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

Amstrong, M., S. Cowan, and J. Vickers (1994). _Regulatory Reform - Economic Analysis of British Experience,_ MIT Press, Cambridge, Mass.

Baumol, W.J. (1977). "On the Proper Cost Tests for Natural Monopoly in a Multiproduct Industry". _American Economic Review, 65, 5,_ pp. 810-822.

Baumol, W. J.; J. C. Panzar and R. D. Willig (1982). _Contestable Markets and the Theory of Industry Structure._ New York: Harcourt Brace Jokanovich.

Borts, G.H. (1960). "The Estimation of Rail Cost Functions", _Econometrica,_ 28, pp. 108-131.

Boyer, K.D. (1997), _Principles of Transportation Economics._ Addison-Wesley. Reading, Mass.

Bradsaw, B. (1996). "The Privatization of Railways in Britain", _Japan Railways and Transport Review,_ 30, pp. 15-21.

Braeutigam, R. (1980). "An Analysis of Fully Distributed Cost Pricing in Regulated Industries", _The Bell Journal of Economics,_ 18, pp. 182-196.

BIE. (1995a), Bureau of Industry Economics). _Rail Freight 1995. International Benchmarking,_ Report 95/22, Canberra, Australian Government Publishing Service.

BIE (1995b), Bureau of Industry Economics. _Issues in Infrastructure Pricing,_ Research Report 69, Canberra, Australian Government Publishing Service.

Button, K. J. (1993). _Transport Economics._ Edwar Elgar Publishing Limited. New York.

Button, K.J. (1994), "Privatization and Deregulation: Its Implications for Negative Transport Externalities", _The Annals ofRegional Science,_ 28, pp. 125-138.

Carbajo, J.C. and A. Estache (1996), "Railway Concessions - Heading down the Right Trac,k in Argentina", _Viewpoint,_ 88. Finance and Private Sector Development Department, The World Bank, Washington D.C.

Carbajo, J.C., Estache, A. and D. Kennedy (1997), "Regulating the Quality of Privatized Transport Services", mimeo., The World Bank, Washington D.C.

CEMT (1996). _Evolution des Transports._ OECD Countries (1970-1994). Paris.

De Rus, G (1998). _Price Regulation in Transport._ First International Training Program. Privatization and Regulation of Transport Services. January 5-13. Washington, D.C.

- 63 -
Policy Research Working Paper 9270

# **Abstract**

Hardly any literature exists on the relationship between equivalence scales and poverty dynamics for transitional countries. This paper offers a new study on the impacts of equivalence scale adjustments on poverty dynamics in the Russian Federation, using equivalence scales constructed from subjective wealth and more than 20 waves of household panel survey data from the Russia Longitudinal Monitoring Survey. The analysis suggests that the equivalence scale elasticity is sensitive to household demographic composition. The adjustments for the equivalence of scales result in lower estimates of poverty lines. The study decomposes poverty into chronic and transient components and finds that chronic poverty is positively related to the adult scale parameter. However, chronic poverty is less sensitive to the child scale factor compared with the adult scale factor. Interestingly, the direction of income mobility might change depending on the specific scale parameters that are employed. The results are robust to different measures of chronic poverty, income expectations, reference groups, functional forms, and various other specifications.

This paper is a product of the Development Data Group, Development Economics and the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Subject expenditures (% of endowment)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Loans are NOT available Loans are available<br>Expenditures (% of endowment)<br><!-- End of picture text -->

**Figure 6: Subject expenditure by treatment and income strata – Conspicuous consumption treatments**

Note: The error bars reflect mean the standard error of the mean. The figure reports data from the conspicuous consumption treatments (unequal endowments with observability) with and without loans.

To establish the effects of this finding on inequality, we construct a measure of inequality as an individuals’ income as a share of the group’s total income.<sup>17</sup> We construct this measure in two ways, once before the consumption phase (which is the initial relative income directly attributable to the effort task), and once after the consumption phase (which reflects both inequality due to the effort task as well as the consumption and borrowing decisions). The difference between these two measures gives us the change in income share due to consumption decisions (purchasing and interest payments). Figure 7 displays the change in income share in our conspicuous consumption treatment, broken out by income strata, with and without loans. When loans are not available, the income shares of all income groups generally do not significant change after consumption. However, when loans become available, the income shares of poorer strata decrease, implying worsening inequality. For the lowest income strata, the availability of loans significantly reduce their income share ( _p<0.05_ ). We observe no significant reductions in any other income strata, with the third highest income strata lower, but not significant ( _p=0.349_ ), the second highest income group showing a significant increase in income share ( _p<0.05_ ), and the highest income strata displaying an increase, but this is not significant ( _p=0.679_ ). What is clear, however, is that due to the lowest income group borrowing to signal status, loans exacerbate inequality.

> 17 For example, persons 1 and 2 have 200 tokens each, persons 3 and 4 have 300 tokens each. Persons 1 and 2’s income as a share of the whole group’s income is 20%; persons 3 and 4’s income as a share of the whole group’s income is 30%.

23
|Male#Pay Group 5|-1.088***<br>(0.025)|-1.088***<br>(0.025)|-1.108***<br>(0.034)|
|---|---|---|---|
|**_Random assignment age_**||||
|Age 35|0.029**|0.029**|0.032*|
||(0.014)|(0.014)|(0.018)|
|Age 45|0.044***|0.044***|0.060***|
||(0.014)|(0.014)|(0.017)|
|Age 55|0.052***|0.052***|0.057***|
||(0.014)|(0.014)|(0.018)|
|Female Respondent||-0.007||
|||(0.018)||
|Constant|4.032***|4.036***|4.010***|
||(0.024)|(0.026)|(0.032)|
|Sample||||
|Observations|23,880|23,880|14,008|
|R2|0.303|0.303|0.314|
|Adjusted R2|0.303|0.303|0.314|

notes: Standard error in parenthesis; .01 - ***; .05 - **; .1 - *; Columns 1-3 report results of ordinary least squares regression; Dependent variable: 1=Severely overpaid, 2= Somewhat overpaid, 3= Fair, 4=Somewhat underpaid, 5=Severely underpaid; Controls for survey round not shown. Base category for gender is female, Base category for age is 25, Base category for pay is the lowest pay.

**Figure 4: Coefficients of interacted treatments for age and pay group**

<!-- Start of picture text --> Decrease in "too low," increase in "too high" Increase in "too low," reduction in "too high"<br>0.40 0.25<br>0.20<br>0.00 0.20<br>-0.20<br>-0.40 0.15<br>-0.60<br>-0.80 0.10<br>-1.00<br>0.05<br>-1.20<br>-1.40<br>0.00<br>Female Male Female Male<br><!-- End of picture text --> _Notes: change relative to the missing category (the missing blue column)._ Broad occupational categories corresponding with common groupings of “blue” and “white” collar occupations were deliberately split evenly in the experimental treatment. All respondents were given vignettes for an equal number of both white- and blue-collar occupations. Blue collar occupations included store clerk, farm worker, receptionist, and taxi driver. White collar occupations included software engineer, manager, medical doctor, and schoolteacher. The results provided in table 5 (column 1) show that respondents were significantly more likely to say that vignette subjects were underpaid (or 12
POi-icY RFSEAR wH WXORKING PAPiR 2278

# Summary findings

Budina. Garrersen, and de _jfong_ use firm level data on Bulgaria to investigate the impact ol liquidlity constraints on firms' investment performance.

Internal funds are an important determinanit of investmnent in rnost industrial economies. The authors use a simple accelerator model of investment to test wshether liquidity constraints are relevant in Bulgaria's case. Their estimates are based on data for 1993-95, before Bulgaria's financial crisis of 1 996-97.

It turns out that Bulgarian firms are liquidityconstrained and that firms' size and financial structure help to distinguish between firms that are inore and less liquidity-constrained.

In the authors' view, liquidity constraints in transition economies should be interpreted in different ways than those in indusstrial economies. In Bulgaria, liquidity constraints and hence access to external funds should be seen in the context of soft budget constraints and the financial system's failure to enforce the efficient allocation of funds.

The relationship between liquidity constraints and firm characteristics may actually be thie opposite of what is nurmally the case in industrial countries. In Bulgaria, lack of liquidity constraints may be a sign of financial weaklness.

This paper - a prodtict of Macroeconomics and Growth. Development Research Group - is part of a larger effort in **the** group to study transition economies. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactNina BEudina, room MC3-353, telephone 202-458-2045, fax 202-522-3518, emrail address obudina(oiworldbank.org. Policy Researclh Working Papers are also posted on the Web at www.worldbank.org/ research/ivorkingpapers. The other authors may be contacted at h.garretsen(Wjbw.kun.nl or e.dejong(@xbw.kun.nl. January 2000. (28 pages)

> _The Policy Research Workinmg Pater Series dissemninates the fin dings of uork in progress to encourage the exchange of ideas about tdeveloposent issnes. .An obective of the series is to get the find ogs or!toquickly, even if the presentations are less than fully_ **_polished. The_** _paperscarry the nanes ofthe authors oand sbhold he cited accordingly. The findings, interpretations. and conclutsionts expressed in this cnIontries eaper are they represent. entirely those of the anthors. They do not necessarily represent the iwew of the World Bank, its_ **_Execuotive Directors, or the_** I

Produced bv the Policy Research Dissemination Center
-32-

46 Asian economies. The table shows that the strong positive correlation between per capita growth and savings and investment rates. To ,,catch up" with the EU, Hungary would need to produce higher growth rates than Greece, Ireland, Portugal, or Spain. Saving rates of 11% (the level reached in 1994) will not be sufficient to attain this target.<sup>47</sup>

**Table 10: Growth, Savings, and Investment in Selected Countries**

||Real GDP Growth Per<br>Capita,<br>1985 - 92|National Saving<br>(% of GDP),<br>1985 - 91|Investnent<br>(% of GDP),<br>1985 - 91|
|---|---|---|---|
|Chile|5.1|25.9|24.9|
|South Korea|9.2|35.8|_33.5_|
|Malaysia|5.1|33.7|27.9|
|Singapore|5.7|42.3|33.1|
|Thailand|7.4|28.8|33.8|
|Greece|0.8|14.9|19.2|
|Ireland|3.7|17.0|17.6|
|Portugal|3.2|20.4|28.5|
|Spain|2.8|21.6|23.3|
|Czech Republic (1994)|2.8|21.1|20.0|
|Poland (1994)|4.8|18.0|19.0|
|Hungary (1994)|2.7|11.6|21.0|

# Source: SachslWarner, 1996.

Can the tax regime contribute to higher national savings? Neither theoretical analysis nor empirical studies provide a definite answer to this issue. It depends on the impact of several effects which can counteract each other. The income effect of a reduction of the tax rate will be negative (households need to save less to attain a given post-tax capital income) but the substitution effect positive (savings are increased because opportunity costs decreased). Low tax rates might lead to higher net savings, because incentives to deduct interest expenses against high marginal tax rates will be reduced. But even if household savings increase, lower tax rates might reduce government revenue, increase deficits and, hence, reduce national savings.

46 National saving equals public plus private saving. Since catching up to, at least, reach the level of the _less wealthy_ member countries of the EU is the primary issue, we concentrate on a comparison with Greece, Ireland, Portugal, and Spain.

47 Hungarian saving rates increased between 1989 - 1992 (mainly due to precautionary savings), but fell again thereafter. Even in 1992, when savings reached their peak at 13% of GDP, the rate was still considerably lower than in the Asian economies (see Monthly Report of the National Bank of Hungary, 2/1994). Empirical studies show a strong positive and robust relationship across countries between savings and growth. However, this does not imply any direction of causality: whether savings drive growth, growth drives savings, or both simultaneously. (For recent reviews, see Schmidt-Hebbel/ Serven/ Solimano, 1994, and Deaton, 1995).
points to a key disadvantage in relying on informal credit networks: the risk of being vulnerable to fraud and the consequent losses.

The current study has implications for anti-fraud policy. Current policies on combating fraud victimization emphasize anti-fraud campaigns and increasing financial literacy, or more generally in the case of containing crimes, strengthening law enforcement and improving the legal environment were thought to be effective to defeat crimes (Ehrlich, 1996; Levitt, 1997; Di Tella and Schargrodsky, 2004). We provide complementary perspective and evidence that policies focusing on the provision of financial services and credit to households may be as important. When encountering credit-related fraud schemes, sufficient access to credit provided to households would greatly reduce the risk of exposure to fraud schemes and allow these households to exhibit more patience—or be less subject to the temptation of quick payoffs.

# **References**

- Alves, L. M., & Wilson, S. R. (2008). The effects of loneliness on telemarketing fraud vulnerability among older adults. Journal of elder abuse & neglect, 20(1), 63-85.

- Anderson, K. B. (2007). Consumer fraud in the United States: The second FTC survey. Washington, DC: Federal Trade Commission.

- Anderson, K. B. (2013). Consumer fraud in the United States, 2011: The third FTC survey. Washington, DC: Federal Trade Commission.

- Aneja, A. P., & Avenancio-León, C. F. (2019). No Credit For Time Served? Incarceration and Credit-Driven Crime Cycles, working paper.Angrist, Jushua, and Jorn-Steffen Pischke. 2009. _Mostly Harmless Econometrics_ . Princeton: Princeton University Press.

- Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. Quarterly Journal of Economics, 121(2), 635-672.

- Beshears, J., Choi, J. J., Harris, C., Laibson, D., Madrian, B. C., & Sakong, J. (2015). Self control and commitment: can decreasing the liquidity of a savings account increase deposits? (No. w21474). National Bureau of Economic Research.

- Bisin, A., & Hyndman, K. (2020). Present-bias, procrastination and deadlines in a field experiment. Games and economic behavior, 119, 339-357.

Brenner, L., Meyll, T., Stolper, O., & Walter, A. (2020). “Consumer fraud victimization and financial well-being”. Journal of Economic Psychology, 76, 102243.

- Busse, M. R., Knittel, C. R., & Zettelmeyer, F. (2013). Are consumers myopic? Evidence from new and used car purchases. American Economic Review, 103(1), 220-56.

- Button, M., Lewis, C., & Tapley, J. (2009). Fraud typologies and the victims of fraud: Literature review, National Fraud Authority report.

26
What Are the Impacts of Syrian Refugees on Host Community Welfare in Turkey? A Subnational Poverty Analysis Joao Pedro Azevedo Judy S. Yang Osman Kaan Inan December 7, 2015 The World Bank<sup>1</sup> JEL Codes: F22, I3, O15, R23 Keywords: Turkey, Syria, Poverty, Refugees, Forced Migration This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Social Protection and Labor Global Practice and the Social, Urban, Rural, and Resilience Global Practice. The team thanks Martin Raiser, William Wiseman for their guidance and support. We are also thankful for comments and assistance with data received from JoAnna P. De Berry, Ximena Del Carpio, Doreen Triebe, Zeynep Durnev Darendeliler, Jose Montes, and Minh C. Nguyen. The usual disclaimer applies. This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice. This piece is part of a collaboration with the Urban, Rural, and Social Development GP supporting Turkey’s Regional Development Strategy (RDS) 2014-2023.

> 1Contacts: Joao Pedro Azevedo (Lead Economist, jazevedo@worldbank.org); Judy S. Yang (ET Consultant, jyang4@worldbank.org); Osman Kaan Inan (Junior Professional Associate, oinan@worldbank.org)

> This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Social Protection and Labor Global Practice and the Social, Urban, Rural, and Resilience Global Practice. The team thanks Martin Raiser, William Wiseman for their guidance and support. We are also thankful for comments and assistance with data received from JoAnna P. De Berry, Ximena Del Carpio, Doreen Triebe, Zeynep Durnev Darendeliler, Jose Montes, and Minh C. Nguyen. The usual disclaimer applies.  This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice. This piece is part of a collaboration with the Urban, Rural, and Social Development GP supporting Turkey’s Regional Development Strategy (RDS) 2014-2023.
The wetness index has surprising opposite effects in the probit and conditional on positive equations that are both statistically significant.

# **_3.2  Robustness_**

Table 5 considers several variants on the equations in Table 4. First, we consider two alternative measures of intensity of dam building activity: the total reservoir capacity and the total height of dams in the subbasin country area.<sup>17</sup> The estimated coefficients with these new dependent variables are similar to those for the counts of dams: the presence of a different downstream country in the basin raises the intensity of dams. The coefficients are only weakly statistically significant for both measures.

Column 3 of Table 5 returns to using the count of all dams, but excludes areas with no downstream subbasins. These areas are on a coast and can never constitute a shared water resource, by our definition. Without these coastal areas, the point estimate on the presence of a downstream country remains similar in magnitude but is only weakly significant. The coefficient on the count of downstream basins rises to very nearly zero with these coastal areas excluded, so the tendency we observe for dams to be placed in downstream areas seems to be explained by a higher density of dams near the coast.<sup>18</sup> 17 Both measures are sums of the respective values across all dams in the subbasin-country area. The GRanD project calculated reservoir capacity, so these data are available for all but a handful of dams. By contrast, the dam heights are missing for 7% of the dams. These missing heights are treated as zero as a conservative assumption. Reservoir capacity has a very dramatic upper tail, so a few observations may be very influential in these equations.

18 Some dams lie on the border between two countries and thus the two countries must coordinate to exploit the resource. With our coding system, areas where rivers form the border will almost always be coded as having a different country downstream. If free riding is less likely on border rivers because of the need for cooperation in dam construction, the presence of dams on border rivers will therefore introduce a conservative bias into our estimates of the extent of free riding.

13
_Shareholder Rights_ is a measure of shareholder rights protection in the country. It is an index of ‘anti-director” rights ranging from 0 to 6 from LLSV (1998) which is formed by aggregating six important shareholder rights. _Law and Order Score_ is a measure of the law and order tradition in the country ranging from 1 to 10 from the International Country Risk Guide. _Judicial Efficiency_ is another index scored 0 to 10 from the country-risk rating agency Business International Corporation that is an assessment of the efficiency of the legal environment from the investors’ perspective.

On all three measures, I find that firms need a larger portion of cash flow rights to control 100% of the firm in countries with good legal rules and enforcement. In comparison, the ratios are significantly lower for cross-listing firms from countries that are not protective of investors.

In addition to shareholder protection variables, I investigate if the ratio of cash flow to voting rights in cross-listing firms also varies with the level of financial development of their home country. Lins, Strickland and Zenner (2003) and Reese and Weisbach (2002) show that firms from emerging markets are more likely to cross-list. I use three different but comparable measures from the Financial Structure Database constructed by Beck et. al (2000) : Higher values of _The Index of Financial Structure_ indicate a more market based financial system. _Developed_ is a dummy that takes the value 1 if the firm’s home market is developed and 0 if it is an emerging market firm. _Overall Size_ is a variable indicating the overall size of the financial system (both the stock market and the banking sector). See Appendix A for a detailed description of the variables definition and their sources.

I find that ratio of cash flow to voting rights is significantly lower for emerging economies and for firms coming from countries with small financial systems. Firms from countries with more market based economies have higher cash flow to voting rights indicating that financial structure of the home market is a significant determinant of the extent of control benefits in firms.

17