rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10714

# **Abstract**

This paper uses data from 2003–19 on 2.47 million test takers of a national high stakes university entrance exam in Ethiopia to study the impacts of temperature on learning outcomes. It finds that high temperatures during the school year leading up to the exam reduce test scores, controlling for temperatures when the exam is taken. The results suggest that the scores of female students are less impacted by higher temperatures compared to their male counterparts. Additionally, the analysis finds that the scores of students from schools located in hotter regions are less impacted by higher temperatures compared to their counterparts from cooler regions. The evidence suggests that the adverse effects of temperature are driven by impacts from within-classroom temperatures, rather than from indirect impacts on agriculture.

This paper is a product of the Development Research Group, Development Economics. 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 abehrer@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
1%) and Nigeria (1.7%). However, the poorest households in Eryuan, which have an annual income lower than 4000 yuan and account for about 25% of the total population, are willing to pay more than 5% of their income for their solid waste disposal. This implies that the poor households in Eryuan have very strong demands for public solid waste management services.

Further analyses of the survey data show that the poor households may have stronger demand for the proposed improved SWM service than rich ones, not only in a relative sense to their incomes but also in absolute WTP values. In the areas of new coverage where no public SWM services are available, the marginal WTP of the poor households is significantly higher than that of the middle income and rich households, according to the econometric analyses, and the average WTP of the poor households is also higher, even though the differences may not be statistically significant. One reason could be that the poor households in those areas were suffering seriously from the solid waste problem, while the rich ones had private coping measures.

A consistent phenomenon is also found about the rich households in Eryuan. If there have been regular solid waste cleaning services in their neighborhood, they are in general willing to pay more for the proposed SWM project. If the solid waste problems around their houses are thought to be serious, they are willing to pay less for the proposed project. This may be understood as if the rich households have already taken some private preventive measures on the solid waste problems around their houses when the problems are thought to be serious, and therefore the potential benefits that they can enjoy from the proposed improved SWM project are reduced.

Among household and individual characteristics, income is found to be a dominant determinant of WTP for SWM services, with an elasticity of 0.22. As expected, those who

> 20 The WTP estimated in this study is only the total value of perceived benefits of those households located in the project area. Methodologically,
information on the macroeconomic indicators, including growth rates, inflation, and population. PBS publishes data on population growth rates, inflation, and the GDP series between FY20 to FY22.<sup>6</sup> Growth rates, along with inflation forecasts, between FY23 andFY25 are projected by the World Bank, and published through the semiannual Macro Poverty Outlook.<sup>7</sup> While headline inflation is projected until FY25, Classification of Individual Consumption According to Purpose (COICOP) category-level projections are not available beyond FY23. Additionally, Table 2 showcases one of the main development challenges in Pakistan: The need for substantial growth to outpace population increases. This can be seen in the situation in the FY25, when real GDP growth reached 2.4 percent, but no per capita changes were achieved.

**Table 2: GDP and Sectoral Growth Rates, Inflation, and Population**

||FY20|FY21|FY22|FY23*|FY24*|FY25*|
|---|---|---|---|---|---|---|
|GDPgrowth rates,nominal(%)<sup>a</sup>|8.8|16.8|21.1|24.7|32.0|19.7|
|GDPgrowth rates,real(%)<sup>a</sup>|-0.9|5.8|6.1|-0.6|1.7|2.4|
|GDPper capitagrowth rates, real(%)<sup>a</sup>|-2.6|3.8|4.1|-2.5|-0.6|0.1|
|_Sectoral growth rates, real_<sup>_a_</sup>|||||||
|Agriculture|3.9|3.5|4.3|
Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
|Disruptive|0.164***|0.135***|0.179***|0.149***|
||(0.016)|(0.011)|(0.017)|(0.013)|
|Disruptive_×_Africa|-0.134***|-0.103***|-0.152***|-0.114***|
||(0.018)|(0.013)|(0.020)|(0.016)|
|Disruptive_×_LAC|-0.093***|-0.072***|-0.101***|-0.079***|
||(0.024)|(0.016)|(0.030)|(0.021)|
|_R_<sup>2</sup>|0.214|0.185|0.215|0.186|
|Observations|25464|25464|25464|25464|
|Countries|33|33|33|33|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, and the number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is a dummy variable that takes a value of one if the firm received funding. Standard errors clustered at the country level.

14Tables A2 and A3 in the appendix re-estimates the tables 2 and 3 using a different definition of start-up: firm no older than
**Figure 6. Wealth losses distribution**

<!-- Start of picture text --> Percentage of Asset Index Loss Percentage of Asset Index Loss, by Asset<br>Index Quintile (if Asset Loss>0)<br>35%<br>30%<br>25%<br>20% February<br>15% June<br>10%<br>-100 -50 Loss Percentage0 50 100 5%<br>February 0%<br>June 1 2 3 4 5<br>kernel = epanechnikov, bandwidth = 4.1864 Quintile<br>.04<br>.03<br>.02<br>.01<br>0 Percentage Lost<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Asset index weights are calculated using pre-earthquake assets.

To go further in the evaluation of assets, we follow Moser and Felton (2007) and construct five categories of assets. The categories considered are: physical capital (divided into two categories: housing quality and household durables), financial / productive capital (income sources and productive durables), human capital (education), social capital (association participation) and agricultural capital. Using these categories, it is possible to evaluate physical capital, productive capital and agricultural capital before and after the earthquake.<sup>16</sup> Social and human capital can only be evaluated in June, but they can be considered as invariant over the period.

> 16 The agricultural index is estimated using agricultural production related good and facilities such as irrigation. The weights given to each variable are obtained from a multiple component analysis and are presented in Table 3 (in Appendix).

13
schooling provision according to their ideology (Columns 5 to 8). There is no clear-cut definition of control by the insurgents, with discrepancies between the classifications used by the People’s Army and the government (Hatlebakk, 2007), and so I use two alternative classifications. In Columns (5) and (6), I consider controlled by the Maoists only those districts recognized as such by both the Maoists and the government. In Columns (7) and (8), I use the government’s classification, which includes more districts and is somewhat more convincing according to Hatlebakk (2007). The choice of definition affects the magnitude and significance of estimates, but altogether the message is that primary schooling became _more_ prevalent over time in areas controlled by the Maoists, for both genders.

Caution should however prevail when interpreting the findings on Maoist abductions. Since these abductions specifically targeted school children and teachers, abductions are more likely to be correlated with unobserved variables that are also correlated with schooling outcomes. There may even be reverse causality of an ambiguous direction running from educational attainment to abductions. On the one hand, abductions may be more likely where educational attainment is higher (e.g., if Maoists target schools in areas where attendance is high to abduct large numbers

> 7 76 schools were destroyed according to the data provided by INSEC.

24
representative of each economy’s formal private sector, covering establishments with five or more employees, thus our findings are less narrow than many previous analyses, which have tended to focus only on larger manufacturing firms.

The remainder of this paper proceeds as follows: section 2 outlines the methodology used to measure management practices and to analyze the relationship between management and performance; section 3 presents the data and descriptive statistics; section 4 presents the empirical findings; and, finally, section 5 discusses the results and provides some brief concluding remarks.

# 2. Methodology

## 2.1 Measuring and scoring management practices

Work to systematically quantify management practices has been pioneered by Bloom and Van Reenen (2007, 2010). The United States Census Bureau adapted the survey questions of Bloom and Van Reenen and in 2010 conducted a wide‐scale survey of management practices via the Management and Organizational Practices Survey (MOPS). Based on the MOPS, and in collaboration with Bloom and Van Reenen, these variables have been modified and implemented as part of the standard Enterprise Survey that the World Bank implements throughout the world.

The Enterprise Survey asks about five specific components of management practices, these are as follows: (1) action taken when a problem arose in production or service provision; (2) the number of production or service provision performance indicators monitored; (3) the level of ease or difficulty to achieve production or service provision targets; (4) personnel's knowledge of production or service provision targets; and (5) the basis of managers' performance bonuses. For each component, establishments receive a score between zero and one, where higher scores indicate more structured practices. Take, for example, the component of management practices that examines how establishments respond to problems in production or service provision (action when a problem in production or service provision arose). The least structured practice would be “No action was taken”, while the most structured would be “We fixed it and took action to make sure it
Table C1: Summary Statistics of pooled household-level data

||N|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Consumption (per day USD in 2017 PPP terms):**|||||||
|Real per capita consumption expenditures|453,632|5.62|3.56|9.48|0.00|2289.78|
|Nominal per capita consumption expenditures|454,631|5.35|3.21|10.05|0.00|2521.83|
|**Spatial defator**<br>|||||||
|Spatial defator<br>|453,659|1.00|0.99|0.13|0.76|1.93|
|**Demographic:**<br>|||||||
|Household size|454,728|5.70|5.00|3.26|1.00|62.00|
|Age of household head|454,623|43.15|43.00|17.04|0.00|119.00|
|Household head is male|454,674|0.81|1.00|0.39|0.00|1.00|
|**Marital status (of household head):**|||||||
|Married|454,664|0.80|1.00|0.40|0.00|1.00|
|Never married|454,664|0.03|0.00|0.17|0.00|1.00|
|Living together|454,664|0.05|0.00|0.21
- Miyauchi, Y., Nakajima, K., and Redding, S. (2020). Consumption Access and Agglomeration: Evidence from Smartphone Data. Working paper, _Princeton University_ .

- Monte, F., Redding, S. J., and Rossi-Hansberg, E. (2018). Commuting, Migration, and Local Employment Elasticities. _American Economic Review_ , 108(12):3855–3890.

- Moreno-Monroy, A. I. and Posada, H. M. (2018). The effect of commuting costs and transport subsidies on informality rates. _Journal of Development Economics_ , 130(C):99–112.

- Pérez Pérez, J. (2018). City minimum wages. _Unpublished paper, Brown University, http://jorgeperezperez. com/files/Jorge_Perez_JMP. pdf (viewed July 19, 2018)_ .

- Perry, G. E., Maloney, W. F., Arias, O. S., Fajnzylber, P., Mason, A. D., and Saavedra-Chanduvi, J. (2007). _Informality : Exit and Exclusion_ . Number 6730 in World Bank Publications. The World Bank.

- Ramírez, S. B., Rojo, M. H., and Gault, D. A. (2017). Decisiones e Implementación en la Construcción de las Primeras Once Líneas de la Red del Metro en La Ciudad de México Hacia la Desorganización del Metro (1967-2000). Working Paper, _Centro de Investigaci ón y Docencia Económicas CIDE_ .

- Redding, S. J. and Turner, M. A. (2015). Transportation Costs and the Spatial Organization of Economic Activity. In Duranton, G., Henderson, J. V., and Strange, W. C., editors, _Handbook of Regional and Urban Economics_ , volume 5 of _Handbook of Regional and Urban Economics_ , chapter 0, pages 1339–1398. Elsevier.

- Restuccia, D. and Rogerson, R. (2008
0|2.8|3.5|
|Russian<br>Fdti|Consumptionper capita(US$)*|2,124|3,506|4,436|5,384|6,415|7,682|9,260|11,217|14,925|32,099|9,698|
|eeraon|Share of tobacco expenditures**|1.7%|1.2%|0.9%|1.0%|0.8%|0.8%|0.8%|0.6%|0.7%|0.5%|0.9%|
||Share of smoker households***|30.4%|38.8%|35.0%|40.8%|38.6%|46.5%|43.3%|43.0%|42.9%|40.7%|40.3%|
||Population|5,791,693|5,791,742|5,797,492|5,785,215|5,790,442|5,791,336|5,793,315|5,791,618|5,790,950|5,789,259|57,913,059|
||Household size|5.9|5.2|4.7|4.3|3.8|3.4|3.0|2.7|2.6|2.4|3.5|
|South Africa|<br>Consumptionper capita(US$)*|365|641|886|1,140|1,456|1,904|2,544|3,692|6,248|16,689|3,556|
||Share of tobacco expenditures**|4.5%
First, the infrastructural limits and connectivity hiccups within a given tax administration inevitably affect the capacity of taxpayers to use a technology. As a recurring theme across the selected countries, when connectivity is poor, taxpayers are often forced to revert back to manual practices. For instance, when EFDs are not functioning, firms end up recording transactions manually, limiting the benefits coming from this technology. In Ethiopia, up to recent times, due to poor internet connectivity, taxpayers were forced to go to the local tax office to file electronically.

The same pattern emerges in Eswatini, where e-filing became mandatory in 2021, but taxpayers in remote, poorly connected areas still have to visit the local offices or internet kiosks to access the web portal and file their returns (see also Santoro, Amine and Magongo, 2022). In Rwanda, on the other hand, the RRA introduced the USSD-based M-declaration application that works on simple feature phones as a simpler tool for tax declaration and payment for presumptive taxpayers (Section 5) instead of the e-tax platform that requires internet connection, with the clear policy intent of reaching less connected taxpayers and facilitating their compliance. The app also easily enables tax payments through a direct link to mobile money platforms, which function smoothly on USSD phones. Second, IT literacy and high adoption costs must be factored in when implementing a new technology. In Uganda, for instance, we learned that lack of sufficient consultation with taxpayers on the new e-registration system, integrated with the national ID database, resulted in overwhelming feedback from taxpayers that URA’s registration process was too cumbersome. As another example from Uganda, the biggest challenge with e-filing is that the average taxpayer does not have adequate knowledge of the tax system and familiarity with IT tools to fill in online forms. Also, language barriers play a role, as e-returns are only in English. This means that most taxpayers need a middleman or tax agent to register or file returns, a situation that the interviewees fear may lead to agents preying on ignorance of taxpayers
it may have been driven by other unobserved factors such as an increase in spare capacity. Nevertheless, the fact that TFP growth was still negative in the 2017-2019 period seems to suggest that its decline in recent years has not only been due to cyclical factors (Figure 2).

International comparisons conrm that Brazil's negative TFP growth stands out in comparison to other emerging and developed economies (Figure 3). Whereas TFP declined at an annual rate of 1 percent per year between 1997 and 2019 in Brazil, it increased around 2 percent per year in China and 2.3 percent in India. Even though there was also a TFP

> 1See Bonelli et al. (2017)for a review and analysis of the productivity evidence in Brazil.

> 2Figures 1to 5 are updated versions of gures presented in Qian et al. (2018)and Dutz (2018).

> 3The data used to perform the growth decomposition exercises was obtained from the Penn World Table 10.0. The human capital index is constructed using the average years of schooling for the population aged 15 and older and rates of return based on cross-country Mincerian wage regressions.

3
given the mass of values at zero (Bellemare and Wichman, 2019; Chen and Roth, 2022; Mullahy and Norton, 2022). For these reasons, we report the (non-transformed) levels of hours worked in each activity in the main text but also provide the IHS-transformed results in the Appendix.

The descriptive statistics appear in six panels. All panels are limited to individuals that appear in the data in the 2018-2019 round and in at least one prior GHS round, meaning the sample roughly corresponds to panel specifications with the inclusion of an individual-level fixed effect. The GHS documentation notes that there was a change in the covered enumeration areas with the addition of a refresher set of enumeration areas in the 2018-2019 round. By limiting this sample to individuals who appear in the 2018-2019 round and at least one prior GHS round, any sample attrition originates from individuals who dropped from the sample for reasons other than changes in the areas enumerated.<sup>27</sup> The sample is also restricted to individuals who are at least 5 years old as of their first appearance in the data, as this is the age threshold for applying the labor module in the GHS. Panel A shows the descriptive statistics for the full sample in the post-planting round; Panel B the same, but in the post-harvest GHS data. Likewise, Panel C and D show the same figures for men, in both post-planting and post-harvest periods, respectively. Panels E and F show the same for women.

Some patterns are immediately evident.<sup>28</sup> A larger proportion of men, compared to women, work in agricultural work, in both seasons. Both men and women are more likely to work in agricultural work in the post-planting season, compared to the post-harvest season. At the same time, a larger proportion of women work in self-employment or household-enterprise work; and such work is comparatively lower in the post-harvest period (though this is driven by the reductions in such work among women). Both men and women
this intervention was implemented. This is the case for 5.4% of teachers in locally-managed schools that extended the winter break, and for 1.3% of teachers in the comparison municipalities. Even though the percentages are small, DiD could be slightly biased if these teachers took better managerial practices to the locally-managed schools. We then add as control the percentage of teachers in each one of the locally-managed schools that also work in a state-managed school that implemented the

> 22School Census, 2009.

19
Policy Research Working Paper 10663

# **Abstract**

Closing the gender digital divide by ensuring equal access to and benefit of the internet may reduce economic inequalities and close the gender gap in employment by providing new economic opportunities and facilitating access to market information. This paper estimates the impact of digital inclusion, measured by the Inclusive Internet Index on the female-to-male labor force participation ratio, while controlling for other economic and social factors. Using data from the World Development Indicators, the Economist Intelligence Unit database, and the World Bank’s Women, Business and the Law database for 13 countries in the Middle East and North Africa region for four years (2018 to 2021), a pooled cross section dataset is constructed. The model is estimated using generalized least squares to control for heteroskedasticity. The results show that an inclusive internet environment would reduce the gender gap in the labor force. Other key drivers include the structure of the economic growth, norms, and gender roles in the society. These results are relevant for the United Nations Sustainable Development Goals agenda, mainly goals 5 and 10.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 racha.ramadan@feps.edu.eg.

_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