rafmacalaba/datause-encoder-data huggingface dataset:
Intervention group 1, the invitation may serve as a cue to act and increase the individual’s sense of efficacy by clarifying how and where to screen.

**_Box 2: Personal invitation with peer group information (Intervention group 2)_**

Dear [Name, Surname], We cordially invite you to visit [Name of the medical facility] of [Name of the community] at the following address [Address] to be screened for diabetes and hypertension. According to official records in your personal medical card, in past 12 months you have not been screened for diabetes and hypertension at a medical facility. In terms of prevention and control of diseases, your visit to the medical facility is extremely important to ensure your personal healthcare.

<!-- Start of picture text --> We would like to inform you that based on official statistics for your community among your peers, X men and Y<br>women have already taken this screening, and what about you?<br>X Y<br>Please, prior to the visit make a call to your doctor for a proper appointment, using the telephone number presented<br>in the bottom of this invitation.<br><!-- End of picture text -->

**_Note:_** for a credible measurement of diabetes you must visit the doctor fasting since midnight, which means you should not eat or drink anything except water. The screening is free-of-charge, painless and not time-consuming. We highly recommend that you visit the medical facility soonest possible and to use the free-of-charge medical services of the primary healthcare facility.

|Looking forward to seeing you,|
|---|
|**DATE**|
|**DOCTOR**|
|**SIGNATURE**|

**Personal invitations with a labeled pharmacy voucher (Intervention group 3)** were delivered in person by study field workers and consisted of a verbal message and printed letters, signed by a physician, inviting an individual to be screened for hypertension and diabetes and a pharmacy voucher that was offered unconditionally (Box 3). These invitations included all the information in the personal invitations under Intervention group 1. In addition, the intervention offered a pharmacy voucher, valued at AMD 5,000 (~US$10) which could be redeemed once for medicines and other supplies available in selected pharmacy chains, and was not conditioned on being screened. The letter identified the two pharmacy chains where the vouchers could be redeemed, the individual’s choice of products, and the monetary value. A unique identification

8
# **Global Data Set on Education Quality (1965-2015)**

Nadir Altinok*, Noam Angrist, Harry Anthony Patrinos<sup>(1)</sup>

**Key words:** Quality, Human Capital, Education, International, Achievement, Database, PISA, TIMSS, SACMEQ, PASEC, LLECE.

**JEL Classification:** C8, I2, N3, J24, O15

(1) * Corresponding author, Nadir Altinok, BETA, CNRS & University of Lorraine (France), Address: BETA, <mark>UFR Droit, Sciences Economiques et Gestion 13 place Carnot C.O. 70026 - 54035 Nancy cedex, F</mark> rance. Tel: +33 372 748 452. Email nadir.altinok@univ-lorraine.fr Noam Angrist, Oxford University, Email noam.angrist@bsg.ox.ac.uk Harry Patrinos, The World Bank, Email <u><mark>Hpatrinos@worldbank.org</mark></u> Support from the World Bank’s Research Support Budget is gratefully acknowledged. The views expressed here are those of the authors and should not be attributed to the World Bank Group.
shows that the health benefits of PM10 reductions are roughly an order of magnitude higher than those of ozone.

**Table E.4: Benefits from Reducing Air Pollution: Four Scenarios for Ozone and PM,o** (in million US$ per year, 2010 value in 1999 prices, 3 percent discount rate)

|||||_Sce_|_nario_||||
|---|---|---|---|---|---|---|---|---|
||_10_|_%_|_20_|_%_|_AQ_|_SI_|_A_|_QS2_|
|_Incomeelasticity_|_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|_1.0_|_0.4_|
|Ozone|||||||||
|Healthbenefitestimate1,including<br>morbidity(Prod.Loss+COI +WTP)<br>andWTPformortality|116|183|232|365|717|1129|794|1250|
|Healthbenefitestimate2,including<br>morbidity(Prod.Loss+COI+WTP)<br>andhumancapitallosses for mortality|75|114|151|228|465|706|515|782|
|Healthbenefitestimate3,including<br>morbidity(Prod.Loss+COI)<br>andhumancapitallosses for mortality|18|18|35|35|109|109|121|121|
|Environmentalcontingenciesbenefits|36|36|45|45|45|45|45|45|
|PM,0|||||||||
|Healthestimate1,including'<br>morbidity(Prod.Loss+CO+WTP)<br>andWTPfor mortality|1451|2549|2903|5098|3186|5595|6793|11931|
|Healthbenefit estimate2,including:<br>morbidity(Prod.Loss+COI+WTP)<br>andhuman capital losses formortality|644|1184|1289|2367|1414|2598|3016|5540|
|Healthbenefit estimate3,including:<br>morbidity(Prod.loss+COI)<br>andhumancapitallossesformortality|96|96|191|191|210|210|448|448|
|Environmentalcontingenciesbenefits|4|4|4|4|4|4|4|4|
|Prod.loss=Productivitylosses;COI=|Direct|Costof|Illness|;WTP|=Willin|gness|toPay.||

_iv_
# **ONLINE APPENDIX: NOT FOR PUBLICATION**

# **OA1. Additional Results on Residual Squared**

**Table A.1: Intersections of Geography and Gender**

||**CH**|**INA **|**IN **|**DIA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
||YrsSch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|
||||**Urba**|**n Sons**|||
|Father’s Edu|0.312***<br>(0.012)|**-0.400*****<br>**(0.062)**|0.524***<br>(0.008)|**-1.007*****<br>**(0.035)**|0.443***<br>(0.020)|**-0.714*****<br>**(0.096)**|
|Constant|8.152***<br>(0.131)|**15.510*****<br>**(0.604)**|6.456***<br>(0.084)|**20.780*****<br>**(0.309)**|7.640***<br>(0.208)|<br>**16.389*****<br>**(0.808)**|
|Observations|17,823|17,823|16,697|16,697|2649|2649|
|R-squared|0.136|0.007|0.307|0.058|0.224|0.028|
|Mean ofY|9.732|13.48|9.264|15.39|11.043|10.902|
||||**Urban D**|**aughters**|||
|Father’s Edu|0.402***<br>(0.015)|**-0.640*****<br>**(0.059)**|0.597***<br>(0.008)|**-0.384*****<br>**(0.049)**|0.489***<br>(0.019)|**-0.694*****<br>**(0.075)**|
|Constant|6.956***<br>(0.180)|**18.774*****<br>**(0.610)**|4.463***<br>(0.085)|**20.284*****<br>**(0.309)**|6.884***<br>(0.190)|**17.225*****<br>**(0.739)**|
|Observations|17,485|17,485|14,519|14,519|3270|3270|
|R-squared<br>|0.184<br>|0.018<br>|0.336<br>|0.007<br>|0.247<br>|0.026<br>|
|Mean ofY|9.013|15.50|7.504|18.33|10.667|11.851|
||||**Rura**|**l Sons**|||
|Father’s Edu|0.270***<br>(0.016)|**-0.631*****<br>**(0.053)**|0.592***<br>(0.008)|**-0.793*****<br>**(0.039)**|0.431***<br>(0.015)|**-0.323*****<br>**(0.083)**|
|Constant|6.267***<br>(0.166)|**16.681*****<br>**(0.551)**|4.972***<br>(0.058)|**20.436*****<br>**(0.199)**|6.901***<br>(0.137)|**15.577*****<br>**(0.596)**|
|Observations|28,968|28,968|30,004|30,004|5909|5909|
|R-squared|0.077|0.017|0.227|0.021|0.146|0.004|
|Mean ofY|7.208|14.49|6.608|18.25|9.273|13.798|
||||**Rural D**|**aughters**|||
|Father’s Edu|0.375***<br>(0.014)|**-0.275*****<br>**(0.069)**|0.580***<br>(0.009)|**0.793*****<br>**(0.050)**|0.500***<br>(0.017)|**-0.231*****<br>**(0.089)**|
|Constant|4.281***|**16.972*****|2.431***|**13.383*****|15.530***|**5.816*****|
||(0.179)|**(0.212)**|(0.052)|**(0.227)**|(0.596)|**(0.134)**|
|Observations|29,883|29,883|25,528|25,528|6528|6528|
|R-squared|0.128|0.004|0.260|0.022|0.177|0.002|
|Mean ofY|5.632|15.98|3.997|15.52|9.273|13.798|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category.

37
operational PSBR takes out both monetary correction and exchange rate devaluation from the change in the non;inal stocks, the real change in NDPS takes out the effective inflation rate from the change in the nominal stocks. Year to year discrepancies between monetary correction and/ or exchange rate devaluation and inflation produce different movements in the two concepts. The sears 1983 and 1986 provide opposite examples of the differences in measurement. In 1983 there was a 30X real devaluation, while in 1986 there was a 15X real appreciation of the exchange rate. In 1983 monetars correction was close to inflation, while in 1986 the government "de-indexed" the financial assets, obtaining a capital gain on its outstanding debt. As a consequence, the operational PSBR was much smaller than the real NDPS in 1983 and higher in 1986. From an economic point of view, the eventual capital gains and losses on the public debt should be included, to the extent that them alleviate or aggravate future potential deficits. This makes the real NDPS a better concept than the operational PSBR as a long term measure of the deficit.

The difficults with the NDPS concept, as it is measured in Brazil, derives from the equal treatment given to assets and liabilities of the public sector. The assets of the public sector are basically domestic credit advanced bY the Central Bank to the financial sector (here included Banco do Brasil and the commercial banks owned by the state governments). These credits refer mostls to subsidized credit programs of the Federal government administered by Banco do Brasil and to financial assistance to state-owned banks. It is at least doubtful whether these credits are fulls recoverable or a substantial discount should be imposed on them. The nominal and real NDPS are presented in Tables 2 and 3 for the period 1981-87.

The methodological base for the real NDPS concept derives from the consolidation of the non-financial public sector with the Central Bank, Producing the following equation:

-7-
Table 1. Institutional Structure of the Chilean Social Insurance System

||**I'ir,a**<br>P'illar|SsCe(iii(l 'illar|**TIhirdl1'i1hr**|**-Zer**<br>P'ill,ar"<br>\A>st,kinv<br>**(El**<br>d.l<br>I**!**,,di<br>nt,i|
|---|---|---|---|---|
|Nature of<br>Institution<br>and<br>financing|PublicMandate,<br>PublicManagement,<br>EarmarkedTaxes|PublicMandate,<br>Private Management,Own<br>Savings|Voluntary,<br>PrivateManagement,Own<br>Savings/Insurance|Non-contributoryTransfer,<br>GeneralTaxes|
|OldAge<br>Income<br>Security|MinimumPension<br>Guarantee:<br>*<br>"Topup"by<br>govemmentto<br>workerscannot<br>affordaminimum<br>annuity<br>*<br>Formen/womenaged<br>65/60,with20years<br>ofcontnbutionsto<br>the secondpillar(an<br>AFP)<br>*<br>Minimumannuity<br>benchmarkis<br>inflation indexed<br>*<br>Topupfinancedfrom<br>earmarked, general<br>taxes|AFP_Adminastradoras de_<br>_FondosdePensiones_<br>*<br>Specialized<br>retirementfund<br>managers<br>*<br>Individualretirement<br>accounts financed<br>with100/%ofworkers'<br>salary<br>*<br>Additional3%<br>financesgroup<br>disability andlife<br>insurancecoverage<br>forcontnbutors<br>*<br>Mandatoryforall<br>employees- Self<br>employedand<br>employers can<br>choose whetherto<br>participateornot<br>*<br>Retirement benefit at<br>65/60(men/women)<br>asanannuityor<br>scheduledwithdrawal<br>*<br>Govemment<br>mandatesthata<br>minimum portionbe<br>annuitised- to<br>financeamonthly<br>benefitequaltothe<br>minimum pension<br>|Private Employersand<br>Contractual Savings<br>Institutions<br>*<br>Employerprovided<br>plans<br>*<br>Privateannuities<br>*<br>Other market<br>insurance- life<br>insurancewith<br>savingsoptions<br>*<br>Otherselfinsurance<br>(savings) instruments|PASIS:_Pensiones_<br>_Asistenciales_<br>*<br>Means-testedbylocal<br>authorities<br>*<br>Nocontribution<br>required<br>*<br>Onlyeligibleifover<br>theretirementage,<br>belowacertain<br>income,and<br>receivingnoother<br>retirementincome|
|||guarantee|||
|Health|PublicClinicsand<br>HospitalsforIndigent<br>FONASA(A&B))<br>*<br>7%ofsalary,for<br>coverageofI00%-<br>75%ofmedical<br>costs<br>*<br>Requiredproofof<br>income- certified<br>health insurance card<br>FONASA (C&D)<br>*<br>7%ofsalary,for<br>coverageoflessthan<br>75%ofmedicalcosts<br>*<br>Selfemployed who<br>wantcoveragemust<br>provecontribution to<br>anAFP|Govemmentmandatesthat<br>employeeswhoopt outof<br>FONASAcontnbuteto<br>ISAPRES (private<br>companies)<br>*<br>7%ofsalaryfor<br>minimumcoverage,<br>withoption forbetter<br>planathigher<br>prmium<br>.<br>Norequirementthat<br>SEbecontributing to|OtherPrivate Health<br>Insurance|Coverageof<br>"Catastrophic"Health<br>Risks|

Note: Only covers institutional arrangements for workers in the private sector and non-military public sector workers. Chile still administers a separate social insurance regime for its military and police.

41
Policy Research Working Paper 6649

# **Abstract**

Focusing on the welfare of the less well off as a measure of real societal progress is the fundamental principle underlying the WBG indicator of “shared prosperity”, namely income growth of the bottom 40 percent in every country. This paper uses a database assembled by the World Bank Group to investigate some basic characteristics of shared prosperity, particularly its relationship with overall economic growth and inequality. Initial estimates using this dataset of 79 countries show that median income growth of the bottom 40 percent (circa 2005–2010) was 4.2 percent, a high number in comparison to the 3.1 percent per capita income growth of the overall population. In addition, the low and lower-middle income countries appear to be trailing the upper middle and high income countries in boosting shared prosperity. Establishing conceptual links between income growth of the bottom 40 percent, the overall growth rate and reviewing existing evidence on how these relate to inequality, the paper discusses two main ideas. First, shared prosperity is strongly correlated with overall prosperity implying that the whole host of policies that are important to generate and sustain growth remain relevant. Second, boosting shared prosperity will also require a concerted effort to strengthen the social contract, particularly in the area of promoting equality of opportunity. Growing evidence suggests that improving access for all and reducing inequality of opportunities— particularly those related to human capital development of children—are not only about “fairness” and building a “just society”, but also about realizing a society’s aspirations of economic prosperity.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. 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 authors may be contacted at anarayan@worldbank.org; jsaavedra@worldbank.org.

_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
**Table 4  Multinomial Logit Regression Results for the Determinants of Migration Decision**

||Foreign|State|Private|Agriculture|
|---|---|---|---|---|
|Education as Stages<sup>*1</sup>|||||
|No primary|.13***|.043***|.38***|1.35|
||(-7.27)<br>|(-7.06)<br>|(-5.88)<br>|(1.18)<br>|
|Primary|.32***|.080***|.63***|1.64***|
||(-8.46)|(-11.84)|(-3.61)|(2.67)|
|Lower secondary|.67***|.27***|.83**|1.43**|
||(-3.70)|(-8.94)|(-2.49)|(2.06)|
|College|.49***|2.81***|.68***|.95|
||(-2.73)|(7.58)|(-2.64)|(-.19)|
|Age|.88***|.94***|.95***|.99***|
||(-19.27)|(-8.15)|(-12.15)|(-4.94)|
|Female|3.25***|1.17*|1.04|1.06|
||(13.08)|(1.64)|(.64)|(.89)|
|Minority status|.28***|.11***|.10***|1.88**|
||(-4.16)|(-4.56)|(-6.17)|(2.34)|
|Unemployed in origin|.64|.87|2.13***|.75|
||(-1.26)|(-.37)|(2.87)|(-.62)|
|Per capita HH expenditure|.73|.24*|.34**|1.37|
||(-.47)|(-1.64)|(-2.52)|(.26)|
|Land holding|.11*|.029**|.0013***|12.39|
||(-1.88)|(-2.29)|(-5.97)|(1.48)|
|Foreign job opportunity in|||||
|origin|4.48***|1.83|2.12|.20|
||(3.35)|(.50)|(.70)|(-1.11)|
|State job opportunity in|||||
|origin|.23|13.34**|.46|.010**|
||(-.90)|(2.22)|(-.72)|(-2.11)|
|Number of Obs.|21354||||
|Pseudo R2|.14||||

Notes: The dependent variable takes discrete values one through five according to the person’s choices. Being non-migrant is the base category. The coefficients are reported as relative risk ratios. *1The upper secondary education is the omitted category. The results for 48 individuals who were unemployed in destination areas are not reported. *, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level respectively. The standard errors in parentheses are based on heteroskedasticity-consistent estimates of the variance-covariance matrix and corrected for within province and urban/rural correlation (clustering). The _t_ -statistics are reported between parentheses.

37
sould be biased upward since SCI_P is the average value in the middle of the sample period. In both cases, however, SCI P is not correlated with economic growth. The results for OECD countries and developing countries are not different (not reported in Table **5).17** The finding that our proxies for initial R&D activities are not correlated with economic growth contrasts sharply with Romer [1989] and Lichtenberg [1992] who use the same UNESCO data set. Romer [1989] finds a positive correlation between the initial number of scientists and engineers employed in R&D activities and subsequent economic growth. Lichtenberg [1992] finds that the ratio of privately funded R&D expenditures to GNP is highly correlated with economic growth in a sample of 59 countries, though government funded R&D expenditures are not.

We believe their results should be treated cautiously because of probable data problems and possible resulting selection bias. Lichtenberg [1992] includes many developing countries that have just one annual observation on the R&D expenditure/GNP ratio, and uses the one observation as an average for the whole sample period. Available data for other countries are often too concentrated around certain periods to represent an average value for the period 1970 and 1985. Both authors include countries whose specifications diverge considerably from the international norms explained in Section II.

The combination of results from Tables 3 and 5 suggests that the positive '<sup>7</sup> Our findings in Table S are not sensitive to White's heteroskedastic error correction, inclusion of other proxies for political stability and market distortions used in Barro [1991], and the choice of R&D proxies. Using the number of scientists and engineers (not denominated by population) or average R&D expenditure in percentage of GDP between 1970 and 1975, without a good theoretical reason, as a proxy for initial R&D capital does not alter the conclusions.

12
**<u>Figure 23. Predicted House Prices Based on Hedonic Estimates from Imputed Rent</u>**

<!-- Start of picture text --> Predicted House Prices, Kigali (implicit rent) Predicted House Prices, Maputo (implicit rent)<br>25,000 16,000<br>14,000<br>20,000<br>12,000 avg. rooms<br>15,000 10,000<br>confidence<br>8,000<br>interval<br>10,000<br>6,000<br>predicted<br>4,000<br>5,000 price<br>2,000<br>0<br>0<br>1 2 3 4 5 6 7<br>1 2 3 4<br>number of rooms<br>number of rooms<br>constant USD, 2005 constant USD, 2005<br><!-- End of picture text --> 38
was done for 3,000 people through physical visits (face-to-face). It was ensured that all individuals listed were responsible for their household’s spending decisions, watched television soap operas, were 20 years of age or older, and belonged to medium to low income groups (i.e. classified as LSM 5 to 7 on the Living Standard Measurement (LSM) scale).<sup>9</sup> To avoid individuals in the sample communicating with each other about the soap operas that they watch (and thus potentially influencing each other), only one interview per home/family was conducted, only one interview per street was carried out, and two streets were mechanically skipped before visiting another household.

Next, the study sample of 1,031 individuals was chosen and randomized into treatment and control groups. The original study design called for stratification by gender, income level, education, and the likelihood of watching soap operas to allow for measuring heterogeneous impacts, however, due to an undetected coding error the randomization occurred unconditionally rather than within strata. Nevertheless, as the summary statistics in Table 1 show, the sample is balanced across treatment and control on these variables.

Since the soap operas were nationally televised, we could not control or restrict viewership. In order to encourage individuals to abide by our assignments and to create a separation between treatment and control groups, a financial incentive was provided for individuals in both groups to watch their respective assigned show. Initial telephone calls were made to explain these details to the study sample. They were told that we would call them every 3-4 weeks during the next 3 months and ask them questions about the soap storyline, and if they answered three out of four questions correctly they would win 60 Rand (US$7) each time. This amount was agreed upon after consulting with the survey and production firm and was deemed high enough to encourage participation. An auxiliary advantage of providing financial incentives to both treatment and

> 9 The South African Audience Research Foundation (SAARF) LSM (Living Standards Measure) is the most widely used marketing research tool in Southern Africa. It divides the population into 10 LSM groups, 10 (highest) to 1 (lowest). The LSM cuts across race, gender, age or any other variable used to categorize people. Instead, it groups people according to their living standards using criteria such as degree of urbanisation and ownership of cars and major appliances.

14