rafmacalaba/datause-encoder-data huggingface dataset:
- 42 -

such a tax on short-term inflows, the magnitude of short-term flows have been more important than longer-term flows. It also appears that part of the short-term borrowings from abroad was due to tax arbitrage reasons. The fact that Eurobonds issued by the Turkish government prior to 1997 were not subject to income tax in Turkey provided a strong incentive for Turkish banks to conduct tax arbitration transactions. Many Turkish banks made substantial investment in government Eurobond issues motivated by tax advantages. In order to finance these investment, banks made short-term borrowings from international banks against which the government bonds were pledged as collateral. Essentially, the banks were borrowing short-term money to hold long term Turkish government bonds.

An important feature of the Turkish banking system is the substantial off-balance sheet contingencies and commitments (Table 20). As of the end of 1997, total contingencies and commitments reached US$85 billion (92% of total on-balance sheet assets), including guarantees and warranties of US$29 billion (of which US$21 billion in foreign currency), commitments (including repos) of US$23 billion (of which US$4 billion in foreign currency), and foreign exchange and interest rate transactions of US$32 billion (of which US$23 billion in foreign exchange). Thus, although the direct foreign liabilities of the banking sector were smaller than those of the non-bank private sector, such on-balance sheet liabilities may only be small part of the banking sector's overall liability to the foreign sector ex post, in the event that such contingent guarantees are called.

||**Cla**<br>(consolidatedcr<br>|**ims ofENS-**<br>oss-border<br>clai<br>Distributi<br>|**Table 19**<br>**Turkey**<br>**Reporting**<br>**Banks b**<br>ms in all currencies a<br>on by maturity|**y Maturit**<br>nd local claim<br>_<br>|**y and Sector**<br>s in non-local curre<br>Distributionby se<br>|ncies)<br>ctor<br>|
|---|---|---|---|---|---|---|
||Total<br>___|Up to and<br>including<br>one year|Over one year|Banks|Public Sector|Non-bank<br>Private Sector|
||in billions of<br>US dollars||in percenta|ges of total c|onsolidated claims||
|mid-1994|17.5|50.4|41.8|29.3|28.6|42.1|
|end-1994|16.2|45.0|46.2|21.7|31.3|47.0|
|mid-1995|17.5|47.6|44.6|23.2|31.8|45.0|
|end-1995|18.6|48.1|44.2|27.9|28.3|43.8|
|mid-1996|20.1|50.3|43.6|28.8|27.1|44.1|
|end-1 996|22.6|50.9|43.5|32.7|22.7|44.6|
|mid-1997|25.1|52.1|42.3|33.0|20.1|46.8|
|end-1997|29.2|56.3|38.5|34.4|17.6|47.9|

Source: BIS, The Maturity, Sectoral and Nationality Distribution of Intemational Bank Lending, various issues.
labor market liberalization—or the potential flip side of increased or maintained protection—on inequality are disputed. On the one hand, some commentators (Arias et al. 2005, Cunningham and Santamaria 2003, Heller and Mahoney 2003) argue that the current set of labor regulations does not serve workers or firms well and also have negative effects through maintaining or increasing the size of the informal sector. On the other hand, liberalization does not appear to have a positive effect on inequality either—one World Bank report concludes that liberalization in Latin America and the Caribbean has led to “on balance mild disequalizing effects” (World Bank 2004, p. 221). One possibility is that liberalization may lead to income growth but not lower inequality, as de Janvry and Sadoulet (2000) find for 12 Latin American countries between 1970 and 1994 (where they find reductions in poverty but not in inequality). Microsimulations may help us elucidate where there are countervailing factors as well as which factor tends to prevail to cause a net change in inequality.

A more difficult challenge for the microsimulation methodology, at least given current data constraints, is how to add in factors like differential access to social networks to gauge their effects on economic inequality. It is likely that social networks, and the different results they can generate by gender and racial/ethnic group, are a key factor in generating inequality.<sup>24</sup> To the extent that processes such as these are not included in formal modeling, then the modeling results will always be open to criticism for not being able to model fully the complex dynamics that generate inequality. This will be an important task for future researchers.

Our conclusions may be viewed as radical by those who have considered inequality in Latin America to have a large racial and/or ethnic dimension. Clearly other dimensions of inequality need to be considered besides earnings inequality, and shortcomings of the available data that we use need to be considered as well in terms of their ability to capture the full range of economic outcomes that people experience. Nevertheless, our results, based on large survey data, relatively consistent across four countries, and utilizing a range of calculations in order to provide some robustness check, set up a challenge for those who would draw inferences based on alternative data that may be actually less rather than more representative of the actual situation in Latin America. We hope that others will follow our path of considering how to develop quantitative measurements of the extent and nature of inequality along this and other dimensions

> 24 Se Lunde et al. (2007), which looks at social networks in five countries and their role in generating income for indigenous peoples.

17
# Table of Contents

|Introduction..<br>..........................................|4|
|---|---|
|I. Financial Systems Under Perfect Markets ...........<br>.. .. . .. ..|. .. .. . .<br>.<br>6|
|II. Financial and Real Development: An Overview ........<br>..........|......<br>8|
|III. Financial Factors in Growth and Development: Theory ..................|11|
|lEa. The premium for external finance.................|..........12|
|IMIb. Financial Structure .....................................|17|
|IIIc. Borrower Net Worth and Financial Efficiency<br>..................|22|
|Hid. Summary implications for finance and growth ...................|26|
|IV. Problems of Developing Countries ...............................|29|
|NVa. Financial Repression .<br>..................................|29|
|Nb.<br>Financial Liberalization ..........<br>......................|32|
|V. Evidence ............................................|34|
|Va.<br>Data .<br>...................................|35|
|Vb. Results ..........................................|36|
|VI. Concluding Remarks .............|40|
Table A.1 Swanmnda Health Insurance Plan Menbership Rates 1986 1989

|.........................<br>.............................|..................<br>1986<br>....|.............<br> <br>1987<br>...............................|<br>1988<br>...........|1989<br>....... .....................|
|---|---|---|---|---|
|Neffbers|32750|58100|80595|81142|
|Total Population|118612|125480|130000|134680|
|Percentag**e**nrolled|27.6|<br>46.3X|<br>62.02|60.2X|
|Change inenrollmen<br>rate**(prcentage)**<br>......................|t<br>18.72<br>............................|15.72<br>..............|-1.72|................|

*1989 population estimated based on growth **rate of** 3.6X

patients. Table V.1 in Annex V analyzes missing data to show how various ambiguities in the survey were resolved.

**Table** A.2 Survey of 50 Bwamanda HospitaL Patients: Frequ ncies

|.................................................<br>Xof Respon<br>|..<br>....<br>dents:N<br>|umbers<br>|
|---|---|---|
|Insured Un|nsured|used|
|<br>Characteristic<br>(0a29)<br>|<br>(0Zi)||
|<br><br>..................<br>......................................||.....<br>......................|
|Gender:<br> <br>|||
|FemaLe<br>69<br>X<br>|43X|S0|
|Nale<br>31X|57X||
|Whatdo you think of the idea of<br>|||
|health insurance?<br> <br>|||
|Good<br>1002<br> <br>|61X|S0|
|Don'tknow<br>02|10l||
|Could you pay 10,000 Zaire for an<br>illressepisoderequiring minor surgery<br>||50|
|in a hospital?<br> <br>|||
|Yes<br>522<br>|43X<br>||
|No or no response<br>48X|482||
|How would you pay thisunot?<br>||35|
|Installment<br>252<br> <br>|272||
|Cash<br>752|73X||
|**Uhere**would you get the money?||48|
|Cah from houehold<br>72|102||
|Sav!ngclubor association<br>42<br>|02<br>||
|By selling household itwn<br>_32<br>|**252**||
|outside the hou shold<br>|||
|Contribution of a fmi lym*bers<br>7r<br>|402||
|other<br>362|**252**||
|No**response**<br>32|02||
|Did you**go**to school?<br> <br>||so|
|Not**at all**<br>**662**<br>|**382**<br>||
|Pr1mary school, 1-3**years**<br>17X<br>|192||
|Primry school, 4-6 years<br>102<br>|142||
|**Secondary**school<br>**7r**|29X||

14
- Becker, G. S. and E. P. Lazear (2013). A market solution to immigration reform. _Wall Street Journal 2_ .

- Becker, G. S. and G. Nashat (1997). _The Economics of Life: From Baseball to Affirmative Action to Immigration, How Real-World Issues Affect our Everyday Life_ . McGraw Hill.

- Binswanger, J. and D. Schunk (2012). What is an adequate standard of living during retirement? _Journal of Pension Economics and Finance 11_ (2), 203–222.

- Bureau of Labor Statistics (2018). _Work Experience of the Population_ . US Department of Labor.

- Capps, R., J. Gelatt, J. Van Hook, and M. Fix (2018, 09). Commentary on “the number of undocumented immigrants in the united states: Estimates based on demographic modeling with data from 1990-2016”. _PLOS ONE 13_ (9), 1–10.

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- Chiswick, B. R. (1982). _The Impact of Immigration on the Level and Distribution of Economic Well-being_ . The Gateway: US Immigration Issues and Policies, American Enterprise Institute for Public Policy Research, Washington DC.

- Clemens, M. A. and M. Mendola (2020, August). Migration from Developing Countries: Selection, Income Elasticity, and Simpson’s Paradox. Working Papers 539, Center for Global Development.

- Clemens, M. A., C. E. Montenegro, and L. Pritchett (2019, 05). The Place Premium: Bounding the Price Equivalent of Migration Barriers. _The Review of Economics and Statistics 101_ (2), 201–213.

- DeVoretz, D. (2008). An auction model of canadian temporary immigration for the 21st century. _International Migration 46_ (1), 3–17.

19
Table 5: Effects of mining on the probability of the respondent or the respondent’s partner being a miner.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|_RMG m_<br>Woman<br>is miner|_ine data_<br>Husband<br>is miner|_USGS m_<br>Woman<br>is miner|_ine data_<br>Husband<br>is miner|_Diamond _<br>Woman<br>is miner|_mine data_<br>Husband<br>is miner|
|active (20 km)|0.004*<br>(0.003)|0.046***<br>(0.011)|||||
|inactive (20 km)|0.011<br>(0.009)|0.005<br>(0.021)|||||
|usgs mine (20 km)|||0.001<br>(0.001)|0.008***<br>(0.003)|||
|diamond mine (20 km)|||||-0.000<br>(0.002)|0.037***<br>(0.012)|
|Observations|259,114|149,692|264,695|152,228|264,695|152,228|
|R-squared|0.026|0.071|0.026|0.069|0.026|0.070|
|F test: active-inactive=0|0.406|2.913|||||
|p value|0.524|0.0879|||||

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest. *** p<0.01, ** p<0.05, * p<0.1 16
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**<u>Table 1. Natural Wealth is Crowding Out Saving and Investment Leading To a Resource Curse</u>**

||||<br>|_Dependent Va_<br>|_riables_|||||
|---|---|---|---|---|---|---|---|---|---|
||Ln_Y2002_|Sa|vings (Sa|v94)||Inve|stment (Inv|94)||
|_Explanatory_||||||||||
|_Variables_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|NatK94(0.11)|-7.62***|-0.39***||-0.22***|-0.18***||-0.19***|||
||(-7.86)|(-5.20)||(-2.29)|(-3.40)||(-2.70)|||
|Aid94(0.17,<br>0.18, 0.17,|||-<br>0.27***|-0.19***||0.04|-0.02|||
|0.18)|||(-5.61)|(-3.11)||(0.73)|(-0.46)|||
|Sav94(0.09)||||||||0.24***<br>(3.26)|0.38***<br>(7.13)|
|Constant|9.29|0.23|0.21|0.23|0.23|0.23|0.24|0.18|0.15|
|_R_<sup>_2_</sup>adjusted|0.43|0.24|0.21|0.3|0.11|0|0.13|0.06|0.29|
|_N_|82|83|115|63|83|110|63|141|121|

Source: Papyrakis and Gerlagh (2006). Note: Standard deviations for independent variables in parentheses. For Aid variable, standard deviations refer to regressions (3), (4), (6) and (7), respectively; t-statistics for coefficients in parentheses. Superscripts *, **, *** correspond to a 10%, 5% and 1% level of significance. Data on natural capital and GDP per capita are provided by the World Bank.

Motivated by existing literature and the recent discoveries of the Pre-Salt oil fields in Brazil, this paper aims to contribute to the literature by adding the possibility, in the context of Brazil, for government to choose how much of the oil revenue it will devote to public investment and public consumption, respectively. This has not before been attempted, to our knowledge, in the literature.

Existing literature has assumed that all oil revenue has been consumed by the government — which therefore, naturally, leads to a resource curse since capital is crowded out. However, maybe by investing merely a relatively small fraction of oil revenue the government can escape the resource curse; maybe by investing the full amount of oil revenue can even promote growth and welfare; and, finally, maybe the effect on different generations could be positive at the same time if oil revenue to some extent is invested. These are crucial questions that have not before been formally addressed in the economic literature.

A dynamic OLG model is developed in this paper that is capable of analyzing the _efficiency–equity_ implications. A key policy relevant relationship is stressed: The efficient investment-share depends endogenously to the manner in which the government chooses to consume whichever share that is not invested. The model incorporates natural resource revenues in a new way, by combining existing approaches with an approach of public investments of revenues that directly stimulates capital accumulation. This framework will be combined with a version of the model that features population growth, productivity growth, and life expectancy 4
- 3 -

which is often a main cause of urban traffic congestion, generally depends on the marginal transportation cost and where they are located (e.g., Muth, 1969; Mills, 1972). Thus, the residential locational choice is dependent on the housing price gradient in a city (e.g., Atack and Margo, 1998; Gedal and Ellen, 2018). Therefore, whether people commute to work is essentially determined by transport connectivity and residential land values.

4. The modal choice literature has also extensively been discussing travel decisions by commuters for a very long time. In addition to pioneer studies in this area (e.g., McFadden, 1974a, 1974b; Hensher, 1986; Hensher and Greene, 2002), for example, Bento et al. (2005) examine the commute mode choices in relation to urban structure and public transit supply in the United States, showing that poor people and women are more likely to use public transportation for commuting. In particular, bus ridership is found to increase with the supply of bus transit services and the relative spatial concentration of jobs to housing. In their analysis, notably, a choice of nonmotorized transit is included, that is, walking and bicycling. Interestingly, as jobs-housing balance increases, people are more likely to walk to work, which is often referred to as the compact city.

5. Related to the above, some studies discuss job accessibility in connection with car ownership. In particular, where population density is low, quality public transportation services are often difficult to provide, and therefore, car dependence must of necessity increase. In Barcelona and Madrid, it is found that the reduction of travel time to jobs reduces the number of second or third car owners and substantially increases the number of households without a car (Matas et al., 2009). The findings in New York by Salon (2009) look consistent: New Yorkers are sensitive to travel time and costs to decide how many vehicles they own.

6. From the gender point of view, labor economics discusses gender differences in access to jobs in the labor market. Gobillon et al. (2015) analyze micro employment data in France and find that females have significantly lower access to jobs, especially, high-paid jobs, such as executive job positions. There are two different arguments: First, as Gobillon et al.
# **V. References**

Demsetz, Harold (1968), “Why regulate utilities?” _Journal of Law and Economics_ , 11, pp. 55-65.

Dailami, M. (2004), Korea’s PPI market: Financing source, growth potential and new challenges, mimeo.

Foster, V. (2005), “Ten years of water service reform in LAC – toward an Anglo – French Model”, January, Water Supply and Sanitation Sector Board Paper No 5 Harada, Fumiyo (2004), “Legal framework for private participation in infrastructure in the selected East Asian Countries – Comparative study on Japan, Korea, and the Philippines”, 28 pages, mimeo.

Jose Luis Guasch (2004), Granting and renegotiating infrastructure concessions – doing it right, WBI Development Studies, The World Bank Kerf, Michel and al (1998), Concessions for infrastructure – A guide to their design and award, World Bank Technical Paper No 399, 131 pages.

Klemperer, Paul (2002a), How (Not) to run Auctions: the European 3 G telecom auctions, _The European Economic Review_ , Volume 46, Issues 4-5, May, pp. 829-45.

Klemperer, Paul (2002b), “What really matters in auction design”, _Journal of Economy Perspectives_ , 16 (1), pp. 160-189.

Laffont, Jean-Jacques and Tirole, Jean (1993), <u>A theory of incentives in procurement and regulation, MIT Press.</u> Michael Klein (1998), “Bidding for concession”, World Bank Policy Paper Series 1957 National Treasury South Africa (2004), PPP manual – module 5: PPP procurement PICKO (2004), Developing best practice for Korea’s PPI market – with a focus on PSC, mimeo, 370 pages Williamson, O. E. (1976), “Franchise bidding for natural monopolies – in general and with respect to CATV”, _Bell Journal of Economics_ , 7 (1), spring.

World Bank (1998), Bidding for private concessions – the use of World Bank guarantees, RMC Discussion paper 120.

30
Most firm owners in our sample financed the start-up and ongoing capital investments through own savings and loans from family. Our survey contains two measures of the household internal capital market: the number of paid wage workers in the household, and an index of household durable assets constructed as the first principal component of baseline indicators of ownership of durable assets such as fans, radios, cameras, televisions and motorcycles. Table 6 shows that on average female owners have more wage workers and higher household assets to draw upon, measured in the baseline survey. As a result, we would expect females to be less constrained, and hence earn lower returns.

To investigate whether differences in access to capital can explain the gender difference in treatment effects, we examine the gender interaction term after controlling for interactions between measures of access to credit and the treatment amount. That is, we estimate the following equation, for a given set of _H_ potential explanators Xs’ We demean all the X’s, so that β will give the mean treatment effect for males, and γ the differential effect for females, evaluated at the mean of the other potential explanatory variables. We then examine how sensitive γ is to the inclusion of these other variables, to determine whether the gender interaction is really reflecting gender differences in access to capital. We use profits adjusted for the value of the owner’s labor input, as described above.

Table 7 shows the results. The first column repeats the basic result from Table 2. Columns 2 through 4 then show the impact of controlling for treatment interactions with access to bank loans, the number of wage workers, and household assets. Each of these controls shows a negative interaction effect, consistent with less constrained owners earning lower returns. However, controlling for these interactions does not lead to a significant change in the size of the negative female interaction term. We still find high returns for males, and can not reject the return being zero for females after these controls 21

-  -
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21