Bertrand Koebel Phu Nguyen-Van Fre´de´ric Ollanddocs.gip-ecofor.org/public/SEHS_22sept_ComExt_BKoebel1.pdf · Total factor productivity in the French wood sector Bertrand Koebel

Total factor productivity in the French wood sector

Bertrand Koebel Phu Nguyen-Van Frederic Olland

BETA, CNRS & Universite de Strasbourg

ECOFOR, Paris, 22.09.2015

KNO TFP ECOFOR, Paris, 22.09.2015 1 / 21

Motivation and Objectives

Lack of competitiveness of the French wood sector. Why?

Sectoral data are not sufficient to answer the question (Koebel et al.

2015).

Firm data appear to be a good alternative

Research questions:

◮ What is the productivity of the French wood sector?◮ How is the dynamics of the productivity of the French wood sector ?





2015).


Research questions:






2015).


Research questions:






2015).


Research questions:






2015).


Research questions:






2015).


Research questions:



Findings

The production exhibits decreasing returns to scale

Productivity is improved over the period of study

A catching-up phenomenon for firms with low productivity

Firm productivity converges in the long-run


Findings






Findings






Findings






Outline

1 Literature

2 Deterministic and stochastic technological changes

3 Data

4 Estimation results (production function, TFP)

5 Dynamics of TFP

6 Concluding remarks


Literature

Deterministic versus stochastic technological change (Solow 1957)

Production function and productivity (Olley and Pakes 1996, Levinsohn

and Petrin 2003, Ackerberg et al. 2006)

Growth and convergence (Quah 1996, Johnson 2000)


Literature






Literature






Deterministic and stochastic technological changes

Consider the production function

Yit = AitKβk

it Lβl

it Mβm

it eεit Ait = A0eαt+ωit

Kit capital stock

Lit employment

Mit intermediate inputs

Ait productivity (TFP)

t time trend (autonomous technological change)

ωit unobserved productivity shocks (stochastic T.C.)

Objective: consistent estimation of elasticities of capital (βk), labor (βl),

intermediate inputs (βm), and two components of TFP (α, and ωit).




Yit = AitKβk

it Lβl

it Mβm


Kit capital stock

Lit employment










Yit = AitKβk

it Lβl

it Mβm


Kit capital stock

Lit employment








Data

Firm data in the French wood sector

Period: 1996-2007

NACE:

◮ woodworking: 2010, 2020, 2030, 2040, 2051, 2052 (2378 firms)◮ pulp and paper: 2111, 2112, 2121, 2122, 2123, 2124, 2125 (1022 firms)◮ wooden furniture: 3611, 3612, 3613, 3614, 3615 (1302 firms)

In total: 4673 firms, 27697 observations


Data


Period: 1996-2007

NACE:




Data


Period: 1996-2007

NACE:




Data


Period: 1996-2007

NACE:




Data


Period: 1996-2007

NACE:




Data


Period: 1996-2007

NACE:




Data


Period: 1996-2007

NACE:




Production function estimation

Table : Estimation results

Woodworking Wooden Furn. Pulp & Paper Whole

Coefficient Estimate Estimate Estimate Estimate

Elasticities

βk 0.067 0.105 0.028 0.050

βl 0.123 0.030 0.116 0.121

βm 0.496 0.505 0.841 0.563

Other coefficients

α 0.010 0.003 0.004 0.007

Returns to scale βk + βl + βm 0.686 0.640 0.985 0.734

Number of firms 2378 1302 1022 4673

Number of observations 12340 8103 7254 27697


Stochastic TFP estimation (1/2)

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.0

0.5

1.0

1.5

TFP

Den

sity

199720022007

(a) whole sector

6 7 8 9

0.0

0.5

1.0

1.5

TFPD

ensi

ty

199720022007

(b) woodworking products

Figure : Distribution of stochastic total factor productivity in 1997, 2002, and 2007.


Stochastic TFP estimation (2/2)

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.0

0.5

1.0

1.5

TFP

Den

sity

199720022007

(a) wooden furniture

2 3 4 5 6

02

46

810

TFPD

ensi

ty

199720022007

(b) pulp and paper products

Figure : Distribution of stochastic total factor productivity in 1997, 2002, and 2007.


Growth rate of TFP (1/3)

Ait is composed of two parts, a deterministic part (αt) and a stochastic

part (ωit).

The growth rate of Ait is

Ait

Ait

= α+ ωit.



Ait is composed of two parts, a deterministic part (αt) and a stochastic

part (ωit).

The growth rate of Ait is

Ait

Ait

= α+ ωit.



1998 2000 2002 2004 2006

−0.

010.

000.

010.

020.

030.

04

year

TF

P g

row

th r

ate

(a) whole sector

1998 2000 2002 2004 2006

−0.

020.

000.

020.

040.

06

year

TF

P g

row

th r

ate


Figure : Growth rates of total factor productivity. The horizontal line represents α,

the coefficient of deterministic productivity. The solid curves correspond to the first

(25%), second (median), and third (75%) quartiles of TFP growths Ait/Ait.



1998 2000 2002 2004 2006

−0.

04−

0.02

0.00

0.02

0.04

0.06

year

TF

P g

row

th r

ate


1998 2000 2002 2004 2006

−0.

005

0.00

00.

005

0.01

0

year

TF

P g

row

th r

ate


Figure : Growth rates of total factor productivity. The horizontal line represents α,

the coefficient of deterministic productivity. The solid curves correspond to the first

(25%), second (median), and third (75%) quartiles of TFP growths Ait/Ait.


Dynamics of stochastic TFP (1/7)

gτ (y | x) is the conditional density of current TFP given past values of

TFP.

g = transition matrix of TFP between two periods (e.g. between 1997

and 2002, and 2002 and 2007).

We also compute the long-run (ergodic or stationary) distribution of TFP.

Application to 5-year interval data: 1997, 2002, 2007 (to avoid business

cycle effects).




TFP.


and 2002, and 2002 and 2007).



cycle effects).




TFP.


and 2002, and 2002 and 2007).



cycle effects).




TFP.


and 2002, and 2002 and 2007).



cycle effects).



past TFP

current TFP

conditional density

(a) surface plot

past TFP

curr

ent T

FP

0.5

0.5

1

1

1.5 1.5

2

2

2.5

2.5

3

3

3

3

3

3.5 4.5

5.5 6.0 6.5 7.0

5.5

6.0

6.5

7.0

(b) contour plot

Figure : Surface and contour plot of conditional density of stochastic TFP

g(current TFP | past TFP), whole sector.



past TFP

current TFP

conditional density

(a) surface plot

past TFP

curr

ent T

FP

0.5

0.5

1

1

1.5

1.5

2

2

2.5

2.5

2.5

3

3

6.2 6.4 6.6 6.8 7.0 7.2 7.4

6.5

7.0

7.5

(b) contour plot


g(current TFP | past TFP), woodworking products.



past TFP

current TFP

conditional density

(a) surface plot

past TFP

curr

ent T

FP

0.5 0.5

1

1

1.5

1.5

2

2

2

2

2

6.0 6.5 7.0 7.5 8.0

6.0

6.5

7.0

7.5

8.0

(b) contour plot


g(current TFP | past TFP), wooden furniture.



past TFP

current TFP

conditional density

(a) surface plot

past TFP

curr

ent T

FP

2

2

2

2

2

2

2

2

2

2

4

4

4

4

4 4

4

4

4

4

4

4

6

6

6

6

6

6 6

6

6

6

8

8

8

8

10

2.0 2.2 2.4 2.6 2.8 3.0

2.0

2.2

2.4

2.6

2.8

3.0

(b) contour plot


g(current TFP | past TFP), pulp and paper products.



5.5 6.0 6.5 7.0

0.00

0.01

0.02

0.03

0.04

TFP

ergo

dic

dens

ity

(a) whole sector

6.2 6.4 6.6 6.8 7.0 7.2 7.4

0.00

00.

005

0.01

00.

015

0.02

00.

025

TFPer

godi

c de

nsity


Figure : Ergodic distribution of stochastic TFP.



6.0 6.5 7.0 7.5 8.0

0.00

50.

010

0.01

50.

020

0.02

50.

030

TFP

ergo

dic

dens

ity


2.0 2.2 2.4 2.6 2.8 3.0

0.00

0.02

0.04

0.06

TFPer

godi

c de

nsity


Figure : Ergodic distribution of stochastic TFP.


Concluding remarks

Production in the French wood sector exhibits decreasing returns to scale

Productivity converges

Further developments:

◮ Robustness of the results: other functional forms (CES, etc.)◮ Fixed inputs requirements, fixed costs◮ Trade


Concluding remarks






Concluding remarks






Concluding remarks






Concluding remarks






Concluding remarks






Documents

Bertrand Koebel Phu Nguyen-Van Fre´de´ric Ollanddocs.gip-ecofor.org/public/SEHS_22sept_ComExt_BKoebel1.pdf · Total factor productivity in the French wood sector Bertrand Koebel