Memorandum by Professor Paul C Cheshire,
London School of Economics (RG 100)
CITY REGIONS, REGIONAL GOVERNMENT AND URBAN
ECONOMIC GROWTH
1. BACKGROUND
1.1 It has long been recognised that the
functional reality of cities has extended far beyond historical,
political and administrative boundaries. More than 50 years ago
the US—the first country to experience substantial suburbanisation
driven by cars, lorries and motorways with the construction of
the Interstate Highway system—defined Standard Metropolitan
Statistical Areas. This was in recognition of the fact that to
generate useful data for analysing cities and making meaningful
comparisons between them they needed a new definition of "city'
that encompassed the whole city-region. The cores of these were
defined in terms of densities of activity and employment concentrated
on non-agricultural activities; to these were added hinterlands
defined on the basis of commuting patterns. The idea was to identify
the complete economic and social sphere of influence of a given
employment concentration. The data for these metropolitan regions
has provided the main resource for advanced research on urban
economies ever since. One of the disadvantages of this is that
a significant part of the energies of British and European researchers
has been diverted to analysing US cities, operating in a US institutional,
economic and regulatory environment.
1.2 There are a number of reasons why such
"functionally" rather than administratively defined
cities are essential for comparative purposes or analytical research
but the most important stem from that fact that unless city boundaries
are defined on patterns of actual behaviour, rather than historical
boundaries, "cities" are not self-contained. Because
of commuting and population decentralisation, a large but varying
fraction of the jobs which are still concentrated in the urban
core, are not all held by the people who live there.
1.3 Perhaps the most obvious error this
gives rise to is if one is trying to estimate productivity or
GDP per capita for sub national units, such as cities. These indicators
are by far the most commonly used to make comparisons of prosperity
or economic success. They are used, for example, by the EU to
determine eligibility for regional aid. But for sub national units
these measures are distorted in varying degrees according to the
extent of net commuting. The most extreme case in Europe is caused
by the splitting of London into two "regions"—Inner
and Outer London—for statistical reporting purposes. This
grossly overstates prosperity in Inner London and understates
it for Outer London and parts of the Home Counties. Such distortions
are repeated across Europe—Bremen and Hamburg provide two
well documented examples. A second and related reason why functionally
defined city-regions are essential for making comparisons is because
of patterns of residential segregation and the fact that such
patterns differ between cities and, particularly, they differ
systematically between cities in different countries. In most—but
not all—British cities, the poorer inhabitants are concentrated
in the inner areas of the city. The opposite is true in many continental
European cities, most obviously Paris. If comparisons are not
made between "complete" city-regions, therefore, there
are varying degrees of bias arising as proportionately more or
less of the unemployed/employed or rich/poor are included within
the administrative boundary of the particular "city".
1.4 These problems are particularly acute
in England, both because local government units tend to be relatively
small (compared, for example, to those in Spain or Germany) and
because of the effects of our land use planning system. Our core
cities have had relatively rigid growth boundaries since 1947.
Where they were successful and grew, they tended to jump over
their growth boundaries and create, in effect, freestanding dormitory
suburbs and sub centres, separated from the original city, sometimes
by extensive stretches of agricultural land. London is the most
obvious case with a commuting catchment area now covering most
of South East England. In functional terms most of the "villages"
of the Home Counties are the equivalent of high-income suburbs
in the US: functionally, places like Reading or Northampton have
much in common with the Edge Cities of the US.
2. IMPLICATIONS
FOR POLICY
2.1 This matters in terms of government
because many strategic functions need to be provided at the level
of the whole functioning city. Applying the first principles of
welfare economics and public finance we know that there is a strong
case for "internalising externalities": in this case
that means ensuring that the same group which bears the costs
of a policy receives the benefits the policy generates. One of
the problems of our land use planning system, for example, is
that it frequently fails to achieve that. The costs of physical
development are highly localised and are significant for those
that bear them (noise, dirt, disruption and congestion while construction
is in progress; and loss of amenities and perhaps congestion after
the development is completed); the benefits of development are
widely spread and small for any given recipient (more affordable
housing, more job opportunities, access to new infrastructure).
Moreover, since gains are thinly spread, those who benefit have
little individual incentive to lobby for development. Losses,
however, are heavily concentrated on local residents. In addition,
in the case of planning, the local residents are the voters who
control the system; planning decisions are in most cases taken
at the lowest tier of local government, so the political process
is biased in favour of those who lose from physical development.
The gains of most physical development, even building houses on
a large scale, extend far beyond the confines of a District—typically
the first point of planning decision-making. Since one cannot
have economic development without physical development, one function
for a city-regional tier of government would be that of strategic
planning. Its residents would (typically—there may be some
developments where the beneficiaries are even more widespread
so that national decision making is appropriate) also be the beneficiaries,
reducing the inbuilt bias in favour of NIMBYism inherent in the
present system.
2.2 Thus there are general principles which
help us decide functions which should most appropriately be carried
out at what level of government. If the city-region is intentionally
defined in functional terms, so that its boundaries contain those—or
the great majority of those—who live and work in it—then
it will be as self-contained in economic terms as it is possible
to be[9].
If job opportunities or incomes improve, for example, then the
beneficiaries will be mainly those who live and vote within the
city-region.
3. STRUCTURE
OF LOCAL
GOVERNMENT AND
LOCAL ECONOMIC
GROWTH
3.1 Together with a former PhD student I
have applied these ideas to help understand the differences between
city-regions in their rates of economic growth. To do this rigorously
it is necessary to use statistical techniques and so have a large
enough sample. Our sample has been all the large city-regions
of the former EU of 12 countries. For reasons already given, these
were defined to be as self-contained as possible and we used commuting
patterns rather than administrative divisions to define their
boundaries. Not only did using all the large city-regions of the
EU12 give us an adequate sample but it also meant there was substantial
variation on most measures which helped identify the significant
relationships. The data related to 121 city-regions.
3.2 If we accept that it is at least possible
for local policies to increase (or impede) growth then we can
identify conditions favouring the emergence of growth promoting
policies and helping to make them more effective. I should clarify
from the outset that I am not conceiving of "growth promotion
policies" in the narrow sense in which their advocates sometimes
speak of them: as policies aimed at the direct attraction of mobile
investment. I have a much broader definition in mind. Effective
policies could include: having a concern for efficient and transparent
public administration so that risk and uncertainty for private
sector investors is reduced; making sure relevant infrastructure
is provided and maintained; co-ordination between public and private
investment; providing education and training which is relevant
to the needs of the local economy and is effective; and ensuring
that land use policies are flexible and co-ordinated with infrastructure
provision and the demands of private sector investors. It could
also involve giving a higher priority to output growth as opposed
to redistribution. I am trying to gauge outcomes, not make judgements.
It is possible that policies which promote growth may not be seen
by everyone as promoting public—or at least their own—wellbeing.
3.3 More effective policies to promote growth
in the sense outlined above, need not involve spending more, even
on infrastructure, so a simple measure of local expenditure is
unlikely to be an appropriate measure of the efficacy of growth
promotion efforts—even were such a variable available. "Grand
projects" such, perhaps, as the Guggenheim museum in Bilbao,
London's Millennium Dome or a trophy metro system in Toulouse—may
be expensive but not cost effective in terms of generating more
economic growth for the city-region as a whole[10];
efficient public administration and reduction of uncertainty for
private investment by rapid decision-making, clearly defined land
use policies and infrastructure planning, may cost less than their
inefficient alternatives. A tax on congestion might ensure a more
efficient use of road space and resources than road construction.
3.4 The "output" of effective
policies is the impact they have on the growth performance of
the city-region as a whole, since that is the area which, because
it is economically as self-contained as possible, definitionally
maximises the capture of growth for local voters and businesses.
Effective local growth policies can be viewed as the provision
of a pure local public good (in both a general sense, and in the
strict sense implied in the jargon of economics)[11].
It will be next to impossible to exclude agents who have not contributed
to the policies from any benefits generated; and there will be
a zero opportunity cost in consuming the benefits or "output":
if your rents rise, so do mine and the increase in yours is not
a "cost" to me; if your employment opportunities improve
that, too, is not a "cost" to mine.
3.5 As a local public good, we know markets
will not provide the optimal quantity because markets will not
generate effective incentives for individuals or firms to provide
them. If an individual firm promotes the growth of a city, typically
only a tiny fraction of the benefits—higher incomes, revenues
or rents—would accrue to it. Growth policies need local
political support and active lobbyists or promoters to generate
an agency to implement them. Typically such an agency involves
some mix of public and private sector interests but it does not
emerge out of thin air; and once formed it serves some constituency.
3.6 This suggests we can analyse the incentives
and costs of providing such policies for groups of agents—whether
firms, local governments or public-private partnerships—and
find some common factor(s). The most obvious is the extent to
which the boundaries of the functionally defined city-region coincide
with those of the largest local government unit which represents
part of, or includes the whole city-region. The single most important
actor in establishing effective growth promotion is typically
a unit of local government. So the closer the match of boundaries
of the government providing such policies is with those of the
city-region within which their impact is contained, the less will
be the spatial "spillovers" to non-contributors: those
who contribute towards the policies (including providing the political
basis for pursuing local economic growth) will include those that
benefit from the effort. In addition, the larger is the central
unit of government of a city-region relative to the size of that
region as a whole, the lower will be the transactions costs inevitably
incurred in building a "growth-coalition". This is because
the central administrative unit of a city-region will inevitably
have to join in any effective growth promoting effort. For a given
potential growth gain for a city-region, the expected payoff for
any individual administrative unit (say a London Borough or the
City of Manchester) will fall as the size of its territory falls
in relation to that of the city-region within the boundaries of
which it is located. The spillover losses to other areas of the
city-region will increase as the proportion of the city-region
the governmental unit in question does not control increases;
and the transactions costs necessary to establish an agency and
formulate and implement effective policies, will rise with the
political fragmentation of the city-region.
3.7 Arguments such as these led Cheshire
and Gordon (1996, page 389) to conclude that growth promotion
policies would be more likely to appear and be more energetically
pursued where "there are a smaller number of public agencies
representing the functional economic region, with the boundaries
of the highest tier authority approximating to those of the region...".
4. THE STATISTICAL
EVIDENCE
4.1 Applying these ideas one can find a
statistical variable closely reflecting this feature of city-regions:
that is simply the ratio of the total population of the largest
unit of local government within—or including—the territory
of the city-region, to its total population[12].
In the statistical analysis, we assumed this would be the government
unit with the largest population, usually the central administrative
unit of the city-region, but in European countries with free-standing
cities and a regional tier of government—such as Spain—the
government unit was sometimes larger than the city-region itself.
In practice, the value of the variable ranged from only about
0.1 (in Nantes or Valenciennes) to about 2 (in Murcia or Frankfurt).
In Britain the range was from around 0.2 in Newcastle and Manchester
to 1.4 in Glasgow. All these measures relate strictly to the time
period of the analysis. In fact, the local government units covering
the city-regions of both Frankfurt (the Umlandverband—a
unique strategic planning region created by a confederation of
local governments but now abolished) and Glasgow (the former Strathclyde
region) have since disappeared.
4.2 We found that this indeed had been a
significant factor in explaining the different growth performance
of large city-regions in the EU12 over the period we could analyse—1979-94[13].
Many other factors contributed to differences in growth performance
and most were clearly outside the influence of local policy makers.
Some statistical results are shown in Tables 1, 2 and 3 attached
to this memorandum. Table 3 shows the results for various tests
for the statistical validity of the results. From these we can
see that there are no apparent technical problems—so we
can rely on the results. Table 1 defines the variables used (including
models explaining population growth not discussed here) and Table
2 shows the statistical results for two different models. Apart
from the variable described in Para 4.1—the Policy Incentive—which
measures the coincidence of the boundaries of the government unit
relating to the city compared to the functional city-region itself,
the other variables over which policy might have some influence
are those relating to highly qualified labour and to the population
density of the whole city-region. The city' external economic
context—measured by the performance of the national economy
excluding all its large city-regions—was the single most
influential factor in explaining differential growth rates. The
city's economic inheritance was also important. The additional
variables included in the "Best" model capture systematic
spatial influences, including economic linkages with neighbouring
city-regions, which together account for the extent to which cities'
economies tended to perform according to their location within
Europe.
4.3 It is reasonable to draw some conclusions
for policy from these results. They certainly support the conclusion
that local policy can influence local economic performance and
also that city-regions are to some extent the natural building
blocks of national economies. But there needs to be some caution
in interpreting them as providing a simple "policy lever".
It is true that local and regional government boundaries and functions
could be restructured and, given that a significant element of
the disadvantage city-regions which had fragmented local government
structures face resulted from the problems of spillovers and transaction
costs entailed in pursuing effective growth policies, the outcome
should be more effective growth policies all round. A problem
is that, of course, "effective" local growth policies
at present, in circumstances in which not all city-regions are
equally well endowed with the incentive to develop them, may be
significantly competitive and diversionary. Some local growth
may be the result of successfully diverting growth from other
cities, so the success of the successful may significantly be
a function of the poor performance of the unsuccessful. There
might not, therefore, be a symmetric growth gain all round as
a result of creating a city-regional tier of government with appropriate
powers and functions. Far from all policies effectively promoting
local growth are diversionary, however. It is reasonable to expect
that there could be net efficiency gains for the urban system
as a whole if government boundaries—for the highest strategic
tiers of local government—were aligned more closely with
those reflecting economically relevant patterns of behaviour and
spatial economic organisation. Since government areas in relation
to the size of city-regions is smaller in Britain than in most
countries of continental Europe (except Italy) then there could
be particular gains both for British cities and the British economy
overall.
REFERENCES
Cheshire, P C and I R Gordon (1996) "Territorial
Competition and the Logic of Collective (In)action", International
Journal of Urban and Regional Research, 20, 383-99.
Cheshire, P C and S Magrini, (2000) "Endogenous
Processes in European Regional Growth: Implications for Convergence
and Policy", Growth and Change, 32, 4, 455-79.
Cheshire, P C and S Magrini, (2006a) "Population
Growth in European Cities: weather matters—but only nationally",
Regional Studies, 40, 1, 23-37.
Cheshire, P C and S Magrini, (2006b) "European
Urban Growth: now for some problems of spaceless and weightless
econometrics', Working Paper.
Cheshire, P C and S Sheppard, (2005) "The
Introduction of Price Signals into Land Use Planning Decision-making:
a proposal" Urban Studies, 42, 4, 647-663.
These papers are all available from p.cheshire@lse.ac.uk
Table 1
VARIABLE
DEFINITIONS
| No | Variable Name
| Description |
| Constant |
|
| 1 | Ln Population | Natural log of population in 1979
|
| 2 | Population density | Density of population in FUR in 1979
|
| 3 | Industrial Emp 1975 |
% of labour force in industry in surrounding NUTS 2 region 1975
|
| 4 | Coalfield: core | A dummy=1 if the core of the FUR is located within a coalfield
|
| 5 | Coalfield: hinterland |
A dummy=1 if the hinterland of the FUR is located in a coalfield
|
| 6 | Port size 1969* | Volume of port trade in 1969 in tons
|
| 7 | Agric Emp 1975* | % of labour force in agriculture in surrounding NUTS 2 region 1975
|
| 8 | Unemployment 1977-81* |
Mean FUR unemployment rate 1977 to 1981 |
| 9 | Nat Ex-FUR GDP Growth 1979-93
| Annualised rate of growth of GDP pc in the territory of each country outside major FURs between 1978-80 and 1992-94
|
| 10 | Nat Ex-FUR Pop Grow 1980-2000
| Annualised rate of growth of population in territory of country outside major FURs between 1980 and 2000
|
| 11 | Policy Incentive* | Ratio of FUR population to that of the largest governmental unit associated with the FUR (1981): see below for details.
|
| 12 | University Students emp ratio 1977-78-79*
| Ratio of university students 1977-78 to total FUR employment 1979
|
| 13 | R&D Facilities per million population*
| R&D laboratories of Fortune top 500 companies per million population 1980
|
| 14 | South within Country |
Distance south of centre of FUR from national capital city (Amsterdam taken as capital of Netherlands; Bonn of Germany)
|
| 15 | West within Country |
Distance west of centre of FUR from national capital city (Amsterdam taken as capital of Netherlands; Bonn of Germany)
|
| 16 | South within EU | Distance south of centre of FUR from Bruxelles/Brussel
|
| 17 | West within EU | Distance west of centre of FUR from Bruxelles/Brussel
|
| 18 | Frost frequency* | Ratio of frequency of days with frost between FUR and national average (1970s and 1980s)
|
| 19 | Wet days* | Ratio of wet day frequency between FUR and national average (1970s and 1980s)
|
| 20 | Maximum temperature* |
Ratio of maximum temperature between FUR and national average (1970s and 1980s)
|
| 21 | Integration Gain* | Change in economic potential for FUR resulting from pre-Treaty of Rome EEC to post enlargement EU with reduced transport costs
|
| 22 | Peripherality dummy |
Dummy=1 if FUR more than 10 hours time distance from Brussels
|
| 23 | University Student density employment
| Sum of university students per 1,000 employees in all FURs within 150 minutes travel time discounted by distance with 600 time penalty added for national borders
|
| 24 | R&D Facilities density population
| Sum of R&D Facilities per million population in all FURs within 150 minutes travel time discounted by distance with 600 time penalty for national borders
|
| 25 | Unemployment 1977-81 density
| Sum of differences between the unemployment rate (average between 1977 and 1981) of a FUR and the rates in neighbouring FURs up to 60 minutes away discounted by time-distance with a 600 minute time-distance border penalty.
|
| 26 | Interaction `79-'91 |
Sum of the differences in the growth rate of employment in the FUR and in all FURs within 100 minutes travelling time discounted by distance over the period 1979-1991
|
Table 2
DEPENDENT VARIABLE: ANNUALISED RATE OF
GROWTH OF REAL GDP p.c. MEAN 1978-80 TO MEAN 1992-4: 4=BASE MODEL
WITHOUT SPATIAL VARIABLES, 5=BEST
| Model 4 | Model 5
|
| R2 | 0.6785 | 0.7555
|
| Adjusted R2 | 0.6372 | 0.7095
|
| AIC | -10.8686 | -11.0440
|
| LIK | -671.552 | -688.488
|
| Observations | 121 | 121
|
| Constant | -0.03200 | -0.0262573
|
| se | 0.00937
| 0.009193 |
| Nat Ex-FUR GDP Growth 1979-93 | 0.94416
| 0.902537 |
| se | 0.10238
| 0.097571 |
| Coalfield—core | -0.00621
| -0.005213 |
| se | 0.00120
| 0.001287 |
| Coalfield—hinterland | -0.00418
| -0.003176 |
| se | 0.00160
| 0.001526 |
| Port Size | -0.00147 | -0.000922
|
| se | 0.00040
| 0.000379 |
| Port Size squared | 0.00008
| 0.000045* |
| se | 0.00003
| 0.000024 |
| Agricultural Employment | 0.00051
| 0.000484 |
| se | 0.00016
| 0.000159 |
| Agricultural Employment squared | -0.000013
| -0.000012 |
| se | 0.000004
| 0.000004 |
| Unemployment Rate | | -0.00031
|
| se | | 0.000136
|
| Population Size | 0.002118 |
0.001611 |
| se | 0.000600
| 0.000557 |
| Population Density | -0.0000015
| -0.0000013 |
| se | 0.0000007
| 0.0000006 |
| University Students | 0.0000309
| 0.000031 |
| se | 0.0000116
| 0.000011 |
| R&D Facilities | 0.000808
| 0.000845 |
| se | 0.000285
| 0.000275 |
| Policy Incentive | 0.007500
| 0.008562a |
| se | 0.00335
| 0.003455 |
| Policy Incentive squared | -0.002089
| -0.002647*a |
| se | 0.001580
| 0.001554 |
| Integration Gain | | 0.005162
|
| se | | 0.001430
|
| R&D Facilities Density |
| 0.262331 |
| se | | 0.094307
|
| Peripherality Dummy | |
0.005411 |
| se | | 0.001318
|
| University Students Density |
| -0.010527 |
| se | | 0.003797
|
| Unemployment Rate Density |
| -0.134403* |
| se | | 0.069318
|
Italics indicate not significant at 10%: all variables significant
at 5% except where indicated with an asterisk where only 10%.
aSignificant at 10% only but F test indicates they should not
be excluded as a pair at 5% level.
Table 3
REGRESSION DIAGNOSTICS
| REGRESSION DIAGNOSTICS (SpaceStat)
| Model 4
| | Model 5
|
| MULTICOLLINEARITY CONDITION NUMBER
| 80.62 |
| | 100.87
| | |
| TEST ON NORMALITY OF ERRORS
| DF | Value
| Prob | DF
| Value | Prob
|
| TEST=Jarque-Bera | 2
| 3.3273 | 0.1894
| 2 | 1.2374
| 0.5386 |
| DIAGNOSTICS FOR HETEROSKEDASTICITY
| | | |
| | |
| RANDOM COEFFICIENTS |
DF | Value
| Prob | DF
| Value | Prob
|
| TEST=Breusch-Pagan |
13 | 19.3825
| 0.1117 | 19
| 20.8169 | 0.3470
|
| DIAGNOSTICS FOR SPATIAL DEPENDENCE
| | | |
| | |
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance with infinite national border effect
|
| TEST | MI/DF
| Value | Prob
| MI/DF | Value
| Prob |
| Moran's I (error) | 0.04344
| 1.8729 | 0.0611
| -0.04784 | -0.1457
| 0.8842 |
| Lagrange Multiplier (error)
| 1 | 0.9212
| 0.3372 | 1
| 1.1171 | 0.2905
|
| Lagrange Multiplier (lag)
| 1 | 6.6183
| 0.0101 | 1
| 1.4510 | 0.2284
|
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance squared with infinite national border effect
|
| Moran's I (error) | 0.05593
| 1.4068 | 0.1595
| -0.06140 | -0.1916
| 0.8480 |
| Lagrange Multiplier (error)
| 1 | 0.6996
| 0.4029 | 1
| 0.8432 | 0.3585
|
| Lagrange Multiplier (lag)
| 1 | 7.1177
| 0.0076 | 1
| 1.9795 | 0.1594
|
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance with 600 minute national border effect
|
| | |
| | | |
| Moran's I (error) | 0.0303
| 2.8693 | 0.0041
| -0.01504 | 0.6940
| 0.4877 |
| Lagrange Multiplier (error)
| 1 | 1.5984
| 0.2061 | 1
| 0.3938 | 0.5303
|
| Lagrange Multiplier (lag)
| 1 | 5.8394
| 0.0157 | 1
| 0.9660 | 0.3257
|
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance squared with 600 minute national border effect
|
| | |
| | | |
| Moran's I (error) | 0.06620
| 1.7888 | 0.0736
| -0.03589 | -0.1484
| 0.8820 |
| Lagrange Multiplier (error)
| 1 | 1.3233
| 0.2500 | 1
| 0.3888 | 0.5329
|
| Lagrange Multiplier (lag)
| 1 | 7.1366
| 0.0076 | 1
| 1.5291 | 0.2162
|
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance with zero national border effect
|
| | |
| | | |
| Moran's I (error) | 0.0143
| 2.3972 | 0.0165
| -0.01538 | 0.4386
| 0.6610 |
| Lagrange Multiplier (error)
| 1 | 0.5553
| 0.4561 | 1
| 0.6440 | 0.4223
|
| Lagrange Multiplier (lag)
| 1 | 2.4908
| 0.1145 | 1
| 0.4333 | 0.5104
|
| FOR WEIGHT MATRIX (row-standardized)
| Inverse of time-distance squared with zero national border effect
|
| | |
| | | |
| Moran's I (error) | 0.0573
| 1.7963 | 0.0724
| -0.02911 | 0.1375
| 0.8906 |
| Lagrange Multiplier (error)
| 1 | 1.3549
| 0.2444 | 1
| 0.3498 | 0.5542
|
| Lagrange Multiplier (lag)
| 1 | 2.8781
| 0.0898 | 1
| 0.1902 | 0.6627
|
*Results in italics are significant at 10% level; Results in bold
are significant at 5% level
9
In reality cities, even defined as city-regions, are never completely
self-contained and differ in the degree to which their economies
interact with neighbouring cities. Research shows that this is
significantly a function of the distance between them but that
cultural and national borders reduce the degree of interaction.
Ignoring trade and external property ownership, the primary source
of interaction seems to be changes in commuting patterns but the
extent of communication may also be important. Where city-regions
are densely packed (Liverpool and Manchester, for example, or
Sunderland and Newcastle or the Midlands) if job opportunities
increase in one, some residents living in another city-region
but within commuting reach, will take some of the jobs because
changes in commuting patterns are, on the margin, almost costless.
Where city-regions are "free-standing" (Aberdeen, Hull
or Plymouth, for example) the only source of labour market interaction
is migration which is far more disruptive and costly, and responds
only to substantial incentives. Back
10
Although they can certainly shift the location of growth, so that
the area close to, or benefiting from, the project grows. That
local growth may be purely diversionary from elsewhere in the
city-region, however. Back
11
The local public good, non-excludable and non-rival in consumption,
is, of course the growth they may produce. Resources employed
in the promotion of growth are simply a cost. Back
12
The unit of government of course has to have significant powers
to be relevant in this context. Thus the GLA or a London Borough
would be "relevant" but the South East would not. In
addition, if a region (such as Scotland or Andalusia) includes
more than one major city-region we assume the next lower tier
of government below the region is the "relevant" one. Back
13
5urostat changed the way in which it calculated sub national GDP
data in 1995 and despite strenuous attempts to reconcile the data
series our conclusion is that it is misleading to try to compare
growth rates using that data for the pre and post 1995 break.
It will soon be possible to analyse the period since 1995 but
as yet there data are still not available for enough years for
that to be valid.
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