2008-5-1VERC Conference,WLU,Canada1 Location Choices of the Pharmaceutical Industry in Europe after 1992 Prof. Frances P. Ruane The Economic and Social

2008-5-1 VERC Conference,WLU,Canada 1

Location Choices of the Pharmaceutical Industry in Europe after 1992

Prof. Frances P. RuaneThe Economic and Social Research Institute, Ireland and The Institute for

International Integration Studies, Trinity College Dublin, Ireland

Xiaoheng ZhangThe Institute for International Integration Studies and the Department of Economics,

Trinity College Dublin, Ireland


• The Single Market Programme (1st, January, 1993) removed the non-tariff barriers between EU Member States to allow free movement of goods, capital, people and services.

• Multinationals rationalize their production by consolidating production facilities within a country or across countries to fully utilize economies of scale.

• Pharmaceuticals become a focus because it is an important industry to the EU economy and European people.

• We expect its significant response to the Single Market because this industry is able to benefit a lot from rationlization due to high increasing returns to scale.

Context


• Measure the geographic concentration of pharmaceutical production across 14 EU countries using Theil Index and Location Gini coefficient (1993 to 2002)

- Data source: OECD STructural ANalysis (STAN) database

Pharmaceutical production and employment at country level

where is the production in country i in the country set that under investigation, and n is the number of countries.

Location Gini coefficient of concentration is defined as the area between the Lorenz curve and 45 degree line in a space where , the pharmaceutical production share of country i in the data set that under investigation, is cumulated on the Y-axis and the number of countries cumulated on the X-axis with equal interval of width 1/N. Countries are ranked by .

1ln( )

Ni i

i

x xTI

N x x

ix

Geographic Concentration Trend

iS

iS


• Theil Indices of Geographic Concentration of Pharmaceutical Production EU15

(OECD STAN data, EU 15, exclude Luxembourg)

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Theil Index for Employment

Theil Index for Gross Output

Theil Index for GDP(deflated)


• Location Gini coefficient (OECD STAN data, EU 15, exclude Luxembourg)

0.2300

0.2400

0.2500

0.2600

0.2700

0.2800

0.2900

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Location Gini for Employment

Location Gini for GrossOutput


• New Economic Geography (NEG) theories Theories to analyse the spatial distribution of the economic activities

between two or more regions. The subject is increasing returns to scale industry.

Krugman (1991), Venables (1996), Baldwin (1997), Baldwin (2002), Puga (1999)

• Two different predictions on the relationship between trade costs and agglomeration.

Theoretical explanations of the agglomeration/dispersion


Krugman (1991): Monotonic relationship Puga (1999): Bell-shaped curve

X-axis: trades costs level Y-axis: share of the industry in each of two regions


• Dispersion trend in the pharmaceutical industry and low trade cost imply that the agglomeration process of this industry may be at the left half of the Bell-shaped curve: high wages and congestion in the agglomerated region drive the industry to the less agglomerated regions.

• Empirical question - What are the determinants of pharmaceutical multinationals’ location choice?

- Main focuses

Country level agglomeration Corporate tax rate Market size

Implication to the EU Single Market and the Pharmaceutical industry


A Discrete-choice Framework (I)

• Multinationals choose a country from a set of alternative countries to expand their production or build up new facilities. Selected country is supposed to be able to maximize the multinational’s profit. Profit depends on the observable attribute of the alternative countries.

• The Conditional Logit Model (CLM) McFadden (1974)

ij ij ijU X

1

exp( )Pr( |1,..., )

exp( )ik

J

ijj

Xy k J

X


• The problems with CLM

- Simple but restrictive assumption on error term

The ratio of probabilities of any two alternatives being

chosen is independent on any other alternatives. This is

called “Independence from Irrelevant Alternatives” (IIA).

Individual taste behaves as an individual effect

correlation between error terms of alternatives violation

of IIA and inconsistent ML estimation

- Not able to accommodate complicated individual structure in our

case : several location choices made by the same MNE

ij


A discrete-choice framework (II)• The Mixed Logit Model (MXL) Train (2003)

Rabe-Hesketh et al. (2004)

- Coefficient follows a normal distribution (random effect)

- Control for MNE parent-Subsidiaries hierarchy

i

ij ij i ijU X

1

exp( )Pr ( |1,..., )

exp( )ik i

i J

ij ij

Xy k J f d

X

f


Data

• Subjects: Pharmaceutical MNEs’ subsidiaries in 11 out of EU15 countries

• Data source: Amadeus data – Collection of European firm’s accounts

• Samples: High-performance sample – 224 existing

pharmaceutical firms experiencing

above-median expansion of turnover b/w 1995

and 2003

New firms sample – 119 firms that were

established after 1993


Major Explanatory VariablesVariable Description Expected sign Source

PHAR/PHAR2

Absolute agglomeration of pharmaceutical production in eachcountry. Number of employees / Gross output.

? OECD STAN

PHARS-HARE/PHAR2SHARE

Relative agglomeration of pharmaceutical production. Theshares of one country’s pharmaceutical production to its totalmanufacturing production.

? OECD STAN

Similar variables for the Chemical industry. + OECD STAN

CDRUG Market size - National consumption of drugs and medicines. +OECD Health

Data

EATRNational effective average tax rate (per cent) created byDevereux and Griffith (2003).

-The Institute for

Fiscal Studies

LCOSTNational labour compensation per worker in the pharmaceuticalindustry (euros).

- OECD STAN

EDU3Education level - National share of workers with a tertiary leveleducation in manufacturing workforce (per cent).

+ Eurostat

GOV The Governance indicator. + World Bank

FAM

Dummy variable =1 if for a firm, there are at least one other firm

from the same MNE existed in a country, or a firm is located inthe same country with the parent company.

+ The Amadeus


Empirical Models• High-performance Sample

• New-firm Sample

• For both CLM and MXL. Only agglomeration variables, tax rate and market size are treated as random-effect variables.

1 2 3 4 5

6 7

Pr( |1,..., )

( ln ln l

......

n ln

3 ln ). ,

y k J

PHAR CDRUG EATR DIST LCOST

EDU GOV

1 2 3 4 5

6 7 8 9 10

Pr( |1,..., )

( ln ln ln l

.......

n

ln 3 ln ),

y k J

PHAR CDRUG EATR DIST AREA

LCOST EDU COMP GOV FAM


• Effective tax rate, market size, agglomeration of the

pharmaceutical industry matter.

• Firm heterogeneity shows up through interaction terms.

• Hausman test rejects IIA for Germany, Portugal, Spain and Sweden if they are excluded.

• CLM and MXL show similar results.

Results – High-performance SampleCLM MXL

lnPHAR -1.225** -1.306**

lnCHEM -0.508 -0.506

lnCDRUG 1.638*** 1.757***

EATR -0.083*** -0.099***

lnDIST 0.296* 0.301*

lnLCOST 1.444* 1.372

EDU3 0.041** 0.040**

lnGOV -1.406** -1.355**

EU ParentPrefer less agglomerated

countries, and low labour costsN/A

US Parent No particular effects N/A

Top MNEsLess prefer high education level

N/A

Size Effects are very weak N/A

Log-likelihood -437.0826 -436.7510


Results – New-firm Sample

• Only distance to Brussels and familiarity matter.

• Firm heterogeneity isn’t found.

• Hausman test cannot rejects IIA

• CLM and MXL show similar results.

CLM MXL

lnPHAR 0.428 0.375

lnCHEM 0.499 -0.381

lnCDRUG -0.394 -0.304

EATR -0.018 -0.020

lnDIST -1.103** -0.956*

lnLCOST -1.282 -0.630

EDU3 0.041 0.055

COMP -0.013 -0.014

lnGOV -0.700 -1.034

FAM 1.008*** 1.004***

EU Parent No particular effects N/A

US Parent No particular effects N/A

Top MNEs No particular effects N/A

Size No particular effects N/A

Log-likelihood -227.3379 -226.9356


Future Improvement

• Endogeneity in estimation of High-performance sample

Petrin and Train (2002) a control function approach Lewbel (2004) “very exogenous variable” approach

• Adding variables to the models to test the assumptions of NEG models

Krugman’s assumption : inter-region labour mobility use skilled pharma workers in neighbouring countries to proxy potential labour flow

Venerable/Puga’s assumption : intra-region labour mobility use workers in Chemical industry in the same country to proxy potential intra-region labour flow


• Evidence is found to support Puga and Venables models of a non-monotonic relationship between industrial agglomeration and trade costs.

• The expansion in production at existing plants in Europe may contribute to Europe-level geographic dispersion of pharmaceutical production.

• The use of the conditional logit model in this research is justified by comparing its performance with those of the mixed logit models.

Any comments and critiques are welcome!

Conclusions

Documents

2008-5-1VERC Conference,WLU,Canada1 Location Choices of the Pharmaceutical Industry in Europe after 1992 Prof. Frances P. Ruane The Economic and Social