rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 6: Level of data policy restrictiveness and ICT‐services imports (2018) <!-- Start of picture text --> Data policy index & ICT-services imports<br>JPN<br>SGP<br>KOR<br>MNG THA<br>HKG<br>IDN<br>PHL<br>CHN<br>KHM<br>0 .2 .4 .6 .8 1<br>Restrictiveness index (0-1)<br>60<br>50<br>40<br>30<br>20<br>Computer services (% of commercial service imports)<br><!-- End of picture text --> Source: ECIPE and World Bank Development Indicators. Note: Digital services include computer, communications and digital services such activities as international telecommunications, and postal and courier services; computer data; news‐related service transactions between residents and non‐ residents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. The trend line is plotted with China included.

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Privatization Investment Funds (PIFs) and banks), consumers, workers, and the general public-both understand the basic concepts of competition and regulation, and are motivated to act in such a way as to capitalize on the role that competitive forces can play in fueling economic prosperity and growth. There are several areas where this challenge is partiCularly proncunced and where there is a priority for action-in particular mobi:lizing training, pragmatic policy advice and public education about competition and regulation.

_Monopoly Register._ One of the primary instruments by which the AMC implements competition policy is through its Register of Monopoly Enterprises - a policy instrument common to Russia and. many other CIS countries.<sup>4</sup> Uzbek enterprises that are deemed as "dominant"defined by statute as generally having a market share of at least 65 percent, and under certain conditions a market share of at least 35 - percent are listed on AMC's Register and therefore must declare (register) their prices and prolfits for AMC approval. In addition, registered monopolists, once they agree with their input sellers and output buyers on volumes, delivery times, and other conditions (as well as prices) must register these transaction terrms with the AMC along with their expected profits. In certain cases-typically for infrastructure ("utility") monopolies-the AMC directly determines prices and profits.

As of October 1, 1999 there were 716 enterprises and 1,924 products listed in the" AJMC's Monopoly Register, of which 91 enterprises and 205 products were registered as monopolies at the national (Republic) level, with the remaining (and vast majority) of monopoly firms and products registered in the AMC's 14 regional offices. Table 1 indicates the sectoral distribution of the registered monopolies. In Uzbekistan there are approximately 176,800 firms with business licenses, the vast

> 4 A description of Russia's competition policy framework is contained in Broadman (2000).

2
|Province|Laid-off workers<br>(10,000 persons)|People trained (10,000<br>persons)|Reemployment rate (%)|
|---|---|---|---|
|Heilongjiang|62|46|65%|
|Liaoning|35|19.7|40%|
|Chongqing|11|4|71%|
|Guangdong|45|24|70%|
|Hubei|32|11|58%|
|Hunan|44.8|8|72%|
|Fujian|3|1.3|46%|
|Henan|23.7|13.4|46%|
|Ningxia|3.5|2.6|30%|
|Xinjiang|4|1.5|27%|

Source: China Employment Training Center, 1998.

The results of these training programs were variant from province to province (table 15), for example, in Hunan, 72% of people were reemployed after training while in Xinjiang, only 27% were reemployed after training. An evaluation study by the research group at the World Bank on the retraining programs in Shenyang and Wuhan also showed mixed results.<sup>70</sup> In general, retraining programs had a positive impact on the reemployment prospects of trainees in Wuhan, while in Shenyang they seemed to be ineffective. In both cities, training didn’t put any impact on trainees’ earnings. Training results were also different for workers of different gender, age and education backgrounds. The study also showed that the design of training programs had an impact: the longer-duration programs usually yielded better results. Meanwhile, more positive results can be seen from those programs where trainees had to pay at least part of the training costs. In addition, a survey conducted among the unemployed in August 2001 by the Ministry of Labor and Social Security in 10 large cities showed that 70 percent of
threatened – if interviewed by a government official. These risks can be mitigated if enumerators are hired among FDPs from the same background. However, this can create legal challenges if FDPs do not have work permits. In some cases, it might also be necessary to have UNHCR or IOM officials accompanying enumerators or even conducting the interviews themselves.

The implementation of FDP-specific socio-economic surveys can be done in parallel with UNHCR’s verification exercises. The verification exercise is a census that visits all registered refugees to update their data. In Kenya, an additional socio-economic questionnaire was administered to a random subset of refugees participating in the verification exercise (Pape et al., 2019b). The parallel implementation reduced costs substantially but the implementation requires close coordination between the verification exercise and the socio-economic survey. Ideally, no redundant data would be collected but in reality most questions from the verification exercise are asked slightly differently than in the socio-economic survey, which usually aims to be consistent with the latest national survey, necessitating different instruments for the same concept to ensure comparability.

# Poverty measures

Poverty measures the level of deprivation in a population. The most widely used concept defines as poverty headcount the number of individuals living below a threshold, usually called the poverty line (Foster, Greer and Thorbecke, 1984). Since the poverty headcount only reflects the proportion of poor people, it is usually complemented by a measure of the depth of deprivation, the poverty gap, which estimates the average gap between poor individuals and the poverty line. All poverty measures constructed in such a way are based on an underlying welfare metric. In most cases, the metric is defined at the household level and needs to be transformed into an individual measure. The transformation either divides household welfare by household size, providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure.

To measure household welfare, different metrics have been proposed and can generally be classified either
# **Figure C5.1. Impact of Political Turnover on Competition** _–_ **time varying industry characteristics**

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The results of our sensitivity tests are quite revealing, and show that our estimates are indeed robust to other measures of spatial inequality. In our first test, the results are also robust to using both constant and average mineral prices in the analysis. Of course, the magnitudes of coefficients do change considerably across MLD, Theil, and relative lights per capita (RLP), but the qualitative interpretations remain the same when compared with the baseline Gini. Worth noting, however, is a change in the R-squared across these different specifications: from 0.763 on the baseline Gini to 0.627, 0.677, and 0.464 on MLD, Theil, and RLP respectively. In our second test, when we model the data using multilevel analysis, although the magnitude varies across the different specifications, the qualitative interpretation of the results (unreported)<sup>24</sup> remains the same and robust.

Overall, although the aggregated analysis suggests that mining activities increase spatial inequality in favour of districts with natural advantages in mineral endowment, disaggregating the analysis to accommodate individual minerals shows that changes in inequality within districts and across countries are sensitive to the types of minerals and to whether mining activities are active or closed, small or large scale, of low or high value, and extractive or transformational. Based on these findings, we argue that mining activities can be both a curse and a blessing.

# **6 Conclusions**

This paper analysed the effects of mining activities on spatial inequality in 2,182 districts over 653 regions across 38 African countries. Our study employed novel spatial data enabling analysis at the local scale. District inequality was measured using night light data from the National Oceanic and Atmospheric Association’s National Geophysical Data Centre, while mineral production data came from the United States Geological Survey.

Our study offers three main conclusions. First, when the analysis is aggregated, mining activities significantly increase spatial inequality in Africa, a finding that is consistent with the standard resource curse literature.
To carry out this comprehensive fiscal incidence analysis, the following data was used:

- _Household-level data_ : This includes the 2021 Survey on Income and Living Conditions (SILC) for the income year 2020 and the 2021 Household Budget Survey (HBS)<sup>14</sup> . These datasets provide information at the individual and household level, allowing for a granular examination of the tax/benefit system's impact on different households.

- _Administrative data_ : Information on the number of beneficiaries per program and the number of taxpayers is essential. This data helps in understanding the specifics of the social protection system. The EUROMOD Bulgaria country report for the years 2018–2021 and the European System of Integrated Social Protection Statistics (ESSPROS) serve as sources for this data.

- _Data on the tax system_ : Data regarding the tax system is sourced from the Ministry of Finance (MOF) of Bulgaria and the EUROMOD. This data provides insights into the structure and characteristics of the tax system, including revenue collection mechanisms.

- _Fiscal data:_ Data from various sources such as the International Monetary Fund (IMF), Organization for Economic Co-operation and Development (OECD), and Eurostat is utilized. The IMF's GFS Main Aggregates, GFS Revenue datasets, and the OECD's Tax Revenue Statistics contribute to the fiscal data. Eurostat, a statistical office of the European Union, also provides relevant data for analysis.

**A comprehensive evaluation of Bulgaria's tax/benefit system can be conducted by combining the datasets above and employing micro-simulation analysis techniques.** This analysis enables a comparison of the system's performance before and during the COVID-19 pandemic, specifically for the years 2018 and 2020.

**The model encompasses a wide range of taxes and government expenditure items - comparable to other countries- capturing a comprehensive view of the fiscal system.** This analysis covers 60% of the total expenditure and 69% of the total revenues for Bulgaria in 2020. It uses a CEQ scenario where
<!-- Start of picture text --> importanceof IPA services<br>Assistance to handle issues and problems,<br>and resolve grievances with the government<br>Informationand assistance in setting up<br>Efforts to improve the business environment<br>in the country<br>Meetingswith agency officers to<br>Exhibitionsabout the country at trade<br>shows and other events<br><!-- End of picture text --> Critically important [| Important [MJ Somewhat important [Mf Not at all important [| Don’t know Source:Note: Computationbased on the Git Survey. the question,The questi “H **o** wns on IPAImportantservicesare high-qu were **a** nsweredlity servicesby 632andressu **p** portondentsfrom Thesethe respondentscountry’s IFA In answeredyour decision somewhatto Invest important, In dewaloping important,countries?or criticallyA =ImportantInvestment on promation agency.
Public versus Private Protection against Crime: The Case of Somali Piracy<sup>_∗_</sup> Quy-Toan Do Lin Ma Claudia Ruiz World Bank National University of Singapore World Bank : _Keywords_ Crime, protection, deterrence, displacement, externalities _JEL Codes_ : K42, O12

> _∗_ We are grateful to Abhijit Banerjee, Jishnu Das, Sebastian Galiani, Cl ́ement Gorrissen, Hanan Jacoby, Cl ́ement Joubert, Ted Leggett, Andrei Levchenko, Dilip Mookherjee, Ben Olken, Martin Ravallion, Debraj Ray, and seminar participants at the 31st BREAD Conference (Georgetown), Duke, Georgetown, Harvard and MIT, Paris School of Economics, University of Maryland, and the World Bank for helpful discussions. Special thanks go to Cyrus Mody and the IMB for providing us with the dataset on piracy incidents. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.
agriculture, fossil fuel production and waste disposal.

Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and a current spatial resolution of 5.5 x 3.5 km. We use the S5P L2 Offline georeferenced measures of XCH4 (the column average dry air mixing ratio of methane<sup>11</sup> ), corrected for bias associated with XCH4 dependence on surface albedo.<sup>12</sup> Recent global research by Sha et al. (2021) has shown that S5P CH4 measures correspond very closely to independent measures taken at ground stations maintained by TCCON (Total Carbon Column Observing Network).<sup>13</sup> Sentinel-5P’s bias-corrected XCH4 data exhibit a difference of

> 10 The remaining 5-decile GGUs do not contain any 5-decile EDGAR cells.

> 11 The total atmospheric column between the surface and the top of the atmosphere normalized to the corresponding dry air column.

> 12 For more technical details, see the documentation at

> https://sentinel.esa.int/documents/247904/3541451/Sentinel-5P-Methane-Product-Readme-File

> 13 Current TCCON sites are located in the United States, China, Canada, Germany, Poland, France, Japan, Australia, New Zealand, the Republic of Korea, Réunion, and Ascension Island.

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22 scaled by the size of the corresponding economies (i.e., by their gross domestic product), the largest (deepest) financial system is still some 110 times the smallest (least deep) one. And even if the top and bottom 5 percent of this distribution are taken out, the ratio of the largest to the smallest is about 28 – a large degree of disparity, considering that these are not raw figures but ratios relative to the size of economy. Similar orders of magnitude are obtained for the other characteristics of financial systems.<sup>14</sup> In other words, when one examines country-level data, there are vast differences in financial sector depth, as well as in the other characteristics. The cross-country differentiation along the key characteristics of financial systems can be seen from the scatter plots in Figure 4 as well as from cartograms such as the one shown for illustration in Figure 6. The scatter plots and the cartogram underscore the large cross-country differences. The measurement framework underscores that financial sectors in jurisdictions such as the United States and Korea exhibit a relatively great financial market depth, as one would expect. United States have less deep financial institutions, reflecting a less bank-centric (and more market-based) nature of the U.S. financial system. Several European countries exhibit relatively great financial depth.

Financial systems have changed substantially in the 2000s in the run-up to and during the crisis. As illustrated in Figures 7 and 8, the most visible change is the observed declines in the stability index, which in turn reflects the increased volatility in returns by financial institutions in some countries and in most financial markets. But the charts also illustrates that stability has not been the only dimension in decline and that to some extent it has been accompanied also by difficulties along other characteristics, such as reduced depth and access to finance and in some cases also reductions in efficiency, particularly in financial markets.

Overall, the data from the Global Financial Development Database suggest that the key disparities among countries in
building heights available for 2012 from CLARIN<br>and INDEC. The value of 0.7 calibrated is very<br>close to the value of 0.65 found for France in<br>(Combes,Duranton,and Gobillon 2021).|
|**Share of**<br>**household**<br>**income net of**<br>**transport costs**<br>**spent on housing**|β<br>|0.4|0.1 – 0.6|Calibrated. This figure is consistent with official<br>statistics from INDEC which place average<br>expenditure shares at 15.2% and 17.7%<br>respectively for transport and housing and<br>expenditures at 164% of income(INDEC 2013).|
|**Real interest rate:**<br>**opportunity cost**<br>**of capital**|i<br>|0.02|0.005 – 0.1|Assumed as a reasonable value. It is difficult to<br>ascertain a real interest rate that would reflect<br>the annual financial burden of construction in an<br>urban area which has developed over centuries.|
|**Unit cost of time**<br>**as a fraction of**<br>**hourly income**|t<br>|0.65|_|Calibrated. The value of 0.65 falls within the range<br>of values - [0.5, 1] - reported by most papers<br>(Small and Verhoef 2007; Glaeser, Kahn, and<br>Rappaport 2008).|
|**Depreciation rate**<br>**of capital**|ρ|0.01|_|Assumption that structures on average last for<
Table 2. Harmonized welfare indicators

|**Topic**|**Indicator**|**Countries**|**N**|
|---|---|---|---|
||Respondent was currently working at time of survey|77|471,967|
||Respondent stopped/started working at time of survey rel-<br>ative to prepandemic|75|443,902|
||Respondent changed job, sector or employment type relative<br>to prepandemic, if working before the pandemic and at the|Job: 69<br>Sector: 54|184,324<br>115,828|
|**Employment**|time of survey|Type: 43|96,152|
||Respondent gained employment during the pandemic, if not<br>working in a previous HFPS wave (panel respondents)|52|136,478|
||Respondent recovered employment during the pandemic,<br>if stopped working relative to prepandemic in a previous<br>HFPS wave (panel respondents)|52|78,019|
||Household income decreased relative to the prepandemic<br>reference period|54|186,073|
||Household received no or partial payment for wage work in<br>the past week, if the respondent was a wage worker|47|36,421|
|**Income**|Household gained income during the pandemic since the<br>date of the previous HFPS wave|29|102,091|
||Household recovered income during the pandemic – income<br>increased since the date of the previous HFPS wave – if the<br>household reported income loss relative to prepandemic in<br>a previous HFPS wave (panel households)|23|56,360|
|**Food security**|Household adults went a full day without eating in the last<br
# _Discussion_

The median prevalence of any functional difficulty among adults age 15 and older stands at 10% in the countries under consideration. This result is overall consistent with earlier estimates of prevalence among adults for LMICs (WHO-World Bank 2011; Mitra and Sambamoorthi 2014).

The prevalence of at least a lot of difficulty ranges from under 1% in Vanuatu to 6% in Papua New Guinea. The median household level prevalence is at 23%, which is consistent with earlier estimates (e.g. Mitra 2018). Overall, these results show that functional difficulties affect sizeable shares of individuals and households in LMICs.

They tend to affect older individuals and people in rural areas more, as well as women more often than men. More research on gender is needed given the higher prevalence found among women in this study and in other studies (WHO-World Bank 2011; Mitra and Sambamoorthi 2014). Functional difficulties are significantly associated with aging in the countries with increasing prevalence for older age groups. More research is also needed on older adults in LMICs, for whom knowledge about their situation is scarce.

The prevalence estimates in this paper likely offer a lower bound estimate of prevalence given that only four to six functional difficulties are measured. More data collection efforts are needed for instance using the WG Enhanced short set or the Extended set of questions of the WG to capture psychosocial and mental health-related functional difficulties. Further, the use of the WGSS or similar questions can address issues of prevalence and, through disaggregation, inequality. They do not, however, address questions regarding why such differences might exist. Surveys that can collect detailed information on the environment such as the Model Disability Survey (Cieza et al 2018) would provide information to help understand the determinants of functional difficulties.

Because functional difficulties affect sizeable shares of individuals and households in LMICs, a study of the correlation of such difficulties with inequalities is thus warranted and is presented in the rest of this paper.

# **Educational attainment**

Next, we present several educational
5.** _Learning by doing._ Even in cases where researchers have some power to detect a treatment effect with one post-treatment survey round, it can be useful to plan multiple survey rounds relatively close together in time. This allows researchers to quickly analyze the early results from the first follow-up survey, and based on these, ask followup questions in the next post-treatment survey to explore new hypotheses generated by the results.

**6.** _How do we know what ρ is?_ Power calculations already require substantial insights from researchers in terms of what the likely mean, standard deviation, and treatment effect size of interest are. Pilot surveys or existing data are often used to give some sense of likely

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