rafmacalaba/data-use-mentions-extended huggingface dataset:
to the number of individuals with an account by using their estimate that, on average, each individual has 3 deposit accounts.<sup>4</sup> As a result of this exercise we revise the estimate by Kendall _et al_ . (2010) for the number of unbanked: as of December 2008, we predict that approximately 2.8 billion adults are unbanked, 175 million of which are in high-income countries, and 2.63 billion are in the developing world. These numbers imply that 58 percent of the world is unbanked (21 percent of adults in highincome countries, and 66 percent in developing countries). The numbers slightly improve by the end of 2009. A total of approximately 2.75 billion adults around the world are predicted to be unbanked, out of which 138 million live in high-income countries and 2.61 billion live in developing countries. Overall, 56 percent of the world is unbanked by the end of 2009, with 17 percent of adults unbanked in high-income countries and 64 percent in developing countries. Hence, about 50 million more adults in the world have bank accounts, and most of the improvement is seen in high-income countries. Note that, at the same time, the total adult population in the world increased by approximately 79 million.

# **3 Access to financial services**

This section presents the state of access to financial services in 2009. We consider deposit and credit services only, as these are the basics. For both deposit services and credit services, we provide an overview of the global picture and summarize the results of econometric analyses. We also compare banks to non-bank financial institutions (NBFIs) in terms of deposit and loan penetration, although data on NBFIs are limited. Next, we consider the outreach of the financial system. An overview of the state of retail networks, including branches, ATMs, and POS terminals as well as urban vs. rural, and bank vs. non-bank divides are presented.

Note that due to limitations in data
In summary, our results for the Madrid GGU indicate significant reduction in methane emissions during the past four years, coupled with continuing progress during the most recent year. While the Madrid landfills undoubtedly remain high methane emitters among Western European sites, the evidence from our database suggests that their emissions are at least headed in the right direction.

# **5.2 Irrigated Rice Production in Karnal, Haryana, India**

Our second case focuses on irrigated production of rice, the staple crop for the majority of the world’s population (Adhya et al. 2014). Table 1 shows that Agricultural Soils account for about 10% of global methane emissions, and irrigated rice production accounts for most of those (Smith, Reay and Smith 2021). In 2013, rice was harvested on 165 million hectares of land in 100 countries, with 90 percent of global production in Asia. Irrigated fields occupy about 80 million hectares and produce 75 percent of the global crop (FAO 2014; Fischer et al. 2014). Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methane-producing bacteria.

We identify rice paddy flooding months using RiceAtlas, a spatial database of global rice calendars developed by Laborte et al. (2017). RiceAtlas distinguishes up to three rice cultivation seasons and identifies the first flooding and harvest days for each season by day-of-year. We have chosen Karnal district in India for this case because it is identified by RiceAtlas as an area with only one of three potential rice cultivation seasons. This permits a clear illustration of the utility of the 5 km methane database for tracking methane emissions across seasons and years. In Karnal, fields are typically flooded from early summer through October.

Figure 9 provides summary information about Karnal. Figure 9(a) displays the GGU (Karnal District, Haryana) along with the sampling area for Sentinel-5P data (bordered in blue). Figure 9(b) draws on our 5-km global database
even though regulations do not mandate them.

Most firms wish to have their securities listed and traded on a stock exchange, which will charge its own fees based on security type and size. The firm must also pay a depository fee if there is a central depository facility available.

Finally, the firm will incur marketing and publishing costs. Some forms of publishing are forced by law, and give the company a minimum standard for creating public awareness. For marketing purposes, the company will have to print prospectuses and offering memorandum to deliver to potential clients. Finally, the company bears the costs of any road shows, either domestic or international, used to market the deal.

# **B.  Methodology**

The cost data was gathered from a combination of web site searches of stock exchanges and regulatory bodies, security information from Bloomberg and Bondware, and heavily relied on interviews with securities lawyers, investment bankers, regulators, and companies themselves (typically the CFO). The process itself, when applied across countries, helped to formulate a questionnaire to be followed in the interviews.<sup>2</sup>

> 2 This questionnaire is available from the author on request.

4
<!-- Start of picture text --> California schools Education - PISA<br>Shoreline Unified Luxembourg<br>San Pasqual Valley Unified Norway<br>Carmel Unified Denmark<br>‘St. Helena Unified United States<br>Warner Unified Switzerland<br>Lone Pine Unified Austria<br>Butte Valley Unified Sweden<br>Geyserville Unified France<br>Klamath-Trinity Joint Unified Belgium<br>Sierra-Plumas Joint Unified ttaly<br>0 5,000 10,000 15,000 0 5,000 10,000 15,000<br>Inefficiency Inefficiency<br>OECD health systems Vietnam hospitals<br>United States 601.5.6<br>Switzerland 603.5.4<br>Denmark 405.5.9<br>Norway 403.5.36<br>Germany 401.5.39<br>Sweden 7115.44<br>Luxembourg 601.5.4<br>Netherlands 113.3.12<br>Iceland 09.5.5<br>Canada 701.5.51<br>0 500 1,000 1,500 2,000 2,500 0 200 400 600 800 1,000<br>Inefficiency Inefficiency<br><!-- End of picture text --> Notes: California data refer to 313 unified school districts, and cover the period 2003/2004 - 2008/2009. The PISA data refer to 49 countries, and cover some or all the years 2000, 2003 and 2006. The OECD health system data refer to 29 countries, and cover the period 1960-2005. The Vietnam data refer to 795 district hospitals with between 50 and 500 beds, and cover the period 1998-2000. Units are averaged over the sample period. Vietnam hospital codes refer to region and hospital identifier.
loudspeaker announcements. Applicants who came to the registration centers were asked to provide information about their address and dwelling characteristics, such as whether the dwelling had access to running water, whether it had a dirt floor, the number of rooms in the dwelling, and household appliances and other durable goods available to the household. We used these characteristics and demographic information to compute a household poverty index, which was used to determine an applicant’s eligibility for enrollment in the program. The weights attached to each characteristic in the household poverty index were predetermined using a poverty regression similar to that described by Ravallion (1996). The methodology was public (Reglas de Operación 2002), but the specific weights were not.

Applicants initially found to be eligible received a household visit during subsequent weeks to verify the information provided, after which a final determination on eligibility was made. Because of budget and capacity constraints at the level of program offices, offices were sometimes closed when certain quotas were reached, and not every household initially found eligible was considered for a household visit. This process likely led to errors of exclusion. Moreover, some households that were initially classified as eligible at the registration center and found to be ineligible after the verification did, in fact, enroll in the program, leading to errors of inclusion (Martinelli and Parker 2009).

# **<<Place table 1 about here>>**

Our dataset was the ENCELURB (Encuesta de Evaluación de los Hogares Urbanos 2002), the survey used to evaluate the performance of _Oportunidades_ in urban areas. Shortly after enrollment but before beneficiary households began to receive _Oportunidades_ transfers, a sample of residential blocks in the program’s area of operation was selected, and all households in these selected blocks (20,859) were visited. An initial screening survey found 4,649 _Oportunidades_ beneficiary households. All of these households were included in the evaluation sample. Additionally, a subsample (5,776) of 11
Another caveat with the ENOE industry classification is that the codes are so broad that I cannot separate out only 34 IMSS industries that I use to estimate the effect of the wage subsidy program. Instead, I have to use employees in all durable goods industries. Despite these shortcomings, I believe that the evidence from the ENOE data can shed further light on the impact estimates I obtained with IMSS data.

The ENOE asks individuals whether their job provides them with access to medical services through IMSS or other government institutions vs. no access to such services. Figure 6 plots the number of individuals working in eligible industries who state they have access to IMSS, and separately the number of individuals who state they have no access to government medical care, in each quarter from 2005 to 2014.<sup>11</sup> On average across these quarters, the ENOE includes about 4,000 individuals who work in eligible industries and have access to IMSS, and only about 130 individuals who work in eligible industries and have no access to government medical care. The number of individuals with access to IMSS drops during the crisis, hitting a low in 2009, and increases steadily thereafter. In contrast, the number of individuals without access to government medical care remains constant throughout all quarters. This constant pattern suggests that the wage subsidy program did not simply shift workers from informal to formal employment and that the impact estimates in this paper reflect changes in actual employment instead.

# **4.6 Displacement effects**

There is evidence that labor market programs can have displacement effects, i.e. the program may shift employment from ineligible to eligible jobs instead of creating more jobs overall (Crépon et al. 2013). That is, employment in the control group may be negatively affected by the program and impact estimates may thus overstate its true effect. In the context here, displacement effects are probably not a great concern during the program and in the first years after the program since the subsidy was paid for maintaining existing
* The data related to the statutory tax rates, comprised by tax brackets and rates of Personal Income Tax, Corporate Income Tax, Social Security and VAT, comes from the OECD Tax Database for OECD countries ( _www.oecd.org/tax_ ); from the annually tax books “Individual Taxes: Worldwide Summaries” and “Corporate Taxes: Worldwide Summaries”, published by _PriceWaterhouseCoopers_ until 2005; from the _PWC_ tax webpage which has the latest statutory tax information ( _www.pwc.com/gx/en/worldwide-tax-summaries_ ); and also from the _KPMG_ Individual and Corporate Tax Rate Survey from 2006-2009.

- a. **Personal Income Tax** is generally progressive and composed by several brackets, but there are a few exceptions of countries and years in which a flat rate applies (Bulgaria and Czech Republic after 2008).

- b. **Corporate Income Tax** is usually given by a flat rate on profits, although there are also a few exceptions. Brazil has a progressive tax schedule with two brackets and Bulgaria had a progressive tax schedule in the years 19992001. For Lithuania (after 2003), Cyprus (1995-2004), Malaysia and Thailand have different tax rates according to the firm size.  In Argentina and Colombia, if the resulting chargeable corporate tax is small enough, there is a complementary tax on assets.  According to the perceived regressiveness/progressiveness of the tax schedule the imputed profit elasticities in those cases vary from 1.0 to 1.1.

- c. **VAT (or Sales Tax)** although there are many deductions according to the nature of each product, we consider the standard flat rate.  In the USA, where there are State, County and Municipal sales taxes, we consider the national average sales-tax rate.

- d. **Social Security** usually has regressive tax rates with several brackets.  UK is the only exception, since a high tax credit offsets the effect of regressiveness from the tax brackets.  The income elasticities of
Intelligence Network database provides data on vessel-specific fixtures, namely time charter and spot rates. Vessel-specific rates are not particularly representative of a given market segment (i.e., containerships, dry bulk, tankers, auto carriers, etc). For example, Brancaccio et al. (2020) report that Clarksons tend to oversample newer and medium-sized vessels in dry bulk freight markets. That said, Clarksons does provide a representative summary of the freight earnings time series (from an annual to a weekly reporting frequency) for main trade routes and vessel benchmarks: e.g., Feeder Containership 2,7500 TEU gearless 6-12 month Timecharter rates ($/day) or SCFI Shangahi-West Coast America (spot) freight rates ($/FEU).

Another readily available data source of transport costs for large cross-sections of countries is the Doing Business (DB) survey, compiled by the World Bank.<sup>18</sup> It contains information on international transport for 189 countries since 2006. The section "Trading across Borders" provides data on the number of documents, time, and import and export costs. More specifically, it reports the US dollar cost container to and The data in the DB is collected a World per import export. survey using Bank questionnaire completed by trade facilitators and freight-forwarders across countries. For the comparability across countries, the survey presents the respondents with a detailed, stylized import or export case. The cost per container is for dry-cargo, 20-foot, full container load, and the products shipped do not require any special standards. Given the description in the survey, the most likely product categories are textile yarn and fabrics (SITC 65), articles of apparel and clothing accessories (SITC 84), and coffee, tea, cocoa, spices (SITC 07). The respondents are also asked to identify the likely port of loading (Djankov et al., 2010). Therefore, the variation in transport costs across products, routes, or firms is rather limited (Blonigen and
Policy Research Working Paper 10619

# **Abstract**

Based on global datasets, 4.5 billion people were exposed to extreme weather events (flood, drought, cyclone, or heatwave) in 2019, an increase from 4 billion in 2010. Among exposed people in 2019, 2.3 billion people lived with less than $6.85 per day and about 400 million lived in extreme poverty (on less than $2.15 per day). This paper presents a methodology to estimate the number of people who are _at high risk from extreme weather events,_ defined as the people who are exposed to these events and highly vulnerable to them. Vulnerability is proxied by a set of indicators measuring (1) the physical propensity to experience severe losses (proxied by the lack of access to basic infrastructure services, here water and electricity) and (2) the inability to cope with and recover from losses (proxied by low income, not having education, not having access to financial services and not having access to social protection). Estimates from 75 countries for which data on all indicators are available suggest that, in 2019, 42 percent of the total population (and 70 percent of people exposed) are at high risk from extreme weather shocks, if one indicator is enough to be considered as highly vulnerable. If high vulnerability is defined based on being vulnerable on two dimensions or more, then 12 percent of the total population (and 20 percent of people exposed) are at high risk from extreme weather shocks. The trend between 2010 and 2019 can be explored in a subset of countries covering 60 percent of the world population. In these countries, even though the population exposed to extreme weather events has been increasing, the number of people at high risk has declined. The exception is Sub-Saharan Africa where the number of people at high risk has increased between 2010 and 2019.

This paper is a product of the Poverty and Equity Global Practice and the Climate Change Group. It is part of
to perform on a large scale (e.g., Gibson 2005; Fiedler et al. 2013; etc.).<sup>7</sup> In the absence of these difficult-to-collect and expensive data, researchers often turn to household consumption and expenditure surveys (HCES’s) to measure the quantity and quality of foods consumed (e.g., Wiesmann et al. 2009; etc.). However, estimates from HCES’s are subject to a substantial amount of measurement

> 7For example, one method is observed-weighed food record data. See Gibson (2005) for details.

6
of the impacts of EE projects in developing economies, whose energy systems and associated challenges often differ fundamentally from those in advanced economies (Urban et al 2007; Ouedraogo 2017). Recent studies on low and middle income countries (LMIC) find preliminary evidence that investments focused on more efficient lighting can be cost-efficient (Iimi et al. 2019; Carranza and Meeks 2021). For example, transitioning from kerosene lighting to solar lighting reduces energy expenditures by 42 percent in rural households in Kenya (Rom and Günther 2019). There is, however, at best mixed evidence in the literature as to whether these interventions cause energy savings,<sup>3</sup> and most of the existing evidence is based on small-scale interventions with unknown external validity and potential for scaling up. As noted by Fowlie and Meeks (2021), there is “ _tremendous value in ex post evaluations of these interventions. Empirical research that objectively evaluates the impacts of these and other programs can inform the course of future policy initiatives aimed at improving energy efficiency._ ” This paper contributes to addressing this gap in the literature by providing quasi-experimental evidence on the impacts of a large-scale EE project in Malawi which was supported by the World Bank between 2015 and 2018. Our estimation strategy is based on a difference-in-differences (DiD) approach using a combination of remote-sensing data from satellite images and data from national household surveys. The estimation strategy takes advantage of the geographical variation in the implementation of different project components, including variation across districts, subdistricts (cities), and individual households. Various project components were implemented in different areas across the country,

> 1 In this paper, EE refers to reductions in the amount of energy required to provide the same output or level of service. For example, such reduction can result from the adoption of improved technologies or practices that help to save energy or reduce energy losses.

> 2 For example, the IEA (2017) estimates that, without the improvements in EE achieved since 2000, the world would have
period from the analysis. We find that the impacts of tariffs on average wages are positive, similar in magnitude, but not statistically significant; in contrast, the impacts of tariffs on the skill premium remain negative and statistically significant.

Non-tariff barriers were fully eliminated from 1988 to 1991. Indeed, the elimination of quantitative restrictions was a pre-requisite to the negotiations of the common external tariff of Mercosur (Berlinski, 1994; 2003). Thus, an additional robustness check of the link between tariffs and the skill premium is to run the model on the 1992-2001 sample. Active trade policy during this period comprises only tariff changes. Results in column (5) confirm our previous findings. Tariffs have a positive impact on average wages; this effect is highly significant during the 1990s. Further, tariffs impact negatively, and highly significantly, on the skill premium.

A final concern with the analysis is the potential endogeneity of sectoral tariffs to wages (as in a model of political economy or unionization). In our setting, the case for the endogeneity of tariffs is relatively weak because our regressions include a number of control variables that ameliorate this problem, namely time-varying returns to schooling and tenure, individual characteristics, industry effect, time effects, and sectoral trends. The temporal variation in our data is critical to support this claim. For instance, the endogeneity of tariffs caused by sectoral unionization, industry lobbies, or political economy is unavoidable in a cross-section but can be accounted for, to a large extent, with the industry dummies, the time dummies, and the sectoral trends in the pooled historical data. Once we control for all these variables, the level of protection is mostly determined by two factors: the worldwide trend towards trade liberalization and the initial level of protection (so that sectors with higher tariffs would face larger tariff cuts, on average).<sup>11</sup> We argue that these two factors can reasonably be thought of as exogenous to the level of current wages in our econometric models.
respondents from these cities was employed a year later. Elsewhere the rate of job finding for this education category was 56 percent. A similar picture is found for the much larger group with ISCED 3C education (see Appendix Table 18). On the other hand, an extra year of schooling after vocational training does not appear to have improved job finding performance in the country in general. Perhaps jobseekers in this category are less willing to accept blue-collar jobs than their counterparts with just vocational training.

**Figure 16.  One-Year Probability of Finding a Job by Age Group and Education Level**

<!-- Start of picture text --> UE<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>15-19 20-24 25-34 35-44 45-54 55-ret ret<br>higher posts econdary vocational<br>s econdary secondary after vocational<br>basic<br><!-- End of picture text --> Notes: _Secondary_ includes both general and technical secondary education. Controls for field of education, gender, ethnicity and residence are included. “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Technological skills are in high demand.** Two broad fields of education which make job search most successful in Latvia are: (i.) transport or communications, and (ii.) engineering, technologies, construction, computer science and mathematics (Figure 17).

23
>−0.348***|0.103|−0.364***|0.128|−0.415***|<sup>−0.185*</sup>|
||(0.018)|(0.086)|(0.018)|(0.093)|(0.018)|(0.089)|(0.009)|(0.110)|
|Controls|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|1470|1470|1160|1160|1404|1404|864|864|
|Log likelihood|−653.93|−597.64|−524.11|−482.46|−625.26|−568.37|−341.32|−338.75|
|Sampleperiod|1986–|2009|1993–|2009|1986–|2009|1986–|2009|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

> <sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del