[PDF] - The Spatial Impact of Globalisation in Germany Johannes Brcker PDF Document

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The Spatial Impact of Globalisation in Germany

Johannes Bröcker Henning Meier

Christian-Albrechts-Universität zu Kiel Institute for Regional Research and Department of Economics

18th Uddevalla Symposium 2015 Sønderborg, June 11-13

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Globalisation: German exports, share in GDP Index, =1 in 1993

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Globalisation: German imports, share in GDP Index, =1 in 1993

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Research Question: What has been

the impact on German regions?

Agenda
1. Theory
2. Model
3. Calibration
4. Results
5. Test
6. To do

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Marius Brülhart, The spatial effects of trade
penness: a survey, Review of World

Economics 147 (2010), finds:

1. “Whether trade promotes concentration or

dispersion depends on subtle modelling choices among which it is impossible to adjudicate a priori.

2. Second, empirical evidence mirrors the

theoretical indeterminacy: a majority of cross-country studies find no significant effect of

penness on urban concentration or regional

inequality.

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Theory

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Marius Brülhart, The spatial effects of trade
penness: a survey, Review of World

Economics 147 (2010), finds:

3. Third, the available models predict that, other

things equal, regions with inherently less costly access to foreign markets, such as border or port regions, stand to reap the largest gains from trade liberalisation. This prediction is confirmed by the available evidence. Whether trade liberalisation raises or lowers regional inequality therefore depends on each country’s specific geography.” … and regional sector structures.

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Theory

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Three effects from increasing openness need to

be considered (vice versa for decreasing

penness):
1. Demand linkage: Right (or upward) shift of

foreign demand for domestic goods drives

utput and factor prices up.
2. Supply linkage: Cheaper (better, more

diversified) imports reduce input cost of firms and consumer prices.

3. Competition: Cheaper (better, more diversified)

imports threaten domestic competitors.

Regional impact? It all depends …

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Theory

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Static, perfect competition “iceberg” SCGE,

small (big) version

15 (96) domestic regions, 22 (64) industries, 11

(82) foreign countries or groups of countries

One representative firm per each region-industry

combination

One representative household per region,

representing all domestic demand

Simplified interregional transfer system
Constant returns to scale technologies, NCES

form, uniform across regions

NCES preferences, uniform across regions

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Model

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The CES nest

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intermediate inputs factors labor capital

. . . . . . product

utput of product j

 

, F I

. . . . . . from region r

... ...



  ,

0.5

i i

I  ,

j k l

 i I  Arm

i

Elasticities of substitution between labor and capital from Young (2013).
Armington elasticities from Broda & Weinstein (2006).

 ,

j k l

 Arm

i

Model

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After calibrating the model for 2010, I simulate the

effect of changing foreign trade for all years 1993 to 2013.

Method 1:

 I solve for hypothetical 2010 equilibria with trade (relative to GDP) as in years 1993, ..., 2013.  Thereby all parameters held fixed, except for recalibration of shift parameters of foreign demand and supply.

Method 2:

 I backcast or forecast to all years 1993 to 2013  Thereby, all parameters are held fixed, except for recalibration of  shift parameters of foreign demand and supply  a Hicks-neutral productivity parameter.

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Model

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Shift parameters of foreign demand may represent

both, change of demand in foreign countries and change of trade barriers.

Shift parameters of foreign supply may represent

– change in foreign price – change in trade barriers – change in quality – change in variety.

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Model

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Elasticities from literature
Shift parameters fixed by limited information

calibration to reproduce 2010 benchmark with respect to:

– employment by industry and region to calibrate Armington weights for domestic sources – national imports by foreign country or country group to calibrate Armington weights for foreign sources – national exports to calibrate shift parameters for foreign demand shift parameters – national accounts for shift parameters of technologies and preferences.

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Calibration

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Quantifying transport cost mark-up

with

– distance grs on shortest route from r to s in kilometre land distance equivalents – concavity parameter 0.27 from Hummels (2001) . – product-specific weight , depending on weight-to- value ratio vi for product i; function estimated by Hummels (2001), weight-to-value ratios from German export statistics.

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   

0.27

1 ( )

i i rs rs

v g ( )

i

v

Calibration

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Quantifying transport cost mark-up

with arcs a, routes R(r,s) from r to s, and, on respective arcs,

– land distances ta from Open Source Routing Machine, – sea distances sa from BP Shipping Distance Tables, – number of loadings and unloadings lua between sea and land.

Weight 0.14: Per seamile cost (sea) over per kilometre cost

(land) of container transport, from Limao & Venables (2001).

Weight 90: Per load/unload cost of a container over per

kilometer land transport cost of a container, from European Commission (2009).

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   

0.27

1 ( )

i i rs rs

v g

( , ) ( , ) ( , ) ( , )

min 0.14 90

rs R r s a a a a R r s a R r s a R r s

g t s lu

  

              

  

Calibration

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Results

Globalisation effect on wages,

wage 2013 relative to 1993, percentage point deviation from total Germany (5.9 %)

5.9 
3
3 
2
2 
1
1 

0  1 1  2 2  3 3  7.8

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Results

Globalisation effect on wages, percentage wage

growth rate 1993-2013 versus log of wage 1993

2 4 6 8 10 12 14

0.5
0.4
0.3
0.2
0.1

0.0 0.1 0.2 simulated wage growth rate 1993-2013, percent log of simulated wage rate 1993 Pearson‘s r = 0.34

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Globalisation effect on wages, wage relative to 1993,

percentage point deviation from total Germany

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Results

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2
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1 2 Northern Germany Central Germany Southern Germany Eastern Germany Johannes Bröcker & Henning Meier, Impact of Globalisation

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Globalisation effect on wages, wage relative to 1993,

percentage point deviation from total Germany

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Results

1

1 2 Urban regions Intermediate regions Rural regions

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Results

Globalisation effect on wages, wage

2013 relative to 1993, percentage point deviation from total Germany

Northern Central Southern Eastern Total Urban

1.2
0.6

1.4

4.6
0.6

Intermediate 1.1 0.7 2.2

0.7

1.2 Rural

0.7
0.4

1.8

2.2
0.2

Total

0.1
0.3

1.8

2.8

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Results

1 2 3 4 5 6 7 8 9 10

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3
2
1

simulated wage growth rate 2009-2011, percent simulated wage growth rate 2008-2009, percent

Recovery from crisis, trade effect on wages,

percentage changes 2009-2011 versus 2008-2009

Rheinpfalz Osthessen Berlin Braunschweig

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Test

, ,

log of observed wage rate in region at time log of simulated wage rate in region at time

r t r t

w r t w r t

, ,2010 , ,2010 , * , ,2010 , ,2010 , , , 1 , , 1 , , , 1 , , 1

White

ˆ ( ) ( ) ( ) 0.32 2.8 ( ) ( ) 0.42 4.6 ( ) ( ) 0.39 4.3 ( ) ( )

r t r r t r t r t r t r r t r t r r t r t r t r t r t t r t r t r t r t r t t r r

t I w w w w u II w w w w u III w w w w u IV w w w w u           

   

                     

,

0.37 3.8

t * t = 2.1, robust against both, spatial and serial

autocorrelation, following Cameron, Gelbach, and Miller, Journal of Business & Economic Statistics, 29 (2012).

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Test

w w    w   w

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Test

, ,

log of observed employment in region at time log of simulated wage rate in region at time

r t r t

r t w r t

, ,2010 , ,2010 , , ,2010 , ,2010 , , , 1 , , 1 , , , 1 , , 1 ,

White

ˆ ( ) ( ) ( ) 1.51 8.1 ( ) ( ) 1.65 9.8 ( ) ( ) 0.35 3.9 ( ) ( )

r t r r t r t r t r t r r t r t r r t r t r t r t r t t r t r t r t r t r t t r r

t I w w u II w w u III w w u IV w w u           

   

                      0.10 1.3

t

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Test

w w    w w        constant

d d

w    constant

s s

w    w        ˆ   ˆ   w ˆ ˆ ˆ ˆ / ˆ 0.9 and 0.6 1 ˆ           

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Introducing elastic labour supply into the CGE
Inserting controls into the test regressions
Taking account of serial autocorrelation in level

regressions

Taking account of spatial autocorrelation (that

hopefully is not significant, because the model should take account of spatial spillovers)

Sensitivity analysis, in particular with regard to

elasticities

Decomposition by geography, industry structure,

impacts of specific countries or country groups

and more.

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To be done

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Broda & Weinstein (2006): Globalization and the gains from variety, The Quarterly Journal of Economics, 121(2), 541-585. European Commission (2009): Terminal handling charges during and after the liner conference era, Publications Office of the European Union, Luxembourg. Hummels (2001): Toward a geography of trade costs, Purdue University, West Lafayette, mimeo. Limao & Venables (2001): Infrastructure, geographical disadvantage, transport costs, and trade, The World Bank Economic Review, 15(3), 451-479. Young (2013): U.S. elasticities of substitution and factor augmentation at the industry level, Macroeconomic Dynamics, 17, 861-897.