rafmacalaba/data-use-mentions-extended huggingface dataset:
**Based on the final synthetic dataset, we overlay monetary with energy poverty indicators and investigate energy poverty rates by income quintiles.** These types of analyses are not possible without data fusion methods, given that reliable and detailed expenditure and income data are only generated separately in Bulgaria, like in most countries in the EU. By employing statistical matching techniques, we can generate valuable information on who the energy poor are. We find that more than one-third of the energy-poor individuals (using the low-income high-cost measure) are not considered at risk of poverty. As a result, efforts directed at offering financial assistance to individuals in poverty might not adequately reach this particular subgroup. Importantly, these findings vary significantly by the underlying definition of energy poverty.

**This paper makes an important contribution to the increasing body of literature exploring statistical**

**matching techniques.** Researchers and Statistical Offices have become increasingly interested in alternatives to record linkage to combine different data sources, and the number of papers in this area is growing. To the best of our knowledge, we are the first ones exploring the potential of statistical matching techniques in Bulgaria. We are also the first to apply data fusion methods to the area of energy poverty. Lastly, we make a methodological contribution by exploring several different imputation models and comparing their performance to each other.

# **Future research can take advantage of the generated dataset to study energy poverty in Bulgaria in**

**more detail.** Our generated dataset allows us to describe the energy poor in more detail. Based on the new dataset, it is possible to analyze if the energy poor have access to social protection programs, for example, or what could drive their energy poverty status. In addition, having energy expenditure information in the EU-SILC allows for microsimulations that study energy price increases or potential mitigation measures.

**To improve the possibilities for statistical matching between EU-SILC and HBS, policymakers could push for greater harmonization between both surveys and include expenditure-based questions in
Source: Own estimates using CEQ and Bulgaria SILC 2021<br><!-- End of picture text --> _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by comparing the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented. Notice that the coverage and benefit incidence are the same as in Figure 10, but the impacts of generosity, poverty, and inequality differ._

# **Finally, the fiscal cost analysis reveals moderate changes between the baseline and Policy Scenario 1.**

In the baseline scenario, administrative accounts show that the MSA program incurred a cost of 32.3 million BGN and a heating allowance cost of 140.2 million BGN, resulting in 172.5 million BGN. Under Policy Scenario 1, the simulations show a moderate cost increase, with the MSA program cost rising to 230.2 million BGN, heating allowance to 174.5 million BGN, and the total fiscal cost reaching 404.6 million BGN. This reflects a moderate change in fiscal spending, with an increase of 197.9 million BGN for social assistance, 30.3 million BGN for the heating allowance, and a cumulative rise of 34.2 million BGN in the total fiscal cost (table 4). These changes underscore the financial implications of the policy simulation, highlighting a moderate expansion in government expenditure in these support programs.

30
Table 1. Global inequality (in Gini points) in 1990s, according to various authors

|Author|Year|Gini value|National mean<br>incomes from:|National<br>income<br>distributions<br>from:|
|---|---|---|---|---|
|Milanovic<br>(2005)|1993|66|Household<br>surveys|Household<br>surveys|
|Milanovic<br>(2205)|1998|65|Household<br>surveys|Household<br>surveys|
|Bourguignon<br>and Morrison<br>(2002)|1990’s|66|GDI<br>(Maddison)|Household<br>survey<br>estimates|
|Sala-i-Martin<br>(2002)|1998|61|GDI<br>(PennWorld<br>Tables)|Ginis and<br>quintiles from<br>HS|
|Bhalla (2001)|2000|65|GDI<br>(PennWorld<br>Tables and<br>WDI)|Ginis and<br>quintiles from<br>HS|
|Dikhanov and<br>Ward (2001)|1999|68|National<br>consumption<br>(WDI)|Ginis and<br>quintiles from<br>HS|
|Dowrick and<br>Akmal (2001)|1993|71|GDI|Ginis and<br>quintiles from<br>HS|
|Sutcliffe (2003)|2000|63|GDI<br>(Maddison)|Ginis and<br>quintiles from<br>HS|
|Chotikapanich et<br>al. (1997)|1990|65|GDI<br>(PennWorld<br>Tables)|Ginis for HS|

Note: HS=household survey; GDI=Gross Domestic Income. WDI=World Development Indicators (World
the April 2020 Current Population Survey,” Technical Report, National Bureau of Economic Research 2020.

- **Fajnzylber, Pablo, William Maloney, and Gabriel Montes Rojas** , “Microenterprise dynamics in developing countries: How similar are they to those in the industrialized world? Evidence from Mexico,” _The World Bank Economic Review_ , 2006, _20_ (3), 389–419.

- **Frazer, Garth** , “Which Firms Die? A Look at Manufacturing Firm Exit in Ghana,” _Economic Development and Cultural Change_ , 2005, _53_ (3), 585–617.

- **Gollin, Douglas** , “Getting Income Shares Right,” _Journal of Political Economy_ , 2002, _110_ (2), 458–474.

- , “Nobody’s business but my own: Self-employment and small enterprise in economic development,” _Journal of Monetary Economics_ , 2008, _55_ (2), 219–233.

- **Hsieh, Chang-Tai and Benjamin A. Olken** , “The Missing ”Missing Middle”,” _Journal of Economic Perspectives_ , September 2014, _28_ (3), 89–108.

- **Islamaj, Ergys, Young Eun Kim, and Duong Trung Le** , “The Spread of COVID-19 and Policy Responses,” _World Bank. https://openknowledge.worldbank.org/handle/10986/35010_ , 2021.

- **Jayachandran, Seema** , “Microentrepreneurship in Developing Countries,” Working Paper 26661, National Bureau of Economic Research January 2020.

- **Kraus, Sascha, Thomas Clauss, Matthias Breier, Johanna Gast, Alessandro Zardini, and Victor Tiberius** , “The economics of COVID-19: initial empirical evidence on how family firms in five European countries cope with the corona crisis,” _International Journal of Entrepreneurial Behavior Research_ , 2020.

- **Le, Duong Trung, Edmund Malesky, and Anh Pham** , “The impact of local corruption on business
# **3 Data**

## **3.1 Sample**

This paper makes use of data from the Hohoe Garment Makers Project, collected in Hohoe Municipal District, a mountainous part of the Volta Region in Eastern Ghana near the border with Togo. In February of 2014, the research team conducted a census of all garment making firm owners in the district in preparation for this and other projects, a listing which uncovered 445 active garment making firm owners in Hohoe Town and its outlying suburbs. The activity began with existing lists of firms provided by the leadership of the local chapter of the Ghana National Tailors and Dressmakers Association (GNTDA) and other local trade associations, and continued through snowball sampling until all leads were exhausted. The field team then conducted a final stage of geographic road-by-road canvassing.<sup>7</sup> Of the firms identified in the census, 416 were still operational at the time of the weekly monitoring surveys in March and April of 2015.<sup>8</sup> Of these, 342 firms were verified by the field team to have an electricity connection to their shop or place of business at the time of the weekly surveys.<sup>9</sup> These 342 firms make up the analysis sample.

## **3.2 Data Sources**

In addition to the February 2014 census, supplementary baseline data was collected in the summer of 2014 and recall information in June of 2015 on machinery and equipment in use as of June 2014. Together, these three data sources make up the baseline dataset.

The primary analysis dataset was generated by a weekly panel survey conducted over seven weeks in March and April of 2015. All 445 firms in the Hohoe town census sample were cluster randomly assigned by neighborhood to weekdays for the weekly surveys, in an effort to spread daily recall randomly across days (if, for example, the weekend interlude makes it easier or harder to recall certain information). Data collection began on Thursday
**Table 1:** **<u>Share of Bangladesh’s Exports with Neighbouring Countries*</u>**

|**Country**|||**Y**|**ear **|||
|---|---|---|---|---|---|---|
||**1995**|**2000**|**2005**|**2007**|**2008**|**2009**|
|**Bhutan**|0.00|0.00|0.02|0.00|0.00|0.00|
|**India**|0.79|0.35|1.90|2.31|2.20|1.53|
|**Nepal**|0.03|0.00|0.01|0.01|0.01|0.01|
|**Pakistan**|1.03|0.91|0.87|0.41|0.40|0.48|
|**Sri Lanka**|0.41|0.10|0.13|0.13|0.14|0.12|
|**South Asia total**|**2.26**|**1.36**|**2.93**|**2.86**|**2.75**|**2.14**|
|**Rest of the world**|97.74|98.64|97.07|97.14|97.25|97.86|

*As percentage of total export.

_Source_ : Calculated based on UN COMTRADE.

## **
# **FI dly** background **and** student achiev_nt

Education advocates in Third World governments and international agencies have been buoyed **in recent years** by evidence that the offect of school characteristics on student achievement, when compared with the effect of student family background, **appears** to be greater in Third World countries than within industrialized countries. For example in their widely cited reanalysis of International Education Association (IEA) and other data from 29 countries, Heyneman and Loxley (1983) report that--taken altogether--characteristics of schooLs in developing countries explain a greater share of the variance in student achievement than is explsined by student background characteristics. In addition, a significant negative correlation **was** found between the percentage of variance explained by school characteristics and the nation's level of wealth. As a case in point, they note that 27 percent of the variance in achievement among children in India was attributable to school factors while only 3 percent -as explained by variation in background characteristics. In contrast, achievement levels within industrialized countries were explained mostly by student background. Similarly, a recent review of approximately 60 empirical studies of determinants of achievement in developing countries found that school characteristics were related to student achievement in the vast majority of studies, after holding constant student social class background (Fuller, 1986).

Yet there is strong evidence that student family background contributes significantly to both educational attainment and achievement in developing countries. Family background affects the probability that children enroll in, attend, and complete various levels of education. For example, analyzing historical data from the

**-3-**
report cross-national carbon dioxide Engel curves. We next present data from California and Europe to document that these wealthy areas feature significant numbers of consumers creating a large average carbon footprint. In our last piece of empirical work, we turn to micro survey data to estimate the association between education and support for environmental protection. Together, these various pieces of evidence allow us to provide a consistent explanation for the slow progress in negotiating sharp greenhouse gas emissions reductions.

# **Estimating Engel Curves**

Table 1 lists the nations for which the World Bank has assembled data on household durables ownership. The table also reports the first year the survey data are available. Data on asset ownership come from the World Bank’s Global Monitoring Database (GMD). The original source of the data are country-level household survey data that measure household income or consumption in each country. World Bank teams work closely with National Statistical Offices (NSOs) to ensure that household survey data are of good quality and that technical calculations are robust and aligned with international best practices. However, data on durables ownership are not collected in all national household surveys. Further, the World Bank does not have access to micro data from China’s National Bureau of Statistics; thus, China does not appear in the data set. For each of these nation/year pairs we have five data points for each durable good. These five data points correspond to the quintiles of the income distribution in that nation/year. The World Bank data also provides a measure for each nation/year/income quintile of either the average daily personal consumption or average daily income measured in PPP $ 2011.

For 66% of the sample, we observe the income and for 33% we observe the consumption measure. This variable is the key explanatory variable in our regressions below. In results available on request, we have run our Engel curve estimates separately (so using log(consumption) for 33% of the sample and log(income) for the other 66%
Empirical studies have attempted to test this prediction using data on democracy and economic liberalization. An area that has attracted substantial interest is trade policy. Overall, the existing literature suggests a positive impact of democracy on economic (trade) liberalization (e.g., Banerji and Ghanem 1997; Milner and Kubota 2005; Giavazzi and Tabellini 2005; Eichengreen and Leblang 2008; Giuliano _et al._ 2011). Some studies, however, have argued that this effect is not generally true but depends on a country’s resource endowment (e.g., O’Rourke and Taylor 2007; Kono 2006).

There are several methodological critiques of these studies, such as the problem of spurious correlation between democracy and economic reforms (Eichengreen and Leblang 2008) or the existence of potential feedback effects (Giavazzi and Tabellini 2005; Milner and Mukherjee 2009). An additional problem is that most existing studies have examined the relationship between democracy and trade policy using aggregate trade indices, such as the trade to GDP ratio or the Sachs and Warner (1995) openness index (e.g., Giavazzi and Tabellini 2005; Milner and Kubota 2005; Persson 2005; Eichengreen and Leblang 2008; Tavares 2007). Studies have only rarely employed direct indicators of trade policy, such as tariffs. Moreover, aggregated trade policy indicators may be misleading because different (and possibly offsetting) effects may occur at a disaggregated level (Anderson and Martin 2006). Thus, an examination of disaggregated policies, such as agricultural and food policies, could yield additional insights.

Empirical studies have estimated the impact of political institutions on agricultural policies. Lindert (1991) was the first to document a positive impact of democracy on agricultural protection. Beghin and Kherallah (1994) examine the impact of different political systems (no-party, one-party, dominant party, and multiparty systems) on agricultural protection. They find that political institutions are important and that their effect is nonmonotonic: protection peaks with dominant party systems and then becomes 8
_“Short-run” estimates: The static model_ . Table 3 presents estimates of agricultural per capita GDP using annual data using RE and FE with robust standard errors. We tried various lag structures for the explanatory variables. The results are robust to the method of estimation and to other changes in specification. The sign pattern of the coefficients does not change, though their sizes and standard errors are mildly affected by the actual lags used. We present in Table 3 the estimates using one-year lags for the explanatory variables.

The fact that now we have many more observations allows us to use annual time dummy variables in the regressions. The time dummies control for unobserved common-to-the-countries shocks that vary over time.<sup>25</sup> The pattern of significance of the estimated coefficients and their signs are highly consistent with the “long-run” estimates reported earlier. Consistent with the short-run/long-run interpretation, in general the elasticity estimates using annual data have lower absolute values than the coefficients estimated using the five-year averages.

In particular, the effects of total government expenditures in rural areas are still positive and statistically significant, but their values are about 50% lower than the values estimated using five-year averages. Similarly, the share of government subsidies in total expenditures is now negative and significant, as before, but the estimates are smaller in absolute value. Still, the message is the same: total government expenditures are important in promoting agricultural income, but the structure of public expenditures is even more important. Switching resources from subsidies to public goods can be an extraordinarily effective instrument to achieve faster agricultural growth.

The reduction of the size of the effect of the non-agriculture income is even more pronounced than that of the public expenditures variables. In fact, the effect of non-agriculture GDP falls by about 66% when annual data is used compared to five-year averages, though the coefficient is still significant. This drastic reduction in the size of this coefficient may reflect not only the short-run/long-run dichotomy, but also a possible reduction of the size of
|6.56|7.36|
|**2016**|11,557|7.39|8.98|8.12|9.78|5.52|6.30|6.08|6.64|
|**2017**|11,557|7.30|8.94|8.01|9.73|5.30|5.89|5.82|6.18|
|**2018**|11,557|7.28|8.92|7.98|9.71|5.14|5.54|5.65|5.85|
|**2019**|11,557|7.27|8.91|7.98|9.70|5.05|5.36|5.56|5.68|
|**2020**|11,557|7.20|8.59|7.90|9.40|4.97|4.77|5.48|5.14|

Note: Table presents main summary statistics of the Russian tariffs computed for products at ten-digit level of the Russian tariff classification. An ad valorem rate ignores the specific component of the tariff. An applied MFN rate includes both ad valorem and specific components of the tariff. Specific component of the tariff is translated into the ad valorem equivalent according to the formula that is explained in the methodology section and using import statistics of Russia in 2006-2010. For 2011-2020, the ad valorem equivalent is computed using import data of 2009-2011. Weighted averages are computed using weights computed as the share of the product line _h_ in the total import at time _t_ .

**Table A2.  Calculation of the Russian Simple Average Tariff on Matched Tariff Lines only in 2011-2020.**

||**Number of matched tariff lines**|**un-weighted average tariff*
nological opportunities might be particularly low in countries such as Indonesia and Mexico, where heavily subsidized prices result in energy-efficiency being not a major concern for the average producer. Our finding that the positive impact of fuel prices on investment is muted for foreign and exporting plants provides further support to the market frictions and bounded rationality hypothesis, as information frictions and deviation from profit-maximizing behavior are likely to be more limited for these The rest of the paper is organized as follows. Section 2 discusses some of the key institutional features of the case studies; Section 3 describes data; Section 4 sets out the identification strategy; Section 5 provides the results, and Section 6 concludes.

# **2 Institutional context**

Indonesia and Mexico are particularly suitable contexts to study the relation between fuel prices and firms’ performance. First, they are two of the largest emerging countries, which together account for 2.6% of world’s CO2 emissions.<sup>11</sup> A significant part of the emissions is due to fuel consumption, which is sustained by long-standing policies of subsidized fuel prices in both countries, including for industrial users. These subsidies translate into some of the lowest fuel prices among a large sample of countries for which this data are available (panel a) in Figure 1).<sup>12</sup> The low prices have contributed to relatively high consumption of fossil fuel by domestic industries in both countries. Fuels account for roughly 65% of total energy consumption in the average manufacturing plant in Indonesia and 68% in Mexico, but only 40% in France (Marin and Vona, 2017), where fuel prices are considerably higher in purchasing power parity terms. On the other hand electricity prices in both countries are in the middle range within the same sample

> 11The figure refers to 2014 according to World Bank Data on emissions.

> 12In Indonesia the subsidy has been largely phased out in the public budget at the end of 2014, but energy prices continue to be implicitly subsidized
25 On the other hand, US opponents of NAFTA saw primarily the traditional "losses" from free trade; that is, downward pressure on the wages of relatively low-skilled workers in the US and reduced investment in labor-intensive industries.

# _B._ **_Europe Agreements_**

The Europe Agreements between the CEE countries (the Czech Republic, Slovakia, Bulgaria, Poland, Hungary and Romania) and the EU, concluded in 1993, are an interesting contrast to NAFTA. They are similar in a number of respects:

- they were between much larger developed economies and much smaller emerging economies;

- they "contain significantly fewer concessions by the large countries to smaller countries than vice versa"<sup>24</sup>

- the direct effects of the trade liberalization provisions were relatively small.

However, as will be seen, there are also some important differences.

As with NAFTA, the pure trade liberalization provisions of the Europe Agreements were not likely to have a substantial effect on CEE-EU trade. While Wang and Winters (1991) and Hamilton and Winters (1992) found that EU-CEE exports were much smaller - by a factor of 2 to 10 - than could be expected on macroeconomic and geographic grounds, trade barriers were not the primary cause: using 1989 data, Aghion et al. (1992) estimated that the potential effect of complete removal of trade barriers would only be an expansion of about 40 percent in CEE exports to the EU, more than half of which was in agriculture, while other estimates were even lower.

However, between 1989 and 1992, without complete liberalization, trade expanded by more than 50 percent, most of which was in sectors like manufactures where tariff barriers were not that high in the first place<sup>25</sup> This suggests that EC trade policy has not been the primary factor behind the observed trade expansion. This does not in itself

> 24 Perroni and Whalley (1994).

> **_25_** Portes (1993).