Allocative Efficiency and Finance

1. Allocative Efficiency and Finance
Andrea Linarello — Andrea Petrella — Enrico Sette
Bank of Italy
Paris — January 10, 2018
Joint OECD-IMF-BIS Conference
“Weak productivity: the role of financial factors and policies”
The analysis and conclusions expressed in this paper are those of the authors and should not
be interpreted as those of the Bank of Italy

2. Motivation
Productivity is key for economic growth
Finance affects firms choices (capital, labor, investment) ⇒
productivity
Large literature looks at the effect of bank supply shocks on
productivity:
credit constraints become more binding
↓ firms investment (employment) and hence productivity
entry/exit decisions of firms
⇒ ↓ aggregate productivity
However, bank supply shocks may also foster allocative efficiency:
reallocation of resources from least to most productive firms
⇒ ↑ aggregate productivity

3. What we do
Do bank supply shocks affect aggregate productivity growth?
we use a unique data on the universe of Italian manufacturing firms
covering the period 2000–2015 (BdI-ISTAT)
guided by aggregate Melitz-Polanec (2015) decomposition we assess
the effect of credit supply shocks on productivity through different
margins:
1. intensive
⇒ average productivity and OP covariance among incumbents firms
2. extensive
⇒ the contribution of entry and exit

4. What we find
1. we find significant effects of bank supply shocks mostly during the
crisis (after 2008)
2. we find that credit supply shocks affect aggregate productivity
growth through different channels:
⇒ − average productivity
⇒ + reallocation
⇒ + exit
⇒ − entry
3. credit supply changes contributed to:
⇒ 1
4 drop in contribution of average productivity
⇒ 1
2 increase in the contribution of the within-sector reallocation

5. Related literature
Literature on productivity and allocative efficiency (Hsieh and
Klenow 2009, Bartelsman et al. 2009, Andrews and Cingano, 2014,
Gopinath et al. 2017, Sebnem Kalemli-Ozcan et al. 2012)
Literature on reallocation along the business cycle (Schivardi 2003,
Saks and Wozniak 2011)
Measures of allocative efficiency: Olley Pakes 1996, Bartelsman et
al., (2013) Melitz and Polanec (2015)
Growing literature on credit frictions and misallocation (Midrigan
and Xu 2014, Larrain and Stumpner 2015, Gopinath et al 2017,
Schivardi, Sette, Tabellini 2017, Manaresi and Pierri 2017)

6. Plan of the talk
1. Data and definitions
2. Decomposing productivity growth
3. Constructing and validating the bank shocks
4. Industry level analysis
5. Firm level evidence

7. Data
We use 2 data sources:
1. Firm data: ASIA – universe of Italian firms, including sole
proprietorships - focus on manufacturing.
2. Loans data: Credit registry. All firm-bank relationships above 75,000
euros. Amount granted and drawn – we look at granted, better
measure of credit supply.
⇒ sample period: 2000–2015
⇒ productivity measure: revenue per worker
⇒ caveat: we cannot match firm data with the credit registry

8. Descriptive stats: Firms
# firms # employees avg. size sales sales p.w.
000s 000s 000s 000s
2000 501 4435 8,8 957 215
2001 502 4454 8,9 957 214
2002 508 4523 8,9 930 205
2003 497 4549 9,1 894 196
2004 487 4464 9,2 924 207
2005 482 4410 9,1 925 209
2006 477 4393 9,2 970 220
2007 473 4431 9,4 1008 227
2008 459 4387 9,6 979 223
2009 438 4152 9,5 814 196
2010 426 4000 9,4 866 216
2011 425 3981 9,4 894 224
2012 417 3897 9,3 856 219
2013 407 3782 9,3 830 219
2014 396 3703 9,3 831 224
2015 389 3627 9,3 855 235

9. Comparison between National Accounts and Firm dataset
(a) trillion Euros at current prices
.7
.8
.9
1
1.1
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Value of production (National accounts)
Total sales (ASIA)
(b) growth rates
−20
−15
−10
−5
0
5
10
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Value of production (National accounts)
Total sales (ASIA)

10. Decomposing productivity growth
1. using granular data covering the universe of firms we are able to
mimic aggregate patterns
−20
−15
−10
−5
0
5
10
200120022003200420052006200720082009201020112012201320142015
VA (national accounts)
Sales per worker (ASIA)
2. we use the Melitz-Polanec (2015) decomposition
Φt − Φt−1 = (∆φSit + ∆cov(φSit, ωSit))
Incumbent
+ ωEt(ΦEt − ΦSt)
Entry
+
+ ωXt−1(ΦSt−1 − ΦXt−1)
Exit

11. Reallocation, entry and exit components
Avg. prod. Reallocation Entry Exit Total
2001 -1.2 2.2 -1.9 1.9 1.0
2002 -2.6 2.5 -2.1 1.5 -0.6
2003 -3.9 0.9 -1.4 2.4 -2.0
2004 -0.2 3.0 -1.5 2.6 3.9
2005 -2.8 3.0 -1.5 2.2 0.8
2006 1.3 3.8 -2.0 2.4 5.6
2007 -0.4 3.1 -2.2 2.6 3.1
2008 -3.7 0.3 -2.4 3.1 -2.6
2009 -18.0 2.0 -1.8 3.7 -14.0
2010 1.3 7.8 -2.6 4.0 10.5
2011 -3.1 4.8 -2.2 2.6 2.1
2012 -7.6 1.8 -1.9 3.1 -4.6
2013 -4.8 5.4 -2.2 3.4 1.8
2014 -2.9 5.6 -2.2 3.5 4.0
2015 0.4 4.0 -2.3 3.6 5.7
Total -3.2 3.4 -2.0 2.9 1.0

12. Allocative Efficiency and Finance
(a) contribution of reallocation to aggregate productivity growth
0
2
4
6
8
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(b) total credit granted to manufacturing firms
−10
−5
0
5
10
200120022003200420052006200720082009201020112012201320142015

13. Building the credit supply shock (Greenstone et al.; 2014)
We aggregate credit granted by each bank at the sector-time level and
we estimate the following model:
∆ ln(Lbst) = αbt + γst + bst
where:
∆ ln(Lbst) is the log change in credit granted by bank b to sector s
at time t.
αb,t are a set of bank*time fixed effects
γst are a set of sector*time fixed effects.
Then take weighted average of the αb,t at the sector level using as
weights shares of credit of each bank as of 1999 for the years 2000–2007
and as of 2006 for the years 2008–2015.

14. Credit supply shocks and bank balance-sheet characteristics
(1) (2) (3)
capital 0.0854 0.150*** 0.122**
(0.0605) (0.0541) (0.0560)
liquidity 0.165*** 0.0923*** 0.104***
(0.0297) (0.0329) (0.0347)
roa 1.471** 0.157 0.371
(0.586) (0.608) (0.601)
interbank -0.298*** -0.0991** -0.110**
(0.0531) (0.0470) (0.0471)
non-performing -0.747*** -0.556*** -0.518***
(0.101) (0.0916) (0.0863)
size -0.00200 0.000254 -0.00301
(0.00165) (0.00176) (0.00213)
d(mutual) -0.0235**
(0.0101)
Constant 0.0108
(0.0194)
Year FE N Y Y
Observations 7,158 7,158 7,158
R2
0.071 0.156 0.158

15. Empirical specification
We start our analysis by applying the aggregate productivity
decomposition to Sectors (2-digit Nace) and then we run regressions that
closely speak to this decomposition to investigate the effect of credit
supply shocks.
We will estimate the following equation:
yst = βCSSs,t + γt + δs + εst (1)
where:
yst is the dependent variable of interest at sector-level (2-digit)
CSSs,t is the credit supply shock
γt are time fixed effects
δs are sector fixed effects

16. Main results: sector level results
Total Avg. Prod. Reallocation Entry Exit
CSSs,t -0.254 0.407*** -0.594** 0.0828* -0.151***
(0.176) (0.0986) (0.239) (0.0463) (0.0532)
Sample Split: 2000–2007
CSSs,t -0.300 -0.0323 -0.171 -0.0502 -0.0465
(0.450) (0.373) (0.479) (0.0958) (0.218)
Sample Split: 2008–2015
CSSs,t -0.194 0.325** -0.566*** 0.121*** -0.0731*
(0.141) (0.131) (0.200) (0.0203) (0.0376)
Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

17. summing-up
average productivity and reallocation reacts to credit shock during
crisis periods
↓ CSSs,t ⇒↓ average productivity
↓ CSSs,t ⇒↑ reallocation
entry and exit significant only in sector-level regressions during crisis
↓ CSSs,t ⇒↓ aggregate productivity via entry
↓ CSSs,t ⇒↑ aggregate productivity via exit
Back of the envelop calculation — during the crisis CSSs,t contributed:
1
4 drop in contribution of average productivity
1
2 increase in the contribution of the within-sector of reallocation

18. Focus on incumbent – work in progress
Exploit firm-level data to:
1 provide micro-evidence in support of the effect of credit shock on
aggregate productivity via reallocation
2 characterize drop in productivity conditional on productivity and size
distribution
Caveat:
size and produtivity are two important dimension of heterogeneity
Preliminary findings:
1 ↓ employment among large and unproductive firms
⇒ ↑ reallocation
2 ↓ productivity all firms, more strongly among small and productive
⇒ productive firms do not adjust employment, but fall in output

19. Conclusion
Using data on the universe of Italian manufacturing firms, we analyze the
effects of credit supply shocks on aggregate productivity growth
Overall effect of credit shocks are negligible ⇒ Why?
− In line with existing literature, we find a negative impact of credit
supply shock on productivity (average productivity and entry)
+ These effects are counterbalanced by the reallocation of resources and
the exit of least productive firms
How large are these effects?
According to our estimates, during the crisis credit shocks contributed:
1
4 drop in contribution of average productivity
1
2 increase in the contribution of the within-sector of reallocation