rafmacalaba/datause-encoder-data huggingface dataset:
the Education Assessment System (SAEB).<sup>10</sup> Students take the test at the end of the school year (between October and November). In 2007, children took the test between November 5 and 20; and in 2009 between October 19 and 31 (approximately, two months after the school shutdowns). For fifth-graders, proficiency in reading has a scale ranging from 0 to 325, and math has a scale ranging from 0 to 350 (SAEB scale). The student’s proficiency can be classified as insufficient, basic, or advanced.

In addition to answering reading and math questions, students filled out a socioeconomic questionnaire with information on their parents’ educational attainment; incentive from the family to pursue an education; time watching TV, time spent on the internet, time dedicated to reading books, and to homework; if they already dropped out or repeated a grade; and if they attended kindergarten.

Data from the School Census and _Prova Brasil_ are used to calculate the National Education Development Index (IDEB), the most important educational indicator in Brazil, that monitors students’ grade promotion and learning levels.<sup>11</sup> Brazilian State and municipal governments use this indicator to monitor the quality of public education and compare the schools’ performance within and between municipalities.

# **4 Empirical strategy**

To estimate the treatment effects of the 2009 school closures due to the H1N1 outbreak on fifthgraders proficiency in reading and math, we explore the policy variation between and within the municipalities of the state of São Paulo. We focus the analysis on grade five because only five of the 13 municipalities that opted for extending the winter break had proficiency scores available for grade nine in 2007, our ’baseline’ year.

To estimate the impact of the shutdowns on learning, we first look at the impacts on locally-managed schools. To do so, we explore the policy variation between municipalities

> 10SAEB stands for _Sistema de Avaliação da Educação Básica._

> 11 IDEB stands for _Índice de Desenvolvimento da Educação Básica_ . To compute the index, the student’s reading and math performance is transformed into a standardized proficiency ranging from 0 to 10. The index is then multiplied by the student’s grade promotion rate (on a scale from 0 to 1) to obtain the IDEB at school, municipal, state, and country levels. For example, for primary education, the index is the product of the standardized performance of fifth-graders and the average grade promotion from first to fifth grade.

7
# **Development Banks: Role and Mechanisms to Increase their Efficiency**

# **Eva Gutierrez**

**Heinz P. Rudolph**

**Theodore Homa Enrique Blanco Beneit**<sup>**1**</sup>

**The World Bank**

## **JEL: G28, G21**

> 1 Eva Gutierrez is a Sr. Financial Sector Specialist in the Latin America and Caribbean Region (egutierrez2@worldbank.org). Heinz P. Rudolph is a Senior Financial Sector Specialist in the Private and Financial Sector Development Department (hrudolph@worldbank.org). Theodore Homa is a Managing Partner of the Consulting Division of the Business Development Bank of Canada (Theodore.HOMA@bdc.ca). Enrique Blanco Beneit is the Deputy Director of Intermediation Banking at the Spanish Official Credit Institute (ICO) (enrique.blanco@ico.es ) . This paper has been produced with the support of the Spanish Trust Fund for Latin America and the Caribbean.  The authors are grateful to David Scott, Jose de Luna, Rafael Gamboa, Tony Randle, Lily Chu and the participants of the April 2011 meeting of the World Bank working party on state financial institutions (where an earlier version of this paper was presented) for valuable comments,.
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Section 6 presents insights into why parallel systems exist and section 7 discusses addressing parallel systems. Section 8 recommends approaches to address the problem and a final section concludes.

# 2. METHODS AND SAMPLE

The study used qualitative and quantitative research methods and was conducted in two states, A and B.<sup>5</sup> Both methodologies adopted multiple data collection mechanisms to ensure reliability of the data and information collected. The qualitative findings were used to inform the design of the quantitative surveys and later validate or complement the quantitative findings. Field research was conducted in late 2011 and early 2012.

Multiple qualitative methods were applied to triangulate information from various sources. Focus group discussions were conducted with policy makers and administrators at the state and district levels, and facility physicians (Medical Officers - MOs) in three sampled districts in each state. In-depth interviews were conducted with 39 and 37 medical officers and health officials in States A and B respectively, including officials in the state secretariat. Secondary data and documents such as service records and government orders pertaining to the HR practices that are central to the study were collected from the study districts and respective State Department of Health (DH).

Quantitative methods consisted of a perception survey (PS) and Unmatched Count Technique survey (UCT). The findings from the focus groups and interviews were used to develop the questionnaires for the PS and UCT surveys. While we aimed to avoid ambiguous words and phrases; questions asking two or more opinions; or manipulative statements (Recantini, Wallsten, and Xu: 2000), based on information collected in the qualitative research, the PS and UCT surveys contained some sensitive questions used to gather and quantify respondents’ _views_ on the occurrence and workings of parallel systems.<sup>6</sup> While selfreporting perception surveys are commonly used for eliciting views of public servants on administrative processes and organizational performance, they have limitations for research on sensitive topics (Tourangeau, Roger, and Ting Yan. 2007; Barnett, 1998; Lee, 1993). The UCT methodology addresses this limitation by mixing non-sensitive with sensitive statements, isolating the latter.<sup>7</sup> Unlike most UCT instruments, however, we did not ask respondents about _their_ behaviors or participation in parallel systems. This would have required a much larger sample of respondents requiring considerably more 5
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# FIGURE 5: TRENDS IN CONTRACEPTIVE PREVALENCE, BY REGION

<!-- Start of picture text --> 1960-65 1983<br>80 74<br>Percent<br>practicing<br>contraception<br>60 56<br>45<br>40 34<br>20 14<br>9 7<br>5<br>0<br>All East Ltin South Africa<br>Developing Asia America Asia<br>Countries<br><!-- End of picture text --> <!-- Start of picture text --> Source: United Nations, 1989<br><!-- End of picture text -->
- 12 -

When removing monthly periodicities in a deterministic manner, hydrologic and weather series indeed come to exhibit clearer stationarity. Figure 5 depicts the seasonally adjusted time series of the Vishnugad Pipalkoti case, for example. Some extreme observations— compared with the long-term monthly mean—remain striking; however, the seasonality disappears. Of particular note, in this case the seasonally adjusted precipitation series may have a declining time trend especially in the past ten years, while no clear trend can be detected graphically in hydrology and temperature series.<sup>3</sup> Figure 5. Seasonally Adjusted Time Series: Vishnugad Pipalkoti, India <!-- Start of picture text --> (Hydrology) (Temperature)<br>1970m1 1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1 1970m1 1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1<br>Year and month Year and month<br>(Precipitation)<br>1970m1 1975m1 1980m1 1985m1 1990m1 1995m1 2000m1 2005m1<br>Year and month<br>2000 15<br>1000 10<br>(mil. m3 per month) 0 5<br>Seasonally adjusted hydrology<br>Seasonally adjusted temperature (oC)<br>0<br>-1000<br>400<br>200<br>0<br>Seasonally adjusted precipitation (mm per month)<br>-200<br><!-- End of picture text --> 3 There might be a much longer term trend in climate time series, which is not taken into account in the current analysis. Goswami _et al_ . (2006) show that India’s precipitation time series exhibits a long-term trend over the last 50 years.
information on inputs expenditure shares is readily computed from firm-level production data, the production function needs to be estimated in order to recover output elasticities.

# **3.3 Estimation method**

To obtain _θit_<sup>_v_=</sup><sup>_{θ_</sup> _it_<sup>_m, θ_</sup> _it_<sup>_l}_,rewriteequation(A.1)intermsoflogarithmsandallowfor</sup> log-additive measurement error and/or unanticipated shocks as where _qit_ is the production level for firm _i_ at time _t_ , **x** _it_ is a vector of inputs, specifically, of labor, materials, and capital; **_β_** is the vector of production function coefficients to be estimated; _ωit_ is firm-specific productivity; and _εit_ is an idiosyncratic error term.

Ignoring input price differences in the production function estimation can lead to biased estimates of the output elasticities. Taking advantage of firm-specific input and output prices, this article adopts a recent approach developed by De Loecker et al. (2016) to estimate the production function. Equation (A.2) becomes: where ** ̃x** _it_ denotes the vector deflated (log) inputs and **w** _it_ is a vector of firm-specific prices. To control for input price variation, the following observables were used: firmspecific prices, market share, location, and export status. The input price vector becomes where ** ̃x**<sup>_c_</sup> _it_<sup>=</sup><sup>_{_1</sup><sup>_,_</sup><sup>** ̃x**</sup><sup>_it}_;and</sup><sup>**_δ_**isanadditionalparametertobeestimatedtogetherwiththe</sup> production function parameters **_β_** . Next, the selection bias in equation 9 is corrected for by defining a productivity threshold the guides the selection mechanism. A translog specification of the production function is estimated separately for each two-digit sector level: Once the coefficients **_β_**<sup>**ˆ**</sup> = ( _β_<sup>ˆ</sup> _l, β_<sup>ˆ</sup> _k, β_<sup>ˆ</sup> _m, β_<sup>ˆ</sup> _ll, β_<sup>ˆ</sup> _kk, β_<sup>ˆ</sup> _mm, β_<sup>ˆ</sup> _lk, β_<sup>ˆ</sup> _mk, β_<sup>ˆ</sup> _lm, β_<sup>ˆ</sup> _lkm_ ) are obtained, then output elasticities can be computed as: _θ_<sup>ˆ</sup> _it_<sup>_m_=</sup><sup>_β_ˆ</sup><sup>_m_+ 2ˆ</sup><sup>_βmmmit_+ ˆ</sup><sup>_βlmlit_+ ˆ</sup><sup>_βmkkit_+ ˆ</sup><sup>_βlmklitkit_,</sup> for materials and _θ_<sup>ˆ</sup> _it_<sup>_l_=</sup><sup>_β_ˆ</sup><sup>_l_+ 2ˆ</sup><sup>_βlllit_+</sup><sup>_β_ˆ</sup><sup>_lmmit_+</sup><sup>_β_ˆ</sup><sup>_lkkit_+</sup><sup>_β_ˆ</sup><sup>_lmkmitkit_forlabor.</sup> A detailed description of the procedure to estimate the production functions and derived output elasticities is reported in the supplementary appendix. Once the production function has been estimated and the output elasticities recovered, the variables markups, the joint parameter of market imperfections, and the degree of monopsony power can then 11
the WBES is that it includes not only a module measuring management practices of firms but an additional “Green Economy” module that solicits responses on firms’ energy use, type of pollution emissions, and (a limited set of) practices relating to energy conservation (e.g., energy target-setting, monitoring, and external audits).

Our analysis advances the literature on the energy-management nexus in the following ways. _First_ , we characterize the variation in energy intensity with respect to generic management practices as well as energy-centric management practices. _Second_ , the WBES Green Economy Module provides information on the physical quantity of energy used, which allows us to measure the association of management practices with a close proxy for environmental impact. _Third_ , the presence of information on both general and energy-specific management practices within the same survey allows us to consider their separate and combined influence, since either could be an omitted variable when examining relationship with energy intensity. _Fourth_ , our data allows us to explore the relationship of generic and energy-centric management practices and firm outcomes in services, a sector that remains largely ignored in such discussions. _Finally_ , of the sample of 31 countries in WBES dataset, eight are in the “Lower Middle” or “Low” income categories. This enables us to also explore the similarities and differences in the relationship between management practices and energy intensity in advanced and developing economies.

We report four main findings. _First_ , while generic management practices negatively correlate with energy expenditure, they bear either a positive or no relationship to physical energy use, suggesting that management can help firms save energy costs without significantly reducing environmental impacts. _Second_ , among the structured management practices examined, target-setting practices are most strongly associated with reduced output-indexed energy expenditure. In both cases, these results are mainly driven by manufacturing firms, while the relationship between management practices and energy intensity is weaker in services.<sup>4</sup> _Third_ , general management practices are correlated with greater discipline around

> 4The coefficient on target-setting practices was negative but insignificant for services firms, with much noisier estimates.

4
bread consumed by the households. Next, following Abdalla and Al-Shawarby (2018), we assume a 30 piaster per loaf of subsidy received by households. Finally, HIECS 2015 also provides information on the items purchased using bread points.<sup>31</sup> Using an estimate of the value of the items purchased using a market reference price, we also impute such value as a transfer of the food program to the beneficiary household.

# **Energy subsidies and indirect taxes**

Energy subsidies for electricity and fuels and indirect taxes (sales taxes) are imputed based on household consumption expenditure records. In other words, when households record purchases of energy or goods or services that attract the sales tax, the subsidy or indirect tax payment implicit in this purchase is imputed based on the relevant subsidy or tax schedule. For example, if a household records US$50 in kerosene expenditures over a month, and the known subsidy rate on kerosene is 10 percent, the household is imputed to have purchased US$55.55 of kerosene, US$5.55 of which was actually an expenditure made by the government (via its subsidy policy) on behalf of the household.

The electricity subsidy (and the electricity tariffs) in Egypt is worth describing in further detail. In FY15, electricity tariffs were based on seven consumption brackets (Table 3). Using a cost-recovery rate estimate obtained from the Ministry of Petroleum, we first simulate for each bracket what would have been the corresponding “full recovery” or no subsidy tariff. The difference between the two provides an estimate of the per kilowatt hour (kWh) subsidy received by the household. Next, using HIECS 2015 data it is possible to estimate the electricity consumption volume from household expenditure records and the imposition of Egypt’s 2015-era block-tariff structure. For each household we have information on the bimonthly expenditure on electricity. After subtracting connection fees, we can estimate the corresponding volume that would be feasible based on the potential tariffs faced by households. Finally, we multiply the per-kWh subsidy to the corresponding estimated electricity consumption to get at the total benefits received by the households.

**Table 3. Electricity Tariffs Faced by Households, by Consumption Bracket**

|Residential (PT/kWh)|FY15<br>tariffs|
|---|---|
|upto 50|7.5|
|51–100|14.5|
|0–200<sup>a</sup>|16|
|201–350|24|
|351–650|34|
|651–1,000|60|
|above 1,000|74|

_Source:_ FY15 Ministry of Petroleum.

_Note:_ Tariffs in piasters per kWh (PT/kWh) of bimonthly consumption. For instance, a household consuming 80 kWh would pay 50 kWh at a rate of 7.5 piasters and 30 kWh at 14.5 piasters.

a This tariff applies to all kWh consumed when households consume more than 100 kWh and less than 200.

> 31 The unconsumed allotment of bread is transformed into points that can be used to purchase items in family stores.

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Why is so much left out? In practice, the choice of dimensions for measuring poverty will naturally be constrained by the data. When following the Alkire-Foster approach, the options are constrained further by the fact that one must obtain all the data for the same sampled household. So they must all come from the same survey.<sup>6</sup> Yet most surveys do not cover all the things one would like to know in a comprehensive assessment of poverty. This restricts the set of dimensions that can be included in the MIP. Nor can it be presumed that the dimensions that can be measured this way are representative of some subset of dimensions, within some seemingly reasonable taxonomy (such as “consumption poverty,” “health poverty” or “education poverty”). There will often be other data available on the selected dimensions, and other data on other relevant dimensions, but only from different surveys.

This aspect of the Alkire-Foster approach suggests that we will inevitably fall back in practice on the standard approach I described at the outset in which we use multiple indices rather than a single index. If one chooses not to form the composite at household level but to look instead at the separate dimensions of poverty then one is in a better position to span the relevant dimensions and to choose the best available data on each.

While this aspect of the Alkire-Foster methodology comes at a cost in terms of the coverage of the relevant dimensions of poverty, it can be acknowledged that it has an advantage too in that one can get some idea of the joint distribution of the multiple dimensions of poverty— to what extent the different dimensions of poverty that can be identified are shared by the same people. This adds something that cannot be easily identified when using multiple surveys (though simulation methods are sometimes used for that purpose). When a survey for a specific country does span multiple dimensions there can be much interest in exploring their joint distribution, though a MIP is not the only tool available for that purpose.<sup>7</sup> Another data constraint also points to the need for multiple measures in practice, namely that the data we typically use in measuring poverty do not tell us much about consumption within the household. To use such data we need to make assumptions about intra-household

> 6 Unique identifiers can in principle link households across two or more surveys, but this is relatively rare in practice, especially in developing countries.

> 7 See, for example, the study by Lokshin and Ravallion (2005) of the joint distribution of wealth and power. Lokshin and Ravallion use standard statistical tools for this purpose, including correlation and regression methods and contingency tables and related statistics for ordinal categorical data.

6
675 and Newell, 2008; Armitage et al., 2023). Future work could also quantify the additional economic cost 676 of missing technology and knowledge gains as a result of delaying climate policy. Finally, while our 677 numerical examples take a global view, it would be interesting and useful to apply this framework to 678 country-level nationally determined contributions to the Paris Agreement to determine how the con679 stitution of an individual nation’s economy either exacerbates or nullifies the increase in policy costs 680 described here; this is especially the case as the emissions intensity of each sector can vary substantially 681 across countries, meaning that the costliest sectors to delay are bound to change based on the specifics 682 of the economy being examined.

## 683

# **VII Conclusion**

684 In this paper, we elucidated how the “timing versus allocation” trade-off underscores climate policies 685 under PECs, and how these PECs impact economy-wide decarbonization dates, optimal carbon prices, 686 the temporal distribution of abatement spending, and aggregate policy costs. We presented four distinct 687 political economy-constrained policy suites to explore qualitative and quantitative changes in climate 688 policies to navigate political challenges. We close by reemphasizing two major themes of this work. 689 Firstly, we showed, both theoretically and in numerical experiments, that preserving the optimal 690 timing of abatement investment outweighs preserving the optimal allocation of emissions across sectors 691 for climate policies under PECs. We found that implementing an uncoordinated policy suite across 692 sectors is more cost-effective than delaying action to improve coordination, and can substantially lower 693 the cost burden of assuaging political headwinds. Implementing a sub-optimal carbon price in politically 694 challenged sectors,<sup>28</sup> with a modest increase in the carbon price for other sectors, can lead to both 695 delayed decarbonization in the politically challenged sector (thus complying to political constraints) 696 and only moderate overall policy cost increases; our numerical experiments estimate, at the maximum, 697 a 1.7% increase in aggregate policy costs as a result of delaying challenged sectors’ decarbonization by 698 a decade using this policy approach. On the other hand, doing nothing in a set of politically challenged 699 sectors or economy-wide leads to an increased cost of policy relative to doing something; indeed, our 700 numerical experiments suggest that overall policy costs could increase by as much as 50% if all policies 701 are delayed by a decade.

> 28Note that throughout we use a carbon price as a proxy for emission reduction in general, but in practice these reductions may be actualized through other policy instruments. For example, if the optimal building energy efficiency standard (that would create a similar marginal cost of abatement as other sectors) is politically infeasible, it is better to implement some improved standards rather than delaying standards altogether.

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