rafmacalaba/data-use-mentions-extended huggingface dataset:
32 child deaths for every COVID-19 fatality averted.

Our findings hold general lessons for pandemic mitigation, past and future. Empirical evidence has shown repeatedly that in lower- and middle-income countries, infant and child

> 2We take the perspective of the initial months of the COVID-19 pandemic when governments were first confronted with designing pandemic responses in the absence of effective vaccines or therapeutics. We implement only non-pharmaceutical interventions because in the event of a new but similar pandemic, the same tradeoffs will be relevant as new vaccines and/or therapeutics would most likely not be immediately available. According to the Oxford Blavatnik COVID-19 Government Response Tracker, every single one of the 175 countries that it covers imposed at least some lockdown policy. Lockdown severity was remarkably similar across countries at different levels of development. On a scale of 0 to 100, the mean Oxford–Blavatnik lockdown stringency index was 79 in the low-income countries, and 78 in the high-income countries. (These averages are as of April 2020, the most globally synchronized phase of the lockdown. We use the World Bank classification of countries into income groups.)

3
statistical capacity. The analysis also demonstrates that CDR data are unlikely to substitute traditional data, such as administrative and survey data, as linkages between telecommunication data and individual level and household characteristics need to satisfy strict technical and ethical requirements.

This application focuses on a well-defined use case, and further work is necessary to scale up the existing structure and ensure interoperability. This paper demonstrates that the analysis of CDR data can support decision making during a crisis situation. This scaling up will require a commitment to include the analysis of CDR data into the standard set of planning instruments, including for the allocation of human capacity and financing.

Future work in The Gambia will build on the existing partnership and experience. While valuable, CDR data on its own can only offer limited insights. We propose to build on this analysis by overlaying it with additional data sets. This includes validation of observed patterns against mobility data from Facebook. We also propose to overlay the mobility trends against price data to infer whether shifts in population drove food prices up in rural areas relative to urban areas. Finally, we would draw on recently available survey data, including the 2019 migration survey, to unpack the correlation between ward level mobility metrics and underlying patterns of internal migration. Notably, did the areas that depend most on internal migrants see a large number of returns? This would allow us to infer how the COVID19 induced lockdown differentially impacted vulnerable populations in rural and per-urban areas.

From a policy perspective, the future analysis of CDR data could inform urban planning, particularly investments into infrastructures such as roads, schools, and hospitals. In addition, the private sector has also shown interest in using this information to better understand commuting and clustering patterns in order to exploit untapped market potential.

**19** | P a g e
to GDP approach a healthy 15-20 percent range. In order to evaluate the efficiency of the VAT system, the paper examines the VAT revenue productivity ratio.* This ratio contains information on both the coverage of the VAT and the efficacy of the tax administration to prevent evasion. Especially since 1990, this productivity rate has improved in most countries in the region, and particularly in Argentina, Bolivia, Costa Rica, Paraguay, and Peru. For most countries, however, there is still much room for further progress. Only a few countries -- Brazil, Chile, Bolivia, Paraguay, El Salvador, and Guatemala -- have achieved moderate VAT productivity rates between 40 percent and 50 percent in contrast to the rates for OECD countries, which are normally between 50 percent and 70 percent.” (4) The fourth area of structural reform deals with governance and public institutional reform. \n this area progress has been more modest and less widespread that in the previous three reform areas prevously mentioned. The approach taken in this paper to assess progress in governance has been to rely on subjective indicators provided by two independent international country risk evaluators. They are International Country Risk Guide (ICRG) and Business Environment Risk Intelligence (BERI). These sources provide information related to two aspects of good governance. The first is the quality of public institutions (evaluated by the variables “corruption in government” and “quality of the bureaucracy”), which describes the ability of the state to implement its policies through an appropriate structure ofgovernment and through honest and technically competent bureaucrats. The second is the quality of public services (evaluated by the variables “rule of law’, “enforceability of contracts”, and “expropriation risk”), which determine an environment where contractual obligations are respected by both the state and private agents, where legal disputes are effectively resolved in the judicial system, and where legal rules for investment, employment, and taxation are not arbitrarily modified by the state.

The assessment of progress in these aspects
**Table 1. Description of Variables and Data Sources**

|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|Capital<br>share<br>(�)|In the production function of the<br>modern economy, the capital<br>share measures the share of<br>national income that accrues to<br>owners of capital.|We use a constant estimate of 0.5. This corresponds<br>roughly to the median value of country‐specific<br>estimates we derived from labor shares presented<br>in the Penn World Tables (averaged from 1990‐<br>2012). These country‐specific estimates are used<br>directly in the appendix where, if a country is<br>missing data, we use ��0.5. All other variables<br>used in the appendix are derived using this<br>alternative country‐specific capital share.|
|Subjective rate<br>of<br>time<br>preference (�)|The marginal rate of substitution<br>between<br>present<br>and<br>future<br>consumption.|Using the equation that determines the rate of<br>capital growth in the modern economy and the<br>average growth rate of capital from 2000‐2012, we<br>|
|Capital<br>depreciation<br>rate (�)|The rate at which the economic<br>value<br>of<br>the<br>capital<br>stock<br>decreases over time.|obtain the implicit depreciation rate and the<br>subjective rate of time preference. We set a<br>minimum value of 1 percent.|
|Capital<br>stock<br>(K)|
those amounts to poor and non-poor households. Therefore, the Takaful program was relatively less “effective” and less efficient (given the Takaful program’s budget) at protecting poor and vulnerable households than the Karama program (given the Karama program’s budget). The food credit program, energy subsidy expenditures, and public education expenditures reach a very small proportion of their potential to reduce inequality; this is due primarily to the large amounts spent on those programs combined with their near-universal coverage.

**Figure  18. Fiscal Gains to the Poor Effectiveness Indicator**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Takaful Karama Food credit Subsidies Education Health<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget data.

# **CONCLUSIONS**

This study implements the CEQ methodology for the case of Egypt. Having unprecedented access to the administrative data of the GoE, this study is able to map several of the country’s main policies and assess their impact on poverty and inequality. From the expenditure side, the exercise accounts for social spending (including pension fund contributions, the food subsidy program, the Baladi bread program, and the recently implemented Takaful and Karama programs); we also account for education and health spending, as well as expenditures on fuel and electricity subsidies. For the revenue side, the exercise includes the direct personal income tax, the alcohol and tobacco excise, the fuels excise, and the direct and indirect burdens created by the GST.

Fiscal policy in Egypt reduces inequality and poverty through a series of fiscal policy elements. The flagship, nearly universal program of the food smartcard and Baladi bread allowances of the Tamween program has a
expenditures because they could not find consistent data on these expenditures for a large sample of countries in their panel.<sup>17</sup> Public infrastructure is measured with the number of telephone mainlines per capita in a country (Canning, 1998). Alternative proxies of public infrastructure are energy generation capacity and transport facilities. Loayza _et al_ . (2005) found these to be highly correlated with each other and concluded that results would be quantitatively similar for any of them.

Governance is measured with the first principal components of four indicators from the International Country Risk Guide (ICRG): prevalence of law and order, quality of bureaucracy, absence of corruption, and accountability of public officials. All of them enter with approximately identical weights in their first principal component. There are other measures of governance but these are typically highly correlated with each other. Loayza _et al_ . determined that the correlation coefficients between the ICRG index used in their study and the Gastil’s index of civil liberties and the Business Environment Risk Intelligence index were 0.79 and 0.85, respectively.

The set of long-run growth determinants _X_ ’ includes the following stabilization indicators: inflation, cyclical volatility, real exchange rate overvaluation, and the number of years in which a country underwent a systemic banking crisis. Inflation – an indicator of macroeconomic stability in many cross-country growth studies<sup>18</sup> – is measured by the consumer price index (World Bank 2002). The inflation rate is positively correlated with other indicators such as fiscal balance (0.24) and black market premiums (0.26) (Loayza _et al._ 2005). Cyclical volatility, which reflects the lack of output stability, is computed as the standard deviation of the output gap for the period. Real exchange rate overvaluation – a measure for external imbalances and the risk of balance-of-payments crises – is given by the

> 17 Loayza _et al_ . note that the presence of initial GDP per capita and the governance indicator in regression model
support and the behavior of students in terms of performance, part-time employment, and career choice will be empirically investigated in this paper.

# **3. Institutional Setting**

Before turning to the SOFES loan program in more detail, let us first briefly describe the Mexican higher education sector. In 2000, almost two million students were enrolled in higher education. The enrollment rate in tertiary institutions is 21% in 2000/2001 (data from UNESCO; that defines enrollment rates as a percentage of the 5-year age cohort following on from the upper secondary-school leaving age).<sup>3</sup> Educational attainment shows an upward trend in Mexico. The relative supply of skilled workers with higher education was about 40% higher in 1999 than in 1987 (cf. De Ferranti et al., 2002).

Estimates of the (annualized) private return to higher education in Mexico ranges from 13% (De Ferranti et al., 2002) to 24% (Chapman 2003b). The relative wages of workers with tertiary education increased substantially over the past 15 years in Mexico, as well as in most other Latin American countries (De Ferranti et al., 2002). In combination with the observation that the relative supply of high-skilled workers has also increased, this points to the emergence of skill-biased technological change possibly related to increased

> 3 This is lower than in Argentina (48% in 1999/2000), Bolivia (36%), Chile (38%), Colombia (23%), Uruguay (36%), and Venezuela (29%), but higher than in Brazil with a tertiary enrollment rate of only 17%.

4
East Asia and Pacific<br>‐0.1 0 0.1 0.2 0.3 0.4 0.5 0.6<br>For women For men<br><!-- End of picture text --> _Source_ : WWBI, unweighted means across countries so, we reinforce some findings in the literature, while also obtaining more surprising results. As in other studies, we find that the public sector pays a wage premium. However, this premium disappears tin most developing countries when public sector workers are compared to private sector formal workers. In that case, the premium is higher for women, lower skilled employees, and employees with lower unexplained wages. These patterns suggest that that the public sector premium for public sector workers may be smaller in magnitude than the private sector penalty for private sector workers, who tend to be less well‐educated and have lower‐paid jobs than their public sector counterparts. Furthermore, there is no significant relationship between country income levels and the size of the wage premium, either compared to all wage employees or only formal sector wage employees.

The paper points to several questions for future research. First, what is the appropriate private sector comparator given the heterogeneity in premiums across employee characteristics? Administrative data sets that identify transitions between public and private sector employment, or panel household surveys with rich information on workers’ skills, can be potentially valuable sources of data to explore this issue. The appropriate comparator may also differ for different types of workers. Second, what are the determinants of the cross‐country heterogeneity in the size of the wage premiums given that country income levels are not a major factor? The literature emphasizes the strength of public sector employee unions, but there are no studies to our knowledge that explore the role of unions in a large sample of countries. Other possible explanations could be the strength of political business cycles, the 12 In Turkey, Tansel (2005) finds public sector wage premiums for women are higher
preted as a more positive attitude. Both the game and video treatments have a positive and significant impact of 8.9 pp and 10.5 pp, respectively, on the assessment that circumstances beyond a Venezuelan migrant’s control relate most to poverty (vs. lack of effort), which amount to an increase of 11.2 and 13.2 percent, respectively. Although the point estimates are positive, the treatments did not significantly influence attitudes about equal job opportunities for Venezuelan migrants and natives or attitudes about the right to segregate migrants from neighborhoods. Finally, regarding policy attitudes about government support for and increased spending on Venezuelan migrants, both treatments had positive and significant effects. Thus, both interventions were effective in shifting attitudes towards increased public support for migrants. Moreover, both treatments improved the attitudes index by 0.33 sd.

Panel B of Table 4 presents the effects of each intervention duration in minutes. The estimates affirm the programs’ effects in enhancing attitudes across the same variables. Additionally, they indicate that the video was at least four times more effective in improving attitudes toward migrants per minute spent compared to the game. Furthermore, the effects between treatments per minute of duration are statistically distinguishable.

# **7 Treatment Effects on Beliefs about Forced Migrant Impacts**

We also conducted an exploratory analysis (not prespecified) on respondents’ beliefs about the impacts of migration on the economy and on society. It is important to note that the treatments do not provide explicit information on the effects of migration on crime or on the labor markets. Yet, to the extent that they reflect the realities of migrants who are hard-working, honest individuals, it is possible that treated participants may generalize this information to other dimensions of the migrant experience and thus change their beliefs. Nonetheless, as shown in Panel A of Table 5,<sup>21</sup> the interventions had no statistically significant impact on commonly held beliefs that Venezuelan migrants compete with natives for jobs or that they increase crime rates
|Table 8: Ind|ices of Po|verty He|ad-Cou|nt, Gap, S|everity in|Indone|sia, 1996-1999||
|---|---|---|---|---|---|---|---|---|
|||1996|||1999||Percentage Ch|ange|
||Urban|Rural|Total|Urban|Rural|Total|Urban Rural|Total|
|Head-Count Index (P0):<br>Percent population in<br>poverty|3.82%|13.10%|9.75%|9.63%|20.56%|16.27%|152.3% 56.9%|66.8%|
|Poverty Gap ndex<br>(P1): Total expenditure<br>gap of those below<br>poverty line as percent<br>of total expenditures|0.53%|2.12%|_1.55%_|1.51%|3.61%|2.79%|183.0% 70.5%|80.2%|
|Mean expenditures of<br>the poor as a percentage<br>of the poverty line<br>(1- PI/PO)|86.01%<br>|83.84%|84.15%|84.30%|82.44%|82.87%|||
|Poverty Severity Index<br>(P2): Squared poverty<br>gap|0.12%|0.54%|0.39%|0.37%|0.99%|0.75%|201.6% 83.6%|91.9%|

This deterioration was also in line with a significant increased in the severity index
<mark>constructed by combining Aerosol Optical Depth (AOD) satellite retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a geographically weighted regression. The resolution of 0.01 degree (equivalent to about 1.1 kilometer at the equator) is well suited to capture regional variation in</mark> concentrations, though not granular local variations.

<mark>As a global modeled data set, some uncertainty is to be expected, though sensitivity tests suggest good agreement with ground measurements (van Donkelaar et al, 2021). For more spatially nuanced analyses, for instance at the neighborhood or street level, alternative data based on local measures would be required. Moreover, it should be noted that the chemical composition of PM2.5 particles can differ by pollution source (Thurston, et al 2022). PM2.5 particles associated with the combustion of fossil fuels are more toxic due to higher acidity levels (e.g. sulfuric particulate matter from coal burning). The global PM2.5 can inform on total particle concentration, but not the spatial variation in the chemical composition (i.e. acidity) of particles.</mark>

**Population density:** We estimate the location of people using the WorldPop Global High Resolution Population data set (WPGP), produced by the University of Southampton, the World Bank, and other partners. It offers global coverage and is available yearly from 2000 to 2020. While WorldPop provides several data sets (including poverty, demographics, and urban change mapping), we use the population density map (WorldPop-PPP-2020). In a raster format, this data set provides the number of inhabitants per cell, with a resolution of 3 arcseconds, thus specifying the distribution of population. This information is based on administrative or census-based population data, disaggregated to grid cells based on distribution and density of built-up area, which is derived from satellite imagery (Freire et al
, Japan and Switzerland account for another 56 percent.

It seems reasonable to think that both the general investment environment and the specific institutions related to carbon markets should influence the location of investments. However, in the case of ECA these relationships are hard to discern. For example, figure 19 reports on six key business environment indicators that are based on firm surveys. Two of the indicators reflect firm views about governance: whether the head of the firm thinks that the domestic court system is fair and impartial; and whether corruption is a problem for doing business. The remaining four indicators have to do with firm characteristics: whether any foreign investors have an ownership stake in the firm; whether a third-party certifies the quality of the goods or services produced by the firm; whether the firm has a line of credit available to it; and whether the firm reports a financial statement that has been audited by an outside accounting firm. The bars in the graph correspond to the percentage of surveyed firms that answer in the affirmative.

All things equal, direct foreign investment should be higher when governance is good and where firms have access to credit and where third parties verify the quality of their output and the veracity of their financial statements. The number of firms with foreign participation is an outcome, which hints at the overall climate for foreign investors. However, when averages are calculated over the countries that host investment projects and those that do not, it is difficult to distinguish between the two sets of averages. Nor is there a clear distinction between ECA and other regions.

The same is true when a measure of climate-related institutions is used. Figure 20 reports on the Climate Laws, Institutions and Measures (CLIM) index, ―a comparative assessment of the extensiveness and quality of climate change mitigation legislation, policies, measures and institutions project investment under the CDM and JI programs‖ (EBRD 2011, p. 61).<sup>53</sup> The index is bounded between zero and one, with a higher value
economies, only China experienced a marginally higher drop in FLFP rate from 1990 to 2013. However, in comparison to India, China’s FLFP rate remained considerably higher at 64 percent. Pakistan, which had lower FLFP than India in 2013, experienced a sharp rise in women’s participation in the labor force during 1990-2013. Further, for the first time in recent history, estimates suggest that between 2004-05 and 2009-10, not only was there a decline in India’s FLFP rate, but also a shrinking of the total female labor force.

Using several rounds of NSSO and census data in India, this paper conducts an empirical examination to better understand the socioeconomic and demographic dynamics of this downslide in FLFP during the post-liberalization era after 1991, a period marked by economic growth. This paper contributes to the existing empirical literature on FLFP in India in several key areas. First, it provides an analysis of the patterns and trends in FLFP in India using several rounds of NSS data. Second, the paper attempts to identify various cross sections of the female population—such as age cohorts, economic groups, and social groups, both in isolation and in combination—that can account for the magnitude of the drop in FLFP, thus illuminating the underlying anatomy of the phenomenon. Finally, the paper tries to identify the relative contribution of the various determinants of the FLFP decision in both urban and rural areas during the same period, and examines whether such information can provide insights into the driving factors of the recent drop in FLFP.

This paper is organized as follows. In the second section, we examine trends in FLFP in India during 1993-94 to 2011-12 and estimate the extent and magnitude of the drop. Since our calculation is based on inter-censual projections, we compare our calculated FLFP figures with those of various other authors for validation of both the projection method used and the measure of the size of the labor force. In the third section, we analyze the contributions of various age 3