rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 10.**|**Financial Information on Village Funds, 201**|**0**<br>For VF re|porting positiv|e values|Average|
|---|---|---|---|---|---|
|||di||b|ll|
|||Mean|Mean|No. os.|over a VFs|
||**Sources of capital (Liabilities and Net Worth)**||_in thousan_|_ds of baht_||
|V4_8|Capital inflow from VF|1,000|1,165|2,751|1,165|
|V4_9|Loans received from institutions|1,000|929|651|220|
|V4_10|Savings deposits received from members|102|236|1,873|161|
|V4_11|Revenue from shares|13|71|921|24|
|V4_15|Donations from village organization|10|77|126|4|
|<br>V4_16|<br>Donations from NGOs|8|101|49|2|
|<br>V4_17|Funds from other sources|3|86|255|8|
||Balancingitem: net worth||||186|
||**Uses of capital (Assets)**|||||
|V4_29|Amt held on deposit at BAAC/GSB/KTB/Oth banks|220|564|2,441|501|
|V4_18|Value of loans disbursed by VF|1,130|1,309|2,567|1,222|
|V4_22|Value of saving repaid to savers (excl. int.)|28|201|443|32|
|V4_23|Value of saving repaid to savers who quit/died|6|27|843|8|
|V4_25_1|Repayment of shares (baht)|8|17|1,233|8|
|V4_20|Value ofprincipal repaid to institutions|1,000|971|530|187|
||**_Memo item_**|||||
|V4_12|Total principal repaid by borrowers (on time)<br>**Revenue**|1,059|1,068|2,363|917|
|V4_13|Total value of interest earned on loans|64|89|2,359|76|
|V4_14|Fees|2|22|633|5|
||**Expenditure**|||||
|V4_19|Value of interest paid to institutions|50|56|537|11|
|V4_21|Value of interest paid to savers|12|22|921|7|
|V4_28_2|VF “profit”|60|89|2,393|77|
||**Uses of profit**|||||
|V4_24|Payments to shareholders|6|12|1,889|8|
|V4_27|Payments for welfare programs|6|9|1,749|6|
|V4_26|Payments to VF committees|15|17|2,158|12|
|V4_25_2|Repayment of shares|1|5|465|1|
|V$_28_1|Share of VF reporting profit(%)||92.3|2,631||

Source: Village Fund Survey, 2010. Totals do not always add up exactly, because the samples vary somewhat from item to item. Includes only those VFs that reported the amount of lending.

# **6. Measuring Village Fund Performance**

Our main interest in this paper is in examining why some Village Funds perform better than others. For this we need a measure of performance (Yaron 1992). One approach is to measure the impact of VF borrowing on income or expenditure per capita, but this issue has already been examined elsewhere (Kaboski and Townsend 2005, 2009; Boonperm et al.. 2011; Chandoevwit and Ashakul 2008; Sukantha 2008; Laohong 2005). An alternative approach, and the one we take here, is to measure the performance of a Village Fund in more immediate ways, by looking at the profit rate, the loan recovery rate, the credit rating earned by a VF, or the proportion of people (or poor people) who borrow from the fund. Summary statistics for these measures are shown in the top panel of Table 11, and we discuss each in more detail below, where we estimate their determinants with a series of regression models.

18
||||(3)|(4)||(6)|||
|---|---|---|---|---|---|---|---|---|
|Average age of entrance into the<br>labor market (in years):|16.6|16.3*|16.4*|16.2*|18.0|16.6*|14.4|15.2*|
|Average working experience in the<br>labor market (in years):|18.5|19.9*|19.0*|20.7*|22.0|21.6|23.1|20.0*|
|Average potential experience in the<br>labor market (in years):|15.6|16.9*|16.5*|17.3*|17.5|16.1|23.3|18.3*|
|Proportion who were self-<br>employed/employers:|0.21|0.20*|0.17*|0.22|0.22|0.22|0.30|0.26*|
|Proortion who had ariculture job:|002|002*|012*|002|001|002|014|003*|
|p g|.|.<br>Sec|.<br>tor of activi|.<br>ty:|.|.|.|.|
|Agriculture|0.02|0.02*|0.01*|0.02|0.01|0.02|0.14|0.03*|
|Extractive industries|0.00|0.01*|0.01*|0.01*|0.00|0.01|0.00|0.00|
|Manufacturing|0.12|0.15*|0.16*|0.15*|0.07|0.17*|0.14|0.17*|
|Construction|0.03|0.03|0.02|0.03|0.02|0.04*|0.09|0.05*|
|Commerce and repair|0.22|0.16*|0.16*|0.17*|0.10|0.13*|0.19|0.21*|
|Lodging and food|0.03|0.02*|0.02*|0.02*|0.01|0.02|0.04|0.04|
|Transportation, warehousing and<br>communications|0.05|0.04*|0.05|0.04*|0.03|0.03|0.05|0.06*|
|Financial services|0.03|0.03|0.02*|0.03|0.05|0.05|0.01|0.02*|
|Real estate activities and service<br>activities|0.10|0.12*|0.12*|0.11*|0.13|0.15|0.06|0.10*|
|Public administration|0.11|0.10*|0.09*|0.12|0.15|0.09*|0.05|0.07*|
|Education|0.14|0.13*|0.07*|0.17*|0.27|0.17*|0.05|0.07*|
|Health and social services|0.06|0.14*|0.22*|0.08*|0.12|0.06*|0.03|0.05*|
|Others collective, social and<br>personal services|0.04|0.04*|0.03*|0.04|0.03|0.05|0.04|0.08*|
|Household activities|0.03|0.01*|0.01*|0.02*|0.00|0.00|0.10|0.05*|
||||Occupation:||||||
|Management|0.10|0.10|0.09*|0.11*|0.16|0.19|0.05|0.06*|
|Sciences and arts professional|0.19|0.15*|0.10*|0.19|0.52|0.39*|0.07|0.08*|
|Technician with secondary level of<br>education|0.13|0.31*|0.38*|0.25*|0.13|0.18*|0.06|0.12*|
|Administrative services|0.17|0.15*|0.15*|0.15*|0.10|0.12|0.08|0.13*|
|General services|0.12|0.08*|0.09*|0.08*|0.02|0.03|0.23|0.22*|
|Sales and commerce services|0.12|0.08*|0.07*|0.08*|0.03|0.04|0.11|0.11|
|Farming|0.02|0.01*|0.01*|0.02|0.01|0.01|0.14|0.03*|
|Production/manufacturing|0.12|0.09*|0.09*|0.08*|0.02|0.04*|0.24|0.20*|
|General maintenance and repair|0.01|0.02*|0.03*|0.02*|0.00|0.01|0.02|0.03*|
|Military|0.02|0.01*|0.01*|0.01*|0.01|0.01|0.01|0.01*|
|Others|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Proportion of male:|0.50|0.51|0.5|0.52*|0.44|0.56*|0.62|0.55*|
|Average age (in years):|35.2|36.2*|35.4|36.9*|40.0|38.2*|37.5|35.2*|
|Average squared age (in years):|1,371.3|1,420.9*|1,362.6|1,472.0*|1,729.4|1,577.8*|1,586.5|1,383.7*|
|Proportion of white:|0.59|0.59|0.57*|0.61*|0.73|0.75|0.45|0.50*|
|Average of years of education:|12.6|12.3*|12.0*|12.6|15.5|15.0*|7.3|9.9*|
|Proportion who was attending<br>tertiary education:|0.12|0.14*|0.13*|0.15*|0.00|0.00|0.04|0.07*|
|Proportion who completed tertiary<br>education:|0.29|0.22*|0.15*|0.28|1.0|1.0|0.09|0.11*|

13
Decline in Wage Inequality in Brazil: A Survey<sup>_∗_</sup> _Insper Institute of Education and Research, Brazil_ _Insper Institute of Education and Research, Brazil_

**Keywords:** Brazil, Wage Inequality, Income Distribution

**JEL Codes:** D31, J31, O15, D63

> _∗_ This study was commissioned by the Poverty and Global Practice of the World Bank. The authors thank Liliana Sousa and Joana Silva for their valuable contributions. Views expressed are those of the authors and do not represent official views of the World Bank.

> _†_ `firpo@insper.edu.br`

> _‡_ `alyssonlp@al.insper.edu.br`
and 28% in Honduras (Lagaarde, Haines and Palmer, 2007). Targeting, conditioning, and administrative costs are amongst the overhead categories.

## **Cost effectiveness**

None of the studies examined the cost-effectiveness of their DSF interventions, with the exception of the Nicaraguan STI voucher scheme, which was found to be cost-effective, with a lower cost per STI patient effectively cured costs compared to before ($118 compared to the status quo of $200).

# **Funding and sustainability**

There is a correlation between the level of development of a country and the funding of their DSF policies – by and large, low income countries have external funding, while for low and upper middle income countries, some or all of the funding comes from local sources. Some policies are quite ambitious: in Mexico, the overall Opportunidades policy absorbs 45% of the entire federal anti-poverty budget (Barber and Gertler, 2008), while in India, almost half of the federal budget for maternal health is now absorbed by the incentive scheme (Walford, 2004). As many of the policies are young, it is hard to assess at this stage their likelihood of being sustained, and indeed it is not yet clear how long they should be maintained in order to meet their objectives. There is a risk that after 2015 many of the policies focused on improving maternal health may lose support.

# **Discussion**

## **Preconditions for effectiveness**

A number of factors for success of these reproductive DSF policies are drawn out here, not necessarily in order of importance.

**1. Correct identification of demand-side barriers to use**

DSF approaches will work best when services are underutilized by the target group for reasons which are predominantly financial.

DSF works on the asumption that supply-side subsidies provided by government may not be effective at targeting those in most need. There is strong evidence that the poor have inferior access and make lower use of publicly allocated resources and services (Demery 2000, Institute of Policy Studies 2001, van Doorslaer, et al. 1993). This is for a variety of reasons, however, many of which are non-financial, including poor physical access to facilities, ignorance of treatment options, poor treatment by providers, and other constraints (cultural, gender, ethnic, caste etc.) preventing health seeking behaviour. Where non-financial barriers predominate, alternative policies may be more effective, or complementary actions to address these wider factors are likely to be necessary to make DSF schemes effective. Reproductive health is an area where social and cultural factors tend to play an important role in health seeking choices.

The assumption that improving the affordability of the service alone will raise demand does not always hold. In the case of Cambodia, the low utilization of delivery vouchers (which covered the full range of costs, including transport) raised the issue of cultural perceptions and other (non-price) barriers.

13
Can Scholarships Increase High School Graduation Rates? Evidence from a Randomized Controlled trial in Mexico<sup>_∗_</sup> Rafael de Hoyos<sup>_†_</sup> Orazio Attanasio<sup>_‡_</sup> Costas Meghir<sup>_§_</sup> _Keywords:_ scholarships, dropout, high school, RCT.

_JEL codes:_ I25, D80, O12

> _∗_ We are especially indebted to Elizabeth Monroy and Juan Manuel Mart ́ınez de la Calle who were responsible for project and data management at the Mexican Secretariat of Public Education (SEP) at the time of implementing the project. Juan Bedoya provided excellent research assistance. The views expressed here are those of the authors alone, and do not necessarily reflect the opinions of the World Bank.

> _†_ Corresponding author, Education Unit for Latin America, The World Bank. E-mail: rdehoyos@worldbank.org.

> _‡_ Department of Economics, University College London. Email: o.attanasio@ucl.ac.uk.

> _§_ Department of Economics, Yale University. Email: c.meghir@yale.edu
**Table A3.9. Macro indicators in 2012 and by simulation in 2030 or averages for 2013-2030 (% of nominal GDP)**

||**2012**<br>|**base**<br>|**infhd+tx i**<br>|**nfhd+eff**<br>**i**<br>|**nfhd+fg in**<br>|**fhd+mix i**<br>|**nf+mix **<br>|**hd+mix pw**<br>|**emin-sd**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Absorption**|166.5|122.7|122.6|122.6|127.0|124.1|124.2|124.1|131.5|
|**Consumption - household**|75.7|63.1|59.1|62.8|63.5|61.8|61.6|62.0|69.4|
|**Consumption - UNMIL**|29.5|2.7|2.7|2.7|2.6|2.7|2.7|2.7|2.8|
|**Consumption - government**|23.5|27.5|29.3|26.5|29.2|28.3|27.5|29.0|28.5|
|**Fixed investment - private**|22.7|17.5|16.6|17.4|17.4|17.2|17.1|17.2|19.5|
|**Fixed investment - government**|15.0|11.9|14.9|13.3|14.2|14.2|15.3|13.3|11.3|
|**Exports**|37.1|66.3|67.2|68.1|63.3|66.3|67.1|65.4|67.5|
|**Imports**|-103.5|-89.0|-89.8|-90.7|-90.3|-90.4|-91.3|-89.5|-99.0|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|10.2|9.8|12.8|9.9|10.0|10.8|11.0|10.7|10.8|
|**GDP at factor cost**|89.8|90.2|87.2|90.1|90.0|89.2|89.0|89.3|89.2|
|**GNI**|88.5|86.9|87.0|86.7|87.5|87.1|87.0|87.1|89.8|
|**GNDI**|147.8|112.5|112.4|112.4|117.1|114.0|114.0|114.1|119.8|
|**Foreign savings**|18.6|10.2|10.2|10.2|9.8|10.1|10.1|10.1|11.7|
|**Gross national savings**|19.1|19.2|21.3|20.5|21.8|21.3|22.3|20.4|19.1|
|**Foreign government debt**|12.1|30.0|29.2|29.6|28.1|29.0|28.9|29.2|35.7|
|**Domesticgovernment debt**|17.6|21.2|20.8|21.0|20.6|20.8|20.7|20.9|22.7|

**Table A3.10. Government budget in 2012 and by simulation in 2030 (% of nominal GDP)**

||**2012**<br>|**base**<br>**in**<br>|**fhd+tx inf**<br>|**hd+eff**<br>**i**<br>|**nfhd+fg inf**<br>|**hd+mix **<br>|**inf+mix**<br>|**hd+mix pw**<br>|**emin-sd**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Receipts Domestic transfers**|1.5|1.5|1.5|1.5|1.5|1.5|1.5|1.5|1.5|
|**Foreign transfers**|24.3|17.1|17.0|17.2|21.5|18.6|18.7|18.6|20.2|
|**Direct taxes**|3.9|11.6|13.4|11.7|11.0|12.1|12.1|12.0|7.6|
|**Import tariffs**|6.4|6.6|6.7|6.7|6.7|6.7|6.8|6.6|7.3|
|**Other indirect taxes**|3.8|3.2|6.1|3.2|3.3|4.1|4.1|4.1|3.5|
|**Domestic borrowing**|-0.2|1.0|1.0|1.0|1.0|1.0|1.0|1.0|1.0|
|**Foreign borrowing**|2.2|2.1|2.1|2.1|2.1|2.1|2.1|2.1|2.5|
|**Total**|42.0|43.1|47.9|43.4|47.0|46.2|46.4|46.0|43.6|
|**Spending Education**|5.3|5.5|6.6|6.7|6.5|6.6|5.4|7.7|5.6|
|**Health**|4.8|5.1|6.2|6.2|6.0|6.2|5.0|7.2|5.2|
|**Water-sanitation**|1.7|1.7|2.1|2.1|2.0|2.1|1.6|2.5|1.7|
|**Energy**|3.9|4.4|5.2|5.2|5.1|5.2|6.3|4.3|4.5|
|**Roads**|5.1|5.6|6.8|6.8|6.6|6.8|8.3|5.5|5.7|
|**Other infrastructure**|2.1|2.3|2.8|2.8|2.7|2.8|3.4|2.2|2.3|
|**Other**|15.6|14.8|14.6|10.0|14.5|12.8|12.8|12.9|14.8|
|**Domestic transfers**|2.9|2.9|2.9|2.9|2.9|2.9|2.9|2.9|2.9|
|**Domestic interest**|0.4|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|
|**Foreign interest**|0.1|0.3|0.3|0.3|0.3|0.3|0.3|0.3|0.4|
|**Total**|42.0|43.1|47.9|43.4|47.0|46.2|46.4|46.0|43.6|

38
Finally, the radiant heat of each flare at each observation was averaged to obtain the flarelevel yearly average radiant heat for 2012 to 2015. In calculating the averages, observations where the site was cloudy were excluded. Last, the yearly averages of all sites linked to a field were summed to obtain the field’s yearly radiant heat. If flares from the same field have heterogeneous productivity, the log of the sum does not equal the sum of the logs. To address this concern, we repeated the calibration estimation with the subsample of sites with only one flare so that there was no aggregation.

# **A.4.3 Estimation results**

Table A.8 shows the result of the estimation of equation 2. In column (1), we do not include GOR in the regression, while column (2) shows the full specification shown in equation 2. Column (3) excludes fields with more than one flare site.

Table A.8: Oil output and radiant heat - estimation results

||(1)<br>(2)|(3)|
|---|---|---|
|VARIABLES|Logarithm oil output<br>Logarithm oil output|Logarithm oil output|
|Logarithm of radiant heat|0.984***<br>1.017***<br>(0.101)<br>(0.123)|0.958***<br>(0.170)|
|Logarithm of oil to gas ratio|0.264<br>(0.311)|0.383<br>(0.473)|
|Constant|0.776**<br>-0.775|-1.870|
||(0.308)<br>(2.001)|(2.931)|
|Observations|52<br>48|28|
|R-squared|0.733<br>0.731|0.788|
|Linearitytest(p-value)|0.874<br>0.894|0.894|
||Robust standard errors in parentheses<br>*** p_<_0.01, ** p_<_0.05, * p_<_0.1||

Note: Sample includes 22 unique oil fields in Syria and Iraq pooled over four years. Column (2) includes an additional four observation of Syria aggregated. Column (3) limits the sample to only fields with one flare site.

The R-squared of the regression is .73 with the full sample and .78 with only single flare 41
production processes and, therefore, lower energy intensity. Garbaccio, Ho, and Jorgenson (1999) find that technical change rather than structural change explains most of the decline in China’s energy intensity from 1987–1992. Using logarithmic mean Divisia index techniques to examine changes in energy use per unit from 1980–2003, Ma and Stern (2008) also find technical change to be the most important factor explaining energy intensity decline.

Foreign direct investment (FDI) has also contributed to the decline in energy intensity. FisherVanden et al. (2004) find the energy intensity of foreign firms in China, on average, to be lower than that of local firms. Empirical results in Fisher-Vanden et al. (2009) show that spillover effects of FDI tend to be energy-saving. He and Wang (2007) also provide empirical evidence to suggest that foreign capital has had an effect on lowering the energy intensity of Chinese enterprises.

This paper contributes to the literature in a number of ways: First, except for Fisher-Vanden et al (2004), previous studies examining China’s decline in energy intensity have been at the sectoror regional-level. Like Fisher-Vanden et al (2004), we will utilize a data set of Chinese industrial enterprises in order to identify the factors contributing to lower energy intensity at the firm-level. Second, we extend the analysis of Fisher-Vanden et al (2004) by (a) focusing on a longer time period (1999-2004) over which a number of policies related to energy use have occurred and (b) focusing on four specific industries which will allow us to examine how the impacts of common industrial policies differ across industries.

Given the above review of the literature and summary of policies affecting firm-level energy intensity, a number of hypotheses emerge which we will test in this paper. We organize these 11
# Annex

|Table<br>**S/N**|A1: Websites of Nation<br>**Economy**|al Statistical Agencies in MENA<br>**National Statistical Agencies**|**Website**|
|---|---|---|---|
|1|Algeria|The National Office of Statistics|https://www.ons.dz/|
|2|Bahrain|National statistical office of Bahrain<br>Information and eGovernment Authority|http://www.data.gov.bh;<br>http://www.iga.gov.bh|
|3|Djibouti|National Institute of Statistics of Djibouti|www.instad.dj|
|4|Egypt, Arab Rep.|Central Agency for Public Mobilization<br>and Statistics(CAPMAS)|https://www.capmas.gov.eg/|
|5|Iraq|Central Statistical Organization|http://cosit.gov.iq/en/|
|6|Iran, Islamic Rep.|Statistical Centre of Iran|https://www.amar.org.ir/|
|7|Jordan|Department of Statistics|http://dosweb.dos.gov.jo/ar|
|8|Kuwait|Central Statistical Bureau|https://www.csb.gov.kw/|
|9|Lebanon|Central Administration for Statistics|http://www.cas.gov.lb/|
|10|Libya|Bureau of Statistics and Census Libya|http://www.bsc.ly/|
|11|Malta|National Statistics Office|https://nso.gov.mt/en/|
|12|Morocco|Direction de la Statistique|https://www.hcp.ma/Direction-de-la-<br>statistique_a716.html|
|13|Oman|National Centre for Statistics and<br>Information|https://www.ncsi.gov.om/Pages/NCSI.aspx|
|14|Qatar|Planning and Statistics Authority|https://www.psa.gov.qa/en/Pages/default.aspx|
|15|Saudi Arabia|General Authority for Statistics|https://www.stats.gov.sa/en|
|16|Syrian Arab Republic|Central Bureau of Statistics|http://cbssyr.sy/index-EN.htm|
|17|Tunisia|National Institute of Statistics (INS)|http://www.ins.tn/en/statistics-tunisia-<br>national-institute-statistics|
|18|United Arab<br>Emirates|Federal competitiveness and Statistical<br>Center|https://fcsc.gov.ae/en-us|
|19|West Bank and Gaza|Palestinian Central Bureau of Statistics|http://www.pcbs.gov.ps/default.aspx|
|20|Yemen, Rep.|Central Statistical Organization|http://www.cso-yemen.com/|

33
1998, the same author found that Japanese firms have a tendency to favor investment in countries where Japan has agreements to claim foreign tax credits for income taxes that they would have paid to foreign governments in the absence of tax holidays. 1<sup>2</sup> From a policy perspective, these two findings seem to indicate that tax incentives are more effective when they apply to firms from countries whose governments do not tax their foreign activities.

Some recent evidence has shown that the home country's taxation system is likely to influence the way their multinational companies do business abroad. Hines and Hubbard (1990) and Grubert (1998) found that it is attractive for U.S. firms to use debt to finance foreign investment in high tax countries (compared to the U.S.) and equity in low tax countries. The argument is that the debt generates interest deductions for the subsidiary and so reduces its taxable income in the host country (note that the parent firm has to pay additional taxes, but at a lower rate, in the U.S.). Harris (1993) uses firm-level data to illustrate that the Tax Reform Act of 1986 in the United States pushed U.S. firms with higher equipment/structure ratios to invest abroad more heavily because their tax regime encouraged such an action. A series of other recent studies have found similar results for preferred stock issuances (Collins and Shakelford (1992)) or domestic versus foreign borrowing (Atshuler and Mintz (1995). An interesting finding is that the 1986 Tax Reform has also influenced the form of business organization that the multinational will select in the foreign country. For example, since 1986, American investors have had fewer tax incentives to participate in joint ventures, particularly in low tax foreign countries, and the number for this type of foreign investmnents fell sharply as reported by Desai and Hines (1999).

Finally, the importance of the home-country tax system can also be illustrated by the efforts of tax authorities to prevent the transfer of multinationals' headquarters or other specific activities (such as R&D) to other countries. Many governments negotiate

> 12 Hines shows that Japanese firms are subject to 23 percent lower tax rates than their American counterparts in countries which whom Japan has agreements. In other world, Japanese firms have a greater propensity to invest when they can benefit from tax incentives than when they cannot not because they would have to pay taxes in Japan.

17
Economic Parameters of Dcforestation 46 <!-- Start of picture text --> Base Case<br>Timber Price Path<br>Prce<br>. I I I I I 1 . I I I I ,,,,,,I<br>Land Use<br>Lad CLIS<br>rm~~~~~~~Tm<br>0 Agricuture M Managed Foest U Unmanaged Forest<br><!-- End of picture text --> Figure 5
Development Economics. Oxford University Press.

Joshi, R., Kumar, S. (2019). Structural Transformation in Rural Rajasthan: Evidence from Panel Data Analysis. Indian Journal of Agricultural Economics, 74(3), 347-361.

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