rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

According to the International Energy Agency (IEA), the world’s share of China’s and India’s combined CO2 emissions grew from 9.4% in 1990 to 24.4% in 2006 (IEA, 2008). These two populous countries together accounted for 51.8% of the world’s total growth of CO2 emissions over the period. China accounted for almost as much CO2 emissions as the United States in 2006 (5,607Mt vs. 5,697Mt), and informal reports of the acceleration of this trend suggest that China may have since surpassed the U.S. to become the world’s largest emitter of greenhouse gases (GHG).

Local air pollution is the main environmental concern in China. Beijing, China’s second largest city, is already one of the world’s most polluted cities in terms of air quality. In Gurjar et al.’s (2008) ranking of ambient air quality in 13 of the world’s megacities, Beijing places second for sulfur dioxide (SO2), second for nitrogen dioxide (NO2), and fifth for total suspended particulates (TSP). Similarly, the Millennium Cities Database (MCD, 2001) identifies Beijing as the world’s most congested city as measured by average road speed. According to the World Bank (2007), the estimated cost of health damages associated with urban air pollution (i.e., from sickness and premature death) ranges from 1.2 to 3.8% of GDP, making air pollution the costliest pollution problem faced by China. The transport sector emissions in China have grown by 457% over the 1990-2006 period (IEA, 2008). The Asian Development Bank (2006) projects China’s transportation energy to grow by 6% - 9% per year through 2025. Given the massive urbanization and real income growth, which result in sharp increases in private vehicle ownership, the air quality is certain to get worse.

Urbanization has potentially complex effects on the global efforts to curb GHG emissions. On the one hand, with increased urbanization and higher incomes, suburban and exurban sprawl accelerates with urban areas spreading out in low density patterns that favor mobility over longer distances by private motorized vehicles rather than by public transit or by bicycle (Ingram, 1998). On the other hand, high densities that can be achieved in urbanized areas support potential investments in rail transit systems that could greatly reduce reliance on the automobile, than if the same population were spread over more but smaller cities, each unable to support the economies of scale inherent in rail mass transit. Although it is a widely held perception that sprawl in land use causes more aggregate car miles to be driven, recent results from modern theoretical models of the urban economy in which both jobs and residences decentralize with 2
on Scholastic Achievement. _The Quarterly Journal of Economics_ , 114(2):533–575, 1999. URL https://ideas.repec.org/a/oup/qjecon/v114y1999i2p533-575..html.

Rosario Maria Ballatore, Margherita Fort, and Andrea Ichino. The Tower of Babel in the Classroom: Immigrants and Natives in Italian Schools. CEPR Discussion Papers 10341, C.E.P.R. Discussion Papers, January 2015. URL https://ideas.repec.org/p/cpr/ceprdp/10341. html.

- Anders Bohlmark. Age at immigration and school performance: A siblings analysis using swedish register data. _Labour Economics_ , 15(6):1366–1387, December 2008. URL https://ideas. repec.org/a/eee/labeco/v15y2008i6p1366-1387.html.

- Maria Branden, Elisabeth Birkelund, and Ryszard Szulkin. Does school segregation lead to poor educational outcomes? evidence from fifteen cohorts of swedish ninth graders. The Institute for Analytical Sociology Working Paper Series, 2016.

- Thibault Brodaty. Peer effects in education: A literature review. _Revue d’ ́economie politique_ , 120 (5):739–757, 2010. doi:10.3917/redp.205.0739.

- Giorgio Brunello and Lorenzo Rocco. The effect of immigration on the school performance of natives: Cross country evidence using PISA test scores. _Economics of Education Review_ , 32(C): 234–246, 2013. doi:10.1016/j.econedurev.2012. URL https://ideas.repec.org/a/eee/ ecoedu/v32y2013icp234-246.html.

- Scott E. Carrell, Richard L. Fullerton, and James E. West. Does Your Cohort Matter? Measuring Peer Effects in College Achievement. _Journal of Labor Economics_ , 27(3):439–464, July 2009.

URL https://ideas.repec.org/a/ucp/jlabec/v27y2009i3p439-464.html.

42
Table 7. Main features of global recoveries (by country group)

||Glo|bal reco|veries (in|itial yea|rs)|Globa|l recove|ries (first t|hree ye|ars)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1976|1983|1992|2010|Average|1976-78 1|983-85|1992-94 2|010-12|Average|years|
|**Advanced economies**||||||||||||
|Output|4.8|3.1|2.0|2.9|3.2|4.2|3.8|2.1|1.9|3.0|3.3|
|Output per capita|3.9|2.4|1.3|2.3|2.5|3.5|3.2|1.4|1.3|2.3|2.4|
|Industrial production|7.8|2.3|-0.3|7.6|4.3|5.5|3.9|1.3|3.4|3.5|3.0|
|Trade|10.7|2.8|5.0|12.0|7.6|7.2|5.8|5.5|6.5|6.3|5.9|
|Unemployment rate|0.0|0.3|0.6|0.3|0.3|0.0|-0.1|0.3|0.0|0.1|0.0|
|Output (PPP)|4.9|3.2|2.1|3.0|3.3|4.3|3.9|2.2|2.0|3.1|3.3|
|Output per capita (PPP)|4.1|2.5|1.4|2.4|2.6|3.6|3.2|1.5|1.4|2.4|2.5|
|Credit|3.6|4.7|1.7|-1.2|2.2|4.5|5.9|1.8|-0.6|2.9|4.7|
|Government expenditure|3.4|2.8|5.1|0.4|2.9|3.9|3.1|3.1|-0.1|2.5|4.2|
|Policy rate|0.1|-2.0|-1.0|0.0|-0.7|0.2|-1.0|-0.9|0.0|-0.4|0.0|
|**EMDEs**||||||||||||
|Output|6.7|1.6|0.9|7.4|4.2|5.0|2.8|2.1|6.3|4.0|4.7|
|Output per capita|4.5|-0.5|-0.8|6.0|2.3|2.9|0.7|0.5|4.9|2.2|2.8|
|<br>Industrial production|...|...|1.4|11.0|6.2|...|...|3.6|7.9|5.7|5.0|
|<br>Trade|4.6|-1.2|0.5|13.2|4.3|5.1|1.4|5.0|9.8|5.3|5.6|
|Unemployment rate|...|-0.5|0.3|-0.3|-0.2|...|-0.3|0.3|-0.2|-0.1|0.0|
|Output (PPP)|6.7|2.2|1.4|7.5|4.5|5.0|2.9|2.4|6.3|4.2|4.8|
|Output per capita (PPP)|4.6|0.2|-0.3|6.1|2.6|2.9|0.9|0.7|4.9|2.3|2.9|
|Credit|6.0|-6.8|15.8|12.5|6.9|3.3|-0.2|13.8|12.4|7.3|7.8|
|Government expenditure|12.0|-5.2|4.7|6.0|4.4|6.9|2.0|6.3|6.7|5.5|5.8|
|Policy rate|0.2|-0.2|-1.3|-0.1|-0.3|0.3|0.1|-0.5|0.2|0.0|0.0|
|**LICs**||||||||||||
|Output|0.9|1.3|-1.7|6.9|1.9|0.7|2.0|-0.4|6.4|2.2|3.8|
|Output per capita|-1.5|-1.4|-4.5|4.0|-0.8|-1.7|-0.8|-3.3|3.5|-0.6|1.1|
|<br>Trade|-2.3|0.9|-6.3|15.1|1.8|4.7|7.9|-0.8|13.8|6.4|6.0|
|Output (PPP)|1.3|1.4|-1.3|7.2|2.1|1.0|2.1|0.0|6.3|2.3|3.9|
|Output per capita (PPP)|-1.1|-1.4|-4.1|4.2|-0.6|-1.4|-0.7|-2.9|3.3|-0.4|1.2|

Note: All variables show percent changes, except in unemployment rates and policy rate, in which percentage-point changes of these variables are reported. See footnote of Table 6.

55
# **2 Approach and Methodology**

## 2.1 Approach

The study takes the dominant state-owned utility—the dominant state-owned distribution company if the utility is not vertically integrated—in each country and estimates QFDs under two cost scenarios, while maintaining the same average tariff levels:<sup>3</sup>

1. Current cost structure

2. Cost structure if operational efficiency is improved to limit aggregate T&D and collection losses to 10 percent and target staffing at benchmark levels.

There are five cases in SSA where the utility has a high level of private sector participation. In four cases, the reference utility is under a long-term concession with a private company (Cameroon, Côte d’Ivoire, Gabon, and Uganda). While the term “quasi-fiscal deficit” is not strictly accurate in these cases, the state still plays a significant or leading role in making investments in the power sector. The financial statements of the reference utilities are available in these cases and used in the analysis. In Nigeria, all distribution utilities have recently become majority-privately owned and utility financial statements are not yet available. A specific approach is taken for Nigeria, which is explained in section A1.5 in annex 1.

The factors responsible for the deficits are analyzed by calculating the components below: Hidden costs = underpricing + T&D losses + under-collection of bills + overstaffing.<sup>4</sup> Table 2 shows the distinction used in the AICD and in this paper between T&D and bill collection losses. These losses consist of technical and nontechnical losses. Technical losses are for electricity consumed by T&D lines and transformers (but not by customers) during transmission of electricity to consumer’s premises, whereas non-technical losses are for electricity consumed by customers that is not billed due to actions external to the power system—such as theft, meter tampering, and deliberate under-reading of consumption—and under-recording of consumption due to lack of meter maintenance and calibration. Both forms of losses are disproportionately concentrated in the distribution segment, and represent losses that utilities cannot recover because the electricity “lost” has never been nor will it ever be billed to customers. It is important to note that unmetered customers can still be billed, which is the case in Nigeria. That is, not metering doesn’t automatically translate to not billing the unmetered customer. Bill collection losses occur after the point of sale, and utilities can recover them in principle through debt collection mechanisms.

> 3 This is a departure from the classic studies on QFDs, which defined benchmark performance to include costrecovery tariffs, making QFDs zero by definition at benchmark performance.

> 4 It is important to note this formula includes accrual and cash accounting items. Uncollected bills become receivables and eventually a loss only once recognized as uncollectable.

11
<!-- Start of picture text --> S e India:IPV }justification<br>=.<br>i<br>=z><br>Be<br>i)<br>g<br>°<br>5<br>Sa<br>§<br>fn<br>PS W 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>-— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> § Bangladesh:IPV justificationee<br>on<br>5<br>28<br>Bo<br>Peal<br>c<br>5<br>5<br>.<br>E<br>De<br>ii 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintie2 t—— Quintile3<br>+—— Quintile 4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 5 Nepal:|PV justification<br>8<br>Oe<br>z-<br>><br>2<br>So<br>i<j<br>5<br>&<br>no<br>or<br>a<br>ey5<br>c<br>()<br>i 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 +——— Quintile 3<br>-—— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->

<!-- Start of picture text --> 5 Pakistan:|PV justification<br>gx<br>ra<br>=<br>><br>Bo<br>6<br>im<br>S.<br>gr<br>ne]<br>e<br>a<br>5%<br>&<br>on<a<br>ii 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2_ +——— Quintile 3<br>+— Quintile4 +——— Quintile 5<br>Generated by regressing ipv justification on wealth, urban, higher education and interactions<br>with year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
of the 2,208 entrepreneurs at 2 months (72%, 66% control, 75% treatment) and 1,613 at 6 months (73%, 70% control, 74% treatment). Appendix B shows that the sample answering the surveys remain balanced on baseline characteristics. We account for possible bias due to selective attrition in several ways. Our main specification (noted below) uses the postdouble-selection lasso of (PDS lasso) of Belloni et al. (2014). This selects covariates that either predict the outcome of interest (which can potentially improve power), or that predict treatment status (which could arise from unbalanced attrition). Appendix B shows our results remain robust to alternative approaches to accounting for attrition, such as probability re-weighting, or using bounding approaches.

We supplement these quantitative surveys with a qualitative survey of 20 treated firms, selected to comprise of a sample of 10 firms that had attended training and had improved their sales and business practices a lot by the 2-month survey, and 10 firms that had attended training but not shown improvement. We also use our own observations from observing training sessions to provide more qualitative information on content and process.

The main primary outcomes of interest are those that are the focus of the majority of the business training literature: whether training gets business owners to adopt new business practices, and whether it improves business performance in terms of profits and sales. Our AEA registry includes a short pre-analysis plan specifying these measures. We estimate the effect of being assigned to training using the following specification for outcome _Y_ for firm _i_ in randomization stratum _s_ : This regression includes the lagged outcome variable ( _Y0_ ) where available, dummies for the different randomization strata, and a set of control variables _X_ selected via PDS Lasso. Robust (Eicker-White) standard errors are used. The coefficient of interest _β_ corresponds to the intention-to-treat effect, which is the effect of being offered the live Zoom training, compared to just being offered the asynchronous version in the control group. We also run a stacked version of this equation which pools both survey rounds and allows us to test for equality of treatment effects over time.

# **3 Feasibility, Cost, and Effectiveness**

We start by examining whether Zoom training is feasible in a developing country microenterprise setting, then discuss the costs of providing this training, before turning to measuring training effectiveness.

## **3.1 Feasibility, Take-up, and Attendance**

Women who signed up for the course knew that it would be an online program and had managed to watch the short information session telling them some details about the train7
# **Table of Contents**

|**1.**In|troduction ..........................................................<br>1|
|---|---|
|2.|The data: Livings Standard Measurement Study Surveys in fifteen countries .....................**.**.......<br>3|
|3.|Who has infrastructure services? .........................................................<br>.<br>6|
|3.1.|Global infrastructure coverage ..........................................................<br>6|
|3.2.|<br>Coverage in urban and rural areas ..........................................................<br>8|
|3.3.|Coverage by country ..........................................................<br>9|
|4.|Who has access to services? Who has access and chooses not to connect?<br>.<br>.<br>.<br>10|
|_5._|Household coverage: a multivariate analysis .<br>........................................................<br>11|
|6.|Are other service options filling the gap? .........................................................<br>13|
|6.1.|The energy sector ...................<br>14|
|6.2.|<br>Water ....................<br>15|
|6.3.|<br>Sanitation ...................<br>17|
|6.4.|Telecommunications ...................<br>18|
|7. Co|nclusions ...................<br>20|
|Refer|ences ....................<br>23|
|Table|s ....................<br>24|
|Figur|es ....................<br>32|
## **Figure 3. Total own-price demand elasticity in the transportation sector for alternative**

## **time periods**

<!-- Start of picture text --> LPG NG Electricity Diesel Gasoline 87<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>2000-2004 All sample 2008-2012<br><!-- End of picture text -->

# **5. Concluding remarks**

This paper aims to compare the efficacies of two important policies to reduce GHG emissions from the transport sector: (a) fuel efficiency improvement, and (b) fuel substitution. For this purpose, we estimated, using a translog cost function, the partial as well as total own-price elasticity and cross-price elasticity using 15 years’ time-series data from 33 industrialized economies. While most existing studies (e.g., Greening, 2004; Lu et al. 2007; Timilsina and Shrestha, 2008; Timilsina and Shrestha 2009) have used a decomposition approach to understand the role of various factors, such as energy efficiency improvement and fuel switching, on changes in transport sector CO2 emission intensities, this study uses an econometric approach, which is 18
# Contents

|**List of figures**|**iii**|
|---|---|
|**List of tables**|**iv**|
|**Synopsis**|**v**|
|**1 Introduction**|**1**|
|Why infrastructure matters|1|
|Why regional integration matters|3|
|The state of ECCAS’s infrastructure|6|
|**2 Transport**|**8**|
|Surface transport|8|
|Roads|18|
|Railways|18|
|Ports|21|
|Air transport|24|
|**3 Power**|**33**|
|**4 Information and communication technologies**|**44**|
|**5 Regional infrastructure funding**|**54**|
|**Bibliography**|**57**|
|General|57|
|Financing|57|
|Growth|57|
|Information and communication technologies|57|
|Irrigation|58|
|Power|58|
|Transport|58|
|Water supply and sanitation|59|
|Other|59|

# List of figures

|Figure 1.1a. Infrastructure‘s historic contribution to economic growth, 1995–2005|2|
|---|---|
|Figure 1.1b. Infrastructure‘s potential future contribution to economic growth (% GDP per year)<br>Figure 1.2a. Topographical profile of the ECCAS region|3<br>5|
|Figure 1.2b. Spatial distribution of economic activity within ECCAS<br>Figure 2.1a. The condition of ECCAS‘s regional road network|6<br>8|
|Figure 2.1b. Traffic on ECCAS‘s regional road network<br>Figure 2.2. ECCAS‘s main regional road corridors|9<br>12|
|Figure 2.3. Time required to import goods from Douala to Ndjamena via road and rail<br>Figure 2.4. Cost of importing goods from Douala to Ndjamena via road and rail|15<br>15|
|Figure 2.5. Time required to import goods by road through alternate gateways*|16|
|Figure 2.6. Cost of importing goods by road through alternative gateways|17|
|Figure 2.7.ECCAS‘s regional railway network|19|
|Figure 2.8. Traffic densities on African railways|20|
|Figure 2.9.Ratio of current demand to reported capacity|22|

iii
Policy Research Working Paper 7855

# **Abstract**

Climate-driven water variability is a natural phenomenon observed across river basins, but predicted to increase due to climate change. Environmental change of this kind may aggravate political tensions, especially in regions that are not equipped with an appropriate institutional apparatus. This paper argues that attempts to assess the ability of states to deal with variability in the future rests with considering how river basins with agreements have fared in the past. The paper investigates whether basins governed by treaties witness less tension (and by extension more cooperation) over shared water in comparison with those basins not governed by treaties, using the 1948– 2008 country dyads event data from the Basins at Risk project. The results provide evidence to suggest that the presence of a treaty promotes cooperation. Furthermore, the number of agreements between riparian countries has a significant positive effect on cooperation, which is robust across different specifications controlling for a broad set of climatic, geographic, political, and economic variables.

This paper is a product of the Development Data Group, Development Economics. 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 bblankespoor@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
and reducing subsidies (especially those that encourage environmental degradation and inefficient use of scarce resources).

**Table 1: Trade Taxes in MENA Countries, 1993**

||**Share of Import**<br>**Duties in Total**<br>**Government**<br>**Revenue**|**Share of "Other"**<br>**Taxes in Total**<br>**Import Tax**<br>**Revenue**|**Average Collected**<br>**Tariff**<br>**(revenue/**<br>**imports)**|
|---|---|---|---|
|Morocco|17.7|52|17.5|
|Tunisia|28.3|46|18.7|
|Egypt|10.0|8|14.9|
|Jordan|35.9|40|17.8|
|Syria|10.0|25|16.4|
|Oman|3.2||3.0|
|UAE||||
|Yemen|20.2|3|19.1+|
|Bahrain|9.2||4.0|
|Israel|1.0||1.2|
|Turkey|4.4|78|2.5|
|Mexico (1990)|5.1||4.8|
|Chile|9.9||9.7|
|Indonesia|5.2||4.9|
|Malaysia|13.6|31|4.9|
|Korea|4.8|-_|4.4|

Notes: -- zero or negligible; Exclude **s** tamp duties; + Valued at the average parallel market exchange rate, the average collection rate was around 8 percent. The nominal rate of 19% is indicative of the Government's intentions, however.

Source: IMF Government Finance Statistics Yearbook, 1994; International Financial Statistics, 1994.

Effective rates of protection of manufacturing generally exceed the level of nominal protection by a substantial margin. In addition to the nominal tariffs, imports are often also subject to a variety of additional taxes. In the case of Jordan, these include a 5% import license fee, a tax earmarked for universities (4%), another tax for municipalities (2%), an additional consolidated fee of 6%, a customs service fee of 0.2%, and a surcharge of either 3% on non-zero rated goods or 5% if no

**_5_**
The soil water holding capacity (Smax) was taken from FAO’s geonetwork. Values are dependent largely on soil texture. For areas of open water, values are set at 1000 mm. The original dataset was organized at the 0.08<sup>o</sup> scale. The data were aggregated (by taking the numeric mean) into 0.5<sup>o</sup> grid cells.

A gridded drainage network at 0.5<sup>o</sup> resolution was used to simulate flow paths for routing runoff generated within cells to the oceans. The network, based on topography, attributes an ‘average flow’ direction to each grid cell (Figure 6). This is the direction that any runoff generated within the cell will move. Where rivers exist, the drainage network broadly corresponds to the river network (Figure 6). The simulated topological network used was the STN-30p network developed by the University of New Hampshire (Fekete et al. 2000). This dataset has been validated against several independent atlases and station-based attribute sources (Vörösmarty et al. 2000).

# _2.2.4 Model calibration data_

The model was calibrated against simulated average monthly runoff generated on a 0.5<sup>o</sup> grid by the University of New Hampshire (UNH) (see Section 2.3). The mean annual runoff is shown in Figure 7. The equivalent data are available for each month of the year. Several ‘holes’ were discovered in the gridded datasets developed by UNH, including nine holes in the continental land mass of Africa. It was not clear why these holes occurred. They did not correspond to water bodies or other natural features. Consequently, they were ‘filled’ with assumed runoff values of zero.

# **_2.3 Model calibration_**

In WatBal, hydrological processes are simulated, as described above (Section 2.1), on the basis of a conceptual approximation. Consequently, it is necessary to adjust or optimize parameters until the model output is an acceptable estimate of the observed runoff regime. In order to do this it is necessary to have runoff data against which to calibrate parameter values. In the gridded version of the model, three parameters are calibrated: 12