rafmacalaba/data-use-mentions-extended huggingface dataset:
C to 5.3 ̊C, and the duration of heatwaves is projected to increase by 18 to 214 days by 2085.<sup>8</sup> Along the Atlantic coast of Brazil, Uruguay, and Argentina, the temperature increase is expected to be less marked. For the LAC region as a whole, the most pronounced temperature increases are projected to occur in September and October, where the median estimates for the whole region could increase by 4.2 ̊C (Figure 3). Warmer temperatures have negative consequences for economic activity. The IMF estimates that a one-degree increase in temperature in emerging market economies lowers growth in the same year by 0.9 percentage point, where the contractionary effects are statistically significant even seven years after a weather shock (IMF 2017).

LAC’s dry regions will get drier and wet regions will get wetter. According to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), the Caribbean, Central America, and central Brazil are the areas where precipitation is expected to decrease the most, in the range of 20 to 40 percent (Figure 4).<sup>9</sup> On the flip side, extreme precipitation events’ frequency and intensity are expected to increase, particularly for the tropical and subtropical Pacific coastline and southern Brazil. Water scarcity and increased mean temperatures are projected to decrease water supplies and affect most ecosystems and agroecosystems. On the other hand, an intensified rainfall could significantly increase landslide risks, particularly in sloping terrain, which is often occupied by vulnerable populations.

> 5 EM-DAT estimates damage as the value of all damages and economic losses directly or indirectly related to the disaster.

> 6 Economic damages estimates are sourced from EM-DAT and expressed in today's currency. 7 Ibid footnote 6.

> 8 <u>https://climateknowledgeportal.worldbank.org/country/brazil/climate-data-projections</u>

> 9 <u>https://climateknowledgeportal.worldbank.org
</mark> (Najnin _et al._ , 2019 <mark>). A two-stage cluster RCT with 60 villages in Kenya also showed that</mark> children <mark>in schools</mark> wher <mark>e hand washing and drinking water stations</mark> were mad <mark>e available were consistently less likely to have an acute respiratory infection over the following 2-10 months than in schools without (2% versus 3%, Estimated Difference in Medians -2%, 90% CI-3% to -1% in rounds 1-17) (</mark> Hoekstra _et al._ , 2012).

Taken together, it is therefore plausible that handwashing could impact the number of hospitalizations and deaths from COVID-19 via two pathways: 1) by reducing transmission and incidence of COVID-19 and 2) by reducing the incidence of underlying respiratory illness in a population, thereby reducing susceptibility to severe COVID-19. If a host who becomes infected with SARS-CoV-2 already has a respiratory infection, the chances of that developing to severe COVID-19 disease are increased. What we focus on in the analysis presented in this paper is therefore the second, more indirect pathway described above, to impact on prevalence of severe COVID-19 cases.

This study contributes to the literature in three main ways. First, it contributes to the growing body of spatially disaggregated approaches to COVID-19, but focusing on clinical risk data. Spatially disaggregated models thus far have made use of mobility data to examine transmission dynamics (Mu, Yeh and Zhang, 2021; Zachreson _et al._ , 2021) and examined the impact of government policies (Guaitoli and Pancrazi, 2021), through lockdowns, or compared population characteristics in their response to government measures (Macharia, Joseph and Okiro, 2020; Gauvin _et al._ , 2021). However, to the best of our knowledge, this study is one of the few, particularly in the Sub-Saharan African context, that have used available spatially disaggregated data on clinical risk factors to map
income or consumption and are often more appropriate for country-specific analysis. However, despite their many advantages, national official poverty measures are not comparable with the approaches used in other counties, in part because they are so tailored the specific country context. Thus, comparisons across countries (for instance, for the purposes of monitoring or benchmarking) require a harmonized approach, such as that conducted by the World Bank.

An additional reason that the World Bank uses a stand-alone approach to measure internationally comparable poverty rates is to account for differences in the cost of living between countries. To address this issue, the World Bank participates in the International Comparisons Program (ICP), a global effort to measure differences in the amount of goods and services a unit of one country’s currency can purchase in another country. The ICP exercise was most recently completed in 2011, which led to important updates to previous PPP measures.

The original dollar-a-day line was created in 1991 using 1985 PPPs and was estimated by taking an average of the national poverty lines in the world’s poorest countries. When a new set of PPPs was published in 1993, the line changed to $1.08 per day. PPPs were revised again in 2005, and the new line was estimated at $1.25.<sup>2</sup> For the most recent revision in 2011, the new line was estimated again using the national poverty lines of the poorest countries, as was the case in previous rounds. The result was a line set at $1.90 a-day. However, poverty by this measure is quite rare in Central Asia and is therefore excluded from the analysis that follows.

But in addition to the international “low income” poverty line, the World Bank also uses income class poverty lines which facilitate comparisons between countries at similar stages of development. The income class poverty lines are defined for the lower middle-income and upper middle-income countries and are based on the national poverty lines of the countries in each group. As such
have the most specialized industrial structures. For example, Hungary is highly specialized in refined petroleum products, while Bulgaria shows high specialization in mining and quarrying (European Commission, 2011). This specialization effect dominates other, potentially positive effects of trade on energy productivity, such as technological spillover and increased competition.

One concern with the above analysis is that the causal effect between economic growth and energy intensity is likely to be two-way. Energy efficient technologies can improve productivity (Cleveland et al, 2000); Reductions in energy demand could also free up capital required for energy requirement and cut expenses. Both effects can spur economic growth. It is also likely that non-residential energy prices are affected by the amount of energy consumed in the industry - that is energy price may be endogenously determined by energy intensity. Furthermore, the above analysis assumes an instant response of energy intensity to income and energy price

> 12 This is consistent with findings of Metcalf (2008) based on US data. However, to be noted, there has been no consensus about the sign of the elasticities of substitution between energy and capital. Some authors suggest the need to disaggregate capital inputs into working capital and physical capital, and find that energy is generally complement to physical capital and a substitute for working capital (Field and Grebenstein, 1980).  Solow (1987) demonstrates either relationship is possible when using aggregated capital data.

10
designated team. Lastly, countries with lower GDP per capita levels are more likely to have their regulators work effectively on promoting access to finance by SMEs (Table 21).

# **5 Conclusion**

Using the _Financial Access_ database by CGAP and the World Bank Group, this paper (i) counts the number of unbanked adults around the world, (ii) analyzes the state of access to deposit and loan services as well as the extent of retail networks, and (iii) discusses the state of financial inclusion mandates around the world.

The findings indicate that there is yet much to be done in the financial inclusion arena. Fifty-six percent of adults in the world do not have access to formal financial services. The situation is even worse in the developing world with 64 percent of adults unbanked. Nevertheless, highincome countries also have to worry because approximately one in every five adults is unbanked. On the contrary to conventional wisdom, poor people indeed need and use financial services, albeit in small amounts and usually from informal sources as it is costly for formal providers to provide services for such small amounts. Anecdotal evidence suggests that informal financial 16
agency for instance, the <mark>Ministry of Statistics and Programme Implementation</mark> MOSPI, has a long history of running national sample surveys dating back to the 1950s when they were initiated by Professor <mark>Mahalanobis, the father of Indian statistics, and of publicly releasing the anonymized microdata. On MOSPI’s website microdata sets are available for download dating back to as far as 1975. Other known sources of downloadable microdata sets are the World Bank’s (WBs) microdata library,</mark><sup>5</sup> <mark>the DHS</mark><sup>6</sup> <mark>and MICS</mark><sup>7</sup> <mark>websites, the labor force surveys curated by the ILO,</mark><sup>8</sup> <mark>and IPUMS</mark><sup>9</sup> <mark>which publishes (samples of) population censuses.</mark> Others, like EUROSTAT, make microdata available in two formats: Public and Scientific Use Files. The Public Use Files (PUFs) can be downloaded immediately. They are subsamples of the Scientific Use Files (SUFs) which allow researchers to explore data sets and build their code. These PUFs cannot be used for publications. For this the SUF files are needed. SUF files are also made

> 4 See https://unstats.un.org/unsd/dnss/gp/FP-Rev2013-E.pdf

> 5 https://microdata.worldbank.org/

> 6 https://www.dhsprogram.com/

> 7 https://mics.unicef.org/surveys

> 8 https://www.ilo.org/surveyLib/index.php/catalog/LFS/about

> 9 https://usa.ipums.org/usa/

4
to navigate and adapt to the challenges posed by the pandemic.

**_E-commerce and Other Strategic Backward and Forward Linkages_** The COVID-19 crisis accelerated the trend toward e-commerce and digital solutions, with the largest firms investing significantly in automation and digital technologies to streamline operations and reach consumers directly. For instance, research using extensive e-commerce data from China (Guo et al., 2021, 2022, 2023) finds that the COVID-19 pandemic has led to substantial growth in online sales of agricultural products, particularly fresh and perishable produce, which customers are more likely to repurchase. Additionally, firms have invested in genetic development and other R&D to not only survive but thrive in an increasingly uncertain global landscape.

**_Concentration of Activity within Major Agribusinesses_** Strategic investments have led to changes in the structure of the value chain, with increasing concentration upstream and downstream, and a hollowing out of the middle of the chain. Vertically integrated suppliers, capable of upgrading from simple production to packing, exporting, and importing, became direct service providers for demanding retail outlets, further consolidating the global supply chain around large, well-financed suppliers.

The extensive adaptations by the fruit and vegetable sector to COVID-19 lockdowns underscore the importance of large agri-food corporations in economic stabilization during crises. Their long-term strategy of building backward and forward linkages may have also played a critical role. Specifically, the firms that led the adaptation of the fresh produce industry were large, multi-product conglomerates from both developed and developing countries like Chile, Peru, South Africa, Morocco, and the Arab Republic of Egypt, all with robust crisis management capabilities. They have decades of experience navigating the uncertainties of weather shocks, diseases, and natural disasters, which they leveraged during the pandemic to maintain operations, adapt to changing market channels, 6
# **2 Background and Data**

## **2.1 Regional trade agreements and agreement provisions**

Regional trade agreements have proliferated in the past three decades. The number of RTAs, which had remained low and stable since the 1950s, increased from 50 in the early 1990s to roughly 300 by 2020. Over this same time period, RTAs have also expanded their scope. While the average RTA in the 1950s covered 8 policy areas, in recent years they have averaged 17 (Hofmann _et al._ , 2017). Trade agreements that used to regulate areas such as tariffs, customs and trade remedies, increasingly cover non-trade areas such as competition policy, investment or intellectual property rights. The number of commitments that countries undertake in the context of RTAs has also increased considerably, most notably since 2000. And the deepening commitments have been accompanied by an increase in regulatory requirements, namely on enforcement. The changing nature of RTAs is documented by a new database by the World Bank informing on the detailed content of 295 RTAs signed between 1958 and 2018—i.e. all RTAs in force and notified to the WTO (Mattoo _et al._ , 2020).

Our data on environmental provisions in RTAs have been collected as part of the broader World Bank project on the content of trade agreements and are described in detail in Monteiro and Trachtman (2020). This is the most extensive effort to date to document environmental provisions in trade agreements. First, the coding covers environmental provisions included in the environmental chapter or in other chapters of trade treaties and, when present, in side agreements on the environment.<sup>4</sup> Second, the environmental provisions coded include environmental goals, specific commitments, compliance with multilateral environmental agreements, enforcement mechanisms, and external assistance and collaboration. Within this set of environmental provisions, we select two that are specifically important to protect forest resources: _•_ Does the agreement require measures to prevent deforestation and/or require sustainable trade
they capture virtually the same context as characteristics of some households change slowly over time.

These _Xhm_ sets include characteristics of individuals, households, and dwellings. The set _Zm_ considers some of these variables to be aggregated at the municipality-level along with data on the coverage and availability of public services and infrastructure.<sup>7</sup> This helps raise the precision of the estimates by minimizing the ratio of the variance of the error _ηm_ relative to the variance of the total error _μhm_ , that is, the share of the variance of errors that results from unexplained differences across municipalities. The parameters of equation (1) are estimated using the whole sample in each round of the ENIGH. The income distribution is then simulated based on 200 repetitions in the corresponding census dataset, each covering Mexico’s total population. The only exception is the census data in 2015, which represent a sample of 5.9 million households; yet, this dataset has sufficient statistical power to provide reliable statistics at the municipality level.

Based on the simulated income distribution, poverty and inequality indicators were computed across municipalities and validated through several tests.<sup>8</sup> The income concept used is household net per capita income, which includes labor income, income from businesses owned by the household, nonlabor income, such as public and private transfers, and an estimate of the imputed rent of owner-occupied dwellings, selfconsumption, and in-kind transfers and gifts received. The measurement of poverty was based on the Foster et al. (1984) family of indexes by comparing this income concept with three poverty lines: food poverty, defined as the inability to acquire a basic food basket; capabilities poverty, defined as the inability to cover the value of the food basket, plus expenditures on health and education; and assets poverty, defined as the inability to acquire the latter plus expenditures on clothing, housing, and transportation. The municipalities’ inequality levels were computed through an array of well-known indicators such as the Gini coefficient.

This
this group (Richards and Waterbury (1996)).

Economic sanctions may also affect children’s education through changes in relative prices. In addition to the reduction in household income, rising prices decreased households’ spending capacity. Over the years of sanctions, prices of many commodities spiraled upwards and inflation reached 35%. However, the magnitude of this change is different across goods and services. In particular, the prices of tradables (typically goods) have risen significantly relative to non-tradables (typically services). Thus, the relative prices and so the budget shares of the different commodities have changed.<sup>23</sup> Although education prices doubled, the changes are not as much as other commodities. The Education Price Index (EPI) increased on average 8% less than the overall rate of inflation.

# **3 Data**

The main data source is the Iranian Households Income and Expenditures Surveys (HIES).<sup>24</sup> These surveys, which are conducted yearly by the Statistics Centre of Iran (SCI), are designed to be representative of the population of Iran and cover near 40,000 households every year. HIES include extensive data on expenditures of households including education spending according to the Classification of Individual Consumption According to Purpose

> 23The budget shares of the various commodities are related to the real total expenditure and relative prices (Deaton and Muellbauer (1980)). Indeed, the descriptive analysis showed that sanctions significantly changed the households’ consumption patterns. The most significant change is related to the expenditure share allocated to food. While food prices became sixfold in 2013 since expenditure on food is necessary expenditure and unsubstitutable, expenditure share on food increased by 6% (from 40% to 46%).

> 24HIES are publicly available on www.amar.org.ir

10
products—using the 6-digit Harmonized System (HS) codes—, destinations/origins, and quantity. Export values are Free on Board (FOB) and import values are cost, insurance, and freight (CIF). To benchmark Argentina’s export performance, we employ the Exporter Dynamics Database (Fernandes, Freund, and Pierola 2016). The data set contains an array of indicators computed from firm-level information on the characteristics and dynamics of exporters at different levels of disaggregation.

We compute the following corresponding indicators for Argentina at the country-year level: number of exporters; average/median size of exporters; number of destinations per exporter; export market shares concentration; entry and exit rates; and survival rates.<sup>3</sup> This is the information used in the next section. In order to estimate the export premiums of exportersimporters, we split the sample between only-exporters and exporter-importers. Following recent literature (Arkolis, Costinot, and Rodriguez-Claire 2012; Pierola, Fernandez, and Farole 2018), we identify exporters that directly import intermediate products or capital goods, identified according the United Nations Broad Economic Classification (BEC). The rest of exporters are considered only-exporters. Figure 1, panel a, shows the evolution over time of the number of firms, by type. The total number of exporters declined significantly between 2007 and 2015. The total fell by 30 percent—around 5,100 firms stopped exporting altogether. Since 2015 the total number of exporters has stagnated at around 9,500 firms.

Exporter-importers dominate the trade landscape in Argentina, in terms of both number of firms and export value. The share of firms that export and import increased from 57 percent in 2007 to 63 percent in 2018. These firms represent the bulk of export value. In 2007 they accounted for 92 percent of export value, whereas in 2017 they represented 94 percent of the export value (figure 1, panel b).

> 3Details on the cleaning procedures for the Argentina raw data are presented in appendix A
Furthermore, although formally independent, it is materially conducted by present and future World Bank employees (Denizer, Kaufmann, & Kraay, 2013). Finally, the nature of the test is descriptive, because it does not account for nonrandom locations of projects to mission areas. This means that it is possible to draw suggestive, but not definitive, conclusions about project

# **3.2 Survey Data on Development Outcomes**

The second empirical strategy attempts to overcome the limitations of the first by relying on a quasi-experimental setup and on direct measures of economic development. To identify effects of World Bank aid, the strategy exploits the longitudinal dimension of the aid data, obtaining a panel of cellsyears spanning the period 1995–2014. Equipped with these data, it is possible to compare development in cells that received aid at different points in time, exploiting information on the dates of project approval and completion. The regression equation to be estimated by OLS has the form where _i_ , _k_ , and _t_ are indices for cell, country, and year, respectively.

The outcome variable _Yikt_ is a measure of economic development (e.g., electrification or access to water). ActiveAid _ikt_ is a binary indicator of the presence of at least one active project in year _t_ .<sup>26</sup> As the effects of aid will not necessarily materialize immediately in the years of project implementation, regressions also include CompletedAid _ikt_ , which is a binary indicator of the past presence of a project. (The indicator is equal to 1 in every year after completion of the first project.) EverAid _ik_ is a binary indicator that equals 1 if cell _i_ ever received at least one World Bank project in the sample period, and Mission _ik_ is an indicator that equals 1 if at least one historical mission was located in the cell. Fixed effects at the country-year level ( _λkt_ ) are always included.

Specification (3) amounts to a triple-differences setup (difference-in-differences-in-differences): EverAid _ik_ , Mission _ik_ ,
||PPR|Working Paper Series|||
|---|---|---|---|---|
||Title|Author|Date|Contact|
|WPS36|Means and Implications<br>of Social<br>Security Finance in Developing||||
||Countrles|Douglas J. Puffert|August 1988|R. Blade-Charest<br>33754|
|WPS37|Black Market Premia, Exchange Rate<br>Unification<br>and Inflation In<br>Sub-Saharan<br>Africa|Brian Pinto|July 1988|R. Blade-Charest<br>33754|
|WPS38|<br>Intergovernmental<br>Grants In<br>Developing<br>Countries|Larry Schroeder|||
|WPS39|Fiscal Policy In Low-income<br>Africa|Stephen A. O'Connell|<br>July 1988|R. Blade-Charest<br>33754|
|WPS40|Financial Deregulation<br>and the||||
||Globalization<br>of Capital Markets|Eugene L. Versluysen|August 1988|R. Blade-Charest<br>33754|
|WPS41|Urban Property Taxation in<br>Developing<br>Countries|William Dillinger|August 1988|R. Blade-Charest<br>33754|
|WPS42|Government<br>Pay and Employment<br>Policies<br>and Government<br>Performance in||||
||Developing<br>Economies|David L. Lindauer|August 1988|R. Blade-Cha-est<br>33754|
|WPS43|Tax Administration<br>in Developing<br>Countries: Strategies<br>and Tools||||
||rf Implementation|Tax Administration<br>Division|||
|WPS44|The Size and Growth of Government<br>Expenditures|David L. Lindauer|||
|WPS45-<br>WPS60|Assigned to WDR||||
|WPS61|