rafmacalaba/datause-encoder-data huggingface dataset:
and remaining hydropower resources in Bhutan<sup>15</sup> depends on regional electricity trade in South Asia. One would argue that the higher capacity available for trade in Nepal could be the result of lower capacity requirement projected by this study for meeting Nepal’s domestic demand by 2040.<sup>16</sup> Even if WECS’s (2017) reference case, that assumes 7.2% average annual GDP growth rate through 2040, is considered, Nepal would need 29 MW for its domestic demand. This means, the firm capacity available in Nepal for trade would be 32GW instead of 52GW. Note however that due to other flexibilities that the regional trade can bring, such as inframarginal trade possibilities, reserve sharing, peak load management discussed in the earlier section, the actual potential available in Nepal for trade by 2040 would be higher than 32 GW.

modeling exercise). Considering the availability of new technologies, increasing access infrastructure to potential hydropower sites and so on, the economic potential today would be much different as compared to that estimated two decades ago. Therefore, we did not stick with the available economic potential estimate. New studies are needed to assess the technical as well as economic potential of hydropower in Nepal and other South Asian countries to have a better measure of this resource’s contribution to regional electricity supply.

15 Note that half of the total technical potential of hydropower resources will be exploited under the existing bilateral electricity trade agreements between India and Bhutan (or in the baseline).

16 Based on the 2010 electricity load forecast available when the model was run, the capacity requirement to meet Nepal’s domestic demand was estimated to be 9 GW. This is certainly at the lower side, more recent study (WECS, 2017) shows that Nepal’s domestic requirement would be 19 GW under the business as usual case (5% annual average GDP growth) and 29 GW under reference GDP growth scenario (7.2% annual average GDP growth).

28
level and did not allow for the examination of poverty and welfare levels at lower administrative levels. Therefore, we decided to develop and field the ‘Service Access and Perception Survey in the Municipality of Cali’ or _Encuesta De Acceso y Percepcion de los Servicios Ofrecidos por el Municipio de Cali (EPSOC)_ .

The requirements for the EPSOC survey were multiple. First, apart from providing a basic poverty profile for the city, it had to answer the specific policy questions on the characteristics of poverty, the distribution of city resources, satisfaction with public services delivery, and priorities for budget decisions. It was hence required to collect both quantitative and qualitative data. Second, the sample had to be large enough to provide reasonably good estimates of poverty and social characteristics at sub-city levels. Third, data collection and analysis had to be rapid as the City Development Strategy formulation was on a very tight time schedule (linked to political necessities in Cali). Fourth, it had to be affordable as it was to be a pilot for the municipality with a potential to be conducted and financed by the city itself in the future.

The final EPSOC survey instrument consisted of nine different modules. With comments from many local organizations, we designed and planned the questionnaire in close cooperation with the Centre on Socio-economic Research and Documentation of the Universidad del Valle in Cali.<sup>5</sup> The _household module_ collected information pertaining to size, composition, function (headship), gender, civil status and origin of all household members. The _housing and living condition module_ recorded housing material and space, basic service supply (including Cali-specific questions on access to garbage disposal), service satisfaction levels, ownership arrangements as well as consumer goods possession. An extensive _education module_ included questions on literacy, school attendance (by type of school), highest school degree and year obtained, as well as private educational costs. But it also asked about problems in the schools, reasons for absenteeism, and overall satisfaction levels of students. Similarly, the _health module_ recorded both the standard information about illness patterns, health service utilization (including birth attendance, pre-natal care provision), and health insurance access as well as satisfaction levels (and reasons for dissatisfaction) with the health service provision. A _nutrition and childcare_ module inquired about access to, and satisfaction with, assistance programs, distinguishing a number of public, private and

> 5 Although the survey included many questions specific to the social situation in Cali, it did build significantly on similar surveys conducted in other cities including a survey fielded in parallel in Kampala, Uganda (see Nord (2002) for a description and use of the Kampala survey).  From the Universidad del Valle, the major collaborators where Fernando Urrea and Carlos Ortiz.

8
<!-- Start of picture text --> Real Effective Exchange Rate<br>(1980=100)<br>300<br>200 - - - -<br>1S0- N/Nggeria \ Malawi<br>100 -<br>Ghana .<br>0<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993<br><!-- End of picture text --> Most developing countries have been undertaking unilateral liberalization since the mid 1980s to benefit from the rapidly globalizing market. This wave of liberalization represents an effective shift in development strategy from an inward-oriented importsubstituting framework designed strategically to reduce dependence on the outer world, to an outward-oriented export-promoting framework designed to create a virtuous cycle of higher integration and faster growth with expanded opportunities. However, dragging the legacy of the old frarnework-the anti-export bias that is typically manifested in overvalued currency and real exchange rate appreciation-when one enters the rapidly globalizing market is dangerous, even suicidal. A real exchange appreciation not only prices exports out of world markets, on the demand side, but also takes resources out of the tradable (export) sector by increasing relative price of nontradables to tradables, on the supply side. Aggregate effects of the appreciation on investment are ambiguous; however, this appreciation unambiguously reduces investments in export-oriented sectors. The failure to foster productivity and export competitiveness by eliminating anti-export bias in the open framework has resulted in balance of payment difficulties, forced contraction, and lower levels of growth. Countries situated in the lower-right quadrant in Chart 17 (Chart 4 reproduced) are the ones that followed an unbalanced path of integration. Balanced integration into the rapidly globalizing world market calls for sound growth of exports or for intertemporal collateral of future export growth. This can only be attained by a prudent combination of complementary domestic and border policies that encourage long-term productive investment, supported by savings drawn in by higher expected rates of return due to efficiency gains from reforms that encourage exports.

35
random variable with variance _σ_<sup>2</sup> _u_<sup>sothat</sup><sup>_u ∼N_(0</sup><sup>_, σ_2</sup> _u_<sup>).</sup> Since I am interested in capturing wage price-elasticities that differ by labor skills, I interact prices with educational dummies, which are represented by the vector **ed** _i_ . Three labor categories are considered: unskilled (primary education), semi-skilled (secondary education) and skilled labor (college education). The wage price elasticities are denoted by the three-element vector **_β_** _w_ . In the estimation, I include prices interacted with the three educational dummies; an equivalent procedure would be to include prices separately and add two interactions with the skill dummies. Introducing the three interactions provides direct estimates of the wage-price elasticities and it is thus more convenient.

� I add a comprehensive set of controls in (1). Wages depend on a vector **x** _i_ which includes individual characteristics like age, (and age squared), gender and marital status, as well as on a separate set of educational dummies that accounts for differences in returns to schooling not related to the trade shocks. To control for technical change and other preference and profit shifters, I follow Roberts and Tybout (1997) and introduce a trend in (1). I interact the trend with the educational dummies so as to capture differential technological change by skill intensity, too.

These trends also capture other macroeconomic factors that may have affected wages. During the period under study, Argentina adopted a currency board in 1991 that led to a huge crisis in 2001. While in the estimation I only use data from 1992 to 1999, so that the crisis period is excluded from the estimation, there is concern that some of the macroeconomic tensions that gave rise to the crisis were present even before 2001. To further control for these factors, and on top of the aggregate trends, I include an index price of both import 9
Policy Research Working Paper 8546

# **Abstract**

This paper uses a popular web mapping and transportation service to generate information for more than 22 million counterfactual trip instances in 154 large Indian cities. It then develops a methodology to estimate robust indices of mobility for these cities. The estimation allows for an exact decomposition of overall mobility into uncongested mobility and the congestion delays caused by traffic. The paper first documents wide variation in mobility across Indian cities. It then shows that this variation is driven primarily by uncongested mobility. Finally, the paper investigates correlates of mobility and congestion. Denser and more populated cities are slower, in part because of congestion, especially close to their centers. Urban economic development is generally correlated with better uncongested mobility, worse congestion, and overall with better mobility.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/research. The authors may be contacted at eghani@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
**Panel 1.  Medium-term Fiscal Projection: Baseline Scenario**

<!-- Start of picture text --> Real GDP Diamond Production and Real Profit<br>140 15<br>1000 40<br>120<br>10 35<br>100 800<br>30<br>80 5 600 25<br>60 0 20<br>40 400 15<br>20 -5 200 10<br>5<br>0 -10<br>0 0<br>Mineral GDP (right) Non-mineral GDP (right)<br>GDP growth (left) Production vol. (right) Real profit per carat (left)<br>Government Operation (% of GDP) Government Revenue (% of GDP)<br>50 30<br>40 25<br>30 20<br>20 15<br>10<br>10<br>5<br>0<br>0<br>-10<br>-5<br>-20<br>Fiscal balance Revenue and Grants Mineral SACU<br>Expenditures and Net Lending Net BoB transfer Non-mineral, non-SACU<br>Financing (% of GDP) Government Debt & Guarantees (% of GDP) /1<br>25 45<br>40<br>20<br>Saving/Repayment 35<br>15 30<br>10 25<br>20<br>5 15<br>0 10<br>5<br>-5<br>0<br>-10<br>Dissaving/Borrowing<br>-15<br>External debt Domestic debt GIA External Domestic<br>GIA Balance (% of GDP) /2 Government Net Financial Position (% of GDP) /3<br>80 80<br>70<br>60<br>60<br>50 40<br>40<br>20<br>Unfinanced<br>30<br>fiscal gap 0<br>20<br>10 -20<br>0<br>-40<br>-10<br>-20 -60<br>(ml. 2000/01 Pula perct.)<br>Production vol. ( ml. of ct.)<br>GDP (in billions of 2000/01 Pula) Real GDP growth (in percent p.a.) Profit<br><!-- End of picture text --> 18
|Table G4<br>|. Regulatory Bur<br>|den (cont)<br>|<br>|
|---|---|---|---|
|Code|Table|Row|Concepts measured|
||(Appendix 1)|||
|Non-repr|esentative sourc|es (cont)||
|GCSA|A 1.8|15|Extent of market competition|
|||16|Effectiveness of anti-trust policies|
|||39|Costs of uncertain rules, laws, or govemment policies|
|||1|Negative impact of tariffs on costs and availability of<br>equipment and materials|
|||2|Negative impact of hidden barriers to trade|
|||47|Openness of public sector contracts to foreign investors|
|||48|Policies for dividend remittances as obstacles to development|
|||49|Dominance of state owned or state controlled enterprises|
|||50|Regulatory burden|
|||52|State interference in private business|
|||53|Regulatory discretionality (vagueness of regulations)|
|||54|The tax system hinders business competitiveness|
|||57|Protection of domestic banks from foreign competition|
|||59|Regulations for starting a business as an obstacle to<br>business development|
|||60|Price controls as an obstacle to business development|
|||61|Regulations on foreign trade as an obstacle to business<br>development|
|||62|Foreign currency regulations as an obstacle to business<br>development|
|||63|Transfer costs associated with exporting capital as an<br>obstacle to business development|
|||64|General uncertainty on costs of regulations as an obstacle to|
||||business development|
|WCY98|A 1.12|20|Legal regulation of financial institutions|
|||2|Protectionism as an obstacle to imports from abroad|
|||3|Controls on foreign investors ownership of companies|
|||4|Obstacles to foreign bidders on public contracts|
|||11|Political system as an obstacle to development|
|||5|Real personal taxes as a burden to work initiative|
|||7|Real corporate taxes as a burden to entrepreneurial activity|
|||9|Legal framework as an obstacle to competitiveness|
|||15|Custom's administration as a burden to intemational trade|
|||16|Price controls|
|||17|Competition laws as an obstacle to fair competition|

57
have successfully stabilized prices in Asian rice markets, as described by Dawe (2001), Timmer (2011), and Cummings (2012). When evaluating current policy versus possible alternatives, it is thus important to have a complete picture of how prices are determined.

We use data on prices, exports and harvests to evaluate the drivers of glutinous rice prices in Lao PDR, focusing on the role of exports to Thai and Vietnamese markets. Both Thailand and Vietnam produce and consume large quantities of glutinous rice, and share long and porous borders with Lao PDR. The price data for glutinous rice (also called rice in the rest of the paper) are monthly averages of consumer prices for the period January 2001December 2012. We went to great lengths to compile this database, which to our knowledge has not been used in any previous studies. There is a lack of detailed historical data on international trade, but information is available on official exports for the period after 2005. We combine these with harvest data to analyze how they jointly determine changes in Lao rice prices.

Our main findings are three-fold: First, cointegration analysis and innovation accounting (impulse responses and variance decomposition) provide compelling evidence that Lao PDR is importing its rice price changes in the long run by exporting its produce to Vietnam and Thailand. While there are feedback effects from Lao to Vietnamese prices, shocks to Vietnamese prices explain a much larger part of changes in Lao prices than vice versa. This finding is corroborated by regressions of (official) exports to Vietnam on Lao price changes, which confirm a positive association.

Second, adding Lao harvests to error correction models reveals a pattern where good harvests initially lead to lower prices, as expected, but then tend to raise prices as stocks are depleted over the year. While we have few data points, it appears that good 4
# **Table** 7 Savings Functions for the Panel Data of Latin American Countries, 1960-90

|||Depen<br>(t-|dent Variab<br>Statistics in|le: Savings ra<br>parenthesis)|te||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|GDP per capita growth|4.752<br>(4.76)|5.613<br>(5.65)|4.448<br>(4.29)|5.442<br>(5.46)|4.652<br>(4.71)|6.140<br>(5.81)|4.145<br>(3.76)|5.433<br>(5.07)|
|Lagged GDP growth per capita||0.039<br>(0.04)||0.299<br>(0.35)||1.044<br>(1.11)||0.056<br>(0.06)|
|Dependency ratio|-1.259<br>(-3.90)|-1.201<br>(-4.32)|-1.370<br>(-4.72)|-1.227<br>(-4.78)|-1.032<br>(-3.13)|-1.005<br>(-3.20)|-1.210<br>(-3.96)|-1.126<br>(-4.28)|
|Lagged Savings Rate||0.421<br>(4.10)||0.427<br>(4.18)||0.469<br>(4.42)||0.446<br>(3.97)|
|Overall Education Stock|3.090<br>(3.90)|1.525<br>(2.00)|||||||
|Lagged Overall Education Stock|-3.517<br>(-4.40)|-1.481<br>(-2.61)|||||||
|Primnary Education Stock|||2.564<br>(3.31)|1.109<br>(1.51)|||||
|Lagged Primary Education Stock|||-3.189<br>(-4.03)|-1.698<br>(-2.31)|||||
|Secondary Education Stock|||||0.561<br>(1.31)|0.124<br>(0.38)|||
|Lagged Secondary Education Stock|||||-0.595<br>_(-1.51)_|-0.179<br>(-0.60)|||
|Tertiary Education Stock|||||||0.720<br>(4.697)|0.345<br>(2.07)|
|Lagged Tertiary Education Stock|||||||-0.816<br>(-4.75)|-0.458<br>(-2.65)|
|AdjR<sup>2</sup><br>|0.318<br>|0.439<br>|0.303<br>|0.434|0.194|0.394|0.292|0.422|
|DW|1.66|2.02|1.65|2.03|1.63|2.08|1.71|2.05|
|Observations|100|100|100|100|100|100|100|100|

All variables are expressed in log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixedeffects have been introduced to account for countries' characteristics.
where _θt_<sup>_c_and</sup><sup>_αn_are,respectively,thecropproductivityindexandthevalue</sup> share of land in production of crops at the benchmark time 0 _._ The parameter _ρn_ = _<u>σnσ−n</u>_ <u>1</u> is a CES function parameter proportional to the elasticity of substitution of agricultural land for fertilizers, _σ_<sup>_n_</sup> _._ The amount of food crops (measured as the global physical production of agricultural crops) and global expenditures on food crops in 2004 come from the FAOSTAT database. The elasticity of substitution of nitrogen fertilizers for agricultural land is based on Hertel et al. (1996) estimates for the US corn production over the 1976-1990 period. We obtain the economic rent of global cropland from GTAP v.7 database. The values of crop technology index and shares of agricultural land and fertilizers in 2004 are calibrated from known values of agricultural output, fertilizers, and the agricultural land as described in Rutherford (2002).

We obtain the non land expenditures of producing food crops from GTAP v.7 database (sectors 1-8), and normalize them by the total expenditures on other primary resources. To compute the normalized average cost of producing food crops, _c_<sup>_o,c_</sup> _,_ we divide normalized expenditures on the amount of food crops produced in 2004.

# B.1.2.4 Biofuels

In the model baseline we dene the rst-generation biofuels as a grain-based ethanol. The production of biofuels, _x_<sup>_b_</sup> _,_ is a chemical engineering process that can be described by a linear production function: where _θ_<sup>_b_</sup> is the rate of conversion of biofuels' crops, _x_<sup>_c,b_</sup> , to biofuels. The amount of biofuels produced in 2004 comes from BP Statistical Review of World Energy database (2013). The values for biofuels conversion rate and cost for ethanol are taken from Taheripour and Tyner (2011). Following Winston (2009) we adjust the quantity of rst generation biofuels produced by 0.7 to match the energy content of liquid fossil fuels. To compute the normalized average cost of conversion of fossil fuels to fertilizers, _c_<sup>_o,b_</sup> _,_ we multiply the average conversion cost of biofuels by its expenditure share in other primary resources.

14
<!-- Start of picture text --> Volume in Monitored Outlets: Vermicelli<br>(01/21/92-08/25/92)<br>28 . . -7000<br>5X; t .. ......... ................................... ............. ............. .... ..... 60<br>24.... . * -- ----------- . r- --------------------------- .- S ......... -------------------- oN<br>_ 5,20 * < ------- ------- -5000<br>t ;<br>> ,2... ..... . .......... ........ 2-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4 ; --------s---------------------- j~* I------- ---------I---------<br>20..... '---- 'rS = 8 20.*~~~~~~~~~~~~~~.a.4~~~~~~-000 'I .<br>21 00~~~~~~~ 0-'°°°<br>SD S ** S D at<br>--- - 2o40/8 ---- oe1 40 -------- *511 ------ %................... ---------<br>10I T T IT I to Y<br>S 58 S<br>MaktVolume . ... State Volume<br><!-- End of picture text -->

**_58_**
**Table 8: Land Rights, Decision-Making and Permissions Variables by Cluster (Malawi)**

||Male La<br>|ndowners by<br>|Cluster<br>|Female L<br>|andowners b<br>|y Cluster<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(6)|(7)|(8)|
||Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|Mostly Not<br>Hold<br>(base)|Mostly<br>Joint<br>Rights|Mostly<br>Exclusive<br>Rights|
|Document Owner|0.04|0.06|0.07|0.03|0.02|0.02|
||(0.20)|(0.23)|(0.25)|(0.17)|(0.15)|(0.15)|
|Plot Decision Making|0.78|0.93***|0.92***|0.86|0.90|0.90|
||(0.41)|(0.26)|(0.27)|(0.35)|(0.30)|(0.30)|
|Economic Owner|0.76|0.98***|0.97***|0.85|0.96***|0.96***|
||(0.43)|(0.15)|(0.17)|(0.36)|(0.19)|(0.21)|
|Right to Improve|0.71|0.98***|0.98***|0.57|0.9***|0.98***|
||(0.45)|(0.14)|(0.13)|(0.50)|(0.31)|(0.15)|
|Need permission for at least 1 right#||0.83***|0.42***||0.98***|0.44***|
|||(0.38)|(0.49)||(0.15)|(0.50)|
|Need permission for some rights (at||0.77***|0.32||0.88***|0.33|
|least 3 out of 5 inc. right to improve)||(0.42)|(0.47)||(0.33)|(0.47)|
|Need permission for all rights||0.67***|0.22***||0.75***|0.22***|
|||(0.47)|(0.41)||(0.44)|(0.42)|
|Perc. of rights needing permission||0.77***|0.32***||0.87***|0.35***|
|||(0.38)|(0.42)||(0.24)|(0.43)|
|Observations<br>Notes:Permission statistics for non-h|165<br>olders were n|90<br>ot provide|289<br>d as very fe|416<br>w individuals|76<br>in this cate|319<br>gory held ri|

<u>Notes: Permission statistics for non-holders were not provided as very few individuals in this category held rights.</u> 25