Tuesday, January 23, 2018

Bitcoin and Illegal Activity

One of the main attractions of bitcoin is its anonymity, which is worth the most to those who are carrying out questionable or illegal transactions. But how much of bitcoin used is tied to illegal activity? Sean Foley, Jonathan R. Karlsen,  and Tālis J. Putniņš tackle this question in their January 2018 working paper, "Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?" available at the SSRN website.

I've tended in the past to view bitcoin and other digital cryptocurrencies as a fascinating sideshow: that is, a combination of the deeply interesting blockchain technology, but at a relatively small scale. The authors point out that the scale has been rising substantially (footnotes omitted).
"Cryptocurrencies have grown rapidly in price, popularity, and mainstream adoption. The total market capitalization of bitcoin alone exceeds $250 billion as at January 2018, with a further $400 billion in over 1,000 other cryptocurrencies. The numerous online cryptocurrency exchanges and markets have daily dollar volume of around $50 billion. Over 170 “cryptofunds” have emerged (hedge funds that invest solely in cryptocurrencies), attracting around $2.3 billion in assets under management. Recently, bitcoin futures have commenced trading on the CME [Chicago Mercantile Exchange] and CBOE [Chicago Board Options Exchange], catering to institutional demand for trading and hedging bitcoin. What was once a fringe asset is quickly maturing."
I'm not sure where the dividing line is, but as cryptocurrencies seem headed toward exceeding $1 trillion in total value, greater attention will need to be paid. The focus of these authors is on links from bitcoin to illegal activity. Their research uses a key fact about the technology of bitcoin: the transactions carried out by bitcoin are all publicly available and observable, but the names of the participants are not. For example, you can observe that accounts A, B, and C all made a simultaneous payment to accounts D and E--but you don't know who those parties are. The authors write:

"We extract the complete record of bitcoin transactions from the public bitcoin blockchain, from the first block on January 3, 2009, to the end of April 2017. For each transaction, we collect the transaction ID, sender and recipient address, timestamp, block ID, transaction fee, and transaction amount. ... The data that make up the bitcoin blockchain reveal “addresses” (identifiers for parcels of bitcoin) but not the “users” (individuals) that control those addresses. A user typically controls several addresses. ... Our sample has a total of approximately 106 million bitcoin users, who collectively conduct approximately 606 million transactions, transferring around $1.9 trillion."

However, there are cases where the anonymity of blockchain has been broken, or at least dented. For example, law enforcement may expose who actually controls a certain address. Or certain addresses may be escrow accounts for firms operating on the "dark web." Or one can look at darknet forums, where anonymous parties may in some cases reveal their bitcoin address--for example, because they are complaining that they never received what they paid for.

Once you have a list of bitcoin addresses linked to anonymous activity, you can then track the transactions to and from those addresses. By looking at the patterns that emerge, you can build up a "cluster" of accounts and transactions that seem likely to be illegal. The authors compare the size of this cluster to the total number of bitcoin transactions. They write:
"We find that illegal activity accounts for a substantial proportion of the users and trading activity in bitcoin. For example, approximately one-quarter of all users (25%) and close to one-half of bitcoin transactions (44%) are associated with illegal activity. Furthermore, approximately one-fifth (20%) of the total dollar value of transactions and approximately one-half of bitcoin holdings (51%) through time are associated with illegal activity. Our estimates suggest that in the most recent part of our sample (April 2017), there are an estimated 24 million bitcoin market participants that use bitcoin primarily for illegal purposes. These users annually conduct around 36 million transactions, with a value of around $72 billion, and collectively hold around $8 billion worth of bitcoin.
"To give these numbers some context, a report to the US White House Office of National Drug Control Policy estimates that drug users in the United States in 2010 spend in the order of $100 billion annually on illicit drugs.5 Using different methods, the size of the European market for illegal drugs is estimated to be at least €24 billion per year. While comparisons between such estimates and ours are imprecise for a number of reasons (and the illegal activity captured by our estimates is broader than just illegal drugs), they do provide a sense that the scale of the illegal activity involving bitcoin is not only meaningful as a proportion of bitcoin activity, but also in absolute dollar terms."
As the authors note, these amounts are large enough to suggest that cryptocurrencies have the potential to shift how black markets operate. Many bitcoin accounts make only a single transaction, and then are never active again. And unsurprisingly, we are also seeing  "the emergence of alternative cryptocurrencies that are more opaque and better at concealing a user’s activity (e.g., Dash, Monero, and ZCash)." In the past, I have tended to believe that if law enforcement really wanted to break the anonymity of a cryptocurrency account, and devoted sufficient time and energy to a combination of old-fashioned and cyber-police work, it could do so. But the technology for anonymity keeps moving ahead.

For a recent, readable, and fairly short overview of bitcoin and the underlying blockchain technology, see "A Short Introduction to the World of Cryptocurrencies," by Aleksander Berentsen and Fabian Schär, in the Federal Reserve Bank of St. Louis Review (First Quarter 2018, pp. 1-16) For previous discussions of Bitcoin and blockchain technology on this blog, see:

Monday, January 22, 2018

Textiles: Your Clothes are Pollutants

"[T]he way we design, produce, and use clothes has drawbacks that are becoming increasingly clear. The textiles system operates in an almost completely linear way: large amounts of non-renewable resources are extracted to produce clothes that are often used for only a short time, after which the materials are mostly sent to landfill or incinerated. More than USD 500 billion of value is lost every year due to clothing underutilisation and the lack of recycling. Furthermore, this take-make-dispose model has numerous negative environmental and societal impacts. For instance, total greenhouse gas emissions from textiles production, at 1.2 billion tonnes annually, are more than those of all international flights and maritime shipping combined. Hazardous substances affect the health of both textile workers and wearers of clothes, and they escape into the environment. When washed, some garments release plastic microfibres, of which around half a million tonnes every year contribute to ocean pollution – 16 times more than plastic microbeads from cosmetics. Trends point to these negative impacts rising inexorably ..."

This, from the Executive Summary, is one of many jolting statements from A new textiles economy: Redesigning fashion’s future, published by the Ellen MacArthur Foundation in November 2017. The report seeks to envision a "circular" model of textile production: "In a new textiles economy, clothes, textiles, and fibres are kept at their highest value during use and re-enter the economy afterwards,
never ending up as waste."

Sales in the textile industry are growing rapidly as world incomes rise, and as people expand their wardrobes and wear each fewer times.

A few comments from the report on the environmental consequences, which are rising, too (footnotes omitted throughout):
"Large amounts of nonrenewable resources are extracted to produce clothes that are often used for only a short period,4 after which the materials are largely lost to landfill or incineration. It is estimated that more than half of fast fashion produced is disposed of in under a year. ..."
"Worldwide, clothing utilisation – the average number of times a garment is worn before it ceases to be used – has decreased by 36% compared to 15 years ago. While many low-income countries have a relatively high rate of clothing utilisation, elsewhere rates are much lower. In the US, for example, clothes are only worn for around a quarter of the global average. The same pattern is emerging in China, where clothing utilisation has decreased by 70% over the last 15 years. Globally, customers miss out on USD 460 billion of value each year by throwing away clothes that they could continue to wear, and  some garments are estimated to be discarded after just seven to ten wears. ..."

"Less than 1% of material used to produce clothing is recycled into new clothing, representing a loss of more than USD 100 billion worth of materials each year. As well as significant value losses, high costs are associated with disposal: for example, the estimated cost to the UK economy of landfilling
clothing and household textiles each year is approximately GBP 82 million (USD 108 million). Across the industry, only 13% of the total material input is in some way recycled  after clothing use ..." 
"The textiles industry relies mostly on non-renewable resources – 98 million tonnes in total per year – including oil to produce synthetic fibres, fertilisers to grow cotton, and chemicals to produce, dye, and finish fibres and textiles. Textiles production (including cotton farming) also uses around 93 billion cubic metres of water annually, contributing to problems in some water-scarce regions. ... [I]t is recognised that textile production discharges high volumes of water containing hazardous chemicals into the environment. As an example, 20% of industrial water pollution globally is attributable to the dyeing and treatment of textiles. ..."
Much of the report is given over to discussion of four broad areas in changes could be made, with numerous examples of what is happening in each area: "1. Phase out substances of concern and microfibre release; 2. Transform the way clothes are designed, sold, and used to break free from their increasingly disposable nature; 3. Radically improve recycling by transforming clothing design, collection, and reprocessing; 4. Make effective use of resources and move to renewable inputs."

I found especially interesting the vision in which clothes are designed for greater durability, combined with business models in which consumers rent a far greater share of their clothing. Once you start thinking along these lines, market segments where this approach might work well (aided by the ability of clothing providers to know your measurements in advance) become apparent. Small children? Maternity wear? That ski outfit you only wear a few times a year? As the report notes:
"Subscription models allow customers to pay a flat monthly service fee to have a fixed number of garments on loan at any one time. These models can provide an attractive offering for customers desiring frequent changes of outfit, as well as an appealing business case for retailers. ... Subscription models are already disrupting the market, with brands such as Le Tote, Gwynnie Bee, Kleiderei, and YCloset. This demonstrates that there is a willingness to pay monthly subscriptions for clothing, with YCloset in China securing a USD 20 million investment to scale up in March 2017. Another successful model is Rent the Runway, initially set up for online short-term rental of clothing for occasion wear and high-end luxury garments, which expanded to include a monthly rental subscription model in 2016. ... YCloset is riding the wave of popularity for sharing economy services in China, gaining customers in over 100 Chinese cities since their app launched in 2015. They target mid-market urban customers who want to access variety and a fresh look, but who lack the budget to buy midrange or luxury clothing. ..."
"The Danish company Vigga, established in 2014, allows parents to access igh-quality baby clothing for a fraction of the cost of buying new, with bundles of 20 appropriately sized baby clothing items provided at a time through a subscription service. By increasing durability, centralising washing and quality control, and streamlining operations through RFID (Radio Frequency Identification) tagging, on average Vigga circulates their baby clothes to five families before they are visibly used and go into
recycling, and they are working on increasing this number. Similar services have emerged in other countries, for example Tale Me in Belgium. Subscription services have also been introduced for pregnant women through companies such as Borrow For Your Bump, attempting to better address a woman’s needs for maternity wear. ..."
"Houdini Sportswear has offered customers the option to rent their outdoor sports shells since 2013. This creates an attractive financial model for both the brand and the customer, who can afford high-quality performance sportswear for one weekend or week for 10–25% of its retail price, rather than buying a cheaper, low-quality version or needing to store the garment for the rest of the year. At the same time, Houdini achieves higher overall margins by combining rental and resale. ..."
The report especially appealed to me for a couple of reasons. One is that I tend to think of textiles as an important but rather sleepy industry, gradually being transformed by robots and automated production. The report persuaded me that the opportunities for innovation in textiles are far greater than I had imagined. Also, it seems to me that even among those who drive hybrid cars and recycle religiously, the environment effects of clothing choices are often not much considered. This report should help to open up a new dimension of environmental awareness.

For a different angle on these issues, see "Quandaries of Global Trade in Secondhand Clothing" (May 22, 2015).

Friday, January 19, 2018

Attributing Economic Outcomes to Presidents: Year One of Trump

The US economy has performed well on a wide variety of measures in the year since President Trump was inaugurated on January 20, 2017.  The unemployment rate was 4.8% in January 2017 and 4.1% in December 2017. The unemployment rate among black Americans has fallen to its lowest level in the 45 years that regular statistics have been kept. The most recent estimates of GDP growth (which are preliminary and subject to later revision) show GDP growth of 3.1% in the second quarter of 2017 and 3.2% in the third quarter. Stock market indexes like the S&P 500 have risen dramatically.

The new year has brought a wave of economic good-news stories in the business press.  Business investment spending seem to be on the rise. By one measure, US manufacturing in 2017 has its best year since 2004. News sources not known to be overly friendly to the Trump administration, like the New York Times, are reporting stories like "The Trump Effect: Business, Anticipating Less Regulation, Loosens Purse Strings." US carbon emissions declined in 2017, which the US Energy Information Administration attributes in substantial part to fewer days of hot weather than in 2016--thus reducing the need for heavy use of air conditioning.

How much credit does President Trump deserve for this showing? Trump's critics quickly point out that an enormous economy like the United States has considerable momentum. For example,  Paul Krugman says that Trump gets "essentially zero" credit for US economic performance in 2017.  I agree that the effect of presidents--and especially newly elected presidents--on the economy is often overrated. But the observation that a US president has only a modest effect on the economy during a first year of office often includes a heavy dose of partisan bias.

Maybe I missed it, but when Trump had been elected and was headed toward taking office in January 2017, I didn't hear a lot of his critics say: "Well, the US economy is really set up for a strong year in 2017, but when it happens, Trump won't deserve any credit." Instead, there were predictions of grave difficulties ahead. Moreover, if the US economy had headed south early in 2017, with rising unemployment, sluggish output, stagnant investment, and a falling stock market, I strongly suspect that Trump critics like Krugman would place the blame on Trump's election. And in that case, it would be Republicans and Trump sympathizers arguing that the new president had inherited an unexpectedly poor situation and should receive essentially zero blame.

It's interesting to reflect back on previous presidencies, and apply the standard of "for the first year a president is in office, what happens (for good or bad) is largely what they inherited."  For example, the Great Recession ended in June 2009, six months into President Obama's first term. By this standard, the exit from the Great Recession should be credited to the economy and policies inherited from the previous Bush administration. The 2001 recession arrived in March of that year, just two months after President George W. Bush had assumed office. By this standard, that recession should be attributed to the economy and policies inherited from the Clinton administration. The incoming Clinton administration in 1993 inherited an economy with a falling unemployment rate, which by this standard should be attributed to the outgoing Bush administration.

I'm sympathetic to the argument that the first year of any presidential administration is essentially inherited, but this only sharpens the question of why the US economy performed so well in 2017. I'd suggest three possibilities.

First, the election campaign of 2016 seemed to involve all the candidates talking down the economy. But the national unemployment rate had fallen to 5,0% in September 2015, and has stayed at or below that level since then.  Here are the quarterly rates of real GDP growth since 2000.  The annualized growth rates of slightly more than 3% in Q2 and Q3 of 2017 are just fine, but they don't really stand out from a number of previous quarters since about 2010.  Overall, the US economy has considerable forward momentum at this point. It has continued to grow despite a succession of interest rate increases, and despite some terrible weather-related events in 2017. Growth across the main sectors of the economy has been quite balanced, rather than tilted toward a sector like housing or high-tech in a way that can lead to instability.
Second, although Americans like to think of our economy as an outpost remote from the rest of the world, we are in fact tied into the global economy in a number of ways. The World Bank argues that 10 years after the Great Recession, the global economy is at last again producing at its full potential. When an economic pattern happens across a number of nations at the same time, it's wise to suspect that there is a common underlying force that goes beyond national policies. For example, the fact that income inequality has been rising across many nations of the world, not just the US, suggests that the reasons for that increase are deeper economic patterns that affect many countries, not specific national actions. Similarly, with unemployment rates falling and stock markets rising in many countries around the world during 2017, it suggests that the reasons for that increase are patterns that cross many countries, not specific national actions.  My own sense is that many firms and banks had been holding their breath for a few years, waiting to be sure that the carnage of the Great Recession is behind them--and now they are stepping up.

Third, even with the US domestic momentum and full-potential output of the world economy taken into account, it does feel to me as if the Trump presidency was in some way an inflection point. The Trump administration's two main economic policy changes in 2017 involve a much more hands-off regulatory environment and the recently released tax bill. On the merits of these policies, I've expressed some concerns about both. Regulatory reform can be a positive step for an economy, and the UK and Canada have shown some ways to carry it out, but reform that does more to sort out regulations that justify their costs from those that don't is one thing, while just blocking and negating regulations willy-nilly is something else. The recent tax reform bill has many moving parts, but to me, the crucial question is the extent to which lower tax rates and other changes that benefit corporations pay off in a demonstrable surge in investment and wages. There are a bevy of recent anecdotes of companies announcing such changes, but in the next year or two, it will be interesting to check the follow-through on those promises--or whether most firms just cash the tax breaks, pay big bonuses to executives, and continue their investment and and wage-paying along much the same trajectory.

But whatever the merits of  these changes, it's not possible that their effects would have been directly felt early in 2017. Instead, firms would need to be reacting to the expectation of an improved business climate in the future. Like a lot of economists, I mistrust using "business climate" or "business confidence" as an explanation.  I'd prefer to be able to trace back "business confidence" to specific measurable parts of the economy, and to focus on those instead. But just because something is hard to measure doesn't mean it isn't real. It seems at least plausible that firms in a number of industries felt that the US business climate was not supportive, and interpreted Trump's election as a sign that polies more likely to support profit-seeking firms were on their way.

In thinking about business confidence, it may also be that some of Trump's important policy steps were the ones not taken. There were concerns that Trump might trigger a trade war. But while he has been hostile to additional trade agreements (as were the main Democratic party contenders for President in 2016), he did little to add impediments to trade in 2017.  Similarly, there was concern that Trump might replace Federal Reserve chair Janet Yellen with someone who didn't have the necessary trust and connections in financial markets, but the selection of Jerome Powell seemed to calm those concerns.

There's an old line commonly attributed to John Naisbitt (I don't have a citation) that "leadership involves finding a parade and getting in front of it." Politicians often excel at this kind of leadership, and in that spirit,  I don't blame President Trump for claiming excessive credit for the good news of the US economy in 2017. When it comes to economic outcomes, presidents are a bit like the coaches of professional sports teams--that is, they often get an outsized share of the credit for success and the blame for failure.

Thursday, January 18, 2018

The Global Output Gap Has Closed: What Next?

A decade after the global financial crisis circa 2008, the global economy has finally recovered. Tthe Global Economics Prospects 2018 report just published by the World Bank, subtitled "Broad-Based Upturn, but for How Long?" tells the story. 
"The global financial crisis tipped the global economy into a deep recession that affected first the advanced economies but spread—especially with the subsequent collapse of commodity prices—to emerging market and developing economies (EMDEs). Recoveries have been slow, but by 2018 the global economy is expected to return to its potential for the first time in a decade as the global output gap is expected to be closed. This in turn could mean a continued withdrawal by advanced economies of the extraordinary policy accommodation that was provided during the crisis, with important spillovers to EMDEs through trade and financial linkages. ...

"A broad-based cyclical global recovery is underway, aided by a rebound in investment and trade, against the backdrop of benign financing conditions, generally accommodative policies, improved confidence, and the dissipating impact of the earlier commodity price collapse. Global growth is expected to be sustained over the next couple of years—and even accelerate somewhat in emerging market and developing economies (EMDEs) thanks to a rebound in commodity exporters. Although near-term growth could surprise on the upside, the global outlook is still subject to substantial downside risks, including the possibility of financial stress, increased protectionism, and rising geopolitical tensions. Particularly worrying are longer-term risks and challenges associated with subdued productivity and potential growth. With output gaps closing or closed in many countries, supporting aggregate demand with the use of cyclical policies is becoming less of a priority. Focus should now turn to the structural policies needed to boost longer-term productivity and living standards. A combination of improvements in education and health systems; high-quality investment; and labor market, governance, and business climate reforms could yield substantial long-run growth dividends and thus contribute to poverty reduction." 
The report offers considerable detail across countries and regions, for the reader who wants to delve further. But at a time when the global economy is again, at long last, producing near its potential output, it's worth emphasizing that the formula for long-run growth is fairly clear: gains in education and human capital, gains in capital investment, and research and development for gains in technology, all interacting in an economic environment flexible enough to offer meaningful incentives for innovation. The report puts it this way:
"Global productivity growth has slowed over the past two decades. Some of the underlying drivers of this slowdown may fade over time, such as policy uncertainty and crisis legacies. Others, however, are likely to persist: the decline in labor force growth and population aging; a levelling-off of productivity-enhancing innovations in information and communication technologies; and maturing global supply chains. Policies to address these persistent factors include better education for improved learning in aging populations and initiatives to stimulate investment in physical capital and research and development. Other measures, such as regulatory reform and trade liberalization, could raise productivity by reducing informality and increasing competition."
It's also worth remembering that one reason for the current health of the American economy is that  US economic growth is being bolstered and supported by growth from the rest of the world. 

Wednesday, January 17, 2018

Snapshots of Economic Inequality Around the World

Compiling data on economic inequality from countries all around the world is a hefty task, which has been shouldered by a group of more than 100 researchers around the world who contribute to the efforts of the World Inequality Lab and the World Wealth and Income Database. The World Inequality Report 2018, written and coordinated by Facundo Alvaredo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, provides an overview of their findings. Here are a few of the figures that jumped out at me.

This figure shows the share of income going to the top 10% of the income distribution in a number in some prominent countries and regions. Inequality in the US-Canada area (blue line) is clearly rising, but so is inquality across all of these areas. In particular, economic development in China and India has made some parts of those economies much better-off than others, so inequality his on the rise. The rise of inequality in Russia during the 1990s is also apparent.

This is a similar graph, but with a different set of comparison regions. The blue line for the US-Canada area remains the same. But as you can see, inequality in the Middle East, sub-Saharan Africa, and Brazil have long been above US-Canada levels, and by this measure, India has now passed the US level of inequality.
This figure is known as the "elephant graph," because if you squint a little, you can imagine that the bump on the left is the top edge would trace out the elephant's head, and then the upward movement on the right would be the top edge of the elephant's trunk. As the text explains: "On the horizontal axis, the world population is divided into a hundred groups of equal population size and sorted in ascending order from left to right, according to each group's income level. The Top 1% group is divided into ten groups, the richest of these groups is also divided into ten groups, and the very top group is again divided into ten groups of equal population size. The vertical axis shows the total income growth of an average individual in each group between 1980 and 2016."

For example, the figure shows that an adult who was in the 20th percentile of the world income distribution in 2016 had an income that was about 120% higher than an adult who was in the 20th percentile of the world income distribution in 1980. The "head" of the elephant shows that the gains to those in the 20-40th percentiles of the world income distribution were substantial. The drop in the middle shows that gains were smaller for those from the 50th-80th percentiles of the world income distribution. And on the far right, the top percentile is divided up into smaller slices. The gains for the top percentile were substantial, but comparable to those in the 20th-40th percentile. However, the gains the top 0.01% and the 0.001% were substantially larger. Of course, these groups at the very top are also for much smaller groups, and thus are  harder to measure, and probably also involve more turnover year-to-year.

Underlying these overall patterns are some shifts in regional economic patterns that are fairly well-known, but remain striking. For example, this figure looks at average incomes in Africa and across Asia, and  how they compare to the average world income. In 1950, Africa was well ahead of Asia relative to average world income, but that pattern has dramatically reversed.
As a similar exercise, China lagged far behind Latin America relative to world income back in 1950. But Latin America has underperformed the world economy, and China has outperformed it, and China appears to be on its way to outstripping Latin America in average incomes in the next few years.
This volume is a rich resource, with lots of information on inequality of incomes by country and by region,  inequality of wealth, shifts in public wealth, and other topics. The policy discussion is relatively brief (better education, progressive taxation, rethinking labor institutions), but that was  fine with me. The fundamental point of this exercise is to generate a common fact base, and then let the policy discussion build upon it.

Monday, January 15, 2018

Some Economics for Martin Luther King Day

On November 2, 1983, President Ronald Reagan signed a law establishing a federal holiday for the birthday of Martin Luther King Jr., to be celebrated each year on the third Monday in January. As the legislation that passed Congress said: "such holiday should serve as a time for Americans to reflect on the principles of racial equality and nonviolent social change espoused by Martin Luther King, Jr.." Of course, the case for racial equality stands fundamentally upon principles of justice, not economics. But here are a few economics-related thoughts for the day from the archives:

1) Inequalities of race and gender impose large economic costs on society as a whole, because one consequence of discrimination is that it hinders people in developing and using their talents. In "Equal Opportunity and Economic Growth" (August 20, 2012), I wrote:

A half-century ago, white men dominated the high-skilled occupations in the U.S. economy, while women and minority groups were often barely seen. Unless one holds the antediluvian belief that, say, 95% of all the people who are well-suited to become doctors or lawyers are white men, this situation was an obvious misallocation of social talents. Thus, one might predict that as other groups had more equal opportunities to participate, it would provide a boost to economic growth. Pete Klenow reports the results of some calculations about these connections in "The Allocation of Talent and U.S. Economic Growth," a Policy Brief for the Stanford Institute for Economic Policy Research.

Here's a table that illustrates some of the movement to greater equality of opportunity in the U.S. economy. White men are no longer 85% and more of the managers, doctors, and lawyers, as they were back in 1960. High skill occupation is defined in the table as "lawyers, doctors, engineers, scientists, architects, mathematicians and executives/managers." The share of white men working in these fields is up by about one-fourth. But the share of white women working in these occupations has more than tripled; of black men, more than quadrupled; of black women, more than octupled.

Moreover, wage gaps for those working in the same occupations have diminished as well. "Over the same time frame, wage gaps within occupations narrowed. Whereas working white women earned 58% less on average than white men in the same occupations in 1960, by 2008 they earned 26% less. Black men earned 38% less than white men in the typical occupation in 1960, but had closed the gap to 15% by 2008. For black women the gap fell from 88% in 1960 to 31% in 2008."

Much can be said about the causes behind these changes, but here, I want to focus on the effect on economic growth. For the purposes of developing a back-of-the-envelope estimate, Klenow builds up a model with some of these assumptions: "Each person possesses general ability (common to
all occupations) and ability specific to each occupation (and independent across occupations). All groups (men, women, blacks, whites) have the same distribution of abilities. Each young person knows how much discrimination they would face in any occupation, and the resulting wage they would get in each occupation. When young, people choose an occupation and decide how
much to augment their natural ability by investing in human capital specific to their chosen

With this framework, Klenow can then estimate how much of U.S. growth over the last 50 years or so can be traced to greater equality of opportunity, which encouraged many in women and minority groups who had the underlying ability to view it as worthwhile to make a greater investment in human capital.

"How much of overall growth in income per worker between 1960 and 2008 in the U.S. can be explained by women and African Americans investing more in human capital and working more in high-skill occupations? Our answer is 15% to 20% ... White men arguably lost around 5% of their earnings, as a result, because they moved into lower skilled occupations than they otherwise would have. But their losses were swamped by the income gains reaped by women and blacks."

At least to me, it is remarkable to consider that 1/6 or 1/5 of total U.S. growth in income per worker may be due to greater economic opportunity. In short, reducing discriminatory barriers isn't just about justice and fairness to individuals; it's also about a stronger U.S. economy that makes better use of the underlying talents of all its members.

2) The black-white wage gap--and the share of the gap that is "unexplained"-- is rising, not falling. Here's part of what I wrote about in "Breaking Down the Black-White Wage Gap (September 6, 2017):

Mary C. Daly, Bart Hobijn, and Joseph H. Pedtke set the stage for a more insightful discussion in their short essay, "Disappointing Facts about the Black-White Wage Gap," written as an "Economic Letter" for the Federal Reserve Bank of San Francisco (September 5, 2017, 2017-26). Here are a couple of figures showing the black-white wage gap, and then seeking to explain what share of that gap is associated with differences in state of residence, education, part-time work, industry/occupation, and age. The first figure shows the wage gap for black and white men; the second for black and white women.

Here are some thoughts on these patterns:

1) The black-white wage gap is considerably larger for men (about 25%) than for women (about 15%). Also, the wage gaps seem to have risen since the 1980s.

2) The three biggest factors associated with the wage gap seem to be education level, industry/occupation, and "unexplained."

3) The "unexplained" share is rising over time time. As the authors explain: "Perhaps more troubling is the fact that the growth in this unexplained portion accounts for almost all of the growth in the gaps over time. For example, in 1979 about 8 percentage points of the earnings gap for men was unexplained by readily measurable factors, accounting for over a third of the gap. By 2016, this portion had risen to almost 13 percentage points, just under half of the total earnings gap. A similar pattern holds for black women, who saw the gaps between their wages and those of their white counterparts more than triple over this time to 18 percentage points in 2016, largely due to factors outside of our model. This implies that factors that are harder to measure—such as discrimination, differences in school quality, or differences in career opportunities—are likely to be playing a role in the persistence and widening of these gaps over time." The authors also cite this more detailed research paper with similar findings.

4) In looking at the black-white wage gap for women, it's quite striking that this gap was relatively small back in the 1980s, at only about 5%, and that observable factors like education and industry/occupation explained more than 100% of the wage gap at the time. But as the black-white wage gap for women increased starting in the 1990s, an "unexplained" gap opens up.

5) It is tempting to treat the "unexplained" category as an imperfect but meaningful measure of racial discrimination, but it's wise to be quite cautious about such an interpretation. On one side, the "unexplained" category may overstate discrimination, because it doesn't include other possible variables that affect wages (for example, one could include previous years of lifetime work experience, or length of tenure at a current job, scores on standardized tests, or many other variables). In addition, the variables that are included like level of education are being measured in broad terms, and so it is possible that, say, a blacks and whites with a college education are not the same in their skills and background. On the other side, the "unexplained" category could easily understate the level of discrimination. After all, education levels and industry/occupation outcomes don't happen in a vacuum, but are a result of the income, education, and jobs of family members. For this reason, noting that a wage gap is associated with some different in education or industry/occupation may reflect aspects of social discrimination. The kinds of calculations presented here are useful, but they don't offer final answers.

In short, the black-white wage gap is rising, not falling. The wage gap is also less associated with basic measures like level of education or industry/occupation than it was before. I can hypothesize a number of explanations for this pattern, but none of my hypotheses are cheerful ones.


3) The patterns in which speeding tickets are given for those just a little over the speed limit can  reveal discrimination. I discuss some evidence on this point in "Leniency in Speeding Tickets: Bunching Evidence of Police Bias" (April 5, 2017):

Imagine for a moment the distribution of speed for drivers who are breaking the speed limit. One would expect that a fairly large number of drivers break the speed limit by a small amount, and then a decreasing number of drivers break the speed limit by larger amounts.

But here's the actual distribution of amount over the speed limit on the roughly 1 million tickets given by about 1,300 officers of the Florida Highway Patrol between 2005 and 2015. The graph is fromFelipe Goncalves and Steven Mello, "A Few Bad Apples? Racial Bias in Policing," Princeton University Industrial Relations Section Working Paper #608, March 6, 2017. The left-hand picture shows the distribution of the amount over the speed limit on the speeding ticket given to whites; the right-hand picture shows the distribution the amount over the speed limit on the speeding tickets given to blacks and Hispanics.

Some observations:

1) Very few tickets are given to those driving only a few miles per hour over the speed limit. Then there is an enormous spike in those given tickets for being about 9 mph over the speed limit. There are also smaller spikes at some higher levels. In Florida, the fine for being 10 mph over the limit is substantially higher (at least $50, depending on the county) compared to the fine for being 9 mph over the limit.

2) The jump at 9 mph is sometimes called a "bunching indicator" and it can be a revealing approach in a number of contexts. For example, if being above or below a certain test score makes you eligible for a certain program or job, and one observes bunching at the relevant test score, it's evidence that the test scores are being manipulated. If being above or below a certain income level affects your eligibility for a certain program, or whether you owe a certain tax, and there is bunching at that income level, it's a sign that income is being manipulated. Real-world data is never completely smooth, and always has some bumps. But the spikes in the figure above are telling you something.

3) Goncalves and Mello note that the spike at 9 mph is higher for whites than for blacks and Hispanics. This suggests the likelihood that whites are more likely to catch a break from an officer and get the 9 mph ticket. The research in the paper investigates this hypothesis in some detail ...

In the big picture, one of the reminders from this research is that bias and discrimination doesn't always involve doing something negative. In the modern United States, my suspicion is that some of the most prevalent and hardest-to-spot biases just involve not cutting someone an equal break, or not being quite as willing to offer an opportunity that would otherwise have been offered.


4) Many of the communities that suffer the most from crime are also the communities where the law-abiding and the law-breakers both experience a heavy law enforcement presence, and where large numbers of young men end up being incarcerated. Here are some slices of my discussion from "Inequalities of Crime Victimization and Criminal Justice" (May 20, 2016):

And law-abiding people in some communities, many of them predominantly low-income and African-American, can end up facing an emotionally crucifying choice. One one side, crime rates in their community are high, which is a terrible and sometimes tragic and fatal burden on everyday life. On the other side, they are watching a large share of their community, mainly men, becoming involved with the criminal justice system through fines, probation, fines, or incarceration. Although those who are convicted of crimes are the ones who officially bear the costs, in fact the costs when someone needs to pay fines, or can't earn much or any income, or can only be visited by making a trip to a correctional facility are also shared with families, mothers, and children. Magnus Lofstrom and Steven Raphael explore these questions of "Crime, the Criminal Justice System, and Socioeconomic Inequality" in the Spring 2016 issue of the Journal of Economic Perspectives. ...

It's well-known that rates of violent and property crime have fallen substantially in the US in the last 25 years or so. What is less well-recognized is that the biggest reductions in crime have happened in the often predominantly low-income and African-American communities that were most plagued by crime. Loftrom and Raphael look at crime rates across cities with lower and higher rates of poverty in 1990 and 2008:
"However, the inequality between cities with the highest and lower poverty rates narrows considerably over this 18-year period. Here we observe a narrowing of both the ratio of crime rates as well as the absolute difference. Expressed as a ratio, the 1990 violent crime rate among the cities in the top poverty decile was 15.8 times the rate for the cities in the lowest poverty decile. By 2008, the ratio falls to 11.9. When expressed in levels, in 1990 the violent crime rate in the cities in the upper decile for poverty rates exceeds the violent crime rate in cities in the lowest decile for poverty rates by 1,860 incidents per 100,000. By 2008, the absolute difference in violent crime rates shrinks to 941 per 100,000. We see comparable narrowing in the differences between poorer and less-poor cities in property crime rates. ... "
It remains true that one of the common penalties for being poor in the United States is that you are more likely to live in a neighborhood with a much higher crime rate. But as overall rates of crime have fallen, the inequality of greater vulnerability to crime has diminished.

On the other side of the crime-and-punishment ledger, low-income and African-American men are more likely to end up in the criminal justice system. Lofstrom and Raphael give sources and studies for the statistics: "[N]nearly one-third of black males born in 2001 will serve prison time at some point in their lives. The comparable figure for Hispanic men is 17 percent ... [F]or African-American men born between 1965 and 1969, 20.5 percent had been to prison by 1999. The comparable figures were 30.2 percent for black men without a college degree and approximately 59 percent for black men without a high school degree."

I'm not someone who sympathizes with or romanticizes those who commit crimes. But economics is about tradeoffs, and imposing costs on those who commit crimes has tradeoffs for the rest of society, too. For example, the cost to taxpayers is on the order of $350 billion per year, which in 2010 broke down as "$113 billion on police, $81 billion on corrections, $76 billion in expenditure by various federal agencies, and $84 billion devoted to combating drug trafficking." The question of whether those costs should be higher or lower, or reallocated between these categories, is a worthy one for economists. ... Lofstrom and Raphael conclude:
"Many of the same low-income predominantly African American communities have disproportionately experienced both the welcome reduction in inequality for crime victims and the less-welcome rise in inequality due to changes in criminal justice sanctioning. While it is tempting to consider whether these two changes in inequality can be weighed and balanced against each other, it seems to us that this temptation should be resisted on both theoretical and practical grounds. On theoretical grounds, the case for reducing inequality of any type is always rooted in claims about fairness and justice. In some situations, several different claims about inequality can be combined into a single scale—for example, when such claims can be monetized or measured in terms of income. But the inequality of the suffering of crime victims is fundamentally different from the inequality of disproportionate criminal justice sanctioning, and cannot be compared on the same scale. In practical terms, while higher rates of incarceration and other criminal justice sanctions may have had some effect in reducing crime back in the 1970s and through the 1980s, there is little evidence to believe that the higher rates have caused the reduction in crime in the last two decades. Thus, it is reasonable to pursue multiple policy goals, both seeking additional reductions in crime and in the continuing inequality of crime victimization and simultaneously seeking to reduce inequality of criminal justice sanctioning. If such policies are carried out sensibly, both kinds of inequality can be reduced without a meaningful tradeoff arising between them."

5) An "audit study" of housing discrimination involves finding pairs of people, giving them similar characteristics (job history, income, married/unmarried, parents/not parents) and sending them off to buy or rent a place to live. In "Audit Studies and Housing Discrimination" (September 21, 2016), I wrote in part:

Cityscape magazine, published by the US Department of Housing and Urban Development three times per year, has a nine-paper symposium on "Housing Discrimination Today" in the third issue of 2015. The lead article by Sun Jung Oh and John Yinger asks: "What Have We Learned From Paired Testing in Housing Markets?" (17: 3, pp. 15-59). ...

There have been four large national-level paired testing studies of housing discrimination in the US in the last 40 years. "The largest paired-testing studies in the United States are the Housing Market Practices Survey (HMPS) in 1977 and the three Housing Discrimination Studies (HDS1989, HDS2000, and HDS2012) sponsored by the U.S. Department of Housing and Urban Development (HUD)." Each of the studies were spread over several dozen cities. The first three involved about 3,000-4,000 tests; the 2012 study involved more than 8,000 tests. The appendix also lists another 21 studies done in recent decades.

Overall, the findings from the 2012 study find ongoing discrimination against blacks in rental and sales markets for housing. For Hispanics, there appears to be discrimination in rental markets, but not in sales markets. Here's a chart summarizing a number of findings, which also gives a sense of the kind of information collected in these studies.

However, the extent of housing discrimination in 2012 has diminished from previous national-level studies. Oh and Yinger write (citations omitted): "In 1977, Black homeseekers were frequently denied access to advertised units that were available to equally qualified White homeseekers. For instance, one in three Black renters and one in every five Black homebuyers were told that there were no homes available in 1977. In 2012, however, minority renters or homebuyers who called to inquire about advertised homes or apartments were rarely denied appointments that their White counterparts were able to make.

Friday, January 12, 2018

The Problem of Questionable Patents

The theoretical case for patents is clear enough: if you want people and companies to have an incentive for investing money and time in seeking innovations, you need to offer them some assurance that others won't immediately copy any successful discoveries.  But with the power of patents comes the risk of gaming the patent system and of patents being granted when the proffered invention is either not new, or obvious, or both. Michael D. Frakes and Melissa F. Wasserman tackle these issues in "Decreasing the Patent Office’s Incentives to Grant Invalid Patents" (Hamilton Institute Policy Proposal 2017-17, December 2017). Also, Jay Shambaugh, Ryan Nunn, and Becca Portman offer some useful background information in "Eleven Facts about Innovation and Patents" (Hamilton Project, December 2017).

The Shambaugh, Nunn, and Portman paper offers a few background figures on patents that, as you look, at them, can raise your eyebrows a bit. The background here is that three main patent-granting agencies in the world--the US Patent Office and Trademark Office, the Japanese Patent Office, and the European Patent Office--are sometimes referred to as the Trilateral Patent Offices. The usual belief is that "compared to the USPTO, the JPO and EPO are believed to apply stricter scrutiny to applications." Getting a patent from all three of these offices is called a "triadic" patent, and the number of triadic patents is sometimes used as a measure of quality. Now consider a couple of comparisons.

The number of patent applications in the US had more-or-less doubled since 2000. In that time, the number of patent applications in Japan has dropped by one-quarter, while the number in Europe has risen by about 50%. One possible interpretation of this pattern is that the US economy is the grip of a massive wave of innovation far outstripping Japan and Europe, which may foretell a productivity boom for the US economy. An alternative interpretation is that it's so much easier to apply for a patent in the US, and to have a patent granted, that the US Patent Office is attracting lots of low-quality and invalid patent applications, and some of those are sneaking through the system to receive actual patents.

Here's a figure that poses a similar question. This graph shows the share of GDP spent on research and development on the horizontal axis. The vertical axis is a measure of the number of "high-quality" patents, which in this figure refers to an innovation that is patented in at least two of the three Trilateral Patent Offices. The US level of R&D spending is a bit below that of Germany and Japan, but similar. However, when measured in terms of high-quality patents filed, the US lags well behind. Again, this could be the result that US firms aren't bothering to apply for European and Japanese protection for all their great patents. Or it could be a signal that the rise in US patents includes a greater of low-quality or even invalid patents than those in Japan and Europe.
Frakes and Wasserman lay out how the US Patent Office works in greater detail, in a way that for me sharpens these concerns. For example, they write (citations omitted):
"There is an abundance of anecdotal evidence that patent examiners are given insufficient time to adequately review patent applications. On average, a U.S. patent examiner spends only 19 hours reviewing an application, including reading the application, searching for prior art, comparing the prior art with the application, and (in the case of a rejection) writing a rejection, responding to the patent applicant’s arguments, and often conducting an interview with the applicant’s attorney. Because patent applications are legally presumed to comply with the statutory patentability requirements when filed, the burden of proving unpatentability rests with the Agency. That is, a patent examiner who does not explicitly set forth reasons why the application fails to meet the patentability standards must then grant the patent." 
The US Patent Office is funded by the fees it collects, which fall into several categories, as Frakes and Wasserman explain:
"The overwhelming majority of Patent Office costs are attributed to reviewing and examining applications. To help cover these expenses, the Agency charges examination fees to applicants. These fees fail to cover even half of the Agency’s examination costs, however. To make up for this deficiency, the Agency relies heavily on two additional fees that are collected only in the event that a patent is granted: (1) issuance fees, paid at the time a patent is granted; and (2) renewal fees, paid periodically over the lifetime of an issued patent as a condition of the patent remaining enforceable. Combined with examination fees, these fees account for nearly all of the Patent Office’s revenue. ... In fiscal year 2016 the Patent Office estimated that the average cost of examining a patent application was about $4,200 . The examination fee that year was set at only $1,600 for large for-profit corporations; at $800 for individuals, small firms, nonprofit corporations, or other enterprises that qualify for small-entity status; and at $400 for individuals, small firms, nonprofit corporations, or other enterprises that qualify for micro-entity status."
An obvious concern is that if the US Patent Office relies heavily on fees that are collected only after a patent is granted, then there is an obvious incentive to grand more patents. Indeed, they cite studies to show that when the Patent Office is facing financial troubles, it tends to grant more patents. 

An additional concern is that the US Patent Office doesn't really reject patents, at least not permanently, because you can apply repeatedly. "Considering that about 40 percent of the applications filed in fiscal year 2016 are repeat applications (up from 11 percent in 1980), a substantial percentage of the Patent Office’s backlog can be attributed to its inability to definitively reject applications." To put it another way, an applicant for a patent can just keep applying until it gets assigned to a less-experienced examiner during a budget crunch, and improve your odds that it will eventually be granted.

With these thoughts in mind, Frakes and Wasserman offer some practical solutions, which include: 1) increase patent examination fees and abolish "issuance" fees, to reduce the financial incentive to grant patents; 2) limit repeat applications, perhaps by charging higher fees; 3) give patent examiners more time (and charge higher fees to support that additional time as needed).

But the key economic insight between these proposals and others is that in a economy whose future is based on innovation and technology, the danger of granting a substantial number of patents which should not have been allowed has important costs. As Frakes and Wasserman write: 
"Although patents encourage innovation by helping inventors to recoup their research and development expenses, this comes at a cost—consumers pay higher prices and have less access to the patented invention. Although society can accept such consequences for a properly issued patent, an invalid patent imposes these costs on society without providing the commensurate benefits from additional innovation because, by definition, an invalid patent is one issued for an existing technology or an obvious technological advancement. Invalid patents provide no innovative benefit to society because the public already possessed the patented inventions.
"In addition to this harm, erroneously issued patents can stunt innovation and competition. Competitors might forgo research and development in areas covered by improperly issued patents to minimize the risk of expensive and time-consuming  litigation. There is growing empirical evidence that invalid patents can increase so-called patent thickets—dense webs of overlapping patent rights—that in turn raise the cost of licensing and complicate business planning. Because a firm needs a license to all of the patents that cover its products, other firms can use questionable patents to opportunistically extract licensing fees. There is mounting evidence that nonpracticing entities—commonly known as patent trolls—use patents of questionable validity to assert frivolous lawsuits and extract licensing revenue from innovative firms. Invalid patents can also undermine the business relations of market entrants because customers might be deterred from transacting with a company out of fear of a contributory patent infringement suit. Finally, erroneously issued patents can inhibit the ability of start-ups to obtain venture capital, especially if a dominant player in the market holds the patent in question."
For some other thoughts on the economics of patents, the interested reader might check: