rafmacalaba/data-use-mentions-extended huggingface dataset:
percentage points (pp) decrease in the probability of being (asset) poor (column 3).

# **6.2 Child Health**

Conditional on the positive income effect of coffee mills in the catchment area, a proximate outcome is an improvement in the health of children living within the catchment areas. We test

> 19However, disentangling these effects is challenging due to data constraints.

> 20Rwanda’s Integrated Household Living Conditions Survey (EICV) has direct measures of household income, however, the dataset is not geocoded hence limiting its usage in the context of this study, especially as it pertains to identifying households in treated and control communities. In fact, the only location identifier in the (publicly available version) of the dataset is at the district level.

> 21The DHS measure of poverty is a measure of the relative (asset) wealth of a household. A household is classified as poor if its wealth score is within the two lowest quintiles of the wealth distribution in the country.

17
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Notable are the absence of private investments in the power sector (figure 25). Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of GDP, while the most successful country in this regard, Guinea-Bissau, has captured in excess of 3.0 percent of GDP.

**Figure 25. Zimbabwe needs to attract more private investment**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ World Bank’s Private Participation in Infrastructure Database, 2010. _Note:_ GDP = gross domestic product; ICT = information and communications technology.

Attracting required private sector funding is a challenge. The uncertain and difficult investment climate in Zimbabwe, where private property itself seems frequently jeopardized, makes it difficult to attract investors. Furthermore, most of the infrastructure providers are parastastals that require restructuring before any private participation can take place; poor parastastal performance currently poses too high a risk for private investors, and the regulatory framework needs to be revised and properly implemented. However, the potential scope of private participation in infrastructure (PPI) in Zimbabwe is highly promising, and therefore the country must take all possible means to tap this potential.

Given the size of the funding gap, clear principles for prioritization and sequencing of infrastructure investment decisions are essential. Since infrastructure is only a means to an end, it is important to define infrastructure development plans in close coordination with the national growth vision. Zimbabwe is currently envisaging a growth strategy based around key development corridors that anchor economic activities in
rates or deferred payments are not counted as expenditures everywhere—they tend to be counted in high-income countries but rarely in LMICs. In low-income countries, tax expenditure reports don’t always cover all special tax regimes, sometimes explicitly stating so.<sup>10</sup> The reporting weakness in official data is reinforced by the observation that poorer countries display lower tax gaps than richer countries, which could reflect incomplete measurement rather than true differences. Compared to the data from those reports, our CIT expenditure estimates are homogeneous and comparable.

# **3.2 Country-Level ETR-Firm-Size Curves**

Which firms benefit from the sizable aggregate tax expenditures documented above? We rank firms based on their size within their country and assign each firm to a percentile of revenue from 1 to 100. To study the behavior of the largest firms, we further separate the top 1 percentile of firms into five bins, each representing 0.2% of firms. We then measure the average ETR across firms in each quantile. We show average ETRs by quantiles for two samples of firms. The first sample considers all firms, including zero-profit and loss-making firms to which we assign a zero ETR. The second sample is restricted to profitable firms (keeping the quantiles of revenue constant).

Figure 2 shows the pattern of ETRs in each of the 13 countries, for all firms. We rank countries by their top statutory tax rate (STR), ranging from 9% for Montenegro to 30% in half of the countries. We show the average ETR in each firm-size quantile and a polynomial fit capturing the ETR-firm-size relationship. The grey shaded area corresponds to the top 1% of firm size, which is graphically expanded to zoom in on the largest firms’ ETRs. We observe two main patterns. First, in every country the ETR rises between the first and ninth decile of firm size. The difference in ETRs between the first and ninth deciles is large in some countries—e.g. 19 percentage points in Costa Rica
# _2.2. Data and stylized facts_

We use firm-level information from Bureau van Dijk’s Orbis global database, the largest cross-country database containing information on firms’ financial statements, production activity, and firm ownership (Kalemli-Ozcan et al., 2019). We consider all industrial sectors, including financial companies and services (see table A.1 in Appendix A). We observe European firms over a full economic cycle between 2010 and 2016. To clean up the data for our intended purposes, we follow Cusolito and Didier (2020) and select firms for which we have information on the key variables in our study, namely total sales, compensation of employees, interest payments, taxes, paid-in shareholder equity, total debt and date of establishment. Our final sample comprises information on approximately 7.8 million observations across 24 European countries.

Table 1 reports the number of countries and firms in our sample. Of the 24 countries, according to the 2021 World Bank classification of countries into income groups, 18 are high-income countries and 6 are upper-middle-income countries. Italy is the country with the largest number of firms (25% of the sample) and Luxembourg the lowest (0.04% of the sample). On average, more than 60% of total assets are financed through debt. Liabilities to total assets range from 41% (Ukraine) to 74% (Italy). The ratio of value-added (VA) to total assets is on average around 0.9, varying between a minimum of 43% (Bosnia and Herzegovina) to a maximum of 143% (Bulgaria).

Table 2 summarizes the cost of financing through debt of firms classified according to their size. In all years, smaller firms face a higher cost of financing through debt. Moreover, not only does the average cost of finance appear to be higher for smaller firms, but also there is a higher dispersion, suggesting potentially greater misallocation. Table 3 shows similar patterns but with some distinctions. The cost of equity financing
food safer as well as reduces the frequency of grocery shopping and cooking. Studies analyzing data from household surveys have found positive effects of electrification on income (Kumar and Rauniyar 2011; Khandker et al. 2012; Khandker, Barnes, and Samead 2013; Van de Walle et al. 2013), employment (Dinkelman 2011; Khandker et al. 2012; Grogan and Sadanad 2013; Van de Walle et al. 2013), and education (Kumar and Rauniyar 2011; Khandker et al. 2012; Khandker, Barnes, and Samead 2013; Van de Walle et al. 2013).

In order to understand better why so many households in Africa do not have access to electricity despite its many benefits, some have studied possible constraints facing households. Komives et al. (2005) analyzed electricity access by quintile and by poverty status. The study covered about 40 countries, and predictably found that access was much lower for poor households. However, only three countries in Africa were included in the study, and most other countries had much higher rates of access. The study assessed the effectiveness of subsidies embedded within the tariff structure and the potential improvements that could be made by redesign of the tariff structure.

Komives et al. broke down access into two factors in assessing whether subsidies could benefit low‐income households: the “community access rate” and the “connection rate” for those living in communities with 2 In international initiatives to achieve universal access, access has come to mean having electricity at home, and this paper uses the word in that sense. However, some literature has defined access to mean availability of electricity in the community and uses the term “coverage” to mean having electricity at home. The difference between percent coverage and percent access is the percentage of people or households who live in communities with electricity but have chosen not to have it at home (for example, have chosen not to connect to the grid), most usually for financial reasons. To the extent that anyone can have
6 considering the asset replacement cost, such as is considered, for example, in current costs accounting (CCA).

The measure of the long-term avoidable cost obtained from the cost information submitted by the universal service provider will give the costs incurred by that operator. However, it is not necessarily the case that this measure represents an efficient degree of avoidable costs. In the event that a finance mechanism is determined, the other operators should not have to pay for the universal service provider's inefficiency. Thus, an efficiency adjustment must be applied to the long-term avoidable costs incurred so as to obtain an estimate of the efficient level of costs.<sup>5</sup> The financial approach uses the proper definition of costs, and introduces some interesting concepts into the analysis of the universal service burden (like the indirect financial benefits of being a universal service provider). It seems, though, that financial models share some of the undesirable properties of historical embedded cost (HEC) models (accounting auditing approach). Both types of models rely on firm-reported cost data; the major difference is that the financial model uses a current cost accounting methodology rather than the HEC standard.<sup>6</sup> Engineering-economic models have been developed in recent years as an alternative to the traditional econometric and accounting approaches to cost assessment. Engineering models offer a more detailed view of cost structures than is possible using econometric data. The engineering models (also known as _cost proxy models)_ could enable the regulator to estimate the forward-looking economic cost of the service without having to rely on detailed cost studies that otherwise would be necessary. Proxy models can be useful for many regulatory purposes, such as determining levels of universal service support in high cost areas and the pricing of unbundled network elements (e.g. interconnection). An economic cost proxy model begins with an engineering model of the physical local exchange network, and then makes a detailed set of assumptions about

> ' For the case of BT (British
responses to the threat of foreign multinational competition by upgrading productivity, raising innovation, investment and wage rate, and altering product composition.

# 2.3 The Determinants and Impacts of Chinese Outward FDI

Within the broad FDI literature, a new emerging strand of research focuses on China’s outward FDI.<sup>5</sup> In its earliest stage, research in this area is primarily descriptive in nature. For example, Cai (1999) proposes that Chinese firms invest overseas to seek markets, natural resources, technology, managerial skills, and financial capital. Wu and Chen (2001) suggest two additional motives, namely, to transfer excessive production capacity overseas and to diversify production lines. A study by Morck, Yeung and Zhao (2008) shows that Chinese multinational firms tend to perform better than other foreign firms in environments with weak domestic institutions, possibly because Chinese firms are better at dealing with governments and operating in a country with inefficient domestic institutions.<sup>6</sup> A new wave of empirical work examines the determinants of Chinese ODI at the aggregate level. For example, Cheung and Qian (2009) find Chinese ODI tends to be attracted to countries with a larger market size and richer resources. Buckley et al (2007) find that Chinese ODI is positively correlated with the levels of host-country political risk and cultural proximity to China. Ramasamy et al (2012) use annual report data from the 200 largest Chinese firms listed in Shenzhen and Shanghai stock markets to compile Chinese ODI measures. They find that while state-controlled firms are attracted to countries with greater natural resources and more risky political environments, private There are also studies analyzing Chinese ODI from the home country perspective. At the regional level, You (2015) finds that Chinese local government policy measured by willingness to approve local ODI and investment in R&D has a positive impact on regional ODI. Another home country characteristic found to have a significant effect is lagged inward FDI. Yao et al (2016) find
For our indicator of attitudes toward migration and asylum, we use the share of votes in each canton that express a preference for a more restrictive regulation of migration and asylum. A first challenge is that each vote concerns a different project. Therefore, it is difficult to carry out comparisons between votes over time. We solve this problem by standardizing each vote outcome: what matters for our indicator is each canton’s _relative_ position at each moment in time. An important implication of this standardization is that our indicator cannot measure the change over time in average attitudes (at the country level). This is, however, not a problem for our empirical analysis, since we use year fixed effects in all our regressions.<sup>20</sup> We use the voting data to create our yearly indicator in the following way. When there is more than one vote within a year, we take the average of these vote outcomes. Then we use a simple method to fill in the missing values: for each missing value, we take the simple average of the two observations that are closest in time.<sup>21</sup> Figure 4 shows the resulting data for all 26 cantons over the period 1998–2018. Standardized vote outcomes are shown as black dots, whereas the filled-in data appears in red. Several patterns are clearly visible. First, the French-speaking cantons (FR, VD, VS, NE, GE, JU) generally exhibit less restrictive attitudes toward migration and asylum than Italian-speaking (TI) or German-speaking cantons. However, the attitudes in most French-speaking cantons have become more restrictive over time, whereas several German-speaking cantons (especially those with big cities, ZH and BS) have evolved toward more open attitudes. There is also a third group of cantons, including some small conservative cantons and Italian-speaking Ticino (TI), where initially restrictive attitudes have become even more restrictive over time.<sup>22</sup>

# **4.2 Empirical Strategy*
<!-- Start of picture text --> Figure 3c. Algeria and Tunisia ‐ Inflation (Annual %)<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Algeria Tunisia Military coup Onset<br>Annual inflation (%)<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Tables 9.0, International Financial Statistics Database (IMF), and Historical Public Debt Database (IMF)/Abbas et al. (2011).

The two comparisons between conflict‐afflicted and non‐conflict neighboring countries provide both commonalities as well as contrasts. In both, the conflict‐afflicted economies (Nicaragua and Algeria) were characterized by rising current public expenditures and public debt as a share of GDP relative to their non‐ conflict neighbors (Costa Rica and Tunisia, respectively). However, in one case, inflation shot up in Algeria while inflation was gradually falling in Tunisia, whereas the contrast in the inflation dynamics of Nicaragua and Costa Rica was less clear. These differences could be due to regional or global trends of economic adjustment and reforms that coincided with the onset of armed conflict. The following section discusses our econometric strategy, which aims to disentangle the contrasting macroeconomic trends around the onset of conflict by systematically comparing conflict economies with non‐conflict economies for as many episodes of conflict as possible.

# **_III. D. Empirical strategy_**

The purpose of our estimation strategy is to identify the potential differential effects of armed conflict on the three macroeconomic variables of interest. The econometric strategy aims to identify these potential effects with a difference‐in‐difference estimator: The subscripts c and t denote countries and
day with 3.3 million lifted above the poverty line, followed by the FTAAP (2.1 million) and TPP15 (1.3 million). At the same time, the shape of the growth incidence curve also suggest that more ambitious agreements tend to concentrate larger gains in richer segments of the population, particularly in the case of the most ambitious of all, i.e. the EU-IDN CEPA (Figure 13a).

In contrast, RCEP and CPTPP (TPP-11) scenarios offer lower gains in terms of poverty reduction but better distributional outcomes. The RCEP scenario generates a reduction in poor people by 0.3 million for the PPP$3.20 a day line and by 0.8 million for the PPP$3.20 a day line **(** Figure 14b). The growth incidence curve (Figure 13b) shows that CPTPP might have a negative impact on the middle-class, sending more people - 0.3 million -below the poverty at the PPP$5.50 a day.

# **Figure 13. Growth incidence curves in simulated scenarios in 2030.**

<!-- Start of picture text --> 13a. EU-IDN, FTAAP and TPP-15 scenarios 13b. RCEP and CPTPP scenarios<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>EU-IDN FTAAP TPP-15<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>RCEP TPP-11<br
Failing to fully capture the consumption of the middle and upper classes—in SSA they occupy the top two deciles (African Development Bank 2011; Shimeles and Ncube 2015; Corral Rodas et al. 2019)— may lead to an underestimation of the welfare of this group. While this does not pose a problem for poverty measurement, it does mean that consumption is less well suited to measuring inequality. Furthermore, this problem is exacerbated when attempts are made to measure inequality changes over time, since the creation or consolidation of national middle classes in SSA pushes a greater proportion of households into that part of the distribution that is poorly captured by consumption measures (Kharas 2010; Ravallion 2010; Ncube and Lufumpa 2014; Schotte et al. 2018)—potentially leading to an underestimation of the rise of inequality. This, we argue, calls for adjusting the way we measure welfare in the region.

Our proposal to overcome this problem is to adjust the top of the consumption distribution by using insample information from a corresponding income distribution (Clementi et al. 2020). In a nutshell, our approach is to recalibrate the consumption figures for the middle-class segments by imputing information from the shape of the income distribution of the same households. This recalibration of the consumption distribution for a sample of selected African countries is made possible by constructing an ad hoc database that combines information on consumption with information obtained from household-based surveys maintained by the Food and Agriculture Organization (FAO) of the United Nations, which provides several important indicators on rural livelihoods but also constructs income estimates at the household level.

The paper is organized as follows. Section 2 details the data and the methodology used to produce our results. Section 3 presents unequivocal evidence of inequality underestimation by means of multivariate statistics and econometric analysis. Section 4 suggests original solutions to overcome the problem, also providing the empirical results. Section 5 concludes.

# 2 Data

Various data sources are used for this paper. The general overview on consumption versus income (section 3) is conducted using grouped
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