rafmacalaba/datause-encoder-data huggingface dataset:
- 14 -

|**Table 2. Summary Statisti**|**cs**||||||
|---|---|---|---|---|---|---|
|Variable|Abbr.|Obs|Mean S|td.Dev.|Min|<br>Max|
|Investment amount (JPY million)|_INV_|68|265.6|265.0|17.8|1738.5|
|Annual savings of energy spending (JPY million)|_SAVE_|68|32.3|35.6|0.9|171.0|
|Annual payment to ESCO (JPY million)|_PAY_|68|30.1|32.0|1.0|145.0|
|Contract duration (months)|_DUR_|68|121.9|42.1|36.0|180.0|
|Score attached to investment criteria (on a scale of 0 to 100)|_wINV_|68|23.7|6.0|12.5|<br>44.4|
|Score attached to energy savings criteria (on a scale of 0 to 100)|_wSAVE_|68|24.2|4.8|12.5|<br>37.5|
|Score attached to contract duration criteria (on a scale of 0 to 100)|_wDUR_|68|3.8|3.2|0.0|<br>8.3|
|Score attached to payment criteria (on a scale of 0 to 100)|_wPAY_|68|0.6|2.1|0.0|<br>8.3|
|Number of ESCOs applying for public tender|_N_|68|3.2|2.0|1.0|<br>11.0|
|Dummy variable for shared saving contracts|_dSSC_|68|0.79|0.41|0|<br>1|
|Dummy variable for packaged contracts|_dPCKG_|68|0.09|0.29|0|<br>1|
|<br>Annual electricity consumption (GWh)|_KWH_|68|5.0|6.8|0.2|<br>41.9|
|Age of building (years since the first establishment)|_AGE_|68|27.9|12.2|11.0|<br>85.0|
|Area of floor (1,000 m2)|_AREA_|68|32.3|33.5|2.1|171.2|
|Dummy variable for administrative offices|_dOFFC_|68|0.35|0.48|0|<br>1|
|<br>Dummy variable for hospitals|_dHOSP_|68|0.24|0.43|0|<br>1|
|Dummy variable for schools, university and museums|_dSCHL_|68|0.18|0.38|0|<br>1|
|Dummy variable for lighting systems|_dLIT_|68|0.91|0.29|0|<br>1|
|Dummy variable for air conditioning|_dAC_|68|0.97|0.17|0|<br>1|
|<br>Dummy variable for hot water boilers|_dHOTW_|68|0.37|0.49|0|<br>1|
|<br>Dummy variable for cogeneration systems|_dCGS_|68|0.29|0.46|0|<br>1|
|Number of ESCO contracts that were secured in the past|_AWAD_|68|1.31|2.17|0|<br>9|
|Dummy variable for lease companies|_dLEAS_|68|0.06|0.24|0|<br>1|
|Dummy variable for equipment manufacturers|_dMANU_|68|0.26|0.44|0|<br>1|
|<br>Dummy variable for oil, gas and electricity companies|_dENEG_|68|0.22|0.42|0|<br>1|
|Dummy variable for energy saving target announcement|_dTAGT_|68|0.87|0.34|0|<br>1|
|Dummy variable for reserve price announcement|_dRESV_|68|0.88|0.32|0|<br>1|
|<br>Dummy variable for risk sharing matrix disclosure|_dRISKMT_|<br>68|0.26|0.44|0|<br>1|
|<br>Dummy variable for walk through implementation|_dWALK_|68|0.93|0.26|0|<br>1|
|Dummy variable for typo errors born by public sector|_RISKTYPO_|68|0.97|0.17|0|<br>1|
|Dummy variable for regulatory risk born by public sector|_RISKREG_|68|0.24|0.43|0|<br>1|
|Dummy variable for inflation risk born by public sector|_RISKINF_|68|0.16|0.37|0|<br>1|
|Number of ESCOs expressingtheir initial interest|_NEOI_|68|4.5|5.1|1.0|<br>38.0|
Table 7: Estimated statistics for the fourth quintile

|**Estimator**|**Estimated value**|
|---|---|
|Estimated average|8503.57|
|STE: Analytical approach (without considering the implicit variability)|20.26|
|STE: Analytical approach|67.37|
|STE: Bootstrap (number of replications = 400)|68.57|

where _STD_ ( _V_ ) is the standard deviation of the variable _V_ , and _f_ ( _._ ) and _F_ ( _._ ) denote respectively the density function and its cumulative.

Is the estimated standard error of _μ_ ( _τ_ 1 _, τ_ 2) significantly different form that when we omit to take into account the implicit variability, i.e. when we assume that: _V ar_ ( ˆ _α_ 1) = _N_<sup>_−_1</sup> _STD yI_ [ _y ≤ Q_<sup>ˆ</sup> ( _τ_ 1)] ? To answer this question and to validate the proposed method for � � the estimation of the STE, we use a sample of 6000 randomly generated observations.<sup>17</sup> Table 7 shows the estimates for the forth quintile. The estimated STE with the bootstrap approach is close to that estimated with the proposed analytical approach. Instead, omitting the correction for the implicit variability will give a wrong estimate of the STE.

# **5.2.3 Pro-poor curves and stochastic dominance**

Having a method for statistical inference at the quantile level, we can now use it to draw and compare pro-poor price reforms curves. There are different interpretations of what pro-poor means and this has been the object of debate in the welfare economics literature with some arguing that pro-poor means that the poor outperform the non-poor and other arguing that a propoor reform is one that simply makes the poor better off, whether they outperform the non-poor or not (Ravallion and Chen (2003), Kakwani and Pernia (2000) Son (2004), Duclos (2009)). Irrespective of these different views, assessing pro-poorness implies comparing welfare impacts across quantiles and this can be done with what is known as pro-poor curves. One can then use a standard stochastic dominance approach to determine whether one distribution dominates another or not. In our case, each pro-poor curve can be constructed using the pre and post price reform for each welfare estimation method. We can then check for the pro-poorness of the impact with the different welfare measurements by using stochastic dominance theory. Recall that a pro-poor curve for a welfare estimation is first order welfare dominant if and only if: where _ρ_ is simply the average impact of the price reform and _τ_<sup>_∗_</sup> is a given critical percentile. The critical value marks the limit of the test, which is simply the highest possible proportion of poor. Recall here that, among the Social Welfare Functions (SWFs) that obey to the first order dominance constraint, some do not attribute more importance to the poor. For unequivocal comparisons, we need a second order dominance test as follows: 17 We used the Stata command _gen imp_ _~~w~~ el =_ _~~n~~ *(3+0.05*uniform())_ 45
Policy Research Working Paper 6571

# **Abstract**

This paper analyzes annual accounting data for a sample of 5,000 publicly traded manufacturing firms from Germany, France, Italy, Japan, and the United Kingdom. The analysis uses data from 1997 to 2011 and finds an increasing trend of excess savings (defined as the difference between gross saving and capital formation) and a gradual decline of gross capital formation. This trend is accompanied by a steady deleveraging process and a decrease in the share of operating assets in total assets. This process is more acute among the more credit constrained, the more volatile, and the less dynamic firms.

This paper is a product of the Financial Advisory and Banking Department of the World Bank Treasury. 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://econ.worldbank.org. The authors may be contacted at leandrobrufman@gmail.com.

_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
- 6 -

# Table 1 (cont'd): THE CILSS QUESTIONNAIRE

-SECOND ROUND-

|Qu|estionnaire<br>Section|Obtained Information|
|---|---|---|
|*|Farm and Livestock<br>Activitien|Area of land utilized, purchases and<br>sales In the last 12 months. Harvest and<br>disposition of the crops. Age of<br>perennials. Farm inputs. Income and<br>expenditures<br>of farms and activities that<br>transform farm products. Inventory of<br>livestock, purchases and sales during the<br>last 12 months. Income from the sales of<br>animal products. Mutual aid. Farm<br>equipment and tools. Sharecropping.|
|*|Non-farm Self-Employment<br>Activities|Income, expenditures,<br>capital goods for<br>the three main non-farm enterprises of<br>the household.|
|*|Expenditures<br>and<br>Inventory<br>or Durable<br>Goods|Daily expenditures<br>in the last two<br>weeks. Non-food expenditures<br>in the last<br>two weeks and the last 12 months.<br>Inventory of durable goods. Expenditures<br>of family support.|
|*|Food Expenditures<br>and<br>Consumption<br>of Home<br>Produced Food|Food expenditures<br>in the last two weeks<br>and the last 12 months. Consumption<br>of<br>home produced food in the last 12<br>months.|
|*|Fertility|Pertains to a woman aged 15 or more,<br>randomly chosen during the first round.<br>Number of children, number of<br>pregnancies,<br>and utilization of maternity<br>services during the last pregnancy.|
|*|Other Incom,_|Income from family support, and other<br>income not yet accounted for in previous<br>sections.|
|*|Credit 'nd Savings|Loans and savings of the household.|
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22 Finally, the sex ratio for U.S. net immigrants in the previous World Bank estimates seems too high for 1980-1985. The sex ratio of legal immigrants for FY1982-1986 has been 101.2. Even if we assume that 55 percent of emigrants are female, we would need to assume that 60 percent of illegal immigrants are male in order to reach the World Bank's previously published sex ratio estimate of 119 in 1980-1985. The World Bank's most recent estimate of 112 for 1985-1990 seems closer to the mark. I would recommend that a sex ratio in the vicinity of 112 be maintained for the entire period 1980-2000.

# Australia

International migration statistics in Australia are generally timely and of very highly quality. According to Appleyard (1987:3), "Australian migration statistics are arguably among the most accurate and detailed available." The accuracy of international migration data is due, in large measure, to Australia's isolation, the absence of direct land litks with cther countries, the limited number of ports of entry, and a long tradition of interest in demographic data in general and immigration in particular (Choi and Ward, 1987). Immigration and emigration data are based primarily on passenger cards that are filled in by all persons entering or leaving Australia. This source of data is relatively complete since illegal immigration is not considered to be a major component of international migration in Australia. In fact, it has been estimated that in 1980 there were only about 60,000 illegal immigrants in Australia (Storer, 1982).

The migration assumptions that have been used for the official population projections from the Australian Bureau of Statistics have been increased substantially in the last few years as additional information on recent international migration trends has become available. The large differences in the migration assumptions between the 1982 and 1986 sets of projections "underscore the difficulty involved in predicting long-term migration trends" (Zlotnick, 1988:25). Within a four-year period, for example, the prospective level of net migration for 1986-1987 increased from 70,000 to 106,000. Moreover, the 1986 set of projections assumed that net migration would increase to 125,000 in 1991-1992, whereas the newest projections (1988) assume that net migration will be more than 140,000 in 1991-1992.

Part of the reason for these differences is the considerable annual fluctuation in the level of admission of permanent settlers (see Figure 4). There has been a strong upward trend in settler arrivals since 1984 and the latest figures for 1988 confirm the continuation of that trend. Settler arrivals annually since 1975 are shown in the first column of Table 8. Arrivals increased steadily from 52,750 in 1975/1976 to 118,030 in 1981/1982. They then dipped to a low of 68,810 in 1983/1984 after which they began a dramatic recovery to a level of 143,490 in 1987/1988. Recent settlers are divided almost equally between males and females. The sex ratio of settlers for the last five years has hovered around 100 and has ranged from a low of 95.4 in 1983-1984 to a high of 105.8 in 1932-1983 (Hugo, 1988:15).
constant reserves and low labor share) but with 1 percent oil TFP growth, so aggregate TFP is also 1 percent, as in the standard LTGM. The excessive TFP growth leads to extra 0.6ppts of growth on average over 2023-2050.

# _B. The Effects of Oil Price Shocks in Angola_

In this section, we assess the effects of oil prices on Angola’s long-term growth. Note that oil prices shocks have potentially very large effects on short-medium run growth through Keynesian effects. However, those mechanisms are not part of neoclassical growth models like the LTGM-NR, and while important, are not considered here. Moreover, it can be the case that the best fiscal rule for long-run growth produces excessive volatility in the medium term.

More specifically, we consider a ten-year boom-and-bust cycle, in which the price of oil increases from $50 to $80/barrel from 2025 to 2030 and then returns to $50 by 2035, remaining at that level thereafter (see Figure 4, Panel A). This scenario is assessed relative to the baseline oil price of $50 until 2050. We also set $50/barrel as the reference price to determine structural revenues.<sup>45</sup> To isolate the price effect, we assume that oil reserves are held constant in per worker terms by setting discoveries equal production (both in per worker terms) and the structural production of oil is set equal to actual production.

In this section, most variables are expressed as deviation from the baseline. For example, Panel B of Figure 4 displays incremental GDI as percent of baseline GDI, defined as: 100 x [(Scenario real GDI in US$)/(Baseline real GDI in US$) − 1] Each line represents the simulation of GDI under a specific fiscal rule (BBR, SSR, BBR-HR, and SSR-HR) and submodel (Default and External Balance). Likewise, panels C, E, and F display incremental GDP, public and private investment, respectively. Finally, panel D shows incremental GDP growth _,_ defined as the difference in growth rates between scenario and baseline, in percentage points. Below we briefly discuss the impact of oil prices in each variable. Appendix Table 6 through Appendix Table 8 provide a detailed summary of the results.

> 45 These assumptions are based on the estimated forecast distribution of oil prices, using the World Bank’s Commodity Markets Outlook data from 1960 to 2019. The price of $80 is the 75<sup>th</sup> percentile of the distribution and $50 is the unconditional mean. See Appendix Figure 5 for details.

33
**Table 4: Implicit** **<u>tax rate on consumption (</u>** in %<sup>*</sup> <u>)</u>

||**1995 - 1999**|**2000 - 2004**|**2005 – 2007**<br>**(ranking in brackets)**|
|---|---|---|---|
|Hungary|28.6|26.4|26.4(1)|
|Bulgaria|-|20.2|25.1(2)|
|Slovenia|24.2|23.7|23.8 (3)|
|Estonia|19.6|19.9|23.3(4)|
|Czech Republic|20.2|19.8|21.6 (5)|
|Slovak Republic|23.8|20.5|21.0(6)|
|Poland|19.9|17.9|20.4(7)|
|Latvia|19.4|18.1|20.0 (8)|
|Romania|15.9 (1999)|16.5|17.9 (9)|
|Lithuania|18.9|17.3|17.0 (10)|
|EU-25 average|21.3|21.1|22.0|

<u>* The implicit tax rate is calculated by dividing consumption tax revenue by the macroeconomic tax base.</u> _Source:_ EU Commission.

**Table 5: Implicit tax rate on labor (** in %<sup>*</sup> <u>)</u>

||**1995 - 1999**|**2000 - 2004**|**2005 – 2007**<br>**(ranking inbrackets)**|
|---|---|---|---|
|Czech Republic|40.3|41.1|41.4(1)|
|Hungary|42.9|40.2|39.5(2)|
|Slovenia|37.5|37.6|37.3(3)|
|Poland|36.1|32.9|34.1(4)|
|Estonia|38.4|37.2|33.9 (5)|
|Lithuania|37.0|38.5|33.6(6)|
|Latvia|36.8|36.9|32.4(7)|
|Bulgaria|35.9 (1999)|35.5|31.7(8)|
|Slovak Republic|38.3|36.1|31.4(9)|
|Romania|37.6 (1999)|30.7|29.5 (10)|
|EU-25|35.9|35.4|34.8|

<u>* The implicit tax rate is calculated by dividing labor tax revenue by the macroeconomic tax base.</u> _Source:_ EU Commission.

**Table** **<u>6: Implicit tax rate on capital (</u>** in %<sup>*</sup> <u>)</u>

||**1995 - 1999**|**2000 - 2004**|**2005 – 2007**<br>**(ranking in brackets)**|
|---|---|---|---|
|Czech Republic|22.8|23.9|25.7(1)|
|Slovenia|-|17.3|22.4(2)|
|Poland|21.2|20.7|22.2(2005-2006) (3)|
|Slovak Republic|30.1|21.6|18.4(4)|
|Hungary|-|16.5|16.5(2005-2006) (5)|
|Latvia|18.9|9.8|11.7(6)|
|Lithuania|8.9|6.9|10.9 (7)|
|Estonia|10.9|6.6|8.9 (8)|
|EU-25 average|24.7|24.2|26.5|

<u>* The implicit tax rate is calculated by dividing capital tax revenue by the macroeconomic tax base.</u> _Source:_ EU Commission.

13
11

# **Trends in Emissions and Ambient Concentration of Polutants**

Table 2 shows trends in the overall emissions of SOx, particulate matter, NOx, CO, and lead in selected countries. Based on this sample of countries, there has been a decreasing trend in overall emissions of air pollutants in industrialized countries during the period 1973 to 1984, whereas in developing countries there has been an increasing trend. The share of transport in overall emisions is not well-documented, particularly in developing countries.

Air quality in major urban areas around the world has been monitored by the Global Environmental Monitoring System (GEMS/Air), which operates worldwide networks to monitor air quality under the auspices of World Health Organization (WHO) and United Nations Environmental Program (UNEP). The concentration levels in 1985 and trends from 1973 to 1988 in selected cities are shown in Table 3. There has been a decreasing trend in the concentration of sulfur oxides and particulate matter in many cities in industrialized countries, but urban areas in developing countries have experienced sharp increases in these contaminants. For example, New Delhi experienced a 20% increase in the concentration level of sulfur oxides from 1973 to 1985. Except for Singapore the concentration levels of NO2 increased in all cities shown in Table 3, in both industrialized and developing countries.

The concentration levels of SOx in many cities such as Beijing, Sao Paulo, Rio de Janeiro, and Seoul exceeded the WHO guidelines of 40 to 60 ug/cu. m, in 1985. The particulate level in almost all the developing cities shown in the Table 3 exceeded the WHO guideline of 60 to 90 ug/cu. m. Although the average levels of NO2 in most cities in developing countries were below the US national air quality standard of 100 ug/cu.m, in Sao Paulo this limit was exceeded in some years. Peak concentrations of NO2, however, are more significant from a health standpoint than annual mean values, as intermittent exposure to high concentrations contributes more to toxicity of NO2 than the total dose. Data on CO levels in cities in developing countries are generally insufficient; Bangkok had a level which is about one-half of the WHO guideline of 10 mg/cu. m. while ambient levels in Sao Paulo were almost twice as high. Lead level in Singapore was very close to the maximum limit of the WHO guidelines.
<!-- Start of picture text --> — — oo<br>% _<br>an —_—<br>~a<br>&a<br>.<br>& —<br>D<br>ga a<br>— —_<br>“<br>—<br>oO<br>2000 2005 2010 2015<br>year<br>——._ P75. - Develaping — — - P75 - Developed<br>———. P50 - Develaping — — - P50 - Developed<br>——— P25 - Develaping — — - P25 - Developed<br><!-- End of picture text -->
<!-- Start of picture text --> 0.5<br>0.45<br>0.4 Commercial Banks<br>0.35<br>Investment Bank/Securities<br>0.3<br>House<br>0.25<br>Non-banking Credit Institution<br>0.2<br>0.15<br>Other<br>0.1<br>0.05<br>0<br>1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br><!-- End of picture text -->

**Figure 6. Trend of non-deposit funding share by categories of bank type.**

This plot shows the trend of non-deposit funding share by bank categories from 1999 to 2007. Bank categories are commercial banks, investment banks or securities houses, non-banking credit institutions and other. Subcategories of _commercial banks_ include bank holdings and holding companies, and commercial banks. Subcategories of _other_ are cooperative banks, Islamic banks, medium and long term credit banks, real estate mortgage banks and savings banks. The data are from Bankscope.

62
# that "price influences choice among providers and does so more strongly than its influence on

whether or not to use services at all' _The larger the private sector, the smaller the impact ofpublic spending._ Using a recent update of health expenditures data, Table 4 shows that in nearly all poor countries the largest part of health spending is made by private individuals. The share of health expenditures that is made by private individuals is 63 percent in the low-income country group, and reaches almost 75 percent in South Asia.

Table 4: Percent of total health expenditures that are private

||Average|Number of countries|
|---|---|---|
|All countries|53.6|115|
||Income groups||
|Low income|<br>63.1|32|
|Low-middle income|42.8|36|
|High-middle income|54.0|15|
|<br>High income, non-OECD|62.0|10|
|<br>High income, OECD|32.6|22|
||Regions||
|East Asia and Pacific|53.2|14|
|Latin America and Caribbean|58.1|32|
|Middle East and North Africa|47.4|11|
|South Asia|74.8|_5_|
|Sub-Saharan Africa|59.6|19|
|Rest of World|29.3|34|

Notes: Average are population weighted means of countries from each region/group that have non-missing data.

Much of these private expenditures are likely made to private health care providers.

Indirect evidence of this flows from the fact that public care is free, or close to free, for the user in many countries, especially the poorer ones. Direct evidence is more rare. A recent study of health care in five Indian states estimated the share of the private sector in health care provision and the results summarized in Table 5 show that: a large share (82 percent) of illness episodes result in a visit to a private provider, that expenditures for non-hospital 34