The three indexes are normalized to mean zero and a standard deviation of one in each year. Higher values correspond to more advanced democratic institutions in a country.

Other variables used in the analysis are the level of real GDP per capita, from Eurostat and World Development Indicators (World Bank 2022a), and the size of the informal sector, sourced from the Informal Economy Database produced by the World Bank (Elgin and others 2021). Table 1 summarizes the descriptive statistics.

> cycle depends on the outcomes of previous elections (Alesina and Passarelli 2019). By using an exogenous variation in the electoral cycle, we avoid the bias driven by these effects.

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# **2.3. Health sector approaches to GBV**

## **2.3.1  Overview of GBV and the health sector**

As more epidemiological evidence has emerged highlighting the links between GBV and a wide variety of serious health problems, international attention is increasingly focused on the role of the health sector in addressing violence. In 1996 the World Health Assembly declared violence to be a public health priority, and followed this up in 2002 with the publication of the World Report on Violence and Health, with in-depth discussions of intimate partner and sexual violence (World Health Organization 2002). The World Health Organization has carried out a multi-country study with detailed information on the prevalence and health effects of violence in ten countries. This report, which will be published in late 2004, will provide the first in-depth overview of violence using standardized methods.

Health services, and particularly reproductive health programs, provide a unique window of opportunity to address the needs of abused women, since most women come into contact with the health system at some point in their lives. Because violence presents serious risks to women’s health, routine screening for violence among all women who attend health services, regardless of the reason for the visit is increasingly considered the standard of care in the United States and many industrialized countries (American Medical Association, 1992; Buel, 2001).

In the United States, despite extensive training of providers, progress in implementing family violence screening policies has been slow. Research indicates that although battered women use primary and secondary health services more than non-abused women, a very small percentage of them are identified by health workers (Cohen, De Vos et al., 1997; Sagot, 2000) In developing countries violence is even less likely to be detected within health services. A Demographic and Health Survey in Nicaragua found that over one-third of women who had experienced partner abuse had never told anyone about their situation, Although 57% of the women had suffered one or more injuries as a result
China in 1998–2005 (Hsieh and Klenow, 2009). There were also large gaps in the returns to capital— between SOEs and private firms, between regions, and between sectors within China in 2002– 04 (Dollar and Wei, 2007). At the same time, TFP and the returns on labor of SOEs converged to those of private firms over 1998–2007, while the SOE returns on capital remained about 40 percent lower than private ones (Hsieh and Song, 2015). Differences in the marginal products of factor inputs (or returns to factor inputs) indicate the overall distortions some firms face within industries, such as firm-specific tax rates, subsidies, credit constraints, and trade barriers.

This paper updates through 2013 productivity estimates for China’s manufacturing sector that were originally made for 1998-2007 (Brandt et. al, 2012, 2017). The basis of these estimates is the firm-level data that are collected as part of the NBS annual Industrial Enterprise Survey. The survey covers all industrial firms with annual sales above RMB 5 million before 2008 and above RMB 20 million thereafter. The estimation methodology and how we deal with several measurement challenges, including inconsistent data at the individual firm level between 2008 and 2009, lack of data on firm-specific prices, and no data for 2010, are described in the appendix. The new estimates allow a comparison of productivity growth in industry before and after the global financial crisis.

Average annual TFP growth in manufacturing fell from 2.0 percent in 1998-2007 to 1.1 percent in 2007-13. This decline was spread across industries: 24 of 28 sectors at the two-digit level had lower TFP growth in 2007–13 than in 1998–2007 (Figure 8). More than a quarter of all sectors also experienced a decline in productivity in levels. Large reductions were observed in metal products, food processing, timber processing, and petroleum refining.

> 9 IMF (2009) provides an overview of the literature explaining the link between crises and productivity growth. 10