Exploring External Conditions and Emergency Savings in OECD Countries

Background. Research has shown that internal capabilities such as financial knowledge and self-efficacy are positively related to the ability to afford an unexpected financial need, also known as emergency savings (Babiarz and Robb, 2014). While some have suggested a relationship between external conditions and financial behaviors (Sherraden, 2013), limited empirical evidence exists. The purpose of this study is to investigate the role that external conditions play in shaping emergency savings across 21 Organization for Economic Cooperation and Development (OECD) countries.

Methods. The study design is cross-sectional and pools micro data from household surveys with aggregated country data. At the individual level, we used the World Bank’s 2014 Findex database that includes survey data on how individuals save, borrow, and manage risks. At the country level, we accessed the Comparative Welfare States (CWS) dataset compiled by Brady, Huber, Stephens (2014). The CWS includes country level data for several OECD countries on income distributions, social expenditures, and demographics. Data across individuals and countries were merged for a final sample of 20,857 individuals across 21 OECD countries. Emergency savings was measured as the ability to pay for an expense in the amount of 1/20 of gross national income per capita in local currency. Responses were coded into dichotomous indicators (1= emergency savings; 0 = no emergency savings). Country-level factors were based on comparative welfare state literature and included per capita gross domestic product (GDP), an index of welfare generosity (Scruggs, 2013), and income inequality measured by the Gini coefficient. Multilevel mixed probit models were estimated to systematically examine the relationship between external conditions and emergency savings after controlling for individual covariates age, gender, education, and household income.

Results. Emergency savings varied across the 21 countries with the lowest rate observed in Portugal (64%) and the highest in Germany (93%). In the random-intercept multilevel models, about 8% of the variation in emergency saving was accounted for by country-level external conditions. Although they exhibited strong bivariate correlations, country level measures per capita GDP and welfare state generosity were not statistically significant in multilevel models. A country’s income inequality as measured by the Gini coefficient had the strongest relationship: a one standard deviation increase in the Gini coefficient was associated with a .027 reduction in the probability of emergency savings (p < .01). 

Conclusion. This first cross-national study of emergency savings generates at least three research and policy implications. First, in documenting that GDP per capita is not a strong correlate of emergency saving, we establish a need for more complex explanations. Second, the generosity of a country’s welfare policies does not explain emergency savings. Third, a country’s income inequality has a strong negative relationship to emergency savings, which adds to the evidence base on the potential consequences of rising inequality. Future research will need to examine the extant to which inequality itself is driving this relationship versus other policy mechanisms correlated with it. Future research might also consider how policy trends over time (as opposed to cross-sectional) relate to emergency savings.