rafmacalaba/data-use-mentions-extended huggingface dataset:
24 countries. Nonetheless, we find stronger correlations for NRXGDP than for MFGSERV.

We also know the main structural material used to construct the tall buildings in the data set. Steel is more durable, resistant and safe than concrete but also more expensive. Cols. (2)-(3) show the results with the dependent variable being log urban height density for buildings whose structural material is steel vs. concrete, respectively. Interestingly, we find strong correlations between steel use and MFGSERV (but steel could be cheaper in MFGSERV countries) and between concrete use and NRXGDP.

Third, we examine if the correlations are stronger for larger cities. In col. (4), the dependent variable is log height density considering only buildings in the capital city. We find a stronger correlation with NRXGDP than MFGSERV, although the differences with respect to MFGSERV is not significant. If we consider both the capital city and the largest city, we find a weaker correlation ((5)). For other cities, we also find a possible gap, indicating higher height density in NRXGDP than MFGSERV countries ((6)).

The excess of tall buildings in NRXGDP countries may be accompanied by lower occupancy rates. Since we do not have information on occupancy rates, we instead examine how the overall construction sector is potentially affected. For 80 countries, we know the annual cement production from 1970 to date.<sup>32</sup> Cement – the main ingredient of concrete – is typically not traded (World Cement, 2013). Using UN COMTRADE data we find that the world trade of cement (tons) only accounted for 3.5% of world cement production (tons) during the period of study, and for just 2.5% today. For this reason, cement production should be a very good proxy for cement use.

We on the same model as for tall but use as our rely long-difference buildings dependent variable the log sum of cement production over the period 1970-2020 while controlling
|6.3|

Source: Authors’ calculations; ESS 2018/19data.

In general, looking at the final income, we see that the fiscal actions reduced poverty and inequality for both men and women. Moving along the income concepts, we see the changes in poverty and inequality due to different taxes and transfers. For example, from market to gross income, there is evidence that direct transfers reduce both absolute and relative poverty; absolute poverty falls by 0.8 pp for both women and men (Table 3). These positive distributional effects could be due to PSNP, the large transfer program. Apparently, direct transfers are sufficient to offset the povertyinducing impact of direct taxes for both relative and absolute poverty lines. Indirect taxes worsen 19
these services to gather speed if appropriate policy actions are taken.

The issue of the _quality_ of employment among lower-end service activities, which are the large employment creators for unskilled labor, is particularly relevant in light of the labor market polarization resulting from changes in technology and trade (Autor 2015). Evidence suggests that labor compensation in the industrial sector is somewhat higher than in services for comparable workers. In a sample of 20 advanced economies, for example, the median difference in labor earnings between industry and services for high- and low-skilled workers is about 6 percentage points and 9 percentage points, respectively (IMF 2018). Similarly, in the United States, lowerwage workers in manufacturing earn about 11 percent more than their peers in other sectors, while high-wage workers earn just 4 percent more (Helper, Krueger, and Wial 2012). Using data from India, Nayyar (2011) finds that similar workers earn less in wholesale and retail trade, hotels and restaurants, transport services, and community and personal services than in manufacturing.

Yet, some valuable nonwage attributes of manufacturing jobs appear less widespread in other sectors. Manufacturing jobs tend to be characterized by formal employment arrangements with associated benefits for workers, such as access to minimum wages, labor codes, retirement plans, paid holidays and sick leave, and health and life insurance. They also tend to provide relatively stable arrangements, relying less on part-time or temporary contracts than other sectors, and may offer collective bargaining via unions (Jaumotte and Osorio Buitron 2015). Recent experimental evidence from Ethiopia also indicates that not all manufacturing jobs are better than selfemployment in services: in the studied factories, there is no evidence of a significant industrial

> 6 That said, recent studies suggest that the domestic demand for services exhibiting strong productivity growth may increase in relative terms over time as they become more affordable (IMF 2018).

12
income distribution for the sub-sample (not reported here) does not show any signs of truncation. Less than 3 percent of the observations are dropped which is sufficient to produce a sizable decline in inequality (see Table 8): the estimated Gini drops from 36 _._ 4 (full sample) to 31 _._ 9 (sub-sample).<sup>19</sup> Table 7 presents the estimates of _β_ 1, _α_ , _θ_ , _λ_ and _P_ . Note that _β_ 1 is estimated using the sub-sample of households, _α_ is estimated using the imputed rent data for the full sample (which now stand in for the house price database), while _θ_ , _λ_ and _P_ are estimated using a combination of the two data sources. Observe that the estimates of the Pareto tail index _α_ obtained using the imputed rent data from the survey are markedly higher than the estimates obtained using the house price database (see Table 3 in the main text). This is consistent with the hypothesis that top income households are under-represented in the original survey. The estimates for _β_ 1 are in the same range as those obtained using the full sample.

|sub-group|ˆ_β_1|ˆ_α_|ˆ_θmix_|ˆ_θsvy_|ˆ_λprop_7|ˆ_Pprop_7|
|---|---|---|---|---|---|---|
|Cairo|0.692|1.385|2.001|2.910|0.058|0.015|
|Alexandria|0.676|2.567|3.795|3.900|0.021|0.003|

Table 7: Estimates of _β_ 1, _α_ , _θ_ , _λ_ and _P_ The corresponding estimates of total inequality for urban Egypt are shown in Table 8. While we do not perfectly reproduce the original estimates of inequality, we find the results encouraging. All measures of inequality considered show a clear upward adjustment. We slightly over-estimate the Gini,
based on the presence of active mining activities. Columns 1 and 2 show that the average impacts of post-accession on annual per capita growth in the treated compared to control districts were statistically not different from zero. Columns 3 and 4 show the average impacts on migration. Similar to growth impacts in columns 1 and 2, the impacts on migration are also statistically insignificant. Columns 5 and 6 show the average impacts on spatial inequality. Similar to Table 5a, Column 5 shows a statistically significant 3 percent reduction in spatial inequality in the treated compared to control districts. However, this effect is not different from zero when the relevant controls are accounted for in column 6. Columns 7 and 8 show the welfare impacts. Column 7 shows a statistically significant gain of 1.10 index points on welfare in the treated relative to the control districts. When the regressions take into account the relevant controls, in column 8, this effect is reduced to 0.744 index points. The effects on mineral prices and expectation effects are similar to those in Table 5a.

# _D. Robustness Checks_

Tables 6a–6d, in Appendix II, report the robustness checks to our main results. Columns 1-4 present the estimates using USGS historical average prices as controls while columns 5-8 use the World Bank’s real prices data. Tables 6a and 6b replicate similar estimates as in Tables 5a and 5b, respectively. Tables 6c and 6d re-estimate the baseline model with different outcome variables; 18
elites face greater social and economic pressures from below– the elites’ tax contributions tend to increase (Boix, 2003). Moreover, these dynamics can be self-reinforcing, with greater economic equality giving way to more democratic systems. As Boix (2003) argues, “since the tax [that the holders of the most productive assets] will pay in a democratic regime finally becomes smaller than the costs of repression that they would have to bear to exclude the majority of the citizens, they accept the introduction of a system of universal suffrage” (Boix, 2003, p.10).

# **3 Arithmetics and politics of redistribution rules: A framework**

To develop a method to measure the potential DRM constraints related to taxable capacity, this paper looks at the _arithmetics_ of DRM from the perspective of the income of the potential tax base. It seeks to measure if that income would be sufficient to eliminate poverty using simple income redistribution rules and proposes a Poverty Eradication Capacity Index. It builds in particular on three recent efforts to quantity this capacity.

5
-|---|---|---|---|
|_Bangladesh_|cyclical components|0.33*|28|-0.25|24|-0.07|28|
||growth rates|0.31|27|-0.09|23|0.06|27|
|_Bhutan_|cyclical components|0.36*|28|-0.33*|28|0.19|28|
||growth rates|0.36*|27|-0.13|27|0.06|27|
|_India_|cyclical components<br>|-0.28|28|0.04|28|0.66***|28|
||growth rates|0.02|27|0.05|27|0.65***|27|
|_Nepal_|cyclical components|0.40**|28|-0.44**|28|0.48**|28|
||growth rates|0.33*|27|-0.33*|27|0.43**|27|
|_Pakistan_|cyclical components|0.37**|28|0.16|28|0.20|28|
||growth rates|0.37*|27|0.16|27|0.25|27|
|_Sri Lanka_|cyclical components|0.05|28|0.11|27|0.27|28|
||growth rates|0.12|27|0.01|27|0.12|27|

**Note:** Based on annual data from 1990 to 2017. Cyclical components are calculated as the deviation of the actual data from a trend computed using the Hedrick-Prescott filter with the standard smoothing parameter for annual data (6.25). *
(0.11)|(0.30)|(0.07)|(0.72)|(2.49)|(0.47)|
|Observations|2555|2555|2555|2542|2555|2555|
|No. of households|512|512|||512|512|
|R-squared|0.12|0.11|||0.05|0.02|
|||**Su**|**bsample with 75**|**/25 sharing rule**|||
|Barga land|-0.213***|-0.185***|-1,025.248***|-15.191***|-0.055**|-0.002|
||(5.33)|(5.54)|(3.30)|(5.54)|(2.11)|(0.10)|
|Plot size (acre)|0.002|0.008|313.166|28.356***|0.002|0.000|
||(0.55)|(0.98)|(0.24)|(5.53)|(0.78)|(0.07)|
|Distance to|0.014|0.001|191.388|0.134|-0.007|0.019|
|homestead (km)|(1.11)|(0.05)|(0.89)|(0.08)|(0.46)|(1.04)|
|Observations|2292|2292|2292|2283|2292|2292|
|No. of households|451|451|||451|451|
|R-squared|0.16
# **1 Introduction**

Electricity generation is a water-intensive process, with most power plants requiring water to spin hydroelectric turbines or cool thermoelectric generators (Sanders, 2015). In Latin America and the Caribbean (LAC), approximately half of the total power generation comes from hydropower, while the other half comes from combustion power, including coal, oil, gas, and biomass (IEA, 2021). Because hydrological droughts predominantly limit the generation capacity of hydropower plants, droughts can shift generation to combustion power plants. A plausibly important but understudied consequence of this shift in generation is the worsening of local air quality. Changes in air quality are a first-order concern, as exposure to fine particle air pollution (PM2 _._ 5 _, <_ 2 _._ 5 μm) is detrimental to human health (Dockery et al., 1993; Pope III et al., 2002, 2009; Cohen et al., 2017), with recent studies showing that even low levels of exposure lead to adverse health outcomes (Weichenthal et al., 2022). With nearly half a billion individuals in LAC residing near a combustion power plant and climate change leading to more frequent and severe hydrological droughts in the region, a key question is determining how air quality changes in response to this shift in electricity generation.

In this paper, we fill this gap by quantifying the relationship between hydrological droughts and increased PM2 _._ 5 concentrations around combustion power plants in LAC. We begin by assembling a monthly frequency power plant-level panel covering the 2000 to 2020 period. The panel provides information on both PM2 _._ 5 concentrations in the proximity of combustion power plants and marketlevel measures of the level of hydropower generation capacity in water stress. These market-level measures are derived from granular watershed-level measures of hydrological drought. Our preferred market-level measure is the fraction of hydropower generation capacity affected by drought (FHD). The panel also provides information on an extensive set of meteorological factors, emissions from wildfires, proxies of
welfare impacts of crises through simulation tools that can then be used to simulate such impacts on poverty and inequality in short periods of time.</mark> In general, the literature showcases a wide variety of approaches used to quantify the distributional impacts of shocks.<sup>2</sup> In this paper, we follow a methodology similar to that employed by Ajwad, Aran, Azam and Hentschel (2013) and use a hybrid approach that combines the results of macroeconomic projections of a sectoral slowdown of the Tunisian economy over the whole year of 2020 with micro-simulation techniques. Unlike Ajwad, Aran, Azam and Hentschel (2013), however, we introduce the effect of price increase while doing the simulations. In this regard, <mark>our study is different from estimates of IFPRI and UNDP as these are based on CGE modeling and focus much more on the macroeconomic impact of the pandemic, and not so much on the distributional implications of the pandemic, which is the key focus of our simulation exercise.</mark> We focus on quantifying the magnitude of the impacts of COVID-19 on poverty and inequality in Tunisia through the labor income and consumption channels using the Tunisia household budget survey conducted in 2015 entitled _Enquête Nationale sur le Budget, la Consommation et le Niveau de Vie des Ménages_ (henceforth referred to as EBCNV 2015). The objective of this analysis is three-fold: first, to assess the pre-COVID situation in order to identify and profile the subgroups of population that are most vulnerable to getting infected by COVID-19 and impacted by the associated government measures; second, to simulate the impacts of COVID-19 on consumption, poverty and inequality based on sectoral growth performances over the whole year of 2020 and analyze the distribution of individuals expected to fall into poverty due to COVID-19; and finally, to simulate how the impacts would vary with or without mitigation measures adopted by the Tunisian government.<sup>3</sup>

> 2 Refer to Ajwad, Aran, Azam and Hentschel (2013) for a
the Random Digit Dialing method, using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The refugee samples were drawn from the UNHCR’s proGres database, which covers all registered refugees in Kenya.

Two national lockdowns were put in place during the survey periods. The first one started in mid-March 2020 and covered the first months of the first wave of the survey. A second lockdown was introduced in late March 2021, wrapping the fifth wave of the survey. The data was collected by phone interviews with a nationally representative sample of households using cell phones in Kenya, covering both urban and rural areas. The households were called every two months for five survey rounds, to track the impact of the pandemic over time.

The data consists of a wide range of information including the knowledge of COVID19 and mitigation measures, changes in behaviors in response to the COVID-19 pandemic, employment, income, food security, as well as perceptions of the government’s response.

We use the household head gender as the main indicator of gender. Compared to the male-headed households, the female-headed ones are more likely to receive transfers prior to the COVID-19 crisis (see Table 4 and Figure 2). Moreover, they have a higher childcare burden and smaller livestock values. However, these two types of households are not significantly different with regards to their poverty status.

In Kenya, about 37.4 percent of the households are female-headed (The World Bank, 2021 _a_ ). The female household heads are more likely to be separated, divorced or widowed relative to the male household heads. This indicates that it is likely that those female heads became the household head after the male head left the household due to death or divorce.

There are in total three categories of coping strategies by the household. The first was coping using the household internal wealth or resources. These include coping by reducing meals, selling assets or livestock, and taking loans. The second kind of coping would be coping based on help
are to be expected because, as with goods, the marginal returns to services liberalization should be greater for more regulated and protected countries. They find their results are robust to different estimation methods and to different sub-samples of the data.

Very interestingly, they find that the productivity gains from services liberalization are more pronounced for domestic and small firms, which emphasizes for policymakers the importance of services sector liberalization for the development of small and medium size domestic firms. Again this is consistent with theory, as large firms can often hire service sector specialists, like lawyers, accountants, truck drivers and courriers as employees. Small and medium size firms find full time employees too costly, and have to rely to a greater extent on the market for services.

The authors note that the EBRD indices of services sector liberalization for their sample period of 2001-2007 show that there was substantial liberalization in Ukrainian services during this time period, including toward foreign direct investment. In 2001, 48 percent of the inward FDI stock in Ukraine was in manufacturing. By 2007, the share of the FDI stock in the services sector (excluding utilities) had reached 53 percent, while the share of the FDI stock in manufacturing had declined to 30 percent. As a result, the output produced by foreign-owned services providers grew in almost all services sub-sectors. Likely spurred by foreign ownership, labor productivity in the services sector more than doubled between 2001 and 2007.

**Methodology.** In view of the important methodological innovation of the Shepotylo and Vakhitov paper, it is worth devoting some additonal space to its methodology in this summary chapter. Their paper builds on the methodology developed in the heretofore frontier papers by Arnold, Javorcik and Mattoo (2011) and Fernandes and Paunov (2011).  Shepotylo and Vakhitov, however, extend the methodology by applying the newly developed methodology of

9
and salary jobs are being created, much of the labor force cannot access these jobs due to their low education levels. In rural areas, not only is the labor force poorly educated, but the remote locations do not favor investments which would create these jobs. The only opportunity the labor force has to access the non-farm sector is through the creation and development of household enterprise employment.

**Figure 5 Difficulty of doing business is not correlated with high number of informal HEs**

<!-- Start of picture text --> 47.0 Ghana<br>42.0 TanzaniaUganda Burkina Faso<br>37.0<br>Cameroon Congo, Rep.<br>32.0 Mozambique<br>27.0 Rwanda<br>22.0<br>17.0<br>12.0<br>0.0 10.0 20.0 30.0 40.0 50.0<br>Ease of Doing Business Rank<br>Share of Households with HE<br><!-- End of picture text --> _Source: WB Doing Business 2011. Note: a higher rank indicates a higher difficulty for doing business_ Contrary to results from Latin America (Loayaza et al, 2009), more employment in HEs is not associated with a bad business environment. Figure 5 shows that employment in HEs has no correlation with the country rankings of the business environment shown in the Doing Business data base (www.doingbusiness.org). No doubt some aspects of the reported poor business environment in SSA countries reduced investment in labor intensive enterprises (which would create more wage and salary jobs), but these factors do not seem to be primarily responsible for the growth in informal non-farm enterprise employment.

A final point on recent employment trends: in OECD countries it is unusual for adults who have entered employment to engage in more than one income earning activity at a time. But in SSA countries, it is not unusual for
expectations to formulate their decision. Since subjects do not know the nationality of their counterparts, their expectations of others is dependent upon the information they have about their current institution. Individuals from corrupt countries may assign a higher subjective probability to the issuance/acceptance of a bribe by their counterpart. In Banuri and Eckel (2012b) this probability becomes known (through repeating the game), the only factor which can differ is the values (or what Rose-Ackerman terms the “moral costs” of bribing). Hence, if values differ across cultures, then we can expect differences in behavior when a game is repeated in different settings.

In an experiment related to the one carried out by Cameron et al. (2009), Alatas et al. (2009) use the data from their four-nation study to investigate the effect of gender and culture on corruption. Their results are striking in that they find the expected result of females being less corrupt to hold only in Australia. Behavior of both males and females in India, Indonesia, and Singapore was statistically indistinguishable. This is a particularly worrisome result, since it points to the difficulty of generalizing lab studies carried out in western cultures on corruption. It casts doubt on the policy recommendation of female participation in order to reduce corruption: of course one can still argue that participation should increase to reduce discrimination rather than an anti-corruption tool. This suggests that the importance of gender for corruption also varies by culture. They further extend the analysis by implementing a neutral frame with Australian subjects, and find that the gender differences hold when instructions are loaded, but not when instructions are neutral.<sup>17</sup> The one thing we do note is that of the four countries studied, Australia ranks the highest on Hofstede‟s (1980) masculinity index, suggesting that differences between genders would be starker in those cultures. Indeed, Gneezy et al. (2009) show that gender differences are subject to cultural forces.

Banuri and Eckel (2012b) implement an