rafmacalaba/data-use-mentions-extended huggingface dataset:
<sup>5</sup> from the OSM data set for all clusters at the global scale and then evaluate the relative completeness of tertiary and residential roads based on the extracted road network. For each settlement cluster, we compare road network density (total road length divided by the area of the cluster’s convex hull, unit: km/sqkm) between two types of road network: a) drivable road network excluding tertiary and residential road types and b) tertiary and residential road networks only.

We evaluate the percentile rank of network density for both types among all clusters as well as the percentile ratio–the quotient of road network density percentile rank (tertiary and residential roads excluded) and its tertiary and residential network density percentile rank) (Fig. 3a). Percentile rank measures the percentage of network density values in the frequency distribution that are equal to or lower than it. For example, if a cluster’s tertiary and residential road network density is greater than 50% of the densities of all clusters, then it is at the 50<sup>th</sup> percentile, where 50 is the percentile rank. A higher percentile rank suggests higher network density relative to other settlement clusters. Percentile ratio measures the inequality between the two network types aforementioned based on the distribution of network densities. A percentile ratio of 1 suggests no difference between the percentile ranks of the two network types. A percentile ratio greater than 1 suggests that the percentile rank of road network density excluding tertiary and residential roads is higher than that of tertiary and residential roads alone. This translates to the potential of insufficient

> 4 It should be noted that only roads with the following tags in the OSM road database were included in the completeness evaluation study: “motorway”, “motorway_link”, “trunk”, “trunk_link”, “primary”, “primary_link”, “secondary”, “secondary_link”, “tertiary”, “residential”, “road”, “unclassified”,
flows requirement which is our proxy used for the sustainability of water withdrawals (SDG6), though the local impacts in other regions such as East Asia and Pacific may be significant.

Consideration of these multiple trade‐offs with SDGs helps to determine the regions where investments could be targeted with limited adverse impacts. According to our findings (Table 1), the most relevant regions for such large‐scale irrigation investments would be Europe and Central Asia, Middle East and North Africa, and South Asia where substantial benefits are to be expected with limited negative effects.

Table 1. Summary of the difference in impacts by indicator for Investment Scenarios relative to ZeroInvest in 2050 with color‐ coded SDG scoring

||Irrig.<br>Area|Investment<br>Cost|Crop<br>prices|Food<br>availability|GHG<br>AFOLU|Cropland|Other Nat<br>Land|Forest|Env. Flow<br>Requirem<br>ent|
|---|---|---|---|---|---|---|---|---|---|
|**Invest**|Mha|$ Billion<sup>1</sup>/<br>year|%<br>change|kcal/cap/<br>day|MtCO2eq|Mha|Mha|Mha|% of EFRs<br>at risk<sup>2</sup>|
|AFR|8.8|3.7|‐2.0|9.9|‐4.6|1.5|0.1|‐0.3|0.7|
|EAP|36.7|6.4|‐2.3|13.5|68.6|1.5|‐0.8|‐1.2|0.8|
|ECA|5.4|
# **Abstract**

This paper studies the long-term impact of genocide during the period of the Khmer Rouge (1975-79) in Cambodia and contributes to the literature on the economic analysis of conflict. Using mortality data for siblings from the Cambodia Demographic and Health Survey in 2000, it shows that excess mortality was extremely high and heavily concentrated during 1974-80. Adult males have been the most likely to die, indicating that violent death played a major role. Individuals with an urban or educated background were more likely to die. Infant mortality was also at very high levels during the period and disability rates from landmines or other weapons are high for males who, given their birth cohort, were exposed to this risk.

The very high and selective mortality had a major impact on the population structure of Cambodia. Fertility and marriage rates were very low under the Khmer Rouge but rebounded immediately after the regime’s collapse. Because of the shortage of eligible males, the age and education differences between partners tended to decline. The period had a lasting impact on the educational attainment of the population. The education system collapsed during the period, so individuals—especially males—who were of schooling age during this interval have a lower educational attainment than the preceding and subsequent birth cohorts.

# **Contents**

|I.<br>Introduction................................................................................................................ 1|
|---|
|II.<br>Mortality ....
_Note_ : Data on GDP, population and SPI are retrieved from the World Bank’s World Development Indicators For GDP per capita, the figure indicates an elasticity of 0.5, meaning that a 10% increase in GDP is associated with a 5% increase in academic articles using data. The elasticity with respect to population is 0.67, and population alone explains 59% of the variation in academic articles using data. The overall SPI score is also predictive of academic output using data. A 10-point increase in SPI scores, which is approximately the same as moving from the median SPI score to the 65th percentile, translates into around a 0.6% increase in the number of articles using data.

Table 2 shows coefficients from a regression of the log number of academic papers using data on log GDP per capita, log population, and the SPI scores. The first column shows a specification including just log GDP per capita and log population. These two indicators alone explain roughly 75% of the variation in the production of articles using data. The second column includes the SPI overall score as an additional control. The performance of a country’s statistical system is associated with greater academic output. Conditional on GDP per capita and population, a tenpoint increase in SPI overall scores (on a scale of 0-100) translates into a 0.2% increase in academic output.

It is possible that omitted variables are behind this result. For example, it could be the case that countries that are of general interest to the research community also happen to have high statistical performance. Das et al. (2013) and Porteous (2022) control for a range of variables which may influence the relationship sought after, such as whether English is a first language, international tourist arrivals, etc. We leverage that we have an estimate of the number of articles not using data – referred to as qualitative articles – to control for all factors that are likely to influence overall research output in a country unrelated to statistical
|3.8|4.2|4.9|5.3|5.4|5.6|6.0|
|Rwanda|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|
|Senegal|23.8|23.8|23.8|23.8|24.2|24.8|25.1|25.2|25.3|25.7|
|South Africa|—|2.4|3.6|4.8|5.4|6.0|6.3|6.4|6.5|6.8|
|Sudan|—|—|—|—|—|—|—|—|—|—|
|Tanzania|3.2|5.5|6.7|7.9|8.5|9.0|8.8|8.8|8.8|8.6|
|Uganda|19.5|20.7|21.4|22.0|22.3|22.6|22.8|22.8|22.9|23.1|
|Zambia|4.2|3.3|2.9|2.4|2.2|2.0|2.1|2.1|2.1|2.5|

# _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

_Notes:_ Average residential consumption level is highlighted in bold — Not available.

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# I. Introduction

Georgia faces high economic and social costs of health. Health spending as a share of GDP was 8.9 percent in 2016, like countries with higher income levels (OECD 2018). Consumption of tobacco is among the leading causes of disease and deaths in Georgia. Smoking prevalence in the Georgian population is among the highest in the European region, and it has continued to increase over the last decade (NCDCPH et al. 2016). Consumption of tobacco is estimated to account for 7,000 to 11,000 annual deaths in Georgia (The Tobacco Atlas 2019; UNDP et al. 2018).

Efforts by the Government of Georgia (GoG) to control tobacco have received international recognition. Recently introduced legislation has been called “one of the strongest single comprehensive tobacco control laws in the world” (The Tobacco Atlas 2018). Despite these efforts, meeting the target reductions in smoking rates poses a challenging policy goal. Taxes are recognized as an effective measure to reduce tobacco consumption among smokers, and to dissuade initiation (World Bank 1999). However, concerns over potentially regressive effects are raised in opposition to higher taxes on tobacco. It is often argued that poorer households suffer the most from a tax increase, as tobacco products represent higher shares of the budgets.

This paper addresses the policy concern of the welfare and distributional impact of increasing taxes on cigarettes. It seeks to determine whether the claims of the regressive effects of tobacco tax policies have empirical validity in the case of Georgia. An Extended Cost Benefit Analysis (ECBA) is performed to account for the negative effects on household budgets as cigarette prices increase, as well as the indirect welfare benefits derived from the health benefits of reducing smoking. Decile-specific price elasticities of demand for cigarettes are used to incorporate heterogenous behavioral responses by different income groups of the population.

The estimates of price-elasticities suggest that -when faced with higher cigarette prices- poorer households in Georgia tend to reduce their consumption more intensely than higher-income peers. The magnitudes (under
across the whole income distribution, while in others like Mexico or Brazil, universal access is now almost the rule.

While this has in some cases led to large progress in access to energy consuming devices such as refrigerators or washing machines, different patterns emerge. In Brazil, ownership of refrigerators did increase in lower income deciles following connection to the electric grid, but the process was much slower in Mexico. Similarly, comparing El Salvador and Peru, two countries with similar profiles of electrification, reveals quite different evolutions. Access to refrigerators stagnated and if anything progress was greater at the top of the income distribution in the latter, while the former witnessed large increase in ownership.

Next, we use the complete panel, based on the global quantiles distribution, to analyze how the income elasticity of infrastructure access varies across services and countries, and within these, at different levels of the income distribution over the 1992-2012 period.

Table 5 presents two sets of results using the household survey data. First, in Panel A, we report an average Latin American elasticity given by the following estimation: where _D_<sup>_A_</sup> _dct_ is the average access / ownership rate for infrastructure asset A, in country c, by households in income decile d, at time t, _log(income)dct_ is the average decile income, and _θct_ are country-time fixed effects.<sup>10</sup> This simple specification assumes that infrastructure demand indicators at the decile level are a function of average income in that decile, and of the price of infrastructure as proxied by country-time fixed effects. The underlying assumption is thus that this price is uniform within countries in a given period, but varies over time. This is much milder than proxying prices through country dummies in the country-level panel, in effect assuming that prices are invariant across time period. In addition, the country-time fixed effects absorb any other relevant countrylevel determinants, such as the ones discussed above.

Second, in Panel B, we differentiate elasticities by
Panel B of Table 6. By endline (Panel B), the impacts on marriage and desired fertility had dissipated, while the estimates for enrollment and time preferences remained similar to midline but were no longer statistically significant (all FDR-adjusted q-values _>_ = 0.143). We note that the 12-month follow-up surveys were conducted by phone due to the COVID-19 pandemic (as opposed to in person for the rest of the data collection rounds), so the difference in the impact of IPT-G on human capital between the 12- and the 24-month follow-ups could be in part due to differences in data collection methods.

# **3.3.2 Impact of adding cash to IPT-G**

Finally, Table 10 presents the marginal impact of providing cash transfers over and above the therapy intervention on human capital outcomes at the 12- and 24-month follow-up surveys. Consistent with the fact that the mental health effects of cash were negative (compared to the IPT-G arm), individuals in the IPT-G+ arm were less likely to be enrolled in school, had higher desired fertility, and lower expectations of being in paid work at age 25. Most marginal impact estimates are not statistically significant, but it is clear that the addition of lump-sum cash transfers to IPT-G was not an effective intervention. While we focused on the marginal effects of cash over IPT-G here, we also note that outcomes (mental health or

> 22The set of outcomes at the _rapid resurvey_ differ from those for later rounds. As it was deemed unlikely for human capital accumulation to have materialized so shortly after the therapy, data on outcomes that may be prognostic of future investments in human capital were collected in this round.

16
# **4. Empirical strategy**

Our aim is to causally quantify the effect of refugee presence on host communities and then discuss the potential coping strategies employed by refugee-hosting households. Specifically, we investigate; whether refugees’ influx impacts households’ welfare; the distributional effects induced by the presence of refugees; and the potential channels through which refugees affect the welfare of households. The household welfare is proxied by the consumption aggregate which is adjusted for household demographic composition in terms of sex and age.<sup>10</sup> Hence, we use the consumption aggregate per adult equivalent scales for Ugandan households as our proxy for household welfare. Then we weave out the coping strategies implemented by households in reaction to experienced refugee shocks.

We assess the impact of the presence of refugees at the year of interview between 2009 and 2012. To that purpose, we construct a refugee index which weights the number of refugees in the closest refugee settlements by the inversed distance from those settlements to the clusters.<sup>11</sup> We only consider refugee settlements within a certain distance (buffer). Given that the distribution of households as a function of distance to the closest refugee settlement (Figure 7), we first adopt a 50kilometers buffer. An alternative threshold at 100 kilometers will be explored as further robustness check.

> 10 Uganda Bureau of Statistics (2013). Uganda National Panel Survey 2010/2011 Wave II report. June 2013. 11 As discussed by Maystadt and Verwimp (2014), the parameter of the decay function can be modified to give more or less weight to proximity. We will follow their robustness check in dividing the number of refugees by the squared or the square root of the distance of their settlements to the clusters.

16
15 is disrupted, i.e. we consider all values of _ν ∈_ [0 _,_ 1]. Later we discuss which values are most likely for each country group, depending on school closures, technology, etc.

# **3.1.2. Employment Shock**

**Incidence** The employment shock affects mainly workers with sub-college education and the young. We assume that, if the unemployment rate among adults without college education rises by a certain amount _x_ , then unemployment among college-educated adults rises by one half of that amount (0 _._ 5 _x_ ). Youth unemployment is more severely affected so we assume that if unemployment among adults without college rises by _x_ youth unemployment rises by 2 _x_ . We explain in detail in Web Appx .Section D how we arrived at these factors, based on ILO (2020) data over the COVID-19 pandemic.

**Severity** We examine an increase in overall unemployment rates ranging from zero to ten percentage points. This is achieved by varying _x_ between 0 and the value that leads to overall unemployment increasing by 10 percentage points in each economy. Web Appx. Section D explains in detail how we arrived at this range, but note that the ILO (2020) nowcasting model estimates that total working hours (including unemployment, inactivity and reduced hours) lost worldwide in 2020 were 8 _._ 8 percent, varying from 6 _._ 7 percent in LIC up to 9 _._ 5 percent in MIC, making 10 percent a reasonable upper bound. **Duration** Hall and Kudlyak (2021) find that recoveries from unemployment shocks regularly occur following a proportional factor _ρ ∈_ (0 _,_ 1) – that is, given an initial employment shock _x_ 0, thereafter _xt_ +1 = _xt × ρ_ . Based on employment data from across the OECD, we calculate a proportional factor of _ρ_ = 0 _._ 7143 (see Web Appx. Section D).
direct measures of individual welfare (based on household surveys), and constitute a move away from focusing exclusively on low-middle-high income country status or GDP growth.<sup>48</sup> 41 Standard inequality measures, such as the Gini index, are relative or scale-invariant. This means that they are unchanged by a proportional transformation, such as a doubling of all incomes. However, such a change would increase the absolute gap between individuals, and thus increase absolute measures of inequality. An absolute measure is translation-invariant, i.e. it is unchanged by an additive change, such as adding $100 to everyone’s incomes.

42 The absolute Gini is defined as ���������������������, where ������� is the relative Gini and �� is the mean.

43 The empirical evidence as to whether individuals perceive inequality as absolute or relative is mixed (Amiel and Cowell, 1999; Ravallion, 2004).

44 Below we use GDP and GNI per capita interchangeably, as the main point we make pertains to the use of national accounts based measures more generally.

45 Because GDP per capita does not reflect within-country inequality, it cannot be used to derive a measure of regional interpersonal inequality. This is an example of a concept 1 type measure, using the Milanovic (2005) classification reviewed in the introduction.

46 National accounts based measures of per capita household consumption typically exceed those measured in household surveys (Deaton, 2005). Especially relevant for our paper, there exists a substantial strand of the global inequality literature which anchors a country’s mean income to GDP per capita (Bourguignon and Morrisson, 2002; Sala-i Martin, 2006; Chotikapanich et al., 2012; Pinkovskiy and Sala-i-Martin, 2014; Chotikapanich et al., 2015). Anand and Segal (2008) argue that GDP per capita is not an appropriate measure of household income. Furthermore, Lakner and Milanovic (
Mexico), the age profiles of excess deaths are similar to those of official COVID-19 deaths, and those under age 65 account for on average 37 percent of excess deaths.<sup>5</sup> Figure 3 shows the hypothetical age distribution of COVID-19 deaths according to official reports, as well as excess deaths, applying the United States standardized age distribution. This controls for the differing population age distributions across countries. Unsurprisingly, as compared to the actual, observed distributions, these figures show an older profile of deaths in low- and middle-income countries. However, the pattern of a younger profile of deaths in less wealthy countries remains, both with official data and with excess mortality data. The numbers shown in Figures 2 and 3 can also be found in Appendix Tables A1 and A2.

Notably the United States has a much younger profile of death—using both official COVID-19 death counts and excess mortality—than countries with similar income levels. This is the case even after controlling for differences in population age distribution. The age distribution of deaths in the United States is roughly similar to that of Chile. In both countries, 27 percent of excess deaths were among those under age 65.

Figure 4 presents a visual representation of all the mortality rates by age group across all countries and economies with available data. The vertical axis represents mortality rates per 100,000, and it is important to note that the scale varies widely across countries, reflecting the different levels of exposure to the pandemic and to its related mortality across the globe. Overall, mortality rates increase with age, albeit with different gradients. With the excess mortality data, a few deviations from this gradient are likely due to the relatively low mortality as well as the overall small number of deaths (e.g. Iceland). Australia, New Zealand and Taiwan, China have close to zero official COVID-deaths and show negative estimates of excess mortality rates, especially for the older age groups, likely reflecting averted deaths due to isolation policies.

>
_comprehensiveness of diagnosis in the typical project discussed The section closes with an examination of the extent to which pre-project work discusses lessons from earlier experience._ In a recent Bank study, Evans and Battaile (1998) present evidence that good Bank performance is a key determinant of successful project outcomes, being dominated only by borrower performance. The most important ingredient of good Bank performance identified in the study is the quality-at-entry of a project. Thus the importance of thorough pre-project preparation and diagnostic work.

In order to evaluate the way in which Bank projects have incorporated institutional concerns in their design, we have analyzed existing documentation against a framework summarized in the matrix in Table 6. In this framework,<sup>5</sup> Performance is a function of Capacity, Institutions and Exogenous constraints. Furthermore performance is disaggregated into policy formulation, accountability and service delivery with the latter being divided further into effectiveness and efficiency. Effectiveness refers to the quality and quantity of services provided while efficiency deals with the unit cost of service provision.<sup>6</sup> Tables Al through A6 in appendix report the extent to which different diagnostic areas were examined in the 43 projects with major tax/customs administration components. A summary, which also shows the overall comprehensiveness of diagnostic exercises in the typical project, is in Table 7.

It should be made clear that, in compiling project data, no judgement was made about the adequacy or completeness of the diagnosis in any category in SARs or MOPs since other preproject documents, if any, were largely unavailable for this review.<sup>7</sup> On the basis of the data in table 7, we can infer the following conclusions:

- _Tax policy institutions and tax structure:_ Tax policy making and analysis institutions received attention in only about one quarter of projects in the sample. Regarding tax structure and revenue performance, diagnosis was typically limited to standard tax features such as the tax to GDP ratio; mix of taxes; tax rates, bases and exemptions