Metropolitan Region Competitiveness

Geoffrey J.D. HewingsRegional Economics Applications LaboratoryUniversity of Illinois, Urbana, IL 61801-3671, USAwww.real.illinois.edu hewings@illinois.edu

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 ServicesFlows

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

89.96%

2.97%1.44%5.63%

2.40%

90.30%

1.49%5.81%

2.17%2.77%

89.81%

5.25%

2.21%2.83%1.38%

93.58%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CBD R of Chicago Suburbs Outer Suburbs

CBD R of Chicago Suburbs Outer Suburbs

7

Total Spatial Interdependence• Substantial interdependence when all interactions

considered

48.90%

5.97%

18.98%

26.15%

11.29%

47.47%

11.57%

29.67%

17.48%

5.69%

49.87%

26.96%

13.82%

6.60%

14.69%

64.89%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CBD R of Chicago Suburbs Outer Suburbs

CBD R of Chicago 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

(Miernyk-Leontief)• 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 Forecasts12

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

16

Relationship Between Total Sectoral Outputs and Intermediation, 1975-2011

110000

160000

210000

260000

310000

360000

410000

460000

510000

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

Total Intermediation

Total Sectoral Outputs

Gap between Local Production and Local Supplies increasing

over time

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

18

1960s/1970s 1990s/2000s

Raw Materials State 1 Raw MaterialsIntrastate Inital transformation State 2 Inital transformation

exchangeSecondary transformation State 3 Secondary transformation

Finished product State 4 Finished product

Interstate Internationaltransport

Delivery to market Delivery to market

Supply Chain Impacts

19

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

Supply Chain Issues

• “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

low Potential for innovation high

Resources Initial Secondary Penultimate Finishing Deliverytransformation transformation finishing to market

higher Bull-whip effect lower

Spatial Interdependence: Job Losses in the Recession

Change in Impacts inMetroArea

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 input-output 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.

230 20 40 60 80

LuxembourgNorway

NetherlandsIreland

SwedenCzech Republic

DenmarkIceland

HungaryEstonia

KoreaBelgium

Slovak RepublicAustralia

AustriaFinland

GermanyIsraelSpain

CanadaSlovenia

FranceSwitzerland

JapanItaly

PolandNew Zealand

United KingdomPortugal

ChileMexico

United StatesTurkeyGreece

Demographic Changes in Illinois 2000-2030

24

Note:Significant decline in 25-44 age cohort

Significant increase in >45

Cohort Number %Under 18 13,662 0.4

5-17 -41,976 -1.818-24 17,468 1.425-44 -302,690 -8.045-64 373,007 14.065+ 912,152 60.8

Change 2000-2030

2000

Population Pyramids of Illinois

2030Percent of Total Population

5 4 3 2 1 0 1 2 3 4 55 4 3 2 1 0 1 2 3 4 5

0 - 4 5 - 910 - 1415 - 1920 - 2425 - 2930 - 3435 - 3940 - 4445 - 4950 - 5455 - 5960 - 6465 - 6970 - 7475 - 7980 - 84 85+

Male FemaleMale Female

Impact on Forecasting of Demand

25

70 80 90

100 110 120 130 140 150

2003 2006 2009 2012 2015 2018 2021 2024 2027 2030

1HH -24 25-34 35-45 45-54 55-64 65-

RepresentativeHousehold

Older households

Younger households

Does Trickle-Down Work?

26

Age group of income origin16-24 25-44 45-64 65+ Total

Age group of income receipt: 200916-24 1.055 0.037 0.035 0.045 1.17225-44 0.423 1.292 0.286 0.383 2.38445-64 0.378 0.263 1.259 0.349 2.24965+ 0.030 0.021 0.021 1.028 1.100

Total 1.886 1.612 1.601 1.806 6.905Age group of income receipt: 2020

16-24 1.043 0.028 0.027 0.035 1.13325-44 0.362 1.249 0.244 0.326 2.18245-64 0.440 0.304 1.299 0.404 2.44765+ 0.040 0.028 0.027 1.036 1.131

Total 1.884 1.610 1.598 1.801 6.892Changes in indirect & induced impacts (%): 2020-2009

16-24 -22.3 -22.7 -22.8 -22.7 -22.625-44 -14.4 -14.6 -14.6 -14.9 -14.645-64 16.3 15.7 15.5 15.5 15.865+ 30.7 30.9 31.1 31.3 31.0

Total -0.25 -0.48 -0.55 -0.58 -0.45

Age composition of employment

Average propensity to consume

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