rafmacalaba/data-use-mentions-extended huggingface dataset:
, thus giving a higher number of observations for study, and more variation in their economic structure and performance. Some regions have been more successful than others, and we can we learn from the growth patterns that have characterized different types of regions. We focus especially on low-income and low-growth regions, as the subset of regions which are the highest priorities for Cohesion Policy.

**We structure the analysis according to three research questions: on location, economic dynamism, and correlates of growth.** Our research questions are the following: (i) where are NUTS3 level lagging regions located, and how similar are these locations to NUTS-2 lagging regions? (ii) have NUTS-3 lagging regions been growing and converging with non-lagging regions? (iii) what are the correlates of growth among NUTS-3 regions, and do these correlates differ for lagging regions?

The rest of the paper is organized as follows. Section 2 provides an overview of the empirical framework by describing the data set employed, discussing the taxonomy adopted to classify regions, and presenting the empirical modeling. Section 3 presents some stylized facts on the economic geography and dynamism of regions in the EU. Section 4 presents the empirical results. Section 5 concludes the work by discussing the main results and drawing some policy implications.

# **2. EMPIRICAL FRAMEWORK**

# **2.1. Data Set**

**We use data on NUTS-3 regions, with special focus on industrial structure, innovation, and inward FDI.** According to the 2016 NUTS classification, there are 1,348 NUTS-3 regions in the EU.<sup>11</sup> Specifically, we aim to analyze the drivers of regional economic growth – defined as yearly growth of GDP per capita – by focusing on three key economic dimensions: industrial structure, innovation, and inward FDI, while controlling also for population dynamics and agglomeration forces. The choice of these three economic dimensions is for three main reasons. First, the empirical literature has identified industrial structure,
Toward Environmentally Sustainable Public Institutions: The Green Government IT Index _Michael Lokshin and Eduardo Widmar_<sup>_*_</sup>

**Keywords:** ICT, Government, Sustainable Development, Green agenda **JEL:** C43, Q55, Q58, L86

*The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We are grateful to James Foster for his guidance on the methodological approach used in the paper. We thank Denis Robitaille for his support of this initiative and Frank Batler, Rajan Bhardvaj, Ronald Fortin, Paulo Motta, Frederico Shinohara, and Varun Sondhi for their support and contributions on the design of GGIT instrument and general comments.
|60|12|13|36|
|**Main actors**|**AEP, AES, CLP,**<br>**Duke, E.ON, EDF,**<br>**EDP, ENDESA,**<br>**ENTERGY, UF,**<br>**HEG, Intergen,**<br>**PSEG, Reliant,**<br>**Southern, Tractabel**|AES,<br>EDF,<br>UF,<br>Intergen,<br>PSEG|AES,<br>CLP,<br>Duke,<br>EON, EDF<br>HEG, UF,<br>Intergen,<br>PSEG,<br>Tractebel|AEP, AES,<br>Duke, EDF,<br>EDP,<br>ENDESA,<br>UF, Intergen,<br>PSEG,<br>Tractabel|AEP, AES,<br>Duke, E.ON,<br>ENTERGY,<br>PSEG,<br>Reliant,<br>Southest,<br>Tractabel|AES,<br>EDF,<br>Entergy<br>E.ON,<br>PSEG,<br>Tractebel,<br>UF|AEP, AES,<br>Duke, E.ON,<br>EDF, EDP,<br>ENDESA,<br>UF, Intergen,<br>PSEG,<br>Reliant,<br>Tractabel|

At the regional level, a strict adherence to the rule suggested above would however only clear North America and Eastern Europe of any market power. Both Latin America and Western Europe should be sources of concern for an hypothetical supranational competition agency as indicated by the HHIs and hinted at by the large market shares of the operators present in the regions. For Western Europe,
operated on highly-suitable soils, potential gains would increase to USD 0.21 per capita per day on average. Enhancing the technology set and achieving the GAEZ highinput use potential yield on highly-suitable soil would increase gains to USD 0.79 per capita per day. Although these estimates of potential economic gains from agricultural production are only for maize, maize makes up 66 percent of cultivated land across all households.

The findings hint that realizing agricultural production potential alone, given the current set of inputs and soil constraints, may not be sufficient for significant welfare gains. In order for agriculture to act as a key mechanism for poverty reduction, policies can include (i) significantly boosting the quantity and quality of inputs used by smallholder farmers, and (ii) implementing crop-specific agricultural interventions based on high-resolution soil data with the aim of increasing crop-specific soil suitability. Addressing specific soil deficiencies that render the land sub-optimally-suitable for a given crop, which can be identified with this dataset for example, can result in gains in agricultural productivity and associated income. Future research may include the analysis of interventions aimed at triggering a shift from one soil-suitability class to the next, and whether, considering the costs required for the shift, that would result in net gains for smallholder farmers. Additional work, in partnership with agronomic specialists, should be conducted on the application of an unequal weighting scheme in the suitability classification framework to confirm 28
- Non-precarious areas. These are neighborhoods where homes are self-constructed, constructed by stages, or constructed with the intervention of a contractor. They are also neighborhoods built entirely by the state for social housing in accordance with urban standards (drainage system, access to water and electricity, tracing roads, and so on) and habitat.

Each EA is then classified as either precarious or non-precarious by the knowledge of local experts based on the definition above (Figure 1).

The sample was drawn from the following seven strata: (a) low-density non-precarious zone, (b) medium-density non-precarious zone, (c) high-density non-precarious zone, (d) low-density precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021a for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

The analysis of job accessibility draws on the 2017 JICA Commuter Survey, which was conducted in 2017 with the aims of informing the Urban Transport Master Plan toward 2030 and identifying priority projects. The study area of the survey mainly covers urban parts of Kinshasa Province and spans over 1,450 km<sup>2</sup>
3 Gine and Townsend, 2004; and Townsend and Ueda, 2006). For example, financial development by enhancing economic activity could boost the demand for labor. If this increased labor demand falls primarily on low-skilled workers, this indirect effect of financial development could increase the income of the poor and reduce income inequality. Thus, even if financial development does not increase the direct use of financial services by the poor, it might alleviate poverty and tighten the income distribution by creating job opportunities for the poor.

Similarly, financial development could intensify the competition in the non-financial sector by reducing the barriers to the entry of new firms. According to Becker (1957), this increased competition could reduce discrimination in hiring workers and expand the economic opportunities of disadvantaged groups, i.e. the poor.

Empirically, many studies looking at micro data find evidence of a positive correlation between access to finance and poverty. Jacoby (1994), for instance, finds that lack of access to credit perpetuates poverty in Peru because poor households cannot afford to provide their children with appropriate education. Jacoby and Skoufias (1997) show that households from Indian villages without access to credit markets tend to reduce their children’s schooling when transitory shocks reduce their income. Similarly, Dehejia and Gatti (2003) and Beegle et al. (2003) show that child labor rates are higher in countries with poorly functioning financial systems.

Rosenzweig and Wolpin (1993) and Rosenzweig and Binswanger (1993) find evidence to support the risk diversification view. Their results suggest that low-wealth households without access to services that would help to manage their risks choose lower-return, lowerrisk activities compared to households without these constraints.

Turning to the job creation channel, Burgess and Pande (2005) look at India’s policy on bank branches that led to the opening of thousands of new rural bank branches in states with initially low levels of financial development. Their results suggest that opening a bank branch led to faster growth in wages of agricultural workers, while the wages of
subject to the budget constraint where E( _pj_ )( _sz_ ) is the expected price paid for the good _j_ and _Nz_ is the total number of products consumed in the hierarchy. The expected price depends on the shopping intensity _sz_ in the following way. A household that chooses to spend time _sz_ in shopping samples one (two) price(s) in the retail market with a probability _α_ ( _sz_ ) (respectively 1 _− α_ ( _sz_ )). We assume that there is perfect recall and the household sampling two prices always chooses the minimum price when purchasing the good _j_ .<sup>2</sup> Thus, conditional on the shopping intensity _sz_ , the expected price paid for the product _j_ is In the above Equation (3), the first term captures the expected price paid by consumers if they meet only one retailer in the goods market, while the second term denotes the expected price paid by households who meet two retailers. Differentiating the above expression with respect to the shopping time _s_ yields where the second line directly obtains from integrating the term in brackets by parts. The observation that expected price paid for any product _j_ is decreasing in the shopping time follows immediately from the facts that _α_<sup>_′_</sup> ( _s_ ) _<_ 0, while the term inside the bracket is always positive. Thus, households that spend more time on shopping have a higher probability of finding a retailer who offers a product _j_ at a lower price compared to its competitors, and pay

> 2We make the assumption that there is no retailer-level dispersion in the variety of goods, and that each retailer can supply all categories _j_ demanded by the households. Thus, once a household-retailer link has been established through a matching process, quotes for all prices become available to the household. While this assumption may be theoretically restrictive, and indeed is adopted for expositional convenience, it is empirically supported by the data. In the IRI sample, each retailer, on
||||-0.233<br>|-0.119<br>|
|can work in the same industries as||||||(0.564)|(0.555)|
|men**Y:1 N:0**||||||||
|Exports (%, cell average)*WBL|||||||0.043|
|Index|||||||(0.363)|
|No. of Workers (logs)*WBL|||||||-3.295*|
|Index|||||||(1.880)|
|No. of Workers (logs)|0.518**|0.665***|0.716**|0.861***|0.869***|6.010**|6.143**|
||(0.245)|(0.258)|(0.282)|(0.289)|(0.288)|(2.527)|(2.532)|
|GDP per capita (logs)|19.350***|19.859***|21.294***|16.245***|6.741|8.508|7.740|
||(5.010)|(5.226)|(5.237)|(5.461)|(5.343)|(5.484)|(5.494)|
|Herfindahl-Hirschman Index||-6.955*|-6.569*|-9.727***|-6.613**|12.845*|13.158*|
|||(3.557)|(3.
.046)|(0.046)|(0.050)|(0.079)|(0.051)|(0.020)|
|Country FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|436|436|436|436|441|1,001|
|R-squared (within)|0.483|0.580|0.577|0.552|0.674|<br>0.848|
|Countries|40|40|40|40|40|40|

Notes: Standard errors clustered by country in parentheses. Dependent variables are from the September 2018 update of the Worldwide Governance Indicators (estimates of GE = Government Effectiveness; RQ = Regulatory Quality; RL = Rule of Law; CC = Control of Corruption), various editions of the Corruption Perceptions Index (CPI), and the July 2018 version of the V-Dem dataset (ECI = Executive Corruption Index, reversed). See data appendix for full variable definitions and sources. *** p < 0.01, ** p < 0.05, * p < 0.1.

12
# **3. The data set**

This study evaluates the targeting and impact of FONCODES investments using available sources of data; no new data were collected. The data for the analysis come from two main sources: (i) information on the geographic distribution of FONCODES allocations and expenditures, kept by FONCODES itself; and (ii) information from the 1994 and 1997 Peru Living Standards Measurement Survey (LSMS), and a household survey conducted by the Peruvian Statistical Institute (INEI) in 1996.

Since 1992, FONCODES has used an "allocation rule" to direct resources to small geographic jurisdictions-provinces, for 1992 through 1995, and districts since 1996. Specifically, the population of each province or district is weighted by an index of unmet tbasic needs. This index is an ad hoc composite of various measures-including access to school 'n, electricity, water, sanitation, adequate housing, and measures of chronic malnutrition and illiteracy (Schady, 1998). Provinces or districts with higher values of the FONCODES index have more unmet bas,ic needs. The index used by FONCODES has evolved over the years: until 1993, it was based on information from the 1981 population census, but it has been updatedv with information from the 1993 population census since 1994. The variables and weights in the index also changed somewhat between 1992 and 1995, but have remained constant since then. Infornation on the FONCODES index, the allocation rule, and the corresponding allocatio ris is available for 1992 through 1998.

FONCODES also keeps monthly records on the number of projects and aggregate amo-unts spent in each district. Three points are worth noting about these data. First, because expenillures are recorded in the month in which a project is _approved,_ there is a lag of about two weeks iefore _disbursement_ of the first installment of funds, and of several months before the second (anct final) disbursement. Second, only expenditures on projects are included, and not, for example, administrative costs or expenses for general overhead. Third,
<u>Figure A7.1</u> in the Annex presents similar results, accounting for the reduction in total tobacco-related medical expenses (including out-of-pocket, as well as public and other private expenses). The long-term value (upper-bound scenario) of increasing the price of cigarettes by 50 percent can exceed 2 percent of income for the bottom 10 percent of the population.

Heterogenous effect of increasing the specific excise The two major types of special taxes that countries impose on tobacco are _specific_ (quantity-based) and _ad valorem_ (value-based) excise taxes. While both schemes have strengths and drawbacks, the literature and international experiences suggest that a _specific_ excise tax is often “technically superior” (Marquez and Moreno-Dodson 2017). Implementing a _specific_ excise can lower administrative burdens, the risks of noncompliance, and the incentives for the tobacco industry to keep consumer prices low (Ibidem).

Additionally, raising the specific tax may bring further distributional considerations. If poor households consume lower-priced cigarettes, augmenting the price of all cigarettes by a specific amount will translate to a higher price shock for poor households. To test this scenario in the case of Georgia, consumption prices are analyzed for different brands in the tobacco market. The available tobacco price data for 2014 suggest that a pack of 20 cigarettes was 3.2 GEL for the premium brand, and 2.2 for the most sold brand. However, the cheapest brand was only 80 tetri per pack (WHO 2015). A hypothetical price shock is simulated, assuming that those price ratios are held, and that smokers in the 20 poorest percent of the population purchase the cheapest brand only, while smokers in the two wealthiest deciles consume premium brands only. The rest of the population is assumed to smoke the most sold brand. The simulated price shock is also calibrated to achieve comparability to a 50 percent price shock. Hence, it is assumed that deciles I and II face a 100 percent price shock, deciles III through VIII are affected by a 36 percent price increase,
questions, where several layers of phrasing separate the respondent from the subject of the question, reticent respondents report that sensitive acts are committed more often than do other respondents. One interpretation of this result is that those respondents identified as reticent do have something to hide and therefore assess the prevalence of sensitive acts as being higher than do other respondents. Once these respondents feel protected from inference by the wording of the question, they give an unbiased (but higher) estimate of that prevalence.

AM did not find the variation of the size and significance of the reticence effect that we find for Nigeria, where the effect of reticence is much weaker for questions that refer less directly to the respondent. One possible reason for this difference is that AM's survey in Romania was directly focused on corruption and every respondent must have felt that answers to every question could provide inferences about the respondent's own conduct. The Nigerian survey was much more general and probably did not create this impression. Hence, surveys focused on corruption might in fact provide less accurate information on corruption than more general purpose surveys that have a few questions on corruption.

Our results suggest that there is a fundamental tradeoff in survey design. On the one hand, respondent reticence is more of a factor in responses to questions that are worded in more personal and specific ways, questions that most likely imply personal misbehavior of the respondent and those asking about direct personal experiences with sensitive acts. On the other hand, because such questions refer to direct personal experience, they should in principle provide better measures of firms' actual experiences. The results here suggest that there might be at least a partial way out of this tradeoff, which is to try to identify and correct for reticence biases using methods like those in this paper.

16
household. Total consumption is the sum of consumption expenditures and consumption taken from households’ stocks per adult equivalent. In column (1) and (2), _Di_ only included ex-combatants cross-checked with official registers. In column (3) and (4), _Di_ adds matches. In column (5) and (6), all self-reported ex-combatants are included. Odd-numbered columns do not include control variables or fixed effects; the remaining columns include both. We report the _p_ -values of F-tests assessing whether the coefficients associated with CNDD+ and FNL variables are of similar size, but of opposite sign. Controls include past violence, whether the household head changed, his sex, age, matrimonial status, education, and occupation. Outliers are excluded (as explained in Appendix B.1). Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 economic outcomes. This table relies on the second definition of demobilized ex-combatants, namely the one using self-reported information cross-checked with the official demobilization registers and supplemented with matches.<sup>25</sup>

> 25 _DCNDDi_ +2 and _Di_<sup>_FNL_2</sup> in table 2. The use of other definitions leads to similar results. When self-reported information is used, the results are weakened by the presence of noise.

19
foreign affiliates) every six months to learn about the possibilities of upgrading their products. Suppliers provide the necessary inputs and often prepare a new formula for the product based on these inputs.

> 2Several studies show that when firms have access to imports of high-quality and diverse inputs, there are significant gains in productivity (Amiti and Konings, 2007), export quality (Bas and Strauss-Kahn, 2015), and product scope (Goldberg et al., 2010).

> 3 See a survey by Javorcik (2008) on Czech manufacturing firms showing that about 40% of domestic suppliers receive some kind of assistance from their multinational customers. Interestingly, becoming a supplier was the incentive to improve product quality for about half of these domestic firms. Javorcik (2004) also shows spillovers from FDI to the supplying industries. In addition, Javorcik et al. (2018) use the Hidalgo and Hausmann (2009) product complexity indicator to show that domestic firms are more likely to produce more complex products as a result of the increased FDI presence in the supplying sectors.

> 4Recent evidence from the Russian Federation show that firms with foreign affiliates have better management and better performance (Grover and Torre, 2019).

2
2. All-cause deaths by age drawn from national vital statistics records, aggregated in the Short-Term Mortality Fluctuations harmonized data series assembled as part of the Human Mortality Database.<sup>11</sup> Additional all-cause death data from Colombia, Mexico, Brazil and Peru was obtained from national statistical offices. Population by age figures for each country from 2020 were taken from United Nations estimates.<sup>12</sup> A more detailed description of data sources is provided in an annex.

# _Standardized population_

A standardized population analysis was conducted to assess the extent to which variation across countries in the age distribution of COVID-19 deaths was driven by variation in the population age distribution. First age-specific mortality rates were calculated by dividing the number of deaths attributed to COVID-19 for each age group by the population in each age group. These calculations were conducted for the following age groups: under 45, 45-54, 55-64, 65-74, 75-84, 85 and above. A hypothetical age distribution of deaths for each country was calculated by multiplying the age-specific COVID-19 mortality rates by corresponding population shares from the United States. A parallel calculation was carried out using estimates of excess mortality by age.

# _Excess mortality calculations_

Excess mortality methods compare the number of deaths to the number of expected deaths during a period of interest. Excess mortality is often treated as the “gold standard” in analysis of COVID-19 mortality.<sup>13</sup> However, there are two issues that are often overlooked in the analysis of excess mortality.

The first issue is that estimates are sensitive to the choice of method. Many COVID-19 excess mortality calculations have estimated expected deaths as the mean number of deaths over a corresponding period in previous years. If the number of deaths was trending upward in previous years, this common approach will over-estimate excess mortality.

This issue is illustrated in Figure 1 for five Latin American countries. All five countries show an increase in the number of annual deaths over 2015-19. Excess death estimates based on 2