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Journal of Financial Services Research

Erschienen in:

01.06.2014

The Benefits of Political Connection: Evidence from Individual Bank-Loan Contracts

verfasst von: Yan-Shing Chen, Chung-Hua Shen, Chih-Yung Lin

Erschienen in: Journal of Financial Services Research | Ausgabe 3/2014

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Abstract

This paper investigates whether political connections improve the access of firms to financing. We propose three hypotheses to prove that political benefits exist. First, do politically connected firms obtain preferential treatment for bank loans? Second, if these firms do obtain preferential treatment, do they benefit from government-owned banks (GOBs) more than from privately owned banks? Third, is the preferential treatment from GOBs enhanced during presidential election years? We examine these three questions by using detailed data on political connections and 69,332 individual bank-loan contracts for listed firms in Taiwan from 1991 to 2008. The empirical results generally support our hypotheses.

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For example, Khwaja and Mian (2005), Faccio (2006), Faccio et al. (2006), Fan et al. (2007), Goldman et al. (2009), and Francis et al. (2009).

During the sample periods, 30 Non-PC firms are reclassified as PC firms when the connections of top managers are observed. In contrast, five firms are reclassified as non-PC firms after the departure of the connected CEO or chairman in our sample. Both reclassifications allow us to estimate the coefficient for the PC and the firm fixed effects at the same time.

The modified Z-score equals (1.2Working capital + 1.4Retained earnings + 3.3EBIT + .999Sales)/Total assets.

We adopt the TEJ rating score because it provides the most complete rating data for all listed firms during our sample periods. We also try to use rating data from international rating agencies, such as Standard and Poor’s (S&P). However, the S&P only rates ten listed firms excluding banks, financial holding companies, insurance firms, and utilities.

If the type of loan has a floating interest rate, we use the mean of the upper and lower bounds as our interest rate.

Throughout the paper, bank × year and firm effects represent their fixed effects.

Although we have controlled for some firm characteristics, these are not sufficient to rule out the endogenous problem stemming from the omitted variable problem.

In the first stage, we estimate a structural model for all of the loan characteristics other than the Spread. The dependent variable is the loan characteristics (LoanPeriod, D_Secured, etc.), and the independent variables comprise the PC and the firm characteristics. In the second stage, we regress the Spread on the PC, firm characteristics, and the predicted values of the loan-characteristic variables obtained from the first-stage regression.

The GOBs are banks in which the government has shares exceeding 20 %.

See Shen and Wang (2005) for details about this data.

Our sample accounts for around 65 % of the total loan contracts from 1991 to 2008.

We exclude two firms that have been connected to both KMT and DPP parties in the same period. We also drop three firms switching their political connections from one party to another in our sample periods. However, our results remain the same when our sample includes these firms.

In the regression settings from columns 1 to 4, we drop the bank-year dummy for the China Trust bank, the largest private bank in Taiwan, in the year of 1991 to avoid the multicollinearity problem. Thus, the coefficient for the constant term represents the fixed effect for the loan contracts extended by the China Trust bank in 1991. In addition, we also drop the firm dummy for the SouthEast Cement Corporation when we add the firm fixed effect in column 5.

To address the concern that our results might be driven by the high degree of small-sized loans in our sample, we also follow KM to weight the regression by the loan size. In this setting, we can interpret the coefficients for the PC as the additional loan rate per dollar of the borrowed amount for firms with connected CEOs. As a result, we still find a significantly negative coefficient for the PC.

Notably, the Rating and Z-score are both measures that examine the default risk of firms, so the coefficient for the Z-score becomes positive because of the enormous positive coefficient for the Rating. That is, the coefficient for the Z-score is just for reference because the default risk information is captured by the Rating variable.

LoanSize Ratio means the ratio of loan sizes over book value of assets. Log (Lender) means the total number of lenders in a firm in a given year.

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Titel: The Benefits of Political Connection: Evidence from Individual Bank-Loan Contracts
verfasst von: Yan-Shing Chen
Chung-Hua Shen
Chih-Yung Lin
Publikationsdatum: 01.06.2014
Verlag: Springer US
Erschienen in: Journal of Financial Services Research / Ausgabe 3/2014
Print ISSN: 0920-8550
Elektronische ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-013-0167-1