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When I reverted to Islam in March 2005, I have to admit I was afraid. Okay, perhaps nervous is a better choice of word, as I wasn’t scared or frightened. And I know I’m not alone in admitting this feeling, especially with female converts. The process of transitioning into Islam from a previous faith/belief system (because face it, even if you don’t believe in God, you believe there is no God) is daunting:

What will my friends think? How will I be received by the public? Does this mean I have to start dressing like an Arab or East Asian-er? Do I have to start my life over from the beginning, rethinking every choice I’ve ever made?

While all of those are valid concerns, and ones that I did contemplate at some point in time post-reversion, they weren’t what I was afraid of. My fear came from telling my father.

Not my family. Not my Mother.

My Father.

Now, before you start thinking my dad is this overbearing and close-minded totalitarian who lives for controlling others’ lives, he’s NOT. In fact, he’s the polar opposite. He’s one of the most open-minded individuals I’ve ever known in my life. And if there is a perfect antonym for overbearing, that describes him, too. I mean, for Heaven’s sake, the man used to sit and logically discuss with me the reasons I should pick up my toys when I was 3 years old. If there’s anything my dad is not, it’s overbearing and close-minded.

So, why was I scared of telling my dad I had become Muslim?

My father has a strong head on his shoulders (don’t confuse strong with stubborn). His choice of worship was not made based on how he was brought up (Nazarene). He didn’t look to his parents to tell him how he should worship God or practice his religion (Christianity). Instead, he went to a Christian college, studied the history and lineage of the Bible and Christianity, and majored in Bible Studies. His goal: to become a preacher.

When he became a member of the Church of Christ denomination, he did so knowing full-well that it represented the beliefs he personally held based on his extensive studying. To him, it was correct.

Now there I was, his 23-year-old daughter, midway through my graduate school program, and I’d converted to Islam. And I had to tell my Father. The same father who responded to my 16-year-old self’s idea of becoming Baptist with, “I’ve failed as a father!”

So, one day while my parents were in town for a wedding, my father and I drove over to the beach at Gulf Shores. We had lunch, talked about religion a little bit, and mostly discussed general life topics. (My father is also a severe introvert, like me, and idle conversation is not a forte of his.)

After lunch, we walked out on the beach. I’d planned my delivery. I asked him what it was exactly that he believed about life and death. He started out with the history of religion (he always starts with the history behind the pertinent question), and then he transitioned into his personal beliefs. Once he finished, I offered my part. I told him nobody had ever really asked me what I believe. It was always just assumed because I was part of a certain family or church that I shared the same beliefs. But, obviously, I didn’t.

Then came the time to deliver my blow. I told him I was thinking about becoming Muslim. (I couldn’t own up to it full-force yet; I needed time to let the idea sink in for him.) Surprisingly, he didn’t stop walking. He didn’t yell (not surprisingly). He just said one thing, and his response has stayed with me every day since. It has had my back when people were against me. It has given me conviction along my chosen path. And those words were:

As your father, it is my job to let you know that I think you’re wrong. But you’re an adult. And if you chose to believe something just because I told you so, that would be just as wrong.

It was all I needed. I didn’t need an “I support you” or a “That’s wonderful”. And I know he still doesn’t like my choice. And I know there have been many tears shed on his side on my behalf. But I also think both he and my mom have come to conclusion that after nearly a decade, a husband and a child, I’m not going through a phase.

And as each day goes by, I never lose hope that one day my family will join me in truly understanding the history, relevance and authority of our beautiful Islam, insha’Allah. Until that day comes, I will continue to enjoy the avid discussion my father and I have about our beliefs, and I will rest easy knowing that despite our differences, we still respect each others’ beliefs … and rights to have them.

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Ghazala Khan, the mother of a fallen U.S. soldier of Muslim faith, is responding to Donald Trump’s speculation that she didn’t speak at last week’s Democratic convention due to her religion.

“I can say that my religion or my family or my culture never stopped me saying whatever I want to say,” Khan said in an interview with CNN’s “New Day.” “And my husband is very supportive of me in these things that I have all the rights as a wife, as a mother, as a daughter.”

After Khan and her husband, Khizr, took the stage at the Democratic National Convention last week to deliver an emotional speech denouncing Trump’s proposed Muslim immigration ban, the GOP presidential candidate suggested that Mrs. Khan wasn’t allowed to speak because of her Islamic religion.

Also Read: 'The Simpsons' Derides Donald Trump, Theorizes Dog Toupée (Video)

“If you look at his wife, she was standing there. She had nothing to say. She probably, maybe she wasn’t allowed to have anything to say. You tell me,” Trump said.

The Republican candidate received backlash for his comments, notably from Mrs. Khan.

“I have done very well saying my mind out, but that time was different. And anybody can see it was different that time when I was standing there in front of America,” Khan said.

Also Read: Ann Coulter Hammered by Conservatives for Smearing US War Hero's Dad as 'Angry Muslim'

The Khans’ son, Army Capt. Humayun, had served in Iraq and died during a suicide car bombing. They said Trump’s ban would have prevented their son from serving his country.

MOSCOW (Reuters) - Russia’s postal service was hit by Wannacry ransomware last week and some of its computers are still down, three employees in Moscow said, the latest sign of weaknesses that have made the country a major victim of the global extortion campaign.

A man walks out of a branch of Russian Post in Moscow, Russia, May 24, 2017. REUTERS/Maxim Shemetov

Wannacry compromised the post office’s automated queue management system, infecting touch-screen terminals which run on the outdated Windows XP operating system, one of the workers said. Terminals were still blank in some parts of Moscow this week but it was not clear exactly how many branches had been affected.

A spokesman for Russian Post, a state-owned monopoly, said no computers were infected, but some terminals were temporarily switched off as a precaution. “The virus attack did not touch Russian Post, all systems are working and stable,” he said.

Other institutions in Russia have said they were infected by the virus, highlighting Moscow’s readiness to show it too is a frequent victim of cyber crime in the face of allegations from the United States and Europe of state-sponsored hacking.

The Interior Ministry, mobile operator MegaFon (MFON.MM) and state rail monopoly Russian Railways all reported infections, with employees locked out of their computers and the creators of the virus demanding ransoms of $300 to $600.

The Russian central bank said on Friday the virus had also compromised some Russian banks in isolated cases.

That the infected post office terminals ran on Windows XP - which Microsoft stopped supporting in 2014 - points to the widespread use of outdated software in Russia, which experts say left the country disproportionately vulnerable to the attack.

Of 300,000 computers infected worldwide, 20 percent were in Russia, according to an initial estimate by cybersecurity researchers last week.

Globally, few ransoms have been paid after many victims found they could restore their systems from backups.

The post office outages also illustrate what investigators say is a common misconception about Wannacry: infected computers are more likely to be part of antiquated systems not deemed important enough to update with the latest security patches, rather than machines integral to the company’s core business.

“Many companies in Russia use outdated unpatched systems and older anti-malware solutions,” said Nikolay Grebennikov, vice president for R&D at data protection company Acronis. “In big companies upgrades are hard to perform and avoided because of budget and scale.”

SCRUTINY

Russia’s relationship to cyber crime is under intense scrutiny after U.S. intelligence officials alleged that Russian hackers had tried to help Republican Donald Trump win the U.S. presidency by hacking Democratic Party servers. Moscow has denied the allegations.

Investigators are yet to track down Wannacry’s criminal authors, saying they likely used a hacking tool built by the U.S. National Security Agency (NSA) and leaked online in April.

It has not previously been reported that the Russian postal service, which employs more than 350,000 people, had been hit by the virus.

“The head guys rang on Thursday and said we had to turn off the terminals immediately. They said this extortion virus had infected them,” a worker at a branch in northwest Moscow said, declining to be identified discussing internal company matters.

“They rang again yesterday and said we could turn them back on. We did that, but you can see they still don’t work.”

Employees at a second post office confirmed the electronic queuing system was broken but said they did not know why.

Two sources at Russian Railways said the company had suffered a “huge” cyber attack and a small number of computers were infected without damaging any important files.

The extent of the damage had been limited, one of the sources said, because a lot of computers were turned off at the end of the working week. “We were lucky it was a Friday night,” he said.

Megafon, which is Russia’s second biggest mobile operator, declined to comment on how the virus had got into its system.

It said the virus had caused a temporary outage of its customer support services. “Our sales points suffered worst of all because Windows, which had the exploited vulnerability, is more widely used in retail,” a company statement said.

COMPUTER PIRACY

The frequent use of pirated software in Russia also helped spread the Wannacry infection, investigators said, as unlicensed products do not receive security updates.

Reuters has found no evidence any of Russian companies infected with the Wannacry virus were using unlicensed software.

But computer piracy is a long-standing issue for technology companies in Russia, one which has as become increasingly acute as the country’s economic slump and falling earnings make licensed products prohibitively expensive.

A woman walks past a branch of Russian Post in Moscow, Russia, May 24, 2017. REUTERS/Maxim Shemetov

Data compiled by the BSA Software Alliance trade group shows 64 percent of software products in Russia were pirated in 2015 - a black market industry worth $1.3 billion - compared to a global average of 39 percent.

“Piracy is still wide spread in Russia, especially if we are talking about home users,” Grebennikov said. “This is because of poverty. If an operating system costs say 500 rubles, people would buy it.”

Microsoft’s Windows 10 operating system currently costs around 8,000 rubles ($140.92) in Russia, around a fifth of the average monthly wage of 39,000 rubles. Online, the same product can be illegally downloaded for free.

Executive summary

This paper reviews the empirical literature on the employment effects of increases in the minimum wage. It organizes the most prominent studies in this literature by their use of two different empirical approaches: studies that match labor markets experiencing a minimum-wage increase with an appropriate comparison labor market, and studies that do not. A review of this literature suggests that:

The studies that compare labor markets experiencing a minimum-wage increase with a carefully chosen comparison labor market tend to find that minimum-wage increases have little or no effect on employment.

The studies that do not match labor markets experiencing a minimum-wage increase with a comparison labor market tend to find that minimum-wage increases reduce employment.

A better understanding of which approach is more rigorous is required to make reliable inferences about the effects of the minimum wage. This paper argues that:

Labor market policy analysts strongly prefer studies that match “treatment” with “comparison” cases in a defensible way over studies that simply include controls and fixed effects in a regression model.

The studies using the most rigorous research designs generally find that minimum-wage increases have little or no effect on employment.

Application of these findings to any particular minimum-wage proposal requires careful consideration of whether the proposal is similar to other minimum-wage policies that have been studied. If a proposal occurs under dramatically different circumstances, the empirical literature on the minimum wage should be invoked with caution.

Introduction

President Harry Truman famously joked that he wanted to hire a one-armed economist because all of his staff economists would resort to “on the one hand… but on the other hand…” formulations when giving policy advice. Truman just wanted a straight answer. Today, policymakers and the public also seem to want a one-armed economist in discussions of the minimum wage. Minimum-wage policy in the United States is made at the federal, state, and local level. The federal government imposes a minimum wage nationally (currently $7.25 an hour for most workers) that Congress can raise. Many states and even local governments set minimum wages that are higher than the federal minimum. One group of well-regarded economists contends that increases in the minimum wage reduce employment by raising labor costs, while another group insists the evidence shows that minimum-wage increases do not reduce employment, likely due to factors such as reduced turnover, increased productivity, and small price increases. Responsible economists understandably mention both strands of the literature. Nevertheless, it would be helpful if there were some way to determine which side has the more persuasive case, something a little closer to Truman’s one-armed economist.

There are many criteria that could be used to make sense of the empirical literature on the employment effects of the minimum wage. This report focuses on the distinction between studies that use what I will refer to as “matched comparison groups” to estimate these effects, and those that do not. The term “matching” is used here in a relatively broad way, to describe a family of methods that identify a comparison group as an appropriate match for a treatment group, thus mimicking a randomized experiment. A matching design is strongly preferred by economists working on a variety of applications because it is often the closest study design to randomized experiments available. Whether or not a study uses matching is a broad criterion, but an important one for discriminating between studies and clarifying who provides more persuasive evidence in the minimum-wage debate.

The first section of this report reviews the two major approaches to studying the minimum wage—studies with and without matched comparison cases—and compares the major findings from these two approaches. The second section makes an argument for preferring studies that use matching over studies that do not. The report concludes with a discussion of the implications of this research for policy.

Two approaches to studying the minimum wage

The empirical literature on the impact of the minimum wage is large, but much of it (and all important recent studies) can be classified into one of two categories: one, studies that match and compare cases involving an increase in the minimum wage with a similar control group, and two, studies that do not match cases of a minimum-wage increase to a similar control group. This distinction is only one of many possible ways of thinking about the empirical literature, but it is critical for answering the question of who is right about the employment effects of the minimum wage.

Matching studies

Analyses of the minimum wage that use matching first received wide attention with David Card and Alan Krueger’s 1994 paper on an increase in New Jersey’s state minimum wage from $4.25 to $5.05. Card and Krueger were concerned with distinguishing changes in employment at fast food restaurants that would have happened anyway from changes occurring in response to the minimum-wage increase. Their solution was to use comparable restaurants in Pennsylvania immediately across the border from New Jersey as a control group of establishments operating in a similar environment, but not subject to the minimum-wage increase. These Pennsylvania establishments provided a baseline for determining what would have happened in New Jersey if the minimum wage had remained constant. Deviation from that baseline in the New Jersey restaurants could thus be safely attributed to the minimum wage. A true experimental design would have randomly assigned increases in the minimum wage in order to control for alternative influences, but in the absence of random assignment the authors identified the next best alternative: a close match.

The Card and Krueger study concluded that there was no evidence that the minimum-wage increase in New Jersey reduced employment in that state relative to the comparison group of Pennsylvania restaurants. Criticisms of the quality of the study’s phone survey data were raised at the time, which led the authors to analyze more reliable administrative payroll data from New Jersey and Pennsylvania. Card and Krueger (2000) confirmed the original finding that the minimum-wage increase in New Jersey had no discernable employment effect.

The matching approach pioneered by Card and Krueger has been applied with increasing sophistication and stronger data sources than the initial phone survey data in the 20 years since the New Jersey analysis. The most notable advance in matching has been in the work of Arindrajit Dube with several coauthors, which uses counties that neighbor each other across state borders as control cases. Rather than a restricted analysis of one state’s minimum-wage increase, Dube, Lester, and Reich (2010) compare every pair of neighboring counties along every state border in the country (similar study designs are used in other papers by Dube and his colleagues). By exploiting variation in the minimum wage across the country and over the course of 16 years, this research estimates minimum-wage effects from a larger sample than earlier matching studies, and produces estimates that are more representative of the typical response to a minimum-wage increase and not the special circumstances of a particular local labor market.

Dube and his colleagues consistently find no evidence for reduced employment as a result of regular increases in the minimum wage using the county pair match. In fact, even before using county pairs, as Dube, Lester, and Reich (2010) add increasingly more precise geographic matching into their models, the negative impact of the minimum-wage increase identified in the nonmatching literature (discussed in more detail below) gradually evaporates. Table 1 reports Dube, Lester, and Reich’s (2010) estimates of the percentage change in employment resulting from a percentage change in earnings as a result of an increase in the minimum wage. The authors analyze two different samples of employment data: one that includes all counties (the first column), and one that includes pairs of neighboring counties (the second column), with county pair matching performed on the latter sample.

Table 1 Percentage change in employment for each percentage change in earnings due to a change in the minimum wage All county sample County pair sample No matching -0.784* -0.482** No matching, control for Census division differences -0.114 — No matching, control for state differences 0.183 — No matching, control for MSA differences 0.211 — County-level matching — 0.079 * Statistically significant at the 10 percent level.

** Statistically significant at the 5 percent level. Source: Estimates drawn from Dube, Lester, and Reich (2010), Table 2 (this is not a reproduction of their Table 2) Share on Facebook Tweet this chart Embed Copy the code below to embed this chart on your website. Download image

The first row in Table 1, which presents results when no matching is done, is representative of most study designs before Dube, Lester, and Reich (2010), and many since. When no matching is done, the minimum-wage increase is estimated to have a negative effect. However, as the comparison is increasingly narrowed to more similar counties, first in the same Census division, then the same state, then the same metropolitan statistical area (MSA), the statistically significant negative effect of the minimum-wage increase is eliminated. In the analysis that uses actual pair-matching of bordering counties to construct a comparison group (the last row), the higher minimum wage has an estimated positive effect on employment. However, because this result is statistically insignificant it cannot be statistically distinguished from a finding that the minimum wage has no effect on employment. In any case, the stronger designs that use matching strategies clearly contradict the theory that minimum-wage increases reduce employment. Other examples of this approach include Addison, Blackburn, and Cotti (2009; 2012), which have conclusions that are similar to Dube, Lester, and Reich (2010) and other matching studies.

One possible critique is that by over-parameterizing (i.e., adding too many controls to) their models, Dube, Lester, and Reich (2010) are mistakenly attributing true employment-discouraging effects of minimum-wage increases to other variables in their model, or that statistical significance is lost due to the difficulty of estimating such a complex model. However, the authors point out that these fears can be easily dismissed by comparing estimates of the impact of the minimum wage on employment with estimates of the impact on earnings. Only the estimate of the impact on employment becomes positive—and loses statistical significance—as more rigorous matching strategies are introduced. The effect of the minimum wage on earnings stays consistent across these models. Since the same statistical model with the same risks of over-parameterization is being used regardless of the dependent variable (earnings in one case, employment in the other), the case that specification problems are driving the result is harder to justify.

There are many different explanations for the lack of substantial disemployment effects in matching studies. One suggestion is that employers exercise “monopsony power,” or bargaining power associated with being one of a small population of buyers in a market (an analog to the monopoly power exercised by sellers). Just as a monopoly will not reduce its output in response to an imposed price reduction, a monopsonist can absorb a price increase (such as a minimum-wage increase) without reducing demand for workers. Although such theoretical explanations are possible, a more straightforward argument is that an increase in the minimum wage does not have a disemployment effect because the increased labor costs are easily distributed over small price or productivity increases, or because fringe benefits are cut instead of employment levels. Less work has been done on the impact of the minimum wage on these outcomes than on the employment impact. Alternatively, disemployment effects might be avoided due to reduced fixed hiring costs as a result of lower turnover.

The most comprehensive and best known matching studies find that a higher minimum wage does not have a negative impact on employment, but this finding is not unanimous. Some matching studies do find disemployment effects. For example, Sabia, Burkhauser, and Hansen (2012) find negative effects on employment when they compare New York state with several comparison states, and Hoffman and Trace (2009) find that a minimum-wage increase in Pennsylvania reduced the employment prospects of “at-risk” workers relative to comparable workers in New Jersey. Perhaps the best quality study using matching methods that identifies a disemployment effect is that of Singell and Terborg (2007), who find negative effects associated with much larger increases in the minimum wage in Oregon and Washington. Finally, Neumark, Salas, and Wascher (2013) use a “synthetic control method” and find negative minimum-wage effects. This important contribution to the matching literature is discussed in more detail below.

Each of these studies is open to criticism. Hoffman (2014) shows that rectifying questionable data choices eliminates Sabia, Burkhauser, and Hansen’s (2012) negative result. Finally, all of these analyses use state-wide data, which arguably provide a weaker match than Card and Krueger (1994), Dube, Lester, and Reich (2010), and other studies that match neighboring counties rather than states. Even if these negative results are taken at face value, the strongest studies investigating the widest range of minimum-wage increases by Dube and his colleagues find that on average, minimum-wage increases have little or no effect on employment.

Studies without matching

The alternative to a matching approach is to run a model using state-level or individual-level panel data (i.e., data collected over time) on employment levels to estimate how employment changes after states enact a higher minimum wage. These models have a number of valuable features, most notably their ability to control for idiosyncratic differences between states or individuals that do not change over time. These stable differences are called “fixed effects,” and the models are therefore referred to as fixed-effects models. Regardless of whether fixed-effect models use state or individual-level data, they rely on variations in the minimum wage among states to determine the effect of the policy.

Notably absent from the fixed-effects models is any matching of comparison cases to treatment cases. While Dube, Lester, and Reich (2010) used counties immediately across a state border as comparison cases, the fixed-effects models implicitly treat every state not experiencing a minimum-wage increase as a coequal comparison case to every state that does have a minimum-wage increase. This potentially introduces “selection bias” into the results. Minimum-wage laws are not imposed under experimental conditions. This means that states that “select into” higher minimum wages by enacting increases may be systematically different from states that do not. Fixed-effects models can handle this problem if the researcher has data on the factors that are associated with the differential adoption of minimum-wage laws or if these factors do not change over time (in that case, the inclusion of fixed effects controls for the nonrandomness that is introduced due to the lack of a true experiment). However, if factors correlated with the adoption of minimum-wage laws vary over time and across states, fixed-effects models will produce biased estimates of the effect of the minimum wage.

This sort of bias is very plausible in practice. Many states in the South and Central United States are experiencing rapid population and economic growth. In contrast, communities in the Midwest and Northeast are already densely populated and in many cases undergoing a structural transition associated with the decline of manufacturing. None of these changes are the result of the minimum-wage policy, but all are correlated with the minimum wage, which tends to be lower in the South and Central United States and higher in the Midwest and Northeast. Other trends specific to states or counties rather than regions are also conceivable. Some of these trends may be controlled for in certain studies, but fixed-effects models are not structured to capture the more comprehensive set of state-specific trends that matching studies can account for. State-specific time trends that are not accounted for will move a fixed-effects model further away from results that would have been estimated by a randomized experiment.

The economists most closely associated with the fixed-effects model approach to studying the minimum wage are David Neumark and William Wascher. In 2007, Neumark and Wascher conducted a thorough review of 102 minimum-wage studies, covering policies implemented both inside and outside the United States, and at the federal and state level. They identified a subset of studies that they deemed “credible,” most of which fall into the category of state and individual-level fixed-effects models. This subset of studies, selected for special mention by the most prolific authors who use the fixed-effects method, is therefore an excellent vantage point for understanding the consensus of this literature. Most of the studies mentioned below come from this list. Neumark and Wascher’s most recent minimum-wage study with J.M. Salas is not a standard fixed-effects model. This is discussed in more detail in the next section.

A typical state-level fixed-effects approach is offered by Neumark and Wascher (1992), published two years before the great disruption of the Card and Krueger (1994) study. This research estimated that a 10 percent increase in the minimum wage reduced teenage employment by 1 to 2 percent and young adult employment by 1.5 to 2 percent. These findings were notable because they were comparable to earlier estimates from the time series literature, which relied on variation over time rather than across states to estimate employment effects.

Neumark and Wascher (1996), Neumark (2001), and others soon extended the fixed-effects modeling framework to individual-level data to understand the impact of the minimum wage on specific vulnerable groups. The authors find in both cases that increases in the minimum wage reduce employment for the population of interest (typically teenagers or low-skill workers). These studies use the same design as the state-level studies, relying on variation among states and over time to estimate how changes in the minimum wage affect employment. As such, they are vulnerable to the same criticisms outlined above. Individuals in a high-minimum-wage state may experience lower employment rates, but it is difficult to determine whether that is the result of fundamentally different local labor market conditions that are unrelated to the minimum wage.

The most comprehensive exploration of the sensitivity of the fixed-effects model results to their ability to control for differences among states is by Allegretto, Dube, and Reich (2011). This study uses Neumark and Wascher’s preferred fixed-effects modeling framework, but includes controls for Census division and state-specific labor market trends that Dube, Lester, and Reich (2010) suggest might be driving the strong negative employment effects in most fixed-effects analyses. After controlling for these trends, the standard disemployment effects become statistically indistinguishable from zero effects. What is notable about Allegretto, Dube, and Reich’s (2011) contribution is that the result of little or no disemployment effects of the minimum wage is not generated from models related to the matching studies described in the previous section. Instead, the study uses the methods that are usually employed by Neumark and Wascher.

The method has also been extended beyond standard employment outcomes for the United States. Couch and Wittenburg (2001) use a fixed-effects model to assess the impact of the minimum wage on hours worked, while Neumark and Wascher (2004) use these techniques to understand how labor market institutions are relevant for international differences in the effect of the minimum wage. Both studies find the traditional negative impact. Meer and West (2013) use state fixed-effects models and numerical examples to argue that matching studies that include location-specific time trends (discussed in more detail in the next section) may provide inappropriate employment estimates if the principal impact of changes in the minimum wage is on employment growth rates.

Which approach makes more sense?

Matching cases of minimum-wage increases to a control group is essential because it is often the closest social scientists can get to the gold standard of an experiment using random assignment. Although the minimum-wage literature as a whole is divided on the question of the impact of minimum-wage increases, the strongest studies that use matching strategies find little or no evidence that such increases have a negative impact on employment.

It is difficult to overstate how uncontroversial it is in the field of labor market policy evaluation to assert the superiority of matching methods to the nonmatching approaches described above. The seminal evaluations of the effects of job training programs, work-sharing arrangements, employment tax credits, educational interventions, and housing vouchers all use at least some sort of matching method, if not an actual randomized experiment. In their widely cited survey article on non-experimental evaluation, Blundell and Costa Dias (2000) do not even mention state-level fixed-effects models when they list the five major categories of evaluation methods. In a similar article, Imbens and Wooldridge (2009) do mention fixed-effects models as a tool for policy evaluation, but clarify that these were used before more advanced methods were developed, noting that the modern use of fixed-effects models is typically in combination with other more sophisticated techniques. For example, Dube, Lester, and Reich (2010) also use a fixed-effects model, but more importantly it is a fixed-effects model that utilizes rigorous matching strategy to identify the effect of the minimum wage. Sometimes fixed-effects models are the best available option if no natural experiment or other matching opportunity emerges to provide a more rigorous approach. Well specified fixed-effects models can still be informative. But faced with the choice between a well matched comparison group and a fixed-effects model, the former is unambiguously the stronger study design.

Given the unanimity of the evaluation literature on the importance of these methods, how is it possible that so many minimum-wage studies use only state-level fixed-effects models? One possible answer is that unlike many of the programs studied in the evaluation literature, everyone is subject to the minimum wage. The minimum wage is not like a training program or a tax credit where some people receive it (are treated) and others do not. It is instead just one of many “rules of the game” in the labor market. As such, economists may not think of the minimum wage in the context of the evaluation literature and the methods of that literature.

Potential signs of progress

In the immediate aftermath of the Card and Krueger (1994) study, many critics simply dismissed the finding as an abandonment of sound economic theory. Fortunately, today these reactions are less common (though still not unheard of), and the major voices in the discussion seem to be developing a mutual appreciation for the importance of hammering out credible study designs. An excellent example is the recent exchange between Neumark, Salas, and Wascher (2013) and Allegretto et al. (2013). Instead of advancing new work in the tradition of a state-level fixed-effects model, Neumark, Salas, and Wascher (2013) raise criticisms of the county matching approach of Dube and his colleagues, and then go on to offer an alternative matching approach that they feel to be more appropriate. They suggest that a better method is the “synthetic control” approach of Abadie and Gardeazabal (2003), which generates weights for a number of comparison cases that together provide a good match to the treatment case. After running models using the synthetic control method, Neumark, Salas, and Wascher (2013) find evidence for negative effects of a higher minimum wage on employment, consistent with their work with state-level fixed-effects models. Allegretto et al. (2013) responded by defending their county-pair approach and further developing the synthetic control method, including rectifying problems in Neumark, Salas, and Wascher’s (2013) work. In a separate paper, Dube and Zipperer (2013) argue that Neumark, Salas, and Wascher (2013) fail to properly implement the synthetic control method, using an approach that is quite different from the earlier literature in that tradition and much less defensible. Allegretto et al. (2013) and Dube and Zipperer (2013) conclude that across both methods (their contiguous county approach and a properly executed synthetic control method), the minimum wage does not have substantial disemployment effects.

The most important development in this recent work is not that it has resulted in agreement on the impact of the minimum wage. Numerous econometric disagreements remain, and of course Neumark, Wascher, and others continue to defend fixed-effects studies on the grounds that the biases in these analyses are not substantial. The critical advance has been that Neumark, Salas, and Wascher (2013) appear to concede that some sort of modern matching approaches are essential for evaluating the effect of minimum-wage increases in the absence of a randomized experiment. The authors continue to disagree on the best way to implement such a study, but the more recent focus on credible non-experimental designs is a step forward.