Raising the Floor

President Obama, in his State of the Union speech, made two proposals that will reduce inequality, and that have strong research support—raising the minimum wage and universal pre-school. The main difference is that raising the minimum wage is a short-term, direct solution to inequality. It proposes to help the working poor by ensuring that they make more money. Universal preschool, which I'll discuss in a future post, is about making kids more socially mobile 20 years from now. Not surprisingly, more things can go wrong with that plan.

The current federal minimum wage is $7.25 per hour, which is $15,080 per year—well below the federal poverty level for a family of three, and well below the minimum wage in most industrialized countries. The value of the minimum wage in this country peaked in 1968 at $10.56 per hour in inflation adjusted dollars. But worker productivity has risen sharply since 1968. If the minimum wage had kept pace with productivity growth, it would be $16.50 per hour. Obama proposes to raise it gradually to $9 (24%) by 2015—hardly a radical proposal—and index it to the rate of inflation thereafter, so that it increases with the cost of living without requiring action by Congress. For comparison, the top 1% increased their real income (adjusted for inflation) by 281% between 1979 and 2007. Although every income group lost money during the Great Recession, during the economic recovery (2009-2011), income increased by 11.2% for the top 1%, but declined by -.4% for the bottom 99%.

In a 2012 survey, raising the minimum wage to $10 in 2014 and indexing it to inflation thereafter was favored by 73% of Americans, with 20% opposed and 7% undecided. Support is strongest among Democrats and those who would be helped most by the proposal—women, minorities and young adults.

Unfortunately, there is an incorrect argument about the effect of raising the minimum wage that appeals to the conventional wisdom. It states that raising the minimum wage causes employers to hire fewer workers or lay off existing workers. House Speaker John Boehner reacted to the President's proposal by saying, “When you raise the price of employment, guess what? You get less of it.” Fortunately, this is an empirical question, and Boehner is wrong.

The fact that the minimum wage has stagnated since 1968, while bad for the country, has been a boon to research on its effects. Some states and cities have raised the minimum wage above the federal level. This variability makes it possible study whether raising the minimum wage depresses employment. The modern history of this research begins with a before-after comparison group design by Card and Kreuger. New Jersey increased its minimum wage in 1992 while Pennsylvania did not. The authors did a telephone survey of employment in fast food restaurants in counties along the NJ-PA border. (Restaurants were chosen because they are the business that experiences the greatest increase in cost when the minimum wage goes up.) They found no evidence that NJ's minimum wage reduced employment relative to PA.

Of course, this could be an atypical case. But Dube, Lester and Reich published a study in 2010 comparing restaurant employment in 318 state borderline pairs of counties in which the minimum wage differed, essentially replicating the Card and Kreuger study over a much larger sample of times and locations. They found no employment effects. In recent years, there have been two meta-analyses of minimum wage studies, one of which summarizes 1492 separate tests of the minimum wage hypothesis. They find no significant overall effect on employment among low income workers, teenage workers, or anyone else. (By the way, 80% of minimum wage workers are adults.)

These studies are now well accepted by economists, but they raise the question of why the conventional wisdom is wrong about the effects of raising the minimum wage. The most likely reason is that, for most owners, the cost of increasing the minimum wage is small relative to other costs affecting their business. Schmitt has suggested 11 “adjustment channels” that might explain the lack of a minimum wage effect. Here are the four that he feels have the greatest research support.

• Increasing the minimum wage reduces turnover, an important cost savings for employers.
• Workers who are paid more increase their productivity, either on their own, since the job is more important to them, or in response to employer demand.
• The cost is passed on to consumers in the form of increased prices. Does this cause inflation? Yes, but not very much. One study found that a 31% increase in the minimum wage increased restaurant prices between 1% and 2%, at worst increasing the cost of a $10 meal to $10.20.
• In the long run, employers compensate by reducing wages paid to higher wage workers. This results in “wage compression,” or less wage inequality within the organization.

Of course, Boehner's comments suggest that the President's plan is dead on arrival in Congress. But let's imagine we lived in a country where some wage relief for the working poor were possible. If the minimum wage is to be indexed to inflation, it is important that it start from a baseline that is high enough to be fair to low income workers. The data in the second paragraph suggest that $9 is too low. Wicks-Lim has suggested that businesses could easily adjust to a 70% rise in the minimum wage to $12.30 per hour. Raising the minimum wage could be seen as a form of reparation for the harms caused to low wage workers by the successful class warfare waged by the rich for the past 30 years.

Laffing All the Way to the Bank

For more than three decades, Republicans have argued that higher taxes, especially higher taxes on the rich, hurt the economy by discouraging work and investment, or alternatively, that the economy will be stimulated by tax cuts. This prediction was illustrated by the Laffer curve, named for economist Arthur Laffer, who claimed that under most circumstances government revenue increases when taxes are cut, since economic growth more than compensates for loss of revenue due to the tax cut itself.

That this rhetoric is still central to the Republican message is illustrated by Sen. Marco Rubio's rebuttal to President Obama's State of the Union speech:

[A]s you heard tonight, his solution to virtually every problem we face is for Washington to tax more, borrow more and spend more. . . . And the idea that more taxes and more government spending is the best way to help hard-working middle-class taxpayers—that's an old idea that's failed every time it's been tried.

True to his word, Rubio was one of eight senators who voted against last month's “fiscal cliff” agreement, presumably because it raised taxes on Americans earning over $400,000 per year ($450,000 for couples).

What's the actual relationship between marginal tax rates and economic growth? An article by economist Gerald Friedman in the latest issue of Dollars and Sense addresses this issue with two important charts.

As I've noted before, when it is impossible to do a controlled experiment to test a hypothesis about social policy, we must turn to two types of quasi-experiment. In a time series design, you look at how the outcome variable (economic growth in the United States) changes from before to after a change in the social policy (a tax cut) that is hypothesized to affect it. This table shows the relationship between tax rates on the wealthy and gross domestic product (GDP) growth during all the presidencies after World War II.

The biggest tax cuts came under Reagan and Bush II. You might object that it takes time for tax cuts to stimulate the economy, but Bush I and Obama presided over even lower GDP growth than their predecessors. The main counterargument to a time series design is that the results might be explained by other historical changes that happened to coincide with tax policies.

The alternative is a comparison group design. Since tax policies are national decisions, the United States must be compared to other similar countries that have different marginal tax rates. The historical change argument is partially negated by the fact that the countries are compared over the same time period.

This chart is particularly stark in its condemnation of U. S. tax policy. The main counterargument to a comparison group design is that the countries are simply not comparable—for example, that all these other countries have some advantage that the U. S. lacks which accounts for their greater economic growth. Really?

I don't mean to suggest that these two charts exhaust the arguments against low taxes or tax cuts for the wealthy. For example, Friedman also shows that there is no relationship between top marginal tax rates and investment. Returning to Sen. Rubio's argument, it appears that it is not tax increases but tax cuts that have failed every time they have been tried.

The evidence that higher taxes and tax increases do not harm the economy seems so clear that it may be time to call into question the media's policy of false balancing, or quoting statements like Rubio's without comment or evaluation. It is difficult for public attitudes to change when the media merely act as a conduit for false information.

Get the Lead Out, Part 1

The rate of violent crime in this country has been declining for decades, and although there several plausible hypotheses, no one really knows why. Kevin Drum argues in the January Mother Jones that changes in lead emissions from automobiles have been the primary influence on violent crime in this country. Why didn't I think of that?

Here's the argument, in brief. Violent crime began to increase in the '60s, peaked in the early '90s, and has been declining ever since. Neither demographic changes, i.e., increases and decreases in the number of young men, nor changes in the economy can fully explain this pattern. Lead in the environment comes from two major sources: lead paint, which was gradually phased out during the last century, and gasoline. Following World War II, Americans began driving a lot more, and the oil companies added lead to gasoline, allegedly to improve engine performance. In the mid-'70s, due to evidence that lead exposure reduced I. Q., government forced the oil companies to switch to lead-free gasoline. Lead has its greatest influence on the developing brains of children. The results of lead on violent behavior becomes apparent when people are in their early twenties. Here are the data (originally compiled by by Rick Nevin) showing the relationship between the concentration of lead in the environment and the rate of violent crime 23 years later.

It's obvious that there is a correlation. But like many social scientists, I've spent my career telling students that “correlation does not mean causation.” Whenever a variable, A (lead), is correlated with another variable, B (violent crime), there are three possibilities: A causes B, B causes A, or some third variable, C, is responsible for the apparent relationship between them. Since no one is arguing that violent crime causes lead to be deposited in the environment, the real issue is whether confounding variables (Cs) have been ruled out.

Correlational arguments can be strong or weak. They are relatively strong if they have been replicated using several different data sets and research methods, and if alternative explanations can be ruled out. The two primary methods of determining whether a social policy, such as lead abatement, influences a behavioral variable are time series and comparison group designs. In a time series design, you look at whether a change in the policy is followed, at an appropriate interval, by a change in the behavior. Those are the data in the above graph.

In a comparison group design, you compare different spatial locations that have different levels of the presumed cause to see whether they also have different incidences of the presumed effect. The switch to unleaded gasoline was not uniform among the 50 states. Reyes found that states that switched to unleaded sooner saw their violent crime rate drop sooner. Nevin has examined the relationship between lead and crime in several countries, and has found that lead predicts differences in crime rates both within and between nations. Mielke has compared lead concentrations in various U. S. cities with the same results. Mielke has also measured lead concentration in New Orleans soil samples—which is quite unevenly distributed. He finds that it predicts crime rates at the neighborhood level. There are also data relating the crime rate to the distance one lives from a major highway.

Turning to alternative explanations, all these studies do a reasonable job of statistically controlling for confounding variables such as race, gender, socioeconomic status, family demographics, and unemployment rates. Of course, the number of possible alternative explanations is theoretically infinite, so you can never anticipate all of them. It is important to note that these researchers are not suggesting that lead is the only variable that influences violent crime, only that it is much more important than has been generally realized.

The relationship between lead and violent crime has been confirmed at the individual level in longitudinal studies. Researchers at the University of Cincinnati have followed a cohort of children for 30 years. Those with higher levels of lead in their bloodstreams as children are more likely to be arrested for violent crimes as adults. Lead exposure is also related to lower I. Q., attention deficit disorder, and higher rates of teen pregnancy.

Furthermore, plausible physiological mechanisms to explain the lead hypothesis have been proposed. The Cincinnati group compared the MRI brain scans of adults who had high or low lead exposure as children. Those exposed to more lead had less gray matter in the prefrontal cortex, the area of the brain associated with “executive function”—attention, verbal reasoning and impulse control. They also have a thinner myelin sheath around the synapses which connect adjacent neurons, suggesting that their communication channels within the brain are slower and less reliable.

Tomorrow:  Part 2