Transcript regional development

REGIONAL DEVELOPMENT
WEEK 2
Recap
• Last week we have mentioned the evolution
path of regional economic models.
• Traditional trade theory
• New trade theory
• New Economic Geography Models
• This week we will talk about some important
models that are quite important in regional
economic theory and policy in detail.
Traditional Tools for Measuring and Evaluating
Regional Economic Performance
• Regional economic development policy is
basically about the allocation or reallocation of
resources to enhance the economic
performance of industries. Planners and policy
makers need to be able to measure and
evaluate that performance.
Thus it is necessary to:
• Measure the degree to which economic activity
and employment in a region is related to serving
local demand as against serving demand external
to the region (i.e. exports).
• Assess a region’s overall performance relative to
that of other regions.
• Assess which industry sectors are performing
better in the region.
• Assess a given sector’s efficiency relative to other
industry sectors’ performance in the region.
Measures of Concentration
• There are several measures used in empirical
studies to investigate geographical and
industrial concentration within and across
countries/regions.
• We will only consider some basic measures.
Herfindahl Index
• H=
• Where; sijc denotes share of employment in
industry i in region j in total employment of
industry i:
• The Herfindahl index is a measure of industrial
concentration. Its main advantage is the
computational simplicity. On the other hand
Herfindahl index does not take the areas of the
region into account, it assumes they all have
same sizes and it is also sensitive to the
number of firms in each industry
The Dissimilarity Index for Regional
Specialization
• DSRj=
• Where; sijs denotes share of employment in
industry i in region j in total employment of
region j and si denotes share of total
employment in industry i in total employment
and calculated as follows:
The Dissimilarity Index for Industrial
Concentration
• DCRi=
• Where; sj denotes share of total employment in
region j in total employment and calculated as
follows;
Krugman Specilization Index
• KSI=
where k and l are two different
regions.
• The Krugman specialization index, compares
two regions and identifies how specialized or
despecialized these regions are.
Gini Coefficient for Regional
Specialization
• GINIjs=
• where, Ri=
• λi indicates the position of the industry i in the
ranking of Ri in descending order.
Gini Coefficient for Industrial
Concentration
• GINIic=
• where, Cj=
• λj indicates the position of the region in the
ranking of Cj in descending order
Economic Base Theory
• Economic base theory (EBT) is an easily
understood traditional body of thought in the field
of regional economic development.
• EBT views an economic system as composed of
two parts:
• one, called non-basic, is viewed as producing for
local consumption;
• the other, called basic, is viewed as producing
goods and services primarily for external
consumption, (that is for export from the region).
• Economic development theorists believe that
the critical cornerstone of a regional economic
system is its basic economic activities.
• Thus by expanding (export) base activities, the
local regional economy not only expands
employment and earnings in the region
directly, but also expands employment and
earnings indirectly.
• As a consequence, the primary focus in
sectoral targeting analysis is on basic
economic sectors and activities.
• A complimentary approach is entitled import
substitution, whereby goods and services are
imported to support basic and, in some cases,
non-basic production.
• With this approach, industry sectors that are
insufficiently developed to support local basic
activity are targeted for investment and
development. By expanding these sectors, the
relative importance of basic sectors often can be
increased.
Measuring the Economic Base of a
Region
• A variety of techniques have been developed to
separate economic systems into their basic and
their non-basic parts. The simplest method is to
sort industry sectors into those that are primarily
basic and those that are primarily non-basic.
• Subsequent efforts have relied on location
quotients, which are measures estimating the
importance of industry sectors to the local
economy relative to their importance in a larger
reference economy
Two types of location quotients:
• One is measured the minimum requirements
approach, where that locality which has the least
employment (earnings or some other indicator of
scale) in a sector becomes the base against which
the same sector in all other regions is compared.
• Alternatively, location quotients can be computed
in terms of some reference area, (e.g. the nation),
whereby the contribution to the basic part of the
economy is measured as the part that is greater
than the proportional amount found in the
reference area.
Calculating Location Quotients
• Location quotients are well known measures of
the relative importance of sectors compared to
their importance in a larger frame of reference
as described above. Location quotients (LQ)
are computed as follows:
• LQir = (Eir/Er)/(EiN/EN)
• Measures of scale other than employment can
be used; for example earnings and gross
regional product GRP
• LQ>1, means a higher concentration in the
region than in the country and LQ>1.25
considered as an initial indicator of regional
specialization.
An Example: Industrial Targeting in
Northern Virginia
• A case study application of industrial targeting
analysis is a study of the Northern Virginia
region in the United States.
• The time period for this study was 1988–1993.
• One objective of the study was to identify and
evaluate the performance of the primary
technology intensive industry sectors.
An Example: Southeast Anatolia
Region (1980-2000)
High point cluster 1980
Driver industries
Food, beverages and tobacco
Slaughtering,
preserving meat
Textile
Chemicals
LQ
Share in employment (%)
6.55
6.13
Dairy products
2.08
0.98
Vegetable and animal oils and fats
2.40
3.79
Grain mill products
1.59
1.96
Prepared animal feeds
2.40
0.96
Distilling, rectifying and blending
spirits
16.78
5.98
Tobacco
1.37
9.06
Spinning, weaving and finishing
textiles
1.63
29.39
Carpets and rugs
4.07
4.5
Petroleum refineries
33.37
28.31
2.06
2.97
Plastic products
elsewhere
preparing
not
and
classified
High point cluster 1980
Driver industries
LQ
Share in employment (%)
Textile
Spinning, weaving and finishing
textiles
3.77
55.08
Made-up textile
wearing apparel
1.58
5.16
11.89
8.60
Carpets and rugs
goods
except
Mediterranean Region
High-point cluster 1980
Driver Industries
LQ
Share in employment (%)
Textile
Spinning, weaving and finishing
textiles
2.63
47.49
Basic metal industries
Iron and steel basic industries
3.67
24.79
High-point cluster 2000
Driver industries
LQ
Share in employment (%)
Textile
Spinning, weaving and finishing
textiles
2.08
30.37
Manufacture of wearing apparel
except leather and fur
1.24
9.95
Petroleum refineries
1.65
0.73
Manufacture of plastic products not
classified elsewhere
1.49
3.95
Manufacture of glass and glass
product
3.06
3.70
Manufacture of cement, lime and
plaster
1.39
1.31
Iron and steel basic industries
4.15
17.57
Chemicals
Non-metallic mineral products
Basic metal industries
Shift-Share Analysis
• A simple descriptive, quick and relatively
inexpensive technique for analyzing regional
growth and decline over time is shift-share
analysis.
• This technique enables the assessment of a
region’s overall performance relative to other
regions.
The Traditional Shift-Share Model
• The traditional shift-share model measures
regional growth or decline by decomposing it into
three components:
• National share (NS): that is, that part of change
attributable to overall national trends
• Industrial mix (IM): that is, that part of change
attributable to the industrial composition or mix
of the region
• Regional shift (RS): that is, that part of change
attributable
to
regional
advantage
or
competitiveness.
• Early shift-share models outlined in Perloff et
al. (1960) focused on total regional
employment and had only two components:
• Total shift (TS), expressed as:
• Differential Shift (DS), expressed as:
• where:
• ei and Ei respectively are regional and national
employment in industry i;
• e and E respectively are regional and national
total employment in all industries; and
• t-1 is the initial period and t the end period (e.g.
inter-censal dates) of the analysis.
• Dunn (1960) introduced to the model differential
rates of growth in individual industries, to give
what is known as the ‘proportionality effect,’
which is equivalent to the industry composition or
mix (IM) effect referred to above.
• Ashby (1967) introduced a three-component
model of regional change, incorporating national
share (NS), industry mix (IM), and regional shift
(RS)
• This classical shift-share model—which has
been used extensively by economists,
geographers, regional scientists and planners in
regional analysis— thus emphasizes not only
the role of regional change for a regionspecific industry, but also the regional shift or
competitive component as a measure of the
relative performance of the region for a
specific industry.
• A position shift is interpreted as being
associated with the comparative or competitive
advantage of the region for that industry, or
vice versa. The partition of regional change
into the three components—NS, IM and RS—
was intended to enable researchers to study the
sources of change separately.
An Example: Regional and Axial Shifts in the United
States
Spatial Economy
• In the early 1980s the United States, the Northeast and
Midwest regions were loosing out to growth in the
Southeast, South, Southwest and West. This was
evidenced by population movements as well as shifts in
industrial location and employment. The Northeast
always was a net out-migration region. What was of
significance was the sudden change in pace and
destination of population movement beginning in the
1970s. While the West and Southwest showed gains in
population, the dramatic growth appeared in the South.
At least some observers interpreted this as a direct
transfer from the (old) North to the (new) South
• Shift-share analyses were computed for all
States and for seven primary transportation
industries in order to compare the rates of
growth among the different states and in
particular
to
compare
the
regional
competitiveness of the states in these
industries.
• Total Transportation, Communications and
Public Utilities
• Local and Interurban Passenger Transit
• Trucking and Warehousing
• Water Transportation
• Air Transportation
• Pipelines
• Transportation Services
• The general pattern among the results was that in
all transportation sectors job loss was occurring
due to industry mix and further loss in the
Northeast and Midwest due to a loss of State
competitive share (see Table 3.4).
• Transportation services were the highest growth
part of the transport sector (see Table 3.5). The
general trend shows a loss in competitive share
from North to South and West even though, in
absolute terms, employment remained highest for
most transport sectors in the North, because of the
heavy industrial concentration there.
• Two criteria were used to identify high
performer States:
• the competitive share had to at least equal the
national growth component;
• the absolute growth in employment must be at
least 2 percent of absolute growth for the
industry nationally.
Critiques and Extensions of Traditional Shift-Share
Analysis
• Despite its continued widespread use, shiftshare analysis has been heavily criticized for
having
temporal,
spatial,
industrial
aggregation, theoretical content and predictive
capability deficiencies
Shortcomings
• Dawson (1982) lists six shortcomings of the traditional shiftshare model:
• Changes in the industry mix in the national economy are not
taken into account. This is a weighting problem as changes
from the beginning to the end of the period of time over which
change is being measured may have quite different weights or
opposite signs for the industrial mix and the competitive
effects.
• Results are sensitive to the degree of industrial and regional
disaggregation.
• The differential industry component is unstable over time, and
the degree of instability varies among industries.
• Growth resulting from inter-industry linkage and secondary
multi-sector effects are not explicitly isolated but are
included in the competitive component (RS), whereas they
should be included in the industry mix component (IM).
• The differential component (RS) may be influenced by
relatively spurious causes, including the incorrect
classification of firms, product heterogeneity within firms,
and transfers of production between separate sites of
individual firms.
• The technique provides no information on the capacity of a
region to retain growing industries or on how to attract them
in the first place (Richardson 1978).
Modifications and Extensions
• Researchers have incorporated the shift-share
model into other statistical forecasting methods,
including:
• (a) Analysis of Variance (ANOVA) models
(Berzeg and Koran 1984, 1978).
• (b) A multiplicative model of shift-share (Theil
and Gosh 1980; Kurre and Weller 1989).
• (c) Univariate autoregressive integrated moving
average (ARIMA) time series models.
• (d) A linear model of shift-share analysis
(Knudsen and Barff 1991).
• Another trend in shift-share analysis is the use
of econometric models developed by
Emmerson et al. (1975) and by Berzeg and
Koran (1978). These are early forms of the
information-theoretic approach developed by
Theil and Gosh (1980).
Next Week
• Total Factor Productivity Approach (3.4.3)