rafmacalaba/datause-encoder-data huggingface dataset:
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40
Table 5: Total Residential Energy Consumption (by source and quintiles of household consumption) Panel A: Absolute terms Panel B: Relative terms (in %)

||Gasoline<br>(million<br>liters)|LPG<br>(1,000s<br>tons)|Diesel<br>(million<br>liters)|Electricity<br>(GWH)||Gasoline|LPG|Diesel|Electricity|
|---|---|---|---|---|---|---|---|---|---|
|Quintile 1|1|80|1|587|Quintile 1|0.3|15.3|1.6|12.5|
|Quintile 2|5|99|4|761|Quintile 2|1.7|18.9|6.8|16.2|
|Quintile 3|18|107|8|881|Quintile 3|6.3|20.5|12.9|18.7|
|Quintile 4|54|122|12|1033|Quintile 4|18.7|23.4|19.4|22.0|
|Quintile 5|213|114|37|1440|Quintile 5|73.0|21.9|59.4|30.6|
|Total<br>|292<br>|521<br>|63<br>|4702<br>|Total|100|100|100|100|

_Source:_ WB staff calculations using SUBSIM.

Similarly, when it comes to the per capita consumption of energy, Table 6 unequivocally confirms that the top consumption quintiles, the richest quintiles, consume much more than the poorest quintiles. Consumption differences are largest for gasoline, followed by diesel (panel A). On average, an individual from quintile 5 consumes 200 times more gasoline than someone from the poorest quintile. That ratio is still a whopping 38 to 1 in the case of diesel. Much narrower differences are observed for electricity and LPG. A richer individual consumes 4.5 times more electricity and 1.4 times more LPG than an individual from the poorest household. Individuals from the fourth quintile consume more LPG on average than anyone else in the distribution. When the analysis is conducted for households—rather than individuals—(panel B) very similar ratios and distributions are observed, confirming results for individuals.

Table 6: Per Capita and Per Household Consumption of Subsidized Energy (in quantity) Panel A: Per individual Panel B: Per household

||Gasoline<br>(liter)|LPG<br>(kg)|Diesel<br>(liter)|Electricity<br>(kWh)||Gasoline<br>(liter)|LPG(kg)|Diesel<br>(liter)|Electricity<br>(kWh)|
|---|---|---|---|---|---|---|---|---|---|
|Quintile 1|0.46|36.50|0.45|37.41|Quintile 1|2.53|200.75|2.47|205.75|
|Quintile 2|2.30|45.35|1.95|49.59|Quintile 2|11.5|226.75|9.75|247.95|
|Quintile 3|8.45|49.08|3.70|61.23|Quintile 3|38.02|220.86|16.65|275.53|
|Quintile 4|25.02|55.99|5.58|86.58|Quintile 4|100.08|223.96|22.32|346.32|
|Quintile 5|97.74|52.39|17.07|167.20|Quintile 5|342.09|183.36|59.74|585.2|
|Total|26.79|47.86|5.75|80.40|Total|107.16|191.44|23|321.6|

_Source:_ WB calculations using SUBSIM.

In terms of expenditures, Figure 3: Household Expenditure on Energy (% of total household expenditures by quintiles of consumption) shows that the expenditure of energy represents 13
from much less than one at the end of January and February to 5.84, 8.80, 6.41, and 12.60 at the end of March, April, May and June, respectively. The distributions across states show considerably more variation over space and time. New York State was the epicenter of cases at the end of March, with 37.1 reported cases per 100K – more than six times the national average. New York State had fewer cases in subsequent months, with Arizona having the highest number of new infections at the end of June with 41.2 per 100K.

We next consider the authors’ index of government activity restrictions, a sum of various constraints on activities that ranges from zero to 10. The national average was exactly zero at the end of January and only 0.023 at the end of February with a single restriction in Washington State at that time. The national average across states rose to 6.85 at the end of March, peaked at 7.56 at the end of April, and fell slightly by the ends of May and June to 6.78 and 6.41, respectively. There was not as much variation across states in the restrictions as there was in the infections reported just above. Individual states hit maximums of 10 at the ends of March and April, with the maximums falling to nine and eight at the ends of May and June, respectively.

Not surprisingly, public health and government restrictions shocks had devastating effects on the U.S. economy. A recession was underway by late February and conditions worsened until April. The seasonally adjusted U.S. unemployment rate increased from 3.5% in January and February 2020, the lowest rate in many decades, to 4.4% in March to 14.8% in April, the highest rate since the Great Depression.<sup>6</sup> Real U.S. GDP, which is measured in quarters, declined at an annualized rate of 5.0% in 2020:Q1 and a record rate of 31.4% in 2020:Q2.<sup>7</sup> The economic decline

> 6 <u>https://fred.stlouisfed.org/series/UNRATE</u>

> 7 The annualized rate of decline of 31.4% amounts to about an 8% decline in the quarter.

> <u>https://fred.stlouisfed.org/graph/fredgraph.xls?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&gr aph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width</u> =412&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=A191RL1Q225SBEA

13
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22
barely; at the upper bound of its 95% confidence interval, the survey estimate accounts for 99% of registered participants. This does not suggest that there is likely to be a serious concern about undercounting of Jefes participation in the EPH relative to its sample frame.

There is a question of how _Jefes_ “eligibility” should be defined in terms of the EPH data. In practice, the status of being unemployed and a head of the household was certified via a signed statement by the beneficiary. However, the only signal of unemployment status that could be reliably checked by the authorities was whether an individual was participating in the formal labor market. It is thus of interest to consider a definition of eligibility that is close to what could be enforced in practice by the program administrators. We will define a sampled adult as “eligible” if he or she is not employed in the formal labor market and lives in a household with a child (under 18 years and belonging either to the head or the spouse) or a handicapped person. We will however point out some important differences between this “practical eligibility” definition and the official “theoretical eligibility” definition.

Table 1 compares _Jefes_ participants from the EPH with households satisfying the program’s eligibility criteria. About one third of those receiving the program did not satisfy the eligibility criteria. And about 80% of those active individuals who were eligible (though not necessarily poor) did not receive the program. Notice that applicants not yet receiving the program were more likely to be ineligible than current recipients.

Tables 2 and 3 give a broader set of descriptive statistics; Table 2 is for the full crosssectional sample in October 2002 while Table 3 gives results for the baseline sample of October 2001. In both cases, we split the sample between current participants and applicants who have not yet joined the program; we will draw a comparison group from the latter, so the comparison 10
Policy Research Working Paper 5264

# **Abstract**

Agriculture including food products is of particular interest for Ukraine. However, in free trade agreements involving the European Union, agriculture is always given special treatment and subject to less and slower liberalization than other sectors. This paper employs the standard Global Trade Analysis Project model in order to assess how World Trade Organization accession affects agriculture in Ukraine, and how potential bilateral tariff cuts may interact with potential productivity gains within Ukrainian agriculture. The results indicate that, due to trade liberalization, Ukraine can expect gains from a more efficient allocation of its resources in line with comparative advantage, leading to an increase of production and exports of wheat, other grains, and oilseeds, but also of several processed food products that benefit from less expensive intermediate inputs. However, Ukraine’s exports are concentrated on a small number of destinations, especially Russia and some other Former Soviet Union countries because they fail to meet quality standards elsewhere. When Ukrainian production of these products increases due to increased allocative efficiency, exports to Russia increase further and prices there fall, generating negative terms of trade effects that largely offset the allocative gains. Ukrainian imports of agricultural products increase as well, partly because Ukrainian consumers switch to higher quality imported goods even though domestic production increases. Regarding free trade agreement negotiations with the European Union, these results highlight for Ukraine the fact that improved agricultural productivity will help to get most out of improved market access. However, the results also highlight for Ukraine the great importance of adopting internationally accepted quality standards in order to diversify its export structure.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group; and Europe and Central Asia Region—is part of a larger effort by the Bank to support Ukraine’s further regional and global economic integration. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at scramon@gwdg.de.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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Policy Research Working Paper 10514

# **Abstract**

Using detailed monthly firm-level trade data from Indonesia from February 2019 to June 2021, this paper shows that firm-level exports were overall more resilient than imports during Covid-19. Firms that participated in global value chains were more resilient to the Covid-19 shock beyond the immediate short-run compared to firms that did not. However, among global value chain firms, those that faced certain types of non-tariff measures on their import products, notably port of entry restrictions, on average faced larger reductions in export quantities and number of transactions compared to firms that did not face such restrictions, consistent with the evidence of major port congestion during Covid-19. Therefore, although international connectedness could be a source of vulnerability to global shocks in the immediate short run, policies that enable firms to be more globally engaged through global value chains could enhance resilience. Relatedly, tackling measures such as port of entry restrictions can ensure fast and efficient port and customs procedures, especially during periods of high port congestion, as global value chain trade requires goods to cross borders many times.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **II. Sample and Measures**

Our sample includes 3,153 children in 158 parishes in six provinces in Ecuador.<sup>4</sup> We briefly describe some important features of the Ecuador setting and of our sample, and then turn to a discussion of the measures of cognitive development, SES, child health, and parenting quality.

<u>Sample: Ecuador is a lower-middle income country. In 2002, its per capita GDP was US</u> $ 1,796, about half the population-weighted Latin American average of US $ 3,740. Inequality is high (the Gini coefficient is 0.44), although not especially so by Latin American standards. Poverty is widespread. An estimated 18 percent of the population lives on less than a dollar per person per day, and more than 40 percent live on less than two dollars per day (World Bank, 2004).

Overall health indicators for Ecuador are roughly on par with those of other Latin American countries. Life expectancy is 72 years, and infant mortality is 33 per thousand live births—very close to the mean values for the region. Ecuador does worse on measures of child nutrition: One of every four children under the age of five has low height-for-age (stunting), a figure that is comparable to other “Andean” countries in the region (Bolivia, Peru) but is far above the Latin American average of 15 percent, and one in ten children has low weight-forheight (wasting), a very high figure by regional standards. Preventable, communicable diseases such as diarrhea and acute respiratory infections are the main source of morbidity and mortality among children in Ecuador (Vos et al., 2004). Intestinal parasitic infections (IPIs) are also widespread: One recent study finds that more than 90 percent of children in a study area in the rural highlands of Ecuador were infected with one or more pathogenic IPIs, and more than onehalf the children were infected with helminths (Sackey, Weigel, and Armijos, 2003). We are not aware of any published studies that measure hemoglobin levels or cognitive development among pre-school children in Ecuador.

The sample we use contains urban and rural children, and is drawn from a survey conducted between October 2003 and September 2004. This survey is intended as a baseline for an evaluation of the _Bono de Desarrollo Humano_ , the largest social transfer program in Ecuador.

> 4 The provinces are Azuay, Loja, and Pichincha in the sierra or highlands region of the country, and Esmeraldas, El Oro, and Los Ríos on the coast. Parishes, or “parroquias,” are the smallest administrative units in Ecuador. There are 1,149 parishes in the country.

4
feeling that the social order is somewhat fragile, although, the government has recently undertaken steps to address these sentiments.

_Table A3_ provides detailed institutional comparisons. Transparency International‘s Corruption Perception Index, which relates corruption perceptions of various countries‘ government, indicates considerable differences between Botswana and South Africa: Botswana‘s score is some 20 percent higher (better) than South Africa‘s. These results are consistent with the Quality of Governance Index provided by Kaufmann et al. (2004). Botswana has higher control of corruption values than South Africa. Similarly, the Rule of Law Index, which measures the degree of agents‘ confidence in and compliance with social rules, is more than three times larger in Botswana. Consequently, the respect of citizens for the state and the institutions that govern economic and social interactions is higher in Botswana. The capacity of the government to effectively formulate and implement sound policies (represented as the government effectiveness and regulatory quality) is higher in Botswana, which also has a higher level of political stability and absence of violence. Only voice and accountability are higher in South Africa, referring to the process by which governments are selected, monitored and replaced. Overall, the values of these six governance dimensions for the periods 1998 and 2000 clearly indicate a higher level of institutional quality in Botswana compared to South Africa. These results are also supported by the _International Country Risk Guide_ (ICRG). _Table A3_ also shows that political rights and the level of civil liberty are similar in both countries. To measure the variable income inequality – we use the newest available data set, Estimated Household Income Inequality (EHII), constructed by Galbraith and Kum (2005)<sup>7</sup> . The GINI coefficients indicate that income inequality is slightly greater in Botswana. The Index of Economic Freedom clearly indicates a higher fiscal burden<sup>8</sup> for South Africa, but also less government interventions in South Africa. The Polity IV data set shows, in line with previous data sets, that Botswana has more

> 7 Galbraith and Kum (2005) estimate gross household income inequality from a regression between the Deininger and Squire (1996) inequality measures and the UTIP-UNIDO pay inequality measures. 8 The index of fiscal burden measures the burden a government imposes on its citizens. The following variables have been integrated in the index: top income tax rate, tax rate an average taxpayer faces, top corporate tax rate and government expenditures measured as a percentage of GDP. To get the index, the scores of the income tax rate and the corporate tax rate are measured separately and then averaged to get a single taxation score. The final score for the fiscal burden consists of the averaged scores for income and corporate taxation and for government expenditures.

28