rafmacalaba/datause-encoder-data huggingface dataset:
_21_

**_Intertemporal_**

**_Intertemporal I would like to comment on four points that have actually imbalances..,_** _been raised In connectlon with the Issue of lntertemporal Imbalances. On the one hand,_ **_it_** _is argued that the Germans and the Japanese, because of their respective characteristics, save a lot. It just happens that twenty years ago the Germans and the Japanese were saving as much, and possibly_ **_more,_** _wlthout probably really thinklng about these demographic problems at the beginning of the next century or by the year 2020._

_Thus the foresight_ **_Implicit_** _In thls attitude I flnd somewhat far-fetched. But quite apart from that, are these positions really balanced ones? Leslie Lipschltz suggests that_ **_If_** _the Japanese want to Invest in Korea and Taiwan, well that_ **_Is_** _flne. But Korea and Taiwan save a lot themselves, and In fact, they have got current account surpluses. They happen to be putting_ **_It_** _all In the United States. K.owever, the US as a debtor country may not want_ **_It._** _There is also a problem on the creditor's side because the Germans and the Japanese don't like the real exchange rate implications of their huge surpluses. Moreover, there_ **_Is_** _a problem of the allocation of these savings. Even_ **_If_** _there was this intertemporal balance which one might want to maintain, then the money shouldn't be going to the United States;_ **_It_** _should be going to the LDCs, but of course_ **_It_** _won't. And crowning_ **_It a711,_** _there is_ **_this_** _enormous exchange market uncertainty, which tends to be driven_ **_by_** _day- to-day considerations._ _Even if, in an Ideal world, Japanese savings ought to be exported to Latin America, we are not in that Ideal world; we are In the presence of major imbalances, whatever intertemporal shifting might lead one to expect._ _Is it, therefore, inevitable that the next few years, 1989, 1990 will see a recession? And if we see a recession, will_ **_It_** _be coupled with inflation, or will we at least be able to avoid one or the other of these two possibilities?_ _MR. LARSEN: I was very stimulated by Professor Boltho's conclusion that there seemed to be a consensus that the issue of the imbalances and their resolution are ab"'olutely essential for the long-term outlook. But if you would allow me to be a little provocative, I think I would probably dissent to a degree, from that_ **_vlew._**

**_Resolution of_** _It is, of course, quite possible that the resolution of_ **_the imbalances_** _the Imbalances ultimately might result in slowdown in growth. I don't and_ **_the long-_** _think it is necessarily inevitable that it will. This is, of course, term growth... very difficult to judge, but I think a much more important question is: Will such a cyclical event, which I would consider it to be, necessarily affect the long-term growth path of the world economy, of the industrial countries or the developing countries?_

**WP/PROCISF:Jaq**
- 6 -

Very high rates of dollarization have been recorded in some countries in all regions of the world, with the overall maximum of almost 95 percent in Cambodia approached closely by the 93 percent recorded in Bolivia, and upwards of 80 percent in Bosnia-Herzegovina, Angola, and Georgia, for example and almost 70 percent in Lebanon. Clearly, however, the dollarization phenomenon has not affected all countries equally, with some countries remaining nearly untouched. In some cases, such as in Brazil, Colombia,<sup>8</sup> Thailand, Venezuela and some of the Caribbean islands, official prohibition of, or restrictions on, dollarization has limited dollarization.

With some exceptions, de facto dollarization has gained ground across the board in recent years. Annual trend growth in dollarization across the world during the past decade is estimated at about 1 percentage point per year (Box 1). All in all, 64 countries displayed a clear upward trend throughout the sample period. Yet, there have also been important variations in dollarization trends, both across regions and across countries. Dollarization increased most sharply in Latin America and the transition countries (Table 1; Figure 1); it also increased, albeit somewhat more moderately, in Africa, Asia, and the Middle East; it remained constant on average (and low) in the Caribbean region and the industrial countries. The evolution of dollarization over time has also varied substantially across countries (Box 2). A handful of countries, including Egypt among the developing countries, Israel and Mexico among the emerging market countries, and Poland, Hungary, Slovenia and Latvia among the transition economies, have experienced important reductions in dollarization in recent years.

Table 1. Evolution of Average Foreign Currency Deposits to Total Deposits (in percent, unless otherwise indicated)

|Regions|Numberofcountries|1996|1997|1998|1999|2000|2001|
|---|---|---|---|---|---|---|---|
|SouthAmerica|8|45.8|46.1|49.4|53.2|54|_55.9_|
|TransitionEconomies|26|37.3|38.9|43.5|44.3|46.9|47.7|
|Middle East|7|36.5|37.2|37.7|37.5|38.2|41.9|
|Africa|14|27.9|27.3|27.8|28.9|32.7|33.2|
|Asia|13|24.9|28|26.8|28.8|28.7|28.2|
|CentralAmericaandMexico|7|20.6|20.8|22|22.1|22.5|24.7|
|Caribbean|10|6.3|7.6|6.8|6.7|6.1|6.2|
|Developedcountries|14|7.4|7.5|7.5|6.7|7|6.6|

Sources: IFS, EDSS, and central banks' statistical publications.

**8** Like Chile, Brazil and Colombia have also developed alternative indexation mechanisms, such as priceindexed or floating rate instruments.
gathering. Figure 1 presents some regional comparisons on these technologies, both in levels as of 2003-04 and in changes between 2003-04 and 1994-96.

Since these technologies are rather recent, the comparisons based on levels and changes are quite similar. The OECD far surpasses any developing region. The typical OECD country has 5 times more personal computers per capita than the typical East Asian developing country, 10 times more than the typical Latin American or Middle Eastern countries, and about 50 times more than the typical Sub-Saharan African country. Regarding internet usage, the gaps are smaller but still considerable. The typical OECD country leads the typical East Asian developing country by 25%, while the other regions --Latin America, the Middle East, and specially Sub-Saharan Africa-- lag much farther behind.

**Figure 1. Technological Innovation per World Region**

<!-- Start of picture text --> Personal Computers (per 1,000 people) Internet Users (per 1,000 people)<br>Level Change Level Change<br>LAC SSA MNA EA OEC LAC SSA MNA EA OEC LAC SSA MNA EA OEC LAC SSA MNA EA OEC<br>500 500<br>300 400<br>400 400<br>300<br>200<br>300 300<br>200<br>200 200<br>100<br>100<br>100 100<br>0 0 0 0<br><!-- End of picture text --> Notes: 1. LAC: Latin America and the Caribbean; SSA: Sub-Saharan Africa; MNA: Middle East and North Africa; EA: ASEAN+2 (South Korea and China) excluding LICs; OEC: OECD. 2. Median value of each country group. 3. Level indicates an average of 2003-2004 by country, and change means the difference between the periods 2003-2004 and 1994-1996. 4. Data on Personal Computers and Internet Users are from World Development Indicators.

These regional differences are clearly related to income levels, providing some evidence on the importance of the developmental barriers mentioned above. What about regulatory barriers? Figure 2 shows some evidence that they are also potentially important. Using the Fraser index of business regulatory freedom, we divide countries into three groups. For each of 7
concluding that “the growth rate is substantially positively related to the starting amount of human capital.* (Barro, 1991, p.22}] Similarly, one should cautiously interpret Barro’s (1991) conclusions that lower fertility rates are correlated with faster growth. For some specifications, population growth enters with a significantly negative coefficient, but it enters with a positive but insignificant coefficient with other plausible 2-variables.

The extreme bounds of the I-variables are also computed using the investment share as the dependent variable. As Table 1 illustrates, none of the coefficients are robust. In fact, the coefficient on initial income takes-on both significant positive and negative values. Interestingly, the finding of a non-robust relationship between initial income and the investment share combined with the finding of a robust negative partial correlation between initial income and growth (when controlling for population growth, the investment share, and initial human capital) suggest that per capita income convergence does not operate primerily through increases in domestic savings or international capital flows.

# IV. Macroeconomic Variables and Growth

# A. Illustrative Overview

This paper's primary aim is to evaluate the degree of confidence one should have in the partial correlations between growth and popular macroeconomic indicators. This introductory subsection uses two relatively comprehensive studies of growth and some simple correlations to illustrate this paper's two major themes: many indicators of policy - taken individually or in groups - are with average growth rates.

ll
and Scarpetta, 2009, 2013; Hsieh and Klenow, 2009; Restuccia and Rogerson, 2008, 2017). This paper contributes to this literature in two primary ways. First, it is the first paper to our knowledge to apply the Olley-Pakes decomposition methods to African data. To carry out this decomposition, we have generated new longitudinal data for Ethiopia (2012-2014) and Tanzania (2008-2012) and gained access to new panel data from Côte d’Ivoire.<sup>1</sup> In the case of Ethiopia, our dataset adds to the existing panel which covers the period 1996 to 2009.<sup>2</sup> Plus, we have generated a short panel for Bangladesh that covers every other year between 1995 and 2001. Using these data, we examine the sources of manufacturing productivity growth including the relative importance of factor reallocation for African countries.

Several interesting findings emerge from this analysis. First, there are clear differences in the size and sources of productivity growth across both countries and plants within countries. In terms of country performance, Ethiopia outperformed both Côte d’Ivoire and Tanzania on nearly every metric that we estimated. For example, aggregate manufacturing productivity increased by 75 percent in Ethiopia between 2006 to 2016 while it increased by 6 percent in Côte d’Ivoire over a similar period (2004 to 2016). Aggregate manufacturing productivity fell in Tanzania over the period 2008 to 2012. Second, we find several similarities between Ethiopia’s pattern of productivity growth in recent years and that of Bangladesh during its early years of industrialization. For example, Bangladesh’s aggregate manufacturing productivity increased by 52 percent between 2001 and 2012 which is comparable to that achieved by Ethiopia between

> 1 We’d like to thank Nouhoum Traore for sharing the Ivorian panel data with us.

> 2 In addition, we have establishment-level data for 2010, 2011, 2015, and 2016.  We hope to match the establishments in those years to our existing dataset so that we have a panel covering the period 1996 to 2016.  Similarly, we have one additional year of data for Tanzania (2013) and hope to extend the Tanzanian panel from 2008 to 2013.

3
Using the data sets discussed above, we construct a number of variables. The dependent variable, **_Employment growth_** , is calculated as the annual change in the number of permanent employees divided by the average value over the two periods.<sup>12</sup> In Orbis, we calculate growth rates year over year, while in the ES sample, employment growth rates are annual changes over a two year period. To nullify undue effects from outliers, we truncate the top and bottom 5 percentile observations from the employment growth distribution in Orbis. As the distribution of employment growth is much tighter in the ES sample, we only remove the top and bottom 1 percentile of observations.

In the ES, firms were asked whether they have access to a loan or line of credit from a financial institution. We use the responses to this survey question to construct a dummy variable **_Access to a loan_** which takes the value 1 for firms with access to a loan or line of credit and 0 for firms without access. We measure financial access in Orbis using the amount of loans and long term debt. If any of these amounts are positive, we code the **_Access to a loan_** dummy as 1, and 0 if both are 0 or if either loans or long term debt is 0 and the other is missing. Thus, the financial access measure in Orbis captures access to both short-term and long-term financing. As alternative measures, we calculate the **_Growth of finance_** in Orbis as the annual growth rate<sup>13</sup> of the sum of the loans and long term debt, and **_Debt ratio_** as the ratio of this sum to the total assets. We again truncate the top and bottom 5 percentile of the distribution of these two variables so as to diminish the influence of outliers.

> 12 Employment growth rates calculated using the base year or the average value over the periods as the denominator can yield substantially different growth rates, especially for micro and small firms. We are using the latter method because, like log growth rates, it naturally bounds the employment growth rates so that the regression analysis is not skewed by big jumps from micro and small firms. For more discussion on implications of the two definitions, see Haltiwanger et. al. (2013).

> 13 We calculate the growth rate as before as the difference divided the average value over the two years.

9
paid-up capital. Medium firms have been defined as firms with paid-up capital up to Rs. **_5_** lakhs (table 2), while large firms are firms with paid-up capital of Rs. 1 crore and more (table 3). The ICICI data relates to medium as well as large firms (table 4).

# **(11) Financing patterns**

# **(a) Indian data**

# **(i) RBI data**

**Sources and uses of funds:** RBI data on medium and large firms for the 1972-1991 period (table 2) suggest that on an average, internal finance contributed about 42 **percent of total funds** and external finance the remaining 58 percent. While external equity made up about 4 percent of all funds, long-term borrowing contributed 29 percent. With regard to the uses of funds, gross fixed assets accounted for about 50 percent of the funds used.

The data for the large firms shown in table 3 reveal a similar picture. While internal finance provided about 38 percent of the funds, external finance made up the remaining 62 percent. While external equity contributed 6 percent of total funds, long-term borrowing made up 33 percent of total funds.

It is interesting to note that in the case of medium as well as large firms, the evidence in tables 5 and 6 suggest that external finance has become more important in the 1980s compared to the 1970s. This finding is consistent with the result of Roy and Sen (1994) based on national accounts and flow of funds accounts for the 1970-1989 period.'<sup>9</sup> Further, tables 2 and 3 suggest that the increasing importance of external finance is due to debt as well as equity; in particular,

**19** This is also consistent with the evidence of Roy Choudhury (1992). Based on data for the 1955-56 to 1986-87 period, Roy Choudhury (1992) has concluded that the dependence of the private corporate sector on external funds for investment has continued and increased.

18
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||Height-for-|age z-scores|||
|_Structure of house (_|_reference cate_|_gory: Kacha)_|||||
|Pacca|-0.033|-0.040|-0.032|-0.036|-0.032|-0.036|
||(0.151)|(0.151)|(0.151)|(0.151)|(0.151)|(0.151)|
|Mixed|-0.070|-0.063|-0.065|-0.063|-0.067|-0.071|
||(0.143)|(0.142)|(0.142)|(0.142)|(0.142)|(0.142)|
|_Asset index (referen_|_ce category: p_|_oorest)_|||||
|Asset index 2|0.166|0.155|0.164|0.151|0.162|0.173|
||(0.193)|(0.194)|(0.194)|(0.193)|(0.193)|(0.193)|
|Asset index 3|0.056|0.044|0.043|0.033|0.050|0.059|
||(0.177)|(0.177)|(0.178)|(0.179)|(0.177)|(0.176)|
|Asset index 4|0.047|0.016|0.032|0.004|0.034|0.056|
||(0.189)|(0.193)|(0.192)|(0.199)|(0.191)|(0.189)|
|Asset index 5|||||||
|(wealthiest)|0.265|0.231|0.252|0.217|0.234|0.273|
||(0.226)|(0.227)|(0.227)|(0.241)|(0.235)|(0.224)|
|Child suffered|||||||
|from diarrhea|-0.084|-0.084|-0.083|-0.094|-0.083|-0.081|
||(0.216)|(0.216)|(0.216)|(0.216)|(0.216)|(0.216)|
|Other illness|-0.443***|-0.440***|-0.443***|-0.439***|-0.448***|-0.445***|
||(0.132)|(0.132)|(0.132)|(0.132)|(0.132)|(0.132)|
|Rural|-0.109|-0.093|-0.117|-0.090|-0.108|-0.114|
||(0.154)|(0.154)|(0.152)|(0.154)|(0.153)|(0.153)|
|Punjab|-1.080***|-1.068***|-1.090***|-1.092***|-1.088***|-1.096***|
||(0.279)|(0.279)|(0.281)|(0.280)|(0.280)|(0.281)|
|Sindh|-1.388***|-1.365***|-1.382***|-1.384***|-1.391***|-1.393***|
||(0.294)|(0.295)|(0.294)|(0.295)|(0.295)|(0.296)|
|Khyber Pkahtoon|||||||
|<br>Khaw|-1.715***|-1.675***|-1.710***|-1.733***|-1.720***|-1.734***|
||(0.319)|(0.320)|(0.319)|(0.322)|(0.320)|(0.323)|
|Constant|<br>-0.883**|<br>-0.907**|<br>-0.859**|<br>-0.843**|<br>-0.869**|<br>-0.861**|
||(0.419)|(0.419)|(0.421)|(0.422)|(0.420)|(0.423)|
|Observations|1,648|1,648|1,648|1,648|1,648|1,648|
|R-squared|0.039|0.039|0.039|0.040|0.039|0.039|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure in total expenditure.

79
# <u>Data on economic, political and international relations</u>

We use several sources to construct our economic, political, geography, and international relations variables. We will explain the processes we used in order to calculate each of these variables in the context of a basin/country or in the context of a basin (containing the area of the basin for the two riparians).

# **_Democracy and governance_**

We employ 4 variables that measure level of democracy and governance in a country, using data from Neumayer (2002a:145-146). The variables include a combined index of political rights and civil liberties, a combined index of democracy and autocracy, Vanhanen’s index of democracy, and a combined governance indicator, based on seven other indicators that measure governance quality. Three of the democracy/governance variables also have a dummy version. The variables are (1) a combined index of political rights and civil liberties ( _Freedind_ ); a combined index of democracy and autocracy ( _Politind_ ); Vanhanen’s index of democracy ( _Autodemo_ ); and a combined governance indicator, based on seven other indicators that measure governance quality ( _Voiceind_ ). Three of these variables also have a dummy (0-1) version ( _Freeddum_ , _Politdum_ , _Voicedum_ ). The exact definition of the democracy variables can be found in Neumayer (2002a).

Variables in the democracy and governance categories are expressed as indexes or as dummies and are calculated for each country in the basin. We expect that some of the democracy variables and the governance variables are correlated somehow due to the nature of the specification of several of the democracy variable (political rights and civil liberties; governance quality). Therefore, we will not use democracy and governance as independent variables in the same equation.

# **_Trade and diplomatic ties_**

These variables pertain to two riparian states in each basin and thus they are calculated as basinlevel variables.

14
# PCM 2100, Large Farms

<!-- Start of picture text --> N<br>+<br><!-- End of picture text --> %Change of Ag Income IMP_farm.D100_pcm_b WM - 1.7157 --0.3000 WN -0.2099 - -0.2000 MN -0.1999 --0.1000 I -0.0999 - -0.0500 [)) -0.0499 -0.0000 [I 0.0001 -0.0500 (I 0.0501 -0.1000 I 0.1001 -0.2000 HE 0.2001 -0.3000 BE 0.3001 -0.9900

**L** I LSI S— Sd: Decimal Degrees 0 4 8 16 24 32

NIGGOL SEO, YALE UNIV.
Policy Research Working Paper 10827

# **Abstract**

Addressing energy poverty has emerged as one of the main challenges for Bulgaria’s poverty and social inclusion policy, particularly in the context of the European Green Deal and the current crisis in Ukraine. To tackle the adverse impacts of energy poverty effectively, a crucial initial step involves accurately defining and measuring this issue. Identifying households affected by energy poverty is essential for shaping and implementing targeted policies. This study explores various definitions of energy poverty within the Bulgarian context by (1) systematically reviewing current methodologies and measures employed in the EU context; (2) assessing the feasibility of implementing these measures in Bulgaria based on data availability, comparing the incidence of energy poverty using alternative measures, and presenting characteristics of energy poverty to inform potential policy instruments; and (3) providing policy recommendations for the measurement and monitoring of energy poverty. The way energy poverty is measured and the overlap with income poverty shape the types of policy solutions perceived to be possible and appropriate to address it. The evaluation supports the need to shift from single indicators to multidimensional approaches in measuring energy poverty. Additionally, enhancing the granularity, quality, and frequency of expenditure and income surveys can contribute to easier operationalization of these concepts and a better understanding of the demographics of energy poverty. The study proposes exploring alternative data generation methods, such as smart meters, further to enhance insights into the dynamics of energy poverty.

This paper is a product of the Poverty and Equity 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 mrobayo@worldbank.org and brude@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