rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3: Level of education age 25+; compared with regional average (%)**

<!-- Start of picture text --> 9 5 3 5<br>6 22 25 11 8 8<br>34<br>18<br>29<br>15<br>Secondary or Higher<br>85 85 89 87 Primary<br>60<br>51 47 None<br>IDPs Refugees Returnees Bamako Gao Timbuktu Kidal<br>(INSTAT) (INSTAT) (INSTAT) (INSTAT)<br><!-- End of picture text --> (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014.

With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal.

**Figure 4: Asset ownership compared with regional average**

<!-- Start of picture text --> 600<br>Mobile Phone<br>500<br>Car/Motorized Vehicle<br>400 Motorbike/scooter<br>300 Bicycle<br>Refridgerator<br>200<br>TV<br>100 CD/DVD Player<br>Radio/Casette Player<br>0<br>IDPs Refugees Returnees Bamako Gao Timbuktu Kidal AC<br>(Instat) (Instat) (Instat) (Instat)<br>Percentage<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT).

The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37% of refugees and 34% of returnees. 18% of the refugees 10
As discussed, SBM was introduced in non-TEEP schools after 2006. In this process, TEEP divisions were partnered with their neighboring non-TEEP divisions to assist the implementation of SBM. Schools are required to set up a school improvement plan (SIP) for the coming five years as well as more specific annual improvement plans (AIP) that describe their plan for a school year. Based on significance of their SIP/AIP, SBM grants were allocated to schools. Thus, SBM grants reflect both school-level eagerness and division-level priority. The information on when SBM grants were received is available to us from the Department of Education.

The outcome measure used in this study is national achievement test (NAT) scores (percentage scores). Grade 6 students are required to take the exam each year. NAT data are available for all elementary schools in each school year.

Two years, 2006 and 2009, are used to define treatment and control groups. In 2006, non-TEEP schools started receiving SBM grants. To have an enough span to detect its impact on students’ achievements, measured by NAT scores, I decided to compare schools that received SBM grants in 2006 (treatment) and 2009 (control). Therefore, the treatment schools had received SBM grants three years earlier than the control schools.<sup>12</sup> As discussed in the next section, propensity score matching is used to control the endogeneity of SBM grant receipt in 2006 against 2009. BEIS data in SY2005 are used to control school types, resources: pupil-classroom ratio, pupil-teacher ratio, and total enrollment, and principal and teachers’ experience (i.e., capacity measured by salaries, correlated with their ranks). I suppose that schools that are well

> 12 The data show that no school received the grant in 2008. The numbers of recipient schools are comparable in 2006 and 2007. The analysis uses, as treatment, the schools that received the grant in 2006, rather than 2007, since this gives a 3-year period to assess the impact of SBM on test scores. However, the results reported below can be subject to potential selectivity that may be caused by years chosen for this study. It is also important to note that there are many schools that had not received the grant during the period of 2006 to 2010.

9
# **A Methodology for Updating International Middle-Class Lines for the Latin American and Caribbean Region**<sup>**1**</sup>

Jaime Fernandez, Sergio Olivieri, and Diana Sanchez<sup>2</sup> JEL code: C53, D31, O15, I32 _Keywords: 2017 PPP, vulnerability line, middle-class, LASSO; Multiple Imputation; Synthetic panel_

> 1 This paper has benefited from discussions with Hugo Nopo, Ximena del Carpio, Carlos Rodriguez Castelan, Dean Jolliffe, Daniel Gerszon, Christoph Lakner, Luis Felipe Lopez-Calva and Eduardo Ortiz-Juarez.

> 2 Jaime Fernandez is Consultant with the Poverty and Equity Global Practice, World Bank, and Associate Professor at Pontificia Universidad Catolica del Ecuador. Sergio Olivieri is a Senior Economist with the Poverty and Equity Global Practice, World Bank (solivieri@worldbank.org). Diana Sanchez  is Research Analyst with the Poverty and Equity Global Practice, World Bank.
percentage change in male and female employment AC. That is for males = (M empl. AC- M empl.)/M empl. BC; for females = (F empl. AC- F empl.)/FempBC. This value differs from the % change given in columns 3 and 6 of Table 1.

While low levels of female participation in manufacturing persist, the focus of this paper is on the moderate changes that COVID-19 has brought about in terms of the employment gap and the underlying explanatory factors in Egyptian manufacturing. On account of the vast use of lockdown measures, COVID-19 has induced both a demand and a supply side shock. In this paper, we look at these separately as well as the effect of the combined demand and supply shock on the gender gap. The demand shock is inferred from reported declines in domestic and foreign quantities demanded, and the supply shock from reported declines in production and in capacity utilization. The combined shock combines the two. Figure 2 shows that the demand shock (Figure 2a. as opposed to the supply one has hit men harder resulting in relatively larger improvements in the gender employment gap. The overall effect of the combined shock is also to improve the gap (Figure 2c).

<!-- Start of picture text --> Figure 2: The Gender Gap (median) pre- and post-COVID-19 by type of shock<br>a. Demand shock b. Supply shock c. Combined shock<br><!-- End of picture text --> Source: Authors’ own elaboration using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

Despite the overall improvement in the gender gap, it is possible to find heterogenous effects of firm characteristics. ‘Status variables’ such as formality or export status, size and sector could have varying effects on the gap. For example, while the gap is considerably larger in the formal sector (Figure 3a) it has improved in the sector post-COVID compared to a worsening of the gap in the informal sector. Similarly, while large firms account for a much larger employment gap, the gap has disproportionately improved for larger firms compared to their SME counterparts (Figure 3b).

**Figure 3: The Gender Gap (median) pre- and post-COVID-19 by informality and size**

<!-- Start of picture text --> a. median by formality status b. median by firm size<br><!-- End of picture text --> Source: Authors’ own elaboration using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

7
concerns of reverse causation, as expected higher future growth of a specific county is unlikely to affect state-level political decisions.<sup>29,30</sup> A somewhat related differences-in-differences approach is suggested by Rajan and Zingales (1998), who conjecture that the effect of financial development should vary by sector or industry according to the financing need of each sector or industry. They thus assess the finance and growth link by focusing on a specific channel through which financial development should foster economic development, i.e. the channeling of society’s savings to industries with the highest demand for external finance. Specifically, they use variation across industries in their dependence on external finance and variation across countries in their level of financial development to assess the impact of finance on industry growth, and apply the following specification:<sup>31</sup> _g_ ( _i_ , _k_ ) = α( ) _i_ + λ( _k_ ) + β( _External_ ( _k_ ) * _f_ ( ) _i_ ) + _yShare_ ( _i_ , _k_ )+ ( _Industry_ ( _k_ ) * _Country_ ( ) _i_ )δ + ε( _i_ , _k_ ) (23) where _g_ is growth of value added in industry _k_ in country _i_ ; _α_ and _λ_ are vectors of country and industry dummies; _Share_ is the initial share of industry _k_ 's value added in total manufacturing value added of country _i_ ; _External_ is the external dependence of industry _k_ ; _f_ is a measure of financial development in country _i_ ; _Industry_ is a vector of other industry characteristics that do not vary across countries; and _Country_ is a vector of other country characteristics that do not vary across industries. By including industry and country specific effects, the coefficient _β_ measures the differential growth impact of financial development on high-dependence industries relative to low-dependence industries. When redefining this exercise in terms of a controlled experiment, we could see industries (rather than states) as the treated objects, some of which (high external

> 29 This argument, however, is only valid if there is sufficient variation in growth across different counties within the state.

> 30 Given the lack of randomness of the sample relative to the population, Huang (2008) constructs critical values from a distribution of the effects of fictitious placebo treatments on county pairs on non-event borders, taking into account spatial correlation across counties along the same borders. Only if 95% of all placebo treatments result in a growth difference below a certain value can this value be considered a significant growth difference for a real world treatment at the 5% significance level.

> 31 Rajan and Zingales (1998) compute the industry-level dependence on external finance from data of listed firms in the U.S, i.e. firms that should have the least problems in raising external finance and thus face a perfectly elastic supply curve, to get measures of industry-level demand for external finance. They conjecture that demand for external finance measured in this way proxies for the industry-inherent demand for external finance, rather than country- or firm-specific characteristics, in the U.S.

24
Table 5. The Effect of School Types on Labor Market Outcomes: Full sample pooled

||LFP<br>LPM|Unemployment<br>LPM|**Men**<br>Formal<br>LPM|Wage<br>OLS|Wage<br>LAD|LFP<br>LPM|<br>Unemployment<br>LPM|**Women**<br>Formal<br>LPM|Wage<br>OLS|Wage<br>LAD|
|---|---|---|---|---|---|---|---|---|---|---|
|Public Vocational|0.013*|-0.006|0.036**|0.009|0.032|0.023|-0.017|0.032|0.087|0.133***|
||(0.007)|(0.011)|(0.017)|(0.056)|(0.044)|(0.029)|(0.012)|(0.025)|(0.075)|(0.049)|
|Private general|0.013*|-0.003|-0.042*|-0.171***|-0.278***|-0.076**|0.016|-0.052*|-0.047|-0.202***|
||(0.007)|(0.008)|(0.025)|(0.062)|(0.045)|(0.032)|(0.010)|(0.028)|(0.076)|(0.064)|
|Private vocational|0.005|0.010|0.019|-0.203***|-0.188***|-0.032|0.004|0.007|-0.014|-0.048|
||(0.008)|(0.012)|(0.020)|(0.064)|(0.057)|(0.034)|(0.013)|(0.029)|(0.081)|(0.058)|
|Average among public general graduates|0.971|0.051|0.575|||0.693|0.045|0.566|||
|R-squared|0.090|0.171|0.559|0.230||0.175|0.232|0.584|0.314||
|Observations|6084|5931|5642|5065|5065|5330|3452|3288|2681|2681|

Notes: *** 1% significance, ** 5% significance, * 10% significance; standard errors in parentheses, they are robust to heteroskedasticity and clustered at subdistrict level; LPM stands for Linear Probability Model, OLS stands for Ordinary Least Squares, and LAD for Least Absolute Deviations. In all cases, the sample is rebalanced by reweighting observations by the estimated inverse probability of attending their school type, in addition to standard individual cross-sectional weights. Robust standard errors are reported. All estimates are based on equation (2) in the text. Wage LAD estimates include provincial instead of district fixed effects. Standard errors for LAD estimates <u>are obtained from an unweighted bootstrap procedure.</u> 17
<!-- Start of picture text --> Effectively Applied Import Tariff<br>°vt +<br>*<br>©<br>8 e ~<br>r= e :<br>80 * .<br>5N “a<br>a o-@--@-@ 4<br>o_o ~e—_2—@<br>eS * a oe rs<br>. O—_» 6<br>Oo<br>1990 1995 2000 2005<br>Year<br>——®-—— Weighted average —-®-- Simple average<br><!-- End of picture text --> <!-- Start of picture text --> Effectively Applied Export Tariff<br>x<br>?.<br>e / /<br>NX “ ° .<br>2 »-* ; &<br>€ \ ~~ \.<br>8 @ +-¢+- o~<br>ae3 ¥ ~*e *»<br>mg Ca<br>o ° ~e a<br>e 2. _@~<br>t . oe ° e oe °<br>v<br>1990 1995 2000 2005<br>Year<br>——®— Weighted average —-®-—- Simple average<br><!-- End of picture text -->
# **6. The Alternative Scenario Sheets**

The sheet “Table” also summarizes the four alternative scenarios and the six bound tests. The template contains the following alternative scenarios: _Historical Average Scenario_ : This scenario presents an alternative evolution of the debt ratio under the assumption that all key variables (real growth rate, real interest rates, inflation, real exchange rate, primary balance to GDP ratio, seigniorage revenue to GDP ratio are constant and equal to their historical averages over the projection period. The template calculates averages over a ten-year period, and uses that information to project debt dynamics five years ahead.<sup>11</sup> This scenario is a rough test of the “realism” of baseline projections; the baseline projections may be argued to be overly optimistic when it differs remarkably from predicted debt evolution if historical patterns were followed. Of course, this scenario may be somewhat misleading if significant changes in economic policies have taken place in recent history—using the early years of transition, for instance, would bias the “historical” value of certain parameters and thus might result in too high a debt ratio. More generally, circumstances reflecting credible changes in policies (and thus lower baseline debt ratio projections) may look anomalous in comparison. In any case, using this scenario—and justifying any large anomalies—could usefully discipline baseline projections.

The “A1_historical” sheet (Table 5) reports the result of this scenario. Key real macroeconomic variables are assumed to be at their historical average, as calculated in the bottom section of the sheet (cells B71:L76, Fig. 4). The table reports in red the variables modified with respect to the baseline scenario: primary balance, primary expenditure, seigniorage revenue, the real and nominal interest rates on domestic, foreign debt and NFA of the Central Bank and growth of real primary spending.

> 11 If country teams wish to change the parameters used in this scenario, changes should be made in the “Table” sheet, and justification provided.

29
_- 15 -_

**Table 2: Simulated indicators of student flow at selected per capita GNP, circa 1993**

|Indicator|||Per cap|ita GNP,|1993 $|||R|2|
|---|---|---|---|---|---|---|---|---|---|
||200|400|800|1,500|3,000|10,000|20,000|(1)|(2)|
|Dropping out, primary cycle<br>%grade I entrants reaching the end|62.4|68.1|73.8|79.0|84.7|94.6|100.3|0.45|0.16|
|Dropout-related efficiency indexal|74.4|80.3|84.9|88.3|91.5|95.9|97.9|0.38|0.11|
|Grade repetition, primary cycle||||||||||
|% repeaters|17.2|13.7|10.9|8.8|6.9|4.2|3.0|0.31|0.11|
|Repetition-related efficiency indexb/|82.7|86.3|89.1|91.2|93.2|95.8|97.1|0.31|0.11|
|Among pupils exiting before end of grade 8<br>% leaving between prim. and sec. cycles|46.4|58.8|68.7|76.0|82.8|92.1|96.4|0.61|0.27|

a/ Measured relative to a value of 100 which corresponds to a system in which no pupils drops out before completing the cycle. b/ Measured relative to a value of 100 which corresponds to a system with no repeaters.

Note: The R<sup>2</sup> corresponds, in column (1), to a regression of the indicator concerned to the natural logarithm of the per capita GNP and its square, based on data for the whole sample; and in column (2), to the same regression based on data for countries with a per capita income below $4,000 in 1993.

The last line in the table pertains to the efficiency of student flow from grade 1 to grade 8, using as an indicator the percentage of school leavers who exit from the system in the interval _between_ the primary and lower secondary cycles rather than _within_ either cycle. The larger the percentage, the more children complete their studies rather than abandon them halfway, and the more efficient the pattern of student flow. An index of 100 means that all selection takes place between the two cycles.'<sup>6</sup> As countries grow richer, the efficiency of intercycle student selection improves: the share of pupils leaving between cycles is only 46.4 for countries at $200 in per capita, compared with 96.4 for countries at $20,000. The R<sup>2</sup> statistic is 0.61 for the whole sample, suggesting the efficiency of student selection rises in a predictable fashion as incomes increase. Among countries with per capita income below $4,000, however, the R<sup>2</sup> statistic drops to only 0.27, indicating much greater diversity in the efficiency of student selection, as well as greater scope for countries to influence the outcome through policy choice.

_Efficiency of resource use in education._ To assess cross-country differences in this regard, we first relate education outcomes to the input of public resources in the sector, defining outcomes in two ways: (a) years of school life expectancy; and (b) scores on

> **16** Because dropping out within the secondary cycle tends to occur less frequently than at the primary level, a small index may usually be interpreted as signifying significant dropping out within primary education.
we would expect accelerated outmigration from newly-impacted communities and, perhaps, deceleration in communities where cyclone strikes have declined. These changes would reflect both damage from actual impacts and revised expectations about future impacts.

In this context, robust tests of expectations formation have two requirements. The first is a georeferenced panel database of the type assembled for this exercise. The second is significant temporal and spatial variation in cyclone impacts, which enable them to serve as effective statistical controls in multivariate analyses of household and community changes over time.

To determine whether our data meet the second requirement, we perform an intertemporal assessment using the seven previously-defined periods for 1877-2016. Within each period, we consider the full set of spatially-ordered coastline points numbered 0 (the southernmost coastal point of Odisha) to 11,327 (the southernmost coastal point of Bangladesh). We register each coastal landfall at the closest point of initial strike and compute distribution statistics for spatial order numbers. Figures 3 and 4 display our results for the landfalls of 178 cyclonic storms from 1877 to 2016.

Figure 3 provides a geographical perspective by mapping median landfall points for each period. Two conclusions are immediately clear. First, the locus of cyclone landfalls has been far from stable during the past 140 years. The seven median landfall locations in Figure 3 are distributed in a broad arc from the central coastline of Odisha to the western coastline of Bangladesh. Second, the inter-period variation has been far from random: Median landfalls are in Odisha from 1877 to 1920, the Odisha/West Bengal border area from 1921 to 1960, and the West Bengal/Bangladesh border area from 1961 to 2016.

Figure 4 provides statistical perspective, with coastal point order numbers configured on the y-axis from 0 (southernmost coastal Odisha) to 11,327 (southernmost coastal Bangladesh). The 9
<!-- Start of picture text --> Kyoto Protocol***<br>yes<br>Price of oil** mno<br>Transmission Investment***<br>0 0.2 0.4 0.6 0.8<br><!-- End of picture text --> <!-- Start of picture text --> Kyoto Protocol***<br>yes<br>Price of oil** Hino<br>Transmission Investment***<br>0 100 200 300 400 500 600 700<br><!-- End of picture text -->
<!-- Start of picture text --> Policy, Research, and External Affairs<br><!-- End of picture text -->

# Fnanlal Policy and Systems

WPS 654 Thispaper-ajolntproductoftheFinancialPolicy and Systems Division, Country Economics Department and the Country Operations Division, Country Department III, Europa, Middle East, and North Africa - Regional Office is part of a larger effort in PRE to analyze the role of financial policy in the growth and adjustment process of developing countries. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Raggambi, room N9-04 1, etension 37657 (28 pages, with charts, figures, and tables).

Previous approaches to credit policy and its role in the stabilization and adjustment of developing countries have emphasized either the role of the availability of credit or the role of its price - that is, the Interest rate. Dailami and Giugale argue that effective credit policy in developing countries must take into account both: terest rate and credit channels.

The authors develop their argument in the context of the link between credit policy and private investment, using a model of firms' investment behavior in an economy with exogenous, time-varying borrowing constraints. The model incorporates a credit ceiling linked to the fims' net worth and the state of the credit market.

The state of the credit market depends on factors - such as credit and interest rate policy, regulatoty and supervisory practices, and market sentiments - that banks consider in making lending decisions. These factors affect banks' decisions independent of a borrower's creditworthiness. Thus, in times of tight money, firms that would otherwise have received loans may be denied them and have to postpone or cut back investment plans.

Dailami and Giugale use their model to specify an equation relating aggregate private investment to aggregate output and to two credit market variables - the real interest rate and aggregate credit. They estimate the equation for five developing countries (Brazil, Colombia, India, Korea, and Turkey) using annual data for 1965-85, and test the joint significance of both interest rate and credit supply conditions. Their findings show that interest rates and credit volume exert a joint influence on the behavior of private investment in the countries examined The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and External Affairs Complex. Anobjectiveoftheseries is to getthese fmdingsoutquickly, even if presentations are less than fully polished. The findings. interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

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