Transcript Geoffrey Hewings` presentation

Metropolitan Region
Competitiveness
Geoffrey J.D. Hewings
Regional Economics Applications Laboratory
University of Illinois, Urbana, IL 61801-3671, USA
www.real.illinois.edu [email protected]
Introduction to the
Regional Economic Applications Laboratory (REAL)
• Provide monthly employment analysis Illinois; monthly index
leading indicators for Chicago economy each MSA; housing
market analysis and forecasts
• Encourage students to be schizophrenic – talk to other
academics and policy-makers
• Annual forecasts for Illinois, Chicago and other Midwest state
economies through 2040
• Developed models for states and regions in EU, Brazil,
Colombia, Chile, Japan, Korea, Indonesia.
• Participants in 2015 from: Chile, Brazil, Indonesia, Korea,
Japan, Colombia, Italy, Turkey, Spain, Poland, Mexico,
Nicaragua, Peru
• Provided support (2 years or more) for >40 doctoral
dissertations in economics, agricultural economics, urban and
regional planning and geography
• “bolsa sanduiche” program with University of São Paulo
2
Three Issues
• What makes a city competitive and what
factors are especially important in the
process?
• How can cities' competitiveness be
evaluated?
• What policy conclusions can be drawn from
the research?
3
Diagnosis before Prescription
• First, my focus is on the city-region (metropolitan region) rather
than just the city de jure
• Reflects our research that has shown that within metropolitan
areas, the degree of interdependence is very large but often
unmeasured and therefore under appreciated
• Consider the case of the Chicago metropolitan region
• Divided it into four areas as shown on the next map
• Explored linkages between industries within and across areas
• The evaluated role of households
• As suppliers of labor
• Recipients of wage and salary income
• Consumers of goods and services
4
Spatial Division of Chicago
5
Chicago Intra Metropolitan Flows
Goods and Services
Flows
Wages and salaries
Flows of commuters
and their incomes by
zone
Household expenditures
Flows of expenditures by
zone
6
Interindustry Interdependence
• Limited connections across regions
100%
5.63%
1.44%
2.97%
5.25%
5.81%
1.49%
90%
80%
70%
60%
93.58%
50%
89.81%
90.30%
89.96%
40%
30%
20%
10%
1.38%
2.83%
2.21%
2.77%
2.17%
2.40%
0%
CBD
R of Chicago
CBD
Suburbs
R of Chicago
Suburbs
Outer Suburbs
Outer Suburbs
7
Total Spatial Interdependence
• Substantial interdependence when all interactions considered
100%
90%
26.15%
29.67%
26.96%
80%
64.89%
70%
11.57%
18.98%
60%
5.97%
49.87%
50%
40%
47.47%
14.69%
30%
48.90%
5.69%
6.60%
20%
17.48%
10%
13.82%
11.29%
0%
CBD
R of Chicago
CBD
R of Chicago
Suburbs
Suburbs
Outer Suburbs
Outer Suburbs
8
Embracing Interdependence
• Attention to a city’s competitiveness – even in
comparison to other cities – fails to acknowledge
the dominant role of interdependence in the
economy
• Establishments in cities are increasingly part of
spatially extensive value chains
• The competitiveness of any firm is dependent on
the efficiency of its suppliers an on those firms
that use its products (unless the firm is a producer
of final goods)
9
Regional Competitiveness: Policy Evolution
• Last 70+ years witnessed change in foci
• Structural –Beveridge – unemployment variance across
regions (“Misery generates hate”)
• “Carrot” and “Stick” policies of 1960s (exclusion and
incentives)
• Growth poles/centers
• Keys sectors (Hirschman-Rasmussen), key firms (MiernykLeontief)
• Portfolio theory
• Clusters (industrial complex analysis in previous
nomenclature)
• Import substitution vs hollowing out
• Creative Class
• Smart Specialization
• Degree to which policies were ex post or ex ante is important
10
Public Policy Decision-Making
• Public Policy Decision-Making requires access to more
sophisticated tools of analysis
• Medical care analogy
• Needs range from
•
•
•
•
•
Short-term impact analysis
Strategic forecasting
Ex-post impact evaluation
Evaluation of alternative development strategies
Broadly based planning, especially related to infrastructure
• Policies need to be evaluated before they are enacted;
feelings and intuition are wonderful but they are not
substitutes for careful formal analysis
11
Our Portfolio of Models
(1) Econometric Input-Output Impact and
Forecasting Models (annual forecasts through 2040)
•
•
•
•
•
6-region (WI, IL, IN, OH, MI and Rest of US)
2-region (5 Midwest states and Rest of US)
11-region MW model
Individual state models
Chicago Metro area
(2) Computable General Equilibrium Model
• Chicago Metro area
• 2-region (Midwest and Rest of the US)
(3) Indices and Business Cycle Analysis
• Chicago and IL metro areas
• 5 Midwest states and US
(4) Housing Market Analysis and Forecasts
12
What we do with the models
• Who are our major trading partners?
• How has this changed over the last decade?
• What do the forecasts suggest in terms of significant
changes (winners and losers)?
• Demographic changes
• Ageing of the population
• In- and out-migration
• By skills
• By income
• Feed data into a fiscal module to help explore state’s
(precarious) financial condition
13
Focus on four major issues of
competitiveness
• Smart specialization
• Complementarity in production – a
missing element in the competitiveness
debate
• Demographic hollowing out – the
implications of inequality on regional
competitiveness
• Investment in public and human capital
14
Smart Specialization
• In development literature, tension between exploiting competitive
advantage and diversifying to avoid sectoral cyclical swings generating
devastating impacts on an economy
• As we explore this debate, we see the way in which cluster
development, notions of complementarity, investment in capital and
inequality intersect.
• Consider the Chicago economy 1970-earlt 2000s
• Multipliers declining but total production and employment increasing
• Transfer local inputs from mfg to services
• But some increases in functional specialization that saw reverse trend –
increasing sectoral interaction
• But overall – region now more dependent on markets for its products
outside and for outside markets as a source of inputs
• Conclusion
• activities that remain in the region do so because they are competitive in
the value chain or interactions locally enhance competitiveness of small
sets of product groups (e.g. rubber and plastic)
• Smart specialization in local clusters accompanying important
substitution
15
Economic Hollowing Out
Relationship Between Total Sectoral Outputs and Intermediation, 1975-2011
510000
Total Intermediation
460000
410000
Total Sectoral Outputs
360000
310000
Gap between Local Production
and Local Supplies increasing
over time
260000
210000
160000
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
110000
16
General Evidence
• Krugman was right - growth in interregional trade exploited scale
economies as a result of decreases in transportation costs
• An Increasing share of interregional flows are intra-industry
flows
• Look at effects of the recession in MW
17
Interregional Trade Increasing More Rapidly
that GDP
1960s/1970s
Intra
state
exchange
1990s/2000s
Raw Materials
State 1
Raw Materials
Inital transformation
State 2
Inital transformation
Secondary transformation
State 3
Secondary transformation
Finished product
State 4
Finished product
Interstate
transport
International
Delivery to market
Delivery to market
18
Supply Chain Impacts

Location in the supply chain generates different
response rates and opportunities – the bullwhip
effect
Red line – consumer demand

Impact on regions/state provides important
insights into their economic performance
19
Supply Chain Issues
low
Resources
higher
Potential for innovation
Initial
transformation
Secondary
transformation
Bull-whip effect
high
Penultimate
finishing
Finishing
Delivery
to market
lower
• “location” of a city’s firms within supply chain helps explain
reaction to changes in demand and the impacts of business
cycles
• Innovation potential greater closer to final goods production?
20
Spatial Interdependence: Job Losses in
the Recession
Change in
Metro
Area
Impacts in
Chicago Indianap. Detroit
C
I
D
C
M
9.36%
5.78%
4.54%
7.91%
5.98%
5.73%
6.47%
3.64%
Columbus Madison
4.70% 5.13% 3.85%
6.19% 12.00% 2.33%
13.10% 5.06%
8.24%
1.98%
8.35% 5.00%
-
Rest of
Midwest Total
19.66%
29.88%
29.66%
21.24%
24.91%
Rest of
US
80.34%
70.12%
70.34%
78.76%
75.09%
21
Demographic Hollowing Out and Impact
on Competitiveness
• Books by Okun many years ago, the continuing work of Atkinson
and now Piketty, Saez, Stiglitz and other drawing attention to
problems of worsening income inequality – but link with economic
growth is not clear-cut
• Madland (2015) in a recent book has attempted to provide
explanation through what he refers to as the hollowing out effect of
middle classes
• In fact, demographic forces affecting the economy – and not just
competitiveness – have not been fully embraced
• Ageing – impact of declining workforce
• Decline in the Middle Class in terms of job opportunities for skilled
manufacturing workers
• Household Consumption as a share of GDP (diagram)
• Migration
• Need to focus on more than net flows
• Skill exchange/income
22
Households and the Economy
• Personal consumer expenditure
account for 70 percent of GDP in
the US.
• Most economic model persist in
aggregating
all
household
heterogeneity
into
“one
representative household sector”
while
industries
are
often
represented by 50-500 sectors.
• The regional econometric inputoutput model (REIM; Conway,
1990; Israilevich et al., 1997) has its
roots
in
an
empirical
macroeconometric model with an
input-output component.
• However, private consumption in
the REIM is limited to a
representative consumer.
Greece
Turkey
United States
Mexico
Chile
Portugal
United Kingdom
New Zealand
Poland
Italy
Japan
Switzerland
France
Slovenia
Canada
Spain
Israel
Germany
Finland
Austria
Australia
Slovak Republic
Belgium
Korea
Estonia
Hungary
Iceland
Denmark
Czech Republic
Sweden
Ireland
Netherlands
Norway
Luxembourg
23
0
20
40
60
80
Demographic Changes in Illinois 2000-2030
Population Pyramids of Illinois
Percent of Total Population
2000
2030
Male
5
4
Cohort
Under 18
5-17
18-24
25-44
45-64
65+
85+
80 - 84
75 - 79
70 - 74
65 - 69
60 - 64
55 - 59
50 - 54
45 - 49
40 - 44
35 - 39
30 - 34
25 - 29
20 - 24
15 - 19
10 - 14
5-9
0-4
Female
3
2
1
0
1
2
Change 2000-2030
Number
13,662
-41,976
17,468
-302,690
373,007
912,152
3
4
%
0.4
-1.8
1.4
-8.0
14.0
60.8
5
Female
Male
5
4
3
2
1
0
1
2
3
4
5
24
Impact on Forecasting of Demand
Older households
150
140
130
Representative
Household
120
110
100
90
Younger households
80
70
2003
1HH
2006
2009
-24
2012
25-34
2015
35-45
2018
2021
45-54
2024
55-64
2027
2030
65-
25
Does Trickle-Down Work?
Age group of income origin
16-24
25-44
Age group of income receipt: 2009
16-24
1.055
0.037
25-44
0.423
1.292
45-64
0.378
0.263
65+
0.030
0.021
Total
1.886
1.612
Age group of income receipt: 2020
16-24
1.043
0.028
25-44
0.362
1.249
45-64
0.440
0.304
65+
0.040
0.028
Total
1.884
1.610
Changes in indirect & induced impacts (%): 2020-2009
16-24
-22.3
-22.7
25-44
-14.4
-14.6
45-64
16.3
15.7
65+
30.7
30.9
Total
-0.25
-0.48
Average propensity to consume
Age composition of employment
45-64
65+
Total
0.035
0.286
1.259
0.021
1.601
0.045
0.383
0.349
1.028
1.806
1.172
2.384
2.249
1.100
6.905
0.027
0.244
1.299
0.027
1.598
0.035
0.326
0.404
1.036
1.801
1.133
2.182
2.447
1.131
6.892
-22.8
-14.6
15.5
31.1
-0.55
-22.7
-14.9
15.5
31.3
-0.58
-22.6
-14.6
15.8
31.0
-0.45
26
Investment in Public and Human Capital
• Role of public capital in enhancing competitiveness generally
understood
• Appears limited evidence for crowding out (in Spain – crowding
in)
• Human capital
• What kind and do we need to provide of incentives?
• Role of higher education institutions (declining state support in US
and many developed economies)
• Reinvestment – how will this be done –critical factor to address
retraining issues
• Migration issues – Parisian banlieus; 500K unskilled in Chicago by
2025
• Positive impact on GRP – counter effect of ageing and shrinking
labor force but need to be trained
27
Implications
• Focus on enhancing competitiveness – notions of mutuality
often overlooked
• Net results: each state/metropolitan region becoming at one and
the same time more competitive and more complementary
• Spatial spillovers increasingly important - unlike nefarious activities in
Las Vegas (what goes on there stays there), not true for regional
economies
• How do we show/prove that policy x has made a difference?
• Comparative analyses – region with and without policy
• E.g. essential air service program and economic growth
• Identified “sister” region with no air service based on propensity
matching
• But comparison based only on internal structure fails to highlight
potential differences in external linkages
• Have we linked the policy to outcomes of interest to policy-makers?
28
Smart Specialization/Clusters/Competitiveness
• New wine in old bottles or old new wine in new
bottles or new wine in new bottles?
• Identification: explore Renstaller’s work
(WIFO) on using density functions (product
spaces) to identify detailed commodities in
which Austria appears to have international
competitive advantage
• What is the appropriate spatial scale (casting
doubt on a single region focus)
• What role does ownership play in
competitiveness?
29
Smart Specialization/Clusters/Competitiveness (2)
• Michael Batty (2014) New Science of Cities
• Not about location per se but the location based
on interactions
• What happens in locations (cities) is a synthesis of
what happens through networks and how activities
interact with each other
• Interactions (f) of networks and network evolution
is a (f) of interactions
• Alan Wilson – DNA of cities based on infrastructure
(to which I would add the human capital DNA)
30
Other issues
• Role of failure – have we spent enough time
exploring this?
• Have we learnt from previous policies about what
did and did not work?
• E.g. have there been a meta analysis of
competitiveness-based policies so we can say that it
has made a difference (and in what sense)?
• How does the outcome vary by the type of
intervention
• Direct (e.g. subsidy)
• Indirect (infrastructure, investment in human
capital etc.)
31
Evaluation
• Need to assess on an expanded project appraisal
basis
• Costs (policy intervention, assembling and
evaluating data, running models etc.)
• Benefits (outcomes – in terms of useful metrics)
• Calculating ROI?
• Sustaining the initiative?
• Have we spent enough time understanding how
regional economies work and have we
communicated this effectively to policy-makers?
32