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Credit and ProductivityBackground material for DIA 2009
Roadmap
• Stylized Facts • Financial development and TFP
– Analytical discussion – Empirical evidence
• Macroeconomic volatility and TFP– Analytical discussion – Empirical evidence
• Ex post volatility• Ex ante volatility
• Final remarks
Roadmap
• Stylized Facts • Financial development and TFP
– Analytical discussion – Empirical evidence
• Macroeconomic volatility and TFP– Analytical discussion – Empirical evidence
• Ex post volatility• Ex ante volatility
• Final remarks
Stylized Facts
Stylized Facts (cont.)
Correlation TFP and FD: LAC=0.80; Asia=0.54
Stylized Facts (cont.)
-.00
50
.00
5.0
1.0
15
.02
TF
P G
row
th
-.5 0 .5 1 1.5Rajan & Zingales
(mean) gltfp2 Fitted values
TFP Growth and Rajan & Zingales - United States
-.02
0.0
2.0
4T
FP
Gro
wth
-.5 0 .5 1 1.5Rajan & Zingales
(mean) gltfp2 Fitted values
TFP Growth and Rajan & Zingales - LAC
-.02
0.0
2.0
4T
FP
Gro
wth
-.5 0 .5 1 1.5Rajan & Zingales
(mean) gltfp2 Fitted values
TFP Growth and Rajan & Zingales - Asian Tigers
Stylized Facts (cont)
Volatility of GDP Growth (relative to industrial countries)
0
0.5
1
1.5
2
2.5
3
3.5
4
1970-1979 1980-1989 1990-1999 2000-2006 all sample
Period
Rat
io o
f S
tan
dar
d D
evia
tio
ns
of
GD
P g
row
th r
ates
LAC
Asia
Stylized Facts (cont.)Volatility of TFP Growth
(relative to industrial countries)
0
0.5
1
1.5
2
2.5
3
3.5
1970-1979 1980-1989 1990-1999 2000-2006 Full Sample
LAC
ASIA
Stylized Facts (cont)Volatility of the Real Effective Exchange Rate
(relative to industrial countries)
0
0.5
1
1.5
2
2.5
1980-1989 1990-1999 2000-2006 all sample
Period
Rat
io o
f st
and
ard
dev
iati
on
s o
f re
al e
xch
ang
e ra
tes
LAC
Asia
Roadmap
• Stylized Facts • Financial development and TFP
– Analytical discussion – Empirical evidence
• Macroeconomic volatility and TFP– Analytical discussion – Empirical evidence
• Ex post volatility• Ex ante volatility
• Final remarks
Financial Development and TFP
• Literature on financial development and TFP growth goes as far as Bagehot (1873) and Schumpeter (1912)– Financial markets promote efficient capital
reallocation across productive units
• Hsieh and Klenow (2007), Restuccia and Rogerson (2007), Buera and Shin (2008), Buera et al (2008) point in a similar direction.
Financial Development and TFP
• Examples of the channels:– Collateral constraints limit entrepreneurship– Financial underdevelopment limits the
possibility of entering in highly productive sectors with high fixed costs
– Credit market imperfections reduce long term investments (prod. enhancing) vis a vis short term ones
Financial Development and TFP
• Empirical evidence:– Cross country is abundant (Beck et al,
Levine and Servos, Rioja and Valev, Acemoglu, Aghion and Zilibotti, …).
– Sectoral level studies focus more on channels (FD vs elasticity of investment to GDP, FD and sensitivity of R&D expenditure to shocks, firm growth) and less on the final impact (TFP).
Financial Development and TFP
• We add: – Sectoral data: impact of credit availability
on TFP– Firm level data: Survey data + Colombia
country study
Sector Level Evidence: TFP Estimation
• Unido Dataset: panel 77 countries, 26 manufacturing sectors, annual data 1970-2003.
• Compute series of capital stock using the perpetual inventory method. (Caselli 2005)
• Assume Cobb-Douglas technologies:
tcitcitcitci LKAY ,,,,,,,,
Sector Level Evidence: TFP Estimation (cont.)
• TFP: Regression residual
• TFP1: Fixed-cost shares
• TFP2: Industry-specific cost shares (Fleiss 2008, Bernanke and Gurkaynak, 2001)
tcicitcii
itcii
itci lky ,,,,
26
1,,
26
1,,,
tcitcitcitci lkyltfp ,,,,,,,, 7.03.01
tciKitciKitcitci lkyltfp ,,,,,,,, )1(2
Sector Level Evidence: TFP Estimation (cont.)
gltfp gltfp1 gltfp2gltfp 1
gltfp1 0.997 1gltfp2 0.996 0.995 1
ltfp ltfp1 ltfp2ltfp 1
ltfp1 0.568 1ltfp2 0.358 0.556 1
Levels
Notes: ltfp is estimated by OLS, ltfp1 using fixed input shares for the whole sample, and ltfp2 allows input shares to vary per industry.
Growth Rates
Sector Level Evidence: Methodology
• Estimation Equation (1)
• Estimation Equation (2)
tcitctitcitcitci FDRZShareTFPgr ,,,,,,,,,_
tcititctctcitcitci XFDFDRZShareTFPgr ,,,,,2,1,,1,,_
Sector Level Evidence: Results
Sector Level Evidence: Results (cont.)
Sector Level Evidence: Results (cont.)
Firm Level Evidence: WBES
• Using the WBES we construct measures of TFP for firms in 54 developing countries (17 LAC)
• We construct three measures of TFP based on cost shares and a prod function including labor, capital and intermediate inputs.– Cost shares are the same across countries and
industries (TFP)– Cost shares are the same across countries but
differ across industries (TFPj)– Cost shares differ across countries and industries
(TFPij)
WBES TFP EstimationsAll countries
1.90
2.00
2.10
2.20
2.30
2.40
2.50
2.60
2.70
LARGE MEDIUM SMALL
TFP TFPj TFPijLatin America
1.90
2.10
2.30
2.50
2.70
2.90
3.10
3.30
3.50
3.70
LARGE MEDIUM SMALL
TFP TFPj TFPij
WBES: Access to credit
Size
Access to credit line
or overdraft
% of WK financed
with formal lending sources
% of WK financed with trade
credit
% of WK financed with other lending sources
% of Inv financed with bank lending
% of Inv financed
with C.Mkts.
% of Inv financed with trade
credit
% of Inv financed with other sources
Allsmall >=5 and <=19 28.84 6.85 11.70 16.29 9.16 0.68 4.25 15.23medium >=20 and <=99 51.03 16.76 12.26 13.71 22.06 1.53 4.84 10.97large >=100 61.40 25.15 10.60 9.89 30.48 1.89 3.74 10.80
LACsmall >=5 and <=19 59.11 16.56 15.02 10.54 16.04 1.00 7.95 10.62medium >=20 and <=99 76.12 27.78 16.35 8.11 23.64 1.77 8.05 6.40large >=100 83.79 34.41 14.88 5.58 33.26 1.88 7.10 3.05
Marginal impact of access to credit line
Regression: TFP(ij) = f(export, size, access, size*access, CI-FE)
Instrument by: past firm growth, share of firms with access in cluster, share*size
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Large Medium Small
TFP
TFPj
TFPij
***
***
***
***
******
***** **
Roadmap
• Stylized Facts • Financial development and TFP
– Analytical discussion – Empirical evidence
• Macroeconomic volatility and TFP– Analytical discussion – Empirical evidence
• Ex post volatility• Ex ante volatility
• Final remarks
Macroeconomic Volatility and TFP – Some Related Literature
Crisis, or ex-post volatility I:
• Recent literature suggests a close connection between crisis and TFP performance:
– Calvo et al (2006): Phoenix miracles: Collapse in TFP performance
– Fernandez Arias et al (2007): TFP seldom recovers to trend
– Cerra-Saxena (2007): output does not recover to pre-crisis trend levels
• Periods of financial crisis are associated with large RER depreciation and RER volatility:
– Calvo et al (2004): 63% of large RER depreciations in EMs associated to Sudden Stops
– Calvo et al (2006): RER volatility (relative price of tradables vis-à-vis non-tradables) increases with Sudden Stops
Macroeconomic Volatility and TFP: sector level evidence
(1) (2) (3)gltfp gltfp1 gltfp2
RZ * RER_Vol -0.111** -0.134*** -0.124***[0.046] [0.045] [0.045]
Industry Share -0.664*** -0.864*** -0.618***[0.173] [0.139] [0.160]
Observations 10316 9840 10316R-squared 0.425 0.429 0.425
(1) (2) (3)gltfp gltfp1 gltfp2
RZ * RER_Vol -0.110** -0.127** -0.123**[0.049] [0.049] [0.048]
Industry Share -0.648*** -0.837*** -0.600***[0.183] [0.153] [0.170]
Observations 7663 7355 7663R-squared 0.409 0.417 0.409
Full Sample
Developing Countries
Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. All specifications include country-time and industry-time fixed effects.
Macroeconomic Volatility and TFP – Some Related Literature
Crisis, or ex-post volatility II:
• Connection between crisis and productivity through credit markets:
– Caballero et al (1994): The now standard view of recessions: A cleansing effect
– Barlevy (2003): If credit frictions exist, there could be “uncleansing” effects
– Efficient but credit-constrained firms with loose connections to credit markets (or little collateral) could be wiped out, leaving larger but less efficient incumbents in the market
– This connection between macroeconomic volatility, credit markets and TFP is what we are working on at the firm level for the case of Colombia.
Macroeconomic Volatility and TFP – Some Literature
• Exposure to frequent crises and large RER fluctuations also raises ex-ante volatility issues:
– Calvo (2005): Greater price volatility increases the profitability of more malleable, less productive technologies
– Goldberg (2001): Exchange rate volatility affects the share of foreign direct investment in total investment
– We take from Calvo the idea that price volatility conspires against the choice of more productive technologies, and from Goldberg the idea that volatility affects the composition of investment, and ask:
– Can volatility affect the sectoral allocation of investment away from what TFP differences would indicate?
Ex-ante Volatility – How does volatility introduce distorsions in investment allocation?
• Where
: Investment ratio, country j sector i over investment country j
: Tfp ratio, country j sector i over Tfp country j.
: Measure of volatility in country j
: Country time and Industry time fixed effects
ijt
jt
Inv
Inv
, 1 , 11 2 , 1
, 1 , 1
ijt ij t ij tjt it j t ijt
jt j t j t
Inv Tfp Tfp
Inv Tfp Tfp
, 1
, 1
ij t
j t
Tfp
Tfp
j
1 ,jt 2it
Relationship Between Investment Ratio and TFP ratio: An Example
212211k,k
rkrk)k(fpa)k(famax21
(2))('
(1))('
:F.O.C.
22
11
rkfap
rkfa
)4()()('
)3()()('
: then;"degree"ofshomogeneou),(Consider
222
111
kfnkkf
kfnkkf
nkf i
Consider the case of a 1 period model in which a firm decides on investment in two activities with different productivity levels
Relationship Between Investment Ratio and TFP ratio: An Example
21
1ratio heConsider tkk
k
orkfapkfa
kfa
kk
k,
)()(
)(
:(4) & (3) (2), (1), from
2211
11
21
1
)()( 1221
1
21
1
kfkfapa
a
kk
k
(i.e, investment in activity 1 over total investment)
I. Pooled OLS
(1) (2) (3) (4) (5) (6) (7) (8)vol1 vol2 vol3 vol4 vol1 vol2 vol3 vol4
0.352 1.11*** 0.438* 1.07*** 0.173 0.679*** 0.227 0.644***Tfp [0.226] [0.19] [0.23] [0.196] [0.142] [0.145] [0.146] [0.178]
0.119 0 0.057 0 0.223 0 0.119 0
-1.32 -3.01*** -1.93 -2.92*** -0.382 -1.8*** -0.829 -1.71***Tfp*volatility [0.857] [0.588] [0.858] [0.633] [0.58] [0.453] [0.582] [0.472]
0.123 0 0.024 0 0.511 0 0.155 0
Observations 10430 10766 10239 10766 9991 10317 9807 10317R-squared 0.512 0.519 0.511 0.518 0.531 0.534 0.53 0.534
legend: b/se/p
Tfp2
Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1.. All specifications include industry-time and country time fixed effects. vol1=ave~12, vol2=ave~f, vol3=end~12, vol4=end~f.
Tfp1Dependent Variable: Investment Ratio
t-1
t-1
II. IV-PanelNote 1. According to Exogeneity test (C and Hansen J statistic) we can consider Tfp to be exogenous, in each of the eight models.
Note 2. However, due to perpetual inventory methodology used to construct the capital series which involves past Investment values, we could think if Investment ratios exhibit enough persistence, it could influence the Tfp path, that is why we conduct an IV-Panel regression.
(1) (2) (3) (4) (5) (6) (7) (8)vol1 vol2 vol3 vol4 vol1 vol2 vol3 vol4
0.476** 1.07*** 0.511** 1.08*** 0.226* 0.669*** 0.271* 0.663***Tfp [0.21] [0.21] [0.235] [0.204] [0.135] [0.138] [0.142] [0.137]
0.023 0 0.029 0 0.095 0 0.057 0
-2.21** -2.88*** -2.42*** -2.95*** -0.734 -1.77*** -1.1** -1.76***Tfp*volatility [0.943] [0.453] [0.804] [0.47] [0.594] [0.329] [0.52] [0.352]
0.019 0 0.003 0 0.217 0 0.034 0
Observations 9050 9382 8860 9382 8669 8991 8486 8991
legend: b/se/p
Tfp1 Tfp2
Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1.. All specifications include industry-time and country time fixed effects. vol1=ave~12, vol2=ave~f, vol3=end~12, vol4=end~f.
t-1
t-1
Marginal Effect(Different Volatility Levels)Model (4)
-4
-2
0
2
Fin
al Im
pact
0 .25 .5 .75 1 1.25Volatility Value
Marginal Effect MinMax
Tfp1_end_f
IV. Dynamic Panel
(1) (2) (3) (4) (5) (6) (7) (8)vol1 vol2 vol3 vol4 vol1 vol2 vol3 vol4
-0.332* -0.342* -0.346* -0.343* -0.505*** -0.444** -0.506*** -0.444**Investment(-1) [0.176] [0.204] [0.181] [0.204] [0.175] [0.193] [0.18] [0.193]
0.059 0.094 0.058 0.094 0.004 0.021 0.005 0.021
0.012 0.443*** 0.008 0.465*** 0.018 0.393*** 0.069 0.434***Tfp [0.062] [0.148] [0.068] [0.176] [0.057] [0.142] [0.063] [0.160]
0.843 0.003 0.908 0.008 0.757 0.006 0.272 0.007
0.599 -1.198*** 0.263 -1.341** 0.431 -1.077** -0.219 -1.27**Tfp*volatility [0.706] [0.439] [0.511] [0.57] [0.561] [0.425] [0.440] [0.521]
0.397 0.006 0.604 0.019 0.443 0.011 0.619 0.015
Observations 10430 10766 10239 10766 9991 10317 9807 10317
legend: b/se/p
Tfp1 Tfp2
Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1.. All specifications include industry-time and country time fixed effects. vol1=ave~12, vol2=ave~f, vol3=end~12, vol4=end~f.
Systemic Sudden Stops: Total Factor Productivity in
EM Collapses & the US Great DepressionCollapses in EM Economies
Collapse Recovery
100
102
104
106
108
110
t-2 t-1 t t+1 t+2
GD
P
100
102
104
106
108
110
TF
P
TFP
GDP
US Great Depression
Collapse Recovery
95
100
105
110
115
120
125
130
135
140
19
29
19
30
19
31
19
32
19
33
19
34
19
35
19
36
GD
P
95
100
105
110
115
120
125
TF
P
TFP
GDP
Case study: Colombia
• We use plant level data to estimate TFP and combine this data with a sectoral level data base to identify access to finance (firm level panel 1995 – 2005).
• Our general questions refer to:– Relationship between productivity and crisis/volatility
at the firm level, analyzing the role played by credit constraints. Does credit access help smooth shock?
– Relationship between entry-exit and firm productivity. Is there a cleansing effect of crisis/volatility? Does credit play a role in the way that crisis affect firms with different productivities?
Case study: Colombia
• We estimate regressions of the sort to estimate the impact of access to credit on productivity in general and during crisis:
• We do this at a firm level and at a sectoral level
ititotiit CCa *
ittititotiit crisisCCCCa *** 1
Case study: Colombia
• To estimate the impact of access to finance on firm survival:
• On entry at a sectoral level
ititstitpstotsit aCCaCCe ****)1Pr( 2
StSttststotsSt acrisisCCCCe 132 ****
StStstStstotsSt aCCaCCe **** 1211