rafmacalaba/data-use-mentions-extended huggingface dataset:
43 with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see Appendix. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.

**Table 6: Regression results for infant mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0124|0.0121|0.0157|0.0128|0.0136|0.0152|0.0080|
|High malaria risk zone (HMRZ)|0.0720***|0.0701***|0.0774***|0.0761***|0.0743***|0.0757***|0.0473**|
|Interaction LMRZ and ITN ownership|0.0222|0.0224|0.0190|0.0207|0.0200|0.0193|0.0225|
|Interaction IMRZ and ITN ownership|-0.0097|-0.0098|-0.0125|-0.0083|-0.0061|-0.0076|-0.0113|
|Interaction HMRZ and ITN ownership|-0.0440**|-0.0439**|-0.0477**|-0.0438**|-0.0447**|-0.0470**|-0.0506***|
|2008 survey|-0.0145|-0.0119|-0.0072|-0.0087|-0
# COVID-19 Mortality in Rich and Poor Countries: A Tale of Two Pandemics?

Philip Schellekens<sup>1</sup> Diego Sourrouille<sup>2</sup> May 2020

**Keywords** : Pandemic; Demography; Health; Mortality.

**JEL** : I10, J0

* We would like to thank for helpful comments and inputs Syud Amer Ahmed, Pablo Cafiero, Marcio Cruz, Carl Dahlman, Indermit Gill, Huade Huo, Matthew Jones, Fabrice Lockefeer, Andrew Mason, Bryce Quillin, Juan V. Sourrouille, Alfred Watkins, Juan Wisnivesky and Shahid Yusuf. The findings, interpretations, conclusions and any remaining errors are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors or the governments they represent.

1 Senior Economic Advisor to the Office of the CEO, International Finance Corporation. Correspondence: pschellekens@ifc.org. This work is inspired by the Global Monitoring Report on demography and development, which I managed when previously at the World Bank.

> 2 Financial Analyst, World Bank. Correspondence: dsourrouille@worldbank.org.
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Responsibility for capital investment and O&M in Congo, by sector**

<!-- Start of picture text --> 3.5 operations capital<br>and expenditure<br>3.0 maintenance<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>public enterprises government<br>Source: Briceño-Garmendia and others 2008.<br>Figure 14. Budgetary spending on infrastructure, 2004–06<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2004 2005 2006 2007 2008 2009<br>ICT power roads water irrigation other transport<br>% of GDP<br>US$ millions<br><!-- End of picture text --> _Source:_ Republic of Congo budget book. _Note:_ 2004-08 data are actual, 2009 data is budgeted.

30
was likely driven by the Asian and Russian crises that seem to have generated a desire to hold funds that can invest more freely around the world. At the end of 2005, there were 490 global funds and 186 specialized funds. In terms of assets under management, the differences are even starker. Global (specialized) funds managed US$29 ($7) billion in 1991 and US$781 ($160) billion in 2005. This pattern holds across more specific fund types. Foreign funds had the most noticeable increase, with assets under management increasing from US$10.3 billion to US$540 billion between 1991 and 2005. In sum, the data show a

> 21 See Appendix Table 1 for a detailed description of the sample coverage of the price data for each mutual fund family.

12
originating from 187 origin countries that are ultimately filed in 82 different destination countries. The fact that there are fewer destination countries than origins in our data is a result of the set of patent offices covered in the PATSTAT data set.<sup>7</sup> Figure 2 depicts changes in average patent shares and volumes in the years before and after the entry into force of three types of IPAs and another simple form of policy protection. Our definition of which trade agreements constitute IPAs is as follows. First, does an agreement include at least three of what we call “core TRIPS-Plus requirements,” which are key elements of

> 6 In the trade-gravity context, the inclusion of intranational trade volumes has been shown to be an important consideration for recovering accurate estimates of trade-policy impacts (Yotov, 2022). For instance, Larch et al. (2018) find that the inclusion of intranational trade volumes in the empirical gravity setting leads to substantially different estimates on the impact of countries’ accession to the Eurozone relative to specifications based solely on international trade flows.

> 7 Details on extracting these data are explained in Appendix B.

9
data remains a blunt instrument for examining progressivity. Indeed, the limitations exhibited in this section further illustrate the need for obtaining microdata to more precisely measure the progressivity of tax systems where possible.

# 5.1 Potential aggregate tax revenue-based metrics

The section draws on aggregate tax revenue data from the UNU-WIDER Government Revenue Dataset covering 68 of 74 countries currently eligible to receive IDA resources (59 IDA-only and 15 blend countries).<sup>26</sup> Three potential indicators are presented:

1. The ratio of direct tax revenue to indirect tax revenue;

2. The ratio of income tax (PIT + CIT) revenue to total tax revenue; and 3. The ratio of income tax (PIT + CIT) revenue to GDP.

The ratio of direct-to-indirect tax revenue is a commonly used aggregate metric, adopted on the basis that direct taxes can be expected to exhibit more progressivity than indirect taxes. The second and third indicators focus specifically on the PIT and CIT on the basis that these are likely to be the predominant sources of progressivity in tax systems. In the calculations, direct taxes are specified as PIT (including labor and capital income), CIT, and property taxes; while indirect taxes are specified as VAT, sales taxes, excise taxes and trade taxes.<sup>27</sup> For brevity, a detailed discussion of the tax incidence literature is not provided here. However, income taxes are considered a primary source of progressivity based on the inherently progressive design of most PIT systems, and, in the case of CIT, following the conventional view that capital owners bear the majority of the CIT (and given that capital income is disproportionately earned by high-income households).<sup>28</sup> In contrast, while empirical results can be mixed for indirect taxes, none are typically considered to achieve a significant degree of progressivity.<sup>29</sup> Property taxes, while included in the numerator for the first indicator, are excluded from the second and
Furthermore, in tracking the impacts of the COVID-19 pandemic, there were a very large number of surveys that included the FIES as the _only_ food access measure (e.g., FAO 2020; United Nations 2020; World Bank 2020; Adjognon et al. 2021; Amare et al. 2021; etc.). Part of the reason for the extensive reliance on the indicator is likely its inclusion as an official measure for tracking progress towards Sustainable Development Goal 2 (zero hunger).

However, the FIES needs to be more extensively validated beyond the work that

> 1For example, in the nationally representative survey analyzed here — the 2018/19 Nigerian Living Standards Survey (NLSS) — the only food access modules included are the Food Insecurity Experience Scale (FIES) and the Food Consumption Score (FCS).

> 2See ”https://www.fao.org/in-action/voices-of-the-hungry/background/en/” for a list of countries that have or are in the process of adopting the FIES.

2
drivers of deforestation near the camps? Are Rohingya refugees directly causing forest cover loss in the region? Or has increasing economic activity associated with the refugee camps’ presence driven forest change?

Remote-sensing data opens up new opportunities to investigate whether the areas near camps face higher rates of natural capital losses and whether camp residents primarily drive such transitions. Using the US Geological Survey’s Landsat satellite imagery at 30-meter resolution, we conducted an LULC change detection using data from 2010, 2014, 2017, and 2020. We first provide a full accounting of forest loss and expansion of other land cover types following the camp openings. Our descriptive analysis reveals forest cover reductions and settlement 3
But protection was provided through the highly depreciated exchange rates, as well as through exchange controls.

As countries started to introduce their own currencies and stabilization programs started to take hold in 1994-1996, and as they also initiated broader market oriented reforms, the different trade regimes that are in place today started to emerge. The transition had several _4 Michalopoulos and Tarr, 1994 contains a detailed discussion of trade policies in these countries through 1994, based on seven country case studies._ 6
perceptions deviate from fundamentals, sometimes abruptly and significantly. An implied default probability measure can capture the market’s views as they change, which would otherwise not have been picked up in low-frequency variables connected to fundamentals. As will be shown below, the index captures a key ingredient in a DSA by essentially inverting a ”fan chart” to produce a measure of how far a country is from its maximal effort to implement discretionary fiscal policies in an attempt to reduce debt.

The third aspect is expert evaluation of risks. Using standard DSA debt indicators, we evaluate for each country the number of variables that exceed thresholds of risk as defined by the DSAs into a **Count Index** . DSAs have been a staple input for evaluating debt risks. Economists at the IMF and the WBG use judgment and history as a guiding principle to assess whether a country is in debt distress. The Count Index reflects some of this judgment.

The fourth and final aspect captures what ought to be. Using a theoretical framework that embeds endogenous default, we calculate time varying debt default thresholds for each country to construct our **Theory Index** . The theoretical model is used to find debt ratios that are indicative of risk. These theoretical thresholds are endogenous and hence different than DSA guidelines on debt. They are time varying and embed the possibility of a self-fulfilling debt crisis - i.e., when market perceptions deteriorate significantly from fundamentals.

These four indices are constructed for each country and then used as explanatory variables for debt default. Figure 4 illustrates the objective. The red circle represents the unit disk (i.e. 1 = default), while the blue area represents the space spanned by the four angles of debt.

**Figure 4:** Angles of debt vulnerability

<!-- Start of picture text --> Vulnerabilityindex<br>Implieddefaultindex<br>Capturesfundamentals<br>Capturesperceptions<br>Theoryindex Capturesjudgement<br>Captureswhatoughttobe<br>Countindex<br>Unitcircle= Default
The goal of the rest of the paper is to estimate the relevant parameters that allow us to compare changes in Bangladesh to changes predicted by the models above. We begin with a description of the relevant data.

# 3. Data Description

Following McCaig and Pavcnik (2018), we start with labor force surveys and combine them with trade data. After describing these data, we present the empirical methodology that starts with the Bartik (1991) approach and extends with an approach to estimate the general-equilibrium predictions of the theory above. We first describe the trade data and trends in trade and production and then describe the labor market data and relevant labor market characteristics.

## Trade and Production

We begin our description of the changes in Bangladesh’s trade and production patterns using annual, bilateral commodity trade data from COMTRADE using the 4-digit International System of Industrial Classification (ISIC) Rev. 3.1. Our main focus is on United States and European Union imports from Bangladesh over the 1990-2016 period. The main point is that exports have expanded significantly, and apparel and textiles have played the leading role _._ Like many developing countries, Bangladesh followed an import-substitution-industrialization (ISI) strategy for much of the 20<sup>th</sup> century but turned and implemented liberalizing reforms in the 1990s. The government reduced the maximum import duty of 350 percent in 1993 to 32.5 percent in 2003 and 25 percent in 2005. Bangladesh also reduced the number of tariff bands from 15 in 1993 to 4 in 2016 _._ Between 1992 and 2008, the unweighted average tariff rate declined from a high of 70 percent to low of 12.3 percent. During the 1990s several measures were designed to reduce the cost of imported inputs, including reducing tariffs, subsidized interest rates on bank loans, cash subsidies, exemptions from value added and excise taxes, bonded warehouse facilities, a duty drawback facility, duty-free imports of machinery and inputs for export industries, an export credit guarantee scheme, and income tax rebates for
|16.1||
|||_A lot_|47.6|36.3||
||Math is important in life|_Somewhat_|38.6|48.0|.115***|
|||_Never_|13.8|15.7||

_Note:_ *p < .05; **p < .01; ***p < .001.

**Table A1.2. Percentage of students** **_not born in Saudi Arabia_ attending schools of various types (parent reports), by school gender**

|||**Male(%)**|**Female(%)**|**_φ/φc_**|
|---|---|---|---|---|
||Government school|85.0|95.2||
|Grade 4|Quran school|1.1|2.2|.206***|
||Private school|13.9|2.6||
||Government school|87.7|96.3||
|Grade 8|Quran school|1.3|1.0|.168***|
||Private school|11.1|2.7||

_Note:_ *p < .05; **p < .01; ***p < .001.

39
import or export licenses, to import intermediate inputs, raw materials or capital goods and to export their final production. Finally, rebates under duty drawback schemes and for value-added taxes are often slow, sometimes for less than the full amount and even can require additional informal payments or bribes in some cases.

Although it is difficult to quantify all aspects of trade and customs regulations, the investment climate surveys include some questions that address these questions. The most direct

> 7 In the sample of firms in this surveys exporters are more likely to use imported intermediate inputs than non-exporters.  Imports account for 50% of inputs for exporters, compared to 37% for non-exporters.  This is consistent with results for Tanzania in Grenier _et al_ . (1999)

7
remain unchanged since 2013<sup>28</sup> ; IDR 450,000, 750,000, and 1 million for each beneficiary at primary, junior secondary, and senior secondary level, respectively (World Bank, 2018). Poor households sometimes choose between sending their sons and daughters to school when resources are limited. Data from Susenas 2017 indicate that among households with out-of-school children, more than half of households with out-of-school boys (52 percent) report that economic condition is the major reason for not going to school, while only 44 percent of households with out-of-school girls stated the same reason (see also MoWECP and BPS 2018a).

**Table 3. Main reasons for dropping out from school by sex (percent)**

|Main Reason|<br>|Indonesia<br> <br>||
|---|---|---|---|
||Male|Female<br>|Total|
|Insufficient funds|33.01|30.68|31.99|
|Working|18.74|13.68|16.52|
|Marriage|0.37|12.27|5.59|
|Taking care of the household|0.37|3.65|1.81|
|Assume that current education is enough|4.26|5.24|4.69|
|Shame of Poor Economic condition|2.88|1.9|2.45|
|School is far|3.71|3.26|3.51|
|Disability|5.39|4.82|5.14|
|Other|31.27|24.51|28.3|
|Total|100|100|100|

Source: Susenas, 2017.

The same data source also shows that economic status is positively correlated with length of schooling. This applies for both males and females (Table 4). The
|Yes|Yes|
|Cohort fixed effects|No|No|No|No|No|No|Yes|Yes|Yes|Yes|
|County-cohort fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|4432|4484|3741|3789|21382|21654|12239|12261|5111|5111|
|Number of countries|163|165|151|154|158|160|145|145|168|168|
|Adjusted R-square|0.997|0.997|0.999|0.999|0.997|0.999|0.986|0.993|0.998|0.999|

Source: Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note: Business cycles defined as deviation of real GDP from Hodrick-Prescott-filtered trend. Sample includes unbalanced panel of 35 advanced economies and 133 EMDEs for 1987-2020. p-statistics are shown in parentheses.

52
# **Conclusion**

Increases in the level and volatility of food prices over the past several years have led to a severe erosion of purchasing power in developing countries where the poor often spend the majority of their budgets on food. Increases in the price of staple foods in particular can have deleterious effects for households living at subsistence levels.

In this paper, we use the case of Afghanistan during the 2007-08 global food price crisis to examine the impact of high staple food prices on household food security. We estimate the marginal effect of increases in wheat flour prices on measures of total food consumption, caloric intake, and dietary diversity. We use the unconditional quantile regression estimator, proposed by Firpo, Fortin and Lemieux (2009b), to examine the marginal effects at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> quantiles of the unconditioned distribution of each food security-related dependent variable, as well as the marginal effects associated with key policy-relevant thresholds.

The key findings of our analysis illustrate the value of distribution-sensitive analysis. We find large differences in the behavioral response of a household to wheat flour price increases based on its location on the unconditional distribution of each of our measures of food security – differences that are obscured when using OLS estimation techniques. Households at the 75<sup>th</sup> quantile of the distribution of real food consumption experience the largest percentage decline in real food consumption for a given increase in wheat flour prices – over two and a half times larger than that of households at the 25<sup>th</sup> quantile. Analogously, those at the 75<sup>th</sup> quantile of the caloric intake distribution experience the largest percentage declines in caloric intake for a given increase in wheat flour prices. Households at the bottom of the caloric intake distribution make very small reductions in caloric intake due to the price increases. Such households live near subsistence levels and
Year<br>Boys Girls<br>Reading Score<br><!-- End of picture text --> Source: PISA, OECD, various years In mathematics, boys’ mean performance in OECD countries is on average five points higher than girls’, and girls outperformed boys in science just by two score points (OECD 2019, PISA Insights and Interpretations). In Indonesia, however, girls scored higher than boys in mathematics by 10 score points, and girls outperformed boys in science by seven score points (OECD 2019, Indonesia Country Note). These gendered trends in performance start early in Indonesia. Based on a sample of more than 10,000 children between the ages of 6 and 9 in rural Indonesia, a World Bank study found higher levels of cognitive skills for girls on language and mathematics test scores. For example, at age 6, girls on average have a significantly higher score than boys by 0.25 standard deviation in Bahasa Indonesia and 0.24 standard deviation in mathematics. The same study found that 8-year-old girls perform significantly better than boys in non-cognitive skills, with higher scores on the social competence (0.25 standard deviation) and 3
This model presents two groups of predictions. The first group is “static” in the sense that it predicts that exporting firms are larger and have more productivity. The second group is “dynamic” in the sense that the model can be used to decompose the expected change in employment from an increase in the foreign price (i.e. following a trade agreement) into that due to changes in the extensive and internal margins. The model predicts that most of the employment gains from a trade agreement come along the extensive margin. Existing firms increase employment, but less than new firms entering the export market. In addition, we would expect employment to be higher among firms that have always exported, while lower for firms that are new exporters. We can compare these predictions with firm-level data described below.

# **_Data_**

We use the World Bank Enterprise Surveys (ES) to evaluate the link between exports and employment at the firm level. The ES are representative samples of an economy’s privatesector firms based on interviews with business owners and top managers about several topics, including trade and employment. In Morocco, ESs conducted in 2007, 2013, and 2019<sup>11</sup> cover businesses from manufacturing, construction, motor vehicle sales and repair, wholesale, retail, hotels and restaurants, storage, transportation, communications, and IT sectors. They only consider firms with more than five workers, and a minimum of 1 percent of private ownership.<sup>12</sup> The ES reveal significant firm-level heterogeneity in Morocco. From the 1,096 interviewed firms in 2019, 28 percent were large (307 firms), 35 percent medium (384 firms), and 37 percent small (405 firms). By sector, 11 percent operate in the food sector, 16 percent in garments, 16 percent in other manufacturing, 17 percent in retail, and 41 percent in other services. In fact, 63 percent of firms have been in operation less than
(Mathieu et al. 2021). The dataset is publicly available through the Our World in Data GitHub repository.<sup>4</sup> We additionally access a second source of administrative data stemming from the WHO’s COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).

Lastly, we use data from the World Bank’s Statistical Performance Indicators (SPI) available through the World Bank’s Open Data library (World Bank n.d.). The SPI is a composite index between 0 – 100 scoring countries’ statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI’s indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).

> 3 Defined as the share of the total population that has completed the initial vaccine protocol: two doses or more of a two-dose vaccine (such as Comirnaty by BioNTech and Pfizer) or one shot of a single-shot vaccine (such as Janssen by Johnson & Johnson).

> 4 https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations

6
agricultural GDP in 2016, according to the Statistical Yearbook (General Statistics Office, 2016b). Fishery-related activities also make up a considerable share of the economics in the central coastal of Vietnam. At the four-digit Vietnam Standard Industrial Classification (VSIC) level, the single sub-industry of saltwater fishery accounts for 3.8 and 7.3 percents of total employment and income in coastal districts<sup>2</sup> of Ha Tinh, Quang Binh, Quang Tri, and Hue in 2015 (General Statistics Office, 2016a). Second, it is worth noting that most of the Vietnamese fishery sector is small and medium scale, where fishers predominantly operate nonpower or outboard-engine (i.e. low-powered) boats. This puts a considerable restriction on the length and types of the fishing voyage, and often makes fishery earnings highly vulnerable to the uncertainty in fishing conditions, such as unfavorable climates or extreme, albeit rare, incidents like _Formosa_ . Because we are interested in examining the immediate impact of the crisis, we confine the scope of the analysis to the period before any

> 2 District is a second-tier administrative unit, subordinated to a province.

2