rafmacalaba/datause-encoder-data huggingface dataset:
To directly test Hypotheses 5-1 and 5-2, it is necessary to separately estimate the effects of an increase in input costs and an increase in windfall revenues during the contractual period. Domestic fuel prices are not observable, but country-level variations of the changes in input costs and windfall revenues can be constructed by weighting the global commodity price series by the compositions of input resources and resource rents. The global prices of three major commodities (oil, natural gas, and coal), which have risen dramatically since 2000 as shown in Figure 4, are used to construct country-specific variables of the _average_ input and end-product price shocks as follows.

They are the weighted average of the log difference of the global prices of three primary commodities _c_ for country _i_ at year _t_ . The share of a source of electricity production (either oil, natural gas, coal, or others) φct is used to construct a variable of input price change, while the average value of exports of commodities (either oil, natural gas, mineral, or others) ωct is used for measuring an increase in windfall revenue during contracts. The data on the source of electricity production are from the World Bank’s World Development Indicators, and the data on the value of commodity exports are from the United Nation’s UN Comtrade database. Both φct and ωct are country-specific characteristics, which are exogenous to BOC risk.

In some countries, there is one dominant fuel for electricity generation, and in others there is a mix of multiple fuels.<sup>14</sup> Depending on sources of electricity generation, the level of exposures to fuel cost increase would be different by country, which helps me identify the impact of cost increase in BOC risk.

# _5.1.2.  Heterogeneous Impacts of Commodity Price Shocks_

To test the heterogeneous impact of commodity prices on BOC risk by sector (Hypothesis 5-1 and 5-2), the interaction terms of sector dummies and two price shock variables (specified in Eq. (2)) are included. The

> 14 Woodhouse (2006) and his research project in IPPs (see <u>http://pesd.stanford.edu/ipps) detailed case studies on the</u> structure of IPPs and the linkage with contractual disputes.  Dominant fuel sources substantially differ by countries.  In China, there are 32 coal-fired plants selling electricity to state utilities where contract enforcement is very weak.  In Egypt, 3 gas plants sell electricity at low retail price ($2.3 cents/kWh) reflecting highly subsidized gas price from state gas monopoly.  In Phil lines, more than 40 IPPs exist which vary in fuel choice, investor composition, and the identity of the off-taker.  Finally in India, rough 22 IPPs have been built to sell electricity to state utilities which vary across critical variables including the mode of solicitation, fuel arrangements, and the type of sponsor.

13
_Table 2: Aid for Trade Facilitation Broad Definition: Bilateral-5Year Fixed Effects and Year Fixed Effects Included_

||1<br>|2<br>|3<br>|4<br>|5<br>|6<br>|7<br>|
|---|---|---|---|---|---|---|---|
||Baseline<br>– all<br>positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only;<br>trade with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-<br>donor<br>trade|Base-line<br>(2) with<br>lags|
|ln_tariff|-0.201***|-0.202***|-0.421***|-0.225***|-0.219***|-0.203***|-0.321***|
||[0.035]|[0.035]|[0.067]|[0.039]|[0.043]|[0.036]|[0.073]|
|ln_rep_gdp|0.692***|1.604***|-1.416*|1.525***|1.595***|1.540***|3.075***|
||[0.045]|[0.322]|[0.856]|[0.367]|[0.468]|[0.327]|[1.011]|
|ln_part_gdp|0.253***|-0.688**|-4.187***|-1.509***|-1.558***|-0.748***|-0.032|
||[0.036]|[0.270]|[0.924]|[0.338]|[0.416]|[0.274]|[0.612]|
|ln_itfa|0.011***|0.011***|-0.023|0.017***|0.013***|0.011***|0.010*|
||[0.003]|[0.003]|[0.015]|[0.004]|[0.003]|[0.003]|[0.005]|
|ln_etfa|-0.001|-0.001|0.021|-0.001|-0.005*|-0.001|0.002|
||[0.002]|[0.002]|[0.016]|[0.003]|[0.003]|[0.002]|[0.003]|
|ln_rep_gdp_cap||-0.908***|2.310***|-0.828**|-0.788*|-0.843***|-2.711***|
|||[0.319]|[0.852]|[0.364]|[0.454]|[0.323]|[1.015]|
|ln_part_gdp_cap||0.944***|4.149***|1.691***|1.695***|1.003***|0.333|
|||[0.264]|[0.913]|[0.331]|[0.404]|[0.267]|[0.600]|
|L.ln_itfa|||||||0.059***<br>[0.009]|
|L2.ln_itfa|||||||0.004<br>[0.006]|
|L.ln_etfa|||||||0.003<br>[0.003]|
|L2.ln_etfa|||||||-0.001<br>[0.003]|
|Observations|108304|107806|29421|86913|57822|104639|32592|
|Number of Bilat - 5year F.E.|39309|39125|13284|32882|25231|38388|13461|
|Robust standard errors in brack|ets|||||||

* significant at 10%; ** significant at 5%; *** significant at 1%

/1/ A4TF “Aid for Trade Facilitation’ Includes Bilateral- 5 year fixed effects and year fixed effects 27
instrument political instability, but unfortunately finding credible instruments is hard. Instead, we recognize this problem and caution that our results should be interpreted as conditional associations, rather than reflecting causal relationships.<sup>10</sup> A related concern is omitted variable bias, which we try to minimize by including as many relevant economic variables as we could obtain quarterly data for, notably inflation, changes in the nominal exchange rate, industrial production and changes therein.<sup>11,12</sup> While we would ideally have liked to control for a richer array of macroeconomic factors, high frequency economic data are unfortunately not widely available.

Last but not least, the presence of a lagged endogenous variable creates a potential upward endogeneity bias when estimating equation (1) using Ordinary Least Squares, due to a correlation between the time invariant unobserved fixed effects and explanatory variables. By contrast, fixed effects estimates tend to be biased downwards because of the so-called Nickell bias (Nickell, 1981; Kiviet, 1995) – a non-negligible correlation between the transformed residuals and the transformed error term. However, Monte Carlo evidence suggests that bias-reduction methods, developed by Bun and Kiviet (2003) and Bruno (2005), work well when T is relatively large and the serial correlation relatively modest as appears to be the case in our data. The raw correlation between greenfield FDI and lagged greenfield FDI is 0.49 and we have on average 36 time series observations per country. These least squares dummy variables bias-corrected estimators are our preferred estimation method for they allow us to control for time-invariant unobservable variables while minimizing the Nickell bias.

> 10 We also do not control explicitly for potential spillovers across countries, although the ICRG political instability index has a component capturing external conflict and the risk of foreign interference and therefore does reflect the impact of unrest in the region on the political stability in the host economy. 11 In the cases of countries without industrial production data (Bahrain, Lebanon, and Yemen), we use quarterly exports data. Since we are only looking at changes within countries over time this does not yield any major problems. 12 Ideally, we should have used quarterly Gross Domestic Product (GDP) data, but such series were not available for the majority of countries in the region

14
# BANK RESTRUCTURING IN MALAYSIA. 1985-88

# TABLES

1. Principal macro-indicators

2. Aqsets of the Financial system 3. Banking system: Outstanding Interest-in-suspense and bad dobt provisions 4. Commercial banks: Average loan margins 5. Losses of the 24 deposit-taking co-operatives 6. Key data of three ailing commercial banks 7. Summary of Rescue Plan for 24 deposit-taking co-operatives

# FIGURES

1. Terms of trade and current account deficit

2. Fiscal deficit and nominal GNP growth 3. Exchange rate and lending to property 4. Money supply and real GDP growth 5. Pre-tax profits of the banking system 6. Real interest rates This is a revised and updated edition of the paper "Financial Adjustment in a period of Disinflation: The Case of Malaysia", a background study prepared for the World Development Report 1989. The author acknowledges with gratitude the research assistance of Tan Yit Seong, Tan Gaik Looi and Chan Kok Seong of the Bank Regulation Department, Bank Negara Malaysia and the secretarial assistance of Zaini Osman. I am also grateful to Alan Gelb, Ignacio Mas, Alfredo Thorne, and Yoon Je Cho of the World Bank for their helpful comments, and Karin Waelti for her typing assistance.
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

# 5   Interpretation of results and implications for spatial investment

The coefficients of the variables for travel time to the nearest city would seem to imply a very large potential response of agricultural production to infrastructure investments and policy changes that reduce travel times. For example, if marketing margins account for 50 percent of the urban price (for example, 100 shillings of a 200-shilling urban consumer price) and if 80 percent (80 shillings) of the marketing costs are due to factors that are proportional to travel time, then a 10 percent reduction in travel time would reduce the marketing margin by 8 percent (8 shillings in this example). If consumer prices are unchanged (due to a perfectly price-elastic demand), then producers reap the entire benefit of the lower transport costs and producer prices rise by 8 percent (from 100 to 108 shillings).

Assuming an elasticity of supply of 0.3, production in this hypothetical scenario would increase by 2.3 percent, and the elasticity of production with respect to travel time would be 0.023/–0.10 = –0.23 (table 5.1). But if the product faces demand constraints (simulated using an own-price elasticity of demand of –0.4), the urban price falls by 3.6 percent and the rural price increases by only 0.8 percent. In this scenario, production rises by only 0.2 percent, so that the elasticity of production with respect to travel time is –0.02.

## **Table 5.1 Travel-time elasticities with endogenous market price effects**

|Own-price elasticity of supply|
|---|
|Own-price elasticity of demand|
|Urban price (% change)|
|Rural price (% change)|
|Production (% change)|
|Travel time (% change)|
|Elasticity of supply with respect to travel time|

### _Source:_ Own calculations.

Both of these travel-time elasticities are significantly below the estimates for various crops for all Sub-Saharan Africa or East Africa. The estimated travel time elasticities in Sub-Saharan Africa (all cropping systems) are –2.0 for the six-cereal aggregates and –3.3 for maize (table 4.2); the corresponding elasticites for East Africa are smaller in absolute magnitude (–1.8 and –0.8, respectively, table 4.5), but still much larger than the estimates using an elasticity of supply with respect to price of 0.3. To approximate even the relative small magnitude of the travel time elasticity for maize in East Africa (– 24
and disabled pensions, the income cut-off requires that households have a per capita income that is less than one-third of the minimum wage. If the income of the household is deemed to have risen above this threshold, it loses access to the benefit. For most of the regular programs that provide ongoing support, such as the programs for the widows and families of soldiers and the CCT programs, the requirement is that the household should have no formal income (i.e., that the household is not registered in the social security system). The government has begun to introduce a series of measures that try to reduce the impact of a sudden loss of benefits on a household, with the aim of improving the incentives of household members to work. In 2015, for example, a change was made such that if a household member who receives the CCT benefit finds a job, the household is entitled to keep receiving those benefits for an additional year.

**Figure 1. Adequacy of Social Assistance Benefits by Poorest Quintile (%)**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>LAC ECA Turkey*<br>Income<br>% of pre-transfer Household<br><!-- End of picture text --> _Source:_ The Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE) (database), World Bank, Washington, DC (accessed March 20, 2015), http://datatopics.worldbank.org/aspire.

Several features of the design of social assistance in Turkey suggest that there may be significant impacts on household decision making in selecting benefits over employment, or in selecting employment in the informal sector over that in the formal sector. These include the fact that (i) access to benefits is dependent on income earned, (ii) in some programs, if a household member is working in the formal sector s/he is disqualified from eligibility, and (iii) benefits are removed in their entirety should income rise above a relatively low threshold. An important consideration in determining whether these impacts are likely to be sizable is the value of benefits provided. Notwithstanding the rapid increases in spending over the past decade, the overall value of benefits provided to households remains low. Figure 1 uses survey data from Turkey and other comparator countries to estimate the value of benefits as a percent of pre-transfer household income. For Turkey, the value of benefits is 7
> obtain, and few alternatives to subjective indicators are available for measuring these aspects of

> governance. Second, perceptions of the quality of governance may often be as important as objective differences in institutions across countries. While a country may nominally enjoy a set of sound institutions according to certain standards, the country's residents must have confidence in these

> institutions if they are to contribute to good governance.<sup>6</sup>

**Table 2. Sources of Governance Data, 2000-01**

|_Source/Institution_|_Publication/Survey_|_Code_|_Type_|_Country_<br>_coverageRe_|_presentative_|_Newin_<br> _2000-01_|
|---|---|---|---|---|---|---|
|Business EnvironmentRisk|BusinessRiskService|BRI|Poll|50|||
|Intelligence|||||||
|Columbia University|StateCapacityProject|CUD|Poll|109|x|x|
|EconomistIntelligenceUnit|CountryRiskService|EIU|Poll|115|x||
|EuropeanBankfor|TransitionReport|EBR|Poll|26|||
|Reconstructionand||D|||||
|Development|||||||
|FreedomHouse|NationsinTransition|FHT|Poll|27|||
|Freedom House|FreedomintheWorld|FRH|Poll|192|x||
|GallupInternational|GallupMillenniumSurvey|GMS|Survey|60|||
|Heritage Foundation/Wall|Economic FreedomIndex|HWJ|Poll|161|x||
|Street Journal|||||||
|Institutefor Managementand|WorldCompetitivenessYearbook|WCY|Survey|49|||
|Development|||||||
|Latinobar6metro|Latinobar6metroSurveys|LBO|Survey|17||x|
|PoliticalEconomic Risk|AsiaIntelligence|PRC|Survey|14|||
|Consultancy|||||||
|PoliticalRiskServices|InternationalCountryRiskGuide|PRS|Poll|140|x||
|PriceWaterhouseCoopers|OpacityIndex|PWC|Survey|35||x|
|Standardand Poor'sDRI|CountryRiskReview|DRI|Poll|111|x||
|McGraw-Hill|||||||
|World Bank|BusinessEnterprise Environment<br>Survey|BPS|Survey|18||x|
|WorldBank|WorldBusinessEnvironment<br>Survey|WBS|Survey|81|x||
|WorldEconomic Forum|GlobalCompetitiveness Report|GCS|Survey|75|||

> We distinguish sources according to (I) whether they are polls of experts or surveys of businesspeople or citizens in general and (2) the extent to which the sample of countries included in the

6. In this context, it is noteworthy that nowadays almost every country in the world has adequate anticorruption legislation, yet the actual variation in corruption control across countries is enormous. The conceptual soundness of many of the supposedly objective governance measures being advanced is another weakness. It is unclear, for instance, whether a high number of jailed criminals per capita indicates a high or low level of rule of law (Russia and the United States are tied for first place in this indicator). This contrasts with subjective indicators emerging from polls of experts and surveys, in which the questions are a priori designed to capture the normative range between a bad and a good outcome.

8
The second set, consisting of nine skills listed in Table 5, is based on Deming and Kahn (2018). The left column in the table presents skill categories, and the right column lists keywords and phrases related to corresponding skill categories.

<!-- Start of picture text --> Table 5. Skill categories based on Deming and Kahn (2018)<br>Skills Keywords and phrases<br>Cognitive Problem solving, research, analytical, critical thinking, math, statistics<br>Social Communication, teamwork, collaboration, negotiation, presentation<br>Character Organized, detail oriented, multitasking, time management, meeting deadlines,<br>energetic<br>Writing Writing<br>Customer service Customer, sales, client, patient<br>Project management Project management<br>People management Supervisory, leadership, management (not project), mentoring, staff<br>Financial Budgeting, accounting, finance, cost<br>Computer Computer, spreadsheets, common software (e.g., Microsoft Excel, PowerPoint)<br>Source : Adapted from Deming and Kahn (2018).<br><!-- End of picture text -->

# 3.3 Salaries

This section examines how salaries are associated with job characteristics and qualifications.

## **_Q. How do salaries differ depending on required qualifications, occupations and industries?_**

## **_A. Regression analysis finds that higher education degrees, more experience and higher occupational levels are associated with higher salaries. By industry, the ICT sector advertises the highest average salaries. By firm size, larger firms advertise higher salaries._**

We conduct a regression analysis of the correlations between salaries and job characteristics. The regression results are summarized in Figure 16. The dots and numbers in the figure indicate differences in salaries from a reference category. For example, in the case of industries, the reference category is manufacturing, and the dots and numbers indicate how much the average salaries in the other industries are different in proportion from the average salary in manufacturing.

18
Colombia Mayor, which puts them at 718,376 and 1,259,004, respectively. The data of the 2013 round of the Encuesta Nacional de Calidad de Vida (National Survey of Quality of Life, ENCV) used in the analysis were collected during September and October of 2013. The Consorcio Colombia Mayor also provided us with figures for October of 2013, putting the number of beneficiaries at 1,012,724. The numbers implied by the ENCV are a little lower, but not by much. A weighted total of the number of beneficiaries declared by each household yields 828,738 beneficiaries. If most of the program’s expansion occurred in the second half of the year, this calculation seems fairly reasonable.

Table 1 presents this last total, together with other estimates of the target population that are also derived as weighted totals from the 2013 round of the ENCV. We present totals for different agegroups and subgroups by socioeconomic status. The first age-groups refer to the minimum age of eligibility for the program (52 or older for women, 57 or older for men), followed by the 60–65 agegroup. For socioeconomic status, we first consider whether an appropriately aged person would qualify for the benefit based on the household’s Sisben score. We have also created indicators for whether a household’s per capita income puts it below Colombia’s poverty line and below the extreme poverty line (indigence). For this, we followed the Ministry of Labor’s own methodology.<sup>3</sup> The main insight is that the total number of the poor in each age-group is only slightly lower than the number of people living beneath the Sisben eligibility threshold. This bodes well for the Sisben criterion in providing a good proxy for poverty. Moreover, given the enrollment numbers supplied by the ministry, the poor 65 years of age or older should be largely covered.

**Table 1: Estimates of the Target Population according to ENCV 2013**

|Total beneficiaries|828,738|
|---|---|
|_Age eligible_||
|Total|7,383,638|
|Sisben eligible|2,083,818|
|Poor|2,066,330|
|Indigent|699,141|
|_60 years of age or older_||
|Total|4,947,172|
|Sisben eligible|1,669,819|
|Poor|1,476,454|
|Indigent|525,100|
|_65 years of age or older_||
|Total|3,414,864|

> 3 See “Estadísticas por Tema: Pobreza y Condiciones de Vida” (database) (accessed August 4, 2015), National Administrative Department of Statistics, Bogotá, Colombia.

9
Taking Stock of Wholesale Power Markets in Developing Countries: A Literature Review Taking Stock of Wholesale Power Markets in Developing Countries: A Literature Review<sup>1</sup> Hugh Rudnick & Constantin Velasquez<sup>2</sup>

**Keywords** : Electricity markets; Competition; Developing countries; Emerging markets **JEL codes** : L13, L94, L98, Q4, K21

1This paper is a product of the “Rethinking Power Sector Reform” knowledge program of the Energy & Extra ctives Global Practice of the World Bank. Any views presented here are the authors alone and should not be a ttributed to the World Bank or any other person or institution. The authors are very grateful for financial supp ort from the Energy Sector Management Assistance Program (ESMAP) and the Public Private Infrastructure Advisory Facility (PPIAF). Thanks are due to Vivien Foster, who supervised the work, and Debabrata Chattophadyay and Pedro Sanchez who acted as peer reviewers. Any shortcomings are the sole responsibility of the authors. 2 Department of Electrical Engineering, Pontificia Universidad Católica de Chile i
# **11b. Most important source of funding (i.e. ranking) Commercial SOFIs**

<!-- Start of picture text --> Government funding Multilateral FI funding<br>Deposits general public Corporate deposits<br>Securities Interbank market<br>Central Bank<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **Development SOFIs**

<!-- Start of picture text --> Government funding Multilateral FI funding<br>Deposits general public Corporate deposits<br>Securities Interbank market<br>Central Bank<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **11c. Most important source of funding (i.e. ranking)**

# **Benchmark EU**

<!-- Start of picture text --> Government funding Multilateral FI funding<br>Deposits general public Corporate deposits<br>Securities Interbank market<br>Central Bank<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **West ECA**

<!-- Start of picture text --> Government funding Multilateral FI funding<br>Deposits general public Corporate deposits<br>Securities Interbank market<br>Central Bank<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **East ECA**

<!-- Start of picture text --> Government funding Multilateral FI funding<br>Deposits general public Corporate deposits<br>Securities Interbank market<br>Central Bank<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> 28