rafmacalaba/datause-encoder-data huggingface dataset:
Andr ́ee

# 6.2.5 Linear analysis of case hospitalization rates

The main results provided evidence for increased COVID-19 incidence in areas where populations are more exposed to air pollution. However, it is possible that the estimated association between PM2 _._ 5 concentrations and COVID-19 incidence can be attributed to worse respiratory health in polluted areas, which then leads to more severe symptoms and higher case detection. The analysis tried to control for this using health data and the percentage of confirmed cases that resulted in hospitalization as controls. This did not impact the results. A second way to further test this theory is by analyzing the association between PM2 _._ 5 and the case hospitalization rate because worse respiratory health would lead to more severe COVID-19 disease (Ruan, 2020). The suspect correlation is investigated below using step-wise AIC variable selection keeping PM2 _._ 5 in the variable set, followed by the full spatial specification. Model 6 uses March 31 confirmed COVID-19 hospital admissions as a percentage of March 22 confirmed COVID-19 cases, model 7 uses March 30 confirmed cases. The analysis finds that age, male gender, and the share of population with overweight are positively associated with increased case hospitalization rates which follows earlier identified risk groups (Ruan, 2020).

Table 8: Dependent Variable: Confirmed COVID-19 related hospital admissions as a percentage of COVID-19 cases.

|**Variable**|**Model 6**|**Model 7**|
|---|---|---|
|(Intercept)|-587.36*|-93.71***|
||(296.95)|(35.24)|
|Population density||-2.08*|
|||(1.26)|
|Share of male population|935.03**||
||(487.40)||
|Share 45 to 64|9.17***|1.05*|
||(3.82)|(0.51)|
|Share of unmarried||0.57*|
|||(0.33)|
|Share of households without children|-7.74***<br>(2.58)||
|Average household size||7.70|
|||(5.51)|
|Share of western immigrants|-1.83||
||(1.96)||
|Share of non-western immigrants||0.46*|
|||(0.25)|
|Share of water surface||14.25**<br>(5.62)|
|Share of overweight|8.74***|0.57**|
||(1.92)|(0.25)|
|Share exposed to noise above 50kmh|-10.16**||
||(4.22)||
|Mean PM2_._5|-6.70|0.40|
||7.79|0.63|
|_λ_|0.46*|-0.40**|
||0.13|0.17|
|_ρ_|-0.60***|0.61***|
||0.16|0.10|
|R2|0.20|0.26|
|AICc|3938.38|3019.20|
|N|355|355|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 22
14 whether African countries would experience net trade gains or losses in the US and Japan.

The Trade Effects of **MFN Tariff** Cuts The previous analysis showed a EEC MFN tariff liberalization would have adverse consequences for all African countries' exports due to preference erosion, and many could also experience net trade losses in the United States and Japan. This raises the key question of how large would the overall OECD displacement be, and which African countries would be most seriously affected? For answers, a trade projection model developed by the World Bank was used to simulate the effects of MFN tariff cuts (and preference erosion) on African exports. A full description of the model (named SMART -- Software for Market Analysis and Restrictions on Trade) can be found in World Bank (1992) or Erzan and Yeats (1992).'3 Table 5 summarizes projections of the effects of a full removal of MFN tariffs on goods exported by African countries to the EEC, Japan and United States."<sup>4</sup> The table '<sup>3</sup> An extensive literature exists on the empirical modelling of trade preferences. The study most often cited is Baldwin and Murray (1977). Other frequently referenced studies include Ahmad (1978), Clague (1971), Pomfret (1986) and Sapir and Baldwin (1983). The SMART model incorporates many features of the models used in these analyses. One important point to note is that none of these models account for the effects of linkages between the export sector and other supporting sectors of the economy. As such, they understate the importance of export changes on overall changes in the level of economic activity. An offsetting factor, which also is not accounted for, is that a libera;ization of OECD trade barriers should accelerate economic growth in these countries. This, in turn, would increase the demand for African exports.

'<sup>4</sup> Although the precise magnitude of the Uruguay Round tariff cuts are not yet known, it is expected that they might lower existing MFN tariffs by as much as 50 percent. In this case, the African countries' EEC trade losses would be about one-half the figures reported in Table 5. A roughly proportional relationship exists between the depth of the eventual Uruguay Round tariff cuts and the projections for the EEC shown in the table. Thqt is, if the Round were to liberalize tariffs by (say) 30 percent the African trade losses would be about one-third those reported. This relation does not hold for the United States and Japan where a 50 percent MFN tariff reduction might produce larger African losses than those shown in the table -- see footnote 16 for an explanation of why this occurs.
**<mark>Table C.2a: Statistics on goodness-of-fit for estimated models for relations with suppliers</mark>**

|Model|Npar|LL|p-value<br>ofL<sup>�</sup>|BIC|CAIC|AWE|Entropy<br>R<sup>�</sup>|Classification<br>AWE|ICL-BIC|
|---|---|---|---|---|---|---|---|---|---|
|3 classes: no correlations|74|-18580.0|1.00|37760.7|37834.7|38583.3|0.791|39998.9|39176.2|
|4 classes: no correlations|99|-18174.4|1.00|37152.4|37251.4|38252.9|0.811|39952.7|38852.2|
|5 classes: no correlations|124|-17964.1|1.00|36934.6|37058.6|38313.1|**0.814**|40151.0|38772.5|
|6 classes: no correlations|149|-17776.5|1.00|36762.4|36911.4|38418.8|0.812|40445.5|38789.2|
|3 classes: 1-2 corr|90|-17834.5|1.00|36399.5|36489.5|**37400.0**|0.728|**38653.4**|37652.9|
|4 classes: 1-2 corr|115|-17667.0|1.00|36267.3|36382.3|**37545.7**|0.720|39825.6|38547.2|
|5 classes: 1-2 corr|140|-17541.5|1.00|36219.3|36359.3|37775.6|0.735|39967.4|38411.1|
|6 classes: 1-2 corr|165|-17421.5|1.00|36182.2|36347.2|38016.4|0.724|40567.7|38733.4|
|3 classes: 4-5 corr|90|-18400.7|1.00|37531.8|37621.8|38532.3|0.776|40091.1|39090.5|
|4 classes: 4-5 corr|115|-18013.3|1.00|36960.0|37075.0|38238.4|0.798|40099.6|38821.2|
|5 classes: 4-5 corr|140|-17799.3|1.00|36735.0|36875.0|38291.4|0.812|40225.6|38669.3|
|6 classes: 4-5 corr|165|-17595.8|1.00|36530.9|36695.9|38365.1|0.814|40372.2|38537.9|
|3 classes: 1-2, 4-5 corr|106|-17699.4|1.00|36259.2|36365.2|**37437.6**|0.718|**38664.5**|37486.1|
|4 classes: 1-2, 4-5 corr|131|-17518.6|1.00|36100.5|36231.5|37556.8|0.709|39809.4|38353.1|
|5 classes: 1-2, 4-5 corr|156|-17410.9|1.00|36088.0|36244.0|37822.2|0.721|40120.5|38386.3|
|6 classes: 1-2, 4-5 corr|181|-17321.2|1.00|36111.6|36292.6|38123.7|0.740|40465.6|38453.5|
|3 classes: 1-2, 2-3, 3-4, 3-5, 4-5 corr|154|-17365.1|1.00|**35980.2**|**36134.2**|37692.1|0.713|38919.6|**37207.6**|
|4 classes: 1-2, 2-3, 3-4, 3-5, 4-5 corr|179|**-17222.0**|1.00|**35897.0**|**36076.0**|37886.9|**0.846**|**38850.9**|**36861.0**|
|5 classes: 1-2, 2-3, 3-4, 3-5, 4-5 corr|204|**-17140.4**|1.00|**35936.6**|**36140.6**|38204.4|**0.847**|39389.4|**37121.6**|
|6 classes: 1-2,2-3,3-4,3-5,4-5 corr|229|**-17063.8**|1.00|35986.3|36215.3|38532.0|0.776|40724.8|38179.1|

Note: p-values of L<sup>�</sup> were obtained using the bootstrap chi<sup>2</sup> procedures of Latent GOLD (Vermunt and Magidson 2016, p. 52).

Appendixes, page 16
theoretical model: it is the policy-selectivity of the _overall_ or _global_ aid budget that affects recipients’ incentives to improve policies. Importantly, the policy-selectivity of the overall aid budget is exogenous to recipient country policies and to other recipient country factors that may affect its policies. The choices of any one recipient country are unlikely to affect the policy-selectivity of the global aid budget. We find economically modest but statistically significant positive incentive effects. We show that a 10 percent increase in the global share of aid that is given policy selectively increases policy for the average recipient country by about 1.34 percent. Furthermore, we present evidence that is consistent with comparative static results of our model, namely that a large resource influx by a donor that allocates aid unconditional of policies reduces the incentive effect. We show that the incentive effect disappears for recipient countries that received a large amount of investments from China.

The current paper relates to the growing literature that analyzes the policy selectivity of donors’ aid allocations (Dollar and Levine, 2006; Knack, Rogers, and Eubank, 2011; Annen and Knack, 2018; World Bank, 2005; Annen and Moers, 2017). The policy-selectivity of aid allocations received more scrutiny after the publication of World Bank (1998) and a working paper version of Burnside and Dollar (2000), which both argue that aid is effective conditional on good policy. This message has been very influential among policy makers and development practitioners, as Easterly (2003) convincingly shows. Most of this literature, however, addresses policy-selectivity from a donor perspective. Here, we also consider the recipient perspective, as it is not clear whether policy-selectivity by donors translates into a better-policy-more-aid relationship for a typical recipient country that receives aid from many donors, of whom not all are policy-selective. In addition, we ask whether policy-selective aid leads to better policies, which may be expected when recipients are rewarded with more aid if they improve their policies.

Previous studies of this question relied on somewhat dubious identification strategies, and arrived at very mixed results, based on somewhat different methods, time periods, and policy measures. Burnside and Dollar (1997) report no relationship between aid and an index of macro policy distortions; Alesina and Weder (2002) similarly conclude aid has no effect on corruption. Knack (2001) finds aid impairs the quality of governance, measured by an index of bureaucratic quality, rule of law and corruption, but Tavares (2003) reports the contradictory finding that aid reduces corruption. Heckelman and Knack (2008) find aid reduces scores on a broader policy index of “economic freedom.” These studies do not attempt to distinguish policy selective aid from other aid. Ohler,<sup> ̈</sup> Nunnenkamp, and Dreher (2012), in 3
# III. CAUSES OF INEFFICIENCY IN PUBLIC TRANSPORT

16. Public transport enterprises often have explicit goals which may be quite different from the efficient provision of services. Enterprises may have multiple objectives such as: providing jobs with secure salaries as a type of employment program,30/ maximizing passenger miles, maintaining a network of services, providing low cost services to rural and urban poor, etc.31/ These objectives may be explicit goals of the enterprise, or imposed by government on the enterprise through regulation. The pursuit of these other goals can cloud the issue of cost minimization. For example, a railroad whose goal is to provide both employment and railway services will not minimize costs in the commonlyunderstood sense.32/ The railroad will employ more labor in the provision of railroad services than with the loptimal" combination of inputs which minimizes costs, given market wages and prices. The pursuit of explicit goals other than welfare maximization, such as those mentioned above, result in the lack of cost minimization in public transport and can be readily identified as causes of high operational costs. Furthermore, theoretical work in the fields of mechanism design, industrial organization, and privatization provide reasons to believe that public sector production may be inefficient even in the mist restrictive case when government has the single goal of maximizing consumer welfare.

30/ See Alice Galenson, Labcr Redundancy in the Transport Sector, The World Bank, PPR WPS-158, February 1989, p. 27-32.

31/ Eric W. Beshers, A Survey of Restrictive Practices on Railways in Developing Countries, p.11. Forthcoming INUTD publication.

32/ Given the goal of creating employment opportunities, the relevant question becomes not the efficiency of the operation, but the effectiveness of the enterprise in accomplishing this goal. Arturo Israel discusses the difference between "effectiveness" and 'efficiency" in Institutional Development, Incentives to Performance, The John Hopkins University Press, Baltimore, 1987, p. 12-14.

13
OECD (2009). World Economic Outlook, OECD, Paris, France.

Pattillo Catherine, Sanjeev Gupta and Kevin Carey (2006). “Sustaining and Accelerating Pro-Poor Growth in Africa”. The International Monetary fund, Washington DC.

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Stiglitz Joseph (2002). Globalization and Its Discontents, W.W. Norton & Company Inc. New York, NY 10110.

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Tchundjang Pouémi Joseph (1979). « Monnaie, Servitude et Liberté : La Répression Monétaire de l’Afrique ». Edition Jeune Afrique Conseil.

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25
strongly related to lagged, but not contemporaneous WEF corruption ratings. The simplest explanation for this result - although not one mentioned by Lambsdorff - is that the EIU assessments may systematically incorporate the most recently available WEF ratings. Interdependence does not even require, however, that sources directly check each others’ ratings; it could also result merely from sources relying on the same publications for their information about conditions in countries.

In contrast to most expert assessments, surveys of firms and households generate data likely to be largely independent from other judgments. Most respondents in business surveys such as the BEEPS are unlikely to know the TI ratings for the country in which they operate, and even for the few that do know, it is unlikely to influence their response to a question on the share of their firms’ revenues paid in bribes. The WEF “Executive Opinion Survey” differs from BEEPS in several respects, however, that could make it less independent. First, the sample of executives is deliberately chosen to elicit the views of “business leaders” with extensive international experience. These executives are more likely than those in the BEEPS to be aware of the TI and other cross-country ratings. Second, the WEF survey questions deliberately are phrased in such a way that respondents will “compare their own environment to a world standard, rather than thinking in national terms.” Some respondents may consult other cross-country rankings in order to provide a seemingly better-informed response. Third, the WEF and IMD both implement similar executive surveys, with samples selected by “partner institutes.” The WEF and IMD share many of the same partner institutes, so many of the same executives are likely to be included in both sets of surveys.<sup>20</sup>

> 20 The WEF and IMD have at least one partner institute in common in 12 of the 51 countries that are included in both sets of surveys.  These two organizations jointly published the “World Competitiveness

22
**Commonwealth of Independent States**

Tax regulations and/or high taxes (question 12.g) was the single most important obstacle for doing business in CIS countries according to responses of surveyed businessmen. The quantitative value of this obstacle was 1.41 times higher then for the second obstacle - policy instability (question 12.i). This is the 12.i).Thisra is the **t** highest ratio for all regions.

|and/or high taxes<br>(question 12.g) was<br>thesingle most<br>important obstacle<br>|||g.Tax regulations<br>and/or high taxes<br>**i.**Policy instability<br>|
|---|---|---|---|
|for doing business<br>in CIS countries<br>|||n.Corruption|
|according to<br>responses of|||m.Crime and theft|
|surveyed|||IGeneral uncertainty|
|businessmen. The<br>quantitative value of<br>this obstacle was|||on costs of regulations|
|1.41 times higher|||c. Regulations<br>on|
|then for the second<br>obstacle - policy|||foreign trade (exports,|
|instability (question|1?||**k.**Inflation|
|12.i). This is the<br>12i)This**t**<br>ra is the|||**h Inadequate**<br>**supply of**|
|..<br> <br>highest ratio for all|||**.**<br> <br>infrastructure|
|regions.|||f. Foreign currency|
|||CIS|<br>regulations|
|As previously stated<br>|+|||
|policy instability|**la**<br>policy instability||**euain**<br>**o**<br>starting business/new|
|was the second||||
|majorobstacle for|||b. Price controls|
|doing business in||||
|the countries of CIS.|||e. Labor regulations|
|This is the highest||||
|ranking this obstacle|||o.Terrorism|
|received among all||||
|regions. The second|||j.Safety or|
|highest ranking for|l<br>l<br>l<br>l|I|environmental|
|policy instability|1.5<br>2.0<br>2.5<br>3.0<br>3.5<br>4.0<br>4.5|5.0|5.5|
|was 6 in Latin<br>America.||||

Corruption received rank 3 followed by crime and theft (question 12.m).

The lowest on the list of obstacles is safety or environmental regulations (question 12j). This ranking is also the lowest among the regions.

The quantitative value of the top obstacle (tax regulations and/or high taxes) is almost two times higher than the value of the lowest obstacle (safety or environmental regulations).

27
**OECD.** 2022 _b_ . “Employment and Labour Market Statistics.” `https://www.oecd-ilibrary.org/employment/data/ oecd-employment-and-labour-market-statistics_lfs-data-en` .

- **OECD.** 2022 _c_ . “Enterprises by Business Size.” `https://data.oecd.org/entrepreneur/ enterprises-by-business-size.htm` .

**Paradisi, Matteo.** 2021. “Firms and Policy Incidence.” _Mimeo_ .

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- **Rodrik, Dani, and Charles F Sabel.** 2022. “Building a Good Jobs Economy.” In _A Political Economy of Justice_ . , ed. Danielle Allen, Yochai Benkler, Leah Downey, Rebecca Henderson and Josh Simons, 61–95. University of Chicago Press.

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- **Rothstein, Jesse.** 2010. “Is the EITC as Good as an NIT? Conditional Cash Transfers and Tax Incidence.” _American Economic Journal: Economic Policy_ , 2(1): 177–208.

- **Rovigatti, Gabriele, and Vincenzo Mollisi.** 2020. “PRODEST: Stata Module for Production Function Estimation Based on the Control Function Approach.” Boston College Department of Economics Statistical Software Components S458239.

- **Saez, Emmanuel, Benjamin Schoefer, and David Seim.** 2019. “Payroll Taxes, Firm Behavior, and Rent Sharing: Evidence from a Young Workers’ Tax Cut in Sweden.” _American Economic Review_ , 109(5): 1717–63.

- **Saez, Emmanuel, Manos Matsaganis, and Panos Tsakloglou.** 2012. “Earnings Determination and Taxes: Evidence from a Cohort-Based Payroll Tax Reform in Greece.” _Quarterly Journal of Economics_ , 127(1): 493–533.

- **Shapiro, Carl, and Joseph E Stiglitz.** 1984. “Equilibrium Unemployment as a Worker Discipline Device.” _American Economic Review_ , 74(3): 433–444.

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42
composition of these different samples is shown in the second and third columns of Table 1.

As is well known there are substantial difficulties in comparing income distribution data across countries.'<sup>0</sup> Countries differ in the coverage of the survey (national versus subnational), in the welfare measure (income versus consumption), the measure of income (gross versus net), and the unit of observation (individuals versus households). We are only able to very imperfectly adjust for these differences. We have restricted our sample to only income distribution measures based on nationally representative surveys. For all surveys we have information on whether the welfare measure is income or consumption, and for the majority of these we also know whether the income measure is gross or net of taxes and transfers. While we do have information on whether the recipient unit is the individual or the household, for most of our observations we do not have information on whether the Lorenz curve refers to the fraction of individuals or the fraction of households." As a result, this last piece of information is of little help in adjusting for methodological differences in measures of income distribution across countries. We therefore implement the following very crude adjustment for observable differences in survey type. We pool our sample of 418 observations separated by at least five years, and regress both the Gini coefficient and the first quintile share on a constant, a set of regional dummies, and dummy variables indicating whether the welfare measure is gross income or whether it is consumption. We then subtract the estimated mean difference between these two altematives and the omitted category to arrive at a set of distribution measures that notionally correspond to the distribution of income net of taxes and transfers.'<sup>2</sup> The results of these adjustment regressions are reported in Table 2.

# **3.2 Estimation**

> **10** See Atkinson and Brandolini (1999) for a detailed discussion of these issues. " This information isonly available for the Chen-Ravallion dataset which exclusively refers to individuals and for which the Lorenz curve is consistently constructed using the fraction of individuals on the horizontal axis.

> 12 Our main results do not change substantially if we use three other possibilities: (1) ignoring differences in survey type, (2) including dummy variables for survey type as strictly exogenous right-hand side variables in our regressions, or (3) adding country fixed effects to the adjustment regression so that the mean differences in survey type are estimated from the very limited within-country variation in survey type.

14