rafmacalaba/datause-encoder-data huggingface dataset:
created a major bottleneck. This has been solved in part with the access program. Nonetheless, the program itself has a major limitation: the expansion of the network has not been accompanied by an equal effort to increase connectivity. As a result, while 55 percent of the population resides in areas served by the network, less than 25 percent is connected to electricity services. Connections have lagged behind for several reasons, including the absence of a robust program and dedicated resources to roll out connections; lack of affordability; and capacity constraints at the utility level in handling a growing customer base. Given the massive investments mobilized under the program, results have been disappointing. In 2015, against the US$245 million spent, only 65,000 new customers were added to service, resulting in a cost per connection of US$3,770.

As new generation plants come online and as the number of connections increases, total system sales will increase and the cost of service per kWh billed is therefore expected to reduce dramatically from an estimated US$0.18 per kWh billed to less than US$0.10 per kWh billed. Average tariff levels are expected to increase as exports increase in the share of total sales, helping to reduce the financial gap in the sector over the medium to long term.

# 5.2 Distribution of quasi-fiscal deficit between consumer categories

Sales data are available for a subset of 16 countries to analyze the distribution of QFD between consumer groups. For the purposes of this analysis, it was assumed that industry and export customers have an average cost of service US$0.035 less than other customers. This is because industrial and export customers are typically supplied at high voltage, thereby avoiding the use of medium and low voltage distribution networks. In most cases the source of data for breakdown of sales by consumer group (revenues and energy sold) were different to the sources used in the base-case QFD estimates. In these cases, it is assumed that the sources used in the base-case QFD estimates contain the correct data at an aggregated total level, and that the sources used for the breakdown provide the correct average tariff data by consumer group and the correct revenue profile i.e. percentage of LCU sales to each consumer category. These assumptions are used to estimate the breakdown of total reported revenue and energy sales by consumer group.

The results are summarized in Figure 8. Residential customers account for 42 percent of the QFD, and 38 percent of consumption in the sample of countries included in this analysis, suggesting other customer categories marginally cross-subsidize residential customers. In these 16 countries, tariff schedules indicate that only a small number of customer categories (e.g., commercial customers in Mauritius, government customers in Niger) pay above the cost of service.

38
Brophy, J. (1999). Toward a model of the value aspects of motivation in education: Developing appreciation for particular learning domains and activities. _Educational Psychologist_ , 34(2), 75-85.

Bruns, B., De Gregorio, S., & Taut, S. (2016). Measures of Effective Teaching in Developing Countries. Research on Improving Systems of Education (RISE) Working Paper, 16(009).

Bruns, B. and Luque, J. (2014). _Great Teachers: How to Raise Student Learning in Latin America and the Caribbean_ . The World Bank: Washington, D.C.

Buhl-Wiggers, J., Kerwin, J. T., Smith, J. A., and Thorton, R. (2017). The impact of teacher effectiveness on student learning in Africa. Research on Improving Systems of Education (RISE) Working Paper.

Burchinal, M. (2018). Measuring Early Care and Education Quality. _Child Development Perspectives_ , 12(1), 3-9.

Burchinal, M., Howes, C., and Kontos, S. (2002). Structural predictors of child care quality in child care homes. _Early Childhood Research Quarterly_ , (17), 87-105.

Burnett, P. C. (2003). The impact of teacher feedback on student self-talk and self-concept in reading and mathematics. _The Journal of Classroom Interaction_ , 11-16.

Carrell, Scott E., Mark Hoekstra, and Elira Kuka. (2018). The Long-Run Effects of Disruptive Peers. _American Economic Review_ , 108 (11): 3377-3415.

Casabianca, J. M., McCaffrey, D. F., Gitomere, D. H., Bell, C. A., Hamre, B. K., and Pianta, R. C. (2013). Effect of observation mode on measures of secondary mathematics teaching. _Educational and Psychological Measurement_ , 73(5), 757-783.

Casabianca, J. M., Lockwood, J. R., and McCaffrey, D. F. (2015). Trends in classroom observation scores. _Educational and Psychological Measurement_ , 75(2), 311-337.

Castillo Castro, C. (2017). Teacher Practices in Primary Schools with High Value-Added Scores and Engaging Lessons in Disadvantaged Communities in Rural Mexico. A dissertation submitted to the University of Cambridge for the degree of Doctor of Philosophy in the Faculty of Education.

Carver, S. M., and Klahr, D. (Eds.). (2001). Cognition and instruction: Twenty-five years of progress. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.

Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Whitmore Schanzenbach, D., and Yagan, D. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project Star. _Quarterly Journal of Economics_ , 126(4).

Chetty, R., Friedman, J. N., and Rockoff, J. E. (2014a). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. _American Economic Review_ , 104(9): 25932632.

29
|Port|
|---|
|Rural Credit Project|
|Rural Credit Project|
|Rural Credit Project|
|Rural Credit Project|
|Rural Credit Project|
|Rubber Development Project|
|Rubber Development Project|
|Rubber Development Project|
|Rubber Development Project|
|Rubber Development Project|
|Rubber Development Project|
|Rubber Development Project|
|Industrial Credit Project(02)|
|Industrial Credit Project(02)|
|Industrial Credit Project(02)|
|Industrial Credit Project(02)|
|Industrial Credit Project(02)|
|Industrial Credit Project(02)|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Polytechnic and Television UniversityProject|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|Agricultural Education Project(02)|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|KaramayPetroleum Project|
|Rural Health and Medical Education Project|
|Rural Health and Medical Education Project|
|Rural Health and Medical Education Project|
|Rural Health and Medical Education Project|
|Rural Health and Medical Education Project|
the demographics of the poor, who have a higher dependency ratio (2 working adults with mean household size of 5.8), the wage rate would need to be Rs 42.<sup>11</sup> The second approach is to base the calculations on the existing (State-level) minimum wage rates. This has been advocated by supporters of the EGS in India. At the time of writing, a wage rate has not been set for the proposed national EGS, though supporters have proposed a wage rate (in 2005) of Rs 60 per day, being an average of the State-level statutory minimum wage rates. Adjusting only for inflation (using the Consumer Price Index for Agricultural Laborers) this is equivalent to Rs 55 in 1999-2000. However, this is a deceptive comparison. While the statutory minimum wage rate refers to a full working day, and might be taken to reflect a reasonably high level of effort, the wage rates calculated from surveys average across workers with a range of effort levels and lengths of the working day.<sup>12</sup> An alternative source of wage data is the series Agricultural Wages in India collected by the Ministry of Agriculture, based on their monitoring of male agricultural wages for a full working day, at district level. Aggregating up the state level estimates for 1999-2000 (weighting by the shares of agricultural workers by state), the mean male agricultural wage from this source is Rs 51 per day, which is 13% higher than the male wage rate of Rs 45 from the NSS. Deflating the Rs 55 wage rate by the same factor gives a wage of Rs 49. Rounding off, we will use an EGS wage rate in 19992000 of Rs 50 per day as the comparator to the Rs 60 for 2005.

So one can defend at least two possible reference “living wage” for an EGS in 1999-00, namely around Rs 40 per day (which happens to also be the average wage rate) and Rs 50. We include both, and we test sensitivity to other wage rates over the range from Rs 25-55 per day.

> 11 The daily household expenditure requirement is calculated as household size times one thirtieth of the monthly poverty line appropriate to that household (depending on its location).  The “living wage” is then the expenditure requirement for the day divided by the number of adults in the labor force.

> 12 Schedule 10 distinguishes a “half day” from a “full day” though the former could be much less than four hours.  (We take account of the full day-half day distinction in calculating wage rates.)

13
# **2.3 Services are becoming more tradable**

No more than 10 percent of service value added is currently exported, which could be compared to 90 percent of goods exports in industrial value added. However, growth in service tradability has been considerable (Mishra, Lundstrom and Anand, 2011). There are now a host of new services that can be traded, and more countries are taking advantage of the globalization of services. NMS has been the very successful in growing its service exports, especially since mid-2000s (see Figure 4). It is still far from star performers such as Indian and China but is doing well compared to the rest of the world. EU-15 has also made progress while ACC is falling behind the rest of the world.

**Figure 4: Development of Service Export (1998=100).**

<!-- Start of picture text --> 1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>eu-15 new member accessions developing upper middle china india<br>Source: Balance of Payments, IMF. 2011.<br><!-- End of picture text -->

**Figure 5: Service exports as a share of service value added (%), 1990-2007.**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>EU-15 NMS ACC Developing<br>Upper middle India China<br><!-- End of picture text --> _Source_ : Balance of Payments, IMF and World Development Indicators, World Bank. 2011.

7
- 36 -

project the aggregate results to the national industrial sector in Nigeria. The report in the references states (World Bank, 1993, page 92) that a total of 2,121 GWh of electricity was sold to manufacturers in 1988. This amounted to 29% of NEPA's total sales in that year and the percentage has remained roughly constant over time. NEPA purchases by the firms in our sample amounted to 837 GWh or 39.5% of the total sold to industries in 1988. Our finns are 160 in number or 12.4% % of the total number of 1,294 manufacturers in the five Nigerian cities included in the study. The largest firm in our sample was responsible for the purchase of 662.72 GWh of power from NEPA. This is 79% of the total power use by the 160 finns in our sample. Clearly, this largest firm is an unusually power intensive unit. However, even exclusion of this firm from our sample leaves a total consumption of 175 GWh of NEPA power by the remaining 159 firms which is still 8.2% of the total sold by NEPA in 1988. Nevertheless, a few rough calculations can be made to provide an upper bound for aggregate savings.

Consider, for example, that under 1988 conditions the optimal tariff with endogenous reliability (Table 9) would have resulted in a total cost saving of 245.4 million naira by the firms included in our sample. As a crude guess, if aggregate gains are ten times this number (based on 12.4% sample), then the aggregate benefit for the five cities is about 2.5 billion naira annually. However, we cannot overemphasize the fact that this is a very rough projection. A more meaningful way to evaluate the significance of the estimated cost saving is to consider it as an annual return on the present value of investments in privately owned generators. As reported in Lee and Anas (1992a), the manufacturers in our sample had investments in generators worth 169.1 million naira and spent 26.4 million naira annually on the maintenance and operation of these generators. At a 10% rate of discount, the present value of investment and annual operating outlays was 393.7 million naira. Under 1988 conditions, the 245.4 million naira in savings is a 62 % annual return on this private investment in generating capacity. Under
household. The transfer is then calculated by comparing the assessment of residential property value per household of the municipality against the simple average assessment of residential property value per household for municipalities across the province.

# Pattern of Distribution

On a per household basis, the level of fimding is significantly higher in the north than in the south. While this is largely the result of the NSG, the result is also reinforced by the fact that most northern municipalities receive significant funding under the REG and revenue guarantee. Moreover, northem municipalities tend to receive higher levels of conditional grants.

Table 2. Ontario: 1988 Provincial Transfers to Municipalities per Household ($)

||Unconditional|Conditional|Total|
|---|---|---|---|
|South||||
|Metro Toronto|217|936|1,153|
|Co. Cities|258|581|839|
|Regions|210|709|919|
|Counties|169|804|973|
|North||||
|Regions|635|687|1,322|
|Dis. Cities|590|921|1,511|
|Districts|400|1,081|1,481|
|Total|238|784|1,022|

Source: Advisory Committee (1991).

# III. **State-Municipality** Revenue Sharing **in Brazil32**

Brazil has a federal system with three levels of government: the federal government, the state governments, and municipalities. The federal government assumes exclusive responsibility for the taxes on income, payroll, wealth, foreign trade, banking, finance and insurance, rural properties, hydroelectricity, and mineral products. The federal government allows states to levy supplementary rates up to 5 percent on the federal bases for personal and corporate incomes. The main state taxes include the general value added tax on goods and services, tax on inheritance and gifts, and tax on motor vehicles registration. These three taxes consist 32 Based on Anwar Shah, 1991, The New Fiscal Federalism in Brazil, World Bank Discussion Paper, No. 124.

72
# **Appendix Table 2: Correlations between variables used in Regression Analysis**

||**SME**<br>**Loans/Total**<br>**Loans (%)**|**Investment**<br>**Loans/SME**<br>**Loans (%)**|**Log Total**<br>**loans**|**GCC**<br>**Dummy**|**State**<br>**Ownership**<br>**Dummy**|**Maximum**<br>**Coverage**<br>**Registry**<br>**or Bureau**|**Log**<br>**Number**<br>**of**<br>**Branches**|**Separate**<br>**Unit for**<br>**SME**<br>**Clients**<br>**Dummy**|**Legal**<br>**Rights**<br>**Index **|**Time to**<br>**Register**<br>**Property**|**Time to**<br>**Enforce**<br>**a**<br>**Contract**|**Recovery**<br>**rate for**<br>**Closing a**<br>**Business**|**Time to**<br>**Close a**<br>**Business**|**Outstanding**<br>**Credit**<br>**Guarantee**<br>**% of GDP**|**Lag**<br>**Outstanding**<br>**Credit**<br>**Guarantee**<br>**% of GDP**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Investment Loans/SME**<br>**Loans (%)**|0.26***|1||||||||||||||
|**Log Total loans**|-0.47***|-0.29***|1|||||||||||||
|**GCC Dummy**|-0.50***|-0.35***|0.53***|1||||||||||||
|**State Ownership Dummy**|-0.01|0.17***|0.07|0.02|1|||||||||||
|**Maximum Coverage**<br>**Registry or Bureau**|-0.30***|-0.1|0.40***|0.59***|-0.02|1||||||||||
|**Log Number of Branches**|0.02|0.09|0.55*|-0.07|0.12**|0.02|1|||||||||
|**Separate Unit for SME**<br>**Clients Dummy**|-0.04|-0.07|0.31***|0.27***|-0.17***|0.13**|0.22***|1||||||||
|**Legal Rights Index**|-0.13**|-0.23***|0.43***|0.58***|-0.14***|0.42***|0.18***|0.49***|1|||||||
|**Time to Register Property**|-0.07|0.05|0.04|-0.32***|0.04|0.01|0.16***|-0.10*|-0.21***|1||||||
|**Time to Enforce a Contract**|-0.12*|0.06|0.06|-0.47***|0.04|-0.19***|0.19***|-0.22***|-0.34***|0.60***|1|||||
|**Recovery rate for Closing a**<br>**Business**|-0.10|0.01|0.10|0.28***|-0.09*|0.46***|-0.22***|0.06|0.02|-0.08|-0.23***|1||||
|**Time to Close a Business**|-0.22***|-0.01|-0.18***|0.04|0.08|-0.23***|-0.18***|-0.09|0.07|-0.01|0.15***|-0.69***|1|||
|**Outstanding Credit**<br>**Guarantee % of GDP**|0.30***|0.27***|-0.01|-0.40***|-0.15***|-0.04|0.22***|0.03|-0.10**|0.11**|0.13***|-0.18***|-0.10*|||
|**Lag Outstanding Credit**<br>**Guarantee % of GDP**|0.29***|0.26***|-0.02|-0.4***|-0.15**|-0.07|0.22***|0.04|-0.08|0.17***|0.14**|-0.19***|-0.06|0.99***|1|
|**Median Coverage Ratio of**<br>**PCG Scheme**|0.43***|0.29***|-0.42***|-0.3***|-0.26***|-0.31***|-0.25***|0.14***|0.23***|-0.44***|-0.39***|-0.21***|0.32***|0.60***|0.63***|

* significant at 10%; ** significant at 5%, *** significant at 1%.
# **I. Introduction**

Traditional static analysis of labor markets provides evidence on stocks of workers found in different labor markets states, but can tell us nothing about where those workers arrived from, how long they will stay, or where they will go next. The importance of answering these questions and developing the tools to do so has been increasingly apparent in the mainstream literature, for example, on the causes of unemployment, (whether due to shedding of labor by firms or reduced hiring) or in understanding the different motivations behind being unemployed vs. out of the labor force (see, for example, Flinn and Heckman 1982, Blanchard and Diamond 1989, Shimer 2005 and Hall 2005). Increasingly, panel data sets are becoming available in the developing world that facilitate greater understanding of how those labor markets function and how they may differ from advanced country markets.

This paper discusses and develops a set of statistics based on the estimations of continuous time Markov transition processes and employs them to study and compare labor market dynamics in three developing countries Argentina, Brazil and Mexico. The methods of estimating instantaneous probabilities (intensities), durations, and probabilities conditional on separation (propensities) as well as the discussion of the embeddability of the estimated transition matrices follow closely the work of Fougère and Kamionka (1992 a,b,c). We discuss the importance for inference of conditioning on both rate of separation and job turnover in the destination sector and show that a statistic that adjusts for both has the interpretation of workers’ revealed comparative advantage for particular sectors. The estimates suggest broad commonalities among the three countries, and establish numerous common patterns of worker mobility among sectors of work and inactivity. As such, the paper offers some of the first comparative work on labor dynamics.<sup>1</sup>

> 1 See Bosch and Maloney (2005) for the first application of continuous time Markov processes to Argentina, Mexico and Brazil and more recently, in a discrete context, Duryea et. al (2006) that examine both Eastern European and Latin American data.

1
plans that have proliferated in Australia, New<sup>Zealand,SouthAfricaandtheUnited</sup> States).<sup>5</sup>

**Determinants of Mutual Fund Growth.** The growth<sup>ofmutualfundsinthe</sup> United States and other high-income countries<sup>has stimulateda large andeverincreasing</sup> literature on the factors that explain the<sup>performanceof mutualfunds.Mostof these</sup> studies follow the structure-conduct-performance<sup>paradigmandareusuallyfocusedon</sup> the performance of mutual funds in one country.<sup>Thereisalsogrowinginterestinthe</sup> impact of international fund investment on emerging<sup>markets(Kaminskyetal2000).</sup> Very few studies have examined the development<sup>andperformanceof mutualfundsin</sup> several countries. An interesting exception<sup>is thestudyby OttenandSchweitzer(1998)</sup> that compared the US and European mutual<sup>fundindustries. OttenandSchweitzer found</sup> that the European mutual fund industry<sup>is lagging theAmerican industrywithregardto</sup> total assets, average fund size and capital market<sup>importance.European investorshavea</sup> preference for fixed income mutual funds,<sup>whilemutual fundmarketsinindividual</sup> European countries are dominated by a few large<sup>domesticgroups,mostlybank-centered,</sup> possibly implying a lower level of competition.<sup>Otherpapers, such as Walter(1999) and</sup> Davis (2001), have looked at the European asset<sup>andpensionmanagementindustries</sup> respectively rather than mutual funds _per se._

Seen as financial institutions that serve the<sup>needsof households,the growthof</sup> mutual funds is likely to be determined by a<sup>numberof factors.Firstandforemostis the</sup> level of income and wealth of the residents<sup>of acountry.Conceptually,investingin</sup> mutual funds, like purchasing life insurance<sup>and savingfor retirement, shouldbe seenas</sup> a luxury good with a positive income elasticity of<sup>demand.Inpractice,however, the</sup> relationship between per capita income (used as<sup>an indicatorof economic development</sup> and wealth) and holdings of mutual fund assets<sup>(expressedasapercentageof national</sup> income) is not always positive.

The availability or not of substitutes as well as complements<sup>alsogreatly affects</sup> the growth of mutual fund assets. Houses are distant<sup>substitutesof mutual fundshares in</sup>

> 5 Reported Australian statistics on mutual funds registered<sup>a verylarge jumpin 1999,the most likely</sup> explanation of which is the inclusion of the mandatory<sup>pension plans that operate on mutual fulnd</sup> principles.

6