Shepotylo eerc may_2016_policy_and_firms_performance

Policy liberalization and �rms' performance

O. Shepotylo1

1Department of Economics

University of Bradford

40th EERC Research Workshop

O. Shepotylo (Department of Economics University of Bradford )Liberalization and �rms EERC, Kyiv 2016 1 / 17

Introduction

After this talk you will learn

how productivity responds to policy liberalization

how non-tari� measures in�uence quality

how wages are linked to market power and productivity


Modeling approach

Consider industry

Firms di�er in productivity

Competition is imperfect

Monopolistic Competition

Each year

�rms expand and contract market share

some �rms shut down

and some �rms enter


Trade policy, quality and productivity. Channels


Does policy liberalization increase growth?

Theoretical and empirical results show that liberalization is bene�cial

for the strongest, most productive �rms

It is also known as Matthew e�ect:

For unto every one that hath shall be given, and he shall

have abundance: but from him that hath not shall be taken

even that which he hath. �Matthew 25:29, King James

Version.

But, it is bene�cial for the whole economy!









Version.










Version.



Did WTO accession help Ukraine?

Ukraine joined WTO on May 16th 2008

Small reduction in tari�s

Large changes in services sectors!

Results

Shift from manufacturing to services

Increase in productivity in manufacturing


Evolution of services

Source: Shepotylo and Vakhitov (2015)

Foreign Direct Investment (FDI) in services

Shepotylo and Vakhitov (2015)


Gains in productivity

Shepotylo and Vakhitov (2015) More productive �rms gained more as a

result of services liberalization


Services liberalization in Ukraine

Services liberalization and productivity (Ukraine; Shepotylo and

Vakhitov, 2015)

An increase in services liberalization index by a standard deviation leads

to a 9.2 percent increase in productivity among manufacturing �rms

Matthew e�ect: More productive �rms bene�ted more - leads to

e�cient reallocation of resources

E�ect is stronger for domestic and small �rms.


NTM and quality

Study of �rms in food industry in Ukraine 2001-2010 (Movchan, Shepotylo

and Vakhitov, 2015)

NTM (restrictions on technology) in the �nal good sector:

some �rms to upgrade their quality:

purchase better inputs

upgrade capital

improve human capital quality

NTM in the intermediate goods sector

Firms with lower quality inputs switch to better ones or shut down


NTM and �rm performance

More stringent health regulations

force �rms to purchase ingredients of better quality and play a role of

a positive demand shifter

NTMs are likely to increase costs and change composition of inputs

from the upstream industries

which have a positive e�ect on exports of more productive �rms


Results

More NTM on inputs has overall negative e�ect on quantity

imported, neutral for import prices

But: with NTM, �rms buy more expensive ingredients and capital

equipment, but cheaper packaging

Quality e�ect: More stringent NTM regulations for �rm's inputs

increase output prices

Conditional on the level of NTM protection, less productive �rms are

more likely to exit


Policy liberalization and wages

Shepotylo, Vakhitov, and Uschev 2015

Build a model that links wages and market power

Show that labor market bargaining power crucially depends on inverse

demand elasticity

Test the predictions of the model at �rm level

Demonstrate that

market power and productivity are important determinants of wages

Firms with more market power pay lower wages

Firms with higher productivity pay higher wages


Barganing power and �rm size


Wages, productivity and markups

Dependent variable: D. ln(w)(1) (2) (3) (4) (5) (6)

D.ln(LP) 0.163∗∗∗ 0.151∗∗∗ 0.151∗∗∗ 0.151∗∗∗ 0.148∗∗∗ 0.148∗∗∗

(0.006) (0.035) (0.035) (0.035) (0.036) (0.036)

D.Markup -0.251∗∗∗ -0.145∗∗∗ -0.146∗∗∗ -0.145∗∗∗ -0.148∗∗∗ -0.148∗∗∗

(0.012) (0.039) (0.039) (0.039) (0.039) (0.039)

D.ln(h) -0.008 -0.019∗ -0.019∗ -0.019∗ -0.024∗∗ -0.024∗∗

(0.009) (0.011) (0.011) (0.011) (0.012) (0.012)

Industry × Year Yes Yes Yes Yes Yes Yes

Region FE Yes Yes Yes Yes Yes Yes

Observations 31224 29019 29019 29019 29019 29019

Hansen J statistics 4.532 4.600 4.573 4.408 4.514

p-value 0.209 0.204 0.206 0.221 0.211

R2 0.300 0.300 0.300 0.302 0.302

Thank You!

Questions?

[email protected]


Shepotylo, O. and Vakhitov, V. (2015). Services liberalization and

productivity of manufacturing �rms. Economics of Transition,

23(1):1�44.


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