rafmacalaba/data-use-mentions-extended huggingface dataset:
of the business climate, the Global Competitiveness Index ranks the MENA region better than upper-middle-income countries in the areas of infrastructure, health, primary education, and higher education.

The literature finds that the presence of trade agreements between MENA countries and the rest of the world does not stimulate trade. On the other hand, domestic reforms are effective in stimulating trade by inducing a supply response from improved governance, regulations, openness, and labor markets (Freund, Portugal-Perez, 2013). The significance of institutional quality<sup>9</sup> towards promoting trade has also been documented by Francois and Manchin (2007) and Anderson and Marcoullier (2002).

As regards financial sector development, MENA exhibits low levels of financial inclusion. Only 27.1 percent of MENA firms have a loan or line of credit, compared to 43 and 47 percent in the Eastern Europe and Central Asia and Latin America and Caribbean regions, respectively.

> 6 Intangible assets are assets with lower ability to be transferred between signatories to a contract (e.g. from borrower to creditor) when the terms of the contract are broken. Examples of intangible assets are goodwill, R&D, associated human capital, organizational capital and even accounts receivables, cash, inventory or related investments. Examples of tangible assets, those amenable for use as collateral, are real estate, machinery, industry plants. “The higher the proportion of tangible assets a firm or entrepreneur has the higher the effective protection the outside investor has, and the more likely the relationship will be stable and become feasible in the first place” Braun, 2003.

> 7 See Behar and Freund (2011), Bhattacharya and Wolde (2010), and Iqbal and Nabli (2007).

> 8 The World Bank’s Enterprise Surveys is a firm-level survey, conducted in over 110 economies, that provides useful insights on firm perceptions and experiences. It is important to note that most of the MENA Enterprise Surveys do not have weights, and the survey statistics
through October.

Figure 9 provides summary information about Karnal. Figure 9(a) displays the GGU (Karnal District, Haryana) along with the sampling area for Sentinel-5P data (bordered in blue). Figure 9(b) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022.

Our 5-km database contains 2,747 observations for the Karnal GGU sampling area. Figure 10 plots the observations against time, with mean monthly observations superposed. The plot reveals the close correspondence between rice paddy flooding months and annual peak periods for mean CH4 anomalies.

Table 8 reports estimates for the Karnal GGU from OLS, Random Effects and Fixed Effects models. Results for the two models align in all cases. For Karnal the monthly trend (γ1) is positive and highly significant, yielding an estimated increase of about 0.40 ppb/month (or about 19.2 ppb over four years). The final-year deviation is also positive and highly significant, yielding an estimated difference of about 7.8 parts per billion between 2022 and the previous three years.

21
CBDF. After China comes Vietnam with a score of 0.82, and then third both Thailand and Indonesia which both have a level of restrictiveness that scores 0.64. The least restricted country is Hong Kong SAR, China, with a score of 0.09 and is therefore almost virtually open. It only shows some minor restrictions related to IPR and intermediate liability. Japan is the second least restricted country with a score of 0.20. Together the set of East Asian countries allow for substantial variability in our data policy index as illustrated in Figure 2 and Figure 4.

Figure 3 shows how the full index of data policy restrictiveness has evolved over time between the years 2009 and 2019. The line is computed as the weighted average of the 15 East Asian countries covered by the index with their respective GDP used as weights. The reason for doing so is that in order to get a non‐biased trend of restrictiveness for the entire region, countries’ restrictions should be corrected for their individual developments. A small country such as Vietnam might be very restricted but compared to China or Indonesia has a much smaller economic impact in the region. Treating all countries equally would therefore give a distorted picture of the aggregate level of restrictiveness for the entire area. As one can see, there is a clear upward trend reflecting the fact that data policies in the East Asian region have become more restrictive over time.

# **3.4 Descriptive Analysis**

Before turning to the econometric assessment using firm‐level data of innovation, we first provide some descriptive analysis of our data policy index and show how it relates to existing variables of innovation that are computed at the level of country and sector.

We first do so by taking one of the firm‐level innovation variables from the World Bank Enterprise Survey and average this binary information by country and sector. Admittedly, doing so has problems as the variable is initially dichotomous and would much depend on the number of firms included in the sample. Nonetheless,
Africa (Platform for Collaboration on Tax 2017), future research drawing on micro level administrative information, including Country-by-Country reports of large MNEs, could potentially help fill this gap To assess the robustness of our findings and evaluate their potential underlying drivers, we examine the role of treaty shopping in driving nominal investment flows and provide initial empirical evidence on the importance of treaty-shopping in the region. Our analysis suggests that the conclusion of DTTs with investment hubs increases the rerouting of income flows. These results confirm that avoidance opportunities have a major impact on the overall costbenefit ratio of tax treaties. Ongoing policy reforms and international initiatives to include or improve anti-abuse provisions in tax treaties, including those signed with Mauritius, may help address this challenge.

The findings of this paper are relevant for tax policy makers in Sub-Saharan Africa when balancing revenue and investment promotion objectives and for wider discussions on the international tax architecture. Tax treaties can be beneficial where their benefits outweigh costs. Our paper raises further doubts on this being the case for treaties concluded with investment hubs.
of land in less desirable suburban areas and it decreases the area of floor space that can be built in areas where there is a high demand for high quality high density urban development. But not allowing higher maximum FSI in nonresidential areas, it intensifies the competition between households and businesses for welllocated land. While businesses may happily sacrifice a spacious, bucolic environment for an efficient well designed business centre, uniformly low maximum FSI forces them to consume open space they don’t want or need. This drives up the price of open space everywhere, and raises the costs of these amenities to households. In aggregate low maximum FSI increases the land required for housing and commercial development, and drives up its price—thereby driving up the cost of any housing—since they all need land.

Beyond its substantial impacts on housing affordability, the current maximum FSI policy affects the structure of the city in undesirable and possibly unforeseen ways. Figure IV.7 shows for example that some of the lowest densities in the city are in the heart of the central business district (CBD) on the west side of the river. The modern service sector CBD that emerged in the 1980s along Ashram road around Nehru bridge is now anemic because the low FSI doesn’t allow the construction of a modern CBD. Instead, small office and commercial centers are being built in suburban areas along ring roads and radial roads. By failing to distinguish between commercial and residential land, the current 1.8 FSI imposed on Ahmedabad contributes to dispersing commercial activities into distant suburbs. This dispersion of business and commercial activities will have consequences for the modernization of Ahmedabad’s transport system.

21 This analysis can be updated to 2011 once ward level population data are available from the 2011 census.
# **6. Concluding remarks**

This study investigates whether Twitter data can be used to infer attitudes towards COVID vaccination with an application to the Arabic speaking world. Our objectives are three-fold. First, to estimate the prevalence of anti-vaccine attitudes using Twitter data. Second, to identify which messengers and what arguments are found to be most effective. Third, to assess how Twitterbased estimates of vaccine hesitancy compare to estimates obtained from survey data.

The following findings stand out. At first glance, anti-vaccine sentiment estimated from Twitter data is surprisingly low in comparison to estimates obtained from survey data. Only about 3 percent of Twitter accounts in our database are identified as anti-COVID-vaccination (compared to 20 to 30 percent of survey respondents). The most effective messages on the anti-vaccine side highlight claims that the vaccine causes serious life-threatening side effects. In the pro-vaccine camp, tweets containing content showing public figures receiving the vaccine are found to have the largest reach by far. This observation sits well with the earlier study by Alatas et al. (2019), which finds that Twitter endorsements of vaccination by celebrities are most effective. The study furthermore finds that celebrity endorsements can influence beliefs about vaccination. With respect to messengers in our application to the MENA region, we find that state-news accounts receive the overwhelming majority of retweets in our database.

To explain why our estimates of anti-vaccine attitudes are visibly lower than survey-based estimates, which are found to range approximately between 20 to 30 percent, we consider three candidate sources of bias. First, Twitter accounts include both individuals and organizations, whereas survey respondents consist of individuals only. The organizations in our sample are primarily news organizations, NGOs, private companies, law firms, government entities, and international bodies – all of which are more likely to express overtly positive attitudes towards vaccination. Individuals, on the other hand, are more likely to hold anti-vaccine attitudes and express them explicitly online. Second, Twitter users are a selective sub-sample of the population biased towards higher levels of education, higher income
From a policy perspective, the comprehensive environmental and tax structure of the IRENCGE-DF model makes it well suited for analyzing a wide range of policy scenarios. In particular, the “Fit for 55” package is analyzed in the model by limiting the level of emissions and letting the model calculate the endogenous price consistent with this cap. Thanks to the extensive focus on tax modeling, the IRENCGE-DF model is able to properly fit scenarios with new structures of tax rates based on the energy content and environmental performance of the fuels and electricity and also to simulate broader taxable bases by including more products in the scope and by removing some of the current exemptions and reductions. Several simulation scenarios will be discussed in the following sections.

The remainder of the paper is organized as follows. In Section 2, we present the data and methodology, reviewing the main aspects of the IRENCGE-DF model. In the Section 3, we present the results of our policy simulations. The final section concludes with some policy implications.

# **2 Data and Methodology**

# **2.1 SAM Construction**

The model is calibrated on the 2017 SAM. We update the benchmark data to 2020 by using macroeconomic variations resulting from the latest public economic and financial documents (i.e. Italian annual budget law, etc.).

The information contained in the SAM is obtained by combining national account data, such as the national account matrix for 2014, supply-use tables for 2017, and Eurostat data. In addition, we integrate missing information with data from tax returns available at the Department of Finance.

National accounts data provide detailed information on the final and intermediate consumption at activity and commodity levels, though they do not contain detailed information on taxation. Hence, we use the tax return data to distribute taxes and subsidies per commodity. In Table 1, we provide a description of the sets and structure used in the analysis. For the purpose of this study, the SAM distinguishes between 77 activities, 68 commodities, 10 household groups and
23 on a demand study. How to pick a method also depends on this, as much as on the data availability and the type of mode the regulators is focusing on.

Assuming that money is not an issue, in addition to the specific traffic type to be forecasted and the specific type of nfrastructure concerned, the ideal model will depend on a data availability, on how much detail is needed and on the specific goals of the forecast. Data provided by the historical operators of the service will help. For the most aggregated data this is generally possible but for more detailed data this can be a problem. Indeed, many operators will consider a lot of the relevant information to be confidential and would see as a threat to their commercial viability the obligation to disclose some data on their markets and clients. But on the other hand, precisely in the context of privatization, demand studies should be undertaken with the historical operator in preparation for the valuation of the assets to be "privatized"; this will also constitute a good initial data base for a regulator. In most countries, a useful compelemtn is data published by public (statistical offices) or private entities monitoring the sector. It is surprising to causal observers the number of think tanks or private survey firms collect and sell relevant information collected through regular surveys. Finally, all this can be compelemented by data produced through field surveys. The enthousiams for revealed and stated preferences is also generating vasts databases which often end up under a pile of dust after their initial use. A better organization of data bases should allow these surveys to become good sources of information to any regulator. Ultimately, the choice of methods will be driven by the data availability and for many of the most promising methods they new surveys are the way to go and do not require more than a couple of months to collect field data.

While data matters, the amount needed depends often on how much details are really needed from a regulatory viewpoint The devil can often be in the details but
_Source:_ Authors’ calculations based on World Bank-Burning Glass online Malaysia job postings data (2016-18). The means and ranges for the main skills categories (e.g. digital or cognitive) are higher than the statistics for the subsets (e.g. advanced or basic) since an occupation may require skills from more than one subset within a main skills category.

The average relative frequency of digital, cognitive, and socioemotional skills is in the range of 8-13 percent. The relative frequency of digital, cognitive, and socioemotional skills is similar (8-13 percent) while nearly three quarters of skills are classified other skills. Basic digital skills have a higher relative frequency than intermediate or advanced digital skills, though occupations requiring intermediate or advanced digital skills will implicitly require basic digital skills, as well.

When ranking occupations by their digital skill level, the ordering is similar to a ranking of these occupations based on the O*NET digital skill level, but with significant variance. In other words, our measure of the demand for digital skills in occupations generally tracks, but differs from, the O*NET measures. Thus, our estimates of the digital intensity of occupations may return results that are closer to the Malaysian – and perhaps the Southeast Asian – reality than the O*Net would have allowed.<sup>19</sup>

# _Defining occupation digital levels_

We use cluster analysis to assign each occupation to a digital occupation level. The cluster analysis allows the data to endogenously sort the occupations into groups with similar digital skills profiles. Since cluster analysis is an unsupervised machine learning technique, informed selection of the variables must be done to increase the likelihood of the clustering model to generate meaningful groups. We use the 13 variables as input to the model.

Five “raw” variables are included. The values for the “share of advanced, intermediate, and basic digital skills” (1) are the fundamental characteristics that encode an occupation’s digital level. The sum of these three,
affected experienced a decrease in consumption of similar magnitude and mainly correspond to poor households since the incidence of poverty in Mogadishu was 74% in 2017-18. Households in the top 20% of the consumption distribution are likely to have savings or other sources of income, allowing them to smooth the shock from a terrorist attack and preventing them from reducing their consumption levels.

Figure 5: Spatial variation of the impact on consumption in Mogadishu <!-- Start of picture text --> 0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Distance (Km) from the incident<br>Estimated impact on log of core consumption<br><!-- End of picture text --> Source: Authors’ calculations based on data from the SHFS and ACLED. Note: The vertical lines depict 95% confidence intervals.

Finally, we also relax the spatial criterion for the definition of treated households and classify as exposed or treated those households located from 1 to 6 kilometers away from the incidents.<sup>29</sup> Figure 5 presents the estimated coefficients for the effect on core consumption, considering as exposed those households located within the radius of each cutoff point.<sup>30</sup> The impact on consumption is similar for households located between 1 and 4 kilometers from the incident. After this threshold, the estimates become insignificant. The results suggest the immediate negative effect is clustered within a 4 kilometer radius from the attack. Households located within this radius suffer a decrease in consumption of similar magnitude
Policy Research Working Paper 11007

# **Abstract**

Public procurement in North Macedonia amounted to 16 percent of total government expenditure in 2018, or 5 percent of gross domestic product. The country’s public procurement is also vulnerable to corruption risks, which typically push prices up, leading to overspending. To support better budget policies, this paper maps corruption and state capture risks using administrative data on public procurement and examines their impact on prices. The analysis finds that overall corruption risk decreased slightly in 2011–22, with, for example, the single bidding rate remaining around 30 percent. Estimated overpricing at the contracting stage due to corruption risks ranged between 5 and 6 percent of spending throughout the period. The highest savings potentials are presented by lowering single bidding, increasing the use of a simplified open procedure, and advertising tendering opportunities longer. Network analysis of contracting relationships reveals that the 2017 change in government weakened the centrality of high corruption risk groups of organizations, leading to an overall higher integrity core.

This paper is a product of the Economic Policy Global Department—Trade. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at FazekasM@ceu.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the
of households is close to 50% as well.

**Less affluent, older, rural and unemployed people show a more negative perception of economic mobility.** Taking advantage of the information available in the survey for 2006, we obtained that these characteristics were associated with perceptions of downward economic mobility.

**Current income can only explain only a small fraction of the observed variation in perceptions.** Welfare explains only a minor fraction of the variation in perceptions. Consumption can only explain 5% of the variations in perceptions, while the asset index explains only 8%, as measured by using the R-squared of the regressions

**Factors beyond objective well-being, including those related to trust and fairness are associated with perceptions of mobility.** Households in countries where income distribution has become more unequal have a more negative perception of their situation. Further, findings show that trust in public institutions, perceptions of fairness in the economy and preference for a market economy are associated with perceptions of improvement.

> 16 In the Western Balkans, the public sector employs 48% of the workforce, close to the 47% regional average. 17 In Central Asia, private employment accounts for 46% of the employment, below the regional average of 53%. The agricultural sector employs a share considerably higher than the regional average: 31% of employed workers vs. a regional average of 10%.

21 | P a g e
<br>-0.5<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: World Bank, Bloomberg, Consensus Economics.

Note: Gray areas represent the gobal financial crisis of 2008-09, the intensification of the Euro area debt cris in 2010 and 2012. A. The implied volatility of option prices on the S&P 500 is recovered using the Black-Scholes formula from 6- and 18-monthahead put and call option contracts. B. The skewness of option prices on the S&P 500 is approximated using (25 Delta put volatility-25 Delta call volatility)/50 Delta volatility where Delta stands for the degree to which an option is exposed to shifts in the price of the underlying asset. This skewness measure is scaled down by a factor of 3 to match the equivalent skewness parameter of the risk neutral density function (Mixon 2011). C.D. The degree of uncertainty (proxied by dispersion) and balance of risks (proxied by skewness) of current and next-year term spread forecasts are compiled from monthly surveys of professional forecasters .

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