rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Summary Statistics for Unsatisfactory Projects in First Half

|**Variable**|**Mean**|**(Std. Dev.)**|**N**|
|---|---|---|---|
|Dummy for FCS Country|0.395|(0.49)|392|
|CPIA Rating|3.173|(0.426)|382|
|GDP per capita growth|1.476|(3.912)|372|
|Dummy for DPO|0.028|(0.165)|392|
|Share of project in largest sector|0.668|(0.236)|391|
|Project Size - $ m|36.341|(46.046)|392|
|Project length (yrs)|7.141|(1.997)|392|
|Dummy FPD project|0.099|(0.3)|392|
|Dummy HDN project|0.321|(0.468)|392|
|Dummy PREM project|0.089|(0.286)|392|
|Dummy SDN project|0.487|(0.5)|392|
|Supervision Costs per Yr - $’000s|116.39|(104.19)|281|
|Preparation Costs - $’000s|576.542|(457.602)|366|
|Preparation time - mths|23.445|(17.119)|392|
|Total Staff Weeks per Yr|30.288|(17.378)|238|
|No. of Mission Days per Yr|60.884|(42.949)|130|
|No. of Consultant Days per Yr|32.867|(65.907)|238|
|Staff + Consultant Days per Yr|184.305|(124.335)|238|
|Size of Country Offce|9.367|(9.377)|264|
|IRS Ratio of Country Offce|0.469|(0.312)|258|
|No. of Risk Flags per Yr|1.806|(0.991)|392|
|Approval-Effectiveness time (mths)|7.322|(5.647)|392|
|TTL Experience (yrs)|6.278|(5.567)|392|
|TTL Quality|0.622|(0.295)|372|
|<br>Dummy for TTL onsite|0.092|(0.225)|392|
|No. of words in PDO|55.636|(58.207)|349|
|No. of PDO Indicators|1.407|(2.504)|349|
|No. of Intermediate Indicators|4.493|(7.733)|349|

39
# School Meals, Educational Achievement and School Competition: Evidence from a Randomized Evaluation

Christel Vermeersch<sup>�</sup> and Michael Kremer<sup>yz</sup> November 1, 2004

## Abstract

This paper examines the e¤ects of subsidized school meals on school participation, educational achievement, and school ...nance in a developing country setting. We use data from a program that was implemented in 25 randomly chosen preschools in a pool of 50. Children’s school participation was 30 percent higher in the treatment group than in the comparison group. The meals program led to higher curriculum test scores, but only in schools where the teacher was relatively experienced prior to the program. The school meals displaced teaching time and led to larger class sizes. Despite improved incentives, teacher absenteeism remained at a high level of 30 percent. Treatment schools raised their fees, and comparison schools close to treatment schools decreased their fees. Some of the price e¤ects are due to a combination of capacity constraints and pupil transfers that would not happen if the school meals were o¤ered in all schools. The intention-to-treat estimator of the e¤ect of the randomized program incorporates those price e¤ects, and therefore it should be considered a lower bound on the e¤ect of generalized school meals. This insight on price e¤ects generalizes to other randomized program evaluations.

> �Nu¢ eld College, University of Oxford; Email: vermeersch@post.harvard.edu

> yDepartment of Economics, Harvard University, The Brookings Institution and NBER; Email: mkremer@fas.harvard.edu

> zThe analysis in this paper is based on the data that were available as of April 15th, 2004. Future versions will incorporate new data as they become available. Results might change accordingly. We would like to thank Francesco Caselli, Gary Chamberlain, Makhtar Diop, Caroline Hoxby, Gary Chamberlain, Edward Miguel, Michael Murray, Mark Rosenzweig and various participants in seminars for helpful comments. We are indebted to the sta¤ of Internationaal Christelijk Steunfonds, to Alicia Bannon, Elizabeth Beasley, Pascaline Dupas, Matthew Jukes, Sylvie Moulin, and Jonathan Robinson for their generosity. The usual disclaimer applies. The project implementation was funded by ICS. We gratefully acknowledge the ...nancial support of the MacArthur Network on the E¤ects of Inequality on Economic Performance, the Colin-Wauters fund at the Belgian-American Educational Foundation, the Flemish Science Fund, the University of Antwerp. The World Bank o¢ ce in Nairobi provided partial funding under its program for supporting policy oriented economic research on Kenya. The paper was presented at the World Bank O¢ ce in Nairobi on January 13th, 2004. NOTE: The ...ndings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.

1
predicts an earlier reversal. When both hypotheses are combined, the reversal is predicted to occur for an even lower value of the total enrollment rate (0.20), which is the closest to what is empirically observed. According to the Barro-Lee data, the average female-to-male ratio in tertiary enrollment reached one when the global enrollment rate in tertiary education was between 0.14 and 0.17, for our baseline sample of 98 countries.

# **6.1.2 Secondary completion**

The lower panel of table VI reports the model estimates for secondary education. In contrast with tertiary education estimates, the R-squared of the tail hypothesis specification is higher (0.51) than that of the mean hypothesis (0.45). The tail hypothesis has therefore stronger explanatory power compared to the mean hypothesis for secondary education dynamics. In addition, the R- squared reported in column (1) and (3) are identical. Thus, the mean hypothesis parameter adds little explanatory power compared to the specification with the tail hypothesis only. Additionally, the magnitude of the mean hypothesis parameter _μf_ in column 2 decreases more than threefold from 0.022 to 0.06 once the tail hypothesis parameter is also accounted for, although it remains statistically significant at the 5% level. These results overall indicate that the mean dynamics hypothesis adds little additional explanatory power to the tail hypothesis for secondary school completion.

In the restricted sample where GDP per capita is also available, as in the baseline estimations, the R-squared is noticeably larger in the tail hypothesis’ specification in column 4 (0.53) compared to the mean hypothesis’ specification in column 5 (0.45). The R-squared of the specification with both hypotheses’ parameters (column 6) is also identical to that of the specification with the tail hypothesis only (column 4). The mean hypothesis parameter _μf_ also decreases more than twofold once the tail hypothesis is also accounted for. The R-squared is virtually unchanged by the inclusion of GDP per capita. The coefficient on GDP per capita is however positive and statistically significant at the one percent level, and the magnitude of both the tail and mean hypotheses parameters is to some extent reduced, suggesting that our baseline estimates were to some extend capturing the influence of overall economic development. Our main results are however quite 25
The increased investment in schooling by federal and state governments in the Northeast in the beginning of the 1980s seemed to have been successful. The lack of school attendance in Rio Grande do Norte (and the Northeast) are due to low school attainment by parents more than poor quality of schools in the region (Barros, Firpo and Mendoça, 1999). Furthermore, findings by Barros and Mendoça (1998) show that teachers in Rio Grande do Norte have completed 10.4 series while the average for the entire country is 10.9 series (the respective figure for São Paulo is 13.0). The difference of salaries of teachers in Rio Grande do Norte and São Paulo is substantial. Average earnings of teachers in Rio Grande do Norte and São Paulo are 3.1 and 9.4 (1990) minimum salaries, respectively. The national average is 4.8 (1990) minimum salaries. The large difference in teacher incomes however also reflects the fact that 71 percent of teachers in São Paulo have some degree of higher education, the respective figure for Rio Grande do Norte is 23 percent.

School attendance has improved substantially over the last decades in Brazil. In 1998 (IBGE Social Indicators, 1999) only 5 percent of the 7 to 14-year-olds did not go to school. The respective figure for the Northeast and Rio Grande do Norte is 8 percent. School enrollment for secondary age children (15 to 17-year-olds) is much lower, 23 percent (27 percent, 31 percent) of this age group do not attend school in Brazil (Northeast, Rio Grande do Norte). While there appears to be only a slight urban-rural difference for 7 to 14-year-olds, this gap widens for 15 to 17-year-olds. Twenty percent (23 percent, 23 percent) of secondary school-age children do not go to school in urban areas of Brazil (Northeast, Rio Grande do Norte), while the respective figures for the rural areas are 35 percent, 35 percent and 39 percent. However, we have to keep in mind that these figures represent school attendance rates (total enrollments by age group independent of being adequate or not).

19
currency-based approaches to estimate the growth of domestic vis-à-vis international bond issuances. The results are robust to using these two alternative measures.

For some parts of the analysis we use equity and syndicated loan data, which also come from SDC. The equity data include 43,196 issuances by 13,860 firms (Table 1, Panel B). We classify equity issuances into domestic and international using the residence-based approach. We use syndicated loans because, unlike typical bank loans, they are available for all economies. Moreover, syndicated loan markets capture a sizable share of bank financing and are the most relevant comparison to capital markets in terms of transaction size (amount raised per issuance) and terms of financing, such as debt maturity (Ivashina and Scharfstein, 2010; Cerutti et al., 2015). The syndicated loan data include 25,493 issuances by 9,606 firms (Table 1, Panel C). To distinguish between domestic and international (crossborder) syndicated loans, we compare the nationality of the lead bank that arranges the deal with the residence of the issuing firm. Domestic loans are those in which only domestic banks lead the syndication, whereas international syndicated loans entail the participation of at least one foreign bank acting as a lead arranger.

# _2.3. Firm Size Measure and Firm-level Data_

To study the types of firms issuing in domestic and international markets, we focus on size. We follow the literature that typically uses firm size or collateral to measure access to debt across markets, firms, and over time (Pagano et al., 2002; Beck and Dermirguc-Kunt, 2006; Claessens and Schmukler, 2007; Beck et al., 2008; Campello and Larrain, 2016; de la Torre et al., 2017).

The literature on firms’ issuance activity tends to study the size of issuers through balance sheet data using, for example, firms’ assets.<sup>5</sup> The downside of this approach is that balance sheet data

> 5 See, for example, Pagano et al. (2002), King and Segal (2009), Adrian et al. (2013), Becker and Ivashina (2014), Didier et al. (2015), and Bruno and Shin (2017).

10
**Figure 6: Increase in demand for factors resulting from trade liberalization (GAFTAEU) is reflected in higher factor prices (%)**

<!-- Start of picture text --> 7<br>6.65<br>6.15<br>6<br>6<br>5.33<br>5.03<br>5 4.75<br>4<br>3<br>2<br>1.28<br>1 0.72 0.69 0.78 0.93 0.84<br>0<br>ROMIDE RONAF MOR TUN<br>Capital UnSkLab SkLab<br><!-- End of picture text -->

**Figure 7: Increase in demand for factors resulting from trade liberalization (GAFTA) is reflected in higher factor prices (%)**

<!-- Start of picture text --> 1<br>0.93<br>0.9<br>0.79<br>0.8<br>0.74<br>0.7<br>0.6<br>0.5<br>0.4 0.39<br>0.4<br>0.33<br>0.3<br>0.3 0.28 0.27<br>0.2 0.17 0.17<br>0.14<br>0.1<br>0<br>RONAF ROMIDE MOR TUN<br>Capital UnSkLab SkLab<br><!-- End of picture text --> 15
prices. The passthrough is less pronounced in oil-exporting countries. A few countries, such as Côte d’Ivoire and Ethiopia, have at one point or another suspended the use of formulas in favor of less frequent and ad hoc adjustments. Many countries have also tried to protect the poor by limiting price increases and taxation of kerosene. For 14 African countries for which data are available, petroleum subsidies increased from 0.75 percent of GDP in 2003 to 1 percent in 2005. On average, petroleum taxes are important sources of revenue and constitute close to 2 percent of GDP in indirect taxes.

In a recent Energy Sector Management Assistance Program (ESMAP) study<sup>33</sup> of 31 developing countries, the amount of passthrough in gasoline and diesel was found to be positively correlated with a country’s terms of trade and inflation (GDP deflator) and negatively correlated with a country’s oil vulnerability, overall fiscal position, and per capita income or growth; interestingly, it is also negatively correlated with whether a country is an oil producer or exporter. The debt-to-GDP ratio affects gasoline pricing negatively but not diesel pricing. The 31 developing countries in the sample showed less passthrough of oil cost when compared with industrial countries, particularly with respect to diesel (table 1.10). However, the results for 11 African countries confirmed that African countries have not been delaying adjustment to their fuel prices, even when compared with developed countries; hence, the risks of an unsustained fiscal position appear to be less than in past episodes of oil price shocks. Overall, diesel prices tended to be protected from increases to protect the poor—although such increases are implemented relatively less often in African countries. Outside of a few instances in which market mechanisms were directly suspended, African governments have been actively intervening in the fuel markets in other ways to help the poor or consumers: by adjusting taxes and subsidies, regulating margins, mandating conservation measures and cash transfers, and so forth.<sup>34</sup>

> 33 See Bacon and Kojima (2006).  ESMAP is a global technical assistance partnership that has been administered by the World Bank and sponsored by bilateral official donors since 1983.

> 34 See Bacon and Kojima (2006) for various case studies of African countries.

29
# **6 Conclusion**

Most empirical analyses of migration decisions and patterns are based on static models. A potential migrant is assumed to make a single decision between alternative destinations and his current place of residence based on location-specific utility differences and bilateral mobility costs. Once migration takes place, the payoff and costs are realized and, most importantly, there are no additional movements. The data indicate that this simplistic view is quite off the mark. Many migrants’ paths involve multiple countries and are likely to be the outcomes of complicated dynamic decision processes. Historical evidence and current data provide ample evidence that many migrants live in a series of countries for different lengths of time, experience repeated migration episodes, or have circular migration paths.

There is a relatively new and exciting strand of the literature that is based on innovative dynamic models. They rely on detailed survey data on individual migration histories from single origin or destination countries to estimate the determinants of return or circular migration behavior. Unfortunately, the absence of detailed individual data at the global level prevents us from shedding light on some of the other processes. Our contribution is to construct a novel dynamic model of global migration with the goal of incorporating and explaining transit migration patterns, using the available data. In our model, agents decide to stay in their current location or move to another one every period, taking instantaneous utility payoffs and bilateral mobility barriers into account. This dynamic structure leads to an option value associated with each location, making it attractive as it provides easier access to other locations in future periods. This attractiveness is exactly the basis of the transit migration behavior we observe.

Our empirical analysis relies on transit migration data from the American Community Survey, which asks migrants their place of birth and where they were living the year before they came to the United States. This data indicate that transit migration—people coming to the United States from places other than their birth countries—is actually high, especially among highskilled migrants coming from other high-income OECD countries. We combine the American 30
Nonnemberg, M., Bragaand Mario, and J.C. de Mendonça (2005). “The Determinants of Foreign Direct Investment In Developing Countries.” Estudos Econômicos, Sâo Paulo, 35(4):631-655. Also at <u>http://ideas.repec.org/p/anp/en2004/061.html</u> (August 22, 1007).

- Nunnenkamp, Peter (2002). “Determinants of FDI in Developing Countries: Has Globalization Changed the Rules of the Game? Kiel Working Paper No. 1122, Kiel Institute for World Economics, Kiel.

- Olsen, Karen Holm (2007). “The Clean Development Mechanism’s Contribution to Sustainable Development: A Review of the Iterature.” _Climatic Change_ 84:59-73.

- Polacheck, Solomon W., Carlos Seiglie, and Jun Xiang (2007). “Globalization and International Conflict: Can FDI Decrease Conflict?”  Paper presented at the ASSA Meeting, Chicago, IL, January 5-7.

- Pollins, Brian (1989). ‘Conflict, Cooperation, and Commerce: The Effect of International Political Interactions on Bilateral Trade Flows’, American Journal of Political Science 33: 737-761.Pollins, 1989

- Russett, Bruce and John Oneal (2001). Triangulating Peace: Democracy, Interdependence, and International Organizations. New York, NY: W.W. Norton & Company.Russett and Oneal, 2001

Seres, Stephen (Climatesolutions.ca), (2007). Data Processing of capital cost in CDM Projects.

- Siegel, Jordan I., Licht, Amir N. and Schwartz, Shalom H., 2008. “Egalitarianism, Cultural Distance, and FDI: A New Approach.” (January 2, 2008). Available at SSRN: <u>http://ssrn.com/abstract=957306</u>

- Sigman, Hillary (2004). ‘Does Trade Promote Environmental Coordination? Pollution in International Rivers’, Contributions to Economic Analysis and Policy 3:Article 2.Sigman (2004)

- Sirohi, Smita (2007). “CDM: Is it a ‘Win-win’ Strategy for Rural Poverty Alleviation in India?” _Climatic Change_ 84:91-110.

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**Table A.9: Robustness of treatment results with alternative definition for correct treatment for unstable angina**

||**(1)**<br>Representative sample<br>**All (compare**|**(2)**<br>Dualpractice sample<br>**with table 4)**|**(3)**<br>Representative sample<br>**Unstable angina only (c**|**(4)**<br>Dual sample<br>**ompare with table A8)**|
|---|---|---|---|---|
||Correct treatment|Correct treatment|Correct treatment|Correct treatment|
|**Panel A: SP fixed effects**|||||
|Is a private provider|-0.014|0.119*|-0.112|0.206*|
||(0.063)|(0.069)|(0.088)|(0.121)|
|R-squared|0.075|0.086|0.092|0.070|
|Number of observations|440|201|217|67|
|Mean of public|0.421|0.520|0.459|0.417|
|Mean of private|0.421|0.653|0.360|0.613|
|Mean of sample|0.421|0.587|0.367|0.507|
|**Panel B: SP and market/district f**|**ixed effects**||||
|Is a private provider|0.001|0.122*|-0.065|0.195*|
||(0.069)|(0.070)|(0.118)|(0.115)|
|R-squared|0.196|0.095|0.298|0.186|
|Number of observations|440|201|217|67|
|**Panel C: SP and market/district f**|**ixed effects**||||
|Is a private provider|-0.009|0.126*|-0.203|0.190|
||(0.070)|(0.075)|(0.141)|(0.122)|
|Has MBBS|0.340***||0.233||
||(0.081)||(0.147)||
|Has some qualification|0.164***||0.139||
||(0.057)||(0.095)||
|Age of provider|0.000|-0.004|0.002|-0.006|
||(0.004)|(0.004)|(0.004)|(0.006)|
|Gender of provider (1=Male)|0.256|0.007|0.334**|-0.165|
||(0.158)|(0.111)|(0.170)|(0.162)|
|Patient load during visit|-0.030***|-0.006|-0.022**|-0.034|
||(0.008)|(0.018)|(0.010)|(0.021)|
|R-squared|0.244|0.106|0.352|0.240|
|Number of observations|423|183|208|61|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant. Observations are standardized provider-patient interactions. Columns (1) and (2) also include case fixed effects. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample. Alternative definition for Unstable Angina adds "referral" and "referral for ECG" as correct treatment.

69
|**Table A1: Baseline S**|**urvey Summary**<br>Number of|**Statistics**<br>Low p|**by Airtel Pri**<br>riority<br>|**ority Rati**<br>High|**ng**<br>priority<br>|P-value of<br>|
|---|---|---|---|---|---|---|
||households|Mean|Standard<br>deviation|Mean|Standard<br>deviation|difference in<br>means|
|_Background characteristics_|(1)|(2)|(3)|(4)|(5)|(6)|
|Household head is male|7112|0.79||0.73||0.998|
|Household head's age|7110|42.21|15.05|42.08|14.59|0.604|
|<br>No household member completed primary|7373|0.44||0.45||0.081*|
|Experienced negative shock in last 6m|7399|0.84||0.8||0.962|
|<br>Has access to a phone|7399|0.38||0.45||0.727|
|_Mobile money usage and remittances_|||||||
|Is aware of mobile money|7399|0.58||0.62||0.011**|
|Used mobile money in last 3 months|7399|0.16||0.14||0.663|
|<br>Saved money in last 6 months|||||||
|Any type of savings|7399|0.81||0.78||0.59|
|<br>In mobile money account|7399|0.08||0.06||0.764|
|Received money in the last 6 months|||||||
|Via any channel|7399|0.14||0.15||0.133|
|Via mobile money|7399|0.06||0.07||0.228|
|Transportation costs (Ugandan Shilling)|1075|10913.74|110585.3|3567.5|15173.15|0.749|
|_Occupation_|||||||
|Does non-farm work|7379|0.09||0.12||0.444|
|Self-employed (non-farm)<br>Note: The sample size varies from question t<br>households who received money via any cha<br>in treatment and control group means, clust|7379<br>o question due<br>nnel in the last<br>ered at the EA c|0.04<br>to nonres<br>6 months.<br>luster leve|ponse. Tran<br>Column 6 s<br>l and condit|0.05<br>sportatio<br>hows the<br>ional on s|n costs are f<br>p-value of t<br>trata dumm|0.942<br>or<br>he difference<br>ies.|

29