rafmacalaba/datause-encoder-data huggingface dataset:
In summary, our findings show there are no health or education benefits of implementing school-based IST with AL in a low to moderate transmission setting such as this study site, as a high proportion of children screened do not require treatment and those who do largely live in focal high transmission regions, where rapid re-infection occurs between screening rounds, and results in no lasting gains from treatment. Nevertheless, our results do highlight a potential role for schools as screening platforms. School screenings using RDTs could provide an operationally efficient method to initially identify transmission hotspots for targeted community control [72]. School surveys have proved a useful platform for defining heterogeneities in _Plasmodium_ transmission over large geographical areas in a more rapid and low cost manner than community surveys [73,74]. The results from this study’s screening rounds present a case for the use of schools in also depicting local transmission heterogeneities, which can be extrapolated to the local community [75] and aid in developing targeted community-wide comprehensive interventions, such as localized indoor residual screening and larviciding, with biennial school screenings used to monitor the success of these interventions. The use of schools in this way is a focus of our current research.

# **Acknowledgements**

We are grateful to all the teachers, children and parents who participated in this trial and to all the members of the survey teams for their tireless effort and excellent work. We acknowledge the support provided by the ministries of education and health at national and district levels, and thank Elizabeth Juma, Charles Mwandawiro, Kelvin Kinyua, Bob Ogada, Veronica Wambugu, Siân Clarke, Tansy Edwards, Caroline Hagelskamp, Sharon Wolf, Geetha Mathews, Chandana Jasti, and Elizabeth Adelman for their contributions and support. Thanks go to Abdunoor Mulokozi for advice on surveillance of adverse events and to Teun Bousema and David Schellenberg for their comments on a draft manuscript. We would also like to extend our sincere thanks to the members of the World Bank’s Development Impact Evaluation Initiative ( <mark>Arianna Legovini</mark> **<mark>,</mark>** <mark>Jed Friedman, Marcus Holmlund and Edit Velényi), the s</mark> tudy advisory group (David Schellenberg, Greg Fegan, Don Bundy and Diana Elbourne) and the data monitoring committee (Feiko ter Kuile, Penelope Phillips-Howard and Carole Torgerson).

15
The census tracts listed show a great deal of variation in the number of Nikkei. The mean size of a census tract was 301 households. The mean (median) number of Nikkei households in a census tract was 11 (8). Fifty-nine of the 75 census tracts each had less than 15 Nikkei, including three census tracts with no Nikkei households (with 758 households between them). Two census tracts had more than 50 Nikkei households, one with 58 and the other with 92.

**TABLE 2: LISTING OF HOUSEHOLDS IN SAO PAULO AND PARANA STATES**

||Sao Paulo <br>State|Sao Paulo<br>City|Parana<br>State|Curitiba<br>City|Combined<br>Sample|
|---|---|---|---|---|---|
|Number of Municipios surveyed|28|1|14|1|42|
|Number of Census tracts surveyed|50|16|25|9|75|
|Average Size of Census tract|285|314|332|381|301|
|Number of residential units listed|14239|5025|8300|3425|22539|
|Number of nikkei households listed|559|206|280|78|839|
|Number of nikkei where household member interviewed|305|86|223|58|528|
|Percent of household listed that are nikkei|3.9|4.1|3.4|2.3|3.7|
|Percentage of nikkei households where:||||||
|Interview obtained|54.6|41.8|79.6|74.4|62.9|
|Interview refused|18.3|13.6|7.5|18.0|14.7|
|Family was travelling|2.7|1.5|1.1|0.0|2.2|
|No one was home during three visits|24.5|43.2|11.8|7.7|20.3|
|_Number of households in 2000 Census_|11886|3902|6698|2294|18584|
|_Predicted number of Nikkei households in 2000 Census_|1209|395|532|215|1741|
|_Listed households/Census households_|1.20|1.29|1.24|1.49|1.21|
|_Listed Nikkei/Censuspredicted Nikkei_|0.46|0.52|0.53|0.36|0.48|

The bottom of Table 2 shows that the number of Nikkei listed was only 48 percent of the number of Nikkei predicted on the basis of the 2000 Census, despite the overall number of households growing 21 percent. There are three main reasons for this difference. First, part of the difference reflects the misclassification of Chinese, Korean and Taiwanese households as Nikkei in predicting the number of Nikkei in the Census. Secondly, given that six years had passed between the Census and our survey, the difference could also partly reflect population dispersion, if Nikkei households are moving out of the more traditional neighborhoods over time. Finally, part of the difference could also be due to the long form of the Census being used only for 10 percent of households, and thus to sampling error in predicting the number of Nikkei in a census tract from the 10 percent sample.

17

-  -
# **Bank ownership and credit over the business cycle: Is lending by state banks less procyclical?**<sup>**1**</sup>

Ata Can Bertay World Bank Asli Demirgüç-Kunt World Bank Harry Huizinga Tilburg University and CEPR

**Key words** : state banks, lending, procyclicality **JEL Classification** : G21, H44

> 1 Bertay: World Bank (Abertay@worldbank.org); Demirguc-Kunt: World Bank (Ademirguckunt@worldbank.org); Huizinga: Tilburg University and CEPR (Huizinga@uvt.nl). This paper‟s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
**Table 4: Impact of reform on shares of land used for different purposes**

||**Agricul**|**tural land**|**Construct**|**ion land**|**Unutilized**|
|---|---|---|---|---|---|
||**arable**|**‘other’**|**non-residential**|**residential**|**land**|
|**Panel A**||||||
|Chengdu*After|0.025***|-0.022***|0.012**|-0.009***|-0.006|
||(0.006)|(0.005)|(0.005)|(0.002)|(0.004)|
|Observations|1,842|1,842|1,842|1,842|1,842|
|R-squared|0.110|0.025|0.039|0.082|0.207|
|**Panel B**||||||
|Chengdu*After|0.003|0.009*|0.001|-0.002**|-0.010**|
||(0.003)|(0.005)|(0.002)|(0.001)|(0.004)|
|Observations|912|912|912|912|912|
|R-squared|0.266|0.092|0.037|0.137|0.183|
|**Panel C**||||||
|Chengdu*After|0.032***|-0.028***|0.010*|-0.009***|-0.005|
||(0.006)|(0.005)|(0.005)|(0.002)|(0.005)|
|Chengdu*After*Time|0.027***|-0.006|-0.014**|0.004***|-0.011***|
||(0.006)|(0.005)|(0.005)|(0.001)|(0.004)|
|Observations|1,842|1,842|1,842|1,842|1,842|
|R-squared|0.130|0.031|0.050|0.088|0.210|
|Spatial fixed effects|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Village fixed effects are included. Village time-variant characteristics are controlled for throughout but not reported. These include total area and population, share of the households with access to pension and health schemes as well as the amount of agricultural subsidy, distance to the nearest county road, surface of the village road, and indicator variables for primary schools and medical stations within the village. Time is z score of google travelling time from a village to the center of Chengdu ( _μ_ =1.43h, _σ_ =0.40h). Standard errors adjusted for spatial correlation and serial correlation are in parentheses. If spatial fixed effects are included, otherwise they are adjusted for serial correlation within the village. *** p<0.01, ** p<0.05, * p<0.1.

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Figure 12. Savings Rate at a Financial Institution <!-- Start of picture text --> Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>60 60<br>53.1<br>48.5 49.5 50.5<br>50 50<br>41.5 42.4<br>40 36.2 40<br>33.7 34.0<br>30.6<br>30 30<br>20 20<br>13.8<br>9.1 9.6<br>10 3.7 2.2 3.8 2.0 4.2 2.8 4.3 2.0 4.5 2.9 10 6.6 3.3 4.1 2.3 6.0 2.9 1.4<br>0 0<br>Senegal Argentina Turkey Algeria Mali Senegal Argentina Turkey Algeria Mali<br>Coverage Dissimilarity EACR<br>Shapley Decomposition of Dissimilarity, (Individual level, FINDEX)<br>2011 2014<br>100% 100%<br>13.0<br>90% 17.7 90%<br>25.7<br>80% 7.7 49.3 33.2 47.2 80% 41.7 34.9 35.9 33.9<br>70% 9.8 70%<br>17.2<br>60% 60% 9.7<br>60.3<br>24.6<br>50% 1.3 34.9 9.3 50% 14.8 25.7<br>40.9<br>40% 40%<br>31.4 14.4 13.0 38.2<br>30% 57.4 30% 20.1<br>20% 19.2 20% 27.6 4.1<br>29.1 30.6<br>10% 18.0 12.6 23.5 10% 17.3 10.8 22.7 21.4<br>0% 0%<br>Senegal Argentina Turkey Algeria Mali Senegal Argentina Turkey Algeria Mali<br>Age Education Gender Income<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS The decomposition of differences in the Equity Adjusted Coverage Rate (EACR) between countries summarizes if differences are driven by composition, equalization, or scale effects (Figure 13). For example, the EACR in Mali is 2.9 compared to 2.8 in Turkey. The net difference between the EACR values is only 0.1, but Figure 13 displays that different three different factors have individually larger impacts that neutralize each other. The higher EACR in Mali is driven by the scale effect, but diminished by equalization and composition effects. Savings has a higher coverage overall in Mali, however the equity of coverage among individuals in Mali contributes to a lower EACR. Compared to Turkey, Argentina has a lower EACR (2.8 vs 2.0 respectively). This lower EACR is explained by composition and equalization effects. In Argentina, equity of coverage in formal savings is poorer than in Turkey and the composition of the individuals have a negative effect on the EACR.

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- Rosar, F. and E. Schulte (2012): “Optimal Test Design under Imperfect Private Information and Voluntary Participation,” Manuscript, University of Bonn.

- Rosenberg, M. (1965): _Society and the Adolescent Self-Image_ , Princeton University Press.

- Schöer, V., M. Ntuli, N. Rankin, C. Sebastiao, and K. Hunt (2010): “A Blurred Signal? The Usefulness of National Senior Certificate (NSC) Mathematics Marks as Predictors of Academic Performance at University Level,” _Perspectives in Education_ , 28, 9–18.

- Schöer, V., N. Rankin, and G. Roberts (2014): “Accessing the First Job in a Slack Labour Market: Job Matching in South Africa,” _Journal of International Development_ , 26, 1–22.

- Statistics South Africa (2016): _Quarterly Labor Force Survey Quarter 4 2016_ , Pretoria: Statistics South Africa.

- Taylor, S., S. Van Der Berg, V. Reddy, and D. Janse van Rensburg (2011): “How Well Do South African Schools Convert Grade 8 Achievement Into Matric Outcomes?” Working Paper, Stellenbosch University.

- Wheeler, L., E. Johnson, R. Garlick, P. Shaw, and M. Gargano (2019): “LinkedIn(to) Job Opportunities? Experimental Evidence from Job Readiness Training,” Working paper, Duke University.

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Separate studies using Global Entrepreneurship Monitor (GEM) and WBGES data have found contradictory results: While no relationship is found between GEM data and administrative barriers to starting a business, a significantly negative effect is found with WBGES data (van Stel, et al. 2007 and Klapper et al. 2007, respectively).<sup>2</sup> It is possible that this – and similar contradictory results in the empirical entrepreneurship research – can be attributed to some degree to the differences in _what_ the data captures. For this reason, we compare the two popular datasets designed to capture entrepreneurial dynamics.

In this paper, we compare the GEM dataset for early-stage entrepreneurial activity and the WBGES dataset for formal business registration. We find two important trends when the data are compared descriptively. First, GEM data tends to report significantly higher levels of early-stage entrepreneurship in developing economies than does the World Bank business entry data. Second, the World Bank business entry data tends to be higher than GEM data for developed countries.

There are at least three possible ways to interpret for this discrepancy. First, the datasets simply measure different dynamics related to “entrepreneurship.” The WBGES measure rates of entry in the formal economy, and even more specifically, entry in the form of LLC establishments. The GEM data is perhaps more reflective of entrepreneurial intent and what some might call “entrepreneurial spirit.” For this reason, GEM data captures _informality_ of entrepreneurship, particularly in developing countries. In particular, firm _formation_ does not necessarily mean firm _registration._ Second, this discrepancy can also be interpreted as the spread between individuals who could _potentially_ operate businesses in the formal sector – and those that _actually_ do so. If this is the case, then GEM data may represent the potential supply of entrepreneurs, whereas WBGES data would represent the actual rate of entrepreneurship. This is interesting especially in the context of the allocation of talent (Murphy et al., 1991) and the allocation of entrepreneurship (Baumol, 1990). In the allocation of talent model, the stock of talent is relatively constant but its allocation towards a range of activities can change.

> 2 This is also consistent with Klapper, et al. (2006), who find a significant relationship between business registration in 35 European countries and entry barriers.  De Soto (1989) and Djankov, et al. (2002) find that costly regulations impede the setting up of businesses and stand in the way of economic growth. Djankov et. al. (2002) find that high costs of entry exist in most countries, and that countries with more corruption have larger unofficial economies.

2
Table 2: Out-of-School Children (Formal School)

||(1)|(2)|
|---|---|---|
|Any Treatment (T1 or T2)|-0.063***<br>(0.013)||
|Videos (T1)||-0.063***<br>(0.014)|
|Videos + Mobile apps (T2)||-0.064***<br>(0.014)|
|Observations|9,201|9,201|
|R-squared|0.239|0.239|
|Covariates|YES|YES|
|Enumerator FE|YES|YES|
|Control Mean in F-up|0.148|0.148|
|T1 vs T2(p-value)||0.899|

Note: Linear probability models with clustered standard errors at the school catchment area level. The covariates considered in these regressions are: age of target child, gender of target child, education of household head (baseline), availability of food (baseline) and enumerator fixed effects. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 0.63 _σ_ respectively.<sup>14</sup> When comparing both components, we confirm these effects were driven by the combined intervention of aspirational videos plus the m-Learning add-on (T1 vs T2: _p-value_ =0.000). The aspirational videos alone did not have significant impacts on learning skills.<sup>15</sup> Tables A2 and A3 in Appendix show results for disaggregated levels of competences assessed by EGRA and EGMA modules. The combined intervention ( _T_ 2) improved scores for almost all literacy and numeracy competence levels, with statistically significant increases ranging from 0.16 and 0.67 _σ_ . The largest impacts are observed in foundational skills for both literacy and numeracy. For literacy, the largest impact is observed in ‘letter recognition’ and ‘letter dictation’, where children living in _T_ 2 school catchment areas scored 0.42 and 0.41 _σ_ higher than the control group, respectively. This is followed by ‘listening comprehension’ (0.23 _σ_ ), ‘oral passage reading’ (0.18 _σ_ ) and ‘word dictation’ (0.16 _σ_ ).

> 14Using data from the Uwezo literacy assessments, which are simpler tests designed for low-literacy populations, we find qualitatively similar results in terms of literacy gains. These results are not shown in the paper.

> 15Our learning outcomes have been standardized using the mean and the standard deviation of the control group at baseline as in Banerjee et al. (2007) and Muralidharan et al. (2019) to facilitate the comparison of treatment effects across studies. See cost-effectiveness analysis in Section 6.6.

22
the absence of the program.<sup>5</sup> While fifth graders in the treatment group did better in math and language tests than those in the control group, particularly in rural areas, program effects for ninth graders in both subjects are negative. Yet, the validity of the findings of this paper is limited for at least two reasons. First, if program effects do exist, they are probably difficult to identify due to the lack of enough statistical power in their analysis. Sample sizes for the nonparametric models of the paper are very low, ranging from 100 to 300 observations depending on the age groups. Second, and perhaps more troublesome, the nationally administered tests used by the authors to infer the academic performance of children (known as ― _Pruebas Saber_ ‖) are only representative at the school rather than at the individual level.<sup>6</sup> This paper seeks to contribute to the understanding of the effects of CCTs on school completion and learning outcomes by early adulthood. In particular, this study adds to the existing literature in three ways. First, the analysis focuses on the dynamics of program impacts in the long run as it tracks different cohorts of treatment children who have been in the program from one to nine years. Most of the few studies that measure program effects on intermediate and final outcomes in education did so with children who have been exposed to the treatment for no longer than two years. Second, we use three different data sources and samples (the baseline survey of the evaluation of the program from 2002, the program‘s information system, and a census of poor households) to perform two different methodological approaches which allows for a comparison of the findings across methodologies. Additionally, by using data from the program‘s information system for impact evaluation purposes, this paper highlights the opportunities for research that may arise from using monitoring and evaluation systems, as these are becoming increasingly popular tools to administer CCTs and other safety net programs. Finally, this paper also investigates the extent to which the final educational outcomes of older children that are not eligible to the program could be influenced by the participation of their siblings.

> 5 Treated children were then matched to children in the control group based on the propensity to stay in school, derived from those in the control group that were enrolled both before and after program implementation.

> 6 Schools could be comprised of children that take the test and others that do not take the test.

8
For the impact evaluation of the concurrent trial, one primary or secondary health facility was randomly chosen per ward in each of the districts, for a sample of 786 facilities out of the 1,389 facilities participating in the trial. At each facility, two health workers who routinely provide antenatal or under-five curative care from the roster of health workers present on the day of the survey were randomly sampled for an in-depth interview. We conducted our experiment in all 691 primary health facilities in the survey sample of the impact evaluation. All health workers in a selected facility were eligible to participate in the experiment. The experiment was conducted simultaneously with participating health workers in the same facilities being interviewed in different rooms to prevent information sharing.

The survey instrument captured a range of health worker characteristics, including their education, when they have been last trained in labor and delivery, and their professional grade. In addition, the survey included two standard assessments of worker knowledge and skill (see for instance, Das and Gertler, 2007) in the preceding non-experimental section. First, it included a protocol-based vignette based on the standard WHO antenatal care protocol (Villar et al., 2001) in which participants were read a narrative about a pregnant woman seeking antenatal care and were asked to list everything that they would do during that visit.

Second, the survey provides a measure of real effort—i.e. one with costs to the health worker— through direct observations of actual patient-provider interactions in the context of antenatal care. As described in Kandpal et al. (2019), direct observations were performed at a randomly-chosen third of the sampled primary health centers. In each health center, one randomly selected health worker was observed while she was providing antenatal care to two patients. We thus have direct observation data for 339 of the 1,363 health workers in our experiment. The observation was conducted using a structured, quantitative checklist and the data were collected by enumerators trained in the direct observation of antenatal care provision. Enumerators recorded whether the health worker performed actions listed in the standard WHO protocol, and whether she performed five standard screening tasks to assess the woman’s risk for developing serious pregnancy complications. The latter are closely related to our experimental task of identifying emerging complications and guiding interventions. Moreover, like in our task, the screening can be performed in any setting whereas the physical actions may require supplies or equipment, such as a stethoscope or lab kits for 14
variation for some municipalities, notably Cáceres, Campo Verde, Pedra Preta and Primavera do Leste, where land in agricultural use is far more valuable than pasture land. In these cases, INCRA assigns two values. To adjust for further intra-municipal variation in land value, we regressed mean municipal land values on agricultural suitability category, biome and roaddistance category. With the objective to assess the role of land value in private deforestation decisions, only sample points of nonprotected areas of Mato Grosso were used (i.e. where deforestation can be authorized). Based on the SEPLAN land use information, municipalities with two land values became separate observations.<sup>14</sup> We then used the parameters of the municipal level regression to impute land values for each sample point.

In order to spatially impute land values, we assume that plot-level land value is a linear function of land characteristics. Then, by linear aggregation, the INCRA mean municipal land value is a linear function of the proportion of municipio land with each relevant characteristic. We estimated this function by linear regression. Six observations with mean land value above 1700 Reais/hectare presented outliers in our dataset. These were the agricultural land areas in the municipalities of Alto Garças, Alto Taquari, Campo Verde, Itiquira, Pedra Preta and Primavera do Leste, all situated in the old agricultural frontier, mainly in south-east Mato Grosso. The outliers were extracted and the sample used for estimation was limited to observations with mean land value below R$1700/hectare. Table 17 depicts the summary statistics and Table 18 the outcome of the robust regression model. A simple exploratory functional form (chosen for ease of imputation) found that land values reflect locational rents from road proximity and soil quality, confirming the first hypothesis. For subsequent imputation purposes, the omitted observations with INCRA land values greater than R$1700 hectares were left with their original land value; we do not believe that these values are in error but rather reflect characteristics we are unable to measure.

> 14 In fact, the municipio-level data set was derived from aggregating point level information. Hence, additional municipio observations were created by assigning all points of municipio x in agricultural land (pasture land) the INCRA land value for agricultural land (pasture land).

35