rafmacalaba/data-use-mentions-extended huggingface dataset:
local effects ignoring effects transmitted to other communities or network effects. Notwithstanding these shortcomings, they provide useful benchmark figures for investment appraisal. For example, there are ongoing efforts to estimate the poverty impact of investment projects in Vietnam (Larsen and Rama 2003).

**IV. Incidence analysis** _._ Cross-country regressions and CGE models establish, in very broad terms, the links between expenditure components and poverty reduction. Another set of tools help clarify whether expenditure components indeed reach their intended final beneficiaries, whether these are individuals or local providers of services. These studies have been typically used to assess the efficiency and equity of the allocation of resources within sectors and, to a very limited extent, across sectors.

- _Benefit incidence analysis_ . Benefit incidence analysis (BIA) use household survey data and information on public expenditure to assess the distribution of benefits among different groups, such as households at different income levels. BIA has been typically used to identify who benefits from public spending on education and health care (see Demery 2003, for a review of the literature). There are existing summary household survey data on access to water, sanitation, and other infrastructure by income quintiles (World Bank 2003a) that lend themselves to a benefit incidence analysis of infrastructure but studies of benefit incidence of expenditure items other than education and health care are rare.

36
States (score of 4.33).<sup>15</sup> Also, results from quantile regression show that the score of the United States is consistently higher than China and Vietnam at all relevant deciles; more specifically, the (negative) difference in managerial quality in relation to the United States appears greatest among the better managed (higher deciles) firms for both Vietnam and China (Figure 18).

**Figure 17. Dispersion in managerial quality: China and Vietnam vs United States**

<!-- Start of picture text --> 1 2 3 4 5 1 2 3 4 5<br>management management<br>United States China United States Vietnam<br>1<br>.6<br>.8<br>.4<br>.6<br>Density D ensity<br>.4<br>.2<br>.2<br>0 0<br><!-- End of picture text --> Combined K-S tests: p value= 0.000 Combined K-S tests: p value= 0.000 Source: Own elaboration based on WMS data

**Figure 18. Coefficients of quantile regression (China and Vietnam against United States) across deciles**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Quantile Quantile<br>-0.30<br>-0.20<br>-0.40<br>-0.40<br>-0.50<br>country=China country=Vietnam -0.60<br>-0.60<br>-0.70<br>-0.80 -0.80<br><!-- End of picture text --> Source: Own elaboration based on WMS data

> 15 According to World Bank
since in some locations, international actors were not present when the LPA was signed).<sup>72</sup> The ILA and Return Index also utilize different methodologies, varied timing for data collection, different indicators for similar issues, and different questions for similar indicators. However, despite these limitations, the combined data sources still offer significant contextual information that enables us to identify key markers associated with IDP return, security, and social cohesion in the eight areas before and after each agreement.

Another limitation in comparing LPAs was the absence of data related to the return of IDPs with perceived ISIL-affiliation, since this information is not available in public datasets and is not recorded by humanitarian actors. In addition, data by ethnoreligious background is not systematically collected, so it is not possible to disaggregate population movements (or other indicators) by ethnoreligious group. Instead, the number of returned IDPs refers to the overall returns to the subdistrict,<sup>73</sup> while the reported return of ISIL-affiliated families pursuant to each agreement is captured via interview data and secondary sources.

> 71 This indicator measures how comfortable people feel in engaging in public spaces. 72 We rely on the ILA datasets for agreements signed prior to mid-2018 (since no other datasets are available), while indicators for more recent agreements are drawn from the Return Index. 73 Genat, M. (2020).

13
Kose, A., and M. Terrones. 2015. Collapse and Revival: Understanding Global Recessions and Recoveries. Washington, DC: International Monetary Fund.

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Monacelli, T., and L. Sala. 2009. “The International Dimension of In fl ation: Evidence from Disaggregated Consumer Price Data.” Journal of Money, Credit, and Banking 41(s1): 101-20.

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A further break down of the ENE data is presented in Table 2.2, where we consider among other things, the sector of activity and the participation of women and men separately over time in the rural labor force. Of the working-age population in rural Mexico, the share of people engaging in agricultural activities is slowly falling while the share engaging in nonfarm activities is slowly rising. In 2003 the vast majority of women (75 percent) worked in nonfarm related activities while the majority of men worked in farm related activities (67 percent).

Table 2.2 also reveals information on the labor status of the rural population. In 2003, employers and the self-employed constituted 40.9 and 36.5 percent of the male and female employed, employees 39.3 and 34.2 percent, and unpaid family workers 16.0 and 25.0 percent, respectively. Partly because of high unemployment in the formal labor sector, the number of informal-sector workers ballooned during the 1990s, reaching 27.5 and 17.8 percent of the male and female employees respectively in 2003.

The nonpoor people are relatively more likely to be employed in nonagricultural activities than the poor are (see Verner 2004). While 61.3 percent of the poor working population is employed in nonagricultural activities, more than 78 percent of the nonpoor are active in this sector; up from 42.3 and 50.6 percent respectively in 1992. This may indicate that nonfarm employment offers a route out of poverty in rural Mexico.

The average years of education of the employed 9.3 million people in the rural labor force increased from 4.4 years for both genders in 1995, to 5.1 and 5.4 years for males and females respectively in 2003 (Table 2.2). The level of attained education of the work force is rapidly increasing (Figure 2.2). The male and female workers that completed lower secondary education increased by 62.2 and 83.6 percent, respectively
We call researchers’ attention to the fact that COVID-19 also did not result in any longlived crises in financial markets, although these markets are not the main focus of this article. U.S. stock markets fell sharply initially, and demonstrated a strong negative reaction in the skewness and total market price of risk, more negative even than the GFC (Delis, Savva, and Theodossiou, 2021). The contagion effects across stock markets in different nations was also quite pronounced during COVID-19 (e.g., Iwanicz-Drozdowska, Rogowicz, Kurowski, and Smaga, 2021). However, the market came back even stronger during the pandemic, likely due in part to the policy interventions that helped the real economy and Federal Reserve policies that boosted financial markets. The S&P 500 index achieved a record closing high of 3,386 on February 19, 2020. However, 16 sessions later, the market closed at 2,481 on March 12, 2020, a decline of about 26 percent, a bear market. Nonetheless, financial markets have now recovered, with the S&P 500 closing at 4,163 on May 17, 2021. However, Demirguc-Kunt, Pedraza, and Ruiz-Ortega (2021) analyze stock price data to show that banks around the world, particularly those that operate in countries with little fiscal space, were put under stress in 2020 despite many policies introduced to moderate this impact.<sup>20</sup> We recommend that future research extend this analysis to monitor these vulnerabilities and developments across the world.

# **5. Key Reason for the “Economic Surprise” – Government Stimulus Programs during COVID-19**

We argue in this section that a key reason why the COVID-19 economic outcomes in the U.S. became a favorable “surprise” may be the large government stimulus programs that were quickly put into place during the crisis. Both the COVID-19 and GFC crises featured many stimulus programs in the U.S. from government payments to households, to aggressive Federal
A number of teams have recently undertaken analysis of likely learning losses stemming from COVID-19. Most have focused on the United States and other high-income countries<sup>11</sup> but estimates have also been developed for a selection of low- and middle-income countries.<sup>12</sup> These analyses have focused on a range of grades and subjects. The effects of these analyses have mostly

> 8 In all three scenarios the paper utilizes a conservative estimate of school dropouts based exclusively on expected losses to national income derived from global macro projections such as the World Bank Macro Poverty Outlook (MPO) from June 2020. These dropout-income elasticities are computed for children 4-11 as well as for children 1217. This dropout effect would likely be higher if it were to include non-income related channels such as school safety concerns and school disengagement. However, available data do not allow either of these channels to be quantified convincingly at a global level. We are also making assumptions on availability, take-up, and effectiveness of remote learning, which are based on the scarce literature on the effectiveness of remote learning and household information on access of alternative learning modalities such as television and internet using the PISA, DHS, and MICS household questionnaire. In addition, we are also making assumptions regarding the expected learning observed in one school year, these are made based on the literature on school productivity, unexpected school closures, and summer learning loss (for more information see Annex A.1).

> 9 See for instance the policy response options described in Rogers and Sabarwal, 2020.

> 10 Although much of the same framework used here would still hold, allowing for a future extension to include an explicit component of remediation.

> 11 Kuhfeld and Tarasawa (2020), Kuhfeld et al. (2020) and Dorn et. al (2020).

> 12 Cummiskey et al. (2020).

4
This raises a further econometric issue: Assuming that cognitive and noncognitive skills continue to develop beyond adolescence and depend on accumulated work and life experience, then equation [2] is subject to simultaneous equations bias, i.e., earnings and employment depend on skills, and skills also depend on a person’s work experience.<sup>21</sup> The implicit assumption in using cross-section data as we do is that some skills are relatively unchanged over the work life, or that a permanent component of skills developed early in life dominates later development and is a good enough measure of current skills.<sup>22</sup> As discussed in the previous section, this potential source of simultaneity bias implies that noncognitive skills acquired in childhood and adolescence determine adults’ earnings and employment also through their impact on academic performance.<sup>23,24</sup> As we do not have earlier 21 Using data on skills at a young age may minimize the problem of reverse causation, of work experience itself contributing to the formation of observed cognitive or noncognitive skills, but studies have also found that some noncognitive skills continue to develop throughout the life cycle. Several studies on advanced countries—for example, Heckman, Stixrud and Urzua (2006), Fortin (2008), Hall and Farkas (2011), and Mueller and Plug (2006) for the U.S., Braakmann (2010) for Germany, and Viinikainen et al. (2014) for Finland—use data on cognitive and/or noncognitive skills that were collected at an earlier time than when earnings were observed (e.g., end of high school or at labor market entry). Keeping in mind that individual noncognitive skills are correlated and that there may indeed be an ordering of skills, we conducted an exploratory principal components analysis to draw out this ordering. However, our results indicated the variables of nine distinct noncognitive skills were characterized more by “uniqueness” rather than being correlated.

22
Looking first at the graphical results, direct taxes appear progressive in each country as average tax rates increase at higher income deciles, though to differing degrees.<sup>23</sup> That said, Honduras appears arguably only slightly progressive as even the top deciles pay very little tax. More generally, the graphs highlight clearly two points about the PIT in many developing countries: the PIT is paid predominantly by high income households, and the overall levels of taxation are low. For example, in Uganda, only the top three income deciles pay any significant amount of tax. The overall low levels of taxation are evident when compared to the United States, where average tax rates are significantly higher right across the income distribution, and even lower decile household pay significant levels of tax.

While the graphs provide a broad indication that direct taxes in these countries are progressive, the Kakwani index enables the degree of progressivity in each country to be assessed and compared. Among the IDA countries, Tanzania and Uganda are shown to have the highest degrees of progressivity, driven by the substantially higher tax burdens on the top decile and minimal tax burdens elsewhere. The lowest degree of progressivity amongst IDA countries is in Honduras where the top six deciles pay very low levels of tax, and the bottom four deciles pay virtually no tax. Interestingly, though, the degree of progressivity in Honduras is still slightly higher than in the United States.

The Reynolds-Smolensky index results provide further insight. While direct taxes in the United States exhibit less progressivity than in the IDA countries, they achieve significantly more redistribution. The reason for this is shown clearly by the average tax rate in the United States which is vastly greater than in any IDA country. In contrast, Uganda, which has the second most progressive tax system, achieves the lowest degree of redistribution because the average tax rate is extremely low.

More generally, the decomposition of the redistributive effect into its progressivity and average tax rate components shows that a highly progressive system on its own is not
|Figure 11_._Building Structure and Rental Tenure in Selected Transition Countries...................................60|
|Figure 12. Tenure Demand, Life Cycle, and Rent-Mortgage Cost Ratio Distortion ..................................78|

|**Tables**|
|---|
|Table 1. Household Survey Data Descriptors.............................................................................................13|
|Table 2. Survey Specific Definitions of Dwelling Status ...........................................................................14|
|Table 3. Tenure Structure in Studied Countries by Settlement Category ...................................................16|
|Table 4. Ownership of Apartments and Buildings in Studied Countries as % of Housing
mobility is negatively affected by strikes in Bangladesh, which reduces output growth relative to a typical workday.

# C. Health and Environmental Externalities

Transport involves various social and environmental externalities, including road accidents, air and noise pollution, and associated emotional stress and health issues. Car accidents and related injuries have been found to increase with traffic density (Bauernschuster et al., 2017), leading to higher automobile insurance premia (Edlin and KaracaMandic, 2006). The health effects of transport-induced pollution—mainly from nitrogen oxides (NOx), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter (PM), and unburned hydrocarbons—are well documented in the literature. These effects include cardiovascular and pulmonary problems (Bauernschuster et al., 2017), low birth weight, prematurity and increasing risks of infant mortality (Currie and Walker, 2011; Knittel et al., 2016). These health effects, in turn, may have economic implications. Empirical evidence from London (Duffy and McGoldrick, 1990) and Los Angeles (Evans and Carrère, 1991) suggests that exposure to highly congested traffic increases the level of psychological stress, which may negatively affect task performance (Stokols et al., 1978) and proofreading abilities (Schaeffer et al., 1988). In contrast to these findings, however, a recent study using a large sample of driving instances from the American Time Use Survey (ATUS) finds that congestion during rush hour commuting has little impact on happiness, sadness, stress, and fatigue.

With the rise of big data, the opportunity to better understand congestion-induced social and environmental externalities has emissions can drastically improved. Traffic now be tracked with more precision using urban sensors in combination with crowdgenerated points of interest (Thakuriah et al., 2017). For example, Nyhan et al. (2016) analyze GPS data from a taxi fleet of over 15,000 vehicles in Singapore to estimate emissions from different sources of pollutants at high spatial resolution and temporal scale.
practice, by definition, not all countries can pursue a policy of real undervaluation at the same time. Beyond the empirical answer, a policy of “forced savings” can only be conducted in a certain political economy context.

# 5. Transitional macro‐financial programming scenario

Implementing the new vision while maintaining macroeconomic economic stability would require solid control over the macro‐financial flows and good anticipation on the immediate financial effects. The current NBE financial programming framework would need to evolve and incorporate the main elements of reforms discussed above in new medium‐term financial programming. This revised financial program would have significant implications on the financing of the government, the SOEs and the private sector.

## 5.1. Current NBE financial programming framework

The current NBE monetary policy framework aims at directly controlling monetary aggregates and credit allocations.<sup>111</sup> This involves several steps. First, given the real GDP growth projection and inflation rate target, the NBE determines the growth of reserve money supply in line with the nominal GDP growth rate. Since the NBE does not directly control broad money, it determines the increase in reserve money consistent with the targeted growth of broad money supply assuming a gradual increase in the money multiplier. Second, based on the balance of payment financing requirements and sources projections,<sup>112</sup> the NBE forecasts the net foreign assets (NFA) that would be available in the monetary survey, and by difference with broad money, the net domestic assets (NDA) that could be made available to the economy. Third, the NBE allocates the NDA by prioritizing the credit to the government (direct advances and bonds) and credit to the CBE and DBE based on their respective cash flow needs. The CBE cash flow needs are projected based on the financing requirements of major SOEs.<sup>113</sup> The DBE cash flow assumes that the DBE outflow does not exceed its inflows, which comprises the NBE bills proceeds (27‐percent rule
.37**|
||**Coefficient**|**t‐statistic**||
||Years of Education<br>0.05|5.24|*******|
||Age<br>0.03|6.01|*******|
||Age, Squared<br>0.00|‐4.51|*******|
||Male (Dummy=1)<br>0.61|20.01|*******|
||Married (dummy=1)<br>0.05|2.51|*******|
||Literate (dummy=1)<br>0.03|0.10||
|Default is:|Education type, General<br>‐0.08|0.09||
|Professional|Education type, Technical<br>‐0.13|0.11||
||Private Employee (dummy=1)<br>0.10|1.87|*****|
||Salaried worker<br>0.07|3.01|*******|
|default sector is:|Primary Sector<br>‐0.14|0.18||
|Tertiary|Secondary Sector<br>0.12|0.04||
||constant<br>2.32|17.08|*******|
|*** =Significant at|1% ;** =Significant at 5%;* =Significant at10%|||

**_Source_ : Authors’ calculations based on Core Welfare Survey, 2006.**

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