rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 8: Full-Cost Recovery and Affordability of Subsistence Consumption for the Bottom 40%**

<!-- Start of picture text --> 8.00%<br>Philippines<br>Uganda<br>7.00%<br>6.00%<br>5.00%<br>4.00%<br>Kenya<br>3.00%<br>Tanzania<br>2.00%<br>Dominican Colombia<br>India - Andhra Republic Morocco Peru<br>1.00% Tajikistan<br>Pradesh Pakistan Ukraine<br>India - Rajasthan India - Odisha<br>0.00% Senegal Vietnam<br>30% 50% 70% 90% 110% 130%<br>Egypt, Arab Rep. Full-Cost Recovery<br>Cost of 30kWh per month as share of GNI for bottom 40%<br><!-- End of picture text --> _Source:_ World Bank staff.

41. Such cross-subsidies are important means for governments to maintain affordability and political legitimacy of power sector reforms. The critical question is starting from what level cross-subsidization starts impeding cost recovery. One way to quantify cross-subsidization is to look at the area under a Lorenz curve, which shows cumulative shares of consumption and revenue by customer class (see Figure 9).<sup>12</sup> A straight diagonal would mean that all customers contribute shares of revenue equal to their shares of consumption, and a high degree of curvature would mean that customers’ shares of revenues are not well aligned with their shares of consumption. Many utilities show a relatively even distribution of shares of revenue and consumption by customer class. This even distribution does not mean that cost recovery is being met or that there are no cross-subsidies; it only means that all customer classes are paying similar tariff rates for their consumption. Some countries with flatter distributions may not be recovering costs through tariffs on any customer class (as is the case with Rajasthan). In addition, it is appropriate for customers which impose lower costs on the system (such as industrial customers) to pay lower tariffs than other customer groups, so a completely flat curve is not ideal. A high degree of curvature, however, can point to a serious misalignment of costs incurred to serve each customer group and the customers who are paying those costs. The three Andhra Pradesh distribution companies show the greatest disparity in the share of consumption and the share of revenue contributed by each customer class. The distribution

> 12 Specifically, the level of cross-subsidization is quantified here as the area between the 45-degree line and the curve defined by the cumulative shares of consumption and revenue by customer class. The formula is the same as that of a ‘Gini-Coefficient‘ of inequality.

22
(developed, emerging, or frontier). Then, it selects a number of securities that fall within the scope and meet the size, market capitalization, liquidity, and other requirements. Each of these securities gets a loading (or inclusion factor) in the index portfolio assigned by the index producer according to how much it meets the index-construction criteria and how accessible it is to investors (given by the free-float market capitalization, restrictions to foreign investors, and so forth). The return of the index consists of the returns of its constituent securities, using various approaches to aggregate fluctuations in individual instruments (e.g., Laspeyres, chain-weighting). Namely, each index captures the market capitalization weighted returns of all constituents included in the index. The indexes are periodically rebalanced to ensure their continuity and representativeness (MSCI Barra, 2013a,b).

We then study how benchmark weights are related to country characteristics by analyzing the factors that affect the probability of including a country in a benchmark (the extensive margin) and the weight it receives (the intensive margin). We start with a cross-sectional analysis and relate these two margins to common market and country characteristics, namely, market capitalization ( _MC_ ), gross domestic product ( _GDP_ ), GDP per capita ( _GDPPC_ ), the composite ICRG index of country risk ( _RISK_ ), the Polity index of quality of institutions ( _INST_ ), and the Chinn-Ito index of capital account openness ( _KOPEN_ ).

We estimate the parameters of the following specifications for the extensive and intensive margins.

- Extensive margin:

( ) (

- Intensive margin:

( ̅ ) where is the time average of the weight of country _c_ in benchmark _B_ , the country level variables (indexed by _c_ ) correspond to the average over 1996-2012, and the errors are clustered at the country level. The extensive margin specification estimated by Probit and Ordered Probit considers only the composition of the MSCI benchmarks (whether a country is in an index or not and whether a country is in the developed or 10
- 25 -

# Appendix **1:** The Distributions

# The Household Consumption and Caloric Intake Data

Past poverty assessments for Indonesia have generally been based on the distributions of household consumption per person from the SUSENAS expenditure surveys done by the Central Bureau of Statistics. We follow this method, though noting that it has both advantages and disadvantages. The following points should be particularly noted:

- (i) The SUSENAS is widely recognized as a household consumption survey of relatively high quality which has used sound and consistent survey methods over many years. The 1984 and 1987 surveys appear to be fully comparable in terms of the questions asked, and the methods of sampling and interviewing. The estimates obtained will depend in part on the date of interview, particularly in rural areas where past work has found evidence of seasonality in consumption (as well as incomes).1 9 The 1984 and 1987 SUSENAS surveys used here were done at approximately the same time of year (February and January respectively) in comparable agricultural years,<sup>20</sup> and so this is unlikely to be a problem for comparisons between those years.

- (ii) The SUSENAS has the advantage over expenditure surveys fer a rumber of developing countries that it is a consumption based survey i.e., it does not just ask how much was spent on variuus goods and services, but also asks how much was actually consumed from various sources, including of course, market expenditure but also including consumption from own production and transfers (van de Walle, 1988).
19 To arrive at the poverty line, an allocation has to be made for non-food expenditure. In both countries this is done based on the typical share of non-food expenditure of households who are close to the food poverty line. The idea is that at this expenditure level, non-food enters as a basic need, because it competes directly with food itself. Hence, the poverty line can be written as:

# where θ is the share of basic non-food expenditure.

Looking at the topping up in the two countries, non-food expenditure in Ghana adds two-thirds to the food poverty line and in Mozambique only around one-third (+31, +41, +36% in the respective years 1997-2003-2009). Also here, notable differences occur when looking at the actual shares of non-food expenditure across population groups.

Because observed consumption patterns might reflect preferences or an adaptation to changing prices, one may consider a CSPL, say one poverty line for a rural context and one for an urban context. The fact that the Mozambique survey makes a distinction between 40 basic foods, while the Ghanaian diet is based on 73 foods, offers a window of opportunity to modify the food poverty line. A common upward (downward) adjustment of the urban (rural) food poverty line adjusts for higher (lower) food prices and, possibly, also for urban (rural) preferences for relatively expensive (cheap) foods. The allocation for non-food also usually involves an urban bias, in response to the fact that expenditures for clothing, housing, transport, etcetera are much more common in the cities.

For the purpose of the paper, the urban-rural poverty line is constructed as an adjustment of pfood for rural-urban difference in the median calorie cost of a typical diet of the poor, while keeping similar non-food share θ for urban and rural households alike. Adding further specificity by lowering (expanding) non-food share θ in rural (urban) area and by adapting to the calorie cost in the various provinces or regions has also been done, in an attempt to see how adding more context-specificity changes the picture. The resulting details of the CSPL from national to urban-rural to spatial domains are given in Annex 3 and shows that urban lines (Accra; Maputo) exceed rural lines (Upper West; Rural Zambezia and Nampula) by a factor 2 to 3.

# _Quality vis-à-vis quantity of foods: Flat calorie Engel-curves and elastic calorie prices_

Before turning to the poverty patterns corresponding to a different poverty line, we consider the issue of food quality through estimation of food Engel curves. We employ a double-log model and
Table 3c. Likelihood of Choosing Job A with alternate unemployment insurance coverage offers/ earnings, total and by Current and Previous Employment Status

||Employers prio<br>|r to current job<br>|Other Jobs<br>|Currently?<br>|
|---|---|---|---|---|
||None|1 or More|No|Yes|
|Payment of RM 800 per month for 6 months, 0.5%<br>|0.261***|0.158***|0.174***|0.208***|
|lower income|[0.0471]|[0.0280]|[0.0355]|[0.0340]|
|Payment of RM 800 per month for 6 months, 0.2%<br>|0.169***|0.166***|0.196***|0.145***|
|lower income|[0.0413]|[0.0277]|[0.0377]|[0.0286]|
|Payment of RM 1200 per month for 6 months, 0.5%<br>|0.229***|0.105***|0.148***|0.142***|
|lower income|[0.0464]|[0.0234]|[0.0316]|[0.0311]|
|Payment of RM 1200 per month for 6 months, 0.2%<br>|0.277***|0.130***|0.144***|0.201***|
|lower income|[0.0494]|[0.0253]|[0.0335]|[0.0330]|
|N|337|701|466|572|
|_Standard errors in brackets_<br>_* p<0.05, ** p<0.01, *** p<0.001_|||||

# Retirement Savings

Table 4 below shows the coefficients of the LPM with dependent variable is the dummy variable for the choice of Job A (= 1 if the respondent chooses job A, 0 if she chooses job B). In the case of retirement income, Job A includes contributions to a retirement savings fund as per EPF’s current rules for contribution and retirement savings, and earnings are set at the same level as the respondents’ current income. Two options of Job B (B1 and B2) offer no retirement savings fund but offer 5% and 10% higher earnings instead. The other two options (Jobs B3 and B4) offer a monthly pension plan after retirement instead of the current model which offers a lumpsum payout at retirement. Earnings through Jobs B3 and B4 are offered as 5% and 10% lower than respondents’ current earnings, respectively.

13
within households exhibits uniform patterns across all asset classes. Nuances of ownership, including exclusive versus joint ownership, vary substantially across countries, and it appears more common that households fall in either only exclusively-owned arrangements or only jointly-owned arrangements across asset classes, as opposed to combinations of exclusive and joint ownership.<sup>12</sup>

# 3.2 Constructing individual-level wealth measures and addressing issues related to measurement error

Following the UN guidelines that were discussed in Section 2.2, we use <u>respondents’ self-reported valuations of each asset they own, based on the potential sales value, to construct aggregate valuations of</u> their wealth. Data on asset valuation, however, can be subject to different forms of measurement error, that might affect an overall understanding of inequality within populations. There are two measurement issues with the valuation data that we address in the paper: (1) A key measurement challenge is that in Ethiopia and Tanzania, there was a substantial number of respondents who did not report values for specific assets, such as land and livestock (see Appendix Table A4). In Ethiopia, for example, 43 percent of men and 56 percent of women who were residential landowners did not report a potential sales value. In Tanzania these shares were 19 percent of men and 48 percent of women. Appendix Table A4 shows that among those with missing valuations, women were generally more likely to have missing data, and the most common reason among men and women was not knowing the value of the asset. In Malawi and Cambodia, on the other hand, the shares of respondents with missing valuations across asset classes was much lower — 5 percent or less among landowners in Malawi, and on average 15 percent in Cambodia.

> 12 In Cambodia, a high share of households owning bicycles (28 percent) and motorcycles (62 percent) are listed in column (5) since the survey did not distinguish across exclusive versus joint ownership, but joint ownership is likely given the country’s civil law governing marital assets.

16
|Head completed primary|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|& lower secondary|-0.345***|-0.0854*|-0.244***|0.154*|-0.157**|-0.544***|-0.327***|-0.314***|-0.102|-0.364***|
||(0.0684)|(0.0461)|(0.0594)|(0.0872)|(0.0749)|(0.0758)|(0.0477)|(0.0907)|(0.0783)|(0.0715)|
|Head passed O/L|-1.184***|-0.162**|-0.355***|-0.0194|-0.440***|-1.352***|-0.466***|-0.398***|-0.165|-0.435***|
||(0.190)|(0.0631)|(0.0959)|(0.123)|(0.120)|(0.191)|(0.0644)|(0.141)|(0.105)|(0.104)|
|Head passed A/L or|||||||||||
|higher|-2.100***|0.139*|-0.333**|-0.0446|-0.0151|-2.026***|-0.249***|-0.587***|-0.584***|-0.993***|
||(0.461)|(0.0748)|(0.136)|(0.160)|(0.143)|(0.358)|(0.0704)|(0.190)|(0.136)|(0.142)|
|Enrolled in school|-0.479***|-0.559***|-0.493***|-0.404***|-0.513***|-0.512***|-0.698***|-0.289***|-0.462***|-0.486***|
||(0.0774)|(0.0405)|(0.0607)|(0.0761)|(0.0731)|(0.0846)|(0.0424)|(0.0907)|(0.0699)|(0.0673)|
|pens_d|<br>-1.033***|<br>-0.182***|<br>-0.318***|<br>-0.351**|<br>-0.147|<br>-1.167***|<br>-0.225***|<br>-0.886***|<br>-0.373***|<br>-0.437***|
||(0.284)|(0.0695)|(0.122)|(0.154)|(0.138)|(0.314)|(0.0668)|(0.179)|(0.113)|(0.135)|
|Constant|-9.909***|-7.515***|-8.932***|-10.44***|-9.093***|-9.289***|-7.366***|-9.675***|-10.23***|-9.244***|
||(0.273)|(0.139)|(0.219)|(0.349)|(0.245)|(0.270)|(0.148)|(0.346)|(0.283)|(0.224)|
|Observations|48,866|48,866|48,866|48,866|48,866|50,748|50,748|50,748|50,748|50,748|
|Pseudo R-squared|0.287|0.287|0.287|0.287|0.287|0.294|0.294|0.294|0.294|0.294|
|Standard errors in<br>parentheses<br>*** p<0.01, ** p<0.05, *<br>p<0.1|||||||||||

40
Employment shares in non-farm activities have grown since 1983, reaching their peak in 2004/5 (Table 3), with state level employment shares ranging 22-32% (Appendix Table 1). In each respective survey year, wage and salary employment has tended to account for about 11-16% of overall employment, with the balance made up by selfemployment/own-enterprise activities. There are large differences across states in terms of the importance of the non-farm sector. In Kerala, non-farm employment shares were as high as 77% in 2004/5, and the importance of regular employment in this state grew significantly over time (from under 25% to nearly 40%). In states such as Madhya Pradesh, Bihar, Chattisgarh and Maharashtra, the sector has still to make its presence felt.

At the all-India level, there has been a modest expansion of this sector over time. However, importantly, it has become the main source of employment growth in rural areas. In the 1980s, of the nearly 40 million additional jobs generated, the majority (6 out of every 10) were in the farm sector. Between 1993/4 and 2004/5, non-farm employment growth had outstripped agriculture, as 6 of every 10 new jobs in rural India were generated in the non-farm sector. The largest increase in incremental employment attributed to the non-farm sector took place between 1999/0 and 2004/5.

# _Non-Farm Employment and Consumption Quintiles_

The distinction between regular, casual and self-employment in the non-farm sector is well reflected in Tables 5a-5c, documenting the relationship between non-farm employment and consumption quintiles in each of the respective survey years.<sup>8</sup> In all five survey years, regular non-farm employment occurred disproportionately in the top quintile of the per capita consumption distribution (Table 5a). While overall employment shares in regular non-farm employment hovered around 6% throughout this period, the relative frequency of such employment in the top quintile was two or more times higher, while in the bottom quintile it was below 3%.

Overall employment shares in casual non-farm activities are only slightly higher than for regular non-farm employment shares (Table 5b). But the distribution across consumption quintiles is quite different. Casual wage employment in the non-farm sector

> 8 Lanjouw and Shariff (2003) observe very similar patterns across income quintiles in NCAER data for 1993/4.

11
Table 8. Required increments of tax rates for experiments 2.1-2.6

|||2000|2005|2010|2015|2020|2025|2030|2035|2040|2045|2050|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Exp.2.1|Corporatetax|0|1.12|1.27|1.28|1.23|_1.15_|1.07|0.96|0-82|0.62|0.39|
|Exp. 2.2|Value-added tax|0|0.59|0.66|0.65|0.62|0.57|0.53|0.47|0.40|0.30|0.18|
|Exp. 2.3|Personal income tax|0|0.72|0.80|0.78|0.73|0.68|0.63|0.56|0.48|0.34|0.18|
|Exp. 2.4|Final demand tax rate|0|0.56|0.61|0.60|0.56|0.52|0.48|0.43|0.37|0.27|0.15|
|Exp. 2.5|Corporate tax|0|0.41|0.46|0.46|0.44|0.40|0.37|0.33|0.28|0.21|0.12|
||income tax|0|0.27|0.31|0.31|0.29|0.27|0.26|0.24|0.20|0.15|0.09|
||Value-added tax|0|0.15|0.17|0.17|0.16|0.14|0.13|0.12|0.10|0.07|0.05|
|Exp. 2.6|Corporate tax|0|0.30|0.34|0.34|0.32|0.30|0.28|0.25|0.21|0.15|0.09|
||Incometax|0|0.20|0.23|0.23|0.22|0.20|0.19|0.17|0.15|0.11|0.07|
||Value-added tax|0|0.12|0.13|0.12|0.12|0.11|0.10|0.09|0.07|0.06|0.04|
||Final demand tax rate|0|0.14|0.16|0.16|0.15|0.14|0.13|0.12|0.10|0.07|0.04|

Source: The authors.

Table 10. Balanced contribution rate

||1995|2000|2005|2010|2015|2020|2025|2030|2035|2040|2045|2050|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Baseline|25.0|23.3|27.2|31.2|34.5|37.7|35.8|34.3|33.3|33.4|34.2|35.1|
|Exp 1.1|25.0|22.1|23.0|25.3|27.2|29.2|29.4|29.6|30.1|31.5|33.2|34.7|
|Exp 1.2|25.0|22.1|23.0|25.1|25.4|26.3|26.5|27.0|27.2|28.3|30.1|31.9|
|Exp 1.3|25.0|22.1|23.0|25.1|25.4|25.6|20.8|18.8|17.9|17.7|18.2|19.5|
|Exp. 2.5|8.3|7.8|9.1|10.4|11.5|12.5|11.9|11.4|11.1|11.1|11.4|11.7|
|Exp. 3.1|8.3|7.8|10.0|11.1|11.7|12.2|11.5|11.0|10.7|10.9|11.2|11.6|
|Exp.4.1|8.3|7.8|10.0|10.5|11.1|11.4|11.1|10.7|10.5|10.7|11.1|11.6|
|Exp. 5.1|8.3|7.8|10.6|10.7|11.5|11.9|11.7|11.3|11.1|11.4|11.8|12.1|
|Exp. 5.2|8.3|7.8|10.6|10.6|10.6|10.5|10.4|10.2|9.9|10.0|10.4|10.8|
|Exp. 5.3|8.3|7.8|10.6|10.6|10.6|10.2|8.1|6.9|6.3|6.0|6.0|6.2|

Source: The authors.

35
Table 7: Shares of Total Consumption, Income, and Wealth by Rural and Urban Residency, Malawi (ISA 2010)

# (A) Rural Residency

# (B) Urban Residency

|Bo|ttom(%)||Q|uintile|s||To|p(%)||All|
|---|---|---|---|---|---|---|---|---|---|---|
|0-1|1-5<br>5-10|1st|2nd|3rd|4th|5th|10-5|5-1|1|0-100|
|B1)|Income Parti|tion:<br>Sh|ares o|f Tot|al (%)||||||
|1|2<br>3|12|13|15|19|41|9|15|6|100|
|0|0<br>0|2|5|8|14|72|11|26|25|100|
|1|1<br>1|5|13|10|12|59|10|28|11|100|
|0|1<br>1|4|21|27|25|23|5|6|5|100|

||Bot|tom(%)|||Q|uintil|es||To|p(|%)|All|Bo|ttom(%)|Qu|intile|s|Top|%|)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0-1|1-5<br>5-10|1st<br>|2n|d|3rd|4th|5th|10-5|5-|1<br>1|0-100|0-1|1-5<br>5-10|1st<br>2nd|3rd|4th<br>5th|10-5<br>5|-1|1|0-100|
|(A1) Income P|artition|:|Sha|re|s o|f Tot|al (%)||||||(B1)|Income Partit|ion:<br>Shares of|Tota|l (%)|||||
|Consumption|1|2<br>3|12|1|5|19|22|33|8|1|0<br>3|100|1|2<br>3|12<br>13|15|19<br>41|9<br>|15|6|100|
|Income|0|0<br>1|3||7|12|20|57|11|1|8<br>14|100|0|0<br>0|2<br>5|8|14<br>72|11<br>|26|25|100|
|Wealth|0|2<br>2|10|1|3|15|22|39|9|1|4<br>4|100|1|1<br>1|5<br>13|10|12<br>59|10<br>|28|11|100|
|_▷Land_|0|2<br>3|11|1|4|17|26|32|8||9<br>2|100|0|1<br>1|4<br>21|27|25<br>23|5|6|5|100|
|(A2) Wealth P|artition|:|||||||||||(B2)|Wealth Partit|ion:|||||||
||||Sha|re|s o|f Tot|al (%)||||||||Shares of|Tota|l (%)|||||
|Consumption|1|3<br>4|15|1|6|18|21|31|7|1|0<br>2|100|2|1<br>3|11<br>14|17|19<br>39|9<br>|14|4|100|
|Income|1|2<br>3|11|1|5|17|20|36|8|1|3<br>3|100|1|0<br>2|7<br>8|14|16<br>54|12<br>|24|9|100|
|Wealth|-0|0<br>0|2||6|10|18|63|11|2|1<br>17|100|-0|0<br>0|0<br>1|4|10<br>84|12<br>|29|32|100|
|_▷_Land|0|0<br>0|2||7|12|21|59|11|1|9<br>13|100|0|0<br>0|0<br>1|5|20<br>74|24<br>|20|8|100|
|(A3) Land Part|ition:||||||||||||(B3)|Land Partitio|n:|||||||
||0-10||Sh|ar|es|of To|tal (%)||||||0-75||Shares o|f Tot|al (%)|||||
|Consumption|16|-<br>-|20|1|6|19|20|25|6||7<br>2|100|75|-<br>-|-<br>-|75|5<br>20|4|6|3|100|
|Income|16|-<br>-|20|1|4|17|20|28|7||8<br>2|100|72|-<br>-|-<br>-|72|4<br>24|7|7|4|100|
|Wealth|6|-<br>-|8||8|14|19|51|10|1|6<br>13|100|64|-<br>-|-<br>-|64|3<br>33|7<br>|13|7|100|
|_▷_Land|0|-<br>-|1||5|10|19|65|12|2|2<br>18|100|0|-<br>-|-<br>-|0|3<br>97|15<br>|46|26|100|

_Notes:_ All variables are averages in current USD. The construction of the measures of household consumption, income, and wealth is discussed in Section 2, with further details in the online appendix.
-4 -

protection created by the VERb both with and without labor market distortions and, with and without imperfectly competitive behavior. This step-by-step approach helps isolate the contribution of each distortionary component. Section 5 asks what would be the likely benefits of optimal wage and tariff policies in the auto and steel industries. Conclusions follow in section 6.

# **2.** Modelling **Labor and** Product **Markets in Autos and Steel**

The auto and steel sectors are among the sectors taat pay the highest wages in US manufazturing. Steel worker (auto worker) compensation was 63 (53) percent above the compensation of the average US manufacturing worker in 1984.4/ Using the 1984 Current Population Survey, Krueger and Summers (1988) estimate that, after controlling for human capital and demographic factors such as sex, age, race, marital state and education, workers in the t-ansport equipment sector earn a premium of 27 percent above the industrywide average, and workers of fabricated metal a premium of 26 percent. They also find that these statistically significant premia are stable across time and space. These wage premia are viewed as too large and persistent to be explained by compensating differentials such as attractiveness of work. Similar results were found by Katz and Summers (1989b).

In view of this (and other) evidence, we assume that workers in auto and steel earn a premium. We model this premium in two different ways. In one variant, we assume that the premium is exogenous ti > 1, i.e. Wi - W#i, where W is the wage earned in sectors where workers do not receive rents. Workers in all industries receive the value of their marginal product at the wage rate Woi. Since ti > 1 is a premium, we choose 0 - 1 for all sectors other than