rafmacalaba/data-use-mentions-extended huggingface dataset:
8 hand, shorter recall periods are subject to the “telescoping” effect by which health expenditures are compressed into a shorter time to fit the recall period (Beegle et al. 2012). Exploiting the two recall periods for inpatient care in the WHS, Lu et al. (2009) find that the 4-week recall period leads to a larger (annualized) estimate of out-of-pocket spending than the 12-month recall period. Fourth, surveys vary in <u>how they collect out-of-pocket expenditure data. Sometimes, the data are collected</u> in the expenditure section, and the respondent is asked to provide information on the household’s total out-of-pocket expenditures for a specific item. In other cases, the data are collected in the health section, and the respondent is asked to go through each household member in turn and report the household member’s expenditures for the specific item. In some surveys, out-of-pocket expenditures are collected in both sections. The choice of which approach to use may well affect the reliability of results.<sup>5</sup> Fifth, different adaptations of a survey may have different values of the out-of-pocket expenditure variable or variables. For example, the outof-pocket health expenditure variables in the US public-release Consumer Expenditure Survey data are top-coded.

# <u>Measuring consumption and income</u>

A consumption aggregate should capture consumption across a broad range of categories, should capture home-produced food and other items, and should also capture the use value of durables (Deaton and Zaidi 2002). Housing should also be included, measured by the value of the flow of services that the household receives from occupying its dwelling (Balcazar et al. 2014). An income aggregate should capture both wage and nonwage income, wage income including earnings from dependent activities, and nonwage income capturing all other income including household crop and livestock production, self-employment earnings and transfer income (Carletto et al. 2007). While in the past it was unusual to find income aggregates for developing-country household surveys,
externalities to other firms: for example, firms might hesitate to train workers if there is a chance that the workers will then leave and start their own firms or go to work for competitors. A few of these programs focus solely on the export sector, with the aim of getting firms to diversify into other markets or other products, but most of these matching grant programs are open to a wide variety of sectors.

Table 3 uses data from World Bank project completion reports to highlight the number of firms participating in these projects. A typical project seems to involve giving out matching grants totaling $1-5 million to between 100 and 500 firms, and has a duration of around 5 years. Projects focusing on exports have fewer firms: with a range of 13 to 149 firms seen in the data. Within some of these programs there can be considerable heterogeneity in both the size of the 5
- (ii) Labor productivity channel

High temperature and humidity place the human body under strain, resulting in health risks to workers (particularly those in high physical exertion roles) and reduced physical and cognitive performance.

We utilize heat stress threshold values based upon the US Association Advancing Occupational and Environmental Health non-acclimatized standard by the National Institute for Occupational Safety and Health (NIOSH) and measured on the WBGT scale (Centers For Disease Control, 2018). We derive lost working hours (LWH) by applying productivity loss functions to WBGT heat stress values modeled through the UrbClim simulations. Separate productivity loss functions are applied for occupations with low, medium and high physical intensity (Figure 3).

We model labor productivity loss under climate scenarios in several steps. The working-age population for each city is divided into occupational sector based on the Periodic Labor Force Survey (PLFS, 2023). Sectors are categorized into low, medium and high work intensity (see Supplementary Figures – SF3). Average daily earnings by category are identified from the PLFS (2023) and city-specific sources. Foregone work output is calculated by applying productivity loss functions to sector-wise economic output and expected future losses are adjusted to present-day real prices to account for inflation.

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during the period 1994-2003, Le et al. (2012) study 110 countries during the period 1994-2009, and Khwaja and Iyer (2014) study 61 countries during the period 2000-2010. We contribute to this literature by providing new evidence on relative efficiency in DRM across countries based on a recently launched, comprehensive data set covering 182 countries (including 118 low- and middle- income countries which we focus on as our core sample) in the period 2008 to 2019.<sup>3</sup> As such, our analysis may be considered as providing an updated view of past analyses of revenue potential across countries based on the largest data set available to date.

In addition, by using non-parametric data envelopment analysis to estimate countries’ domestic revenue potential, we add a new approach to the range of methodologies used in the existing literature on revenue potential. Using non-parametric frontier analysis to evaluate countries’ performance in DRM offers several benefits compared to alternative methods for performance assessments, such as regression-based approaches. In particular, DEA has the advantage that it involves fewer assumptions about the structure of the data than parametric methods (e.g., regression analysis). Consequently, the results generated by DEA tend to be easier to interpret and are not subject to the same econometric challenges that regression-based methods typically face.<sup>4</sup> Moreover, DEA estimates efficiency scores for a given unit (e.g., country) based on the performance of a subset of efficient units in the sample with similar input factors than the considered unit. This differs from the estimates obtained from regressionbased approaches, which rely on comparisons relative to the average performance across all units in the sample. If the observed units are heterogeneous—as is clearly the case when analyzing countries from different parts of the world with different sizes and stages of development—then an important advantage of DEA is that the estimates for any given country are not determined by other countries with fundamentally different characteristics and prospects, but only by those
# **5. Why unbanked adults do not have an account**

To understand why the unbanked do not have an account, the Global Findex 2021 survey asked the unbanked directly why they did not have an account. Specifically, globally, unbanked adults are asked why they do not have an account at a financial institution (excluding mobile money).<sup>7</sup> Moreover, in Sub-Saharan African countries, where mobile money accounts are widespread, the survey also asks the unbanked why they do not have a mobile money account.

# **_5.1_ Reasons for not having a financial institution account**

“Lack of money” was, unsurprisingly, the most common answer given by 62 percent of unbanked adults for not having a financial institution account (excluding mobile money). However, only 12 percent said that not having enough money was their only reason for not having an account (Figure 3). Respondents were allowed to offer multiple reasons, and, on average, most gave two. These findings suggest that if, for example, the costs of opening and maintaining an account or a minimum balance requirement were reduced, adults might be able to have an account.

The second most common answer, given by 36 percent of unbanked adults worldwide, is that financial services are too expensive. High costs have an even larger impact in Latin America and the Caribbean, where 60 percent of unbanked adults say that costs keep them from having an account. This is a region with less bank competition, greater bank concentration, and higher bank overhead cost-to-total assets ratios, which are associated with higher costs for banking services

> 7 This question is limited to barriers to account ownership at a bank or similar financial institution (excluding mobile money) since worldwide, 76 percent of adults reported having any account in 2021, including 74 percent who reported having an account at a bank or similar institution.

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the most important source of variation in inspection frequency within districts, since blocks and clusters without supervisory staff are much less likely to get inspected.

The second source of variation in inspections is the diligence of the concerned supervisory officer. Even if all the positions of supervisory staff were filled, there would be variation in the zealousness with which these officers visited villages/schools, which might lead to some areas being inspected more often than others based on whether they were in the coverage area of a more diligent officer or not. However, since supervisors are typically assigned a coverage area of clusters or blocks that comprise many villages, variation in monitoring frequency that is driven by supervisor-level unobservable characteristics is unlikely to be correlated with other village-level characteristics that are also correlated with absence. Of course, this

> 24This module was designed to complement the school surveys by allowing us to create quantitative measures of district-level education governance. Unfortunately, the non-completion rate for these interviews was very high (over 40 percent) due to non-availability, and non-response of district-level administrators. Since this non-response is clearly not random, we do not use the quantitative measures in regressions. Nevertheless, important qualitative insights can be obtained from these interview transcripts. These results are summarized in a companion policy report (Centre for Policy Research, 2012).

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- _β_ : Coefficient of interest, measuring the impact of TRA officials’ presence on the outcome.

- _εiw_ : Error term.

Standard errors were clustered at the ward level to account for intra-ward correlation in responses.

# **4 Results**

We collected data from a random sample of firms across the country in two waves. We surveyed one individual representing each firm in December 2022 at the same time as the treatment was implemented. After this primary data collection, we matched each ward with administrative tax records. A second wave of data was collected to understand the first wave results as well as persistence in May 2024.

## **4.1 Data Collection Wave 1: Survey**

We report findings for different subgroups of outcomes for our treatment - the presence of agents of the state when conducting surveys in the field.

Our first set of outcomes is about the firm’s own behavior. We find that our treatment led to an increase in tax morale in general (Table 3). We create an index using four different measures of own tax morale: whether firm pays on tax obligations, whether not paying taxes is considered wrong and punishable, whether firm would pay taxes even if the probability of being caught for non compliance is low, and firms should not refuse to pay taxes until services improve.

We find no effect on the first three outcomes, but find a statistically significant effect on not refusing to pay tax in the absence of services. We estimate a coefficient of 0.086 over a control mean of 0.395. This shows the agreement with this statement is already high 15
policies in the past decade are Ecuador, Uruguay, Indonesia, Tunisia, Thailand, Colombia, Costa Rica and Pakistan (decrease by at least 5 points on average). Underlying these patterns of overall liberalization across economies are differences in the sectoral pattern of liberalization which are discussed in greater detail below.

**Figure 7 Changes in STRI 2008-11/2016 and level of income, 55 economies**

<!-- Start of picture text --> IND PANPAN<br>IDN<br>THA<br>IND IDN<br>CHN<br>PHLPHL MYSMYS<br>TUN THA<br>EGY<br>BGD LKALKAEGY CHN GRC FRA FINBEL DNK<br>VNM TUN CRI URYARGARG PRT OMN<br>KENKENBGD PAKPAKVNMNGANGA UKRUKR COLCOLZAFZAFDOMDOM BRABRACRI MEXMEXURY TURTURCHLCHLKAZKAZ POLPOLRUSRUSHUNHUNLTULTUPRTGRCCZEKORKORESPESPNZLNZLITAITAFRAGBRGBRFINBELCANCANOMNDEUDEUAUTAUTSWESWEDNKNLD USAUSAIRLIRL<br>PERPER CZE JPN AUSAUS NLD<br>ECU JPN<br>ECU<br>8 9 10 11<br>Avg income per capita (2008-16)<br>Note: STRI 2008-11 in blue, STRI 2016 in orange.<br>70<br>60<br>50<br>40<br>STRI 2008-11, 2016<br>30<br>20<br><!-- End of picture text --> The broad trend towards liberalization described above is also evident when considering changes in the STRI by sector and income group (Table 2). Green shaded cells highlight a reduction in the level of restrictiveness, with dark green pointing to bigger reductions. Grey cells point to insignificant changes in STRI levels.

**Table 2:** Change in STRI by sector and income group:

|Sector|Low|Lower Mid<br>Income|Upper Mid<br>Group|High|
|---|---|---|---|---|
|Distribution|-1.69|-4.45|0.24|-2.78|
|Financial|
# Kenya’s Infrastructure: A Continental Perspective

## **_Cecilia M. Briceño-Garmendia and Maria Shkaratan_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably, Johannes Zutt (country director), Sumila Gulyani (sector leader), Paivi Koljonnen (power), Anil Bhandari (transport), James Karuiru (water and sanitation), Mark Williams (ICT), Praveen Kumar (macro), as well as Ayub Osman and Jane Kiringai (local consultant).
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 13 obtained from the GHS layers. Natural amenities were calculated using several GIS layers: Water bodies were used to calculate dummy variables for location near the sea (coast). We used the 250 meters resolution raster MODIS Water Mask (Carroll et al., 2009) for this purpose. Finally, we used the Lloyd’s lists of maritime and fluvial ports to account for the presence of ports. Table 5 presents the control variables.
(Table 2, first row). The negative effect of climate change is lower for large firms in low and lower-middle income countries but still sizable and statistically significant at 14%, suggesting that large firms in LMCs are better able to adapt but cannot fully mitigate the adverse impact of higher temperatures on firm revenues.

The negative effects of higher temperatures disappear among firms in upper-middle income (UMCs) and high-income countries (HCs). The result may reflect that countries in both income groups face substantially lower average temperature levels. That is, while the average temperature is 23<sup>_◦_</sup> C in LMCs, it is only 17<sup>_◦_</sup> C in UMCs and 15<sup>_◦_</sup> C in HCs. In other words, although it is possible that firms in higher income countries face fewer constraints to adapt to rising temperatures, the results may also indicate that the costs are larger in hotter regions, in line with earlier studies using macroeconomic data (Dell, Jones, and Olken 2012; Burke, Solomon M Hsiang, and Miguel 2015). Notably, all sizes of firms in high income countries—which face relatively low average temperatures of 15<sup>_◦_</sup> C in our sample— benefit from positive temperature deviations, which is consistent with the finding of R. S. Tol (2021). Our results remain stable when we include all covariates that displayed statistically significant correlations with temperature differences as additional controls (see Table A1).

The baseline results establish that temperatures exceeding location-specific historical means significantly slow private sector growth in LMCs, disproportionally affecting small and medium firms. The findings thus highlight substantial heterogeneity in the effects of rising temperatures depending on firm-level and location-specific characteristics, emphasizing the importance of using granular data to assess the economic impact of climate change.

To isolate the effect of high temperatures, we estimate the impact of extremely hot days on firm performance. This also
employment information at higher disaggregation levels, the common practice is to equally weight all occupations at 8-digits merged to the same 4-digit occupation. Once the crosswalk is done, there are two options to categorize employment into green and non-green: a) assume that the 4-digit occupation is green if it contains at least one green task, or b) assume that only the share of green tasks within the 4-digit occupation represents green employment. If we compute estimates using the O*NET GEP methodology (green enhanced occupations and green new and emerging occupations), then 104 4-digit occupations are green or partially green,<sup>61</sup> which represent between 28 and 15 percent of employment in Indonesia, depending on whether a) or b) is assumed (Figure 4.2). Similarly, when carrying out the text analysis method and applying the narrow green dictionary to the O*NET occupational database, 164 4-digit occupations are green, which represent between 36 and 20 percent of employment, respectively. The employment level is higher using this latter method since the text analysis captured more green tasks and occupations in the O*NET taxonomy than the O*NET GEP.

These results imply that, given the level of greening of the economy in Indonesia, the text analysis applied to the ISCO-08 taxonomy may better reflect Indonesia’s level of greening of the economy.

**Figure 4.2 Green employment using O*NET taxonomy, O*NET GEP and text analysis applied to O*NET taxonomy**

<!-- Start of picture text --> 15 20<br>28<br>36<br>85 80<br>72<br>64<br>GEP GTI with O*NET taxonomy GEP GTI with O*NET taxonomy<br>Occupation at 4-digit level is green if contains at least Only the share of green tasks is considered green<br>one green task employment<br>Non-green Green<br><!-- End of picture
- B. Scientific and creative property

- Research and Development (R&D) in science and technology (spending for the development of new products and production processes, usually leading to a patent or license)

- Mineral exploration (spending for the acquisition of new reserves)

- Copyright and license costs (spending for the development of entertainment and artistic originals, usually leading to a copyright or license); and development costs in the motion picture, radio and television, sound recording, and book publishing industries

- Other product development, design, and research expenses (not necessarily leading to a patent or copyright), such as new product development costs in the financial services industry, new architectural and engineering designs, and R&D in the social sciences and humanities

# C. Economic competencies

- Brand equity (advertising expenditures and market research for the development of brands and trademarks)

- Firm-specific human capital (spending on developing workforce skills; for example, on-the-job training and tuition payments for job-related education)

- Organizational capital (costs of improvement in organizational structures).

In the US, the Income and Product Accounts (NIPA) already provide data on business expenditures on computer software (own account spending and purchases) and databases (own account spending). For other intangible expenditures, CHS estimations are based on a mixture of public data, private data, and derivative measures.

Official firm surveys are an important source of direct information for CHS. For example, the Census Bureau‘s Services Annual Survey (SAS) gives firm-level data on purchases of computer databases. Surveys conducted by the Bureau of Labor Statistics (BLS) collect data on firm expenses on workforce skills development (in-house trainers, outside trainers, tuition reimbursement, and outside training funds). R&D data are from firm R&D surveys conducted by the National Science Foundation (NSF).

But several intangible expenditures are not elicited in any firm survey in the US. In some cases, CHS use data on the revenue of the industry which produces
as well. By definition, all bonds in the sample are denominated in GBP or USD, so we need historical inflation data for the United Kingdom and the United States to compute real returns. For this purpose, we rely on the historical inflation indices provided by the Bank of England (for GBP bonds) and by the US Bureau of Labor Statistics (for USD bonds). For a USD bond, the monthly inflation rate is measured as U = U U U , so that the real ex-post return for this USD bond is given by ,mm = ππ ,mm U . A comparable process applies to UK bonds.,mm−1 ,mm−1<sup>35</sup> ii,jj,mm �CCNNCC −CCNNCC �/CCNNCC rr jj,ii,mm ,mm RR −ππ To arrive at yearly global portfolio returns,<sup>36</sup> we compute the monthly weighted global average across all bonds actively traded in month _t_ .

> 35 Appendix B5.5 shows that the results are similar when using inflation rates 12 months ahead.

> 36 Our results are similar when comparing bond prices at the beginning and end of a year (adding all within-year coupon payments) instead of annual averaging.

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) a company A, which owns company B (by 10% or more), which also owns company C.

> 6 The WB BOS database does not provide information on golden shares or veto powers, so the level of participation and thresholds proposed are used as a proxy of control and influence of the state over the firms.

> 7  The ORBIS dataset does not provide a variable to directly identify those with state participation  Although a proxy variable in ORBIS denoted as the Global Ultimate Owner can serve to identify some companies with state shareholding, it provides an underestimation of the real footprint of the state in the markets and suffer from omission errors that could misclassify SOEs as private firms (Dall'Olio, et al., 2022).

> 8 Yet, there is also variation of coverage within these countries as documented by (Bajgar, Berlingieri, Calligaris, Criscuolo, & Timmis, 2020).

6
within the agricultural sector, from ―traditional subsistence‖ to new, higher value export crops, the use of livestock, and modern marketing channels.

To see the first transformation, the change in the structure of primary <u>employment by sector</u> is broken down by type of employment within the sector (see Figure 2). While the reported primary sector of employment of those in the labor force

> 9 We found significant inconsistencies in the 2009/10 employment data, and suspect that the 2009/10 questionnaire undercounted non-wage employment, especially secondary employment. We made some adjustments to correct for this, ad are reasonably confident about the data on primary employment by sector shown here.

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cross-city, cross-country business-level database of informal businesses is one of the contributions of this paper. The data combines surveys that cover a wide range of topics through interviews that are administered to a representative sample of informal businesses rendered through the enumeration process (for more details, see the companion paper Aga et al., 2022). Two databases are prepared and made publicly available through the website of the World Bank's Enterprise Analysis unit: one combining raw data where question-wording and response categories have been carefully compared and mapped across surveys and standardized, if possible; and another, the indicators database transforming raw data into a set of indicators that aggregate the results in an easily analyzable format.

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