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Modelling Long-Range Dependence and Non-linearity in the Infant Mortality Rates of African Countries

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Abstract

Infant mortality rates in 34 Sub-Saharan African countries (1960–2016), obtained from the Federal Reserve Bank of St. Louis database, were examined in this paper by focusing on the degree of persistence and non-linearities in the growth rate series. Persistence deals with the degree of association between the observations. Non-linearity occurs when departing from the linear assumption as in a time trend. These two issues are relevant in this context because they are intimately related. Based on the high degree of persistence observed in the series examined, instead of investigating structural breaks, which produce abrupt changes in the data, a non-linear approach was used based on Chebyshev polynomials in time, producing smooth rather than abrupt changes. This approach has never been examined in a unified framework in the treatment of infant mortality rates. The results indicate that half of the countries examined display non-linearities and the orders of integration of the series are extremely large in all cases, being around two in the majority of them. Looking at the growth rate series, significant negative trends were observed for: Chad, Equatorial Guinea and Mozambique. Evidence of mean reversion and thus transitory shocks, were observed for Lesotho, Rwanda, Botswana and Mozambique. Time dynamics of the series were expected to persist in order to ascertain the decline in mortality rates. Therefore, serious government interventions are required in managing infant health in these countries.

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Notes

  1. Vogelsang (1998) constructed a t-statistic based on Equation (3) in the presence of serial correlation.

  2. Allowing for higher orders (m = 4) produced an insignificant θ4 coefficient in all cases.

  3. Though the samples differ regarding the starting period, the analysis was also conducted with all series starting in 1975 (except Angola, Equatorial Guinea and Somalia) and the results were qualitatively very similar to those reported herein.

  4. Since the growth rate series of IMRs was used in the linear trend model specification of Robinson (1994), the slope of the trend line was expected to be statistically insignificant, indicating constancy in the rate of decline of IMRs over the years. This represents progress in the the health management of IMRs. Alternatively, a significant positive slope in the IMR growth rate implies a slower initial mortality decline from the beginning of the sample, resulting in a faster decline towards the end of the sample. Analytically, this means that the health management strategy suddenly improved beyond expectation. This is difficult to achieve by health management since IMRs change more slowly as they decline. When the linear trend slope is significantly negative, it implies retardation in the decline. Such IMRs series may stop declining after a few years which signals danger. Details of the model results indicating negative trends in those three countries (Chad, Equatorial Guinea and Mozambique) are available on request.

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Comments from the Editor, Board of Editors and various anonymous reviewers are gratefully acknowledged.

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Correspondence to OlaOluwa Simon Yaya.

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Yaya, O.S., Gil-Alana, L.A. Modelling Long-Range Dependence and Non-linearity in the Infant Mortality Rates of African Countries. Int Adv Econ Res 26, 303–315 (2020). https://doi.org/10.1007/s11294-020-09796-y

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