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Applying spatial techniques: What can we learn about theory?. Henry G. Overman LSE, CEP & CEPR. Lecture for the 19 th Advance Summer School in Regional Science. Publishing papers in spatial economics. Types of paper: Methodological Applied

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Applying spatial techniques what can we learn about theory

Applying spatial techniques:What can we learn about theory?

Henry G. Overman


Lecture for the 19th Advance Summer School in Regional Science

Publishing papers in spatial economics
Publishing papers in spatial economics

  • Types of paper:

    • Methodological

    • Applied

  • For applied papers the key question is do we learn anything new about:

    • Theory

    • Policy

Some casual empiricism
Some casual empiricism

  • Based on a spatial econ workshop (Kiel ’05)

  • 60 papers at the conference

    • 12 methodological

    • 48 empirical

      • 10 growth in EU regions

  • Theoretical and empirical issues

    • Econometric theory and empirical work

    • Economic theory and empirical work

      • What do we learn from spatial econometric papers about theories of economic growth and location?

Some less casual empiricism
Some less casual empiricism

  • Abreu, Groot and Florex ‘space and growth’

    • 63 papers between 1995 and 2004

    • Data

      • 68% EU

      • 11% country

      • 8% US/Canada

    • Relationship to theory

      • 63% standard spatial

      • 11% derive explicit models from theory

Lessons from less casual empiricism
Lessons from less casual empiricism

  • Spatial econometrics literature should think about underlying reasons for spatial dependence

  • Non-spatial literature should worry about spatial dependence of residuals

  • Spatial economics literature unduly concentrated on methodological issues

    HGO: What new things do we learn about growth?

Space as nuisance
Space as nuisance

  • “For better or worse, spatial correlation is often ignored in applied work because correcting the problem can be difficult”

    Wooldridge, p. 7

  • Key assumption

    • We know the relationship we want to estimate

  • Conclusion

    • We should use spatial econometric toolbox to correct residuals where appropriate

An analogy
An analogy

  • The returns to education

    • Wage = f (ability, education)

    • Ability unobserved but correlated with education

       Fixed/Random effects estimation to get coefficient on education

  • Slightly unfair comparison because dealing with spatial correlation harder

    • FE/RE maintains i.i.d. assumption

    • Need different asymptotic theory etc

The challenge
The challenge

  • The problem

    • Way too many papers focus on space as nuisance

    • Standard spatial techniques to correct the coefficient estimates (63%)

    • Important to understand these techniques but …

    • … revised coefficient estimates often do not tell us anything new!

  • How can we use spatial data or spatial techniques to learn something new?

The empirics of location
The empirics of location

  • Four types of papers on the location of economic activity (or people):

    • Descriptive papers

    • Empirical models

    • Class of model approaches

    • Structural approaches

Descriptive work
Descriptive work

  • Good descriptive work should

    • Give us a feel for the data

    • Give us a feel for patterns in the data …

    • .. Without getting too hung up on the details

    • Hopefully tell us something about theory …

    • … Without claiming to tell us lots about theory

    • Give us a feel for how we might best analyse the data

Location patterns
Location patterns

  • For concreteness consider something specific – the spatial location of economic activity.

  • First important point – define your terms:

    • Are places specialised in particular activities?

    • Are activities localised in particular places?

  • Second important point – plot the data (GIS)

    • Cross check from statistical results to data plot

First generation location measures
First generation – location measures (2005)

  • Typical way to proceed is to calculate some summary statistic for each industry/location

    • Specialisation: Is the production structure of a particular region similar or different from other regions?; how different is the production structure?

    • Localisation: Is economic activity in a particular activity broadly in line with overall economic activity or is the activity concentrated in a few regions?; how concentrated is the economic activity?

A typical paper
A typical paper (2005)

  • Variety of measures to capture spatial location patterns

  • Discussion of why some measures better than others

  • But, no systematic attempt to outline criteria by which to assess these methods

  • Arguments usually statistical and one dimensional

Measuring localisation 5 key properties
Measuring localisation: (2005)5 key properties

  • Comparable across industries

    • (e.g. can Lorenz curves be compared)

  • Conditioning on overall agglomeration

  • Spatial vs. Industrial concentration

    • (The lumpiness problem)

    • Ellison and Glaeser (JPE, 1997) dartboard approach; Maurel and Sedillot (RSUE, 1999); Devereux et al (RSUE, 2005)

Measuring localisation
Measuring localisation (2005)

  • Scale and aggregation

    • Dots on a map to units in a box

    • Problem I – scale of localisation

      • Cutlery in Sheffield versus Motor cars in Thames valley

    • Problem II – size of units

      • California 150 x Rhode Island

    • Problem III – MAUP

      • Spurious correlations across aggregated variables

    • Problem IV – Downward bias

      • Treat boxes separately

      • Border problems

  • Significance

    • Null hypothesis of randomness

Spatial point pattern techniques solve these problems
Spatial point pattern techniques solve these problems … (2005)

  • Select relevant establishments

  • Density of bilateral distances between all pairs of establishments (4)

  • Construct counterfactuals

    • Same number of establishments (3)

    • Randomly allocate across existing sites (2)

  • Local and global confidence intervals (5)

And we learn something
… and we learn something (2005)

  • Excess localisation not as frequent as previous studies

    • Significance versus border bias

  • Highly skewed

    • Some sectors very localised;

    • Others weakly

    • Many not significantly

  • Scale of localisation

    • Urban/metropolitan

    • Regional for 3d

  • Broad sector effects

    • 4d behave similarly within 3d

  • Size of localised establishments

    • Big or small depending on industry

1 st generation concentration regressions
1 (2005)st generation: Concentration regressions

  • Get measures of industry characteristics and run a “concentration regression”

  • CONC(s) = a + bTRCOSTS(s) +

    cIRS(s) + dLINKAGES(s) + eRESOURCE(s) +


Conceptual limitations
Conceptual limitations (2005)

  • Theory tells us nothing about the relationship between indices and industry characteristics when more than two regions

  • Given availability of shares, why throw away lots of information by calculating only one summary statistic?

Using industry shares
Using industry shares (2005)

Harrigan (1997) classical trade theory + simple translog revenue function + hicks neutral technology

  • a and r vary across industries, technologies and factors

Location theory
Location theory (2005)

  • Ellison and Glaeser (1999) – sequential plant choice + expected profits depend on location specific and spillovers

  • Expected shares a non-linear function of:

    • Interaction of industry/country characteristics

    • No theoretical justification for using intensities

Industry intensities
Industry intensities (2005)

  • Midelfart et al (2002) CRS + CES preferences + differentiate goods + Armington + transport costs + # of industries proportional to country size

Some comments
Some comments (2005)

  • Number of firms in industry s, region r as a function of interaction between industry and regional characteristics

  • E.g. first expression interacts vertical linkages intensity (mu), sectoral labour intensity (phi) with regional wages

  • Problems

    • Hardly any data available

    • No firm movement (short run)

    • End up estimating sectoral transport variable

An improvement over first generation
An improvement over (2005)first generation?

  • A much clearer link from theory to the empirical specification that is estimated

  • Spatial interactions modelled explicitly

  • But could still be spatial correlation in the residuals

    • Get out the spatial econometrics toolbox?

    • 2nd order issue relative to first order issue of identification

What do we learn about theory
What do we learn about theory? (2005)

  • Harrigan is a straightforward neo-classical trade model

  • E&G is a very stylised geography model with black box assumptions to get to functional form

  • Midelfart et. al. has some geography effects but no IRS

  • Gaigne et. al. have a functional form that is very far from what they estimate

An alternative strategy
An alternative strategy (2005)

  • Take one particular class of models and test whether the data are consistent with the model

  • Even better – nest one class of models within another class of models and test whether the data allow us to reject the implied restrictions

Testing agglomeration
Testing agglomeration (2005)

  • Agglomeration has two senses:

    • A process by which things come together

    • A pattern in which economic activity is spatially concentrated

  • Two paths approach

    • Test mechanisms

    • Test predictions

  • We will consider NEG models

Defining and delimiting neg
Defining and delimiting NEG (2005)

  • NEG (here) = theories that follow the approach put forward by Krugman’s 1991 JPE article

  • Five key ingredients

    • IRS internal to the firm; no tech externalities

    • Imperfect competition (Dixit-Stiglitz)

    • Trade costs (iceburg)

    • Endogenous firm locations

    • Endogenous location of demand

      • Mobile workers

      • I/O linkages

Antecedents novelties
Antecedents & Novelties (2005)

  • Ingredients 1-4 all appeared in New Trade Theory literature  home market effects in Krugman 1980

  • Key innovation of NEG relative to NTT is assumption 5

  • With all 5 assumptions, initial symmetry can be broken and agglomeration form through circular causation

Testing neg predictions
Testing NEG predictions (2005)

  • Leamer and Levinsohn (1995)

    “Estimate don’t test”

  • Empiricists need to take theory seriously, but not too seriously

  • False confirmation – housing prices very expensive in areas with concentrated activity

  • False rejection – Kruman’s prediction of complete concentration

Neg predictions
NEG predictions (2005)

  • Access advantages raise factor prices

  • Access advantages induce factor inflows

  • Home market / magnification effects

    • Lower t.c. increase HME

    • More product differentiation (IRS? – same parameter) increases HME

  • Trade induces agglomeration

    • Increases for high IRS, high diff

    • t.c. inverted u?

  • Catastrophe

    • Small change t.c. large change location

    • Temporary shocks can have permanent effects

Strategy (2005)

  • Take these predictions to the data

  • Empirical specifications that are “close” to the underlying theory

  • Allows us to assess whether these mechanisms and predictions are consistent with data (not prove that these are the mechanisms)

Empirical neg
Empirical NEG (2005)

  • Papers that model spatial linkages explicitly consistent with “class of models” approach

    • Redding and Venables (2004): income across countries

    • Davis and Weinstein (2004): testing for home market effect

    • Davis and Weinstein (2005): Catastrophe for location of Japanese industry

Lessons from neg work
Lessons from NEG work (2005)

  • Methods should connect closely to theory but not be reliant upon features introduced for tractability or clarity rather than realism

  • Better to have a limited number of parameters to distinguish models?

    • e.g. beta/sigma convergence

  • Much more work needed on observational equivalence

    • 1st order issue

      • A more accurate estimate of (say) a beta coefficient?

      • Discriminating between alternative models of differences across space?

Structural estimation
Structural estimation (2005)

  • Estimation of specification directly derived from the theoretical model without any further simplifying/function form assumptions

  • Clear identification of which variables are endogenous

  • Interpretations easier?

  • Computation of the model parameters: possible simulation of the model on real data

Lessons from structural models
Lessons from structural models? (2005)

  • Endogeneity

    • Structural econometric specification identifies precisely which variables are endogenous

    • In simpler situations (eg neighbourhood effects) may get through intuition

  • Which variables should be on RHS/LHS

    • Working with structural theory suggests these are more complicated than expected

  • Structural identification of parameters

The downside
The downside (2005)

  • Do we really believe that the world looks like a NEG model plus some random shocks?

  • Two issues here

    • Is the world NEG?

    • What are the shocks?

Estimation versus testing
Estimation versus testing (2005)

  • Estimation – assume NEG model is valid and estimate its parameters under this assumption

  • Need to be confident that the model is true before estimating it

    • A crazy model (D-S) might not be so bad an approximation

    • Models place restrictions on parameters

    • Reality checks with parameter values

  • Testing requires nested structural models

An alternative approach
An alternative approach (2005)

  • Structural estimation works well in simple situations where we can observe agents actions and where the real world is close to the model (e.g. some IO situations)

  • A bounds approach can work well in situations which are very complicated, but where different classes of models consistently place restrictions on the relationships between variables (Sutton)

Lessons (2005)

  • Mainstream economics increasingly recognising importance of space

  • Huge scope for geo-referenced data to increase our understanding of socio-economic processes

  • Spatial econometrics providing a rapidly expanding toolbox for dealing with some problems encountered with spatial data

Lessons cont
Lessons (cont) (2005)

3. Too much emphasis on application of methods [c.f. heteroscedastic robust errors]

4. Too little attention on issues of role of theory and importance of identification

  • Why include a spatial lag?

  • If answer to (a) is

    • “robustness for particular parameter estimate” see (3)

    • “spatial interactions” then identification is everything

      5. Class of models approaches to identification may be better than structural