Phenomenal World

Phenomenal World

January 16th, 2020

Macro Modeling in the Age of Inequality

—   Kathryn Holston


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Macro Modeling in the Age of Inequality

—   Kathryn Holston

 
 

Recent years have seen the revival of academic conversation around rising wealth inequality and its distributional consequences. While applied, microeconomics-oriented fields like public and labor economics have long engaged with inequality, macroeconomics has historically paid less attention to these questions—particularly as they relate to business cycles. Instead, it has focused on the relationships between aggregate macroeconomic outcomes—such as unemployment, income, and consumption—and how they fluctuate during booms and recessions. As a result, research on rising income and wealth inequality in the United States tends to overlook the macroeconomic consequences of these developments, as well as the long-term macroeconomic trends which have contributed to their rise.

In order to assess what rising inequality means for our society, and what policies we should enact to mitigate its effects, we must understand its relationship to the economy as a whole. What macroeconomic forces have contributed to rising inequality, and how might elevated levels of inequality be shaping our economy? We need macroeconomic research to fully understand how income and wealth inequality have evolved in the United States. Particularly, we need a range of macroeconomic models, each of which can capture meaningful differences in household income or wealth but emphasizes different, potentially relevant features of the economy.

Why model?

Macroeconomic models are, by construction, abstractions of the world that distill our complex economy into simple factors. We know from the Great Recession and financial crisis that aspects which are omitted in the name of tractability are often crucial to understanding the economic mechanisms at play. Knowledge of these mechanisms—which tell us how and why economic phenomena are occurring—is essential if we want to use research to inform policy.

The failure of macroeconomists to predict the Great Recession caused many academics, policymakers, and critics to question the discipline’s utility. In a piece titled “How Did Economists Get It So Wrong?”, Paul Krugman outlined one condemnation of the field: economists’ tendency to assume individuals were rational and markets were efficient. An Economist article from the same year asked “What Went Wrong With Economics?”, putting forth another common criticism: financial market frictions were absent from macroeconomic models, so it was impossible for these models to capture problems in the macroeconomy arising from financial markets.

While useful, these criticisms do not justify neglecting what we can learn from the slate of macroeconomic models at our disposal. Rather, they demand a renewed commitment to a robust approach. The newfound awareness of the importance of financial markets sparked the development of macroeconomic models with financial complexities. More broadly, the crisis highlighted the need to study important macroeconomic questions using a slate of models, and this approach must be applied when studying inequality as well. By looking at a range of models that bring different mechanisms and aspects of the economy—such as channels of monetary policy or returns to superstar firms—into focus, economists can vigorously investigate questions surrounding inequality.

To answer these questions in a robust manner, economists need macroeconomic models that can capture general equilibrium effects: the direct and indirect effects of changing a variable on the macroeconomy. As opposed to partial equilibrium analysis, which considers the impact of a change within one market, general equilibrium theory aims to characterize the whole economy by looking at several markets simultaneously and finding the interaction of supply and demand in all of them. Considering general equilibrium effects is key to capturing not only the relationship between income inequality and macroeconomic variables such as consumption in isolation, but also the feedback relationships that arise as the effects of a change in the income distribution propagate through the economy. Using a general equilibrium model, we can investigate, for example, the overall changes in consumption and real interest rates caused by rising inequality.

As economists and policymakers consider solutions to combat rising inequality, it is essential to study the general equilibrium effects of new policies as well. Pilot studies and microeconomic evaluations are key elements of assessing the efficacy of a policy, but large-scale economic policies will change the landscape of our macroeconomy in ways that might be hard to anticipate. General equilibrium models can provide insights here.

Heterogeneity in macro models

A long-standing explanation for the dearth of research examining questions of inequality in general equilibrium models is the commonly-used representative agent assumption, which postulates that all agents in the model (e.g. household or firms) can be represented by one agent (i.e. the representative household or firm) who solves a corresponding optimization problem. This is a useful simplification: researchers only need to model and solve the decision problem for one individual. There are also theoretical justifications for making this assumption; as Krusell and Smith have shown, a benchmark model with multiple agents produces similar results to its representative agent counterpart. Nevertheless, because it so drastically simplifies the economy, the representative agent assumption fails to capture meaningful differences between households which, when compounded, are likely to have significant macroeconomic consequences in a more sophisticated model.

The representative agent assumption is not just unrealistic: it constrains the economic questions we can ask. Income inequality cannot be studied using a model with a representative agent, because it assumes all households are identical—including having identical incomes. The model has no notion of income inequality, meaning that the income distribution does not factor into the macroeconomic outcomes that the model predicts. For this reason and others, researchers have turned to heterogeneous agent models, in which a representative agent is not assumed and there are instead many households that can differ by, for example, income, wealth, asset holdings, age, or education. These models have experienced a resurgence in the past ten years, due in large part to differential experiences of households during the Great Recession and increased academic attention to inequality. Thanks to advances in computing power, researchers are also able to solve much more complex models and work with much larger datasets.

In a fantastic post on the history of heterogeneity in macroeconomic modeling, Beatrice Cherrier explains that it is not only computing power or a lack of suitable models that prevented examination of these questions. She argues that heterogeneous agent models have been a part of the macroeconomic modeling toolbox for the past few decades, but “back [in the 1990s], studying inequality in wealth, income, and wages was not the main motive for developing these models.” The motive of studying inequality is therefore engendering a push to revolutionize existing models.1

Recent research

Recent research demonstrates how sophisticated heterogeneous agent models can be used to tackle macroeconomic questions surrounding inequality. A 2018 paper by SeHyoun Ahn, Greg Kaplan, Benjamin Moll, Thomas Winberry, and Christian Wolf, aptly titled “When Inequality Matters for Macro and Macro Matters for Inequality,” highlights the importance of the two-way interaction between inequality and the macroeconomy. The authors develop a method for solving complex models that can reasonably match the distributions of income and wealth observed in the United States economy, and these models outperform representative agent models in predicting consumption responses to changes in income. Using their new model, Ahn et. al show that inequality can have substantial effects on macroeconomic aggregates.

Kaplan and Violante (2018) examine when and how heterogeneity matters for business cycle analysis specifically. They build a heterogeneous agent New Keynesian model and use it to study the interaction between inequality and business cycles. Because the model has heterogeneous agents, they are able to consider income inequality as well as differences in unemployment risk, credit, liquidity, and other drivers of the Great Recession. Nesting this heterogeneity in a New Keynesian model—which generates effects from aggregate demand and monetary policy—means that they are able to study business cycle forces as well. Using their model, Kaplan and Violante find macroeconomic responses to monetary and fiscal policy shocks that are sizably different from those in a representative agent New Keynesian model. By comparing New Keynesian models that are similar—with the exception of having either representative or heterogeneous agents—they illustrate that the inclusion of heterogeneity matters for the model’s predictions about economic fluctuations.

Kaplan and Violante also emphasize the importance of studying interactions between household heterogeneity and business cycles for understanding the Great Recession: during this period, collapsing home prices led to a fall in household wealth, but the decline in wealth was very different for different households. Changes in household spending in response to the fall in wealth also varied depending on their respective marginal propensities to consume (the fraction of an extra dollar that the household would spend). There were (and still are) relevant secular trends occurring, such as the rising income and wealth inequality and the declining real interest rates. Without including household heterogeneity in a model, macroeconomists are unable to study these dynamics.2

A recent paper by Ludwig Straub provides further support for the importance of studying these dynamics. Straub uses a heterogeneous agent model to show that rising income inequality can account for a significant portion of the decline in real interest rates over the past few decades. Because aggregate consumption and savings depend on the income distribution in the model, it is able to capture the impact of rising income inequality on these variables. Specifically, because richer households save a higher fraction of their income, greater income inequality leads to more aggregate saving, which lowers the real interest rate.

In identifying the causes and consequences of rising inequality, economists would benefit from having a range of models—including ones used in these papers—at their disposal, each of which includes heterogeneity in household income or wealth but is tailored to study particular mechanisms or features of the economy. Policymakers should be informed by research that considers the impacts of a given economic policy in a range of models, under various assumptions. If a given policy has similar general equilibrium effects across the range of models, policymakers can have a higher level of confidence in the likely outcomes. For this to be possible, macroeconomists must develop several models that can incorporate household heterogeneity.

These papers represent an exciting frontier of macroeconomic research, but it is important to note that challenges remain: large-scale heterogeneous agent models are still in the early stages of development and are computationally challenging to solve. Despite offering a more nuanced insight into economic behavior, they remain rough abstractions of the world, in which numerous assumptions and simplifications are made. In some cases, the assumptions are necessary to make the model solvable. But even in the absence of these necessary simplifying assumptions, a model by definition cannot capture every element of the economy: it's necessary to leave things that are less relevant out in order to gain clarity about the aspects that are particularly relevant for the question of interest. As Joan Robinson wrote in 1962: “A model which took account of all the variegation of reality would be of no more use than a map at the scale of one to one.” Models are useful because they exclude some features of the economy, allowing the most pertinent ones to come into focus.

Deciding which aspects of reality are relevant to a given question is a complicated process. By using a variety of macroeconomic models, economists can address the fact that models require trade-offs. But we must also acknowledge that it matters who is making these choices, how they understand the trade-offs between parsimony and accuracy, and how they ultimately decide to resolve these trade-offs. Economics aims to be objective, but subjective elements run throughout the research process, from the selection of the research question and formation of a hypothesis to the modeling choices and assumptions. Beyond diversifying the types of models we use, it is therefore also vital to diversify the economics profession itself.

In order to know where to look for compelling research questions and how to tackle them, researchers must take inequality seriously as a macroeconomic topic. Developing a range of macroeconomic models that incorporate meaningful differences across households is a prerequisite to understanding how rising inequality impacts our economy and what policies we should adopt in response.


  1. As documented by Kaplan and Violante (2018), until recently, the study of income and wealth distributions within macroeconomics was largely separate from analysis of business cycles. Heterogeneous agent models have historically been used primarily to study long-term trends, but not aggregate fluctuations over the business cycle, while business cycle analysis has primarily relied on representative agent models. Early one-asset heterogeneous agent models include Aiyagari (1994) and Krusell and Smith (1998). 

  2. For further explanation on this point, see Kaplan and Violante (2018). 

Title Macro Modeling in the Age of Inequality
Authors Kathryn Holston
Date 2020-01-16
Collection Analysis
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