Micro, Macro, and the Minimum Wage

Meme showing man in office with coffee mug looking skeptical saying "Yeah, I'm going to have to go ahead and disagree with you there."Economists disagree. It’s so common that there are jokes about.  For example,

If all of the economists in the world were laid end-to-end they would scarcely reach a conclusion.


Economics is the only field in which two people can get a Nobel Prize for saying the opposite thing.

Why?  I can’t explain all of economists’ disagreements here (I don’t have enough pixels!), but I can explain some of the disagreement over questions of raising the minimum wage.  There are numerous calls for Congress to raise the minimum wage, yet Congress has remained bitterly divided on the issue. Their disagree to a large extend reflects the disagreement among economists. To non-economists, the disagreement seems to either indicate that there really isn’t any science in economics and it’s all opinion, or that some economists must be lying or deliberating obfuscating.  In truth, though, there’s another reason for the apparent disagreement: the difference between micro and macro level economic analyses.

First, let’s establish some historical perspective on the debate. I want to clarify the difference between “normative” and “positive”.  Positive arguments are statements or conclusions about what the predicted effects of a proposal will be without taking a stand on whether those effects are desirable or tolerable. (note that the word “positive” here denotes “factual or likely”, not “good”) Normative arguments are when someone argues whether a proposal should be done. Normative arguments typically are based upon a combination of predicted outcomes and a value judgement as to whether those outcomes are desirable or tolerable compared to the alternative.  For a long time in economics, economists were actually largely in agreement on the positive science, or the predicted effects, of a rise in the minimum wage.  It was generally agreed that raising the minimum wage would give larger incomes to those who continued to work at minimum wage (i.e. low skilled) jobs but that the rise would decrease the numbers of those jobs and thus raise unemployment rates among those seeking low-skilled jobs. Historically the disagreement over minimum wage hikes was over the normative aspects: was the rise in unemployment and loss of jobs worth the increased incomes to others.

The agreement over the predicted positive effects wasn’t always unanimous. There have always been some dissenters. But in the early 1990’s Card and Krueger studied a “natural experiment” by comparing fast food restaurants on two sides of a state line when one state raised the minimum wage and the other didn’t. Their results started a fierce debate that still rages over the predicted effects of rises in minimum wages.  On one side there are now many economists who side with Card & Krueger in saying that raising the minimum wage, even if raised very significantly such as to $15 per hour from less than $8, will not decrease employment and will have a very large increase incomes. On the other side, maintaining the older stance are those such as Don Boudreaux who doggedly argue that any rise in minimum wage must increase unemployment significantly.  Like most topics in economics the practicality of measuring and analyzing the empirical data is somewhat equivocal.  Although there have been numerous studies since Card and Krueger that have buttressed their results, the empirical data along always leaves enough room for some argument.  So what does the theory say?

Don Boudreaux and others of the “increases in minimum wage MUST increase unemployment” camp, would have use believe that theory is unequivocal. The essence of their argument is that low skilled labor is a commodity sold in a market. It has a demand (firms want to buy it) and a supply (low skill workers want to sell it and get paid).  The wage that gets paid is the price of this labor commodity. The most basic supply-and-demand analysis tells us that if the government forces the price up somewhat artificially by setting a price floor (i.e. a minimum wage) below which transactions cannot occur, then there will a smaller quantity of hours of labor demanded. In other words, firms will hire and pay fewer workers. There’s often an appeal to the concept that if the price (cost) of an input or resource goes up, then the firm’s profits will go down and the firm will be less inclined to produce that good or service and therefore will buy less (hire fewer workers).

How can good theory-toting economists dispute this?  Isn’t it supply-and-demand, the most basic micro economic concept as taught in the first few chapters of any principles of econ text?  It’s easy actually. The key is that this supply-and-demand theory as argued against a rise in minimum wage has three major flaws. Two flaws are the result of the theory as applied being too simple (there’s more chapters in the micro text!) and the other  flaw reflects the difference between micro and macro in economics.

The first flaw in the simple supply-and-demand model application to minimum wage type jobs is that there’s really very little evidence that labor markets behave like commodity markets or that they conform to the assumptions necessary to use a supply-and-demand model.  Most jobs, including minimum wage jobs, are more like long-lasting relationships. They aren’t commodity, transaction based like a market for selling widgets or apples or even theatre tickets.  There are dramatic transaction costs involved. Put another way, it’s expensive to hire people (and to fire them and then replace them). Minimum wage jobs aren’t homogenous (they aren’t all the same) the way the theory requires.  Further, the wage paid affects the productivity of the worker, which in turn affects the value of that worker’s output to the firm. When the wage is boosted, workers work harder, stay longer on the job, quit less often, and gradually acquire more productivity and skills. Firms often find that when forced to pay the higher wage, the firm’s total costs, including hiring costs, etc, stays level or even declines.  This is the essence of Arindajit Dube’s studies.

The second flaw is in focusing on the cost of the worker’s wages as if it were the sole consideration in the firm’s decision of how much to produce. The standard theory of the firm and production, which is covered in-depth just a few chapters later in the same economics textbooks after the supply-and-demand model makes it clear:  a firm will produce whatever quantity makes it the most profit. The primary constraints on the output are the demand for the end product, the pricing of the end product, and the core technology used. In other words, if the firm can still sell the output to consumers, it will produce it and the technology (means of production) will require it to hire the necessary labor. A rise in the price of a particular input does not necessarily mean a drop in the quantity produced.

The other two flaws in the arguments against minimum wage increases require shifting to a macro perspective. Micro economics is often described as studying individuals and individual products/markets.  That’s only partially true. Actually micro is a methodology. It’s more properly called “comparative statics using partial equilibrium analysis”.  Micro theories and models explicitly focus on only one particular shifting variable (the wage in this case) and it assumes that all other variables or influences are held constant or unchanged. (Economists call this the ceteris paribus assumption).  In contrast, macro theories are often described as focusing on large aggregate phenomena such gross national product or the inflation rate or the national unemployment rate.  But again, there’s actually a methodological difference. Macro theories require a general systems approach accounting for multiple effects and ripples of many variables that are interrelated.  Let’s look at minimum wage increases as an example of these differences in methodology between macro and micro.

In micro, there’s really only the price (i.e. the wage itself), the quantity of jobs offered, and the quantity of workers available, all of it in the low skill arena.  That’s it. So the micro analysis sees that when the minimum wage is boosted, the firm pays more per worker and each employed worker gets more. End of story.  The only micro question is how it all affects the quantities of jobs.

Macro, however, recognizes that nothing happens in isolation in the economy. There’s a circular flow. Workers are also simultaneously customers. So when the minimum wage goes up, yes, the workers get paid more and firm pays out more money. But what do those workers do with the additional money income? They buy things. Who do they buy them from? Firms that sell and produce products. So the firms not only pay out more money to workers, the firms also get to collect more money by selling more to the increased consumer demand.  But, you say, Acme’s newly enriched minimum wage workers don’t buy that much stuff from Acme. Doesn’t matter. The workers spend it somewhere. And that firm uses the additional money and additional demand to buy more inputs and pay more profits. And those firms and workers then experience income increases and so on and so on as the money circulates throughout the economy. Eventually even Acme sees an increase in sales and revenue collected which in turn helps pay for the wage boosts.  Macro looks at the whole system.

In recent years, many cities and some states have taken it on themselves to raise the minimum wage, often to a so-called “living wage”.  The empirical results have pretty clearly supported the macro analysis. Rises in minimum wages tend to not depress employment and actually tend to stimulate the local economy.  This is the macro analysis.

Sometimes economists just disagree and sometimes they let their ideological and political biases color their professional arguments. Some of that happens in the debates on minimum wage increases.  However, much of apparent disagreement arises from the choice of whether to view the issue through a micro lens or a macro lens.

To read more about the economic analysis of minimum wage increases see these earlier posts:



Sometimes Methodology Isn’t Everything

Brad Delong points us to a study published in the British Medical Association jounal BMJ and quotes from it:

Smith and Pell: Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials 327 (7429): 1459 — bmj.com:

No randomised controlled trials of parachute use have been undertaken

The basis for parachute use is purely observational, and its apparent efficacy could potentially be explained by a “healthy cohort” effect”

The full journal article is well worth following at the link he quotes.  Besides the laugh (warning: the positive health effects of laughing haven’t been proved by randomized controlled trials either), the authors suggest by parody an excellent point.  Sometimes rigid adherence to one single methodology in science is sometimes not only uncalled for and useless, it can also be immoral.  Some Austrian and New Classical economists might want to take note.

Employment News, A Muddle – Part 2

In this post, I’m going to look at the methodology that produces our monthly employment reports because with today’s report, it’s particularly timely. As mentioned in the first post, the employment situation report for January 2011 is a real muddle, full of apparent contradiction. The obvious contradiction is 36,000 new jobs (an incredibly weak number) vs. a 0.4 point drop in unemployment rate from 9.4 to 9.0% (an encouraging number if we can believe it).  But once you read the full report, there’s even more confusion to be had. The report says that only 36,000 jobs were created but it also elsewhere says 117,000 more people were employed. Huh? Well it turns out there’s reasons. First, words matter – a job is not the same thing as an employed person (people can have two jobs). Second, while all the data is published each month as a single “employment situation report”, in fact the data being reported are estimates, not exact counts. This is not a monthly employment census, it’s a survey-based estimate/analysis. And the estimates depend crucially on five inputs:

  1. A sample survey of households (people) done by BLS.
  2. A sample survey of businesses that employ people, also done by BLS.
  3. Monthly estimates of the demographics and population of the nation provided by the Census Bureau.
  4. Econometric models that the BLS uses that forecasts the number and size of new businesses created each month and the number that failed. A model is used since reliable actual data isn’t available for a long time after the month it happens.
  5. The seasonal adjustment method.  In trying to seasonally adjust the data, this month’s SA data is in effect dependent upon the seasonal pattern exhibited in the past. If this year is different from the past, a temporary distortion enters.

This month we’ve got a near perfect storm of noise and confusion coming from at least 4 of these inputs.

First, the two surveys sharply disagreed. This could be a fluke occurence, an outlier of sampling, or it could indicate beginnings of a trend such as the number of jobs held person changing (normally stable), or it could be..who knows?  Garth Brazelton at Reviving Economics tries to explain through his exasperation:

…Typically always true from November – January imop.
But this one is particularly f***ed up.

News is reporting that according to BLS only 36,000 jobs were added in January (about 1/4 of what was expected).

Meanwhile, BLS reports the unemployment rate falls by another 0.4% – 2 months in a row – to 9.0%.

In any case, it is important to note that BLS reports 2 surveys:
The first is the household survey (which surveys people, duh) – that is where we get the unemployment rate.
The other survey is for businesses and the government – that is where news organizations usually report jobs gained or lost.  (36,000 more employed in Jan. compared to Dec. 2010)

The unemployment rate simply cannot be compared to establishment based employment payroll changes because the two numbers come from 2 completely different surveys, measuring slightly different things:

Reported change in civilian employment from Dec. 2010 to Jan. 2011 (private): 117,000  (HOUSEHOLD SURVEY)

Reported change in total private non-farm employment from Dec. 2010 to Jan. 2011: 36,000  (ESTABLISHMENT SURVEY)

The unemployment rate comes from the survey of the former statistic, not the latter.  Media does everyone a dis-service by not pointing this out.

So, you know what you should do with all these news reports and the January statistics?  Throw them in the garbage, and wait for February and March.

Unlike Garth, I don’t think we need to throw the report in the garbage (see my post here), but we need to think critically about it.  Calculated Risk explains:

This data comes from two separate surveys. The unemployment rate comes from the Current Population Survey (CPS: commonly called the household survey), a monthly survey of about 60,000 households. The payroll jobs number comes from Current Employment Statistics (CES: establishment survey), a sample of approximately “140,000 businesses and government agencies representing approximately 410,000 worksites”.

These are very different surveys: the CPS gives the total number of employed (and unemployed including the alternative measures), and the CES gives the total number of positions (excluding some categories like the self-employed, and a person working two jobs counts as two positions).

A couple of key concepts (from the BLS):

The CES employment series are estimates of nonfarm wage and salary jobs, not an estimate of employed persons; an individual with two jobs is counted twice by the payroll survey. The CES employment series excludes employees in agriculture and private households and the self-employed.

And the CPS:

The CPS estimate of employment is for the total number of employed persons. Included are categories of workers that are not covered by the Current Employment Statistics (CES) survey: self-employed persons, private household workers, agriculture workers, unpaid family workers, and workers on leave without pay during the reference period. Multiple jobholders are counted once in the estimate of total employed.

Unemployed persons include those who did not have a job during the reference week, had actively looked for work in the prior 4 weeks, and were available for work. Actively looking for work includes activities such as contacting a possible employer, contacting an employment agency or employment center, having a job interview, sending out resumes, filling out job applications, placing or answering job advertisements, and checking union or professional registers.

In January the headline CES number showed a gain of 36,000 non-farm jobs (by the definitions above). The CPS showed an increase of 117,000 employed people.

These two surveys are almost always different, and both are useful for understanding the employment situation. 

Second, another problem confounding things this month is what’s called the “benchmark adjustment”.  Put very simply, the survey data is used to establish some proportions and ratios among those sampled. Then these ratios and proportions are scaled up to national numbers by multiplying by the latest population estimates from the Census bureau. But the Census bureau monthly estimates of population and demographics are themselves estimates that get revised yearly and of course, definitively every ten years. So once each year, the BLS has to adjust it’s data series on employment and jobs to keep it in line with the annual revision of population from Census. This year, these “benchmark adjustments” were very large (around 500,000).  Again I turn to Calculated Risk:

On Feb 4th, with the release of the January employment report, the BLS will make the following three changes / revisions:

1) Annual Benchmark revision to the Establishment Survey Data

With the release of January 2011 data on February 4, 2011, the Current Employment Statistics survey will introduce revisions to nonfarm payroll employment, hours, and earnings data to reflect the annual benchmark adjustments for March 2010 and updated seasonal adjustment factors. Not seasonally adjusted data beginning with April 2009 and seasonally adjusted data beginning with January 2006 are subject to revision.

Last October the BLS released the preliminary annual benchmark revision of minus 366,000 payroll jobs. Usually the preliminary estimate is pretty close to the final benchmark estimate. 

Finally, we have the issue of seasonality and winter. The data series on employment and labor force must be seasonally adjusted to be meaningful and informative since there’s a huge month-to-month swing in jobs that’s very predictable: summer hiring for kids, Christmas season hiring, July shut-downs in some industries, etc. The raw, non-seasonally adjusted data (NSA) just changes too wildly each month to be easily interpreted. So seasonal adjustment (SA) is necessary. But SA can distort numbers if this year’s seasonal change happens at a slightly different time than it normally has in the past.  This year, Christmas and New Years were on a weekend. More importantly, much of the nation (fortunately not Southeast Michigan, IMO) was wracked by snow and winter storms that were out-of-normal. So the severe winter and snow may have distorted the numbers. After all, snow doesn’t just keep kids home from school. It also keeps job hunters from being active. It keeps people out of stores and causes retailers to temporarily lay-off workers (i.e. tell’em not to come to work). It keeps businesses from hiring (temporarily) since you can’t hire somebody when both interviewer and interviewee are stuck in a snow drift. Old man winter could have had an impact on the numbers, but we have no certain way of knowing. This is particularly true since the surveys do not happen during the entire month. Instead the two surveys (CPS and CES) are done during a few particular days during mid-month. If that’s during a blizzard, well, you can guess the impact. Right now a lot of commentators (see  are willing to write-off the bad numbers in this report to winter snow. They assume the numbers will bounce back next month.  Maybe. Maybe not. We’ll see.  Meanwhile, let’s see what conclusions we can take away from report in the next post.


The History of Modern Macroeconomics

A good piece from Brad Delong.  At the core, modern macroeconomic theory is relatively empty and vacuous when it comes to the major crises: last year’s melt-down, the Great Depression, the many bubbles, etc.  I have to agree with Brad’s conclusion that macro theory must re-connect with economic history ( I would suggest micro theory needs too, also).  Otherwise it becomes an imaginary trip through a fantasy world of math models (what Deidre McCloskey once called a “hyperspace of assumptions”).

Economic History and Modern Macro: What Happened?

Economic History and the Recession

If you ask a modern economic historian—like, say, me—if I know why the world is currently in the grips of a financial crisis and a deep downturn, I will say that I do know and I will give you this answer:

This is the latest episode in a long history of similar episodes of bubble—crash—crisis—recession, episodes that date back at least to the canal bubble of the early 1820s, the 1825-6 failure of Pole, Thornton, and company, and the subsequent first industrial recession in Britain. We have seen this process at work in many other historical episodes as well—1870, 1890, 1929, and 2000, for example. For some reason asset prices get way out of whack and rise to unsustainable levels. Sometimes the culprit is lousy internal controls in financial firms that overreward subordinates for taking risk; sometimes it is government guarantees; sometimes it is the selection of the market as a long run of good fortune leaves the financial market dominated by cockeyed unrealistic overoptimists.

Then the crash comes. And when the crash comes the risk tolerance of the market collapses: everybody knows that there are immense unrealized losses in financial assets and nobody is sure that they know where they are. The crash is followed by a flight to safety. The flight to safety is followed by a steep fall in the velocity of money as investors everywhere hoard cash. And the fall in monetary velocity brings on a recession.

I will not say that this is the pattern of all recessions: it isn’t. But I will say that this is the pattern of this recessions—that we have been here before.

Macroeconomic Theory and the Recession

If you ask the same question of a modern macroeconomist—like, say, the extremely sharp Narayana Kocherlakota of the University of Minnesota—you will find that he says that he does not know: Continue reading

We need a better measure than GDP

Nobel winner Joseph Stiglitz explains why GDP is NOT a good measure of society’s well-being and offers ideas on better measures.  It’s a good critique of GDP.

National income statistics such as GDP and gross national product were originally intended as a measure of market economic activity, including the public sector. But they have increasingly been thought of as measures of societal well-being, which they are not. Of course, good statisticians have warned against this error. Much economic activity occurs within the home – and this can contribute to individual well-being as much as, or more than, market production.


What we measure affects what we do. If we have the wrong metrics, we will strive for the wrong things. In the quest to increase GDP, we may end up with a society in which most citizens have become worse off. We care, moreover, not just for how well off we are today but how well off we will be in the future. If we are borrowing unsustainably from this future, we should want to know.

I strongly recommend reading the entire article here:   FT.com / Comment / Opinion – Towards a better measure of well-being.

Krugman on How Did Economists Get It So Wrong? – Excellent

Long, but excellent reading on the recent (last few decades) of the history of macro thinking.  I think Krugman understates some issues, but much of it is good.  I recommend.

It’s hard to believe now, but not long ago economists were congratulating themselves over the success of their field. Those successes — or so they believed — were both theoretical and practical, leading to a golden era for the profession. On the theoretical side, they thought that they had resolved their internal disputes. Thus, in a 2008 paper titled “The State of Macro” (that is, macroeconomics, the study of big-picture issues like recessions), Olivier Blanchard of M.I.T., now the chief economist at the International Monetary Fund, declared that “the state of macro is good.” The battles of yesteryear, he said, were over, and there had been a “broad convergence of vision.” And in the real world, economists believed they had things under control: the “central problem of depression-prevention has been solved,” declared Robert Lucas of the University of Chicago in his 2003 presidential address to the American Economic Association. In 2004, Ben Bernanke, a former Princeton professor who is now the chairman of the Federal Reserve Board, celebrated the Great Moderation in economic performance over the previous two decades, which he attributed in part to improved economic policy making.

Last year, everything came apart.

For the complete article, see:  http://www.nytimes.com/2009/09/06/magazine/06Economic-t.html?_r=1&pagewanted=all

Krugman’s article caused quite a stir in academic and professional economics.  Many have agreed with him, such as Jeff Frankel who posted this:

The Queen of England during the summer asked economists why no one had predicted the credit crunch and recession.   Paul Krugman points out that, inasmuch as economists can almost never predict the timing of recessions (and don’t claim to be able to), the real questions are worse.  The real questions are, rather how macroeconomists (most of us) could have gotten it so wrong as to believe that: (i) a severe recession like this was not even looming ahead as a danger, and (ii) a breakdown of many of the world’s most liquid financial markets, in New York and London, was not possible.To anyone wondering about these questions, I recommend Krugman’s essay in the New York Times Sunday magazine, September 6:  “How Did Economists Get it So Wrong?” . I think he has it exactly right.

I would only add that he is modest in skipping over one point:  during Japan’s lost decade of growth in the 1990s Paul forcefully drew from the Japanese experience the implication that a severe economic breakdown was, after all, possible in a modern industrialized economy – a breakdown that both was reminiscent of the Great Depression and was outside the ken of modern macroeconomic theory.   But macroeconomics went on as before.   (Likewise with the stock market correction of 1987, the LTCM crisis of 1998, and the dotcom bust of 2000-01.   I do think, however, that our field did a better job with the emerging market crises of 1994-2001, in part because it was considered permissible to argue that financial markets in this case were highly imperfect.)

Even the cartoons in the NYT article are good…  except that I have never seen Olivier Blanchard in a double-breasted suit.    But Robert Lucas definitely merits a place there:   when given one page to defend orthodox economists regarding the crisis in a recent  Economist essay, he actually thought it was a useful rebuttal to point out that critics are repeating arguments they have made before.  And he also thought it was useful to explain: “The term “efficient” as used here means that individuals use information in their own private interest. It has nothing to do with socially desirable pricing; people often confuse the two.”  — as if it is not the latter question that the public is wondering about.

I am pleased that at least some in the profession are waking to the serious methodological and intellectual problems that have crept into economics (not just macro) in recent decades.  There’s a long way to go though before we return to math only being a tool to check/explore the logic instead of the centerpiece.  We need to put real-world economies back in focus as the topic of our theorizing and research.

Macro: an awful mess today

I was not alone.  Apparently Tim Harford of FT.com was also confused and disappointed while learning the modern macro models and theories.  I however figured it was a bunch of nonsense.  The assumptions made by modern macro models, particularly the Rational Expectations stuff of New Classical and New Keynesian theory are the problem.  By making assumptions that enable them to use their elegant math, they assume away any possibility of the model being useful.  The assumptions are not just “simplifying”, they are contra-reality.  It is as if I were to create a theory of flying in physics by first assuming that gravity cannot exist.  There’s a lot of work to be done in macro now to repair this failed research program.

I am struck by the soul-searching that has gripped the profession in the face of the economic crisis. The worry is not so much that macroeconomists did not forecast the problem – bad forecasts are more a sign of a complex world than intellectual bankruptcy – but that macroeconomics seems unable to provide answers. Sometimes it cannot even ask the right questions.

Willem Buiter, a former member of the UK’s Monetary Policy Committee who blogs for the FT, complains that macroeconomists have simply discarded the difficult stuff to make their models more elegant: “They took these non-linear stochastic dynamic general equilibrium models into the basement and beat them with a rubber hose until they behaved.”

Mark Thoma offers additional insight at Economist’s View.