rafmacalaba/datause-encoder-data huggingface dataset:
progressive increase in tariffs will lead to a moderate increase in poverty, with poverty increasing by 0.5 percentage points. This reform will, however, have a rather equalizing impact on distribution reducing the Gini coefficient by 0.2 percent.

|**Table 15. The impact of electricity subsid**|**y reform an**<br>|**d cash tr**<br>|**ansfer o**<br>|**n povert**<br>|**y and ineq**<br>|**uality**<br>|
|---|---|---|---|---|---|---|
||Poverty<br>level, %|Change,<br>pp|Poverty<br>gap, %|Change,<br>pp|Gini<br>coefficient|Change,<br>%|
|**Pre-reform**|13.0|.|2.4|.|33.66|.|
|**Post-reform:**|||||||
|Scenario 1: 2015 tariffs|13.0|0.0|2.4|0.0|33.64|-0.04|
|**Post-reform: Scenario 2:**|||||||
|full elimination of electricity subsidies, flat|15.4|2.4|3.2|0.7|34.28|1.9|
|full elimination of electricity subsidies<br>progressive|14.7|1.7|2.9|0.5|33.66|0.0|
|**Post-reform: Scenario 3**:|||||||
|semi-progressive increase in tariffs|13.5|0.5|2.6|0.1|33.59|-0.2|
|Source:Authors’calculations based onextrapol|atedHEIS201|0 data.|||||

The impact of the electricity reform on government revenues The largest savings from electricity reform will come from the second scenario assuming full elimination of subsidies. The government can save the largest amount (estimated at JD473 million) from full removal of subsidies under the scenario of flat tariffs (table 16). However, this reform will also have the largest impact on poverty as shown in table 15. To get to the pre-reform poverty and poverty gap levels around JD319 million will be required. Therefore, the net gain will be JD158 million. Under progressive subscenario, the costs of the transfer to compensate the poor will be smaller and the government will save about JD174 million. In the third scenario with semi-progressive increase in tariffs, overall gain from higher tariffs will be JD 162 million. From this amount JD70 million have to be transferred back (assuming universal transfer) to bring poverty to pre-reform level leaving the government with JD92 million (table 17).

|**Table 16: The impact of the electricity subsidy reform on the government expenditures, mln. JD**|
|---|

|**Quintiles**|Scenario 1,<br>2015 tariffs|Scenario 2,<br>su<br>|full elimination of<br>bsidies<br>|Scenario 3,<br>semi-progressive|
|---|---|---|---|---|
|||flat|progressive||
|Quintile 1|-0|-64|-44|-14|
|Quintile 2|-0|-77|-55|-19|
|Quintile 3|-0|-87|-66|-25|
|Quintile 4|-0|-102|-82|-33|
|Quintile 5|-3|-143|-144|-71|
|Total|-4|-473|-391|-162|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes: Population quintiles based on consumption per capita before the reform.</u> 15
40 structural component of pollution intensity change was considered. However, it also alerts us to the possibility that each subsector has become more polluting during the period, rather than less.

- 4.6 EnerQy Intensity of Mexican Industry in an International Perspective

Although the changes in energy intensity of Mexican industry appear to be poorly correlated with real price changes of energy inputs, one may still defend the position that the fact that mexican energy intensity increased while, at the international scene, there was a pervasive trend towards energy saving is determined by the low pricing practices for energy goods in Mexico in comparison with the international markets.

To test this hypothesis an index of industrial energy intensity was constructed for petroleum fuels, natural gas and electricity for the OECD countries. The necessary information was collected from the OECD energy balances and indicators of industrial production. These OECD indexes were compared with similar energy intensity indexes for Mexico derived form the information contained in table 11. The results are brought together in table 13.

From the table it appears that, although Mexico's industrial energy intensity grew by only 5.7% over the period 1970-1990, in comparison with industrial energy intensity of the OECD countries
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# PPR Working Paper Series

||Title|Author|_A_|Contact<br>forpape|
|---|---|---|---|---|
|WPS294|Irreversibility, Uncertainty, and<br>Investment|Robert S. Pindyck|October 1989|N. Carolan<br>61737|
|WPS295|Developing Country Experience<br>in Trade Reform|Vinod Thomas|October 1989|S. Fallon<br>61680|
|WPS296|How Serious is the Neglect of<br>Intrahousehold Inequality?|Lawrence Haddad<br>RaviKanbur|November 1989|J. Sweeney<br>31021|
|WPS297|Effects of the Multi-Fibre<br>Arrangement on Developing<br>Countries' Trade: An Empirical<br>Investigation|Refik Erzan<br>Junichi Goto<br>Paula Holmes|November 1989|<br>L. Tan<br>33702|
|WPS298|Evaluation and Validation of a Multi-<br>Region Econometric Model: A Case<br>Study of MULTIMOD: A Forward-L<br>Macroeconometric Model|Ahmad Jamshidi<br>ooking|||
|WPS299|The External Effects of Public<br>Sector Deficits|Carlos AHf redo Rodriguez|November 1989|<br>R. Luz<br>61588|
|WPS300|How the 1981-83 Chilean Banking<br>Crisis was Handled|Mauricio Larrain|||
|WPS301|Myths of the West: Lessons from<br>Developed Countries for Developm<br>Finance|Collin Mayer<br>ent|November 1989|<br>WDR Office<br>31393|
|WPS302|Improving Support Services for<br>Rural Schools: A Management<br>Perspective|Sherry Keith|||
|WPS303|Is Undernutrition Responsive to<br>Changes in Incomes?|Martin Ravallion|November 1989|C. Spooner<br>30464|
|WPS304|The New Pr-itical Economy:<br>Positive rconomics and Negative<br>Politic,,|Merilee S. Grindle|||
|WPS305|Between Public and Private: A<br>Review of Non-Governmental<br>Organization Involvement in World<br>Bank Projects|Lawrence F. Salmen<br>A. Paige Eaves|||
|WPS306|A Method for Macroeconomic<br>Consistency in Current and<br>Constant Prices|Ali Khadr<br>Klaus Schmidt-Hebbel|||
# **Table 9:** **_Economic Logic Underpinning Decentralization in the Infrastructure Sectors_**

|**Infrastructure Sect**|**or**|**Level of Jurisdiction**|**Economic Determinants**|**Institutional Arrangements**|
|---|---|---|---|---|
|**WATER**|**Catchment (often the**<br>**case for groundwater as**<br>**well)**|Central or Regional|cross-jurisdiction externalities or open access<br>issues<br>_Central and regional governments or water user_<br>_associations should regulate (i.e. catchment,_<br>_groundwater and water shed management to_<br>_ensure equitable distribution across jurisdictional_<br>_boundaries)._|_State or Regional Water utility;_<br>_responsible line ministry; national /_<br>_regional water agencies, user_<br>_associations_|
||**Distribution**|Local *|allocative efficiency, economies of scale|_Local sector entities_|
|**SANITATION**|**on-site and off-site**|Local *|cross-jurisdiction externalities; allocative<br>efficiency|_Local sector entities, private household_<br>_level solutions_|
|**SEWERAGE**<br>**TREATMENT**|**On-site and off-site**<br>**(depends on collection)**|Local *|cross-jurisdiction externalities and addressing<br>local public bads|_Local sector entities, private household_<br>_level solutions_|
|**FLOOD CONTROL**|**Catchment**<br>**Urban**|Central or Regional<br>Local *|cross-jurisdiction externalities and addressing local<br>public bads|_national / regional water agencies_<br>_Local authorities_|
|**POWER**|**Generation**|Central, Regional, Local *|Economies of scale and cross-jurisdiction<br>externalities|Para-statal, State Company, National<br>Agency, Local communities; individual<br>user|
||**Transmission**|Central or Regional|Economies of scale and cross-jurisdiction<br>externalities|Para-statal, State Company or National<br>Agency|
||**Distribution**|Local|Economies of scale; allocative efficiency|Private or public entity|
|**ROADS**|**Highways**|Central, Regional, Local**|Economies of scale; club good issues (i.e.<br>congestion)|National Highway Administrators|
||**Rural Roads**|Local *|Club good issues; Allocative Efficiency|State or local government / communities|
|**SOLID WASTE**|**Collection**|Local|Allocative efficiency|Local government|
||**Disposal**|Regional|cross-jurisdiction externalities; economics of scale|Metro agencies, Local government<br>associations|
|**ICT**|**Landlines**|Central, Regional|Economies of scale; network externalities|Regulated Private Sector|
||**Mobile**|Central, Regional|Economies of scale; network externalities|Regulated Private Sector|

_Source:_ Andres, Biller, Schwartz (2013).

_Note:_ * While operation and implementation should be local, the central and regional governments could play an important role in regulation. ** The jurisdiction for urban highways could be local.

31
18 equivalents (Szuba, 1964) Simlilarly, a study of the pharmaceutical market in Thailand showed that for 18 commonly-used pharmaceuticals, the average brad name drug was 5.4 tmes more expensive than the generk equValknt; for ampicillin, the range was 175 times more crpenMive, and for reserpine 275 times more expensive. (Sepulveda-Aharez, 1985) Sri Lanka procurement authorities found price ratis of brand name drup to generls of 2.2 for mltremo e, 10.2 for propranoloL and 62 for diazepa (Jayawardene, 1980) Another experiece, from Mali, shows the potential pins from concentrating procurement improvements on high-value, high-priority dru and from buying **generi.** Injetible ampicklin represented about 30% by value of drugs consumed at a large health center, and was widely used througbout MalL The price being paid for a brand name ampicillin wa 23-3 times the international price. As a result, if the procurement strategy could be chanpd for this one drug alone, it would be posble to sav from 15-20% of the drugs budget. (WHO, 1965) A simila Result was found in anoter Aian country, where cOe author estimated that use of generics oDuld sive 30% of the total drug budget. (Yudkin, 1978) _Competive Bidding, Drect Purcuse, Or Purchasing Agency?_ International competitive bidding is often thought of as the most effent and cbeapest way of procuring drugs. For **large** countries, and depending on the volume involved, this is probably cortcL However, orpnizing competitive bidding requires special administrative skills and a knowledge of the international pharmaceuticals market which many of the smaller countries lack and cannot be expected to develop. A solution, especially for smaller amounts of essential, generic drugs, is to use a not-for-profit international procurement agency (UNICEF Supply Division, International Dispensary Association, ECHO, etc.) which does have the specialized skills required, and which uses international competitive bidding for its own purchases. These agencies buy such large quantities of drugs that they obtain very low prices, and their operating margins are very modest. They provide an attracive alternative to international competitive bgddin Their main drawback is that they require payment in foreign **exapnge** eitber upon delivery, or in some cases, upon placement of the order.

Direct purchase from the manufacturer is rarely the most economic type of procurement, and should probably be used only for drugs which are absolutely essential and available from no one ele. LesS than 10% of the drugs on the WHO Model List of Essential Drugs fall into this category. It may be possible to drive prices for brand name drugs down by using
Figure 16. Recoveries with financial booms: duration, amplitude and slope <!-- Start of picture text --> Duration<br>(in quarters)<br>6<br>Recoveries associated with Recoveries associated without<br>5<br>4<br>3<br>***<br>2<br>1<br>0<br>Credit Booms House Price Booms Equity Price Booms<br>Amplitude<br>(in percent)<br>10<br>*** Recoveries associated with<br>8<br>Recoveries associated without<br>***<br>6<br>4<br>2<br>0<br>Credit Booms House Price Booms Equity Price Booms<br>Slope<br>(in percent)<br>3<br>Recoveries associated with<br>*** Recoveries associated without<br>2<br>***<br>1<br>0<br>Credit Booms House Price Booms Equity Price Booms<br>Source: Claessens et al (2012a).<br><!-- End of picture text --> Note: The amplitude and slope correspond to the sample medians. Duration corresponds to the sample means. Booms are the highest 25% of upturns by amplitude. ***, ** and * imply significance at the 1%, 5% and 10% levels, respectively. Significance refers to the difference between recoveries with and without booms. For other definitions, see the notes to Figures 13 and 14.

76
countries. Therefore, development and use of the CV method can be very important for costbenefit analyses of transport projects in developing countries.

This paper reports a contingent valuation study of an NMT project in Pune, India. Recently, it has been proposed to have the World Bank finance an NMT project under India’s Sustainable Urban Transport Program (SUTP) which aims to improve road conditions for pedestrians and cyclists. A CV survey is developed and applied to potential beneficiaries of the Pune NMT project<sup>3</sup> , and an internal validity test on the WTP estimation is conducted. This study has also tested the heteroskedasticity assumption in WTP modeling which has been mostly neglected in the literature.

The paper is organized as follows: in the following section, we briefly review existing literature on CV from various threads, especially the application of CV method in transportation sector and the evolution of heteroskedasticity treatment in recent years. In section 3, we introduce the policy context, goal and proposed activities of Pune NMT project and the potential benefits this project may generate. Section 4 summarizes the survey design and implementation, the WTP elicitation strategy, and the descriptive statistics of major questions in the questionnaire. In section 5, we present our analytical framework that accommodates heteroskedasticity assumption in payment card (PC) elicitation strategy. The estimation results are also shown in this section. Section 6 discusses and concludes the paper.

# **2. Literature Review**

The CV method is principally developed and established in the area of environmental economics; however, application of the CV approach in the transport sector has become more widespread over the past decade. Feitelson (1996) examined the effects of aircraft noise following an airport expansion by using the open-ended (OE) CV approach. Verhoef (1997) asked

> 3 The survey was applied to the original version of the project proposal, dated as of December, 2008. The final version of the project design has been changed significantly after the survey was conducted.

3
# Appendix E. Unweighted Results

## **Table E.1. Conflict** **<u>and food insecurity, unweighted regression</u>**

|||||Event|type|Per|petrator ty|pe|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|||||||||
|Conflict events|0.052***<br>(0.017)|0.045***<br>(0.017)|0.045***<br>(0.015)||||||
|Conflict events property||||0.081***<br>(0.029)|||||
|Conflict events violence|||||0.062*<br>(0.033)|<br>|||
|Insurgents||||||0.051**<br>(0.023)|||
|Bandits/criminals|||||||0.247***<br>(0.080)||
|Pastoralists/nomads||||||||0.006<br>(0.016)|
|Observations|3,550|3,457|3,457|3,457|3,457|<br>3,457|3,457|3,457|
|R-squared|0.015|0.025|0.033|0.032|0.031|<br>0.032|0.034|0.031|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are from the six visits of the GHS and telephone survey for conflict. No weights are used in any of the regressions. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xviii
Table 4b Ghana: Decomposition of the percent change in poverty incidence by locality GLSS2-GLSS3

|||_ASj_|A|(K)|_-_|_A?P_||
|---|---|---|---|---|---|---|---|
||Actual|Weighted|Actual|Weighted|Actual|Weighted|Total|
|Accra|-1.4|-0.05|52|1.9|-2|-0.08||
|Other Cities|-3.7|-0.73|2.4|_0.5_|-12|-2.5||
|Rural Areas|1.6|1.2|*3.8|2.9|-22|-17||
|Ghana|||||||_-13.7_|

Figure 13 Contribution of migration, inequality and mean expenditure to poverty reduction in Ghana, GLSS1-GLSS3 <!-- Start of picture text --> 70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>-10.00<br>-20.00<br>Accra Other cities Rural areas<br>Eg Population * Inequality n Mean Expenditure<br><!-- End of picture text --> Since the rural areas performed much better over the period 1988-92, both in terms of mean expenditure per capita and its distribution, it is not surprising to see from Table 4a that there was some redistribution of population from the urban to the rural areas. The shift, however, seems to have occurred more from Other Cities than from Accra, although the mean expenditure increased in the Other Cities and decreased in Accra. A couple of reasons could explain this. First, although expenditure decreased in Accra, it is still the region which has the highest mean expenditure per capita. Second, public services such as education and health are much better in Accra than in rural areas, which makes urban to rural migration unlikely unless the income difference is sufficiently large. Third, it is also possible that there is more integration between the economies of the rural areas and the Other Cities than between the rural areas and Accra.

In summary, the poverty reduction in Ghana over the five-year period between 1988-1992 occurred mostly from the increase in mean income from Rural Areas and Other Cities, as well as from an improvement in distribution in both regions. In Accra, not only mean expenditure has a big decrease, the inequality also worsened greatly. The comparison of between 1989 and 1992, however, shows that poverty reduction is mainly a result of an increase in income, but not an improvement in distribution as shown in the GLSS1 and GLSS3 comparison (see Table 4b).

14
<!-- Start of picture text --> than the corresponding cost data. We therefore re-estimate our baseline formulation using alterna-<br>tive Doing Business data on the number of procedures required for market entry, and the number of<br>documents required for exports. Results in the third column of Table 3 are qualitatively identical to<br>the baseline, although the coefficient on export costs is now marginally significant at the 10% level<br>(prob. = 0.100). The signs on EU tariffs and GDP per capita change, but these coefficients remain<br>statistically insignificant.<br>4.3 Additional Independent Variables<br>While the set of explanatory variables used in our baseline formulation accords well with the<br>theoretical model developed in this paper, we are conscious that the literature discloses a number<br>of additional factors that might be expected to impact export diversification. For instance, Feenstra<br>and Kee (2006) include measures of factor endowments as instruments for export variety. Given<br>that the mechanism driving diversification in our theoretical model is investment related, it may also<br>be appropriate to take account of macroeconomic conditions. We therefore augment our baseline<br>model to include data on the percentage of manufacturing and agriculture in GDP-as a proxy for<br>economic structure as determined by factor endowments—plus the GDP deflator and real interest<br>rate as indicators of macroeconomic performance. Results are presented in the last two columns of<br>Table 3, and are qualitatively identical to those from our baseline formulation as regards trade and<br>market entry costs. Moreover, results in the last two columns indicate that a larger manufacturing<br>sector and a lower GDP deflator are both associated with greater export diversity, and that these<br>effects are statistically significant at the 1% level. The two other additional variables are statistically<br>insignificant. While the size of the agricultural sector carries the expected negative sign in column<br>4, both that variable and the real interest rate have unexpected positive signs in column 5.<br>4.4 Narrower Country Samples<br>The definition of developing country that we have used thus far is a wide one: all countries that<br>are not members of the OECD or the EU-25. It is important to ensure that our results continue<br>26<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4353

# **Abstract**

This paper presents unique evidence that orphanhood matters in the long run for health and education outcomes, in a region of Northwestern Tanzania. The paper studies a sample of 718 non-orphaned children surveyed in 1991-94, who were traced and re-interviewed as adults in 2004. A large proportion, 19 percent, lost one or more parents before the age of 15 in this period, allowing the authors to assess the permanent health and education impacts of orphanhood. The analysis controls for a wide range of child and adult characteristics before orphanhood, as well as community fixed effects. The findings show that maternal orphanhood has a permanent adverse impact of 2 cm of final height attainment and one year of educational attainment. Expressing welfare in terms of consumption expenditure, the result is a gap of 8.5 percent compared with similar children whose mother survived till at least their 15th birthday.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study the socio-economic impact of health shocks in low income countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@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