I wrote earlier this month about innovation and equality, based on two papers that documented the correlation between parents’ income and the likelihood someone files a patent, even after controlling for an individual’s ability.
Abstract The misallocation of talent between routine production versus innovation activities is shown to have a first-order impact on the welfare and growth prospects of an economy. Surname level empirical analysis combining patent and inventor micro-data with census data reveals new stylized facts: (1) People from richer backgrounds are more likely to become inventors; but those from more educated backgrounds are not. (2) People from more educated backgrounds become more prolific inventors; but those from richer backgrounds exhibit no such aptitude. Motivated by this discrepancy, a heterogeneous agents model with production and innovation sectors is developed. Individuals compete against each other for scarce inventor training in a tournament setting. Those from richer families can become inventors even if they are of mediocre talent by excessive spending on credentialing. This is individually rational but socially inefficient. The model is calibrated to match the new stylized facts via indirect inference. A thought experiment in which the credentialing spending channel is shut down reveals that the rate of innovation can be increased by 10% of its value. Optimal progressive bequest taxes serve to increase social welfare by 6.20% in consumption equivalent terms.
In the long run, prosperity depends on a society’s ability to generate and apply good ideas – especially in science and technology. Unfortunately, those ideas are getting harder and harder to find. Economists estimate that scientific research is becoming more expensive — the amount of time and effort required for each new breakthrough is increasing. In response, some experts fear that modern economies have eaten “all the low-hanging fruit” and therefore will struggle to maintain economic growth.
The challenge for the U.S. and other advanced economies is to somehow overcome this, to innovate more without paying more.
My editor wisely advised I get right to the point, but I wanted to post those paragraphs here because it’s an important topic. If you want to know more about the research suggesting ideas are getting harder (more expensive) to find, read John Van Reenen’s research brief or this paper or this Economist article.
The thinking in starting the inventors/taxes piece with this line or research was that if new ideas are getting harder to find, one way to find more of them more efficiently would be to reduce barriers that keep talented would-be inventors from inventing. If society were more equitable, you wouldn’t just get more inventors, you’d get better ones.
But there’s one other big idea out there about how to improve the efficiency of invention: use AI. The idea is that AI isn’t just a normal invention; it’s an invention that helps you invent new things. That’s an idea I hope to explore more, here and at HBR.
This paper dramatizes a conflict between intuition and evidence. On the one hand, many people see strong intuitive reasons for believing that the rise of national tax-based social transfers should have reduced at least GDP, if not true well-being. On the other, the fairest statistical tests of this argument find no cost at all. Multivariate analysis leaves us with the same warnings sounded by the raw historical numbers. A bigger tax bite to finance social spending does not correlate negatively with either the level or the growth of GDP per capita. How can that be true? Why haven’t countries that tax and transfer a third of national product grown any more slowly than countries that devote only a seventh of GDP to social transfers?
The keys to the free lunch puzzle are:
(1) For a given share of social budgets in Gross Domestic Product, the high-budget welfare states choose a mix of taxes that is more pro-growth than the mix chosen in the United States and other relatively private-market OECD countries.
(2) On the recipient side, as opposed to the tax side, welfare states have adopted several devices for minimizing young adults’ incentives to avoid work and training.
(3) Government subsidies to early retirement bring only a tiny reduction in GDP, partly because the more expensive early retirement systems are designed to take the least productive employees out of work, thereby raising labor productivity.
(4) Similarly, the larger unemployment compensation programs have little effect on GDP. They lower employment, but they raise the average productivity of those remaining at work.
(5) Social spending often has a positive effect on GDP, even after weighing the effects of the taxes that financed the spending. Not only public education spending, but even many social transfer programs raise GDP per person.
(6) The design of these five keys suggests an underlying logic to the pro-growth side of the welfare state. The higher the social budget as a share of GDP, the higher and more visible is the cost of a bad choice. In democracies where any incumbent can be voted out of office, the welfare states seem to pay closer attention to the productivity consequences of program design. In the process, those countries whose political tastes have led to high social budgets have drifted toward a system that delivers its tax bills to the less elastic factors of production.
The takeaway is that it depends on how you structure the taxes and what you spend the money on. To take an extreme example, if you taxed carbon dioxide emissions and spent the money on education you’d have every reason to expect growth to increase as a result. If you taxed land and spent the money on R&D, once again you’d absolutely expect growth to increase. These are not examples from the paper, but they illustrate the point. Lindert argues that actual welfare states often tax and spend in ways that are pro-growth or at least not terribly anti-growth. As he writes:
The overriding fact about the cases of costly welfare states, then, is that they never happened
One final reason this case study is illustrative: one of Netflix’s key advantages is tech; Disney’s is IP.
Their competitive advantage rests largely on questionable (at best) extensions of copyright law.
What publications have the best economics coverage? I’ve been thinking about this related to a side project, but it’s also relevant in light of the debate over “fake news” and journalistic quality, and as more and more publications go behind paywalls and thereby force consumers to make choices. Which sites are most worth paying attention to?
I have opinions, of course, as I cover economics for HBR. But I was curious to see which publications and sources dominate the elite economics conversation. To do that, I decided to see which sites are most commonly recommended by two major economics bloggers with somewhat different ideological positions: Tyler Cowen and Mark Thoma.
I used the Bing search API to get links to posts in which those two recommend things to read (which they’ve both done daily for over a decade), then I extracted the links from their posts, then I counted up the most common domains. (My code is here. If you notice any errors please leave me a comment on Github! This was a quick weekend project.)
There are a ton of caveats to this approach. It’s not a complete sample; it’s based on whatever links Bing does or does not return. And I don’t try to account for the fact that not every link points to something about economics; Cowen in particular often links to stuff about literature, food, etc. Finally, this is just an indication of what two particular economists found worth recommending; it’s not a comprehensive account of the field or an objective measure of what’s “good”. (If you know of others who’ve recommended links on their blogs in similar fashion, my code could be revised to include them. I’d love to diversify my sample.) Finally, by looking back ten years, my analysis biased toward sites that have been around that whole time as opposed to newer outlets like Quartz, Vox, or Fivethirtyeight. Despite all of this, I hope others find this analysis to be useful.
An observation before I get to some of the top sites: Cowen’s list of most-recommended domains in my sample is far newsier, whereas Thoma’s has far more blogs by individual economists.
With that in mind, here are the domains that show up in the top 50 most linked for both Cowen and Thoma in my sample, not counting platforms like Twitter or YouTube (and consolidating across sub-domains, though my code does not):
Finally, I checked the most common links within my sample just from recommendations this year (2017), and looked at the overlapping domains. Nothing surfaced that wasn’t already on one of these lists.
You could set the cutoffs wherever you prefer (again, here’s the code), and I can’t emphasize enough that lots of caveats apply. But if you’re looking for a places to go for good economic analysis, these lists aren’t a bad start.
I was recently asked, in an interview, for the single most important thing we could do to increase productivity growth. After a pause, I answered: upgrading Americans’ skills. Within minutes of finishing the interview I was regretting the answer — not because it’s wrong, exactly, but because, as I said to the interviewer afterwards, skills are overrated by the business elite as a cause of inequality, wage stagnation, etc.
It is a standard practice in policy circles to claim that technology is the primary culprit in the rise in inequality over the last four decades. As the story goes, computers and other new technologies have placed a premium on highly skilled labor while substantially reducing the need for the physical labor done by less-educated workers. This raises the pay of the highly skilled while lowering the pay of everyone else.
By making technology the culprit, this story relieves policy of responsibility for inequality. It also effectively makes the alternative to rising inequality suppressing technology, which presumably few would want to do.
In fact, in a closed door event just a couple days before that interview, I made the point that skills alone do not dictate labor market outcomes. An obvious point, perhaps, but again one that many “elites” discount.
If asked again, I’d say that to improve productivity growth we ought to invest more in science, technology, and people. That makes the point about the importance of human capital, but spreads the focus around a bit.
So, my answer wasn’t terrible. If you waved a magic wand and increased by an order of magnitude the number of people with basic digital skills — or who can create software, or who can build machine learning systems — you absolutely would increase productivity growth. It may or may not rank as the most important factor. The one thing we know for sure, though, is that it’s not the entire story.
Even the assumption that bureaucratic “red tape” holds back startups is less obvious than it sounds… What evidence we do have squarely challenges the intuition that it’s government that holds back startups.
But if Wilkinson is going to acknowledge the entrepreneurial benefits of the welfare state, liberals ought to at least consider the possibility that regulations do hamper innovation.
Putting entrepreneurship and innovation aside for a second, it seems clear that the net benefits of regulation vary considerably depending on which ones you’re talking about. The Clean Air Act seems to have had largepositive effects. On the other hand, overzealous land use regulations that prohibit building have had largenegative effects.
The same is likely true of regulation and entrepreneurship. Plenty of regulations probably aren’t a big impediment; some even help. But plenty of others probably do hold back innovation.
Generalizing about the economic effects of regulation was hard, it seemed to me, since there are cases of regulation that are obviously net positive and cases that are arguably net negative. Now, that’s true of other government interventions, too; there’s better and worse welfare state programs, for instance. Nonetheless, the aggregate evidence that the welfare state and redistribution have been net positive seems reasonably compelling.
But Wilkinson’s Niskaten colleague Ed Dolan has a nice post in which he does some rough-and-ready statistical analysis exploring the relationship between regulation indices and measures of prosperity and well-being. In the absence of clear aggregate evidence on the effects of regulation (that I know of) it is quite interesting:
When all is said and done, our search among the economic freedom data from Heritage and Fraser for evidence of the effects of the regulatory state has been frustrating. We are left with the following conclusions:
Simple correlations do find positive and statistically significant relationships between measures of regulation and commonly used measures of prosperity and personal freedom.
Half or more of the relationships implied by simple correlations turn out to come from the strong correlations of regulation, prosperity, and personal freedom indicators with GDP per capita. Controlling for income, wealthy countries with light regulation have only slightly better freedom and prosperity outcomes than wealthy countries with average regulation.
In multiple regressions that account for the interaction of regulation with other components of economic freedom, the statistical power of the Fraser and Heritage regulation indicators to explain cross-country variations in prosperity and personal freedom evaporates altogether.
Close examination reveals serious methodological problems in the way both the Fraser and Heritage regulation components are constructed. Neither makes adequate efforts to distinguish between helpful and harmful aspects of regulation. Both include some indicators that fit poorly with common notions of what the regulatory state really is and does, and both exclude important aspects of regulation (especially of international trade).
Dolan, who does worry about the negative effects of at least some regulation, sees this largely as exposing the flaws of the most common regulatory indices. And it reiterates my almost tautological starting point: there’s good regulation and bad. But it also suggests that, broadly, regulation is not on average and in general a huge factor holding back our economies.
This post is the first part of a series in which I’ll attempt to sketch out some thinking that I’ve found useful in terms of understanding organizations. A lot of my work as an editor and writer focuses on how organizations work, and here I’m hoping to synthesize ideas I’ve found valuable, and present them as I think about them myself. I’ll try to cite my main influences in each post, but I’m borrowing from a wide range of sources and will inevitably fail to credit each and every influence.
This post is about how to think about organizations at the highest level. The next will be about how organizations are, well, organized. And the last one will be about what makes them successful.
What is an organization?
Here’s a definition by Richard Scott, a sociologist at Stanford:
“Organizations are groups whose members coordinate their behavior in order to accomplish shared goals or to put out a product.”
Three ways to think about organizations
Scott has a different way of breaking up theories of organizations, which I’ve included at the bottom. What follows is my own thinking on simple models for analyzing organizations:
The market view
Firms respond to incentives in the market. The emphasis here is on external conditions and financial pressures; firms come into being to capture market opportunities, and change strategy to maximize profits. The individuals in the firm are less important than the incentives the firm faces and perhaps the structure of the market. Per Ronald Coase, the boundaries of the firm are shaped by transaction costs. Managers exist to serve the interests of shareholders, which by and large means maximizing financial returns.
The managerial view
Organizations pursue specific goals, as directed by management. Power is formalized, and the perspectives and priorities of leadership matter in terms of how the organization will act. Conflicts and differing incentives can exist between members of the leadership team, leading to rival factions and internal politics, but it’s still the top management that matters most in shaping how the organization behaves.
The sociological view
The actions of an organization are deeply shaped by its culture, and by the preferences, incentives, and coalitions not just of management but of all the organization’s participants. Culture often influences organizational structure, rather than just the other way around. And management’s decision-making is shaped and constrained by the rest of the organization.
Using the three models
In the market model, standard microeconomic thinking helps explain an organization’s behavior and predict its actions. In the simplest econ 101 view, the organization (a private sector firm, typically) enters and exits markets in response to opportunities. In more complicated analyses, game theory helps explain how the rational actor (org) competes against other rational actors.
In the managerial view, the statements by management help explain and predict what an organization will do. If the founder of a company cares deeply about a project, it will go forward. If the board decides that a technology is the future of an industry, expect investment. Organizations that are “well run” — managed by competent people — succeed, and those run by incompetent managers fail.
In the sociological view it’s most important to understand the organizational culture and the internal politics. What do employees think the organization’s purpose is? Are they excited about a new market? Who in the organization stands to benefit if it adopts a new technology, and who might stand in its way?
These models may be biased toward understanding standard for-profit firms, but I’d argue they’re also useful lenses for thinking about other types of organizations, from nonprofits to nontraditional organizations like open source collaborations.
Here’s Richard Scott’s mapping of organizational theories, also via McFarland’s course. You’ll see a lot of the same issues are raised, but my thinking does not map clearly onto his. My managerial view is a combination of his rational and natural theories; my sociological view is a mix of his natural and open theories; and he doesn’t really have a corollary to the market view, though aspects of both rational and open overlap with it.
Another good set of theories about organizations comes from Graham Allison’s seminal book on the Cuban Missile Crisis, The Essence of Decision. Here’s the Wikipedia entry, which gives an overview of his three theories of organizational decision-making.
If you’re new to economics and want an introduction to what I called the market view above, check out Marginal Revolution University’s introductory video on competitive firms. More videos here.
And, of course, working at HBR plenty of what we’ve published has influenced my thinking on all of the above. I’ll draw on some of that more directly in future posts.
I’m moderating an event on digital technology and productivity later this month, and Noah Smith just published a great column on the topic, based largely off of a new paper by Erik Brynjolfsson, Daniel Rock and Chad Syverson. Here’s a key bit:
Often, when a very versatile new technology comes along, it takes a while before businesses figure out how to use it effectively. Electricity, as economist Paul David has documented, is a classic example. Simply adding electric power to factories made them a bit better, but the real gains came when companies figured out that changing the configuration of factories would allow electricity to dramatically speed production.
Machine learning, Brynjolfsson et al. say, will be much the same. Because it’s such a general-purpose technology, companies will eventually find whole new ways of doing business that are built around it. On the production side, they’ll move beyond obvious things like driverless cars, and create new gadgets and services that we can only dream of. And machine learning will also lead to other new technologies, just as computer technology and the internet led to machine learning.
That’s also why I think Brynjolfsson and Andrew McAfee’s latest book, Machine, Platform, Crowdis so valuable. Redesigning organizations isn’t just about machine learning; when you combine ML with crowdsourcing and other newer models, you end up with fundamentally different kinds of organizations, like Numerai:
Numerai is a hedge fund managed by an anonymous community of data scientists. It encrypts its data and allows anyone in the world to continuously apply machine intelligence to the set and anonymously submit price predictions back. Numerai turns these predictions into trades and compensates the best performing models with bitcoin.
One open question: how does the wilddivergence of productivity between firms fit in, given that it’s driven largely by digital technology? Is it the case that the winners so far are the ones who’ve really organized around these technologies? Or are they just better at the lesser early adoption that barely moves the needle, and will be toppled by a new era of AI-full-stack startups?
Some entrepreneurs and some libertarians (or “liberaltarians”) appear to be warming to redistribution and the welfare state. But there’s a reexamination happening in left-of-center policy circles, too. I offer no opinion on that conversation here, but want to clip together a few references…
The limits of redistribution
Franklin Foer on Elizabeth Warren and the future of the Democratic party:
Nor is Warren’s driving obsession wealth redistribution. That’s important politically, because many Americans simply don’t begrudge wealth, and “inequality” as a clarion call hasn’t stuck… Rather, Warren is most focused on the concept of fairness. A course she taught early in her career as a law professor, on contracts, got her thinking about the subject. (Fairness, after all, is a contract’s fundamental purpose.) A raw, moralistic conception of fairness—that people shouldn’t get screwed—would become the basis for her crusading. Although she shares Bernie Sanders’s contempt for Wall Street, she doesn’t share his democratic socialism. “I love markets—I believe in markets!” she told me. What drives her to rage is when bankers conspire with government regulators to subvert markets and rig the game. Over the years, she has claimed that it was a romantic view of capitalism that drew her to the Republican Party—and then the party’s infidelity to market principles drove her from it.
Suppose we raised marginal tax rates on the highest income households from 39.6 percent to 50 percent… the increase would raise taxes by an average of $6,464 for those in the 95-99th percentiles (those with average incomes of $321,000 in 2013). Households in the top 1 percent (with average incomes of $1.571 million in 2013) would pay an additional $110,968 and those in the top 0.1 percent an additional $568,617… Now imagine that all of the revenue collected from this change was distributed evenly to the bottom 20 percent. The total revenue raised is $95.6 billion and allows each household at the bottom to have an extra $2,650 in post-tax income.
Between 1979 and 2012, the share of all household income accruing to the top percentile of U.S. households rose from 10.0% to 22.5% (8, 9). To get a sense of how much money that is, consider the conceptual experiment of redistributing the gains of the top 1% between 1979 and 2012 to the bottom 99% of households (10). How much would this redistribution raise household incomes of the bottom 99%? The answer is $7107 per household—a substantial gain, equal to 14% of the income of the median U.S. household in 2012. (I focus on the median because it reflects the earnings of the typical worker and thus excludes the earnings of the top 1%.)
He goes on to say that, in terms of total dollars, the rise in the college wage premium has been more significant:
This increase in the earnings gap between the typical college-educated and high school–educated household earnings levels is four times as large as the redistribution that has notionally occurred from the bottom 99% to the top 1% of households. What this simple calculation suggests is that the growth of skill differentials among the “other 99 percent” is arguably even more consequential than the rise of the 1% for the welfare of most citizens.
A predominant Democratic view is that the economy is mostly fine; it’s just a matter of adjusting and correcting it to ensure everyone has access. Deeper, structural, changes are put to the side in favor of taxes, transfers, and behavioral nudges to help people out.
On trade, for example, the consistent Democratic narrative in 2016 was that we need to “compensate the losers” of trade. The phrasing alone tells us everything we need to know. Which voters want to be identified as losers? Democrats may mean something more abstract when they speak of “losers” in a globalized economy, but the language carries the connotation of personal blame.
But what role does individual agency play when global capital flows upend communities? And why are we treating the economy as a natural phenomenon — one whose consequences we simply must accept — when voters know it’s a series of laws, trade agreements, and businesses making decisions? If this is the best Democrats can offer, it’s not surprising workers aren’t interested.
Here’s the shameful secret not uttered in our favorite futurists’ TED-style presentations. The reason they adore UBI isn’t to do with their commitment to lift a growing underclass out of poverty; that’s just a bedtime story that helps the super-wealthy sleep. Instead, it’s more to permit spending on their goods by what remains of the American middle class. No one on a stagnant wage can currently buy the things that Musk—and the rest of Silicon Valley—wants to sell them. These billionaires champion a scheme whose prime result will be their profit.
This paper suggests redistribution from rich to poor has been the historical norm for the last hundred years, and this paper suggests it improves life satisfaction. That’s broadly consistent with the historical data presented here on “social spending” improving human well-being, which admittedly may not map well onto today’s debates between redistribution and “pre-distribution” policies.
Rich countries are using their governments to redistribute enormous amounts of their total national income, through health care, pensions, poverty assistance and other measures. Sometimes, this doesn’t do much to equalize income distribution — for example, in the U.S., Social Security payments are roughly proportional to how much people pay into the system. But as Lindert shows, these social transfers are doing a lot to equalize incomes, generally cutting the Gini coefficient — a common measure of income or wealth inequality — by between a fifth and a third.