rafmacalaba/datause-encoder-data huggingface dataset:
|Rowland and<br>Torres (2004)|Dependent variables: sovereign rating (Institutional Investor rating), spread<br>Explanatory variables: GDP growth rate, inflation rate, external-debt to GDP ratio, external<br>debt to exports ratio, debt service as a share of GDP, the level of international reserves as a<br>share of GDP, and the openness of the economy (exports and imports as share of GDP) and<br>a dummy that takes value one for the years in which a country is in default.<br>Number of observations: 225<br>R<sup>2</sup>: 0.62<br>Pooled cross section and time series of 15 emerging market (developing) countries for<br>1987-2001.|
|---|---|
|Rowland<br>(2005)|Dependent variables: sovereign rating (Moody’s, S&P, Institutional Investor rating), spread<br>Explanatory variables: GDP per capita, GDP growth rate, inflation rate, external-debt to<br>GDP ratio, debt-service ratio (ratio of external debt service to current account receivables),<br>the level of international reserves as a share of GDP, and the openness of the economy<br>(exports and imports as share of GDP)<br>Number of observations: 49<br>Adjusted R<sup>2</sup>: 0.58 for Moody’s, 0.69 for S&P<br>Pooled time-series and cross-section data|
|Sutton (2005)|Dependent variables: sovereign rating (average of Moody’s and S&P)<br>Explanatory variables: Corruption index, international reserves, ratio of short-term bank<br>claims to total claims, external-debt to exports ratio, external-debt to GDP ratio, years since<br>resolution of last default, and a dummy for whether the country was admitted to the EU<br>Number of observations: 32<br>R<sup>2</sup>: 0.87<br>Cross-section of 32 developing countries in 2004|
|Mora (2006)|Dependent variable: sovereign rating (average of Moody’s and S&P)<br>Explanatory variables: GDP per capita (PPP), GDP growth, inflation rate, budget balance<br>(% of GDP), current account balance (% of GDP), ratio of external debt to exports of goods<br>and services, an indicator for the level of economic development (dummy for OECD),<br>dummies for default on bonds and bank debt, and lagged spread.<br>Number of observations: 705<br>R<sup>2</sup>: 0.58-0.68<br>Pooled cross-section and time series of 88 countries for 1986-2001.|

11
in terms of diversification).<sup>4</sup> Moreover, recent meta-analyses of studies of financial education generally show positive impacts of such training on financial outcomes (Fernandes et al., 2014; Miller et al., 2015). Although much is still to be learned about how the content, timing, and delivery of that training affects financial outcomes, a number of the most recent studies that employ rigorous identification methods such as randomized control trials (RCTs) also find positive impacts. Financial training is also likely to be potentially relevant in many environments, since cross-country evidence shows that a large share of the population struggles to understand basic financial concepts, even in developed countries (Lusardi and Mitchell, 2012).

These factors also appear to be important in the Chinese context. Li, Rozelle, and Zhang (2004) find that a minimum level of educational competence is necessary for understanding the terms of micro-loans in China (durations, interest rates, and repayment conditions) and for reading the passbooks linked to those micro-credit accounts. And recent RCT evidence shows that financial education for rural farmers in China increased their uptake of insurance (Cai et al., 2015). We find that better financial knowledge is associated with loan portfolios that are tilted toward formal sources at the expense of informal loans. However, taking financial classes and college education is associated with higher interest rates on bank loans for urban residents, suggesting perhaps that financial knowledge coincides with greater demand for credit in areas with more economic opportunity.

Household characteristics also play an important role in describing variation in usage of financial services. First and foremost, we find that household wealth (or its proxies) tends to be affiliated with better access to finance. While this is not surprising, it raises questions about how wealth can be pledged as collateral or whether wealth is simply a summary statistic for

> 4 See Miller et al. (2015) for an overview.

5
Quantifying the Magnitude and Severity of Absolute<sup>Poverty</sup> in the Developing World in the Mid-1980s*

# by

Martin Ravallion, Gaurav Datt, Dominique van de Walle, and Elaine Chan

# Table of Contents

|1.<br>Introduction|1|
|---|---|
|2.<br>Approaches<br>to Defining an "Absolute Poverty Line"|2|
|3.<br>An International<br>Comparison of Poverty Lines|4|
|4.<br>Poverty Measures and their Estimation<br>from<br>Distributional<br>Data|8|
|5.<br>Extrapolations<br>When Distributional<br>Data are Unavailable|15|
|6.<br>Ine Estimates of Aggregate Poverty in the<sup>Developing</sup><br>(ountries|17|
|7.<br>An Alternative<br>Estimate Based on the Berry et al.,<br>World Lorenz Curve|20|
|8.<br>Scme Implicaticns|21|
|9.<br>Conc!usions|26|
|Tables|31|
|Appendices|34|
|Ntes|37|
|References|39|
|Figures|41|

* This was prepared as a Background Paper for the World Bank's _World Development Report 1990._
**<u>Table 2. Lower threshold of the middle class: income level associated with 4 percent probability of</u>**

|<br>**falling into poverty**<br>_Income level(2017_<br>|<br>_PPP dollars)_|<br>|
|---|---|---|
|Country<br>2005–08|2015–18|% change<br>between<br>2005–08 &<br>2015–18|
|_Eastern Europe_|||
|Bulgaria<br>30|51|69|
|Czechia<br>38|36|–6|
|Estonia<br>44|50|14|
|Hungary<br>48|43|–9|
|Lithuania<br>45|45|0|
|Latvia<br>42|52|23|
|Poland<br>50|46|–8|
|Slovenia<br>45|44|-1|
|_Continental Europe_|||
|Austria<br>56|72|29|
|Belgium<br>48|51|6|
|Cyprus<br>62|51|–17|
|Denmark<br>61|73|19|
|Spain<br>57|65|13|
|Finland<br>52|54|5|
|France<br>50|50|0|
|Greece<br>48|56|16|
|Italy<br>53|63|19|
|Luxembourg<br>69|67|–2|
|Malta<br>44|47|7|
|Netherlands<br>49|59|20|
|Norway<br>63|85|35|
|Portugal<br>50|46|-8|
|Europe(average)<br>50.5|55.5|10|

_Source_ : Authors’ calculations using data from the EU-SILC UDB-L. _Note_ : Europe is average of countries listed in the table.

21
# 3.3 Population

Knowing the size of the rural population in the year 2000, P1,1, as well as N1<sup>c</sup> ,1<sup>/(1−q</sup> 1<sup>ae</sup> ,1<sup>),</sup> N1<sup>a</sup> ,1<sup>andN o</sup> 1,1<sup>,wecanobtainF1,1from</sup> Using eq. (2), we then obtain F1,0.<sup>18</sup> Similarly, for the urban population, we have which yields F2,1; F2,0 then follows from eq. (6). For any sequence {Mt<sup>a}t</sup> t<sup>=</sup> =1<sup>T,itis</sup> then possible to calculate all the demographic variables Fi,t and Pi,t (i = 1, 2) in all subsequent periods. Note that the mortality rates in the early and late phases of adulthood, qi,t<sup>aeandq</sup> i,t<sup>al,areassumedtobeequalforalliandt.</sup> holds in both sectors spent ‘almost the same amount’ (Birr 49 compared to Birr 42). 18 c Values of qi,0<sup>andq</sup> i,<sup>a</sup> 0<sup>arealsorequired.Thesearecalculatedusingthefollowingmortalityrates</sup> from World Bank (2003):

||Adult female|Adult male|Infant|Under-5|
|---|---|---|---|---|
|1980|400.98|490.97|143.00|213.00|
|2000|535.00|594.00|117.00|174.00|

where qi,t<sup>ais the average over the two sexes.Child mortality in 1980 is proportional to child mortality in</sup> 2000, that is, q1<sup>c</sup> ,0<sup>= q</sup> 2<sup>c</sup> ,0<sup>= (213/174)·0.062 = 0.0759.Similarly, for premature adult mortality in 1980,</sup> assuming that the sectoral mortality profiles do not differ: q1<sup>a</sup> ,0<sup>= q</sup> 2<sup>a</sup> ,0<sup>= [(400.98 + 490.97)/(535.00 +</sup> 594.00)] · 0.200 = 0.158. Our data indicate fewer children in period 0 than in period 1, but the World Bank (2003) reports total fertility rates of 6.60 in 1980 and 5.65 in 2000. Note that Pi,t corresponds to the population aged 5 or more years.

19
Policy Research Working Paper 9669

# **Abstract**

In April 2015, the Government of Ukraine abruptly raised the tariffs of natural gas to residential customers, which were previously well below the cost of acquiring gas and delivering it to households. The tariff increase—700 percent—caused considerable distress to the population and led the government to scale up its existing energy assistance program, the housing and utilities subsidy program. This paper examines the welfare effect of the program and potential redesigns of the program. Using several waves of Ukraine’s Household Budget Survey, the analysis finds that electricity, gas, and fuels account for a considerable share of household income. After the tariff hike, the average household that did not receive the housing and utilities subsidy spends 11 percent of its income on electricity, gas, and fuels, implying that it meets the definition of “fuel poor.” The average share for households that do receive the subsidy is 6–8 percent. The housing and utilities subsidy cuts the rate of fuel poverty in half. It also brings considerable consumer surplus gains of 6–7 percent of income. This comes at a high price tag for the government, as the budget for the housing and utilities subsidy is 1–2.5 percent of gross domestic product. Considerable savings would be achieved with only a small loss of consumer surplus if the housing and utilities subsidy was cut in half. Linking the subsidy solely to income would also attain considerable savings, but at a high loss of welfare. The housing and utilities subsidy could also be paired with social tariffs, or an energy efficiency subsidy, with major savings for the government.

This paper is a product of the Social Protection and Jobs Global Practice. 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 aalberin@umd.edu and numapathi@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
Figure 4: Decomposition of Synthetic GDP data (left panels), GDP growth (right panels).

(a) Synthetic GDP data (left panel), GDP growth (right panel).

<!-- Start of picture text --> 0 10 20 30 40 50 0 10 20 30 40 50<br>Time Time<br>(b) MODWT scalogram of the synthetic GDP: (a) growth and (b) acceleration.<br>(a) Growth (b) Acceleration<br>0.8<br>6<br>0.6<br>4<br>0.4<br>2<br>0.2<br>0.0 0<br>−0.2 −2<br>10 20 30 40 50 10 20 30 40 50<br>Time Time<br>17 8<br>16 6<br>15 4<br>14 2<br>13 0<br>Synthetic GDP Synthetic growth<br>12 −2<br>11 −4<br>4 4<br>3 3<br>Levels<br>2 2<br>1 1<br><!-- End of picture text --> GDP. The acceleration scalogram reveals that for the long-term shock the decline was at a slower pace than the following recovery (see the red shade followed by a cluster of green around 30). The short-term shock results in sharp changes in the acceleration on a short-term scale (level 1), not visible in the long-term.

A potential limitation to the use of wavelet techniques for growth and volatility analysis in practice is limited data availability. The sample size of the analysis, i.e. the length of the time series, influences the power of the tests and estimation uncertainty as in the other statistical techniques. The methodology chosen here does not depend on the starting point of the time series. Figure 5 repeats the synthetic data examples for growth 11
and cassava is important in all regions except the Western region. Given their importance, the analysis presented focuses on the prices for maize and beans, but other prices were also used in preliminary analyses.<sup>7</sup> For the most part, it is crops that are produced for household, domestic and regional consumption that dominate crop income. Coffee is important for some households, but does not comprise more than 10 percent of crop income in any region in either 2011 or 2012. Given that coffee plays a relatively small role, and given the difficulty of examining the impact of coffee prices on coffee income with only four data points (all households in Uganda face farm-gate prices driven by the same international coffee price as documented by Fafchamps and Hill (2008)) the role of coffee prices is not considered in the analysis. Sunflowers produced for commercial production has increased in importance in recent years, particularly in the north, but it is still a relatively small share of crop income and is also not considered further. The growth of sugarcane, particularly in the Eastern region has been reported, but by 2012 it was not comprising more than 1 percent of crop income in that region.

Even though food crops dominate crop income, crop sales are important and increasingly so. The share of household income coming from crop sales has increased from 2006 to 2012. Figure 1 shows that the share of crop income marketed has increased over time for the bottom 40 percent. The share of households in the bottom 40 percent selling crops has increased from 60 percent in 2006 to 72 percent in 2012.

**Figure 1: Share of crop income derived from crop sales, bottom 40 percent, 2006-2012**

<!-- Start of picture text --> 1<br>0.8<br>0.6 2006<br>2010<br>0.4 2011<br>2012<br>0.2<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Share of bottom 40 percent<br>sales<br>Share of crop income from crop<br><!-- End of picture text --> Source: Staff calculations using RIGA income aggregates calculated from UNPS 2006-2012 Figure 2 presents data on weather, price and conflict by region across the years considered in the study. Weather conditions were in general good, with rainfall deficits less than 20 percent in most cases. However, 2010 was a more challenging year for households and higher losses were observed (although no higher than 30 percent). The four years of data presented suggest that rainfall is more volatile in the north and east than in the center and west. The data also suggest larger losses on average in the west across the four years, but this may be on account of the fact that a maize model has been used to calculate the losses while this is not a crop grown in the west. The inclusion of regional dummies or household fixed effects controls for this persistent difference in the analysis.

> 7 Including prices of matooke, cassava, potatoes, rice, sorghum and coffee.

7
Che et al., 2020; Hayakawa and Mukunoki, 2021a).

We contribute to two sets of literature. The first covers studies on how firms’ participation in GVCs played a role in the impact of the Covid-19 pandemic. A growing body of literature has analyzed the effects that the pandemic had on different firms (Ayres and Raveendranathan, 2021; Cirera et al., 2021; Bruhn et al., 2021; Bricongne et al., 2012; Brucal et al., 2021; de Lucio et al., 2022) and the role of GVCs as a potential transmitter of the Covid-19 induced consequences (Bonadio et al., 2021; Egger and Zhu, 2022; Baqaee and Farhi, 2020; Gerschel et al., 2020; Heise et al., 2020; Chakrabati et al., 2021; Lafrogne-Joussier et al., 2022; Rose et al., 2021; Arenas et al., 2022). Most scholars agree that the initial impact was stronger on international firms (Constantinescu et al., 2022; Pimenta et al., 2021; Borino et al., 2021) and characteristics like high imports or connections to China made firms worse off (Lafrogne-Joussier et al., 2022; Meier et al., 2020). However, authors also argue that internationally trading firms were more resilient and recovered faster (Constantinescu et al., 2022; Borino et al., 2021; Hyun et al., 2020; de Lucio et al., 2022). Our paper contributes to this literature by providing novel evidence of various performance indicators of GVC firms compared to non-GVC firms following Covid-19 up to a year and a half into the pandemic, especially since GVC data is usually available with a lag.

Second, we contribute to the literature on the role of trade policy when such a shock takes place, particularly non-tariff measures. Until now, only a few studies have focused on Covid-19 and trade policy, mainly looking at the trade policy responses to the pandemic (Baldwin and Evenett, 2020). We contribute to this literature by focusing on pre-existing trade policy measures and how they amplify the effect of the pandemic on GVC firms. Our use of a novel data set for NTMs, an updated version of that used in Cal`ı et al. (2022), makes such an exercise possible.

The rest of the paper is organized as follows: Section 2 describes the data used, Section 3 analyzes the trends prior to and during the Covid-19 crisis, Section 4 outlines the empirical strategy and results from the regression analysis and Section 5 concludes.

# **2 Data**

The trade data is obtained from Panjiva, which is a private data provider collecting daily highfrequency customs data. We obtain the daily firm-to-firm import and export data for Indonesia from February 2019 to June 2021. The data is checked and cleaned rigorously with respect to firm identities, data completeness, quantity units, product codes, and partner countries, as detailed in Appendix subsection A1. Further, we validated the data quality by comparing the annual aggregates with the World Integrated Trade Solution (WITS) and found that Panjiva’s exports and imports data in 2019 captured about 94% and 79% of WITS, respectively. A summary of the firm-level data is given in Table A1.

4
- -- (1990b), UNESCO, UNICEF, WORLD BANK World Conference on Education for All/Basic Working Document. "Meeting Basic Learning Needs: A New Vision for the 1990s." March 5-9, 1990 (draft A). Thailand.

- UNITED NATIONS INTERREGIONAL SEMINAR ON WOMEN AND THE ECONOMIC CRISIS. Centre for Social Development and Humanitarian Affairs (UN), Branch for the Advancement of Women. Vienna: 1988.

- VANDERSLICE, Lane. "Structural Adjustment Lending: An Evaluation." Bread for the World. Testimony prepared for delivery before the International Development Institutions and Finance Subcommittee of the Banking, Finance and Urban Affairs Committee, U.S. House of Representatives. May 4, 1988.

- WOODHALL, Maureen. "Education and Training under Conditions of Economic Austerity and Restructuring." A Background Paper prepared for ILO-UNESCO Interagency Task Force: Austerity, Adjustment and Human Resources: A Role for the ILO and UNESCO. November, 1989.

- WORLD BANK (1988a). "Adjustment Lending: An Evaluation of Ten Years of Experience." 1988.

- -(.... 1990a). "Adjustment Lending Policies for Sustainable Growth." Policy and

- Research Series No. 14. 1990.

- ... (1990b). "Analysis Plan for Understanding the Social Dimensions of Adjustment."

- World Bank. Report No. 8691-AFR. July 1990.

- --------- (1987a). "Country Assessment and Policy Issues Paper: Republic of Zambia." SDA Unit. September, 1987.

- -- -- (1986a). "Economic and Social Development Prospects: Pakistan." Report No.

- 5962-PAK February 18, 1986.

- --------- (1988b). "Education Sector Study: Philippines." Report No. 7473-PH. December,

- 1988.

- ---(1986b). "Education and Training Sector Survey: Turkey." Report No. 6249-TU.

- September, 1986.

- ---- (1988c). Education in Sub-Saharan Africa: Policies for Adjustment. Revitalization. and Expansion. 1988. With Updated Statistical Tables. 1989.

- -------- (1985a). "Ghana: Towards Structural Adjustment." Report No. 5854-GH. October 7, 1985.

---------- (1988d). "Interim Report on Adjustment Lending." 1988.

66
on manufacturing firms in a broad set of countries (mainly in Eastern Europe) found that the negative correlation between management practices and energy (expenditure) intensity was much higher in magnitude when energy prices are low (subsidized) compared to when they are high (Schweiger and Stepanov, 2019). The basic argument is that management’s effect on energy intensity depends on factor costs; better managed firms conserve in the face of high prices but intensify use to take advantage of lower prices.

Firms in developing countries, where the energy-management nexus has been rarely explored, may show a different relationship when compared to their counterparts in the developed world. _First_ , industry composition often varies with the level of development. Developing countries may possess inexpensive labor or natural resources, which may encourage relatively energy-intensive industrial production and discourage energy-saving investments. _Second_ , firms in developing countries face more heavily subsidized costs of fossil energy. Nine out of the top ten countries that accounted for 61% (US $272 billion) of the total fossil fuel subsidies globally in 2017 were developing or emerging markets (Taylor, 2020). _Third_ , firms in developing countries may prioritize growth over environmental protection, although they may also be more sensitive to input costs. Evidence from a randomized controlled trial in Shandong Province, China, suggests that firms rarely considered energy efficiency interventions with a payback period longer than one year (Karplus and Zhang, 2020). In Ryan (2018), an experiment that raised energy productivity of Indian manufacturing plants led to longer operating hours and greater demand for skilled labor and electricity, which given the country’s high reliance on coal generation likely increased environmental impact.

The purpose of this study is to develop a deeper understanding of the link between management practices (both generic and energy-centric) and energy intensity in a large and diverse sample of firms operating globally. We use the data collected in a recent wave of World Bank Enterprise Surveys (WBES), which provides a representative cross-section of firms in 31 countries. We observe complete entries for over 6,000 out of more than 20,000 total firms, a sample three times larger than prior studies. A unique feature of this wave of 3