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 Capital Gains Taxation, Learning and Bubbles                                                              Download!

Abstract. Why have there been more bubbles since the 1980s? This paper argues that the fall of the Capital Gains Tax (CGT) has been a crucial factor. In a model of learning about prices, I show that lower taxes make prices more responsive to changes in investors’ beliefs, raising the likelihood of self-fulfilling booms and busts. The model can account for several hard-to-explain facts about the US stock market when using the observed path of tax cuts. In particular, it replicates 60% of the increase in excess volatility despite the decline in macroeconomic risk. It also explains 75% of the equity premium. Even with the drop in the safe real interest rate, the model predicts that the rise in volatility would have been largely avoided if tax cuts had not been implemented. Finally, I investigate the normative implications as asset price fluctuations are not fully efficient in the model. Optimal policy prescribes a CGT that counteracts too optimistic/pessimistic beliefs, preventing belief-driven business cycles. It can be implemented through a CGT equal to 100% and a subsidy to physical capital productivity.

Presented at: University of Oxford, Bocconi University, University of Mannheim, UC3M, Universitat de Barcelona, Universitat Autònoma de Barcelona, Barcelona School of Economics, ENTER, Universitat de les Illes Balears, Winter Meeting of the Econometric Society, SAEe2020 and 2022, EconMod2021. 

Work in progress

The Fiscal Channel of Quantitative Easing (with Eddie Gerba and Luis E. Rojas)                                [Draft]

Abstract. Fiscal surpluses/deficits must balance out the losses/gains caused by Quantitative Easing (QE). The general equilibrium effects of QE depend critically on how this fiscal adjustment is made. Following Wallace (1981), it is commonly assumed that only lump-sum taxes are adjusted, making QE irrelevant. We deviate from this premise. When governments also adjust public spending or taxes are distortionary, QE changes the real allocation of resources in the economy. As a result, forward-looking agents adjust their savings-consumption choice, influencing aggregate demand and asset prices. This is the QE’s fiscal channel. We show that adjusting spending is optimal in relevant environments, including Wallace’s one. Finally, we exploit this channel to show that a targeted QE, such as Green Corporate Bond Programs or the Transmission Policy Instrument, implies fiscal redistribution and risk-sharing.

Presented at: Bank of England, T2M Conference London 2022, UAB MacroClub, 53rd  MMF conference, SAEe2022*, 2023 ASSA meeting*.

Dematerialization in part explained by the burst of the housing bubble (with Marina Requena[Draft]

Abstract. We document that a set of countries grew their GDP while decreasing their Material Footprint (MF) over the 2007-2017 decade, breaking the previous trend. This paper analyzes the drivers of this absolute dematerialization. In accounting terms, it simply reflects a reduction in Material Intensity (MI) typically associated with technology. Nonetheless, we show that a wide range of variables related to technology can only explain between 2% and 10% of the MI variation. Alternatively, we hypothesize that the observed dematerialization is in part a cyclical phenomenon resulting from the housing prices bust that depresses construction activity and, under some conditions, hits MF harder than GDP. Indeed, the data analysis reveals that housing prices and construction explain between 19% and 46% of the dematerialization variance. Besides, we show that the absence of the housing boom would have accelerated the dematerialization, although being insufficient to bring MF within their sustainable limits.

Heterogeneous Expectations and the Anatomy of Stock Market Cycles (with Adrian Ifrim)              [Draft]

Abstract. This paper shows that a model of learning about capital gains with heterogeneous expectations can jointly explain several old and new facts about stock prices, portfolio adjustments and survey expectations. Our key innovation is to model the whole distribution of expectations in a way consistent with many survey stylized facts: perpetual disagreement, procyclical expectations/disagreement and forecast error predictability. Using this model we replicate hard-to-reconcile facts regarding market volatility, expected returns, disagreement and trading. A typical boom would follow this sequence: i) an income or sentiment shock make investors more willing to invest in equities, driving up prices ii) the initial price increase make all investors more optimistic, reinforcing the cycle iii) however, certain conservative investors are reluctant to be drawn by this wave of optimism iv) this heterogeneous reaction of expectations raises disagreement and trading. Therefore, disagreement and trading appear as a consequences of a bullish market and not as a driving force.

Solving asset pricing models with learning through the Parameterized Expectations Algorithm