rafmacalaba/data-use-mentions-extended huggingface dataset:
to be not covered, see footnote 13);

- (ii) In Russia, Ukraine, Bulgaria, Romania, Slovenia, Latvia, the United Kingdom, and Austria  the two groups are of comparable size;

- (iii) In remaining countries (i.e., Portugal, Spain, and Italy; the four Central European countries; Estonia and Lithuania; the Nordic countries; as well as Switzerland, Netherlands, Germany, France, and Belgium) the informal sector is dominated by the self-employed.

The latter finding calls for a closer look, given that, according to anecdotal evidence, in countries with more restrictive Employment Protection Legislation a large share of selfemployed are hired as „self-employed service providers‖ and doing work that is in every way identical to a formal dependent worker‘s. However, even if this is the case, in a survey such workers might describe their status as „an employee without a contract‖ or even as „an employee with a contract‖ (the contract being not an employment one though). Fortunately, ESS data allow to distinguish between ̳true self-employed‘ and ̳quasi-self-employed‘, using 15
We find robust evidence that gatekeeping costs affect providers’ choices: patients are nine percentage points (35 percent) more likely to redeem a voucher on Patient as compared to Doctor Voucher days. This implies that provider and patient preferences are frequently misaligned. We also find consistent evidence that excess demand for vouchers on Patient Voucher days is driven by _patients’ preferences for simple treatment_ . First, providers are 4 percentage points more likely to write an antimalarial prescription (for simple or severe treatment) on Patient Voucher versus Doctor Voucher days, and patients are 6 percentage points more likely to purchase malaria treatment. Second, the excess demand for antimalarials on Patient Voucher days – is driven by patients with the fewest malaria symptoms antimalarial purchases by this group are 9 percentage points (23 percent) higher. This suggests providers prefer to withhold treatment from these marginal low-risk cases, but accede to demand when patients know that treatment is free.

In contrast, we find no evidence of doctor-driven demand for severe malaria treatment. There are no significant differences in rates of severe malaria treatment across Patient and Doctor Voucher days, and we find no evidence that providers use their information advantage in the Doctor Voucher condition to increase clinic revenues.<sup>4</sup> Next, we assess the match between underlying illness and treatment with data from follow-up home-based malaria tests with patients. In the Control group, we estimate that 57 percent of patients received “appropriate” malaria treatment, in that they either were malaria positive and purchased malaria treatment, or were negative and did not purchase treatment. The Patient Voucher condition reduced the match by 8 percentage points, compared to only 4 percentage points in the Doctor Voucher condition.<sup>5</sup> Over half of the worsening of the match in the Patient Voucher condition is driven by a change in providers’ prescribing behavior that is not observed in the Doctor Voucher condition; in other words, patient-driven demand for ACTs leads to overtreatment.<sup>6</sup> 4Although doctors
# 2. Data

This section provides an overview of the data types and sources that are crucial for assessing the impacts of flooding on the urban economy. For this purpose it briefly discusses flood maps (Section 2.1), employment data (Section 2.2), Open Street Map network data (Section 2.3), and firm-level data from the World Bank’s Enterprise Surveys (Section 2.4).

## 2.1. Flood maps

Flood risk information is typically communicated through flood maps that describe the spatial extent of flooding. For the purpose of this study, three different flood maps were used. This section will briefly present each of them: i) flood maps produced through the SSBN global flood model, ii) flood maps produced by a tailored flood model, and iii) empirical flood maps produced through community mapping. Table 2 in Annex 5.1 provides an overview of the availability of different flood maps for 13 cities in SubSaharan Africa.

### 2.1.1. SSBN-modelled flood maps

The main flood data used in this analysis are city-level flood maps derived from the SSBN global flood map.<sup>1</sup> The underlying flood model has a resolution of 90 meters, and considers floods that are either fluvial (i.e. caused by overflowing bodies of water) or pluvial (i.e. caused by a saturation of the absorption capacity of the ground or drainage systems). In this analysis, fluvial and pluvial maps were combined to assess the full extent of potential flooding.

SSBN flood maps are available for several return periods, that denote the average repeat interval of an event with a certain intensity. Here, return periods of 10, 50, and 100 years are considered. Flood model estimates provide the extent of flooding as well as the expected flood depth in each grid cell for each return period. By using a common methodology in all cities, SSBN flood maps are useful for analyses comparing the effects of
and Gardeazabal (2003) estimate a decline of 10% in GDP per capita over two decades as a result of terrorism in the Basque Country. Some authors have analyzed the consequences of terrorism on other outcomes. Nitsch and Schumacher (2004) associate terrorist actions with a reduced volume of trade across various countries, while Enders and Sandler (1991) report an annual reduction of FDI inflows by 13.5% and a loss of 140,000 tourists between 1970 and 1988 in Spain. All these negative effects can ultimately limit economic growth of a country. Gaibulloev and Sandler (2009) use panel data for Asian countries and find that <mark>terrorist incidents reduce private sector investment and increase government spending, which leads to a decline in GDP per capita growth by 1.5%. Similarly, R</mark> uiz Estrada, Park, and Khan (2018) conclude that terrorist attacks have slowed economic growth in Turkey between 1990 and 2016.<sup>9</sup>

> 9 See Frey, Luechinger, and Stutzer (2007) for a summary of the literature on the economic impact of terrorism.

5
18 2.00-4.50 - 1.10-1.50 - - 0.7-1.0 - 1.10-2.50 p 19 Notes: a Data sources are 1 (Tibrewal et al., 2023); 2 (Abbas et al., 2023); 3 (Eil et al., 2020); 4 (Valdes et al., 2020); 5 (Nepal et al., 2019); 6 (Deore et al., 2019); 7 (TERI, 2017); 8 (FNCCI, 2017); 9 (Kumar and Maithel, 2016); 10 (Weyant et al., 2016); 11 (MinErgy and FNBI, 2015); 12 (Kamyotra, 2015); 13 (Maithel et al., 2014); 14 (Manandhar and Dangol, 2013); 15 (Maithel, 2013); 16. (Maithel et al., 2012); 17 (Premchander et al., 2011); 18 (Heierli and Maithel, 2008); and 19 Rapid Survey study.

b P is the primary source; S is the secondary source; V is the various sources; and U is the source unknown. The number in the parenthesis is a reference number from a data source.

c Fuel types and their share by kiln technology and by data source are as follows: For BTK: 0.19 MJ/kg (100% coal), 1.26 MJ/kg (coal dominated), 1.33 MJ/kg (biomass dominated), and 1.43 MJ/kg (100% biomass); For ZZK: 1.02 MJ/kg (100% coal), 1.24 MJ/kg (coal dominated), and MJ/kg 1.32 (100% biomass); CK: 1.64 MJ/kg (low capacity), 1.51 MJ/kg (medium capacity), and 1.07 MJ/kg (high capacity
their firms, IMSS also provided us with industry-level calculations of transition probabilities, which we use to calculate the degree to which industries are related to each other. These transition probabilities were calculated using the entire country, not just data from the states that border the United States, and therefore provide a more robust indicator of inter-industry mobility.

The resulting data can be used to illustrate the relationship between Mexico-U.S. trade and employment in Northern Mexico during 2007-2009, and the resulting trends provide strong motivations for the discussion of the empirical strategies. Figure 4 shows quarterly total employment in tradable industries (i.e., those industries for which we found a match with the trade data under the HS classification) and Mexican exports to the United States. A positive correlation appears very strong during the period 2007q3-2009q1. Figure 5 plots the same export series together with total employment in non-tradable industries. Surprisingly, the correlation between exports and employment in non-tradables appears to be even stronger, especially during non-crisis periods, although it is also strong during the crisis. We interpret this surprising finding as suggestive evidence that trade shocks affect employment decisions in industries that are not necessarily directly engaged in trade, which lends credence to the idea that inter-industry labor mobility or other inter-industry linkages can play an important role in determining the empirical correlation between labor-market outcomes and trade.

During the crisis period, however, tradable-sector employment fell proportionately more than employment in non-tradable industries, as shown in Figure 6. Despite the important differences in magnitudes, changes in employment in tradable and non-tradable industries are also highly correlated. Figure 7a shows trends in real log wage changes for tradable and nontradable industries for workers that remained with their employers for consecutive quarters (i.e., 10
are large enough for them not to be useful in terms of the desired monitoring exercise for IDA20. If they are to be used, they should be complemented by additional indicators where possible, particularly of the progressivity of the PIT.

# 6 Measuring tax progressivity in IDA countries

Drawing on the preceding analysis, this section proposes a set of indicators for measuring progressivity in IDA countries over FY23-25. The proposal is guided by the following principles:

- The need to assess progressivity in IDA countries in FY23-25, concurrently, if possible or with minimal lag.

- The desire to assess progressivity of the entire tax system, not just the personal income tax.

- Acknowledgment of data limitations: specifically, the lack of availability of income survey microdata in most IDA countries, and difficulty in accessing consumption microdata.

- Acknowledgment of the limitations of different progressivity metrics.

- Acknowledgment of resource constraints.

As highlighted by the preceding sections, there are strengths and weaknesses with different measures of progressivity and redistribution. It is therefore proposed that a suite of metrics be employed to assess progressivity in IDA countries over FY23-25. Aggregate tax revenue metrics would be relied upon to provide a basic assessment of the progressivity of the overall tax system, while the degree of progressivity of PIT systems would be investigated in further detail utilizing both effective progressivity and progressive capacity metrics. In light of the large number of countries involved, a simplified modelling approach is

> 33 Even in flat rate PIT systems, the existence of a basic allowance will still create some progressivity (i.e., the average tax rate will increase with income). However, this will be significantly less than in a typical country applying a progressive tax rate schedule.

28
country coverage during our period of analysis is significantly smaller.<sup>12</sup> The bottom panel of Table A6 reports the number of conflict events by type over our period of analysis.

Our measures of conflict exposure may be subject to measurement error, as the ICEWS dataset shares common limitations of large-scale datasets compiled from multiple sources. One such issue is the presence of duplicated events, which we address by excluding entries with identical characteristics (e.g., date, location, actors). Another potential source of measurement error is related to possible biases in event reporting, which may favor certain locations. To mitigate this concern, we account for potential (time-invariant) geographical differences in reporting by including firm and area fixed effects in our analysis. Additionally, we cross-validate ICEWS conflict event data with information from the ACLED dataset. The correlation between the number of conflict events reported by the two datasets is very high (0.77) within the same sample. Robustness checks further confirm that our main results hold when using ACLED as a dataset for conflict events.

> 11See Table A6 in the Online Appendix for details on this classification.

> 12Until 2016, ACLED collected data only for African countries and a limited number of Asian countries. For details on the ACLED coverage see: `https://acleddata.com/acleddatanew/wp-content/ uploads/dlm_uploads/2019/07/Country-and-Time-Period-coverage_updOct2019.pdf` .

7
# **Appendix 1: Timeline**

# _Applications_

February 9, 2016: BIG Platform launched to start receiving applications December, 2016: BIG Platform registrations close for the first year

# _Induction workshops_

Lagos workshops held in March, May, August and December 2016 Abuja workshops held in May, August, and November 2016 One firm located in Abuja also attended the Enugu induction workshop in November 2016

# _Interventions_

Training workshops: April/May 2017 to June/July 2017 for most firms.

Consulting: Phase 1 (Needs assessment and growth plan) – January-April 2017 for most firms Phase 2 (80 hours assistance) – May 2017-February 2018 Insourcing and Outsourcing: Grant signing events – March 13 and 24, 2017 Subsidies received – March/April 2017 to December/March 2018 for most firms

# _Surveys_

First follow-up: March-June 2018 - corresponds to approximately one year after interventions started Second follow-up: February-June 2019 (86% in February/March) - corresponds to approximately two years after interventions started, and over one year after interventions ended Business Service Provider survey – 333 providers interviewed November 2017-June 2018 _GEM Project closing:_ March 29, 2019.

_Social media quality measured:_ October 2019.

||2017|201|8||2|019||
|---|---|---|---|---|---|---|---|
|2016 J|F<br>M A M J<br>J<br>A S<br>O N D J|F<br>M A M J<br>J|A S|O N D J|F<br>M A M J|J<br>A S|O N D|
|Applications||||||||
|Induction workshops||||||||
|_Interventions_||||||||
|Training workshops|||||||
||(-0.44)|
|El|12|12|0|12|0|0.137|0.116|||-0.020|
|Salvador||||||||||(-0.64)|
|Mexico|8|0|0|0|0|-0.030|-0.011|||-0.035|
|||||||||||(-4.40)|
|Paraguay|7|0|0|0|0|0.088|0.042|0.09|0.04|-0.043<br>(-3.76)|
|Peru|64|24|0|24|0|0.007|-0.001|0.04|-0.01|-0.151<br>(-6.05)|
|Uruguay|8|0|0|0|0|-0.037|-0.063|-0.05|-0.08|-0.081<br>(-9.53)|
|Venezuela|36|23|0|0|23|-0.029|-0.282||-0.33|0.036<br>(0.97)|

Notes: t-ratios obtained from heteroskedasticity-consistent standard errors are in parentheses. PC = price cap; HR = hybrid regimes; RoR = rate of return; H = household prices; I = industrial prices; LP = labor productivity.

Panama was excluded from the analysis since there is data for only one period.

All KWh of electricity prices were converted into 2001 price levels using the US Consumer Price Index and expressed in terms of purchasing power parity.

# **Concluding remarks**

In this study we compare the relative performance of public and privatized Latin
2. Available survey data for India**

The Consumption Expenditure Surveys (CES) by the National Statistics Office are the main source of poverty and inequality statistics in India. These surveys have also informed the World Bank’s poverty monitoring and are used to track progress towards the Sustainable Development Goal (SDG) number 1, which is focused on ending poverty. The release of the 2017/18 round of the consumption expenditure survey was eagerly anticipated, given that the last available expenditure survey dates to 2011/12. As indicated above, the government decided to withhold these data and hence we explore alternative data sources to provide updated estimates of poverty in India.

Table 1 lists several recent household surveys, all of which are nationally representative and include estimates of household consumption. As indicated above, the CES is the official source for poverty estimation. It includes around 400 questions covering expenditures on a comprehensive

> 5 This estimate underpins the World Bank’s estimate of global poverty in 2017, as reported in World Bank (2020). Also see Castañeda Aguilar et al. (2020).

> 6 The interval for the pass-through method [10.0, 10.8], calculated utilizing the confidence interval of the 0.67 passthrough rate, is also within the confidence band of the survey-to-survey method.

3
**Figure 3: Economically active and inactive population by sex (% of population 15-64 years old)**

<!-- Start of picture text --> 100.0<br>80.0 31.3 20.9 40.7 26.4 23.9 28.7 24.0 18.5 29.1 21.5 18.3 24.4<br>60.0<br>40.0 58.3 71.8 68.5 71.6 65.6 61.3 67.9 55.0 70.5 74.0 67.3<br>20.0 46.1<br>0.0<br>All Male Female All Male Female All Male Female All Male Female<br>Ethiopia (ESS 2018/19) Malawi (IHPS 2019) Nigeria (GHS 2018/19) Uganda (UNPS 2019)<br> Currently working Temporarily absent Unemployed Economically inactive<br><!-- End of picture text --> _Source_ : Pre-COVID-19 face-to-face LSMS-ISA surveys.

**Figure 4: Working respondents by economic sector (% of population 15-64 years old currently working)**

<!-- Start of picture text --> 100.0 5.49.0 11.7 14.9 14.6<br>80.0 15.6 8.2<br>23.8<br>60.0<br>40.0 78.0<br>63.8 57.9<br>20.0 40.7<br>0.0<br>Ethiopia (ESS 2018/19) Malawi (IHPS 2019) Nigeria (GHS 2018/19) Uganda (UNPS 2019)<br>Agriculture Manufacturing & Utilities<br>Construction, Transport & Professional Act. Commerce<br>Public Administration Services<br><!-- End of picture text --> _Source_
and opportunities to reach out to those who are hesitant. As such, our data can inform strategies that national vaccination campaigns may pursue to turn vaccines into vaccinations in Africa.

# **Limitations**

While our data stands out in its informational richness, national scope, and robust survey methodology, it is still subject to the challenges and limitations of (phone) survey data collection on vaccination. These include sample selection at the household level due to under-coverage, non-response, and attrition, as well as within the household arising from the purposive selection of respondents.<sup>19,20</sup> The implications of these issues for findings on vaccine hesitancy should be the subject of future research as should be the reliability of survey data on vaccination in the context of COVID-19.<sup>29–31</sup> Survey data, regardless of mode, necessarily relies on respondent self-reporting which is susceptible to respondents’ incentives, misreporting, and misperceptions.

# **Main findings and policy recommendations**

We find that in our study countries a majority remains willing to get vaccinated but that hesitancy among those unvaccinated is a non-negligible issue. As vaccine coverage in much of SSA is still below 20 percent, vaccination campaigns should focus first on getting those who are willing but yet unvaccinated to take up the vaccine. The main barriers keeping this group away from the vaccination sites are country-specific but commonly relate to the ease with which vaccines can be accessed within communities. Therefore, it is indispensable that vaccination sites become more widespread at the local level.<sup>28</sup> Furthermore, a continuation of communication campaigns about the ongoing risk of COVID-19 and safety of vaccines will be pivotal: We find that the protection vaccines afford to one’s own health is the main reason why people take up the vaccine and that hesitancy mostly relates to concerns about the vaccine’s side effects.<sup>24</sup> As this was already the main concern among the hesitant in 2020
we obtained GDP per capita from the World Bank’s World Development Indicators,<sup>3</sup> and cultural dimensions data from Geert Hofstede’s website.<sup>4</sup> We capture the environmental, social, and governance dimensions that represent ethical practices by constructing three binary variables from the ES. Specifically, the environmental dimension is captured by a binary variable that takes the value of one if the firm has monitored its CO2 emissions over the past three years (“ _mon_emi”_ ), and zero otherwise.<sup>5</sup> The social dimension is captured by a binary variable that takes the value of one if the firm offered formal training programs for permanent, full-time employees (“ _training_ ”) in the last fiscal year, and zero otherwise. The governance dimension, related to the employment of female top managers (a gender issue) is captured by a binary variable that takes the value of one if a company employs female top managers (“ _top_man_fem_ ”), and zero otherwise.

To identify digital-tech-oriented firms, we build two binary variables. First, we exploit the R&D intensity classification at the two-digit level by Galindo-Rueda and Verger (2016). We construct a first binary variable that takes the value of one for firms in sectors with at least medium technology orientation as implied by their R&D intensity classification, and zero otherwise (“ _tech orientation”_ ).<sup>6</sup> Second, we compute a binary variable that takes the value of one if the establishment has its own website or a social media page, and zero otherwise (“ _digital orientation”_ ). The product of tech

> 3 Data can be retrieved from the following web-link: https://databank.worldbank.org/source/world-development-indicators (last accessed: November 2024).

> 4 See: Dimension data matrix, version 2015 12 08 0-100, available at the following link: <u>https:
<!-- Start of picture text --> Figure 4: Correlation between difference in urban shares and<br>average population density across countries ( )<br>tcd<br>nerssd RR 22 = 00. 5<br>nam caf mli mdg swzlso<br>sdnsom<br>bwamrt gab cog zmbago ericodmozzwe zaflbrgincmrtzagnbbfacivsenkenethbensle ghatgo mwisycgmb<br>uganga<br>cpv<br>com mus<br>bdi<br>rwa<br>1 2 3 4 5 6<br>ln pop. density, 2015<br>Country Fitted<br>60<br>40<br>20<br>0<br>-20<br>-40<br>Urban share diff. (DB minus DoU)<br><!-- End of picture text -->

**Notes:** Data on average population densities taken from the World Bank’s World Development Indicators (WDI) database <u>(https://databank.worldbank.org/source/world-development-indicators). Results exclude São Tomé Príncipe.</u>

It follows from the above discussion that while the dartboard and degree of urbanization approaches agree on some of the broad patterns of urbanization in SSA – most notably that most SSA countries are more urbanized than official data would have us believe – they can produce, what appear to be, dramatically different results for individual countries. These differences, however, reflect the different perspectives – absolute versus relative – on urbanization that underpin the two approaches, both of which, especially when taken together, yield useful information. As such, the two approaches should be viewed as complements rather than substitutes.

# **7. Summary and Conclusion**

Sub-Saharan Africa has, over the last six decades, been among the world’s fastest urbanizing regions. And, by 2050, the number of people living in its towns and cities is projected to expand by a further 0.95 billion