The UK Intranational Trade Cycle

byMichael Artis, University of Manchester and CEPR
andToshihiro Okubo, Kobe University, RIEB

Abstract: The paper uses annual data on real GDP for the UK regions and 12 manufacturingsectors to derive regional and regional/sectoral business cycles using an H-P filter. The
cohesion of the cycles is examined via cross-correlations and comparisons made with theregional cycles for Japan, the United States and the EuroArea. The UK emerges as especiallycohesive and efforts to explain the overall cross-correlations of regional GDP not very
successful owing to the low variance of the explicand; when attention is turned to thesectoral/regional cycles, with their greater variance it is possible to demonstrate that
economic variables such as distance, dissimilarity in structure and level of output play asignificant role in explaining the variance in the cross-correlations. A significant feature of
the cross-correlations in relation to those of EU countries is that whilst they continue toprovide support for the “UK idiosyncrasy” they no longer do so as strongly as they did in
earlier data samples.
Keywords: intranational business cycle; regional business cycles; income convergence; Hodrick-Prescott filter;
Euro-sympathy
JEL: E32; E41; R11
The paper was prepared and substantially completed while the authors were at the Manchester Regional
Economics Centre, Institute for Political and Economic Governance, Humanities Bridgeford building,
Manchester University, Oxford rd., Manchester M13 9PL. Professor Artis is currently Welsh Assembly
Government Visiting Research Professor in the Department of Economics at the University of Swansea,
Singleton Park, Swansea SA2 8PP E-mail: Michael.Artis@manchester.ac.uk; Dr Toshihiro Okubo is Associate
Professor of Economics at the Research Institute for Economics and Business Administration, Kobe University(RIEB), Rokkoudai-cho 2-1, Nada-ku, Kobe, 657-8501, Japan. E-mail:Toshihiro.Okubo@rieb.kobe-u.ac.jp

Acknowledgments: The authors would like to acknowledge, without implication, the helpful assistance of Marianne
Sensier and Matthias Hoffmann. This paper was presented at 5th Eurostat Colloquium on Modern Tool for Business
Cycle Analysis on September 30th 2008. The authors appreciate participants for their helpful comments.

The UK Intranational Trade Cycle
byMichael Artis and Toshihiro Okubo

1.

Introduction

This paper analyzes the business cycles that can be identified for the regions of the UK – theintranational business cycle of the UK, in short. An analysis of this type can benefit from thehuge attention paid in recent years to the international business cycle in terms of themethodologies that can be used and some of the questions that can be examined.

In

particular in this paper we use the Hodrick-Prescott filtering techniques popularized in the
RBC literature to identify the trend and make heavy use of the cross-correlations between the
resultant series of cyclical deviates as measures of business cycle synchronicity. Explaining
these, as in many of the studies stimulated by the real business cycle literature and the
optimal currency area literature which use international data, is a particular aim of the paper.
Precursor studies of the intranational business cycle are relatively few: notable ones for theUnited States include papers by Wynne and Koo (2000); Hess and Shin (1997 and 2001), andmost recently HM Treasury (2003). For Japan there is a paper by the present authors (Artisand Okubo, 2008) and for the UK a study by Barrios, et al. (2003). In most of these cases anoptimal currency area (OCA) concern can be seen as a driver of the study, the United States
sometimes being presented as a feasible if not formally “optimal” currency area and thecoherence of its regional business cycles as some kind of benchmark for monetary union. We
compare the convergence of UK regional business cycles with those in Japan, the UnitedStates and the EuroArea, finding it to be particularly high compared to what can be found forthe last two of these entities. Current concerns about the optimality, for Scotland, ofbelonging to the UK currency area do not find any resonance in the cross correlation
evidence presented here. There are other reasons for being concerned with regional businesscycles, though. One of these is the issue of how far the regions demonstrate specialization or
localization, and how spillovers distribute through time the effect of shocks attracted fromone specific region to another. Because the data we have at our disposal are annual infrequency we cannot usefully date the cycles we detect nor explore in requisite detail the2effect of monetary policy or external shocks upon the regions. However, the data come withan industrial breakdown and we are able to use sectorally disaggregated series; moreover we
can examine in some detail the convergence of regional output trends and the (non-)convergence of per capita incomes across the regions.
The data we use for the greater part of this study are the annual series of real gross value
added (GVA) (at 2002 prices) for NUTS1 regions that are provided by Cambridge
Econometrics; here we use a sample that ranges from 1970 to 2004. This is a different data
set than the one that was constructed for use in their own study of the UK’s regional cycles
by Barrios et al. (2003) – which spanned the period from 1966 to 1997. Barrios et al deploy
a data set in which deflation of regional current price GDP to obtain a real GDP series isaccomplished by using an estimated regional retail price index. Our data set is available forthe 12 NUTS1 regions and has the advantage of coming with considerable industrial sector
detail (12 manufacturing industrial sectors are distinguished for example). The original datafor regional GVA come from the Office of National Statistics (ONS) in current price form;
constant price GVA series for the regions are constructed by Cambridge Econometrics fromthe deflated national industrial sector series aggregated as appropriate for each region. Table
1 identifies the regions and sectors by number, which will be a useful reference for what
follows.

<< Table 1 >>In the next section of the paper we explain how we identify the business cycle in the regions;
we then move on to derive the regional business cycle cross-correlations that characterize the
UK intranational cycle, making comparisons with cross-correlation evidence drawn fromother countries and groupings (the United States, Japan and the EuroArea).
Subsequently we come to an econometric estimation of the variance in regional businesscycle synchronization between regions and over time, using a GMM panel data estimation
framework. In principle we should expect that the degree of business cycle synchronization
would depend to some extent on (evolving) differences in industrial structure between theregions and as a preliminary to the estimation we devote a short section of the paper to a3

discussion of this issue. It is apparent that the sectoral developments are a lot less smooththan the evolution of GVA as a whole and we take advantage of the added variance inregional-sectoral GVA to revisit the explanation of the cross-correlations with more varianceto bite on and more opportunity for revealing explanations to be identified In the remaining
sections of the paper we revert to the cross-correlation evidence presented at the outset andextended to incorporate cross-correlations with EuroArea countries. This is done to confirm(or otherwise) the findings of Forni and Reichlin (2001), confirmed by Barrios, et al. (2003)
on the “UK idiosyncrasy” – broadly, the finding that the UK economy possesses a highdegree of internal cohesion with none of its regions displaying an outstanding affiliation withthe business cycle of any EU country. Current discussion of further devolution for Scotland
points up a particular dimension of this question. There are two appendices: the first of these
discusses data and related issues whilst the other takes up the issue of per capita income
convergence across the UK regions.

2. Identifying the Cycle.
Traditional business cycle analysis recognizes two types of cycle. There is the “classical”
cycle, which can be recognized from the fact that it involves an absolute decline in economic
activity from the peak and an absolute rise in activity from the trough. The NBER for the US
and the CEPR for the EuroArea provide chronologies of such cycles. Clearly such cycles do
not exist in growth economies and they are relatively rare for European economies and forJapan. The other type of cycle is a deviation or growth (occasionally growth rate) cycle
where the underlying idea is that the business cycle can be identified as a cycle relative to atrend. Thus some kind of filter is required to provide a measure of the trend, and the cycle isidentified as the deviation from this trend. In our case, where the original data are annual,
there is a reasonable presumption that the high-frequency noise (seasonal and the like) isalready filtered out by the annualization of the data. On this basis we use a Hodrick-Prescottfilter with a lambda value (dampening factor) set at 6.25, following the suggestion of Ravn
and Uhlig (2002): this corresponds to a maximum periodicity of the cycle of 10 years just as
the popular lambda value of 1600 does for data at a quarterly frequency.1 The filter has been
1

There remains a degree of controversy about the procedure, as exemplified most recently in the paper by
Meyers and Winker (2005), following earlier papers by Harvey and Jaeger (1993), Burnside (1998) and Canova

4

applied to the log of the GVA series for each region and subsequently for each industrial
sector.

<<Figure 1>>

Figure 1 shows the business cycles for the individual regions identified by the numbers
shown in Table 1. Because the data we are using are annual, dating the cycle is notundertaken here: a higher frequency series of economic activity is necessary for such an
endeavour. The Economic Cycle Research Institute (ECRI) reckons to give a monthly datingof the growth rate cycle based on their coincident index. The turning points they give (which
are largely similar to those identified by Artis (2002) on monthly GDP data using a nonparametric dating algorithm) are consistent with the peaks and trough apparent in Figure 1
given that the regions move fairly closely together. Indeed, the Figure gives the strong
impression that the cycles are quite closely synchronized in general with only a few singularfeatures: the cycle in the South West has a strikingly higher amplitude in the early years thando other regions’ cycles; there is generally less synchronization in the first half of the sample;
the second half of the period shows signs of “the great moderation” – since 1994 there has
been much less volatility and synchronization appears more marked (our data series do notinclude 2008).
The second part of the figure shows the cycle identified on data for manufacturing industry,
traditionally regarded as the more cyclical and volatile component of total output: thisapprehension is borne out in the tabulation of standard deviations accompanying the Figure:the values for manufacturing industry are in the order of twice as much as those for overall
GVA. The bilateral cross-correlations measured over the period as a whole between theregions’ cycles and (in the bottom row) between those for each region and the national cycle
are shown in Table 2.
<<Table 2>>(1998) among others. However, an effective counter-criticism can be found in Kaiser and Maravall (2001,
2002).

5

<<Figure 2>>

There are no negative cross-correlations to be observed and very few that fall below 0.5; themain exception to this picture is for region 12 (i.e. Northern Ireland) which has the lowest
correlation with the nation as a whole and quite a number of low correlations with otherindividual regions. The data are conveyed in the form of a histogram in Figure 2.

<<Figure 3>>

Figure 3 provides a plot of the 5-year moving averages of the variance and the unweighted
mean of the bilateral regional cyclical cross-correlations of overall GVA over our sample
period. A definition of a coherent intranational cycle would have a low variance and a highmean as components. As can be seen these are characteristic, most of the time, of the UK.
How does the UK experience compare with the cross-correlations that can be observed forthe regional cycles within other countries? Although the comparison depends in detail on the
period concerned – and should also depend in principle on the relative size and sovereigntyof the regions in the comparison2 - the picture conveyed in Figures 4-6 deserves comment.
Figure 4 shows the histogram for Japan computed for four separate sub-periods whilst
Figure 5 shows comparable information for the states of the United States in two separate
periods (1990-1997 and 1997-2005). Finally, Figure 6 shows the histogram of crosscorrelations between EuroArea member countries over the period from 1975 to 1995.3<<Figure 4>>2

Smaller-sized regions seem likely to show more specialization, hence lower cross-correlations than larger
sized regions. On this basis the Japanese prefectures (of which there are 46 excluding Okinawa) might be
expected to show the lowest cross-correlations ceteris paribus and those for the Euro-Area among the highest.
Sovereignty may work towards less specialization as nations prefer a degree of all-round self-sufficiency.
3
The countries concerned are those that made up the “Euro-12” after the accession of Greece to the monetary
union and before that of Slovenia. The bulk of the data used here of course predates the operation of themonetary union itself.

6

<<Figure 5>><<Figure 6>>

The histogram of the bilateral cross-correlation coefficients for the EuroArea shows aconcentration in the positive area, a little below the values where the values concentrate in
the case of the UK (in the range 0.4 to 0.8 rather than 0.6 to 0.9), with a couple of negativevalues and a scattering of low values. The histogram for the United States has the bulk of its
observations in the positive area, but there are many negative values to be found and –especially in the second period – the histogram shows less of a tendency to peak at very highvalues than does that for the UK. The Japanese experience, finally, offers for all periods thebulk of observations in a strongly positive area, with negative observations growing fewer
through the periods shown and a strengthening tendency to demonstrate a concentration at
the highest values. Of all these results the cohesion of the UK and (especially in later periods)
the Japanese regions stands out in some contrast to the experience reported for the UnitedStates. That the latter should be exemplified as an “optimal” currency area is not somethingthat these observations alone could support (the extent of labor mobility, the federal income
tax and expenditure system and the degree of financial integration may be the more importantfeatures).

3. Industrial Trends
A significant ingredient in the cyclical experience of a region will depend from its industrial
structure and changes in that structure may significantly alter the overall cyclical experienceof a region and its relationship to the cycle in other regions. As in other countries there havebeen, in the UK, some very large changes in industrial structure in the past 25 or so years. A
traditional signifier of this is the ratio of the manufacturing sector to the total economy – data
for this are shown in Figure 7. There are some exceptions to the almost monotonic decline
in the ratio shared by all the regions – with a prolonged revival in Wales for example and7more resistance to decline in Northern Ireland. Some of the fluctuations are due to changes
in trend and some to cyclical variation around that trend.

<<Figure 7>>

Figures 8-1 and 8-2 deploy data derived from the H-P filtering and show changes in theoverall trends - notably these changes are relatively smooth for total GVA with relatively
little movement between the regions whereas for individual sectors (four are chosen for
illustration), some quite large movements and changes in relative position as between theregions are noticeable. This is consistent with our econometric findings, below, that reflect a
relative success in explaining the high variance of the sector-regional cyclical crosscorrelations compared with a disappointing inability to provide an interesting account of theoverall GVA cross-correlations. It is to some econometric work that we now proceed.

<<Figure 8-1>><<Figure 8-2>>

4. Estimation
4.1. Estimation Strategy
In this section we address the issue of explaining the variance in the cross correlation
coefficients over time and by region. A panel data estimation framework appears the mostappropriate, with GMM estimation in view of the likely endogeneity of the variables

8deployed.4 Because the set of bilateral cross-correlations is bounded by -1 to +1, Fisher’s ztransform of the left hand side (LHS) variable is used: otherwise we would have to deal withlimited dependent variable estimation as well.

To anticipate our strategy, it is first toconcentrate on overall measures of GVA and their cross correlation between pairs of regions;and, subsequently, to expand the set of observations and their variance by defining cycles inthe same way as before, but for industrial sectors in the regions. Then the cross-correlations
are defined for sector k in each pair of regions i and j. Then, suppressing time subscripts forconvenience the general form of the estimation is as follows:

1 ⎛⎜ 1 + ρ ijk ⎞⎟log= β t X ijk ... + Dk + Di + D j + ε ijk
2 ⎜⎝ 1 − ρ ijk ⎟⎠

[

]

(1)

where the ρijk are the cross-correlation coefficients between all pairs of regions i, j, for sectork, Xijk are a set of explanatory variables, whilst the D represent region and sector fixed effectdummies. At first pass, we estimate this equation without reference to sector, so dropping thesubscript k and the corresponding fixed effect dummies; then at a second stage we define thecross correlations for sector GVAs and the sector dummies become relevant. The selectionof variables in the explanatory set X is relatively sparse compared with the long lists thatappear in comparable exercises designed to explain the variance in international bilateralbusiness cycle cross-correlations. This is for two reasons: first, the availability of data is less– in particular, data on trade between regions are not available; secondly, variables that
register differences between nations in labor and product markets, fiscal systems andfinancial structure are not appropriate in interregional comparisons where all regions sharethe same fiscal systems and financial structure and participate in unified, national labor andproduct markets. The basic motivating ideas we can use are two-fold: first, although explicit
trade data are not available the ideas behind the gravity model of trade appear relevant in thissetting. This means that we can use variables that measure “mass” and geographical distance.
In this case “mass” is represented as the sum of GVA in each of the two regions in the pair.
Distance is measured in two alternative ways – either as physical distance between theprincipal cities or towns in each pair of regions or as the time taken to travel (by roadtransport) between these cities. That the one is not a simple transform of the other is due to4

Our estimations use two or three subsample periods (1971-1987 and 1988-2004 for two subsample case and1971-1981, 1982-1992 and 1993-2004 for three subsample case). In our GMM estimations, independent
variables before one period are used as instruments.

9the presence of physical barriers between regions in some cases, notably where Northern
Ireland is involved. The distance measures are supplemented by a contiguity dummy forshared borders. The second idea we use is based on the notion of a business cycle as the
product of a shock and a propagation mechanism; differences in industrial structure between
regions stand for a different vulnerability to shocks, so that a variable measuring dissimilarity
of industrial structure should help to explain differences in the cross-correlations of thebusiness cycle deviates for those regions. In fact, we employ two such measures, one which
defines the difference in a region’s industrial structure with respect to the national average,
which we term DISSIM (and where in estimation we employ the difference between thevalues of this measure between each of the two regions in every pair), the other which,following Krugman (1991) measures directly the difference in industrial structure between
each pair of regions, which we term SPECIALIZATION. The appendix gives more detail on
the explanatory variables we use. Time is divided into two or three subsamples – 1971-1987
and 1988-2004 in the first case and 1971-1981, 1982-1992 and 1993-2004 in the second, allvariables as appropriate being averaged over those periods. As the GMM estimation requires
instruments it is convenient to nominate the values of the variables in the previous period as
the instrument for the current one. Evidently this reduces the number of periods being
examined from two and three to one and two.

4.2. Estimation Results

Before showing estimation results, we need to check the presence of spatial autocorrelation
because our data are regional level and regions are spatially linked each other. Classicalstatistical inference would be invalidated in the presence of substantial spatial autocorrelation.
We test for this by calculating Moran’s I statistic. The values of Moran’s I are low andinsignificant in our case so that we can proceed with the estimation in the normal way. We
present some of the calculations with an illustrative diagram in the first appendix to thispaper (Figure A-1 in Appendix A).
Table 3 shows a set of results for overall GVA; it is evident that it is not just the significanceof the region dummies that is responsible for the poverty of significant estimates on the
explanatory variables for these are insignificant even when the region dummies are10

suppressed (as in the top half of the table). With only regional fixed effects significant noneof the variables suggested by economic theory has anything to contribute.5 Much the same
result was evident in Barrios et al. (2003). More interesting results were retrieved, in theircase, by extending the sample to include correlations with a number of European countries.
Both specialization and dissimilarity index are not significant at all. While overall GVA areclosely correlated (Figure 2), cross-correlations of sectoral GVA are quite different acrosssectors (see Appendix C and Figure C). Overall GVA seems to absorb heterogeneous
sectoral fluctuations as well as different location patterns across sectors, which results in poor
estimation outcomes. We can say that overall GVA cannot capture heterogeneous sectoral
behaviors. Thus we now turn to estimations using sector-GVA correlations.

<<Table 3>>

In our case, we find that extending the sample to examine sector-GVA cycles in the regions
not only adds more observations, but increases the variance to be explained, whereupon the
explanatory variables suggested by economic theory become significant (with the expectedsign) in every case – even while fixed effects for both regions and industrial sectors are also
highly significant. Tables 4-7 show these results; it is also observable that when different
time subsamples are used the coefficient estimates are not greatly disturbed. The extensionof the analysis to examine sector-GVA cycles by region is in this sense highly successful.

<<Table 4>><<Table 5>><<Table 6>>5

We also tried estimations using 3-subsample period data. However, results cannot be improved at all. The
paper omits the report of the results in 3-subsample overall-GVA case.

11

<<Table 7>>

By decomposing aggregate level analysis to sectoral level, our sector-GVA estimations cancapture heterogeneous sector-GVA movements and geographical location patterns and candefinitely improve estimation results: significantly positive average sector-GVA andsignificantly negative geographical distance and dissimilarity index. Sector-GVA crosscorrelations are higher as two regions are closer in geographical distance and similar inindustrial structure. By contrast to the overall-GVA results shown in Table 2 and evidences
shown in previous studies (e.g. Barrios, et al. 2003), we have all significant and reasonable
results.

4.3 The OCA criterion recalled

At this point we turn to a further consideration of the way in which the data we havecollected reflect on the UK’s suitability to join the Eurozone – and the possible suitability ofa further devolved Scotland as a Euro-member. As we noted earlier in the paper cyclicalcross-correlation evidence has been extensively used in the empirical counterpart of the
optimal currency area discussion. In the case of the UK, the “five tests” promoted by the UK
government as economic criteria which should be satisfied before the government could
make a case, to be tested by referendum, for the UK to join the Eurozone, included anargument that could be translated as a requirement that the correlation of business cycles in
the UK and the EuroArea should be highly correlated. In fact, HM Treasury (2003) includes
an extensive consideration of this issue - with a negative flavour, “the UK business cycle
idiosyncrasy” being at the time a well-recognized phenomenon. The idiosyncrasy embraces
two aspects – first that the UK cycle as a whole is not in tune with the cycle that dominates incontinental Europe and, second, that there is no region of the UK that is closely associatedwith that cycle. Barrios, et al. (2003) noted both these aspects of the UK case. They support
the finding of Forni and Reichlin (1998) that the UK and Greece were the only two
European countries not to contain at least one region that was prominently tied to the12European cycle. Today, in the light of the possibility of Scottish devolution paving the way
for that country to secede from the sterling area and to join the EuroArea, the UK
idiosyncrasy takes on an additional flavour. Our findings in fact suggest that with the passageof time the extent of the UK idiosyncrasy has diminished without having disappeared.
Figure 9 shows the histograms of the UK regional cross-correlations alongside those for the

EuroArea countries and for the joint distribution of the two. Evidently, the UK histogram istighter than either of the others and has a higher average; when the UK and the EuroArea are
mixed the result is to reduce the average for the EuroArea.

<<Figure 9>>

However, when the data are interrogated on the question of possible regional affiliations with
EuroArea countries – as in Figure 10 – it emerges that many UK regions enjoy quite highcyclical correlations with individual EuroArea countries, with the prominent exception ofGermany. It is not the case, though, that Scotland’s affiliation is more notable than others’; ifanything it is London and the South East which exhibit the highest cross-correlations.

<<Figure 10>>

5. Conclusions
The UK intranational cycle is a highly cohesive one, when judged by the size of the crosscorrelations between its regional cycles and in comparison with similar evidence for the US
and the EuroArea; Japan is a similarly cohesive economy when evaluated in the same way.
Explaining the cross-correlations between the regions yields few results of interest, perhaps
because the variance of the explicand is in fact quite small. The basic GVA data set does
however contain considerable sectoral detail and, when attention is turned to explaining thesectoral/regional cycles, results can be obtained which are of greater economic interest.

13

GMM estimation discloses that variables measuring distance, GVA size and indicators ofstructural difference are significant in explaining the pattern of cross-correlations. The UK
business cycle idiosyncrasy, whereby the UK cycle is found to stand apart from thecontinental European one and no individual regions are affiliated to continental European
state cycles is confirmed on this data set, yet in weakened form. Whilst the UK regional
cycles en bloc are not strongly affiliated to the cycles that can be measured for the EuroArea
economies, most regions are quite well connected to most continental economies (with theprincipal exception of Germany). Current interest in Scottish devolution cannot be
rationalized in a finding that the Scottish cycle is more closely synchronized than are otherUK regions to EuroArea countries’ cycles as there is in fact rather little difference between
them - London and the South East might appear to be a better candidate.

References
Artis, M. J. (2002) “Dating the Business Cycle in Britain”, National Institute Economic Review,
no.182, 4, October 2002, 90-95.
Artis, M. J. and T. Okubo (2007) “The Intranational Business Cycle: Evidence from Japan”,
CEPR Discussion Papers, No 6686. London: CEPR.
Barrios, S., Brulhart, M., Elliott, R. J. and M. Sensier (2003) “A Tale of Two Cycles: Cofluctuations between UK regions and the Eurozone”, The Manchester School, 71, June,
265-292.
Burnside, C. (1998), " Detrending and business cycle facts: A comment", Journal of
Monetary Economics, 41(3), 513-532.
Canova, F. (1998), " Detrending and business cycle facts: A user’s guide", Journal of
Monetary Economics, 41(3), 533-540.
Bernard, A. and S. Durlauf. (1995) “Interpreting tests of the convergence hypothesis”,
Journal of Econometrics, 71, 161-73.
Forni, M. and L. Reichlin. (1998) “Let’s get real: a factor-analytical approach todisaggregated business cycle dynamics”, Review of Economic Studies, 65, July, 453-73.

14Gruben, W., Koo, J. and E. Mills (2002) How much does international trade affect businesscycle synchronization?”, Federal Reserve Bank of Texas Research Department
Working Paper, 0203.
Harvey, A..C. and A. Jaeger (1993), “Detrending, stylized facts and the business cycle”,
Journal of Applied Econometrics, 8, 231-247.
Hess, G. D. and K. Shin. (1997). “International and intranational business cycles”, Oxford
Review of Economic Policy, Vol. 13, 93–109.
Hess, G. D. and K. Shin. (2001).Intranational business cycles in the United States, Journal ofInternational Economics 44 (1998) 289–313.
H.M Treasury (2003) The United States as a Monetary Union, EMU Study.
Hodrick, R. J., and Prescott, E. C. (1997), ‘Postwar US Business Cycles: An EmpiricalInvestigation’,

Journal of Money, Credit and Banking, 29(1), 1–16.

Kaiser, R. and A. Maravall (2001) Measuring Business Cycles in Economic Time Series,
Lecture Notes in Statistics 154, New York:Springer-Verlag.
Kaiser, R. and A. Maravall (2002) A complete model-based interpretation of the HodrickPrescott filter: spuriousness reconsidered, at
http://www.bde.es/servicio/software/tramo/mhpfmodel.pdf.
Krugman, P. (1991) Geography and Trade. MIT Press: Cambridge, Mass.
Meyers, M. and P.Winker (2005)”Using HP filtered data for econometric analysis: someevidence from Monte Carlo simulations”, Allgemeines Statistiches Archiv, 89, 303-320.
Pedroni, P. and J. T. Yao (2006) “Regional Income divergence in China”, Journal of Asian
Economics, 17, 294-315.
Ravn, M., and H. Uhlig (2002) “On Adjusting the HP Filter for the Frequency of
Observations”, Review of Economics and Statistics 84 (2): 371–376.
Wynne, M. and J.Koo (2000) “Business Cycles under Monetary Union: a comparison of the
EU and US”, Economica, 67, 347-374.

15

Data Appendix: Data sources and definitions
1. GDP and GVA data sets

GVA: GVA data are taken from Cambridge Econometrics (Regional Economic Prospects,
February 2006). The data are all real GVA (£mn 2002 prices) from 1971 to 2004 for 12 UK
regions. We use the data for total GVA, main sector GVA, total manufacturing GVA and 14
manufacturing sectoral GVAs.
The main sectors are agriculture, coal, “oil and natural gas, etc”, other mining, “electricity,gas, water”, construction, distribution, retailing, “hotel and catering”, “transport andcommunications”, “banking and finance”, insurance, other business services, “public
administration and defence”, “education and health”, “manufacturing” “other services”.
In our estimations for manufacturing sectors, we single out 12 manufacturing sectors (as seenin Table 1) from 14: “motor vehicles”, “textiles, clothing and leather”, “food, drink andtobacco”, “rubber and plastic products”, “printing and publishing”, “other transport
equipment”, “chemicals and man-made fibers”, “electronics, electrical, inst.eng”, “nonmetallic mineral products”, “basic metals and metal products”, “mechanical engineering” and“wood and paper”. We dropped two minor sectors: “manufacturing fuels” and “othermanufacturing”.
Our (NUTS 1) 12 regions are composed of London, South East, East of England, South West,West Midlands, East Midlands, Yorkshire and Humberside, North West, North East, Wales,
Scotland and Northern Ireland (See Table 1).
The GDP data set for 17 European countries and the United States is taken from World
Development Indicator (Edition September 2006) (World Bank). GDP is constant 2000 US
dollars. The US GSP (gross state product) data sets for the cross-autocorrelations shown in
Figure 6 are taken from the Bureau of Economic Analysis, US Department of Commerce.

The unit of real GSP is millions of chained 2000 dollars. Japanese prefectural GDP data
(real) used in Figure 4 are taken from Fukao and Yue’s “Japanese prefecture database”(Hitotsubashi University, Tokyo, Japan)(http://www.ier.hit-u.ac.jp/~fukao/japanese/data/index.html).

16

2. Estimation variables

We estimate the gravity augmented equation using sectoral data sets.
We use GMM and 2SLS estimations to resolve endogeneity problems. The sample is split
into two and subsequently three sub-sample periods, i.e. from 1971 to 1987 and from 1988 to
2004 and then 1971-1981, 1982-1992, and 1993-2004.
Dependent variable

The paper focuses on the cyclical deviates of HP-filtered GVA, where the dampening
parameter (λ) is set at 6.25. After computing cross- correlation, we apply the Fisher ztransformation. The transformation is aimed at expanding the limited variation (from -1 to 1)
in the cross correlation measure. Fisher’s z transformation is a one-by-one mapping utilising⎛1+ ρ ⎞⎟⎟ for -1<ρ<+1.
a uniformly increasing monotone function f, defined as f = 0.5 ln⎜⎜⎝1− ρ ⎠
Independent variables:
GVA: The product of GVA average of two regions. GVA average is taken for each region ineach sector in each period. The variable is expressed in logarithmic form.
DISSIM: This stands for a dissimilarity index computed from all sector information for theregions. The dissimilarity index in region A is defined as
2⎛⎞⎜
GVAjA ⎟∑GVAjA⎟
Dissim A = ∑ ⎜⎜− j
⎟ where i denotes region and j denotes the sectors (allj⎜⎜ ∑ GVAji ∑∑ GVAji ⎟⎟
i
j
⎝ i
⎠
14 manufacturing sectors plus agriculture, mining and services). Then, DISSIMMAN isderived as DISSIMMAN = DissimA + DissimB . The DISSIM index is the summation of thesquared differences between the shares of industries in a region and the average acrossregions. This measures how different the distribution of industries in the region is from theaverage. It can be termed a concentration index.

17

DISSIMMAN: Using the definition of DISSIM, this index is calculated for just the 14

manufacturing sectors.
SPECIALISATION: This stands for the value of an index of specialisation between two

regions. We use the Krugman (1991)specialisation index:

SPECIALISATION AB = ∑
i

GVAAj

−

GVABj

∑ GVA ∑ GVA
ij

j

ij

j

This sums the absolute difference of the industrial structures of the two regions and can bethought of as a measure of specialisation between regions.
SPECIALISATIONMAN: Using the definition of SPECIALISATION, this index is

calculated for just the 14 manufacturing sectors.
DIST: driving distance between principal cities of two regions, expressed as log km.
Driving: driving time between principal cities of two regions measured in (log) minutes.

(In the case of Northern Ireland, a ferry is taken).
Distance and driving time in our estimations are calculated by programs of “Multi-map”,"motoring" "get direction" --"driving" and "quickest" for UK regions
http://www.multimap.com/motoring/?t=r&map=54.06188,-0.20642|6|4
Northern Ireland distance and time is calculated by "AA.com" route planner
http://www.theaa.com/travelwatch/planner_main.jsp?database=B
Neighbor: the dummy for contiguity
Sector 1-12: Our estimation uses 12 sectors out of 14 manufacturing sectors. These aresectoral dummies (12 sectors). See the code numbers of the 12 sectors in Table 1.
Region 1-12: These are regional dummies (12 regions). See the code numbers of the 12

regions in Table 1.

18

Appendix A: Moran’s I (Spatial Autocorrelation)
These statistics are aimed at studying (global) spatial autocorrelation in terms of GVAs
across regions. Figure A-1 shows the value of Moran’s I calculated over the (log of) firstdifferences in GVA in the 12 regions. I-statistics are bounded in value between -1 and +1.
We used geographical distance as a weight matrix, W. The formula for Moran’s I is given as
n

I=

n

∑∑W ( X − X )( X − X )i =1 j =1
n
n

ij

∑∑Wij
i =1 j =1

i

j

1 n
∑ ( X i − X )2
n i =1

where Xi (Xj) is GVA in region i (j), X is average GVA across regions. Values for the Istatistics value closer to 1 indicate clustered (spatially concentrated) data points with similar
characteristics, whilst the values close to -1 imply gathering data points with totally different
characteristics. When the value is zero, it is randomly distributed in space: there is no spatial
pattern in the distribution of characteristics.

<<Figure A-1>>

Appendix B: Testing for Convergence
In this appendix we explore the evidence for convergence of income levels across the regionsof the UK. To do so, we invoke the test procedures suggested by Bernard and Durlauf (1995)
which draw on the concept of cointegration and unit-root testing. Our tests will be for
pairwise cointegration: we do not attempt to draw on the recent literature which tries toaddress the question of convergence within a whole panel (e.g. Pedroni and Yao, 2006). It is
doubtful whether such an extension could add anything in our context.
It makes little sense to direct the unit root tests to regional aggregates as such. From awelfare point of view it is only per capita measures that are meaningful as the object of
examination. This obliges us to draw upon a data set for an earlier period than the one usedin the main body of the paper (in fact, the one used in Barrios et al., 2003) because consistent19

population estimates are not available for us to use for our preferred data set.6 Instead, thedata used here, besides referring to an earlier period (1971-1991) also involve a slightly
different set of defined regions.
Our convergence tests correspond to those that have become known in the literature as β- andσ- convergence. The former refers to the idea that poorer regions need to grow faster thanricher ones if per capita income levels are to be equalized. Hence we can examine whether
the growth gap has a unit root or not. If it does, then the presumption is that convergence oflevels is not occurring – whilst the growth gap remains the same the levels (in logs) gap
remains the same. (But note that rejection of the unit root is a necessary but not sufficientcondition for equalization – it could be compatible with the existence of increasing
divergence). Alternatively we can apply the tests directly to the levels (in logs) gap. Here thefinding that the unit root cannot be rejected is a finding that convergence is unlikely to behappening. In either case we can apply the augmented Dickey-Fuller (ADF) test. It is wellknown that this test has weak power against the alternative (i.e. against the null of
cointegration) so that the results may be somewhat indecisive. The results are reported inTables B-1 and B-2. In Table B-1 we find that the augmented Dickey-Fuller test whenapplied to the levels gap can provide only three cases in which the unit root can be rejected.
In Table B-2 we report that the test finds only one case where the unit root cannot be rejected
for the growth gap. In terms of the growth gap, almost all pairs are found to have a unit root
and thus per-capita GVA growth convergence may be occurring.

<<Table B-1>><<Table B-2>>

6

The regional population data are taken from the ONS. The regional classification employed in the ONS
population data is 11 UK regions, which is different from 12 regions in the GVA data we used in the main text
(Table 1) but corresponds exactly to the GVA data Barrios et al. used. To be consistent in regional classification,
we adopt Barrios et al.’s GVA and ONS population data sets.

20

Appendix C: Sector-GVA Cross-correlations
Here sector-GVA cross-correlations in some representative sectors show Figure C.
Compared with overall-GVA (Figure 2), cross-correlations have larger variance in every
sector and also have quite different features across sectors. This indicates that sectors aresubstantially heterogeneous and differently distributed across regions in geographicallocation. The heterogeneity impacts the idiosyncrasy of intranational trade cycle.

<<Figure C>>

21

Figure 1: UK Regional GVA Cycles (HP Filtered GVA)

Total GVA Cycles
0.08

0.06

0.02

22
0
1971 1975 1979 1983 1987 1991 1995 1999 2003
-0.02

-0.04

-0.06

Total GVA Standard Deviation
0.025Standard Deviation

0.04

1
2
3
4
5
6
7
8
9
10
11
12

0.02
0.015
0.01
0.005
0
1

2

3

4

5

6

7

UK Regions

8

9

10

11

12

Manufacturing GVA Cycle
0.2

0.15
Manufacturing GVA Standard Deviation

0.05

23
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03

0

-0.05

-0.1

-0.15

1
2
3
4
5
6
7
8
9
10
11
12

0.05
0.045
Standard Deviation

0.1

0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
1

2

3

4

5

6

7

UK regions

8

9

10

11

12

Figure 2: UK 12 Regions Cross-correlations
14
12

24

Frequency

10
8
6
4
2
0
-0.25

-0.05

0.15

0.35
0.55
Correlations

0.75

0.95

Figure 3: Average and Variance of GVA
Correlations (moving average periods)
1.2
1
0.8
0.6
25

averagevariance

0.4
0.2
0
-0.2

1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
time periods

Figure 4: Japanese GDP cross-correlation
Histogram(Period: 1955-1964)

Histogram(Period:1965-1974)
350

200
180
160
140
120
100
80
60
40
20
0

300

Frequency

Frequency

250
200
150
100
50
0

-1 -0.8 -0.6 -0.4 -0.20

0.2 0.4 0.6 0.8

1-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Correlations

Correlations

1

26
Histogram(Period: 1985-1996)

Histogram(Period:1975-1984)
180
160
140
120
100
80
60
40
20
0

250

Frequency

Frequency

200
150
100
50
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Correlations

10-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Correlations

1

Figure 5: US state GSP Cross-correlation
Histogram (Period: 1997-2005)

27

180
160
140
120
100
80
60
40
20
0

160
140
120
Frequency

Frequency

Histogram (Period 1990-1997)

100
80
60
40
20
0

-1 -0.8 -0.6 -0.4 -0.2 0

0.2 0.4 0.6 0.8

Correlations

1-1 -0.8 -0.6 -0.4 -0.2 0

0.2 0.4 0.6 0.8

Correlations

1

Figure 6: EU Currency Members Crosscorrelation: 1975-1995
16
14

28

Frequency

12
10
8
6
4
2
0
-1

-0.8 -0.6 -0.4 -0.2 0
0.2 0.4
Correlations

0.6

0.8

1

Figure 7: Manufacturing Ratio

0.35

LondonSouth East
East of EnglandSouth WestWest Midlands
East Midlands
Yorks & the Humber
North West
North East
Wales
ScotlandN Ireland
United Kingdom

0.3
0.25
0.2
0.15
0.1
0.05

Year

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

0
1971

29

(Manufacturing GVA)/(Total GVA)

0.4

9

10

11

12

Figure 8-1: Total GVA-trend (after filtering)

1970

1980

1990
var1

30

hp_ln_GDP_2_sm_1
hp_ln_GDP_4_sm_1
hp_ln_GDP_6_sm_1
hp_ln_GDP_8_sm_1
hp_ln_GDP_10_sm_1
hp_ln_GDP_12_sm_1

2000
hp_ln_GDP_3_sm_1
hp_ln_GDP_5_sm_1
hp_ln_GDP_7_sm_1
hp_ln_GDP_9_sm_1
hp_ln_GDP_11_sm_1
hp_ln_GDP_13_sm_1

2010Figure 8-2: Trends in Sectors
Electoronics

5

5

6

6

7

7

8

8

Textile82010

hp_ln_GDP_2_sm_1
hp_ln_GDP_4_sm_1
hp_ln_GDP_6_sm_1
hp_ln_GDP_8_sm_1
hp_ln_GDP_10_sm_1
hp_ln_GDP_12_sm_1

1970

Machineryengineering

1980

1990
DATES

hp_ln_GDP_1_sm_1
hp_ln_GDP_3_sm_1
hp_ln_GDP_5_sm_1
hp_ln_GDP_7_sm_1
hp_ln_GDP_9_sm_1
hp_ln_GDP_11_sm_1

2010

hp_ln_GDP_2_sm_1
hp_ln_GDP_4_sm_1
hp_ln_GDP_6_sm_1
hp_ln_GDP_8_sm_1
hp_ln_GDP_10_sm_1
hp_ln_GDP_12_sm_1

4

6

5

2000

5

3

31

6

2000

8

1990
DATES

hp_ln_GDP_1_sm_1
hp_ln_GDP_3_sm_1
hp_ln_GDP_5_sm_1
hp_ln_GDP_7_sm_1
hp_ln_GDP_9_sm_1
hp_ln_GDP_11_sm_1

7

Motor

1980

7

1970

1970

1980

1990
DATES

hp_ln_GDP_1_sm_1
hp_ln_GDP_3_sm_1
hp_ln_GDP_5_sm_1
hp_ln_GDP_7_sm_1
hp_ln_GDP_9_sm_1
hp_ln_GDP_11_sm_1

2000
hp_ln_GDP_2_sm_1
hp_ln_GDP_4_sm_1
hp_ln_GDP_6_sm_1
hp_ln_GDP_8_sm_1
hp_ln_GDP_10_sm_1
hp_ln_GDP_12_sm_1

2010

1970

1980

1990
DATES

hp_ln_GDP_1_sm_1
hp_ln_GDP_3_sm_1
hp_ln_GDP_5_sm_1
hp_ln_GDP_7_sm_1
hp_ln_GDP_9_sm_1
hp_ln_GDP_11_sm_1

2000
hp_ln_GDP_2_sm_1
hp_ln_GDP_4_sm_1
hp_ln_GDP_6_sm_1
hp_ln_GDP_8_sm_1
hp_ln_GDP_10_sm_1
hp_ln_GDP_12_sm_1

Figure 9: Cross-correlations in UK Regions and EURO Countries
Intra-UK Regions Cross-correlations

EURO countries and UK regions Cross-correlations
18

9

12

16

8

14

7

12

6

Frequency

86Frequency

1410Frequency

Intra-EURO Countries Cross-correlations

10
8

5
4

6

3

4

2

2

2

1

0

0

4

-0.25

-0.05

0.15

0.35
0.55
Correlations

0.75

0.95

0

-0.25

-0.05

0.15

0.35
0.55
Correlations

0.75

0.95

-0.25

-0.05

0.15

0.35

0.55

Correlations

0.75

0.95

32

Figure 10: Correlations in 12 UK regions with EURO Countries
0.7
1
2
3
4
5
6
7
8
9
10
11
12

0.5
0.4
0.3
0.2
0.1
Finland

Portugal

Austria

Netherlands

Italy

FranceSpain

Greece-0.3

Luxembourg

33

-0.2

Ireland-0.1

Gerrmany0
Belgium

Correlations with EU countries

0.6

EURO Countries

UK Regions
Belgium
Gerrmany
Ireland
GreeceSpain
France
Italy
Luxembourg
Netherlands
Austria
Portugal
Finland1
0.45990.04130.10340.28760.47080.4916
0.53680.51870.32520.2821
0.48190.651820.44150.11240.33450.52350.53330.62650.48930.4461
0.38170.3626
0.59380.594330.38120.1796
0.36140.57630.49310.59790.433
0.46020.4396
0.34980.45360.440740.5306-0.0067
0.28820.01670.62730.63080.6287
0.52650.461
0.38
0.50750.562850.18450.11
0.20930.35130.31270.44080.35440.39480.31780.23670.19970.34186
0.3218-0.10140.14130.1146
0.4632
0.39350.41530.34990.23740.167
0.27330.54947
0.26350.02650.13660.24090.43310.42660.368
0.42480.30290.22710.30340.356380.22660.184
0.14860.544
0.37090.445
0.25250.34770.22540.24660.34010.225790.26520.14430.01380.51750.36920.28970.23860.22520.054
0.188
0.41570.2709100.07240.04230.13220.5904
0.19870.31180.12780.11990.01480.09190.24860.3339110.32010.0001-0.00630.18430.48490.46750.31010.32920.146
0.26410.3716
0.355412-0.1126
0.12380.05550.1308-0.0470.13990.04780.19
0.15930.0275-0.1804
0.0001

Figure A-1: Moran's I Statistic0

Statistics-0.05

1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004

-0.1
-0.15

34

-0.2
-0.25
-0.3
Year

Figure C: GVA Cross-correlations in Some Sectors

ChemicalBasic Metal

10
Frequency

Frequency

8
6
4
2
0
-1

-0.8 -0.6 -0.4 -0.2

0

0.2

0.4

0.6

0.8

8
7
6
5
4
3
2
1
0

1

-1

Correlations

35
Frequency

Frequency
00.2

Correlations

0.2

0.4

0.6

0.8

1

0.4

0.6

0.8

1

Electoronics

7
6
5
4
3
2
1
0
-0.8 -0.6 -0.4 -0.2

0Correlations

Mechanical Engineering-1-0.8 -0.6 -0.4 -0.2

0.4

0.6

0.8

1

16
14
12
10
8
6
4
2
0
-1

-0.8 -0.6 -0.4 -0.2

00.2

Corerlations

Table 1: Sector and Region Codes

36

12 UK region code
1 London
2 South East
3 East of England
4 South West
5 West Midlands
6 East Midlands
7 Yorks & the Humber
8 North West
9 North East
10 Wales11 Scotland
12 N Ireland
13 United Kingdom(total)

sector code
1 food2 textile
3 printing
4 chemical
5 rubber
6 mechanical engineering
7 electoronics
8 motor9 basic metal
10 wood
11 other transport eq
12 non-metal

Table 2: HP Filtered Total UK GVA correlation
1
2
3
4
5
6
7
8
9
10
11
12
total uk

1

2

3

4

5

6

7

8

9

10

11

12

0.81080.69320.70420.644
0.73490.6692
0.63830.63350.60550.67230.28520.85960.92950.72840.72350.65880.683
0.82370.63710.76720.65770.30220.92410.69560.80790.68450.74370.91140.63070.77460.69450.47920.92870.66270.83730.78020.60670.31140.3546
0.58160.35070.78510.78550.82980.76380.46440.70050.61870.79070.86490.89640.69480.52680.508
0.71210.57960.84520.82030.55930.563
0.72870.67870.87

0.76310.79620.76060.54520.90650.7406
0.76860.23030.72070.6521
0.39880.78920.448
0.80890.541337

Table 3: Regional Overall GVA Regressions
Dependent variables are HP filtered regional overall GVA

38

GVA
DISSIM
DISSIMMANSPECIALISATIONSPECIALISATIONMAN
Distance
Neighbor
region1
region2region3region4region5region6
region7region8region9region10
region11
region12
Constant
Estimation Methods

The number of samples
The number of groups
Wald Chi squared
MSE
Hansen J
R-squared (overall)

1
Coefficients z-value
0.018
1.77 *-0.719-0.65

2Coefficients z-value
0.017
2.03 **-2.0783
Coefficients z-value-0.004-0.2

4Coefficients z-value-0.041-1.33

5
Coefficients z-value
0.008
0.35
-1.865 -0.87

-0.66
1.107

0.86

1.477

0.92

6
Coefficients z-value-0.038-0.99-4.015-0.73-0.090
0.127

-1.00
1.24

-0.147
0.105

-1.44
1.02

-0.119
0.152

-1.22
1.23

2.678
-0.0960.238

1.71 *-0.93
1.67 *-0.069
0.201

-0.56
1.31

3.038
-0.1640.238

1.66 *
-1.14
1.53

1.259

2.36 **

1.584

2.66 **

1.416

2.46 **

1.260

2.05 **

1.116

1.53

1.640

1.95 *2SLS random-effects IV panel

2SLS random-effects IV panel

2SLS random-effects IV panel

2SLS random-effects IV panel

2SLS random-effects IV panel 2SLS random-effects IV panel

132
66
36.15

132
66
35.91

132
66
25.07

132
66
24.79

132
66
20.4

132
66
22.33

0.16830.15820.028

0.01770.00580.00387
Coefficients z-value
0.081
0.86
-0.459-0.9

GVA
DISSIM
DISSIMMANSPECIALISATIONSPECIALISATIONMAN
Distance-0.075Neighbor-0.019region1
0.473
region20.665
region30.723
region40.535
region50.752
region6
0.584
region70.724
region80.769
region90.522
region100.543
region110.708
region12
0.287
Constant(dropped)

39

Estimation Methods

The number of samples
The number of groups
Wald Chi squared
MSE
Hansen J
R-squared (overall)

8
Coefficients z-value
0.012
0.15
0.518

9
Coefficients z-value
0.056
0.79

GMM
Time dummy is omitted.

-0.075-0.019
0.444
0.697
0.755
0.550
0.746
0.587
0.739
0.757
0.522
0.547
0.666
0.274
(dropped)

11
Coefficients z-value
0.076
0.81
-0.204 -0.36

0.31

-0.75
-0.24
1.57
2.22 **
2.47 **
1.83 *
2.72 **
2.17 **
2.65 **
2.68 **
1.64 *
1.82 *
1.86 *
0.84

GMM
Time dummy is omitted.

12
Coefficients z-value
0.084
0.9
-1.741-0.422-0.74-0.23
1.65 *
2.06 **
2.3 **
1.76 *
2.77 **
2.13 **
2.54 **
2.71 **
1.6
1.78 *
1.93 *
0.87

10
Coefficients z-value
0.036
0.56

-0.074-0.029
0.499
0.713
0.732
0.528
0.743
0.574
0.716
0.752
0.528
0.548
0.638
0.301
(dropped)

-0.99
-0.75
-0.36
1.73 *
2.28 **
2.43 **
1.8 *
2.87 **
2.17 **
2.63 **
2.74 **
1.66 *
1.86 *
1.82 *
0.92-0.3571.187-0.088-0.001
0.401
0.751
0.788
0.588
0.726
0.651
0.806
0.819
0.568
0.607
0.723
0.267
(dropped)

1.17
-0.84
-0.01
1.32
2.28 **
2.45 **
1.86 *
2.51 **
2.28 **
2.73 **
2.8 **
1.71 *
1.93 *
1.93 *
0.78-0.075-0.027
0.503
0.698
0.723
0.525
0.742
0.574
0.714
0.756
0.527
0.546
0.663
0.302
(dropped)

-0.74

-0.74
-0.75
-0.33
1.74 *
2.16 **
2.35 **
1.77 *
2.84 **
2.15 **
2.59 **
2.75 **
1.64 *
1.84 *
1.9 *
0.92

GMM
Time dummy is omitted.

GMM
Time dummy is omitted.

GMM
Time dummy is omitted.

1.460-0.091
0.003
0.398
0.741
0.778
0.596
0.763
0.666
0.810
0.859
0.575
0.616
0.717
0.271
(dropped)

1.19
-0.86
0.04
1.29
2.22 **
2.4 **
1.86 *
2.61 **
2.28 **
2.7 **
2.82 **
1.72 *
1.92 *
1.89 *
0.78

GMM
Time dummy is omitted.

132

132

132

132

132

132

0.275400.275
00.268500.291500.269100.29790

Table 4: UK Intra-national RBC Results (GMM)

1
Coefficient z

40

GVA
DISSIM
DIST
Driving
Neighborhood
region1
region2region3region4region5region6
region7region8region9region10
region11
region12sector1
sector2
sector3
sector4
sector5
sector6
sector7
sector8
sector9
sector10
sector11
sector12

0.026-1.840-0.092

Observations
Root MSE
Hansen J

1584
0.4724
0

0.352
0.502
0.519
0.462
0.459
0.487
0.475
0.607
0.330
0.302
0.391
0.310

2
Coefficient z
8.08 **-2.77 **
-2.83 **

0.065
-10.959-0.315

3
Coefficient z
3.57 **-2.59 **
-4.91 **

3.74 **
5.03 **
5.14 **
5.00 **
5.17 **
5.59 **
5.31 **
6.53 **
3.22 **
3.2 **
3.41 **
2.86 **-0.191
0.435
0.308
0.036
0.493
0.238
0.491
0.017
0.287
0.299
(dropped)
0.397

Constant is dropped* denotes statistically significant at 10%** denotes statistically significant at 5%

Observations

Root MSE
Hansen J-2.72 **
6.26 **
4.54 **
0.56
7.08 **
3.52 **
7.02 **
0.25
3.91 **
4.31 **
5.49 **

1584
0.53650Constant is omitted

0.025-1.678-0.092

0.374
0.528
0.546
0.488
0.482
0.513
0.502
0.626
0.358
0.330
0.418
0.337
-0.4450.138
0.034-0.2400.201-0.0350.226-0.2790.019(dropped)-0.2940.090

Observations

Root MSE
Hansen J

4
Coefficient z
8.89 **-2.91 **
-3.06 **

4.15 **
5.50 **
5.65 **
5.62 **
5.74 **
6.22 **
5.82 **
7.05 **
3.69 **
3.65 **
3.87 **
3.31 **
7.78 **
2.36 **
0.6-4.43 **
3.62 **-0.61
3.91 **-5.06 **
0.31

5
Coefficient z

0.026-1.8408.08 **-2.77 **

0.064
-10.8103.41 **-2.46 **

-0.107-2.79 **

-0.248-4.99 **

0.380
0.522
0.547
0.479
0.473
0.508
0.499
0.634
0.347
0.329
0.412
0.436

3.62 **
4.82 **
4.88 **
4.81 **
5.00 **
5.32 **
5.04 **
6.19 **
3.16 **
3.13 **
3.33 **
2.84 **-0.191
0.435
0.308
0.036
0.493
0.239
0.492
0.017
0.287
0.299
0.000
0.397

-0.73
1.55
1.14
0.13
1.78 *
0.88
1.84 *
0.06
1.05
1.06
0
1.4

-5.04 **
1.5

1584
0.42460Constant is dropped

Observations

Root MSE
Hansen J

1584
0.47240Constant is dropped

Observations

Root MSE
Hansen J

1584
0.53410Constant is omitted.

6
Coefficient z

7
Coefficient z

8
Coefficient z

41

0.025
-1.6788.89 **-2.91 **

0.026
-1.5878.43 **-2.57 **

-0.107-3.03 **

0.402
0.548
0.574
0.506
0.496
0.534
0.526
0.652
0.374
0.356
0.438
0.463
-0.4450.138
0.034-0.2400.201-0.0350.226-0.2790.019(dropped)-0.2940.090

4.04 **
5.31 **
5.39 **
5.42 **
5.59 **
5.95 **
5.54 **
6.70 **
3.63 **
3.57 **
3.79 **
3.25 **
7.77 **
2.36 **
0.6-4.42 **
3.62 **-0.61
3.91 **-5.06 **
0.31

-0.125-0.041
0.446
0.601
0.626
0.551
0.539
0.580
0.573
0.697
0.423
0.404
0.493
0.530
-0.4460.138
0.034-0.2400.201-0.0350.225-0.2790.019(dropped)-0.2940.090-2.3 **-0.4
3.16 **
3.71 **
3.88 **
3.94 **
4.07 **
4.2 **
4.07 **
5.01 **
2.8 **
2.73 **
2.91 **
2.54 **-7.8 **
2.37 **
0.59-4.44 **
3.63 **-0.61
3.92 **-5.08 **
0.3

Observations

Root MSE
Hansen J-5.04 **
1.5

1584
0.42460Constant is dropped

Observations

Root MSE
Hansen J-5.05 **
1.5

1584
0.42470Constant is dropped

0.026-1.558-0.111

8.39 **-2.49 **
-2.33 **

-0.055
0.423
0.589
0.605
0.542
0.533
0.566
0.555
0.676
0.415
0.383
0.482
0.393
-0.4460.138
0.033-0.2400.201-0.0350.225-0.2790.019(dropped)-0.2940.090-0.48
3.26 **
3.79 **
4.01 **
4.03 **
4.13 **
4.31 **
4.2 **
5.24 **
2.84 **
2.79 **
2.96 **
2.65 **-7.81 **
2.36 **
0.59-4.45 **
3.63 **-0.62
3.92 **-5.07 **
0.3

Observations

Root MSE
Hansen J-5.04 **
1.5

1584
0.42480Constant is dropped

Table 5: UK Intra-national RBC Results (2SLS)

1
Coefficient t-value

42

GVA
DISSIM
DIST
Driving

0.025-1.678-0.092

region1
region2region3region4region5region6
region7region8region9region10
region11
region12sector1
sector2
sector3
sector4
sector5
sector6
sector7
sector8
sector9
sector10
sector11
sector12-0.043
0.110
0.128
0.071
0.064
0.095
0.084
0.208
-0.060-0.088(dropped)-0.081-0.5350.048-0.056-0.3300.111-0.1250.136-0.368-0.071-0.090-0.384(dropped)

Observations
F
R-square8.74 **-3.05 **
-3.19 **

-0.95
2.64 **
3.15 **
1.61
1.30
2.01 **
1.86 *
3.89 **-1.49-2.09 **
-2.06 **
-10.12 **
0.91
-1.06
-6.25 **
2.11 **-2.36 **
2.58 **-6.99 **
-1.34
-1.7
-7.29 **

1584
26.22
0.2817

Constant is omitted
Sector dummies are omitted* denotes statistically significant at 10%** denotes statistically significant at 5%2
Coefficient t-value
0.025-1.6788.74 **-3.05 **

-0.107-3.19 **

-0.061
0.085
0.110
0.043
0.033
0.070
0.063
0.189
-0.089-0.107-0.025(dropped)-0.5350.048-0.056-0.3300.111-0.1250.136-0.368-0.071-0.090-0.384(dropped)

-1.00
1.46
2.03 **
0.67
0.46
1.04
0.98
2.69 **-1.58-1.84 *
-0.51

Observatio
F
R-square-10.12 **
0.91
-1.06
-6.25 **
2.11 **-2.36 **
2.58 **-6.99 **
-1.34
-1.7 *-7.29 **

1584
26.22
0.2817

Constant is omitted
Sector dummies are omitted

Table 6: 3-subsample period UK Intra-national RBC Results (GMM)

1
Coefficient z

2
Coefficient z

43

GVA2
GVA3
DISSIM2
DISSIM3
DIST
Driving
DIST2
DIST3
Neighborhood
region1
region2region3region4region5region6
region7region8region9region10
region11
region12sector1
sector2
sector3
sector4
sector5
sector6
sector7
sector8
sector9
sector10
sector11
sector12

0.011
0.110
-2.244-20.759

2.64 **
2.86 **-2.63 **
-2.44 **

-0.294-4.31 **

Observations
Root MSE
Hansen J

2376
0.7489
0

0.008
0.027
-1.553-2.208-0.093

0.409
0.538
0.541
0.480
0.488
0.513
0.494
0.646
0.375
0.362
0.454
0.317
-0.2860.314
0.278-0.084
0.412
0.181
0.427
0.025
0.143
0.195
0.000
0.382

3
Coefficient z
1.99 **
6.88 **-1.92 *
-2.91 **
-3.04 **

4.51 **
5.7 **
5.65 **
5.53 **
5.7 **
6.22 **
5.82 **
7.29 **
3.89 **
4.02 **
4.23 **
3.09 **

-0.8
0.81
0.75
-0.23
1.08
0.49
1.17
0.06
0.39
0.51
0
0.98

Constant is dropped

Observation
Root MSE
Hansen J

2376
0.558
0

Constant is omitted

0.008
0.025
-1.588-1.875-0.093

0.467
0.602
0.607
0.546
0.550
0.577
0.559
0.700
0.441
0.428
0.519
0.383
-0.531-0.001(dropped)-0.3590.110-0.0960.165-0.281-0.125-0.118-0.3070.054

Observatio
Root MSE
Hansen J

4
Coefficient z
2.17 **
6.92 **-2.11 **
-2.71 **
-3.19 **

5.27 **
6.51 **
6.46 **
6.42 **
6.62 **
7.2 **
6.73 **
8.11 **
4.72 **
4.84 **
5.01 **
3.87 **-9.57 **
-0.02
-6.55 **
2.03 **-1.77 *
2.76 **-5.61 **
-2.26 **
-2.02 **
-5.36 **
0.99

2376
0.52050Constant is dropped

5
Coefficient z

6
Coefficient z

0.008
0.025
-1.588-1.8752.18 **
6.92 **-2.11 **
-2.7 **

0.050
0.138
-2.309-3.747

3.16 **
4.35 **-2.87 **
-4.19 **

0.050
0.136
-2.312-3.726

3.17 **
3.41 **-2.88 **
-3.78 **

-0.074-1.91 *

-2.79 **
-2.82 **

0.73
4.32 **
5 **5.1 **
5.11 **
5.18 **
5.53 **
5.23 **
6.3 **
3.8 **
3.96 **
3.97 **
3.26 **-12.53 **-2.85 **
-2.76 **
-9.54 **
-1.02
-4.79 **

-2.79 **
-3.63 **
0.1
-4.66 **
-3.85 **
0
-4.96 **
-3.58 **
-4.12 **
-4.7 **
0-7.14 **
-7.44 **
-5.34 **
-6.73 **
-9.85 **
0.91

-0.089-0.243

0.026
0.497
0.622
0.630
0.574
0.577
0.604
0.587
0.726
0.466
0.456
0.537
0.409
-0.696-0.166-0.165-0.524-0.055-0.261(dropped)-0.446-0.290-0.283-0.472-0.111-0.089-0.2450.003-0.221-0.149-0.143-0.197-0.176-0.175-0.1970.000-0.282-0.299-0.219-0.299-0.5890.055(dropped)-0.3570.156-0.1020.141-0.226-0.145-0.054-0.2550.138-0.079-0.0050.000-0.054-0.032-0.031-0.0530.143-0.139-0.156-0.077-0.158-0.5890.055(dropped)-0.3570.156-0.1020.141-0.226-0.145-0.055-0.2550.137-1.94 *
-0.11
0
-1.12
-0.73
-0.63
-1.06
0
-2.85 **
-3.18 **
-1.58
-2.86 **
-9.7 **
0.88

Observatio
Root MSE
Hansen J

2376
0.52040-8.86 **
-5.23 **
-4.82 **
-8.22 **
-2.03 **

Constant is dropped-6.3 **
2.75 **-1.84 *
2.32 **-4.17 **
-2.55 **
-0.88
-4.14 **
2.36 **-6.3 **
2.72 **-1.84 *
2.32 **-4.13 **
-2.54 **
-0.88
-4.1 **
2.27 **

Observatio 2376
Root MSE 0.523
Hansen J
0

Observatio
Root MSE
Hansen J

Constant is dropped

Constant is omitted.

* denotes statistically significant at 10%** denotes statistically significant at 5%
Note: GVA2 is the average GVA in the second subsample period (1982-1992) and GVA3 is the one in the third period (1993-2004). The same notations are applied to DIST2/DIST3 and DISSIM2/DISSIM3.

2376
0.52310

Table 7: 3-subsample period UK Intra-national RBC Results (2SLS)

1
Coefficient t-value

44

GVA2
GVA3
DISSIM2
DISSIM3
DIST2
DIST3

0.050
0.136
-2.312-3.726-0.089-0.243

2.91 **
3.18 **-2.7 **-3.61 **
-2.54 **
-2.64 **

region1
region2region3region4region5region6
region7region8region9region10
region11
region12sector1
sector2
sector3
sector4
sector5
sector6
sector7
sector8
sector9
sector10
sector11
sector12

0.079
0.153
0.158
0.104
0.126
0.127
0.105
0.301
0.019
0.002
0.081
(dropped)-0.726-0.082-0.137-0.4940.019-0.2390.004-0.363-0.282-0.192-0.392(dropped)

1.62
2.92 **
3.13 **
1.88 *
2.08 **
2.03 **
1.72 *
5.23 **
0.42
0.04
1.94 *

Observations
F
R-square-9.9 **-1.53-2.25 **
-8.15 **
0.35
-3.87 **
0.06
-6.72 **
-4.36 **
-3.59 **
-7.24 **

2376
19.97
0.175
Constant is dropped* denotes statistically significant at 10%** denotes statistically significant at 5%
Note: GVA2 is the average GVA in the second subsample period (1982-1992) and GVA3 is the one in the third period (1993-2004).
The same notations are applied to DIST2/DIST3 and DISSIM2/DISSIM3.

Table B-1: GVA per capita Regional Convergence Unit Root Tests (1971-1997)
Dickey-Fuller test
South East East Angli South Wes West MidlaEast Midla Yorkshire &North WestNorth
South East
East Angli
U
South West
(X)
X
West MidlaU
UU
East Midla
U
U
U
(X)
Yorkshire & Humberside U
U
UUU
North West
U
U
U
U
U
U
North
U
U
U
U
U
U
U
Wales
U
U
U
U
U
U
U
U
Scotland
U
U
U
U
U
U
U
U
Northern Ireland
U
U
U
U
U
U
U
U
(X) denotes p<0.05, i.e. unit root is rejected.
X denotes p<0.01: i.e. Unit root is rejected
Otherwise, there is unit root
Note:ln(GVApercapita_A)-ln(GVApercapita_B)
Note: Different regional classification of UK 12 regions from main text (Table1)

Wales

ScotlandU
U

U

Northern Ireland

45

Table B-2: GVA per capita growth Regional Convergence Unit Root Tests (1971-1997)
Dickey-Fuller test

First difference GVA (Growth gap)
South East
East Angli
South WestWest Midla
East Midla
Yorkshire & Humberside
North West
North
Wales
ScotlandNorthern IrelandSouth East East Angli South Wes West MidlaEast Midla Yorkshire &North WestNorth

Wales

Scotland

X
X
X
X
X
X
X
X
X
X

X
X

X

X
X
X
X
X
X
X
X
X

X
X
X
X
X
X
X
X

X
X
X
X
X
U
X

X
X
X
X
X
X

X
X
X
X
X

X
X
X
X

Note that X denotes rejection of unit root (convergence).
Note that U denotes unit root (p-value is more than 0.01).
Note: ln(GVApercapita_A_t)-ln(GVApercapita_A_t-1)-( ln(GVApercapita_B_t)-ln(GVApercapita_B_t-1))
Note: Different regional classification of UK 12 regions from main text (Table1)

X
X
X

Northern Ireland