rafmacalaba/data-use-mentions-extended huggingface dataset:
# I. Introduction

## **Despite recent improvements, Bulgaria continues to have some of the highest levels of income poverty**

**in the European Union (EU).** From 2016 to 2020<sup>1</sup> , economic growth led to improved living standards for the average household and the poorest 40 percent, resulting in a significant decrease in absolute poverty (using the USD 6.85 2017 PPP poverty line). During this period, Bulgaria's economic growth accelerated, benefiting the entire income distribution, particularly the poor, and reducing international poverty by 5.9 percentage points (Figure 1, panel a). Real incomes moved steadily towards the EU average, surpassing 50% of that level. In 2021, this trend reversed, with poverty increasing by 1.2 percentage points due to the pandemic's lingering effects, the expiration of temporary COVID-19 measures, inflationary pressures, and a decline in employment among less-educated individuals. Despite these positive trends, poverty levels remain high by EU standards (Figure 1, panel b).  Moreover, the anchored at-risk-of-poverty (AROP)—the proportion of individuals with income less than 60 percent of the national median equivalized disposable income (after social transfers), anchored using the 2019 threshold —followed similar trends3 (Figure 1, panel c). Substantial declines in poverty are also observed when using higher poverty lines.  Despite the recent economic slowdown, incomes grew rapidly among the bottom 40 percent of the income distribution (6.6% average annual rise over 2016-2021) and even faster among the bottom 20 percent (7.4% average annual rise), outpacing the median household income growth rate (5.8% annually).

**In addition to high rates of monetary poverty, the country has consistently reported high measures of non-monetary poverty; the incidence of energy poverty and food insecurity is also high for EU standards.** On all the measures of economic strain, Bulgaria consistently ranks higher than
124|
|Student [=1]|0.13|0.34|242|
|Political Interest [=1]|0.86|0.34|242|
|Voted in 2019 Mayoral Elections [=1]|0.75|0.43|242|
|Voted in 2002 Presidential Elections [=1]|0.81|0.39|242|

# **VII. A.2 Once informed, do natives change their voting intentions?**

To evaluate the effects of the program, we estimate the following specification: where _i_ represents the individual and _Y_ denotes the primary outcomes of interest as preregistered. These outcomes include: (i) voting intentions; (ii) self-reported measures of social capital (positive reciprocity, negative reciprocity, altruism, and trust);<sup>18</sup> (iii) experimental measures of altruism toward migrants through a dictator game; and (iv) measures of political attitudes toward migrants. Finally, _X_ includes controls for the stratification 18All the questions have been validated in Colombia and taken from Gallup surveys as reported in Falk et al. (2018).

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<!-- Start of picture text --> 7000<br>700 0<br>6000<br>600 0<br>5000<br>500 0<br>400 0 4000<br>300 0 3000<br>200 0<br>2000<br>100 0<br>1000<br>0<br>0 50 100 150<br>Precipitation 0 0 1000 2000 3000 4000 5000 6000 7000<br>Active Model Predicted Flow<br>Active Model<br>Figure 2.1 : Average of all flow (m3/s per month, Figure 2.2 : Predicted flow (m3/s per month, x-axis)<br>y-axis) and precipitation (mm per month, x-axis) versus calculated average flow (m3/s per month, y-<br>in sample catchment area of the Mekong river axis) in sample catchment area of the Mekong river<br>basin in a nonlinear regression. gression. ression. basin from precipitation model.in from precipitation model.om precipitation model.m precipitation model.precipitation model.recipitation model.ecipitation model.ipitation model.pitation model.itation model.tation model.ion model.on model.n model.odel.l..<br>Observed Flow<br>Observed Flow<br><!-- End of picture text -->

**Figure 2.1 : Average of all flow (m3/s per month, y-axis) and precipitation (mm per month, x-axis) in sample catchment area of the Mekong river basin in a nonlinear regression. gression. ression.**

**Figure 2.2 : Predicted flow (m3/s per month, x-axis) versus calculated average flow (m3/s per month, y- axis) in sample catchment area of the Mekong river** **<u>basin from precipitation model.in from precipitation model.om precipitation model
asset, the largest effects are found for type of floor, washer, and cellphone. The largest effects are again found for the youngest cohort. To test for dependency on government aid, or graduation from social programs, we use, as dependent variable, a dummy equal to 1 if the household receives transfers from the government. In this case, overall the evidence suggests that longer exposure to PROSPERA decreases the dependency on government aid. Unfortunately, this measure does not distinguish PROSPERA from other type of government aid. Finally, we look at proxies for vulnerability and food security: two different measures directly addressing food consumption. One question asks whether any adult, in the household, went hungry at least once during the last month. The second question asks whether not enough money was available to purchase enough food variety. We take these two questions as proxies for food security, vulnerability, and quality of the diet. For the younger cohorts we have positive effects on this measure of food security, as those exposed longer are less likely to go hungry (although the estimates are not precise). As for food variety, larger effects are found for the youngest cohort. For them, the likelihood of not being able to buy enough variety decreases between 1 and 2 percentage points for those with longer exposure.<sup>20</sup> These latter two effects on

> 19As explained in section V.B, these estimates are part of our current research where we seek to use an RDD design with the original rule that established that participating localities would be enrolled to the program based on a marginality index.

> 20The food scarcity variables are separately asked for children and adults in the household. Similar estimates

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>**4.012**||31 0.397|0.330|
|1993 THREAT|est|−0.019|0.008|−0.197||0.226|31 0.109|0.010|
||_t_|−0.183|0.848|−0.858||1.478|||
|1996 THREAT|est<br>_t_|0.993<br>**12.014**|0.023<br>1.510|−0.446<br>**−1.921**|−1.872<br> **−2.138**||33 0.312|0.241|
|1996 THREAT|est<br>_t_|0.801<br>**5.732**|0.020<br>1.214|−0.379<br>−1.559||0.229<br>1.040|33 0.232|0.152|
|1999 THREAT|est<br>_t_|0.938<br>**9.527**|0.008<br>0.363|−0.264<br>−0.589|−2.335<br>−1.599||38 0.182|0.110|
|1999 THREAT|est|0.878|0.001|−0.125||−0.041|38 0.121|0.044|
||_t_|**6.121**|0.064|−0.274||−0.188|||

Notes:

1. OLS estimates. Sample is cross-section of 5-digit HS agricultural products for 93, 96, 99. NTM data (from TRAINS database) available for those years
in the sample is 11. There are two concerns regarding the accuracy of the hospitalization data. The AIH form is also completed if a patient requires a procedure; so, some nonovernight hospitalizations may also be entered in the system. However, few procedures are associated with assault and are thus unlikely to bias the results. The other concern is that hospitals cannot report more patients than the number of beds they have registered with the centralized health care system. The system may therefore be underreporting incidents in crowded hospitals.

_Violent incident reporting by health workers._ In 2011, Brazil standardized and expanded its mandatory notification system (VIVA) for cases of domestic violence and violence against women, children, and the elderly to every type of health care facility in the country (Ministry of Health 2016). The reports are filled out at the health care units by the attending medical personnel (for example, doctors, nurses, dentists, psychologists, and social workers) and taken to the municipal health care administrative office, where they are digitized and entered into the Information System for Notifiable Diseases <mark>(Sistema de Informação de Agravos de Notificação</mark> , SINAN).<sup>7</sup> These are to be filled out whether the patient admits to being a victim or if the patient is merely suspected of being a victim. Although psychological, financial, sexual, and physical violence may be reported, the analysis only included SINAN cases involving sexual or physical violence that was reported because these cases are included in the ICD-10 codes used in creating the hospitalization and homicide variables. Observations are dropped from the analysis if the wound is described in the report as self-inflicted because suicides are not included in the homicide data.

Because the number of facilities reporting has risen since the passage of the 2011 law, only mandatory reports of physical violence ae included that have been supplied by facilities that had at least one such report in 2011. Focusing on facilities that have already submitted the mandatory reports avoids confounding an increase
**Figure 14: Revealed Comparative Advantage of Korean Industry**

<!-- Start of picture text --> 16<br>14<br>12<br>Clothing<br>10<br>8<br>Footwear<br>6<br>4 Textiles<br>2 Electronics<br>0<br>62 67 72 77 82 87 92 97 02 07<br>Note: RCA = (The share of an industry in the economy’s exports) / (its share in global exports).<br>Source: Comtrade.<br>Transport<br><!-- End of picture text --> <!-- Start of picture text --> Figure 15: Trade Specialization Index of Korean Electronics<br>1.00<br>Home appliances<br>0.80<br>0.60<br>0.40 Comm. & Industrial<br>0.20<br>Parts & Components<br>0.00<br>74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06<br>-0.20<br>-0.40<br>-0.60<br>Note: TSI = (Export - Import) / (Export + Import) for each industry.<br>Source: Korea Electronic Association.<br><!-- End of picture text --> 19
) is a year in which the<br>CAPB improves (deteriorates) by<br>at least 1.5% of GDP.|If the cumulative reduction of the debt-to-GDP ratio 3<br>years after the beginning of a fiscal adjustment is<br>greater than 4.5% (the value of the 25th percentile of<br>the change of the debt-to-GDP ratio empirical density in<br>all episodes of fiscal adjustments).|<br>OLS, growth regressions<br>using only periods<br>where an adjustment<br>took place.|<br>OECD, 1970-<br>07|
|Biggs et al.<br>(2010)|CAPB improves by at least 1.5% of<br>GDP and data of Devries et al.<br>(2011).|Debt to potential GDP ratio has declined three years<br>following the first year of the consolidation by at least<br>4.5%.|No.|21 [15]<br>(OECD), 1970-<br>07 [1980-07]|

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education and the new level. This is a relative effect. The return to primary education increases by 1 percentage point, Figure 8. With no unemployment, the return to secondary education increases substantially, 4 percentage-points. This is a result of the positive level effect strengthened by a positive relative effect. Workers with secondary education suffer most from joblessness in Colombia. The return to tertiary education decreases by a half percentage point as the relative unemployment effect (compared to secondary education) dominates, since graduates with secondary diploma tend to face higher unemployment than graduates with a tertiary diploma. The findings show that if the unemployment rate declines in Colombia, the return to secondary education would in particular increase.<sup>12</sup>

**Figure 8 - Impact of Unemployment on Rates of Return**

<!-- Start of picture text --> 5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>age 25 age 35 age 45 age 25 age 35 age 45 age 25 age 35 age 45<br>-1%<br>Primary Secondary Tertiary<br>Impact on Rate of return<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000 _<u>Taxation.</u>_ The tax rate only influences the rate of return if costs or benefits are taxed differently. In our base scenario, only returns to tertiary education changes with the tax rate as we assume that tuition cost cannot be deducted. The impact of taxes seems marginal for the returns to education. The impact of paying the top marginal tax rate of 23 percent compared to paying no taxes does not exceed 2 percent-points. The reduced impact and the already existing option of tax deduction of educational costs could 12 In contrast to the Colombian labor market, no unemployment increases returns to tertiary education for the five OECD for which we have
<!-- Start of picture text --> Average annual rate of stunting change (%)<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>e Malaysia<br>1 ; 2006-2016<br>Pakistan<br>0 ° 3001-2012 an<br>Burundi Egypt, Arab Philippines Timor-Leste<br>©2000-2016 Rep. 2000-2014 @ 2003-2015 2002-2013<br>-1 © Jamaica Yemen, Rep. 2000-2013Indonesia e<br>2000-2014 2003-2013<br>-24 Haiti2000-2012 Guinea-Bissau Azerbaijan<br>ee emp0.2014 © Myanmar 2000-2013<br>Mexico. 2000-2016<br>3 2006-2015<br>© Thailand ® Vietnam<br>-4 United States 2006-2016 2000-2015<br>2001-2012 ® Equatorial<br>5 Guinea<br>® 2003-2014 El Salvador 2000-2011<br>-6 Cote d'Ivoire @Peru 2000-2016<br>e©* 2006-2016 West Bank and<br>-7 2000-2012Algeria Gaza 2002-2014<br>© sao Tome @China 2000-2013<br>“8 and2006-2014Principe ® Mongolia<br>2000-2013<br>“9 kazakhstan2006-2015<br>-10 ©@ Paraguay<br>2005-2016<br>-1 0 1 2 3 4 5 6 7 8 9 10 11<br>Average annual rate of GDP per capita change (%)<br><!-- End of picture text --> Other ® Outliers © Philippines ® Regional comparator Source: World Bank World Development Indicators. Notes: Only countries with at least 3 stunting estimates between 2000 and 2016 are considered. Labeled countries are either Philippines' regional comparators or 'outliers' - countries with the 5 highest and 5 lowest growth elasticities of stunting as well as those with average annual reductions in stunting of below -6% per year and average annual increases in per capita growth of 8% per year. The line indicates the average relationship between changes in growth and
mortality go through interventions or policies which affect infant survival after the first month of life. Moreover, analysis of potential health mechanisms suggest that the improvements in infant mortality are driven by improved access to vaccines early in life and during mothers’ pregnancies. Immunization rates against Tuberculosis and Polio among children born to poor, uneducated, and rural mothers go up 4 to 6 percent of the sample coverage rates in these groups and Tetanus immunization rates among poor, uneducated, and rural pregnant mothers increase with about 9 percent of the sample means. The improvement in infant mortality takes place at Decision Point with no additional effect at Completion Point where full debt relief is granted. Results are robust to various country-specific confounders and other sensitivity tests.

The rest of the paper is structured as follows. Section 2 gives a brief background of the HIPC Initiative. I present the data on child health and the empirical specification in Section 3 and the results and various sensitivity analyses in Section 4. Section 4 also includes health mechanisms analysis through which the HIPC Initiative may affect child health. Concluding remarks are given in Section 5.

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awareness of the program was not widespread during its first year.<sup>24</sup> For subsequent cohorts, program impacts are still large and statistically significant, ranging from 11 to 32 percent.<sup>25</sup> Impacts of comparable magnitude have been found in similar female-targeted CCTs in Bangladesh, Cambodia, and Malawi (Khandker and others 2003; Baird and others 2009; Filmer and Schady 2006).

An interesting result is that program impacts on female enrollment increase with school grades for the same cohort. Several factors may be behind this. First, the program might have incentivized girls who had left the education system to re-enter school. However, this does not seem to explain the changes for middle school grades since re-entry is not only very low (less than 0.5 percent) but also similar across stipend and non-stipend districts. Second, and more plausible, the program may have differential effects in retaining girls in schools, perhaps more in higher grades. Dropout rates in Punjab (as in most developing settings) increase with grades and age. In fact, a simple DD analysis between 2003-04 and 2007-08 using household survey data shows a decrease of 2-3 percentage points in drop-out rate in middle and high school grades in stipend districts, relative to non-stipend districts, while there are not changes in dropout rates for noneligible grades. Finally, the expansion of the FSSP in 2006-07 to cover grades 9 and 10 may provide additional incentives for girls to finish middle school and transition to high school.

The findings also show that the increases in enrollment attributed to the program remain as long as girls are enrolled in middle school. However, such effects are not continued to grades 9 and 10. Point estimates of the effects on enrollment in grade 9 only become positive and statistically significant for some econometric specifications in the academic year 2007-08 and 2009-10 when the expansion of the FSSP to high school was fully implemented. This may suggest that this second phase of the program has positive impacts on enrollment in high school
The key macro-economic drivers of the baseline are based on a number of existing baselines. Population and labor growth are calibrated to the United Nation’s Population Division 2015 projection, the medium variant.<sup>28</sup> The near-term GDP projections, through 2020, use the latest World Bank Global Economic Prospects (GEP) forecast.<sup>29</sup> Beyond 2020, the baseline is calibrated to the so-called Shared Socio-Economic Pathway (SSP) 2, or SSP2. The SSPs, of which there are 5, have been developed by the Integrated Assessment Modeling (IAM) Community to provide a macroeconomic framework for quantitative analysis of the economics of climate change.<sup>30</sup> Three economic modeling groups have quantified global GDP projections: the Organisation for Economic Co-operation and Development (OECD), the International Institute for Applied Systems Analysis (IIASA), and the Potsdam Institute for Climate Impact Research (PIK). All three teams harmonized to the same demographic projections provided by IIASA’s demographic unit. For our purposes, we are using the OECD-based SSP2 projection. SSP2, also referred to as the ‘middle of the road’ scenario, is being treated by many modeling groups as a business as usual scenario.

Labor force growth is equated with the growth rate of the population cohort ages 15-64, sometimes referred to as the working-age population. The GEP forecast is used to project real GDP growth from 2014-2020. The SSP2 projection is used to project real per-capita GDP growth, complemented by the UN population projection.<sup>31</sup> We target real GDP growth by calibrating labor productivity in the baseline. We allow for sector differences in labor productivity growth, with a (fixed) higher rate in agriculture and manufacturing, relative to services. Other factors that impact calibrated labor productivity include an exogenous improvement in energy efficiency, agricultural yields, and international trade and transport margins.

The baseline also incorporates the following list of exogenous assumptions:

- Bilateral tariff rates
The survey included the standard WFP food security module that captures food coping strategies and household information, as well as an access to basic services module inserted into the WFP survey by the World Bank. The module was designed to capture access to a number of key services and dimensions of welfare that are captured in the World Bank’s proposed multi‐dimensional poverty index. In particular, access to water, electricity, municipal sanitation services, and food were all captured in the survey.

Figure 5 demonstrates that immediately following the blockade, there was an increase in the reliance on all five food coping strategies captured by the WFP survey. The increase was most noticeable for households surveyed during the full blockade, but there was still a larger reliance on food coping strategies for those interviewed during the partial blockade. The most surprising part of this result is the speed with which the blockade affected household behavior, where the effects began the day after the onset of the blockade. However, it is not clear whether these results were driven by price increases, shortages of food, or anticipation of the blockade continuing. Regardless of the actual cause, these results can offer an explanation for why food insecurity is equally poor in the north of the country where supply chains are more disrupted, regardless of the level of violence.

In addition to investigating the effects of the blockade on access to food, the survey can also be used to identify how access to a variety of basic services was also affected. Importantly, the survey allows one to investigate the degree to which access to each of these basic and necessary services changed, as well as the degree to which these deprivations overlap. Although estimates of monetary poverty have dramatically increased following the start of the conflict (e.g., World Bank 2017), monetary poverty potentially has less meaning in a setting where nearly everyone is poor, and the vast majority of people are facing extreme challenges in sustaining life. However, each of these non‐monetary dimensions of welfare each has an
# Decentralization in Regional Fiscal Systems in Russia: Trends

# and Links to Economic Performance

Lev Freinkman Plamen Yossifov' The World Bank* Ph.D. student in Economics at the University of Delaware. The work on this project was conducted during a summer intemship at the World Bank (ECSPE) in the summer of 1998.

- The views in this paper are those of the authors and should not be attiibuted to the World Bank We are gratefiu to Daniel Treisman for fruitful discussions of various aspects of the analysis presented here and to Alexei MN Lavrov for sharing with us the database on Russian regional budgets. Comments by Martha De Melo were also quite helpful.
measures of income). To reduce the number of hypothesis tests, we combine related measures into mean effects summary indexes, one for each major outcome.<sup>30</sup> We do

> correlates of attrition. Of the 298 non-responses (of 3,896), we (i) had no location information (75%); men were mentally incapacitated (1%); died (8%, or 9 men); were in prison (12%); or refused (3%). Covariates associated with higher attrition include better mental health and income.

> 2919 in control, 16 in therapy, 15 in cash, and 16 in therapy then cash. Sampling was purposeful, based on variation in key baseline measures: economic success, crime, drug use, and present bias.

> 30We take averages of our outcome measures, coded to point in the same direction, akin to the approach by Kling et al. (2007). Note that the outcomes used to create the summary index may themselves be composites of many survey questions, such as consumption (a composite of many goods) or an aggressive behavior index (a composite of many types of aggressive behavior, a standard way that psychologists measure aggression). We do so because it is typically the composite itself rather than its component survey questions that we have theoretical interest or priors. In most cases this is reflected in the survey design, where the survey questions in each composite measure comprise a separate survey section. Also, to create an index by averaging the

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