Time to tighten the belts? Exploring the relationship between savings and obesity

<div><p>Background</p><p>Literature suggests that the higher the rate of time preference people have, the less likely they are to save for the future. Likewise, it has been hypothesised that rising rates of being overweight/obesity are associated with an increase in peoples’ marginal rate of time preference.</p><p>Aim</p><p>To investigate the relationship between being overweight/ obese and the rate of time preference in an older English population, using savings as a proxy for time preference.</p><p>Methods</p><p>Three different econometric methods—Random-effects Probit Estimation, Fixed-effects Estimation, and Generalised Method of Moments Estimation—were used to explore the link between being overweight/ obese and rate of time preference in the English Longitudinal Study of Ageing dataset. Six waves of panel data spanning eleven years provided the data to test whether savings variables are related to being overweight/ obese.</p><p>Results</p><p>The decision to save was shown to hold a statistically significant negative relationship with body mass index but only in the Generalised Method of Moments model. Placing savings in safe, low risk investments was significantly related to a lower probability of being obese but only in the random-effects Probit model. The proportion that people saved relative to their income was not found to be significantly associated with the probability of being overweight/ obese in any of the models.</p><p>Conclusion</p><p>There is an unclear relationship between saving behaviour and being overweight/ obese in an older English population. A financial variable such as savings is a potentially appropriate but imperfect proxy for the rate of time preference of the population. Further research is required to clarify the relationship in order to help develop strategies for obesity prevention. The inconsistency in the results between methods highlights the importance of using a wide range of alternative techniques before implementing important policy decisions.</p></div

Random-effects Probit model for the probability of being overweight—Split sample by age.

<p>Random-effects Probit model for the probability of being overweight—Split sample by age.</p

GMM models.

<p>GMM models.</p

Summary statistics for those not reporting BMI value.

<p>Summary statistics for those not reporting BMI value.</p

Further regression analysis: Safe savings ratio (Random-effects Probit model for the probability of being obese).

<p>Further regression analysis: Safe savings ratio (Random-effects Probit model for the probability of being obese).</p

Two sample T tests.

<p>Two sample T tests.</p

Random-effects Probit model for the probability of being obese.

<p>Random-effects Probit model for the probability of being obese.</p

Variables for regression analysis.

<p>Variables for regression analysis.</p

Random-effects Probit model for the probability of being overweight.

<p>Random-effects Probit model for the probability of being overweight.</p

Additional file 1 of All-cause, premature, and cardiovascular death attributable to socioeconomic and ethnic disparities among New Zealanders with type 1 diabetes 1994–2019: a multi-linked population-based cohort study

Additional file 1: Supplemental Table 1. Technical notes for slope index of inequality and relative index of inequality. Supplemental Table 2. Comparison of Demographic Characteristics Between DCSS Type 1 Diabetes Population and the National Registry of Type 1 Diabetes. Supplemental table 3. Excess mortality for ethnicity and socioeconomic inequality by overall, sex, enrol age, and clinical measurements. Supplemental table 4. Adjusted Mortality rates ratio and excess mortality for ethnicity and socioeconomic inequality. Supplemental Table 5. Population attributable fractions of mortality restriction by socioeconomic deprivation and ethnicity. Supplemental Table 6. Mortality rates ratio (MRR) of clinical events among people with type 1 diabetes in DCSS between 1994-201