rafmacalaba/datause-encoder-data huggingface dataset:
after the 1995 Bosman ruling by the European Court of Justice (ECR _I-04921)_ , which increased the opportunities for African soccer players to play in Europe, the skill level of African soccer leagues most likely to supply players to Europe increased.<sup>4</sup> These case studies share two notable features. First, these studies consider populations whose migration is constrained by policy barriers, so that not all who would like to migrate are able to. A brain gain effect can occur even with open borders, if people acquire education to give them the option value of migrating, and then do not all exercise this option (Katz and Rapoport, 2005). However, the brain gain is more likely when policy barriers limit the number who can migrate. Second, these studies consider cases in which the supply of schooling can easily absorb the added demand for more education. These factors are likely to apply to the cases of secondary education and undergraduate tertiary education in many countries, but may not hold when it comes to talking about doctors, scientists, and other high ability professionals.

Infrastructure constraints and government restrictions on private education limit the supply of medical and graduate education in many countries, particularly in sub-Saharan Africa. If the government only allows one medical school with a fixed quota of students, then clearly it is not possible for more students to be induced to become doctors when migration possibilities open up.<sup>5</sup> In contrast, in countries like the Philippines, private providers allow the supply of nursing education to adapt quickly to increases in demand for such training as a result of migration opportunities for nurses abroad. Furthermore, given the skill-selective nature of immigration systems in many key destinations, the likelihood that a well-qualified doctor or scientist is able to migrate if desired will be substantially higher than for someone with just one year of tertiary education. As a result, there are reasons to believe the brain gain channel will not be as powerful for the most highly skilled as it is for the moderately skilled.

Direct evidence as to whether this channel operates among the most highly skilled comes from surveys we have done of the ―best and brightest‖ individuals who were at the very top of their high school classes in their countries (Gibson and McKenzie, 2010). Among those top

> 4 The effect need not always be positive. In Mexico, McKenzie and Rapoport (2011) use variations in the likelihood of migrating to the United States induced by historic migration networks to show household migration can actually lower educational attainment of children, which they attribute to the low returns to education for illegal migrants in the United States.

> 5 In a study of medical brain drain from 69 countries, using four three-year time intervals between 1991-2004, Bhargava et al. (2011) find that while the lagged physician brain drain rate does predict the growth rate of physicians, the coefficient is too small for the number of physicians remaining in the developing country to rise as the brain drain rate is increased.

13
CAPMAS, 2010. Electronic Database, Central Agency for Public Mobilization and Statistics, www.capmas.gov.eg.

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27
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29
These problems with democracy are compounded by the fact that democracies offer more channels of protest and influence on policymaking to subordinate groups than authoritarian regimes; and that they create more favorable conditions for the development of strong and independent sector and non-government organizations capable of resisting reform efforts (Remmer, 1986). Lastly, democratic rule may fragment decision-making authority among branches of government, allowing opponents of reform to interfere more easily with program design.

In contrast, authoritarian governments have less need to respond to either popular opinion or vested interests and hence can more readily base their decision on criteria of economic rationality. They are better able to make long-run plans than are democratic governments tied to electoral cycles; and have greater centralization of power that facilitates the implementation of reforms.

However, these supposed advantages of authoritarianism are not without their problems. For example, to the extent that policy information and feedback are vital to the design of reforms, democracies may have an advantage (Remmer 1986); autocratic rulers do not have to worry about re-election and hence it is not clear what their incentive to reform is; losers from reforms may comply with their defeat because they believe that the institutional framework that organizes the democratic competition will permit them to advance their interest in the future (Przeworski 1991).

In this paper we provide analysis of the link between democracy and reforms. In particular, we study the relationship between micro-economic reforms, shown in previous studies (for example, Klapper, Laeven and Rajan (2006) and Barseghyan (2008)) to expand growth, and the level of democracy. The analysis is done on a sample of 147 3
countries. In a later study, Hanushek and Luque (2003) use data from the Third International Mathematics and Science Study (TIMSS) to compare performance in about 40 countries. They find that additional resources may impact learning, but effect is small and appears to be no greater in developing than in industrialized countries.

# <u>Methodology</u>

We first estimate cognitive achievement production functions that relate students’ achievement to individual, family and school inputs. We then decompose the indigenous/nonindigenous test gap into an explained component, accounting for family characteristics and school characteristics, and an “unexplained” component, using the traditional Oaxaca (1973)Blinder (1973) decomposition method.

The model specification for the estimation of the production function for cognitive achievement is as follows: where Tija is the observed test score of student _i_ in household _j_ at time _a_ (time of the test), **F** ija is a vector of individual and parent inputs, **S** ija is a vector of school-related inputs and єija is an additive error, which includes all the omitted variables including those which relate to the history of past inputs, endowed mental capacity and measurement error. Its linear specification (after dropping subscript _a_ ) is given by: where β0 to β2 are coefficients to be estimated.

7
# Table 6. EFFECT OF IE ON RATE OF DISBURSEMENT ADJUSTED BY SECTOR AVERAGE UP TO MIDTERM

|||DID|-OLS|||DID|-RE|||DID|-FE||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|0.74***|0.48***|0.74***|0.48***|0.46***|0.40***|0.44***|0.40***|0.42***|0.41***|0.40***|0.40***|
||(5.60)|(4.03)|(5.54)|(4.00)|(3.88)|(3.03)|(3.68)|(2.93)|(3.62)|(3.04)|(3.39)|(2.91)|
|**D (project with IE==1)**|-0.46***|-0.21|-0.46***|-0.21|-0.30|-0.21|-0.29|-0.21|||||
||(-3.84)|(-1.53)|(-3.81)|(-1.53)|(-1.44)|(-1.02)|(-1.38)|(-0.99)|||||
|**_TTL switch dummies_**|Y|N|Y|N|Y|N|Y|N|Y|N|Y|N|
|**_TTL UPI dummies_**|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|**_Polynomial of sector averages_**|N|N|Y|Y|N|N|Y|Y|N|N|Y|Y|
|**_Observations_**|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|
|**_Outcome mean_**|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|
|**_Effect in %_**|74.0%|48.0%|74.0%|48.0%|46.0%|40.0%|44.0%|40.0%|42.0%|41.0%|40.0%|40.0%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle, sector, fiscal year when project became effective, and country.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 33
7 Table 3 Distribution of the Sample by Type of Secondary Schooling (Means and standard deviations)

|Characteristics|Whol<br>|e Sample<br> <br>|Vg<br>|ocational<br>|<br>A<br>|cademic<br> <br>|
|---|---|---|---|---|---|---|
||Mean|<br>sd.|Mean|<br>ed.|Mean|<br>sd.|
|Number obaservations|2,743||407||2,336||
|Age|29.04|<br>9.52|30.18|<br>6.72|28.84|<br>9.64|
|Experience|13.25|<br>9.34|12.87|<br>8.56|13.32|<br>9.47|
|Monthly earnings<br>(Cruzados)|18,495|18,635|23,686*|21,545|17,590*|17,932|
|Schoolina|||||||
|Secondary academic|0.84||||||
|<br>Secondary vocational|0.16||||||
|Years of schooling|9.78|1.43|11.31*|<br>0.55|9.51|<br>1.37|
|<br>Yrs. Industrial|||11.23|<br>0.77|||
|Yrs. accounting|||11.37|<br>0.48|||
|<br>Yrs. other vocational|||11.19|<br>0.43|||
|Occupation|||||||
|Farm worker|0.02||0.01*||0.02||
|Production worker|0.27||0.17*||0.28||
|Sales worker|0.12||0.12||0.13||
|Soevice<br>worker|0.13||0.04*||0.15||
|Clerical worker|0.26||0.35*||0.25||
|Professional/technical|0.13||0.25*||0.12||
|Non-defined occupation|0.07||0.04||0.04||
|Location|||||||
|North|0.01||0.01||0.01||
|North-east|0.13||0.17*||0.12||
|South-east|0.64||0.53||0.66||
|South|0.17||0.19||0.16||
|Central-west|0.05||0.09*||0.04||
|Other variables|||||||
|Social socurity|0.92||0.96*||0.91||
|<br>Migrant|0.55||0.55||0.54||

Signicant at the .001 level or better, Differences between means were tested employing tbtests,
# **Informal Firms and Financial Inclusion: Status and Determinants**

# **Subika Farazi**<sup>**1**</sup>

Keywords: Informal sector, financial inclusion, firm registration JEL codes: O12, O17, L20 Sector Board: Financial Sector (FSE)

> 1 Development Economics Research Group and Financial and Private Sector Development, The World Bank. Email: sfarazi@worldbank.org. The opinions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors or the countries they represent. This paper was prepared as part of the background work for the 2014 Global Financial Development Report. I would like to thank Oya Pinar Ardic Alper, Nancy Claire Benjamin, Miriam Bruhn, Martin Cihak, David McKenzie, Martin Hommes, Anushe Khan, and Claudia Ruiz Ortega for their valuable comments. I would also like to thank Judy Yang and Mohammad Amin who provided very useful insights pertaining to data from the survey of informal firms. The usual disclaimer applies.
**Figure 2.4. PIQ-D index by EMDE groups and regions**

<!-- Start of picture text --> A. PIQ-D by groups, simple averages B. PIQ-D by regions, simple averages<br>Index Index 2009-2013 2017-2021<br>2009-2013 2017-2021<br>0.08 0.15<br>0.10 0.09<br>0.039<br>0.04 0.06<br>0.028 0.022 0.026 0.05 0.05 0.04 0.04<br>0.012<br>0.008 0.02<br>0.00 0.00<br>0.000 0.00 -0.01<br>-0.009 -0.05 -0.02 -0.03 -0.03<br>-0.04 -0.06<br>Commodity Commodity EMDEs LICs -0.10<br>exporters importers without LICs EAP ECA LAC MNA SAR SSA<br>C. PIQ-D by groups, GDP-weighted averages D. PIQ-D by regions, GDP-weighted averages<br>Index 2009-2013 2017-2021 Index 2009-2013 2017-2021<br>0.08 0.071 0.15<br>0.11<br>0.041<br>0.04 0.027 0.020 0.10 0.09 0.08 0.08<br>0.002 0.05<br>0.05<br>0.00 0.01<br>0.00<br>-0.04 -0.039 0.00 -0.01<br>-0.08 -0.055 -0.05 -0.04 -0.05<br>-0.10<br>-0.12 -0.099 -0.12 -0.10<br>Commodity Commodity EMDEs LICs -0.15<br>exporters importers without LICs EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# _Source:_ Authors’ estimates.

_Note:_ EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LICs = low-income countries; MNA = Middle East and North Africa; SAR = South Asia; SSA = Sub-Saharan Africa. Strongly balanced panel of 83 EMDEs. Higher index values indicate better public investment quality. PIQ-D index ranges from -0.7 to 0.6. A.C. The sample includes 51 commodity-exporting EMDEs, 32 commodity-importing EMDEs, and 19 LICs. B.D. The sample includes 8 EAP, 18 ECA, 15 LAC, 7 MNA, 7 SAR, and 28 SSA countries. C.D. GDP-weighted averages are based on real GDP averages over the respective periods.

14
(4.1) above, these together imply that parents underestimate a daughter’s academic ability ̃ ̃ _φ_<sup>_d_</sup> _< φ_<sup>_s_�</sup> , and the daughter’s also face disadvantages in the school � _q_<sup>_d_</sup> _< q_<sup>_s_�</sup> .<sup>33</sup> � However, the evidence that _θ_<sup>ˆ</sup> 1<sup>_d_=</sup><sup>_θ_ˆ</sup> 1<sup>_s_does not add to our understanding without bringing</sup> in additional restrictions imposed by the estimates of the parameters of the intergenerational mobility equation. This is because, in contrast to urban India, the evidence on functional form of the CEFs reject the null hypothesis that _δ_ 5 = 0.<sup>34</sup> So we turn to the evidence from the intergenerational mobility equation below to see if that helps narrow down the possible explanations.

**Learning from the Intergenerational Mobility Equation** Since _θ_<sup>ˆ</sup> 1<sup>_d_=</sup><sup>_θ_ˆ</sup> 1<sup>_s_,theevidencethat</sup><sup>_ψ_ˆ</sup> 2<sup>_d<ψ_ˆ</sup> 2<sup>_s_impliesthefollowinginequality(denotingthe</sup> common value of _θ_ 1<sup>_j_by</sup><sup>_θ_</sup> 1<sup>_c_):</sup>

Now, note that � _q_<sup>_d_</sup> _< q_<sup>_s_�</sup> imply that � _δ_ 2<sup>_d−δ_</sup> 2<sup>_s_</sup> � _>_ 0, and from the functional form evidence we know that � _δ_ 5<sup>_s−δ_</sup> 5<sup>_d_</sup> � _>_ 0. So a sufficient condition for inequality (24) to be valid is that � _δ_ 4<sup>_s−δ_</sup> 4<sup>_d_</sup> � _≤_ 0 _,_ but it is not necessary. This is in contrast to the evidence on urban India where it is necessary that � _δ_ 4<sup>_s−δ_</sup> 4<sup>_d_</sup> � _<_ 0 _._ Next, we consider the evidence _ψ_<sup>ˆ</sup> 1<sup>_d>ψ_ˆ</sup> 1<sup>_s_whichimpliesthefollowinginequalitywhen</sup> combined with the evidence that _θ_<sup>ˆ</sup> 1<sup>_d_=</sup><sup>_θ_ˆ</sup> 1<sup>_s_and</sup><sup>_θ_ˆ</sup> 0<sup>_d_=</sup><sup>_θ_ˆ</sup> 0<sup>_s_:</sup> A necessary condition for inequality (25) to be satisfied is that _δ_ 3<sup>_d>δ_</sup> 3<sup>_s_becausewhen</sup> � _q_<sup>_d_</sup> _< q_<sup>_s_�</sup> we have � _δ_ 1<sup>_s−δ_</sup> 1<sup>_d_</sup> � _>_ 0 and � _δ_ 2<sup>_d−δ_</sup> 2<sup>_s_</sup> � _>_ 0. This is similar to what we found earlier for urban India.<sup>35</sup>

> 33The implication of the alternative assumption about parental belief regarding returns to education are also the same.

> 34Note that with _δ_ 5 _>_ 0, we cannot sign the slope of the _θ_ ˆ1<sup>_j_(</sup><sup>_Rcj_)function.</sup>

> 35But, unlike urban India, we cannot determine whether the _marginal direct effect_ of father’s education _δ_ 3<sup>_j−_2</sup><sup>_δ_</sup> 4<sup>_jHp_�</sup> is higher for daughters with low educated fathers because the evidence does not pin down � the sign of � _δ_ 4<sup>_s−δ_</sup> 4<sup>_d_</sup> � in the case of urban China.

29
deeper understanding of the issue, we use firm level census data to compare the manufacturing sectors in Ethiopia and Vietnam.

# _Ethiopia versus Vietnam: A Case of the Missing Huge Firms_

Ethiopia is the second most-populous country in Africa. Its government has pursued an aggressive industrialization strategy, which largely revolves around attracting investment in laborintensive manufacturing for export. As of 2021, Ethiopia’s strategy mirrors to a large degree the strategy pursued by Vietnam; as noted earlier, both countries have been heavily influenced by China’s use of special economic zones.

In some ways, the manufacturing sectors of Ethiopia and Vietnam look similar. In both countries, annual average employment growth between 2000 and 2017 was between 7 and 8 percent. In both countries, employment growth was driven almost entirely by the entry of new firms, not the expansion of existing firms. Employment growth in both countries is more rapid in firms with between 10 and 49 employees at 7.5 percent in Ethiopia and 12.1 percent in Vietnam. Average annual labor productivity growth in Vietnam was 7.5 percent, higher but not that much higher than the 5.2 percent registered in Ethiopia.

There are high rates of entry and exit in both countries indicating a significant degree of dynamism. Capital-labor ratios are comparable in both, yet Ethiopia is much poorer than Vietnam – a point to which we will return. In both countries, public sector firms played a significant role in the early stages of industrialization. Public-sector firms are still involved in manufacturing in both countries today but by 2017, the employment share of public-sector firms was considerably lower at around 8 percent in Ethiopia and 5 percent in Vietnam (in 2017).

There are of course some striking differences. The most notable is the sheer size of the formal manufacturing sector in Vietnam relative to Ethiopia. An 8 percent increase in formal sector manufacturing in Vietnam adds an additional 520,000 jobs while an 8 percent increase in formal sector manufacturing in Ethiopia adds around 28,000 jobs. If employment in Ethiopia’s manufacturing sector continues to grow at 8 percent per year, it will take Ethiopia 38 years to catch up to the level of employment seen in Vietnam today. Part of this difference has to do with the fact that Vietnam had a much larger industrial base when it embarked on its strategy of export-led manufacturing development. In 1986, at the start of the “Doi Moi” reform period in Vietnam, the country already registered 2.5 million employees in the manufacturing sector. By contrast, when the Ethiopian government embarked 11
# Do Patients Value High-Quality Medical Care? Experimental Evidence from Malaria Diagnosis and Treatment

Carolina Lopez Anja Sautmann Simone Schaner<sup>∗</sup> Originally published in the Policy Research Working Paper Series on _April 2024._ This version is updated on _November 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL Codes: I11, I12, O15 – Keywords: malaria treatment, demand for medication, patient satisfaction, patients vs. provider beliefs, Mali

> ∗We thank the staff of IPA Mali and especially Alassane Koulibaly for expert field coordination. This project would not have been possible without the advice and support of Dr. Seydou Doumbia and Dr. Issaka Sagara (Malaria Research and Training Center, University of Bamako) as well as Dr. Seydou Fomba (Programme National de Lutte contre le Paludisme (PNLP)). We thank the Ministry of Health and the participating community clinics for allowing us to conduct this research. The study was funded by ESRC/DFID Development Frontiers Award ES/N00583X/1. The research protocol was approved by the IRB of La Faculté de Médecine de Pharmacie et d’Odontostomatologie de Bamako (FMPOS) (protocol No. 2016/47/CE/FMPOS) and registered in the AEA RCT registry (Sautmann and Schaner, 2016). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. Lopez: World Bank, carolina_lopez@worldbank.org, Sautmann: World Bank and BREAD, asautmann@worldbank.org, Schaner: University of Southern California, BREAD, and NBER, schaner@usc.edu.