Discussion Paper Series, 13(1): 1-22
Spatial Variations of Greek Manufacturing
Employment Growth: The Effects of Specialization
and International Trade
Georgios Fotopoulos
Economist, Assistant Professor of Applied Microeconomics
University of Patras, Department of Economics
e-mail: [email protected]
Dimitris Kallioras
Economist (MBA, PhD), University of Thessaly
Department of Planning and Regional Development
e-mail: [email protected]
George Petrakos
Economist, Professor of Spatial Economics
University of Thessaly, Department of Planning and Regional Development
e-mail: [email protected]
Abstract
This research addresses the effects on regional employment growth in
Greece of both trade and specialization. In doing so it follows Markusen
et al (1991) in applying a trade-adjusted shift share analysis. The effect
of specialization is dealt with an extended econometric shift-share
analysis. The results obtained indicate that international trade had a
disparate effect on regional employment growth whereas specialization
had a negative effect.
Key words: Greek
international trade
manufacturing
employment
growth,
specialization,
March 2007
Department of Planning and Regional Development, School of Engineering, University of Thessaly
Pedion Areos, 38334 Volos, Greece, Tel: +302421074462, e-mail: [email protected], http://www.prd.uth.gr
Available online at: http://www.prd.uth.gr/research/DP/2007/uth-prd-dp-2007-01_en.pdf
Spatial Variations of Greek Manufacturing Employment Growth
3
1. Introduction.
The aim of this research is to investigate patterns of manufacturing employment growth
across Greek regions and examine possible effects of regional specialization and
international trade. Both effects may be important.
Krugman, (1991) elaborating on Marshall’s (1920) views identifies three sources of
increasing returns due to spatial proximity (localization economies). These involve labor
market pooling, the provision of non-traded inputs specific to an industry in a greater
variety and at lower costs, and increased information flows and technological spillovers.
Increasing returns in production theorized in such a fashion might, in turn, carry
significant implications for differences in interregional growth patterns. Apart from
localization economies, external economies to both firms and industrial sectors
contained within a spatial unit may also exist: agglomeration economies. Such
economies stem from a large number of economic activities being concentrated in
space. The main difference compared to localization economies is that emphasis is now
given in economies attained across and not within industries (Henderson, 1986).
Therefore agglomeration economies need economic variety in an area (diversification).
A higher degree of diversification implies a higher variety of skills available locally. Skill
and diverse working experiences can, in turn, give way to higher entrepreneurial choice
and opportunity, especially since there should be some degree of transfer of individuals
between not only firms but also industries. The latter might work as a safeguard.
Downturn movements in some sectors would not be as harmful to the local economy
because human and other resources are diverted to existing more secure alternatives.
Moreover, higher degrees of diversification could ensure that emerging opportunities
due to, say increasing demand, may not go unexploited locally, if even a small number
of firms in the industry producing the product are in the area. Thus, increasing returns
can operate at three levels: the first is the firm level, the second takes place outside the
firm but within an industry (localization economies) and the third takes place between
industries (agglomeration economies) in a locality. Although it is difficult to distinguish
the effect of localization from that of urbanization since both operate locally, it seems
that the first is associated by increased industry concentration in a locality and the
second with increased diversity of economic activities in a locality.
However, the role of these external economies at the regional level is adjusted by
international trade depending on regional economic structure. Deteriorating trade
conditions in some sectors affects regions specialized in these sectors and changing
trade conditions reshape, where possible, the regional economic structures.
To examine the effects of trade and specialization on regional employment growth, this
research resorts to extensions of shift-share analysis. In a first step a trade-adjusted
shift share analysis is performed following Markusen et al (1991). The basic advantage
of the method is that it requires information on exports, imports, domestic demand and
Discussion Paper Series, 2007, 13(1)
4
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
output only at the national economy-manufacturing branches (sectoral) level. A
disadvantage, on the other hand, is the non-theoretical character of the shift-share
analysis. Moreover, in the present form, the method does not allow for any statistical
hypothesis testing.
In a second step shift-share econometric extension is used to address the effect of
regional specialization on employment growth following and elaborating on the
pioneering work of Weeden (1974). This method, while maintaining the shift-share
analysis structure, allows for proper hypothesis testing.
Some recent studies have also turned to shift-share analysis proper and extensions to
address the effect of structure on regional growth. Esteban (2000) finds that
interregional differences in labor productivity in the European Union (EU) are primarily
determined by uniform industry productivity gaps across regions and that regional
specialization plays only a minor role. These results seem to contrast those found by
Garcia-Mila and McGuire (1993) for employment growth at the State level in the US.
The authors maintain that the industrial mix is important in explaining differences in
growth rates and their variability across States. In Eastern Germany the results obtained
by Blien and Wolf (2002) point to restructuring and spatial deconcentration as important
determinants of regional disparities in employment growth. In addition, shift-share like
components have been used to test the neoclassical economic growth model and
account for differences in steady states (Barro and Sala-i-Martin, 1991 and 1992) as
well as for the effects of structural change on economic growth (Paci and Pigliaru,
1997).
The data used refer to the 1984-1988 period and refer to 20 2-digit industry sectors and
to 13 NUTS II Greek regions1. These data may be admittedly dated however they are
the most detailed data available since they come from manufacturing censuses
conducted by the National Statistical Service of Greece (NSSG). Although more recent
data are available from the Annual Industrial Survey conducted also by the NSSG, they
cover only larger firms (with more than 10 and for some years more than 20
employees). This essentially involves loss of information as, even at 2-digit sectoral
classification, a lot of sectors are not represented across in all regions (NUTS II) at
smaller firm size levels. Consequently, use of these data arises out of necessity.
Nevertheless, they do have an advantage as they present the most accurate data
available for the period that followed the Greek accession to European Economic
Community in 1981. Recently the EU has been enlarged and more countries wait to join.
Central to the current debate is whether European integration will affect the
specialization patterns of economic activity across the regions of the enlarged Union
and the patterns of spatial concentration of economic sectors as both have implications
1
Nomenclature of Statistical Territorial Units (NUTS).
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
5
for the cohesion of the EU. In this sense the results obtained here describing the
experience of a small older member state might be useful to newcomers.
Overall, the results obtained reveal that international trade conditions at the sectoral
level may have a disparate effect across regions. However, the magnitude of the effects
set aside, the vast majority of regions would suffer employment losses if increasing
import-penetration at the sectoral is to be transmitted at the regional level through a
shift-share analysis allocation fashion. In the same analytical context there was no
industrial-mix at the regional level able to recover jobs lost to increased international
competition and competitiveness losses of domestic producers. In terms of labor
productivity, regional specialization has been, in the vast majority of cases, in sectors
recording productivity setbacks. Thus, it seems that, overall, employment losses cannot
be attributed to productivity gains. As far as the results of the shift-share covariance
model are concerned it appears that in regions specializing in an industry, a growing
industry does not tend to grow fast and a declining industry tends to decline faster.
The paper is organized as follows. In the next section the trade-adjusted shift-share
analysis is described and the results obtained from its application are presented. In
section 3, the shift-share econometric extension explicitly testing for the effects of
regional specialization is presented along with the results of the estimation. The final
section offers the conclusions obtained from this research exercise.
2. Regional manufacturing employment change: an
international-trade adjusted shift-share analysis.
Markusen et al (1991) modify the traditional shift-share analysis method in an attempt to
deal with criticism that the use of the national economy as the norm against which one
measures the sub-national economies is not appropriate as international trade becomes
increasingly important to both national and consequently regional economies. In doing
so they propose a shift-share formulation where the conventional national-growth and
industry mix components are further disaggregated to account for regional employment
growth resulting from changes in exports, imports and domestic demand. In addition,
since output has been used as the base against which the relative importance of both
imports and exports has been measured, the national-growth and industry mix
components have been further extended to account for possible effects on employment
due to productivity gains. That is, it represents hypothetical losses in employment in
cases where output growth leads to disproportionately smaller employment growth.
In Markusen et al (1991) there have been some typographical errors that prevent the
reader to fully comprehend the proposed methodology. These errors have offered the
opportunity for a fertile discussion of this methodological proposition in the literature
Discussion Paper Series, 2007, 13(1)
6
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
(Dinc and Haynes, 1998a; 1998b). Noponen et al (1998) account for these errors and
respond to the comments raised in the literature.
2.1 The standard shift-share method for employment change and its
trade adjusted counterpart.
This section describes the methodology proposed by Markusen et al (1991) and further
clarified in Noponen et al (1998).
Let E be standing for employment, i for manufacturing branches, r for regions, 0 for base
year, t for terminal year. The change in regional manufacturing employment is given by:
Eitr − Eir0
ΔE = ∑ E e , where e =
. The regional employment change can be
Eir0
i
r
r
i
r r
i0 i
further decomposed as:
(
∑E
ΔE r ≡
r
i0
e
i
123
national −component
∑E − E
=
∑E
r
it
where ei
)
+ ∑ Eir0 (ei − e ) + ∑ Eir0 eir − ei (1),
i
i
1
42
4 43
4 1
4
4244
3
industrial −mix
r
i0
r
r
ir
competitive− shift
∑∑ E − E
and e =
∑∑ E
r
it
i
r
i0
r
r
ir
i
r
.
r
The trade adjusted shift share analysis is funded on the relationship:
Q = D + X − M (2),
where Q is the value of manufacturing production, D is domestic demand
( D = Q − X + M , apparent consumption), X stands for exports and M for imports.
The national component of the trade adjusted shift-share analysis is given by:
∑E
r
i0
i
e = ∑ Eir0 (e + q − q ) = ∑ Eir0 q + ∑ Eir0 (e − q ) (3).
i
i
In the above relationship q =
output.
The
term
i
Qt − Q0
is national-level growth of total manufacturing
Q0
∑E
r
i0
q
can
be
further
decomposed
as
i
∑E
r
i0
i
⎛ D
D − D0
X
M ⎞
represents growth in
q = ∑ Eir0 ⎜⎜ d 0 + x 0 − m 0 ⎟⎟ , where d = t
Q0
Q0 ⎠
D0
i
⎝ Q0
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
m=
Xt − X0
X0
x=
domestic demand,
7
growth in total manufacturing exports, and
Mt − M0
growth in total manufacturing imports.
M0
The national component is fully decomposed as:
∑E
r
i0
i
⎛ M ⎞
⎛ X ⎞
⎛ D ⎞
e = E0r ⎜⎜ d 0 ⎟⎟ + E0r ⎜⎜ x 0 ⎟⎟ − E0r ⎜⎜ m 0 ⎟⎟ +
E0r (e − q )
(4).
1
424
3
Q
Q
Q
0 ⎠
0 ⎠
0 ⎠
⎝
⎝
⎝
14243 14243 14243 national labour productivity
national exports
national demand
It can be confirmed that
q=d
national imports
D0
X
M
+ x 0 −m 0 .
Q0
Q0
Q0
The industry-mix component of the trade adjusted shift-share analysis is obtained by the
following relationship:
∑ E (e
r
i0
i
i
The
− e ) = ∑ Eir0 (qi − q ) + ∑ Eir0 ((ei − e ) − (qi − q )) (5).
i
term
i
∑ E (q
r
i0
i
− q)
can
be
further
decomposed
yielding
i
∑ E (q
r
i0
i
i
⎡⎛ D
X
M ⎞⎤
X
M ⎞ ⎛ D
− q ) = ∑ Eir0 ⎢⎜⎜ d i i 0 + xi i 0 − i 0 ⎟⎟ − ⎜⎜ d 0 + x 0 − 0 ⎟⎟⎥ . The latter
Q0 Q0 ⎠⎦
Qi 0 Qi 0 ⎠ ⎝ Q0
i
⎣⎝ Qi 0
with some rearrangement becomes:
⎛ M
⎛ X
⎛ Di 0
D ⎞
X ⎞
M ⎞
⎜⎜ di
− d 0 ⎟⎟ + ∑ Eir0 ⎜⎜ xi i 0 − x 0 ⎟⎟ − ∑ Eir0 ⎜⎜ mi i 0 − m 0 ⎟⎟
Qi 0
Q0 ⎠ i
Qi 0
Q0 ⎠ i
Qi 0
Q0 ⎠
i
⎝ 442
⎝ 42
⎝ 42
1
44
4443
144
4443
144
4444
3
∑ E (q − q) = ∑ E
r
i0
i
i
r
i0
domesticindustrialmix
exports industrialmix
importsindustialmix
Thus, the industry-mix component of the trade-adjusted shift-share analysis can be fully
decomposed as:
⎛ Di 0
⎛ X
⎛ M
D ⎞
X ⎞
M ⎞
⎜⎜ di
− d 0 ⎟⎟ + ∑ Eir0 ⎜⎜ xi i 0 − x 0 ⎟⎟ − ∑ Eir0 ⎜⎜ mi i0 − m 0 ⎟⎟ +
Q0
Q0 ⎠ i
Q
Q0 ⎠ i
Q0 ⎠
i
⎝4
⎝4
⎝ 44Q
1
44
42i4
444
3 144
42i 04444
3 144
2i 04444
3
∑ E (e − e) = ∑ E
r
i0
i
i
r
i0
domesticindustrialmix
exports industrialmix
importsindustialmix
+ ∑ Eir0 ((ei − e) − (qi − q))
i
1
444
424444
3
Lab.Prodindustialmix
The competitive shift component in the trade-adjusted shift share analysis remains the
same as in the original version of the method. As far as the interpretation of the
components of the trade-adjusted shift-share analysis is concerned, it should be noted
that: the national component has four subcomponents, namely “national exports”,
Discussion Paper Series, 2007, 13(1)
(6).
8
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
“national imports”, “national domestic demand”, and “national labor productivity”. These
would represent the hypothetical effect if employment were to expand proportionately to
national exports, the effects on employment through national imports substituting for
domestic production, the effects on employment through a residual effect of national
demand shifts, and a correction factor as productivity gains may lead to employment
losses if output growth leads to disproportionately smaller job growth.
The industry-mix component of the modified shift-share method also has four
subcomponents: “exports-industry-mix”, “imports-industry-mix”, “domestic-industry-mix”,
and “labor-productivity industry-mix”. The first of them represents a hypothetical
employment effect as if a region’s industries expanded proportionally to national export
sales in those industries. The second provides for the hypothetical employment effect
through import substitution for local industries. The third accounts for the residual effect
of domestic demand on local industries, and the fourth accounts for possible effects on
employment in cases where a region’s industrial structure has outperformed or lagged
behind the nation’s productivity growth.
It becomes evident that the employment effects attributed to domestic demand, exports
and imports shifts are all hypothetical. The basic assumption is that output-based
measures are translated into jobs as if employment to output ratios had remained
constant over the period studied. The labor productivity components come into play to
account (as correction factors) for possible shifts of employment-output ratios during the
period. Thus, a national labor-productivity component may be negative (positive) if over
the study period output growth has outpaced (lagged behind) employment growth at the
national level.
2.2 Results for traditional and trade adjusted shift-share analyses.
The international trade adjusted shift-share analysis is applied for the analysis of
regional manufacturing-employment changes in Greece for the 1984-1988 period. To
facilitate interpretations results of the conventional shift share analysis are also
provided. In Table 1 and Table 2 that follow the results of the conventional and the
trade-adjusted shift-share analysis are presented. Over the study period (1984-1988)
total manufacturing employment grew at about 2,5%, manufacturing output declined at
about 6,5%, and manufacturing imports increased about 11%, whereas manufacturing
exports declined at about 23% and domestic demand grew at 1,6%. These facts may
help the interpretation of the results presented in Table 2.
Table 1 demonstrates that in five out of thirteen regions there was positive net
employment change in manufacturing. Namely these regions are: Central Macedonia,
West Macedonia and Thrace, Attiki and Crete. Thessalia’s positive employment change
is solely attributed to national component as both the industrial-mix and competitive
shifts are negative. This contrasts the case of East Macedonia and Thrace and Central
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
9
Macedonia where all components are positive but the competitive shift is most
pronounced. In Attiki the competitive shift has been quite large and negative whereas in
Crete the positive net employment change is attributed mainly to large positive
competitive shift.
Particularly large appears the negative industrial mix component for West Macedonia
indicating overrepresentation of nationally declining sectors in this region.
Table 1: Shift-share analysis of regional employment change: 1984-1988
Region
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
Net Job Change
4.812
15.544
-424
533
-471
-164
-2.143
-2.647
-368
1.909
-471
-550
1.461
National Growth
826
3.514
509
1.026
314
137
858
1.195
664
7.123
167
231
455
Industrial Mix
1.400
3.064
-3.817
-331
-160
-101
154
-1.550
346
1.515
-245
-18
-257
Competitive Shift
2.587
8.965
2.883
-161
-625
-200
-3.155
-2.292
-1.379
-6.729
-394
-763
1.263
Source: Data from NSSG, own elaboration
Far more interesting are the results of the trade-adjusted shift-share analysis. The
national effect subcomponents reflect increases of domestic demand, imports and
declines in exports and labor productivity at the national level for total manufacturing. It
is, however, the industry-mix decomposition that is worthy of a more detailed discussion.
The values of the export subcomponent are positive in all but one region, namely West
Macedonia. This may lead one to infer that all, but one, regions, overall, specialize in
Xt − X0
is
Q0
X − X0
− t
can be
Q0
export expanding sectors. This is not necessarily always the case. The term
negative for the period considered. Therefore the term
X it − X io
Qi 0
positive either when an industrial sector is export-expanding or when it is exportdeclining but at a lower rate than total manufacturing.
Discussion Paper Series, 2007, 13(1)
10
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
Table 2: Trade-adjusted shift-share analysis of regional employment change: 1984-1988
Region
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
Net Job
Change
4.812
15.544
-424
533
-471
-164
-2.143
-2.647
-368
1.909
-471
-550
1.461
National Component
Exports
Imports
Domestic
Demand
607
2.584
374
754
231
101
631
879
489
5.238
123
170
335
-1.574
-6.699
-970
-1.955
-598
-261
-1.636
-2.278
-1266
-13.578
-319
-441
-868
-1.154
-4.913
-711
-1.434
-438
-191
-1.200
-1.671
-929
-9.958
-234
-324
-636
Labor
Productivity
2.947
12.542
1.815
3.660
1.119
489
3.063
4.265
2.371
25.421
597
826
1.624
Source: Data from NSSG, own elaboration
Table 2 (continued)
Region
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
Domestic
Demand
502
787
-1.731
446
-128
-59
270
-653
-553
421
-104
-153
165
Industry-Mix Component
Exports
Imports
384
2.765
-242
846
159
37
698
703
194
6.640
52
185
231
-548
-3.195
2.425
-1.150
-104
-62
-435
-800
-288
-7.555
49
-128
-303
Labor
Productivity
1.061
2.708
-4.270
-473
-87
-17
-379
-801
993
2.010
-241
77
-350
Competitive
Shift
Component
2.587
8.965
2.883
-161
-625
-200
-3.155
-2.292
-1.379
-6.729
-394
-763
1.263
Source: Data from NSSG, own elaboration
To further clarify this point, the industry-mix export component may be written in the
form:
∑E
i
ir 0
⎛ X it − X i 0 X t − X 0 ⎞
⎛ X − X i0 ⎞
X − X0
⎜⎜
⎟⎟ = ∑ Eir 0 ⎜⎜ it
⎟⎟ − ∑ Eir 0 t
−
Q0 ⎠ i
Q0
⎝ Qi 0
⎝ Qio ⎠ i
(7).
The second right hand side (RHS) term in the above relation is simply the national
exports component. In Table 3 below both RHS terms are presented together with
industry-mix export component.
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
11
Table 3: Decomposition of the industry-mix export component
Region
(1)
∑E
i
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
ir 0
⎛ X it − X i 0 ⎞
⎜⎜
⎟⎟
Q
i0
⎝
⎠
-1.190
-3.935
-1.211
-1.109
-438
-224
-938
-1.575
-1.073
-6.938
-267
-256
-636
(2)
∑E
ir 0
i
(1)-(2)
⎛ Xt − X0 ⎞
⎜⎜
⎟⎟
Q
0
⎝
⎠
-1.574
-6.699
-970
-1.955
-598
-261
-1.636
-2.278
-1.266
-13.578
-319
-441
-868
384
2.765
-242
846
159
37
698
703
194
6.640
52
185
231
Source: Data from NSSG, own elaboration
In the light of the further composition that is presented in Table 3 it becomes evident that
all the positive values of the industry-mix export component that appear in Table 2
reflect that if export performance of each industrial sector at the national level is to be
translated, according to industrial mix, to changes in employment levels, then all regions
would have employment losses. However, these losses would be smaller in comparison
to the case where national exporting performance in manufacturing was applied,
according to industrial mix, to regional employment. Thus, what is recorded as
employment gains is essentially savings in hypothetical employment losses when
compared to the national export component. The negative sign of the industry-mix
export component for West Macedonia (see Table 2) implies that hypothetical
employment losses are even higher when applying sectoral export performance, in
place of national export performance, on the industrial structure of this region.
⎛ M it − M io M t − M 0
−
Qi 0
Q0
⎝
The sign of import industrial-mix depends on − ⎜⎜
⎞
⎟⎟ . The
⎠
differences are again multiplied by E ir 0 and then summed over sectors to get the
relevant figure for each region. Therefore, this subcomponent may be negative if a
region has larger base-year employment levels in sectors outperforming total
manufacturing aggregate in import penetration and positive if a region has larger baseyear employment levels in sectors where imports have been declining or in sectors
where imports have been increasing but slower when compared to total manufacturing
imports.
The industry-mix import component can be written in the following, more informative,
form:
Discussion Paper Series, 2007, 13(1)
12
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
⎛ M − M i0 M t − M 0 ⎞
⎛ M − M i0 ⎞ ⎛
M − M0 ⎞
⎟⎟ = −∑ Eir 0 ⎜⎜ it
⎟⎟ − ⎜⎜ − ∑ Eir 0 t
⎟
− ∑ Eir 0 ⎜⎜ it
−
Q0 ⎟⎠
Q0 ⎠
i
i
⎝ Qi 0
⎝ Qio
⎠ ⎝ i
(8).
In Table 4 the industry-mix import component is further decomposed to its ingredient
parts.
Table 4: Decomposition of the industry-mix import component
Region
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
(1)
(2)
⎛ M − M i0 ⎞
⎟⎟
− ∑ Eir 0 ⎜⎜ it
Qi 0
i
⎝
⎠
⎛ M − M0 ⎞
⎟⎟
− ∑ Eir 0 ⎜⎜ t
i
⎝ Q0
⎠
-1.702
-8.108
1.714
-2.584
-543
-254
-1.635
-2.471
-1.216
-17.513
-185
-452
-939
-1.154
-4.913
-711
-1.434
-438
-191
-1.200
-1.671
-929
-9.958
-234
-324
-636
(1)-(2)
-548
-3.195
2.425
-1.150
-104
-62
-435
-800
-288
-7.555
49
-128
-303
Source: Data from NSSG, own elaboration
This decomposition helps to explain that the positive industry-mix import component for
West Macedonia (see Table 3) is attributable to specializations in import-declining
sectors whereas the corresponding positive value for North Aegean Islands is due to
specializations in sectors characterized by slower import penetration than national
economy.
The industry-mix domestic demand component offers mixed results across regions. This
component is positive only for East Macedonia and Thrace, Central Macedonia, Attiki
and Crete indicating specialization in sectors facing faster domestic demand expansion
than the national economy.
Assuming constant employment-to-output ratios over the study period, shifts in exports,
imports and domestic demand (all of them are output-based measures) translate to
employment changes at the regional level through the mechanism described above.
However, output may increase with less or the same level of employment used if there
are productivity gains. On the other hand, output growth may lag behind employment
growth implying productivity losses. The sign of labor productivity industry-mix
coefficient depends on ((ei − qi ) − (e − q )) . Both terms if negative signify productivity
gains at the manufacturing-branch and total manufacturing levels respectively, whereas
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
13
if positive they imply productivity losses at the corresponding levels. The differences are
multiplied by E ir 0 and then summed over sectors for each region. Therefore a negative
labor-productivity industry-mix figure implies overall productivity gains in the region
(hence employment losses), whereas a positive figure underlines overall productivity
losses (hence employment gains).
Again, there is a more informative way to write the industry-mix labor productivity
component. That is,
∑ E (e
ir 0
i
i
− qi ) −∑ Eir 0 (e − q ) (9).
i
In Table 5 the industry-mix labor productivity component is further decomposed. This
would facilitate the results regarding this component that appear in Table 4.
Table 5: Decomposition of the industry-mix labour productivity component
Region
∑ Eir 0 (ei − qi )
(1)
∑ Eir 0 (e − q )
(2)
(1)-(2)
4.008
15.251
-2.455
3.187
1.033
472
2.684
3.465
3.364
27.431
356
903
1.274
2.947
12.542
1.815
3.660
1.119
489
3.063
4.265
2.371
25.421
597
826
1.624
1.061
2.708
-4.270
-473
-87
-17
-379
-801
993
2.010
-241
77
-350
i
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
North Aegean Islands
South Aegean Islands
Crete
i
Source: Data from NSSG, own elaboration
With the assistance of Table 5 it can be clarified that the negative value of the industrymix labor productivity component for West Macedonia (see Table 4) owes to the
specialization of the region in sectors experiencing productivity growth. In contrast, the
negative signs obtained for the same component for Thessalia, Ipeiros, Ionian Islands,
Western Greece, North Aegean Islands and Crete should be attributable to
specialization in sectors experiencing productivity reductions at a rate lower than that of
total manufacturing.
Discussion Paper Series, 2007, 13(1)
14
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
3. The effects of regional specialization on employment
growth: a shift-share covariance model
The shift-share analysis has been extended to estimable linear models by Weeden
(1974). These extensions pertain to a two-way analysis of variance (ANOVA) and
covariance extensions of the shift share analysis.2
R
J
R
J
∑∑ E - ∑∑ E
ir0
Let Gn =
ir0
r =1 i =1
r =1 i =1
R
be the national growth rate (total manufacturing) for
J
∑∑ E
ir0
r =1 j=1
i = 1,...J industrial branches and r = 1,..., R regions. The following weights need also
J
to be defined: Wr =
∑E
i =1
R
R
ir 0
∑∑ E
r =1 i =1
∑W
i
= 1 , Wir =
i
∑Z
Eir0
∑E
ir0
r
= 1 Σr , Wi =
r
∑∑ E
such as
r =1 i =1
∑W
ir
= 1 , Zir =
i
ir0
Eir0
R
∑E
=
ir0
i =1
ir
r =1
R J
ir0
ir 0
such as
J
∑W
such as
J
∑E
Wir Wr
such as
Wi
r =1
= 1.
r
It follows that G r =
∑W
ir
i
Gir , Gi = ∑ Z ir Gir and Gn = ∑ Wr Gr = ∑ Wi Gi .
r
r
i
Working with growth rates, the shift-share identity becomes:
Gr - Gn = ∑ (Wir - Wi ) G i + ∑ Wir (G ir - G i ) (10).
i 44244
i 442443
1
3 1
industry mix
differential component
In an analysis of variance terms, Weeden (1974) proposes that the linear model
generating Gir is given by:
Gir = ai + br + vir (11),
i = 1,..., J industry dummies and br the
parameters of Br r = 1,..., R regional dummies respectively, vir is a stochastic term
where ai are the parameters of Ai
with zero mean (unsystematic variation). The latter is the amount of variation not
attributed to factors that determine the average growth of each industry nationally ( Ai )
2
See Fotopoulos and Spence (1999 and 2001) for methodological discussion and applications of
econometric model extensions of shift-share analysis in different research contexts.
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
15
and factors that determine the average growth of each region for all manufacturing
( Br ).
Provided that the residual term is normally distributed with zero mean, so that its
expected value is E(Vir ) = 0 , then it follows that the expected value of the regional
growth rate is: E(Gr) =
∑ Wir ai + br ,
the expected value of national growth is:
i
E(Gn) = ∑ Wiai + ∑ Wrbr
i
and
that
their
difference
is:
r
E(Gr − Gn) = ∑ (Wir − Wi )ai + (br − ∑ Wrbr ) .
i
The
derived
r
shift-share
ANOVA
expression
then
becomes:
gr - gn = ∑ (Wir - Wi)âi + b̂r - ∑ Wrb̂r .
i
r
The estimators of the industry-mix and the differential components respectively become:
P̂r = ∑ (Wir − Wi ) â i (industry-mix) and D̂ r = b̂ r − ∑ Wr b̂ r (differential component
i
r
or competition effect).
Conventional shift-share analysis applies a weighting system on combinations of
∑E
ir t
Gi =
r =1
− ∑ Eir0
r =1
∑E
∑E − ∑E
irt
and
Gr =
ir 0
r =1
i =1
ir0
i =1
∑E
to account for the effect of
ir0
i =1
composition and growth effects in analyzing differences between observed regional and
national growth rates. The analysis of variance model, however, uses the effects of
differences between industry
1
Eir t - Eir 0
and also between regional means
∑
R r
Eir 0
1 Eir t - Eir 0
∑ Eir 0 in explaining variation in Gir . This allows some statistical inference to be
J i
drawn on the contribution of the composition and growth effects in explaining differences
between estimated regional and national growth rates. It also requires that both the
composition and growth effects be expressed as linear combinations of the coefficients
of industry and regional effects respectively. This necessarily renders the composition
and growth effects derived from the ANOVA version of shift-share analysis as a result of
solely systematic factors. The estimation of the shift-share ANOVA proceeds as follows.
In a first step, equation (11) is estimated by ordinary least squares. That is,
( )
−1
⎛ â ⎞
ẑ = ⎜⎜ ⎟⎟ = X ′X X ′G ir is estimated where X represents the JR×(J+R) matrix of RHS
⎝ b̂ ⎠
dummy variables and ẑ is a (J+R)×1 vector of coefficients. In a second step vectors c
Discussion Paper Series, 2007, 13(1)
16
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
′
and q of dimensions (J+R)×1 are found such as c r ẑ gives the estimated industry-mix
′
q r ẑ gives the estimated competition component for each region r. The
′
′ −1
2 ′
variance of industry-mix components is given by var(c ẑ) = σ c ( X X ) c and that
component and
′
r
′
r
r
′ −1
of the competition component by var(q r ẑ) = σ q r ( X X ) q r (see Johnston,
2
1973:126). Singularity of the X matrix can be avoided by dropping, say, one regional
dummy,
without
affecting
the
derivation
of
shift-share
components
P̂r = ∑ (Wir − Wi ) â i and D̂ r = b̂ r − ∑ Wr b̂ r since in order to derive the competition
i
r
component for one region, the coefficients of all others need to be taken into account.
An alternative would be to put a priori constraints on the coefficients of the linear model
such as
∑W a
ir
= 0 and
i
∑W b
ir
r
= 0 permitting the inclusion of all industry and
r
i
regional dummies.
Weeden (1974:81) considers the effect of industry specialization on regional growth by
extending the shift-share linear model. In particular he tests for the hypothesis that
“regions perform best in those industries in which they specialize” (ibid.) suggesting that
Gir might be a positive function of Wir − Wi , the degree to which the region specializes
in the industry. Under this hypothesis the basic linear model extends to the following
covariance model:
Gir = ai + λ (Wir − Wi ) + br + ε ir (12),
′
or alternatively Gir = ai + λWir + br + ε ir , where ai′
= ai − λWi , Wir =
Eir,0
J
∑E
is the
ir 0
i =1
share of manufacturing branch i in region’s r total manufacturing employment and
R
∑E
ir 0
Wi =
r =1
R J
∑∑ E
is the share of manufacturing branch i in total national manufacturing
ir 0
r =1 i =1
employment. The resemblance of the measure Wir
coefficient is apparent since the latter is defined as:
− Wi to regional specialization
1
∑ Wir − Wi ∀r . The estimated
2 i
difference between the regional and national growth rates under the specialization
hypothesis becomes:
⎛
⎞ ⎛
⎞
g r − g n = ∑ (Wir − Wi )aˆi′ + λˆ ⎜ ∑ Wir2 − ∑ Wr ∑ Wir2 ⎟ + ⎜ bˆr − ∑ Wr bˆr ⎟ (13).
i
r
i
r
⎠
⎝ i
⎠ ⎝
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
17
In the above formulation the industrial mix component, as a whole, for each region is
⎛
⎞
− Wi )aˆ i′ + λˆ⎜ ∑ Wir2 − ∑ Wr ∑ Wir2 ⎟ . The part of the industrial mix
i
r
i
⎝ i
⎠
⎛
2
2⎞
component that owes to regional specialization is given by λ̂ ⎜ ∑ Wir − ∑ Wr ∑ Wir ⎟ .
r
i
⎝ i
⎠
What remains, ∑ (Wir − Wi )aˆ i′ , is the part of industrial mix component that is purged
given by
∑ (W
ir
i
from the effect of regional specialization. Hereafter the latter is named “rest of industrial
⎛
⎞
⎜ ∑ Wir2 − ∑ Wr ∑ Wir2 ⎟ is a measure of the degree to which a given
r
i
⎝ i
⎠
mix”. The term
region is specialized in certain industries relative to all other regions. To understand this,
note that
∑W
2
ir
is the Herfindahl index of regional specialization and
i
∑W ∑W
2
ir
r
r
is
i
a spatially-weighted average of the Herfindahl index across all regions in the reference
economy. The larger the value of the expression
⎛
⎞
⎜ ∑ Wir2 − ∑ Wr ∑ Wir2 ⎟ the more
r
i
⎝ i
⎠
relatively specialized the region.
The parameter λ is estimated by a linear regression of pooled cross-section (regions) of
cross-section (industries) data with region-fixed and industry-fixed effects and one
covariate (true independent variable) that of specialization. Perhaps some further
comments might be helpful. First, λ̂ is the same for all regions and industries as there
are insufficient degrees of freedom to permit the estimation of different λ for each region.
Second, when extending the theorem used above to derive the standard error and
consequently the t-ratio of this component for each region, the latter essentially
becomes that of the estimated regression coefficient of the estimated equation. Despite
this, the corresponding shift-share component differs from region to region. Thus, for
each region the specialization effect could take on both positive and negative values
depending on the sign of λ̂ and that of the quantity included in parentheses.
It may seem contradictory at a first glance that the “rest of industrial-mix” is positive
whereas the specialization is negative (and vice versa). The “rest of industrial mix” term
is positive whenever a region is specialized in fast growing (at the national level)
sectors. In contrast, the sign of the specialization term depends on the sign of relative
specialization (negative for a relatively less specialized region, positive of a relatively
more specialized region) and the sign of λ̂ . The latter is negative. Thus, the coefficient
of the specialization term is positive for relatively less specialized regions and negative
for relatively more specialized ones.
In Table 6 the results of estimating the shift-share covariance model are presented. The
industry-mix specialization component is positive only for the regions of Thessalia,
Discussion Paper Series, 2007, 13(1)
18
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
Central Greece, and Attiki. The regions are relatively less specialized than other regions
in Greece. Table 7 helps in clarifying this point.
Table 6: Shift-share covariance model: the effects of regional specialization (%): (t-ratios
are given in parentheses)
East Macedonia and Thrace
gr − gn
11,877
16,002
8,360
10,157
Macedonia West
-5,129
-7,469
Thessalia
-1,675
-3,016
Ipeiros
-6,696
-3,953
Ionian Islands
-6,004
-4,055
Western Greece
-9,536
-9,090
Central Greece
-8,711
-9,796
Peloponnisos
-4,491
-2,157
Attiki
-1,266
-2,593
North Aegean Islands
-9,990
-11,416
South Aegean Islands
-8,744
-7,891
5,615
7,605
Macedonia Central
Crete
Industrial-mix
2,735**
(2,197)
2,602***
(4,000)
-35,568***
(-4,771)
1,177**
(1,331)
1,441*
(1,293)
-2,045*
(-1,618)
1,566**
(1,749)
-2,605**
(-1,737)
1,280
(0,894)
0,753
(1,034)
-3,159**
(-2,324)
3,050**
(2,326)
0,752
(0,725)
Competition
effect
total
Estimated
specialization
Actual
Gr − Gn
rest of
industrial-mix
Region
-0,722**
(-1,864)
-0,167**
(-1,864)
-9,359**
(-1,864)
0,363**
(1,864)
-0,860**
(-1,864)
-1,865**
(-1,864)
-0,015**
(-1,864)
0,258**
(1,864)
-1,310**
(-1,864)
1,004**
(1,864)
-1,182**
(-1,864)
-0,745**
(-1,864)
-0,885**
(-1,864)
2,013*
(1,498)
2,435***
(3,364)
-44,927***
(-8,240)
1,540**
(1,757)
0,581
(0,473)
-3,910***
(-2,469)
1,552**
(1,732)
-2,347*
(-1,576)
-0,030
(-0,019)
1,757***
(2,357)
-4,341***
(-3,582)
2,305*
(1,651)
-0,133
(-0,119)
13,989***
(3,051)
7,722**
(1,905)
37,458***
(3,960)
-4,556
(-0,933)
-4,534
(-0,983)
-0,145
(-0,030)
-10,642***
(-2,238)
-7,449**
(-1,485)
-2,127
(-0,453)
-4,350**
(-1,409)
-7,075**
(-1,520)
-10,195***
(-2,137)
7,737**
(1,666)
Hypotheses:
H1:
br1 = br2 = ... = brR −1 = 0
and
ai1 = ai2 = ... = aiJ −1 = 0
H2:
br1 = br2 = ... = brR −1 = 0
, F(12,227)=6,58***
H3:
ai1 = ai2 = ... = aiJ −1 = 0
, F(19,227)=4,49***
, F(31,227)=5,15***
H4: λ=0, F(1,227)=3,42**
*** significant at the 1% level
t ≥ 1,2853
t ≥ 2,3429 ;** significant at the 5% level t ≥ 1,6517 ;* significant at the 10% level
for 227 degrees of freedom
Source: Data from NSSG, own elaboration
Local economic conditions as reflected by the competition component are conducive to
employment growth in East Macedonia and Thrace, Central and West Macedonia, and
Crete. In contrast, Attiki, the most populated region in the country has a negative
UNIVERSITY O THESSALY, Department of Planning and Regional Development
Spatial Variations of Greek Manufacturing Employment Growth
19
competition component most probably due to agglomeration diseconomies. The total
industrial mix component has been positive and significant (at some conventional level)
in East Macedonia and Thrace, Central Macedonia, Thessalia, Attiki and North Aegean
Islands. In contrast it has been negative and particularly significant in West Macedonia
and the Ionian Islands and is less significant in Central Greece. In nine out of thirteen
regions the competition effect dominates (in absolute value) over the total industrial mix
component. Overall, national manufacturing employment growth was surpassed only in
East Macedonia and Thrace, Central Macedonia and Crete. Although Attiki experienced
employment growth, it did so at a rate smaller than that of total manufacturing at the
national level. However only in the first two of the aforementioned regions was this result
was brought about by positive contributions of both total industry-mix and competition
component. In Attiki only the industry-mix component is positive and in Crete only the
competition component.
Table 7: Indices of absolute and relative regional specialization
Herfindahl
Region
∑Wir2
i
Spatially weighted
average Herfindahl
∑W ∑W
r
East Macedonia and Thrace
Macedonia Central
Macedonia West
Thessalia
Ipeiros
Ionian Islands
Western Greece
Central Greece
Peloponnisos
Attiki
Northern Aegean Islands
South Aegean Islands
Crete
2
ir
r
0,124
0,109
0,351
0,095
0,127
0,154
0,105
0,098
0,139
0,078
0,136
0,124
0,128
i
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
0,1046
Relative specialization
⎛
⎞
⎜ ∑ Wir2 − ∑ Wr ∑ Wir2 ⎟
r
i
⎝ i
⎠
0,019
0,004
0,246
-0,010
0,023
0,049
0,000
-0,007
0,034
-0,026
0,031
0,020
0,023
Source: Data from NSSG, own elaboration
These results should be viewed within the context of results of other research studying
the effects of specialization on local economic growth. Thus, in Glaeser et al’s (1992)
study of employment growth between 1956 and 1987 in 170 US standard metropolitan
areas (SMA), specialization was found to slow employment growth. In contrast,
Henderson et al (1995), using more extensive geographical data coverage (224 SMA)
but only eight traditional capital goods industries, found that past own-industry
geographical concentration has a positive effect on an industry’s employment growth
between 1970 and 1987.
On the other hand, in Combes’ (2000) study of economic structure on local economic
growth in France over the 1983-1993 period, specialization was found to have a
Discussion Paper Series, 2007, 13(1)
20
Georgios Fotopoulos, Dimitris Kallioras, George Petrakos
negative effect on employment growth in both manufacturing and services sectors. The
author suggested that this result may be seen in relation to business cycles since
specialization may enhance local employment growth during economic upturns but
contribute to employment decline during downturns. In a somewhat different, but not
unrelated, research context Baldwin and Brown (2004) found that specialization (as an
inverse concept to diversity) has a positive effect on employment volatility (variance of
annual regional employment growth rates) whereas export intensity has a negative
effect of employment volatility in Canadian regions over the period 1976-19973.
However, they note that regions demonstrating higher export intensity (more integrated
to world economy) also tend to be more specialized.
4. Conclusions.
Overall, this research exercise reveals that, although useful, the trade-adjusted shiftshare analysis must be applied with some caution. With this in mind, the results of the
analysis indicate that Greek regions have been vulnerable to deteriorating trade
conditions pertaining to most manufacturing sectors over the study period. It appears
that has been was no industrial-mix at the regional level able to recover jobs lost to
increased international competition and competitiveness loss for domestic producers. In
addition, it seems that, overall, lost jobs cannot be attributed to productivity gains. As far
as the specialization hypothesis is concerned, relatively more specialized regions
performed worse in employment growth terms. This result may not be unrelated to those
obtained by the trade adjusted shift-share analysis. This may recommend that a fruitful
suggestion for future research would be to address both issues, that is, the effects of
international competition and regional specialization, in the same analytical framework.
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