rafmacalaba/data-use-mentions-extended huggingface dataset:
) areas can be combined with a neighboring area or neighboring areas, and large VP areas can be divided into smaller segments. The procedure for merging multiple VP areas should be straightforward; however, segmenting large areas could be costly as it requires some spatial analysis and probably cartographic tasks.

Additionally, the boundaries of PSUs are needed because the enumerator might go beyond the targeted area if they do not know the boundaries. This feature is absent in both sampling frames under investigation in this section since there is no boundary (neither digitally nor physically) available for the Census settlements and electoral precincts. However, it is more disadvantageous for the precinct-based sampling frame because precincts are relatively smaller in area than settlements. The probability of enumerators walking into the nonselected PSUs that neighbor with the selected PSU is thus higher for precincts. Enumerators can walk outside the boundary for some very small electoral precincts, such as the one with 7 addresses, if not provided with a proper map while doing the fieldwork.

The pros and cons of a frame based on the 2023 electoral precincts show that it is relatively better than the frame based on the 2011 Census settlements. Thus, the 2023 electoral precincts have been further evaluated as a sampling frame for representative individual- and household-level surveys by exploring the data on electoral precincts. The size of the electoral precinct is measured by the number of voters or adult population, which leaves out children or individuals younger than 18 years old. **Table 1** shows the distribution of strata size using total and adult population. The strata are similarly defined as earlier, i.e., the combination of _marz_ and settlement type—urban or rural status. The 2020 population is obtained from the WorldPop predicted based on the 2011 Census (Bondarenko et al., 2020). Then the 2020 population has been predicted further to 2022 as shown in Column 1 using strata-level population growth obtained from the Committee of the Republic of Armenia (ArmStat). The estimated total population in 2022 in this data is 2
destruction on educational attainment is the destruction of schools whereas malnutrition is partly behind the estimated impact on health. Minoiua and Shemyakina (2014) use survey data and information on the dates and locations of conflict events to examine the causal impact of the 2002–2007 Ivorian conflict on child health. They find that children from conflict-affected regions suffered significant health setbacks and that victimization in the form of economic losses is the main conflict–impact mechanism.

Conflicts affect aggregate productivity directly through physical health but also indirectly through mental health. Being exposed to violence leads to post-traumatic stress disorder (PTSD) which affects both adults and children. These mental illnesses are characterized by hyper-vigilance, flashbacks and nightmares. Individuals who are exposed to a traumatic event have an increased likelihood of poor self-reported health, morbidity (as indicated by physical exam or laboratory tests), utilization of medical services, and mortality. Li et al. (2011) show that people who experience PTSD are at much higher risk of developing other health problems, including diabetes, heart ailments, depression and addiction. Sibai et al. (1989) study civilians exposed to the civil war in Lebanon and find that patients with severe coronary artery disease had more war-related stress than did either patients with normal arteriographic findings or hospital visitors. Using female Vietnam veterans, Wolfe et al. (1994) show that warzone exposure and PTSD are associated with self-reports of poor health and numerous physical problems. PTSD in early childhood also makes people more vulnerable in later life (The Economist 2015). Rosen and Fields (1988) were among the first to attempt to explain how PTSD could adversely affect physical health. The potential channels can be divided in 3 groups: biological, psychological and behavioral channels (Friedman and Schnurr 1995). From a biological point of view, PTSD can modify cardiovascular reactivity, disturb sleep physiology, enhance thyroid function, etc. Singhal (2019) studies the long-term effects the U.S. war in Vietnam on mental health. He finds that early-life exposure to war
PCT to IFIs in return for competitive lending rates. Levy Yeyati (2009) argues that PCT is related to an insurance motive–namely the expectation that IFIs will extend credit during a crisis. Humphrey (2015) stresses IFIs mutual ownership structure. Risk Control (2017) collects statistics related to these three potential “drivers”of PCT (favorable rates, countercyclical lending, cooperative nature of the institutions) and ...nds evidence that IFIs do indeed extend funds at lower rates than the market, that they are countercyclical, and that they lend in bad times. The paper also compares the degree of “mutuality” of each institution and discusses how that may a¤ect preferential treatment.

Second, within the large empirical literature on sovereign defaults, a subset of papers consider defaults (and the building up of arrears) on IFIs. Schlegl et al (2015, 2019) claim a hierarchy in seniority with the IMF and MDBs at the top. Their analysis is based on World Bank data and covers 127 countries from 1980 to 2006. Considering arrears, bonds appear below MDBs in the pecking order, and then come bilateral lenders, banks and trade credit. The analysis also shows that not all multilateral lenders are treated equally;

> multilateral lenders such as the IFC (the private sector arm of the World Bank Group) do not generally claim preferred status.

> 9 Focusing on emergency lending (and abstracting from lending for consumption-smoothing motives in normal times) provides a clean way to illustrate how a preferred lender could exist as an equilibrium outcome.

2
important to note that the trend of the average within-country Gini changes from declining to increasing, once population weights are used, implying an increasing Gini in populous countries (also see Figure 1 in the introduction). Over the entire period of analysis, the EAP-wide Gini index has increased by almost 11%. By contrast, the (unweighted) average within-country Gini has fallen somewhat by around 2%. In other words, within-country inequality for the average EAP country declined slightly, while the region-wide inequality increased.

The Lorenz curve for 1997 is uniformly to the right of the 1988 curve without crossing (Figure 4), so inequality increased between 1988 and 1997 irrespective of the choice of inequality measure (Atkinson, 1970). This appears to have reversed in the most recent period (2007-2012) when the Lorenz curves moved inwards. Between 1997 and 2012, however, there is no unambiguous ranking of Lorenz curves, so the direction of change in inequality depends on the choice of inequality measure. Similarly, when looking at the entire period between 1988 and 2012, the ranking in terms of inequality depends on the choice of inequality measure. That is, the Lorenz curve for 2012 crosses to the left of the 1988 curve at around the 95<sup>th</sup> percentile – suggesting that inequality increased at the bottom. This is also confirmed by the alternative GE inequality measures in Table 2, Panel A. The bottom-sensitive GE(0) measure increases by 26% between 1988 and 2012, while the top-sensitive GE(2) declines by around 36%. At

> 13 Lakner and Milanovic (2016, fn. 31) explain in detail how these stacked densities are created.

> 14 For the earlier benchmark years, there are only a few countries with a Gini higher than the EAP-wide Gini: Malaysia (although it uses income surveys), the Philippines and Thailand before the 1997 financial crisis, Papua New Guinea in 1997 and Solomon Islands in 2007.

7
Mozambique,and Rwanda|
|SITC_21|321**|Coal|No|37|22|Botswana, Burundi, Ethiopia, Malawi,<br>Morocco, Mozambique, Niger, Nigeria,<br>South Africa, Tanzania, Zambia, and<br>Zimbabwe|
|SITC_22|525**|Uranium|No|2|2|Namibia and Niger|
|SITC_23|663**|Chromite|No|8|6|South Africa, Sudan, and Zimbabwe|
|SITC_24|684**|Aluminium, ammonia,<br>and bauxite|No|5|5|Cameroon, Mozambique, Nigeria, and<br>South Africa|
|SITC_25|728**|Asbestos|No|4|3|South Africa|
|Subtotal||||376|304||
|Base|999|No commodities|n.a.|1806|349||
|Total||||2182|653||

* Names assigned by authors for purposes of analysis and ease of understanding of results. ** These commodities were dropped from the regressions that included mineral prices; this explains the differences in the numbers of countries in the regression tables.

Source: authors’ construction (based on USGS data).

Table 3: Mining sites by districts in Africa, 2001–2012

|Status of mining activiti|es<br>||||||
|---|---|---|---|---|---|---|
||Status|No mine|Active mines|Closed mines|Total||
|Mineral deposits|No|1806|0|0|1806|82.8%|
||Yes|0|333|43|376|17.2%|
||Total|1806|333|43|2182|100%|
|||82.8%|15.3%
_Centrality)._ We then use granular data to explore the co-occurrences of poverty indicators at the household level and build network maps for each country.

Using data from the Global Multidimensional Poverty Index (MPI), we apply these metrics in 67 developing countries to map the interconnected structure of multidimensional poverty and explore changes in its structure over time. Our findings reveal a remarkable similarity in the structure of the Poverty Spaces across countries and their stability over time. Specific indicators like cooking technology at home (cooking fuel, which is associated to living conditions at home) consistently emerge as nodes located at the core of this network, while indicators like child mortality tend to be located in the periphery. To bring a more dynamic perspective and capture pathways for spillover effects, we also explore the association between how central a poverty indicator is and the change in the incidence of people deprived in that indicator over time. We find that more central indicators in the Poverty Space also tend to experience a more significant reduction in their censored headcount ratio over time. Finally, to apply our findings in a policy context, we integrate the Poverty Space network into the Policy Priority Inference (PPI) framework (Guerrero and Castañeda Ramos, 2020). This integration enables us to explore how the structure of poverty could be associated with the potential effectiveness of targeted interventions. PPI serves as a forward-looking, agent-based model that leverages networks of structural relationships to prioritize policy initiatives, originally aimed at achieving sustainable development goals (Guerrero and Castañeda, 2024). By using the information offered by the Poverty Space on the structural relationships among poverty indicators, PPI can be implemented to prioritize different policy interventions, recognizing the potential cascading effects across various indicators of poverty.

Understanding the interconnected structure of multidimensional poverty is critical for maximizing the impact of policies to reduce it. By shedding light on the web of connections within the structure of multidimensional poverty, this paper shows how economic complexity methods can help us to better pinpoint the “key nodes” within that network. These key
industries to ensure maximum compatibility across the two datasets. Around 8.5 percent of the plants in our sample switch industry over the period of analysis. To avoid potential endogeneity of industry choice to robots’ adoption, we drop the observations after a plant switches industry. However, we obtain very similar results if we do not drop such observations, or adopt alternative strategies.<sup>10</sup> We keep only plant-year cells with non-missing observations for all the dependent variables involved in the analysis. Our final plant level (unbalanced) panel includes 22,288 plants operating in thirteen 2-digit manufacturing industries between 2008 and 2015, for a total of 55,417 observations.<sup>11</sup> Table 2 presents summary statistics for the variables used in the analysis.

# **2.4 Empirical Specification**

We use equation (1) for the plant level analysis using the following model: where _Yf,i,t_ is an outcome of plant _f_ in industry _i_ at time _t_ ; _ηf_ are plant fixed effects; _ui,t_ are industry-year effects defined using the same classification as IFR robots’ import data; _If_ is a plant-specific innovation intensity index (measured in the 2006 census year); and _θt_ are time effects.

The plant fixed effects should absorb the impact of unobserved time-invariant characteristics correlated with the available opportunities for automation i.e. plants’ 2006 share of secondary education and the outcome variables. That may be the case for instance as foreign owned plants are usually more productive and have a larger share of routine labor.

> 10The first alternative strategy is assigning robots to plants based on the industry in which it operates the year of the first available information. The second strategy is dropping altogether all switching plants.

> 11In principle, we could run the plant level analysis starting in 2007. However, representative data by regency and industry, which are discussed in Section 4.3, are only available from 2008. Therefore, to be consistent across levels of
# **Introduction**

An important question for understanding the global impact of the COVID-19 pandemic is the disease’s age pattern of mortality across countries. Early in the pandemic, case data from COVID-19 patients in mainland China showed that COVID-19 mortality risk increases rapidly with age.<sup>1</sup> This risk profile would tend to limit the death toll in developing countries, which generally have younger populations. But this prediction only holds if the age profile of COVID19 mortality remains similar across countries.

Our study examines this question by comparing the age gradient of COVID-19 mortality in a broad set of countries using both official COVID-19 death counts and excess mortality estimates for 2020. Vaccine availability was very limited before 2021 and therefore is unlikely to affect the mortality patterns identified in this analysis.

Nearly all studies of the pandemic’s age mortality patterns have used only official COVID-19 death data.<sup>2–8</sup> However, excess deaths at the aggregate population level during the pandemic exceed officially recognized COVID-19 deaths in most countries.<sup>9</sup> This is to our knowledge the first study that estimates COVID-19 excess mortality by age for a wide swath of countries.

Previous studies have found a pattern of flatter COVID-19 age-mortality curves among low- and middle-income countries (LMICs) using only official COVID-19 death counts.<sup>4,8</sup> It was unclear to what extent that finding may have been driven by data reporting issues. In particular, it is possible that deaths among older people in developing countries have been more likely to be under-attributed to the disease in official COVID-19 death counts. This paper addresses that concern by showing that the same pattern is found using both official COVID-19 death counts and excess mortality estimates.

# **Methods**

_Data sources_ The analysis employs two main sources of data:

1. Official reported COVID-19 deaths drawn from the COVerAGE database, with death counts aggregated to 5-year age-groups.<sup>10</sup>
provides extended arguments regarding these measures and shows that the ability of agricultural workers to work from home is limited and, moreover, tends to increase, rather than to fall, with income per capita.<sup>4</sup>

> construct an index with three intensity categories (low, medium and high). They apply the index to the American Community Survey. Mongey, Pilossoph, and Weinberg (2020) use the same O*NET questions as Dingel and Neiman (2020) but construct a continuous index of working from home using the intensity scores as Leibovici et al. (2020). 3 Their sample also includes lower income economies covered by the STEP surveys, where the positive correlation between home-based work and GDP per capita also holds. However, these surveys exclude rural areas in most cases, thereby excluding most agricultural workers.

> 4 Given that agriculture remains an important source of employment in low-income countries, the assumptions on the ability of self-employed agricultural workers to work from home is an important determinant of the relationship between labor market vulnerability to COVID-19 and development. This paper follows the DN2020 approach, but we recognize that the treatment of agricultural workers is very important. One of our main conclusions, that ICT constraints are more likely to bind in low income countries, does not depend on the assumptions about the ability of the self-employed agricultural workers to work from home.

4
**Figure 9: Magnitude of Changes in Education Spending over Five-Year Periods** _a. Distribution of five-year growth episodes for total b. Distribution of five-year growth episodes for spending education spending, 1999–2015 (constant 2015 PPP as a % of GDP, 1999–2015 –2015 2015_

_b. Distribution of five-year growth episodes for spending as a % of GDP, 1999–2015 –2015 2015_ _$)_ _Source:_ World Bank calculations based on UIS and IMF data.

The overall distribution of these 852 five-year growth episodes shows that spending has grown in most cases (Figure 9). Consistent with our findings from Section 2, real public education spending increased in 83 percent of these five-year growth episodes. Over all five-year growth episodes, public education spending grew by a median of 18 percent, but there was wide variation between countries and time periods. The changes ranged from a 71 percent decline in spending in Malta between 2003 and 2008 to a 166 percent increase in spending in Afghanistan from 2006 to 2011. However, public education spending as a proportion of GDP changed only slightly, with the median five-year episode being associated with a 0.1 percentage point increase in public education spending as a percentage of GDP.

There was considerable variation in the characteristics of five-year changes in public education spending across and within income groups. The average increase in public education spending growth was much higher for the low-income group (39 percent) than the high-income group (13 percent) (Table 5).<sup>10</sup> Real public education spending for countries in the top growth quartile<sup>11</sup> grew by an average of 36 percent for high-income countries and 92 percent for low-income countries. Patterns among countries in the lowest growth quartile were less clear and ranged from an average decline of 4 percent for low-income countries to an average decline of 15 percent for the upper-middle-income group (Table 5).

> 10 Annex Table A5 includes details on the second and third quartiles.
# Figure 12.  Country correlations for CE and control of corruption

<!-- Start of picture text --> Low CC High CC<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Low corruption control = 0.50***<br>-0.2<br>High corruption control = 0.31***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongoliaSudan SenegalMali Burkina Faso<br>AlgeriaUzbekistan MyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>NorwayMalawi New Zealand<br>Australia Guatemala<br>Brunei Darussalam<br>United Arab Emirates<br>Canada<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) control of corruption, defined as control of corruption above (below) the sample median. The difference between the two averages is significant at the 10 percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International
of Economic Literature_ , 41(1): 113-58.

- Deaton, Angus. 1985. “Panel Data from Time Series of Cross-Sections.” _Journal of Econometrics_ , 30(12): 109-26

- Devereux, Paul. 2007. “Improved Errors-in-Variables Estimators for Grouped Data.” _Journal of Business and Economic Statistics_ , 25(3): 278-87.

- deBrauw, Alan and John Giles. 2008. “Migrant opportunity and the educational attainment of youth in rural China.” The World Bank, Policy Research Working Paper: 4526.

- Easterly, William. 2007. “Inequality Does Cause Underdevelopment: Insights from a New Instrument.” _Journal of Development Economics_ , 84(2): 755-76.

- Engermann, Stanley, and Kenneth Sokoloff. 1997. “Factor endowments, institutions, and differential paths of growth among new world economies: a view from economic historians of the United States.” In: Haber, Stephen (Ed.), _How Latin America Fell Behind_ . Stanford University Press, Stanford CA.

- Fan, Shenggen, Linxiu Zhang, and Xiaobo Zhang. 2004. “Reforms, Investment, and Poverty in Rural China.” _Economic Development and Cultural Change_ , 52(2): 395-421.

- Forbes, Kristin. 2000. “A reassessment of the relationship between inequality and growth.” _American Economic Review_ , 90(4): 869-87.

- Ferreira, Francisco H. G. 2010. “Distributions in Motion: Economic Growth, Inequality and Poverty Dynamics,” The World Bank, Policy Research Working Paper 5424.

- Frank, Mark W. 2009. “Inequality and Growth in the United States: Evidence from a New State-Level Panel of Income Inequality Measures,” _Economic Inquiry_ 47(1): 55-68.

- Galasso, Emanuela and Ravallion, Martin. 2005. “Decentralizing Targeting of an Antipoverty Program,” _Journal of Public Economics_ , 89(4), pp. 705-27.

- Galor, Oded and Joseph Zeira. 1993. “Income Distribution