The European Regional Growth Process Revisited

Figures & data

Table 1 . Estimation results of models (6) by OLS and (7) by ML with spatial weight matrix W(D1)

	1	2	3	4	5
	OLS-White	ML-ERR	Tests	OLS estimation	ML-ERR estimation
Constant	0.221 (0.000)	0.220 (0.001)	Moran's I	5.867 (0.000)	–
Manufacturing output growth	0.842 (0.000)	0.823 (0.000)	LMERR	25.656 (0.000)	–
Gap	0.061 (0.076)	0.075 (0.083)	R-LMERR	16.308 (0.000)	–
Density	4.73.10^−5 (0.002)	5.70.10^−5 (0.000)	LMLAG	9.399 (0.002)	–
Peripherality	−7.35.10^−5 (0.015)	−9.18.10^−5 (0.053)	R-LMLAG	0.051 (0.820)	–
Lambda	–	0.530 (0.000)	K–B for heteroskedasticity	30.233 (0.000)
Squ.-corr.	–	0.639	White test	40.294 (0.000)
LIK	63.481	73.609	JB test for normality of errors	436.877 (0.000)	–
AIC	−116.964	−137.218	BP test	–	102.178 (0.000)
SC	−99.477	−119.732	Spatial BP test	–	102.192 (0.000)
			LR test on spatial error dependence	–	20.254 (0.000)
			LR test on common factor hypothesis	–	2.242 (0.691)
			Wald test on common factor hypothesis	–	2.187 (0.701)
			LM test on spatial lag dependence	–	0.142 (0.705)
Notes: P-values are in parentheses. OLS-White indicates the use of a heteroskedasticity consistent covariance matrix estimator. Sq. Corr. is the squared correlation between predicted values and actual values. LIK is the value of the maximum likelihood function. AIC is the Akaike information criterion. SC is the Schwarz information criterion. W(D1) corresponds to the inverse of the great circle distance with a cut-off equal to 508 miles.

Table 2 . P-values of the spatial Chow–Wald test and individual stability tests for different break points

		Individual stability test					Weight matrix
	Overall stability	Constant	Growth in manufacturing output	Technology gap	Density	Peripherality	Cut-off distance (miles)
Regime 1: 46 regions Regime 2: 198 regions	(0.000)	(0.000)	(0.000)	(0.410)	(0.000)	(0.138)	508
Regime 1: 47 regions Regime 2: 197 regions	(0.000)	(0.000)	(0.000)	(0.361)	(0.000)	(0.105)	508
Regime 1: 48 regions Regime 2: 198 regions	(0.000)	(0.000)	(0.000)	(0.349)	(0.000)	(0.089)	508
Regime 2: 159 regions Regime 3: 38 regions	(0.000)	(0.194)	(0.046)	(0.995)	(0.706)	(0.037)	500
Regime 2: 160 regions Regime 3: 37 regions	(0.000)	(0.129)	(0.034)	(0.725)	(0.072)	(0.031)	500
Regime 2: 161 regions Regime 3: 36 regions	(0.000)	(0.007)	(0.007)	(0.540)	(0.065)	(0.023)	500
Regime 3: 28 regions Regime 4: 9 regions	(0.000)	(0.000)	(0.000)	(0.148)	(0.397)	(0.115)	511
Regime 3: 29 regions Regime 4: 8 regions	(0.000)	(0.000)	(0.000)	(0.116)	(0.001)	(0.037)	511
Regime 3: 30 regions Regime 4: 9 regions	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	511
Note: Results are obtained by ML estimation. They all are confirmed by GMM estimation.

Table 3 . Spatial Chow–Wald test results for the regimes defined in the text

		Individual stability test
		Constant	Growth in manufacturing output	Technology gap	Density	Peripherality	Cut-off distance (miles)
Regime 1 vs regime 3	(0.002)	(0.097)	(0.877)	(0.050)	(0.355)	(0.020)	380
Regime 1 vs regime 4	(0.000)	(0.190)	(0.000)	(0.431)	(0.096)	(0.003)	517
Regime 2 vs regime 4	(0.000)	(0.021)	(0.000)	(0.453)	(0.000)	(0.003)	495
Note: P-values are in parentheses.

Figure 1.  Distribution of the regions by regime.

Table 4 . Estimation results with spatial regimes (ML estimation) and the spatial weight matrix W(D1)

	Regime 1	Regime 2	Regime 3	Regime 4		Stability tests
Constant	−0.070	0.426	1.012	−0.469	Constant	58.123
	(0.402)	(0.000)	(0.000)	(0.117)		(0.000)
Growth in manufacturing output	0.245	0.441	−0.025	0.948	Growth in manufacturing output	41.969
	(0.003)	(0.000)	(0.815)	(0.000)		(0.000)
Gap	0.108	−0.024	–0.023	0.151	Gap	5.184
	(0.100)	(0.432)	(0.772)	(0.175)		(0.158)
Density	3.99.10^−4	2.49.10^−5	4.34.10^−5	0.001	Density	22.722
	(0.000)	(0.010)	(0.356)	(0.001)		(0.000)
Peripherality	−1.66.10^−4	−1.91.10^−5	5.50.10^−5	6.61.10^−4	Peripherality	17.962
	(0.001)	(0.598)	(0.502)	(0.002)		(0.000)
Lambda	0.348				Chow–Wald test	378.394
	(0.003)					(0.000)
Squ.-corr.		0.844			BP test	137.571
						(0.000)
LIK		186.493			Spatial BP	230.000
						(0.000)
AIC		−332.986			LR test on spatial error dependence	18.032 (0.000)
SC		−263.043			LM test on spatial lag dependence	22.227 (0.000)
Notes: All the results above are confirmed by GMM estimation. W(D1) corresponds to the inverse of the great circle distance with a cut-off equal to 508 miles.

Table 5 . Estimation results with spatial regimes and groupwise heteroskedasticity (GMM estimation) with spatial weight matrix W(D1)

	Regime 1	Regime 2	Regime 3	Regime 4		Stability tests
Constant	0.103	0.403	0.826	−0.347	Constant	53.622
	(0.300)	(0.000)	(0.000)	(0.417)		(0.000)
Growth in manufacturing output	0.195	0.461	0.031	0.835	Growth in manufacturing output	35.079
	(0.038)	(0.000)	(0.582)	(0.000)		(0.000)
Gap	0.072	−0.015	−0.021	0.194	Gap	2.624
	(0.316)	(0.538)	(0.588)	(0.294)		(0.453)
Density	6.66.10^−5	2.18.10^−5	5.24.10^−5	7.79.10^−4	Density	2.400
	(0.672)	(0.017)	(0.061)	(0.246)		(0.493)
Peripherality	1.57.10^−4	−1.71.10^−5	4.43.10^−5	5.09.10^−4	Peripherality	12.569
	(0.003)	(0.515)	(0.220)	(0.088)		(0.005)
Lambda	0.028				Chow–Wald test	455.352
	(0.000)					(0.000)
	0.020 (0.000)
	0.011 (0.000)
	0.004 (0.000)
	0.045 (0.000)
Squ.-corr.	0.859
Note: W(D1) corresponds to the inverse of the great circle distance with a cut-off equal to 508 miles.