rafmacalaba/datause-encoder-data huggingface dataset:
|Institutional Capacityfor Sustainable Fiscal Decentalization TAL|Oscar Calvo-Gonzalez|
|---|---|
|Institutional Capacityfor Sustainable Fiscal Decentalization TAL|Oscar Calvo-Gonzalez|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|MN-Index-Based Livestock Insurance|Stephane Forman|
|PovertyReduction Support Operation(PRSO)|RosalindaQuintanilla|
|PovertyReduction Support Operation(PRSO)|RosalindaQuintanilla|
|PovertyReduction Support Operation(PRSO)|RosalindaQuintanilla|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|Road Maintenance & Rehabilitation 2 Project|Radoslaw Czapski|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|MR:INTEGRATED DEVELOPMENT PROGRAM FOR IRRIGATED AGRIC|.Christian Berger|
|BuildingCapacityin Governance & Revenue Management in Natur|aCraigB. Andrews|
# **3.2 Estimating a utility’s asset base**

We begin our calculations by estimating the value of the asset base for each utility with adequate representation in IBNET. To do so, we first estimate the average efficient asset base per customer for small, medium, and large utilities. Currently SISS regulates 59 utilities varying greatly in size; one is categorized as large (almost 1.7 million customers), seven as medium size (between 200,000 and 700,000 customers), and the remaining as small (fewer than 200,000 customers). Thirty-seven small utilities are excluded from our sample, as they serve fewer than 5,000 customers. Additionally, four medium size utilities are removed (three had yet to present their tariff revision studies, while one was deemed an outlier due to its reported assets), resulting in an overall sample of 15 (see Table 1).

**Table 1. Sample of 15 Chilean water and sanitation utilities**

|**Utility**|**Custo**<br>|**mers**<br>|**Data Sourc**<br>|**e**<br>|
|---|---|---|---|---|
||**Water**|**Sanitation**|**Year of Tariff Revision**|**Year of Data**|
|Aguas Andinas|1,691,195|1,652,946|2015|2013|
|ESSBIO|657,785|585,562|2011|2009|
|ESVAL|576,285|524,750|2015|2013|
|Nuevosur|204,343|193,202|2011|2009|
|ESSAL|192,311|180,554|2011|2009|
|Aguas del Valle|180,457|172,409|2011|2009|
|Aguas Araucania|175,505|164,015|2011|2009|
|Aguas Cordillera|126,613|123,295|2010|2008|
|Aguas del Altiplano|142,155|137,779|2013|2011|
|Aguas Chañar|85,606|82,012|2014|2012|
|Aguas Magallanes|46,415|45,707|2011|2010|
|Aguas Décima|37,017|34,624|2009|2007|
|SEMBCORP Aguas Chacabuco|19,677|18,739|2014|2012|
|Aguas San Pedro|10,292|10,189|2010|2009|
|SEMBCORP Aguas Lampa|5,655|3,939|2010|2008|
|Total|4,151,311|3,929,722|2014|2012|

_Source:_ Water and sanitation clients for each utility were obtained from their latest tariff study as submitted to SISS. The fourth column denotes the year that each tariff study was published, while the final column denotes the year of associated data collection.

In Chile, assets are estimated using a greenfield scenario at the start of every five-year period. To this end, the regulator models each utility based upon its size, network characteristics, services provided, and the sub-activities conducted for each service (e.g., production, treatment, and distribution), taking into account any necessary expansion stemming from demand growth. Efficient, optimized asset values for each service provided (water and/or sanitation) are obtained from calculating the net present value of Information on the efficient asset base for each utility (in Chilean pesos) is obtained from its latest available tariff review.<sup>15</sup> We convert this value to U.S. dollars and apply an inflation factor to express all

> 15 See http://www.siss.gob.cl/586/w3-propertyvalue-6385.html; see also Aguas Andinas (2015).

7
included 1,951 small businesses operating out of a physical structure in and around Cape Town, South Africa.

In stage two, we used the data collected in the screening survey to further narrow our sampling frame. This screening stage involved ranking businesses based on questions covering formal education levels, years in business operation, formal registration status, motivation and commitment, as well as several interviewer impression questions evaluated by the RA (e.g., business aspirations, English level, literacy, and numeracy). Entrepreneurs were then ranked on their composite score.

In stage three, beginning in September 2012, the top 1,500 businesses on our composite scale were invited to attend a registration session to learn more about the next steps for their training and complete additional forms. This number was chosen for two main reasons: first, based on statistical power calculations, we were aiming for an initial sample of 750 businesses (approximately 250 in each of the three groups). Second, we conservatively anticipated a 50% take up rate between the invitations and registration attendance, meaning it was important to ensure the program was oversubscribed.

During the notification call, each of the 1,500 invited participants was told that they had qualified for a free scholarship to receive a two-month business training course offered by our partner organization, Business Bridge, but that they had to attend a registration session in person to pick up their scholarship letter. In total, 852 small business owners attended these registration sessions and completed the baseline survey. This survey was administered in person at the registration sessions.

Randomization was done after the baseline by computer, so that any pre-treatment differences between the groups are due to pure chance. The three groups were not perfectly equal in size because we performed a stratified randomization in order to balance our sample on several variables (gender, education, firm size, and formalization status). Our partner organization, Business Bridge, also indicated a capacity constraint of 13 classes of 20 students each for both finance and marketing courses.

13
12 model and 26 percent in the non-linear logistic model. The proportion of students in the population under the age of 20 is significant and positive. If this proportion increases, the demand for and, therefore, the number of classes increases. The number of teachers is found to be positive and significant in both models. The rationale is that for same classrooms, the number of classes increases if the number of teachers increases. This is the samne as saying that teachers are a variable input. The DEA analysis took into account fixed input only i.e., the number of classrooms. Furthermore, if a percentage of municipal budget spent on education increases, efficiency increases (statistically significant). The percentage of urbanization is also associated with higher efficiency. Typically, in regions with a greater degree of urbanization and, therefore, greater demand, classes are run more than one shift, i.e., there are greater number of classes per classroom. Hence the highly urbanized areas, such as City Sofia displays highest efficiency scores in the utilization of classrooms, as shown earlier in Table 2. Finally, the size of the land area of a municipality is positively associated with efficiency; the association is stronger in the linear model.

# IV. Policy Implications

We analyzed the technical efficiency of classroom use in terms of the number of classes in Bulgarian municipalities, in both urban and rural areas, using the Data Envelopment Analysis to generate efficiency scores for each of 199 municipalities with complete data. Efficiency scores are presented in terms of frequency distribution, and geographic, regional distribution. We then regressed these efficiency scores on several socio-economic variables which intuitively could explain the observed variation in efficiency scores. Significant relationships are found between the proportion of students in the population under age 20 (demand indicator), the number of teachers (variable input), the percentage of municipal budget spent on education, and degree of urbanization. Although the regressions explain a modest amount of the total variation in efficiency scores (between 24 and
# **Appendix Table 22: Children in monetary** **<u>poor households in 2022 at the $2.15 line in 2017 PPP by household size</u>**

|**Household size**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate(%)**|**Share of extreme**<br>**poor(%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Two or less|2.2|6.1|0.7|1.7|
|Three|10.6|4.2|3.2|11.9|
|Four|29.4|6.5|8.8|21.5|
|Five|47.8|11.5|14.3|19.8|
|Six or more|243.6|25.7|73.0|45.1|
|**Total**|333.5|15.8|100.0|100.0|
|**Household size**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate (%)**|**Share of extreme**<br>**poor (%)**|**Share of**<br>**population (%)**|
|No children|302.4|6.4|47.6|69.3|
|Children|333.5|15.8|52.4|30.7|
|**Total**|635.9|9.3|100.0|100.0|

# **Appendix Table 23: Children in monetary** **<u>poor households in 2022 at the $3.65 line in 2017 PPP by household size</u>**

|**Household size**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Two or less|6.2|17.0|0.8|1.7|
|Three|32.8|13.1|4.0|11.9|
|Four|97.9|21.6|11.9|21.5|
|Five|141.8|34.1|17.3|19.8|
|Six or more|540.9|57.0|66.0|45.1|
|**Total**|819.5|38.9|100.0|100.0|
|**Household size**|**Children and**<br>**adults in poverty**<br>**(millions)**|**Headcount**<br>**poverty rate (%)**|**Share of poor (%)**|**Share of**<br>**population (%)**|
|No children|935.2|19.7|53.3|69.3|
|Children|819.5|38.9|46.7|30.7|
|**Total**|1754.7|25.6|100.0|100.0|

# **Appendix Table 24: Children in monetary** **<u>poor households in 2022 at the $6.85 line in 2017 PPP by household size</u>**

|**Household size**|**Children living in**<br>**poverty (millions)**|**Headcount poverty**<br>**rate(%)**|**Share of poor**<br>**(%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Two or less|14.3|39.6|1.0|1.7|
|Three|86.0|34.3|6.1|11.9|
|Four|235.0|51.9|16.5|21.5|
|Five|281.3|67.6|19.8|19.8|
|Six or more|803.3|84.6|56.6|45.1|
|**Total**|1419.9|67.4|100.0|100.0|
|**Household size**|**Children and adults in**<br>**poverty (millions)**|**Headcount poverty**<br>**rate(%)**|**Share of poor**<br>**(%)**|**Share of**<br>**population(%)**|
|No children|2121.5|44.7|59.9|69.3|
|Children|1419.9|67.4|40.1|30.7|
|**Total**|3541.4|51.7|100.0|100.0|

17
# The Spatial Division of Labor in Nepal'

Marcel Fafchamps University of Oxford Forhad Shilpi The World Bank

## Abstract

JEL classification: RI 1, 018 Keywords: economic geography, South Asia, specialization, roads _The Policy Research Working Paper Series disseminates the findings of work_<sup>_in_</sup> _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_<sup>_than_</sup> _fully polished. The papers carry the names of the authors and should be_<sup>_cited_</sup> _accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ We would like to thank Gershon Feder for his constant moral and intellectual support.<sup>Weacknowledge</sup> generous support from the former Chief Economist of South Asia Region John Williamson.<sup>Roaddistances</sup> and population densities were calculated with the help of Uwe Deichmann,<sup>JyotsnaPuriandAndrew</sup> Nelson at the GIS Lab of the Devetopment Economics Research Group (DECRG)<sup>of theWorldBank.We</sup> would like to akowledge Giovania Prennushi and Salman Zaidi for NLSS data and complementary documentation and Lawrence Zhangj and Misuzu Otsuka for excellent research assistance. We also<sup>received</sup> useful comments on an earlier draft from seminar participants at World Bank. Financial support from the Development Economics Research Grout (DECRG) is also gratefully acknowledged.
2 The second stage of enterprise reform involved the restructuring of indirect taxation and the creation of a dual pricing system. Up to 1983 the indirect taxation of enterprises was carried out through the Consolidated Industrial and Commercial Tax (CICT) that had been introduced in 1958, and simplified in 1972. This was a turnover tax that was applied to industrial production, agricultural sales, imports, retailing, transport and communications and services. It was replaced in 1984 by the Product Tax, VAT and Business Tax. The CICT however continues to apply to foreign enterprises and joint ventures.

# Plan of the paper

Section 2, following this introduction, provides a detailed analysis of the structure and reform of indirect taxes in China. Section 3 of the paper is concerned with direct taxes. Section 4 concludes by drawing together the main issues and considering their implications for other de_entralizing socialist economies in transition.

# 2. INDIRECT TAXES

# 2.1 The Current Tax Structure

As is the case with most countries, China has a very large number of taxes but only a small number of them raise substantial revenues. The principal revenue-raising instruments are indirect taxes on goods and services (Product Tax, Value Added Tax and Business Tax) and the Enterprise Income Tax, and it is on those taxes that this paper concentrates.-" The system of price controls is an implicit subsidy to users, financed by an implicit tax<sup>on</sup> producers. Hence price controls on final goods subsidize final consumers at the expense of consumer goods-producing enterprises, while price controls on intermediate goods boost profitability in consumer goods producing enterprises at the expense of those producing intermediate goods. Since indirect taxes must operate within the overall framew,rk of price controls, it is desirable to view taxes and price controls as cone W-uting an integrated system. This perspective is more fully developed in ! tion 2.7.

# Product Tax

Product tax is levied on the manufacture and import of certain<sup>goods.</sup> Goods are valued (for tax purposes) at the price paid by purchasers. This differs from normal international practice, where tax rates are expressed as a percentage of the producer price. It means that, with controlled prices,<sup>taxes</sup> reduce the revenue received by the enterprise and do not increase the prices paid by the consumer.

V The tax system, and its evolution, are described in more<sup>detail in Easson</sup> and Li (1987). A general analysis of its strengths and weaknesses,<sup>including</sup> recommendations for reform, are given in World Bank (1990).
# **Table 7: Wage Changes Among All Workers Observed 2 Periods and the Impact of Leaving Manufacturing, 1983-2002**

Dependent Variable: Log Wage Change Between Periods

|Variable|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Left Manufacturing (1=yes)|-0.034***<br>(0.003)|-0.034***<br>(0.005)|-0.033***<br>(0.005)|-0.029***<br>(0.008)|
|Lagged log of low income affiliate<br>emloment chane|-0.010|-0.016*|-0.004|0.0002|
|py g<br>X Left Manufacturing|(0.006)|(0.009)|(0.018)|(0.021)|
|Lagged log of high income affiliate<br>employment change|0.045***|0.038|0.076**|0.0260|
|<br>X Left Manufacturing|(0.016)|(0.026)|(0.031)|(0.054)|
|Lagged import penetration change|0.093|0.124|0.092|-0.083|
|X Left Manufacturing|(0.243)|(0.335)|(0.250)|(0.564)|
|Lagged export share change|-0.014|-0.189|0.123|0.102|
|X Left Manufacturing|(0.118)|(0.141)|(0.153)|(0.278)|
|Number of observations|162,270|90,989|45,527|25,754|

_Source_ : Sample is composed of CPS MORG workers observed in two consecutive samples and employed in manufacturing in the first period.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. The classification of occupations into routine categories is determined by the proportion of tasks which are routine in each occupation, with low being occupations with more than 2/3, intermediate being between 1/3 and 2/3, and high being occupations with less than 1/3 of tasks designated routine. The regressions are run with the same controls as in Table 2. This includes controls for the worker's age, race, experience, union status, and sex. Fixed effects are included at the industry, year, and state levels. We also control for lagged log price of investment, lagged total factor productivity, lagged log real price of shipments, and computer use rates in the worker's industry.
# 1 Introduction

There is a vast literature documenting the presence of gender gaps in the labor market in both developing and developed countries. Women are often underrepresented in the labor force in general but especially in high-earnings occupations.<sup>1</sup> In this paper we provide some descriptive statistics on the degree of gender inequality in participation the labor market and then use the framework in Cuberes and Teignier (2014) together with survey data to calculate the impact of these gender gaps on income and productivity in several countries of the Balkan Peninsula (Albania, Bosnia-Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, and Serbia) and Turkey. We carry out these calculations for our entire sample and also for specic age groups. The aforementioned countries represent an interesting group to study gender inequality in the labor market because they all belong in the category of middle-income countries and yet they have sizable labor gender gaps. Turkey, for instance, had a labor force gender gap of about 59% in 2007, i.e. out of every 100 men that participated in the labor market, only 41 women did so. We calculate that, for this group of countries, the average gender gap in labor force participation is around 41% in the latest available year, much higher than the one calculated in Cuberes and Teignier (2014a) for the rest of European countries (around 16%).

To our knowledge, there are very few papers that quantify the macroeconomic eects of gender gaps in the labor market.<sup>2</sup> The International Labor Organization provides some estimates of the output costs associated with labor gender gaps in the Middle East and Northern Africa but without proposing any specic theoretical model (ILO, 2014). Another shortcoming of this exercise is that it does not allow one to shed light on the mechanisms through which gender gaps in the labor market may aect aggregate eciency. Cavalcanti and Tavares (2011) construct a growth model based on Galor and Weil (1996) in which there is exogenous wage discrimination against women. Calibrating their model using U.S. data, they nd very large eects associated with these wage gaps: a 50 percent increase in the gender wage gap in their model leads to a decrease in income per capita of a quarter of the original output. Their results also suggest that a large fraction of the actual dierence in output per capita between the U.S. and other countries is indeed generated by the presence of gender inequality in wages. Hsieh et al. (2013) use a Roy model to estimate the eect of the changing occupational allocation of white women, black men, and black women between 1960 and 2008 on U.S. economic growth and nd that the improved allocation of talent within the United States accounts for 17 to 20 percent of growth over this period. Finally, in the model summarized in

> 1See the World Development Report 2012 (World Bank, 2012) for a comprehensive review of these and other gender gaps.

> 2See Cuberes and Teignier (2014b) for a critical literature review of the two-directional link between gender inequality and economic growth.

2