rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1 The Level of indebtedness of Bulgarian Enterprises**

||_1993_|_1994_|_1995_|_1996_|
|---|---|---|---|---|
|**Long term debt to capital**|||||
|a. mean|0.12|0.12|0.06|0.35|
|b. median|0.00|0.00|0.00|0.00|
|**Short-term debt to capital**|||||
|a. mean|0.48|0.57|0.75|1.53|
|b. median|0.20|0.28|0.37|0.82|

Source: Authors' calculations based on AMADEUS data set (Annex 1).

In the case of Bulgaria, the continuation of SBCs and its relevance for the functioning of the financial sector, imply that in our estimation period, 1993-1996, the SBCs provided perverse incentives with respect to the allocation of external funds (notably bank loans). We will discuss this at length in Section 5 and we will find confirmation for this hypothesis. Here, we merely provide some input for this discussion by showing the financial indebtedness of _loss-making_ firms, see Table 2. We do suspect that these companies faced SBCs with the result that they were able to attract more debt despite their losses. Table 2 shows the financial indebtedness (median and mean) of top 25 and top 100 loss-makers. We calculated means and medians for following ratios: _LOSSK -_ economic losses to long-term assets, _LTDEBTK,_ long-term debt to long-term assets, _STDEBTK,_ short-term debt to long-term assets, _STDEBTAK,_ short-term debt arrears to long-term assets and _LTDEBTAK,_ long-term debt arrears to long-term assets.

The long term debt to long-term assets ratio for top-25 loss makers increased during this period, except in 1995. The ratio of short-term debt to long-term assets increased nearly five-fold for this group of firms. This is evidence that these loss-makers had access to expensive short-term debt and were also rolling-over old long term debt. Note the excessive level of indebtedness of these loss-makers in 1996 - the year of the banking crisis.<sup>6</sup> Regarding the top-100 loss-makers, their long-term debt to capital ratio dropped (most probably due to the write-off of bad debt). However, at the same time the ratio of short-term to long-term assets increased steadily and was also accompanied by an increase of short-term arrears ratio (not shown) during the same period.

> 6 In fact, a large share of the short-term bank debt was in arrears in 1996.

9
9 cases where Xhe main objective was to bring down inflatton. The choice of exchange rate regime and rules must be made on the basis of their ability to achieve a given macroeconomic objective, and the costs that they might impose on other, less pressing objectives. For example, a trade off is likely to arise between exchange rate rules that help to reduce inflation, and those that help to maintain the competitiveness of the export and import competing sectors. The need to establish clear priorities is thus necessary to design the best exchange rate policy.

In the next sections we will examine the role of exchange rate rules and regimes in two important aspects of macroeconomic management: (i) their importance to affect the real exchange rate (or the level of competitiveness), and (ii) their impact on inflation in the short and long term.

# IV. Exchange Rate Policies and the Real Exchange Rate

The evolution of the RER Ls many times critical for the success of a trade liberalization process. Countries generally prefer to have undervalued exchange rates during these processes as they help the performance of the tradeable sector, and fortify the balance of payments. However, as we argued in section II, the RER is an endogenous variable and its evolution can not be solely determined by exchange rate policy. Devaluations that are not accompanied by appropriate demand management policies are largely eroded by inflation and end up having little or no impact on the RER. When do Devaluations Work?

There is an extensive literature trying to assess the effectiveness of devaluations to affect the RER. Kamin (1S88) is particularly use"'Al in this respect. His analysis is based on 72 devaluations in developing countries, where he compares the levels of the real exchange rate one before and three years after the devaluation. While cross country analysis of this type usually has a number of limitations, the main finding is surprising: on
accuracy of the estimate is significantly reduced. A monthly beta estimate, for example, requires at least five years' data (60 data points) to be used while a daily beta can be calculated for a single year (around 250 data points). The latter estimate is the more meaningful if a company is subject to changes in its operating environment or regulatory regime, as explained in Appendix A1.4.

Examples shown in Appendix A3 show the extent of the interval problem. Beta estimates for British Gas fall as the interval length is increased while those of the two US water companies rise with the interval length, as expected from the fact that the former is large and frequently traded while the latter are infrequently traded. US gas companies show a mixture of trends, reflecting the variety of sizes of the companies in this group. Beta estimates for the UK RECs rise with the interval length, a slightly surprising result which implies that these companies are relatively infrequently traded compared with the index as a whole. It should be noted, however, that the confidence intervals for the longerinterval estimates are very wide, as shown in Appendix A4. Taking the case of five-year asset beta estimates for BT, daily data produce a 95% confidence interval of 0.81 to 0.92, weekly data give a range of 0.79 to 1.02, while the monthly estimate lies between 0.61 and 1.03.

The response taken here to these problems has been to use daily beta estimates in order to obtain a narrow confidence interval for betas calculated over fairly short periods and to deal with the interval problem in the following ways. As explained in Section 6. 1, a largesecurity index results in a smaller interval-effect bias than an all-share index. Industry averages have been calculated; where there is a range of company sizes, this procedure should overcome the bias involved in using any single estimate. This is indicated by the case of US gas companies given in Appendix A3: the average daily, weekly and monthly betas for the group as a whole are very close at 0.20, 0.21 and 0.25 respectively, while individual beta values vary substantially with the interval length.

26
each case.<sup>12</sup> To estimate supply of infrastructure service _h_ , we used the actual stock of _h_ . We then calculated individual indicators of excess demand for each component of infrastructure services by taking the ratio of supply to “predicted” demand. This ratio gives therefore an indicator of adequacy between supply and demand; a fall in the ratio would indicate excessive pressure on existing infrastructure and therefore a deterioration in quality. Second, we calculated a “composite” excess demand indicator for each country. To do so we used the same procedure used by Hulten (1996) to calculate his quality index, that is, we standardized each of the three series (by subtracting the mean and dividing by the standard error) and calculated the unweighted, arithmetic average of the standardized series.

Continuous annual time series for all three of the infrastructure services referred to earlier were not available for the whole estimation period. For instance, data on roads (in terms of kilometers per capita) were available only since 1990. We therefore calculated the composite indicator with all the information available in the base period to begin with, and added additional series as they became available. Figure 4 shows the behavior of these series, which have been normalized to a value of unity in 1977, to facilitate comparisons across countries. Again, a three-year moving average is used, in order to smooth out spikes possibly associated to measurement errors. In contrast to our ICOR-based indicator, the results appear to suggest that quality improved in all three countries in recent years.

Differences in the behavior of our two quality indicators may appear problematic, given that there is no strong _a priori_ reason for choosing among them. We thus follow the same approach as Calderón and Servén (2004 _b_ ), who define several standard quality indicators (based on electricity losses, percentage of paved roads, and telephone faults, given that they use stocks of electricity, roads, and

> 12In their estimation of demand functions for infrastructure services based on panel data, Fay and Yepes (2003, p. 8) found long-term elasticities of 0.375 for electricity, 0.5 for telephone mainlines, and 0.14 for paved roads. These estimates differ quite significantly from our values of unity. However, the Fay-Yepes estimates refer to low- and middle-income countries in general, so there is no indication that they are adequate for MENA countries in particular—or, for that matter, the three countries in our sample. Moreover, in their regressions, there is no price (or user cost) variable, so their estimated income elasticities may be biased.

17
15 their own individual heating system (as opposed to one in common with the rest of the building). About 58% of those who use gas for space heating live in single-family homes, 20.7% live in apartments in multi-family buildings, and 21.6% has other living arrangements.

For 2017 and 2018, we can compute the quantity of piped natural gas consumed during the year from the expenditure on gas (and the gas HUS, if received by the household) reported in the HBS, because by 2017 the block tariff system had been removed and the gas tariffs were stable in 2017 and 2018. Table 7 summarizes the annual gas consumption levels in 2017 and 2018. They were similar, in part because the price of gas was essentially unchanged over two years and in part because the weather was very similar in those two years (bottom panel of table 7). The average household uses about 800 cubic meters of natural gas a year.

Figure 2 presents the distribution of gas consumption in 2017 and 2018 combined for households who use gas for space heating, distinguishing for households that did and did not receive HUS assistance. This figure conveys an important piece of information: The distribution of gas consumption for HUS recipients is to the right of that for the other households. In other words, HUS recipients tend to consume more gas. This raises the question whether HUS recipients get HUS assistance _because_ they consume more, or increase consumption _because_ they receive assistance, or do a little bit of both.

Table 8 begins to examine this question by displaying the frequencies of households that fall in each quartile of the distribution of (net) income and each quartile of the distribution of gas consumption. This table shows how surprisingly evenly distributed households are in terms of income and consumption: The poorest families (those in the first income quartile) are certainly not exclusively concentrated in the first or second consumption quartile, and the richest families (those in the fourth income quartile) are certainly well represented even in the lowest
# **Using Satellite Imagery to Revolutionize Creation of Tax Maps and Local Revenue Collection**<sup>**¶**</sup>

Daniel Ayalew Ali Klaus Deininger Michael Wild The World Bank, Washington DC <u>dali1@worldbank.org, kdeininger@worldbank.org, mwild@worldbank.org</u> JEL Classification: C21, E31, Q24, R32, R38 Keywords: property taxes, property values, satellite imagery, mass valuation, spatial hedonic model, Rwanda

> ¶ We thank the Rwanda National Resource Authority (RNRA) for data access, and we gratefully acknowledge funding support from the German Ministry for Economic Cooperation and Development through GIZ, the United Kingdom Department for International Development, the International Growth Center (IGC) and the European Space Agency (ESA) through the “Earth Observation for Sustainable Development” Urban project under the ESA/WB partnership program. We would like to thank C. Aubrecht (coordinator of collaborative activities under the ESA/WB partnership) for facilitating the collaboration; and the GAF AG, Geospatial Service Provider, Germany team particularly S. Gomez and T. Haeusler for coordinating the generation of building heights and footprints using high resolution satellite imagery. We are also very grateful to T. Hilhorst and S. Murray (IGC) for their helpful comments and suggestions.
TIJ **I** £T1AVWAI D Ilt I &C OF **fUWS IDEIITIIY**

**Central Ginnut** FInenes ant'd

capitat **EDpwituwe** 3 I **k-I leg,** 3 _ _ _ _ INPg,_ **3** I IIPO.. **k-I** where: lcg - capital expenditure of the central governmt IlPg - goverment lworts k 1 1987. 1988, 1989

# 3ialncef PaI

9 Expt I geq ., tl + 4 gdpfc,).t1 **+** a.pd.) **k-i** lapt w I (~l ti + , gcltc).(l + a-.p4) where: exp - 9 categories of exports: coffee, tea, horticulture, petroleus, mile, mmafacturlng, re-exports, other goods, non-factor services Pd - domestic commodity price

|Ut<br>of**Pe**|**mnt**||
|---|---|---|
|TOTOPCpS<br>|a<br>|_NFA_+Sf<br> <br> <br> <br>|
|TOTOPCaIU|<br>a|Lfg<br>+Lsfg<br>+Lfc+Ifd<br>+<br>Lfp+DFI+Lsfp+Kne|
|TOTBOPCur<br>TOTBoPCurU|Sa<br>|Mfg+Vf.Vpf<br>Iap<br>lsfpI+<br>m<br>Ine+Prof+Yrgf<br>-RC<br>*Tfp .Ip+Sf+_IFA_|
|where:|||
|Sf<br>Lfgt<br>Lsfg|-|current<br>account<br>balance<br>net overn<br>ent<br>capital '(NIt)<br>~~~~net<br>boverneent short-term|

/ Ltfg and Mfg are computed In the debt module using available Information on old and new comitments, and their terms. Standard for utae are used.
# **8 International comparison**

## **8.1 Inequality**

**This section compares fiscal policy effects on inequality across 63 countries assessed in CEQ standard indicators.** We measure this by calculating the reduction in the Gini coefficient from Market Income (prefiscal income) to Final Income (Market Income plus all taxes, transfers, and in-kind benefits from health and education). We refer to the Gini reduction as the “total redistributive impact”. If a country has been assessed multiple times, we include only the most recent datapoint.

**Globally, the total redistributive impact of the combination of taxes and transfers modelled ranges from 0.2 to 20.4 Gini points reduction).** The West Bank and Gaza achieves 6.5 Gini points of redistribution—a moderate result relative to other countries.

### **Figure 8-1 Total inequality reduction in the West Bank and Gaza is moderate relative to countries in other CEQ assessments**

Inequality reduction, Gini coefficient (change in Gini from Market to Final Income) <!-- Start of picture text --> 0<br>-5<br>West Bank and Gaza:<br>-10<br>6.5 Gini point reduction in inequality<br>-15<br>-20<br>-25<br>High incomeHigh income Upper middle incomeUpper middle income Lower middle incomeLower middle income Low inLow in c omeome<br>Source: World Bank estimations for the West Bank and Gaza based on PECS 2016-17. CEQ Data Center for the rest of the<br>countries.<br>Spain Uruguay Panama United States Croatia Mauritius Romania South Africa Argentina Brazil Mexico Namibia Georgia Venezuela Costa Rica Botswana Dominican Republic Colombia China Ecuador Thailand Peru Iraq Iran Turkey West Bank and Gaza West Bank and Gaza Belarus Malaysia Jordan Russia Albania Guatemala Paraguay Indonesia eSwatini Lesotho Zambia Tunisia Kenya Ukraine Honduras El Salvador Mongolia Bolivia India Nicaragua Moldova Egypt Tanzania Ghana Sri Lanka Comoros Cambodia Ivory Coast Uganda Burkina Faso Togo Mali Ethiopia Niger Gambia Tajikistan Guinea<br><!-- End of picture text --> Note: Pensions treated as government transfers in China, Georgia, Guinea, Mali, Mongolia, Niger, and West Bank and Gaza.

## **8.2 Poverty**

**We compare fiscal policy effects on poverty using the 2011 Purchasing Power Parity (PPP) international poverty lines.**<sup>76</sup> Estimates for the economies in each income group use the poverty line appropriate to that income level: US$5.50 per day for high income and upper middle income, US$3.20 per day for lower middle income, or US$1.90 per day for low income. We measure poverty impact by calculating the change

> 76 The International poverty lines have been updated to 2017 PPP but the results are not yet available in the CEQ database. The $5.50 in 2011 (PPP) line is the closest of the international poverty lines to the official national poverty line in the West Bank and Gaza.

53
In Figure 7: Financial Consequences in the U.S. of U.S. Deficit Reduction: Permanent versus Tempoarary versus Anticipated <!-- Start of picture text --> U.S. Nominal Effective Exchange Rote U.S. Inflation<br>I. 2 / | 0.4<br>* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.2<br>-Ia1,- -0£ 7 a -To-wov<br>Year Year<br>U.1., Boenaq 04l (1.490 a D.P IU S Spnd-dg Cut (1.6: of 0.O.P.)<br>Total U.S. Shore Market U.S. 1 0-Year Bond Rate<br>7 ~ ~~~~ ,. ~ ~ ~~~~~~~~~~~~~ oe .. .<br>08a<br>41~~~~~~~~~~~~~~~~~0<br>i~~~~~~~~~~~~~~~~~~~~~~Z -oo- 43 -M Ei.<br>G ~~ ~ ~~ ~ ~ ~~ ~ ~ s-3~~ i} i~~ Te H , : A E rmv<br>-1 -II~~~~~~~~~~~~~~~~a<br>1Q14 14% 61 2000 2t002 2004 2006 2006 2090 012 IS24 10| 1§XS 7X 70 702010 12<br>Year Year<br>U.S. to."" Cut C(162.1% e D.P.) US fmfnjIg Cut (Ioz Ia COP.)<br><!-- End of picture text -->
Policy Research Working Paper 9240

# **Abstract**

There is a crisis of demand brewing around the globe as social distancing becomes the norm to counter the COVID-19 outbreak. So, which parts of the economy are most in the line of fire? Looking at jobs that can be done at home or that require a high degree of face-to-face interactions with consumers can capture complementary but distinct mechanisms to assess this vulnerability. This paper uses data on 900 job titles from the Occupational Information Network (O*NET) database for the United States to demonstrate that there is substantial heterogeneity in vulnerability across industries, income groups, and gender. First, industries vary in whether they emphasize face-to-face interactions and home-based work and the two do not always go hand-in-hand. Second, occupations that are less amenable to home-based work are largely concentrated among the lower wage deciles. Third, a larger share of women’s employment is accounted for by occupations that are intensive in face-to-face interactions.

This paper is a product of the Finance, Competitiveness and Innovation 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 bavdiu@worldbank.org and gnayyar@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
child was breastfed. The _z_ -scores were computed using the US Centers for Disease Control (CDC) growth charts. On average, children have a height-for-age _z-_ score that is 1.2 standard deviations below the US norms, and 23.4 percent of children in the sample are stunted (heightfor-age _z-_ score less than –2). Consistent with other evidence on Ecuador, stunting (low heightfor-age) is more common than wasting (low weight-for-height). Hemoglobin levels were measured in the field using pin-prick tests, and values were corrected for elevation by merging our sample with data on the mean altitude of the parish. Elevation-adjustment was done with the CDC guidelines (CDC, 1989). The average elevation-adjusted hemoglobin level is 11.1, exactly equal to the threshold value that is often used to define anemia for children in this age range. Using this threshold, 48.0 percent of children in the sample are anemic. Breastfeeding duration is based on retrospective questions asked of the mother for each of her children; the average duration of reported breastfeeding is 5.7 months.

We use three measures of parenting quality in our analysis. The first is based on the widely-used Home Observation for Measurement of the Environment (HOME) scale (Bradley, 1993; Caldwell and Bradley, 1984; Bradley et al., 2001). This is constructed from 11 items that are assessed by interviewers at the close of the interview, and measures punitiveness (for example, whether parents yelled at or hit children during the interview), and responsivity to children (for example, whether they responded to and encouraged children in a positive way during the interview). Each item is scored as a dichotomous variable; the final scale ranges from 0 to 11, with higher values corresponding to less responsive and harsher behavior. Low values are more common than high values: The mean HOME score is 2.3, 31.8 percent of children have parents with a score of 0, and 85.9 percent have scores of 5 or less. The second measure of parenting quality is an indicator for whether the child was read to in the week before the survey. Seventy-three percent of the children were not read to, even though only a very small number of mothers in our sample—56 out of 3,153—were illiterate. Finally, we include a variable for the number of other children in the household. More children in the household may imply less parental time per child.<sup>11</sup> Close child spacing, resulting in more children, may also have negative implications for child health and development.

> 11 See Blake (1989) and Parcel and Menaghan (1994) on “resource dilution” caused by additional children in a household.

9
7

# m. Data and Methodology

The analysis is based on the most recent household budget survey: the 1992 Individual Budgets of Households, compiled by the National Statistical Institute (NSI) of Bulgaria. The sample was constructed as a two-tier random sample, involving 2,202 households (or less than 1 percent of households). Of these 2,202 households, 1,386 households (or 63 percent) are urban, the remaining are rural. It is based on a sample frame developed from the 1985 Population Census. The sample was constructed from a sample of 418 sectors or Census districts: each district contains about 90 households and 6 households were sampled from each sector.' Each household was paid a nominal amount, Leva 100 per month (about US$4.00), for participating in the survey. According to the officials of NSI, the sample was "random", and it adequately represented the incomes and expenditures of the Bulgarian population'.

The pattern of effective tax rates, i.e., whether they are progressive, regressive or proportionate, depends not only on the distribution of tax burdens, but also on the concept of income which is used to determine the underlying pattern of income distribution. The concept of income employed in the NSI survey includes seven major sources of pre-tax income - earned income, property income, social insurance, social benefits, income from sales, other 4/The sample of 418 sectors were taken from a 'control' sample of 4,000 sectors which was, in turn, taken from the 1985 Population Census of 40,000 sectors, including approximately, 3.2 million households.

51 Nevertheless, it appears that some minorities, particularly gypsies, were probably underrepresented in the sample.