rafmacalaba/datause-encoder-data huggingface dataset:
# 4.1 Heterogenous mortality under no-intervention

In this section, we perform a cross-country comparison of potential mortality burdens _LY_<sup>_NI_</sup> under the no-intervention scenario. This exercise reveals the large quantitative dierences in the potential number of lost-years between developing and developed countries.

We compute a country's potential number of lost-years under herd-immunity as follows. We assume an 80 % infection rate in all age categories of the population. The number of infected individuals in each age-category is computed from the country's population pyramid. The probability that an infected individual of a given age dies is deduced from age-specic infection-to-fatality ratios (IFR), once again using the IFR estimates for China from Verity et al. (2020) for developing countries, and those for France from Salje et al. (2020) for developed countries. Then, the number of lost-years for a given death is the country's residual life-expectancy at the age of death. Summing the lost-years over all deaths provides the potential number of lost-years.

We consider two no-intervention scenarios. Under the saturation scenario, we assume that contagion is fast and hospitals are overwhelmed by the ow of infected individuals in need of care. In order to reect this, the IFR used assumes that all patients who would need intensive care cannot access ICU and die. This is our reference no-intervention scenario. Under the no-saturation scenario, we assume that contagion is slow and hospitals are not overwhelmed by the ow of infected individuals in need of care. Then, the IFRs used assume that all patients who would need intensive care have access to ICU. See Appendix A2 for more details.

Our estimates of the potential number of lost-years can dier across countries for three reasons: population pyramids, residual life-expectancies and IFR ratio used. We nd that the potential mortality burden is several times larger in high-income countries than in low-income countries. This is despite the use of IFR from China in the latter countries, which implies more lost-years than the IFR from France would. To a large extent, this dierence is the consequence of the concentration of Covid-deaths in oldage categories, as already illustrated in Section 3.1. Hence, younger populations in less developed countries reduce their potential mortality burden. An additional, but related, reason for this dierence is that residual life-expectancies at given ages are smaller in less developed countries.

Table 2 illustrates the heterogenous mortality burdens for two countries: Japan and Zimbabwe. The population of Japan is considerably older than that of Zimbabwe. For instance, a third of Japan's population is older than 60 years, whereas less than 5% of Zimbabwe's population falls into that category. Residual life-expectancy in Japan is larger than that of Zimbabwe in each age-category presented. For instance, individuals in the 60+ population expect on average to live for 16 years in Japan, and only for 11 16
by the Federal Reserve, there has been a trend toward the overall liberalization of capital transactions, notably on capital and money market instruments. Globally, the world is roughly evenly split between countries relying on an exchange rate anchor for monetary policy (mostly small islands and members of currency unions in Africa, for example), and those with more independent monetary policy (including most high-income countries).

4. _Cities and livability_ . Apart from the economic arguments suggesting that agglomeration economies can be significant, “smart cities” have become a dominant theme in the recent economic development literature. Fuller and Romer (2014) argue that “Nothing else [other than the urbanization project] will create as many opportunities for social and economic progress.” The New Climate Economy Report 2014 argues that cities are crucial for sustainable development and that “the stakes for growth, quality of life and carbon emissions could not be higher.” Glaeser (2010)<sup>15</sup> shows a strong link between area density and per capita gross metropolitan product at all income levels. Density is strongly correlated with wages and productivity, as well as with future housing price growth. This seems to reflect the benefits accruing from labor market pooling and the exchange of ideas, rather than from the benefit of reduced transport costs of goods between, for example, suppliers, manufacturers and customers. Skilled labor pooling seems to be particularly important. Unfortunately, East Asian countries have paid scant attention to cities. Among developing East Asia, Bangkok is ranked at 117, Beijing at 118, Manila at 136, Ho Chi Minh and Hanoi at 152 and 153 in the Mercer 2015 City Rankings. Only Kuala Lumpur, at 84, among all cities in developing Asia, breaks into the top 100 global cities.

5. _Cohesion and inequality_ . We had argued that middle-income countries would need to pay special attention to inequality because the shifting growth and urbanization strategies would likely worsen the distribution of labor income, and suggested that fiscal transfers to reduce unequal access to social services would be a good instrument. The last few years have been marked by an escalation of concerns about inequality. Even the IMF has begun to emphasize the links between inequality and growth (Ostry et al. 2014).

Over the last decade, there has been a rapid rise in most indices of country inequality across the world, including in, but not restricted to, middle-income countries. The exception has been in Latin America, where already very high levels of inequality have slowly started to decline. In high-income countries, the concern has been with the slow wage growth of bluecollar workers. In many middle-income countries there is concern with graduate unemployment and the difficulties faced by young people in finding their first job. In some Arab middle-income economies, unemployment rates for graduates are even higher than the national average.

> 15 Glaser (2010) Agglomeration Economics...

16
**Table 6: CIT Incentives and Firm-Level Outcome Indicators, Random-Effects Estimation**

|Variables|(1)<br>Liquidit<br>y|(2)<br>ETA|(3)<br>GFSAL|(4)<br>ROA|(5)<br>Equity<br>Ratio|(6)<br>Capitalizatio<br>n|(7)<br>Turnove<br>r|
|---|---|---|---|---|---|---|---|
||||13311**|||||
|Tax<br>|-97.52*<br>(55.11)<br>963.1**<br>|20,335**<br>(10,225)<br>10,031**<br>|,<br>*<br>(5,016)<br>13,965**<br>|29,980**<br>(15,028)<br>14,359**<br>|30,096*<br>(15,613)<br>14,926**<br>|16,777**<br>(8,385)<br>|8.108**<br>(3.510)<br>14.31**<br>|
|Capital|*|*|*|*|*|8,252***|*|
||(73.41)|(281.0)|(1,060)|(401.3)|(503.2)|<br>(234.3)|(0.780)<br>-|
||||||||5.837**|
|Center|-80.30|-44.16|-3,680**|-519.1|22.75|-32.93|*|
||(187.4)|(1,170)|(1,578)|(1,820)|(1,806)|(961.7)|(1.825)|
|West|-40.39|-766.6|-4,986**|-1,309|-973.4|-627.3|0.787|
||(68.47)|(919.8)|(2,496)|(1,429)|(1,462)|(758.2)|(3.821)|
|East|-230.3<br>(144.5)|-1,436*<br>(775.7)|-6,368**<br>(3,057)|-1,137<br>(993.7)|-2,005<br>(1,278)<br>|-1,185*<br>(636.5)|2.176<br>(3.873)|
|South|70.08|-223.1|357.5|-657.4|-<br>1,060***|-131.5|1.083|
||(63.53)|(241.6)|(612.9)|(498.6)|(375.4)|(201.5)|(1.375)|
|Average Wage|37.28*|-57.61|-869.8*|-102.7*|207.8|-44.66|1.564*|
||(20.15)|(35.36)|(460.5)|(57.66)|(317.6)|(30.24)|(0.921)|
|Equipment|-291.3*|635.9*|-3,426*|953.8*|1,012*|524.8*|-1.417**|
||(154.7)|(383.0)|(2,027)|(573.0)|(528.4)|(314.3)|(0.670)|
|Building|-66.88*|586.5*|826.5*|892.9*|882.0*|481.9*|0.730|
||(34.82)|(302.8)|(442.4)|(460.8)|(452.3)|(249.9)|(0.470)|
|Urban Land|13.99**<br>(6.583)|1,264*<br>(705.3)|-402.2**<br>(193.3)|1,867*<br>(1,044)|1,891*<br>(1,060)|1,040*<br>(580.9)|-0.638<br>(0.466)|
||||||-<br>46,783**||10.74**|
|Constant|3,988*|-26,930*|55,784*|-39,413*|*|-22,231*|*|
||<br>(2,104)|<br>(14,761)|<br>(31,150)|<br>(21,573)|(17,538)|<br>(12,101)|(1.955)|
|Observations<br>Number of|109,383|114,223|43,340|112,240|114,223|114,223|45,122|
|Enterprises|13,612|14,017|11,927|13,880|14,017|14,017|12,169|
|Method|RE|RE|RE|RE|RE|RE|RE|

_Notes._ The treatment variable is the tax incentive. The independent variables are dummies for geographic area, the average value of wage, the value of equipment, the value of building and the value of urban land. Standard errors are reported in parenthesis. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent level.

17
# **REGIONAL INTEGRATION AND TECHNOLOGY DIFFUSION: THE CASE OF** NAFTA

**Maurice Schiff' and Yanling Wangb**

## **World Bank**

JEL Categories: F02, F15, F43, 039

- a. Corresponding author. Mailstop No. MC3-303, International Trade Unit, The World Bank, 1818 H. St. NW Washington DC 20433. Tel. (202) 473-7963; Fax (202) 5221159.. E-mail: MschiffWworldbank.org.

- b. Department of Economics, Georgetown University, 580 ICC, 37th & 0 Streets, NW Washington, DC 20057. Email: wangy6@georgetown.edu.
<!-- Start of picture text --> % productivity change<br>-50 -40 -30 -20 -10 0<br>Oo<br>_—— _———__, Qa<br>ZZ aw<br>+Sne}<br>ro<br>no}fe)c<br>ce)<br><!-- End of picture text --> <!-- Start of picture text --> % productivity change<br>-50 -40 -30 -20 -10 0<br>oO<br>a ~~ Qroe<br>2<br>oo<br>o<br>ne}oo<br>+O<br>me}cfe)<br>ie)<br>2”o<br>eo<br><!-- End of picture text --> <!-- Start of picture text --> % productivity change<br>-50 -40 -30 -20 -10 0<br>Q<br>a of&<br>#<br>3)<br>a@®<br>eS<br>©<br>=<br>3<br>ae)<br>ne}<br>Cc<br>fe)<br>(s)<br>°2®<br>mop)<br>—e— —o— ——<br>Most connected firms Random firms Least connected firms<br><!-- End of picture text -->
Services Make a Difference?,” _Demography_ , 38: 253-66.

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- Glewwe, P., Jacoby, H. and E. King. 2001. “Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis,” _Journal of Public Economics_ , 81:345-68.

- Jayne, T., J. Strauss, T. Yamano and D. Molla. 2002. “Targeting of Food Aid in Rural Ethiopia: Chronic Need or Inertia?,” _Journal of Development Economics_ , 68:247-88.

- King, E. and J. R. Behrman. 2009. “Timing and Duration of Exposure in Evaluations of Social Programs,” _World Bank Research Observer_ , 24(1): 55-82.

- Maluccio, J.A., J. Hoddinott, J.R. Behrman, R. Martorell, A. R. Quisumbing, and A.D. Stein. 2006. “The Impact of an Experimental Nutritional Intervention in Childhood on Education among Guatemalan Adults,” _FCND Discussion Papers No 207_ , IFPRI, Washington DC.

- Martorell, R. 1999. “The Nature of Child Malnutrition and Its Long-Term Implications,” _Food and Nutrition Bulletin_ 20: 288–292.

- Pitt, M., M. Rosenzweig and D. Gibbons. 1993. “The determinants and consequences of the placement of government programs in Indonesia,” _World Bank Economic Review_ , 7:319-348.

24
- de Quidt, Jonathan, Johannes Haushofer, and Christopher Roth. 2018. “Measuring and Bounding Experimenter Demand.” _American Economic Review_ 108 (11):3266–3302. URL `https://www.aeaweb.org/articles?id=10.1257/aer.20171330` .

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- Di Maio, Michele and Nathan Fiala. 2019. “Be Wary of Those Who Ask: A Randomized Experiment on the Size and Determinants of the Enumerator Effect.” _The World Bank Economic Review_ .

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- Hanna, Rema and Benjamin A. Olken. 2018. “Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries.” _Journal of Economic Perspectives_ 32 (4):201–226.

- Haushofer, Johannes and Jeremy Shapiro. 2016. “The Short-Term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya.” _The Quarterly Journal of Economics_ 131 (4):1973–2042. Publisher: Oxford Academic.

- Herbert, Cornelia, Friedrich Meixner, Christine Wiebking, and Verena Gilg. 2020. “Regular Physical Activity, Short-Term Exercise, Mental Health, and Well-Being Among University Students: The Results of an Online and a Laboratory Study.” _Frontiers in Psychology_ 11. URL `https://www.frontiersin.org/article/10.3389/fpsyg.2020.00509` .

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29
|**Outside**<br>**Privacy Team**<br>**Technologies**|$38,700|$30,500|$57,500|$814,200|
|---|---|---|---|---|
|**Other Privacy**<br>**Budget**|$24,700|$84,500|$82,000|$106,200|
|**TOTAL**<br>**PRIVACY**<br>**SPEND**|$257,700|$743,800|$923,400|$1,883,200|

The Ponemon Institute surveyed 1,263 organizations in 2019 on behalf of international law firm McDermott Will & Emery (MW&E). Respondents hailed from the U.S. (544), Europe (371), China (102), and Japan (246).<sup>77</sup> Organizations represented ranged from those with fewer than 500 to 75,000 employees and predominant industries were financial services (18%), industrial (13%), entertainment (11%) and the health sector (11%).<sup>78</sup> The survey found an average GDPR compliance budget of $13.6 million in fiscal year 2019.

# Annual Budgets for GDPR Compliance in 2019, U.S. Dollars

<!-- Start of picture text --> 3% 2%<br>4% > $50M < $500K<br>$26M-$50M 8%<br>$500,001K-$1M<br>12%<br>$21M-$25M<br>13%<br>$1M-5M<br>15%<br>$16M-$20M<br>23%<br>$6M-10M<br>22%<br>$11M-$15M<br><!-- End of picture text --> 79 2. Components of GDPR Compliance

> 77 McDermott Will & Emery, _Keeping Pace in the GDPR Race: A Global View of GDPR Progress in the United States, Europe, China and Japan_ (Ponemon Inst. 2019).

> 78 _Id._ at 38.

> 79 _Id._ at 62.

13
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_transfers to low income countries_ . The objective here is to ensure that resources are provided in such a way as to keep debt distress probabilities at manageable levels, while at the same time ensuring that aid is targetted to countries with good policies and institutions. Clearly, this will involve altering the concessionality of aid by using a mixture of grants and loans, with the mix tailored to the risk of debt distress in each country. This point is especially important in light of the much larger flows of development finance that have been advocated if countries are to meet the Millenium Development Goals. It has been estimated that an additional $50 billion per year in development finance will be required to reach these targets, which would represent approximately a doubling of current flows to developing countries.

If the additional ODA flows envisioned in light of the MDGs are provided primarily as (even concessional) loans, the vast majority of recipient countries will experience very sharp increases in their predicted debt distress probabilities, and are likely to experience serious difficulties repaying these loans in the future. The empirical results in this paper provide rough-and-ready estimates of the scale of this problem, which we illustrate with the following counterfactual exercise. We first take the most recently-available data on low-income countries’ debt burdens, policies, and growth rates, and use it to compute current estimated probabilities of debt distress, as in Figure 4. We then assume that an additional $100 billion in new lending is allocated across lowincome countries in proportion to these countries’ IDA borrowing during the second half of the 1990s.<sup>12</sup> We then compute the new present value of debt relative to exports, assuming that this new lending has the same average concessionality as each country’s stock of past borrowing. Finally, we calculate the new probability of debt distress, assuming that policies and growth are unchanged.

Figure 6 summarizes the results of this calculation. The horizontal axis reports the current predicted distress probabilities, while the vertical axis reports the predicted

> 12 Since IDA lending is already allocated across countries according to a formula that explicitly recognizes good policy and high poverty, this seems like a reasonable basis to allocate new lending in our hypothetical experiment.

27
Overall, the data set covers various observations from 1991 to 2006 for a range of countries. Not all data points are available for all years for all countries and complete for all measures. Most observations reflect more recent years.

There are 385 life and 391 non life index values in the data set including several cases where a state monopoly exists. The average number of observations is around 3.3 readings per country for each of life and non life insurance data sets. The average value for the Herfindahl Index is notably higher for life insurance than non life insurance (refer Table 1).

**<u>Table 1: Data Set Herfindahl Observations</u>**

||No of<br>Observations|No of<br>Countries|Average|Standard<br>Deviation|Maximum|Minimum|
|---|---|---|---|---|---|---|
|_Life_|||||||
|- complete|385|116|2,687|2,506|10,000|110|
|- excluding monopolies|364|111|2,265|1,836|9,768|110|
|_Non Life_|||||||
|- complete|391|119|1,856|2,023|10,000|135|
|-excludingmonopolies|376|113|1,531|1,223|6,590|135|

Source: Staff Analysis The fuller distribution of values in the data set is seen in Figure 1.

**<u>Figure 1: Distribution of Number of Observed Values for the Herfindahl Index</u>**

<!-- Start of picture text --> Life Non Life<br>80<br>40<br>70<br>35<br>30 60<br>25 50<br>20 40<br>15 30<br>10 20<br>5 10<br>0 0<br>Herfindahl Range<br>Herfindahl Range<br>200 800 1400 2000 2600 3200 3800 4400 5000 5600 6200 6800 7400 8000 8600 9200 9800 200 800 1400 2000 2600 3200 3800 4400 5000 5600 6200 6800 7400 8000 8600 9200 9800<br>No of Observations No of Observations<br><!-- End of picture text --> Source: Staff Analysis

# **Observations on the Herfindahl Index**

## **_Is There a Natural Level for the Index?_**

Taking a number of examples where there is a longer history available suggests that lower than average values are followed by an increasing trend and higher values gradually reduce. In other words, the indication from inspection is that there may be a 5
Turning to the firms, female firms are smaller and more likely to operate with no employees (39% of sampled female-led firms have no employees versus 23% male-led firms).<sup>25</sup> On average, 64% of female-led enterprises in the sample have at least one employee, relative to 80% of male-led enterprises. In terms of firm size defined by the number of employees: male enterprises in the sample comprise of: 7.5% medium/large, 33.5% small, 39% micro, and 20% zero employees; and female enterprises comprise of 5% medium/large, 20% small, 39% micro, and 36% zero employees. The survey asked retrospective questions on firm revenues to capture firm size before the pandemic and the pre-pandemic average annual revenues (2019) for a female-led business is USD74,102 which is approximately 35% lower than a male-led business at USD114,392. In terms of the role in the business, 47% of respondents reported to both own and manage their business, 38% own the business; and 15% manage the business. In terms of industry, approximately 45% of male-led firms operate in the services and retail/wholesale sectors compared to 55% of female-led firms; with a greater proportion of men operating in the professional services and ICT sectors compared to women.

The sample of respondents of the Future of Business survey should be considered entrepreneurs with at least some digital access and the survey sample was designed to be representative of the Facebook Business Page administrator population and not of any country’s business population. Nevertheless, the level of geographic coverage and sample size on the variables collected by the survey allow us a unique chance to examine the COVID-19 shock for a group of micro, small and medium enterprises on a global scale. Importantly, this survey was done at a time when traditional data collection methods were not possible. Since internet bandwidth and Facebook coverage have increased dramatically in recent years, with an approximate 2.9 billion monthly active users on Facebook at the time of writing, there is considerable scope to learn from this survey.<sup>26</sup>

> 25Throughout this paper we define the size of an enterprise using the number of employees of the firm in 2019 as follows: no employees if a number of employees is zero, a micro enterprise if number of employees is 1 to 4, small if number of employees is 5 to 49 and medium/large if the number of employees is 50 or over.

> 26Total number of individuals on Facebook are estimated to be equal to the number of monthly active users worldwide as of the 4th quarter 2021 that were using Facebook Source: https://www.statista.com/statistics/264810/number-of-monthly-active-facebookusers-worldwide/

14