FLS on the basis of a chain of the same length.<sup>5</sup> Table 2 displays the marginal posterior probabilities of inclusion of each regressor— _i.e._ , _P_ ( _i_ ). Fig. 2 displays scatter plots of the logarithms of _P_ ( _i ∩ j_ ), _Jij_<sup>_⋆_, and</sup><sup>_Jij_for all possible combinations</sup> of two regressors. Fig. 2 shows that log( _Jij_<sup>_⋆_) and log(</sup><sup>_Jij_) are linearly related for a range of values,</sup> but the relation turns non-linear as _P_ ( _i ∩ j_ ) _→_ 1. The ranking in terms of both jointness measures is, however, identical, so the only issue for identifying jointness is to decide on suitable “critical” values. This will be easier for _Jij_ , given its interpretation as a posterior odds ratio. Another notable feature of Fig. 2 is that both jointness measures tend to be increasing in _P_ ( _i ∩ j_ ). This relationship is less exact for average values of _P_ ( _i ∩ j_ ), but it does seem that extremely high and low values of _P_ ( _i ∩ j_ ) correspond to similar extremes for both jointness measures. The main features of Fig. 2 are closely mirrored by a similar graph for the SDM data set used in the next subsection, thus lending somewhat more generality to our findings.

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> 5 Note that in the applications in this paper, the regressors have been standardized to have (zero mean and) unit variance to ease the comparison between regression coefficients. The dependent variable is expressed in percentage points of annual per capita GDP growth and displays similar behaviour in both datasets—means of 2.1 and 1.8, and standard deviations of 1.9 and 1.8.

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