Economy and elderly population, complementary or contradictory: A cross-continental wavelet coherence and cross-country Granger causality study

Abstract

The aim of this study is to explore the causal relationship between the economy and the elderly population globally as well as continent-wise. This research was designed as a continent-wide study to investigate the differences between several regions simultaneously. The economy was measured by the Gross Domestic Product (GDP) per capita growth rate while the population aged above 65 as a percentage of the total was considered the elderly population. A panel dataset published by the World Bank for a period of six decades from 1961 to 2020 covering 84 countries was used as data for the analysis. Wavelet coherence was the methodology used for the study since it was considered suitable to present causality as well as the causal direction between the two variables for different sections during the six decades. Thereafter, Granger causality was applied for a cross-country analysis to gain further insights on the causality of individual countries over the years. Findings of the study reveal that the causality and its direction have been changing over time for most continents. Negative correlations with the leading variable interchanging with time are evident for the majority of the regions. Nevertheless, results indicate that in a global perspective, elderly population predominantly leads the economic growth with a positive correlation. Research approach allows ascertaining the short-term and medium-term changes that occurred concerning the direction of the relationship throughout the stipulated period of the study, which could not be drawn by any previous study. Even though region-wise literature is available on this topic, global studies for decades have not been conducted yet.

From short to long term: Dynamic analysis of FDI and net export in global regions

“…With the investigation of two time series, Wavelet coherence is ablet to explain the direction from one variable to another. The Wavelet coherence formula [ 63 ], including smoothing factor, s, first variable, y and second variable x, is denoted as follows, …”

“…There are different approaches to testing causality in panel data series. This study used cross-country VAR Granger analysis [ 62 , 63 , 69 , 70 ], to evaluate the causality between FDI and NE for each country undertaken in the study. Initially, the study ran the Augmented Dickey–Fuller test (ADF/dfuller) [ 71 ] and Phillips–Perron test (PP) [ 58 ] to assess the stationary (H 0 : δ = 0) and non-stationary (H A : δ ≠ 0) of the two-time series [ 72 ].…”

“…The equation assumed that β k is the auto regressive coefficients and θ k is the regression coefficients. Those coefficients are constant for k ϵ [1, N ] [ 63 ].…”

“…The Wavelet coherence technique was employed to assess causality and/or correlation in time series data [54]. Initially developed in 1984 [55], the Wavelet coherence approach has been extensively discussed in various studies [35,[56][57][58][59] and has found applications in diverse fields, including medicine [60], finance [61,62], economics [63], and tourism [64].…”

Inhibiting or promoting: Population aging and economic development in China

“…Individuals with strong academic backgrounds or high levels of education who are newly entering old age can positively influence the development of the regional economy [ 28 ] and can partially mitigate the negative effects of the overall elderly population [ 1 ]. In addition to the above factors, Khan Mamun [ 29 ] and Galappaththi Kethaka [ 30 ] have proved that there is a positive correlation between population aging and economic growth. Meanwhile, with the further deepening of research on population aging by scholars around the world, many scholars believe that the "silver hair" economy will help promote economic development [ 31 , 32 ].…”