rafmacalaba/datause-encoder-data huggingface dataset:
**_5_**

of return is described in Claessens, Dasgupta and Glen, 1993). There is a great cross-sectional variation in the monthly rates of return behavior. Autocorrelation coefficients likewise vary over a wide range.

# m. **Test** of Market Segmentation

We first use the model of Stehle (1977), as also applied by Jorion and Schwartz (1986), Errunza, Losq and Padmanabhan (1992) and Mittoo (1992), to investigate the hypothesis of market integration or segmentation separately for each emerging market. The Stehle model assumes that the CAPM holds and that exchange risk is not priced. The test requires running the following regressions. First, we project the rate of return of the domestic IFC market indexes, j, = 1,..., K, on the rate of return on a world portfolio index, here approximated by the Morgan Stanley Capital Internatioral (MSCI) World Index (the net dividends reinvested series), to get the orthogonal component in the domestic index (note that all time subscripts are omitted): where RP is the rate of return on the index in market j, R, is the rate of return of the world index, and Vj., is the component orthogonal to the projection of Rj on R.

We then regress the world rate of return on the various IFC indexes' rates of return to get the orthogonal components here.

## where V,,j is the component orthogonal to the projection of R, on Rj.

The Stehle test then involves two cross-section tests, using the orthogonal components of the regressions (1) and (2). Under full integration, the parameter (denoted here by -y) on the slope coefficients (denoted here by ,B,,,,) of the individual stocks' rates of return on the orthogonal component of the regression of the local index on the world portfolio should not be significantly different from zero. Under complete segmentation, the parameter (denoted here by i2) on the slopes coefficients (denoted here by O,B) of the individual stocks' rates of return on the orthogonal component of the regression of the world portfolio on the local index should not be significantly different from zero. In other words, assuming complete integration or complete segmentation, equation (3) or (4) should hold for the return on stock i in market j:
Policy ReseaRch WoRking PaPeR 4696

# **Abstract**

Under the traditional “competition-fragility” view, more bank competition erodes market power, decreases profit margins, and results in reduced franchise value that encourages bank risk taking. Under the alternative “competition-stability” view, more market power in the loan market may result in greater bank risk as the higher interest rates charged to loan customers make it more difficult to repay loans and exacerbate moral hazard and adverse selection problems. But even if market power in the loan market results in riskier loan portfolios, the overall risks of banks need not increase if banks protect their franchise values by increasing their equity capital or engaging in other risk-mitigating techniques. The authors test these theories by regressing measures of loan risk, bank risk, and bank equity capital on several measures of market power, as well as indicators of the business environment, using data for 8,235 banks in 23 developed nations. The results suggest that—consistent with the traditional “competition-fragility” view—banks with a greater degree of market power also have less overall risk exposure. The data also provide some support for one element of the “competition-stability” view—that market power increases loan portfolio risk. The authors show that this risk may be offset in part by higher equity capital ratios.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study bank concentration and competition. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lklapper@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
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is also positive and significant. Based on the reported coefficients, we find that when an enterprise is solicited for a bribe from any agency covered by the survey (Binary Bribe Index), they are 8% more likely to choose informal finance exclusively, and they are 17% more likely to choose informal finance exclusively if solicited for a bribe by a tax inspector.

In Table 6, the results of measures of tax administration and regulation on enterprise financing preferences are shown. The findings suggest that the administration of taxes is an important factor in enterprise financing patterns, while tax rates are not. In particular, the variables approximating the number of tax payments, and the time needed to complete the revenue collection system (Taxpay and Taxtime respectively) are positively and significantly associated with the preference for informal finance.<sup>7</sup> For every one unit increase in the variable Taxtime, the probability of enterprises choosing informal finance increases by 1%. And this number goes from 8% to 39% between the bottom and top ends of the distribution of taxtime on this variable.

The findings also suggest that enterprises that obfuscate financial reporting are more likely to rely on informal finance. Tax Report Sales and Tax Report Labor (variables that measure truth in reporting to tax authority on sales and employment) have the expected sign (both negative), while the latter variable is significant. Finally, we see that our variable on tax rates has no significant impact on enterprise financing preferences.

Variables on other regulatory procedures were also tested. Table 7 shows the results for land regulations, which are consistent with the findings on corruption and tax regulation. More burdensome and onerous procedures for registering property are significantly associated with informal financing preferences. However, labor regulations, business registration procedures, and licenses costs and procedures were also tested against enterprise financing patterns, and were not found to be significant in the overall sample.

> 7 The Taxpay, Taxtime variables are interacted with an enterprise perception variable on tax administration as a constraint.  Taxrate is interacted with an enterprise perception variable on tax rate as a constraint.

11
27

# **DATA APPENDIX**

The econometric estimation was based on a balanced panel of 420 firms in the manufacturing sector for which there was continuos information for sales, capital stock, value added, profits, and other key variables for the study. Firms selected had to satisfy standard consistency checks, and additional criteria, including non-negative capital stock or value added. The panel is based on information collected by the "Superintendecia de Companias" (SC) of Ecuador. SC is an official agency that controls corporate activities. Among other duties, every quarter SC collects and inspects information presented by companies. By law, firms have to submit accurate information in order to do business in Ecuador and carry out a wide range of activities such as obtaining credit (official loans, as well as regular credit), tax identification numbers, etc.

The data includes yearly balance sheet and profit and losses information for the period 1984-1988. The balance sheets also include information of the revaluation of assets allowed by the Government to account for inflation and exchange rate depreciation. The definition of investment includes plant and machinery, buildings, and others (excluding land), and has been obtained by taking the difference between the gross capital stock at historical cost. The capital stock measure used in the equations is instead the revalued measures of the net capital stock. Our measure of debt includes both long term and short term debt, mainly obtained from banks and other financial institutions. Trade debt is not included. As a proxy for the output price we have used the two-digit wholesale price index. As a proxy for the price of investment goods, we have used the aggregate investment deflator. Both series are regularly published by the Banco Central del Ecuador. The statutory tax rates and depreciation allowances were used in calculating rt and the present value of tax savings associated with a unit of new investment, pt. The personal tax rate, mt has been set equal to 20% and the effective rate on capital gains, zt, equal to zero.

In estimating our model we classify as large those firms with a capital stock larger than 600.000 dollars at 1975 prices. The panel includes 91 large and 329 small firms. We define as young those firms that are born after 1970. The panel has 197 young firms and 223 old firms. For additional details on the data sources see Jaramillo (1992).
# **Annex 2: Some Existing City Indicator Programs**

# **1. UN-HABITAT Urban Indicators**

A first Global Urban Indicators Database (GUID1) was produced in 1996 in order to provide information on urban conditions and trends for the Habitat II Conference in Istanbul that same year. This database provided information on 237 cities worldwide and was perhaps the first representative sample of urban indicators on a global basis.

As part of the statistical analysis of this database, a City Development Index similar to the Human Development Index of UNDP, was developed to serve both as a ranking of cities according to their level of development and as a baseline for comparative study of indicators that designated urban conditions. Subsequent work with updated and enriched versions of this index has shown it to be a valuable tool for assessing the quality of life and the condition of city environments.

The Habitat Agenda and Resolutions 15/6 and 17/1 of the UN Commission on Human Settlements required the development of an indicators system representing the minimum data required to monitor changes in conditions in human settlements post Habitat II. In answer to such need, UNCHS (Habitat) developed an indicators system that contains a set of 23 key indicators and 9 lists of qualitative data. These are considered to be the minimum data required for reporting on shelter and urban development. They are also consistent with the 20 key areas of commitment in the universal reporting format.

Such indicators are designed to measure the performances and trends in the 20 selected key areas, as well as to register progress in the implementation of the Habitat Agenda. These indicators, to be chosen by the various countries, provide a comprehensive picture of cities, in a quantitative and comparative base for evaluating the conditions of cities, as well as the progress towards achieving urban objectives.

The Global Urban Indicators Database 2 (GUID2) prepared for Istanbul+5 included data for 151 cities worldwide. It was intended to assess urban conditions and trends in the target years of 1993 and 1998, and to evaluate progress made in that 5-year period. It provided the main source of information for analyzing urban conditions, trends and progress which were reported at the Istanbul+5 meeting in June 2001.

30
present results for minimum link quality in the other columns of Table (5), but average link quality retains a large elasticity and high significance in the same specifications.

We obtain very strong results for exporter GDP, importer GDP and road network distance. The GDP elasticities are both in the upper range for the previous studies summarized in Table 1, and both have very high significance levels. Road distance is also highly significant, with the expected sign and elasticities that are somewhat larger than previous estimates for West Africa and Latin America.<sup>19</sup> Columns (3) and (4) augment the gravity model with controls for six regional trade agreements that are identified in Table 5 and Appendix Table A7. Three of these agreements – WAEMU (West Africa), CEMAC (Central Africa) and EAC (East Africa) – are among contiguous states with very long-standing monetary and trade relationships.<sup>20</sup> The other three – ECOWAS, SADC and COMESA – are broader and looser aggregates that overlap somewhat with the first three agreements.<sup>21</sup> Our results suggest that the close-knit agreements have had a major impact on trade, ceteris paribus. The highly-significant dummy-variable results for WAEMU and CEMAC, in the range 2.3 – 3.4, suggest trade volumes 10-30 times higher than expected volumes without the agreements. The estimated parameter for EAC is equally large, although it is only 19 Although we have considered panel estimation, we have chosen cross-section estimation for several reasons. First, as we have previously noted, frequent missing values in the data force us to use a 4-year average, even for the cross-section work. At best, we could estimate a pseudo-panel regression with an equivalent average for a previous period in the 1990’s. Even if we adopted this approach, we would confront intractable problems with our road quality index. The cross-country index in Table 2 follows a sensible pattern but, as we have noted, World Development Indicator statistics on percent paved roads exhibit significant short-period fluctuations that seem very unlikely to reflect changes in actual road quality. In any case, we have no time-compatible measures for the CPIA index, which only exists for recent years. The objective of this exercise is to predict the effect of a hypothetical change in road quality which, given the measurement issues, could only enter a pseudo-panel regression as a fixed effect. The same is true for road distance. After considering all of these problems, we have decided against any attempt at panel estimation.

> 20  We cannot test the effect of the South African Customs Union (SACU – Botswana, Lesotho, Namibia, South Africa, Swaziland)) because the relevant inter-country observations are missing in the data. 21  Appendix Table A7 lists member countries of WAEMU, CEMAC, EAC, ECOWAS, COMESA and SADC.

15
# _Appendix Table 1_

_Impacts of Foreign Ownership on Wages by Educational Attainment and by Gender_

# A. Impacts on Male Workers

||||Full Sample<sup>*1</sup>||||Subset of Wor|kers in the Form|al Sector<sup>*2</sup>||
|---|---|---|---|---|---|---|---|---|---|---|
||College<br>and above|Upper-<br>secondary|Lower-<br>secondary|Primary|No Degree|College<br>and above|Upper-<br>secondary|Lower-<br>secondary|Primary|No<br>Degree|
|Foreign dummy|1.12***|.42***|.37***|.23*|-.20|.62***|.32***|.38***|.20|-.64*|
||(.33)|(.098)|(.097)|(.13)|(.21)|(.20)|(.099)|(.11)|(.15)|(.34)|
|Number of obs.|465|856|1182|1005|588|457|643|515|259|99|
|R<sup>2</sup>|.36|.22|.28|.22|.21|.36|.24|.35|.31|.58|

# B. Impacts on Female Workers

||||Full Sample<sup>*1</sup><br>||||Subset of Wor<br>|kers in the Form<br>|al Sector<sup>*2</sup>||
|---|---|---|---|---|---|---|---|---|---|---|
||College and<br>above<sup>*3</sup>|Upper-<br>secondary|Lower-<br>secondary|Primary|No Degree|College<br>and above|Upper-<br>secondary|Lower-<br>secondary|Primary|No<br>Degree|
|Foreign dummy|.26|.40***|.40***|.49***|.45***|.37**|.32***|.18**|.33***|-.014|
||(.22)|(.10)|(.090)|(.073)|(.17)|(.18)|(.083)|(.083)|(.082)|(.015)|
|Number of obs.|384|639|552|517|435|381|562|336|195|67|
|R<sup>2</sup>|.35|.36|.37|.34|.24|.35|.36|.35|.31|.62|

# C. Wald Test Results to Test Whether the Coefficients for Foreign Ownership are Equal between Men and Women

|Chi-square|5.05|.07|.04|3.10|6.33|.89|.00|2.80|.71|4.52|
|---|---|---|---|---|---|---|---|---|---|---|
|_P_-value|.025|.79|.84|.078|.012|.35|.99|.094|.40|.033|

Notes:

- *1The informal wage sector is the omitted ownership category.

- *2The private domestic sector is the omitted ownership category.

*3 A note of caution is appropriate: only three women with an educational attainment of college and above reported working in the informal wage sector, which is the base category.

To conserve the space, only the coefficients for foreign ownership are reported. The control variables included are the same as those in Table 4. The dependent variable is the log of hourly wage.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level, respectively.

The robust standard errors are between parentheses.

35
distributional assumptions on the error terms; it is also more efficient than 2SLS because it accounts for heteroskedasticity (Hall 2005).

We use activity restrictions, banking freedom, and the percent of foreign- and government-owned banks as instruments in the analysis. Schaeck and Cihak (2007) employ the same instruments with a 2SLS technique; we account for the presence of heteroskedasticity by implementing a GMM estimation. Activity restrictions are a key determinant for the scope of operations of banks and are likely to affect the level of competitiveness. This index provides information as to whether banks can engage in securities, insurance, and real estate activities, and whether they can hold stakes in nonfinancial institutions. Further, banking freedom represents a broad indicator for the openness of a banking system, capturing whether foreign banks are allowed to operate freely, whether difficulties are faced when setting up domestic banks, and whether the government influences the allocation of credit. Other instrumental variables for the degree of competition include the percent of foreign- and government-owned banks. These instruments directly impact competition, but cannot be assumed to directly affect loan risk, bank risk, or the capital level. We test for the relevance of these instruments or the endogeneity of market power using the First Stage F test, and we use the Hansen's J test of overidentification to check their validity.

# **IV. Data and Variables Employed in the Tests**

We retrieve bank-level financial data for the years 1999-2005 from the BankScope database provided by Fitch-IBCA (International Bank Credit Analysis Ltd). Our initial sample includes 8,274 banks located in more than 30 developed countries.<sup>9</sup> We apply a number of filtering rules to eliminate non-representative data, reducing our analysis sample to 8,235 banks operating in 23 different industrial countries. Specifically, banks with missing loan-to-asset ratios and income statement data are excluded from the sample, in addition to banks where negative expenses are reported and banks with equity levels in the bottom and top 1% tail of the distribution. Careful consideration is made to drop banks and not bank-year observations in order to sustain and benefit from the panel dimension of the data. Further, income statement variables are winsorized at the top and bottom 1% of the distribution, as are other ratios like loans to assets and equity to assets. Countries with fewer than five banks are also dropped from the original sample.

> 9 The developed nations correspond to the International Monetary Fund definition for "high-income" countries.

10