rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>measuring monthly total consumption and monthly food consumption, using retrospective questions with varying recall periods depending on the item considered (from seven days to one year). The consumption aggregate is compiled by aggregating the different food and non-food items, including</mark> expenditures on education, health, durable assets, and rent. <mark>In the framework of this study, we consider two welfare indicators from the PDM data set. The first is the household total consumption expenditure per capita per month, and the second is the household food consumption per capita per month.</mark><sup>5</sup> For poverty imputation purposes, three data sets are constructed from the ProGres, Targeting, and PDM data sets. The first, which we refer to as “ProGres 2,” is obtained by appending the ProGres data to the end of the PDM data. As the ProGres and PDM data share only demographic variables, ProGres 2 contains demographic variables for all observations, although only the observations from the PDM data have consumption expenditure. The second set of data constructed, “Targeting 2,” comes from appending the Targeting data to the end of the PDM data. Therefore, Targeting 2 contains demographic variables, asset and animal ownership, and coping strategies variables as well as consumption data. The last set of data, “ProGres Targeting,” is obtained by first merging the ProGres and Targeting data (matching 72% of the observations) and then appending these data to the end of PDM data. This set of data is the more complete data set in terms of variables. <mark>The motivation of the construction of these three sets of data is to check whether the different sources of data as well as different sets of variables generate different poverty figures, consequently determining the set that best predicts poverty. To ensure comparability across the three data sets, we restrict the analysis to 16 of the 19 refugee sites, because the PDM data cover only 16 sites. Consequently
It remains of interest, however, to ask how much of a further reduction in poverty could be expected if, rather than being limited to geographic targeting of communities of 1000-5000 households, policymakers could actually target individual households.<sup>15</sup> The “dump-file” underpinning the policy simulations described above can be drawn on to shed light on this question. While this file cannot provide a reliable listing of poor households, it is possible to simulate the change in poverty in a given locality associated with household-level targeting on the basis of exactly the same optimal transfer scheme procedure described above (but where each household _h_ now corresponds to a separate community _c_ ). In the discussion of results below we provide an assessment of the distance (in terms of poverty reduction) between _feasible_ geographic targeting (given the poverty map) and the ideal of perfect household-level targeting. To the extent that this distance is large we can provide a sense of the potential benefit of combining geographic targeting with additional complementary targeting efforts.

# _Budgets and Poverty Lines_

Before turning to a discussion of results, we describe briefly our selection of budgets, _S_ , and our poverty lines. As has been mentioned in Section I, we assume that the budget available for distribution has been exogenously set. As is intuitively clear, the potential benefits from targeting will vary with the overall size of budget, and for this reason we conduct the simulation analyses described above for two different budget sizes. In each of the three countries examined here we identify the per capita consumption value of the 25<sup>th</sup> percentile of

> 15 Relatedly, one could ask how much of a further benefit could one expect if, rather than being compelled to provide only lump-sum transfers to poor communities, policymakers were able to combine geographic targeting with, say, means testing within poor communities.

22
to me in<br>other students. students. am less smart other students. front of others.<br>than I really am.<br>United Arab Emirates Qatar Tunisia Turkey Other countries<br><!-- End of picture text --> Source: Analysis using PISA 2015

# **Deteriorated classroom disciplinary climate hampers student learning in many MENA countries**

Deteriorated classroom disciplinary climate has a negative effect on students’ performance in MENA. On average, improving the classroom disciplinary climate by one standard deviation is associated with an improvement of around 25 to 35 points in mathematics, which corresponds to around three-quarters of a year of schooling (Table 3).<sup>8</sup> Even after accounting for differences in age, gender, and socioeconomic factors between students and between schools, disciplinary climate is one of the variables with the largest explanatory power for student performance. The effect of disciplinary climate on students is more easily demonstrated when considering the extent and frequency of classroom disruptions. There is indeed a robust correlation between the disciplinary climate measure derived from the PISA questionnaire and students’ performance as measured by the same survey. A robust positive relationship between disciplinary climate and students’ performance has been observed since the first wave of the PISA survey in 2000.

> 8 Mathematics performance tends to be less determined by socioeconomic factors and thus more sensitive to changes in pedagogy and features of the education system, which is the reason why mathematics performance rather than reading or average performance is used in this analysis.

8
14 The observed change in the rates of return of schooling and experience when the correction for selectivity bias is performed, is certainly striking. For instance, in 1980 the average return of schooling without correction was 14 percent, while after the correction this value increased to 21 percent<sup>12</sup> In 1985 this value rose from 15 percent before correction to 33 percent after correction.

One important factor in overstating the magnitude of the bias for sample selectivit , is the presence of seeking inactives in our sample. As explained above, this group was included because in a developing country this group is fundamentally formed by discouraged job seekers. However, these individuals **are** also characterized by lower human capital than those actually included in the labor force. As a result, the estimated returns<sup>13</sup> increase substantially when this group is included in a wage equation. As presented in Appendix 2, the results of an exercise using 1987 data show that the inclusion (or exclusion) of seeking inactives from our sample does make a clear difference in terms of the final estimates.1 4 Although the inclusion of seeking inactives introduces an important difference in terms of observed changes in the parameters, it is important to keep them in the sample in order to consider the potential impact of the MW on them. In fact, we believe that a large proportion of seeking inactives withdraw themselves from the labor force because of MWs. Consequently, their elimination from the sample will lead to underestimate the total impact of the MW, which is the central focus in this study.

In order to calculate the total coverage of the MW in the economy, we used two alternatives: the Linneman-Castaneda (L-C) approach and the approach advanced in this paper. Using the L-C method, we computed the percentage of individuals for which the predicted values of wages<sup>15</sup> was less than the
NL-2/2-1-88

- 18 -

or adjustable rate obligations which yield (at least ex-ante) a positive return. On the other hand, in the absence of such instruments a temporary deficit may lend to hyperinflation when foreign finance is not available.l/ Thus, if the increase in government total deficit (domestic plus foreign) leads to excessive finance by seigniorage then the economy may land on a hyperinflationary trajectory.

These considerations are represented by Figure 1. In panel la D and D' represent the government's budget equation for a given deficit under steady state conditions. If this curve intersects the demand curve for money (md) at A then we have an equilibrium with a constant level of inflation. However, if D is above md there is no steady state solution and the economy must embark on a hyperinflationary process.

This last point is illustrated in Figure lb in the space (m,m) which represents real money balances and their rate of change. The underlying model is presented in Kiguel (1986). Each trajectory corresponds to a given level of real seigniorage. In a normal situation the economy is in a stable equilibrium at point A. As the constant seigniorage increases the TT schedule shifts down. However, once seigniorage becomes excessive, there is no stationary rate of inflation that can be used to finance the deficit and the economy will be on TIT, which represents a hyperinflationary motion. This type of development is consistent with the data on inflationary acceleration in Table 1.

- 1/ This argument about the role of indexed bonds should be qualified in view of the possibility that the very introduction of the latter bonds may reduce the demand for money. This implies a shrinkage of the base of the inflation tax and therefore more inflation (if the elasticity of the demand for money is less than unitary). However, if close substitutes for indexed instrument were already in use then the addition of indexed bonds may not affect the demand for money appreciably.
We analyze the relationship between PP regimes and trade policy activism during the first seven months of the COVID-19 pandemic using cross-country information on attributes of precrisis national public procurement regimes (World Bank, 2020), the coverage of PP in trade agreements (Shingal and Ereshchenko, 2020) and data on changes in trade policy for medical products implemented by governments during January-July 2020 (Evenett et al., 2020). A key feature of the trade policy data is that information is available on a weekly basis, permitting analysis of when trade liberalizing and restrictive policy instruments were imposed and removed.

Our results suggest that after controlling for country size, government effectiveness, economic factors and the incidence of COVID-19 cases, restrictions on exports of medical products and import liberalization are positively correlated with pre-crisis attributes of PP regimes. The total number of steps, in particular, and time taken to complete procurement processes are strongly associated with reducing import barriers and imposition of export controls. Membership of trade agreements with public procurement disciplines – both PTAs and the WTO GPA – is associated with greater openness, reflected in actions to facilitate trade in medical products.

The rest of the paper is structured as follows. Section 2 briefly discusses related literature. Section 3 provides an overview of the use of trade policy between January and July 2020. Section 4 presents the empirical methodology. Section 5 discusses the data sources for the explanatory and control variables and provides some descriptive statistics for these variables. Section 6 discusses the estimation results. Section 7 concludes.

# **2. Related literature and hypotheses**

We are not aware of studies that analyze the relationship between public procurement and trade policy responses during public health emergencies. The extant studies on public procurement and COVID-19 include a focus on strategies procuring authorities can (should) use to rapidly ramp up purchases of essential products needed by public health authorities and care providers. Procurement of health care-related products usually involves a prolonged multi-stage process that takes time. In times of crisis procuring agencies may need to dispense with normal practices to
successful international airlines being established. The resulting hull and liability insurance business contributes to premium income, although most of this risk is reinsured internationally.

**Seven countries have shares in excess of 10% under the ‘Other’ heading.** In the case of the oil and gas producing countries this reflects partly liability and engineering business arising from oil and gas extraction and a requirement (particularly in GCC countries) that employers cover for occupational injuries. Workman‟s compensation is compulsory for expatriates in some states.  In the case of Morocco, compulsory workman‟s compensation is handled through the private sector.

**All in all, the insensitivity of non-life and life premiums to income levels suggests that insurance penetration in MENA is still primarily driven by large enterprises, compulsory insurance, and effective enforcement, rather than voluntary purchases by the private sector** . This is true for both the non-life and life sectors.  The countries with partially developed life insurance sectors have historical European connections (Jordan, Lebanon, Tunisia and Morocco).  Most of the remainder has virtually non-existent life markets despite, in some cases, very high income levels.

**Finally, health insurance is becoming an important line of business in some MENA countries, although growing from a low base.** Health insurance is becoming a sizable industry

> 8 The Tunisian ratios reflect an effective reform program while the Egyptian ratios reflect a significant upgrading of claims provisions in the state owned insurers.

12
Chart 14

**Argentina and Brazil** Rate of change of foreign indebtedness

<!-- Start of picture text --> 0.20<br>0.150 rgentina<br>0.10-<br>0.05-<br>0.00 Bai<br>-0.05 '<br>-0.10-<br>-0.15=<br>85 86 87 88 89 90 91 92 93 94 95<br>-<br>ARGFOD ---- BRGFOD<br><!-- End of picture text --> Sources: World Bank Debt Tables and Central Banks 32
_Second_ , a firm-level analysis drawing from ES data was undertaken to assess the diversity of innovation activities; the following developing East Asian countries were covered in the data set: Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Thailand and Vietnam. Results show that product innovation and marketing innovation are the prevalent forms of innovation within this group of countries. Data also suggest that innovation activities occur in all sectors, not only in manufacturing; other service sectors and IT services present the highest innovation rates. In addition, once the novelty of reported innovations is accounted for, the proportion of firms reporting product innovations that are new to the market is positively correlated with income level. This corrects for firms in lower income countries being more likely to report marginal innovation or even imitation as innovation. When it comes to the use of innovation inputs, there is evidence that a smaller fraction of firms undertakes R&D as income level increases, while the spending among these firms doing R&D increases across the income span (when taking Indonesia out of the analysis). The analysis also showed that other innovation inputs, besides R&D, matter to innovation activities: the probability to innovate increases when firms run R&D activities, offer formal training to develop innovative products or processes, and acquire or licenses new technologies, even when controlling for key firm characteristics (such as size, age, ownership, export status) as well as sector and size of the city where the firm operates.

_Third_ , a firm-level assessment drawing from WMS was conducted to shed light on managerial capabilities, a critical innovation input. Results showed that the average managerial quality of China and Vietnam, the only two developing East Asian countries covered by the survey, are ranked respectively at 20th and 24th, among 29 countries. Results also showed that the tail of poorly-run firms is much fatter in China and Vietnam when compared to the United States. In addition, results from quantile regression showed that the score of the United States is
, a vision care research firm, provided estimates on the annual number of cataract procedures. See appendix 3 for the detailed list of sources from which outpatient volume data was estimated.

# _Foreign prices_

Vanbreda International, a Belgium-based employee benefits consulting and administration firm, whose clients include about 124 international organizations with operations in 185 countries, provided the data on international prices for the selected procedures in 21 countries from their own claims (see appendix 4 for details). In order to make a meaningful comparison between US and international prices from a trade standpoint, we added the cost of round-trip airfare to these countries from the US to the international prices. Since the psychological costs of going abroad are hard to quantify, and our focus is on the monetary gains from trade, we do not include the former in our cost comparison. Further, since there can be a lot of intra-procedural variation in prices, we used the average of the three lowest foreign country prices for each procedure as the benchmark foreign price (Column 5, Table 4). This minimizes any price bias arising from the paucity of claims data in certain countries.

> 24 The number of time units can be larger than 4 for a major surgery, while only one time unit may be charged for a minor procedure.

18
# **Supplementary Information**

## **A Data Appendix**

We construct the estimation sample using data from five major sources: 1) EV sales and heavyduty EV sales purchased from EV-volume; 2) vehicle attributes purchased from IHS/Polk and Taobao; 3) financial and non-financial incentives collected by the authors; 4) number of charging ports from the IEA Global EV Outlook; 5) unit labor cost index in construction sector from OECD; and 6) socio-economic data from the World Bank.

The financial incentives are collected and calculated at the country, year, model level. For consistency across countries, we only consider central/ federal EV incentives or subsidies. The financial incentive can be offered in a variety of forms, including direct consumer subsidy, acquisition tax credit or ownership tax credit (Figure 7b). For China, we use the range-based calculation method in Table 2 of Li et al. (2021). The range-based subsidy is year-specific from 2013 to 2018. Starting 2019, central subsidy depends on both driving range and battery capacity. Further, the central subsidy is canceled for models with price above 300,000 RMB in 2020. For the US, consumers receive a federal income tax credit calculated based on battery capacity. Japan’s incentive is provided as direct consumer subsidies at the model-year level. Canada does not have central subsidies to our knowledge, instead we use population weighted average of provincial direct subsidies offered by British Columbia, Quebec, and Ontario.

For European countries, the financial incentives are collected primarily from the European Automobile Manufacturers Association’s (ACEA) guide on purchase and tax incentives for electric vehicles. The financial incentives consists of consumer subsidy and/or acquisition or ownership tax deduction. Switzerland does not have central subsidies to our knowledge, instead we use the population weighted average of metropolitan area tax credits offered by Zuric, Lausanne, Basel, Bern, and Geneva. Each province has its own tax credit determined by cylinder capacity or weight. For the rest
use the MCNA survey and calculate the SSN eligibility of the households in the survey that would be deem eligible under the new SEVAT model.<sup>14</sup> Unlike the previous analysis of the actual database of the beneficiaries, this, using the survey, represents an exploratory analysis and the findings should be interpreted cautiously. Again, given the CLCI’s CT coverage and MCNA survey’s focus, we limit the crosseligibility analysis to the sample from the Northern region that would be deem eligible under the new SEVAT targeting formula (about 14 percent).

Table 9 presents the findings from this exploratory analysis. Results suggest much less overlap between MoLSA targeting and the new humanitarian targeting approach based on the MCNA survey data. Under a small program scenario i.e. targeting the poorest 18 percent nationally, the overlap varies between 0.7 to 13.2 percent depending on the level of confidence. Given equal targeting thresholds suggested by the PPMT, overlaps at high and medium confidence levels are same for a medium size program and increase to 15.4 percent at low confidence level. As expected, the overlap increase under a larger program scenario (between 3.3 to 19.9 percent) but remains significantly less than for the legacy database.

_Table 9. Percentage of likely CLCI beneficiaries in MCNA Survey that are likely to be eligible for MoLSA programs at different program sizes and degrees of confidence_

|Scenario|Program Size|Referral Confidence|Percentage of Households|
|---|---|---|---|
|High confidence, small<br>program|Poorest 18 percent<br>nationally|90 percent|0.7 percent|
|Medium confidence,<br>smallprogram|Poorest 18 percent<br>nationally|70 percent|6.5 percent|
|Low confidence, small<br>program|Poorest 18 percent<br>nationally|50 percent|13.2 percent|
|High confidence, medium<br
# **Table 3. Trends in horizontal and vertical equity in education and health service delivery**

||Quintile|Hor<br>(Wo|izontal/<br>reda le|spatial e<br>vel data:|quity tre<br>2009-2|nds<br>013)|<br>(hou|Vertical<br>sehold<br>data:|equity tr<br>& individ<br>2000-201|ends<br>ual level<br>4)|
|---|---|---|---|---|---|---|---|---|---|---|
|Indicators|groups, ratios<br>& differences|2009|2010|2011|2012|2013|2000|2005|2011|2014|
|NER|All groups|80.6|79.1|83.1|83.2|86.3|32.3|45.7|64.2|65.0|
||q5 Richest|81.9|80.9|83.4|84.8|86.8|68.1|71.3|86.1|82.7|
||q1 Poorest|80.4|79.6|84.4|84.3|84.2|20.8|28.3|51.9|48.3|
||q1/q5|0.98|0.98|1.01|0.99|0.97|0.31|0.40|0.60|0.58|
||q5-q1|1.5|1.3|-1|0.5|2.6|47.3|43.0|34.2|34.4|
|
# Endnotes

1 More fully: an HAI is a localized or systematic condition resulting from adverse reaction to the presence of infectious agent or its toxins acquired from health care settings that was not incubating or symptomatic at the time of admission to the healthcare facility (Horan et al, 2008).

2 Systematic reviews of antimicrobial drug resistance in Sub-Saharan Africa show that antimicrobial resistance varies widely for different first- and second-line drugs and across geographies from 0% to over 80% (Leopold et al, 2014; Tadesse et al, 2017; Kariuki et al, 2021). Murray et al (2022) report a systematic analysis of the global burden of bacterial antimicrobial resistance in 2019 and produce modeled estimates for resistance of several important pathogens to frontline drugs. The data extracted from the nine countries in the study are presented in Annex 1 section 2. In conclusion, the evidence presented above and in Annex 2 indicates significant variation in AMR rates across different bacteria/viruses, across different first line drugs and across different countries and sub-regions. However, given that the analysis is not conducted by different types of HAI, an average rate is needed for the present study. Rates of AMR are seen below 50%, well above 50%, and also around 50%. Hence, in the base case analysis, an average rate of 50% AMR is used.

3 This gave the following total number of inpatient admissions per country in 2022: Angola 1,423,520; DRC 3,680,000; Ethiopia 2,976,000; Kenya 3,188,916; Madagascar 1,184,000; Mozambique 3,607,172; Sudan 1,874,960; Tanzania 2,035,200; and Uganda 3,304,800.

4 Due to lack of published data available on the total costs of treating HAIs, it is necessary to construct the cost based on the cost per hospital bed day, the additional length of stay due to HAI, and the costs of drugs,