rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>probability of being multidimensionally poor. Some EU countries have adopted an integrated approach to social services, encompassing comprehensive support to address diverse needs.</mark><sup>50</sup> How does the extension of the AROPE indicator contribute to our understanding of multidimensional poverty?

# **About half of the population in Bulgaria, Croatia, and Romania are AROPE +, mainly because**

**of the large percentage of individuals who are educationally deprived.** These countries exhibit the highest AROPE + rates, reaching 45.0, 46.2, and 53.6 percent of the population, respectively (Table 7). The proportion of individuals deprived in the education dimension is 29.2 percent in Bulgaria, 34.0 in Croatia, and 38.4 percent in Romania, representing 64.9, 73.6, and 71.7 percent of the AROPE + population, respectively. In addition, Romania has the highest rate of individuals who live in households in which the adults have unmet medical needs (14.7 percent), followed by Poland (11.7 percent) and Croatia (10.3 percent). Romania and Bulgaria have the largest proportion of individuals materially and socially deprived when incorporating the overcrowding indicator.<sup>51</sup> Poland has the lowest AROPE + rate as well as the lowest AROPE rate.<sup>52</sup>

**Table 7.** AROPE + rates and raw headcounts of AROPE + dimensions, by country <u>(2022)</u>

||**Bulgaria**|**Croatia**|**Poland**|**Romania**|
|---|---|---|---|---|
|**Panel(A): national rates**|||||
|AROPE +|45.0|46.2|
Given this limitation, we proceed by combining two extreme assumptions on intergenerational mobility. First, we assume zero intergenerational mobility, which implies that, first, children have the same level of education of their parents and, second, that internet access in childhood can be approximated by the current availability of internet in the household where adult _i_ lives. The other alternative extreme assumption is of perfect mobility. In that case we randomly assign internet access to each adult in a way that is consistent with the overall internet coverage among the population, and also randomly impute parental education, independent of their own level of education.

Finally, we combine these two extreme alternatives to obtain our estimates. Weights for the two alternatives are assigned based on country intergenerational mobility estimates provided by Neidhöfer, Gasparini, and Serrano (2018). In particular, we take the typical measure of intergenerational mobility of education, which arises from a regression of children’s education on their parents’ education. The lower is educational mobility, the higher is the weight we assign to the scenario of zero mobility described above.<sup>15</sup>

# **Dropouts**

As discussed in section 2, the COVID-19 pandemic had a more dramatic effect on young people that go beyond the interruption of classes during several months: it may have implied even a dropout from the education system. We adopt the following strategy to measure the effect of this factor. As above, we assume that without the pandemic the educational structure in the future (2045)

> 15 For instance, the intergenerational educational mobility measure based on regressing children’s education on parental education estimated by Neidhöfer _et al_ . (2018) in Peru is 0.51. This is, in fact, a measure of _persistence_ , so in our simulations we assign the weight 0.51 to the zero-mobility case and the complement (0.49) to the perfect mobility case. For robustness, we also (i) consider the correlation coefficient as a measure of persistence
to incentivize firms to provide training to their employees (67 percent of economies), followed by subsidies (53.4 percent), and cost reimbursement (46.6 percent).

# **3.2. Patterns of UB and ALMP across income groups and regions**

Among the 91 economies with UB schemes, only 8 percent, all of which are HIC, require unemployed persons to meet all six criteria, and 5 percent require just one criterion. The most commonly used number of eligibility conditions is four, followed by three, five, and two (30, 22, 19, and 16 percent, respectively). According to the Jaccard index of similarity,<sup>9</sup> periodic reporting on active job search and participating in training offered by PES are most likely to be used together (with a Jaccard similarity index of 81 percent), followed by registration with PES and periodic reporting on active job search (68 percent), and registration with PES and participation in training offered by PES being used together (61 percent).<sup>10</sup> Regarding the six services provided by PES reported in Table 1, training, vacancy database, and job placement are the most commonly used services. Among the 156 economies that have PES, 29 percent offer all six services, 27 percent offer five services, and about an equal percentage of countries, at about 13 percent, offer 4, 3, and 2 employment services. Only 0.4 percent of economies offer just one employment service. The highest percentage of countries offering all six services are from HIC, at 42 percent of HIC, compared to 26, 22, and 13 percent of UMIC, lower-middle-income countries (LMIC), and low-income countries (LIC), respectively. Interestingly, the largest share of economies offering five services are from LMIC (at 31 percent of LMIC) followed by HIC (27 percent) and UMIC (26 percent), and the majority of countries with
woman who would choose to abort suffers a miscarriage before she is able to actually act on her decision.

Inclusion of these women into the control group is likely to result in overestimating negative effects of bearing a child as a teenager. Women who choose to have an abortion are likely to come from more favored backgrounds. Ashcraft and Lang (2006) show it with the US data; the same is likely to hold for Mexico – although due to underreporting of abortions we cannot demonstrate it with the 17
the other way around (MacKerron, 2012, and references cited therein). Natural experiments which provide exogenous shocks (such as lottery wins in Gardner and Oswald, 2007 or political shifts in Frijters et al., 2004) as well as the availability of panel data may address some of these issues.

This paper does not attempt to settle this debate by providing arguments for one or the other position. We adopt the same view as Ravallion and Lokshin (2002), i.e. that one can learn quite a bit from comparing objective versus subjective data on welfare, and that they should be considered complements, rather than substitutes. In addition, rather than adding to the macro-level research which discusses whether GDP or national happiness should be used as the aggregate index of social progress, our paper contributes to the micro-level research which focusses on distributional issues, such as self-assessed poverty and inequality, or determinants of individuals’ well-being.

Our results extend previous findings of Cojocaru and Diagne (2015) and Ravallion and Lokshin (2002). In more detail, instead of starting, as in these papers, by assuming that the answers to the Cantril’s ladder questions represent a subjective economic welfare variable and then analyzing its reliability, consistency and determinants, our paper’s final objective it to show that this is indeed a valid interpretation. Our paper arrives at 3
Ter-Minassian 2018).

Following the categorization of fiscal risk of Polackova Brixi and Mody (2002), we explore a variety of implicit contingent liabilities that stem from the operation of infrastructure SOEs and from their accumulation of liabilities. We examine the fiscal risk and its key sources in the power, roads, railways, and airlines and airports sectors. We use a sample of 135 firms in 19 countries that come from an original, hand-collected database of SOE subsidies and financials for 2009–18. This sample covers fewer countries and a shorter period than the Bova et al. sample.

Using this database (which we refer to as the World Bank’s Infrastructure SOEs Database), we examine the fiscal risk that stems from the operation of infrastructure SOEs. We provide an overview of the financial performance of these SOEs, their shortfalls in performance, and the factors that constrain them from being profitable. We then build new measures of fiscal injections to SOEs that include not only the flow of subsidies from the government but also changes in the stock of government equity (recapitalizations); changes in the loans SOEs receive from the government, from financial and nonfinancial SOEs; and changes in deferred tax liabilities (when they are used to support an SOE), all net of asset increases. We examine these fiscal injections net of asset increases in order to ensure that we capture only fiscal transfers that 3
This modeling approach held sway for decades,<sup>1</sup> and some recent studies of Covid-19 spread in China have incorporated it (e.g. Kang et al. 2020; Li and Ma 2020). However, two significant weaknesses have been clear from the outset. First, geographic distance is often a poor proxy for travel cost, principally related to travel time, because of great variation in topography and transport infrastructure quality. The difference is most pronounced in the developing countries that are the focus of the present exercise. Fortunately, this weakness has been overcome by global-scale collaborative work in the OpenStreetMaps (OSM 2020) and Open Street Routing Machine (OSRM 2020) projects, which enable realistic estimation of point-to-point road distances and travel times for an arbitrary number of locales at no cost. Recent studies of Covid-19 spread in the European Union have employed road distances (e.g., Felbermayr, Chowdhry and Hinz 2020). This approximation for travel time is unlikely to introduce significant measurement errors in the European Union, given the density of its road network. For global applications, however, we believe that travel time should be the new measurement standard.

The second weakness of epidemic gravity models is structural: They capture the ultimate results of human travel and interaction, but with composite parameters that incorporate five unobserved elements: travel by individuals from locale A to locale B; their infection rate; the frequency of their interaction with people in locale B; the infection rate in locale B; and the likelihood that each interaction of an infected person with an uninfected counterpart will transmit an infection. In principle, technical progress in communications and infection testing has made four of the five elements susceptible to direct measurement. Where the requisite cell phone data are available, they can enable tracking of individuals between locales and (with sufficiently-precise coordinates and timestamps) identification of co-locations that are close enough for transmission to occur. Mass testing in each locale can produce relatively precise estimates of infection rates. If cell phone data
Figure 6: Average Job Quality Ratio Over Time <!-- Start of picture text --> .46<br>.44<br>.42<br>.4<br>.38<br>.36<br>199519961997199819992000200120022003200420052006200720082009201020112012<br>Average Job Quality Ratio<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data. Countries included in each year differ based on availability, but country fixed effects are controlled for. Individuals are counted equally across countries.

Honduras, and Peru have a high concentration of workers with “low quality” jobs, but that do not have “good” jobs either.

We next look at the change in job quality over time and find the average level of job quality in Latin America rose over the sample period. The graph is shown in Figure 6. This graph was constructed by controlling for country specific fixed effects. After a period of stagnation observed during the decade of 1995-2004, job quality steadily rose increasing by roughly 25% between 2004 and 2012. The growth might have a number of explanations. The first is that unemployment rates fell for many countries in Latin America, resulting in more workers with a non-zero level of job quality. In addition, the number of successes in each of the job quality dimensions also grew. The largest growth came from having both retirement and health insurance benefits, and receiving an income above the poverty line. Though we do 21
**Figure 3: Quantile Regression on Business profit**

<!-- Start of picture text --> Impact of assignment to group 2 or 3 (group1 excluded)<br>40,000<br>20,000<br>5,000<br>0<br>-5,000<br>-20,000<br>-40,000<br>5 10 20 30 40 50 60 70 80 90 95<br>Percentile<br>OLS Quantile treatment effect 95% C.I<br>Business profit at endline (CFAF)<br><!-- End of picture text --> Notes: Data source: Endline surveys 2016, N=2905 32
one also included all possible interactions among these economic and demographic characteristics, the R<sup>2</sup> would reach about one-third (regression results are not shown for the sake of brevity).

# Appendix 3. Country share of HtM individuals, share of social transfers reaching HtM individuals, and social transfers targeting

Table A.3.1 shows the country share of HtM individuals, share of social transfers reaching HtM individuals, and social transfers targeting from Global Findex dataset (based on its latest survey for the year 2017).

# Appendix 4. Additional insigths regarding the relative importance of � vis-à-vis � driving large STMs

Table A.4.1 shows on-impact STM values using our benchmark calibration and combinations of � and � which are allowed to vary between 0 and 1 in 0.05 intervals. The shaded cells indicate combinations of � and � such that STT < 0. Recall that since STT � � � �, then STT < 0 implies that �> �.

This exercise provides additional insights as to the relative importance of � vis-a-vis � driving large STMs. In particular, the results show that it is not feasible to obtain truly large on-impact STMs (e.g., STM > 1) for low to moderate values of the share of HtM individuals, �, (e.g., �< 0:4). This implies that, in spite of important social transfers targeting e¤orts, even if � was the maximum possible (i.e., � = 1), in order to be able to achieve truly large on-impact STM, there exists the need to have large propagation forces via high average marginal propensity to consume, which can only be achieved by having a relatively large share of HtM individuals, �. In the extreme, for a su¢ ciently high share of HtM individuals, �, (e.g., �> 0:8) it is possible to have truly large STMs even when � is low enough
Houser, P., Zhang, L., Hou, R., Lan, H., Flynn, C., Lu, M., Hu, T., and Yang, C. (2020). Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in China Using Nighttime Light and Air Quality Data.</mark> _<mark>a</mark> rXiv preprint arXiv:2005.02808_ .

- Mahler, D.G., Lakner, C. Aguilar, R.A.C., and Wu, H. (2020a). The Impact of COVID-19 (Coronavirus) on global poverty: Why Sub-Saharan Africa might be the region hardest hit. _World Bank Blogs: Data Blogs_ , April 20, 2020.

- Mahler, D.G., Lakner, C. Aguilar, R.A.C., and Wu, H. (2020b). Updated Estimates of the Impact of COVID-19 on Global Poverty. _World Bank Blogs: Data Blogs_ , June 8, 2020.

- Masaki, T., Nakamura, S., & Newhouse, D. (2020). How is the COVID-19 Crisis Affecting Nitrogen Dioxide Emissions in Sub-Saharan Africa?.

- <mark>Mills, S., Weiss, S., & Liang, C. (2013). VIIRS day/night band (DNB) stray light characterization and correction. In</mark> _Earth Observing Systems XVII_ _<mark>I</mark>_ <mark>(Vol. 8866, p. 88661P). International Society for Optics and Photonics.</mark>

- <mark>OECD (2021),</mark> _<mark>O</mark> ECD Economic Outlook, Interim Report September 2021: Keeping the Recovery on Trac_ _<mark>k</mark>_ <mark>, OECD Publishing, Paris.</mark>

- <mark>Roberts, M. (2021
00|0.00|0.00|0.00|
|Sri Lanka|0.52|0.21|0.61|-0.09|0.74|0.21|0.53|
|Tajikistan|0.69|0.19|0.69|-0.01|0.66|0.65|0.00|
|Tanzania|1.29|1.03|1.73|-0.44|2.61|0.42|2.18|
|Togo|0.53|0.11|0.69|-0.16|0.55|0.46|0.10|
|Uganda|0.29|0.60|0.81|-0.52|1.19|0.01|1.17|
|Ukraine|0.46|0.10|0.47|-0.01|0.60|0.33|0.26|
|Uzbekistan|0.34|0.08|0.55|-0.21|0.34|0.33|0.01|
|Vietnam|-1.70|0.83|0.05|-1.75|-2.70|-0.59|-2.11|
|Yemen, Rep.|1.11|0.34|1.13|-0.02|1.47|0.67|0.80|
|Zambia|1.66|0.19|1.78|-0.12|1.82|1.48|0.33|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of import tariffs on cereals.

37
retirement age and households with pensioners (Figure 6 and Figure 7). As most of pensioners do not receive any market income, the beneficiaries of policy effect are mostly concentrated in the lower part of the distribution if deciles are defined based on the market income. Other types of households positively affected by changes in social transfers —but to a much smaller degree—were couples with two and three or more children. However, for these groups positive changes were offset by income reduction due to direct and indirect taxes. In terms of geographical location, rural areas had smaller negative effect than metropolis of fiscal policy change during the period under analysis (Figure 6).

# IV. Prospective scenario: A hypothetical tax change

## **Background and the description of “tax maneuver 22/22”**

Informal employment has been growing in Russia, similarly to other East European countries that have undergone transition to a market economy in early 1990s. Informal employment rates in Russia, according to Rosstat, were growing since the early 2000s through 2016, with a short period of stagnation or even decrease in the second half of the 2000s.<sup>10</sup> However, the aging population coupled

> 10 Rosstat’s official measure of informal employment is estimated using the Labor Force Survey (LFS) and includes workers who are employed but not at an enterprise in their primary or secondary job. Informal employment rose from 13

14
.31|22.53|20.62|24.81|26.87|

_Notes_ : LFS imputed data using OLS, regionally deflated. Poverty estimates have standard errors. _Source_ : LFS, SILC 2006-2013; authors’ calculations.

Table 16. Literature on patterns of growth and poverty reduction

|Country|Aggregation|Primary Drivers of Poverty<br>Reduction|
|---|---|---|
|Brazil, 1985-2004<br>(Ferreira,Leite, andRavallion,2010)|Region, Industry, Year|Social Spending<br>Services Sector|
|Indonesia, 1984-2002<br>(Suryahadi, Suryadarma, and Sumarto,<br>2009)|Sector, urban-rural, year|services growth and rural<br>agricultural growth in rural areas|
|Global cross-country<br>(Loayza andRaddatz,2010)||Unskilled labor intensive sectors|
|China, 1984-2004<br>(Montalvo andRavallion,2010)|Province, year|Growth in the agricultural sector|

45
’ demand for different tasks contained in the Chilean Encuesta Nacional Industrial Anual (ENIA). The results from an instrumental variable estimator show that Chilean exporters indeed utilize a higher share of engineers over blue-collar workers than non-exporting firms. The results provide causal evidence of the skilled tasks demanded by exporters relative to non-exporters in Chile.

The rest of the paper is organized as follows. Section 2 sketches a simple model of exports, quality, and the demand for engineers and blue-collar workers. Section 3 discusses the main empirical results. Section 4 concludes.

2
McCullough (2017) aggregates farm labor at the person-level to compare labor productivity between farm and non-farm household enterprises. This illustrates that different levels of aggregation serve different purposes. Collecting data on farm labor at the person-plot level keeps all higher levels of aggregation – i.e. _plot-_ , _person-_ and _household-levels_ – open for analysis.

Despite these advantages, data collection at the person-plot level can be challenging in practice. In a first step, enumerators need to compile a comprehensive list (roster) of household members and agricultural plots. Plot rosters, in particular, are notoriously difficult to compile. While most farmers have an intuitive understanding of what constitutes a plot of land, their understanding does not always align with the surveydefinition of a plot<sup>6</sup> and they may forget to mention marginal plots. Enumerators hence need to probe carefully to ensure that all relevant plots are listed as per the standardized definition used in the survey. Another difficulty is that respondents may not find the concept of farm labor per person per plot intuitively meaningful, and hence face difficulties in reporting on the number of days and hours that individual household members worked on each plot. These challenges can be further compounded by the length of the recall period, as discussed in the next section.

In this paper, the principal unit of data collection and analysis is farm labor per person per plot. However, we also report on higher levels of aggregation, i.e. farm labor per plot, per person and per household, which are, as shown in Table A1, more widely used at the analysis stage. This allows us to

> 4 The Ghana Living Standards Survey (GLSS) 5 uses an even more aggregated approach, asking about the number of male, female and children’s hours of family labor on each plot during the last major/minor seasons.

> 5 Other examples are multiple rounds of nationally representative LSMS-ISA surveys in Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda
|523||||||
|Number of households|294|427|427|427|427|920|
|R-squared|0.017|0.019|0.010|0.007|0.004|0.003|
|Mean of dep. var. for NB in NL vil.|0.103|0.286|0.469|0.153|0.133|0.009|
|P-val of B = NB in L villages|_0.017_|_0.031_|_0.875_|_0.490_|_0.562_|_0.694_|
|Note: In Column 1, we report the estimation<br>individual (household),_j_ indexes the village a<br>specification:_y ij = α ij + β 1 Beneficiary ij + β _<br>dummy that takes value of 1 if the individual_i_ <br>village _j_ were chosen among the eligible hou<br>covariates. The dependent variable in Colum<br>dependent variables in Column 2 and 3 are d<br>using household survey data collected at basel<br>position of or being related to and having regu<br>Column 6 is a dummy variable that takes val<br>Columns 1 and 6 were collected using house<br>asymptotic wild cluster bootstrap procedure d<br>statistical significance at the 1, 5 and 10 percen|of the following specific<br>nd_k_ indexes another ind<br>_2 Lottery j + β 3 Lottery j _<br> in village_j_ is a benefici<br>seholds though a lottery<br>n 1 is a dummy variabl<br>ummy variables that tak<br>ine. In Columns 4 and <br>lar social interaction wit<br>ue 1 if respondent repo<br>hold survey at follow-up<br>escribed in Cameron et <br>t level, respectively. Se|ation:_y