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Shocks, frictions, and inequality in US business cycles

How much does inequality matter for the business cycle and vice versa? This column explores the two-way relationship using a heterogeneous agent New Keynesian model estimated on both the macro and micro data. Although adding data on wealth and income inequality may not materially change the estimated shocks driving the US business cycle, the estimated business cycle shocks themselves are useful for explaining the evolution of US wealth and income inequality from the 1950s to today.

Inequality in income and wealth in the US has substantially increased since the 1980s and is frequently the subject of contemporary public debate (e.g. Piketty and Saez 2003, Kopczuk et al. 2010, Saez and Zucman 2016). Potential policy responses crucially depend on our understanding of the underlying drivers of inequality. In a new paper (Bayer et al. 2020), we argue that business cycles (and the policy responses to them) are an important contributor to inequality dynamics in the short and long run. This business cycle perspective develops the study of inequality, which has typically focused on permanent changes such as the rising skill premium or changes in the tax and transfer system (e.g. Kaymak and Poschke 2016, Hubmer et al. 2016).  More concretely, we find that business cycles can account for 50% of the rise in US wealth inequality, and virtually the entire increase in US income inequality, between 1980 and 2015.

Model and estimation

A new generation of monetary business cycle models that features heterogeneous agents and incomplete markets (known as HANK models) allows us to study inequality through the lens of a business cycle model. In particular, we use a technique that has become the standard practice (at least since Smets and Wouters 2007), extending this technique to the analysis of HANK models. We estimate an incomplete markets model by a full-information Bayesian likelihood approach using the state-space representation of the model. We use this approach to answer two questions: 

  • First, do data on inequality change the estimated shocks and frictions driving the US business cycle? 
  • Second, how important are business cycle shocks for the evolution of US inequality?

Specifically, we estimate an extension of the ‘New Keynesian’ incomplete markets model of Bayer et al. (2019). We add features such as capacity utilisation, a frictional labour market with sticky wages, and progressive taxation. These considerations are held alongside the usual plethora of shocks that drive business cycle fluctuations in estimated New Keynesian models: aggregate and investment-specific productivity shocks, wage mark-up shocks, price mark-up shocks, monetary and fiscal policy shocks, risk premium shocks, and, as two additional incomplete-market-specific factors, shocks to the progressivity of taxes and shocks to idiosyncratic productivity risk.

In this model, precautionary motives play an important role in consumption/savings decisions. Since individual income is subject to idiosyncratic risk (which cannot be directly insured) and since borrowing is constrained, households structure their savings decisions and portfolio allocations to optimally self-insure and achieve consumption smoothing. In particular, we assume that households can either hold liquid nominal bonds or invest in illiquid physical capital. Capital is illiquid because its market is segmented, and households participate irregularly and infrequently. This portfolio-choice component, which gives rise to an endogenous liquidity premium, and the presence of occasional ‘hand-to-mouth’ consumers leads the HANK model to have rich distributional dynamics in response to aggregate shocks.  

To infer the importance of inequality for the business cycle, we estimate the HANK model with, and without, data on inequality. We first estimate the model on aggregate data (as in Smets and Wouters 2007), covering the time period from 1954 to 2015. We then re-estimate the model with two additional observables for the shares of wealth and income held by the top 10% of households in each dimension. This is taken from the World Inequality Database (Kopczuk 2015, Alvaredo et al. 2018). 

US business cycles and inequality

Figure 1 Historical decomposition of the log-deviations of the model implied top 10% income share (a) and top 10% wealth share (b) 

(a) Top 10% income share

Note: Shaded areas correspond to NBER-dated recessions.

First, we find that the inclusion of distributional data does not change what we infer about the aggregate shocks and frictions driving the US business cycle. Second, and in line with the first result, business cycle shocks explain a substantial fraction of movements in inequality because they generate very persistent movements in wealth and income inequality (the black lines in Figure 1). These movements are consistent with the U-shaped evolution of US inequality during the period spanning 1954 to 2015. In the HANK model even transitory shocks have very persistent effects on inequality because wealth is a slowly moving variable that accumulates past shocks. This means that business cycle shocks persistently redistribute across households with different portfolios. 

The historical decomposition of US inequality in Table 1 reveals that changing mark-up levels are the main contributors to the rise of wealth and income inequality from the 1980s to the present day. However, fiscal policies also play a role both by providing liquid assets for self-insurance through government deficits and by changing the incentives to self-insure through progressive taxation. Both factors shift liquidity premia and thus affect the savings incentives of the rich and the poor differentially. Quantitatively, we find deficits to be less important than changes in progressive taxation. For consumption and income inequality, fluctuations in income risk play a significant role that extends beyond simply increasing the dispersion of income, once the higher risk is realised. Wealth-poor (and thus poorly insured) households react to an increase in uncertainty by cutting consumption particularly strongly, while well-insured households (which are already consumption-rich) exhibit far less variation in their consumption behaviour. Consequently, these shocks account for 20% of the cyclical variations in consumption inequality. They also account for 20% of aggregate consumption fluctuations during US recessions.

Table 1 Contribution (in percentage points) of the various shocks to the increase in the top 10% share of pre-tax income, top 10% share of wealth, and the Gini coefficient of consumption from 1980 to 2015 based on our historical shock decompositions

The importance of policy rules for US inequality

Given the estimated shocks, we assess the importance of policy rules in shaping inequality over the business cycle. We find that, broadly speaking, output stabilisation is a key factor in terms of reducing fluctuations in inequality levels.

A more ‘hawkish’ monetary policy (i.e. a stronger reaction to inflation) would have increased inequality in the 1970s and today. Both periods, through the lens of our model, are characterised by high mark-ups. This means that hawkish policy responses lead to output losses and increased inequality. We also consider a ‘dovish’ policy where we double the monetary policy response to output fluctuations. This produces (in general) more stable mark-ups and output, at the expense of higher inflation volatility. (For additional research on this result, see Gornemann et al. 2016).

In our framework, fiscal policy affects output and inequality. This result highlights the role of self-insurance and assets of different liquidity, beyond the traditional Keynesian channels (because it has a strong impact on the return differences between asset classes). When the government runs a larger deficit, it provides the economy with a greater supply of liquid savings devices. Households hold these additional assets only when the return difference between them and real illiquid assets falls. 

In line with this result, we also find that the recovery after the Great Recession would have been faster had US fiscal policy allowed for even larger and more persistent deficits (see Figure 2). An important channel for this faster recovery is that such a policy would have depressed the liquidity premium (the return difference between illiquid and liquid assets) by driving up the nominal and real rates of the latter. A by-product of this is that wealth inequality would have increased by less. Poor households hold more of their wealth in liquid form, meaning they would have been affected particularly significantly from such a policy. This would have provided the lower-wealth demographic with greater incentives to accumulate wealth in the first place. 

In terms of the composition of a fiscal stimulus, we find that putting a larger emphasis on tax cuts instead of spending hikes renders fiscal policy more effective at stabilising output and consumption inequality in general (keeping the deficit response of the government to recessions constant). However, this more aggressive tax policy does increase the liquidity premium in downturns. Therefore, it would not have been a viable option during the Great Recession, as it had made the effective lower bound on interest rates more binding. In other words, cutting back taxes and spending in lockstep is only a viable fiscal policy response to a recession if monetary policy has room to accommodate this policy. 

Figure 2 Counterfactual output, liquidity premium and wealth inequality that the model would predict had the government policies been different

Notes: The lines represent the difference (in percentage points) in the evolution compared to feeding the same shocks through the baseline model. The solid line corresponds to persistence deviations of government and the dotted tax response stimulus. Shaded areas correspond to NBER-dated recessions.


How much does inequality matter for the business cycle and vice versa? To shed light on this two-way relationship, this column estimates an unprecedented New Keynesian business cycle model with household heterogeneity and portfolio choice on macro and micro data. We find that household heterogeneity, and the inclusion of micro data in the estimation, do not materially alter the shocks and frictions within US business cycles.

However, we find that business cycles are important to understand the evolution of US inequality. We show that business cycle shocks and policy responses can account for 50% of the increase in US wealth inequality, and virtually the entire increase in income inequality, since the 1980s. The reason behind this is that wealth (inequality) is a slowly moving variable that accumulates past shocks. Our analysis suggests that price mark-ups and the liquidity premium substantially increased over the last two decades. This has driven down output, but has increased income, consumption, and wealth inequality. 

A more expansionary fiscal policy (that would have allowed government debt to increase by substantially more after the Great Recession) would have had a positive impact on interest rates and helped the economy to escape the effective lower bound earlier, boosting the recovery. At the same time, as this evolution of government debt would have eroded the difference between returns on illiquid and liquid assets, such policy would have helped poorer households to accumulate wealth, driving down wealth inequality. 


Alvaredo, F, L Chancel, T Piketty, E Saez and G Zucman (2018), “World inequality database”, available at

Bayer, C, B Born and R Luetticke (2020), “Shocks, Frictions, and Inequality in US Business Cycles”, CEPR Discussion Paper 14364.

Bayer, C, R Luetticke, L Pham‐Dao and V Tjaden (2019), “Precautionary savings, illiquid assets, and the aggregate consequences of shocks to household income risk”, Econometrica 87(1): 255-290.

Gornemann, N, K Kuester and M Nakajima (2016), “Doves for the rich, hawks for the poor. Distributional consequences of monetary policy”, CEPR Discussion Paper 11233.

Hubmer, J, P Krusell and A A Smith Jr. (2019), “Sources of U.S. wealth inequality: Past, present, and future”, Mimeo, University of Pennsylvania.

Kaymak, B and M Poschke (2016), “The evolution of wealth inequality over half a century: The role of taxes, transfers and technology”, Journal of Monetary Economics 77: 1-25.

Kopczuk, W (2015), “What do we know about the evolution of top wealth shares in the United States?”, Journal of Economic Perspectives 29(1): 47-66.

Kopczuk, W, E Saez and J Song (2010), “Earnings inequality and mobility in the united states: evidence from social security data since 1937”, Quarterly Journal of Economics 125(1): 91-128. 

Piketty, T and E Saez (2003), “Income inequality in the United States, 1913–1998", Quarterly Journal of Economics 118(1): 1-41. 

Saez, E and G Zucman (2016), “Wealth inequality in the United States since 1913: Evidence from capitalized income tax data”, Quarterly Journal of Economics 131(2): 519-578.

Smets, F and R Wouters (2007), “Shocks and frictions in US business cycles: A Bayesian DSGE approach”, American Economic Review 97(3): 586-606.

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