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In fifteen European countries, China, and the US, stocks and business equity as a share of total household assets are represented by an increasing and convex function of income/wealth. A parsimonious model fitted to the data shows why background labor- income risk can explain much of this risk-taking pattern. Uncontrollable labor-income risk stresses middle-income households more because labor income is a larger fraction of their total lifetime resources compared with the rich. In response, middle-income households re-duce (controllable) financial risk. Richer households, having less pressure, can afford more risk-taking. The poor take low risk because they avoid jeopardizing their subsistence consumption.
After the Lehman-Brothers collapse, the stock index has exceeded its pre-Lehman-Brothers peak by 36% in real terms. Seemingly, markets have been demanding more stocks instead of bonds. Yet, instead of observing higher bond rates, paradoxically, bond rates have been persistently negative after the Lehman-Brothers collapse. To explain this paradox, we suggest that, in the post-Lehman-Brothers period, investors changed their perceptions on disasters, thinking that disasters occur once every 30 years on average, instead of disasters occurring once every 60 years. In our asset-pricing calibration exercise, this rise in perceived market fragility alone can explain the drop in both bond rates and price-dividend ratios observed after the Lehman-Brothers collapse, which indicates that markets mostly demanded bonds instead of stocks.
Based on OECD evidence, equity/housing-price busts and credit crunches are followed by substantial increases in public consumption. These increases in unproductive public spending lead to increases in distortionary marginal taxes, a policy in sharp contrast with presumably optimal Keynesian fiscal stimulus after a crisis. Here we claim that this seemingly adverse policy selection is optimal under rational learning about the frequency of rare capital-value busts. Bayesian updating after a bust implies massive belief jumps toward pessimism, with investors and policymakers believing that busts will be arriving more frequently in the future. Lowering taxes would be as if trying to kick a sick horse in order to stand up and run, since pessimistic markets would be unwilling to invest enough under any temporarily generous tax regime.
Modern macroeconomics empirically addresses economy-wide incentives behind economic actions by using insights from the way a single representative household would behave. This analytical approach requires that incentives of the poor and the rich are strictly aligned. In empirical analysis a challenging complication is that consumer and income data are typically available at the household level, and individuals living in multimember households have the potential to share goods within the household. The analytical approach of modern macroeconomics would require that intra-household sharing is also strictly aligned across the rich and the poor. Here we have designed a survey method that allows the testing of this stringent property of intra-household sharing and find that it holds: once expenditures for basic needs are subtracted from disposable household income, household-size economies implied by the remainder household incomes are the same for the rich and the poor.
Most simulated micro-founded macro models use solely consumer-demand aggregates in order to estimate deep economy-wide preference parameters, which are useful for policy evaluation. The underlying demand-aggregation properties that this approach requires, should be easy to empirically disprove: since household-consumption choices differ for households with more members, aggregation can be rejected if appropriate data violate an affine equation regarding how much individuals benefit from within-household sharing of goods. We develop a survey method that tests the validity of this equation, without utility-estimation restrictions via models. Surprisingly, in six countries, this equation is not rejected, lending support to using consumer-demand aggregates.
This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors’ information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the likelihood of rare disasters drop to a much more pessimistic level once a disaster has occurred. Such a shift in beliefs can trigger massive declines in price-dividend ratios. Pessimistic beliefs persist for some time. Thus, belief dynamics are a source of apparent excess volatility relative to a rational expectations benchmark. Due to the low frequency of disasters, even an infinitely-lived investor will remain uncertain about the exact probability. Our analysis is conducted in continuous time and offers closed-form solutions for asset prices. We distinguish between rational and adaptive Bayesian learning. Rational learners account for the possibility of future changes in beliefs in determining their demand for risky assets, while adaptive learners take beliefs as given. Thus, risky assets tend to be lower-valued and price-dividend ratios vary less under adaptive versus rational learning for identical priors. Keywords: beliefs, Bayesian learning, controlled diffusions and jump processes, learning about jumps, adaptive learning, rational learning. JEL classification: D83, G11, C11, D91, E21, D81, C61