rafmacalaba/datause-encoder-data huggingface dataset:
suspended classes at every level of schooling in the country, and stay-at-home orders were issued March 16. This interrupted classes partway through the school year, which was to end in June. Cases continued to rise, and on Monday April 6, the Ministry of Education announced that students would not return to school during the current school year. As of May 4, confirmed COVID-19 cases had reached over 31,000, with 1,564 deaths (Ministerio de Salud Pública, 2020), although these numbers are acknowledged to be undercounts due to a lack of testing (Cabrera and Kurmanaev, 2020).

In partnership with the Ministry of Education of Ecuador, Zone 2 (MINEDUC, Zona 2), we have an ongoing project that aims to teach high school students, in grades 10, 11 and 12, skills relevant for careers in entrepreneurship and science, using an online course called _Showing Life Opportunities_ (DOV for the Spanish acronym) that was taught during class time using computers in schools. To be eligible for our project, schools had to have a reliable internet connection and a computer center with enough computers for one class to study. We cover 126 high schools in Zona 2, and more than 80% of students in the targeted grades. Region 2 of Ecuador covers three provinces: Pichincha, a relatively urban province that includes the capital city of Quito, which has the second-highest number of COVID-19 cases in the country (1,732 as of May 4); and two provinces Orellana (39 cases) and Napo (42 cases) that cover the jungle region and have been less directly affected. We randomly selected a sample of 2,412 students from 88 schools to attempt to contact for our phone survey.<sup>1</sup> We used phone contact information that the students had provided for themselves and their parents in our baseline survey, along with additional contact assistance from teachers and the Ministry of Education. Students are 14 to 18 in age, with an average age of 15.9 years.

The phone survey averaged 20 minutes in length, and a team of 9 enumerators carried out an average of 60 surveys a day, commencing on March 31, with a two-day break over Easter (April 12/13). Students were randomly ordered, and our protocol was for an enumerator to make at least three attempts to reach a student, and if unsuccessful, a second enumerator would also make at least three attempts, and if still unsuccessful, the same would be done by a third enumerator. The

> 1 Our ongoing experiment randomized schools to receive either online entrepreneurial training or online placebo classes in Spanish and English. Our sampling frame for the phone survey consisted of 4,163 students in 177 classes in 88 schools. We stratified sampling by treatment status and class, to sample an equal number of students from both treatment arms and include students from all classes.

4
21. Rudiger Dornbusch and Sebastian Edwards, "The Macroeconomics of Populism in Latin America," Policy, Planning and Research Working Paper t316,<sup>The World</sup> Bank, December 1989; Jeffrey D. Sachs, 'Social Conflict and Populist<sup>Policies</sup> in Latin America,' National Bureau of Economic Research Working<sup>Paper No.</sup> 2897, March 1989.

22. For a review, see John Williamson, ed., Inflation and Indexation: Argentina, Brazil, and Israel (Washington: Institute for International Economics, 1985).

23. This is particularly true for hyperinflations, and a large technical literature has emerged on them, though we do not address it here. The following draws on Rudiger Dornbusch, "Mexico: Stabilization, Debt and Growth,w in Economic Policy 7 (October 1988), pp. 251-262.

24. See Thomas E. Skidmore, "The Politics of Economic Stabilization in Postwar Latin America,' in James Malloy, ed., Authoritarianism and Corporatism<sup>in</sup> Latin America (Pittsburgh: University of Pittsburgh Press, 1977); John Sheahan, "Market-oriented Policies and Political Repression in Latin America,' Economic Development and Cultural Change 28, 2 (1980): 267-291; Karen Remmer, "The Politics of Economic Stabilization: IMF Standby Programs in Latin America, 1954-1984," Comparative Politics 19 (October 1986), 1-25; Stephan Haggard, 'The Politics of Adiustment: Lessons from the IMF's Extended Fund Facility,' in Miles Kahler, ed., The Politics of International Debt (Ithaca: Cornell University Press, 1986); and the review of the literature in Haggard and Kaufman, "The Politics of Stabilization and Structural Adjustment,'<sup>in</sup> Jeffry Sachs, ed., Developing Country Debt and Economic Performance (Chicago: Chicago University Press, 1989), pp. 232-239.

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and sharp contraction of credit followed. (For a detailed description, see Hampl and Matoušek 2000.) Between 1997 and 2000 the credit-to-GDP ratio declined from more than 60 percent to less than 40 percent (Chart 1). Considering the unfavorable situation in the major financial institutions, the authorities decided to resume the privatization process with the goal of finding strong strategic investors. By the end of 2001 the banking sector was restructured and privatized. During the process, all major domestic banks were acquired by banking groups from the European Union. Soon these banks started to extend loans to the segment of households that had been largely ignored by pre-crisis, semi-state banks. With the acceleration of the economic recovery, the performance of the banking sector improved significantly, and the share of nonperforming loans decreased to less than 5 percent in 2003. The financial system simultaneously regained its dynamics, and a strong pickup of credit growth followed. At least part of this dynamic has to be linked to overly optimistic expectations associated with EU membership.

**Chart 3. Year-over-year growth rates of a Chart 4. Bank loans to the private sector in bank loans in the Czech Republic (2001–2011, the Czech Republic (2000–2011, CZK billions) in %)**

<!-- Start of picture text --> 50<br>2200<br>40<br>30 1800<br>20<br>1400<br>10<br>0 1000<br>-10<br>600<br>-20<br>-30 200<br>-40<br>1/01 1/02 1/03 1/04 1/05 1/06 1/07 1/08 1/09 1/10 1/11 -200<br>Total loans I.00 I.02 I.04 I.06 I.08 I.10<br>Non-financial corporations Total loans Nonfinancial corporations Households<br>Households<br>Source: CNB Source: CNB<br><!-- End of picture text --> From the beginning of 2002 until the middle of 2008, credit to the household sector grew by 30 percent or slightly more on a year-over-year basis (Chart 3), albeit initially from a very low base (Chart 4 ). Credit to the nonfinancial corporate sector exhibited a much slower dynamic, but between the beginning of 2006 and the middle 2008 it also rose rather fast (15–20 percent). Unlike the household credit, the nonfinancial corporate credit started from a much higher base, even though its absolute level at the beginning of 2011 was roughly the same as at the beginning of 2000.

Despite comparatively strong credit growth observed in from 2003 to 2007, the stock of bank loans relative to income in the Czech Republic was still relatively low at the end of 2007.<sup>2</sup> This applies not only to a comparison with the advanced euro area economies but also with the most economies from the Central and Eastern European (CEE) region. Chart 5 reveals that in

> 2 One limitation of this comparison is that it is based solely on data on domestic bank loans. This indicator may understate total private credit a bit, as it neglects loans provided by nonbank financial intermediaries and loans provided directly from abroad.

4
Figure 2 Average Reimbursement for Facility Delivery 2008–11 <!-- Start of picture text --> Amount ($)<br><!-- End of picture text --> <!-- Start of picture text --> Complicated<br>Complicat edd elivery,<br>delivery, 2010 – 11,<br>2009 – 10, 88.29<br>Complicated<br>79.54<br>delivery,<br>2008 – 09,<br>61.89<br>Normal<br>Normal<br>Normal<br>delivery,<br>2008delivery, – 09, 2009delivery, – 10, 201019.09 – 11,<br>15.49<br>13.58<br>Normal delivery Complicated delivery<br><!-- End of picture text --> <!-- Start of picture text --> ––<br><!-- End of picture text --> _Source:_ Mazzilli 2011.

The program management cost of the HealthyBaby voucher service was initially higher than that of service reimbursement to health facility deliveries.<sup>1</sup> However, as sales of vouchers increased (indicated by arrow 1 in figure 3), program costs declined and reimbursement costs increased. Reimbursement costs declined between mid-2010 and early 2011 (between arrows 2 and 3) because of the suspension of voucher sales (Mazzilli 2011).

Figure 3 HealthyBaby Voucher Reimbursement versus Program Costs 2008–11

> 1 According to the Cost Analysis Report for the Reproductive Health Voucher Pilot Project in Western Uganda, Report for the World Bank and the Ministry of Health of Uganda, May 2013, the total project expenditures were as follows: 63% as direct reimbursements for service delivery; 29% as Programme Management Overheads, including salaries of all staff; and 8% on BCC and voucher management related costs like production, distribution, and claims processing.

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instructional quality is affected by testing whether the student teacher ratio increases when class sizes increase through the introduction of CCT programs.

# **3. Program Description**

As part of the government of Pakistan’s commitment to the reduction of gender disparity in education, it initiated a conditional cash transfer program targeted to female students in Punjab. Under the program girls receive a stipend of Rs. 200 per month (approximately US$ 3.5) if they are enrolled in grades 6 to 8 in a government school and maintain a minimum attendance of 80 percent.<sup>3</sup> The stipend is paid in quarterly payments to the student via a postal money order. To give this amount context, consider that a day’s unskilled wage for a male in these areas is estimated to be Rs. 60. Thus an unskilled male who works 30 days a month can expect to receive Rs. 1800 a month. The stipend, then, amounts to one-ninth of the average monthly income for an unskilled worker.

The program was announced in 2003. The government did not consider randomization politically feasible and therefore used district-level literacy rates for the population aged 10 and over according to the 1998 census (the most recent census at the time) to determine which districts would be eligible to receive the stipend program. Districts with literacy levels below 40 percent were deemed most in need and hence were eligible for receipt of the stipend program. Those districts with literacy rates higher than 40 percent were deemed ineligible. Implementation of the program reportedly did not begin in full until the second quarter of 2004. Figure 1 reports the literacy rates of the treatment and comparison districts on a spatial map, allowing the reader

> 3 Anecdotal evidence suggests that the 80% attendance requirement is not strictly enforced and that enrollment is a more salient condition.

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experimented with this but found it difficult to obtain reasonable estimates of the effect of money growth on disaggregated consumer prices. Another possibility is to simply allow for a time trend in the regression for each good, to capture the upward trend in domestic prices during the period. A third possibility is to simply ignore the domestically-induced changes in non-traded goods prices and drop them from the regressions.

In any case, denoting the growth rate of the simple average of these items in each region as Δ lnP<sup>ˆ</sup> rtN , I obtain the following empirical specification: (5) Δ lnPirt = γ 0ir + γ 1ir (L) ⋅ Δ lnE t + γ 2ir (L) ⋅ Δ lnC t + γ 3ir ⋅ Δ lnP<sup>ˆ</sup> rtN + eirt where γ 0ir = (1− α ir ) ⋅β0ir is the intercept; γ 1ir (L) = (1− α ir ) ⋅β1ir (L) captures the effect of the exchange rate on consumer prices; γ 2ir (L) = (1− α ir ) ⋅β 2ir (L) captures the effect of foreign costs on consumer prices; γ 3ir = α ir captures the contribution of changes in nontraded goods prices; and eirt = α ir ⋅ Δv irt + (1− α ir ) ⋅Δ uirt is the error term. Since this composite error term is by assumption uncorrelated with the right-hand-side variables, I can estimate Equation (5) by ordinary least squares. In practice, I measure all growth rates as monthly observations on quarterly log differences, and I allow for 3, 6, and 9 month lags of these growth rates in the estimation. Since I have monthly data from July 2000 through July 2005 this gives me 60 monthly data points on which to estimate this specification for each item and region.

# **2.2. Results**

I first calculate the long-run pass-through coefficient as the sum of the coefficients on the current and lagged exchange rate variables, i.e.<sup>γ</sup> ˆ 1ir ()1 , for each of the 160 product-region combinations for which I have data. Figure 2 provides a visual summary of the pass-through estimates, and Table 3 reports some summary statistics. In the top panel of the graph I report pass-through estimates for some aggregate categories, and in the bottom panel I report estimates for disaggregated food items. I organize the pass-through estimates by product, and use box-plots to show the distribution across the 8 regions of our estimates of pass-through for each product. In 7
# **3.2 Risk of low education attainment**

Existing studies have shown that children raised in poor households face a large risk of achieving a low level of educational attainment and dropping out of school. The intergenerational transfer of low levels of education is high in poor households. Parents with little school attainment tend to raise children that also obtain little formal schooling. A large share of the fathers and mothers of the youth have never attended school, 19 and 23 percent, respectively. An even greater share of the parents of the adults did not attend school (48 percent).

The data reveal that 71 percent of the fathers of the youth have not completed primary education compared to 84 percent of fathers of the adults. This compares to 76 percent of the mothers of the youth that have not completed primary education and 88 percent of the mothers of the previous generation. Hence, in poor urban neighborhoods the mothers have acquired slightly more education than the fathers over the past generations.

**<u>Figure 2:   Youth and Adults Education Attainment (percent)</u>**

<!-- Start of picture text --> 60<br>Youth Adults<br>50<br>40<br>30<br>20<br>10<br>0<br>No Schooling Primary Incomplete Primary CompleteSecondary IncompleteSecondary CompleteUniversity IncompleteUniversity Complete<br><!-- End of picture text --> <u>Source: Data collected by authors in poor urban neighborhoods in the Northeast Brazil.</u> 14
<!-- Start of picture text --> Figure 2: Implied Step Sizes<br>0.6<br>0.4 17<br>0.2 7 12 13 15<br>0<br>-0.2<br>-0.4<br>-0.6<br>Years of Schooling<br>1998 2002<br><!-- End of picture text --> Others have also found evidence of thresholds associated with non-certificate years, such as Arabsheibani and Manfor (2001) for Libya, Patrinos (1996) for Guatemala and Griffin and Cox Edwards (1993) for Brazil.

# **Weak versus Strong Screening**

We also test what has come to be known as the “weak versus the strong version of the screening hypothesis.” This hypothesis draws a distinction between the weak and the strong version of the screening hypothesis, depending on whether employers pay irrational wages at the initial hiring point (weak) or continuously thereafter (strong). The empirical strategy is to split the sample into public and private employment and compare the returns by sector of employment. The returns to another year of schooling are also higher in the private sector, but the rate of change is about equal for both sectors between 1998 and 2002 (Table 3). The returns to schooling in 1998 were more than 10 percent in the private sector, and less than 9 percent in the public sector. In 2002, the returns to schooling in the public sector increased to 10 percent, but surpassed 11 percent in the private sector.

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15 In this section we present estimates of the economic returns to implementing a package of nutrition-specific interventions whose costs and stunting impacts have been estimated and reported in a peer-reviewed journal, namely the package devised by Bhutta et al. (2013).<sup>5</sup> At the time of writing, this is, in fact, the only package for which both region-specific costs and stunting impacts have been reported in a public-domain document. We assume a gradual scale-up of intervention coverage from current rates to 90% (the coverage rate assumed by Bhutta et al.). This package is then implemented each year thereafter at 90% coverage. We estimate the benefits on the assumption that in the absence of the program stunting would have fallen at an annual rate of -1.5% p.a.<sup>6</sup>

# _3.1 The Bhutta et al. nutrition package_

The Bhutta et al. package includes 10 interventions, and we assume each is increased to 90% coverage. We estimate the effects on stunting and costs for 34 countries<sup>7</sup> that together account for 90% of the world’s stunted children. The interventions (with, in parentheses, annual aggregate costs across the 34 countries in 2010 international dollars) are: (i) salt iodization ($68m), (ii) multiple micronutrient supplementation in pregnancy including iron-folate ($472m), (iii) calcium supplementation in pregnancy ($1,914m), (iv) energy-protein supplementation in pregnancy ($972m), (v) vitamin A supplementation in childhood ($106m), (vi) zinc supplementation in

> 5 At the time of writing, this is, in fact, the only package for which both region-specific costs and stunting impacts have been reported in a public-domain document.

> 6 We obtain this figure by running a fixed effects growth model (with countries as the fixed effects) on the September 2015 version of the JME data set. The model is of the form: ln( _S_ it)=  +  _t_ + _ui_ + _eit_ , where _Sit_ is the number of stunted children in country _i_ in year _t_ , _t_ is the year, _ui_ is the country fixed effect, and _eit_ the error term. The coefficient  is the growth rate, and the predicted number of stunted under-fives in year _t_ is equal to α��β<sup>�</sup> t�u��, where α� denotes the estimate of  , etc. The country fixed effect model allows us to get an _estimated_ stunting figure for every country for every year, including in years after 2014 for which we have no stunting data.

> 7 The countries are: Afghanistan, Angola, Bangladesh, Burkina Faso, Cameroon, Chad, Congo, Dem. Rep., Côte d'Ivoire, Egypt, Arab Rep., Ethiopia, Ghana, Guatemala, India, Indonesia, Iraq, Kenya, Madagascar, Malawi, Mali, Mozambique, Myanmar, Nepal, Niger, Nigeria, Pakistan, Philippines, Rwanda, South Africa, Sudan, Tanzania, Uganda, Vietnam, Yemen, Rep., and Zambia.
measuring income, most localities use other approaches. The 2007 national policy allowed local officials to classify households in tiers according to their apparent level of poverty and to set fixed benefit amounts associated with each tier. This tier‐classification approach appears to have been widely used (World Bank Social Protection Group 2010).

Several reports have noted that although such flexibility has advantages, it gives local officials and village leaders considerable discretionary power. The program does not appear to have well‐functioning checks and balances, in part because of limited resources at the local level for administration of the program. These features of the program create the potential for irregularities (World Bank 2011). In the Chinese‐language media reports of rural dibao irregularities are numerous, so much so that they have been classified into standard categories: giving dibao on the basis of connections or personal relationships ( _guanxi bao, renqing bao_ ), cheating ( _pian bao_ ), and mistakes ( _cuo bao_ ). An internet search using Baidu yielded many reports of irregularities in multiple localities, including a widely discussed case of dibao corruption in Fang County, Hubei, as well as cases in Shaanxi, Shandong and Guangxi.

Problems with the dibao program are of concern to China’s central leadership and policy circles. In 2012 Guoqiang He, a member of the Politburo Standing Committee and Secretary of the Central Commission for Discipline Inspection, gave a speech about the problem of corruption in China that explicitly mentioned corruption in the dibao program, which he referred to using the phrase “a tide of unhealthy practices in urban and rural dibao ( _chengxiang dibao zhongde buzheng zhi feng_ )” (Zhu 2012). He outlined major reasons for these problems: “first, local village and township cadres don’t do their jobs, they don’t go out to the villages and meet with the people, don’t really understand and grasp which are the households in difficulty; second, dibao work is not sufficiently transparent and open; and third, a few village and township cadres are selfish and looking out for their own benefit, and they give dibao benefits to relatives, friends, or even themselves.” The Ministry of Civil Affairs has openly acknowledged the existence of such irregularities and called for improvements in dibao work. A recent news report published comments by the Minister of Civil Affairs regarding the findings of an internal review of the dibao program. The Minister reported that the review found cases of cheating, mistakes, and awards based on 7