rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1**

# **Individual Characteristics**

||**School Sta**<br>**Sur**<br>|**keholder**<br>**vey**<br>|**2008**<br>|**LSMS**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|**Demographics**|||||
|Average years of education|10.98|3.25|10.73|3.36|
|Average age|40.17|6.39|44.22|17.58|
|Wealth indicator|0.00|0.92|n.a.|n.a.|
|Householdyearlyincome(in 000LEK)|n.a.|n.a.|385|1,894|
|**Survey-based measures of social capital**|||||
|Belongs to an organization (church, political group, sport etc.)|0.02|0.15|0.24|0.75|
|Participated in any community activity in the past year|0.15|0.35|0.11|0.31|
|Thinks village members would cooperate in the case of water shortage|0.72|0.45|0.64|0.48|
|Think that “most people can be trusted”|0.13|0.34|0.27|0.17|
|% of relatives or friends amongotherparents|0.06|0.90|n.a.|n.a.|
|**Involvement with the pupil’s education:**|||||
|Help with homework at least once a week|0.76|0.43|0.54|0.50|
|Number of meetings with head teacher in the previous semester|4.52|3.3|n.a.|n.a.|
|Number of visits to the child's school in thepreviousyear|n.a.|n.a.|8.73|3.39|
|**Information about participatory accountability institutions:**|||||
|Knows about the existence of parent class representatives|0.72|0.45|n.a.|n.a.|
|Knows about the existence of the school board|0.58|0.49|n.a.|n.a.|
|Knows about the existence of participatory accountability institutions at the<br>school level|n.a.|n.a.|0.68|0.47|
|**Participation in the last elections of parent class representatives**|0.50|0.50|n.a.|n.a.|

# **4.2 Parents’ information and participation**

In the two bottom panels of Table 1, we report measures of the extent to which parents are informed about the existence of local-accountability-related institutions, and the proportion of parents who participated in the most recent election of parent class representatives, which represents the first step toward participatory accountability. 28% of the surveyed parents do not know about the existence of parent class representatives, and 42% are unaware of the existence of the school board. The only comparable question in the LSMS education module refers to parents‟ information about the existence of parent-teacher associations or “other means for parents to be involved in school activities”. The percentage of informed parents (68%) is very close to the percentage of parents informed about the class parent representatives (72%).

13
various components of the index provide some information about the economic freedom and therefore a principal components method (PCM) would be preferable.<sup>20</sup> This method was used in the 2001 edition of the Freedom of the World report. But since 2002 the simple averages have been used to derive ratings for each of the five areas, while the aggregated score is a simple average of all five area ratings. Yet, de Haan, Lundström and Sturm (2006) find that the Spearman rank correlations between EFW indices based on simple averages and the remaining aggregation methods are all quite high (the rank correlation coefficient between 0.9 and 0.99). The biggest divergence is recorded for the indices based on the weights assigned by experts as well as between those indices and the principal component aggregation method (Spearman rank correlation of around 0.77-0.87).

**Box 1.  Components of the Fraser’s Institute Economic Freedom in the World Index**

Area 1 Size of government: expenditures, taxes and enterprises

- A. General government consumption spending as a percentage of total consumption

- B. Transfers and subsidies as a percentage of GDP

- C. Government enterprises and investment

- D. Top marginal tax rate

- Area 2 Legal structure and security of property rights

- A. Judicial independence

- B. Impartial courts

- C. Protection of property rights

- D. Military inference in rule of law and the political process

- E. Integrity of the legal system

- F. Legal enforcement of contracts

- G. Regulatory restrictions on the sale of real property

- Area 3 Access to sound money

- A. Money growth

- B. Standard deviation of inflation

- C. Inflation: Most recent year D. Freedom to own foreign currency bank accounts

- Area 4 Freedom to trade internationally A. Taxes on international trade B. Regulatory Trade Barriers

- C. Size of the trade sector relative to expected D. Black-market exchange rate E. International capital market controls

- Area 5 Regulation of Credit, Labor and Business A. Credit market regulations B. Labor market regulations C. Business regulations

the weight equal to the inverse of its standard deviation. The other two methods weight the components based on a survey of experts in the field of economic freedom and on a survey among country experts. 20 The PCM allows for the incorporation of all information comprised in the data without imposing any specific structure on the model.

33
a residual in the national accounts process. Second, this allows for comparison with literature that has compared survey means with GDP (Pinkovskiy and Sala-i-Martin 2014, 2016; Bourguignon and Morrison, 2002).

We extract national accounts data from the World Development Indicators (WDI) database, for both HFCE and GDP, using the series expressed both in current local currency units and in constant dollars. WDI’s data is a compilation of World Bank and OECD national accounts data sets, obtained from official national sources. The per capita estimates are derived using the mid-year population estimates from the World Bank population series data.<sup>6</sup>

# **_Household surveys_**

To assess the gap between surveys and national accounts, we compile a data set of 2,095 national household survey means for 166 countries from 1967 until 2019, together covering countries that account for 97 percent of the world population in 2017. The distribution of surveys by type and over time is illustrated in Figure 1. The vast majority of the surveys come from PovcalNet, the World Bank’s database for monitoring of global poverty (see Ferreira et al., 2016 for a description of data sources and methods used). The database contains income or consumption distributions from nationally representative household surveys typically carried out or supervised by national statistical offices or international agencies, used for national and international poverty monitoring.

For most high-income countries, the survey data available in PovcalNet are for income (rather than consumption), originating from the Luxembourg Income Study and the European Union Statistics on Income and Living Conditions (EU-SILC). To ensure better coverage of consumption surveys from high-income countries in our sample, we supplement with data from other sources. For European countries, we derive consumption means from Eurostat’s 6 From World Development Indicators (WDI), we use the following series for national accounts data: Household final consumption expenditure (current LCU) [NE.CON.PRVT.CN]; Household final consumption expenditure (constant LCU) [NE.CON.PRVT.KN]; Household final consumption expenditure (constant 2010 US$) [NE.CON.PRVT.KD]; GDP (current LCU) [NY.GDP.MKTP.CN] ; GDP (constant LCU) [NY.GDP.MKTP.KN]; GDP (constant 2010 US$) [NY.GDP.MKTP.KD]; and we use Population, total [SP.POP.TOTL] to construct per capita measures where needed. In the levels analysis we make use of the current LCU data, while in analysis of growth rates we use the constant series.

6
||(0.156)|(0.224)<br>(16.261)|(157.584)|
|---|---|---|---|
|Q0.5|0.151|0.638*<br>49.404**|197.589|
||(0.194)|(0.280)<br>(20.477)|(192.043)|
|Q0.75|0.341|0.700**<br>19.499|281.317|
||(0.246)|(0.339)<br>(30.762)|(286.250)|
|Q0.9|‐0.077|0.609<br>‐30.117|‐279.159|
||(0.333)|(0.540)<br>(41.581)|(284.569)|
|Observations|3,955|3,955<br>3,955|3,955|
|Month FE|YES|YES<br>YES|YES|
|State FE|YES|YES<br>YES|YES|
|Controls|YES|YES<br>YES|YES|
|Interacted Controls|YES|YES<br>YES|YES|
|Robust<br>standard<br>errors<br>Column (1) is measured on the ho|in<br>parentheses<br>usehold level. Columns (|(White,<br>1980):<br>***<br>p<0.01,<br>**<br>2‐4) refer to per capita OECD adult equivalents.|p<0.05,<br>*<br>p<0.1.|

Table 8 depicts the results without outliers and indicates a slightly less nuanced pattern. In line with our hypothesis of stronger misreporting tendencies on the extremes, Column (1) indicates significant treatment effects at the 10<sup>th</sup> and 25<sup>th</sup> percentile. Although significant treatment effects among higher quintiles can be found in Column (2) and (3), the coefficients for the 25<sup>th</sup> percentile are quantitatively larger. Finally, with regard to caloric consumption in Column (4) statistical significance vanishes, but the largest coefficient is to be found in the 10<sup>th</sup> percentile. Hence, although the pattern gets weakened when excluding outliers, the prime still significantly affects the reported consumption quantities with stronger effects in the lower quantiles.

_Table 8: Quantile Regressions – without outliers._

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome Variables|ln(Cons. Num.)|ln(Cons. Quant.)|ln(Cons. Val.)|ln(Cons. Cal.)|
|Q0.1|0.124**|0.106|0.085|0.058|
||(0.049)|(0.067)|(0.064)|(0.091)|
|Q0.25|0.045*|0.139**|0.162***|0.042|
||(0.027)|(0.055)|(0.044)|(0.077)|
|Q0.5|0.000|0.065|0.119**|0.037|
||(0.032)|(0.050)|(0.046)|(0.059)|
|Q0.75|0.028|0.077*|0.086*|0.049|
||(0.032)|(0.043)|(0.048)|(0.063)|
|Q0.9|‐0.027|0.064|0.027|0.039|
||(0.023)|(0.039)|(0.049)|(0.051)|
|Observations|3,711|3,605|3,576|3,500|

18
Policy Research Working Paper 6169

# **Abstract**

All-weather rural roads usually improve not only villagers’ terms of trade, but also their educational attainments and health. Obtaining empirical estimates of the benefits generated by the first is straightforward, not so those generated by the others. The object of this paper is to estimate the relative sizes of their respective contributions to total benefits in connection with the all-India rural roads program _Pradhan Mantri Gram Sadak Yojana,_ using an overlapping generations model featuring the production and consumption of goods and the formation of human capital in the presence of both morbidity and mortality. Based on survey evidence from upland Orissa in India and Bangladesh, as well as elements of more usual forms of calibration, the model yields a ratio of commercial to non-commercial benefits of about two-to-one in the first generation, falling to three-tofour in the second. This is broadly consistent with the valuations expressed by respondents in the Orissa survey, who ranked the latter benefits at least on a par with the former.

This paper is a product of the Trasnport Unit, South Asia Region. 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://econ.worldbank.org. The author may be contacted at clive.bell@urz. uni-heidelberg.de.

_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
# **V. METHODOLOGY**

5.01 The education sector is widely recognized as critical for development. It also consumes a large share of government fecurrent expenditures. Therefore, any unevenness of analysis or imbalance in recommended strategies among sectors in PERs is a cause for concern. This section reviews the comprehensiveness of sector coverage, the information and methodology incorporated into the sector analysis, and institutional issues.

A. Sector Coverage 5.02 Prior sector experience. All but 10 reports (all of them multisector) refer specifically to the participation of an education sector specialist on the PER mission(s). Only 56 percent of the reports state that they are building upon Bank experience in the country; and most of these refer to previous Bank studies or other public expenditure or education sector studies. The other reports do not place themselves explicitly in the context of prior CESW by the Bank.

5.03 Comprehensiveness of sector coverage. All of the single-sector and two-thirds of the multisector reports cover the entire formal education system, although the emphasis varies. For example, the Bolivia report concentrates on the primary level, ' Tunisia report on the primary and secondary levels, and the Colombia report on primary and higher educationY1 Vocational and technical training was mentioned in two-thirds of the reports, including all but one of the single-sector reviews.

5.04 Data. All reports except that of the Central African Republic use government data sources, and 28 use Bank-generated data. The Bank data are commonly presented as an addendum or corrective to incomplete or inexact country-generated statistics. In some cases (for example, Niger), Bank spending estimates differ from government budget estimates because of different methods of measuring costs. In other cases, exact estimates are impossible because of ambiguities. In Mozambique, many foreign-financed education projects are not in the budget, and in Pakistan, the extent of federal and provincial costsharing is unclear. Several reports point to inconsistencies in government data. For example, the statistics from one ministry contradict those of another, or subsector expenditures do not add up to the sector total. Half of the reports refer to necessary data (particularly on the wage structure and on unit costs per student) that are not available.

5.05 Single-sector versus multisector reports. The 8 single-sector and 28 multisector reports **in** the sample are only partially comparable because of their different emphases. First, the single-sector reports include more complete data on education (enrollment and repeater rates, unit cost data, breakdowns of information by level of education, and so on). They also provide better analysis of data--which data are inconsistent or missing, and more detailed information on how estimates are derived. Second, they analyze sector issues and priorities in more detail (specific inefficiencies by level of education, intrasectoral !JReports that focused exclusively on a single level were excluded from the review.

41
- Feldstein, M. S. 1967. _Economic analysisfor health service efficiency : Econometric studies of the British National Health Service._ Amsterdam: North-Holland.

- Ferrinho, P., W. Van Lerberghe, M. R. Julien, E. Fresta, A. Gomes, F. Dias, A. Goncalves, and B. Backstrom. 1998. "How and why public sector doctors engage in private practice in Portuguese-speaking African countries." _Health Policy Plan_ 13(3): 332-28.

- Feyisetan, B. J. and M. Ainsworth. 1996. "Contraceptive Use and the Quality, Price, and Availability of Family Planning in Nigeria." _World Bank Economic Review_ 10(1): 159-87.

- Filmer, D., J. Hammer, and L. Pritchett. 2000. "Weak Links in the Chain: A Diagnosis of Health Policy in Poor Countries." _The World Bank Research Observer 15_ (2, August): 199-224.

- F0rsund, F. R., C. A. K. Lovell, and P. Schmidt. 1980. "A survey of frontier production functions and their relationship to efficiency measurement." _Journal of Econometrics_ (Journal of Econometrics ) 13: 5-25.

- Franco, L. M., S. Bennett, and R. Kanfer. 2002. "Health sector reform and public sector health worker motivation: a conceptual framework." _Social Science and Medicine 54:_ 1255-66.

- Frankenberg, E. 1995. "The Effects of Access to Health Care on Infant Mortality in Indonesia." _Health Transitions Review 5(2)._

- Frankenberg, E. and D. Thomas. 2001. "Women's Health and Pregnancy Outcomes: Do Services Make a Difference?" _Demography_ 38(2): 253-65.

- Fried, H. O., C. A. K. Lovell, and S. S. Schmidt. 1993. _The measurement ofproductive efficiency._ New York, Oxford: Oxford University Press.

- Garner, P., J. Thompson, and D. Donaldson. 1990. "Quality Assessment of Health Facilities in Rural Papua New Guinea." _Health Policy and Planning 5(1):_ 49-59.

- Gertler, P., L. Locay, and W. Sanderson. 1987. "Are user fees regressive? The welfare implications of health care financing proposals in Peru." _Journal of Econometrics_ 36(suppl.): 67-88.

- Gertler, P., and J. van der Gaag. 1990. _The willingness to payfor medical care: Evidencefrom two developing countries._ Baltimore and London: Johns Hopkins University Press for the World Bank.

- Gertler, P. J., and J. Molyneaux. 1997. "Experimental Evidence on the Effect of Raising User Fees for Publicly Delivered Health Care Services: Utilization, Health Outcomes, and Private Provider Response." Processed.

- Gilson, L., H. Kitange, and T. Teuscher. 1993. "Assessment of process quality in Tanzanian primary care." _Health Policy_ 26: 119-39.

- Gilson, L., M. Magomi, and E. Mkangaa. 1995. "The structural quality of Tanzanian primary health facilities." _Bulletin of the World Health Organization_ 73(1): 105-14.

- Goddard, M., R. Mannion, and P. Smith. 2000. "Enhancing Performance in Health Care: Theoretical Perspectives on Agency and the Role of Information." _Health Economics_ 9: 95-107.

42
59 _1990's. Anthology serires 27_ , 91-109. Singapore: Southeast Asian Ministers of Education Organization.

Yue, A., Shi, Y., Chang,F., Yang, C., Wang, H., Yi,H., Luo, R., Liu,C., Zhang, L., Chu, J.,&Rozelle, S. (2013). Dormitory management and boarding students in China’s rural elementary schools. _China Agricultural Economic Review_ , 6(3), 523-550.

Zhang, D., & Campbell, T. (2012). An exploration of the potential impact of the integrated experiential learning curriculum in Beijing, China. _International Journal of Science Education_ , _34_ (7), 1093-1123. doi:10.1080/09500693.2011.625057 Zhang, L., Lai, F., Pang, X., Yi, H., & Rozelle, S. (2013). The impact of teacher training on teacher and student outcomes: evidence from a randomised experiment in Beijing migrant schools. _Journal of development effectiveness_ , _5_ (3), 339-358. doi:10.1080/19439342.2013.807862
The PETS data also permit us to examine the relationship between wealth and the types of VL transfers. While we find that the mean transfer to households in the poorest national asset index quintile is 23 percent larger than to households in the richest quintile, the bulk of this comes in the form of milk products (Table 11). For example, the mean value of milk product transfers to the poorest quintile is 135 Soles, and 18 for milk substitutes and other products. Conversely, the mean values of other product received in the other quintiles are between 52 and 100 percent of the mean value of milk product they receive. As such, milk product transfers are generally progressive in the values received by the beneficiaries, while transfers of nonmilk products are not.

**Table 11: Values of Vaso de Leche Transfers to Beneficiaries**

|||_Meanvalue_<br>_(Soles)_||_Shares _|_oftotaltrans_|_fervalue_|
|---|---|---|---|---|---|---|
|_Assetindex_<br>_Quintiles_|_Total_<br>_transfer_|_Milk_<br>_products_|_Other_<br>_products_|_Total_<br>_transfer_<br>_(percent)_|_Milk_<br>_products_<br>_(percent)_|_Other_<br>_products_<br>_(percent)_|
|Poorest|152.6|135.1|17.5|23.1|32.3|7.2|
|2|121.0|71.6|49.4|34.4|32.1|38.3|
|3|141.4|70.7|70.6|33.2|26.3|45.3|
|4|157.2|103.3|53.9|8.3|8.6|7.8|
|Richest|123.7|55.4|68.3|1.0|0.7|_1.5_|
|**Total**|**136.7**|**86.6**|**50.1**|**100.0**|**100.0**|**100.0**|

_Source:_ Public Expenditure Tracking Study, 2002.

As with the mean transfer level, milk transfers appear to be distributed progressively among the bulk of the recipients, unlike nonmilk transfers. While the poorest quintile receives 32 percent of all milk transfers, only 7 percent of other transfers make it to the poorest households. In fact, households in the third quintile receive over 45 percent of all of these other transfers.

# _Marginal Targeting_

Our discussion of targeting thus far has concentrated on average participation rates. The assumption implicit in this analysis has been that an expansion of the VL program leads to increases in benefits to current recipients. In other words, we implicitly assume that all changes would take place at the intensive margin. But two forms of expansion can take place: (1) current recipients can receive larger transfers (intensive margin) and (2) participation rates can increase (extensive margin).

To elaborate on how we might identify the effects at the extensive margin, and in particular how a program expansion might affect participation rates, first note that the _23_
|**Table 13f. Impact of FDI Libera**<br>|**lization, M**<br>Overall<br>_change from b_|**arket Acces**<br>_ase year, %_|**s and Tariff**|**Reform in a**|**CRTS Fram**|**ework**|||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|-0.4|-0.1|-0.4|-0.4|-0.7|-0.4|-0.4|-0.2|
|sigma=1|-0.1|0.3|0.0|-0.1|-0.3|0.0|-0.2|0.0|
|sigma=4|0.1|0.4|0.1|0.0|-0.1|0.3|-0.1|0.1|
|sigma=+inf|0.1|0.4|0.1|0.1|0.0|0.4|0.0|0.2|
|ev/C|0.2|0.8|0.2|0.2|0.0|0.9|-0.1|0.3|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|1.1|1.3|1.0|0.6|0.6|0.3|0.3|0.5|
|Regional exports (% change)|-0.1|-0.1|0.0|-0.1|0.0|-0.1|-0.3|-0.2|
|Real exchange rate (% change)|1.3|1.5|1.4|1.3|1.2|1.1|1.0|1.1|
|International exports (% change)|2.2|2.7|4.7|3.2|3.0|0.5|2.2|2.4|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|1.3|1.7|1.5|1.2|0.9|1.2|1.2|1.2|
|Skilled labor|1.6|1.9|1.8|1.5|1.3|1.3|1.5|1.5|
|Aggregate capital earnings|1.4|1.6|1.5|1.4|1.4|1.2|1.2|1.2|
|Regional mobile capital|1.8|2.6|2.3|1.7|1.6|1.6|1.6|1.9|
|Energy sector resources|1.1|1.4|2.8|1.6|1.6|0.9|1.3|1.1|
|Specific capital in domestic firms|-0.6|-0.7|-0.8|-0.5|-1.0|0.2|0.1|-0.4|
|Specific capital in multinational firms|1.0|0.3|0.4|2.3|1.5|2.7|1.8|1.3|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|0.5|0.5|0.5|0.4|0.5|0.4|0.6|0.6|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|0.4|0.4|0.4|0.3|0.4|0.4|0.4|0.5|

53