Implications for Regional Science of the “Rebuilding Macroeconomic Theory Project”

Abstract

The inability of macroeconomists to anticipate the Global Financial Crisis or reproduce it in their models has led to an important stock-taking of deficiencies in, and necessary modifications to, theories and models used pervasively by researchers and taught to graduate students. This stock-taking—the so-called “Rebuilding Macroeconomic Theory Project,” organized by David Vines and Samuel Wills—has provided an opportunity for economy-wide modelers (who include regional scientists) to consider whether the theories and models they employ are adequate and appropriate to the tasks to which they put them. In this paper I provide a brief report on the project, retrace the development of macroeconomics, and summarize responses by prominent macroeconomists to a set of questions posed by organizers of the project, while drawing implications of these questions and responses for regional science. I then offer original suggestions from a regional scientist’s perspective on what is missing from the “benchmark” macro-model, how financial frictions can be introduced, how behavioral foundations might be modified, how heterogeneity of agents might be captured, and what new stylized facts need to be explained. I proceed to illustrate how several of the suggested changes can be integrated in economy-wide models by drawing on a study of the impacts of monetary policy on consumption by different income groups in Indonesia. I close the paper by posing a number of “big-picture questions” on the implications of the RMTP for economy-wide modelers and regional scientists to ponder and by offering a brief reflection and aspiration.

Keywords

economic growth and development policy and applications poverty income distribution income inequality social and political issues macroeconomic theory global financial crisis economic and financial models economic analysis methods

Introduction

The 2008 Global Financial Crisis (GFC) has proven to be a source of great embarrassment to macroeconomists, who were neither able to anticipate it nor reproduce it in models that they employ to characterize the workings of modern open-market economies. To macroeconomists David Vines and Samuel Wills this state of affairs suggests a need to change or rebuild macroeconomic theory, which is similar in nature to needs experienced in the 1930s, at the time of the Great Depression, and in the 1970s, when inflationary pressures were uncontained (Vines and Wills 2018). For a special double issue of the journal, Oxford Review of Economic Policy (OREP), Volume 34, Numbers 1 and 2, they requisitioned papers from leading macroeconomists, asking them to respond to a common set of questions.¹

Is the “benchmark” Dynamic Stochastic General Equilibrium (DSGE) model—commonly taught to first-year graduate students in economics and now employed by the research departments of most central banks—fit for purpose?

What additions or modifications to this model are necessary to help us understand growth going forward (or should the model be scrapped and a new model developed)?

What are the important inter-temporal complications?

What are the important intra-temporal complications?

Should the above questions be discussed with big or small models?

How should our models relate to data?

(Vines and Wills also asked how macroeconomic theory should be taught to graduate students.)

Why should these questions and answers they have elicited from leaders in the field of macroeconomics be of interest to applied economists more generally? Many spatial, development, and environmental economists work with economy-wide—regional, multi-regional, national, and multi-national—models (CGE, structural or VAR econometric, and DSGE), for various purposes, that are based on principles of general equilibrium. We need to be sure that they are the right (or appropriate) principles.

Much recent work by applied economists has been informed by post-1970s developments in macroeconomic modeling, including “New Keynesian,” micro-foundationalist or representative-agent, and real-business-cycle formulations. If there are flaws in these modeling approaches, applied economists employing them will be prone to committing the same errors that macroeconomic specialists have committed, which, it is argued, have led to blind spots and wrong turns. Many regional scientists, inter alia, are also as guilty as macroeconomists of imposing on regional data a theoretical model of the workings of an economy whose assumptions are grossly false and dangerously in error. Ph.D. students in regional science, following the examples of their advisors, continue to do so.²

Yet, since the early days of the regional science program at the University of Pennsylvania, such regional scientists as Roger Bolton, Stan Czamanski, Norman Glickman, Walter Isard, Benjamin Stevens, and George Treyz—who all worked with the macroeconomic modeler and econometrician, Lawrence Klein—tried to see the spatial economy realistically and whole: as a system of interdependent systems of production, consumption, and institutionalized fiscal and monetary (plus labor and housing market) policies working together (or not).

In the subsequent sections of this paper I will review key developments in the field of macroeconomics and summaries of answers to Vines’s and Wills’s questions provided by the economists they queried. I will also review some suggested changes in general equilibrium specifications, and, arguing that it is important to implement such changes in economy-wide models at all levels of spatial resolution, I will indicate how financial frictions can be introduced, how behavioral foundations might be modified, how heterogeneity of agents might be captured, and what new stylized facts need to be explained. I then illustrate how several of the suggested changes can be integrated into economy-wide models by drawing on a study of the impacts of monetary policy on consumption by different income groups in Indonesia and pose some big-picture questions for both macroeconomists and regional scientists to consider that would seem to be implied by the Rebuilding Macroeconomic Theory Project (RMTP). I conclude with an expression of hope for continued discussion of issues treated in this paper.

Brief Review of the Development of Macroeconomics³

Macroeconomics emerged as a field of study in the early twentieth Century from the partial-equilibrium analysis of different markets—consumer goods, capital goods, money, and labor—by Alfred Marshall, who considered what changes would be needed in each case to bring the respective market into alignment when out of equilibrium. The inspiration for John Maynard Keynes’s (1936) General Theory came from the recognition that Marshall’s partial-equilibrium analysis wasn’t sufficiently systemic to provide an understanding of the crisis of the Great Depression. So Keynes conceived of the consumption function, the investment multiplier, and the notion of liquidity preference. But to demonstrate what difference these conceptual innovations made, he also needed to introduce a methodological innovation—a general equilibrium framework (later characterized by J. R. Hicks (1937) as the IS-LM model) relating the workings of all markets to each other.⁴

The General Theory, which explained why equilibrium unemployment was possible, with various refinements—including those of A.W. Phillips—informed the golden age of post-WWII macroeconomic policy making. When the Great inflation of the 1970s appeared, the IS-LM (investment-savings, liquidity preference-money supply) fixed-price framework no longer explained events or informed policy. Two approaches to modifying the IS-LM model were taken by macroeconomists: an evolutionary approach, which adapted Keynes’s model, and a revolutionary approach.

The evolutionary approach added a Phillips curve to the basic Keynesian framework, allowed for adaptive expectations, created an explicit nominal anchor, and endogenized the supply side of the model. The revolutionary approach proceeded along two fronts—the “rational expectations” revolution of Lucas and Sargent (1979), which called for model-consistent expectations of agents, and the “Real business Cycle” theory of Kydland and Prescott (1982), which attributed economic cycles to technology shocks. The former approach is deemed to have failed because, inter alia, it made unrealistic assumptions about agents’ capabilities to foresee events (Marcellino and Salmon 2002). The latter approach is considered to have failed because it depended on an inadequate formulation of an expectation-augmented Phillips curve (Vines and Wills 2018). The revolutionary approach attempted to show that Keynesian policies were ineffective and unnecessary and insisted that macroeconomists needed to provide micro-foundations—in the form of an intertemporally optimizing private sector for the inflation they sought to explain. Keynesians’ response to the second concern has led to the development of the New Keynesian benchmark DSGE.⁵

In their review of the literature, Vines and Wills acknowledge that the benchmark model “can be used to describe and understand a number of recent experiences going well beyond the 1960s and 1970s, including the Asian financial crisis (Vines and Wills 2018, 16).” Moreover, it helps us to think about the question of “secular stagnation,” raised recently by Lawrence Summers (Rachel and Summers 2019). “Nevertheless, the model has failed when faced with the global financial crisis (Vines and Wills 2018, 16).” The basic elements of the benchmark model, in difference-equation form, are as given in Table 1, below.

In the model presented in Table 1, C, I, Y, L, K, w, R, and Q denote consumption, investment, output, labor supply, the capital stock, the real wage, the gross real interest rate and Tobin’s Q, E_t is the expectations operator, and A_t denotes exogenous periodic technical progress. Equation (1) is the intertemporal Euler Equation for the representative consumer (a.k.a. the Keynes-Ramsey rule). Equation (2) is the intra-temporal labor-supply equation of the representative consumer (equating the real wage to the disutility of labor). Equation (3) gives the aggregate supply of the representative firm. Equation (4) describes the representative firm’s employment policy—i.e., employ labor up to the point where the real wage offsets the marginal product of labor. Equation (5) indicates that the representative firm will carry out investment up to the point at which the marginal product of capital is equal to the real interest rate, plus an allowance for the depreciation of the capital stock, and adjustments for capital gains and costs of capital adjustment. Equation (6) is a balance equation indicating that capital accumulation must equal investment, less depreciation, plus (quadratic) adjustment costs. Equation (7) determines the value of Tobin’s Q (or the market valuation of installed capacity). And Equation (8) imposes the condition that aggregate demand always equals aggregate supply.

Table 1.

Basic Equations of the “Benchmark” DSGE Model.

$\frac{1}{C_{t}} = β R_{t} E_{t} (\frac{1}{C_{t + 1}})$	(1)
$w_{t} = χ C_{t} L_{t}^{ϕ}$	(2)
$Y_{t} = A_{t} K_{t}^{α} L_{t}^{1 - α}$	(3)
$w_{t} = (1 - α) \frac{Y_{t}}{L_{t}}$	(4)
$R_{t} = E_{t} [\frac{α \frac{Y_{t + 1}}{K_{t + 1}} + 1 - δ + (Q_{t + 1} - 1) + \frac{1}{2 ξ} {(Q_{t + 1} - 1)}^{2}}{Q_{t}}]$	(5)
$I_{t} = K_{t + 1} - (1 - δ) K_{t} + \frac{ξ}{2} \frac{{(K_{t + 1} - K_{t})}^{2}}{K_{t}}$	(6)
$Q_{t} = 1 - ξ + ξ \frac{K_{t + 1}}{K_{t}}$	(7)
$Y_{t} = C_{t} + I_{t}$	(8)

Source: Vines and Wills 2018.

The model is solved for the real interest rate, R, which brings about an equilibration of supply and demand, by making savings equal investment. A solution is obtained by log-linearizing the model around the non-stochastic steady state and employing the eigenvalue-eigenvector decomposition approach of Blanchard and Kahn (1980). The initial value of the capital stock, K, is predetermined. Consumption, C, and Tobin’s Q, Q, are jump variables, and $ξ$ is a stochastic shock.

Summary of Answers to Vines’s and Wills’s Questions

In response to the question of whether or not the benchmark model is “fit for service,” most of the macroeconomists queried answered in the negative. The model is unable to explain crises because of its sparse and unrealistic theoretical underpinnings. It fails to allow for heterogeneous agents and is unable to forecast crises because it imposes the efficient market hypothesis and rational expectations. It fails to allow for frictions in finance, trade, and search behavior, which, it has been suggested, are critical to understanding the GFC and macroeconomic performance since. Moreover, to be useful for purposes of policy analysis, the consumption equation must link liquid, illiquid, and housing assets to consumption.⁶ Macroeconomists queried by Vines and Wills suggest that a new core model should include financial frictions, relax rational expectations, introduce heterogeneous agents, and employ more persuasive micro-foundations.^7
–9

The question of how these suggested changes might be incorporated into a new core model elicited a range of responses from the economists queried. Vines and Wills have themselves suggested revising the model’s three behavioral equations—describing consumption, investment, and price-setting behavior—and introducing a gap between the rate of interest set by monetary policy-makers and the rate affecting consumption and investment decisions (a form of financial friction). With respect to consumption, they suggest recognizing finite horizons and introducing liquidity constraints, overlapping generations (to accommodate bequest motives), and a distribution of consumers, while allowing consumers to hold housing as a major asset class. As regard investment, Vines and Wills suggest replacing the equation determining Tobin’s Q by one that allows for liquidity constraints. And concerning price-setting, they would allow for search and unemployment effects, inertia, and relative adjustment between heterogeneous goods.

Regarding intertemporal complications, Vines and Wills observe that

In reality information is limited, balance sheet constraints exist and there are feedbacks from stocks to flows (including from the stock of money to the flow of expenditures). Agents are subject to habits and norms. As a result there are likely to be multiple equilibria, both real and nominal. (p. 30)

Against this backdrop, they ask what the role of policy should be in guiding an economy toward an equilibrium, what the causal connections are between long-run growth and short-run stabilization, and whether in answering these questions the benchmark model can be modified or abandoned. They submit that macroeconomic theorizing and modeling going forward should reflect explicit answers to these questions.

As for intra-temporal complications, macroeconomists note that while the DSGE model recognizes nominal rigidities, it is unable to capture coordination failures resulting from interactions between heterogeneous agents.

In answering the question of whether such complications should be answered with big or small models—i.e., whether the field should aim for completeness or elegance or adopt a “horses for courses” attitude—Olivier Blanchard (2018) has suggested that there are five possible types of models that meet different purposes: foundational, core, policy, toy, and forecasting. On his view, the benchmark DSGE model is suited only for abstract theoretical discussions; structural-equation models estimated with time series data are needed to support policy analyses.

Macroeconomists responding to the question of how models should relate to data (the calibrationist vs econometrician question), recommend eliminating the commonly employed Hodrick-Prescott (data-smoothing) filter (Hodrick and Prescott 1997) that removes low-frequency data—and in the words of one critic, throws the baby out with the bathwater—and returning to the econometric estimation of structural-equation models to ensure the use of appropriate parameter values.

Some Suggestions on Missing Pieces, Financial Frictions, Micro-foundations, Heterogeneous Agents, and Stylized Facts

Missing Pieces

A quick scan of the benchmark DSGE model sketched above reveals that, interalia, there is no government sector, no endogenously determined technical progress, no exports or imports, and no money or financial sector. (And, as Warwick McKibbin and Andrew Stoeckel (2018) complain, there is only one homogeneous industry and commodity.¹⁰) These are institutions and activities without which we simply do not have a macro-economy—a general equilibrium system of systems. It is evident why macroeconomists employing such a model could not anticipate or reproduce a crisis or policy responses to it with so many missing pieces of a plausible basic economic ontology. Such institutions and activities are just as important in characterizing the workings of regional and interregional economies as they are national and international economies.¹¹ In his classic monograph on regional and interregional social accounting, Stan Czamanski nicely portrayed the basic stocks and flows of the regional economy of Nova Scotia in the graphic of Figure 1, reproduced below.¹² And as Azis (2019) has argued, financial flows are absolutely essential to understanding the GFC and its differential impacts on segments of an economy.¹³ As remarked upon above, it is important that we can reproduce the problems we are trying to solve in our applied economy-wide models in order to support credible policy recommendations.¹⁴

Financial Frictions

As noted above, also missing from the benchmark DSGE model are financial frictions, to which many of the problems encountered in coping with the GFC are attributed. Robert Hall (2013) defines financial friction as “the difference between the return businesses earn from capital—plant and equipment—and the market cost of capital (p. 155),” or the short-term interest rate.¹⁵ A more general definition, that acknowledges that households as well as firms are affected by financial frictions, might be the stickiness involved in making financial transactions of any sort. Such stickiness would encompass the time, effort, money and tax implications of gathering information and making a transaction. The amount of wealth invested (and lost, in times of crisis) in relatively illiquid assets, such as housing, certainly represents financial friction.

Figure 1.

Flows in the Nova Scotia Economy, 1965 (from Czamasnki, 1973, p. 31).

Micro-foundations

While not missing from the benchmark model, its micro-foundations are deemed problematic by most macroeconomists questioned and are viewed by Joseph Stiglitz (2018) as the biggest problem of the model. Stiglitz suggests that the assumption of rational expectations, or perfect foresight, on the part of a representative agent is unrealistic to the point of being harmful and urges that insights of behavioral economics and agent-based modeling be implemented.¹⁶ Wren-Lewis (2018) argues for the need to break free of the “hegemony” of micro-foundations altogether.

There are a number of issues to sort out concerning behavioral foundations. As Kirman (1992) has argued compellingly, there are tremendous challenges in aggregating behavior up to some convincing “representative agent,” regardless of the decision-making approach (or approaches) assumed. Moreover, in macroeconomics (and in other general equilibrium pursuits) we are trying to explain the behavior of aggregates (or aggregate behavior). Add to which a lesson learned from complexity theory: some emergent properties of complex adaptive systems simply lack micro-foundations (Durlauf 2005). International economist and regional scientist, I. J. Azis (pers. comm.), suggests that what is most important in macro-modeling is working out the flow-of-funds accounting system at the aggregate level and only then using micro-behavioral theory to describe decision-making behavior of monetary and fiscal authorities, while using standard specifications of aggregate behavior to characterize investment, consumption, labor supply and demand, trade, government spending, etc.¹⁷

Heterogeneous Agents

Related to the question of micro-foundations is that of heterogeneous agents. Economy-wide crises affect different segments of the population differently and elicit different types of response behavior, conditioned on a sub-population’s wherewithal and disposition to act.¹⁸ Batey and Hewings’s contribution to this special issue illustrates the different magnitudes that income elasticities of expenditure can assume for members of different age cohorts in different regions. These differences in responses and their impacts affect the overall macroeconomic performance and need to be accounted for in some manner.

Stylized Facts of Macro-economies

In 1957 Nicholas Kaldor set out his famous six “stylized facts” or regularities of modern industrial economies that he challenged macroeconomic theorists to explain (Kaldor 1957). More recently, Jones and Romer (2010) have augmented them to widen Kaldor’s focus from capital-output ratios and income shares to “ideas, institutions, population, and human capital.” Still, more recently, Gbohui, Lam, and Lledo (2019) have directed attention to the growing inequality within and between regions—as a critical stylized fact—that pervades most industrialized economies. While economists are becoming increasingly aware of this development, and taking it into account in empirical studies (Proost and Thisse 2019; Gurren et al. 2020), they have not yet demonstrated how it affects macroeconomic performance. Still another under-studied manifestation of spatial heterogeneity with macroeconomic implications is the uneven distribution among regions of the effects of climate change (Hardt and O’Neill 2017). In the spirits of Keynes and Kaldor, students of general equilibrium are called to explain, not just observe, contemporary stylized facts. It is arguable that virtually all such stylized facts—and causal explanations of them—are conspicuous by their absence from the benchmark model now in service. This is nothing short of a professional embarrassment. There is much work to be done by students of general equilibria at many levels of spatial resolution. But, we first need to get the relevant stylized facts into our models and then confront our models and the theoretical hypotheses they embody with empirical data to begin to produce the requisite causal knowledge. And, if we are truly regional economists and regional scientists, we also need to acknowledge and represent in our models how spatial proximity or distance of activities from each other affect systems behavior (See Duranton and Puga 2005; Ioannides 2013).

To begin to trace the impacts of factors affecting inequality, Azis (2019) advises:

If welfare, including inequality and poverty indicators, are of interest, the following steps should be taken. First, split the portfolio inflows into the part that goes to productive activities (e.g. infrastructure [projects financed through government bonding]; Azis 2014)… and those staying within the financial market [to pursue high yields]. Second, break down the income flows into incomes related to financial assets and those coming from factor incomes and transfers. Lastly, we need to incorporate a mechanism explaining how different types of inflows end up in different institutions. (p. 118)

And, to use macroeconomic models to begin to target policy objectives concerning the reduction of poverty and inequality, he continues:

It would be highly relevant for many developing countries to have a core macro model with target goals that include poverty reduction. From the aggregate price level, one can make a specific formulation to extract the price of basic needs, hence the poverty line. Combined with the extracted information on incomes of different households, particularly of the poor households, this would allow us to estimate the poverty rate endogenously. (p. 119)

Integrating Some Suggested Changes

Writing this paper (largely, to honor my dissertation advisor, Stan Czamanski, an intellectually honest scholar) and acknowledging the validity of the criticisms of macroeconomics of the RMTP has induced more than a little soul searching on my part. After all, much of the research in which I have been engaged over the last few decades has been focused on operationalizing and estimating representative agent models of open economies with endogenous growth and in chronicling the successes of spatial CGE models, which have been based on the received theories that have left us in the position we now find ourselves.¹⁹ Nonetheless, I believe that I can illustrate how some of the suggested changes to macroeconomic models—heterogeneous agents, differentiated commodities, liquidity constraints on changes in consumption, and financial frictions of various sorts—can be introduced by drawing on ideas presented in a 2016 conference paper by Gunawan Wicaksono, Clifford Wymer, and myself on the impacts of monetary policy on consumer demand of high- and low-income groups in Indonesia.

In this paper we examined how changes in liquidity affected the abilities of high- and low-income consumers in Indonesia to adjust shares of household expenditures to optimize their utility. We specified a dynamic system of demand equations for each income group on the basis of Cooper and McLaren’s (1992) Modified Price-Independent Generalized Logarithmic (MPIGLOG) demand system and Anderson and Blundell’s (1983) disequilibrium adjustment mechanism. In the model, income groups adjust their shares of aggregate expenditures on food, housing, and other items to partial-equilibrium levels, given commodity prices and the groups’ respective allocations of aggregate expenditures. Adjustments of expenditure shares are taken to be functions of the rate of change in the flow of financial services, which is measured as a Divisia aggregate of liquidity. Changes in the rate of growth of the supply of base money, determined by Bank of Indonesia policy, influence income groups’ commodity expenditure levels, hence, levels of welfare derived from the consumption of commodities, through the Divisia broad-money aggregate to which changes in base money contribute. The indirect effects of changes in the growth rate of base money on rates of change in commodity prices can also be endogenized, as can the effects of housing expenditure stress experienced by income groups on adjustments of expenditure shares, although these details did not feature in our 2016 study. Details are provided in the appendix.²⁰

Big-picture Questions

At this moment of professional self-scrutiny, I would suggest that all of us who engage in general-equilibrium modeling of open economies—regional, inter- or multiregional, national, or international—need to ask ourselves a number of big-picture questions. These questions and some subjective responses to them are listed below.

“How do we view the enterprise of macroeconomics (and allied scholarly pursuits, such as regional science)?”

To the extent it can be said to be an applied science, macroeconomics and the policies it informs can be viewed as a societal “coping mechanism”—we are endeavoring to cope with an economy as a system of (complex adaptive) systems (SOS).²¹ While there are differences between the natural and social sciences, and indeed all branches of science, many if not most sciences confront the challenges of conceiving of, observing, and measuring phenomena, identifying causal mechanisms (that explain phenomena), forecasting the occurrence of phenomena, and controlling (or steering) a system toward a desired state.

“Where is the behavior in a model of a macro-economy SOS, at what level(s) is it aggregated, and how should it be represented?”

As the term would suggest, “macroeconomics” concerns the behavior of aggregate measures of economic activity or measures of aggregate economic behavior.

“How many different types of models do we need to understand and influence macroeconomic phenomena?”

As per the suggestions of Blanchard (2018), cited above, there are at least five different types of macroeconomic models, each of which can serve a different purpose. I believe that it is, for now, an open question as to just how many different types are needed to understand and influence which phenomena.

“What is (are) or should be the purpose(s) of a ‘benchmark,’ ‘core,’ or ‘canonical’ model?”

My reading of the literature in the RMTP discussion suggests that the “so-called” benchmark model (or any analogous model) should be employed for teaching purposes but not for research or policy analysis. As participants in a field of study that strives to be problem driven, interdisciplinary, and policy relevant (Isard 1975), it behooves regional scientists to confront this question openly and come to terms with what open-economy general-equilibrium framing should be taught as a core model to graduate students in the field for pedagogical purposes, with appropriate provisos.²²

How should “more realistic” micro-foundations be represented?

More realistic micro-foundations can be represented, it is suggested, by introducing elements of behavioral economics, searching and active learning, more myopic and rule-of-thumb decision making, differences in availability of information and attitudes to risk, neighborhood or network effects (a la Ioannides), and by increasing the number of heterogeneous agents (as in agent-based modeling). The challenge in doing so is to reconcile concerns about aggregation of micro-phenomena and explaining aggregate measures of diverse behavior.

What recent work in, e.g. consumption behavior, investment, finance, endogenous change in technologies, etc. should be introduced to basic models such that the changes they contribute to a general equilibrium framework make sense and are insightful but still parsimonious?

Answers to this question will have to be evaluated on a case-by-case basis, depending on what aggregate behaviors need to be explained or accounted for in coping with particular issues at hand.

How can we find and sustain a balance between empirically grounded (e.g., structural-equation econometric modeling) and theoretically constrained (e.g., Bayesian calibration) modeling work?

I do not know that it is so much a balance that is to be sought as finding the right tool for a specific job—thought experiment, conceptual demonstration, or back-of-the-envelope extrapolation—and not using the wrong tool for the job. My sense is that the question of maintaining a balance between empirical and theoretical considerations has been less of a problem in regional science than in a-spatial macroeconomics.

How do we teach our working knowledge of macroeconomic theory, models, and methods that is relevant to the study of regional and multiregional economies to future academicians and policy practitioners?

Conducting macroeconomic and general equilibrium analyses in various contexts requires both general and specialized knowledge. It would seem to make sense to teach all students in regional science a history of macroeconomic theory and how the theoretical developments of recent decades have mapped on to real-world phenomena (or not). For theoreticians and applied macroeconomists (including regional scientists) to be trained to be competent investigators, there is no evading the higher-level mathematical economics and econometrics coursework that is propaedeutic to being able to conceive and execute research designs that will contribute to the frontiers of knowledge, advance scholarly conversations, and contribute to well formulated policy interventions. It may be necessary to rethink what a course of doctoral (and post-doctoral) studies in applied general equilibrium analysis entails. Ricardo Reis (2018) believes that graduate education in macroeconomics, generally speaking, suffers badly from a lack of good textbooks.²³

What should be the “stylized facts” of urban and regional economic analyses? Should they be imposed or motivated from behavioral foundations of the appropriate order of aggregation?

This question asks for a dialog involving many voices and I will not presume to do more than pose the question. Other questions that might be discussed by colleagues on panels at upcoming regional science conferences (or digital forums) are as follows.

What roles should general equilibrium models in regional science fulfill—pedagogical, causal-explanatory, forecasting, policy-analytical?

Which “necessary fictions” in our models of spatial economies are useful for making sense of reality and which ones are not (possibly distortionary or obfuscatory)?

How should spatial economics models of a general equilibrium nature be “closed” and calibrated? (Different notions of “equilibrium” may be in play.)

Are there “frictions” in spatial economies other than financial ones—e.g., the overcoming of distance, costs of search, and incomplete information—that need to be accounted for in understanding the interaction of markets for goods (of final demand, intermediate inputs, and capital varieties) and services, fixed assets, money and other financial assets, and institutions?

What problems or crises in real-world spatial economies cannot arise within our models nor be solved within them, hence be explained, forecasted, or examined for policy purposes? What divisions of labor between models are appropriate?

Are we (regional scientists) being sufficiently “realistic” in our analytical work to meet our purposes?

In modeling spatial economies, should we strive to develop benchmark models for teaching or public education purposes, focus more on models to meet situation-specific purposes, or both? How should we allocate our modeling efforts?

My sense is that, while we cannot anticipate just what answers may emerge from joined dialog, regional economic theorizing and modeling (of a general equilibrium nature) going forward should reflect, if not explicit answers to these questions, at least consideration of them.

Concluding Reflection and Aspiration

The Rebuilding Macroeconomic Theory Project has provided a valuable service to the macroeconomics profession, but also the field of regional science, by framing a discussion of deficiencies in widely accepted and employed theories and models. The questions posed by Vines and Wills to their colleagues, and the answers these questions have elicited, are worthy of consideration more broadly. To their list of questions I have added others that I hope fellow regional scientists will consider. Papers in this issue were presented (in early form) at a meeting to honor the late Stan Czamanski held over two days in Tel Aviv and Haifa. In his 1975 presidential address on the evolving epistemology of regional science (Czamanski 1976), Stan surveyed a remarkable range of topics that went beyond “standard practice,” on which he displayed great depth of insight. Were Stan with us today, I believe that he would urge us to ponder the questions raised and considered in this paper and endeavor together to produce honest answers.

Footnotes

Appendix

Acknowledgment

Parts of this paper were presented at a workshop at Tel Aviv and Haifa in April 2019 and a workshop at Cornell University in June of 2019.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kieran P. Donaghy

Notes

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Implications for Regional Science of the “Rebuilding Macroeconomic Theory Project”

Abstract

Keywords

Introduction

Brief Review of the Development of Macroeconomics 3

Summary of Answers to Vines’s and Wills’s Questions

Some Suggestions on Missing Pieces, Financial Frictions, Micro-foundations, Heterogeneous Agents, and Stylized Facts

Missing Pieces

Financial Frictions

Micro-foundations

Heterogeneous Agents

Stylized Facts of Macro-economies

Integrating Some Suggested Changes

Big-picture Questions

Concluding Reflection and Aspiration

Footnotes

Appendix

Acknowledgment

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

References

Brief Review of the Development of Macroeconomics³