Greg Kaplan | Macro Paper Warehouse

Microeconomic Heterogeneity and Macroeconomic Shocks

This essay analyzes the role of household heterogeneity for how the macroeconomy responds to aggregate shocks. After reviewing how quantitative macroeconomics incorporated household heterogeneity and market incompleteness over roughly three decades – work that, the authors argue, has been mostly confined to inequality and redistribution questions rather than business cycles, partly because of computational complexity and partly because of a widespread but “inaccurate” reading of Krusell and Smith’s (1998) “approximate aggregation” result as showing heterogeneous- and representative-agent dynamics are essentially the same – the paper outlines an emerging Heterogeneous Agent New Keynesian (HANK) framework, built on Kaplan, Moll, and Violante (2018), that gives households access to both a low-return liquid asset and a high-return illiquid asset subject to a transaction cost. Simulating a consistently calibrated HANK model against its representative-agent (RANK) counterpart, the authors introduce a three-way taxonomy of “strong,” “weak,” and “non-equivalence” between the two frameworks and show, through a discount-factor (demand) shock, a total-factor-productivity shock, a monetary policy shock, and government spending and transfer shocks, that the degree of equivalence depends entirely on which shock is analyzed: demand shocks are strongly equivalent, TFP shocks produce similar aggregate paths through different mechanisms, and monetary and fiscal shocks generate starkly different aggregate responses because HANK consumption is far more sensitive to current disposable income and far less sensitive to interest-rate changes than RANK consumption. The paper’s second message is that heterogeneous-agent models open up macroeconomic questions that representative-agent models cannot even pose – microfounding a fall in aggregate demand through tighter credit limits or higher idiosyncratic income risk, using the cross-sectional pattern of a shock’s transmission to help identify its source, and studying how aggregate shocks and stabilization policy reshape household inequality – and it closes by naming seven directions (real wage and product-market frictions, labor-market microfoundations, gross and nominal balance sheets, time-varying risk premia, financial intermediaries, non-rational-expectations, and optimal policy) along which the still-“infant” HANK framework needs further development.

Monetary Policy According to HANK

This paper revisits the transmission mechanism from monetary policy to household consumption using a quantitative Heterogeneous Agent New Keynesian (HANK) model built to match the empirical distribution of household income, liquid wealth, and illiquid wealth. On the household side, the model extends the standard Aiyagari-Huggett-Imrohoroglu incomplete-markets framework, following Kaplan and Violante (2014), to let households save in a low-return liquid asset and a high-return illiquid asset subject to a transaction cost – a structure that, unlike one-asset incomplete-markets models, can simultaneously match a high aggregate wealth-to-output ratio and a realistically large marginal propensity to consume out of small windfalls. The paper’s central finding decomposes the aggregate consumption response to an interest rate cut into a “direct effect” (intertemporal substitution, operating even absent any income change) and an “indirect effect” (the general-equilibrium rise in labor demand and income that follows from the direct impulse): in representative-agent New Keynesian (RANK) models, direct effects account for nearly the entire response, but in the calibrated HANK model, indirect effects account for about 80 percent of the response and direct effects for only about 20 percent – a result the authors show is highly robust across specifications. The reversal is driven by the coexistence of poor and wealthy hand-to-mouth households (insensitive to interest rates but highly sensitive to income) and by dampened intertemporal substitution even among non-hand-to-mouth households, due to liquidity-constraint risk and portfolio rebalancing toward illiquid assets. A second major finding is that, because the government is a large issuer of liquid assets, Ricardian equivalence fails in this environment, so the specific fiscal response accompanying a monetary shock (transfers, taxes, spending, or government debt absorbing the change in interest payments) materially changes the overall size and timing of monetary policy’s effect on the economy – a dependence entirely absent from RANK models.

The Marginal Propensity to Consume in Heterogeneous Agent Models

This review conducts a systematic investigation of the size and determinants of the aggregate marginal propensity to consume (MPC) in heterogeneous-agent incomplete-markets models – models whose defining features are uninsurable idiosyncratic income risk, a precautionary saving motive, and an endogenous wealth distribution. Motivated by empirical evidence that the average quarterly MPC out of a $500-$1,000 transitory income change is between 15% and 25%, with substantial dispersion across households, Kaplan and Violante ask what model features and calibration strategies allow this class of models to reproduce that evidence while remaining consistent with the observed household wealth distribution. Their central finding is that there is an unavoidable tension in the canonical one-asset precautionary-saving model: calibrated to match aggregate US wealth, it generates an average quarterly MPC of only 3-5%, an order of magnitude larger than in representative-agent models but still far below the data; calibrations that instead target liquid wealth or the empirical share of hand-to-mouth households can match observed MPCs, but only by ignoring more than 98% of aggregate wealth; and extensions with ex-ante heterogeneity in discount factors, returns, or elasticities of intertemporal substitution, or with behavioral preferences (temptation, present bias), can generate realistic average MPCs while matching aggregate wealth, but only by producing an excessively polarized wealth distribution that understates the wealth of households in the middle of the distribution – the “missing middle” problem – with median wealth 5 to 10 times too small. Two-asset models, which separate a low-return liquid asset from a higher-return illiquid asset subject to adjustment costs, can resolve this tension because they generate “wealthy hand-to-mouth” households (holding illiquid but little liquid wealth) alongside poor hand-to-mouth households, but the authors show this requires a sizable gap between liquid and illiquid returns (about 8 percentage points annually in their baseline calibration), and they discuss extensions – direct utility flows from illiquid assets such as housing, or commitment/temptation motives – that can achieve the same fit with a smaller return gap.

When inequality matters for macro and macro matters for inequality

This paper argues that two standard excuses for relying on representative-agent macro models – that heterogeneous-agent models with aggregate shocks are computationally intractable, and that realistic household heterogeneity does not matter much for aggregate dynamics anyway – are both weaker than commonly believed. To address the first, the authors extend Michael Reiter’s discrete-time linearization approach to continuous time: they solve a model’s stationary equilibrium fully nonlinearly using the finite-difference methods of Achdou et al. (2015), take a first-order Taylor expansion of the full discretized equilibrium system around that steady state using automatic differentiation, and solve the resulting large linear system of stochastic differential equations by standard techniques, exploiting continuous time’s tendency to generate sparse transition matrices; a companion model-free dimensionality-reduction method, adapted from the engineering “model reduction” literature, lets the computer – rather than the researcher – identify the low-dimensional information in the cross-sectional distribution needed to forecast prices accurately, generalizing the “approximate aggregation” logic of Krusell and Smith (1998) beyond cases where a hand-picked set of moments happens to work. On the standard Krusell-Smith (1998) business-cycle model, the method is roughly 1,500 times faster and about three times more accurate (by Den Haan’s 2010 error metric) than the best-performing algorithm in the JEDC comparison project, though its accuracy – being a local, linear approximation – degrades as the size of aggregate shocks grows. To address the second excuse, the authors apply their (open-sourced) toolbox to a two-asset incomplete-markets model, calibrated to match the U.S. joint distribution of income, wealth, and marginal propensities to consume, in which “wealthy hand-to-mouth” households arise endogenously from a costly-to-access illiquid asset. This richer model jointly reproduces two features of aggregate consumption dynamics – sensitivity to predictable income changes and relative smoothness – that have long challenged representative-agent and simple spender-saver benchmarks, and, in an extension with capital-skill complementarity, shows that aggregate productivity shocks can generate substantial, shock-specific swings in income and consumption inequality, providing what the authors call “a striking counterexample to the main result of Krusell and Smith (1998).”