This document presents the key findings of a study that develops a theoretical framework to quantify the impact of credit market frictions on aggregate output and productivity. The study assesses these impacts using firm-level data on employment and default risk from UK administrative surveys. The main findings are: 1) Credit market frictions substantially depressed UK output between 2004-2012, reducing it by 3-5% annually on average. This impact worsened during the financial crisis and lingered thereafter. 2) Credit frictions can explain 11-18% of the fall in UK productivity between 2008-2009 and 13-23% of the post-crisis productivity gap in 2012. 3) The results are mainly

1. The Aggregate Effects of Credit Market Frictions:
Evidence from Firm-level Default Assessments
Tim Besley, Isabelle Roland, and John Van Reenen
Joint BIS-IMF-OECD conference
“Weak productivity: the role of financial factors and policies”
January 2018
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2. Role of financial factors in explaining weak productivity
after the Great Recession?
We develop a tractable framework to quantify the impact of credit
frictions on aggregate output and productivity
We assess two channels:
Capital shallowing: Are firms deprived of credit?
Misallocation: Are the most productive firms relatively more deprived
of credit?
We show how the theoretical framework can be taken to the data
with minimal data requirements (employment and default risk)
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3. Role of financial factors in explaining weak productivity
after the Great Recession?
Approach grounded in literature on the aggregate consequences of
firm-specific factor price distortions (e.g. Hsieh and Klenow, 2009)
Firm-specific distortions on the price of capital are a “black box”
measured using data on value added and capital stocks
We try to narrow down these distortions to factors that determine a
firm’s “access to credit” and measure them directly
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4. UK case study for empirical implementation
16% gap between trend and actual labour productivity at end 2015
GDP/hour Q4 2007=100, trend=2.3% p.a.(Q1 1979-Q2 2008 average) Note: Q2 2008=start of recession. Source: ONS
How much of this gap is related to financial factors?
Historical comparison International comparison
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5. Preview of findings
Credit frictions substantially depress output and labour productivity
On average over 2004-2012 UK output was 3% to 5% lower per
annum due to credit market frictions
Impact worsened during the crisis and lingered thereafter
Frictions account for
11% to 18% of productivity fall in 2008-2009
13% to 23% of the productivity gap at the end of 2012
Results mainly driven by deterioration of average default risk (capital
shallowing) as opposed to misallocation of credit (TFP losses)
Results mainly driven by small and medium sized firms (SMEs)
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6. Roadmap
Theoretical framework
Data and measurement issues
Core results
Micro-economic implications
Macro-economic implications
Misallocation versus capital shallowing
SMEs versus large firms
Comparison with traditional quantity-based approach (if time)
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7. Theoretical framework: Firm-level decisions
Production: Ynt = θnt L1−α
nt Kα
nt
η
with η < 1
Factor demands maximize
Πnt = Ynt −
wtLnt
τL
nt
−
ρtKnt
τK
nt
FOCs for L and K imply
Ynt = θ
1
1−η
nt ψ (wt, ρt) τnt
where
τnt ≡ τL
nt
(1−α)η
1−η
τK
nt
αη
1−η
Frictionless world: τL
nt = τK
nt = 1 for all firms n = 1, ..., N → output
solely determined by θnt, α and η, and the frictionless factor prices
Monopolistic competition
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8. Theoretical framework: Aggregate implications
Yt = ψ(wt, ρt)ˆθ
1
1−η
t Θt
where ˆθt is aggregate TFP and
Θt =
N
n=1
ωntτnt
where ωnt = θnt
ˆθt
1
1−η
are relative productivity weights
Θt represents aggregate impact of factor market distortions
Θt = 1 when τL
nt = τK
nt = 1
Equilibrium wage
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9. Capital shallowing or misallocation?
Θt can be decomposed into two parts
Θt = ΘS
t ΘT
t
Scale effect ΘS
t : Reflects the impact of factor market distortions on
aggregate inputs
Reducing aggregate credit distortions induces aggregate capital
deepening and higher output
TFP effect ΘT
t : Reflects how factor market distortions covary with
the productivity levels of firms, i.e. allocational efficiency
Channelling credit to most productive firms induces higher aggregate
TFP and higher output
Technical details
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10. Measurement of relative productivity at the firm level
Method 1: Solow residuals using data on value added, the wage bill,
and capital stock estimates
Method 2: Use theory as a guide
ωnt =
γntΘt
τnτL
nt
where
γnt =
Lnt
Nt
n=1 Lnt
where Lnt denotes the employment of firm n at time t
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11. Output and labour productivity losses
Derive a counter-factual level of output associated with reference
level of distortions ˆτK , ˆτL and ˆΘt
Deviation of actual output from its reference level is given by
ˆYt − Yt
ˆYt
= 1 −
Θt
ˆΘt
1−η
1−αη
Change in labor productivity that can be explained by changes in
distortions is given by
∆ log wt =
1 − η
1 − αη
ln ˆΘt − ln ˆΘt−1
Our reference point is τK
nt = 1, i.e. no credit market distortions
(with or without labour market frictions)
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12. Measurement of credit frictions
How do we measure τK
nt?
Micro-found a measurable proxy for credit frictions
Simple model of equilibrium credit contracts with moral hazard
(unobserved costly managerial effort)
Capital allocation
ΠKt =
ρt
φ∗
nt
where ρt is bank’s cost of funds and φ∗
nt the equilibrium repayment
probability
MPK = Lender’s risk-adjusted cost of funds
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13. Measurement of credit frictions
Simple micro-foundation for credit frictions: τK
nt = φnt
Equilibrium repayment probability φ∗
nt can fall because of
Factors affecting profit function, e.g. more challenging business
conditions
Balance sheet deterioration, e.g. a fall in collateral value
Higher switching costs as lenders are less keen for new business
Lower default risk means higher inputs (labour and capital) and
output, all else equal
Full model
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14. Data and measurement issues
Annual Business Inquiry and Annual Business Survey
Establishment level administrative surveys (ONS)
Census of large businesses and stratified random sample of SMEs
Non-financial market sector
Measure productivity as real gross value added per employee;
estimate capital stocks (PIM)
Use sampling weights to measure aggregate productivity
developments
Estimate default risk using credit scoring model (S&P’s)
Inputs: BvD company accounts, industry, and macroeconomic factors
Output: risk score (aaa, bbb, etc.)
Match risk score to historical default rates to capture historical
information set of lenders
Sample size Productivity developments Sample representativeness
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15. Default probabilities
Aggregate probability of default at the 1-year horizon (in %)
Probability of default systematically larger for SMEs
Increase after 2007 is significant for both types of firms
Aggregate developments largely driven by SMEs
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16. Firm-level implications
Do repayment probabilities affect firm behaviour as suggested by the
theory?
OLS with year and firm fixed effects
Default risk is significant indicator of firm performance
Non-trivial coefficients: e.g. 10pp increase in repayment probability
associated with a 9% increase in investment
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17. Aggregate implications: core results
On average 3% to 5% output loss per annum in 2004-2012
Sustained increase in losses from 2007 onwards, lingers thereafter
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18. Aggregate implications: core results
Scale effect is main driver of output losses (capital shallowing)
Small losses due to misallocation (TFP term)
Increase in misallocation losses since 2009 but relatively small
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19. How much of the productivity gap can we explain?
Real GVA per worker - actual versus trend, 2007=100. Source: ABI & ABS surveys, authors’ calculations.
Had default risks remained at their 2007 level, output would have
been approx. 2% higher in 2012
18% of the gap between actual and trend output by end 2012
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20. SMEs versus large firms
Higher output losses among SMEs
Aggregate deterioration driven by SMEs
Scale effects dominate in both cases
Demand effects
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21. Robustness checks
Results on credit frictions are robust to
Using Solow residuals to measure ωnt
Using empirical factor shares instead of calibrated α
Including labour market frictions
Inferred from the data using the firms’ first-order conditions
τL
nt =
wtLnt
(1 − α)ηYnt
Does not alter the conclusions from the thought experiment τK
nt = 1
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22. Comparison with quantity-based approach
We study specific aspect of credit market frictions
However, capital market imperfections encompass more than what
default risk captures
Traditional measure of capital market distortions (e.g. Hsieh and
Klenow, 2009)
τK
nt =
ρtKnt
αηYnt
Comparison with our direct measure for the manufacturing sector
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23. Comparison with quantity-based approach
Pros
Wider range of distortions, e.g. adjustment costs, capital taxes and
subsidies
Measurement error in default risk: lenders could use other
unobservable information
Cons
All of the measurement error in Ynt and Knt is now attributed to
factor market distortions
Measurement error is a very serious problem, e.g. Rotemberg and
White (2017) show that the conclusions in Hsieh and Klenow (2009)
are very sensitive to standard data cleaning procedures
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24. Comparison with quantity-based approach
Quantity-based approach: average τK
n around 0.14 versus average
repayment probability of 0.89 (default risk is only about 16% of
total distortions)
Larger dispersion in the quantity-based measures
Positively correlated although the R2 is low (0.06) (coeff is 3.32 and
the (robust) se is 0.29)
Much larger losses (as in Gilchrist, Sim, and Zakrajsek, 2013)
If capital market distortions were removed completely, manufacturing
output would be 43% to 45% higher!
Capital distortions appear to be getting worse over time
However, the impact of the financial crisis is much less visible!
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25. Conclusions
Developed a theoretical-empirical framework to quantify the impact
of credit frictions on output and productivity
Data requirements: employment and default risk
UK Case Study with rich administrative firm-level panel data
Substantial output and productivity losses from generalized increase
in default risk
Little evidence of a sizeable contribution of worsening allocational
efficiency
Worsening since 2007 - mainly due to frictions on SME credit markets
Contrasting results of quantity-based approach
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26. UK case study for empirical implementation
Slowdown stands out in historical perspective
Output per worker, 2008-09 recession and previous 3 UK recessions. Pre-recession peak=100. Source: ONS.
UK productivity puzzle
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27. UK case study for empirical implementation
Slowdown stands out in international comparisons
GDP/hour, 2007=100. Source: OECD and ONS.
UK productivity puzzle
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28. Theoretical framework: Firm-level decisions
This is a “Lucas span of control model” where profits are a return to
ownership of technological/managerial capital θ
The model could also be interpreted as a model with monopolistic
competition where
η = 1 −
1
ε
and ε is the elasticity of demand. Firm decisions
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29. Theoretical framework: Aggregate implications
ρt is determined in global capital markets
Exogenously fixed aggregate labor supply L
Equilibrium wage is
wt =
(1 − α)ηψ(wt, ρt)ˆθ
t
1
1−η
t Θt
L
Aggregate implications
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30. Capital shallowing or misallocation?
Yt = TFPt × SCALEt
TFPt ≡ ˆθtΘT
t
SCALEt ≡ ˆθ
η
1−η
t ψ(wt, ρt)ΘS
t
Θt = ΘS
t ΘT
t
Misallocation
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31. Measurement of credit frictions
Firms
Risk neutral
Heterogeneous productivities θn (TFP or demand shocks) and
collateral An
Produce using labor Ln and capital Kn
Borrow Bn from banks and Kn = An + Bn
Output is stochastic - Production takes place or fails (0)
Manager exerts costly effort which determines the probability of
success φn
Effort is not observed by lenders
Solution overview
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32. Measurement of credit frictions
Lenders
Risk neutral
Compete and offer credit terms {Bn, Rn} tailored to a firm’s
characteristics {θn, An}
Access funds at cost ρ > 1
Seize firm’s collateral An if output is 0
Lending contracts - timeline
1 Nature assigns each firm to a bank
2 Banks offer credit contracts {Bn, Rn} given firm’s outside option
U (θn, An) (assume exogenous and binding for now)
3 Manager chooses effort to maximize expected profits
4 Default occurs with probability (1 − φn) in which case firm loses An
5 If there is no default, firm makes labor hiring decisions, produces, and
repays Rn
Solution overview
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33. Measurement of credit frictions
Optimal repayment probability (stage 3)
Choice of default probability maximizes firm’s expected profits given
any credit contract {Rn, Bn} offered
First order condition for incentive compatible effort implies
φn = f (Π (θn, w, An + Bn) − Rn + An)
φn increases in profit and collateral but decreases in interest payment
Solution overview
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34. Measurement of credit frictions
Optimal lending contracts (stage 2)
Credit contract maximizes bank’s expected profits s.t. IC effort
Focus on case where firm’s outside option binds (pins down Rn)
Maximise bank’s profit function with respect to Bn yields
ΠK (θn, w, An, B∗
n ) =
ρ
φ∗
n (An, θn)
MPK = Lender’s risk-adjusted cost of funds
Lower default risk means more capital, all else equal
Model yields a simple micro-foundation for credit frictions: τK
nt = φ
Solution overview
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35. Measurement of credit frictions
Outside option (stage 1)
Suppose there is a switching cost, κ, from moving to another bank
Define the outside option which generates zero profits for a
competing bank as ˜U (An, θn)
This is the best possible terms that another bank would offer
Equilibrium outside option is
U (θ, A) = ˜U (A, θ) − κ
Solution overview
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36. Sample size
Data and measurement issues
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37. Annual labour productivity growth (in %) by firm size and
sector
Data and measurement issues
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38. Sample is representative of aggregate developments
Labour productivity in the “market sector” (2007=100)
Data and measurement issues
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39. SMEs versus large firms: Role of demand effects?
Credit frictions as measured by default risk matter mainly for SMEs
Have large firms suffered larger demand shocks? (exports etc.)
Difference between fundamental and time-varying TFP suggests so
Core results: SMEs versus large firms
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