Beyond Financial Constraints?

Finance Barriers to Growth and Productivity

13th December 2016

Stuart Fraser

Introduction

• There are longstanding concerns that the development and growth of small firms is impeded by financial constraints (see e.g. Wilson Committee, 1979; Breedon, 2012).– Issues leading to supply side “rationing” may result in viable firms

being rejected by finance providers.

• However it is increasingly recognised that demand side “self-rationing” is potentially a bigger issue.– “Self-rationed” aka discouraged borrowers (DBs) are firms with capital

needs but which do not apply for fear of rejection.

• A research program was instigated by BIS and bank stakeholders in October 2013 to investigate the issue of discouragement.– The research led to insights into the “arc of discouragement” (see

Fraser, 2014).

• Based partly on insights from this research, the current research is centred on the effects of previous financing experiences on the development of the firm.

Case study insights• The DB case studies highlighted that the principal factor underlying

discouragement were negative previous financing experiences… “It's [previous experiences] left me reluctant to apply again.” KS, Hotel

owner (£1m turnover), Northern Ireland

• These experiences left business owners both with less capital andfeeling more uncertain going forward…

“You can always find 20 grand, but for example we know we are going to have a working capital shortfall of £250k, so we need to think how we’ll fill that gap, or where we’ll find £1m to rebuild the factory.” D, Seafood distribution (£12m turnover)

• …with the result that the entrepreneur is unsure whether to continue with their growth plans for the firm or give up altogether…

“It gets to a point where you’re thinking ‘I’m earning less than my staff, why am I taking this risk? Why don’t I just work for someone else as the hassle isn’t worth it.’” D

Previous research• There has been much focus in the credit market literature on “good”

(fundable) businesses which are unable to obtain the funding they've applied for:

– Information asymmetries lead to adverse selection/moral hazard problems which potentially lead to market failure (Stiglitz and Weiss, 1981).

• But some “good” firms may not even apply despite having capital needs.

– In Kon and Storey (2003) “good” borrowers under-invest because applying for loans is costly and the bank might mistake them for a “bad” borrower.

• The research literature also points to the benefits of relationship lending for small firms (e.g., Petersen and Rajan, 1994)

– Also, if banking relationships help to reduce screening errors this should decrease discouragement among “good” firms (and increase discouragement among “bad” firms).

• Han et al (2009) present evidence from US small firms supporting the view that discouragement acts as an “efficient self-rationing mechanism”

– Implicitly, banks and firms learn from relationships increasing allocative efficiency.

Previous research• Another strand of the research literature explains

macro/industry/firm dynamics in terms of a process of “experiential learning” (Arrow, 1962; Jovanovic, 1982; Frank, 1988)– Specifically, learning describes a Bayesian process by which the

firm learns about and develops its productivity from experience.– Starting with a prior belief/perception of productivity the firm

updates its perceptions (forming a posterior distribution) with information gathered from running the firm.

• As the firm learns it matures (growth slows) and becomes less risky (uncertainty diminishes)– In contrast, younger (less experienced) firms have higher and

more variable growth rates than older firms (e.g., Jovanovic, 1982).

– Smaller and younger firms (at an earlier stage of the “financial growth cycle”) also find it harder to access external finance due to a lack of track record (and collateral) (see e.g., Berger and Udell, 1998).

Current research• The essence of the model underlying the current research is that

financing experiences impact on the firm beyond traditional financial constraints.

• In addition to affecting the amount of capital available to the firm, financing experiences also have learning effects on the firm.

• In order for learning to take place the firm requires financial capital – insufficient capital may therefore constrain the development of the firm both in terms of its growth and the reduction of uncertainty.

• The model makes new predictions about the effects of previous financing experiences on the development of the firm.

• In particular, learning constraints interact with and amplify the impacts of financial constraints leading to a cycle of financial and learning constraints.

• The model also provides new insights into the issue of perceptions gaps (i.e., the difference between perceived and actual rejection probabilities) and the nature of information asymmetries between firms and finance providers.

Financing experiences

• Financing experiences relate to states of met and unmet capital needs.

• These experiences are comprised of elementary states (binary 1/0 variables) relating to capital needs (𝑐), discouragement (𝑑) and rejection (𝑟)

𝑐 = 0 : “happy non-seeker”𝑐 = 1, 𝑑 = 1 : discouraged borrower

𝑐 = 1, 𝑑 = 0, 𝑟 = 0/1 : successful/unsuccessful seeker

• Capital 𝑘 is the outcome of the firm’s entire history of financing experiences.

Learning and financing states

Three levels of learning underlie how the financing states 𝑐, 𝑑 and 𝑟 evolve:1. 𝑐 = 𝐶 𝜃𝑒 : i.e., capital needs evolve as the firm

develops and updates its perceived productivity, 𝜃𝑒.

2. 𝑟 = 𝑅 𝜃𝑏 : i.e., rejection decisions evolve as the finance provider (‘bank’) updates its perceptions of the firm’s productivity, 𝜃𝑏.

3. 𝑑 = 𝐷 𝜔𝑒 : i.e., feelings of discouragement evolve as the firm updates its perceptions of the probability of rejection, 𝜔𝑒.

Model of financing experiences and the firm

Model of financing experiences and the firm

• The NE quadrant explains the determination of financing events - it provides the “clock-face” of the model.

• Firms with capital needs 𝑐 = 1 have productivity perceptions 𝜃𝑒 > 𝜃𝑒0 .

• However capital needs are only a necessary but not sufficient condition for a finance application to be made.

• Firms with capital needs but which believe their probability of rejection is 𝜔𝑒 > 𝜔0 are discouraged borrowers 𝑐 = 1, 𝑑 = 1 (𝜔0 is a threshold which is decreasing in

application costs).• Firms which apply 𝑐 = 1, 𝑑 = 0 and who the bank

believes are creditworthy 𝜃𝑏 > 𝜃𝑏0 receive funding 𝑐 = 1, 𝑑 = 1, 𝑟 = 0 .

Model of financing experiences and the firm

• The other quadrants explain the demand/supply structure underlying the model - they provide the “clock's mechanism”.

• The NW quadrant explains capital needs in terms of an optimal investment path (“funding escalator”): 𝜃 𝑘

• The SW and SE quadrants relate to the bank’s lending criteria.– Firstly, the bank conducts an affordability check to determine

whether the firm can repay the funds (SW quadrant): the probability of default 𝜋 is increasing in loan size 𝐵 = 𝑘 − 𝑎.

– Secondly the bank determines whether the loan is profitable in terms of a default threshold 𝜏 determined by the bank’s funding costs (SE quadrant): the probability of rejection is 𝜔 𝜏 = 𝑃𝑟𝑜𝑏 𝜋 > 𝜏 .

• Firm borrowing decisions depend not on 𝜔 𝜏 but on the perceived probability of rejection 𝜔𝑒 𝜏 .

Model of financing experiences and the firm

• The (Bayesian) learning processes that determine 𝜃𝑒 , 𝜃𝑏

and 𝜔𝑒 explain the dynamics of the model - they provide the “clock movement”.

• As 𝜃𝑒 evolves the firm moves up or down the funding escalator (e.g. encountering financial constraints after exhausting internal finance).

• As 𝜃𝑏evolves the bank learns about the firm's creditworthiness reducing screening errors.

• As 𝜔𝑒 evolves the firm learns about the true probability of rejection reducing perceptions gaps 𝜔𝑒 − 𝜔; the firm makes more efficient borrowing decisions (leading to “efficient self-rationing”).

• The fly in the ointment is that previous financing experiences involving unmet capital needs may inhibit learning - the clock might run slowly or stop altogether!

Effects of financing experiences (perceptions gaps)

0.71*** 0.71*** 0.69***0.63***

0.52***

0.33***

0.07

-0.18***

-0.31***

-0.58***

0.33***0.40*** 0.40*** 0.37***

0.31***0.20***

0.04

-0.11***-0.18***

-0.35***

0.96*** 0.93*** 0.90***0.84***

0.71***

0.45***

0.10

-0.26***

-0.45***

-0.76***

<1% 2-5% 6-10% 11-25% 26-50% 51-75% 76-90% 91-95% 96-99% >99%

Risk distribution percentiles

Perceptions gaps (perceived minus actual probability of rejection): middle-aged firms

Mean Previous applicant Previous discouraged Significant at:10% level *5% level **1% level ***

Effects of financing experiences (financial and learning constraints)

Cycle of financial and learning constraints

• Unmet capital needs lead to a financial constraint which lowers average performance.• Unmet capital needs also creates a learning constraint resulting in increased uncertainty.• The combination of lower average performance and increased uncertaintyraises the likelihood of future unmetcapital needs.• In essence, having fallen off the fundingescalator, the firm may become trappedin a cycle of unmet capital needs, loweraverage performance and increaseduncertainty which may end in firmclosure.

Financial and learning constraints• To get out of this cycle the firm may revise downwards its

expectations of future productivity (in effect, by cancelling its investment plans).

• This reduces uncertainty and returns the firm to the funding escalator.

• However the firm becomes stuck at a sub-optimal level relative to where the firm would be had finance been originally available.

• This corresponds to a state of stagnation and provides an explanation for ‘discouraged non-borrowers’.

• While the firm no longer has (new) capital needs, it is an ‘unhappy’ rather than a ‘happy’ non-seeker.

• Preliminary analysis indicates that unmet capital needs increase expectations of firm closure and the variability of growth expectations (consistent with the presence of both financial and learning constraints).

Next steps…

• The initial analysis has been based solely on (cross sectional) SME Finance Monitor (SMEFM) data.

• However using the linked SMEFM IDBR data-set (developed by ERC) will facilitate analysis of the causal effects of previous financing experiences on future firm performance, financing decisions and outcomes.

• It is also important to analyse these effects for firms at different stages of the funding escalator and with different needs/expectations (e.g., strategic vs non-strategic firms).

• And where possible to look at non-bank financing experiences – although absence of non-bank discouragement data presents analytical challenges…

0.100.14 0.13 0.12 0.06

-0.04*

-0.18-0.13

-0.95***

0.06 0.09 0.09 0.08 0.05

-0.03

-0.14-0.06

-0.85

0.38*** 0.39***0.34***

0.27**

0.13

-0.08

-0.33***

-0.61*

-1.00***

<1% 2-5% 6-10% 11-25% 26-50% 51-75% 76-90% 91-95% 96-100%

Risk distribution percentiles

Perceptions gaps (perceived minus actual probability of rejection): young firms

Mean Previous applicant Previous discouraged Significant at:10% level *5% level **1% level ***

0.30*** 0.29*** 0.28*** 0.26***0.21***

0.13***

-0.02

-0.13*-0.19**

-0.36***

0.04 0.03 0.02 0.01

-0.01 -0.01

0.00

0.00

0.00

-0.05

0.68***0.65***

0.62***0.56***

0.45***

0.25***

-0.04

-0.24**

-0.37***

-0.66***

<1% 2-5% 6-10% 11-25% 26-50% 51-75% 76-90% 91-95% 96-99% >99%

Risk distribution percentiles

Perceptions gaps (perceived minus actual probability of rejection): mature firms

Mean Previous applicant Previous discouragedSignificant at:10% level *5% level **1% level ***

0.05*

0.02

0.09**

0.060.08

0.04

0.29**

0.25*

0.08**

-0.01

0.16***

0.06

Previous discouraged vs needsmet internally

Previous discouraged vs needsmet by bank

Previous rejected vs needs metinternally

Previous rejected vs needs met bybank

Effects of unmet credit needs on the probability of firm closure (% points)

Young firms Middle aged firms Mature firmsSignificant at:10% level *5% level **1% level ***

0.01**

0.01*0.01**

0.01

0.01*

0.01

0.03

0.02

0.03**

0.01

0.05***

0.03*

Previous discouraged vs needsmet internally

Previous discouraged vs needsmet by bank

Previous rejected vs needs metinternally

Previous rejected vs needs metby bank

Effects of unmet credit needs on the joint probability of firm closure and high growth expectations (uncertainty of outlook) (% points)

Young firms Middle aged firms Mature firmsSignificant at:10% level *5% level **1% level ***