rafmacalaba/datause-encoder-data huggingface dataset:
- 49 -

coal have to be constructed. Finally transport costs for the associated infrastructure (depending on the existing facilities) must be added. Rates of return on oil to coal conversions are calculated for various oil/coal cost differentials and kiln sizes. The detail of this study, which takes into account the specific technology and costs of a particular industrial process confirms that in order to apply the methodology of identifying "switch" points it is necessary to have detailed information on the configurations and costs of all existing plants within an industry.

A Comparison of the Econometric and enainserina Anoroaches to Measuring Fuel Substitutability The purpose of this paper is to evaluate the use of various approaches to measuring the potential for fuel substitution in response to price related market instruments with a view to attainlng a lower level of pollution from the encouraged fuel. It is therefore necessary to be able to assess the degree of fuel substitutability brought about by a given price change and the associated change in pollution.

The econometric approach typlcally takes economy wide data on power, aggregate industry or particular industries and establishes the degree to which aggregate fuel substitution in the past is associated with price changes of the different fuels. The data requirements are several years' information on the prLces and volumes of the different fuels used. For more sophisticated models, in which allowance is made for changes ln the total demand for energy brought about by the impact of indivldual fuel prices on the aggregate price of energy, it is necessary to have information on the price and volume of capital and labor used. For developed countrieo such data are readily available, at least for industry as a whole and for the power sector. For developing countries the data is often likely to be lese comprehensive.

The key assumptions for econometric models are that all fuels are used (or at least all of a subset are always used) and that there is continuous substitut&bility between fuels Ln response to fuel price chcnges. models of fuel shares also ignore capital costs of fuel switching or assume that all
**Table 6: Full Adjustment Elasticities of Demand and Marginal Cost (asymptotic t-ratios in parentheses)**

|Input|Carbon<br>Price|<br>Coal &<br>Oil Price|Natural<br>Gas<br>Prices|Nuclear<br>Prices|<br>Renewab<br>le<br>Prices|Total<br>Output|
|---|---|---|---|---|---|---|
|Carbon|-0.068|-0.119|-0.056|0.052|0.191|2.055|
||(7.7)|(8.6)|(5.6)|(3.6)|(12.2)|(31.2)|
|Coal & Oil|-0.082|-0.127|-0.036|0.068|0.177|1.952|
||(7.3)|(7.3)|(2.8)|(3.8)|(10.7)|(20.8)|
|Natural Gas|-0.066|-0.061|-0.150|-0.022|0.299|3.067|
||(5.1)|(2.6)|(5.3)|(1.4)|(10.2)|(14.2)|
|Nuclear|0.042|0.058|0.054|-0.154|0.000|0.077|
||(12.1)|(13.9)|(12.4)|(16.5)|(2.5)|(17.3)|
|Renewable|0.312|0.539|-0.079|0.028|-0.800|0.413|
||(4.8)|(5.7)|(1.1)|(2.5)|(5.0)|(5.2)|
|Marginal Cost|0.211|0.338|0.284|0.041|0.009|0.239|
||(29.8)|(20.6)|(13.8)|(11.5)|(3.4)|(12.0)|

**Table 7: Full Adjustment Morishima Elasticities of Substitution**

**<u>(asymptotic t-ratios in parentheses)</u>**

|Input|Carbon<br>Price|Coal &<br>Oil Price|Natural<br>Gas Prices|Nuclear<br>Prices|Renewable<br>Prices|
|---|---|---|---|---|---|
|Carbon||-0.014|0.003|0.380|0.110|
|||(3.2)|(0.2)|(5.2)|(9.2)|
|Coal & Oil|0.009||0.066|0.667|0.185|
||(1.8)||(1.9)|(6.2)|(9.1)|
|Natural Gas|0.094|0.114||0.071|0.204|
||(3.0)|(3.1)||(0.8)|(6.7)|
|Nuclear|0.852|0.868|0.778||0.801|
||(4.9)|(4.9)|(4.8)||(5.0)|
|Renewable|0.345|0.331|0.453|0.182||
||(14.0)|(13.3)|(12.3)|(10.0)||

29
# **End Notes**

1. Concerns about the distributional impacts of the growth process have led to a debate. For a recent review, see Chaudhuri and Ravallion (2007).

2. This section draws on Kuijs (2005) and Kuijs and Wang (2006).

3. See Kuijs (2005) and (2009) for details.

4. The exact breakdown of the increase in China’s domestic savings in recent years is not yet fully clear, with the headline national accounts data in the flow of funds difficult to line up with data from the household survey, the industrial survey, and fiscal information. Large discrepancies have also appeared in the flow‐of‐funds data themselves, suggesting that the asset data imply higher enter‐ prise saving and lower household and government saving in recent years than the headline flow‐of‐ funds data shown in figure 2.

5. A 2003 World Bank report concluded that “service sector development suffers from restrictions and regulation and a lingering bias against private ownership.” The OECD (2005) saw similar room for improvement by removing entry and other barriers to the development of services industries.

6. This implication is from Aziz and Cui (2007).

7. The ratio of loans to GDP decreased from a peak of 130 percent in early 2004 to 92 percent in mid‐2008.

8. At the end of 2007, gross external reserves were reportedly almost 700 percent of short‐term external debt by remaining maturity.

9. Until the fourth quarter of 2008, the central bank had been pursuing a tight monetary policy to prevent a spiral of inflation expectations. These inflationary pressures were arising from food and commodity price increases and rising asset prices, and the tight monetary policy aimed to stop overheating in high‐growth sectors. Controls on new lending and credit ceilings (partially voluntary) were put in place.

10. The quick response of government spending partially reflected the fact that a large share of the program had already been drafted for reconstruction after the earthquake in May 2008. 11. Guo and N’Diaye (2009) show that maintaining the current export‐oriented growth over the medium to long term would require significant gains in market share through lower prices across a range of industries. This achievement, without consideration of the lower global demand following the current crisis, would necessitate higher productivity subsidies and lower profits. Evidence sug‐ gests it will be difficult to accommodate such price reductions. Furthermore, experiences from Asian countries also suggest that there are limits to the global share a country can attain. 12. Processing trade refers to importing all or part of the raw and auxiliary materials, parts and components, accessories, and packaging materials from abroad in bond, and reexporting the finished products after processing or assembly by enterprises in China.

28
84

# **_7.3.1  Risk Management Strategies: Market Risks_**

On the asset side of the balance sheet, market risks refer to fluctuations in the value of securities (e.g. stocks, bonds, real estate) as well as to changes in foreign exchange rates. On the liability side, the risk stems form the guaranteed minimum interest rate. To mitigate these risks and to additionally achieve an adequate investment performance, suitable investment strategies and derivatives are used. The following listing presents designated hedging instruments:

- Interest Rate Derivatives

- Interest Rate Swaps

- Currency Derivatives

- Currency Forward Contracts

- Equity Derivatives

As the most hedging strategies use derivatives, having access to a fully developed capital market is necessary to manage market risks efficiently. This condition is obviously satisfied for Switzerland.

# **_7.3.2  Risk Management Strategies: Longevity Risk_**

Pension funds have little scope to set the terms of the annuity contracts in the mandatory part of the system. (They are free to set annuity conditions in the non-mandatory part.) The risks related to the fixed conversion factor are incorrect assumptions about mortality and market returns. These risks can be substantial. If the conversion factor is set too high, it will be difficult to meet the obligations mandated by the law. This has important implications for the asset and liability management of a fund. High conversion factors may induce funds to choose investment strategies with a high expected return and thus more risk to compensate for this.

Almost all pension funds base their estimates on mortality tables provided by the Federal Insurance Fund (FIF), with possible adjustments based on past experience. Note that due to a relatively high degree of homogeneity among the annuitants of a given pension fund (construction workers, teachers, bank employees), the average life expectancy may well differ from the population average.

The implied sorting in the second pillar facilitates tailor-made adjustments of the benefit structure. A recent example in Switzerland is provided by the generous early retirement schemes for construction workers that can be financed to a large extent by a well below average life expectancy. As mortality rates are negatively correlated with lifetime income, the sorting of annuitants prevents extensive redistribution from the short-lived poor annuitants to the long-lived richer annuitants.

Most pension funds do not update their mortality rates on a yearly basis. Often liabilities are computed using the published FIF mortality rates and then augmented by 0.4
**Figure 13. The gender wage gap**

<!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Gender wage gap<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text -->

_Source:_ RLMS (Rounds VI to XXI) _Notes:_ all indicators have been calculated based on the subsample of currently employed wage workers in the 18-60 age group, reporting more than 140 worked hours in the last 30 days and with no missing data in wages. Hourly wages was trimmed at the bottom and top 0.25 percent in each round, to remove outliers. Wages for rounds VI and VII were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). The RLMS was not administered in 1997 and 1999.

Instead, regional differences, including rural/urban variation, may play a more important role in explaining changes in wage inequality. Lukiyanova (2006) finds that differences in the adjustment of wages across regions make up one of the main factors behind the spike in wage inequality following the financial crisis of 1998. In Brazil, the urbanization process and regional variables were responsible for about 15 percent of the reduction in wage inequality during the 1995-2012 period (Ferreira, Firpo and Messina, 2014).

Urban wages are typically higher than rural wages. However, in Russia the rural-urban wage gap has declined from 50 to 30 percent between 2002 and 2012 (Figure 14). Additionally, the share of rural workers out of total employment has increased during the same period, from 19 to 24 percent. The increase in the share of rural employment, coupled with an increase in the rural– urban wage ratio may contribute to explain the reduction in wage dispersion observed in Russia during the last decade.

16
**TABLE 5: Impacts on Parenting Quality - 18-Month Follow-Up**

|||**Pare**|**nting**|**D**|**ependent**<br>**Parentin**|**variable:**<br>**g Quality**|**Subcomp**|**Par**<br>**onents:**|**enting**|
|---|---|---|---|---|---|---|---|---|---|
|||**Qualit**|**y Index**|**Stimu**<br>**In**|**lation**<br>**dex**|**Pos**<br>**Practic**|**itive**<br>**es Index**|<br>**Stres**<br>**(Inv**|<br>**s Index**<br>**erted)**|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|T2 (careiver trainin)||-0.023|-0.018|-0.043|-0.022|-0.023|-0.028|0.018|-0.004|
|g g||(0.078)|(0.079)|(0.072)|(0.073)|(0.067)|(0.067)|(0.078)|(0.078)|
|T3 (T2 + careier incenties)||0.104|0.128|0.043|0.056|0.046|0.052|0.090|0.097|
|gv v||(0.081)|(0.082)|(0.085)|(0.085)|(0.067)|(0.063)|(0.076)|(0.077)|
|T4 (T2 + arentin trainin)||0.267***|0.258***|0.294***|0.294***|0.104|0.097|0.088|0.078|
|pg g||(0.074)|(0.073)|(0.075)|(0.073)|(0.064)|(0.061)|(0.072)|(0.073)|
|Bli Cl Vibl Bli|||0.247***||0.246***||0.179***||-0.135***|
|asene ontro arae (asene)|||(0.028)||(0.030)||(0.025)||(0.027)|
||T2=T3|0.109|0.072*|0.287|0.325|0.255|0.177|0.300|0.140|
|F-test for Equality of Parameters - p-value|T2=T4|0.000***|0.000***|0.000***|0.000***|0.024**|0.027**|0.281|0.213|
||T3=T4|0.032**|0.088*|0.002***|0.003***|0.327|0.415|0.980|0.757|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations||1,938|1,938|1,937|1,937|1,934|1,934|1,938|1,938|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the child level using baseline and 18-month data on Stimulation, Positive Practices, Parenting Stress Index and The Center for Epidemiologic Studies Depression Scale. Standard errors in parentheses are clustered at the CBCC level. The Parenting Stress Index had its score inverted so that a higher score is better. The variable referred to as 'baseline control variable' is the lagged (baseline) value of the dependent variable, except in column (8), where it is the baseline value of the Center for Epidemiologic Studies Depression Scale. We use all children for whom the dependent variable is available for the 18-month follow-up. We weight observations using sampling weights (and tracking weights for the 42 randomly tracked observations at the 18-month follow-up). For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

47
# **Supplementary Appendix References**

- 1 World Bank. World Development Indicators. 2019. https://databank.worldbank.org/source/world-development-indicators# (accessed 3 Mar 2020).

- 2 Global Burden of Disease 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 - ScienceDirect. _Lancet_ 2018; **392** :1859–922.

- 3 Global Burden of Disease 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 - ScienceDirect. _Lancet_ 2018; **392** :1789–858.

- 4 Leonard KL, Masatu MC. The use of direct clinician observation and vignettes for health services quality evaluation in developing countries. _Social Science & Medicine_ 2005; **61** :1944–51. doi:10.1016/j.socscimed.2005.03.043

- 5 Bold T, Svensson J, Gauthier B, _et al._ Service delivery indicators: Pilot in education and health care in Africa. Bergen, Norway: : Chr. Michelsen Institute 2011.

- 6 World Health Organization U. _Model IMCI handbook: Integrated management of childhood illness_ . 2005.

- https://www.who.int/maternal_child_adolescent/documents/9241546441/en/

- 7 Peabody JW, Luck J, Glassman P, _et al._ Comparison of Vignettes, Standardized Patients, and Chart Abstraction: A Prospective Validation Study of 3 Methods for Measuring Quality. _JAMA_ 2000; **283** :1715–22. doi:10.1001/jama.283.13.1715

- 8 Mohanan M, Vera-Hernández M, Das V, et al. The know-do gap in quality of health care for childhood diarrhea and pneumonia in rural India. JAMA pediatrics Published Online First: 2015. doi:10.1001/jamapediatrics.2014.3445

- 9 Leonard KL, Masatu MC. Variations in the Quality of Care Accessible to Rural Communities in Tanzania. _Health Affairs_ 2007; **26** :w380–92. doi:10.1377/hlthaff.26.3.w380

- 10  Das J, Hammer J, Leonard K. The Quality of Medical Advice in Low-Income Countries. _Journal of Economic Perspectives_ 2008; **22** :93–114. doi:10.1257/jep.22.2.93

- 11  World Health Organization. Service availability and readiness assessment (SARA). 2015. http://www.who.int/healthinfo/systems/sara_introduction/en/

67
**Figure 17**

# **Evolution of Dynamic Herding Coefficients**

This figure presents the evolution of the coefficient of the Sias (2004) herding regressions, that is, the regression of the probability of buying an asset at a moment in time on the lagged probability of buying an asset. Panel A and B present the evolution of the coefficients for Domestic Equity and Foreign Investment and Mutual Funds, respectively. The coefficients that are significant at the ten-percent level, according to the two-tailed t-test, are marked on the figure. Significant events that occurred during this period of time are also highlighted: the Asian crisis (July 1997), the Russian crisis (August 1998), the introduction of Fund E and the widening of the minimum return band (October 1999), and the establishment of the multi-fund regime (September 2002). Numbers represent percentages (results are multiplied by 100).

Panel A. Evolution of Dynamic Herding Coefficients - Domestic Equity (1996 - 2005) <!-- Start of picture text --> 80<br>Coefficient<br>Significant at ten percent<br>60<br>40<br>20<br>0<br>-20<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-40<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Herding Coefficient<br><!-- End of picture text --> Panel B. Evolution of Dynamic Herding Coefficients - Foreign Investment and Mutual Funds (1996 - 2005) <!-- Start of picture text --> 120<br>Coefficient<br>100<br>Significant at ten percent<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-100<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Herding Coefficient<br><!-- End of picture text -->
statistics and thus these statistics do not explicitly account for the increase in Internet use. In contrast, our regressions below explicitly examine the relationship between Internet use and paper consumption by seeking to gauge the causal effect of the increase in Internet use on aggregate paper consumption and on paper consumption disaggregated by category.

Our database includes information on yearly trends in Internet penetration by country in addition to data on paper consumption. These data come from the International Communication Union (ITU), a specialized agency for information and communication technologies from the United Nations. Table 2 presents summary statistics from our data on Internet penetration showing that Internet penetration has grown rapidly during our study period. The large standard errors in Table 2 suggest that the growth rate has been heterogeneous across countries (i.e., Internet penetration grew substantially faster in some countries than in others). The large variation in the Internet penetration growth is helpful for our identification strategy, which will essentially derive from comparing changes in paper consumption for countries with slow versus fast Internet penetration growth.

**Table 2. Yearly Evolution in the Internet Penetration Rate**

|**Year**|**Mean **|**Std. Dev. **|**Min **|**Max**|
|---|---|---|---|---|
|**1999**|18.56|14.03|1.86|41.49|
|**2000**|24.2|15.43|3.61|47.38|
|**2001**|28.34|17.20|4.54|56.6|
|**2002**|38.98|22.00|6.58|79.12|
|**2003**|44.6|22.06|8.9|83.14|
|**2004**|48.93|21.39|14.1|83.89|
|**2005**|52.77|21.18|17.8|87|
|**2006**|57.1|20.46|20|89.51|
|**2007**|61.2|18.62|22.2|90.6|
|**2008**|65.57|18.35|23.6|91.04|

Source: International Communication Union (ITU). In order to facilitate the interpretation of the temporal evolution in the growth of Internet this table uses data from a balanced panel including data from 24 countries. However, our econometric analysis below uses all the data in the unbalanced panel.

15
_1VA97._ ln( V ) _=,60_ + _Al 1 ln(IVA821 )_ + _Ih2_ ln(edu;) + _j3_ ln(I _/ GDP,)_ + _/ 4PRSJ_ + e, (3) _IVA82,_ where _ln(I/GDP)_ is the (natural logarithm of the) ratio of aggregate gross fixed investment to GDP. Estimates based on the above equation are reported in Table 2. Specifications 1 through 6 contain PRS variables 1 through 5 and a composite measure of those indexes in each respective equation<sup>4</sup> . With the exception of Institutional Quality indicators, all other variables used in the analysis are from the Statistical Information Management and Analysis (SIMA) database of the World Bank.

The coefficients of primary interest are all positive and comfortably significant. The coefficients on the (logarithm of the) investment rate are positive and significant, rejecting the hypothesis that the rate of capital accumulation are identical across countries. The choice of measure of human capital (or education) turned out to be a crucial one here. We hypothesized that unlike the case of economic growth in general, higher education is more important for industrial growth than secondary education. The results support our proposition. In most regressions the average Secondary School Enrollment ratio failed to provide the sings (and significance) of coefficients predicted by theory<sup>5</sup> . Owing to the lack of reliable data on High Education we included the (logarithm of the) Tertiary Enrollment rate as an explanatory variable to capture the effect of human 4 The composite measure of PRS variables included in the **6th** regression is a simple average of PRS variables lthrough 5. Note that including the PRS Composite measure in an equation is identical to including all 5 PRS variables into the regression and restricting their coefficients to be the same for all 5 variables.

_5_ In another attempt to improve the "performance" of educational indicators, we included Government Educational Expenses (reported by UNESCO) as an explanatory variable. Doing so improved the results but only marginally. The probable cause, we believe, is that (in poor countries in particular) educational expenses might be somewhat biased towards Secondary Education (and against Higher Education and subsequently R&D). Should this be the case the educational expenses would only give us very limited information about the true distribution of Education in the country and thus will not be able to capture the true effect of education on industrial growth.

10