• Optimal Benefit: Talking to Heather Boushey

    How might cleverly crafted research experiments help us devise more egalitarian economic policy? How might increasingly rigorous empirical data help persuade a broad range of Americans that more equal economic distribution benefits us all? When I want to ask such questions, I pose them to Heather Boushey. This present conversation focuses on Boushey’s book Unbound: How Economic Inequality Constricts Our Economy and What We Can Do About It. Boushey is the president and CEO of the Washington Center for Equitable Growth. She is also the author of Finding Time: The Economics of Work-Life Conflict, and the co-editor of After Piketty: The Agenda for Economics and Inequality. Politico has twice named Boushey one of the top 50 “thinkers, doers and visionaries transforming American politics.” She writes regularly for venues including The New York Times, The Atlantic, and Democracy Journal, and makes frequent television appearances on Bloomberg, MSNBC, CNBC, and PBS. Boushey previously served as chief economist for Hillary Clinton’s 2016 presidential transition team, and as an economist for the Center for American Progress, the Joint Economic Committee of the US Congress, the Center for Economic and Policy Research, and the Economic Policy Institute.


    ANDY FITCH: Your book opens by introducing “a new generation of economists” prioritizing not just growth, but more equitable and sustainable growth. So first, could you sketch a preceding generation’s rationale for assuming that some basic trade-off does exist between pursuing optimized efficient economic growth and fair / equitable distribution? And then how might today’s emerging consensus both retain and reject which aspects of the aggregating JFK-era assumption that “a rising tide lifts all boats”?

    HEATHER BOUSHEY: Well, one thing that makes economics so interesting to so many of us is this set of models presenting clear answers to really big and complex questions about production, distribution, and the market transactions that can meet people’s needs and desires. And in the early-20th century, when economics solidified its preeminent place among academic fields for the policymaking community, models often showed that if you can make a series of assumptions about how markets work, and if you then can leave markets to their own devices, then you’ll get optimal outcomes. The research often confirmed the efficacy of these models.

    Every intro-level economics student encounters these basic assumptions about fair, efficient, optimal competition — involving an infinite number of infinitesimally small firms, with no one able to fix prices or have decision-making power over anyone else (and based on a whole series of related assumptions). You can apply these assumptions both to product markets and to labor markets, right? And early researchers could find situations in which these theoretical ideas seemed to play out in practice. If you go to the local farmers’ market with two stands selling strawberries, and if the quality looks about the same, you’ll select the stand with the lower price. And if the price starts going up more generally on strawberries, then demand will start going down.

    So 20th-century economists in the United States and among its Cold War allies began to build consensus around the argument that once you start interfering with this market you create inefficiencies. Arthur Okun, for example, a member of Lyndon Johnson’s Council of Economic Advisers, and the author of Equality and Efficiency: The Big Tradeoff, made the argument that even when policymakers intervene to try to help people, they often end up creating new costs and inefficiencies. The policymaker, in this model, persistently needs to weigh the fact that the market delivers optimal outcomes — meaning that when you go in and tax people in order to redistribute income, you create impediments and distortions.

    As economists gradually put together more sophisticated data, they started to provide more precise measurements and more precise claims about how to optimize growth. This new data and evidence happened to come about at a time when we had a much more equal society. So this evidence seemed to indicate that aggregate national income and aggregate gross domestic product could tell us something really true and valuable about our economy and its presumed impact on everyday Americans. Again, economic research findings seemed to back up these basic claims through the 1970s.

    Then for what most distinctly characterizes the generation of economists that Unbound tracks, could you sketch the role of contemporary data-driven approaches to resolving longstanding debates, reopening long-held assumptions, and / or pinpointing new salient questions to ask? Could you describe how this “data revolution” actually has played out?

    I’ll start with my first labor-economics class, in grad school in the early 1990s. My professor, David Gordon, sounded so excited talking about new research by these two young hotshot economists named David Card and Alan Krueger. Their research focused on an important policy topic, the minimum wage — but even more significantly for the field, Card and Krueger had marshaled new kinds of evidence economists hadn’t traditionally used. They did a survey of fast-food restaurants after New Jersey raised its minimum wage and neighboring states did not, to offer a fresh perspective on a longstanding debate in labor economics: whether raising the minimum wage leads to a decline in employment. They brought to the fore this new method of tracking the states comparatively, and following this natural experiment. Today, going out into the field to study this kind of real-life experiment might sound like an obvious approach to economic research. But at the time it felt revolutionary. And lo and behold, Card and Krueger found that the predicted effect did not occur. Raising the minimum wage did not bring about this commensurate decline in employment.

    Even as economists still debate this policy, Card and Krueger’s research methods were the cutting edge of dramatic changes in economics. In the later-20th century, the US government started conducting surveys of income and employment and hourly earnings and wages, and only in the 1970s did economists really start to have access to this data and start using computers for research purposes. So when Card and Krueger came along with this natural experiment, most of their colleagues still considered those large government surveys a relatively fresh set of data. But Card and Krueger said: “Hey, there’s also all this other data we could access to study natural experiments.”

    In terms of the combined application of copious empirical data and intricately crafted research experiment, could we consider Janet Currie’s work arguing that preceding equality-minded economists had overemphasized later-stage education, whereas a proactive push for prenatal care, paid family leave, pro-family workplace regulations, and early-childhood education would provide a much more dynamic return on collective social investments? How does a “credibility revolution” here happen — perhaps particularly in terms of Currie figuring out how to demonstrate causality, and amid this particularly vulnerable group of research subjects?

    Janet has done so many fascinating papers, but one example is her study of what it means to have one’s skills rewarded in the labor market. She wanted to examine how human capital (the skills and training an individual accumulates) gets developed by a person and then utilized in the economy. Janet started to get especially interested in research showing that children’s birthweights actually correlate to lifelong consequences. For children born at a low birthweight, you can trace health outcomes across the course of their lives, compared to similar children born at an average birthweight.

    So Janet wondered how someone’s condition as an infant might likewise shape their potential for human-capital development. She and a colleague, Hannes Schwandt, did this really interesting study considering both low birthweight and the effects of pollution (an indicator of inequality). They researched people who got exposed to pollution in utero, and found them much more likely to grow up in low-income neighborhoods, near highways and the like. They used the natural experiment of the 9-11 terrorist attack in New York City, which had all of this pollution on September 12th that hadn’t been there on September 10th. They compared children from the same parents and said: “Okay, were the infants born shortly after 9-11 pollution likely to have a lower birthweight?” Their results confirmed this hypothesis.

    They then combined these results with Janet’s preceding research showing that early-childhood and even prenatal conditions can have lifelong consequences not only for health but also for employment and earnings. So again you see economics research that takes a real-life natural experiment that happens to some groups, and compares the results to what happens with otherwise identical control groups, and finds these complex implications for how something difficult to measure (here human capital) develops.

    Alongside these innovative research methods, your book tracks an emergent argumentative consensus confounding, say, reductive mid-20th-century takes on Adam Smith’s invisible-hand metaphor (through which market competition steers private, self-interested pursuits towards a vectored yet broadly positive public outcome, bringing about a flexible and dynamic internal order for efficient market provisioning). Here could we start to outline 21st-century accounts of how, in fact, inequality thwarts possibilities for such idealized market processes to take place — by transforming economic clout into social and political power, so that simply “getting out of the way” and letting the market function “naturally” would mean abetting preexisting disparities as they further distort market operations?

    That gets to the real challenges we face in trying to measure and understand the effects of concentrated inequality. We might intuitively understand that if some people have resources and others don’t, then those without resources will also lack opportunity. And Americans find it relatively easy to have this conversation. We can say: “Okay, we need to open up opportunity for all.” Though then we of course can get stuck on questions of what it really would mean to have equal opportunity, how intergenerational differences in wealth and social capital might get in the way, how much a student from a non-wealthy family receiving a loan or grant for college tuition can offset these dynamics and create a more level playing field.

    One of the main things I’ve learned doing research for this book and running the Washington Center for Equitable Growth for the past six years involves not just recognizing the extent to which wealth has congealed among a smaller and smaller number of families (with a vast disproportion of white families), but how that agglomeration of financial resources has translated into social and political power. Economists think about taxes and revenue. But we need to think much more about intersections between society making certain tax decisions, and which specific policymakers make those particular revenue decisions, and why they make them. And for all of these questions, we really do need to bring in other disciplines. This book cites, for example, some striking research on very wealthy Chicago residents. The researchers interviewed Chicago elites in the top one percent of income earners, and found that close to half of them have personally connected with their US senators. And of course the vast majority of us are like: “Talking to our senator?” Most Americans, especially in big states such as California or New York, never even share the same physical space as their senators.

    And Illinois itself is no small state.

    Right! And yet this very small group of individuals has gained access to power in ways that really help set the agenda. I mean, look at something like the lobbying around the Tax Cuts and Jobs Act passed in December 2017. We saw just enormous spending on that PR campaign by corporate interests, by wealthy elites, essentially for a massive tax cut for themselves that will lead to the long-term under-funding of any initiatives we could ever hope to help equalize opportunity — such as investments in universal child care or in college education or job training. When you look at that full cycle of where potential tax dollars go, and who shapes those decisions, you see how concentrated wealth can have a subversive effect on our whole economy. Beyond basic questions of fairness (exemplified, over the past few months, by news stories of wealthy parents literally buying their children’s admission into college, undermining the meritocracy that we supposedly live in), you see our political process failing more broadly to provide sufficient resources to build the human capital we need for future economic growth.

    Still on these complexities of trying to assess intergenerational transfers of wealth, earning potential, and corresponding political clout, could you also bring in how Raj Chetty’s “applied microeconomic” approach might help to clarify precisely which particular interventions will have the most constructive impact on which specific people — for instance through Chetty’s data-driven (yet nonetheless quite personalized) findings on “environmental exposure to innovation”?

    Sure. My colleagues actually laugh about my personal-favorite example of Chetty’s work, because economists have some ambivalences about the societal value of patents. But Chetty’s research with Alex Bell and a whole host of colleagues shows how compelling the study of patent holders can be, and how relevant to so many of the questions we’ve already discussed. Here Chetty and his colleagues gathered data on certain children, and on whether or not these individuals applied for and received patents as adults, and their income as adults. Chetty’s team matched that data to these individuals’ third-grade standardized test scores (specifically math scores), and to their families’ income at that time. So first, it’s just impressive that these economists could gather and synthesize all of this wide-ranging data, and could show empirically that those kids who scored well on these third-grade math tests were more likely to get a patent when they grew up.

    But then Chetty’s team also could ask: “Okay, so among those kids who scored highest in third grade, what can we say about family income?” And they found the children in the highest income group four times as likely as other children with the same test scores to get a patent. So here again, Chetty’s team figured out how to create a kind of natural experiment, this time with long-term retrospective data. They also figured out a clever way to track how exposure to innovators often makes the difference for those smart third graders who themselves end up innovators as adults. Raj and I discussed this research when it came out, and he emphasized building evidence around the value of mentorship. He stressed the importance of children seeing and connecting with scientists and innovators as really important role models.

    Related research, by Claudia Goldin at Harvard and Tatyana Avilova at Columbia, also hit really, really close to home for me. They looked at gender equity in the field of economics. The economics profession does not have many women — with women more likely to go into math or other STEM fields. So Claudia and Tatyana wanted to better understand why more women don’t go into economics. They started this program trying to get more women to major in economics as undergrads. They then compared what happened in schools that did and did not have these programs. And they found, as with Chetty’s work, that what makes the biggest difference as to whether a young woman might choose economics as an undergraduate major and beyond is whether she’s exposed to women economists. So even just having an 18-year-old girl meet a woman like myself with an economics PhD can still have this strong impact much further down the pipeline. And again, figuring out how to track these complex institutional and social and psychological phenomena takes us to the cutting edge of economics today.

    So these studies by Card and Krueger, Currie, Chetty, Goldin and Avilova, might point to a revolution in research methodologies and foundational disciplinary assumptions — but, as you’ve also hinted, they actually sound like just “common sense” to me. Does this impression simply suggest that my partisan bubble overlaps with yours (obviously with me at the less-sophisticated side of the conversational spectrum)? Or could you unravel a bit the puzzling entwinement of theoretical abstraction / reductionism, of apparent indifference to rigorous empiricism, of apparent intellectual / professional inertia regarding inherited truisms [Laughter], that would have allowed a broader economics field to fend off such insights for so long?

    I’m laughing because it’s a harsh question, but somebody actually asked me that exact same question on Twitter a few months ago. Here I would start from the very real fact that we just didn’t have this data, or have adequate access to it, even just a decade ago. I mean, heck: I can probably do things on my phone today that I couldn’t even do at my school’s computer center during grad school.

    Yeah, and we all have better ideas about how to use data today than we had a few years ago.

    Exactly. And alongside this new access and new approach to data, I would point again to the importance of the historical context in which we forged many of our 20th-century ideas about economics. We developed the national income and product accounts (NIPA for short), from which we still get our GDP numbers, in the 1930s. We’ve maintained the same basic approach here since the 1940s. Similarly, we implemented the income tax in the 1910s, gradually giving us our first glimpses into more detailed data about the US economy, particularly during a mid-century period of historically low inequality and historically strong growth. None of that had anything to do with economists deliberately cherry-picking the data. All of that (including, of course, the strong growth path for ourselves and eventually our Cold War allies following the destruction of World War Two) made it possible to build consensus around a relatively benign conception of aggregate growth, with such growth even seeming to make inequality itself just magically decline.

    From the perspective of seeking to build social and political consensus, if markets truly can deliver these optimal outcomes, if we really can just let the market work its magic and don’t need to engage in tough decisions about distribution, then it seems all the more fair and equitable to just prioritize and support those meritocratic outcomes, right? I mean, of course, when you look back at it now, it all sounds utopian — this whole way of getting around political conflict and the messiness of debate and of power. Today we might see better why, when we dismantle institutions constraining the market, the market itself stops working right. But in a moment of relative income equality, it maybe wasn’t so ridiculous to conclude: “Let’s let the market do its work.”

    Still, why did it take so long for us to see that many of these assumptions no longer applied? Inequality started rising in the 1980s. We started to see a disconnect between productivity growth and what workers’ families received, a gap which wouldn’t make sense in a perfectly competitive market. And here I’d point back to David Card and Alan Krueger’s work. When Card and Krueger re-released their book Myth and Measurement: The New Economics of the Minimum Wage several years ago, they came to the Washington Center for Equitable Growth to discuss it, and David described the harsh academic response that their research received in the 1990s. The famous, most telling criticism, from University of Chicago economist James Heckman, basically argued: “This empirical evidence doesn’t work, because it doesn’t fit with our theory.” I quote Emmanuel Saez in this book making the seemingly more reasonable claim that when the theory doesn’t fit the evidence, you have to change the theory. But we should still think of this as a methodological shift — and an exciting shift away from certain 20th-century paradigms.

    So how else has this gathering of empirical evidence strengthened arguments for thinking of high inequality less as a necessary (perhaps unfortunate) consequence of economic growth, and more as a corrosive force obstructing and subverting and distorting economic growth — increasing both the frequency and the severity (particularly for lower income-earners) of economic downturns?

    Amazingly, we still do get confronted all the time with the argument that we just need to lower taxes to free up capital, and make sure this money gets used for productive investment (already a big assumption), and everything else will take care of itself. I mean, I just testified before the House Budget Committee, and we had that same conversation, just as we did for the 2017 tax cut.

    Wealthy investors supposedly will see a great potential to bring out new goods and services, which will spur economic growth. But in a highly unequal society, with demand constrained especially further down the income ladder, this freed-up money might instead just sit in the hands of the few, who don’t see much incentive to invest, given the limited potential for sales. In our economy, everybody is attached in complex ways to all of these relationships of buying and selling goods and services and time and labor. When we don’t have enough people pursuing consumer demands, every part of this economy eventually gets strained.

    If you decide to make cupcakes and go out on the corner and sell them, and if nobody wants to buy cupcakes with gluten anymore, then you’re stuck, right? Economies cannot just work from one side of this equation. So if you build an economy that just works for rich people, and that doesn’t broaden the base for demand, can you then expect growth and stability? Does anybody across the income spectrum get optimal benefit from that kind of arrangement? Here I find the accumulation of data especially powerful, since the rich of course might go the longest without feeling such an economy fail them. But statistically now, with data from all over the world, we do see that high inequality both slows down an economy’s long-term growth and makes that economy less stable, more prone to financial crises, more vulnerable to drops in consumer demand. And here I want to make a clear distinction from the moral argument that admission to the best schools, and employment in the best jobs, should go to those who work hard and show the most merit — regardless of how much their parents make.

    At the same time, those meritocratic norms certainly did play a crucial role in providing the United States with one of the richest and most productive economies the world has ever seen. We developed such a rich economy because we did allow talent to rise to the top. We did value the contributions of workers up and down the income ladder. We did give people all along the production line a sense of social value and a wage commensurate with their contribution. Of course that wasn’t true in all cases — particularly for many workers of color and for many women. We can and should fix those omissions. But even more broadly, if we let go of basic claims to equity now, we also risk losing the forward-looking, innovative, productive drives that made us so successful in the first place.

    For one acute case of how concentrated inequalities can produce and exacerbate much broader economic instability, could you trace a recent historical arc in which increased disparities (with, on average, one trillion dollars shifting to a small wealthy class of Americans every year since 1974) led to substantial savings increases among this one group (alongside diminished possibility for sturdier middle-class-consumer driven growth cycles), leading to searches for less obvious profit-making opportunities, particularly through (in its most respectable guise) exploitative credit-providing and (in its most conspicuously problematic spin-off) sustained predatory-lending campaigns? Could you point to the damage that would have been done even if this bubble somehow had not popped in such dramatic fashion — as this rent-seeking class continued to skim off those most in need? Could you point to how this credit bubble did make broader economic crisis much more likely? And could you point to how the distorted (supposedly “natural”) market in which such dynamics played out again proves, by comparison, the superior merits of us all starting from a more egalitarian baseline?

    I really do consider this the story for our times. I trained as a labor economist, and spent the early part of the 2000s looking at changes in family economic well-being. Over the 1960s and 70s, as families struggled with wages flat-lining or even falling for workers down the income ladder, you first saw some families increase their labor supply, right? Over those decades, we saw the rising participation of women, of mothers, of caregivers — with their additional earnings not offering any one-to-one correlation with families’ needs, but with these increased labor-force hours making all the difference between family income falling or not. But of course families eventually reached a limit to how much additional time they could put into the labor market. And as families reached those limits, we saw the next generation develop this coping strategy of taking on debt.

    So here, already, we should think of this accumulating debt as not the start of the story. The finance economist Atif Mian, for example, in his research with Amir Sufi, shows quite convincingly that all these individual families didn’t just suddenly decide that they wanted a house in a better neighborhood, or wanted to send their kids to better schools. Mian and Sufi’s research clarifies a more complex sequence of trends, in which first concentrated wealth begins to rise in a limited number of households. This increase in wealth leads to an increase in savings. But this increased wealth and savings do not lead to a commensurate increase in investment. Instead, these trends eventually lead to an increase in the credit supply, and to the financialization of our economy. This money starts flowing through the economy, which Mian and Sufi’s research then ties to the deregulation of lending standards, and to an increased number of ways to lend money especially to households. Their research helps to document that new financial instruments ultimately emerge in response to this newly concentrated wealth needing someplace to go. We see this supply-side story play out in quite destructive ways in different communities across the country.

    And here Mian and Sufi’s research also traces the extent to which this home-mortgage refinancing happened in lower-income neighborhoods, where families saw their homes’ market value rise (temporarily, it turned out), and could extract some much-needed money. Again, the overabundance of available credit made this all possible. So then when this housing bubble collapsed, as bubbles do, that collapse hit these low-net-worth communities especially hard. Spending contracted sharply, and then of course consumption and consumer demand contracted sharply. So, finally, these distortionary market effects hit the economy much more broadly.

    Then returning to your training as a labor economist, and starting to hone in on your calls for fostering increased competition, for combating both consumer-market monopoly and labor-market monopsony: how might breaking up concentrated power not just among a class of wealthy individuals, but likewise among singularly dominant firms, help to promote greater equality of pay throughout a corporation, while also incentivizing innovation and investment in new technologies? How and why should a revitalized antitrust approach, particularly in an era of share-grabbing platform firms and subcontracted “fissured” workplaces, focus on protecting the interests not just of consumers, but of workers?

    We economists have this bad habit of coming up with words difficult to pronounce. But “monopsony” basically describes a situation in which only one buyer exists. Monopoly means only one party selling something. Monopsony flips that around, whether for supply chains or for labor markets. Amid the current General Motors strike, for example, a lot of Detroit-based suppliers pretty much only sell to GM. They make specific parts for specific automobiles.

    In US labor markets, with the recent rise in economic concentration within a lot of different industries, a worker might change jobs but ultimately still end up with the same employer. A nurse may live within commuting distance of four or five different hospitals, but one single corporation might own all of these hospitals. So she finds herself in a monopsony labor market. And if she thinks that her employer does a poor job with safety procedures, or doesn’t pay well, or provide enough vacation or whatever, she doesn’t have much choice. So monopsony significantly decreases worker bargaining power (both in the individual and the aggregate sense), just as it does for suppliers of goods and services.

    Regulators enforcing antitrust rules traditionally have focused on concentrated monopolies, rather than concentrated monopsonies, when deciding whether a merger could go through. But today many economists, especially labor economists, will say: “Hey, we need to watch out for both of these.” Look at the new labor-market data coming out. Unemployment is still going down, but the pace of wage growth has slowed. A lot of people will ask: “Why haven’t wages gone up more, given how low the unemployment rate has been for so long?” Well, when you widen the aperture, when you consider increases in labor-market concentration, and how that affects bargaining power, you see why we need to start investigating these trends quite seriously.

    And then the idea of the fissured workplace comes from the economist David Weil. David’s research shows how these monopsony-like dynamics play out in workers’ daily lives. When you stay in a hotel these days, many workers you encounter might not actually work for that hotel brand. The people cleaning your room, or the people at the front desk, or the restaurant, might work for subcontractors selling their services to the hotel chain. This branded hotel makes its profits by providing its core competency (the customer’s hotel experience), and then everything else can get fissured off. So that creates a different kind of monopsony. This hotel’s restaurant workers might have the option to switch which hotel brand they work in, but not end up switching the subcontracted company they work for. They also won’t have much opportunity to advance at any given hotel. We used to have this adage about the employee who starts off stocking the shelves…

    Or sweeping the floors.

    Right. That person basically has a zero-percent chance of moving up within this hotel chain. That person literally doesn’t work for this hotel chain. And similarly, just as this worker has no chance for vertical advancement, this worker has little chance of pushing her fellow workers to call for collective-bargaining rights. Most other staff in the hotel might work for a completely separate company.

    And these subcontracting firms themselves often can’t squeeze much out of the broader profitability of the branded hotel. These subcontractors have to run on such small profit margins for the branded chains to hire them. So if the big hotel brand starts making a lot of money, well the workers at this hotel can’t take advantage of that, and even their subcontractor employers don’t do much better. These kinds of institutional constraints and structures now set the terms for how workers bargain over wages — and for why they often can’t win in the same way they could a few decades ago.

    In terms of these broader society-wide trends (promoting stifling market environments, benefiting only a select few participants), does “the evidence accumulated by scholars” concerning monopoly and monopsony alarm conservative or classically liberal economists just as much progressive economists — or even more, given neoclassical models’ reliance on virtuous cycles of competition driving broad-based economic growth? Or where might they contest or read differently the data that your book presents?

    Well I would say that the divides you see in economics today often have more to do with methodology than with ideology. The research this book cites comes from the top journals in the field right now, or from the National Bureau of Economic Research, an institute at Harvard that operates by invitation only, and seeks to bring together many of the best and brightest minds in the profession. In both cases, you see economists committed to following the research evidence, and to looking at all of this new data and all of these empirical studies now available to us, and to posing different types of questions. Where fault-lines exist in the field today, they probably trace differences between these recent empirical approaches and more traditional theory-based approaches.

    And I find it just fascinating to watch how this availability of new data and evidence continues changing the way people think. This especially is what makes economics so exciting right now, and so fun. I mean, as a researcher, you get to dig in, test out an hypothesis, work your way to an evidence-based conclusion, and then debate with others about their own research methods and corresponding discoveries.

    So here again, I personally find quite persuasive and quite powerful your bridging of macroeconomic analysis and more micro-focused inequality studies, your calls for systematically tracking what happens up and down the income distribution, in which communities, attached to which industries, in which regions across the country. I appreciate your reformulated prompt for public conversations on economic developments: pivoting away from “Did America’s GDP go up?” or even “Did we have any jobs gains?” to “What is happening right now to family economic well-being?” Where do you see such discussions picking up momentum at present? How can we encourage them to spread? And what, beyond simple generational attrition, will most proactively push our political leaders to factor such conversations into crucial policy decisions?

    I actually just received an invitation to testify before Congress’s bicameral Joint Economic Committee, on rethinking how we should look at national income. So again it’s exciting to see those kinds of changes hopefully coming down the pipeline, and to see Congress taking this very seriously. For over half a century, each quarter, when the Bureau of Economic Analysis releases GDP numbers, we’ve told ourselves that these numbers tell us something important about how well the economy is performing for all of us. Or when you watch the news and find out whether the stock market went up or down today, that seems to tell you something crucial about business performance. When you read a headline about how the economy “only” grew by two percent, you apparently learn whether or not we have met our national expectations.

    Again, these very broad types of measurements marked a great improvement in helping us to understand and to support the mid-20th-century economy. But especially today, with our economic growth so unevenly concentrated, when you try to articulate what national income looks like across the income distribution, or across geographical boundaries, or among different demographic groups, you just can’t find any simple straightforward way to say: “Here’s how the economy’s performing.”

    And it really does shift policy discussions dramatically when we give up on even trying for that kind of single-sentence description. That’s why I find it so encouraging to see Congress move toward allocating funds to make this broader institutional adjustment real, hopefully in this year’s budget. That would be just a remarkable step forward. Numerical data never will tell us everything. But we need to measure much more methodically how the economic landscape has shifted, how we’ve entered this new terrain of high inequality in income and wealth and market concentration. We have to track this shift and make sense of what it means for monetary policy, for antitrust enforcement, for how we think about unions organizing. We have to ask all of those questions, and I think we’ll come to a whole new range of policy answers, because our world has changed in such profound ways.


    Photo Credit: Mark Silva