rafmacalaba/datause-encoder-data huggingface dataset:
non-networked private facilities towards networked hospitals. Moreover, Powell-Jackson et al. (2014) using data from Ghana, Sood and Wagner (2018) from India, and Celhay et al. (2019) from Argentina document that these kinds of substitutions can increase the quality of care. No study to date looks at the impact of health insurance using facility-specific measures of quality: thus, it is possible that the studies that do not find any change in aggregate utilization are still missing changes on this margin.

In contrast to the widespread evidence of increased health care utilization, the impacts of health insurance on health outcomes have been mixed at best. Even when a comprehensive benefit package is offered as in Mexico with the _Seguro Popular_ program, King et al. (2009) fail to detect differences in health outcomes as measured by nine different self-assessments.<sup>9</sup> This basic result of zero to small impact resonates across a number of studies. Levine, Polimeni, and Ramage (2016) report zero impacts from Cambodia; Bauhoff, Hotchkiss, and Smith (2011) report zero results from Georgia; and Fink et al. (2013) from Burkina Faso. Similarly, Powell-Jackson et al. (2014) do not find any health impacts in Ghana and Miller, Pinto, and Vera-Hernández (2013) did not find significant effects of insurance on health outcomes in Colombia, whether these are self-reported health assessments, symptoms (fever, cough, diarrhea, blood pressure), or summary outcomes such as weight (including low birthweight), height, and mortality.

In the handful of studies that do find a positive causal impact on health outcomes, it is usually associated with the increased utilization of higher quality, especially preventive care. Sood and Wagner (2018) investigate the impact of a social health insurance program for the poor in the Indian state of Karnataka and find that it reduced mortality from heart conditions and cancer; they argue that insurance coverage led patients to seek early diagnosis. Likewise, in Camacho and Conover (2013), Balsa and Triunfo (2021), and Celhay et al. (2019), health insurance schemes in Colombia, Uruguay, and Mexico, respectively, are found to lead to reduced infant mortality, which is mostly attributable to increased use of preventive prenatal care.

# The Demand for Health Insurance

The result that health insurance increases utilization but not outcomes is troubling because it suggests that spending is increasing without anything to show for it. That drastic conclusion, though, must be modified both because the statistical power to detect impacts for rare outcomes like mortality requires very large sample sizes that are often unavailable, and because studies to date may have not measured

> 9 King et al.’s (2009) assessment was 10 months after the inception of _Seguro Popular_ , which may be too short a time period for impacts on health outcomes to emerge. Although their study cannot exploit the original randomization, Cohen and Dechezleprêtre (2022) find that 3-4 years after the insurance scheme was introduced mortality rates appear to have reduced.

11
The first four columns in Table 11 indicate that the intervention groups are more likely than the control group to consult teachers, parents, and the regional office for planning and decisions about school expenses. The point estimates in column 4 indicate that the WSD group relies less on the regional education authorities than the Grant-only group, potentially due to the training component of the WSD. Moreover, the WSD group is more likely to conduct fundraisers relative to the control group, whereas this is not the case for the Grant-only group. The WSD treatment has a negative effect on the number of overall PTA meetings: On average, PTAs met 0.41 less in the WSD group than in the Control group (column 7, Table 11). The likely explanation for this finding may be the fact that the WSD creates six sub-committees (as observed in the one-year follow-up data) within the community to deal with different challenges pertaining to the functioning of the school. Parents may participate in sub-committee meetings and so the school may hold fewer overall PTA meetings. Although some of the changes observed may be expected to impact student learning, we observe no impact on student performance (Table 12).

# **6. Final results**

## **6.1. Average Treatment Effects on learning outcomes and participation**

The main outcome variables of interest are the learning outcomes measured by a comprehensive written test. Other outcomes of interest beside student test scores include measures of absenteeism for teachers and for students, and a measure of enrollment. Table 13 presents the estimates of Equation 1 where the dependent variable is a standardized test score in math or English. The estimates show that the interventions have no positive effect on student math and English test scores. The point estimates are mostly negative but small and statistically insignificant. A test comparing the mean score between the WSD and the Grant-only does not reject the null hypothesis that the two interventions have the same effect on test scores.<sup>15</sup> Table 14 presents the same results, controlling for baseline test scores: The significant patterns are identical and all coefficients of interest are within 0.02 of each other.

We run the same model where the outcome variables are student absenteeism and teacher absenteeism. The estimates in the first column of Table 15 indicate that the WSD intervention reduced student absenteeism by about 5 percentage points from a base of about 23% (significant at the 5% level). This corresponds to a nearly 21% reduction in absenteeism. The second column shows that the WSD reduced teacher absenteeism by

> 15 Across both treatment groups, school identified the largest budget item on which the grant was spent: 46% reported teaching and learning materials (including stationery), 23% reported infrastructure (e.g., furniture, building improvements), 20% reported some kind of workshop, 7% reported a radio, while a few reported spending the grant on garden materials.

15
schooling can explain only a small proportion of the variation in children’s schooling (see figure 1, figure 2, and table 4).

Table 5: Countries ranked by average parent-child education correlations

|Country|Coefcient (<sup>ˆ</sup>_β_)|Rank|Correlation (ˆ_ρ_)|Rank|
|---|---|---|---|---|
|Peru|0.88|7|0.66|1|
|Ecuador|0.72|15|0.61|2|
|Panama|0.73|12|0.61|3|
|Chile|0.64|23|0.60|4|
|Brazil|0.95|4|0.59|5|
|Colombia|0.80|9|0.59|6|
|Nicaragua|0.82|8|0.55|7|
|Indonesia|0.78|10|0.55|8|
|Italy|0.67|21|0.54|9|
|Slovenia|0.54|35|0.52|11|
|Egypt, Arab Rep.|1.03|2|0.50|12|
|Hungary|0.61|25|0.49|13|
|Sri Lanka|0.61|24|0.48|14|
|**Nigeria**|**0.93**|**6**|**0.48**|**15**|
|**Madagascar**|**0.72**|**14**|**0.46**|**16**|
|Pakistan|1.00|3|0.46|17|
|United States|0.46|42|0.46|18|
|Switzerland|0.49|39|0.46|19|
|Ireland|0.70|17|0.46|20|
|**Ghana**|**0.55**|**34**|**0.45**|**21**|
|South Africa|0.69|18|0.44|22|
|Poland|0.48|40|0.43|23|
|**Uganda**|**0.68**|**19**|**0.42**|**24**|
|Philippines|0.41|45|0.41|25|
|Vietnam|0.58|29|0.40|26|
|Belgium|0.41|44|0.40|27|
|Estonia|0.54|36|0.40|28|
|Sweden|0.58|32|0.40|29|
|**Malawi**|**0.68**|**20**|**0.39**|**30**|
|Ukraine|0.37|49|0.39|31|
|East Timor|1.27|1|0.39|32|
|Bangladesh|0.58|31|0.38|33|
|Slovakia|0.61|26|0.37|34|
|Czech|0.44|43|0.37|35|
|Netherlands|0.58|30|0.36|36|
|**Tanzania**|**0.50**|**37**|**0.36**|**37**|
|Norway|0.40|47|0.35|38|
|Nepal|0.94|5|0.35|39|
|New Zealand|0.40|46|0.33|40|
|Finland|0.48|41|0.33|41|
|Northern Ireland|0.59|28|0.32|42|
|United Kingdom|0.71|16|0.31|43|
|Malaysia|0.38|48|0.31|45|
|**Guinea**|**0.60**|**27**|**0.30**|46|
|Denmark|0.49|38|0.30|47|
|Kyrgyzstan|0.20|52|0.28|48|
|**Comoros**|**0.73**|**13**|**0.27**|**49**|
|**Rwanda**|**0.58**|**33**|**0.27**|**50**|
|China|0.34|50|0.20|51|
|India|0.64|22|0.52|10|
|Australia|0.30|51|0.31|44|
|Ethiopia|0.75|11|0.10|52|

India data from Azam and Bhatt (2015). Australia data from Ranasinghe <u>(2015).</u> Continued on next <u>page. . .</u> 19
rate, higher values of all the other business environment measures mean that the business environment is more conducive to operating in the formal versus the informal sector. So, the test against the endogeneity problem is that the interaction term between informal competition and profit tax rate is positive while all the other interaction terms are negative.

# 2.5.2 _Exporting and large firms_

For the second group of endogeneity tests, we argue that for a given increase in informal competition, certain types of firms are likely to suffer more than the others. Thus, we check that a unit increase in informal competition leads to a much smaller increase in R&D activity among large firms compared to SMEs, and among exporting firms vs. non-exporting firms. It is wellknown that large firms operate in very different product markets and therefore are unlikely to be much affected by informal competition. The logic can be extended to exporting firms that also operate in very different (international) markets than the non-exporting and the informal firms. We expect the stated predictions to hold if informal competition has a causal impact on R&D activity but there is no strong reason for it to hold otherwise.

# **3. Base Regression Results**

Our base regression results are provided in Table 1. The coefficient values shown in the table are log odds ratios obtained from logit estimation. The associated marginal effects are provided in Table 2 and discussed later in the section.

All the specifications in Table 1 control for industry and year fixed effects. The remaining baseline controls are added sequentially. Table 1 reveals a large and positive relationship between informal competition and R&D activity. The relationship is significant at the 1 percent level in all 25
# **6. References**

Agenor, Richard and Joshua Aizenman (2004): "Savings and the terms of trade under borrowing constraints", _Journal of International Economics_ 63, 32 1-340.

Attanasio, Orazio, Lucio Picci and Antonello Scorcu (2000): "Saving, growth and investment: a macroeconomic analysis using a panel of countries", _Review of Economics and Statistics_ 82, 182-211. Baharumshah, Ahmad, Marwan Thanoon and Salim Rashid (2003): "Saving dynamics in the Asian countries _", Journal of Asian Economics_ 13, 827-845.

Bandiera, Oriana, Gerard Caprio, Patrick Honohan and Fabio Schiantarelli (2000): "Does financial reform raise or reduce saving?", _Review of Economics and Statistics_ 82. Bernanke, Ben S. (2005),<sup>“</sup> The global saving glut and the US current account deficit,<sup>”</sup> remarks at the Sandridge Lecture, Virginia Association of Economics, Richmond, Virginia (March). Birdsall, Nancy, Thomas Pinckney and Richard Sabot (1998): "Why low inequality spurs growth: savings and investment by the poor", in A. Solimano (ed.), _Social Inequality: Values, Growth, and the State_ . Michigan University Press.

Coakley, Jerry, Farida Fulasi, and Ron Smith (1998), "The FeldsteinHorioka puzzle and capital mobility: a review," _International Journal of Finance and Business_ 3, pp. 169-188.

Deaton, Angus (1999), "Saving and Growth", in Klaus Schmidt-Hebbel and Luis Serven (eds.), _The Economics of Saving and Growth: Theory, Evidence and Implications for Policy_ , Cambridge University Press.

De Gregorio, Jose, Alberto Giovannini and Holger Wolf (1994): "International evidence on tradables and nontradables inflation", _European Economic Review_ 38, 1225-44.

Dhayal-Gulati, Anuradha, and Christian Thimman (1997): "Private saving in Southeast Asia and Latin America compared: searching for policy lessons", in J. Hicklin et al (eds.), _Macroeconomic Issues Facing ASEAN Countries_ . International Monetary Fund.

Edwards, Sebastian (1995), "Why are saving rates so different across countries? An international comparative analysis," NBER Working Paper 5097. Eichengreen Barry (2007): "The real exchange rate and economic growth", unpublished manuscript.

31
**Table 6: Poverty estimates**

||Nutrition p<br>|overty line<br>|Basic needs p<br>|overty line<br>|
|---|---|---|---|---|
||Headcount<br>Index|Poverty gap<br>Index|Headcount<br>Index|Poverty gap<br>index|
|_Per adult equivalent_|||||
|_household consumption_|||||
|National|16.69|4.24|42.78|14.26|
||(1.11)|(0.38)|(1.33)|(0.64)|
|Dakar|1.78|0.32|25.57|6.31|
||(0.80)|(0.13)|(2.37)|(0.67)|
|Other urban|9.16|1.98|33.69|9.99|
||(2.05)|(0.58)|(2.93)|(1.19)|
|Rural|29.81|7.81|58.09|21.36|
||(2.04)|(0.71)|(2.12)|(1.02)|
|_Per adult equivalent cell_<br>_consumption_|||||
|National|18.24***|5.12***|46.29***|16.04***|
||(1.00)|(0.37)|(1.26)|(0.64)|
|Dakar|2.07<br>(0.61)|0.47***<br>(0.17)|29.14***<br>(2.02)|7.94***<br>(0.70)|
|Other urban|11.23**|2.93***|37.12***|11.61***|
||(1.95)|(0.66)|(2.53)|(1.11)|
|Rural|32.00***|9.15***|61.60***|23.31***|
||(1.87)|(0.70)|(1.82)|(0.99)|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations.

Note : First panel presents poverty estimates using per capita household consumption, second panel presents poverty estimate using per capita cell consumption. Sample sizes are 1762 households and 4293 cells. Bootstrap standard errors (250 replications) between parentheses. Equivalent scale: 0.5: children 0 to 14 years old; 1: adults. *,**,***: difference with per adult eq. household consumption poverty measure is significant at the 10%, 5%, or 1% level (see Note 12 and Appendix S2 for details).

42
Prior to 2003, fixed asset investment (or capital construction prior to 1981) has been calculated from published data for the infrastructure, government and business sectors. A separate estimate has been made for housing GFCF (Herd _et al_ ., 2013). The sum of these four estimates has then been scaled to be equal to the published data for GFCF.

# _Sectoral estimates from 2003_

From 2003, the movements in the aggregate data for fixed asset investment are no longer a reliable guide for investment in the national accounts. To obtain sectoral estimates of GFCF, the following methodology has been adopted

- Industrial sector investment is set equal to the change in the original value of fixed assets, plus an allowance for scrapping.

- Service sector investment (excluding housing) is set equal to FAI in urban areas, excluding FAI in the asset type _other._

- Rural FAI is added to above urban estimates.

- Housing investment is taken from Herd _et al_ . (2013).

# _Comparison of estimates with official data_

The resulting estimate for aggregate investment is then scaled to be equal to the official estimate of gross fixed capital formation. Given that the estimate of GFCF includes R&D expenditure, this scaling factor incorporates an estimate of R&D expenditure not included in FAI. The reliability of the sectoral estimates can, to some extent, be judged from the overall scaling factors that have to be applied to the data (Figure 4). The scaling factor is quite large prior to the 1980s but it reflects the same error as in published data and cannot be resolved. For the period between 1980 and 2002, the sectoral estimates are close to the GFCF data. After 2002, the method used to estimate sectoral GFCF generates data that is much closer to the GFCF data than the FAI series, which by 2016 was almost double the official data for gross fixed capital formation. By contrast, the difference between the sectoral estimate of GFCF and the published data was smaller at 17% in the same year. Indeed, the cumulated sectoral estimate of GFCF from 2011 to 2016 was only 12% greater than the published estimate of GFCF, while cumulated FAI in the same period was 75% greater than GFCF. The adopted methodology has thus removed most of the discrepancy between the FAI data and that for GFCF.

**Figure 4 Scaling factor applied to estimated sector GFCF and ratio of GFCF to total fixed asset investment**

<!-- Start of picture text -->
1.8<br>1.6<br>1.4<br>Scaling factor<br>1.2<br>1.0<br>0.8<br>ratio GFCF to FAI<br>0.6<br>0.4<br>0.2<br>0.0<br>1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br><!-- End of picture text -->

_Source_ : NBS and author estimates.

13
Table 1: Summary Statistics

|Birth Cohorts|1968 t|o 1970|1988 t|o 1990|
|---|---|---|---|---|
||N|Mean|N|Mean|
|Years of Schooling|1,448|1.477|2,950|4.140|
|Literacy|1,430|0.164|2,857|0.427|
|Number of Births|1,448|5.578|2,950|0.941|
|Births by Age 20|1,448|1.317|1,654|0.813|
|Births by Age 25|1,448|2.741|674|1.749|
|Ideal Number of Children|1,448|7.704|2,950|4.527|
|Currently Working|1,446|0.321|2,945|0.333|
|Sector of Current|Work||||
|Skilled Manual or Professional|1,429|0.082|2,912|0.098|
|Service or Sales|1,429|0.100|2,912|0.138|
|Unskilled Manual or Agriculture|1,429|0.312|2,912|0.240|
|Read About Family Planning|1,448|0.063|2,947|0.115|
|Knowledge of Modern<br>Family Planning Method|1,448|0.920|2,950|0.911|

_Source_ : Author’s analysis based on data for women in the 2005, 2011, and 2016 rounds of the Ethiopian Demographic and Health Survey (DHS). _Note_ : Ideal number of children is censored at 20; no women in the DHS report having more than 18 children, and non-numerical responses are assigned the maximum value. Skilled manual or professional jobs include professional, clerical, and skilled manual occupation groups; the other categories exactly describe the included job groups.

25
# **2.2 What Are the “Impacts” of Consanguineous Marriages on Children?**

With respect to the “impact” of consanguineous marriages on children, most research to date has focused on child mortality. In Pakistan, without correcting for socioeconomic characteristics, Bittles (1994) finds that as a percentage of all reported pregnancies, total pre- and post-natal mortality rose from 16.4 percent in non-consanguineous progeny, to 20.1 percent in second cousins, 22.1 percent in first cousins and 39 percent in double first cousins. Infant mortality was 5.1 percent among non-consanguineous progeny, 6.9 percent for second cousins, 7.9 percent for first cousins and 12.7 percent for double first cousins. Similarly, Shah, Toney and Pitcher (1998) also document a correlation between first-cousin marriages and child mortality in Pakistan. Other examples include Farah and Preston (1982), who find that 37 percent of women in a sample from Sudan are married to cousins and child mortality among this group is 20 percent higher than among families in which the husband and wife are not related by blood (more distant relatives have intermediate child mortality). The magnitude of the effect is quite large; requiring about six additional years of woman’s education to offset the child-survival consequences of marrying a cousin.

Apart from child mortality, consanguinity may also be associated with malnutrition, but there are relatively few studies examining this connection. In one notable exception, Hasnain and Hashmi (2009) surveyed 800 children in rural Sindh, Pakistan and showed that consanguinity (treated as an exogenous variable) is a key predictor of being underweight. Further, descriptive evidence suggests that the offspring of unrelated parents may perform better in cognitive tests than the children of first-cousin marriages. Using a representative sample of 3,203 (grade 4 and grade 6) children from the Arab educational system in Israel, Bashi (1977) finds that the offspring of double-cousin marriages perform the worst on cognitive tests, followed by the offspring of first-cousin marriages and then the offspring of unrelated parents. An important limitation of previous studies on consanguinity is that they do not account for the endogenous decision to marry a relative.

With respect to observed height, which is a proxy for nutritional status in early childhood, and child cognitive ability, there is good reason to believe that the estimated magnitude of any negative biological effects of consanguineous marriage will be biased toward zero. Economic motives driving consanguineous marriages, particularly improved ability to manage idiosyncratic risk, and increased productivity in agriculture and home production, would likely lead to more inputs available for child nutritional support in households with consanguineous unions between parents than in those in which parents are unrelated. A main contribution of the paper lies in identifying the biological effects of consanguinity separately from positive biases associated with economic motives for marrying a relative.

5
- Task ratings in the WH database were consolidated into three categories. High (= 1) corresponds to “extremely effective”, “highly effective” and “very effective” ratings. Medium (= 2/3) to “satisfactory”, “moderately satisfactory”, “effective” and “moderately effective”. Low (= 1/3) is for the rest.

- Google Scholar, Research Papers in Economics, and the Social Science Research Network were used to assess the technical skill of TTLs. Those who showed up in at least two of these platforms were deemed Top (= 1). Those in one only were considered Good (= 2/3), and the rest Unknown (= 1/3).

- The numerous units in charge of delivering knowledge products were consolidated into four groupings: Global Practices – which also includes central Cross-Cutting Solutions teams dealing with topics such as gender, or climate change – DEC, regional Chief Economist offices, and the rest.

The consolidated database of knowledge products assembled through this process is available on request.

# 4. Composition and summary statistics

While the consolidated database is structured around P-codes and Z-codes, this may not be the most informative breakdown to assess the quality of the analytical products in it. Admittedly, knowledgerelated activities are monitored by World Bank based on ASA budget codes, which are a subset of P-codes. Quite often their oversight involves counting how much was spent on them, and how many activities were completed over a specific period. This approach allows disciplining the use of budget resources and ensuring the delivery of committed products.

As for the quality of the analytical work, the conventional wisdom is that it is inversely correlated to the number of tasks management needs to oversee. A new standard monitoring report for ASA products introduced at the beginning of the period covered by this study was used to drive a substantial “cleanup” of analytical products (World Bank 2019). Since then, considerable managerial effort has gone into ensuring that the number of active ASA products is commensurate with some experience-based indicator of the “capacity” of responsible units to deliver.

However, the number of active ASA budget codes may not provide a reliable assessment of the number of knowledge-related tasks being conducted, due to the significant heterogeneity in coding practices across World Bank units. Consider _Doing Business_ , _Global Economic Prospects_ , and the _World Development Report_ – three of the most visible analytical products of the World Bank. Depending on the 15