rafmacalaba/datause-encoder-data huggingface dataset:
# **7.0 - REFERENCES**

- 100 Resilient Cities. 2018a. “Kigali’s Resilience Challenge.” 100 Resilient Cities. 2018. https://www.100resilientcities.org/cities/kigali/.

- 100 Resilient Cities. 2018b. “Lagos’ Resilience Challenge.” 100 Resilient Cities. 2018. https://www.100resilientcities.org/cities/lagos/.

- Al Jazeera. 2018. “Rwanda Landslides: At Least 18 Killed after Heavy Downpour.” Al Jazeera, May 8, 2018. https://www.aljazeera.com/news/2018/05/rwanda-landslides-18-killed-heavy-downpour- <u>180508051710402.html.</u>

- All Kote Lining. 2018. “Cooling Tower Replacement Cost.” All Kote Lining Inc. January 31, 2018. https://www.allkoteliningincaz.com/cooling-tower-replacement-cost/.

- Alvaro, Lara. 2018. “The Pitfalls of Hydroelectric Power in Drought-Prone Africa.” Climate & Environment at Imperial (blog). August 21, 2018. https://granthaminstitute.com/2018/08/21/thepitfalls-of-hydroelectric-power-in-drought-prone-africa/.

- Anderson, Kate, Nicholas Laws, Spencer Marr, Lars Lisell, Tony Jimenez, Tria Case, Xiangkun Li, Dag Lohmann, and Dylan Cutler. 2018. “Quantifying and Monetizing Renewable Energy Resiliency.” Sustainability 10 (4): 933. https://doi.org/10.3390/su10040933.

- APS. 2018. “Palo Verde Generating Station.” 2018.

- https://www.aps.com/en/ourcompany/generationtransmission/generation/pages/home.aspx.

- Ardagh, Michael W., Sandra K. Richardson, Viki Robinson, Martin Than, Paul Gee, Seton Henderson, Laura Khodaverdi, et al. 2012. “The Initial Health-System Response to the Earthquake in Christchurch, New Zealand, in February, 2011.” The Lancet 379 (9831): 2109–15. https://doi.org/10.1016/S0140-6736(12)60313-4.

- Bloodgood, A., and M. Martinez. 2018. “Resiliency by Design.” Siemens Industry, Inc. https://www.siemens.com/content/dam/webassetpool/mam/tag-siemenscom/smdb/regions/usa/company/topic-areas/onsite-powergeneration/documents/puertoricoresiliency-wp-fprint.pdf.

- Bodenhamer, Melisa. 2011. “Mid-Term Review of the Hyogo Framework for Action.” UNISDR, World Vision, , February, 21.

- Bureau of Labor Statistics. 2018. “Effects of Hurricanes Harvey, Irma, and Maria on BLS Data Collection and Reporting : U.S. Bureau of Labor Statistics.” March 20, 2018. https://www.bls.gov/bls/hurricanes-harvey-irma-maria.htm.

- Busari, Stephanie, and Osman Mohamed Osman. 2017. “Lagos Floods: Heavy Rain, Storms Cause Havoc.” CNN, July 10, 2017. https://www.cnn.com/2017/07/09/africa/lagos-floodstorms/index.html.

- Business Journal. 2017. “Nigeria Needs $3tr to Plug Infrastructure Gap.” Business Journal, September 18, 2017. http://www.businessjournalng.com/nigeria-needs-3tr-plug-infrastructure-gap/.

- Byumvuhore, Frederic. 2018. “2017 Disasters Cost Rwanda Rwf6.7 Billion - MIDIMAR.” The New Times, January 8, 2018. https://www.newtimes.co.rw/section/read/227141.

- Campbell, Richard J, Corrie E Clark, and D Andrew Austin. 2017. “Repair or Rebuild: Options for Electric Power in Puerto Rico.” Congressional Research Service. https://fas.org/sgp/crs/row/R45023.pdf.

Central Intelligence Agency. 2018. “The World Factbook: Puerto Rico.” CIA.Gov. 2018.

31
standing beliefs based on the realities of one’s experiences then that new information will be suppressed or simply ignored as some irrelevant fiction.<sup>3</sup> The information campaign will fail.

Alternatively, the campaign may succeed in changing social perceptions relevant to the public program but not individual efficacy in accessing that program. In principle, a persuasive campaign may make one think differently, and more positively, about the local environment in the abstract, but not change the reality for anyone. The campaign creates a “groupthink.”<sup>4</sup> Each individual may instead come to think that he or she is an exception to the norm described by the information campaign. This distortion to beliefs may well come to be corrected in time through the sharing of experience via social interaction. But this will take time.

Among those antipoverty programs in the developing world for which these issues are salient, India’s ambitious National Rural Employment Guarantee Act of 2005 (hereafter the “Act”) surely stands out. The Act created a justiciable “right to work” for all rural households implemented through the National Rural Employment Guarantee Scheme—renamed the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) in 2009. The scheme is huge, and it could well be the largest antipoverty program ever; according to the administrative data, over 50 million households in India participated in MGNREGS in 2009/10.<sup>5</sup> The scheme promises 100 days of work per year to all rural households whose adults are willing to do unskilled manual labor at the statutory minimum wage notified for the program. Work is to be made available within 15 days to anyone who asks for it, failing which the state government is liable to pay an unemployment allowance. Open village meetings ( _Gram Sabhas_ ) are supposed to identify suitable projects and local government institutions ( _Gram Panchayats_ ) are given a central role in planning and implementation.

The most direct and obvious way this scheme tries to reduce poverty is by providing extra employment in rural areas on demand. This can entail both valuable income support for the chronically poor, and insurance against the many risks faced by India’s rural population in their daily lives. Even those who do not normally need such work can benefit from knowing it is available. This can help underpin otherwise risky investments and come with efficiency gains given existing credit and labor market distortions.

> 3  In psychology this is known as the theory of cognitive dissonance, following Festinger (1957). On the economic implications of cognitive dissonance see Akerlof and Dickens (1982).

> 4  The idea of a groupthink is due to Janis (1972). For an economic model of how such erroneous beliefs held by groups of people can emerge and persist for some time see Bénabou (2013).

> 5  See Government of India website for MGNREGS (http:\\nrnic.in).

3
scenarios, key sources of variation in the estimates include the assumed climate sensitivity, the form of the damage function, and the discount rate employed. Tol summarizes these estimates in terms of the distribution of the full sample, in total and broken down according to the assumed pure rate of time preference, and by fitting a Fisher-Tippett distribution to the sample. Table 1 reports figures from Tol (2009), converting the damages to 2010 $ / t CO2. For the fitted distribution, the median estimates of the social cost of carbon vary from a value of $13/t CO2 to $43, depending on the pure rate of time preference, with an overall median value of $33/t CO2.

**Table 1. Estimates of the social cost of carbon, 2010 $ per ton of CO2**

||Sample<br>||PRTP||Fitted<br>||PRTP||
|---|---|---|---|---|---|---|---|---|
||All|0%|1%|3%|All|0%|1%|3%|
|Mean|39|87|32|7|56|55|45|19|
|Standard Deviation|91|162|53|7|101|58|55|23|
|Median|11|32|17|5|33|43|34|13|
|95th percentile|135|220|100|17|200|182|153|77|
|N|232|38|50|66|||||

Source: Selected figures from Tol (2009) converted to 2010 $ / tCO2; PRTP: pure rate of time preference In the US the Interagency Working Group on the Social Cost of Carbon (2010) produced estimates of the social cost of carbon for use in regulatory impact analysis by the executive branch of government. Rather than summarizing published literature, the working group produced their own simulations using the three major integrated assessment models<sup>5</sup> with a common set of assumptions about population and economic growth, an assumed distribution for climate sensitivity, and three assumptions about the discount rate. The distributions of the social cost of carbon were averaged across models for each discount rate to arrive at the figures in Table 2. Because the total atmospheric stock of greenhouse gases is assumed to rise to 2050 in their simulations, there is a corresponding increase in the social cost of carbon. The mean estimates for 2010 lie between $4.7/ton of CO2 and $35.1 /ton, depending on the discount rate.

> 5 DICE, PAGE and FUND – see Interagency Working Group on the Social Cost of Carbon (2010).

6
||ion|Max|5.58|4.10|5.58|5.58|5.58|5.33|5.58|5.58|4.12|4.00|5.58|4.00|4.00|007)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||destinat|v<br>Min|1.40|1.40|1.44|1.44|1.44|1.40|1.46|1.40|2.01|1.66|1.47|1.40|1.66|et al., 2|
||tility at|St. de|1.01|0.61|1.08|1.04|1.09|0.91|1.19|1.01|0.73|0.47|1.16|0.54|0.64|Parsons|
|norms|Fer|Mean|2.58|2.14|2.73|2.55|2.68|2.68|3.17|2.61|2.84|2.22|3.41|2.27|2.50|n data (|
|s and|lity|Max|7.96|3.10|7.95|7.96|7.96|7.77|7.96|7.96|6.08|7.77|7.96|7.77|7.77|igratio|
|y rate|y Ferti|Min|0.90|0.90|1.15|0.98|1.15|0.90|1.84|0.90|2.09|1.23|1.23|0.90|1.23|and m|
|fertilit|me-countr|St. dev|1.72|0.55|1.76|1.66|1.66|1.90|1.73|1.68|1.33|1.07|1.77|1.40|1.63|ndicators)|
|stics on|Ho|Mean|3.19|1.87|3.64|3.10|3.43|3.42|4.24|3.14|3.61|2.76|4.58|2.87|3.20|pment I|
|ive stati|Emig.|rate (%)|11.2|14.8|10.0|10.3|10.5|14.0|7.6|11.4|6.95|19.6|12.1|15.1|18.1|rld Develo|
|Descript|Nb. of|obs.|208|53|155|155|126|53|56|190|13|54|68|48|28|tility (Wo|
|Table 1.||||||(data on remit.)|ata on remit.)|(no data on remit.)|f pop)|of pop)|ies|Carib.|Africa|aci...c||tations based on fer|
||||ntries|come|ping|ntries|ping (d|ntries|s (% o|ics (%|countr|Am. &|haran|sia & P|Asia|compu|
||||All cou|High-in|Develo|All cou|Develo|All cou|Muslim|Cathol|MENA|Latin|Sub Sa|East A|South|Own|

18
Table A.2: Baseline statistics and balance test for endline respondents

||||Test of ra|ndomizati|on balance|(continued|) (N=947)||
|---|---|---|---|---|---|---|---|---|
||Sample|Assigne|d therapy|Assign|ed cash|Assign|ed both|F-Test|
|Baseline covariate|Mean|Coef.|_p_ value|Coef.|_p_ value|Coef.|_p_ value|_p_ value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Age|25.35|-0.23|0.49|0.05|0.89|-0.02|0.96|0.17|
|Married or partnered|0.16|-0.02|0.75|-0.03|0.70|0.03|0.83|0.90|
|# of partners|0.53|0.06|0.43|0.12|0.20|-0.20|0.14|0.20|
|# of children<15 in household|2.23|-0.58|0.06|-0.47|0.19|0.64|0.27|0.28|
|Sees family often|2.36|0.10|0.06|0.22|0.01|-0.27|0.02|0.02|
|Muslim|0.09|0.02|0.56|0.01|0.60|0.00|0.95|0.75|
|Years of schooling|7.73|-0.30|0.51|-0.06|0.89|0.14|0.79|0.54|
|Currently in school|0.06|-0.03|0.09|-0.03|0.13|0.04|0.16|0.20|
|Literacy index (0-2)|1.21|0.08|0.33|0.08|0.47|-0.20|0.14|0.30|
|Math score (0-5)|2.79|-0.18|0.15|-0.09|0.66|-0.08|0.71|0.76|
|Health index (0-6)|4.85|-0.07|0.30|-0.19|0.21|0.28|0.25|0.43|
|Has any disabilities|0.07|0.04|0.29|0.00|0.97|-0.05|0.49|0.22|
|Depression index (0-17)|7.08|0.03|0.90|-0.07|0.82|0.06|0.90|0.88|
|Distress index (0-21)|7.49|-0.01|0.98|-0.10|0.80|-0.15|0.74|0.82|
|Relations to commanders index (0-4)|0.45|0.00|0.99|0.04|0.61|-0.04|0.74|0.88|
|Ex-combatant|0.41|0.06|0.11|0.07|0.19|-0.08|0.19|0.25|
|War experiences index (0-12)|5.86|0.34|0.37|0.01|0.96|-0.61|0.22|0.43|
|Weekly cash earnings (USD)|16.90|-1.94|0.11|-4.33|0.03|4.70|0.00|0.01|
|Summary index of income, z-score|-0.01|-0.20|0.03|-0.11|0.41|0.25|0.14|0.06|
|Homeless in past two weeks|0.25|0.00|0.90|0.01|0.77|-0.03|0.62|0.26|
|# of days slept hungry, last 7 days|1.27|0.26|0.07|0.30|0.02|-0.37|0.04|0.07|
|Savings stock (USD)|32.54|-7.32|0.34|-7.11|0.49|10.34|0.42|0.60|
|Can get loan of 50 USD|0.51|-0.02|0.71|-0.04|0.37|0.04|0.49|0.58|
|Can get loan of 300 USD|0.10|-0.02|0.42|-0.02|0.50|0.05|0.28|0.45|
|Hours in illicit activities|13.22|0.61|0.80|-0.69|0.68|0.03|0.99|0.24|
|Hours/week in agriculture|0.37|0.35|0.33|-0.16|0.21|0.14|0.86|0.02|
|Hours/week in low-skill wage labor|19.34|0.46|0.91|0.78|0.84|-0.28|0.95|0.99|
|Hours/week in low-skill business|11.73|0.18|0.90|-1.56|0.60|5.54|0.15|0.53|
|Hours/week in high-skill work|1.46|-0.19|0.76|1.12|0.03|0.46|0.57|0.02|
|Years of experience in agriculture|0.74|-0.20|0.28|-0.30|0.12|0.27|0.25|0.31|
|Years experience in non-agricultural|3.03|-0.41|0.24|-0.86|0.03|0.98|0.07|0.02|
|business|||||||||
|Years experience in high-skill work|0.93|-0.15|0.13|-0.04|0.87|0.44|0.11|0.03|
|Sells drugs|0.20|0.00|0.97|0.00|0.99|0.02|0.69|0.96|
|Drinks alcohol|0.76|0.06|0.28|0.07|0.30|-0.07|0.18|0.35|
|Uses marijuana|0.59|0.11|0.03|0.08|0.06|-0.14|0.02|0.04|
|Uses marijuana daily|0.44|0.07|0.20|0.04|0.18|-0.08|0.25|0.37|
|Use hard drugs|0.26|-0.02|0.57|0.01|0.88|0.01|0.88|0.87|
|Uses hard drugs daily|0.14|-0.04|0.27|0.01|0.66|0.01|0.92|0.43|

Continued on following page.

iv
These assumptions on sectoral growth pin down the total growth rate for rural and urban areas, but further assumptions are needed to specify the level of the growth and declines. Note that the macro sectoral growth rates in 2020 are largely negative. For households that experienced an increase in income in 2020, we set the size of the increase to match the growth projection prior to COVID-19. Intuitively, if a household managed to grow their income in 2020 (at the same time as mean income was declining), our best guess is the growth rate that was previously projected. With this approach, we will assign each household to one of five different growth rates; two positive growth rates for urban and rural households, two negative growth rates for urban and rural households, and a zero growth rate. In the robustness section we show that our results do not change notably if we add random noise to the growth rates.

# **(iv) Estimates from the literature**

For 26 countries (covering 27% of the global population) we use estimates of distributional changes due to the COVID-19 pandemic available from various studies – including published papers and calculations available from teams in the World Bank. These studies (with the exception of India) are not based on new data collected in 2020, but instead use an econometric model to predict the distributional impact using a household survey from an earlier year. Like with the tabulated data, we often use quantile-specific growth rates from these studies and need to make strong assumptions to apply them to our data. In the case of India, we use estimates from Roy and van der Weide (2022). See Appendix B for further details.

# **(v) Countries without any other data**

For 127 countries, we were unable to use any of the sources presented thus far. Many of these countries are small, such that these countries together account for roughly 18% of the global population. For this group of countries we use three other methods in the following order of preference.

_Use sectoral national accounts growth rates._ As with the HFPSs, we use the sectoral national accounts growth rates to estimate urban and rural growth rates. We apply those growth rates to the 2019 distribution, such that households could only experience two different growth rates in 2020. By design, this will only capture a small fraction of the within-country inequality changes that happened in 2020. This method is applied to 56 countries covering 13% of the global population (half the population in the Middle East, nearly a quarter in South Asia, and close to a fifth in SubSaharan Africa).

_Apply national growth rates._ For some countries we lack sectoral national accounts or information on whether a household resides in an urban or rural area. For those countries, we apply the growth rate in real GDP per capita to all households, which means that we do not allow for any within-country distributional changes. We use this method in 21 countries covering 2% of the global population.

13
3 that equity market liberalization leads to an approximate 1% increase in annual real per capita GDP growth. Alfaro et al. (2004) find that countries with well-developed financial markets gain significantly from FDI and Alfaro and Hammel (2008) identify that stock market liberalizations are associated with a significant increase in the share of machinery and equipment and, therefore, economic growth. This body of evidence suggests that any empirical work to explain GDP growth over time that overlooks financial or trade measures is fundamentally flawed.

A major advance to study globalization has been the comprehensive dataset made available by Lane and Milesi-Ferretti (2007). They have constructed estimates of external assets and liabilities for 145 countries for 1970-2004 and have documented the increasing importance of equity financing and the improvement in the external position for emerging markets, as well as the differing pace of financial integration between advanced and developing economies. This dataset supersedes own measures of financial globalization built by researchers so far.<sup>3</sup> Although there is no theoretical and empirical consensus about the overall impact of IT on output growth, it is well accepted that all IT central banks “not only aim at stabilizing inflation around the target but also put some weight on stabilizing the real economy” (Svensson, 2007, p. 1).<sup>4</sup> Recent theoretical models point at mixed effects. One could mention the ambiguous effects of inflation targeting on growth provided by Gupta (2006), the suboptimal nature of IT (dominated by nominal income growth) put forward

> 3 Albuquerque et al. (2005), for instance, construct a globalization measure that equals the share of explained variation in direct investment attributable to global factors. They show that the measure has increased steadily for developing and developed countries alike. See also Edison et al. (2004) for a detailed description of different measures of capital account openness. 4 Bernanke (2003) also considers that the idea of IT focusing exclusively on control of inflation and ignoring output and employment objectives is a misconception. He suggests that “short-run stabilization of output and employment is more effective when inflation expectations are well anchored” through the constrained discretion that provides an IT regime.
# **VI. Conclusion**

In this paper we study how the formation of the ASEAN Free Trade Agreement, established in 1993, has affected trade flows and trade policies vis-à-vis outsiders. AFTA provides a unique opportunity to analyze the effects of preferential trade integration, in that it involves ten countries that have been lowering tariffs on each other’s imports over time at very different paces across products. Moreover, very detailed data are available, and not only for the pace of actual preferential liberalization, but also for the members’ planned schedule of liberalization at the inception of AFTA in 1992.

We find that AFTA has been broadly benign, in the sense that it does not seem to be promoting trade within the bloc at the expense of trade with nonmembers. Furthermore, we find strong evidence that AFTA members have been responding to lower internal tariffs by reducing also their trade barriers on imports from outsiders. In this sense, AFTA has been clearly beneficial for the promotion of freer world trade.

These findings corroborate those of Estevadeordal et al. (2008) for Latin America (and to a lesser extent also those of Lendle 2007 for some ASEAN members), reinforcing the view that regionalism promotes external liberalization and can be viewed as a “building bloc” toward free trade. On the other hand, our findings contrast sharply with those of Limão (2006) and Karacaovali and Limão (2008) for the United States and the European Union, which imply that regionalism constitutes a break on external liberalization and should be regarded instead as a “stumbling bloc” to global free trade.

Why do we have such discrepant results across studies and trading blocs? While we cannot know for sure, one factor that is very likely behind these divergent results is indeed the stark difference between the countries analyzed in those studies. Since the multilateral system has not enforced much tariff reduction on developing countries, tariffs are relatively high there, creating a large potential for trade diversion. Lower external tariffs moderate that loss. The results of Estevadeordal et al. and of this paper suggest that this force is important in explaining changes in MFN tariffs of developing countries involved in free trade areas. In contrast, Limão focuses 23
**<u>Table A2. OLS Cross Section Estimates of Employment and Export Status</u>**

|Dependent variable|(1)|(2)|(3)|
|---|---|---|---|
|ln(Employment)|2013|2016|2020|
|Exporter (yes=1)|1.066***|1.250***|0.807***|
||(0.11)|(0.13)|(0.09)|
|ln(value of machinery)|0.044***|0.021**|0.176***|
||(0.01)|(0.01)|(0.02)|
|ln(cost per worker)|-0.100***|-0.006|-0.218***|
||(0.04)|(0.05)|(0.04)|
|ln(age of firm)|0.406***|0.342***|0.141***|
||(0.06)|(0.07)|(0.03)|
|Foreign owners (yes=1)|0.475***|0.368**|0.107|
||(0.11)|(0.18)|(0.13)|
|Female owners (yes=1)|0.261**|0.064|0.103|
||(0.11)|(0.12)|(0.08)|
|Registered (yes=1)|0.342***|0.312**|0.165***|
||(0.09)|(0.16)|(0.06)|
|Training (yes=1)|-0.971***|-0.562***|-0.839***|
||(0.11)|(0.11)|(0.11)|
|Foreign tech (yes=1)|0.306**|0.343**|0.355***|
||(0.14)|(0.15)|(0.10)|
|Imported inputs (yes=1)|0.313***|0.633***|0.234***|
||(0.07)|(0.09)|(0.05)|
|Indirect exporter (yes=1)|-0.396***|-0.509**|-0.271|
||(0.15)|(0.22)|(0.17)|
|Constant|3.945***|2.510***|3.509***|
||(0.47)|(0.62)|(0.50)|
|Sector dummies|Yes|Yes|Yes|
|Region Dummies|Yes|Yes|Yes|
|Observations|1,767|1,084|2,676|
|R-squared|0.389|0.443|0.394|

Notes: Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 44
axis from above. In this case, the point of crossing gives the optimal retirement age.<sup>7</sup> In the case of the CNSS the majority of lines are either above or below the horizontal axis. So in the CNSS individuals with a certain propensity for retirement will most likely retire as soon as possible. In the CMR the majority of lines start below the horizontal axis, cross the line from below and then cross the axis again from above. Hence for individuals it is optimal to retire as soon as possible up to age 58. At age 59 it is better to wait one more year. The distorted map that emerges reflects the peculiarity of the benefit formula based on final wages where the accrual rate increases from 2% to 2.5% at age 60. The map of the RCAR corresponds to a system that is more actuarially fair. A majority of individuals will not retire immediately regardless of their propensity to do so. This is because of the upward slopping IRR function.

<!-- Start of picture text --> Figure 3: Utility Maps for the Retirement Decision<br> CNSS CMR<br>0.05 0.05<br>0.04 0.04<br>0.03 0.03<br>0.02 0.02<br>0.01 0.01<br>0 0<br>55 57 5 9 61 63 65 67 69 5 5 5 7 59 61 63 65 67 6 9<br>-0.01 -0.01<br>-0.02 -0.02<br>-0.03 -0.03<br>-0.04 -0.04<br>-0.05 -0.05<br> RCAR<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>55 57 59 61 63 6 5 67 69<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br><!-- End of picture text --> Source: Authors’ calculations.

The information captured in the maps can be summarized by the probability of early retirement, defined here as the probability of retiring at age 55. As suggested by the charts, the probability of early retirement is highest in the CNSS. In that case, for 40% of the individual preference parameter pairs the optimal retirement age is age 55. For the CMR the probability of early retirement is somewhat lower (36%). As previously indicated, the lowest probability of early retirement corresponds to the RCAR (17%). So

> 7 The discussion here is simplified so not all the special cases of the nonmonotone ngu(.) lines with multiple intersections with the horizontal axis are not discussed here; however, the behavioral consequences can be easily determined using the same framework for these individuals as well.

9