D. Acemoglu & S. Johnson

Journal of Political Economy, Vol. 115 No. 6 (2007) pp. 925-85

Principal Research Question and Key Result Have the 21st century’s increases in life expectancy translated into increased economic growth? No statistically significant effect on growth is found and in fact per capita indicators show significant negative association with life expectancy, indicating that any aggregate growth gains are more than offset by increases in population and birth rates.


Theory In neoclassical growth theory increased life expectancy raises that population which initially reduced capital to labour ratios thus depressing income per capita. This may be compensated for in the medium/long run by higher output as more people enter the labour market and more capital is accumulated. This compensation may eventually exceed the initial per capita measures is there are significant productivity benefits from longer life expectancy. This may not occur however if some factors of production are supplied inelastically (not sure I get this last bit). None of this should be taken as an indication that welfare will not be greatly increased from increased life expectancy, only that there is no discernible relationship with GDP.


Motivation There is a growing consensus that improving health outcomes can have indirect payoffs through accelerating economic growth. Whilst Marco studies of these effects are plagued by problems of comparability and also that countries characterized by ill health are also disadvantaged in other ways, micro studies (which show positive effects of health outcomes on growth) cannot account for general equilibrium effects. The most important of these would be that as there are diminishing returns to effective units of labour, micro studies that cannot account for the effect of population growth pursuant to improvements in health, will tend to overstate the economic returns from doing so. This paper takes a novel approach by instrumenting fairly successfully for life expectancy by exploiting what they term the international epidemiological transition that occurred in the 1940s which was essentially the development and diffusion of curative and preventative medicine.


Strategy There is a long differences strategy (primarily to look at effects of life expectancy on other demographic variables), and an IV strategy (to look at effects of life expectancy on wealth variables).

The long differences strategy compares variables in 1940 (prior to the transition) with 1980 (being prior to HIV). By taking differences they are excluding all the country fixed components of the growth model ( technology, initial human capital etc.), with an additional vector of controls. The dependent variables of interest are GDP, population, births, and age composition of the country. The primary explanatory variable is life expectancy.

The IV strategy uses potential mortality to instrument for life expectancy. They collect data comparable data for 15 of the most important infectious diseases, and create time dummies for when interventions in that disease started to occur at the medicinal frontier. The instrument is constructed as follows:

Mit = ∑[(1-Idt)Mdi40 + IdtMdFt

Mdit denotes mortality in country I from disease d at time t. Idt is a dummy for intervention for disease d at time t (and thus equla to 1 for all dates after the intervention). Mdi40 is the pre-intervention mortality from disease d in country I, and MdFt is the mortality form disease d at the technological frontier after the intervention.

(1-Idt)Mdi40this part of the expression will equal the mortality rate in the country before there is an intervention as I only equals 1 when there has been an intervention, and so when there is no intervention 1-I = 1 and 1*Mdi40 equals the mortality rate pre intervention. Then when there is an intervention I turns on to equal 1, and this part of the expression will equal zero.

IdtMdFt – this part of the expression will equal zero in the pre-intervention period, and the mortality rate at the frontier after the intervention turns the dummy to 1.

Critically the value of the predicted mortality derived is in no way dependent upon how successful a country is at implementing the intervention. The dummy switches to one for all countries at the same time, which strengthens the exclusion restriction, as variations in predicted mortality are in no way correlated with the country specific error. Additionally, the Mdft is set to zero such that predicted mortality from a disease equals zero after the intervention, and so is uncorrelated with the error.

They show that there is a strong first stage relationship between the predicted mortality and life expectancy, and this is the case even excluding the richer countries from the sample

Results Long difference: the 1940-80 estimate of the effect of life expectancy on population is 1.6 which suggests that a 1% increase in life expectancy causes a 1.6% increase in the population. The coefficient on births is 2.35 and the on the % of population under 20 is 0.94. These are robust to using longer time windows (1940-2000). When the GDP measures are the dependent variable, there is a 0.85% increase in GDP associated with 1% increase in life expectancy, but this is insufficient to compensate for the population effects, so GDP per capita and GDP per working age population are both significant and negative. These results are only tentative, as there are endogeneity problems i.e. richer countries may invest more in health.

The first stage yields a coefficient implies that improving predicted mortality will improve life expectancy by 21% and this is significant at the 1% level. This is the case then shorter time windows are used also. The run the 2SLS IV estimate with different dependent variables:

  • Population – countries with a larger decline in predicted mortality experienced larger increases in population. The coefficient on the baseline sample is 1.65 and is significant at the 1% level.
  • Births – coefficients vary between 2.15 and 2.9 which are quite high
  • % Population under 20 – the coefficient is large and significant, but using the longer time window becomes insignificant indicating that the new drugs saved the lives of young people initially, but this effect was averaged out over time.
  • GDP – the coefficient on life expectancy is now only 0.32 and is not significant, although given the size of the standard errors, economically significant effects are impossible to rule out. This effect is smaller when only looking at the subsample of poorer countries.
  • GDP per capita – any increase in GDP was more than offset by the population growth as the coefficient is now negative and significant, and this seems to have affected working age population as much as any.


  • They use some alternative instruments which I will not go into, results are similar.
  • In the IV estimates they control for institutions, initial GDP, continent dummies etc. and there are no significant changes to the estimates.
  • They do a falsification test by showing that lead predicted mortality had no effect on life expectancy, and they further see if predicted mortality in 1940 is a good predictor of life expectancy in the period of 1900-1940 which it is not indicating that preexisting trends are not driving the validity of the instrument.
  • The analysis only takes into account mortality. If the epidimiological transition has also greatly reduced morbidity and this has had an effect on individual productivity, then there may have been economic effects not captured by life expectancy measures.
  • If the disease environment in 1940 in some way was a reflection , or cause of the growth trajectory/path of a country, and that trajectory is persistent over time, then using the 1940-80 window may cause endogeneity issues, as outcomes then, and in 1980 will be correlated with this unobserved trajectory variable and this could be biasing results.
  • The results may not be applicable to the world today as the transition was a very singular event, and the disease environment today is very different particularly taking into account HIV which tends to affect adults in the prime of their lives, rather than the diseases analyses here whose burden fell predominantly on young people outside the workforce.
Implications Increasing life expectancy increases the population, and birth rates did not decline sufficiently for there to be any meaningful compensation in terms of increased productivity. Overall this has led to a decrease in income per capita. This would seem to suggest that for economic (as opposed to simply welfare) benefits to be felt from improved health outcomes, there needs to be concurrent efforts to improve productivity such that capital can be accumulated at a faster rate than the population growth diminished capital per worker. Naturally none of this means that promoting health outcomes does not have its own intrinsic value.




  1. C.L.
    Posted March 19, 2013 at 3:14 pm | Permalink | Reply

    What does “lead predicted mortality” mean please ?

    • majorgressingham
      Posted March 19, 2013 at 3:43 pm | Permalink | Reply

      Lead predicted mortality means the predicted mortality of some time that is future in terms of the data period examined. If I remember rightly they test that the predicted mortality of the 1940 period had no explanatory power for the data previous to that point. It’s a falsification test.

  2. C.L.
    Posted March 19, 2013 at 4:29 pm | Permalink | Reply

    Thank you for your quick answer ! But I’m wondering what is the difference between “lead predicted mortality” and “lagged predicted mortality”.
    I’m a french student currently working on a translation of this paper and I have some difficulties to understand those specific concepts. Thanks a lot again !

    • majorgressingham
      Posted March 23, 2013 at 12:36 pm | Permalink | Reply

      Hmmm…. Good point. I’m not sure I remember, is was one year ago I wrote this. It’s probably best if you return to the original paper to try to understand. R

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