rafmacalaba/datause-encoder-data huggingface dataset:
in the context of a capitation-based payment system, quality of care was significantly lower for insured patients in participating health facilities compared to those who were not insured.

These studies are not cherry-picked; they constitute the full corpus of what we have found in the literature on provider behavior in response to insurance in LMICs. The existence of so few studies on this key subject is itself a cause for concern. The fact that every study showed that provider behavior undermined the objectives of the scheme and contributed to an increase in the know-do gap is an even bigger worry.

# Discussion and Conclusion

A considerable literature from LMICs over the last two decades highlights several noteworthy features of health insurance schemes. In terms of the structure, governments have converged on using public subsidies for health insurance premia, which are now nominally priced or free in most countries. On the other hand, governments have diverged in how they reimburse providers for services, using a wide range of payment mechanisms that are frequently revised and overhauled. In terms of outcomes, the schemes have provided financial protection with a decline in out-of-pocket expenditures, but these gains have not translated into demand for unsubsidized health insurance. Furthermore, these schemes tend to increase utilization without a concomitant improvement in health outcomes. Finally, the lack of consistent improvements in outcomes is not because of supply constraints in terms of workload, equipment, or knowledge but instead due to behavioral responses on the part of providers. Health insurance does not systematically improve the quality of existing providers, and often, it seems to make it worse. There is also little evidence to show that health insurance allows patients to visit higher quality providers.

The phenomena of low demand, poor health outcomes and adverse behavioral responses, while seemingly disparate, are consistent with an underlying framework that recognizes the special features of health care as a commodity. While adverse selection is traditionally regarded as the defining unique feature of health insurance, once premia are tax funded, it is less of a concern. What is instead germane here is the credence good aspect of health care, whereby physicians know what patients need but patients (and health insurance companies) do not. This informational asymmetry leads to over-treatment if patients are treated for serious problems when their condition is mild, and under-treatment or incorrect treatment if patients are treated for a mild condition when their condition is serious. Both are inefficient, as insurance pays for unnecessary treatment in the case of over-treatment and patients lose the surplus from good health in the case of under-treatment. Since physicians enjoy considerable latitude in choosing 22
|**TABLE 4: PROVISION**<br>Panel A|**OF REPRODUCT**<br>(1)<br>Skilled<br>delivery|**IVE AND CH**<br>(2)<br>ANC|**ILD HEALTH SERV**<br>(3)<br>Tetanus<br>vaccine during<br>pregnancy|**ICES†**<br>(4)<br>Postnatal care|(5)<br>Modern<br>contraception|(6)<br>Third dose of<br>polio vaccine|
|---|---|---|---|---|---|---|
|Post indicator|0.514|3.213|-16.881***|-3.802*|0.679|-5.280***|
||[0.798]|[3.932]|[5.563]|[2.175]|[1.002]|[1.983]|
|PBF/Post interact|1.374|3.007|21.521***|4.309*|9.240***|4.583**|
||[1.011]|[7.641]|[6.145]|[2.269]|[2.529]|[2.162]|
|Control 1/Post|1.855*|1.545|15.989**|5.513**|5.794***|2.765|
|interact|[1.021]|[5.407]|[6.450]|[2.262]|[1.746]|[2.389]|
|Control 2/Post|0.047|3.993|8.707|3.515|3.321|1.081|
|interact|[1.358]|[5.441]|[7.692]|[2.499]|[2.061]|[3.953]|
|p-value PBF vs. C1|<br>0.581|<br>0.841|<br>0.183|<br>0.190|<br>0.205|<br>0.252|
|p-value PBF vs. C2|0.289|0.894|0.031|0.570|0.046|0.322|
|p-value PBF vs. C3|0.176|0.694|0.001|0.059|<0.001|0.035|
|Baseline mean C3|7.76|20.57|32.84|10.22|3.02|23.90|
|N|2182|2220|2220|2220|2220|2220|
|Panel B|(7)|(8)|(9)|(10)|(11)||
||Meningitis|Measles|HIV testing|PMTCT|ART||
|Post indicator|~~i~~<br>-45.970***|~~i~~<br>-3.736*|4.239|-3.552|1.021*||
||[9.769]|[2.249]|[3.031]|[3.323]|[0.609]||
|PBF/Post interact|19.041|3.758|61.115***|2.084|-1.455||
||[13.471]|[2.552]|[17.817]|[4.011]|[0.888]||
|Control 1/Post|21.931*|1.892|51.466***|2.372|-0.671||
|interact|[11.131]|[2.700]|[13.668]|[3.189]|[0.573]||
|Control 2/Post|<br>8.47|<br>-0.740|<br>6.596|<br>1.648|<br>-0.681||
|interact|[13.547]|[3.546]|[5.757]|[3.156]|[0.595]||
|p-value PBF vs. C1|0.753|0.337|0.656|0.905|0.235||
|p-value PBF vs. C2|0.387|0.135|0.003|0.851|0.252||
|p-value PBF vs. C3|0.159|0.143|0.001|0.604|0.103||
|Baseline mean C3|46.65|20.90|9.98|9.86|0.012||
|N<br>* = p < 0.10, ** p < 0.0<br>†Results from differenc|2220<br>5, *** p< 0.01<br>e-in-differences r|2220<br>egression mod|2220<br>els examining the e|2220<br>ffect of PBF on r|2220<br>eproductive heal|th service|

<mark>† Results from difference-in-differences regression models examining the effect of PBF on reproductive health service provision reported in facility registers. Monthly number of services provided during the six months before the baseline and endline surveys used as the dependent variable. Regression models adjusted for facility controls (type of health facility public/private/religious, urban/rural). Standard errors were clustered at the health facility level.</mark> 37
availability of regular and relevant market prices would reduce the risk of mispricing portfolios or collateral held by mutual funds through repos.

# **Egypt: money market mutual funds as product diversification**

**In Egypt, only money market mutual funds have developed, but their extraordinary growth to LE 50 billion over a five year period (2003-2008) has been the result of arbitrage in the market.** The trigger was the combination of high yield on the Central Bank’s overnight deposit facility, aiming to drain excess liquidity, and the strategy of the major banks to offer higher remunerated substitutes of cash deposits to corporations and high net worth individuals. As banks stated at that time, the higher Central Bank yields were “shared” with mutual fund clients.

**The current money market mutual fund business model in Egypt leads them to invest a large proportion of their assets in bank deposits and, hence their role in diversifying the investor base for public debt is negligible.** According to April 2008 figures, mutual funds could potentially buy 25 percent of outstanding public debt. With appropriate regulations money market mutual funds could shift a large proportion of their holdings from bank deposits into the Treasury bills and the repo market and become significant participants in the public debt market.

**Money market mutual funds could become the first step to develop a more diversified investor base including fixed income mutual funds** . Recently some market participants have started to develop fixed income mutual funds after two regulatory changes that may be acting as triggers for the industry. The first one limits the average maturity of money market mutual funds from thirteen to six months. Thus, longer maturity funds should take the form of fixed income funds. The second regulation bans insurance companies from investing in banking products, including term deposits, thus fixed income mutual funds are currently seen as a substitute.

# **Tunisia: banks and mutual funds**

**Tunisia ranks first in terms of mutual funds holdings of public debt at around 28.6 percent of outstanding debt despite having a very illiquid debt market.** There are 32 fixed income mutual funds with assets of around 7 percent of GDP. However, these figures are not reflective of an independent mutual fund industry since most funds are directly managed by banks. Public debt securities are bought by the funds through OTC transactions from the bank of the same financial group that participates in the auction<sup>45</sup> . In addition, the bulk of government securities is long–term, and there are no reliable market prices to value public debt portfolios.

**Tax and accounting arbitrage seems to be at the heart of the high share of mutual funds and their dependence of bank operations.** For example, mutual funds pay only a

> 45 See further details in Republique Tunisienne: Strategie de Gestion de la Dette publique, Janvier 2004, Groupe Développement Economique et Social (MNSED), Région Moyen-Orient et Afrique du Nord, Banque  Mondiale.

34
**<u>NON-TECHNICAL SUMMARY</u>**

Until recently, there has been no systematic empirical assessment of the economic impact of the brain drain. Despite many case studies and anecdotal evidence, the main reason for this seems to be the lack of harmonized international data on migration by country of origin and education level. An exception is the paper by Carrington and Detragiache (1998) which provided skilled migration rates for 61 developing countries in 1990. This study relies on a set of tentative assumptions. For example, they transpose the skill structure of US immigrants on the OECD total immigration stock.

In this paper, we provide new estimates of skilled workers' emigration rates for about 190 countries in 2000 and 170 countries in 1990, including both developing and industrial countries. Using various statistical sources, we revisit Carrington and Detragiache's measures by incorporating information on immigrants' educational attainment and country of origin from almost all OECD countries. The set of receiving countries is restricted to OECD nations. Our database covers 92.7 percent of the OECD immigration stock.

In absolute terms, we show that the largest numbers of highly educated migrants are from Europe, Southern and Eastern Asia and, to a lesser extent, from Central America. Nevertheless, as a proportion of the potential educated labor force, the highest brain drain rates are observed in the Caribbean, Central America, and Western and Eastern Africa. Repeating the exercise for 1990 and 2000 allows us to evaluate the changes in brain drain intensity. Western Africa, Eastern Africa and Central America experienced a remarkable increase in the brain drain during the past decade.

Our database delivers information that is rich enough to assess the changes in the international distribution of migration rates, to test for the (push and pull) determinants per skill group, to evaluate the growth effects of migration on source and destination countries or to estimate the relationships between migration, trade, foreign R&D and remittances.
Table B.2. Results using Sample of Attrited Individuals

||_Likeliho_<br>|_od of having chil_<br>|_dren of_<br>||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.057***|-0.017|-0.007|-0.001|
||(0.022)|(0.020)|(0.016)|(0.005)|
|Observations|880|880|880|880|
|Wave FE|No|No|No|No|
|HH FE|No|No|No|No|
|Geographic Sampling|Yes|Yes|Yes|Yes|
|Pre-migration controls|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1) but restricted to individuals who were not contacted in VenRePS round 2. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Premigration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table B.3. Attrition using Main Outcomes as Predictors

|_Likelihood of having children of_|(1)<br>Attrited[=1]|
|---|---|
|0 years of age|0.038<br>(0.036)|
|1 year of age|-0.006<br>(0.035)|
|2 years of age|0.020<br>(0.050)|
|Observations|2,232|

_Notes:_ The table presents the correlation between the main outcome variables and the likelihood of attrition at the head-of-household level. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

30
**<u>Annex 1: Returns by educational level and type, most recent survey year (%)</u>**

|E||Oll|||Full dis<br>|counting||||
|---|---|---|---|---|---|---|---|---|---|
|conomy|Y|vera<br>Mii||Private|||Social||S<sup>3</sup>|
||ear|nceran|Prim|Sec|Higher|Prim|Sec|Higher|ource|
|Albania|2012|8.0|||||||Miluka (2015)|
|Algeria|2001|2.2|||||||Arbak (2012)|
|Argentina|2003|11.0|||||||Lopez Boo (2010)|
|Australia|2010|8.3|||||||Mariotti and<br>Meinecke (2011)|
|Austria|2011|||10.4|9.9||8.8|7.1|OECD (2015)|
|Azerbaijan|1995|3.7|||||||Peet et al (2015)|
|Bangladesh|2006|10.0|||||||Taposh and Lin<br>(2009)|
|Belarus|2002|6.9|||||||Yemtsov et al<br>(2006)|
|Belgium|2009||||10.7|||14.5|<br>OECD (2013)|
|Belize|2005|10.0|||||||Hausman and<br>Klinger (2007)|
|Bolivia|2002|10.3|||||||Patrinos et al 2009|
|Bosnia &<br>Herzegovina|2002|8.1|||||||Peet et al (2015)|
|<br>Brazil|2001|15.7|||||||Patrinos et al 2009|
|Bulgaria|2003|6.7|||||||Flabbi et al (2008)|
|Cambodia|2007|6.7|||||||Lall and<br>Sakellariou (2010)|
|Cameroon|1995|6.0|||||||Banerjee and<br>Duflo (2005)|
|Canada|2011|||11.2|12.9||6.9|7.4|<br>OECD (2015)|
|Chile|2011||||14.8||8|15.5|OECD (2015)|
|China|2009|10.3|||||||Ding et al (2013)|
|Colombia|2014|11.3|||||||Tenjo et al (2015)|
|Costa Rica|1992|8.5|||||||Banerjee and<br>Duflo (2005)|
|Cyprus|1994|5.2|||||||Banerjee and<br>Duflo (2005)|
|Czech<br>Republic|2011|||14.2|19.9||22.6|15.3|<br>OECD (2015)|
|<br>Denmark|2011|||14.4|7.9||8.7|3.9|OECD (2015)|
|Djibouti|1996|11.6|||||||Anos-Casero and<br>Seshan (2006)|
|Ecuador|2002|6.5|||||||<br>Peet et al (2015)|
|Egypt, Arab<br>Rep.|2011|3.4|||||||Rizk (2016)|
|<br>El Salvador|1992|7.6|||||||Funkhouser (1996)|
|Eritrea|2002|10.9|||||||Kifle (2007)|
|Estonia|2011|||21.9|17.1||6.6|5.6|OECD (2015)|

3 References are listed on-line in Annex 3 (http://datatopics.worldbank.org/education/files/GlobalAchievement/ReturnsEdAnnex3.docx).

20
|Percent of governing body that belongs to: current students|0.00206***<br>(0.000635)||(0.0396)||
|---|---|---|---|---|
|General or specific knowledge test is an admission requirement|0.0339<br>(0.0260)|0.0249<br>(0.0253)|4.516*<br>(2.521)|0.163<br>(0.178)|
|Interview is an admission requirement||0.0478***<br>(0.0158)|4.977***<br>(1.477)|0.337***<br>(0.110)|
|Min. scores in HS GPA or national entry test is an admission<br>requirement|-0.0141<br>(0.0251)||||
|Student body, program, and HEI characteristics (PCA scores)|✓|✓|✓|✓|
|Noise controls, state- and field-fixed effects|✓|✓|✓|✓|
|Graduation year fixed effect||✓|✓|✓|
|Observations|22,663|7,177|6,827|7,089|
|R-squared|0.126|0.122|0.109|0.135|
|Mean dependent variable|0.303|0.707|53.52|4.791|
|Average wage (USD 2017 PPP)||||715.62|
|Adj R squared|0.124|0.117|0.103|0.131|

_Source_ : Own estimations using individual-level data from administrative sources and program-level data from the WBSCPS for Brazil. _Notes_ : This table shows coefficients from the (second stage) OLS regressions of student outcomes on the quality determinants selected by LASSO in the first stage. The unit of observation is an individual. Outcomes (dependent variables) are graduation within three years of starting the program (col. 1), and the following labor market outcomes pertaining to the 12-month period following graduation: whether the student is formally employed at least one month (col. 2); percent of months she is formally employed (col. 3); and average monthly wage (col. 4). Average monthly wage is computed over the months worked; it is zero if the individual does not work formally at all. Regressions are weighted using WBSCPS sampling weights. All specifications include noise controls as well as fixed effects for graduation year, state, and field. They also control for PCA scores for the following: student body characteristics (based on survey data), student characteristics (based on individual-level data, including proxies for previous labor market experience), program characteristics (based on survey data), HEI characteristics (based on survey data), and peer characteristics (based on individual-level data, including proxies of peers’ previous labor market experience). See Appendix 1 for the list of the variables included in each index. Standard errors clustered at program level are in parenthesis. Significance levels: * p<0.1, ** p< 0.05, *** p<0.0 89
**Table 6:  Country-year Panel Regressions**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|D_dispute|D_dispute|D_dispute|D_dispute|D_dispute|D_dispute|D_dispute|
|**Macro shocks**||||||||
|Real per capita GDP growth (1)|0.152**|0.203*|0.208|0.165|0.265*|0.318*|0.350*|
||(0.0617)|(0.113)<br>|(0.135)<br>|(0.102)<br>|(0.154)<br>|(0.179)<br>|(0.206)<br>|
|Change in TOT (1)||-0.00361<br>|-0.00211<br>|-0.00737<br>|-0.000298<br>|0.0175<br>|0.0412<br>|
|||(0.0408)|(0.0494)|(0.0397)|(0.0332)|(0.0379)|(0.0377)|
|Natural Disaster (1)|0.0543***|0.0515***|0.0510***|0.0560***|0.0365**|0.0332**|0.0466**|
||(0.0115)|(0.0150)|(0.0159)|(0.0150)|(0.0158)|(0.0162)|(0.0183)|
|**Political institution**<br>||||||||
|Democracy||0.00143|||-0.0332|||
|||(0.0145)|||(0.0276)|||
|Democratization|||0.0425|||0.0357|0.0465|
||||(0.0339)|||(0.0380)|(0.0479)|
|Democratization (1)|||-0.0318<br>|||-0.0362<br>|-0.0334<br>|
||||(0.0293)|||(0.0278)|(0.0345)|
|Democratization (2)|||-0.0564*|||-0.0594*|-0.0683*|
||||(0.0294)|||(0.0339)|(0.0391)|
|High state fragility||||-0.00528||||
|||||(0.0190)||||
|Aid/GNI|-0.423***|-0.616*|-0.634*|-0.743**||||
||(0.148)|(0.340)|(0.357)|(0.343)||||
|(Aid/GNI)^2|0.887***|2.152**|2.315**|2.461**||||
||(0.277)|(1.068)|(1.144)|(1.037)||||
|Log of resource rents|0.0115**|0.0152**|0.0154**|0.0123*||||
||(0.00455)|(0.00668)|(0.00709)|(0.00668)||||
|**Other control**||||||||
|Avg. of D_dispute in previous 5 years|0.497***|0.516***|0.514***|0.484***|0.267***|0.226***|0.0890|
||(0.0450)|(0.0541)|(0.0552)|(0.0510)|(0.0639)|(0.0679)|(0.0717)|
|Log income per capita in 2000|0.00235|0.0121|0.0133|0.00854||||
||(0.00800)|(0.0130)|(0.0138)|(0.0146)||||
|**Regional dummies**||||||||
|<br>EAP|-0.00334|0.000367|0.00527|-0.00443||||
||(0.0134)|(0.0266)|(0.0278)|(0.0259)||||
|ECA|0.0436***|0.0540**|0.0634***|0.0430**||||
||(0.0168)|(0.0212)|(0.0229)|(0.0205)||||
|LAC|0.0328**|0.0623***|0.0698***|0.0520***||||
||(0.0140)|(0.0202)|(0.0215)|(0.0196)||||
|MENA|-0.00201|0.00742|0.0110|0.0116||||
||(0.0199)<br>|(0.0236)<br>|(0.0248)<br>|(0.0230)<br>||||
|South Asia|0.0160|0.0692*|0.0704*|0.0703*||||
||(0.0218)|(0.0412)|(0.0408)|(0.0407)||||
|Year|0.0223***|0.0290***|0.0373***|0.0348***|0.0292***|0.0357***|0.0275***|
||(0.00220)<br>|(0.00401)<br>|(0.00457)<br>|(0.00365)<br>|(0.00584)<br>|(0.00663)<br>|(0.00755)<br>|
|Year^2|-0.00064|-0.00079|-0.001|-0.00099|-0.00066|-0.00083|-0.0006|
||(749e-05)|(0000139)|(0000155)|(0000119)|(0000189)|(0000214)|(0000241)|
|Constant|.<br>-0.155**|.<br>-0.310***|.<br>-0.388***|.<br>-0.295**|.<br>-0.162***|.<br>-0.231***|.<br>-0.183***|
||(0.0742)|(0.120)|(0.130)|(0.132)|(0.0398)|(0.0481)|(0.0537)|
|Observations|3,458|2,178|1,982|2,259|2,178|1,982|1,525|
|R-squared|0.180|0.200|0.202|0.188|0.099|0.096|0.063|
|Country FE<br>|||||Y<br>|Y<br>|Y<br>|
|Year FE|||||Y|Y|Y|
|Robustness check|||||||WithoutLACregion|
|Number of country_number|||||100|100|80|
|<br>***p<0.01,**p<0.05,*p<0.1;Robust st|andard errorsinp|arentheses. V|ariablesnoted|with(1) and (2|) beside thena|me take one-ye|arand two-year lag.|

31
developing world as a whole is a major exporter of manufactures and services, many of which compete directly with products made in the industrial countries. The nature of trade and competition between rich and poor countries has fundamentally changed.

# **_2. Accelerated growth and poverty reduction in the new globalizers_**

Some of the debate about globalization concerns its effects on poor countries and poor people. In the introduction I quoted a number of sweeping statements asserting that global economic integration is leading to growing poverty and inequality in the world. The reality of what is happening with poverty and inequality is far more complex, and to some extent runs exactly counter to what is being claimed by anti-globalists. Hence in this section I am going to focus on the trends in global poverty and inequality. Let’s get the facts straight, and then in the next section I will try to link global integration to these facts. The trends that I want to highlight in this section are that (1) growth rates of developing countries have accelerated in the past 20 years and are higher than rich country growth rates; (2) there was a large decline in the number of poor in the world between 1981 and 2001, the first such decline in history; (3) measures of global inequality (such as the global Gini coefficient) have declined modestly since 1980, reversing a long historical trend toward greater inequality; (4) there is no pattern of rising inequality within countries, though there are some notable cases in which inequality has risen; and (5) there is a general pattern of rising wage inequality (larger wage increases for skilled workers relative to those of unskilled workers). It may seem that trend #5 runs counter to trend #4, but I will explain why it does not. Nevertheless, trend #5 is important and helps explain some of the anxiety about globalization in the industrial countries.

_Trend #1. Developing country growth rates have accelerated._ We have reasonably good data on economic growth going back to 1960 for about 100 countries, which make up the vast 12