Carannante, Maria (2016) Effects of Uncertainty on Household Saving Rate. [Tesi di dottorato]

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Abstract

This PhD thesis attempts to investigate the role of economic uncertainty in driving the behaviour of household savings for six European countries. Focusing on three main sources of economic uncertainty Unemployment Risk, Fiscal Policy Uncertainty and Financial Crisis-Investment risk, I construct a Structural Vector Autoregressive (SVAR) model comprising of the Household Saving Rate, main variable of interest; the unemployment rate, to proxy labour income uncertainty and the risk of an income loss; the volatility of financial stock prices per each country, to detect for the presence of financial uncertainty/crisis; a policy uncertainty indicator, using alternatively the Policy Uncertainty Index devised by Baker, Bloom, and Davis (2012), the Debt to GDP ratio or the Government Surplus/Deficit to GDP ratio. A comparison among country-specific cumulative impulse response functions suggests that: 1. Household saving rate’s response to a change in investment risk is ambiguous, due to two counterbalancing effects: higher risk increases the volatility of future consumption and thus stimulates the accumulation of savings, while a more uncertain rate of return reduces the attractiveness of saving since it increases the risk of capital losses. 2. A labour uncertainty shock is detrimental or a booster for saving depending on whether the downward pressures on saving rate due to lower saving from unemployed people, prevails or not over the higher households propensity to save for precautionary reasons. 3. Fiscal policy instruments and related uncertainty influence the savings pattern of the private sector: private saving falls when governments reduce deficits (or the debt level) or run large budget surpluses and vice versa, as suggested by the Ricardian paradigm. I then propose another possible approach to the analysis, a Bayesian estimation of the reduced form VARs for the panel of European countries, as a Hierarchical Linear Model, with the future aim of improving estimation results.

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