rafmacalaba/datause-encoder-data huggingface dataset:
All indirect taxes combined lead to an increase in inequality, as presented in Figure 2. Panel (a) of Figure 11 presents the Kakwani coefficient and the marginal contribution to inequality of each indirect tax taken separately. We find that most indirect taxes are regressive, as shown by a negative Kakwani coefficient. Moreover, nearly all indirect taxes contribute to higher inequality, that is their marginal contribution to the Gini coefficient is negative. There is, however, substantial heterogeneity between taxes. Taxes on alcohol and tobacco, and food and beverages are the most regressive, and, in particular, the SCT on alcohol and tobacco has the largest unequalizing effect. The VAT on motor vehicles is slightly progressive but unequalizing.

The unequalizing effect of indirect taxes on income distribution in Turkey is significantly higher than in nearly all peer countries (panel (b) of Figure 11), even though Turkey’s indirect tax burden is not particularly high. Turkey ranks in the median in terms of revenues collected through indirect taxation, as a share of GDP.<sup>17</sup> Turkey’s indirect taxes are not among the most regressive either. On the one hand there are countries with a more regressive but less burdensome (less revenue) combination of indirect taxes than Turkey, like Russia, and on the other hand there are countries with a less regressive but more burdensome mix, like Brazil. In both cases, however, indirect taxes are less unequalizing than in Turkey.

# **Figure 11. Progressivity and redistributive impact of indirect taxes**

# **a. Indirect taxes in Turkey, 2016**

> 17 Revenue from indirect taxes, in percent of GDP: Turkey 10.3 Croatia 17.0 Poland 10.8 Russia 7.7 Brazil 14.5 Chile 9.8 Mexico 4.4 Argentina 15.3. Source: CEQ Data Center on Fiscal Redistribution.

29
# **TABLE** OF CONTENTS

|||Page No.|
|---|---|---|
|1.<br>INTR|ODUCTION.||
|2.<br>CURR|ENT ISSUES OF PUBLIC POLICY IN THE RAIL INDUSTRY<br>.....|..<br>2|
|2.1|The Proper Criterion for Adequacy of Revenues ..................|4|
|2.2|The Regulatory Problem ..................................|6|
|2.3|The Cost Allocation Problem ...............................|7|
|2.4|No System of Fully Allocated Costs Can Yield||
||Economically Efficient Prices ...............................|8|
|_2.5_|Long-run Marginal Cost and Pricing Efficiency ..................|9|
|2.6|Economically Efficient Pricing ..............................|11|
|2.7|Demand-based Differential Pricing ...........................|12|
|2.8|Ramsey Pricing-Efficiency and Equity .........................|13|
|2.9|The Stand-Alone Cost Constraint ............................|14|
|2.10|Stand-Alone Costs Provide Appropriate Protection||
||Against Excessive Rates ...................................|15|
|2.11|Efficient Pricing Does Not Require Regulatory||
||Control Over the Entire Rate Structure ........................|17|
|2.12|Contestability and the Scope and Structure of Regulation ....<br>.......|.<br>19|
|2.13|The Vertical Structure of Railroad Industry .....................|20|
|3.<br>THE|GENERIC OPTIONS FOR VERTICAL RAILWAY STRUCTURING<br>....|<br>21|
|3.1|Analysis of Monolithic Railway Option ........................|22|
|3.2|The Need for Restructuring ................................|23|
|3.3|Analysis of Structural Separation - Options 4 and 5 ....<br>...........|<br>23|
|3.4|Analysis of the Competitive Access Option .....................|26|
|3.5|Comparing Separation with Competitive Access ..................|28|
|4.<br>CONC|LUDiNG REMARKS .....................................|30|
<!-- Start of picture text --> non-B&R B&R<br>Dp<br>Faas Si<br>82% %<br>ED,<br>ROW<br>ME non-8&R 4M BER<br>ME Row<br><!-- End of picture text --> <!-- Start of picture text --> non-B&R B&R<br>11%<br>64% 5 ;<br>=)<br>ROW<br>ME non-8&R 4M BER<br>ME Row<br><!-- End of picture text -->
satisfactory in terms of normality of the residuals, heteroskedasticity and autocorrelation. For the first difference of log real GDP, statistics for normality and heteroskedasticity are satisfactory, but there is nevertheless some evidence of (higher-order) residual autocorrelation.

**Venezuela: Combined Model for First Differences of Logs of Real Money Balances, Real GDP and Inflation**

**_Key Summary Statistics (T=34)_**

|**Statistic**|**ALog**<br>**RealMl**|**ALog**<br>**Real**<br>**GDP**|**ALog**<br>**Inflation**|
|---|---|---|---|
|Normality(Bowman-Shenton):n_<br>_-22_|0.66|0.07|9.61|
|Skewness:**_s_**<br>_2_|0.42|0.02|6.66|
|Kurtosis:k_- X 2 _|0.24|0.05|2.94|
|Heteroskedasticity:H(1O)_- Flolo_|0.39|0.24|0.14|
|Autocorrelation (upto12'horder)|6.47|16.63|15.03|
|(Box-Ljung):Q(12,6)<br>6||||
|Autocorrelation(firstorder)|1.92|1.85|2.02|
|GoodnessofFit(w.r.t. seasonal mean)|0.70|0.55|0.55|
|GoodnessofFit (ordinaryR<sup>2</sup>)|0.84|0.74|0.26|
|AkaikeInformationCriterion|-3.62|-5.24|-4.88|
|BayesInforrnationCriterion|-1.87|-3.48|-3.13|

The statistics suggest (a) the non-normality of the residuals of the equation corresponding to the change in the log of the inflation rate, and (b) the presence of higher-order residual autocorrelation in the model for the change in the log of the inflation rate. Goodness of fit measures are otherwise also satisfactory, including with respect to the seasonal mean.

Finally, the results of a third model, taking as endogenous variables the first differences of the logs of real MI, real GDP and the rate of nominal currency 21 Money Demand in Venezuela, Mario A. Cuevas
10

# **_Advantages_**

Public allocation intends to promote equity objectives, that is, ensuring water supply to areas of insufficient quantity. It can protect the poor, sustain environmental needs, and provide a given level of water to meet minimal needs in the receiving sector. The physical allocation of water among the users is independent of the charge. Allocation rules in this case can be based on historical facts (such as prior rights), on equal shares in available water volumes, on individual requirements, or even based on political pressure.

The list in Box I details the characteristics of water that establish a role for government intervention and action in water allocation.

**Box** 1: The Case for Government Involvement in Water Management.

Water has several distinguishing features that can define a role for public action:

1. Large, lumpy capital requirements and economies of scale in water infrastructure tend to create natural monopolies, warranting regulation to prevent overpricing. Moreover, many water investments produce joint products, such as recreation, electric power, flood control, and irrigation, which make pricing and allocation decisions difficult.

2. The large size and extremely long time horizons of some investments, given underdeveloped capital markets and the potential for political interference in many water infrastructure investments, reduce incentives for private investments in the sector, 3. The uses of water within a river basin or aquifer are interdependent. Withdrawals in one part of the basin reduce the availability of water for other users; groundwater pumping by one user may lower the water table and increase pumping costs for all users; and pollution by one user affects others in the basin, especially those located downstream. These interdependencies suggest that having all users agree to the rules of the game--or lacking that, imposing government regulations, taxes, or both-could improve the social value of water resources.

4. Certain aspects of water activities, such as the control of floods and waterbome diseases, are (local) public goods, which cannot easily be charged for on the basis of individual use. In such cases, public initiative may be required to ensure that levels of investment are appropriate.

_5._ Water resources are often developed because of their strategic importance for national security and for regional development. Governments thus typically maintain ownership of water thoroughfares, providing services such as the coast guard and traffic regulation. Some regions are subject to periodic droughts. Because water is essential to sustaining life, governments may take control of water.

Source: World Bank 1993.

Box I states clearly that there is no single objective to a public water allocation mechanism. Although a national or state agency may consider MCP approach, public water allocation mechanisms are far more likely to be preoccupied with equity, sovereignty, and an overwhelm-
<!-- Start of picture text --> a. Test scores and growth b. Years of schooling and growth<br>(conditional on initial GDP per capita (conditional on the initial GDP<br>and years of schooling) per capito and test scores)<br>3 3<br>‘2©..i.é°.<br>1<br>‘a°eal. US ‘o: 1 ar. ; * ~ °<br>ra}a© a “ . ° a8 ‘ of ndMe to° “Wo .<br>3 -1 . 3: bd °<br>é<br>a<br>2 -2 é y=1=7.390.00 + 1.59x <2 -2 - y=120.82 0.00+ 0.07x<br>. R e055 R 0.02<br>-15 -! -5 ° 5 1 -2 -! ° 1 2 3<br>Test scores Years of schooling<br><!-- End of picture text -->
Chong, A., Karlan, D., and Valdivia, M. (2010). “Using Radio and Video as a Means for Financial Education in Peru.” Innovations for Poverty Action. Accessed: 14 June 2018. http://www.povertyactionlab.org/evaluation/using-radio-and-video-means-financial-educationperu.

Cole, S., Sampson, T., and Zia, B. (2011). “Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?” _Journal of Finance_ , Vol. 66, Issue 6: 1933-1967, December 2011.

Doi, Y., McKenzie, D., and Zia, B. (2014). “Who you Train Matters: Identifying Complementary Effects of Financial Education on Migrant Households.” _Journal of Development Economics_ , Vol. 109, Issue C, Pages 39-55.

Duflo, E., Hanna, R., and Ryan, S. (2012). "Incentives Work: Getting Teachers to Come to School." _American Economic Review,_ 102 (4): 1241-78.

Fernandes, D., Lynch Jr., J., and Netemeyer, R. (2014). "Financial Literacy, Financial Education, and Downstream Financial Behaviors." _Management Science_ , Vol. 60, No. 8: 1861-1883.

Galiani, S., Gertler, P., and Schargrodsky, E. (2008). “School Decentralization: Helping the Good Get Better, But Leaving the Poor Behind.” _Journal of Public Economics,_ 92(10):2106-2120.

Gertler, P., Patrinos, H., and Rubio-Bodina, M. (2012). “Empowering Parents to Improve Education: Evidence from Rural Mexico.” _Journal of Deveopment Economics,_ 99(1).

Hanushek, E., Link, S., and Woessmann, L. (2013). “Does School Autonomy Make Sense Everywhere? Panel Estimates from PISA.” _Journal of Development Economics,_ 104:212-232.

McCarthy, B. (1994). “Learning Differences: Designing Instruction with the 4MAT System.” _The Video Journal of Education_ .

18
Different earnings risks. While the wage growth of civil servants can be represented by a fixed income instrument, the wage growth of a hedge fund manager would resemble more an equity instrument.

Different life expectancy. People with high incomes tend to have higher life expectancy than people with low incomes. Women also have longer life expectancy than men.

Different family composition. The underlying portfolio of an annuity is different depending on the age profile of the dependents. For example, two males of the same age with one with spouse of the same age, and the other with a spouse 10 years younger, would require a different portfolio composition (in the presence of survivor pensions) if they aim to buy joint annuities to provide benefits for their spouse. The second one would require a longer duration of the fixed income portfolio at retirement age to reflect the fact that the annuity provider will consider the longer life expectancy of the spouse.

While gender is a variable known ex-ante, many of the other variables are not known until later in a person’s working life. It would be ideal to have data on the years of education of individuals, as it is an important variable explaining future income. While gender, education and other socioeconomic variables may help to explain the different income profiles of individuals, policy makers would need to understand the trade-off between simplicity and comparability with the number of benchmark portfolios that would need to be made available to individuals.

Grouping individuals would require strong assumptions and consequently the resulting pensions might not be optimal for each particular person, but overall the portfolio optimization would be better compared with the alternative of pension portfolios invested in short term assets.

As described in Rudolph and de la Torre (2016), it is also important to keep in mind that these portfolios should be considered as default options, and that individuals should be allowed to invest in different alternatives.

# **III. Designing benchmark portfolio in open pension systems**

Designing a benchmark portfolio scheme in an open pension system is not as simple as putting in place different asset allocations for the pension funds. It is essential to have an objective, a tool, and a framework that ensures that the benchmark is resilient to market fluctuations.

Three elements need to be taken into consideration at the time of designing a benchmark portfolio scheme in a defined contribution pension system. These elements, which will be used to guide the asset allocation of pension funds, are:

- a. A clear objective for the pension fund system;

- b. A governance structure for building the benchmark portfolio;

- c. An appropriate methodology for building the benchmark portfolio.

9
# **5.3 Where are we headed?**

Above, we have reviewed different measurement methods for capturing women’s ability to act on their goals, and findings to date on their relative quality. Four key priorities for future work emerges from this review: 1) continuing work on cross-reporting (differences in spouses’ or household members’ reported decision-making roles) and its consequences for understanding women’s agency, 2) exploring varying response options for decision-making questions across domains, 3) capturing the process leading up to final decision-making, and 4) triangulation across the reviewed measurement methods.

First, future research should continue exploring what we can learn from cross-reporting within the household about women’s ability to act. This will include both conducting survey work with multiple members within a household, and continuing the exploratory analytical work presented above. The latter will help shed light on how consistent correlations are between spousal disagreement and particular socioeconomic/demographic characteristics across contexts, and what we can learn about the decisionmaking process through them. Crucially, studies in Asia offer tentative evidence that men and women display different cognitive understandings of decision-making questions, guided by social norms about who ought to control decisions or differences between spouses in perception and identity (Ghuman et al., 2006, Devine et al., 2008). For example, men and women could be using different frames of reference when answering the same question (as discussed for sense of agency in Section 4). Moreover, beyond correlations, it will be important to understand to what extent disagreement over decision-making roles matters for outcomes. For example, how does the relationship between a woman’s decision-making power and outcomes like income, education and nutrition change when she herself vs. the husband is assessing her decision-making power, or when she assigns more power to herself than does her husband, and what does this tell us about decision-making processes in the household? Conducting this work with surveys containing questions on couples’ communication and community norms will be key.

Second, the consequences of using a uniform response structure across domains in decision-making questions, versus ones that are more tailored to decision-making processes specific to those domains, should be explored. For specific domains that are important to program implementers or researchers, valuable information might be missed by not tailoring decision-making questions to the actual process, which could be joint but “sequential” for some domains. For example, Kabeer (1999) cites a 1996 study from the Arab Republic of Egypt, where men were typically the final decision maker over whether to use contraceptives, but women were left to decide what type to use. Moreover, which response options are relevant for decisionmaking questions might vary across domains and contexts. For example, we may be missing valuable information about collective decision-making arrangements by allowing the “joint” decision-making 50
23

|Scenario 7. Increasing retirement ages to 65 w|ith repla|cement ra|tio practi|cally cons|tant||||
|---|---|---|---|---|---|---|---|---|
|1995|1996|1997|1998|2000|2005|2010|2020|2030|
|PAYG Pension Fund (% of formal GDP)|||||||||
|Payroll contributions<br>8.43|9.22|9.63|9.87|10.17|10.77|11.13|11.66|11.75|
|Total expenditure<br>8.48|9.64|10.14|10.36|10.29|8.69|8.29|9.00|9.24|
|Fund balance<br>-0.06|-0.42|-0.51|-0.49|-0.12|2.08|2.84|2.67|2.51|
|Average replacement ratio<br>28.6%|29%|30%|31%|31%|31%|31%|31%|31%|
|Dependency ratio<br>110.8%|117%|114%|110%|103%|83%|77%|81%|80%|
|Share of formal sector employment<br>59%|59%|59%|60%|60%|60%|60%|60%|60%|
|Note: Retirement age increases by one year each yea|r from 1997|to 2001 for|men, to 20|06 for wom|en.||||

|Scenario 8. Increasing retirement ages to 65 w|ith declin|ing forma|l sector a|nd consta|nt replac|ement ra|tio||
|---|---|---|---|---|---|---|---|---|
|1995|1996|1997|1998|2000|2005|2010|2020|2030|
|PAYG Pension Fund (% of formal GDP)|||||||||
|<br>Payroll contributions<br>8.43|9.22|9.63|9.80|9.88|9.29|9.27|9.63|9.64|
|Total expenditure<br>8.49|9.20|9.97|10.39|10.41|9.23|8.91|9.66|9.93|
|Fund balance<br>-0.06|0.02|-0.34|-0.59|-0.53|0.07|0.37|-0.03|-0.29|
|Averagereplacementratio<br>28.6%|28%|29%|31%|31%|31%|31%|31%|31%|
|Dependency ratio<br>110.8%|117%|114%|111%|108%|103%|99%|105%|105%|
|Share of formal sector employment<br>59%|59%|59%|59%|57%|49%|46%|45%|45%|
|<br>Note: This is a "what-ir scenano in the sense that no r|ationale is p|rovided for|dedining f|ormal sect|or.||||

|Scenario 9. Increasing retirement age|s to 65 (<br>|with elimi<br>|nation of<br>|early reti<br>|rements)<br>|and Incre<br>|asing rep<br>|lacement<br>|ratio<br>|
|---|---|---|---|---|---|---|---|---|---|
||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.22|9.62|9.84|10.12|10.68|11.01|11.51|11.61|
|Total expenditure|8.49|9.64|10.60|11.21|11.84|12.45|11.90|12.76|13.38|
|Fund balance|-0.06|-0.43|-0.98|-1.37|-1.73|-1.77|-0.89|-1.25|-1.76|
|Average replacement ratio|29%|29%|31%|34%|38%|47%|48%|47%|49%|
|Dependency ratio|111%|117%|113%|108%|99%|80%|72%|75%|75%|
|Share of formal sector employment|59%|59%|59%|60%|60%|61%|62%|61%|61 %|

Note: Retirement age increases by one year each year from 1997 to 2001 for men, to 2006 for women.

All three simulations show that raising the retirement age has a significant financial impact. However, it would not generate sufficient savings to compensate for the additional expenditures that the desired increase in the overall level of pensions relative to wages would
and credit nations (see the Appandix for data sources). Multiple creditor loans are included if the debtor country imposes a uniform withholding tax rate on all outgoing bank interest payments.

Table 1 breaks down the loans by year of origination, and provides information on average interest spreads and withholding taxes for loans originated during the year. Spread is the spread above LIBOR written into the loan contract, while the variable S is constructed as this contractual spread divided by the 6-month LIBOR rate during the month of loan origination. The LIBOR rate proxies for the bank's cost of funds i*. Note that 166 of the loans were contracted in the years 1977 and 1978 alone. Note also that the average withholding tax rate has tended to fall over time. This trend is not the result of tax rate reductions in the borrower countries. Rather, countries with high withholding taxes, such as Brazil, were able to borrow in the early 1970's, while other countries had to wait till the mid and late 1970's to gain access to international bank credit. Also note the countercyclical pattern in the spread and S variables: they are low before and rise after both the 1973 and 1979 oil crises.

Table 2 provides summary data on loans for each of the 14 borrower countries in the data set. Among the major borrower countries, Korea and Mexico, with high withholding tax rates, generally received low spreads.

Chile, Costa Rica and Honduras, with lower than average tax rates, contracted higher than average spreads. Brazil, with a high withholding tax rate of 25 per cent, received an average spread somewhat above the overall sample mean of 1.51, perhaps reflecting non-tax risk factors.

Table 3 breaks down the set of loans by creditor country. The major creditors are the United State and the United Kingdom with, between the two, 10
**<u>B. By education of the household head (for inequality measured by consumption per capita)</u>**

||**La**|**tvia**|**Hun**|**gary**|**Pol**|**and**|**Rom**|**ania**|**Ru**|**ssia**|**Mol**|**dova**|**Taji**|**kistan**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1998|2002|1993|2002|1998|2002|1998|2002|1997|2002|1998|2002|1999|2002|
|**Theil entropy measure**|**0.198**|**0.254**|**0.149**|**0.126**|**0.206**<br>**Decomp**|**0.217**<br>**osition:**|**0.167**|**0.178**|**0.201**|**0.181**|**0.240**|**0.209**|**0.142**|**0.189**|
|**Decomposition:**<br>**_of which between education_**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|
|<br>**_group_**<br>Of which within primary|**_7%_**|**_22%_**|**_11%_**|**_12%_**|**_13%_**|**_26%_**|**_11%_**|**_20%_**|**_2%_**|**_5%_**|**_8%_**|**_8%_**|**_4%_**|**_4%_**|
|<br>education|15%|9%|23%|19%|18%|14%|32%|25%|5%|2%|24%|21%|28%|22%|
|Of which within second education<br>Of which within vocational|54%|34%|5%|5%|26%|19%|25%|25%|31%|29%|32%|38%|24%|35%|
|education<br>Of which within tertiary|4%|6%|38%|40%|24%|21%|19%|18%|39%|37%|23%|18%|27%|18%|
|<br>education|20%|28%|22%|23%|19%|20%|13%|13%|22%|27%|14%|15%|17%|21%|
||||**Theil en**|**tropy in**|**dex for**|**per capit**|**a consu**|**mption**|||||||
|Within primary education|0.158|0.171|0.136|0.105|0.165|0.181|0.155|0.145|0.256|0.159|0.219|0.166|0.126|0.202|
|Within secondary education|0.189|0.189|0.110|0.095|0.184|0.130|0.139|0.138|0.193|0.176|0.237|0.214|0.141|0.175|
|Within vocational education|0.267|0.195|0.113|0.109|0.163|0.158|0.139|0.139|0.207|0.177|0.220|0.189|0.136|0.176|
|Within tertiary education|0.183|0.224|0.188|0.125|0.208|0.195|0.169|0.153|0.176|0.164|0.203|0.189|0.148|0.178|
||||||**Populatio**|**n shares**|||||||||
|Primary education|23%|21%|30%|28%|24%|22%|40%|39%|5%|3%|31%|30%|34%|22%|
|Secondary education|58%|48%|7%|7%|27%|28%|28%|33%|40%|39%|35%|40%|26%|42%|
|Vocational education|4%|11%|52%|49%|38%|37%|25%|20%|34%|34%|23%|19%|27%|18%|
|Tertiary education|15%|19%|12%|17%|11%|13%|8%|8%|22%|24%|11%|11%|13%|18%|
||**R**|**eal mea**|**ns, relati**|**ve to na**|**tional me**|**an per c**|**apita co**|**nsumptio**|**n=1.00**||||||
|Primary education|0.814|0.644|0.844|0.824|0.827|0.789|0.884|0.789|0.902|0.794|0.842|0.866|0.922|0.919|
|Secondary education|0.980|0.944|1.138|1.079|1.150|1.263|1.084|0.981|0.904|0.880|0.922|0.930|0.942|0.902|
|Vocational education|0.902|0.761|0.962|0.962|0.817|0.692|0.915|1.123|0.997|0.986|1.092|1.061|1.034|1.108|
|Tertiary education|1.377|1.669|1.483|1.365|1.619|1.647|1.621|1.804|1.174|1.217|1.502|1.514|1.239|1.221|

26