Month: April 2013

Each year when the president releases a proposed federal budget, as President Obama did on Wednesday, an \”Analytical Perspectives\” volume is also released with other angles on the budget. This year, Chapter 20 of that volume is about \”Federal Investment.\” Of course, there\’s a certain tendency by those who favor a certain area–from national defense to health care to antipoverty programs–to label as \”an investment.\” But as the budget states: \”The distinction between investment spending and current outlays is a matter of judgment. The budget has historically employed a relatively broad classification of investment, encompassing physical investment, research, development, education, and training.\” In these areas, what is the accumulated value of the federal investments over time?

The total stock of physical capital from federal investment is worth $3.2 trillion in 2013, according to the budget estimates, which look both at investment and at depreciation over time. About 30% of that is defense-related. About 20% is direct federal spending on projects like water and power. The remaining half or so is capital financed by federal grants, and about two-thirds of that ($1.1 trillion) is related to transportation.

The total stock of research and development capital from federal investment is $1.6 trillion in 2013. One way to divide that up is that about 40% is related to national defense, and 60% is not. Another way to divide it up is that about half is basic research, and the other half is applied research.

The total stock of education and training from federal investment is estimated at $2.2 trillion in 2013, with about three-quarters of that being K-12 education, and the rest being higher education.

I\’m sure that the calculations behind these estimates can be critiqued on many grounds, but just taking them at face value, it\’s thought-provoking that the stock of physical capital investment is less than the sum of the education and R&D capital. In discussions of federal \”investment,\” the quick and handy references are usually to fixing roads and bridges. Here in Minnesota, where memories are still fresh of the highway bridge that collapsed in August 2007, I\’d be the last one to denigrate fixing up roads and bridges. But when it comes to long-term economic health of the country, it\’s not the 1950s any more, when the legislation for the interstate highway system was passed. Investments in technology and education, as well as in communications and energy infrastructure, seem likely to be more important drivers of 21st century growth than roads and bridges. 

When the president\’s proposed budget  is released each year, I confess that I tend to ignore the actual and projected spending numbers, and instead head right for the \”Analytical Perspectives\” and \”Historical Tables\” that  volume that always accompany the budget. The president\’s proposed budget is a wish list, which will eventually be compared with the budget proposals from the House of Representatives and from the U.S. Senate–although the Senate has failed to pass an actual budget in the last few years. While that process hashes itself out, the analysis and history are more immediately interesting to me.

For example, Chapter 6 of the \”Analytical Perpectives\” is about \”Social Indicators: \”The social indicators presented in this chapter illustrate in broad terms how the Nation is faring in selected
areas in which the Federal Government has significant responsibilities. Indicators are drawn from six selected domains: economic, demographic and civic, socioeconomic, health, security and safety, and environment and energy.\” A long table stretching over parts of three pages shows many statistics for ten-year intervals since 1960, and for the last few years. For me, tables like this offer a grounding in basic facts and patterns. Here, I\’ll just offer 21 comparisons drawn from the table over the last half-century or so, from 1960 or 1970 up to the most recent data.

• Real GDP per person has roughly tripled in the last half-century, rising from $15,648 in 1960 to 43,352 in 2012 (as measured in constant 2005 dollars).
• Inflation has reduced the buying power of the dollar over time such that $1 in 2011 had about the same buying power as 15 cents back in 1960, according to the Consumer Price Index. 
• The employment/population ratio rose from 56.1% in 1960 to 64.4% by 2000, but since then has sagged back to 58.6% in 2012–roughly the same level as the late 1970s. 
• The share of the population receiving Social Security disabled worker benefits was 0.9% in 1960 and is now 5.8%. 
• The real stock of fixed assets and consumer durable goods has more than quadrupled in the last half-century, rising from $11.5 trillion in 1960 to $51.1 trillion in 2011 (as measured in real 2010 dollars).
• The net national savings rate was 10.3% of GDP in 1960, 8.1% in 1970, 6.2% in 2000–compared with negative 0.7% in 2010 and negative 0.6% in 2011.
• Research and development spending has barely budged over time: it was 2.6% of GDP in 1960 and 2.7% of GDP in 2011, and hasn\’t varied much in between.
• In 1960, 78% of the over-15 population had ever been married; in 2012, it was 68.8%
• Average family size was 3.7 people in 1960, and 3.1 people in 2012. 
• Single parent households were 4.4% of households in 1960, and 9.3% of all households in 2012. 
• In 1960, 11% of those in the 25-34 age bracket were college graduates; in 2011, the corresponding number was 31.5%.
• The average math achievement score for a 17 year-old on the National Assessment of Educational Progress was 304 in 1970, and 306 in 2010. The average reading achievement score for a 17 year-old was 285 in 1970 and 286 in 2010.
• Real disposable per capita income has roughly tripled in the last half-century, rising from $12,457 in 1960 to $37,646 in 2012 (measured in constant 2011 dollars).
• Life expectancy at birth was 69.7 years in 1960, and 78.7 years in 2011.
• Infant mortality was 26 per 1,000 births in 1960, and 6.1 per 1,000 births in 2011. 
• In 1960, 13.3% of the population age 20-74 was obese (as measured by having a Body Mass Index above 30). In 2010, 35.3% of the population was obese. 
• In 1970, 39.2% of those age 18 and older were cigarette smokers. By 2011, this has fallen by half to 19%.
• Total national health expenditures were 5.2% of GDP in 1960, and 17.9% of GDP in 2012. 
• Energy consumption per capita was 250 million BTUs in 1960, and 312 million BTUs  in 2011. 
• Energy consumption per dollar of real GDP (measured in constant 2005 dollars) was 15,900 in 1960 vs. 7,300 in 2011. 
• Electricity net generation from renewable sources was 19.7% of the total in 1960, and 12.7% of the total in 2011.  

 I read these sorts of figures as evidence of substantial and genuine progress in the standard of living–broadly understood–for Americans. But of course, it\’s also easy to see some dangers and warning signs.

The U.S. unemployment rate remains disturbingly high. It first rose above 7.5% in January 2009, and 50 months later in March 2013 remains above 7.5%. For comparison, in the aftermath of the 2001 recession, the unemployment rate peaked at 6.3% and in the aftermath of the 1990-91 recession it peaked at 7.8%. Since the Great Depression, only the \”double-dip\” recessions of the early 1980s offer a comparably sustained rate of high unemployment: unemployment rates reached 7.5% or higher for 7 months in 1980, and then after a mini-recovery,went back above 7.5% for 31 months from September 1981 through April 1984.  After the deep recession of 1974-74, the unemployment rate exceeded 7.5% for 26 months. Here\’s a figure from the ever-useful FRED website run by the Federal Reserve Bank of St. Louis showing the unemployment rate since 1970.

But although the figure shows that the unemployment rate remains high, it also shows that the U.S. labor market is slowly healing itself. For evidence on the internal functioning of U.S. labor markets, beyond the headline unemployment rate, I now and then check the JOLTS report–that is, Job Openings and Labor Turnover Survey–also published by the Bureau of Labor Statistics. The data for February 2013 was just released, and here are a few figures that caught my eye.

The number of actual jobs has been rising fairly steadily since the end of the recession in mid-2009, along with the number of job openings. 

One of the detailed job market indicators I find most intuitively revealing is the ratio of unemployed people to job openings. When the labor market is fairly healthy, like in the early 2000s, this ratio is maybe 2: 1 or 3:1. During the unsustainable bubble economy just before the Great Recession, the ratio fell to about 1.5:1. During the worst of the recession, it exceeded 6:1. It has now dropped back to a bit above 3:1–still uncomfortably high, but not as comprehensively awful as a few years back.

Another intuitively revealing measure looks at how people leave jobs. Do they leave by quitting, which can be viewed as a voluntary departure? Or do they leave by layoffs or discharges, which is surely more of an involuntary departure? In a healthy U.S., labor market, quits exceed layoffs/discharges: that is, more people are changing jobs by choice than by necessity. During the recession, this pattern flip-flopped for a time. But layoffs and discharges have now dropped to lower levels than before the recession, and quits are on the rise.

When it comes to the U.S. labor market picture, it feels uncomfortable to say anything positive. I\’m sure to get emails pointing out that the share of adults no longer in the labor force, and thus not counted as unemployed, is rising, as well as less savory notes accusing me of ignorance and heartlessness and being out of touch with the problem of unemployment. But of course, my point here is not to make the clearly incorrect claim that the U.S. labor market is already bathed in a ruddy glow of good health–only that it has been slowly improving along multiple interrelated dimensions for the last few years.

For years, I\’ve thought about international trade agreements mainly in terms of how high-income countries relate to lower-income countries: for example, would the high-income countries be willing to reduce their support for domestic farmers in exchange for greater intellectual property protection for for their exports? Such issues seem to have stalled the World Trade Organization \”Doha round\” talks, which started back in 2001 and have not yet found a way to reorient themselves toward an agreement. In the meantime, countries around the world have been moving ahead with regional trade agreements.

Nonetheless, I was surprised when President Obama and representatives of the European Union announced in February that they planned to start negotiations on a Transatlantic Trade and Investment Partnership.\”  My presumption was that such talks wouldn\’t offer much in the way of gains, because trade barriers were already low between these economies. Jeffrey J. Schott and Cathleen Cimino offer a useful overview of the issues in \”Crafting a Transatlantic Trade and Investment Partnership: What Can Be Done,\” written as Policy Brief PB13-8 for the Peterson Institute for International Economics.  My own reading of their argument is that the gains from reducing trade barriers between the U.S. and the EU are likely to be perceptible, but low. However, the talks may behave a greater effect if they can kick-start the Doha round of trade talks. And the outcome may ultimately turn on trade agreements already in place with South Korea!

Basic facts first: In recent years, the U.S. has sent 21-25% of its exports to the EU, and received 19-21% of its imports from the EU. The U.S. runs trade deficits with the EU, but they are relatively small is size: for example, just $20 billion in 2009, and up to about $70 billion in 2012. About half of the stock of all U.S. foreign direct investment is in the EU; just over 60% of the stock of all EU foreign direct investment is in the United States. Given this fairly high level of economic interconnectedness, how much more is possible?

Many of the remaining issues seem important to those in certain industries, but not important overall.
For example, in the last couple of years there have been ongoing negotiations between the U.S. and the EU in which the U.S. had three main requests: \”approving lactic acid as a pathogen reduction treatment for processing beef,\”  \”amending regulations for determining the disease status of US hogs exported for breeding,\” and \”conditions for the use of tallow or animal fat in the production of EU biofuels.\”  \”On the European side, the main demands involved regulations designed to (1) open the US market for European beef cleared of “mad cow” disease, (2) facilitate the import of apples and
pears, which are currently prohibited due to the lack of official pest-free status, and (3) apply “regionalization” to animal and plant disease determinations, which would allow the continuation
of overall EU agricultural exports to the United States in the event exports from a certain region were restricted due to disease outbreak.\”

Perhaps fortunately, if these negotiations are to matter, larger issues are on the table, too. The starting point would be to phase out remaining tariffs. But other steps would include: rules for international investment; freeing up international bidding for government contracts; harmonizing a wide range of regulatory issues across finance, insurance, telecommunications, and other industries;  environmental protections; labor rules; customs procedures; competition  policy; and intellectual \”property rights.\”

Schott and Cimino\’s discussion has a lot to say on all the details of a Transatlantic Trade and Investment Partnership in all of these areas, but it barely pauses to mention a few estimates of how such an agreement would affect the U.S. economy. The general tone of these estimates is that removing tariff and nontariff barriers might increase trade flows between the regions by 10% or so, with a lot of that change occuring in service industries. Like most economists, I have a knee-jerk reflexive support of lower barriers to trade, but even for me, this sort of change does not elevate my pulse rate.

More intriguing is this suggestion from Schott and Cimino: \”A successful effort to resolve disagreements across the Atlantic could become a template for the stalled global trade talks in several difficult areas, from agriculture to cross-border rules on services, investment, and regulations.\” For example, if the U.S. and the EU can free up trade in their agricultural sectors, this might create some movement in the Doha round trade talks. If the U.S. and EU can agree on how to address regulatory and environmental and intellectual property issues with each other,  perhaps it will create some flexibility for them to make agreements in these areas in the multilateral trade talks. This approach certainly has enough plausibility to be worth exploring. At a minimum, if the U.S. and the EU can\’t agree with each other on these sorts of issues, it certainly doesn\’t bode well for worldwide trade agreements.

 Oddly enough, the proposed Transatlantic Trade and Investment Partnership between the U.S. and the EU could turn on trade agreements with South Korea. Both the U.S. and the EU have detailed and extensive free trade agreements with South Korea that address many of these issues. Thus, Schott and Cimino explain that U.S. and the EU could essentially work on consolidating their already-approved trade agreements with South Korea–and use the result as a bridge toward their own agreement. When the U.S. and EU are negotiating trade agreements based on pre-existing agreements with South Korea, we have clearly entered the 21st century globalized world economy!

Everyone agrees that entrepreneurs are important to an economy that, like the U.S., relies on innovation and productivity growth. But concrete data on the numbers and characteristics of entrepreneurs are not always easy to come by. The Global Entrepreneurship Monitor project has been seeking to address this issue since 1999 using survey data. The GEM project now surveys 54 countries, and the results for the 2011 U.S. survey of 5800 adults are now available. It is publishes as the National Entrepreneurial Assessment for the United States of America, written by
Donna J. Kelley, Abdul Ali, Edward G. Rogoff, Candida Brush, Andrew C. Corbett, Mahdi Majbouri and Diana Hechavarria.

In general, survey measures suggest that many Americans think of themselves as potential or actual  entrepreneurs. \”Capabilities perceptions in the United States are among the highest of the innovation-driven economies.Over 55% of adults (aged 18–64) believe they have the skills and ability to start a business. This measure shows a relatively stable pattern over time.\”

But measures of actual entrepreneurship show some differing results. One measure is Total Entrepreneurial Activity (red bars in the graph below), which refers to what share of the population is starting or running a new business. By this measure, the U.S. leads the way among high-income economies. Another measure is Entrepreneurial Employee Activity (blue bars in the graph below), which seeks to measure \”employees developing or launching new goods or services or setting up a new business unit, a new establishment or subsidiary for their main employer.\” By this measure, the U.S. performs well, but lags behind Sweden, Denmark, Belgium and Finland. Of course, this difference points out that  organizations that encourage \”intrapreneurs\” may be as important as entrepreneurs who start businesses from scratch.

The report also suggests that entrepreneurship in the U.S. made something of a comeback in 2010 and 2011, after sinking with the economy in 2009. The following graph shows five measures of entrepreneurship in the U.S.

\”1. Intent. Percentage of non-entrepreneurs in the adult population that intend to start a business in the next three years.
2. Nascent. Percentage of the adult population that is in the process of starting a business that has not paid salaries or wages for more than three months.
3. New. Percentage of the adult population that is running a new business (beyond nascent stage), less than 42 months old.
4. Established. Percentage of the adult population that is running an established business older than 42 months.
5. Discontinuance. Percentage of the adult population that has discontinued a business in the last year.\”

As the report points out, the green line \”nascent\” entrepreneurs show the biggest jump. They write: \”Nascent activity accounted for the majority of this activity: 8.4% of the adult working age population—two-thirds of the entrepreneurs—were in the early stages of this process. Additionally, nascent activity accounted for much of the increase in TEA, indicating that a lot of people were jumping into entrepreneurship in 2011.\”

The report has lots more breakdowns of U.S. entrepreneurs by age, education, gender, income, industry, export focus, and other factors. While I find these survey results thought-provoking, I confess that interpreting the results remains difficult for me. When I see data to the effect that 55% of Americans consistently believe that have the skills and ability to start a business, I find myself wondering (a bit cynically, I confess) what percentage also believe that they could be professional athletes–if only they had practiced a little more in high school.

When I seen rates of entrepreneurial activity rising in 2010 and 2011, I find myself wondering if this should be interpreted more as a reaction of people who lacked other opportunities in the dreadful job market of these years and were trying to scrape together some income by working on their own or with a few friends, rather than as a signal of greater entrepreneurial vigor.  There\’s a difference in the economic effects of entrepreneurs who start businesses which grow and hire and individuals or small groups who make a living working independently, but who have no real opportunities or plans for expanding.

I also find myself remembering the arguments by John Maynard Keynes to the effect that entrepreneurs as a group are driven by \”animal spirits,\” not a rational calculation  of costs and benefits. For a a nice overview of what Keynes meant by the term \”animal spirits,\” and the earlier uses of the the term in Descartes\’ analysis of physiology, I recommend this short article by Roger Koppl that appeared in back in the Summer 1991 issue of my own Journal of Economic Perspectives.  (This article, like all JEP articles back to the first issue in 1991, is freely available on-line courtesy of the American Economic Association.) Koppl wrote:

\”Although Keynes would have agreed that animal spirits can lead to bubbles and fads and crashes, he also argued that positive investment generally occurs because of a mistake by the investor, a mistake undertaken because of animal spirits. Keynes argued that since entrepreneurs and investors would often be immobilized if they sought to make rational economic decisions, animal spirits are needed to leapfrog rationality and bolster the economy.\”

Of course, even if entrepreneurship is heavily driven by animal spirits, the ultimate success of entrepreneurial efforts will still depend heavily on the economic, financial, regulatory, and institutional environment.

What is America\’s costliest health condition? I suppose the two obvious guesses might be cancer or heart disease. But the answer may well be Alzheimer\’s disease, according to \”Monetary Costs of Dementia in the United States,\” written by Michael D. Hurd, Paco Martorell, Adeline Delavande,
Kathleen J. Mullen, and Kenneth M. Langa, and published in the April 3, 2013 issue of the New England Journal of Medicine. They write:

\”The estimated prevalence of dementia among persons older than 70 years of age in the United States in 2010 was 14.7%. The yearly monetary cost per person that was attributable to dementia was either $56,290 … or $41,689 …, depending on the method used to value informal care. These individual costs suggest that the total monetary cost of dementia in 2010 was between $157 billion and $215 billion. … By 2040, assuming that prevalence rates and cost per person with dementia remain the same, our estimates suggest that these costs will more than double because of the aging of the population. …

Our estimate places dementia among the diseases that are the most costly to society. The cost for dementia care purchased in the marketplace ($109 billion) was similar to estimates of the direct health care expenditures for heart disease ($96 billion in 2008, or $102 billion in 2010 dollars) and significantly higher than the direct health care expenditures for cancer ($72 billion in 2008, or $77 billion in 2010 dollars). These
costs do not include the costs of informal care, which are likely to be larger for dementia than for heart disease or cancer.\”

The fact that 1 person in 7 in the United States over the age of 70 has dementia now was an unpleasant surprise to me: I would not have guessed the proportion was that high. As the population ages, my guess is that the proportion will tent to rise. The calculations in this study show that only for those in the 71-74 age bracket, just 2.8% have dementia, but that percentage steadily rises to 4.9% for those in the 75-79 age bracket, 13.0% for those in the 80-84 age bracket, 20.3% for those in the 85-89 age bracket, and a frankly terrifying 38.5% for those over age 90.

The cost-per-person of dealing with dementia seem likely to rise, too, like so many other service-related health care costs.   With generally lower birthrates in the last few decades, finding unpaid family caregivers to look after those with dementia will become harder. But pushing all of those with Alzheimer\’s into America\’s ultra-costly health care system isn\’t practical, either. Barring some medical breakthrough that dramatically holds down the number of future cases of dementia, thinking about a model of care for growing numbers of dementia patients that is acceptable and affordable is going to pose some difficult social challenges.

The 2013 Economic Report of the President, published recently by the President\’s Council of Economic Advisers, devotes a chapter to bringing readers up to speed on\”The Challenges and Opportunities of U.S. Agriculture.\” Here are some of the main points that jumped out at me:

Over much of the 20th century, the number of farms was falling, the size of farms was growing, rhe rural share of the population was falling, and the share of GDP from farming was falling–although all of these trends have leveled off in the last decade or so.
 

When it comes understanding farm incomes, the key point is to recognize that there are different types of farmers, like whether the main income come from farming or from outside activities. The report explains:

\”Fifty years ago, average household income for the farm population was approximately half that of the general population. Today, however, farm households tend to be better off than other American households; in 2011, median income for farm households was about 13 percent higher than the U.S. median household income … The difference in income between farm households and the nonfarm households is due in part to the broad Department of Agriculture (USDA) definition of what constitutes a farm, which includes farms where the principal operator is retired or has a main occupation other than farming (“residence farms”). Households operating rural residence farms earn more than the U.S. median household income even though their net cash income from farming is negative. Households operating intermediate farms (farms where the principal operator is not retired and reports farming as his or her main occupation) have on average positive net cash income from their farming operations, but most household income comes from sources other than farming. The sources of income for farm households are increasingly diversified, which means that many of them are less vulnerable to the fluctuations of farm income. In 2011, households operating commercial farms had median household incomes two and a half times the overall U.S. median household income, with most of their income from farming. …  By 2000, 93 percent of farm households had income from off-farm sources, including off-farm wages, salaries, business income, investments, and Social Security. Off-farm work has played a key role in raising farm household income. In 2011, only 46 percent of principal operators of farms reported that farming was their main occupation.\”

Farming remains one of the most useful industries for generating vivid and understandable classroom examples of technological change. Again, some examples from the report:

\”While farm household incomes have become more diversified, farm operations have become increasingly specialized: In 1900, a farm produced an average of about five commodities; by 2000, the average had fallen to just over one. This change reflects not only the production and marketing efficiencies gained by concentration on fewer commodities, but also the effects of farm price and
income policies that have reduced the risk of depending on returns from only one crop or just a few crops….

\”In 1950, the average dairy cow produced about 5,300 pounds of milk. Today the average cow produces about 22,000 pounds of milk, thanks to improvements in cow genetics, feed formula, and management practices. Over that time period, the number of dairy cows in America has fallen by more than half, yet U.S. milk production has nearly doubled. …

\”Livestock operations have undergone dramatic changes in the last 30 years. Farmers now use information technology to adjust feed mixes and climate controls automatically to meet the precise needs of animals in confined feeding operations. Integrated hog operations, for example, sharply reduced the amount of feed, capital, and labor needed to produce hogs as new technologies and organizational forms swept the industry. As a result, live hog prices were nearly a third lower than they would have been without the productivity growth that occurred between 1992 and 2004, and retail pork prices were 9 percent lower. …\”

\”From 1948 to 2009, farm productivity nearly tripled, growing at a rate of 1.6 percent a year. In the early part of that period, increased productivity, measured as output per unit of combined inputs, combined with increased use of equipment and chemical inputs to drive the growth in agricultural output. Between 1980 and 2009, equipment stocks fell along with continued declines in labor and land inputs; chemical use continued to rise, but at a much slower rate. Despite reduced input use, agricultural output grew by 1.5 percent a year in 1980–2009, with increasing productivity accounting for almost all of the growth.\” 

Americans continue to spend more on food.

Finally,the average age of farmers has been rising; for example, farmers under age 35 contribute only 6% of the total value of agricultural production. Clearly, this  raises some issues about who the farmers of the future are likely to be (citations omitted):

\”The average age of U.S. farmers and ranchers has been increasing over time. In 1978, 16.4 percent of principal farm operators were over age 65. By 2007, 30 percent of all farms were operated by producers over 65. In comparison, only 8 percent of self-employed workers in nonagricultural industries in 2007 were that old. One reason the farming sector is relatively older is that farmers are living longer and often reside on their farms. Many established farmers never retire. Additionally, one-third of beginning farmers are over age 55, indicating that many farmers move into agriculture only after retiring from a different career. More than 20 percent of farm operators report that they are retired. Another 32 percent of all farms are operated by farmers aged 55 to 64 years. Farmers aged 55 and older account for more than half of the total value of production. Farmers under 35 contribute only 6 percent of the total value of production. This demographic transition has implications for the future of the U.S. agricultural sector.

When any large group of Americans seems to be moving backward over a sustained period of time, it\’s cause for concern. In Wayward Sons: The Emerging Gender Gap in Labor Markets and Education, a report written for Third Way, David Autor and Melanie Wasserman point out: \”Over the last three decades, the labor market trajectory of males in the U.S. has turned downward along four
dimensions: skills acquisition; employment rates; occupational stature; and real wage levels.\” Moreover, Autor and Wasserman argue that these patterns are intertwined through mechanisms that involve marriage decisions and family structure. (Full disclosure: David Autor is the editor of my own Journal of Economic Perspectives, and in my everyday work life, he\’s my boss.)

Educational achievement for men went backward for a time, and has only recently been recovering back toward the levels of the 1960s. Here\’s a figure showing the pattern for the share of 35 year-olds who have completed a four-year college degree, but similar patterns arise if one looks at completing high school, or completing some college, or other measures of education.

Employment rates for both white men and black men have been sagging since the 1970s, although they took an additional drop during the Great Recession.

Meanwhile, wages have been sagging for low-skilled men workers in particular. This figure shows the percent change in real hourly wage levels over the period from 1979-2010. The left-hand figure shows patterns for younger workers, ages 25-39; the right-hand figure shows patterns for older workers, ages 40-64. The blue bars show outcomes for men, given different levels of education, while the red bars show outcomes for women. Clearly, it\’s been a lousy few decades to be a low-skilled worker. But for men, even being a college graduate hasn\’t been much help for those in the 40-64 age bracket. It\’s also notable that in every age and education category, wages changes for women have outperformed those of men.

Autor and Wasserman offer a multipart hypothesis to tie these fact patterns together. They would be the first to admit that their explanation falls short of a rigorous cause-and-effect demonstration. But as they assemble what evidence is available, their story has considerable plausibility. Here\’s their summary of the argument: 

\”[W]we argue first that sharp declines in the earnings power of non-college males combined with gains in the economic self-sufficiency of women—rising educational attainment, a falling gender gap, and greater female control over fertility choices—have reduced the economic value of marriage for women. This has catalyzed a sharp decline in the marriage rates of non-college U.S. adults—both in absolute terms and relative to college-educated adults—a steep rise in the fraction of U.S. children born out of wedlock, and a commensurate growth in the fraction of children reared in households characterized by absent fathers.

The second part of the hypothesis posits that the increased prevalence of single-headed households and the diminished child-rearing role played by stable male parents may serve to reinforce the emerging gender gaps in education and labor force participation by negatively affecting male children in particular. Specifically, we review evidence that suggests that male children raised in single-parent households tend to fare particularly poorly, with effects apparent in almost all academic and economic outcomes. One reason why single-headedness may affect male children more and differently than female children is that the vast majority of single-headed households are female-headed households. Thus, boys raised in these households are less likely to have a positive or stable same-sex role model present. Moreover, male and female children reared in female-headed households may form divergent expectations about their own roles in adulthood—with girls anticipating assuming primary child-rearing and primary income-earning responsibilities in adulthood and boys anticipating assuming a secondary role in both domains. …

Sorting out the causal factors behind these trends is a challenging but nonetheless central topic for social science research and public policy. A growing body of evidence supports the hypothesis that the erosion of labor market opportunities for low-skill workers in general—and non-college males in particular—has catalyzed a fall in employment and earnings among less-educated males and a decline in the marriage rates of less-educated males and females. These developments in turn diminish family stability, reduce household financial resources, and subtract from the stock of parental time and attention that should play a critical role in fomenting the educational achievement and economic advancement of the next generation.\”

 I\’ll leave it to the reader to contemplate policy alternatives. But of course, the antedilivian prescription that making women economically worse off would some how rescue low-wage men is not a path either to economic growth or to social well-being. However, finding ways to improve the skills and job opportunities of low-skill workers could clearly have substantial social payoffs beyond the labor market.

A friend of mine argues that the roadways are full of people driving as if they had recently knocked back a beer or a glass of wine. But their tipple isn\’t alcohol; instead, it\’s the distractions of cell-phones and texting. There\’s at least some circumstantial evidence that the parallel between how alcohol and texting affect driving is all too real: a few years back, Car and Driver magazine had a few people drive while texting and then drive while drunk, on a closed course. The effects on reaction times  were similar, with texting being a little more disabling. One study has estimated that 2700 deaths per year were occurring in the mid-2000s as a result of texting-while-driving.

Thus, it\’s not surprising that from 2007 through January 2012, 33 states had banned texting while driving. Rahi Abouk and Scott Adams examine the evidence about what happened next in \”Texting Bans and Fatal Accidents on Roadways: Do They Work? Or Do Drivers Just React to Announcements of Bans?\” The paper appears in the April 2013 issue of the American Economic Journal: Applied Economics (5:2, 179–199). The AEJ:Applied isn\’t freely available on-line, but many in academia will have access through library subscriptions.

By looking at data across 33 states, the authors can look for how texting-while-driving bans in one state affect accidents in that state at the time before and after the ban, which helps to sort out the effect of the ban from other issues affecting auto accidents. They can also look at states where the ban is enforce more or less harshly. In particular, they focus on data about single-car accidents, which took a significant jump in the early and mid-2000s as texting became popular. As they explain: \”A driver with passengers might be less willing to put them in danger by texting. Moreover, they may find less need to text if someone is there to speak with them or stop them from texting if they perceived the risk to be dangerous. Multiple vehicle accidents typically are caused by more than one factor since there are multiple drivers.\” Of course, this doesn\’t mean that texting doesn\’t also contribute to multi-car accidents! It just means that if you are seeking evidence in particular about the effect of texting-while-driving bans, single-car crashes are a category where the cause-and-effect connection from legislation is likely to be more clear.

Abouk and Adams summarize their findings in this way: \”Our evidence suggests fatal accidents are reduced by bans if they are enforced as a primary offense and cover all drivers. Alternatively, accidents less likely to be related to text messaging, particularly multiple vehicle or multiple occupant accidents, are not reduced significantly. The strong impact of texting bans on single-vehicle, single-occupant crashes is short-lived. While the effects are strong for the month immediately following ban
imposition, accident levels appear to return toward normal levels in about three months. This suggests that a texting ban immediately saves lives, but the positive effect cannot be sustained. The declining impact of traffic safety policies over time is not uncommon and has been observed in other regulations. Given the large impact of texting bans in the initial months following enactment, however, the evidence of the paper suggests greater enforcement of these laws likely can save more lives.\”

Here\’s are a couple of illustrative figures from their paper that give a sense of their findings. On the horizontal axis, the zero point is when the texting-while-driving ban was passed. Thus, the figures show the pattern of accidents both before and after the ban. At the time of the ban, accidents drop, as shown by the dark line. (The dashed lines are the statistical confidence interval around the main estimates). But then, a few months after the ban, accidents creep back up again. The upper panel shows states with stronger enforcement; the bottom panel shows states with weaker enforcement.

To me, the lesson here is that despite all the states which have passed laws about texting-while-driving, as a society we are still somewhat ambivalent about the practice. We don\’t yet think of it with the condemnation given to drunk driving. Legal enforcement and social opprobrium just aren\’t as fierce in the case of texting-while-driving. But both texting-while-driving and drunk driving pose a real and serious threat both to the driver of the vehicle, and also to all the other drivers on the road.

Long-term economic growth in the United States has a remarkable continuity: indeed, on a certain kind of graph, it almost looks like a straight line. The graphs that follow are from the Measuring Worth website, an extremely useful resource for long-term economic data led by Lawrence Officer and Samuel Williamson. At the website, you will find long-run data on GDP, earnings, prices, interest rates, and other statistics for the U.S., U.K, Japan, and China. One useful purpose of the website is to use long-run data on prices, household consumption, income, and output as ways of putting in perspective what things were worth at different times.

Here, I want to focus on growth of the U.S. economy over time. The first graph shows growth of the real (that is, inflation adjusted) U.S. output from 1790 to the present. The second graph shows growth of real per capita U.S. GDP over the same time–that is, it is calculated on a per person basis and thus adjusts for population growth.

But for the uninitiated, these graphs may not deliver a clear message. It looks on these graphs as if growth was slow in much of the 19th century, but then accelerated in recent decades. It looks as if the most recent Great Recession was similar in depth to the Great Depression of the 1930s.

The problem here arises because of a difference between absolute levels of growth and rates of growth. Imagine, for example, an economy that starts out a size of 100, and grows by 1 every year, so that after two centuries it has reached a size of 300. If you think about it for a moment, the growth rate of this economy is slowing every year: it grew by 1% the first year (1/100), but by less than 1 percent the second year (1/101) and by the end of 200 year is growing at only about one-third of a percent (1/300). Thus, a line of straight slope on the graph above would actually show a steadily declining rate of growth. Now imagine that the economy starts at a growth rate of 100, but grows at 1% each year for 200 years. As this growth rate compounds, with each year building on the previous one, this economy will reach a size of 731 after 200 years. The steady rate of growth over time will look be a larger absolute amount–a difference that will look large after a couple of centuries.

In a logarithmic graph, a straight line shows that the same percentage rate of growth is continuing from year to year. Here is the same data as in the above figure from the Measuring Worth website, but now presented on a logarithmic graph. Notice that both graphs are nearly straight lines–which means that the rate of growth has been fairly consistent over time. Notice also that when expressed in terms of growth rates, the Great Depression is clearly larger than the more recent Great Recession.