Life-cycle effects in small business finance

TLDR: In this article, the authors studied the life-cycle profiles of small firms' cost and use of credit using a panel of Finnish firms and found that firms are more dependent on financial intermediaries in the early periods of their lives.

Abstract: This paper studies the life-cycle profiles of small firms’ cost and use of credit using a panel of Finnish firms. The choice of method matters for the conclusions drawn about the relationship between firm age and financing costs; the cross-sectional age profiles of financing costs are hump-shaped and consistent with hold-up theories, whereas methods that control for cohort fixed effects demonstrate that the financing costs decrease monotonically as the firms mature. The life-cycle profiles of the use of credit also indicate that firms are more dependent on financial intermediaries in the early periods of their lives. Furthermore, the cohorts born during recessions pay higher financing costs and use smaller amounts of bank loans, even after their creditworthiness is controlled for. The recession cohort effect appears to be more related to the experience of starting-up the firm in the recession than to the CEOs growing up in a recession during their early adulthood.

Figures

Table 3 Life-cycle profiles of financing costs: model summary

Table 5 Life-cycle profiles of bank debt: model summary

Table 7 Cost and use of credit: the Finnish Great Depression cohorts Panel A: Financing costs

Table 4 Cross-sectional life-cycle profiles of bank debt Panel A: Years 2000-2006

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Life-cycle effects in small business finance
Ylhäinen, Ilkka
Ylhäinen, I. (2017). Life-cycle effects in small business finance. Journal of Banking and
Finance, 77, 176-196. https://doi.org/10.1016/j.jbankfin.2017.01.008
2017

Accepted Manuscript
Life-cycle effects in small business finance
Ilkka Ylh
¨
ainen
PII: S0378-4266(17)30006-7
DOI: 10.1016/j.jbankfin.2017.01.008
Reference: JBF 5078
To appear in: Journal of Banking and Finance
Received date: 27 June 2014
Revised date: 10 November 2016
Accepted date: 13 January 2017
Please cite this article as: Ilkka Ylh
¨
ainen , Life-cycle effects in small business finance, Journal of
Banking and Finance (2017), doi: 10.1016/j.jbankfin.2017.01.008
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Life-cycle effects in small business finance
Ilkka Ylhäinen
ETLA, The Research Institute of the Finnish Economy, Arkadiankatu 23 B, FI-00100 Helsinki, FINLAND
Jyväskylä University School of Business and Economics, P.O. Box 35, FI-40014 University of Jyväskylä, FINLAND
Email: ilkka.ylhainen@gmail.com, Tel. +358 44 709 8050
This version: November 10, 2016
Abstract
This paper studies the life-cycle profiles of small firms’ cost and use of credit using a panel
of Finnish firms. The choice of method matters for the conclusions drawn about the
relationship between firm age and financing costs; the cross-sectional age profiles of
financing costs are hump-shaped and consistent with hold-up theories, whereas methods that
control for cohort fixed effects demonstrate that the financing costs decrease monotonically
as the firms mature. The life-cycle profiles of the use of credit also indicate that firms are
more dependent on financial intermediaries in the early periods of their lives. Furthermore,
the cohorts born during recessions pay higher financing costs and use smaller amounts of
bank loans, even after their creditworthiness is controlled for. The recession cohort effect
appears to be more related to the experience of starting-up the firm in the recession than to
the CEOs growing up in a recession during their early adulthood.

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JEL classification: G21; G30
Keywords: life-cycle effects; small business finance; cohort effects
1. Introduction
How do the cost of credit and the use of bank finance evolve over the life cycle in small
business finance? The theories of financial intermediation, including that of Diamond (1989),
predict that informational asymmetries are most severe in the early periods of firms’ lives and
that such problems diminish over time as the firms mature (see also Boot and Thakor 1994).
Diamond (1991) also predicts that firms are more dependent on the monitoring provided by
banks early in their lives and switch to other sources of finance when their reputation
improves (see also Berger and Udell 1998). These theoretical frameworks suggest that the
cost of credit would decrease and the availability of finance would improve as the firm gets
older and does not default. Hold-up theories proposed by Sharpe (1990), Rajan (1992), von
Thadden (2004) and Kim et al. (2012) imply an alternative life-cycle profile for financing
costs: In a two-period framework, the competition between banks prompts them to offer low
borrowing rates to new firms in the first period. The firms become locked in after obtaining
the loan, however, as the bank gains an information monopoly over them. The bank then
extracts rents from the firms in the form of higher borrowing rates, which implies rising
financing costs in the next period. Kim et al. (2012) predict the full life-cycle profile. In their
model, there are rising interest rate mark-ups in the early periods of firms’ lives and
decreasing mark-ups for older firms whose quality has been revealed.

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Previous empirical studies that have analyzed the effects of firm age on the availability
and cost of credit have largely used cross-sectional datasets or short panels:
1
For instance,
Petersen and Rajan (1994, 1995) utilize cross-sectional data and find a negative correlation
between firm age and the cost and use of credit. Hyytinen and Pajarinen (2007) study a panel
of Finnish firms over the period 1999-2002 and find that the cost of credit is higher for
younger firms even after controlling for the observed and unobserved creditworthiness of the
firms. Sakai et al. (2010) suggest that the cost of credit is lower for older firms in their panel
of Japanese firms from 1997-2002. Kim et al. (2012) study Norwegian small business data
from 2000-2001, analyzing the life-cycle patterns of interest rate mark-ups. They find
evidence in favor of lock-in theories; young firms face a low mark-up, whereas there is a
rising mark-up for middle-aged firms and a falling mark-up for older firms.
The identification of the life-cycle profiles is difficult, however. To begin with, age
effects cannot be distinguished from unobserved firm-specific heterogeneity, including firm
quality, in cross-sectional data. Importantly, if there are cohort-specific differences in the
firms’ cost and use of credit, it is not possible to distinguish them from the age effects in the
cross-section. Additionally, in the presence of time- and cohort-specific effects, an
identification problem arises in the repeated cross-sections or panel data. Because there is a
linear relationship between age, period, and cohort effects (i.e., age=period-cohort), it is not
1
Degryse et al. (2009) summarize findings from the closely related literature on relationship banking.
Many studies that evaluate the effects of lending relationships on the cost or availability of credit are
cross-sectional. Firm age is a typical control variable in these studies, but there is also another
problem: it is difficult to distinguish bank relationship length effects from age effects (see, e.g.,
Petersen and Rajan 1994).

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Life-cycle effects in small business finance" ?

This paper studies the life-cycle profiles of small firms ’ cost and use of credit using a panel of Finnish firms. Furthermore, the cohorts born during recessions pay higher financing costs and use smaller amounts of bank loans, even after their creditworthiness is controlled for. 

Q2. What have the authors stated for future works in "Life-cycle effects in small business finance" ?

Overall, these findings suggest that recessions and periods of financial instability could have a lasting impact on the perceived riskiness of the firms and their use of external finance in the future. The future literature could further study the scope of the cohort effects in various institutional environments. Such persistent effects, observed even many years after the depression and banking crisis of the 1990s, are intriguing and might call for additional research to further understand their causes. Second, the life-cycle profiles of the cost and use of credit indicate that potential policy interventions would likely have best rationalization when targeting younger firms. 

Q3. How does the identification problem arise in the cross-sectional data?

in the presence of time- and cohort-specific effects, an identification problem arises in the repeated cross-sections or panel data. 

Q4. Why is the inclusion of the aggregated cohort dummies advantageous?

25 The inclusion of the aggregated cohort dummies is advantageous because they can be used to control for other cohort-specific trends in the cost and use of credit. 

Q5. What could explain why the recession cohorts were perceived as less of a quality?

The disruptions in lending relationships and other financial problems could explain why the firms might be perceived as of lower quality from the lenders’ point of view than otherwise identical firms born during stronger economic times. 

Q6. What are the macroeconomic variables that were matched to the dataset?

Several macroeconomic variables were matched to the dataset, including the aggregate country-level unemployment rates, GDP growth, house prices, and consumer prices, which were obtained from the databases of Statistics Finland. 

Q7. What is the key implication from the analysis?

One key implication from the analysis is that the lifecycle profiles estimated from cross-sectional datasets, whose use has been a common practice in the previous corporate finance literature, should be interpreted with caution. 

Q8. What is the coefficient of the recession-born dummy?

AC CEPT EDM ANUS CRIP T48Regarding the use of bank loans, the recession-born dummy is negative and highlystatistically significant. 

Q9. What is the definition of the problem of identifying age effects in cross-sectional data?

Petersen and Rajan (1995, 419) claim that they can identify the age effects in cross-sectional data under certain assumptions, namely the stationarity of the survival process of firms. 

Q10. What is the relevant informational asymmetries for young firms?

Because the informational asymmetries are likely to be the most relevant for relatively young firms, the firms older than 40 years are dropped from the sample.