Examining the Dynamics of Infant Mortality Rates: A Time Series Analysis

Abstract

This paper examines the relationship between infant mortality rates and various independent variables. The Augmented Dickey-Fuller test and unit root tests are employed to assess the stationarity of time series data, ensuring reliable statistical analysis. Stationarity is crucial in time series analysis, as it implies that the statistical properties of a series remain constant over time. The ADF test determines whether a time series exhibits a unit root, indicating non-stationarity. If the test statistic falls below the critical value at a given significance level, the null hypothesis of a unit root is rejected, confirming stationarity. This study utilizes the Autoregressive Distributed Lag (ARDL) model to investigate the relationship between infant mortality rates and key independent variables from 1975 to 2014. Findings reveal significant negative associations between infant mortality and several factors. Increased health expenditure is strongly correlated with lower infant mortality rates, emphasizing the importance of healthcare investment. Additionally, higher female education enrollment contributes to reduced infant mortality, underscoring education’s role in improving maternal and child health. Lower unemployment rates also correspond to decreased infant mortality, highlighting the impact of economic stability. Unexpectedly, inflation exhibits a negative relationship with infant mortality, requiring further investigation. The presence of lady health visitors and rural health centers significantly reduces infant mortality, reinforcing the importance of accessible healthcare services. These results emphasize the multifaceted nature of infant mortality determinants and the need for integrated healthcare, education, and socioeconomic policies to enhance infant health outcomes over time.