rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>4 only learned 31%-40% as much in math as they would have during a normal year (Kim et al. 2021;</mark> <u><mark>W</mark> orld Bank, UNESCO, and UNICEF 2021).</u>

# **Background: What’s New?**

In 2020, the World Bank released COVID-19 learning loss simulations modeling impacts on Learning Poverty (Azevedo 2020), Learning Adjusted Years of Schooling (LAYS),<sup>6</sup> and percent below minimum proficiency in PISA, as well as impacts on future earnings (Azevedo, Hasan et al. 2021). Since then, four major developments have led to revisions of the learning loss estimates and simulations:

- The pandemic has continued. In many countries, school closures have affected two or more school years, and the surge of COVID-19 cases brought about by the Omicron variants indicate we are not out of the woods yet (WHO 2021).

- There is actual data on school closures. At the time of the previous simulations, there was no global data on school closures, and so those simulations relied on uniform assumptions about school closure lengths for all countries under different scenarios. We now have observed country-level data on actual school closures for February 2020 – February 2022 from the <u>UNESCO school closures tracker, which we use for to build the baseline scenario and the different simulation scenarios.</u><sup>7</sup> Therefore, baseline school closure data now differ across countries and regions.

- More up-to-date learning assessment data are available. Internationally comparable data from PASEC 2019, TIMSS 2019, LLECE 2019,<sup>8</sup> and a new regional learning assessment program that took place in East Asia, SEA-PLM 2019, have been released, as have the results from AMPL-b 2021 assessment (in Zambia),<sup>9</sup> and policy linking
slopes. An analysis of the distribution of slope values in a sample of urban extents extracted for the year 1996 suggested that in fact the median slope in the areas considered is 21%; hence, in the analysis we use a threshold of 20% slope to delimitate the undevelopable land areas. Finally, we manually select the largest patch within the circle as the potential urban extent for each date. Figure 3 shows an example of an actual urban extent and its potential shapes in the three dates.

**Figure 3.** Lima 1996 extracted urban extent outline in dark red and potential urban extent areas for 1996 (light red), 2000 (orange), and 2010 (yellow). Base map: © Google.
is heavily influenced by his/her own background, with particular reference to his/her previous job experience; as already discussed in the previous section, on the one hand the importance of previous job experience explains sectoral inertia in entrepreneurship, while on the other the loss of (or the fear of losing) the previous job may trigger the start-up of a

> 13 In a series of my previous studies using different Italian datasets (Vivarelli and Audretsch, 1998; Arrighetti and Vivarelli, 1999; Vivarelli, 2004), the state of actual unemployment or an impending state of unemployment were never found to be a top crucial motivation in determining the decision to start a new business. However, although rather low in the average rankings, the motivation ‘escape from unemployment’ emerged as being quite important in about 15-20% of the examined cases (see also Thurik _et al_ ., 2008, for a study where the role of unemployment in fostering start-ups and the possible job creation effect of new firms are simultaneously considered).

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the Albania Living Standards Measurement Survey (LSMS).

7. The Ministry of Education and Science (MoES) is seeking to further improve governance by strengthening parent and community engagement in participatory accountability structures (the lefthand side of Figure 1). The Ministry has implemented reforms designed to strengthen the independence of school boards and parent committees, and is considering measures to increase school autonomy as well as make education financing more responsive to the priorities of stakeholders. Currently there is little autonomy or flexibility given to school-stakeholders to determine their needs and to make investments decisions. “Conditional transfers” from the national budget are channelled to Regional Education Departments (REDs to finance major inputs like teacher salaries, training, teaching materials and a textbook subsidy scheme. Local governments have discretion over the resources they receive as “unconditional transfers” from Ministry of Finance , which cover the costs of utilities and maintenance. Local governments also manage funds to finance investments which they receive on a competitive basis from MoES. These resources are used for improvements to schools such as rehabilitations, extentions, new buildings, and furniture for classrooms. Figure 2 presents a sylized representation of the _de jure_ service delivery and accountability relationships in Albania‟s pre-university education sector. However, until recently, parents and other community members did not have strong incentives nor effective structures to interact with school directors and teachers, in order to identify their particular issues and come up with solutions.

8. To create an enabling environment for greater parent and community engagement in school governance, the Ministry has given school boards greater responsibilities. The composition of these boards has been changed so that school principals should no longer be part of the boards, to give more representation to parents and community members (see Figure 2). The boards have been given greater responsibility for monitoring the actions of school administrators, and could eventually play a role in the selection of head teachers. There is some interest in providing block
sensing products mismeasure precipitation and temperature.

# **4.1 Extraction Method**

We begin by examining extraction method, following Hypothesis 1 ( _H_ 0<sup>1-differentobfuscation</sup> procedures implemented to preserve privacy of farms or households have no impact on estimates of agricultural productivity). Our null hypothesis rests on two assumptions. First, existing publicly available remote sensing weather products are too coarse a resolution for any of the extraction methods to make a substantial difference in which pixel a household ends up in. Second, even if the obfuscation technique moves a household location from one pixel to another, weather is sufficiently spatially correlated that the shift will not matter. The alternative hypothesis is that obfuscating a household’s true GPS coordinates introduces substantial mismeasurement resulting in researchers matching the obfuscated coordinates with gridded remote sensing data that does not accurately reflect the true weather experienced by the household.

To test Hypothesis 1, we pool the results from the 77,760 regressions (those that do not include linear combinations) and then divide the pool into ten bins, one for each extraction method. We then calculate descriptive statistics for each bin of results. These include the mean adjusted R<sup>2</sup> value and the share of coefficients ( _β_ 1) with _p_ -values of _p >_ 0 _._ 90 _, p >_ 0 _._ 95 or _p >_ 0 _._ 99. For each of these values, we calculate the 95% confidence interval on the mean.<sup>12</sup> We then compare mean adjusted R<sup>2</sup> values or the share of _p >_ 0 _._ 95s across all ten extraction methods and use the 95% confidence interval on the mean to evaluate differences using our weak and strong test criteria. This allows us in turn to make a determination about our Hypothesis: if the preponderance of evidence is such that we see no differences in our heuristics, then
recentered influence function (RIF) regression method was used as proposed by Firpo, Fortin, and Lemieux (2018).<sup>16</sup> This technique allowed re-creation of the Blinder-Oaxaca decomposition at different wage levels. Figures 5 and 6 summarize the results graphically.

# **Figure 5 Coefficient effect of gender wage gap by industry**

> 15 While the TLMPS includes information on number of hours worked, converting monthly wages to hourly wages adds more noise to the data. Therefore, the results are presented using monthly wage data.

> 16 The results of the RIF regressions were calculated for every five percentiles between 2 and 95. To avoid noise from specific outliers, the results presented take those calculations and filter them using a locally weighted regression. By doing so, the trends displayed are more robust to variations in individual values.

36
demand shifts affecting production and the structure of whole or multiple sectors, such as trade liberalization (Pavcnik, 2002) or technological change (Bloom, Draca and Van Reenen, 2015). These factors do not directly affect productivity, but the competitive pressures that accompany them may elicit responses from firms (such as those described above) that do. How firms respond can determine their position in the productivity distribution, whether they move up the productivity ladder and grow or fall back and exit.

Taken together, these firm responses to the two demand drivers, together with new firm entry, are thought to be the key drivers of productivity dispersion within and between firms and, more broadly, productivity growth. These two sets of demand drivers also affect skills demand and the

> 20 The earlier productivity research, which relied on aggregate sectoral data, implicitly assumed that firms were homogeneous and experienced the same productivity growth profile as the aggregate sector. The recently available firm-level data indicates that this assumption is untenable. Research using  firm-level panel data points to considerable heterogeneity in productivity among firms even in narrowly defined sectors, and provides insights into how productivity evolves through a dynamic process of new firm entry and growth of more productive firms, and exit of less-productive incumbent firms.

> 21 Measuring within-industry productivity dispersion at the 90th and 10th percentile, Hsieh and Klenow (2009) find productivity differences of 5 to 1 in China and India as compared to 2 to 1 in the U.S.

15
--> Z-Score<br>1.00<br>0.50<br>0.00<br>-0.50 ROW ECA High Income ECA Emerging<br>-1.00<br>-1.50<br>-2.00<br>-2.50<br>-3.00<br>-3.50<br>-4.00<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: We report the effect on the Z-Score of increasing risk weighted assets (RWA/TA) by 10% for EU ECA countries and the other ECA countries (Emerging ECA). The estimates are obtained after controlling for bank size (log(TA)), bank liquidity (LiquidA/TA), bank profitability (ROA), reliance on short-term funding (ShortFund/TA), and loan ratio (Loans/TA).

Examining the impact of the proportion of Tier 1 in regulatory capital, we find that the quality of capital matters in reducing bank risk. Consistent with the notion that capital that is not Tier 1 can be severely undervalued during times of distress and not able to absorb shocks, we find that capital is less effective in reducing bank risk if a higher proportion of it is in the form of Tier 2 capital. However, as mentioned earlier, what constitutes Tier 1 capital varies across countries and Tier 1 stringency is lower in the ECA region. If supervisors can limit what can be included as Tier 1, we would expect a greater 17
the contribution of entrants to productivity growth.

# **3.3 Reallocations and Productivity Growth**

The reallocation patterns discussed in the previous section suggest several margins through which manufacturing productivity increases. The first channel is _between-industry_ reallocation.<sup>11</sup> A similar argument to that regarding the effect of the structural transformation from agriculture to manufacturing on aggregate productivity may be applied here. The growth rate of aggregate productivity may increase as resources are reallocated to industries with higher productivity levels.<sup>12</sup> As resources are reallocated to more productive industries and used more efficiently, aggregate productivity may increase. Moreover, if growing industries have higher productivity growth rates, the growth rate of aggregate productivity will be increased even more.

The second channel is _within-industry_ reallocations, which are the focus of most studies using establishment- or firm-level micro-data. The logic is similar to that of between-industry reallocations, but this growing literature focuses on reallocations that occur at the firm or establishment level (i.e., _between_ e _stablishments_ within an industry). Empirical literature has been 11 Previous studies using industry-level data implicitly assume that there exists a representative firm for each industry (See Lee (2005) for the detailed argument). For example, Basu and Fernald (1997) examined the cyclical properties of the reallocations between industries. They document that inputs tend to be reallocated toward industries with higher returns to scale and higher productivity levels during a boom in the cycle.

12 It is not clear to compare the levels of TFP between industries, due to the differences in the deflator and production technology (i.e., elasticities of inputs for each industry). We find that growth rates of TFP were higher for growing industries (e.g., transportation and electronics) than for declining industries (e.g., textile and leather).

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12 labor shortages caused by infection, especially so in endemic communities. Since GDEC activities have been typically prevalent in rural areas, the marginal product of labor is estimated as the additional output gained in the agricultural sector through increasing the supply of productive labor input.'<sup>3</sup> In order to estimate the additional output of labor accrued through prevention of Guinea worm infection, methods based on the Cobb-Douglas production function were used.'<sup>4</sup> The following parameters are considered relevant in estimating the additional output of labor in the agricultural sector: the number of cases of Guinea worm infection prevented by GDEC (the difference between the number of cases of Guinea worm without GDEC and with GDEC) in the productive age group, the productivity loss -- in terms of percent of work-year lost -- per case of dracunculiasis, the output elasticity of labor in the agricultural sector, and the length of the effective project horizon. As in the analysis of costs, output figures are measured in 1987 constant dollars for consistency of comparison between costs and benefits. The following subsections describe how each of these parameters was estimated.

# _Cases of Guinea Worm without GDEC_

Global 2000 estimates that in 1986 (i.e., prior to the initiation of GDEC activities) the annual worldwide incidence of dracunculiasis was in the vicinity of 2.2 million cases. For the benchmark case we assume that in the absence of GDEC, in the very least, the annual incidence would have remained at this level.'<sup>5</sup>

- 12 See Shulman (1985) for additional details regarding the coincidence of peak labor input demand with the dracunculiasis cycle and the precipitation cycle. In our paper we assume a work year of 40 weeks.

- _13_ We are assuming unemployment is _not_ a major factor in the analysis. Several surveys of labor markets in Sub-Saharan Africa have indicated that unemployment is largely an urban phenomenon. In fact, as Vandemoortele (1991) indicates, unemployment
expenditure on water is greater than zero, and (iii) there is a specific provider for which we have tariff structure information. For households that pay a fixed rate per cubic meter, the monthly quantity of water consumed equals the total expenditure on water divided by the rate. For households facing unit prices that vary by quantity consumed (such as increasing block or volume-differentiated tariffs), each block within the tariff structure is assigned a maximum expenditure level and associated quantity of water consumed. Then quantities are assigned to the household sample by matching self-reported household water expenditure with the corresponding level of the tariff structure. For the remainder of households, for which (i) the bill is a flat rate unrelated to water quantities consumed or (ii) self-reported expenditure on water equals zero,<sup>11</sup> we assume consumption quantities at the median of those households in the same country for which we could assign quantities. For those households that report a positive expenditure on piped water but are located in areas where they cannot be mapped to a specific provider, we use a median unit price derived from households with data, and recover the quantity of water consumed.

We then define _QP|T_ as the average quantity consumed by the poor, conditional on receiving a subsidy and being connected, and _QH|T_ as the analogous measure for the population as a whole. This variable is related to the subsidy design because the quantity of water consumed by subsidy recipients determines the total value of the subsidy each recipient will receive.

# **Average unit price of water paid among those connected (UP)**

We define this variable as the ratio of (i) water expenditure (as self-reported in household surveys) to (ii) assigned quantities, as described above. For a household with a water expenditure of zero, the unit price would also be zero.

# **Subsidy beneficiaries among those connected (B), and targeting measures (T)**

We define variable _B_ as equal to
El Salvador (Figure 1). Participation rates are higher for men, accounting for 9 out of 10 working-age men in Guatemala and Honduras. However, they are lower in El Salvador, where only 84.3 percent of men are in the labor force. Crime, which can reduce labor supply by both absorbing labor (particularly male labor) and imposing obstacles to participation due to security costs and concerns, is a significant factor in these three countries.<sup>10</sup>

**Figure 1.  Labor force participation and unemployment, by gender 2014**

<!-- Start of picture text --> a) Labor force participation rate b) Unemployment rates, urban only<br>92.0 90.1<br>84.3 6.6<br>5.8<br>51.6 50.2 4.3<br>44.7 3.5<br>2.4<br>1.8<br>El Salvador Guatemala Honduras El Salvador Guatemala Honduras<br>Female Male Female Male<br><!-- End of picture text --> _Source_ : Authors’ tabulations using the Socioeconomic Database for Latin America and the Caribbean or SEDLAC (World Bank and CEDLAS).

_Note:_ The sample is limited to individuals ages 18-65.

Since a large fraction of the workforce is absorbed in informal work, unemployment rates are relatively low in the three countries, and particularly so in Guatemala where urban unemployment rates are 2.4 percent

> 9 Figures in this paragraph are from the World Development Indicators, World Bank.

> 10 Sizeable gangs and other criminal enterprises have resulted in the Northern Triangle having some of the highest homicide rates in the world, with either El Salvador or Honduras recording the highest rates globally each year between 2008 and 2015 (the most recent year for which global data are available).

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(Chi et al. 2022). The Demographic and Health Survey (DHS) is a series of nationally representative surveys conducted in multiple countries, including South Asian countries.

**Firm size in India.** We use the most recent cross-sectional firm-level data from the Sixth Economic Census of India, conducted in 2013, which captures information for over 58 million non-agricultural firms across India, including employee counts for each firm (Government of India

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program school; 80.2 percent of recipients, but only 57.9 percent of non-recipients were attending a JFPR school on the day of the visit; for those whose enrollment status could be established, 89.5 percent of scholarship recipients but only 76.5 percent of non-recipients were enrolled in any school, JFPR or otherwise.<sup>2</sup> Table 1 is consistent with a large program effect on school enrollment and attendance.

We next focus on comparisons of outcomes for girls with different levels of socioeconomic status, as measured by their score. In our sample, girls ranked 45 or lower by the score were 4.3 percentage points more likely to be enrolled at a JFPR school than girls ranked 46 or higher (p-value: 0.04); 3.9 percentage points more likely to be present on the day of the school visit (p-value: 0.08); and 2.0 percentage points more likely to be enrolled at any school, JFPR or otherwise (p-value: 0.29). Note that we would expect that, in the absence of a program, girls with lower SES would be _less_ likely to be enrolled and attending school. These comparisons, which abstract from any possible endogeneity of scholarship receipt, therefore provide further evidence of a JFPR program effect.

Finally, we calculate the mean enrollment and attendance levels, separately for scholarship recipients and non-recipients, by deciles of the score. These results are presented in Figure 1. The lines corresponding to girls who were turned down for scholarships show that, among these girls, there are clear socioeconomic gradients in enrollment and attendance. Figure 2 presents a similar figure based on the 2000 Demographic and Health Survey; here too there is a clear schooling gradient.<sup>3</sup> Figure 1 shows, however, that the gradient in enrollment and attendance has essentially disappeared among scholarship recipients. This is consistent with larger program effects among girls with the lowest SES, a point we examine more
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 37 Productivity

- Cheng, Y; Zhao, L; Wan, W; Li, L; Yu, T; Gu, X (2016). Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. _Journal of Geographical Sciences_ , 26(3), 325–338. https://doi.org/10.1007/s11442-016-1271-6

- Chuvieco, E (2016). Fundamentals of Remote Sensing. An Environmental Approach (2nd ed.). Boca Raton: CRC Press.

- Ciccone A; Hall RE (1996). Productivity and the density of economic activity. _American Economic Review_ , 86: 54–70.

- Couch, C; Ldontidou, L; Petschel-Held, G (2007). Landscapes, land-use change & policy. _Urban sprawl in Europe_ . Blackwell Publishing, Oxford.

- Duranton, G; Puga D. (2004). “Micro-foundations of urban agglomeration economies”, in J.V. Henderson and J.-F. Thisse, eds., _Handbook of Urban and Regional Economics_ , Volume 4, New York: North Holland.

- Ellis, Peter, and Mark Roberts. 2015. _Leveraging Urbanization in South Asia: Managing Spatial Transformation for Prosperity and Livability_ . World Bank Publications.

- Fallah, B; Partridge, M; Olfert, M (2011). Urban sprawl and productivity: Evidence from US metropolitan areas, _Paper in Regional Science_ , 90(3): 451-473.

- Forbes, DJ (2013). Multi-scale analysis of the relationship between economic statistics and DMSPOLS night light images. _GIScience & Remote Sensing_ , 50(5): 83–499. https://doi.org/10.1080/15481603.2013.823732

Freire, S; Pesaresi, M (2015): GHS population grid, derived from GPW4, multitemporal