rafmacalaba/data-use-mentions-extended huggingface dataset:
targeting ability of cash transfer programs. Similarly, reduced VAT rates are unlikely to be a well-targeted way to encourage consumption of merit goods, in part due to their _ad valorem_ nature. Meanwhile, efficiency arguments, with the restricted exception of some substitutes for home production, favor the adoption of a single-rate VAT structure, which would also bring significant administrative benefits.

These findings have significant implications for tax reform in both developed and developing economies. In particular, where countries have the administrative capacity to effectively implement targeted cash transfer programs, they should use these programs to support poorer households instead of using reduced VAT rates. In more limited capacity environments, a simpler universal cash transfer could even be adopted – as this would still better target support to poorer households than reduced VAT rates. Only where there is no administrative capacity to effectively implement a cash transfer program should reduced VAT rates be considered as a tool to target support to poorer households. Even then, the potential impact of informality on the ability to target poorer households should be considered.

Countries should also consider removing reduced VAT rates aimed at non-distributional goals where a more effective instrument is available to achieve the particular policy goal. At a minimum, the merits of these reduced VAT rates should be reassessed in light of their negative distributional impact. Finally, countries that currently do not employ reduced rates in their VAT system should be very cautious

> 1 VAT revenue comprises almost 33% of total tax revenue, on average, in low-income countries, as compared to around 21%, on average, in OECD countries. (3-year average 2019-2021; Government Finance Statistics Database, 2023).

3
task content and different characteristics of occupations, we use two surveys administered by O*NET, the program sponsored by the US Department of Labor to improve the understanding of the nature of work. The O*NET database contains a vast array of standardized, occupation-specific characteristics for almost 1,000 occupations.

# 4. Exposure of Immigrant and Native-Born Workers to Pandemic-Related Risks

## **Measure 1: Non-essential Occupations**

Our first comparison between immigrants and native-born workers focuses on the share of workers in non-essential occupations to assess their relative economic exposure to measures that were put in place to contain the spread of COVID-19. We find that migrants in the EU are less likely than native-born citizens to work in occupations that are defined as non-essential. As shown in Panel A of Figure 1, 58 percent of immigrants and 63 percent of native-born workers were employed in

> 7 At the time of the EU-LFS surveys, the United Kingdom was a member of the European Union, but formally left on January 31, 2020. As such, in most of our analysis, the United Kingdom is included with the high-income EU15 countries.

> 8 The EFTA Member States are Iceland, Liechtenstein, Norway, and Switzerland.

> 9 These are the 13 countries that joined the EU as part of 2004 enlargement and subsequently. The group consists of Bulgaria, Croatia, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovenia, and the Slovak Republic.

> 10 These countries are Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Georgia, Republic of Moldova, Montenegro, the Russian Federation, Serbia, Turkey and Ukraine.

9
and for each asset type, each hazard, and in each scenario, we apply the incremental cost of resilience given by Miyamoto International (2019). For example, in the transport sector we apply a different incremental cost of flood resilience for airports, railways, urban public transit, highways, or rural roads. The overall cost of resilience will thus depend on the types of investments in the initial scenario (a scenario that represents an investment shift towards more rail will have a higher resilience cost than a scenario where transport investments are focused on roads). As a consequence, we present a range of resilience costs that comes from the range of possible infrastructure spending in Rozenberg and Fay’s scenarios.

For each scenario we also assess the overall risk reduction associated with these investments, still using the estimates given by Miyamoto International (2019). Here, again, the benefits depend on the type and quantity of infrastructure investments that are realized.

Because the incremental costs of making assets more resilient can be significant, it is important to target strengthening to areas where exposure to natural disasters is high. Ideally, infrastructure standards and codes should be asset and localization specific. Any effort to design a road should account for the hydrological and hydraulic data and climate model results at the location of the road in order to include in the design the range of impacts that climate change can have on the probability of flood events in the future. For electricity distribution systems—because they are particularly vulnerable to wind from storms, hurricanes, and typhoons—historical data and model results on wind velocities with hour‐level 4
of those age 12. Likewise, 57 percent of 16 year olds and almost one-third of 18 year olds were attending primary school. The magnitude of the over-age problem in the Haitian educational system is also evident from the average age of children attending each grade, shown in Table 5. The average age of children attending first grade is almost 10, while the official age of entry is six. The problem is substantially more severe in rural areas, where the average age of attendance in grade one is 10.3, versus 8.9 in urban areas. At the end of primary school, sixth-grade students are on average almost 16, five years older than the prescribed age for the grade.

The existence of a large number of students who are over the normal age for their grades is a concern because their presence in the classroom may negatively affect other students. Evidence from other countries suggests that age heterogeneity in the classroom may decrease learning (Schiefelbein, 1975 and Schiefelbein and Wolff, 1992). Teachers with multiage classrooms face the challenge of adapting teaching methods to satisfy the interests and attention needs of children of different age groups at the same time.

The presence of over-age children can be driven by a mix of three causes: 1) late entrance to the school system, 2) grade repetition, and 3) dropping out of school and restarting the same grade when older. The fact that attendance rates increase with age over a substantial range suggests that the over-age phenomenon in Haiti is driven chiefly by late entrance to school. As Figure 7 shows, in 2005 attendance rates grew markedly moving from age 6 to 10, and the peak attendance rate was at age 14. We would not expect to see this pattern if the presence of over-age children were largely a result of grade repetition or dropping out and restarting school.

The slight decrease in attendance between 2000 and 2005 occurred during a time of political turmoil and economic decline. During this time international support from the United States, Canada, and other important donors
to 400 basis points over the Central Bank rate, have lowered interest rates, but have caused banks to curtail lending, especially to higher risk SMEs.<sup>24</sup> In the wake of these new limits on interest rates, fund managers interviewed for this report noted that this shift could drive businesses, such as family-owned companies, to substitute debt, which is now harder to come by, with private equity.

> 23 Business Daily (October 28, 2015). "Huduma centres push Kenya up in ease of doing business index". 24 Njini, Felix (May 11, 2017). "Kenya's Largest Bank Expects Rate-Cap Removal in Second Half".

22
-6-

only after a time lag<sup>3</sup> . There are only a handful of countries where fiscal data are adequate for any serious research. Third, given the different time and country-specific coverage of data and policy, fiscal performance cannot be compared across countries in a rigorous fashion. Fourth, because of the linkages with other policies, it is difficult to examine fiscal policy separate from other policy variables.

11. The lack of a common definition of budget deficit, in particular, makes it difficult to compare fiscal performance across countries. Ideally, the appropriate deficit to measure is the consolidated public-sector deficit which takes into account the whole public-sector - the equivalent of the current-account deficit on the external side of the economy. It would take into account not only central government, local governments, and municipal accounts, but non-financial public-enterprises, public-sector banks, the social security system, and the central bank. It would then be easy to review the impact such deficit would have on the "pure" private-sector of the economy. In practice, this may never happen for a number of reasons<sup>4</sup> , not the least of which is that the definition (and calculation) of the deficit is likely to be dictated by data availability and the interest of the researcher. This choice also depends on the role of the public-sector in the economy. Therefore, a definition of budget deficit would need to be specified beforehand. The most comprehensive definition is that given by the IMF's Governnent Financial Statistics. Information from other official and unofficial sources are often more recent, but are not standardized nor systematic with regard to coverage and methodology.

12. Even more important than the coverage of fiscal deficit is the appropriate level of this deficit and the speed of fiscal adjustment. As mentioned earlier, a budget deficit of, say, 4 percent of GDP may be adequate for one country but not for another because fiscal policy is an integral part of the policy framevork. It may
planned with climate change considerations (e.g. where will flood be the worst in the future and how can new infrastructure withstand future levels of flood).

However, current planning approaches in Vietnam have not yet adequately taken these existing and future floods into account (IMHEN and UNDP 2015). For example, the city of Long Xuyen in the Mekong Delta has based its dike infrastructure around the city on historical floods levels only, with no inclusion of future climate change‐induced water levels (This Report, 2016). Qualitative surveys in Long Xuyen suggest existing defenses have already proved inadequate in recent flooding (This Report, 2016). Investments in climate‐informed flood protection taken now reduce flood exposure, but can also save money in the long‐ run by reducing the amount spent on recovery and reconstruction for future floods. And while it is challenging to integrate into project planning, innovative approaches such as decision‐making under uncertainty can provide support to decision‐makers on how to design flood projection with climate change in mind (Hallegatte et al. 2012).

Furthermore, while infrastructure investment can protect certain areas, it may increase exposure in other areas. for example, upstream dams in the Mekong Delta can increase the strength and velocity of downstream floods (This Report, 2016). Thus, flood risk assessment needs to be integrated across policy sectors (agriculture, industry, infrastructure, defense, urban planning) before development decisions are made, and more comprehensive approaches, like integrated coastal zone management and planning, would help to reduce exposure of people and assets in Vietnam.

20
deterioration in investor sentiment could disrupt financial markets and present a downside risk to global growth.

As a robustness check, the constant OLS-derived weights proposed by Kannan and Elekdag (2009) are estimated (Figure 3). The OLS-based approach produces a smaller variance of global growth forecast than the VAR approach. This reflects the presence of other risk factors that are not captured in the OLS estimates of Kannan and Elekdag (2009) but are important residual terms in the VAR estimates.

From the fanchart, the cumulative density function of global growth can be derived. Based on this, the probability of growth falling below any threshold can be calculated (Figure 4). Some of the downside risks have materialized between the January and June forecasts, resulting in forecast downgrades for 2016 and 2017. That said, the probability of global growth being 1 percentage point lower than currently projected in 2017 remains around the average for the decade (19 percent as of May 2016), still well below the probability in 2008 on the eve of the global financial crisis (26 percent).<sup>13</sup> The probability of growth falling to or below 1 percent (the growth rate likely associated with global recession) is somewhat above the 10-year average for 2006-16.<sup>14</sup>

# **5. Conclusion**

A complete assessment of global economic prospects requires baseline forecasts as well as an assessment of risks. The latter conveys to policy makers a sense of the uncertainty prevailing at the time of forecasting, which might vary with incoming data, past forecast performance, and changing expectations. In this working paper, three questions have been addressed:

- What are the selected risk indicators used to assess forecast uncertainty? Three risk factors were chosen for their tight connection with global growth prospects: equity prices, term spreads, and oil prices.  Changes in the distribution of forecasts for these underlying risk factors are mapped into a distribution of risks for global growth.

- How can different risk factors be combined
<!-- Start of picture text --> All workers By male/female<br>i=]g i=]g<br>36<br>32<br>3 8 28 28<br>26 25<br>&>Bo 20 gSS2 i =<br>on£N G<br>13 12<br>13<br>J9<br>°a 8 7<br>o<br>0-20 21-40 41-60 61-80 81-100<br>° 0-20 21-40 41-60 61-80 81-100 =z Male GE) Female<br><!-- End of picture text --> <!-- Start of picture text --> All workers By male/female<br>2oO °o 49<br>44<br>4 i=]Tt 39<br>° =8<br>z” xzg Pag<br>2 24 s<br>£ 2 20<br>an Rg 19<br>&<br>16 13 12<br>o~ 10 7 o~7 95<br>o 0-20 21-40 41-60 61-80 81-100<br>o 020 21-40 41-60 6-80 31-100<br><!-- End of picture text --> <!-- Start of picture text --> All workers By male/female<br>$ $<br>35<br>33 oI 33<br>3 w 8<br>26<br>- g »<br>5 | 18<br>saoa 19 a<br>2 10 9 e Wt 10 5 9<br>J 0-20 21-40 41-60 61-80 81-100<br>° 0.20 21-40 41-60 61-80 31-100<br><!-- End of picture text -->
(whether they went on to graduate or not), whereas we only have information on graduates for Ecuador. Therefore, we will examine graduation outcomes only for Brazil, but will analyze the following labor market outcomes for graduates from both countries over the 12-month period following graduation: (i) whether the student was formally employed at least one month; (ii) what percent of those months she was formally employed; and average monthly wages (calculated as the average over her months of formal work, and equal to zero if she did not work formally at all). Further, we use individual-level data to construct average characteristics and previous labor market experience for peers.<sup>19</sup> Appendix I provides additional information on all variables.

# **3.4 Descriptive statistics of individual-level data**

Table 2 presents descriptive statistics of program graduates in Ecuador and Brazil, for whom we study labor market outcomes. In both countries, the average student is about 25 years old and about 60 percent of students are female (panel A). Students in Ecuador have more educated mothers than in Brazil and 27 percent of them have children of their own. The average student in Brazil has accumulated more formal labor market experience before entering the program than the average graduate in Ecuador (panel C): 64 percent of students in Brazil worked at least one month before entering the program and worked for 46 percent of the time on average, whereas only 26 percent of graduates in Ecuador worked at least one month before graduation and worked for 19 percent of the time on average. Students in Brazil, therefore, have peers with more previous labor market experience than in Ecuador. After graduating (panel D), the average graduate in Brazil is more likely than that in Ecuador to be employed formally for at least one month (70 vs. 35 percent),

> 19 For student _i_ , peer characteristics are the average characteristics of the _other_ students in her cohort (Sacerdote, 2011). Recall that
|Table 28|Charcateristics of university graduates|42|
|---|---|---|
|Table 29|Mean earnings by educational level in Ghana.|43|
|Table 30|Basic Earnings function by years of schooling.|44|
|Table 31|Extended earnings function by levels of education, in Ghana|45|
|Table 32|Marginal returns to education|45|
|Table 33|Unit costs of public education by levels|46|
|Table 34|Private and social returns to education, using the||
||short-cut method.|47|
|Table 35|Returns to education by four levels of education using|47|
||the full method||

# **INDEX OF FIGURES**

|Figure 1|Labor force participation rates in Ghana|10|
|---|---|---|
|Figure 2|Estimated quit rates among wage workers in Ghana|24|
|Figure 3|The trend of real monthly earnings per employee in public and<br>private sector in Ghana|27|
|Figure 4|The trend in employment by sectors in Ghana|27|
|Figure 5a|The trend of employment and real GDP in the agricultural sector|28|
|Figure 5b|The trend of employment and real GDP in the industrial sector|28|
|Figure 5c|The trend of employment and real GDP in the services sector|29|

S
Balance of Payments Statistics based on the Balance of Payments Manual 6 (BPM6). Foreign aid data are from the WDI and refers to net official development assistance and official aid (ODA). Net ODA definition excludes humanitarian aid, development food aid and debt relief grants. Financial flows data (de facto measures of financial integration) are drawn from the IMF’s Balance of Payments accounts and Lane and Milesi-Ferretti (2007) data sets.<sup>8</sup> The database also includes de-jure measures of financial integration and trade openness. For de-jure financial integration, we use Chinn and Ito’s (2006) measure, which offers the most comprehensive coverage of countries and period. Trade openness is defined as exports plus imports to GDP and series are drawn from the WDI. Following earlier work, we drop countries with population less than one million. We also drop countries for which fewer than 10 observations of remittance inflows are available. In the end, for our baseline results we utilize a sample of 79 EMDEs over the 1990-2018 period.<sup>9</sup>

# 2.2. Empirical Methodology

We follow standard literature that has tested various implications of the consumption Euler equation to estimate the extent of international risk sharing in aggregate data (Obstfeld, 1994; Lewis, 1996; Kose, Prasad and Terrones, 2009). These studies employ a simple framework that is based on the predictions of the standard complete markets model and regress country-specific consumption growth on country-specific output growth: j j where ∆ct+1 (∆yt+1) denotes country j consumption (output) per capita growth at time t+ 1; j ∆ct+1 (∆yt+1) denotes world consumption (output) growth at time t+ 1; εt+1 follows a stationary process and represents measurement error; and where β measures the extent of risk sharing. Theoretically, in a model with complete international financial markets and perfect risk sharing, the coefficient β, which captures the
4

# **1. Introduction**

The prices of internationally traded fuels and food surged until mid-2008, fell sharply, and then began rising again, reversing much of the price fall by 2011. By one measure, the prices on the world market have nearly doubled since 2005 (Figure 1). The fiscal breakeven price of oil needed to balance the budget in major oil exporters has risen sharply in recent years, making the prospect of prolonged periods of low prices unlikely in the future. Equally important, the marginal cost of oil production in 2011 was US$92 a barrel for the 50 largest listed oil companies and could rise further if it continues to follow the long-term trend ( _Wall Street Journal_ 2012). Similarly, long-term growth in global demand for food and continuing U.S. ethanol demand for maize and EU biodiesel demand for vegetable oils are expected to keep prices of maize, oilseeds, and many other crops at historically high levels (USDA 2012b).

Figure 1: Monthly indices for fuel and food prices since 2005 in low- and middle-income countries <!-- Start of picture text --> 300<br>Fuel<br>250<br>Food<br>200<br>150<br>100<br>50<br>0<br>Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13<br>Price index, 2005 = 100<br><!-- End of picture text --> _Sources:_ World Bank 2013.

_Note_ : All prices are world prices. Fuel consists of 84.6 percent crude oil, 10.8 percent natural gas, and 4.7 percent coal. Food includes cereals, vegetable oils, meat, sugar, and fruits. Weights are based on developing countries’ export values in 2002–04. Indices are based on nominal prices in U.S. dollars.

Many governments in developing countries control petroleum product prices. In the face of mounting subsidies, a number of
# High-quality health facility–based measurement is crucial for World Bank COVID-19 operations

Facility preparedness and response is integral to addressing current and future pandemics and is a key feature of the World Bank’s response to COVID-19. Specifically, the World Bank’s COVID-19 Strategic Preparedness and Response Program (SPRP) Multiphase Programmatic Approach (MPA)<sup>21</sup> suggests 11 Project Development Objectives (PDO) indicators<sup>22</sup> (plus 12 more Intermediate Results Indicators) for inclusion, the majority of which require facility-based measurement. For example, assessing both baseline (before the operation) and endline availability of diagnostic and treatment inputs in facilities, or the numbers of acute health care facilities with isolation capacity (PDO indicators two and three) requires reliable and objective health facility data. A clear picture of project implementation and impact can only be achieved by high-quality data collection.

# Current measurement tools are not adequate

Unfortunately, current internationally used health facility assessment tools are not designed to comprehensively measure pandemic preparedness. Specifically, the SDI, SARA, and SPA are the three most widely used globally comparable health facility surveys, sponsored by the World Bank, World Health Organization, and USAID, respectively. The ability of the SDI to shed light on pandemic preparedness is discussed in depth above; importantly, 11 of the 18 WHO-recommended items are not captured in the SDI (Appendix Table 1). Similarly, while the SPA survey typically contains more information on PPE availability than the SDI (and recent work<sup>23</sup> documents what can be learned from recent SPA surveys about preparedness to protect health workers from COVID-19), it does not collect information on any more of the 18 domains. For example, no information is available on backup human resources for surge capacity, triage or ambulance dispatch systems, or continency plans or drills.<sup>24</sup> Even the SARA, which is considered the most comprehensive of the three surveys in terms of covering inputs