Table 4

Summary of regression output for the selected eight best predictors

Independent variableOrdinary least squares estimationSpatial* lag model—Maximum likelihood estimationSpatial* error model—Maximum likelihood estimation
R-squared: 0.529
Akaike info: 2093.5
Jarque-Bera prob: 0.000
R-squared: 0.541
Akaike info: 2089.7
Breusch-Pagan test: 0.000
R-squared: 0.537
Akaike info: 2090.43
Breusch-Pagan test: 0.000
Coeff.t-stat.P valueCoeff.z-valueP valueCoeff.z-valueP value
No central heating−32.188−3.8270.000−30.778−3.7490.000−31.776−3.8750.000
WPS recipients – Veterans21.4922.5940.0118.5062.2830.02218.312.2360.025
AFCS recipients – Veterans7.3891.2420.2156.7941.1720.2416.991.2080.227
No qualifications−8.976−1.0950.275−9.342−1.1710.242−9.837−1.2070.227
AFPS recipients – Veterans10.6071.080.28115.1261.5640.11814.6741.50.134
Spatial lag---------0.3722.6720.008---------
  • Breusch-Pagan: tests whether the variance of the spatial errors from a regression is dependent on the values of the independent variables.

  • Spatial lag and the spatially correlated errors (λ) reflect the spatial dependence inherent, measuring the average influence on observations by their neighbouring observations. Both coefficients have a positive effect and are highly significant. As a result, the general model fit improved. The effects of other independent variables remain virtually the same.

  • *Spatial distance weights: (a) Bandwidth: 162 km; (b) Min neighbours: 1; (c) Max neighbours: 203; (d) Mean neighbours: 130.

  • AFCS, Armed Forces Compensation Scheme; SIMD, Scottish Index of Multiple Deprivation; WPS, War Pension Scheme.