rafmacalaba/datause-encoder-data huggingface dataset:
32 the top 30 percent of the population appropriated 55 percent and the bottom 40 percent only 22 percent of total public expenditures on senior secondary schools in 1978, the shares had become 36 and 43 percent respectively in 1987. This is a marked pro-poor shift in benefits from this category of spending. At the tertiary level, by 1987 the wealthiest group's share had dropped from 83 to 60 percent, while the share of the poor increased more than 3 fold to 24 percent and the middle group's share rose by 60 percent to 16, from the shares received in 1978. These shifts represent substantial movements in a pro-poor direction.

It should be noted that the position of the middle 30 percent has altered very little other than at the tertiary level. It is also interesting to note that the poorest group's loss in the share of education subsidies by 3 percentage points at the primary education level appears to cancel out the seemingly large gains at higher schooling levels. This is reflected in the lack of any change in the aggregate share of education subsidies accrueing to the bottom quantile. There appear to be two factors driving these results. The first is that such a large proportion of all subsidies in terms of Rupiahs goes to primary education. Compounding this effect is the assumed change in the proportion of total students attending public versus private institutions across the quantiles.

On the whole, the government's disbursements on education cannot be said, based on these results, to be badly targeted. Indeed, at all except the university levels, the share of total education subsidies going to the poorest 40 percant of the population exceeds their population share.

# _Government Subsidies for Health Care_

The requisite health financing data is taken from a careful compilation of recurrent expenditures on hospitals and health centers (World Bank, 1991a). The same source also estimates cost recovery to have been 3 percent of total recurrent expenditures on the public health center system, and 19.9 percent of recurrent expenditures on public hospitals (Table 5.3).
# L I S T O F F I G U R E S

|Figure1|Contribution of Trade Taxes to Total Tax Revenue . . . .|. . .<br>3|
|---|---|---|
|Figure2|Share of Tax Revenue by Source<br>. . . . . . . . . . . . . .|. . .<br>24|
|Figure3|Dispersion of Tariff Rates . . . . . . . . . . . . . . . . . .|. . .<br>29|
|Figure4|Statutory and Collected Tariff Rates . . . . . . . . . . . .|. . .<br>38|

# L I S T O F TA B L E S

|Table1|Recovery of Taxes in Low-Income Countries,1982-2006<br>. . .|11|
|---|---|---|
|Table2|Illustration of Price and Demand Response in TRIST . . . . .|50|
|Table3|Tariff Rates and Import-based Revenue in Nepal,2008 . . . .|51|
|Table4|VAT Collected on Imports,2005-2010<br>. . . . . . . . . . . . . .|51|
|Table5|Tariff Revenue by Band,2008 . . . . . . . . . . . . . . . . . . .|51|
|Table6|Impact on Revenue of Tariff and Tax Reforms . . . . . . . . .|52|
|Table7|Impact on Revenue of Tariff and Tax Reforms with an Infor-<br>mal Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|53|
|Table8|Impact on Revenue of Regional Free Trade . . . . . . . . . . .|54|
|Table9|Impact on Revenue of Regional Free Trade with Sensitive<br>Lists<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|55|
|Table10|Robustness Tests with Higher Elasticities . . . . . . . . . . . .|56|
|Table11|Change in Price, Production, Revenue, and Protection<br>. . . .|57|
|Table14|Statutory and Applied Tariff Rates<br>. . . . . . . . . . . . . . .|58|
|Table12|Major Exporters to Nepal,2008&2010 . . . . . . . . . . . . .|58|
|Table13|Number of Products in the Sensitive Lists<br>. . . . . . . . . . .|58|
|Table15|Summary of Data used in Table1Regressions . . . . . . . . .|59|
|Table16|List of Countries and Related Tax Data,2002-2006<br>. . . . . .|60|

iv
# Is Inequality Systematically Underestimated in Sub-Saharan Africa? A Proposal to Overcome the Problem

## Fabio Clementi

_University of Macerata, Macerata, Italy_ Michele Fabiani _University of Campania “Luigi Vanvitelli”, Caserta, Italy, and University of Macerata, Macerata, Italy_ Vasco Molini<sup>1</sup> _The World Bank, Rome, Morocco_

## Francesco Schettino

_University of Campania “Luigi Vanvitelli”, Caserta, Italy_

**Keywords:** Inequality, measurement, consumption and income, Africa

**JEL classification:** C46, D31, D63 **Total word count:** 14,236

> 1 _Corresponding author_ . E-mail: vmolini@worldbank.org
# **<u>Table 2. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 1 using 2020/21 Poverty Line, Tanzania (percentage)</u>**

|**Method**|||||**TA 1**|||||
|---|---|---|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|**Model 7**|**Model 8**|**Model 9**|
|1 Nl li i dl|**20.7**<sup>**a**</sup>|**22.1**<sup>**a**</sup>|**21.0**<sup>**a**</sup>|**22.5**<sup>**a**</sup>|**20.9**<sup>**a**</sup>|**20.5**<sup>**a**</sup>|**22.0**<sup>**a**</sup>|**23.1**<sup>**a**</sup>|**23.0**<sup>**a**</sup>|
|) orma near regresson moe|(2.1)|(2.2)|(2.2)|(2.2)|(2.2)|(2.2)|(2.2)|(2.3)|(2.2)|
|2) Empirical distribution of the error|**20.4**<sup>**a**</sup>|**22.0**<sup>**a**</sup>|**21.1**<sup>**a**</sup>|**22.5**<sup>**a**</sup>|**20.7**<sup>**a**</sup>|**20.4**<sup>**a**</sup>|**21.9**<sup>**a**</sup>|**23.1**<sup>**a**</sup>|**22.9**<sup>**a**</sup>|
|terms|(2.1)|(2.2)|(2.2)|(2.2)|(2.2)|(2.2)|(2.2)|(2.3)|(2.2)|
|_Control variables_||||||||||
|Food expenditures|||Y|||||||
|Non-food expenditures||||Y||||||
|Furnishings and household expenses|||||Y|||||
|Health expenditures||||||Y||||
|Education expenditures|||||||Y|||
|Utilities: water, kerosene, lighting||||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.40|0.54|0.85|0.79|0.56|0.55|0.54|0.55|0.44|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|N2(target survey)|711|711|711|711|711|711|711|711|711|
|**True poverty rate in TA 1**|||||21.0<br>(2.2)|||||

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. The imputed poverty rates for TA 1 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

33
day, suggesting a click-through rate of 4.3 percent for abstract reads and 1.1 percent for downloads.<sup>11</sup> This seems consistent with the intuition that as an academic‟s blog expands readership to a larger and larger audience, the additional readers are less likely to be interested in the academic papers.

Finally, we should note that the estimates in Table 1 show the average impacts of being linked to by these blogs. In practice, there appears to be considerable heterogeneity in the spike in blog traffic caused by a particular blog. For example, just taking the difference in abstract views in the month blogged about compared to the mean abstract views over the months before the paper was blogged about gives a range of +33 to +2908 over the 31 papers linked by Marginal Revolution in our sample (25<sup>th</sup> percentile to 75<sup>th</sup> percentile range is 69 to 314). It is likely the size of the increase reflects a combination of the interest in the topic to the general blog reader, and the manner in which the blog links to the paper (e.g. full post about a paper vs. single line link; positive, neutral or negative link, etc.). Unfortunately there are insufficient data in our sample to explore this phenomenon systematically.

# **3. Probit Analysis of Influence**

When economic bloggers discuss what the benefits of blogging are, much of the discussion tends to be self-centered – they view it as something beneficial to themselves. Thus blogs are viewed as providing a space to catalog and flesh out ideas, get feedback and a sense of popular applications for ideas, and to disseminate their own ideas to a wider audience (Nash, 2008). Yet it is difficult to quantify or measure many of these effects, although several bloggers attest that

> 11 Blog traffic statistics from http://www.gongol.com/lists/bizeconsites/ [accessed March 2011 rankings on July 28, 2011]; Marginal Revolution and Freakonomics traffic data from SiteMeter; and <u>www.websitevalue.us. Chris Blattman traffic statistics based on a blog posting in which he</u> said he had 800,000 page views in 2010.

11
Policy Research Working Paper 6123

# **Abstract**

Unbundling power generation, transmission, and distribution is not an end itself, but rather a means to achieve better performance. The key objective of the analytical framework of this paper is to explore the links between alternative market structures and performance (in terms of access, price, quality, and technical and financial performance). The results are crucial for providing policy advice, by offering alternative options to policy makers based on the lessons learned from the taxonomy of different market structures, tailored to different national contexts. The analysis is based on unique data, including a panel of 22 countries for the period beginning in 1989 and extending through 2009. The results of the analysis carried out for this study confirm the following conclusions for policy guidance on power market restructuring for developing countries.

First, unbundling delivers results in terms of several performance indicators when used as an entry point to implement broader reforms, particularly introducing a sound regulatory framework, reducing the degree of concentration of the generation and distribution segments of the market by attracting public and private players and private sector participation. Second, there seems to be a credible empirical basis for selecting a threshold power system size and per capita income level below which unbundling of the power supply chain is not expected to be worthwhile. Finally, partial forms of vertical unbundling do not appear to drive improvements, probably because the owner was able to continue exercising control over the affairs of the sector and hinder the development of competitive pressure within the power market.

This paper is a product of the Energy Unit, Sustainable Energy Department. 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 author may be contacted at mvagliasindi@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
countries. The results remain qualitatively the same when openness and quality of institutions are again added as control variables.

**Table 4.2: Education as Determinant of Growth of Income per Capita, 1960-2000: Sub-samples**

||(5)<br>Developing<br>countries<sup>a</sup>|(6)<br>OECD sample|(7)<br>Low-income<br>countries<sup>b</sup>|(8)<br>High-income<br>countries<sup>c</sup>|
|---|---|---|---|---|
|GDP per capita 1960|-0.262|-0.301|-0.063|-0.294|
||(1.77)|(5.81)|(0.28)|(6.38)|
|Years of schooling 1960|0.025|0.025|0.006|0.152|
||(0.20)|(0.26)|(0.05)|(1.70)|
|Test score (mean)|2.056|1.736|2.286|1.287|
||(6.10)|(4.17)|(6.98)|(5.37)|
|Constant|-5.139|-3.539|-6.412|-2.489|
||(3.63)|(1.96)|(4.52)|(2.86)|
|N|27|23|25|25|
|R<sup>2</sup> (adj.)|0.676|0.830|0.707|0.783|

Dependent variable: average annual growth rate in GDP per capita, 1960-2000.<sup>a</sup> Non-OECD countries.<sup>b</sup> Countries below sample median of GDP per capital 1960.<sup>c</sup> Countries above sample median of GDP per capital 1960.

Second, in columns (7) and (8), we subdivide the sample into countries above and below the sample median of initial GDP per capita. Again, the significant effect of educational quality remains robust in both sub-samples.<sup>48</sup> Here, the effect of quality is considerably larger in the low-income countries, and the difference in the coefficients on test scores is statistically significant at the five percent level. Thus, if anything, the effect of educational quality is larger in developing countries than in developed countries. Furthermore, the robustness of the effect in the sub-samples is remarkable, in particular considering the small sample sizes of the specifications reported in Table 4.2.

When adding an interaction term between years of schooling and test scores to the specification, the interaction term is insignificant in the full sample of countries. But in the sub-sample of lowincome countries (with a GDP per capita below the sample median in 1960), the interaction term is significantly positive. At least in this sample, the returns to increased years of schooling increase with the quality of the education. Once there is a high-quality school system, it pays to keep children longer in school – but it does not if the school system does not produce skills.

> 48 Similarly, the effect of educational quality remains robust in the two sub-samples of countries above and below the median of economic growth over the considered period, with the point estimate larger in the high-growth sample.

36
link between competence and children’s heights and weights. Similarly, Barber and Gertler (2007) show in Mexico that the birth-weight of children increased in villages with conditional cash transfer programs, whereby mothers were given cash if they completed a certain number of doctor visits. What is of particular interest in their study is that the improvement in birth-weight had little do with greater doctor visits—in fact, these barely changed as a consequence of the program. Using data on the percentage of essential tasks completed in maternal check-ups, the authors argue that community meetings “empowered” women to demand better care from their doctors, and that, consequently, there was a large increase in effort and hence the quality of care delivered. Similar results are documented by Bjorkman and others (2006). In a randomized study on the effect of citizen report cards for health care outcomes, the authors document an improvement in child health, which they attribute to greater effort among providers.

A second issue is how to deal with sorting effects, which can arise in a variety of ways. For example, our measure of effort is based on observations of patients who choose to visit the doctor observed. But if those with high incomes visit more competent providers, more competent doctors may provide more effort because they are being paid more, and not because they are more competent. Das and Hammer (2007) use the presence of multiple doctor clinics with random sorting between doctors to show that the correlation between competence and effort was identical in the entire sample and in this more specialized setting. In Leonard (forthcoming), the Hawthorne effect is used as an instrument for quality; patients who receive their consultations immediately after the research team arrived receive better consultations for reasons that are uncorrelated with patient characteristics. This method does not expose the determinants of case mix, but allows the researcher to link effort to outcomes (patient satisfaction, in this case) despite case mix variation. Another possible way to control for case mix is to collect more patient data 27
# TAMLE 1 STRUCTURES OF MECHADISE EXPORTS (percentages of total merchandise exports)

||Fuels, m<br>andm|inerals<br>etals|Other p<br>commo|rimary<br>dities|Total p<br>commi|rimary<br>odities|
|---|---|---|---|---|---|---|
||1965|1985|1965|1985|1965|1985|
|Low-income economies||25||31||56|
|China and India<br>||25<br>||22<br>||47|
|Other|24|23|67|53|91|76|
|Middle-income|34|40|46|19|80|59|
|Lower middle-income|28|51|63|29|91|80|
|Upper middle-income|37|37|38|16|75|53|
|Developing countries<br>|32|39|48|21|80|60|
|Oil exporters|66|84|29|7|95|91|
|Exporters<br>of manufactures|9|13|40|17|49|30|
|Highly indebted<br>countries|<br>38|46|51|26|89|72|
|Sub-Sahara<br>Africa|32|63|60|31|92|94|
|High Income Oil Exporters|98|98|1||99|98|
|Industrial<br>Market Economies|9|11|21|13|30|24|

Source: World Development Report 1987, Statistical Appendix: Table II, p. 222-223
RPOs, providing stable revenue guarantees for investors (similar to the FIT mechanism) while at the same time ensuring that the renewable generation target will be met precisely (similar to an RPO). While auctions have proven to be strong mechanisms for ensuring economic efficiency as they minimize the level of subsidy required, they have also been criticized for their higher transaction costs and subpar performance in terms of deployment rates (delayed or cancelled constructions).

Auction-based schemes aiming to foster renewable generation sources were a relative latecomer to the international scene, and a few early setbacks delayed their widespread adoption by several years. The United Kingdom’s Non-Fossil Fuel Obligation (NFFO) scheme, introduced in 1989, pioneered the usage of this type of mechanism; but the results were not very positive: there were major concerns with underbuilding, as only 391 MW of wind capacity were effectively built out of a total 2659 MW awarded, and the fact that the NFFO was regarded as much more complex than alternative FIT schemes certainly didn’t help matters (Pollitt, 2010). Ultimately, the UK abandoned the auction mechanism, switching to an RPO scheme in 2002.

In large part due to this negative early experience, the international dissemination of auction schemes was very slow at first. However, this stance has changed radically over the past few years, as illustrated in Table 1 – between 2005 and 2013, while the number of countries implementing FIT or RPO schemes roughly doubled in size, countries implementing auction schemes increased six-fold. It is also interesting to remark that much of the renewed interest in auction mechanisms has been led by developing countries – this skew towards upper-middle income countries or lower<sup>5</sup> , as opposed to RPO schemes’ greater concentration in high income countries, is shown in Figure 1.

As a result, auction mechanisms have become an important topic that has caught the attention of multiple investors, governments, and multilateral agencies.

**Table 1 – Number of countries with active renewable policies of various types**

|**Mechanism**|**Number of**<br>**active policies**|**countries with**<br>|
|---|---|---|
||**2005**|**2013**|
|Feed in Tariff/Premiumpayment(FIT)|34|71|
|Electric utility quota obligation/RPS(RPO)|11|23|
|Public competitive bidding/tendering (Auctions)|7|45|

_Source: REN21 (2005), REN21 (2013)_

> 5 Defined as countries with a gross national income per capita equal to 12,475 US$ or lower, according to 2012 World Bank classifications

3
# **Emissions Trading with Offset Markets and Free Quota Allocations**

## **by**

## **Knut Einar Rosendahl***

## **and**

**Jon Strand****

**Key words:** Emission quota markets; offset markets; free quota allocations; updating of allocation rights; global carbon market.

**JEL Classification:** H23; Q41; Q54; Q58.

**Sector boards:** Energy and Mining; Environment

*Senior researcher, Research Department, Statistics Norway, 0033 Oslo, Norway. E-mail: <u>ker@ssb.no. **Extended Term Consultant, Development Research Group, the World Bank,</u> Washington DC 20433, USA. E-mail: jstrand1@worldbank.org. We are grateful for valuable comments from Cathrine Hagem, Ian Parry, Halvor Briseid Storrøsten and Michael Toman. Rosendahl acknowledges financial support from the Research Council of Norway. Views expressed in this paper are those of the authors and should not be attributed to the World Bank, its management, or member countries.
Table 6: Effect of MNREGA on emission rates of pollutants from biomass burning and months exceeding ambient PM2 _._ 5 standard

||E|missions Rates||Ambient PM2_._5 Standard|
|---|---|---|---|---|
||Black Carbon|Organic Carbon|SO2|Share of months _>_ standard|
|Post-MNREGA|0.384<sup>_∗∗∗_</sup>|0.378<sup>_∗∗_</sup>|0.491<sup>_∗∗∗_</sup>|0.014<sup>_∗∗∗_</sup>|
||(0.143)|(0.163)|(0.176)|(0.003)|
|Districts|558|558|558|560|
|Months|144|144|144|144|
|N|80,352|80,352|73,656|80,640|
|Pre-MNREGA Mean|45.81|536.09|52.09|.12|
|District FE|X|X|X|X|
|Year _×_ Month FE|X|X|X|X|
|Weather Controls|X|X|X|X|

Notes: Each column represents separate regressions. In columns 1-3 the outcome is the monthly emissions rate of the pollutant named at the top of the column measured in ng/m<sup>2</sup> s. In column 4 the outcome is the share of months in which the measured PM2 _._ 5 concentration exceeds the annual ambient air quality standard for India set by the Air Prevention and Control of Pollution Act (1981). Concentrations are measured in _μg/m_<sup>3</sup> . All data comes from the MERRA-2 satellite system. In columns 1-3 the coefficient can be interpreted as the approximate percentage change in the outcome after MNREGA was statutorily implemented in a district. In column 4 the coefficient is the change in percentage points in the percent of months that exceed the Indian Ambient standard of 40 _μg/m_<sup>3</sup> . In columns 1-3 the base regression is a fixed effects Poisson of the form _log_ ( E [ _Eimy|_ **X** _imy_ ]<sup>)</sup> = _βPostimy_ + _γi_ + _δmy_ where _Eimy_ is the outcome in district _i_ in month _m_ in year _y_ . In column 4 I use a linear fixed effects specification of the form _Timy_ = _βPostimy_ + _γi_ + _δmy_ where _Timy_ is an indicator for whether district _i_ had PM2 _._ 5 levels that exceed ambient standards in month _m_ in year _y_ . Post _imy_ is a dummy variable equal to one after MNREGA treatment takes effect in district _i_ . _γi_ are district fixed effects while _δmy_ is a year by month fixed effect. In all columns I include controls for the monthly average cloud cover, precipitation and minimum and maximum temperature in district _i_ in month _t_ . N refers to the number of districts _×_ months included in each regression. The sample is a balanced, monthly panel of districts in India from 2003 to 2014. The mean of the outcomes prior to MNREGA for each is presented. Heteroskedasticity robust standard errors clustered at the district level are in parentheses. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

# **10 Figures**

42