rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_1. Introduction_**

Although the region’s dependence on natural-resource exports is legendary, Latin America and the Caribbean (LAC) is composed of heterogeneous countries that differ in terms of size, trade structure and comparative advantage. Partly due to its comparative advantage in mining or agriculture, its history of economic thought is replete with pessimism about the region’s ability to develop (see, e.g., the literature reviewed by Lederman and Maloney 2007). However, the literature has remained silent with respect to the role of entrepreneurship as a driver of export growth in developing countries with diverse trade structures. This paper utilizes a new dataset on export transactions (collected from customs agencies) for a large set of LAC and comparator countries to assess the extent of “export entrepreneurship” during periods of fast export growth (2005-2007) and depressed external demand (2008-2009). Following the broader literature, “export entrepreneurship” is equated with the extensive margin of exports, namely the advent of new exporting firms, new export products and new export-market destinations. The paper provides a series of novel stylized facts on export entrepreneurship in the LAC region by focusing on dimensions of entry into export markets, entry into exporting of new products and entry into new destination markets by incumbent exporting firms. We identify within-regional differences and benchmark LAC’s export entrepreneurship relative to comparator countries in other regions, as well as cross-sectoral differences in export entrepreneurship.

We use novel exporter-level data from customs for 11 countries in the LAC region, including Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru from 2005 to 2009. The analyses consider two separate sub-periods: the steady export growth period of 2005-2007 and the period 2008-2009 characterized by the global financial crisis and the ensuing global recession. By examining the latter, this paper presents the first ever micro evidence on the effects of this major crisis on exporter dynamics in the LAC region. To benchmark the performance of
**Table 4. The estimated effect of the ICDS program on z-scores of children using the child-level data,** **<u>with and without propensity score matching, 1992 and 1998</u>**<sup>**33**</sup>

|||_Un_|_matched_|||_Matched_|||
|---|---|---|---|---|---|---|---|---|
|z-score|treated|controls|difference|std. err.|treated|controls|difference|std. err.|
|||||**India**|**: all children**||||
|**haz**|||||||||
|1992|-1.844|-2.149|0.305***|_0.026_|-1.877|-1.933|0.056|_0.055_|
|1998|-1.787|-1.981|0.194***|_0.027_|-1.807|-1.832|0.024|_0.056_|
|**waz**|||||||||
|1992|-1.894|-2.089|0.195***|_0.017_|-1.917|-1.873|-0.044|_0.038_|
|1998|-1.771|-1.881|0.110***|_0.021_|-1.789|-1.788|0.001|_0.047_|
|||||**In**|**dia : Boys**||||
|**haz**||||
Note: Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

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marginal product of the owner‘s effort in farming relative to other uses of labor time, thus generating a substitution toward non-farm activities. This is a distortion to intersectoral allocation, in the sense that marginal products of labor become unequal between activities. In principle, such an inefficiency could be avoided if the family is well informed about other (nonfarm) income sources, so allowing sharing rules based on total income.

Finally, it is worth underlining that land inheritance can be accompanied by learning within kinship groups, a source of specific human capital that may play an important role in the welfare gains from inheritance. It is widely believed that traditional farming practices in developing countries are characterized by a high degree of farm-specific knowledge, accumulated through experience farming the same land. Rosenzweig and Wolpin (1985) have emphasized the role of family-specific information in explaining inter-generational and intrahousehold land transfers, including inheritance; they find support for the claim that specificknowledge about the family farm entails that land is kept within the family rather than being sold. This could also explain why land markets are often thin or non-existent and also why the extended family is so common in underdeveloped rural economies, given that the older generation will have accumulated greater knowledge about the family farm. As long as the extended family can share knowledge there will be little economic loss at the death of the head of the household, though one can imagine circumstances (including unanticipated deaths) when that is not the case.

The upshot of these observations is that inheritance can have ambiguous effects on welfare and economic activities. The rest of this paper will address these issues empirically using an unusual data set for Senegal.

# **3. Setting and data**

More than half of Senegal‘s population is rural—57% out of a population of about 12.5 million inhabitants in 2009—although the contribution of agriculture to GDP amounts to only 18%.<sup>11</sup> Like other African countries, Senegal has seen considerable population urbanization; at
in the Std→Flex group accepted the switch to the flexible loan.

We test for balance in the second stage randomization by looking at the sample of new clients that initially received a standard offer. Using a combination of data from the recruitment process, data collected by credit officers during the application process as well as the bank’s administrative data, we compare those who received a standard loan with those who were switched to a flexible loan. Appendix Table 4 shows means and standard deviations for the two groups and p-values of the tests of equal means. Out of the 18 variables including loan characteristics (Panel A), socioeconomic characteristics of clients (Panel B) and business characteristics (Panel C), only one difference is significant at the 10% level. The p-value of a joint test of differences across all variables is 0.90.

> 4 Sixty-two percent of the recruits from financial events came from those organized by the mayor’s office, which partnered with private partners to visit different neighborhoods to advertise the availability of existing services such health and education programs, conditional transfers, and microfinance. At an event, prospective borrowers received a "financial inclusion” briefing that included eligibility criteria to apply for a loan.
<!-- Start of picture text --> Figure 1: Among agricultural households experiencing a bad harvest or<br>significant loss of livestock, most bear all the financial risk themselves<br>Adults living ina household where growing crops or raising livestock is the main source of<br>household income (%), 2017<br>ce<br>[ES<br>Fas,<br>aR<br>Zaia<br>0% 10% 20% 30% 40% 50% 60%<br>@ Hada bad harvest or loss of livestock and received compensation<br>© Hada bad harvest or loss of livestock and did not receive compensation<br>@ Didnot have a bad harvest<br><!-- End of picture text --> Source: Global Findex database. Note: The total length of the bar represents the share ofadults living ina household where growing crops or raising livestock is the main source of income. The survey asked respondents if their household had a bad harvest in the past five years.
**The decline in vulnerability to poverty comes along with an increase in the role of risk factors; this could be due to parallel poverty decreases in the country.** As poverty also fell over time, the increased importance of risk factors in vulnerability comes as no surprise. The ratio of risk-induced to poverty-induced vulnerability increased from 1.1 to 1.7 between 2016 and 2019 (see Figure 7). Especially in rural areas, risks have started to play a larger role. While in 2016, vulnerability to poverty was to a larger extent driven by low access to assets, it is the risks that drive vulnerability in 2019. In urban areas, risk factors have started to play an increasing role between 2016 and 2019.

**Additionally, idiosyncratic factors increasingly became more critical than covariate shocks.** This applies to the population as a whole, for which the ratio of idiosyncratic to covariate shocks increased from 1.9 in 2016 to 3.4 in 2019, but also the urban and rural population (see Figure 8). The increasing importance of household-level shocks relative to community-level shocks was more significant in urban areas. This means that, especially in urban areas, households are more subject to individual shocks, such as crime, employment losses, or illnesses.

_Figure 6: The development of vulnerability over time_ <!-- Start of picture text --> 65.0%<br>58.9%<br>52.1%<br>39.4% 41.2%<br>34.6%<br>30.9%<br>24.6% 23.7%<br>19.2% 18.1%<br>13.5%<br>2016 2017 2018 2019<br>All Rural Urban<br><!-- End of picture text --> _Source: World Bank estimates based on EHPM (2016-2019)_ _Figure 7: The development of risk-induced
genders, and ages—the other observed CV characteristics—for all qualified applicants. Besides these two demographic variables, TTC scores are the only consistently measured characteristics of all applicants.

The 2,185 applications come from 1,424 unique individuals, of whom 1,194 have a TTC qualification. The majority (62 percent) of qualified applicants complete only one application, with 22 percent applying to two districts and 16 percent applying to three or more. Multiple applications are possible but not the norm, most likely because each district requires its own exam. Of those applying twice, 92 percent

> 19These data were obtained from the six district offices and represent a census of applications for the new posts across these districts.

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for trade flows and specialization were revised and confirmed only partly in oligopolistic settings (Miyagiwa, 1991) and with imperfect information (McAfee and McMillan, 1989).<sup>3</sup> Within general equilibrium models with increasing returns to scale, Brulhart and Trionfetti (2004) find that trade barriers in government expenditure can actually change the patterns of specialization, while Trionfetti (2001) identifies a significant impact of home-biased government procurement on agglomeration following trade liberalization. In all these papers, home bias in the public sector is treated as a parameter. Trionfetti (2000) provides – a first attempt to quantify this home bias. He uses domestic input-output tables which – are part of the ICIO data for seven European countries to compare import penetration ratios across public and private sectors. Rickard and Kono (2014) uses aggregate trade data in a gravity framework and find that countries with larger government procurement import less, thus suggesting public home bias. We extend this empirical strand of the literature by estimating trade barriers in government procurement directly, by using information from input-output tables, both in absolute terms and relative to the private sector. In doing so, we highlight the importance of the composition of government procurement – i.e., its bias towards services.

In this paper, we infer government procurement purchases from inter-country input-output tables in order to estimate the effect of TAs and their provisions on cross-border purchases. Related work has exploited contract-level data mainly for the U.S. and the EU to assess the local bias in public purchases. Using EU data, Herz and Varela-Irimia (2020) find large border effects both across and within European countries adopting a gravity-style estimation approach, and Kutlina-Dimitrova and Lakatos (2014) provide evidence indicating that product market regulation and policies on Foreign Direct investments (FDIs) affect the probability of awarding a procurement contract to a foreign firm.<sup>4</sup> Fronk (2014) estimates the effect of TAs in a gravity model using U.S. federal – procurement
debt stagnated or even declined. Kenya’s debt to Japan fell 2.98 percent per quarter and France declined 0.58 percent per quarter. Traditional donors must coordinate efforts with China to avoid undermining governance and debt sustainability programs.

<!-- Start of picture text --> 800<br>600<br>400<br>200<br>2010 2011 2012 2013 2014<br>Year<br>Debt (USD Millions)<br><!-- End of picture text --> Figure 33: Kenya’s debt to China is growing quickly (2010-2014) Source: Kenya National Bureau of Statistics Economic Survey 2015 13Here 1 USD = 98.48 KSH. We take this from the UN Operational Rates of Exchange Effective date 30 Jun 2015 37
: an increase of life expectancy and a reduction in infant mortality. However, it is important to place the gains in perspective: over the same time period, global life expectancy at birth increased by 5.1 years, from 66.9 in 1997 to 72.0 in 2016. Restricting the analysis to upper-middle income countries (of which Mexico is one), life expectancy at birth increased by 5 years, from 70 to 75, similar to the increase observed in Latin America and the Caribbean,<sup>5</sup> suggesting that Mexico’s increase of 2.7 years is relatively low.

Mexico has experienced a dissonant epidemiological transition process. Although infant and maternal mortality rates were reduced, mortality among adults associated with noncommunicable diseases and violence has increased, resulting in an increase in the crude mortality rate (Gómez-Dantés _et al_ ., 2016). This is due to social inequalities in health that are manifested as differential access to health, and health outcomes related to socioeconomic conditions of the population (Gutiérrez and García-Saisó, 2016).

Global burden of disease studies capture the national epidemiological profile and its evolution over time. Between 1997 and 2016, changes were observed in the main causes of disabilityadjusted life years (DALYs). In 1997, the top five causes were: lower respiratory infections, neonatal preterm birth, diabetes mellitus type 2, ischemic heart disease, and diarrheal diseases. By 2017, they were: diabetes mellitus type 2, ischemic heart disease, physical violence by other means, physical violence by firearm, and migraine. Diabetes moved from 3<sup>rd</sup> to 1<sup>st</sup> place. In 1997 three of the top five were infectious/neonatal and two were chronic disease. By 2017 none were infectious/neonatal and two were violence.<sup>6</sup>

> 4 CONAPO. Demographic Indicators in Mexico 1990 to 2050. Consulted on 09/15/2018 in http://www
context of estimated crop acreage.<sup>4</sup> Existing studies that validate small area estimates of non-monetary poverty generated from mobile phone or geospatial data have, by and large, focused on extrapolation, or the use of big data to predict welfare in countries that are not surveyed. This differs from small area estimation, in which the objective is to predict poverty within the area covered by the sample, at a more granular level. To do this efficiently, it is crucial to use the survey data not only to train the prediction model, but also as a direct input into the estimates.

This study therefore makes three main contributions. First, it applies the prevailing framework for small area estimation to combine household survey data with geographically comprehensive geospatial indicators in an efficient way. Second, it evaluates the extent to which incorporating geospatial variables at the subarea level improves the precision of small-area poverty estimates. Finally, it assesses which of the commonly used SAE models – unit-level models (Elbers, Lanjouw, and Lanjouw 2003. Molina and Rao, 2010), and the Fay-Herriot area-level model (Fay and Herriot, 1979) – are best suited for combining survey and geospatial data to produce efficient and accurate estimates of both area-level poverty rates and the uncertainty associated with them.

We test these models in the context or generating small area estimates of non-monetary poverty in two developing countries, Sri Lanka and Tanzania. These countries were selected due to the availability of both census and survey data with subarea-level identifiers and matching polygon shapefiles close to the village level. The welfare prediction model uses survey data to estimate the value of a household welfare index as a function of subarea characteristics, and therefore differs from standard small area estimation models that predict welfare using household characteristics. The resulting estimates provide a large efficiency gain compared with direct survey estimates.

We mainly consider Empirical Best Predictor (EBP) models, which have a long history in small area estimation and can accommodate subarea-level auxiliary data to produce estimates of poverty rates and their mean
Policy Research Working Paper 9447

# **Abstract**

Flooding is among the most prevalent natural hazards affecting people around the world. This study provides a global estimate of the number of people who face the risk of intense fluvial, pluvial, or coastal flooding. The findings suggest that 1.47 billion people, or 19 percent of the world population, are directly exposed to substantial risks during 1-in-100 year flood events. The majority of flood exposed people, about 1.36 billion, are located in South and East Asia; China (329 million) and India (225 million) account for over a third of global exposure. Of the 1.47 billion people who are exposed to flood risk, 89 percent live in low- and middle-income countries. Of the 132 million people who are estimated to live in both extreme poverty (under $1.9 per day) and in high flood risk areas, 55 percent are in Sub-Saharan Africa. About 587 million people face high flood risk, while living on less than $5.5 per day. These findings are based on high-resolution flood hazard and population maps that enable global coverage, as well as poverty estimates from the World Bank’s Global Monitoring Database of harmonized household surveys.

This paper is a product of the Climate Change Group and the Global Facility for Disaster Reduction and Recovery, and a background paper for the 2020 Poverty and Shared Prosperity Report. 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 jrentschler@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
Policy Research Working Paper 10839

# **Abstract**

Global supply chains recently faced widespread disruptions. The COVID-19 pandemic caused major disruptions in 2021 and 2022, while in late 2023, geopolitical incidents in the Red Sea and water shortages in the Panama Canal disrupted global shipping routes. Regardless of the cause, delays, or rerouting mean that disruption diffuses at a global scale. To quantify and assess the magnitude of disruptions globally or locally, in 2021, the World Bank developed a proposed metric, the Global Supply Chain Stress Index. The index derives from Automatic Identification System tracking data. It calculates the equivalent stalled ship capacity measured in twenty-foot equivalent units), providing data at the port, country, regional, and global levels. This granular information can inform targeted interventions and contingency planning, improving the resilience of maritime infrastructure and networks. The index explains the observed surges in shipping rates during disruptions, assuming shippers’ willingness to pay for scarcer shipping slots. An increase of 1 million twenty-foot equivalent units in global stress pushes the Shanghai Containerized Freight Index up by US$2,300 per twenty-foot equivalent unit.

This paper is a product of the Macroeconomics, Trade and Investment 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 jarvis1@worldbank.org, crastogi@worldbank.org, ecojpr@tamug.edu and dulybina@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
# **Inferring COVID-19 Vaccine Attitudes from Twitter Data: An Application to the Arabic Speaking World**<sup>**1**</sup>

Roy van der Weide<sup>2</sup> Keywords: COVID-19, health behavior, Twitter, network analysis JEL codes: I12, I15, D85, O33 1 Excellent research assistance was provided by Joey Shea. The authors are grateful to Alexei Abrahams for providing the Twitter data and to Renos Vakis, Mohamad Chatila, and Zeina Afif for sharing the COVID survey data. The authors also thank Johannes Hoogeveen, Ha Minh Nguyen, and Roberta Gatti for their helpful comments and suggestions. Financial assistance was received from the State and Peacebuilding Trust-Fund (SPF). The views expressed here are the authors’ and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.

> 2 The World Bank: rvanderweide@worldbank.org
**Figure 1: Total cases of COVID-19 per million population**

<!-- Start of picture text --> 3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun,<br>2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021<br>Ethiopia Malawi Nigeria Uganda<br>Cases of COVID-19<br><!-- End of picture text --> _Source_ : Our World in Data (2021) To mitigate the spread of COVID-19, countries around the world introduced lockdown measures and Ethiopia, Malawi, Nigeria, and Uganda were no exception. Appendix B describes the government responses in each country. Figure 2 shows Oxford’s COVID-19 Government Response Stringency Index,<sup>4</sup> a composite measure based on nine response indicators, which include school closures, workplace closures and travel bans, rescaled to a value from 0 to 100 (100 = strictest)<sup>5</sup> (Thomas, et al., 2020).

**Figure 2: Government response stringency index**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun,<br>2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021<br>Ethiopia Malawi Nigeria Uganda<br>index<br>Government response stringency<br><!-- End of picture text --> _Source_ :