Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market

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Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market

Khwaja, Asim I. & Mian, Atif. Quarterly Journal of Economics, November 2005

Jo˜ao Vitor Granja de Almeida

EPGE/FGV

March 26, 2014

Jo˜ao Granja (EPGE/FGV) Do Lenders Favor Politically Connected Firms? March 26, 2014 1 / 25

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Outline

1 Motivation

2 Environment and Data

3 Methodology

4 Results

5 Conclusion

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Outline

1 Motivation

3 Methodology

4 Results

5 Conclusion

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Motivation

Corruption often blamed for economic ills: resource waste and/or misallocation

Reasonable, but absence of quantification Rich theoretical literature, limited empirical work

Empirical work: mainly cross-country; cleaner identification here

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Summary

Greatest strenght: very rich (loan level) dataset. More than 110.000 loans, universe of private corporate lending

More: links firms-politicians.

Politically connected firms receive 45% larger loans and have 50% higher default rates

Effect Concentrated in government banks. Increasing in political power and decreasing in political participation

Economic costs: 0.3-1.9% of GDP per year

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Outline

1 Motivation

3 Methodology

4 Results

5 Conclusion

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Environment

Pakistan ranked consistently high in corruption rankings (Transparency International)

Unstable political environment.

By law, top officials in government banks appointed by the government.

These officials, in turn, determine credit and personnel policies Mechanism for political rent extraction

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Data

Two main sources of data:

Credit Information Bureau Database

Quarterly information on theuniverseof corporate lending, 1996-2002 Borrower credit position by lender and quarter

Name, location and directorship of borrowing firms and lending banks

Electoral Information

Names and party affiliations for all candidates in the relevant elections (1993 and 1997)

Info on winner, number of votes by candidate and number of registered voters Use datasets to identify firms that have politicians on their board - defined as thepolitically connectedones. Implied measurement errors leads to

underestimation. Data is “cross-sectionalized”.

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Summary Statistics

TABLE I SUMMARYSTATISTICS

Panel A: Loan-level variables

Variable Mean S.D. Obs.

Loan Size(’000s of 1995

Pak Rs.) 6,669 89,298 112,685

Default Rate(%):

Unweighted 16.85 30.22 112,685

Default Rate(%): Loan

size weighted 17.61 31.06 112,685

Recovery Rate(%):

(conditional on default) 8.55 24.50 24,562

Rate of Return(%) 93.46 35.70 89,223

Interest Rate(%) 14.05 2.90 89,223

Loan Type Fixed

Working Capital

Letter of

Credit Guarantees Other

Percent of total lending 32% 49% 7% 7% 5%

Panel B: Borrower/firm attributes

Politically Connected No Yes

Percent of total firms 77% 23%

Percent of total lending

(of total loans) 63% (74%) 37% (26%)

Size (percentile) 0–50 50–75 75–95 95–99 99–100

(of total loans) 6% (42%) 3% (21%) 13% (23%) 23% (9%) 55% (5%) Location(City Size) Small Medium Large Unclassified

(of total loans) 8% (17%) 12% (15%) 74% (52%) 6% (16%)

Foreign Firm No Yes

(of total loans) (99.8%) 4% (0.2%)

Business Group Size Stand AloneIntermediateConglomerate Unclassified Percent of total lending

(of total loans) 20% (54%) 19% (17%) 39% (10%) 22% (19%) Panel C: Politician level variables for matched politicians (2073 politicians)

Variable Mean S.D.

Win(%) 9.0 26.0

Percentage Votes 9.83 16.33

Victory Margin 20.53 16.50

Electoral Participation

(%) 36.60 10.46

Rate of Return⫽(1⫺Default Rate)ⴱ(1⫹Interest Rate)⫹Default RateⴱRecovery Rate. Politically Connected⫽dummy for whether firm has a politician on its board; Other firm level attributes defined in the Appendix; While we report summary statistics for firm location in terms of city size as defined in the Appendix, in the subsequent regressions firm location controls are introduced as separate dummies for each city. Win⫽politician winning frequency (%); Percentage Votes⫽percentage votes obtained by politician;

Victory Margin⫽Difference in Percentage Votes between the winner and runner up if politician won, 0 otherwise; Electoral Participation⫽Registered votes cast (%).

1381 DO LENDERS FAVOR POLITICALLY CONNECTED FIRMS?

at FundaÃ§Ã£o GetÃºlio Vargas/ SP on February 25, 2014http://qje.oxfordjournals.org/Downloaded from

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Loan Characteristics

III.D. Methodology

The mechanism described in Section II suggests that politically connected firms obtain rents from banks in the form of preferential lending. We examine preference along two margins:

TABLE II

POLITICALLOANCHARACTERISTICS

Political loan Nonpolitical loan Difference

Average city size 1.28 1.45 ⫺0.17

(0.11) (0.10) (0.03)

Average group size 1.68 1.37 0.31

(0.11) (0.07) (0.04)

Loan type share

Fixed 37.72 28.80 8.92

Working capital 46.43 49.82 ⫺3.39

Letter of credit 6.45 7.71 ⫺1.26

Guarantees 6.59 7.27 ⫺0.68

Other 2.81 6.40 ⫺3.59

Political loan Nonpolitical loan

% of total lending

% of Industry

type

% of total lending

% of Industry

type Industry Share

Agriculture 1.4 27.2 2.3 76.0

Chemicals 5.1 53.1 2.6 46.9

Construction 8.3 49.1 5.0 50.9

Engineering/machinery 4.1 20.9 9.0 79.1

Food 11.7 42.8 9.1 57.2

Finance 3.8 23.4 7.3 76.6

Leather 0.5 33.0 0.5 67.0

Paper 2.0 47.4 1.3 52.6

Transport 0.8 19.9 1.9 80.1

Textile 36.6 54.1 18.1 45.9

Energy 1.5 55.8 0.7 44.2

Other 3.1 35.5 3.2 64.5

Firm level attributes (city and group size) are defined in the Appendix. Standard errors are reported in parentheses and clustered at the bank level. The industry shares are percentage of total classified loans and industries classified as follows: Agriculture—Agriculture; Chemical—Ceramics, Foam, Lab, Match, Mineral, Plastic, Rubber, Chemicals, Coating; Construction—Building Material, Construction Metal, Sizing, Storage;

Engineering/Machinery—Appliances, Business Machinery, Electronics, Engineering, Fan, Finishing, Mill, IT, Instruments, Power, Telecommunication, Electric, Pump, Capital Goods; Finance—Export/Import, Fi- nance; Leather—Leather; Paper—Books, Packaging, Paper, Photo, Wood, Packages, Printing; Transport—

Air transportation, Auto, Aviation, Land transportation, Sea transportation, Tourism, Transportation; Tex- tile—Textile; Energy—Energy, Gas, Petroleum; Other—Cycle, Education, Government, Jewelers, Light, Misc. Service, Medical, Military, Sport, Stationery, Watch, Shopping Mall, Advertising, Entertainment.

1384 QUARTERLY JOURNAL OF ECONOMICS

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Outline

1 Motivation

3 Methodology

4 Results

5 Conclusion

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Methodology

Testing for Preferential Treatment

^Results

Yij =αj+β1·Politicali+γ1·X_i+γ2·X_ij+ij (1) Regressions are weighted by loan size.

Endogeneity concern: Political_i might be correlated with unobservables.

Firm-level fixed effects would absorbPolitical_i.

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Methodology

Preferential Treatment by Government Banks

Results-Default Results-Access

Yij =αi+αj+β1·Politicali·GOVj+γ1·X_ij +γ2·X_ij ·GOVj+ij (2) Removes previous identification concerns, since firm fixed effects have been included.

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Methodology

Winning Elections

^Results

Yijt=αij+αt+β1·WINit·GOVj+β2·WINit+ijt (3) Exploit time dimension of the (original) data, using a time-varying measure of political connectedness

WINit is an indicator of whether the firm’s politician holds office during quarter t

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Outline

1 Motivation

3 Methodology

4 Results

5 Conclusion

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Results: Preferential Treatment for Politically Connected Firms

(4) Yijt⫽␣ij⫹␣t⫹␤1䡠WINitⴱGOVj⫹␤2䡠WINit⫹εijt, where the variables are as before and the additional subscriptt specifies the quarter.␣ij are bank-lender (i.e., loan-level) fixed effects;WINit is an indicator for whether the firm’s politician holds office during quartertor not. When we examine changes in electoral success for the politician’s political party, we use a similar indicator for whether the politician’s political party wins or not,WIN-Partyit. The double-difference estimateB1, captures any (additional) lending preference a politically connected firm receives from a government relative to private bank, when its politician or his political party wins. The bank-lender fixed effects imply that this change is for thesameloan (i.e., firm-bank pair) over time.

IV. RESULTS—PREFERENTIALTREATMENT FORPOLITICALLY

CONNECTEDFIRMS

Table III shows the results of estimating (2) for both margins of preference: loan access and price. The regressions nonpara- metrically control for firm and loan characteristics by introducing firm attribute, bank and loan type dummies.

Column (1) presents evidence for political preference in terms of credit usage: loans to politically connected firms are 45

TABLE III

AREPOLITICALLYCONNECTEDFIRMSGIVENPREFERENTIALTREATMENT?

Dependent variable

Log loan size

Rate of return

Default rate

Recovery

rate Interest rate

(1) (2) (3) (4) (5)

Politically connected 0.37 ⫺6.08 6.22 ⫺1.09 0.09

(0.08) (2.46) (1.98) (1.14) (0.05)

Controls YES YES YES YES YES

R² 0.26 0.28 0.29 0.24 0.43

No. of Obs. 112,685 89,223 112,685 24,562 89,223

Results are based on cross-sectionalized data. A unit of observation is a loan (bank-firm pair). There are 89,223 observations instead of 112,685 in columns (2) and (5) as interest rate data are not available for all banks. There are 24,562 observations in column (4) because the data are conditional on a firm having defaulted. Rate of Return⫽(1⫺Default Rate)ⴱ(1⫹Interest Rate)⫹Default RateⴱRecovery Rate.

Standard errors reported in parentheses are clustered at bank level. Regressions in columns (2)–(5) are weighted by loan size. Controls in column (1) include dummy for whether borrower is a foreign firm, 91 bank dummies, 134 dummies for each of the city/town of firm. Columns (2)–(5) include column (1) controls plus 8 dummies for the number of creditors the firm has, 5 loan-type dummies and 3 group size dummies, 5 firm size dummies. Firm-level control variables are described in the Appendix.

Equation

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Results: Political Rents and Government Banks - Default

politically connected firms have slightly lower defaults suggesting either greater monitoring or better selection for politically connected firms by private banks.

An interesting aside is that while the government banks do treat politically connected firms more favorably, they also face high default rates in general (column (1)). By focusing on political connectedness, we are only capturing one source of “influence.”

There may be a variety of other avenues such as alternative forms of status (bureaucracy, army, insider networks, familial ties, etc.) and direct bribes that may also contribute to why government banks face higher default rates. In this paper our focus is only on political rents.

Do government banks face higher default rates because they TABLE IV

AREPOLITICALLYCONNECTEDFIRMSFAVORED BYGOVERNMENTBANKSONLY? DEFAULTRATE

Default rate (%)

(1) (2) (3) (4) (5) (6)

Government banks only

Private banks only

All banks

Firms borrowing from both government and private banks Politically connected 10.92 9.13 ⫺0.02 ⫺0.78 ⫺0.78 —

(4.12) (1.92) (0.27) (0.26) (0.26) Politically connected

ⴱgovernment bank

9.91 1.4

(1.90) (1.04)

Constant 19.87 — 6.05 — — —

(2.60) (2.03)

Controls NO YES NO YES YES^a Firm fixed

effects^b

R² 0.02 0.3 0.004 0.15 0.33 0.78

No. of Obs. 61,897 61,897 50,788 50,788 112,685 18,819 Results are based on cross-sectionalized data. Standard errors reported in parentheses are clustered at the bank level. Politically connected⫽dummy for whether firm has a politician on its board; Government bank⫽dummy for government banks. Controls include 5 loan-type dummies, 5 firm size dummies, dummy for whether the borrower is a foreign firm, 8 dummies for the number of creditors the firm has, 3 group size dummies, 134 dummies for each of the city/town of borrower, 21 dummies for the industry of the firm, and 91 bank dummies. Firm-level control variables are described in the Appendix.

a. Controls also include government bank dummy and all interactions with the government bank dummy.

b. Regression includes a government bank dummy as well. Data are restricted to firms that borrow from both government and private banks.

Columns (1)-(4): Equation (1) Columns (5)-(6): Equation (2)

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Results: Political Rents and Government Banks - Access

nonpolitical firms is that the amount borrowed may differ simply due to a firm’s different credit needs (a demand effect). In other words, the “preferential treatment” in access to credit identified in Table III earlier, may simply reflect a higher credit demand of political firms and not political preference. To argue that there is political preference, one needs to perfectly condition on a firm’s credit demand. The hypothetical comparison would then be between two firms with thesamecredit demand and seeing whether the politically connected firm receives a larger loan from the government bank. Specification (3) allows us to make such a comparison.

Column (1) in Table V shows that while government banks provide larger loans than private banks, they lend even larger amounts—29 percent more—to politically connected firms. The

TABLE V

AREPOLITICALFIRMSFAVORED BYGOVERNMENTBANKSONLY? ACCESS TOCREDIT

Dependent variable

Log loan size

(1) (2) (3)

Data restricted to firms that borrow from both government

and private banks

Government bank 0.07 ⫺1.19 ⫺0.2

(0.03) (0.14) (0.03)

Politically connectedⴱgovernment bank 0.29 ⫺0.21 0.13

(0.05) (0.22) (0.05)

Government bankⴱlog firm size 0.14

(0.02) Politically connectedⴱgovernment bank

ⴱlog firm size

0.041 (0.03)

Government bankⴱfirm default rate 1.9

(0.11) Politically connectedⴱgovernment bank

ⴱfirm default rate

0.56 (0.17)

Firm fixed effect YES YES YES

R² 0.81 0.81 0.83

No. of obs. 10,880 10,880 10,880

Data are restricted to firms that borrow from both government and private banks. Robust standard errors are reported in parentheses. A unit of observation is a firm-bank type (government or private) pair, as all loans of a firm given by the same bank type are summed. There are thus 5,440 firm fixed effects and 10,880 total observations in the regression. Politically Connected⫽dummy for whether firm has a politician on its board; Government bank⫽dummy for government banks; Log firm size⫽Logarithm of a firm’s total borrowing from all banks (private and government); Firm default rate⫽Firm’s average default rate across all banks.

Equation

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Results:Political Strength and Participation

banks, respectively. Finally, column (4) shows that a 10 percent increase in the fraction of the times a politician’s party wins is associated with 3 percent larger loans.²⁰

VI.B. Political Participation

Table VI also examines whether there are any constraints to these rents by asking whether a more active electorate is able to monitor and check its politicians. We run a similar specification

20. We restrict the sample to firms that borrow from both government and private banks in order to use firm fixed effects. We get very similar results when we run these regressions (without firm fixed effects) on firms that only borrow from government banks suggesting that our sample restriction is not a concern.

TABLE VI

TESTING FORPOLITICALSTRENGTH ANDPARTICIPATION

Dependent variable

Log loan size

(1) (2) (3) (4) (5)

Data restricted to firms that borrow from both government and private banks

Government bank 0.07 0.07 0.07 0.07 0.07

(0.03) (0.03) (0.03) (0.03) (0.03) Politically connectedⴱgovernment

bank

0.25 0.26 0.25 0.23 0.67

(0.06) (0.05) (0.05) (0.05) (0.20) Politically connectedⴱgovernment

bankⴱpercentage votes

0.69 (0.47) Politically connectedⴱgovernment

bankⴱwin

bankⴱvictory margin

bankⴱwinparty

bankⴱelectoral participation

⫺1.04 (0.53)

Firm fixed effect YES YES YES YES YES

R² 0.81 0.81 0.81 0.81 0.81

No. of Obs. 10,880 10,880 10,880 10,880 10,880

Data are restricted to firms that borrow from both government and private banks. Robust standard errors are reported in parentheses. A unit of observation is a firm-bank-type pair, as all loans of a firm given by the same bank type are summed. There are thus 5,440 firm fixed effects and 10,880 total observations in the regression. Politically Connected⫽dummy for whether firm has a politician on its board; Government bank⫽dummy for government banks; Win/WinParty⫽politician/political party’s winning frequency (%);

Percentage Votes⫽percentage votes obtained by politician; Victory Margin⫽Difference in percentage votes between the winner and runner up if politician won, 0 otherwise; Electoral Participation⫽Registered votes cast (%).

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Results: The Impact of Winning

tician board member wins an election, the firm partly substitutes borrowing from private banks toward government banks. Win- ning politicians exercise their increased political strength to obtain even greater preferential access to credit from government banks.

Column (2) shows that if a politician’s political party wins, the firm connected to him also benefits by getting greater access to credit from government banks (13.2 percent). Since a politician may both win and his party may also be in power, column (3) introduces the two effects together and shows that they both have independent effects. Column (4) interacts the politician winning with his party winning as well, and shows that there is no additional benefit of both winning and being in the winning party.

TABLE VII

TIMESERIESTEST OFPOLITICALSTRENGTH

Dependent variable Log loan size

Data restricted to politically connected firms that experience change in political status

(1) (2) (3) (4)

In power? ⫺0.120 ⫺0.106 ⫺0.105

(0.027) (0.028) (0.027)

In powerⴱ government bank

0.186 0.170 0.168

(0.032) (0.032) (0.033)

Party in power? ⫺0.132 ⫺0.120 ⫺0.120

(0.028) (0.028) (0.028) Party in powerⴱ

government bank

0.170 0.153 0.150

(0.033) (0.033) (0.036) In powerⴱparty in

powerⴱ government bank

0.008 (0.040) Fixed effects Firmⴱbank-

type, quarter

Firmⴱbank- type, quarter

R² 0.79 0.79 0.79 0.79

No. of Obs. 29,405 29,405 29,405 29,405

Data are restricted to those politically connected firms that actually experience a change in their “power”

status due to elections or their party experiences such a change. There are 2,330 such firms. The data are also restricted to only those quarters when an elected government was actually in power; i.e., we exclude quarters where the old government was disbanded but no new government elected as yet and quarters under military rule. The included quarters are 1996 Quarter 2 and Quarter 3; 1997 Quarter 2 to 1999 Quarter 3. In any given quarter the loans for a given firm from a given bank type (government or private) are summed up. Robust standard errors reported in parentheses. In power⫽dummy for whether politician is in power (won relevant election) during the given quarter; Party in power⫽dummy for whether politician’s political party forms the government for the given quarter (winning parties were different in the two elections in our data period);

Government bank⫽dummy for government banks.

Equation

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Concurrent Explanations

Social Lending, the hypothesis that firms with politicians on their boards are more likely to engage in projects with high social return but low private return, whereas government banks value social returns relatively more. Direct evidence against it is presented (Table VIII)

Efficient lendingcould be going on, assuming that political firms are less productive and private banks have higher cost of funds. Unlikely, since even the average political loan loses money. Moreover, hard to reconcile with differential treatment by government banks and with loans increasing in political power

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The Costs of Rents

DWLof taxation computed as 0.4 (estimated marginal cost of taxation) times the estimated transfer made from taxpayers to borrowers. Result:

7.2-13.7 billion rupees, or 0.16-0.3% of GDP, where lower bound is considered as rent-driven only the political loans and the upper bound considering all loans

Cost of investment distortionis estimated assuming that investment from corrupt lending has zero net return and using a estimated Market to Book ratio of 2.96. Computing (2.96−1)·(InefficientGov.Lending) produces estimates of Rs.35-67, or 0.8-1.6% of GDP. This is conservative since non-defaulted bank lending is also poorly invested (Table IX).

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Outline

1 Motivation

3 Methodology

4 Results

5 Conclusion

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Conclusion

Paper develops analysis of rent provision in financial markets, identifying preferential treatment to politically connected firms in government banks, a in a manner increasing in political power

With respect to policy, the absence of such phenomena in private banks lends credit to (cautious) privatization policies. Caution is advisable since there is little understanding of alternative rent extraction mechanisms

Other promising questions are what are the impacts of the identified phenomena on industry structure and to what extent do firms commit resources to explore the available rent extraction possibilities

Also, to what extent does political competition and electoral participation impose checks on politicians and diminish rent extraction ?

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Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market