ON THE DETERMINANTS OF DIFFERENT TYPES OF FINANCIAL

CONSTRAINTS

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• It is almost common knowledge that young/small firms pay higher interest rates than mature/large firms and that young firms have more difficulties in the access to credit.

• Theoretical and empirical literature explains these two problems as the consequence of informational asymmetries and contract incompleteness.

• It is in general assumed that both types of imperfections are originated in the same problems, and therefore it is expected that the same type of firms will face any of the two problems with the same intensity.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• Empirical literature have for instance tried to identify financially constrained firms using approaches such as investment cash-flow sensitivities assuming that any financial imperfection affects firm’s behaviour in the same way.

• I am using survey responses of firms around the world from the WBES 2000, that with the right methodology can give us a clear identification of firms facing different financial constraints.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• Given the database I’m using (WBES), my results can be partially compared to those of Beck et al (2003,2004), Clarke et al. (2002), Winker (1999).

• My results are also related to those of Petersen and Rajan (1994), that estimate the effect of banking concentration on the access and the price of credit.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• Beck et al used the same database (WBES) to determine the effect of competition on firms’ access to credit and on some other variables that indicate the presence of financing obstacles.

• Their analysis is based on some of the variables of the survey that proxy for financial constraints and firm’s access to credit.

• Petersen and Rajan used an indirect variable, “trade credit discounts not-taken” to proxy for credit rationing.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• I first use a multivariate analysis to construct adequate measures of the presence and intensity of credit rationing(low access to credit) and of high costs of bank credit.

• Once I constructed these new variables, I used them in a regression analysis to determine which are the main determinants of the two constraints.

• I adjust standard errors for a possible common country-level effect in the error term.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• Using this methodology I avoid some econometric problems that would be present otherwise if we use the variables of the WBES that proxy for different financing constraints and for the firm’s access to credit.

• I also have a better indicator of access to credit than those used (for instance) by Petersen-Rajan, and much better proxies for financial constraints than investment cash-flow sensitivities.

• FIRST STEP: CONSTRUCTION OF INDEXES OF FINANCIAL CONSTRAINTS USING CATPCA

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

The variables included in the two CATPCA models are:

• HINT, high interest rates, (1=no problem, 4=high problem)

• LTLOAN, access to long-term loans, (1=no problem, 4=high problem)

• COLL, collateral requirements, (1=no problem, 4=high problem)

• FN_RE, % Retained earnings

• BNK, dummy variable, 1=firm uses bank credit, 0=firm does not use bank credit.

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• The indexes I aim to construct using CATPCA should be as following:

REFNaBNKaCOLLaLTLOANaCREDRAT _4321

REFNbBNKbCOLLbHINTbHIGHCOST _4321

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• I expect that CREDRAT index is related to the following variables:

• AGE, negative

• SIZE, negative

• LEG_RIGHT, negative

• CRED_INF (PUB_REGSTR, PRIV_BUREAU), negative

• CONCENTR, positive/negative

• GROWTH, negative

• SQRT_AGE, SQRT_SIZE, not related

DETERMINANTS OF HIGH COSTS OF DEBT AND CREDIT RATIONING

• I expect that HIGHCOST index is related to the following variables:

• AGE, negative• SQRT_AGE, positive• SIZE, negative• SQRT_SIZE, positive• LEG_RIGHT, negative• CRED_INF (PUB_REGSTR, PRIV_BUREAU),

positive/negative.• CONCENTR, positive/negative• GROWTH, negative

FIRST AND SECOND COMPONENT OF CREDIT RATIONING MODEL:

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

-0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8

FN_RE

BNK

LTLOAN

COLL

UNCONST

CREDRAT

FIRST AND SECOND COMPONENT OF HIGH COSTS MODEL:

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

-0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8

HINT

COLL

FN_RE

BNK

HIGHCOSTUNCONST

• REGRESSION RESULTS WITH ROBUST STANDARD ERRORS

credrat credrat credrat credrat credrat

R2 0,1168 0,1101 0,1127 0,1296 0,1241Prob > F 0,000 0,000 0,000 0,000 0,000

size -0,091 -0,093 -0,361 -0,097 -0,103(0,002)*** (0,003)*** (0,262) (0,001)*** (0,001)***

sqrt_size 0,736(0,394)

age -0,003 -0,003 0,000 -0,003 -0,003(0,002)*** (0,010)*** (0,872) (0,002)*** (0,008)***

sqrt_age -0,043(0,205)

leg_right -0,014 0,003 -0,013 -0,015 0,001(0,526) (0,894) (0,534) (0,476) (0,979)

cred_inf -0,09 -0,086 -0,086(0,000)*** (0,000)*** (0,000)***

pub_regstr 0,001 0,001(0,913) (0,860)

priv_bureau -0,006 -0,006(0,002)*** (0,002)***

conctrtn 0,634 0,697 0,624 0,618 0,685(0,016)** (0,004)*** (0,018)** (0,019)** (0,005)***

growth -0,035 -0,052 -0,034 -0,036 -0,053(0,013)** (0,000)*** (0,015)** (0,009)*** (0,000)***

sccr_d -0,238 -0,239(0,000)*** (0,000)***

Infrml_d -0,12 -0,146(0,027)** (0,006)***

highcost highcost highcost highcost highcost highcost

R2 0,037 0,0293 0,0418 0,0355 0,1397 0,1396Prob > F 0,0001 0,0025 0,0000 0,0000 0,0000 0,0000

size 0,046 0,044 -0,575 -0,5 -0,618 -0,556(0,192) (0,242) (0,075)* (0,167) (0,057)* (0,118)

sqrt_size 1,599 1,384 1,82 1,646(0,061)* (0,146) (0,035)** (0,08)*

age -0,003 -0,003 -0,011 -0,013 -0,011 -0,012(0,063)* (0,073)* (0,000)*** (0,000)*** (0,000)*** (0,000)***

sqrt_age 0,093 0,114 0,09 0,109(0,002)*** (0,000)*** (0,001)*** (0,000)***

leg_right -0,047 -0,047 -0,046 -0,046 -0,04 -0,038(0,019)** (0,031)** (0,017)** (0,029)** (0,041)** (0,068)*

cred_inf 0,065 0,06 0,05(0,020)** (0,031)** (0,063)*

pub_regstr 0,006 0,005 0,004(0,233) (0,290) (0,321)

priv_bureau 0,003 0,002 0,002(0,147) (0,224) (0,256)

conctrtn -0,183 -0,264 -0,137 -0,208 -0,123 -0,188(0,515) (0,455) (0,611) (0,537) (0,662) (0,561)

growth -0,011 0,001 -0,013 -0,001 -0,009 0,001(0,544) (0,940) (0,503) (0,960) (0,615) (0,954)

sccr_d 0,557 0,575(0,000)*** (0,000)***

Infrml_d 0,489 0,503(0,000)*** (0,000)***

• EFFECT OF AGE ON HIGHCOST INDEX ACCORDING TO ESTIMATION RESULTS

-1,500

-1,200

-0,900

-0,600

-0,300

0,000

0,300

0,600

0 20 40 60 80 100 120 140 160 180 200

AGE

hig

hco

st

• EFFECT OF SIZE ON HIGHCOST INDEX ACCORDING TO ESTIMATION RESULTS

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 1 2 3 4

SIZE

hig

hco

st

•  Comparison of Results (Ordinal Probit) Individual Variables

ltloan hint coll gcf ltloan hint ltloan hintPseudo R2

0,0329 0,0124 0,0062 0,0304 0,033 0,0139 0,0406 0,0176

Prob >Chi2 0,000 0,1505 0,0069 0,000 0,000 0,0049 0,000 0,003

size -0,046 -0,001 -0,044 -0,105 -0,526 -0,788 -0,016 -0,823

(0,253) (0,988) (0,248) (0,019)** (0,203) (0,042)** (0,676) (0,037)**

sqrt_size 1,263 2,045 2,178(0,261) (0,045)** (0,036)**

age -0,005 -0,003 -0,004 -0,003 -0,007 -0,01 -0,004 -0,011

(0,003)*** (0,07)* (0,004)*** (0,040)** (0,048)** (0,003)*** (0,007)*** (0,002)***

sqrt_age 0,024 0,082 0,086(0,528) (0,035)**

leg_right -0,048 -0,04 -0,019 -0,032 -0,048 -0,041 -0,044 -0,038

(0,126) (0,258) (0,386) (0,320) (0,127) (0,251) (0,171) (0,284)

cred_inf -0,068 -0,039 0,016 -0,073 -0,069 -0,044 -0,073 -0,047

(0,026)** (0,300) (0,536) (0,002)*** (0,022)** (0,244) (0,016)** (0,214)

pub_regstr 0,763 -0,005(0,038)** -0,991

priv_bureau -0,045 -0,033(0,025)** -0,227

conctrtn 0,789 -0,039 0,22 0,171 0,806 0,001 -0,044 -0,038

(0,033)** (0,930) (0,389) (0,632) (0,028)** (0,998) (0,171) (0,284)

growth -0,046 -0,034 -0,03 -0,048 -0,046 -0,034 -0,073 -0,047

(0,024)** (0,221) (0,07)* (0,011)** (0,025)** (0,211) (0,016)** (0,214)

sccr_d 0,208 0,124(0,000)*** (0,047)**

Infrml_d 0,336 0,188(0,000)*** (0,009)***

Conclusions

• I have found that credit rationing clearly decreases with firms’ age and size and the effect of size and age on credit rationing is only negative and linear .

• The perception about high costs of debt also decreases with age and size, but the effect of these variables is not linear.

• The relationship between high costs of debt and firm age and size is u-shaped (inverted). The perception of high costs is low when the firm is very young, increases initially and finally starts decreasing and is lower for mature/larger firms.

Conclusions• The relationship of age with the perception of high

costs of credit could reflect different costs at different moments or the effect of a learning process of banks and firms about the true risk of firms.

• Very young firms may be willing to pay higher interest rates if they are not aware of their true risk. As they mature, they may find interest rates too high if banks do not learn about their risk as fast as the firms.

• Finally, when both banks and firms have learned about the true risk, firms may finally pay fair-priced interest rates.

Conclusions

• My empirical results also show that access to credit is negatively related with more banking concentration.

• Access to credit increases with more information sharing and decreases with a bad macroeconomic environment.

• “High costs of debt” decrease with a better regulation environment, and increase with more information sharing. However, it is not affected by banking concentration.