TOWARDS A HETERODOX THEORY OF THE SPATIAL ECONOMY

TOWARDS A HETERODOX

THEORY OF THE SPATIAL

ECONOMY

Heterodox Microeconomics: A Foundation for

Urban Economic Theory

PURPOSE:

Three Goals in mind:

1. Extend the Heterodox Model of the Social Surplus Approach (Lee and Jo 2011) to be applicable to: Applied Urban Economic issues

Local Government

2. Begin to incorporate issues that arise due to the structure of space, e.g. spatial dependence.

3. Explain the factors influencing school closure in the US. Interaction between city governments and school

districts.

THE NEED FOR A FRAMEWORK

Traditional Urban Economic Theory:

optimization, spatial equilibrium

Marxist Urban Theory:

Lefebvre, Castells, Harvey, Smith, Santos

Institutional: focus on inner cities but is not

really explicit about space as a concept.

Post-Keynesian: Gary Dymski ; Anthony Downs

Is any one comprehensive enough?

SOME STYLIZED FACTS:

From 2000 – 2012 in the United States (www.nces.ed.gov/fastfacts)

Over 18,870 schools closed

Over 2.7 million students displaced

Over 157,000 teachers displaced

But why are we closing schools? How does this

affect teachers? students? neighborhoods?

What are the factors driving school closure?

In search of a theoretical framework…

http://www.nces.ed.gov/fastfacts

THE TREND IN EDUCATION

0

500

1000

1500

2000

2500

2,194

Public Schools Closed Annually

Source: nces.ed.gov/FastFacts

954

THE TREND IN EDUCATION

Displaced Students from Public School Closure: 1995-2012

Source: nces.ed.gov/FastFacts

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

306,503

173,766

2011-2012: A SUMMARY

Closed 2,194 public schools

226 Charter Schools

Displaced 306,053

Lost 20,879 FTE

40% Primary Schools

13% Middle Schools

20% High Schools

Built 1,520 new public schools

503 Charter Schools

Enrollment of 323,895

19,084 FTE

38% Primary Schools

16% Middle Schools

22% High Schools

HISTORY & THEORY: A NEXUS

All great theorists were once contemporary theorists

Smith sought to describe a system of economic motion

that was free of the tyranny of the Church or the

State.

Keynes sought to free economic theory – and his

fellow economists – from the dogmatic view of a self-

regulating society.

Dr. Lee continues to try and rescue this great art

from an “Incoherent Emperor” that took the consumer

as its king and the individual as its sole force.

THE HETERODOX SOCIAL SURPLUS

APPROACH: MAIN CONTRIBUTIONS

Prices (P) and quantities (Q) are determined independently from one another.

Macro/Micro Consistent with MMT

Agent-causality

The State is far more than a simple referee correcting “market failure.”

The central role of class in determining wages, profits, and output.

INTRODUCING THE LOCAL GOVERNMENT

& URBAN ECONOMY

City A

1. Structure of

Production

2. Social Accounting

3. Financial Structural

Balances

4. Current Financial

Balances

5. Currency user

Country B

1. Structure of

Production

2. Social Accounting

3. Financial Structural

Balances

4. Current Financial

Balances

5. Monopoly Currency

Issuer

THE STRUCTURE OF PRODUCTION WITHIN

A CITY

1. The cost of production is dependent upon local government services:

𝐾 43, 𝐾 1 , 𝑅𝑅1, 𝐹𝐴1, 𝐿𝐵1: 𝐺11𝑃1 + 𝐿11𝑤 + 𝜋 → 𝑄1𝑃1 (1) [Basic]

𝐾 43, 𝐾 2 , 𝑅𝑅2, 𝐹𝐴2, 𝐿𝐵2: 𝐺21𝑃1 + 𝐿21𝑤 + 𝜋 → 𝑄2𝑃2 (2) [Surplus]

Local Government (including county, school board, etc.) has policy space to address prices, wages, and profits.

No smokestack chasing! Invest in the local economy.

Infrastructure spending 𝑃𝑖↓ and/or ↑ 𝜋 via mark-up

Invest in education, including early child-hood ed.

Local min. wage policies

SOCIAL ACCOUNTING IN THE CITY

The demand for domestically produced surplus goods is what

drives urban economic activity. The interaction between

these three components accounts for the stability of City A’s

economy.

𝑄2𝑃2 = 𝑆 = 𝑄2𝐺 + 𝑄2𝐶 + 𝑄2𝐼

𝐴𝑃2 + (𝑄2𝐺+𝑄2𝐶 + 𝑄2𝐼)

𝐵𝑃2 − (𝑄2𝐺+𝑄2𝐶 + 𝑄2𝐼)𝐴𝑃1,2

Therefore, demand for domestic production of surplus goods depends upon

the demands of the ruling class both locally and abroad for these locally

produced items.

A’s Demand for A’s

Production of Surplus

Goods

B’s Demand for A’s

Produced Surplus

Goods

A’s Demand for B’s

Produced Basic and

Surplus Goods

(3)

DYMSKI’S FINANCIAL FLOWS

But there is also a monetary side to these demand

transactions:

∆FAA+ ∆IAB − ∆IBA = (XB – MA) + GPd,h,E (4)

MA = XB + (GPd,h,E +∆IBA) – (∆FAA + ∆IAB) (5)

This shows that for cities to finance imports, they must:

Export goods and services to capitalists in the rest of the

country;

Accept income transfers and external investment from the

ruling class from the rest of the country; or

Spend down the local economy’s accumulated wealth.

MODERN MONEY IN THE CITY

Due to the existence of state money and its inherently

endogenous nature, communities are able to stave off

Dymski’s “wealth de-accumulation” in two ways:

1. The modern state can issue transfer payments

without constraint based on preferred policy choices

(i.e. ELR, school rehab, neighborhood revitalization,

etc.).

2. City’s can generate their own financial wealth via

their own banking institutions – so long as there are

projects to finance. This can generate internal

wealth, which can help to offset funds sent abroad.

OUTPUT & EMPLOYMENT IN THE CITY Given the Leontief/Sraffian system, a change in S will lead to an even greater

change in 𝑄1in the same direction.

𝑄1=[I-A11T]-1A21

TS (6)

L* = 𝑙𝑇1 [I-A11T]-1A21

TS + 𝑙𝑇1S + LBanks (7)

As the A matrix is based on the G matrix, which is augmented by the level of local investment in infrastructure (at ever improving vintages of technology), the maximum eigenvalue will fall faster as S is maintained and/or increased over repeated production cycles.

Given that output and employment are tied to S, we can conclude that urban employment is dependent upon the decisions of the local ruling class plus the ruling class of the country as a whole. This same result also means that the ruling classes establish the network linkages to

other cities within the country.

Given that all output, wages, and profits are in State-money prices, and given that the ruling class governs both the control of State-money and of state investment decisions at all levels of government, the fate of a city can be held captive by the willful decisions of the ruling class outside the city – as is often the case for smaller more rural communities. This is applicable across space within a city as well, depending on the degree of dependency.

A BRIEF SUMMARY OF EXTENSIONS THUS

FAR

𝐾 43, 𝐾 1 , 𝑅𝑅1, 𝐹𝐴1, 𝐿𝐵1: 𝐺11𝑃1 + 𝐿11𝑤 + 𝜋 → 𝑄1𝑃1 (1)

𝐾 43, 𝐾 2 , 𝑅𝑅2, 𝐹𝐴2, 𝐿𝐵2: 𝐺21𝑃1 + 𝐿21𝑤 + 𝜋 → 𝑄2𝑃2 (2)

𝑄2𝑃2 = 𝑆 = XB − MA + G (3)

∆FAA+ ∆IAB − ∆IBA = (XB – MA) + GPd,h,E (4)

MA = XB + (GPd,h,E +IBA) – (FAA + IAB) (5)

𝑄1=[I-A11T]-1A21

TS (6)

L* = 𝑙𝑇1 [I-A11T]-1A21

TS + 𝑙𝑇1S + LBank (7)

Basic Goods

Surplus

Goods

Urban

Networks

Urban

Trade Flow

Condition

Dymski

Condition

Output of

Basic Goods

Employment

Model

INITIAL TAKEAWAYS FROM THE MODEL

Economic Development Policy:

City’s have far more potential policy space than tends

to be assumed.

Invest in infrastructure, education, new technologies (e.g.

Google fiber), and potentially look to set local wage

policies.

Output and Employment within the urban economy

is subject to the desires of its local and external

ruling classes. These interests do not necessarily

need to be in-line.

These ruling class elites also control the connections and

flows of information between other cities.

HOW DOES SPATIAL STRUCTURE

FIT IN?

THE PIN FACTORY HOPEFULLY

HAD AN ADDRESS!

The production of Space

HENRI LEFEBVRE: THE PRODUCTION OF

SPACE

“…social space is socially produced.”

“every society produces a space, its own space.”

“Social relations, which are concrete abstractions,

have no real existence save in and through space.

Their underpinning is spatial.”

LEFEBVRE

The Pin Factory was real. It had an address.

Space is produced & therefore has a history

Implication, space is managed

Managed by whom? For whom?

WHY SPACE MATTERS

Networks that spread information

Contagious/Diffusive – the disasterous effects of perceptions of “disorder.”

Heterogeneity

All production and consumption occurs within socially constructed spaces. Cities impose upon that space, via the administrative elite, rules and regulations that allocate and then distribute spatial rights.

Space is fundamentally another structure that affects production. The ability to re-shape it so as to meet the needs of production – or investment – is critical to success.

SPATIAL CONTROL AND THE CLOSURE

DECISION

Locations of Agency within the model

Two institutions that dominate the urban spatial

landscape: the City and the School District

How do we incorporate their interaction – or lack

thereof – into the model?

SCHOOL CLOSURE BY COUNTY: 2011-

2012

A DYNAMIC STORY OF SPATIAL RE-

ORGANZATION?

All space is owned in the US.

Most privately held space is financed, meaning it

is owned by interests in financial institutions.

Space is regulated by local governments and the

causal mechanisms through which agents alter

spatial segments.

Therefore space is organized to ensure higher

returns.

As time proceeds, the rates of profit within a

community on space falls.

Space must be re-organized. Old space must be

made into new space.

FACTORS DRIVING SCHOOL CLOSURE:

2008-2009

Logistic regression Number of obs = 83760

Wald chi2(11) = 435.07

Prob > chi2 = 0.0000

Log pseudolikelihood = -4953.5763 Pseudo R2 = 0.1081

(Std. Err. adjusted for 12257 clusters in stid08)

------------------------------------------------------------------------------

| Robust

Closed | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

fte08 | .9304691 .0046535 -14.41 0.000 .921393 .9396345***

titlei08 | .9625282 .100877 -0.36 0.716 .7837976 1.182015

chartr08 | .8487812 .1397205 -1.00 0.319 .6147192 1.171965

HISPPCT | 1.004919 .0020588 2.40 0.017 1.000892 1.008963**

BLACKPCT | 1.013971 .0017002 8.27 0.000 1.010644 1.017308***

ulocal1 | 1.063835 .22252 0.30 0.767 .7060415 1.602943

ulocal2 | .9826764 .2467171 -0.07 0.945 .6007619 1.60738

ulocal3 | 1.864095 .4264939 2.72 0.006 1.190469 2.918891***

ulocal4 | 1.103108 .1537998 0.70 0.482 .8393438 1.449759

ulocal7 | 1.197396 .217458 0.99 0.321 .8387868 1.709321

ulocal10 | .8191589 .1100281 -1.49 0.138 .6295585 1.06586

_cons | .0421768 .0053566 -24.93 0.000 .0328827 .0540978***

------------------------------------------------------------------------------

Smaller central cities ( < 100,000), slightly skewed toward minority

schools.

FACTORS DRIVING SCHOOL

CONSTRUCTION: 2008-2009


Wald chi2(11) = 1045.97

Prob > chi2 = 0.0000


(Std. Err. adjusted for 12257 clusters in stid08)

------------------------------------------------------------------------------

| Robust

New | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

fte08 | .9720276 .0027112 -10.17 0.000 .9667282 .977356***

titlei08 | .3518723 .0323725 -11.35 0.000 .293815 .4214017***

chartr08 | 4.342266 .4338637 14.70 0.000 3.569993 5.281599***

HISPPCT | 1.016806 .0013472 12.58 0.000 1.014169 1.01945***

BLACKPCT | 1.013122 .0014065 9.39 0.000 1.010369 1.015882***

ulocal1 | 1.162444 .1453408 1.20 0.229 .9097997 1.485244

ulocal2 | .9936273 .1595202 -0.04 0.968 .7253854 1.361063

ulocal3 | .954336 .2535298 -0.18 0.860 .5669834 1.606321

ulocal4 | 1.076797 .1221828 0.65 0.514 .8620831 1.344989

ulocal7 | 1.137618 .2157903 0.68 0.497 .7843963 1.6499

ulocal10 | 2.996785 .2942182 11.18 0.000 2.472215 3.63266***

_cons | .020951 .0019555 -41.42 0.000 .0174485 .0251566

------------------------------------------------------------------------------

Non-federally funded through Title 1. Strong presence of Charter schools.

Located in rural areas within five miles of an urbanized area.

FACTORS DRIVING SCHOOL CLOSURE: 2011-2012


Wald chi2(11) = 515.84

Prob > chi2 = 0.0000


(Std. Err. adjusted for 12373 clusters in stid)

------------------------------------------------------------------------------

| Robust

Closed | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

fte | .9277881 .0040424 -17.20 0.000 .919899 .9357449***

titlei | .9188991 .0804422 -0.97 0.334 .7740198 1.090897

chartr | 1.641101 .2209695 3.68 0.000 1.260444 2.136718***

HISPPCT | .9950249 .0023623 -2.10 0.036 .9904057 .9996657**

BLACKPCT | 1.01518 .0016256 9.41 0.000 1.011999 1.018371***

ulocal11 | 1.222924 .2442028 1.01 0.314 .8268464 1.80873

ulocal12 | 1.686955 .3008436 2.93 0.003 1.189335 2.392779***

ulocal13 | 1.188237 .1792946 1.14 0.253 .8840245 1.597137

ulocal21 | 1.014854 .1298852 0.12 0.908 .7897016 1.304199

ulocal31 | 1.086921 .275931 0.33 0.743 .6608572 1.787673

ulocal41 | 1.112418 .1326646 0.89 0.372 .8805533 1.405337

_cons | .0235065 .007057 -12.49 0.000 .013051 .0423381

------------------------------------------------------------------------------

Charter schools in the Urban Core of mid-sized Metros

FACTORS DRIVING SCHOOL CONSTRUCTION:

2011-2012


Wald chi2(11) = 607.44

Prob > chi2 = 0.0000


(Std. Err. adjusted for 12373 clusters in stid)

------------------------------------------------------------------------------

| Robust

New | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

fte | .9530533 .0043783 -10.47 0.000 .9445105 .9616733***

titlei | 2.400399 .2452979 8.57 0.000 1.964712 2.932704***

chartr | .2024001 .0249385 -12.97 0.000 .1589757 .2576859***

HISPPCT | 1.007996 .0021545 3.73 0.000 1.003782 1.012227***

BLACKPCT | 1.010103 .0016087 6.31 0.000 1.006955 1.01326***

ulocal11 | 1.665956 .272162 3.12 0.002 1.209499 2.294677***

ulocal12 | 1.379295 .2940517 1.51 0.131 .9082174 2.094714

ulocal13 | 1.358901 .22997 1.81 0.070 .9752975 1.893384

ulocal21 | 1.391157 .197376 2.33 0.020 1.053437 1.837147**

ulocal31 | 1.792355 .6017819 1.74 0.082 .928183 3.461101

ulocal41 | 2.317466 .3053464 6.38 0.000 1.790029 3.000313***

_cons | .0922707 .0269121 -8.17 0.000 .0520953 .1634289***

------------------------------------------------------------------------------

Title 1 funded schools in large urban centers and rural fringe.

TOWARDS A HETERODOX THEORY OF THE SPATIAL ECONOMY

Economy & Finance

Transcript of TOWARDS A HETERODOX THEORY OF THE SPATIAL ECONOMY