Credit and Productivity Background material for DIA 2009

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





• 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



TFP Growth and Rajan & Zingales - LAC

-.02

0.0

2.0

4T

FP

Gro

wth



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





• 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.


• 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


• 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).


• 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





• 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.


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


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


-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


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?


• 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


• 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

Documents

Credit and Productivity Background material for DIA 2009