Disease and Development: A Reply to Bloom, Canning, and Fink
By Daron Acemoglu and Simon Johnson (both MIT)
Bloom, Canning, and Fink (2014) argue that the results in Acemoglu and Johnson (2006, 2007) are not robust because initial level of life expectancy (in 1940) should be included in our regressions of changes in GDP per capita on changes in life expectancy. We assess their claims controlling for potential lagged effects of initial life expectancy using data from 1900, employing a nonlinear estimator suggested by their framework, and using information from microeconomic estimates on the effects of improving health. There is no evidence for a positive effect of life expectancy on GDP per capita in this important historical episode.
Reviewed by Sebastian Fleitas
“The game of science is, in principle, without end. He who decides one day that scientific statements do not call for any further test, and that they can be regarded as finally verified, retires from the game.”
The Logic of Scientific Discovery, Karl Popper, 1934.
Not a long time ago, on April 25, Bill Gates posted an infographic on his blog revealing which is the world’s deadliest animal. Sharks, bugs, snakes and many very scary animals are not even close. The mosquito has the first place by far. They carry terrible diseases, including malaria, which kills more than 600,000 people every year. This infographic is just a reminder of how important it is to improve health around the world. Better health conditions could make millions of people live longer and better lives. But will these better health conditions (and a longer life expectancy) actually cause economic growth? Cross-country regression studies show a strong correlation between measures of health and both the level of economic development and recent economic growth. But, as we know, correlation does not imply causation.
What Acemoglu and Johnson (AJ hereafter) do in their 2014 paper (NEP-HIS 2014-05-17) is just to play the Game of Science. AJ (2007) argue that life expectancy does not cause economic growth and that previous studies had not established a causal effect of health and disease environments on economic growth. Since countries suffering from short life expectancy are also disadvantaged in other ways that are correlated with their poor health outcomes, previous macro studies may be capturing the negative effects of these other unobservable disadvantages. To address this identification problem, AJ (2007) used an instrument for the life expectancy: medical advances that occur at the health frontier, interacted with variation in the prevalence of diseases across the world, used together to construct a predicted mortality variable. The adoption of new medical practices is clearly endogenous, but the authors argue that the technology at the frontier is potentially exogenous. Since there was variation across countries in the prevalence of different diseases, the timing of new medicine advances has a different effect on the predicted mortality for different countries. In other words, the predicted mortality variable satisfies the requirements of a good instrument: it is correlated with the life expectancy in the country, but it is arguably not correlated with other unobservables that determine growth that may be changing at the same time in a country.
Bloom et al. (2013, hereafter BCF) disagree with AJ’s strategy and conclusions. In their paper, which earlier appeared as an NBER working paper, they argue that the problem with AJ’s instrument is that it assumes the predicted mortality to be exogenous and not affected by contemporaneous income shocks. In other words, it implies that the initial mortality rate in 1940 should be unaffected by income levels in 1940, which is difficult to believe. As BCF explain very clearly, the “natural experiment” constructed by AJ is flawed. The “treatment group” that received large health gains from technological innovations is fundamentally different from the “control group” that received low health gains, since the “treatment group” had lower life expectancy initially. Therefore, if initial conditions are important for subsequent economic growth, the results will be biased if these initial conditions in 1940 are not considered. BCF included the level of life expectancy in their econometric specifications (a “partial adjustment model”) and they concluded that exogenous improvements in health due to technical advances associated with the epidemiological transition appear to have increased income levels.
In their reply to the reply, Acemoglu and Johnson (2014) address by different means the concern raised by BCF about their original work. First, in order to capture the long-run effects of the initial life expectancy, they include the level of life expectancy in 1900 interacted with time dummies in their decadal panel data set (which runs from 1940). Second, they estimate the “partial adjustment model” of BCF via non linear GMM, since the linear estimation of BCF’s specification will lead to a great deal of multicollinearity and the standard errors become very large. Finally, they use microeconomic estimates from another paper to calculate potential macroeconomic effects of current life expectancy on future growth and examine the implications of their baseline results. AJ conclude that all these approaches confirm that their main results are robust. There is no evidence that increases in life expectancy after 1940 had a positive effect on GDP per capita growth.
There are three issues in this Game of Science that I would like to comment on. First, the intent to quantify the contribution of health to economic growth is extremely relevant for both scientific and policy-related motivations. The general conclusion of the debate, at this stage of the game, is that health conditions were not a factor that shaped the differences in GDP per capita during the second half of the 20th century. Even more generally, the evidence casts doubts on the views that health has a first-order impact on economic growth. With this in mind, it is important to recognize the limitations in the study, especially to extract conclusions for today’s effect of health on economic growth. This is recognized by AJ, who warn that international epidemiological transition was a one-time event and that the diseases that take many lives in the poorer parts of the world today are not the same as those 60 years ago. Despite these considerations, it is important to notice that no author in this debate has questioned the crucial role of improving health conditions to save and improve the lives of millions of people.
Second, it is important to highlight that the main contribution of AJ is that they provide a sound way to address the problem of endogeneity in order to answer this important question. It is not the first time that Acemoglu and Johnson find a way to design a natural experiment to address some fundamental development questions by using exogenous variation in a country-level panel data setting. In another famous paper, Acemoglu, Johnson and Robinson (2001, AJR hereafter) address the problem of endogeneity that raises in the study of the linkages between income and institutions with the famous instrument of mortality rates of European settlers in different colonies. In both occasions Acemoglu and co-author(s) show us in practice the nuts and bolts of economists’ empirical work, that is, to address the endogeneity concerns by doing good research designs and by finding exogenous sources of variation.
Finally, I see this debate as a privileged example of Popper´s quote. In this short reply to BCF, AJ (2014) present further tests for their results in AJ (2007), overcoming the important point that BCF raise. This is a fair game; both articles are forthcoming in the Journal of Political Economy and the database and programs for AJ papers can be downloaded from Daron Acemoglu’s webpage at MIT. Even more, this is not the first time these authors play the game in the same way. A similar, and also very illustrative debate about AJR (2001) and David Albouy’s critiques can be found in the American Economic Review, or in the NBER working paper. In both debates, Acemoglu and co-author(s) present more evidence on their results that are robust to additional tests, but in both episodes we gain from the debate. We just need to recall that our knowledge is always limited by the evidence we have at the moment, and that this evidence will change over time. After all, in the Game of Science, just like in another famous game, you do not know how it is going to end, even if you read all the books that have been published on the topic.