rafmacalaba/datause-encoder-data huggingface dataset:
39

# Table 4: Rank correlations on author data

||Publication<br>count<br>(_I_<sup>3</sup> =0)|Total citations<br>(_I_<sup>3</sup> =1)|_h_-index(_I_<sup>1</sup>)|Quadratic<br>Influence<br>Function (_I_<sup>2</sup>)|_I_<sup>3</sup> =0.5|Citations of<br>most-cited<br>publication|
|---|---|---|---|---|---|---|
|Publication count (_I_<sup>3</sup> =0)<br>|1.000||||||
|Total citations (_I_<sup>3</sup> =1)<br>|0.477|1.000|||||
|_h_-index (_I_<sup>1</sup>)<br>|0.376|0.678|1.000||||
|QIF (_I_<sup>2</sup>)<br>|0.479|1.000|0.677|1.000|||
|_I_<sup>3</sup> =0.5|0.554|0.991|0.696|0.991|1.000||
|Citations of most-citedpublication|0.399|0.991|0.660|0.991|0.967|1.000|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed author was given full credit for a publication in the case of a co-authored publication. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). All authors were included in calculating these (rank) correlations however few publications in health economics they had to their name.
as number 1 in Newsweek‘s index falls to 17 if I put all the weight on health; Australia‘s rank at number 4 falls to 13 if one put all the weight on education. In exploring the website for Newsweek‘s mashup, the most dramatic impact of re-weighting appears to be for China; if one puts all the weight on ―economic dynamism‖ China‘s rank rises from 66 to 13.

None of their websites make it easy for users to properly assess the sensitivity of these mashup indices to changing weights. Yet it would be relatively easy to program the required flexibility into the current web sites, so users can customize the index with their preferred weights, to see what difference it makes. The only example I know of to date is the OECD‘s Social Institutions and Gender Index. Their interactive website, ―My Gender Index,‖ allows users to vary the composition and weights of the index, and immediately gives the corresponding country rankings and maps them. There are also some useful graphical tools for assessing robustness from the work of Foster et al. (2009). A careful assessment of robustness using such tools would be a more open approach than encouraging users to think that the data have been aggregated in the one uniquely optimal way.

Few of the mashups of development data have said much about data quality, including international comparability. Data constraints are often mentioned, but most of the time the mashups take their data as given with little or no critical attention to the problems; the data often come from others who can be blamed for its inadequacies.<sup>35</sup> Under certain circumstances, forming a mashup index may actually help reduce data concerns, notably when averaging across indicators reduces overall errors. This may have bearing on the choice of indicators, though one finds little sign in the documentation on past mashup indices that this has been considered.

Possibly more worrying than the lack of attention to data quality in existing mashups is how little is done to expose and address the problems in pre-existing data series. The rapid growth in mashup indices will hopefully come with greater attention to these problems, though that may well be little more than hope unless prevailing practices change on the part of mashup producers; greater critical scrutiny and skepticism from mashup consumers would help.

A cavalier approach to data issues appears at times to come hand-in-hand with immodesty in the claims made about new knowledge generated by simply aggregating pre-

> 35  An exception is the DBI, which relies on primary data collected by the team.

24
66

# **Appendix: Topic classification scheme**

# 1. Health and its value

Attributes of health; measurement of health; value of health; value of life; value of avoiding risk of ill-health; utility measures of health-related quality of life; stated and revealed preference methods of measuring willingness to pay; conjoint analysis.

# 2. Efficiency and equity

Welfare, efficiency, equity and possible conflicts; inequality and the socio-economic ̳gradient‘; evaluating efficiency at system level: international comparisons in the developed world; techniques for measuring equity and inequity; evaluating equity at system level: equity in financing arrangements; evaluating equity at system level: equity in service access and delivery; evaluating equity at system level: equity in the distribution of health; institutional arrangements for efficiency and equity; implications for health care financing and delivery systems in the developed world; centralization and decentralization in health care; the role and regulation of markets in developed countries.

# 3. Determinants of health and ill-health

The population health perspective; income and wealth; early determinants of lifetime health (genetics, parenting, schooling, peer effects); aging; occupational and workplace health and safety; addiction: tobacco, alcohol, drugs; the environment: air/water pollution, carcigens, other chemicals, allergens, radiation, etc; economic and social inequality as a determinant of ill-health; epidemics: AIDS, obesity, malaria; prevention, primary and secondary; chronic diseases; infectious diseases; social capital, cultural factors.

# 4. Public health

Public health technologies; public goods and public health; policies towards health and safety at work; health education; financing public health; public health and public behavior; interplay between public health and health care services; interplay between public health and other sectors; economics of public health policy.

# 5. Health and the economy

Global expenditure patterns and their determinants; public expenditure and health care; health in macro models; health and the trade cycle; health and labor force productivity; health and growth; health and economic development; health and employment/unemployment; health and social security; health, savings and investment; health and foreign trade; health and innovation/entrepreneurship.

6. Health statistics and econometrics
Working paper, The World Bank.

- Akresh, R., Halim, D., and Kleemans, M. (2018). Long-term and Intergenerational Eects of Education: Evidence from School Construction in Indonesia. NBER Working Papers 25265, National Bureau of Economic Research, Inc.

- Alesina, A., Hohmann, S., Michalopoulos, S., and Papaioannou, E. (2021). Intergenerational Mobility in Africa. Econometrica, 89(1):135.

- Andrade, S. B. and Thomsen, J.-P. (2018). Intergenerational educational mobility in denmark and the united states. Sociological Science, 5:93113.

- Apouey, B., Nissanov, Z., and Silber, J. (2022). Ordinal variables and the measurement of upward and downward intergenerational educational mobility in european countries. Review of Income and Wealth.

- Asher, S., Novosad, P., and Rafkin, C. (2023). Intergenerational Mobility in India: Estimates from New Methods and Administrative Data. American Economic Journal: Applied Economics. Forthcoming.

- Ashraf, N., Bau, N., Nunn, N., and Voena, A. (2020). Bride price and female education. Journal of Political Economy, 128(2):591641.

- Assaad, R. and Saleh, M. (2018). Does Improved Local Supply of Schooling Enhance Intergenerational Mobility in Education? Evidence from Jordan. World Bank Economic Review, 32(3):633655.

- Azam, M. and Bhatt, V. (2015). Like Father, Like Son? Intergenerational Educational Mobility in India. Demography, 52(6):19291959.

- Aziz, I., J. (1990). INPRES' Role in the Reduction of Interregional Disparity. Asian Economic Journal, 4(2):127.

- Azomahou, T. T. and Yitbarek, E. (2021). Intergenerational Mobility in Education: Is Africa Dierent? Contemporary Economic Policy, Forthcoming.

- Bau, N., Rotemberg, M., Shah, M., and Steinberg, B. (2020). Human capital investment in the presence of child labor. Technical report, National Bureau of Economic Research.

- Bazzi, S., Hilmy, M., and Marx, B. (2020). Islam and the state: Religious education in the age of mass schooling. Technical report, National Bureau of Economic Research.

- Becker, G. (1981). A Treatise on the Family. Harvard University Press.

31
# **Figure 5: Services trade restrictiveness Index**

<!-- Start of picture text --> ETH<br>80.0<br>IND<br>ZWE<br>60.0 IRN<br>QAT<br>PHL<br>IDN MYS<br>DRC CHN PAN KWT<br>MWINPLBGD VNM EGYTUNTHALBN OMN<br>40.0 YEM LKAJORNAMDZABLRVEN SAU<br>20.0 MOZRWAMDGUGAMLITZAGHAZMBLSOKENSENCIVKHMNGAKGZCMRUZBPAKNICMNGHNDMARBOLPRYGTMGEOARMDOMUKRALBPERCOLBRAZAFCRIKAZBGRURYMUSROMTURARGBWACHLMEXRUSPOLLTUHUNPRTTTOCZEKORNZLGRCBHRESPITAFRAJPNAUSDEUFINBELGBRSWEDNKAUTCANNLDIRLUSA<br>ECU<br>0.0<br>4 6 8 10 12<br>Log of GDP per capita, 2007<br><!-- End of picture text --> Note: GDP per capita (constant 2000 US$); 102 countries Source: Gootiiz and Mattoo (2009) + A noteworthy feature of the pattern of services trade and investment policies is that landlocked countries apply more restrictive policies than coastal countries. This appears particularly true in the air transport and telecom sectors, in which landlocked countries have no inherent disadvantage (Borchert, Gootiiz, Grover, and Mattoo 2010). While there are many reasons why being landlocked might lead to lower availability of services and higher prices, restrictive policies contribute to the poor performance in services sectors, beyond the handicap imposed by geography. This suggests that supporting policy reforms to enhance the contestability of “backbone” services in landlocked countries could be a priority area for aid for trade.

To date, however, much of the aid for trade effort has put the emphasis on support for hard infrastructure and improving productive capacity. Less has been done to improve the servicesrelated policies and regulation that help determine the efficiency of (cost of using) infrastructure networks. This is one area where the support and leadership of the G-20 can make a difference. There are two dimensions to this: (i) ensuring that aid for trade assistance includes an adequate focus on pro-competitive regulation and other policies that affect the functioning of producer service markets (Hoekman and Mattoo, 2007); and (ii) doing more to provide access to the knowledge and experience on these matters that exists in the middle-income, emerging market members of the G20, as well as developed economies.

Focusing explicitly on improving the operation and efficiency of services sectors is important in itself from a development perspective, but is also important from a global perspective. As argued by Claessens, Evenett and Hoekman (2010) and Hoekman and Messerlin (2010), rebalancing the world economy – reducing large current account surpluses and deficits – will require improvements in productivity (competitiveness) and domestic absorption in deficit and surplus 15
# TABLE 9 SENSrrIVrTY ANALYSIS OF ASSESSMENTS OF THE CONTRIBUTION OF TAX POLICIES TO KOREAN GROWTH USING THE 1982 BASE MODEL

|Average Annual<br>Growth Rate|Ass<br>in|esnent as<br>Table 7|As in Table 7,<br>but with<br>substitution<br>elasticities<br>in production set<br>equal to 0.75|As in Table 7,<br>but with<br>substitution<br>elasticities<br>in consumption set<br>equal to -0.75|As in Table 7,<br>but with the<br>utility function<br>curvature<br>parameter, z,<br>set equal to 2.50|Actual an<br>growth ra|nual<br>te|
|---|---|---|---|---|---|---|---|
|GDP||0.54|0.24|0.54|0.54|8.65||
|Exports of Manuf|acturers|1.07|1.57|1.06|1.06|35.37||
|Imports of Agicu|lture|1.10|1.60|1.10|1.10|11.94|a|
||1982 base|1982 base|1982 base|1982 base|1982 base|Actual Distri|butionb|
||year model|<br>year model|year model|year model|year model|1962c|1982|
||with 1982<br>policies|with tax<br>policy<br>neutral mix|with tax<br>policy<br>neutral mix|with tax<br>policy<br>neutral mix|with tax<br>policy<br>neutral mix|||
|Distribution of<br>Employment (%)||||||||
|Agriculture|67.35|70.63|69.42|70.63|70.63|63.1|50.6|
|Manufacturing|32.67|29.37|30.58|29.37|29.37|36.9|49.4|

Notes: aFigures are based on imports of food and live animals. bThe distribution is between agriculture and nonagriculture. CBased on the 1963 distribution.
# **Table A2. Sampling frame and survey structure, by country**

|**Country**|**Sampling**<br>**Procedure**<br>(1)|**SCPs**<br>**universe**<br>(2)|**Replace-**<br>**ment rate**<br>(3)|**Assumptions for**<br>**power calc.**<br>(4)|**Stratification levels**<br>(5)|**Sampling Frame**<br>**(Universe) Source**<br>(6)|
|---|---|---|---|---|---|---|
|**Brazil**|Represen-<br>tative<br>sample|2,388|50%|Effect size: 0.08<br>Power: 80%<br>Alpha: 0.05<br>Formal<br>employment at<br>baseline: 9.23%|_State_: Ceara, Sao Paulo<br>_HEI Administration_: Public, private<br>_HEI academic organization_:<br>Universidade, Centro Universitário,<br>Faculdade, Instituto Federal de<br>Educação, Ciência e Tecnologia|Higher Education Census (_Censo_<br>_da Educação Superior_), 2017.|
|**Colombia**|Represen-<br>tative<br>sample|2,130|50%|Effect size: 0.08<br>Power: 80%<br>Alpha: 0.05<br>Formal<br>employment at<br>baseline: 10%|_Region:_Caribe, Centro-Oriente,<br>Centro-Sur, Cafetero-Antioquia,<br>Pacifico<br>_HEI Administration_: Public, private<br>_HEI academic organization_:<br>University, technical center,<br>technological center.|National Information System of<br>Higher Education (_Sistema_<br>_Nacional de Información de la_<br>_Educación Superior_), 2017|
|**Dominican**<br>**Republic**|Census|209|n.a.|n.a.|All programs|Ministry of Economics, Planning,<br>and Development, 2019|
|**Ecuador**|Census|543|n.a.|n.a.|All programs|Secretariat of Higher Education,<br>Science, Technology, and<br>Innovation, 2019|
|**Peru**|Census|387|n.a.|n.a.|All licensedprograms|Ministryof Education,2019|

_Notes:_ For each country included in the WBSCPS, this table shows the sampling procedure (whether using the universe of programs or a representative sample) in column (1), size of program universes in column (2), replacement rate in column (3), and assumptions for power calculations in column (4). To determine the sample size, details on stratification levels used to calculate the number of programs to be included Colombia and Brazil samples and the source of sampling frame (universe or initial list of programs) are in columns (5) and (6). The assumptions to estimate sample size for Brazil were based on Almeida et al (2015). “n.a.” = not applicable.
Figure 2: GHANA <!-- Start of picture text --> (a)<br>fli t<br>6 _ _ _ _ _ _ _ _ _ _ _ _ _ _0.35<br>5<br>4<br>3<br>2<br>0<br>- r3t f principal component<br>I-- - weighted average of principal components<br>(c)<br>rt<br>0 I . . . . . . . .<br>~~r 111~~~~1 le -1491<br>-20 -<br>-30 -<br>-20 t V<br>-40 --<br>-50 -lg<br>-60<br>-70<br><!-- End of picture text --> <!-- Start of picture text --> (b)<br><!-- End of picture text --> <!-- Start of picture text --> Money& Credit<br>O<br>0.25<br>0.2-<br>0.15<br>0 4<br>. . . . - . .. . . . .<br>,GD<br>- (M21GNDI) t - - - (private crsdrtGNDI)<br>(d)<br>(sly) t<br>0.25 -<br>~~~~0.25<br>~~~~~~~~~~~~~0.15-<br>0 -'<br>9i fi b 959O -o-y R 9<br>el c 4b e191l<br>- - adjusted -unadJuste<br><!-- End of picture text --> 54
**Figure 3: Improvements in Asset Ownership**

<!-- Start of picture text --> Kitchen Utensils Jewerly Phone (incld. Mobile) Radio/Cassette Player<br>92% 96% 95% 95% 92% 88% 80%<br>83% 87%<br>70% 80% 66% 70%<br>58%<br>62% 62% 57%<br>Top 20% 36% 45%<br>Middle 40% 43%<br>Bottom 40% 23% 38%<br>24%<br>0% 2% 1%<br>1995-96 2003/04 2010/11 1995-96 2003/04 2010/11 1995-96 2003/04 2010/11 1995-96 2003/04 2010/11<br>TV/VCR/VCD Bicycle Fans Camera/Camcoder<br>77%<br>59% 72% 60%<br>51%<br>44% 44%<br>40% 41% 38% 54%<br>29% 33% 30%<br>23% 34% 32% 34% 22% 21%<br>16%<br>20% 3% 19% 13% 22% 17% 9% 2% 15% 9% 5% 9% 22%6%<br>1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15<br>Motorcycle/Scooter Sewing Machine Refrigerator/Freezer Petromax<br>27% 31% 29% 30%<br>18% 18%<br>4% 15% 5% 10%2% 2%9% 5% 7% 12%4% 3% 14% 3% 7% 4% 2% 8% 8%<br>1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11<br>Computer Heaters Car Washing Machine<br>21% 21%<br>5% 2% 3%0% 4% 7% 12% 1% 2% 3% 3% 0% 0% 2% 3% 3%0%<br>1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 1995-96 2003/04 2010/11 2014/15 1995-96 2003/04 2010/11 2014/15<br><!-- End of picture text --> Source: World Bank Staff Calculation based on Nepal Living Standard Surveys 1995/96, 2003/04, and 2010/11. Question on computer ownership was not available in 1995/96. 2014/15 estimates are added when data is available in the 2014/15 Annual Household Survey.

10
effects of rural-urban migration mainly affecting small establishments.

Let it finally be added (Table 3.1) that wages are highest in public sector establishments whether we look at SII or SMI statistics. The d .fference between public and private establishments has been roughly unchanged over the period and may mainly reflect the fact that public enterprises on balance are larger than private ones.

Granted these facts and the gradual extension of social insurance to small establishments and communities we should. everything else being equal, expect SII wages to be higher than MIS and census wages in 1963, all converging in 1980. This has clearly not been the case. Average daily wages in manufacturing were in 1963 about TL 19.4 according to the SMI series (interpolated) and 18.7 according to the 1963 census (assuming 300 workdays annually) while the SII average was only TL 16.6. In 1980, on the other hand, census and SMI series give us wage averages for manufacturing of TL 888 and 872, respectively, against only 457 for the SII average. Thus, contrary to expectation, SII wages were somewhat lower than SMI and census wages in 1963 and this gap increased over the period. Composition and coverage cannot explain this. We have to search for explanations not related to composition, coverage and size of establishments. Similar remarks apply to mining & quarrying. Considering the statistical disparities for mining &

- 45 -
- ii -

leadership and stakeholder support, which are essential for sustainability, seems to be modest.

> O Evaluations of the quality and relevance of institutional analysis in Bank projects have shown that there is much scope for improvement. Given the potential for good sector work to improve the quality of ID design, the unevenness and gaps in the Bank's sector work is worrisome.

- O While the limited investment in sector work on ID issues and the uneven quality of institutional analysis may have had an adverse impact cn the design of ID components, there has been some organizational learning in this area too. The quality of institutional analysis seems to have improved somewhat in some sectors although adjustment lending lags behind. There is increasing use of the process approach (joint analysis and consultations with relevant groups) in the diagnostic and design phases. ID focus has shifted significantly from PIUs to implementing agencies.

- The widening scope of ID and some of the design features of Bank projects are in.-.onsistent in several respects and might adversely affect ID performance.

- The absence of country-specific sector ID strategies goes counter to the trend of widening scope of ID as there is no map or long-term perspective to guide the design of project ID components.

- The uneven quality of institutional analysis and the limited attention to ID issues in economic and sector work go against the requirements of the widening scope of ID in projects.

- The continuing focus of ID on project implementation weakens the concern for sustainability in Bank projects and limits the attention given to the design of sectoral ID strategies referred to above.

- The more complex sector-wide ID designs and interventions (implied by the widening scope of ID) are being left to Bank sector specialists whose skills and experience are often inadequate to deal with these broader issues.

# The Performance of ID Components

- The achievement of ID objectives in Bank projects has declined somewhat in recent years. The proportion of projects with negligible ID ratings by the Operations Evaluations Department (OED) of the Bank has increased from 15 percent in 1980-1982 to 25 percent in 1986-1988. A recent OED study has found that only 52
Designing Experiments to Measure Spillover Effects<sup>*</sup> Sarah Baird<sup></sup> , J. Aislinn Bohren<sup></sup> , Craig McIntosh<sup>§</sup> , Berk Ozler<sup> ̈¶</sup> February 7, 2014 KEYWORDS: Experimental Design, Networks, Cash Transfers JEL: C93, O22, I25

> * We are grateful for the useful comments received from seminar participants at Caltech, Monash, Namur, Paris School of Economics, Stanford, University of British Columbia, UC Berkeley, University of Colorado, University of Melbourne, and Yale. We thank the Global Development Network, Bill Melinda Gates Foundation, National Bureau of Economic Research Africa Project, World Bank’s Research Support Budget, and several World Bank trust funds (Gender Action Plan, Knowledge for Change Program, and Spanish Impact Evaluation fund) for funding.

>  George Washington University and University of Otago, sarah.baird@otago.ac.nz

>  University of Pennsylvania, abohren@sas.upenn.edu

> § University of California, San Diego, ctmcintosh@ucsd.edu

> ¶ World Bank and University of Otago, berk.ozler@otago.ac.nz
process[es]/negotiated deal[s]” under its transaction advisory work in PPP projects (World Bank 2020, p. 139).

These advantages explain why use of PPPs has been widespread across developing countries. For example, data from the World Bank Private Participation in Infrastructure (WBPI) database indicate that 134 developing countries implemented new PPPs in infrastructure between 2002 and 2011. Beyond infrastructure investments (which includes those in transport, energy, water and waste, telecommunications, and digital networks), PPPs have “increasingly moved into the provision of ‘social infrastructure,’ such as schools, hospitals, and health services,” (World Bank Group, 2014, p. 10), though infrastructure projects continue to dwarf those for social infrastructure in total volume (Inderst, 2020).<sup>9</sup> Overall, PPP investments averaged $30 billion per year in developing countries from 2002-2006 and $79 billion per year from 2007 to 2011 (World Bank Group 2014, p. 10). The report accompanying the 2022 update of the WBPI data noted that “The recovery of PPI that began after the COVID-19 pandemic has continued into 2022. In total, private sector investment commitments reached US$91.7 billion across 263 projects, equivalent to 0.25 percent of the GDP of all low- and middle-income countries” (World Bank, 2022c). While volumes have grown and total investment via PPPs is large relative to guarantees (though smaller than syndicated loans, as discussed below), they too fall far short of the trillions estimated to be necessary to achieve the SDGs. On the other hand, PPPs have been much more focused on the types of investments most likely to raise the marginal product of capital (those with high ‘A’ or ‘H’ in the stylized theoretical framework in section 1) than other financial structures discussed

> 9 For example, Inderst (2020) cites a PwC (2015) study which estimated that 15% of global infrastructure spending in 2012 was in health and education. World Bank Group (2014) estimated that PPPs contributed 15%-20% of infrastructure investment in developing economies (from 2002 to 2011) based on calculations using WBPI data.

20