rafmacalaba/datause-encoder-data huggingface dataset:
# Table 5A

# Impact of PCGNP on Environmental Indicators

|Dependent<br>Variable|Intercept|In PCGNP|Adjusted<br>R<sup>2</sup><br>.|
|---|---|---|---|
|ln Air|2.70<br>(11.93)|0.27<br>(8.70)|0.71|
|ln Water|3.55<br>(22.84)|0.19<br>(8.80)|0.72|
|ln Land|3.79<br>(27.70)|0.17<br>(8.75)|0.72<br>_|
|ln Living|3.73<br>(29.60)|0.16<br>(9.26)|0.74|
|ln Env|4.89<br>(34.80)|0.19<br>(9.78)|0.76|

* t-statistics in parentheses.

# Table 5B

# Impact of ICPGDP on Environmental Indicators

|Dependent<br>Variable|Intercept|ln ICPGDP|Adjusted<br>R<sup>2</sup>|
|---|---|---|---|
|ln Air|1.29<br>(4.06)|0.42<br>(10.59)|0.79<br>.|
|ln Water|2.59<br>(11.53)|0.30<br>(10.30)|0.78|
|ln Land|2.97<br>(14.52)|0.25<br>(9.82)|0.76<br>__|
|ln Living<br>l______________|3.03<br>(13.88)|0.23<br>(8.53)|0.71|
|ln Env|3.97<br>(18.72)|0.29<br>(10.79)|0.79|

14
|Table 8<br>Estimated parametric adjustment<br>Tariffchanges effect estimation|functions|||
|---|---|---|---|
||White collar<br>adjustment|Blue collar<br>adjustment|Capital<br>adjustment|
|Constant|0.026|0.167|0.062|
||[0.030]<br>|[0.030]***<br>|[0.031]**<br>|
|Pos Shortage|0.196<br>|0.116<br>|0.038<br>|
||[0.050]***|[0.047]**|[0.048]|
|(ShortageW)^2|0.201|0.032|0.006|
||[0.015]***<br>|[0.013]**|[0.014]|
|(ShortageW)^2*Pos|-0.076|||
||[0.023]***|||
|(ShortageB)^2|-0.006|0.044|-0.022|
||[0.015]|[0.018]**<br>|[0.016]|
|(ShortageB)^2*Pos||0.026<br>||
|||[0.026]||
|(ShortageK)^2|-0.009|0.033|0.179|
||[0.013]|[0.012]***|[0.013]***|
|(ShortageK)^2*Pos|||-0.119|
||||[0.020]***|
|(ShortageW)*(ShortageB)|-0.047|-0.002|-0.013|
||[0.015]***|[0.013]|[0.018]|
|(ShortageW)*(ShortageK)|-0.025|-0.013|0.016|
||[0.014]*|[0.015]|[0.014]|
|(ShortageB)*(ShortageK)|0.036|-0.040|-0.063|
||[0.018]**|[0.014]***|[0.013]***|
|Constant*Open|-0.019|-0.002|-0.017|
||[0.007]***|[0.006]|[0.007]**|
|Pos Shortage*Open|0.012|-0.007|0.020|
||[0.009]|[0.009]|[0.009]**|
|(ShortageW)^2*Open|0.005<br>|||
||[0.004]|||
|(ShortageW)^2*Pos*Open|0.006<br>|||
||[0.005]|||
|(ShortageB)^2*Open||-0.015<br>||
|||[0.003]***||
|(ShortageB)^2*Pos*Open||0.027<br>[0.006]***||
|(ShortageK)^2*Open|||0.008|
||||[0.004]**|
|(ShortageK)^2*Pos*Open|||-0.008|
||||[0.005]|
|Observations|4278|4278|4278|
|Number of id|627|627|627|
|R-squared|0.33|0.31|0.31|
|Note: Pos Shortage: Dummy=1<br>ShortageK are the shortages for|if Shortage is p<br>white collar. blue|ositive. Shorta<br>collar and capi|geW. ShortageB.<br>tal. Open is the|
|annual change in tariff levels. Sta<br>*significant at10%;**significant|ndard errors in bra<br>at 5%;***significa|ckets<br>nt at1%||

25
**Figure 13:  Dwell Time,** **<u>by Annual Volume of Imports, 2011</u>**

<!-- Start of picture text --> Manufacturing Assembling Domestic Wholesaler<br>Transit Wholesaler Small & Medium Retailer Other<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Volume of importations**

Contrary to the common belief that the volume of imports is an important determinant of cargo dwell time, in SSA the volume of imports is not correlated with dwell time (Figure 14). Hence, shippers that import medium volumes seem to have significant longer dwell time – about one day longer – compared to shippers that import very low, low and large volumes (t(212.253) = 1.669; p = 0.048). And shippers importing large volumes seem to have significant shorter dwell time – approximately one day shorter – than all the other shippers (t(268.041) = 2.218; p = 0.013).More than the size of the company, the type of company and its business model are what matter the most.

**<u>Figure 14: Cargo Dwell Time, by Annual Volume of Imports, 2011</u>**

<!-- Start of picture text --> 12<br>9.80 9.99<br>10 8.76<br>8.16<br>8<br>6<br>4<br>2<br>0<br>Very Low (< 100 Tons) Low (>=100 and <=500) Medium (>= 500 and Large (>2000 Tons)<br><=2000)<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

# **Frequency of Deliveries**

A very important relationship to test through data analysis is the one between dwell time and the frequency of deliveries. It seems that more frequent deliveries - more than 10 deliveries (figure 15) result in about more than two days significant shorter dwell time (t(273.202)= 3.562 p = 0.0002). This reflects the dominant situation of importers in Sub-Saharan African countries who have on average less than 10 deliveries every year and do not have real logistic strategies in place.

25
Second, gender has limited explanatory power and gender differences in labor market vulnerability vary across countries. In Europe and Mexico, women are around 10 percentage points more likely than men to have a job amenable to home-based work, whereas in Brazil the gap widens to 19.4 percentage points. In India, by contrast, there are no gender differences, and in Turkey women are 12.7 percentage points _less_ likely to be able to work from home. However, it is important to bear in mind that these estimates only reflect whether a job can be performed from home and do not consider other dimensions of gender differences in susceptibility to COVID-19 induced labor market stress. Women are disproportionately shouldering caregiving and childcare needs stemming from the shutdown of schools and childcare centers (Alon et al., 2020, Sevilla and Smith, 2020, Wenham, 2020) or the illness of their family members, which constrains their labor supply (Heath et al., forthcoming).

Third, and most important, labor market vulnerability is inversely correlated with educational attainment. Workers with tertiary education are much more likely to be able to work from home in all countries and regions. Education explains a large share of the variation in the ability to work from home as is evidenced by the R2s which consistently exceed 0.2; it is the strongest predictor of who has a relatively safe job among the set of explanatory variables we consider here. While education offers protection in all countries, the probability of having a job amenable to working from home increases least with additional education in India, which is not surprising given that India has fewer jobs that can be performed from home to start with. Interestingly, when using the DN2020 telework variable instead of our home-based work measure, the coefficient of education level in India becomes similar to the one in the other studied countries (see annex 1), which attests to a lack of internet access being the binding constraint on highly educated Indians’ ability to work from home.

Fourth, workers in temporary jobs are less likely to have jobs that can be performed from home. This is worrisome, as they are more susceptible to losing their jobs, and reinforces the conclusion that COVID-19 is likely to exacerbate labor market inequality and will disproportionately impact those least protected. Including all the explanatory variables, together with the regional fixed effects, does not change the significance of specific variables. Education level, age, and job security are still highly important for the ability to perform a job from home even when regional variances are taken into account within each country.

16
# III. RESULTS

# _3.1 What is the impact of attending vocational (versus academic) high school?_

# _3.1.1 Main Results_

According to the results from the OLS analysis, students in vocational schools have different dropout rates and learn both computing and math skills at different rates than students from academic high schools. Specifically, students in vocational schools are 4 percentage points (or about 78 percent) more likely to drop out compared to students in academic high schools (table 2, row 1, column 1). The difference is statistically significant at the 1% level. The OLS regressions also show that students attending vocational high school do not improve computing skills more than students attending academic high school (row 1, column 2). Students in vocational high school scored only 0.02 SDs higher than academic high school students in computing skills (not statistically different from zero). Finally, in terms of math skills, students in vocational high school score far lower (0.44 SDs) than students in academic high school (row 1, column 3). The difference is significant at the 1% level. In summary, students attending vocational versus academic high schools drop out more, learn fewer general skills, and have no measurable advantage in learning specific skills.

The results from our IV analysis generally support the story that vocational high schools do not build human capital (table 3). Vocational high school students are 1.1 percentage points more likely to drop out (although this finding on differences in the dropout rate—unlike the OLS finding—is no longer statistically significant). The IV estimates of vocational schooling on computing and math skills remain consistent with the findings from the OLS analysis. Vocational
- OECD-FAO, (2009), “Agricultural Outlook 2009-2018”, 276 pages, Available at: <u>http://www.agri-outlook.org/pages/0,2987,en_36774715_36775671_1_1_1_1_1,00.html.</u>

- Olinga Mebada, J.-E., (2012) Impact économique des biocarburants au Mali : Une analyse de robustesse, Mémoire de Maîtrise, Département d’économique, Université de Sherbrooke, Sherbrooke.

- Pallière, G. and S. Fauveaud, (2009), « Les enjeux des agrocarburants pour le monde paysan au Mali », GERES, AMEDD, ICD, Available at : <u>http://www.geres.eu/images/stories/publis/Enjeux-jatropha-paysans-maliens.</u>

- Raizon, D., (2009), « Culture intensive de Jatropha : Manne ou catastrophe? », RFI, In line on 23/06/2009, http://www.rfi.fr/sciencefr/articles/114/article_82102.asp.

- Ravallion, M., (1994), « Poverty Comparisons », Harwood academic publishers, 143 pages.

- Ravallion, M. and S. Chen (2003). "Measuring Pro-Poor Growth", World Bank, Policy Research Working Paper 2666.

- République du Mali, (2008), “Orientations stratégiques et priorités d'investissements agricoles », Conférence de Table Ronde des bailleurs de fonds du Mali, Bamako, <u>http://www.tablerondemali2008.org/html/orientation.pdf.</u>

- <mark>Renner</mark> , R., (2007), “Green Gold in a Shrub: Entrepreneurs target the jatropha plant as the next big biofuel”, Green Gold in a Shrub, Scientific American, June. Available at: <u>http://www.scientificamerican.com/article.cfm?id=green-gold-in-a-shrub&page=1.</u>

- Rutherford, T., D. Tarr, and O. Shepotylo. (2005). “Poverty Effects of Russia’s WTO Accession: Modeling ‘Real’ Household and Endogenous Productivity Effects.” World Bank Policy Research Working Paper 3473, Washington, DC.

- Savard, L. (2005). “Poverty and Inequality Analysis within a CGE Framework: A Comparative Analysis of the Representative Agent and Microsimulation Approaches”. _Development Policy Review_ 23(3): 313–332.

- Searchinger, T.; Heimlich, R.; Houghton, R.A.; Dong, F.; Elobeid, A.; Fabiosa, J.; Tokgoz, S.; Hayes, D. and Yu, T. (2008), “Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land use change” _, Science_ Vol. 319, pp. 1238 – 1240.

- Vos, R, De Jong N. (2003) “Trade liberalization and poverty in Ecuador: a CGE macromicrosimulation analysis”. _Economic System Research_ , 15(2): 211-232.

- Word Bank, (2007), “ _Mali - Second poverty reduction strategy paper and joint IDA-IMF staff advisory note_ ”, Report **#** 40724, 241 pages.

# **Appendix**

# **The detailed CGE model developed for the study**

## **Definition of variables, parameters and sets**

### <u>Endogenous variables</u>

_Mim_ : imports _Qi_ : composite good _Xsi_ : sectorial output 30
WPS3972

# **ANNUITY MARKETS IN CHILE: COMPETITION, REGULATION—AND MYOPIA?**

## **Eduardo Walker**

## **School of Business Administration Pontificia Universidad Católica de Chile Santiago de Chile.**

**Abstract:** We study annuity rates in Chile and relate them with industry competition, finding: a) that annuity insurance companies paying higher broker commissions paid _lower_ annuity rates; and b) a structural break of the long-run elasticity of annuity rates to the risk-free rate in 2001. Moreover, this structural break coincided with the submission of a new draft pension law proposing greater transparency in annuity markets and a generalized drop in broker commissions.  The high commissions charged in the 1990s were partly returned to annuitants as informal (and illegal) cash rebates. _Myopic_ pensioners preferred cash rebates over present values. Thus, the legal threat caused the drop in broker commissions, reduced the illegal practice of cash rebates, increased competition via annuity rates, and raised the long-run elasticity to one.

## **JEL Classification Code: G22, G23, G12, L5**

## **Keywords: Annuities, Emerging Markets, Competition, Credit Risk, Myopia**

World Bank Policy Research Working Paper 3972, August 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**Correspondence details:** Eduardo Walker, School of Business Administration, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile; Phone: (56 2) 354 4002; Fax: (56 2) 553 1672; email: ewalker@faceapuc.cl
# **Appendix: Table A.4**

## **Regressions on Mobile Phone Penetration, Upper-Middle-Income Economies**

This table presents OLS regressions of percentage of people using mobile phone to make financial transactions on the set of country-level variables listed below. This table reports the models for all low and middle income countries. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|**% of people using mobile**|||||||
|**phone to:**|**send**|**money**|**receive**|**money**|**pay**|**bills**|
|Ln(Population)|-0.0101|-0.0137|-0.0096|-0.0091|-0.0052|-0.0071|
||(0.006)|(0.009)|(0.009)|(0.012)|(0.005)|(0.007)|
|Ln(Population Density)|0.0015|-0.0014|-0.0030|0.0003|-0.0003|0.0002|
||(0.010)|(0.012)|(0.013)|(0.017)|(0.008)|(0.010)|
|Natural Resources|0.0613***|0.0491***|0.0708***|0.0567**|0.0054|0.0013|
||(0.013)|(0.017)|(0.018)|(0.024)|(0.010)|(0.014)|
|Offshore Center|-0.0232|-0.0258|-0.0293|-0.0212|-0.0066|0.0030|
||(0.043)|(0.043)|(0.059)|(0.059)|(0.033)|(0.036)|
|Ln(GDP per capita)|-0.0566**|-0.0133|-0.0720**|-0.0167|-0.0353**|-0.0132|
||(0.022)|(0.029)|(0.030)|(0.039)|(0.017)|(0.024)|
|Growth GDP Per Capita||-0.3268||-0.7177||-0.4375|
|||(0.460)||(0.632)||(0.386)|
|Inflation||-0.6158**||-0.7192**||-0.2928|
|||(0.249)||(0.342)||(0.209)|
|KKM Index||-0.0626**||-0.0795*||-0.0417|
|||(0.030)||(0.041)||(0.025)|
|Manufacturing/GDP||-0.0090||-0.1797||0.0261|
|||(0.195)||(0.268)||(0.164)|
|Sec./Prim. School Enrollment||0.0179||0.0518||0.0539|
|||(0.098)||(0.135)||(0.082)|
|Sub-Saharan Africa|0.0634*|0.0849**|0.0776|0.1053**|-0.0022|0.0160|
||(0.034)|(0.036)|(0.046)|(0.049)|(0.026)|(0.030)|
|Constant|0.1194***|0.0998|0.1328**|0.1297|0.0867**|0.0506|
||(0.043)|(0.082)|(0.059)|(0.112)|(0.033)|(0.069)|
|Observations|35|34|35|34|35|34|
|Adjusted R-squared|0.5500|0.6090|0.4644|0.5393|-0.0110|-0.0210|
|Root MSE|0.0563|0.0532|0.0777|0.0731|0.0439|0.0446|

21
# **Introduction**

During the last decade, rising concerns about the price and security of fossil fuel supplies and global climate disruption, as well as the large absolute increase in electricity demand throughout the industrialized and developing world, contributed to a resurgence of interest in nuclear power. By the late 2000s, nuclear power was under serious consideration in over 45 countries which did not currently have it (WNA, 2011a).<sup>1</sup> Consideration for building new nuclear capacity was no longer off the table in developed countries like the United States, United Kingdom, and Australia. In fast growing developing economies like China and India, nuclear power became a central component of national energy policy. A "nuclear renaissance"--as it was termed in the press--seemed underway. As of June 2010, 61 units were under construction and around 500 additional reactors were under contract or in the planning stage (figure 1; Arthur D Little, 2010).

**Figure 1 Expected number of nuclear new build units (status June 2010)**

<!-- Start of picture text --> 562 61 61 106 334<br>Units<br>600<br>100%<br>11%<br>500<br>11%<br>400 19%<br>300<br>200 59%<br>100<br>0<br>Total years Construction Contracted Site selected Intended<br>to COD 1.) (≤5) (5 – 10) (10 - 15) (> 15)<br>1.) COD = Commercial Operation Date<br><!-- End of picture text --> _Source_ : Arthur D Little (2010).

An extremely strong record of global nuclear operations (including the absence of any high-profile incidents) over the past two decades led to shifts in perceptions about the 1 In Europe: Albania, Serbia, Croatia, Portugal, Norway, Poland, Belarus, Estonia, Latvia, Ireland, Turkey. In West, Central and Southern Africa: Nigeria, Ghana, Senegal, Kenya, Uganda, Namibia. In the Middle East and North Africa: Iran, Gulf states including UAE, Saudi Arabia, Qatar & Kuwait, Yemen, Israel, Syria, Jordan, Egypt, Tunisia, Libya, Algeria, Morocco, Sudan. In South America: Chile, Ecuador, Venezuela. In Central and Southern Asia: Azerbaijan, Georgia, Kazakhstan, Mongolia, Bangladesh, Sri Lanka. In SE Asia: Indonesia, Philippines, Vietnam, Thailand, Malaysia, Singapore, Australia, New Zealand. In East Asia: North Korea.

2
<!-- Start of picture text --> The gap between the rich and the poor in our country should be reduced<br>100%<br>20.6% 1S:6%569 14.0%:<br>26.8% 28.1% 27.8% 26.5%<br>80%<br>60% 77.3%<br>53.4% 55.8% 37.2% _—=36.5%<br>40% —<br>20% — — — — — — —<br>14.7%<br>ARM TIK KYG GEO RUS UKR MDA BLR<br>m Strongly disagree = Disagree Neither disagree nor agree m Agree m Strongly agree<br><!-- End of picture text -->
In this section, we present a survey of the newer literature, highlighting the key results and the methods used to address some of the biases and methodological flaws in studies before year 2000. Our survey of literature shows that while impact estimates (for different aspects of R&D interventions) tend to shrink compared to earlier, less rigorous studies, the effect of public investment in R&D still remains predominantly positive and significant.

Some of the sources of overestimations in studies about the impact of public support to business R&D can be explained by:

- Specification and Endogeneity : This factor is distinguished by the application of models from a structural- to non-structural-analytical perspective. The former implies that the outcome equation and the selection-into-program are separately modeled in a system of simultaneous equations, and encompasses macro or aggregated outcomes. The latter implies only the inclusion of outcomes for the purposes of analyzing specific sectors or types of firms.

- Data : Models are based on a cross-section dataset, pooled data and/or longitudinal datasets (allowing for dynamic and long-run analysis). Very few studies actually collect data for the purpose of answering specific questions about R&D impacts. The majority use existing survey data and/or administrative records.

- Policy Variables Assessed : Models using a binary policy variable (generally in the form of “subsidized” versus “non-subsidized” units), and models using the policy variable in levels (i.e., in a continuous form) have shown to be irrelevant as it is important to build a statistically robust counterfactual to make valid comparisons between firms. In addition, there are several unobserved attributes that cannot be separated from this variable, which may pose mixed or confounding conclusions.

- Identification Strategy : Papers that robustly evaluate the impacts of R&D by addressing the issue of causality. The majority of the studies used matching techniques, instrumental variables or sample selection correction as part of the non-experimental retrospective approach to evaluate. Very few studies have used regression discontinuity to identify

5
risk, _capital-grant_ contracts alleviate the financing risk. _Annuity-based_ contracts insulate the project company from the demand risk, but the company is still exposed to the full financing risk.

In 2012, an unusually high number of PPP contracts were awarded based on _premium payments_ and about half of them were canceled (figure 13). The data shows that this increase occurred partly because of a decrease in the development of annuity-based projects by the NHAI after 2011 and the bidding behavior of private sponsors. The decrease in the capital-subsidy contracts indicates that private sponsors were more optimistic about the projects offered in 2012 compared to the earlier toll-based projects.

# **Figure 13 Number of national highway public-private partnership projects in India by cancellation status, contract type and financial closure year**

<!-- Start of picture text --> 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014<br>Premium Capital grant Annuity<br> Not canceled Canceled<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source:_ PPI Database and National Highways Authority of India.

_Note:_ Concession agreement year is used if financial closure had not been achieved by the time of cancellation.

To assess which contract features could be associated with cancellations, the following equation is estimated using the maximum likelihood method: 0 p 1 � (7) ii ii,p ii where the dependent variable is the event of cancellation, CCPPaacceePPeeaa = ff�ββ + XX ββ + PP is the logistic function, and p 0 p 1 � is the logistic function, and p are project-specific variables. ii,p ff(. ) XX The contract characteristics are introduced in the regression in three different ways. First, they are introduced as indicator variables for each type of contract in columns (1) through (3) in table 4. Second, the net present value (NPV) of payments to the government is introduced as a continuous indicator of the net cost of the contract to the sponsor in columns (4) through (6).<sup>24</sup> Third, the net present value of payments is rescaled by the amount of investment in columns (7) through (9).

> 24 Annual discount rate of 8 percent has been assumed.

28