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…
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.
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?
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
Logistic regression Number of obs = 83760
Wald chi2(11) = 1045.97
Prob > chi2 = 0.0000
Log pseudolikelihood = -5647.5458 Pseudo R2 = 0.1176
(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
Logistic regression Number of obs = 80917
Wald chi2(11) = 515.84
Prob > chi2 = 0.0000
Log pseudolikelihood = -5327.4963 Pseudo R2 = 0.1120
(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
Logistic regression Number of obs = 80917
Wald chi2(11) = 607.44
Prob > chi2 = 0.0000
Log pseudolikelihood = -3990.099 Pseudo R2 = 0.1402
(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.