rafmacalaba/data-use-mentions-extended huggingface dataset:
- For capex, industry sources were used for generation asset data. The allocation of public to state owned assets may not be accurate because the privatization process was underway during 2014, the reference year for Nigeria, in which case the capex estimates may be overestimated. T&D capex estimates followed the same methodology as that used in the general methodology.

- For opex, the numbers found in the 2015 MYTO model were taken.

# A1.6 Assumptions used in the overstaffing analysis

This annex describes the approach and assumptions used to estimate overstaffing in state-owned utilities by using benchmark employment in generation, transmission, and distribution separately.

## Definitions used for overstaffing analysis

- **Actual number of staff is the number** of staff reported by state-owned utilities supplying electricity, including temporary staff, sub-contractors, and consulting staff.

- **The efficient number of staff** is estimated using reference values for generation, transmission and distribution.

- **Overstaffing ratio** is calculated as the estimated number of excess staff as a proportion of total number of actual staff.

## Data availability

- Employee and customer data are available for 36 countries. Employee data are not available for Lesotho, Nigeria, and Sudan. Ghana can be analyzed only partially because employee data are not available for staff working in state-owned generation utilities.

- A breakdown of staff between generation and T&D was available for 12 countries. These are typically the countries where there is partial or full unbundling in the sector.

- For the 8 utilities providing services other than electricity, such as water and sewerage services, the number of customers taken was the number of electricity customers. If a breakdown of employees is not provided by services, the number of staff working in electricity was pro-rated in proportion to the breakdown of revenues between electricity and other services.

- Fifteen utility companies stated owned with data publicly available on employees and customers in Latin America were studied to obtain reference values for T&D staff. Most also had data available on
Policy Research Working Paper 7607

# **Abstract**

The rise in unemployment during an economic crisis poses a significant concern to policy makers. This paper measures the effect of a program in Mexico that granted firms in certain industries wage subsidies if they decided to keep their workers instead of letting them go during the recent economic crisis. The analysis uses monthly administrative data on employment at the industry level, along with propensity score matching to construct groups of eligible and ineligible durable goods manufacturing industries that display statistically identical pre-program trends in employment. Difference-in-difference results show a positive but not statistically significant effect of the wage subsidies on employment during the program’s eight-month duration, ranging from 5.7 to 13.2 percent in magnitude, depending on the specification. The size of the effect increases to 24 percent after the program ended and the results indicate that employment after the program recovered faster in eligible industries than in ineligible industries.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at mbruhn@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
methodology (Elbers _et al._ , 2003) by combining the 2007 _Encuesta Nacional de Hogares sobre Condiciones de Vida_ and 2007 Census data. This allows us to obtain estimates of poverty incidence at the municipal level. The same sources of information and methodology also allow us to obtain estimates of the average education attainment of the population living in each municipality.

Finally, the _Jurado Nacional de Elecciones_ <u>(www.jne.gob.pe), the public organization in</u> charge of organizing and coordinating electoral processes, kindly made available to us information on a number of characteristics pertaining to the political landscape within each municipality. We have information on the name, political affiliation, and education of the elected mayor in each municipality for the 2006 elections. In addition, we know the share of the vote that the elected mayor received, as well as of all other candidates standing in the mayoral race. We also have this information for the 2002 electoral cycle, which allows us to know if a given mayor was an incumbent when elected in 2006.

# **IV. Setup and results**

We regress the percentage of the allocated budget that is actually spent (or execution rate) by each municipality in 2007, 2008 and 2009 on local characteristics that capture the main hypotheses under consideration – namely, issues related to budget size and allocation process, local capacity, local needs, and political economy. We do so for all fiscal expenditures, capital and current expenditures, and defined capital expenditures alone. We focus most of the analysis, however, on the latter because it is the component of the budget with the lowest rate of execution. Our basic specification is as follows: Where, represents the budget execution rate by municipality _i_ in year _t_ , is 10
Table 1. Sample size and collection of data

||Baseline|Check 1|NAPE 2011|Check 2|Check 3|NAPE 2012|
|---|---|---|---|---|---|---|
|Time|Dec 2010/April 2011|July 2011|July 2011|Sept 2011|Feb 2012|July 2012|
|Head Teachers Survey|||||||
|Treated School|43|48|48|48|49|48|
|Control|41|48|45|46|46|45|
|Teachers|||||||
|Treated School|119||||||
|Control|105||||||
|Students|||||||
|Treated School|944||1230||1467|1268|
|Control|801||1126||1261|990|

Note: calculation of sample size using data collected at baseline, three follow-up (Check 1-3) and two test applications (NAPE) 18
|

.

world imports.’ By the same token, the measure used to evaluate the level of non-tariff restrictions is the weighted percent of tariff-code lines covered by various types of non-tariff barriers (licenses, quotas, prohibitions) as a percentage ofall tariffcode lines, using as weights the respective shares in world trade. The dispersion of tariff and para-tariff rates is measured by their simple standard deviation. Table 1 presents data on each of these indicators for the periods 1984-87, 1988-90, and 1991-93. Unfortunately, only ten countries could be included in the table; they are the largest countries in South America and Mexico. For most of the smaller countries in the region, UNCTAD (1994), the main data source, does not report historical figures for the indicators on trade restrictions.

A summary index was computed to measure the degree of trade liberalization .* This index was constructed in such a way that comparisons over time and across countries are possible. This policy-based index of trade liberalization is presented in Chart 1. It shows that Chile, the pioneer of trade liberalization in the 1970s, has also led the process in the late 1980s and early 1990s. Bolivia, Mexico, Venezuela and, to a lesser extent, Brazil, made considerable progress in trade reform in the late 1980s; all of the remaining countries conducted major trade reforms in the early 1990s. Whereas Brazil and Venezuela liberalized their tariff regime over a few years, Argentina, Colombia, and Peru conducted faster trade liberalizations. (Although not included in the index for lack of comparable data, countries such as Uruguay and Costa Rica in the late 1980s, and El Salvador, Guatemala, Nicaragua, and the members of the Caribbean Community in the early 1990s also conducted important trade liberalizations.”) Closer examination of the components of the index (see Table 1) reveals that the process of trade liberalizationwas accomplished through a balanced reduction of all trade restrictions, that is, a decrease in the level and dispersion of tariffs and para-tariffs and sharp reductions (even elimination
intervention increased both the trust and the infrastructure sub-indices (each by 22%) for the 2011-12 cohorts; and the infrastructure sub-index for the 2013-14 cohorts (24.1%). For both cohorts combined, the intervention increased communal infrastructure by 19.8%.

Panel B of **Table 9** indicates that there were no significant changes in household-level measures of social capital. We find no significant effects for the sub-indices of Trust and Participation, nor for the aggregate index across all samples. In addition, we find no evidence that the increased participation in land-cover work crowded out unpaid contributions to other pro-social community work, such as building roads or maintaining communal infrastructure (see Panel B of **Table 7** ). For the social capital indexes and its sub-indices, we ran similar robustness checks to those we ran for the forest management index; results do not substantially change (see Tables S4 and S13 in the Supplementary information for Alix-Garcia et al., 2018).

One possible concern with the social capital analysis is that the process of applying for and being rejected by the program might impact the controls (e.g., the application process could bring the community together or lead to disharmony lowering social capital among the control communities), which could violate the excludability assumption While we cannot fully test this possible threat to validity as we do not have true pre-treatment measures of social capital, we did examine social capital measures among the control group for those who were rejected once versus multiple times. We found no statistically significant differences or substantial normalized differences among social capital measures for those rejected multiple times (see Table S14 in the Supplementary Information for AlixGarcia et al., 2018). This suggests that the process of applying is not related to underlying social capital.

> 18 These results are also published in Alix-Garcia et al. 2018.

21
aversion to working longer hours exhibited by this class may also reflect the finding that women are more likely to be in this class than in the other classes (Appendix A1 and Figure 3).

# **5. Conclusion**

What specific aspects of formal jobs do workers value the most? This paper used a choice experiment to elicit workers’ WTP for contracts, termination notice, paid leave, preferred working hours, and access to a pension plan. The results suggest that among these attributes, workers most value job stability – that is, the guarantee of longer-term employment ensured by a contract. The baseline results show that the average worker would be willing to forego an increase of about 19 percent of monthly income for a 6- month contract, 27 percent for a 1-year contract and 44 percent for a long-term contract (relative to no contract). Similarly, the average worker would be willing to forego an increase of about 12 percent of monthly income in order to obtain thirty days of termination notice.

The findings are consistent with previous work from other countries. Bonhomme and Jolviet (2009) estimated a job search model for eight European countries, and found that the marginal WTP for job security ranges from 30 percent to 92 percent, making it the highest ranked attribute in five out of the eight countries. Using a choice experiment, Wiswall and Zafar (2018) found that undergraduates at New York University would be willing to give up 2.8 percent of earnings to lower the probability of job dismissal by 1 percentage point. In their choice experiment, Assy et al. (2018) found that youth in Kenya would be willing to pay about 27 percent of the average earnings offered in exchange for additional job security.<sup>xxvii</sup> Thus, there is consistent evidence, arising from revealed and 26
counts statistics (Pinkovskiy and Sala-i-Martin, 2016a; Clark, Pinkovskiy, and Sala-i Martin, 2017; Morris and Zhang, 2019), and to approximate economic activity at small spatial units (Nordhaus and Chen, 2015; Heger and Neumayer, 2019; Chanda and Kabiraj, 2020). A recent survey by Gibson, Olivia, and Boe-Gibson (2020) reviews more than 150 economic studies using nighttime lights and finds that they overwhelmingly use DMSP data.

While more recent VIIRS nighttime light data are understudied in comparison, there are some noticeable exceptions. For example, the higher frequency of these data allowed analysis of India’s demonetization in November 2016 (Beyer, Chhabra, Galdo, and Rama, 2018; Chodorow-Reich, Gopinath, Mishra, and Narayanan, 2020). They have also been used to predict GDP in metropolitan statistical areas in the United States (Chen and Nordhaus, 2019) and to analyze the impact of the recent tariff war between the United States and China on China’s economy (Chor and Li, 2021). More recently, in response to COVID-19, a growing literature uses VIIRS nighttime lights as a subnational indicator of economic activity at high frequency. Among others, the data have been used to assess the impact of COVID-19 in India (Beyer, Jain, and Sinha, 2020; Ghosh, Elvidge, Hsu, Zhizhin, and Bazilian, 2020; Beyer, FrancoBedoya, and Galdo, 2021), China (Elvidge, Ghosh, Hsu, Zhizhin, and Bazilian, 2020), and Morocco (Roberts, 2021). All these studies struggle to convert changes in VIIRS nighttime lights to changes in economic activity. Chodorow-Reich, Gopinath, Mishra, and Narayanan (2020), for example, use an elasticity estimated with annual DMPSOLS data (Henderson, Storeygard, and Weil, 2012), even though their study uses monthly VIIRS data. Most others are abstaining from the conversion and instead focus on differences between subnational entities (Beyer, Franco-Bedoya, and Galdo, 2021),
, as administratively defined, and use the portion falling within the corresponding boundaries as the subject for human judgment.

Relying on information at the level of cities, towns and villages to shed light on important topics in economic geography has precedents in the literature (Eeckhout 2004; Levy 2009; Michaels, Rauch and Redding 2012). Fairly disaggregated administrative units have also been shown to perform as well as gridded cells when dealing with critical topics in the economic geography literature (Briant, Combes and Lafourcade 2010). An admittedly preferable approach would be to let commuting pattern data delineate local labor markets (Duranton 2015; US Office of Management and Budget 2010). However, data of this sort is rarely available in developing countries, and India is not an exception in this respect.

In our methodology, four groups of assessors independently judge whether the Google images from a place correspond to an urban or a rural settlement. The four groups differ in their familiarity with India and in their expertise in urban issues. The most knowledgeable group is made of in-house research analysts. A second group comprises university students from the US who have experience in land use classification but no exposure to India. The last two groups are made of crowdsourced anonymous viewers hired through Amazon Mechanical Turk (MTurk) who are unlikely to have expertise in urban issues. In the third group all viewers are from India while in the fourth one they are all from the US.

Assessments also follow two different protocols. The more structured one requests the assessors to sequentially evaluate the density of construction, the nature of transport infrastructure, and the availability of urban amenities, before making their judgment. All four groups of assessors follow this protocol. But before doing so, the three outsider groups are also asked to have an impromptu judgment of the urban or rural nature of the place, without any guidance.

By proceeding this way, every place in our sample of cities, towns and villages is classified a total of seven classification rounds. We find that the classifications are highly
<br>Coefficent<br>Unconditional Conditional<br><!-- End of picture text --> _Note:_ The reported coefficient is from a regression of log sales on management score<sup>16</sup> , with the conditional regression including controls on sector of activity, city and firm's age. The coefficients should be interpreted as log percentages. The management score is been normalized for each country separately. *** p<0.01, ** p<0.05, * p<0.1

> 15 Besides Mozambique, the Enterprise Survey includes questions on the adoption of management practices in the following countries: Argentina, Belarus, Bolivia, Colombia, Cyprus, Ecuador, Gambia, Greece, Kenya, Lao PDR, Paraguay, Peru, Suriname, Uruguay and West Bank & Gaza.

> 16 The management scores were calculated with a subset of questions that are common for all countries. Some questions asked in Mozambique were not asked in other countries, leading to a slightly different management score for Mozambique (and therefore a slightly different coefficient than found in Column 1 of Table 6).

29
Policy Research Working Paper 8898

# **Abstract**

This paper analyses the degree to which infrastructure reliability and urban economic activity in several African cities is impacted by flooding. It combines firm-level micro data, flood maps, and several spatial data layers across cities through a harmonized geospatial network analysis. The analysis shows that a significant share of jobs in cities is directly affected by floods. It further details how transport infrastructure is subjected to significant flood risk that disproportionally affects main roads in many cities. While direct flood effects are revealed to be significant, this work further shows how knock-on implications for the entire urban economy might be even larger. Regardless of the direct flood exposure of firms, flooded transport networks mean that disruptions propagate across the city and drastically reduce the connectivity between firms. Access to hospitals is also found to be reduced significantly—even during relatively light flooding events: From a third of locations in Kampala, floods mean that people would no longer be able to reach hospitals within the “golden hour”— a rule of thumb referring to the window of time that maximizes the likelihood of survival after a severe medical incident. Overall, this study showcases the use of high-detail citylevel analyses to better understand the localized impacts of natural hazards on urban infrastructure networks.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. 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 jrentschler@worldbank.org and jbraese@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
6

# **II.** **<u>Data description</u>**

The paper uses three main data sources: (i) articles to construct country-specific newssentiment indexes (ii) asset prices and related high-frequency financial variables (such as trading volumes or liquidity measures) and (iii) capital flows data. We detail them in turn.

# **A.** **<u>News articles and Sentiment measures</u>**

# **1.** **<u>News articles</u>**

Our dataset of news articles was acquired from Factiva, a leading provider of news archives. We focused on articles published by Reuters in English, as it offers the most consistent coverage over time and across countries. Each article contains a timestamp, a title, a main text, as well as topics and locations tags generated by Factiva using a proprietary algorithm. We selected all the articles tagged with either “Economic News” or “Financial Market News”, as well as with at least one of the countries included in our sample. Our dataset contains close to 4.5 million daily articles covering 25 countries - 9 Advanced Economies (AEs) and 16 Emerging Markets (EMs) - from 1991 to 2015.

Summary tables and figures reporting (i) the start (and end) of the news coverage by country (ii) the number of articles by country and (iii) the main topics covered in our corpus are provided in Appendix. Overall, the articles we use cover a wide range of economic news (economic policy, government finance etc..), financial news (commodity markets, equity markets, forex etc..), corporate news and political news. The distribution of topics is similar in AEs and in EMs. Quantitatively, US-related articles represent one fourth of our sample (200 articles per day).<sup>4</sup> The distribution of non-US country tags is
transportation, and communication. Of course, all capital expenditures on these categories do not necessarily represent “investment”, as conventionally defined; some components of these outlays may be related to maintenance operations. However, we did not have sufficient information to refine the GFS estimates.

Figure 2 shows the evolution of total capital expenditure in proportion of GDP, and capital expenditure on infrastructure both as a share of total public expenditure (including current spending) and as a share of total capital expenditure only. The figure shows a declining trend in all three ratios since the early 1980s for Jordan and the late 1980s for Tunisia. For all three countries, the behavior of the ratio of total capital expenditure to GDP is consistent with the evolution of total public investment displayed in Figure 1. As a proportion of total capital outlays, the share of capital expenditure on infrastructure has averaged 30 percent for Jordan and almost 40 percent for Tunisia in recent years. For Egypt, by contrast, there are large fluctuations during the past 40 years, but no clear trends. In recent years, the share of capital expenditure on infrastructure in total capital outlays has fluctuated between 45 and 50 percent.

Using our flow data on infrastructure spending, we calculated the stock of public capital in infrastructure, using the perpetual inventory method and the GDP deflator to estimate real values in constant local prices. This procedure is, of course, subject to limitations.<sup>9</sup> Differences in the efficiency of the public sector and the price of infrastructure capital, in particular, means that the same level of capital spending on infrastructure may yield very different results across countries. We account for quality _ex post_ by using various indicators, as discussed below. More generally, the use of perpetual inventory methods to calculate stocks from expenditure flows may introduce systematic errors in stock estimates. At the same time, however, it should be noted that adequate price variables for public infrastructure are difficult to

> 9See Hulten (1990) for a detailed discussion of the