rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> (a) Overall expenditure on roads, by source, 2010-18 (b) Aggregate expenditure, all sources, 2010-18<br>1.5% 2.5%<br>1.3% 2.0%<br>a C)<br>a 1.0% =a 1.5% re<br>° x<br>SX 9.5% 0.5%<br>0.3% 0.0%<br>0.0% rr ———t 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 —— low income Lower middle income<br>——BOOST =——=RF =—— Private -——=Donors == Upper middle income= = = Grand Total<br>(c) Expenditure breakdown for RF countries, 2010-18 (d) Breakdown for largest spenders, 2010-18<br>Private, 6<br>0.01% 6.0%<br>) S 4.0%<br>C)<br>ia)<br>2 2.0%<br>RF, 0.57% 0.0% |<br>BOOST,1.50% s=2SPegggseeegs3ge8= WL £832F<c os© GOs2S$o4€2?35S22325sQreezgstsono8 a<br>“a3° <x-BD 2 ooNs(=)<br>TMDonors MBOOST @Private MRF<br><!-- End of picture text -->
savings and credit associations, was very low, both in terms of outreach and the size of resources transferred. Less than a tenth (10% in Gojjam and 4% in Wollo) of households in the sample applied for a loan from this sector. Although, with some 87% of requestors receiving the requested amount, rejection rates were small, average amounts received were only about one-tenth of the average loan received from the semi-formal sector.<sup>5</sup> The bottom panel of table 1 also points towards marked differences in loan use between the two regions. In Gojjam, one of the country’s crop surplus producing areas, most farmers who received loans from the semi-formal sector (81%) used credit to purchase inputs or tools. By comparison, in Wollo, a food deficit area, more than half of the loans (56%) were used to purchase livestock, followed by consumption (16 %), and start-

> 4 For a reasons such as the segmented nature of formal credit in many developing countries, a high ratio of transaction cost to potential loan size, inability to meet the collateral requirements of formal financial institutions, and high risk which, in the absence of insurance, makes it difficult to use future harvests as a guarantee to obtain formal loans, links to the formal sector are negligible in the sample. We thus do not consider this sector in our analysis.

> 5 A study based on data from 15 villages in rural Ethiopia over the period of 1994-1995 showed that more than 94% of the applicant households obtained their loans from the informal sector with almost three-fourth of the loans were supplied by relatives and friends (Ayalew 2003). A similar pattern was observed for households surveyed in seven major urban centers in 2000. The results indicated that about 79% of the loans were made available by the informal sector and once again three-fourth of which was from relatives and friends (Ibrahim _et al._ 2007). Apparently, there seems to be a
average, the CLASS scores in this sample are somewhat higher than those found in a nationally representative sample of kindergarten classrooms in Ecuador, but substantially lower than those generally found in U.S. settings (Araujo et al. 2016). Table A1 shows that CLASS scores across domains for the same teachers are positively correlated, with correlations that range from 0.43 for _Emotional Support_ and _Instructional Support_ to 0.69 for _Emotional Support_ and _Classroom Organization_ . The fact that the correlation between teacher scores on the different domains of the CLASS are far from unity likely reflects a combination of factors: Different teachers may genuinely excel in the behaviors in different domains, but measurement error would also tend to reduce the magnitude of the correlations.

Within each classroom, we used a random number generator to select a sample of 20 children who would be tested at the end of the year.<sup>6</sup> To measure achievement, we applied two language and two math tests. The language tests were a test of letter and word recognition and a test of receptive vocabulary, while the math tests were a test of number recognition and a test of simple addition and subtraction. To measure receptive vocabulary, we use the _Test de Vocabulario en Imagenes Peabody_ (TVIP)

> 6 With 198 classrooms and 20 children per classroom, our target number of children with test score data was 3,960. However, there were 9 classrooms that had fewer than 20 children in total. Accounting for these classrooms results in 3,909 children. Our final sample, 3,751 children with valid test score data, is 96 percent of the intended sample.

8
# **III. Data**

Our period of analysis spans 1/1/2014 to 10/15/2016. Overall, we have 700 trading days, and hence 699 observations. We obtain daily the WTI crude oil price from the U.S. Energy Information Administration. The WTI crude is chosen instead of Brent because WTI <mark>is the main benchmark for oil consumed in the United States</mark> . The <mark>WTI refers to oil extracted from wells in the U.S. and sent via pipelines to Cushing, Oklahoma</mark><sup>10</sup> <mark>.</mark> We use the Dow Jones U.S. Market Index (DJUS), which represents about 95% of the U.S. market, to capture U.S. equity. We use the Bloomberg bond indices for bond prices. Daily historical Dow Jones U.S. Market indices, Bloomberg HighYield Bond Indices and Bloomberg U.S. Corporate Bond Indices (investment grade) are obtained from Bloomberg. The 10 sectoral stock indices from Dow Jones are Basic Materials, Consumer Goods, Consumer Services, Financials, Healthcare, Industrials, Energy, Tech, Telecom, and Utilities.<sup>11</sup> These 10 indices together make up the Dow Jones U.S. Market Index. In addition, we also examine two important subsectors: transportation and airlines.<sup>12</sup> The S&P 500 and its sectoral indices serve as a robustness check.

The Bloomberg investment-grade corporate bonds are the aggregate index, Healthcare, Tech, Materials, Financials, Communication, Consumer Discretionary, Utilities, Industrials, Consumer Services and Energy.<sup>13</sup> Similarly, the Bloomberg high-yield corporate bond indices are the aggregate high-yield corporate bond

> 10 For 10/10/2016, we opted for future price (March strike date) to account for Columbus’s Day.
# **4. Results**

## **4.1 Cross-sectional analysis**

In our analysis we compare refugee groups to urban rather than rural nationals. Refugees are living in camp-environments in areas with high population density and usually more than 200,000 inhabitants. Job market opportunities and dynamics for refugees are, thus, comparable to urban rather than rural areas.<sup>17</sup> However, using the rural national strata does not change the main results of the analyses as we observe in a robustness check.

Our data highlights large baseline differences between the refugee and national communities that are in line with the findings in previous studies (Betts, Sterck, & Omata, 2018; UNHCR & World Bank, 2020). Refugee households are on average larger (5.6 members compared to 3.8 for nationals) and more likely to be headed by women (48 percent compared to 36 for nationals) both of which are factors that correlate strongly with household poverty levels and vulnerability during shocks (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020; Kumar & Quisumbing, 2014). Refugees are also more vulnerable than nationals. They are on average less educated with 36 percent having completed secondary school compared to 44 percent of urban nationals. They are also more than four times more likely to have their floor made from lower quality material than nationals.<sup>18</sup> Approximately one-third of the refugee sample is engaged in any kind of work compared to almost two-thirds of the nationals’ sample. The large difference is likely due to the lack of required documents to work (birth certificates, IDs and work permits). They are also more likely to be involved in informal employment than urban nationals which tends to be underreported in surveys (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020). Among working refugees, 85 percent work in trade and services while only very few engage in agriculture (8 percent) or manufacturing
.0<br>%<br><!-- End of picture text --> Source: Authors’ calculation using UNESCO Institute of Statistics database. Note: Blue: East Asian countries; Yellow: Pacific Island countries.

Taken together, many countries face the dual challenge of (i) upgrading the qualifications of existing teachers (to at least meet minimum requirements); and (ii) improving the quality of the preservice teacher training provided (to a growing number of teachers) to ensure that all future teachers meet minimum

> 4 Specifically, the indicator shows: “Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country, in a given academic year.”

3
*<br>[0.074]|||||||
|Suitable forest land|0.0320<br>[0.043]|||||||
|Max. possible output value non-<br>forest area||0.5162***|0.4830***|0.5307***|0.4904***|0.4909***|0.4846***|
|||[0.072]|[0.089]|[0.074]|[0.076]|[0.077]|[0.091]|
|Max. possible output value forest<br>area||0.0280|-0.0114|0.0050|0.0328|0.0302|-0.0118|
|||[0.041]|[0.043]|[0.041]|[0.040]|[0.041]|[0.043]|
|Yield gap|0.9486**|1.3042***|0.4959|1.3590***|0.9265**|1.0388**|0.5075|
||[0.376]|[0.404]|[0.519]|[0.442]|[0.472]|[0.439]|[0.511]|
|Land governance index|||-0.2082***<br>[0.049]||||-0.2136***<br>[0.059]|
|Weak investor protection||||-0.0013<br>[0.001]||||
|Control of corruption (%)|||||-0.0072*
to quantify the potential trade cost changes. Additionally, we are also able to predict total export changes in services in the border scenario, while without tariff data we can not obtain total export changes based on tariffs for services.

The rest of the paper is organized as follows. Section 2 presents our estimation and identification methods. Section 3 motivates the focus on the integration of the CEFTA countries in the EU (in Subsection 3.1), and describes the data and sources that we employ to perform the empirical analysis (in Subsection 3.2). Section 4 presents our empirical findings. Subsection 4.1 offers an analysis of our partial equilibrium estimates, while Subsection 4.2 translates the partial equilibrium estimates into GE effects and discusses our findings. Section 5 concludes. The Supplementary Appendix includes additional empirical results and robustness estimates.

# **2 Estimating “Deep”/“Hard” Trade Cost Changes**

This section presents our methods to estimate ex-ante comprehensive “deep”/“hard” trade cost changes in response to a hypothetical policy change. For expositional simplicity, clarity, and to facilitate the interpretation of our results in the following sections, we will focus on a specific application, i.e., the integration of CEFTA and EU.<sup>6</sup> Our departing point is the following econometric gravity model, which we adapt to accommodate the specific goals of our study:<sup>7</sup> Here, as defined earlier, _Xij,t_ denotes nominal trade flows from source/exporter _i_ to destination/importer _j_ at time _t_ . An important feature of the dependent variable is that, consistent with all of the underlying theoretical models that deliver the structural gravity equation, _Xij,t_ in-

> 6The CEFTA consists of seven countries: Albania, Bosnia and Herzegovina, Kosovo, Moldova, Montenegro, North Macedonia and Serbia. We offer further details on the history and relationship between CEFTA and the EU in Section 3.1.

>
” to 52<br>different fueling stations to<br>collect information for<br>demand and price levels|Natural experiment|Energy demand is not very<br>price sensitive along the<br>extensive margin of which<br>fuel to buy even when<br>consumers are reminded of<br>the magnitude of relative<br>price differences|
|Li et al (2013)|United States<br>and Canada|2007-2009|monthly vehicle sales,<br>vehicle MPG data|Difference-in-<br>differences|There is little or no gain in<br>sales from the program<br>encouraging consumers to|

50
ˆ **Figure 7:** Evolution of _EDθa_ ˆ and H, 1990-2019 (where _θ_ = 1 and _a_ = 70).

<!-- Start of picture text --> 60 50<br>50<br>40<br>40<br>30<br>% 30 %<br>20<br>20<br>10<br>10<br>0 0<br>Year<br>Income Deprivation Lifespan Deprivation (70)<br>Headcount ratio ED (70)<br>Lifespan Deprivation share in ED (right axis)<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1990, a newborn expected to be deprived of 58% of its life: 17 percentage points lost because of lifespan deprivation and 41 because of income deprivation. Lifespan deprivation accounted for 30% of expected deprivation in 1990.

wrong ranking whenever life expectancy and headcount diverge (which is the case we focus on). The main interest of robust comparisons is to measure the extent to which PALE allows to improve on a ranking based on the headcount only: in all these situations, irrespective of the value given to _θ_ , the ranking under PALE contradicts the poverty ranking.

# **Inter country comparisons**

To what extent does _PALEθ_ help in robustly ranking countries, as compared to a simple headcount? Figure 8 reports the proportion of all country-pairs comparisons whose ranking based on life expectancy and headcount ratio differs. There are 23% of them.<sup>25</sup> The share of these ambiguous cases for which _PALEθ_ provides a robust answer is equal to 37 percent, independently of the value given to _θ_ . In other words, 37% of these “ambiguous” cases are wrongly classified by the headcount ratio. Note also that the share of ambiguous comparisons that our index unambiguously solves strongly
**The result of this paper is a synthetic dataset that contains all variables in the EU-SILC plus energy spending shares and an energy poverty indicator, which we can ultimately use to overlap energy with monetary poverty, or to describe the energy poor population in Bulgaria.** The resulting dataset from our data fusion exercise is a synthetic version of the EU-SILC, which has information on incomerelated variables I, households' access to social protection programs, imputed expenditure-related information C (the energy spending share and energy poverty indicator), a set of overlapping variables, and all other variables included in the EU-SILC. In the last step, we use the generated dataset to analyze if the energy and monetary-poor overlap. We also explore energy spending shares by income quintiles. Future research can use the generated dataset to conduct detailed studies about the energy poor population in Bulgaria.<sup>14</sup>

# Data Description and Preparation

Data and Target Variable Description

**In this paper, we use two different data sources: the 2019 HBS and the 2020 EU-SILC with reference income from 2019**<sup>**15**</sup> . We focus on the pre-COVID year (2019) because we want to make sure that our data is representative of spending patterns in Bulgaria and not affected by idiosyncratic crisis-related behavior induced by the COVID-19 pandemic.<sup>16</sup> For this reason, we rely on the HBS from 2019. The HBS is a survey conducted every year and collects data on expenditure patterns by households in Bulgaria. The survey consists of 2,952 households and is a quarterly survey, meaning that the information is representative of each quarter of the year.

**The HBS relies on a two-stage random sample design of households and uses expenditure diaries.** In the first stage of the sampling design, census enumeration areas are selected randomly, while in the

14 For a first study of this type, see Robayo-Abril and Rude (forthcoming b).

> 15 In
Chart 15 Contributions of Shocks to Interest Rates: Tightening Phases (Average across G5 economies, percentage points) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>10<br>8<br>6<br>4<br>2<br>0<br>1970s 1980s 1990s 2000s 2020s<br>By structural shocks<br>OP GS GD GMP DS DD DMP<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>1970s 1980s 1990s 2000s 2020s<br>By types of structural shocks<br>Oil and supply Demand MP<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>1970s 1980s 1990s 2000s 2020s<br><!-- End of picture text --> Sources: Authors’ calculations for data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Historical decompositions of domestic policy interest rates around the tightening phases defined in Section II. Estimates based on the FAVAR model that consists of four global variables (global output growth, inflation, interest rates and oil prices) and three domestic variables (domestic output growth, inflation, and interest rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock, “DMP” =domestic monetary policy shock, “Oil and supply” = OP+GS+DS; “Demand” = GD+DD; “MP” = GMP+DMP.

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**Figure 6. Age-transfer profiles across Europe: Average share of transfers (pensions and benefits) over gross income by age group, 2007-2014**

_Source:_ Calculations based on EUROMOD H1.0+.

_Note:_ the light blue line indicates the average tax and social contribution rate, calculated as a share of gross income, for 2007. The navy solid line indicates the same variable for 2014. The blue dashed line indicates the counterfactual rate in 2014 had the tax and transfer system been the same as that of 2007.

So far, the analysis has focused only on taxes and social contributions. Figure 6 instead presents the age profile of incidence of transfers (expressed as a share of gross income) for three examples, Hungary, Spain and Sweden. The patterns are expected to show the highest incidence for the oldest age group, whose income is almost entirely composed of transfers, particularly pensions. Some changes over time can be seen, namely an increase in the incidence among the youngest age group in Spain and a decrease among the older age groups in the three countries, but in all the cases they seem to be entirely explained by changes in market income. The counterfactual simulation - for which the transfer system of 2007 is replicated on top of the market income structure of 2014 - coincides almost entirely with the actual scenario observed in 2014. Only in Hungary did changes in the transfer system explain a small part of the decrease in transfer incidence observed for those of working age. The same pattern as in these three countries – changes in the incidence of transfers explained almost entirely by market forces – is observed throughout Europe.

16
attainment of education. This includes the large number of deaths of teachers as a result of HIV/AIDS (Sany, 2010). Third, NGO-run primary and secondary schools stepped in to fill the education gap in the North in 2004. This could also produce a downward bias in impact of war on education.

All these factors could potentially generate selection bias and confounding errors, thus invalidating the exchangeability assumption. In the presence of these possibilities, it is unrealistic to assume that the incidence of war is randomly assigned and the confounding factors may generate bias in the causal effect of war on education. Since a direct comparison of two groups of individuals may not overcome the problem of identification, we go one step further and employ propensity score matching (Rosenbaum and Rubin, 1983). This means pairing individuals who are identical based on all observable characteristics (including department of birth, other households characteristics and the relevant socio-economic factors) that the rich ENV-2008 survey data offers, except variables that measure war victimization. We discuss it more formally below. Let us denote the binary victimization indicator equals to one if individual _i_ is a war victim and zero otherwise. We are particularly interested in estimating the average treatment effect on the treated (ATT). This can be written as equation (2) below:

# **(2)**

where denotes the potential education outcome (years of education in our case) for each individual _i_ . As the average education level of the counterfactual comparison group - - is not observed, we generate propensity scores to choose a proper substitute from the matched pairs based on propensity scores. Propensity scores are generated by simple probit regression. Individuals are paired and chosen from the war victims (treatment group) and the rest (control group) based on similar propensity scores. Then we calculate the average difference in years of schooling across them. There exists a range of possibilities for matching algorithms; however, the performance of different matching estimators depends largely on the data structure (Zhao, 2000
>OECD CEE SEE FSU1 FSU2 FSU3 FSU4 OECD CEE SEE FSU1 FSU2 FSU3 FSU4<br>% %<br><!-- End of picture text --> Source: World Bank World Development Indicators. Note: CEE = Central and eastern European countries (Poland, Hungary, Czech and Slovak Republics, Slovenia); SEE = Albania, Bosnia and Herzegovina, Bulgaria, Croatia, FYRR Macedonia, Romania, and Serbia and Montenegro; FSU1= Estonia, Latvia and Lithuania; FSU2=Russia, Ukraine, Belarus, Moldova; FSU3 = Armenia, Azerbaijan, Georgia; FSU4 = Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan.

There is also a distinct pattern in labor productivity performance. The CEE, South-East European (SEE) and Baltic states register an increase in productivity, both overall and within services (broadly defined to include government).<sup>2</sup> Conversely, for those other countries where

> 1 See Figure 1 for the definition of country groups used in this paper.

> 2 Output data is measured in constant 1995 US dollars, as reported in the World Development Indicators.

3
## **High-Density Urban Clusters Based on Grid-Level Population Data**

To check the robustness of the main result using other population data, we conducted the same estimation using the alternative population measure based on the grid-level WorldPop data. While the significance level di↵ers, the rest of the subpopulation analysis shows a similar relationship and magnitude as the analysis in the main text (Appendix E).

## **Using Data from Di↵erent Years**

Finally, we run the regressions for the 2006 and 2015 UEUS data separately to examine whether the associations between the town population size and labor outcomes hold in di↵erent years. The overall results remain unchanged (see Appendix F for the results).

## **Sectoral Shifts: Excluding Agricultural Employment**

Although agricultural employment is a small share of urban employment, it could contribute to a negative relationship between city size and self-employment (because agriculture is less prevalent in larger cities). To eliminate the possibility that the estimation result is driven by changes in sector (which tends to be self-employed), this subsection provides an estimate excluding the workers in the agriculture sector. The overall results remain unchanged (see Appendix G for the results).

# **6 Discussion and Conclusion**

Among the channels of urbanization for economic growth and poverty reduction, the transition in the mode of labor is a critically important factor. Focusing on urban Ethiopia, this study empirically examines the nexus between urban agglomerations and employment changes from selfemployment/informal jobs to wage/formal jobs. Overall, our IV regressions based on individual workers in the 2012/13 LFSs confirm such link, providing several important findings. First, town population size is positively correlated with the probability of individuals engaging in wage/formal jobs (as opposed to self-employment/informal jobs). According to our preferred speci- fication, the probability change in an individual’s chance (or the town-level share) of wage employment with respect to the town population size is 4.4