Year: 2014

A new business \”establishment\” occurs when a firm opens up at a new geographical location. A new \”establishment\” can thus occur for one of two reasons: either it\’s a brand-new firm started by an entrepreneur (say, if I start my own fast-food restaurant), or it\’s a new or additional location for an existing firm (say, when Subway or McDonald\’s opens a new store). In \”The Shifting Source of New Business Establishments and New Jobs,\” an \”Economic Commentary\” written for the Fedearl Reserve Bank of Cleveland (August 21, 2014) Ian Hathaway, Mark E. Schweitzer, and Scott Shane make the point that when it comes to new establishments in the U.S. economy, existing firms are playing a larger role.

As a starting point, consider how the rate at which new establishments of both kinds are being born has changed over time. Hathaway, Schweitzer, and Shane note: \”As the figure shows, in 1978, Americans created 12.0 new firms per business establishment. By 2011, the latest year data are available, they generated new firms at roughly half that rate—6.2 new firms per existing business establishment. By contrast, in 1978, Americans created 1.7 new outlets per existing establishment, while in 2011 they created 2.6—an increase of more than half.\”

I wrote about about the decline of the top line–the line showing the entrepreneurs starting new firms–in \”The Decline of U.S. Entrepreneurship\” earlier this month.

Is this shift toward establishments started by existing firms just what one might call a \”Walmart effect\”–that is, big-box stores in the retail industry driving out smaller Mom-and-Pop operations? Apparently not. The share of new establishments that are new outlets of existing firms is rising across across all industries, not just retail

This change has implications for the sources of job creation in the U.S. economy. Hathaway, Schweitzer, and Shane write: \”To give a sense of the magnitude of the changing sources of job creation, we can estimate the number of new jobs that new firms would have created had they continued to generate jobs at the rate they did back in 1978 and the number of new jobs that new outlets would have created had they continued to generate jobs at the rate they did back in 1978. At the 1978 rate of new firm job creation, new firms would have produced an additional 2.4 million jobs in 2011, or 90 percent more. At the 1978 rate of new outlet job creation, new outlets would have produced 828,000 fewer jobs in 2011, or 34 percent less.\”

As to underlying reasons for the change, these authors note that their underlying data doesn\’t allow them to pinpoint any particular cause. However, \”[W]e can offer one hypothesis: Growth in information and communication technologies since the late 1970s have facilitated the coordination of multiple establishments, offering existing businesses an advantage over new firms when setting up new establishments to meet the need for new business locations.\”

This explanation seems plausible to me, but some other possibilities seem plausible, too. The new information and communications technology may also make it easier to establish brand names, so that firms in sectors like Finance, Insurance, and Real Estate or in Services are more likely to be part of a national firm, rather than opening up a stand-alone personal shop. I also suspect that the regulatory burden of starting a business has gotten harder over time, in terms of the rules, regulations, and permits that need to be followed for the physical property of the business, for dealing with employees, for meeting requirements about the qualities of the product that are provided, and for taxes and accounting. There\’s a case for each individual rule and regulation. But when you pile them all up, the burden can become discouragingly high for a potential entrepreneur.

South Africa is the home for 75% of the world\’s population of black rhinos and  96% of the world\’s population of white rhinos. There must be some lessons for conservationists behind those statistics.
Michael \’t Sas-Rolfes tells the story in \”Saving African Rhinos: A Market Success Story,\” written as a case study for the Property and Environment Research Center (PERC).

The story isn\’t just about markets. In 1900, the white rhinoceros had been hunted almost to extinction, with about 20 remaining in a single game preserve in South Africa. The population slowly recovered a bit, and by the middle of the 20th century, there were enough to start relocating breeding groups of white rhinos to other national parks in South Africa, as well as private game ranches. In 1968, the first legal hunt of a white rhino was authorized.

But by the 1980s, Sas-Rolfes reports, a strange disjunction had emerged. In 1982, the Natal Parks Board had a list price for a white rhino of about 1,000 South African rands, but the average price paid by a hunter for a rhino trophy that year was 6,000 rands. Private game preserves were quick to take advantage of the arbitrage opportunity. The Natal Parks Board soon began auctioning its rhinos. In 1989, it was selling rhinos for 49,000 rand, but the average price to a hunter for a rhino trophy had risen to 92,000 rand. There were obvious questions about whether this system of raising and hunting rhinos was a useful tool from a broader environmental perspective.

But property rights and markets enter the story in a different way in 1991.

Before 1991, all wildlife in South Africa was treated by law as res nullius or un-owned property. To reap the benefits of ownership from a wild animal, it had to be killed, captured, or domesticated. This created an incentive to harvest, not protect, valuable wild species—meaning that even if a game rancher paid for a rhino, the rancher could not claim compensation if the rhino left his property or was killed by a poacher. . . . Recognizing the problems associated with the res nullius maxim, the commission drafted a new piece of legislation: the Theft of Game Act of 1991. This policy allowed for private ownership of any wild animal that could be identified according to certain criteria such as a brand or ear tag. The combined effect of market pricing through auctions and the creation of stronger property rights over rhinos changed the incentives of private ranchers. It now made sense to breed rhinos rather than shoot them as soon as they were received.

For a sense of how much difference these issues of property rights and incentives can make to conservation, consider the difference in populations between black and white rhinos. Sas-Rolfes explains: \”Figure 2 shows trends in white rhino numbers from 1960 until 2007. Contrast those
numbers with the black rhino, which mostly lived in African countries with weak or absent wildlife market institutions such as Kenya, Tanzania, and Zambia. In 1960, about 100,000 black rhinos roamed across Africa, but by the early 1990s poachers had reduced their numbers to less than 2,500. . . . Unprotected wild rhino populations are rare to non-existent in modern Africa. The only surviving African rhinos remain either in countries with strong wildlife market institutions (such as South Africa and Namibia) or in intensively protected zones.\”

A strong demand for rhino horn remains, and especially since about 2008, rhinos across Africa face a risk of illegal poachers. Here\’s a figure from the conservation group Save the Rhino showing the level of rhino poaching in South Africa:

Along with the existing choices of \”intensively protected zones\”–which implies costly and not-very-corruptible protectors–and allowing for private game preserves, the other option is to seek to undercut the black market for rhino horn with a legal market. Other more controversial options discussed at the Save the Rhinos website include de-horning rhinos, to make them less attractive to poachers, and perhaps even allowing legal sale of these rhino horns, to undercut the prices paid to poacher. Rhino horns are made of keratin, similar to the substance in fingernails and hair, and the horn could be removed every year or two. There are strong arguments on both sides of allowing legal sale of rhino horn: perhaps rather than undercutting the illegal market, it might also make it easier for poachers to sell their illegally obtained rhino horn. In the end, given that South Africa is now the home to most of the world\’s rhinos, I suspect that South Africa will end up making the decision about whether to proceed with these options.

Those interested in how property rights might be one of the tools for helping to protect endangered species might also want to check this post on \”Saving Jaguars and Elephants with Property Rights and Incentives\” (December 19, 2011).  

\”Fair Trade\” is often little more than a slogan. If you\’d like a look at the analysis and reality behind that slogan, as it\’s working out in the real world, a good starting point is \”The Economics of Fair Trade,\”  by Raluca Dragusanu, Daniele Giovannucci, and Nathan Nunn, in the Summer 2014 issue of the Journal of Economic Perspectives. (Full disclosure: I\’ve worked as Managing Editor of JEP since the first issue in 1987.)

Fair Trade is the practice whereby a nonprofit organization puts a label on certain products, certifying that certain practices were followed in the production of that product. Common required practices include standards for worker pay, worker voice, and environmental protection. The biggest of these certifying organization is Fairtrade International. There is a parallel label for the U.S. called Fair Trade USA. Other labelling standards, each with their own priorities, include Organic, Rainforest Alliance, and others. A producer who joins Fair Trade receives several benefits. When a Fair Trade producer sells to a Fair Trade buyer, they can receive a minimum price, which includes a premium now set at 20 cents per pound for coffee. Fair Trade buyers are also supposed to be more willing agree to long-term purchasing contracts, and to providing credit to producers.

At some level, Fair Trade and other certification programs are just a case of the free market at work. With the certification,  consumers who would are willing to pay something extra to purchase products produced in a certain way become able to find those products. A variety of evidence suggests that at least some consumers value this option. For example, in one study the researchers were able to add Fair Trade labels, or not, and alter prices, or not, for bulk coffee sold in 26 U.S. groceries. they found that at a given price, sales were 10% greater when the coffee was labeled as Fair Trade, and that demand for Fair Trade coffee was less sensitive to increases in price.

So what concerns or issues might be raised about Fair Trade? I\’ll list some of the issues here as I see them, based on evidence from the Dragusanu, Giovannucci, and Nunn paper. As they note, \”the evidence is admittedly both mixed and incomplete\”–so some of the concerns are tentative.

Fair Trade and other certification programs affect a relatively small number of workers.

The most important Fairtrade products, measured by number of producers and workers involved in growing them, are coffee (580,000 producers and workers covered), tea (258,000), and cocoa (141,000). Fair trade standard also cover smaller numbers of producers in seed cotton, flowers and plants, cane sugar, bananas, fresh fruit, and nuts. Obviously, compared to the total number of low-income agricultural producers in developing and emerging economies–measured in billions of workers–the share of production covered by Fair Trade certification is quite small.

Fair Trade does seem to provide higher prices and greater financial stability, at least when farmers can sell at the minimum price. 

A variety of small-scale studies in many countries suggest that Fair Trade farmers do earn more. However, there is a difficult problem of cause-and-effect here. If the more sophisticated and motivated farmers who are well-positioned by their crops and land to carry out Fair Trade practices are the ones who sign up, perhaps they would have been able to receive higher priced even without the certification. There are a variety of methods to adjust for differences across farmers: age of farmer, education of farmer; size of crop; before-and-after entering a certification program, and the like.  After such adjustments, a few studies no longer find that Fair Trade farmers earn more, but the most common finding remains that a price premium continues to exist.

The research in this area also points out that just because a producer is Fair Trade-certified does not mean that the producer can necessarily sell all of their crop as Fair Trade. The buyer determines what quantity of certified product to purchase at the Fair Trade price. In addition, while some buyers provide credit, there is some evidence that buyers who then sell to big firms like Starbucks and Costco are less likely to offer credit or long-term purchasing contracts. Again, farmers overall do seem to gain financial stability from Fair Trade certification, but what they gain in reality is often less than a simple recitation of the guidelines might suggest.

Fair Trade does seem to promote improved environmental practices. 

Again, the evidence is from small-scale studies in various countries, but Fair Trade certified producers do seem more likely to use composting, to use contouring and terraces to reduce erosion, to have systems for purifying runoff from fields, to make use of windbreaks and shade trees, and so on.

While Fair Trade helps producers, the effects on workers and work organizations is more mixed. 

Fair Trade organizations sometimes operate through cooperatives, in which farmers pass their output to the cooperative, which then negotiates the sales. A variety of studies find higher levels of tension between farmers and Fair Trade cooperatives, with farmers complaining about lack of communication and poor decision-making.

In addition, many producers of Fair Trade products hire outside workers, at least seasonally. As Dragusanu, Giovannucci, and Nunn write: \”The evidence on the distribution of the benefits of Fair Trade remains limited, but the available studies suggest that, within the coffee industry, Fair Trade certification benefits workers little or not at all.\” A couple of months ago, I blogged on a recent study making this point in \”Does Fair Trade Reduce Wages?\” However, these is also some evidence that in non-coffee crops, often grown in plantation agriculture, the certification standards can improve working conditions and reduce the use of child labor.

How might entry by producers affect Fair Trade and other certification programs in the long run? 

If producers who operate in a certain way can earn higher profits, then any economist will predict that more producers will choose to operate in that way. But as more producers enter and the supply of the product produced in that way rises, it will tend to drive down the market price, until the opportunities for higher profits are competed away. At least so far, this doesn\’t seem to have happened for Fair Trade. But as Dragusanu, Giovannucci, and Nunn write: \”This link between free entry and rents provides an interesting dilemma for certification agencies. On the one hand, they wish to induce the spread of socially and environmentally responsible production as much as possible. On the other hand, they may also wish to structure certain limits to entry so that they can continue to maintain higher-than-average rents for certified producers.\”

How might entry by additional certification organization affect Fair Trade in the long run?

There is considerable overlap between the various certification organizations: for example, 80% of the Fair Trade-certified producers are also certified as Organic producers. But multiple certifications mean multiple reports and audits, which can be a real burden for farmers in low-income countries. Some for-profit companies are starting their own certification programs, rather than deal with an outside certification organization. At some point, there is a risk that farmers become unwilling to deal with a plethora of organizations, and that consumers become cynical about whether many of these organizations represent something meaningful.

The official unemployment rate predictions, released early each month, are based on a monthly survey. It\’s a good survey, even an excellent survey, but the data is inevitably a month old. In addition, any survey is somewhat constrained by the specific wording of its questions and definitions. Would it be possible to get a faster and reasonably accurate view of labor market conditions by looking at mentions of certain key terms on Twitter and other social media? The University of Michigan Economic Indicators from Social Media has started research program on this topic. The first research paper up at the side is \”Using Social Media to Measure Labor Market Flows,\” by Dolan Antenuccia,  Michael Cafarellab, Margaret C. Levenstein, Christopher Ré, and Matthew D. Shapiro, which is based on data from 19.3 billion Twitter messages sent between July 2011 and November 2013–which is about 10% of all the tweets sent in that time.

For those who want detail on how the official unemployment rate is calculated, the Bureau of Labor Statistics published a short memo on \”How the Government Measures Unemployment\” in June 2014. Basically, the government has been doing the Current Population Survey (CPS)
every month since 1940. In its current form. As BLS describes it:

There are about 60,000 eligible households in the sample for this survey. This translates into approximately 110,000 individuals each month, a large sample compared to public  opinion surveys, which usually cover fewer than 2,000 people. The CPS sample is  selected so as to be representative of the entire population of the United States … Every month, one-fourth of the households in the sample are changed, so that no  household is interviewed for more than 4 consecutive months. After a household is  interviewed for 4 consecutive months, it leaves the sample for 8 months, and then is  again interviewed for the same 4 calendar months a year later, before leaving the sample  for good. As a result, approximately 75 percent of the sample remains the same from  month to month and 50 percent remains the same from year to year. This procedure  strengthens the reliability of estimates of month-to-month and year-to-year change in the data.  Each month, highly trained and experienced Census Bureau employees contact the 60,000 eligible sample households and ask about the labor force activities (jobholding and job seeking) or non-labor force status of the members of these households during the  survey reference week (usually the week that includes the 12th of the month).

Although the headline unemployment rate and total jobs number gets most of the attention, the survey also tries to explore whether those not looking for jobs are \”discouraged\” workers who would actually like a job, but have given up looking, or whether they are part-time workers who would prefer a full-time job.

At present, perhaps the main source of data on labor markets that comes out more frequently than the unemployment rate itself is the data on initial claims for unemployment insurance, which comes out weekly (for example, here). However, this data can be a an imperfect indicator–or as economists would say, a \”noisy\” indicator–of the actual state of the labor market. Not everyone who becomes unemployed applies for unemployment insurance or is eligible for it, and many of the long-term unemployed are no longer eligible for unemployment insurance. So the practical question about using Twitter or other social media to look at labor markets is not whether offer a perfect picture, but whether the information from such estimates is less \”noisy\” and more useful than the data from the initial claims for unemployment insurance. \\

The University of Michigan researchers searched the 19.3 billion tweets for terms of four words or less related to job loss. Some examples would include four-word blocks of text that include the words axed, canned, downsized, outsourced, pink slip, lost job, fired job, been fired, laid off, and unemployment. Some experimentation and analysis is involved in choosing terms. For example, it turned out that \”let go\” was used much more frequently than any other term on this list, presumably because many there were many four-word blocks of text that used \”let\” and \”go\” but weren\’t related to labor market issues.

Each week, the Michigan group plans to publish a comparison between the official unemployment insurance claims data and a prediction based purely on its Twitter-based methodology. Here\’s the current figure:

As you can see, the patterns are similar, which is somewhat remarkable. It shows that social media content provides a similar outcome to the official statistics. The patterns are not identical, which is unremarkable, because they are after all measuring different things. The interesting question then becomes: Is there some additional information or value-added to be gained about the state of the labor market from looking at the social-media based index?

In certain specific cases, the answer seems clearly to be \”yes.\” For example, the authors explain that the official date on unemployment insurance claims showed a big drop in September 2013 that occurred because of a data processing issue in California–that is, it wasn\’t a real effect. The social media prediction shows no decline. More broadly, the authors look at the predictions from market experts a few days before the data comes out on  unemployment insurance claims, and they find that the social media measure would improve these predictions.

The researchers are looking at how social media might reflect various various other measures of labor markets, including job search, job postings, and how labor markets react to short-term events like Hurricane Sandy. Of course, the goal is to develop methods that give a reasonably reliable real-time sense of how the economy is evolving based on immediately available data

 For those interested in doing their own research project based on collecting publicly available data from the web, a useful overall starting point is the article by Benjamin Edelman, \”Using Internet Data for Economic Research,\” in the Spring 2012 issue of the Journal of Economic Perspectives, where I have worked as Managing Editor since the first issue back in 1987. As with all JEP articles, it is freely available on-line compliments of the American Economic Association. Social science researchers are busily writing programs that collect data on search queries, on how prices change in a wide variety of databases, and much more.

Back in the mid-1990s, I thought of the U.S. homeownership rate as fairly constant, holding at about 64-65% most of the time. In the fourth quarter of 1995, for example, the homeownership rate was a bit above this range at 65.1%. But looking back at Census Department data for the fourth quarter of various years (see Table 14 here), the homeownership rate had been 64.1% in 1990, 63.5% in 1985, 65.5% in 1980, 64.5% in 1975, 64.0% in 1970, and 63.4% in 1965.

Since 1995, U.S. homeownership rates have climbed a mountain–speaking graphically–and have now come back down. Here\’s a figure from the Census Bureau\’s July 29 report on \”Residential Vacancies and Homeownership in the Second Quarter 2014.\” The homeownership rate checked in at 64.7% in the second quarter of 2014.

Here\’s a slightly different perspective from the same report, looking at the vacancy rate–that is what share of rental housing and of homes are vacant.

At about the same time that the homeownership rate was rising in the first half of the 1990s, the vacancy rate for homes was also rising–which suggests that an enormous boom in residential construction was occurring at the time.

It\’s worth remembering that as homeownership rates climbed up one side of the mountain from about 1995 to 2004, the change was viewed as a success by  both parties. Bill Clinton had a National Homeownership Strategy  which pushed to make it easier for people with lower incomes to own a home. As Clinton said in announcing the initiative:

You want to reinforce family values in America, encourage two-parent households, get people to stay home? Make it easy for people to own their own homes and enjoy the rewards of family life and see their work rewarded. This is a big deal. This is about more than money and sticks and boards and windows. This is about the way we live as a people and what kind of society we\’re going to have. …  The goal of this strategy, to boost home ownership to 67.5 percent by the year 2000, would take us to an all-time high, helping as many as 8 million American families across that threshold. … Our home ownership strategy will not cost the taxpayers one extra cent. It will not require legislation. It will not add more Federal programs or grow Federal bureaucracy. It\’s 100 specific actions that address the practical needs of people who are trying to build their own personal version of the American dream, to help moderate income families who pay high rents but haven\’t been able to save enough for a downpayment, to help lower income working families who are ready to assume the responsibilities of home ownership but held back by mortgage costs that are just out of reach, to help families who have historically been excluded from home ownership.

The Clinton initiative, together with the booming U.S. economy in the second half of the 1990s,  reached that goal of 67.5% homeownership rate by the year 2000. When George W. Bush became president, he pushed for an \”ownership society,\” with policies to help people with down payments on a home and increase the number of minority homeowners. As Bush  said in a 2003 speech:

\”This Administration will constantly strive to promote an ownership society in America. We want more people owning their own home. It is in our national interest that more people own their own home. After all, if you own your own home, you have a vital stake in the future of our country.\”

When the homeownership rate peaked at 69.4% in the second quarter of 2004, and for some months afterward, there was strong bipartisan support for the policies that had raised homeownership rates. At the time, existing homeowners were largely delighted as well with the swelling price of their homes.

Of course, the underlying problems have now become obvious. It\’s hard to oppose policies that gives low-income people a better chance to own a home. But if those policies involve encouraging those with lower incomes to take out subprime mortgages, so that the people you are claiming to help will be actually be carrying overly large debt burdens and become highly vulnerable to a downturn in housing prices, then this way of pushing for higher rates of homeownership is a poisoned chalice. I\’m very supportive of building institutions and laws that will make it easier for those with low and medium incomes to accumulate financial and nonfinancial assets, including a home. But let\’s focus on ways of encouraging actual saving, not ways of encouraging excessive borrowing.

OK, I admit that it\’s arbitrary to compare countries according to their oil and gas production, setting aside coal, hydroelectric, nuclear, and renewables like solar and wind. Still, as someone who started paying attention to economic issues during the OPEC-related oil price shocks of the 1970s, this figure shows an outcome that I never expected to see. Taking oil and gas together, the U.S. has now surpassed Russia and Saudi Arabia as the world\’s leading producer.

This figure was produced by the Stanford Institute for Economic Policy Research (SIEPR) as part of it annual \”Facts at a Glance\” chartbook. For purposes of this comparison, natural gas has been converted into an energy-equivalent amount of oil: specifically, 5,800 cubic feet is equal to about 1 barrel of oil.

Of course the economic consequences of being the largest energy producer will be different for the U.S. than for Russia or Saudi Arabia. For example, the enormous US economy uses more energy than it produces, and thus remains an energy importer, while the economies of Saudi Arabia and Russia depend on energy exports. But before I try to figure out what it all means. I need to spend some time just wrapping my head around the idea of the U.S. as the world\’s leading oil and gas producer.

How does the level of the minimum wage relative to other wages compare across higher-income countries around the world? Here are a couple of figures generated from the OECD website, using data for 2012.

As a starter, here\’s a comparison of minimum wages relative to average wages. New Zealand, France, and Slovenia are near the top, with a minimum wage equal to about half the average wage. The United States (minimum wage equal to 27% of the average wage) and Mexico (minimum wage equal to 19% of the average wage) are near the bottom.

However, average wages may not be the best comparison. The average wage in an economy with relatively high inequality, like the United States, will be pulled up by the wages of those at the top. Thus, some people prefer to look at minimum wages relative to the median wage, where the median is the wage level where 50% of workers receive more and 50% receive less. For wage distributions, which always include some extremely large positive values, the median wage will be lower than the average–and this difference between median and average will be greater for countries with more inequality.

Here\’s a figure comparing the minimum wage to the median wage across countries.  The highest minimum wage by this standard is Turkey (71% of the median wage) followed by France and New Zealand (about    60% of the median wage). The lowest three are the United States (38%), the Czech Republic (36%) and Estonia (36%).

This post isn\’t the place to rehearse arguments over the minimum wage one more time: if you want some of my thoughts on the topic, you can check earlier posts like \”Minimum Wage and the Law of Many Margins\” (February 27, 2013), \”Some International Minimum Wage Comparisons\” (May 29, 2013), \”Minimum Wage to $9.50? $9.80? $10?\” (November 5, 2012). Moreover, minimum wages across countries should also evaluated in the context of other government spending programs or tax provisions that benefit low-wage families.

However, I will note for US readers that the international comparisons here can give aid and comfort to both sides of the minimum wage argument in this country. Those who would like the minimum wage raised higher can point to the fact that the U.S. level remains relatively low compared to other countries. Those who would prefer not to raise the minimum wage higher can take comfort in the fact that, even after the minimum wage increased signed into law by President Bush in May 2007 and then phased in through 2009, the U.S. minimum wage relative to average or median wages remains comparatively low.

If you calculated that the difference between 2% and 3% is 1%, you are of course arithmetically correct, but in an economic sense, you are missing the point. Herb Stein explained the difference in an 1992 essay about the work of Edward Dennison on economic growth. Stein wrote:

The difference between 2 percent and 3 percent is not 1 percent but 50 percent. That, of course, is not the result of research–at least, not Dennison\’s–but it is an often-neglected and important proposition that he emphasized. Its significance is that what seems a small increase in the growth rate–say, from 2 to 3 percent–is really a large increase. As a first approximatino, such an increase in the growth rate would require an increase of 50 percent in all the resources, effort, and attention that went into generating the 2 percent growth rate.

Dennison had died in 1992, and Stein\’s short remembrance, \”Memories of a Model Economist,\” was published in the Wall Street Journal, November 23, 1992. It was reprinted in On the Other Hand … (pp. 235-239), a 1995 collection of Stein\’s popular essays and writings published by the AEI Press.

One of the challenges of teaching basic economics is to explain why small differences in the annual rate of economic growth are so important. Stein\’s comment from Dennison is one way to focus attention on these issues. In the short run of a single year  the difference between 2% and 3% is indeed 1%, but when the issue is how to bring down the unemployment rate, raising the number of workers needed is a big deal. In the longer run of a decade or two, the key point to remember is that economic growth accumulates, year after year, so losing 1% every year means losing (approximately, not adjusted for compounding of growth rates) 10% after a decade and 20% after two decades.

When a nation falls behind in productivity growth over a sustained period of time, it is a matter of decades to make up that foregone productivity growth. (If you doubt it, consider the experience of the United Kingdom or Argentina during the earlier parts of the 20th  century, or think about a quarter-century of lethargic growth has affected perceptions and reality of Japan\’s economy.) No matter what your public policy goal–more for social programs, tax cuts, deficit reduction, rescuing Social Security and Medicare–the task is politically easier if the growth rate has been on average higher and the economic pie is therefore substantially larger. In the last few years,  U.S. economic policy has for good reason been focused on the aftereffects and lessons of the Great Recession. But looking ahead a couple of decades, the single most important factor for the health of the U.S. economy is whether we create an economic climate so that the rate of per capita growth can be 1 or 2% faster per year.

The idea that an economy functions through a division of labor, in which we each focus and specialize in certain tasks and then participate in a market to obtain the goods and services we want to consume, is fundamental to economic analysis. Indeed, the very first chapter of Adam Smith\’s 1776 classic The Wealth of Nations is titled \”Of the Division of Labor,\” and offers the famous example of how dividing up the tasks involved in making a pin is what makes a pin factory so much more productive than an individual who is making pins.

But what if the division of labor, with its emphasis on focusing on a particular narrow job, runs fundamentally counter to something in the human spirit? Karl Marx raised this possibility in The German Ideology (1846 Section 1, \”Idealism and Materialism,\” subsection on \”Private Property and Communism\”). Marx wrote:

“Further, the division of labor implies the contradiction between the interest of the separate individual or the individual family and the communal interest of all individuals who have intercourse with one another. … The division of labor offers us the first example of how, as long as man remains in natural society, that is, as long as a cleavage exists between the particular and the common interest, as long, therefore, as activity is not voluntarily, but naturally, divided, man\’s own deed becomes an alien power opposed to him, which enslaves him instead of being controlled by him. For as soon as the distribution of labor comes into being, each man has a particular, exclusive sphere of activity, which is forced upon him and from which he cannot escape. He is a hunter, a fisherman, a shepherd, or a critical critic, and must remain so if he does not want to lose his means of livelihood; while in communist society, where nobody has one exclusive sphere of activity but each can become accomplished in any branch he wishes, society regulates the general production and thus makes it possible for me to do one thing today and another tomorrow, to hunt in the morning, fish in the afternoon, rear cattle in the evening, criticism after dinner, just as I have a mind, without ever becoming hunter fisherman, shepherd or critic. This fixation of social activity, this consolidation of what we ourselves produce into an objective power above us, growing out of our control, thwarting our expectations, bringing to naught our calculations, is one of the chief factors in historical development up till now.

Like so much of Marx\’s writing, this passage seems to me to give voice to a difficult concept that contains a substantial slice of truth; indeed, I had this quotation up on my office door for a time. But also like a lot of Marx, it seems to ignore or evade counterbalancing arguments.

Set aside for a few heartbeats the vexed question of just how a minimum wage would affect employment, and focus on a more basic set of facts: What are some characteristics of U.S. workers who receive the minimum wage? The statistics here are from a short March 2014 report from the U.S. Bureau of Labor Statistics, \”Characteristics of Minimum Wage Workers, 2013.\” Of course, the facts about who is receiving the minimum wage also reveal who will be most directly affected by any changes.

How many workers are paid at or below the minimum wage?

The BLS reports that 75 million American workers were paid at an hourly rate in 2013, out of about 136 million total employed workers. Of that total, 3.3 million, or about 4.3%, were paid at the minimum wage or less. A figure from an April 3, 2014,  BLS newsletter puts that level in historical context–that is, the share of hourly-paid workers receiving the federal the minimum wage is lower than in most of the 1980s and 1990s, but it is a little higher than in much of the 2000s. Of course, shifts in the  the share of workers receiving the minimum wage in large part reflect changes in the level of the minimum wage. When the federal minimum wage increase signed into law by President Bush in 2007 was phased in during 2008 and 2009, more workers were then affected by the higher minimum wage.

What\’s the breakdown of those being paid the minimum wage by age? In particular, how many are teenagers or in their early 20s? 

Of the 3.3 million minimum-wage workers in 2013, about one-quarter were between the ages of 16-19,  another one-quarter were between the ages of 20-24, and half were over the age of 25.

What\’s the breakdown of those being paid the minimum wage by full-time and part-time work status? 

Of the 3.3 million minimum-wage workers in 2013, 1.2 million were full-time, and 2.1 million were part-time–that is, roughly two-thirds of minimum-wage workers are part-time.

What\’s the breakdown of those being paid the minimum wage across regions? 

For the country as a whole, remember, 4.3% of those being paid hourly wages get the minimum wage or less. If the states are divided into nine regions the share of hourly-paid workers getting the minimum wage in each region varies like this: New England, 3.3%; Middle Atlantic, 4.8%;  East North Central, 4.3%, West North Central, 4.6%; South Atlantic, 5.1%; East South Central, 6.3%; West South Central, 6.3%; Mountain, 3.9%; Pacific, 1.5%.

The BLS has state-by-state figures, too. There are two main reasons for the variation. Average wages can vary considerably across states, and in areas with lower wages, more workers end up with the minimum wage. In addition, 23 states have their own minimum wage that is set above the federal level. In those state, fewer workers (with exceptions often made in certain categories like food service workers who get tips) are paid below the federal minimum wage. It\’s an interesting political dynamic that many of those who favor a higher federal minimum wage are living in states where the minimum wage is above the federal level; in effect, they are advocating that states who have  not adopted the minimum wage policy preferred in their own state be required to do so.

In what industries are hourly-paid workers most likely to receive the minimum wage? 

Whatever one\’s feelings about the good or bad effects of raising the minimum wage, it seems fair to say that those effects will be disproportionately felt by a relatively small share of the workforce, disproportionately young and part-time, and disproportionately in southern states.