  Data for many nations show a long-run increase, over many decades, of income,
indexed by GDP per capita, and population health, indexed by mortality or life
expectancy at birth (LEB). However, the short-run and long-run relationships
between these variables have been interpreted in different ways, and many
controversies are still open. Some authors have claimed that the causal
relationships between population health and income can be discovered using
cointegration models. We show, however, that empirically testing a
cointegration relation between LEB and GDP per capita is not a sound method to
infer a causal link between health and income. For a given country it is easy
to find computer-generated data or time series of real observations, related or
unrelated to the country, that according to standard methods are also
cointegrated with the country's LEB. More generally, given a trending time
series, it is easy to find other series, observational or artificial, that
appear cointegrated with it. Thus, standard cointegration methodology cannot
distinguish whether cointegration relationships are spurious or causal.
