rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9284

# **Abstract**

School closures due to COVID-19 have left more than a billion students out of school. This paper presents the results of simulations considering three, five and seven months of school closure and different levels of mitigation effectiveness resulting in optimistic, intermediate and pessimistic global scenarios. Using data on 157 countries, the analysis finds that the global level of schooling and learning will fall. COVID-19 could result in a loss of between 0.3 and 0.9 years of schooling adjusted for quality, bringing down the effective years of basic schooling that students achieve during their lifetime from 7.9 years to between 7.0 and 7.6 years. Close to 7 million students from primary up to secondary education could drop out due to the income shock of the pandemic alone. Students from the current cohort could, on average, face a reduction of $355, $872, or $1,408 in yearly earnings. In present value terms, this amounts to between $6,472 and $25,680 dollars in lost earnings over a typical student’s lifetime. Exclusion and inequality will likely be exacerbated if already marginalized and vulnerable groups, like girls, ethnic minorities, and persons with disabilities, are more adversely affected by the school closures. Globally, a school shutdown of 5 months could generate learning losses that have a present value of $10 trillion. By this measure, the world could stand to lose as much as 16 percent of the investments that governments make in the basic education of this cohort of students. The world could thus face a substantial setback in achieving the goal of halving the percentage of learning poor and be unable to meet the goal by 2030 unless drastic remedial action is taken.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jazevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
5 to 8 in turn. Each column corresponds to a different grade. I find evidence to suggest that there might exist a spillover effect of this gender-targeted conditional cash transfer program on boys’ enrollment. Enrollments increase for boys in the treated districts in all the classes reported in Table 8. In percentage terms these gains are largest for boys in class 8 when comparing 2008 to 2004. To be sure there are gains made by boys in all classes for virtually each year of data available. However the estimates vary and are largest for more recent years.

# **7.3 Results: Instructional Quality**

With substantial increases in enrollments for both boys and girls, the logical question to ask is whether quality of instruction is suffering as a result of increasing class size? In the absence of test score data for these students, I proxy for instructional quality using the studentteacher ratio. In this regard the detailed nature of the data is particularly useful. In developing countries in general and in Pakistan in particular, there is often a disparity between the number of teachers a school ought to have and the number of teachers it does have. This is particularly true in hard to reach areas where the positions may have been sanctioned but no suitable or available candidates have filled them. The school census form asks schools to report the number of teaching posts that are filled as of the time of the survey and the number of positions that have been sanctioned. I construct two measures of the student teacher ratio. One uses the number of sanctioned teachers as the denominator and the other uses the actual number of posts filled. This then provides us with an actual student-teacher ratio and an ideal student-teacher ratio (at least as envisioned by the sanctioning authority). The means of these variables are reported in Table 2 for both the average school in the treatment district and the average school in the control district. There is some evidence to suggest that the actual student teacher ratio (regression estimates are 15
Table 7: The Percent of Complete-Destruction Tropical cyclones

|Climate|Current|Future|
|---|---|---|
|Model|Climate|Climate|
|CNRM|1.3%|1.6%|
|ECHAM|6.1%|6.0%|
|GFDL|4.5%|4.4%|
|MIROC|4.7%|6.3%|

There are several sources of uncertainty in the modeling. In order to get a better sense of the relative importance of different assumptions, we undertake a sensitivity analysis. The report has already shown the relevance of different climate scenarios by showing the results from 4 different climate models. In this sensitivity analysis, we examine several other assumptions. A higher population leads to slightly lower damages because of the negative elasticity with respect to population density. Higher GDP leads to higher income which increases baseline damages about 9% and climate change damages about 7 percent. However, the results are quite sensitive to changes in the damage function. A unitary population elasticity of damages increases baseline damages by 25 percent and climate change damages by 23 percent. A unitary income elasticity increases baseline damages by over 250% and climate change damage by 230%. The empirical finding that the elasticity of population and income is less than one is very important. The fact that many earlier analyses simply assumed these elasticities were unitary would have caused these studies to overestimate damages.

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holds could access new credit. If the program had this effect, we would expect this to be reflected in greater investment and increased agricultural productivity. To test whether this is the case, we take advantage of a district level panel on crop yields and commodity prices from the Indian Department of Agriculture. The dataset, which we describe in Section 3 and Appendix E, contains detailed information on agricultural revenue and area cultivated, which allows us to construct time series of agricultural productivity over the time period 2001-2011 for 387 districts in our sample. Table VII, columns [1] to [4], estimate the impact of the stimulus program on agricultural productivity. The results show that the program had no discernible effect on agricultural productivity. Using our preferred specification, the estimated effect is a precise zero (ˆ _γ_ =-0.006, s.e.=0.010), suggesting that the stimulus did not create investment incentives of a magnitude sufficient to affect agricultural productivity.

Second, we investigate the effect of debt relief on real wages, to test for a possible labor market impact of the bailout. Unskilled labor is an important input to agricultural production. Hence, if the bailout had a positive impact on households’ ability to invest in productive inputs, one might expect to find this effect reflected in rural employment. To test for this channel, we construct a panel of real wages for the period 2006-2011. The data capture monthly wages for unskilled agricultural labor in a sample of 327 districts, which we average for all agricultural occupations and over all reported crop seasons to account for seasonal fluctuations in labor demand. In columns [5] to [8], we test for an impact of the bailout on rural wages using the same regression specifications as in columns [1] to [4], and do not find a discernible effect of the bailout using. The coefficient estimate from our preferred specification is negative, close to zero (ˆ _γ_ =-0.07, s.e.=0.099) across all specifications. The result also remains unchanged when we restrict the sample to non-urban districts with a rural population share of 75 percent and above.

Finally, we turn to the effect of the bailout on household consumption. If households perceived the bailout as a permanent change in their access to credit and more productive 24
the respondent. When surveys such as India’s National Family and Health Surveys (NFHS) involve direct questions about sensitive information, as in the case of violence against women, respondents may fail to cooperate—through either nonresponse or dishonest response (Chaudhuri and Mukerjee, 1988; Tourangeau and Yan, 2007). As a result, the quality and reliability of prevalence data tend to be poor. Without correction, such data will likely yield undesirable outcomes such as over- or under-estimation (bias) of the actual prevalence or incidence of violence and inaccurate relationships between the variables of interest. This will result in inaccurate conclusions and recommendations and, in turn, wrong policy choices (Tourangeau and Yan, 2007).

This paper addresses the problem of underreporting prevalence by utilizing the list randomization technique—an alternative to the method of direct questioning—to measure the incidence of domestic violence and physical harassment of women on public and private buses in the Indian state of Kerala. The purpose of this paper is to measure (1) the incidence of domestic violence and physical harassment on public and private buses in Kerala; (2) the extent of underreporting of the incidence of both forms of violence listed; (3) the incidence of both forms of violence among population sub-groups; and (4) the incidence of underreporting of both forms of violence based on respondent characteristics to understand the types of respondents who are more likely to underreport. Some caveats of the study should be noted as well. The levels of incidence of domestic violence and physical harassment on public/private buses that are presented in this study can be viewed as a lower bound of these rates. This is because the incidence that is measured through this study is at the household level and not at the individual level. In cases where there is more than one woman facing domestic violence or physical harassment on public/private buses in a household, the rate of incidence at the individual level can be higher.

Our results indicate that the level of underreporting is over nine percentage points for domestic violence and negligible for physical harassment in public/private buses. Urban households, particularly the urban poor households, tend to have higher levels of incidence of domestic violence. Further, professionally educated and females tend to underreport more. Underreporting is also higher in the 4
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|**MDG**|**Policy Objectives**|**What to do?**|**Link with the Budget**|
|---|---|---|---|
|**_Goal 6. Combat HIV/AID_**|<br>**_S, malaria and other diseases_**|h h idili||
||•<br>Reduce the incidence of the<br>HIV/ADIS, tuberculosis and malaria<br>epidemics|•<br>Strengten te epemoogc<br>monitoring systems, ensure greater<br>availability of treatments and inform<br>onprevention||
|**Target 7.**Havehalted by20|15 and beguntoreverse the spread of|HIV/AIDS||
|_Indicator 18._HIV prevalence (%)<br>**Target 8. **Havehalted by20|15 and beguntoreverse theincidence|of malaria and other majordiseases|•<br>Provide for free certain tests and<br>medicines such as HIV/AIDS tests,<br>condoms, ART, micro-nutrients<br>•<br>Set up centers for anonymous and free<br>HIV testing, as well as services of<br>psychological and social support<br>|
|_Indicator 21._ Prevalence and<br>death rates associated with<br>malaria|||•<br>Allocate enough resources to the<br>implementation of the PNLT<br>•<br>Ensure the availability of anti-<br>tuberculosis vaccines and drugs<br>•<br>Expand the coverage of the TB short,<br>directlysupervised treatments(DOTS)|
|_Indicator 23._Prevalence and<br>death rates associated with<br>|||•<br>Subsidize free supply of insecticide-<br>treated mosquito nets in the endemic|
|tuberculosis(WHO)|||zones_._|
|**_Goal 7. Ensure environme_**|**_ntal sustainability_**|||
|**Target 10.**Halve, by2015,|the proportionofpeople without sustai|nable access to safe drinking waterand b|asic sanitation<br><br>|
|_Indicator 30._Proportion of<br>population with sustainable<br>access to an improved water<br>source, urban and rural (%)|•<br>Promote greater access to water at an<br>affordable price, notably for the<br>poorest segments of the population|•<br>Increase access to potable water and<br>sanitation services, research new water<br>sources and improve governance in the<br>sector|•<br>In urban areas, upgrade water<br>infrastructure in Nouakchott,<br>Nouadhibou, Selibaby and Kaedi<br>•<br>Pursuit the works of the A. Essaheli<br>•<br>Fund water distribution in Nouakchott<br>•<br>In rural areas, build 136 water<br>networks in centers between 500-5000<br>people, and 166 basic water<br>infrastructure in centers between 150-<br>500 people<br>•<br>Construct 145 water points<br>•<br>Rehabilitate 8000 family toilets<br>•<br>Install 1000 toilet mobile blocks|

_Source:_ Authors’ elaboration from Government’s sectoral priority action plans (GIRM 2007b).
in Syria therefore calls for a reduction in the number of tariff bands that are currently applied, from the existing 11 to perhaps 3 or 4 bands, with the lower bound for _ad valorem_ tariffs held at or above the nuisance level of 5%. In addition, the myriad number of fixed and proportional trade taxes can be removed with revenue neutrality ensured by the implementation of an appropriately sized VAT.

# **References**

- Abedini, Javad & Nicolas J. P ́eridy (2008). “The Greater Arab Free Trade Area (GAFTA): An Estimation of its Trade Effects”. _Journal of Economic Integration_ 23(4) (December): 848–872

- Alesina, Alberto F. & Allan Drazen (1991). “Why are Stabilizations Delayed?” _American Economic Review_ 81(5) (December): 1170–1188

- Armington, Paul S. (1969). “A Theory of Demand for Products Distinguished by Place of Production”. _IMF Staff Papers_ 16(1) (March): 159–178

- Bevan, David L. (1999). “Trade Liberalization and the Budget Deficit”. _Journal of Policy Modeling_ 21(6) (November): 653–694

- Brenton, Paul, Christian Saborowski, Cornelia Staritz & Erik von Uexkull (2009). “Assessing the Adjustment Costs of Trade Policy Reform Using TRIST (Tariff Reform Impact Simulation Tool)”. _World Bank Policy Research Paper_ 5045, Washington, DC: The World Bank

- Chemingui, Mohamed Abdelbasset & S ́ebastien C. Dessus (2008). “Assessing Non-Tariff Barriers in Syria”. _Journal of Policy Modeling_ 30(5): 917–928

- Davidson, Carl, Steven J. Matusz & Douglas R. Nelson (2006). “Fairness and the Political Economy of Trade”. _The World Economy_ 29(8) (August): 989–1004

- Davidson, Carl, Steven J. Matusz & Douglas R. Nelson (2007). “Can Compensation Save Free Trade?” _Journal of International Economics_ 71(1) (March): 167–186

- Fern ́andez, Raquel & Dani Rodrik (1991). “Resistance to Reform: Status Quo Bias in the Presence of Individual-Specific Uncertainty”. _American Economic Review_ 81(5) (December): 1146–55

- Gait ́an, Beatriz & Bernd Lucke (2007). “The Barcelona Initiative and the Importance of NTBs: A Dynamic CGE-Analysis for Syria”. _International Economics and Economic Policy_ 4(1) (April): 33– 59

- Grossman, Gene M. & Elhanan Helpman (1994). “Protection for Sale”. _American Economic Review_ 84(4) (September): 833–850

- Ianchovichina, Elena (2004). “Trade Policy Analysis in the Presence of Duty Drawbacks”. _Journal of Policy Modeling_ 26(3) (April): 353–371

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variables themselves. As we. will see below, money, income (consumption), and nominal interest rates are often non-stationary processes (more specifically integrated of order one or I(l) processes). Hence, the "true" long-run relationship among these variables can only be assessed if cointegration holds. Most linear combinations among the level of I (1) variables are I (1), except for the cointegrating vector(s) (if it exists), in which case the linear combination is I (0) (Engle and Granger, 1987). This cointegrating vector captures a long-run relationship among the. variables. Loosely speaking, although the variahles may be moving everywhere as time goes on, there is (are) a relationship(s) among the variables that remains between certain limits. In our case real balances, consumption (or income) and some measure of the opportunity cost of money could be a stationary process. The coefficients of this linear combination describe the demand for money. Money demand relationships have been typiGal example in the development of tests and estimation procedures on cointegration (Engle and Granger, 1987, Stock and Watson, 1989, and Johansen and Juselius, 1990). They have also been recently used for estimation in developing countries (Melnick, 1989 and Laban, 1990).

Another important issue we want to explore is what is the right scale variable: private consumption or GDP. Our model is obtained -from household optimization, in which case the relevant scale variable is private spendinglO. Using the wrong scale variable will produce estimators without clear interpretation. It also has different policy implications. For example, Mankiw and Summers (1986) show in an IS-LM framework that when consumption is the relevant scale variable, a tax cut may be contractionary because of a large

> 10 If money is an input in the production process, it could be the case that firm's demand for money would be a function of output.

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<!-- Start of picture text --> TFP<br>4<br>3.5<br>3<br>2.5<br>2006 2007 2008 2009 2010 2011 2012<br>Non exporting firms Exporting firms<br>Source: Authors calculation based on GeoStat firm level data<br><!-- End of picture text --> At the same time as firms increased their revenues from exporting, they also diversified their export mixes both in terms of products and markets. The average number of exported products per firm grows from 2.2 in 2006 to 3.9 in 2012 while the average number of destinations reached by Georgian firms goes from 2.4 in 2006 to 3.3 in 2012. This is relatively low when compared with a large, developed economy such as the US. In fact, for that country, Bernard et al. (2005) reports that in 2000 the average number of products exported by firms was 8.9 while the average destinations were 3.5 countries. The comparison with other developing countries, such as Peru, Bulgaria and Mexico for example, shows that Georgian firms are relatively more diversified across destinations than products (Table 2). In addition, export revenues increase by a factor of three in nominal terms (when looking at export volumes, rather than values, these double between 2006 and 2012).

However, these figures mask the fact that some firms produce very little of a given product. We also look at the number of “significant” products per firm. A product is considered “significant”, if it explains more than 1% of total revenues of the firm. When we consider significant products only, then, the average number of products firms export is almost halved and remains quite stable across years. Thus, the observed increase in the number of products firms export is mainly due to small transactions while the bulk of firms’ export remains concentrated on few products.

**Table 2: Cross-country comparisons on products and destinations per exporter**

|**Country**|**Avg. num. of**<br>**products per**<br>**exporter**|**Avg. num.**<br>**destination per**<br>**exporter**|
|---|---|---|
|Albania|3.5|1.5|
|Bulgaria|6.2|2.4|
|Mexico|7.2|2.2|
|Peru|7.5|2.6|
|US|8.9|3.5|

Source: Exporter Dynamics Database (EDD); Bernard et al (2005) for the US; Volpe and Carballo (2009) for Peru.

Moreover, despite the fact that firms have been diversifying in terms of products and destinations, most export growth is still accounted for by more exports of the same products to the same destinations. An export growth decomposition into the intensive and extensive margins shows 7
one or two experimental waves. The available sample was the full population of first graders in the Bridge Kenya schools in term 3 of 2020, see section 3.

**Choosing the Research Design.** Even if constraints narrow down the design space, the experimenter may still need to decide whether to use adaptive sampling and choose sample size, wave size, and number of waves to run. An added consideration is that even for a given sample size _N_ , there are some costs to conducting testing in waves.

_Per-wave Implementation Costs._ Maintaining the infrastructure for data collection and interventions for all treatment arms, including the human capital costs of managing the experiment, adds fixed costs _ct_ per wave, on top of any per-unit costs _c_<sup>_k_</sup> _i_<sup>(whichmayvarybytreatmentarm).</sup> _Cost of Delay._ Each new wave adds delay until the gains from the experiment – the average estimated treatment effect of the best arm – are realized for all potential beneficiaries.

Balancing these costs are the efficiency gains from adaptivity. It is computationally involved to estimate these gains, and so the researcher can typically only consider a small number of designs. Here, we briefly discuss two situations that will frequently arise in practice. First, experimenters often have a fixed _N_ available and have to decide whether and how to divide the sample into waves. Second, the experimenter may need to decide at time _t_ whether to run an additional wave in _t_ + 1.

This could be set up as a simple optimization problem. For example, consider choosing the number of waves _T ∈{_ 1 _, . . . , T_<sup>_max_</sup> _}_ for given sample size _N_ , so that the wave size is _Nt_ = _N/T_ (assuming equalsized waves for simplicity). We would expect more efficient learning with more waves and more chances to adapt, and indeed the simulations in Kasy and Sautmann (2021a) with data from three existing experiments show how splitting the sample into 2, 4, and 10 waves monotonically shrinks the expected policy regret. In practice, however, the marginal gains are likely decreasing in _T_ .<sup>9</sup> Moreover, the gains must be weighed against the cost. The experimenter might solve The second term penalizes the cost of increasing _T_ . The first term is the term of interest: the expectation of the number of beneficiaries _M_ times the per-person outcome in the chosen arm _θ_<sup>_k∗_</sup> , discounted by _δ_<sup>_T_</sup> due to the implementation delay.

> 9This is at least in part due to indivisibility issues. As _T_ grows and the wave size shrinks, it becomes harder to implement the adaptive algorithm faithfully, and the actual assignment shares may differ substantially from the exploration sampling shares _qt_<sup>_kN_</sup> _t_<sup>_k_,especiallyifthesampleisalsostratified(seealsosection5).Withmanytreatmentarms,insmallwavessomearmsmay</sup> not be assigned at all, updating about these arms will proceed slowly in terms of _t_ , and the assignment shares may remain far from optimal for a long period of time.

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