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Identifying covariates of population health using extreme bound analysis

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Abstract

Background

The literature is full of lively discussion on the determinants of population health outcomes. However, different papers focus on small and different sets of variables according to their research agenda. Because many of these variables are measures of different aspects of development and are thus correlated, the results for one variable can be sensitive to the inclusion/exclusion of others.

Method

We tested for the robustness of potential predictors of population health using the extreme bounds analysis. Population health was measured by life expectancy at birth and infant mortality rate.

Results

We found that only about half a dozen variables are robust predictors for life expectancy and infant mortality rate. Among them, adolescent fertility rate, improved water sources, and gender equality are the most robust. All institutional variables and environment variables are systematically non-robust predictors of population health.

Conclusion

The results highlight the importance of robustness tests in identifying predictors or potential determinants of population health, and cast doubts on the findings of previous studies that fail to do so.

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Notes

  1. The application of EBA has long been extended to other areas beyond economic growth. For example, Gassebner et al. [19] apply the technique to study the robust determinants of democracy; Bartley and Cohen [4] use it to examine the robustness of the effect of concealed handgun laws on deterring violent crime and inducing substitution into property crime; and Hartwig and Sturm [26] conduct an EBA to identify robust determinants of health care expenditure growth.

  2. We have also used under-five mortality rate. Because of the high correlation between infant and under-5 mortality rates, their results are very similar.

  3. More formally, the extreme upper bound is defined by the group of Z-variables that produces the maximum value of β plus two standard deviations. Similarly, the extreme lower bound is defined by the group of Z variables that produces the minimum value of β minus two standard deviations.

  4. We would like to thank one referee for pointing this out.

  5. To the best of our knowledge, there is no study examining how sensitive an EBA analysis can be to the sizes of X and Z, and the choice of the base variables. As an alternative to relying on the existing literature, Sturm et al. [69] use the general-to-specific modeling approach to select the base variables.

  6. Levine and Renelt [36], on the contrary, limit the size of Z “up to three variables”. This means that, if X is a set of three variables, a regression can have as few as four regressors (three base variables plus one variable of interest). Given that the number of variables being identified as potential predictors of public health is far larger than four, those simpler specifications are likely to suffer omitted variable bias. As a result, we follow Sala-i-Martin [63] to restrict the number of regressors to seven for each regression.

  7. As it can be seen, the result will be the largely same if we replace condition (a) with CDF(0) ≥0.95 for just the unweighted normal distribution—the robustness criterion used by Sala-i-Martin [63] and Sturm et al. [69].

  8. In fact, it might be surprising that a variable excluded from the 0.9 sample is included in the 0.8 sample. However the inclusion/exclusion of a certain variable depends on which other variables are left in the sample. So, suppose that a variable x has a low correlation (<0.8) with most variables in the dataset, but a correlation greater than 0.9 with just a few variables. If these other few variables are included in the 0.9 sample, but excluded from the 0.8 sample, then x might be excluded from the 0.9 sample and still be included in the 0.8 sample.

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Acknowledgments

We thank the three anonymous referees for their helpful comments. Tang would like to acknowledge the funding support from Australian Research Council (DP0878752).

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Authors and Affiliations

  1. University of Queensland, Brisbane, QLD, Australia

    Kam Ki Tang

  2. University of Western Sydney, Sydney, NSW, Australia

    Sriram Shankar

  3. University of Otago, Dunedin, New Zealand

    Eng Joo Tan

  4. Department of Accounting, Finance, and Economics, Griffith University, 170 Kessels Road, Brisbane, QLD, 4111, Australia

    Fabrizio Carmignani

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  1. Fabrizio Carmignani
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  2. Sriram Shankar
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Correspondence to Fabrizio Carmignani.

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Carmignani, F., Shankar, S., Tan, E.J. et al. Identifying covariates of population health using extreme bound analysis. Eur J Health Econ 15, 515–531 (2014). https://doi.org/10.1007/s10198-013-0492-1

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