The debate among researchers about the employment effects of minimum wages remains intense and unsettled. There is clear variation in the magnitude of estimated employment effects across studies, with the debate often being between an elasticity for low-skilled groups equal to (or indistinguishable from) zero, or an elasticity in the range of −0.1 to −0.2. However, there are larger negative estimates in the literature (see Table 1), and occasional large positive estimates (Card and Krueger 1994). That the debate remains unresolved is demonstrated by the recent exchange between Allegretto et al. (2017), who claim no evidence of ‘disemployment’ effects, and Neumark and Wascher (2017), who claim there is job loss.
Table 1 Recent estimates of minimum wage effects on unskilled employment
Notes: The table reports my best attempts to identify the authors’ preferred estimates reported in the papers. The Thompson estimate cannot be compared directly to other elasticity estimates, because there is no population count in the data source used. The Clemens/Wither elasticity is based on a 6.6% decline (p. 27), divided by a 70.2% employment rate (or a 9.4% employment decline), divided by a 9.7% MW increase (50 cents, from p. 14, divided by $5.15). (These numbers are reported in a 2016 version of the study.) The magnitude is likely larger than other studies because it is calculated for a more directly-targeted group of workers (compared to teenagers or restaurant workers, only some of whom are directly affected by the minimum wage). Indeed, the elasticity is smaller when using a treatment group that includes higher-wage workers and hence is “less intensively” treated.
In this column, based a recent paper (Neumark 2017), I suggest questions that, if we can answer them, may prove most useful in making sense of the conflicting evidence.1 I also focus on questions we should consider to better inform policy debate regarding the very high minimum wages becoming more common in the US,2 about which past research is uninformative. I focus on US evidence, which dominates the literature because of significant cross-state variation in minimum wages over many decades.
One key econometric issue that influences the results is the choice of appropriate controls for the area (usually, states) where the minimum wage increases. The standard two-way fixed effects model compares changes in low-skilled employment in states where the minimum wage increased more to states where it increased less (or not at all). Allegretto et al. (2011) and Dube et al. (2010) criticise this approach, arguing that cross-state policy variation is correlated with shocks that also affect outcomes, biasing estimated minimum wage effects. They advocate using as controls only nearby areas that may have experienced the same shocks. When they use only ‘close controls’ they find estimates statistically indistinguishable from zero.
One response to this critique explores – and ultimately casts doubt on – the validity of these close controls (Neumark et al. 2014a). A second develops more advanced econometric techniques to select and construct control areas (Powell 2016), building on synthetic control methods (Abadie et al. 2010). A third pursues alternative identification strategies to distinguish minimum wage increases from potentially correlated shocks.
A natural alternative strategy is to use triple-differences estimators that isolate the effect of the policy change by introducing another group exposed to the same shock but not the policy change. For example, Clemens and Wither (2014) estimate effects for lowest-wage workers whose wages were affected by minimum wages relative to employment changes for workers with slightly higher wages who were not affected by the minimum wage, and find a large employment elasticity for directly affected workers (−0.97 based on SIPP data). Baskaya and Rubinstein (2015) instead use an instrumental variable that purges the estimated minimum wage effect of bias from states endogenously choosing minimum wages in response to shocks to state-level economic conditions, and find an elasticity (for teens) of −0.3 to −0.5.3 Powell’s (2016) synthetic control estimator that does not impose geographic proximity on the controls yields a teen elasticity of −0.44.
A key question is why the close-controls approach yields little evidence of disemployment effects, while research using alternative strategies to address the same problem finds such evidence (see Table 1). Neumark and Wascher (2014b) suggest that minimum wage increases within similar geographic areas could be more endogenous with respect to economic shocks, as other factors that differ across states in different regions and affect minimum wages exogenously – such as unionisation – are less important for close controls. This and other explanations need to be assessed.
Recent research also highlights the sensitivity of estimated employment effects to the inclusion of state-specific time trends.4 Although it is common to include linear trends in assessing the robustness of panel data estimates (e.g. on states over time), it seems that minimum wage effects are unusually sensitive to this robustness check.
There are well-known problems with including linear trends. When pre-treatment periods are short, the trends are largely identified from the post-treatment period and can be hard to distinguish from policy effects.5 In contrast, in long panels the linear specification may be less tenable. Moreover, it can be hard to characterise the counterfactual in a clear way when these trends are included.6 Thus, estimates including these trends are not necessarily the most reliable ones. Triple-difference estimators (and synthetic control matching) obviate the need for state-specific trends – since each state can have an arbitrary pattern of time effects. Finally, evidence that estimates are sensitive to the inclusion of trends is ultimately a sign of our ignorance – more compelling evidence will come from expanding the variables used in minimum wage studies to include the omitted variables the trends are hypothesised to capture.
Economic factors may also help explain variation in estimated employment effects across studies, and understanding their role may enhance our ability to predict the effects of ‘out of sample’ large minimum wage increases.
One factor is whether (and for what share of workers) the minimum wage is binding. Neumark and Wascher (2002) specify labour demand and labour supply curves, and fit a disequilibrium model that estimates the parameters of these curves and the probability that an observation is on the demand curve (the short side of the market when, in the standard model, the minimum wage is set too high), rather than at market equilibrium. Their estimates suggest that some studies finding no effect of the minimum wage (Card 1992a, 1992b) were likely using minimum wage variation in the range where the minimum wage was not binding. However, this approach is based on a market for homogeneous labour. Card’s (1992a) approach of specifying the minimum wage variable as the fraction affected by given minimum wage increases more directly captures worker heterogeneity, and may be more useful for projecting the effects of much higher minimum wages.7
A closely related question is the extent to which studies identify the effects of minimum wages on affected workers. The group most commonly studied is teenagers, who are very low skilled and represent a vastly disproportionate share of minimum wage workers. However, research has begun to study non-teens with wages low enough to be directly affected (e.g. Clemens and Wither 2014). This approach requires information on wages and hence excludes non-workers. Given evidence that minimum wages lower the rate at which workers are hired (Dube et al. 2016, Gittings and Schmutte, 2016), ignoring potential reductions in the flow of workers into jobs can miss a potentially important means by which higher minimum wages reduce employment of low-skilled workers. Longer-term panel data with wages workers earned on previous jobs could prove useful.
Empirical research providing a tighter link between workers affected by the minimum wage and the employment effects they experience can sharpen our understanding of the policy implications of higher minimum wages. For example, perhaps the disemployment effects that most work finds for teenagers are less evident for groups for which policymakers are more interested in increasing incomes – such as single mothers.8
Another economic factor that may partially account for variation in minimum wage effects across studies is labour-labour substitution – substitution from lower-skill to higher-skill workers when the minimum wage increases. Estimated employment effects of minimum wages for samples that extend beyond those whose wages are directly raised by the minimum wage will understate the net effects on the least-skilled when there is labour-labour substitution. There is some evidence of labour-labour substitution from research on minimum wages (Neumark and Wascher, 2003) and on living wages (Fairris and Bujunda, 2008). But no research has directly linked differences in labour-labour substitution to variation in estimated employment effects across studies. Evidence on labour-labour substitution could be informative about the effects of much higher minimum wages, as the ability to substitute between workers is diminished when the minimum wage directly affects a larger share of workers.
Finally, knowing which model best characterises low-wage labour markets may help in trying to make sense of the conflicting literature. Some argue that the monopsony model must be right because it can generate negative employment effects, no employment effects, or positive effects.9 The claim that monopsony models can account for the variation in estimates would be far more convincing if research established that studies find negative, zero, or positive effects in settings where monopsony search models predict these effects. By the same token, the neoclassical characterisation of low-skill labour markets would be enhanced by evidence that variation across studies in estimated employment effects can be explained in the context of this model.10
How can researchers refine our understanding of the employment effects of minimum wages, and perhaps achieve (some) convergence of views? I think progress can come from two types of research. The first is additional studies sorting out the best way to identify minimum wage effects on employment, which includes understanding why different approaches yield different answers, and determining which approaches are most reliable. The second is doing a good deal more than the existing research to recognise that there is not one minimum wage effect, and instead trying to better understand why the employment effects of minimum wages vary across workers, labour markets, time, and the policy environment.11
Abadie, A, A Diamond, and J Hainmueller (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Association, 105 (490), 493-505.
Addison, J T, M L Blackburn, and C D Cotti (2013), “Minimum Wage Increases in a Recessionary Environment”, Labour Economics, 23, August, 30-9.
Allegretto, S A, A Dube, and M Reich (2011), “Do Minimum Wages Really Reduce Teen Employment? Accounting for Heterogeneity and Selectivity in State Panel Data”, Industrial Relations, 50 (2), April, 205-40.
Allegretto, S A, A Dube, M Reich, and B Zipperer (2017), “Credible Research Designs for Minimum Wage Studies”, Industrial and Labor Relations Review, 70 (3), May, 559-92.
Baskaya, Y S, and Y Rubinstein (2015), “Using Federal Minimum Wages to Identify the Impact of Minimum Wages on Employment and Earnings across U.S. States”, Unpublished paper.
Belman, S, and P J Wolfson (2014), What Does the Minimum Wage Do? Kalamazoo, MI: Upjohn Institute.
Brown, C, C Gilroy, and A Kohen (1982), “The Effect of the Minimum Wage on Employment and Unemployment”, Journal of Economic Literature, 20 (2), June, 487-528.
Card, D (1992a), “Using Regional Variation in Wages to Measure the Effects of the Federal Minimum Wage”, Industrial and Labor Relations Review, 46 (1), October, 22-37.
Card, D (1992b), “Do Minimum Wages Reduce Employment? A Case Study of California, 1987-1989”, Industrial and Labor Relations Review, 46 (1), October, 38-54.
Card, D, and A B Krueger (2000), “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania: Reply”, American Economic Review, 90 (5), 1397-420.
Card, D, and A B Krueger (1995), Myth and Measurement: The New Economics of the Minimum Wage Princeton, NJ: Princeton University Press.
Card, D, and A B Krueger (1994), “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania”, American Economic Review, 84 (4), 772-93.
Clemens, J, and M Wither (2014), “The Minimum Wage and the Great Recession: Evidence of Effects on the Employment and Income Trajectories of Low-Skilled Workers”, NBER Working Paper 20724.
Dickens, R, S Machin, and A Manning (1999), “The Effects of Minimum Wages on Employment: Theory and Evidence from Britain”, Journal of Labor Economics, 17 (1), 1-22.
Dube, A, T W Lester, and M Reich (2016), “Minimum Wage Shocks, Employment Flows, and Labor Market Frictions”, Journal of Labor Economics, 34 (3), 663-704.
Dube, A, T W Lester, and M Reich (2010), “Minimum Wage Effects across State Borders: Estimates Using Contiguous Counties”, Review of Economics and Statistics, 92 (4), 945-64.
Dube, A, and B Zipperer (2015), “Pooling Multiple Case Studies using Synthetic Controls: An Application to Minimum Wage Policies”, IZA Discussion Paper 8944.
Fairris, D, and L F Bujunda (2008), “The Dissipation of Minimum Wage Gains for Workers through Labor-Labor Substitution: Evidence from the Los Angeles Living Wage Ordinance”, Southern Economic Journal, 75 (2), 473-96.
Gittings, R K, and I M Schmutte (2016), “Getting Handcuffs on an Octopus: Minimum Wages, Employment, and Turnover”, Industrial and Labor Relations Review, 69 (5), 1133-70.
Liu, S, T J Hyclak, and K Regmi (2016), “Impact of the Minimum Wage on Youth Labor Markets”, LABOUR, 30 (1), 18-37.
Meer, J, and J West (2016), “Effects of the Minimum Wage on Employment Dynamics”, Journal of Human Resources, 51 (2), 500-22.
Monras J (2015), “Minimum Wages and Spatial Equilibrium: Theory and Evidence”, IZA Discussion Paper 9460.
Neumark, D (2017), “The Employment Effects of Minimum Wages: Some Questions We Need to Answer”, NBER Working Paper 23584.
Neumark, D, J M Ian Salas, and W Wascher (2014a), “Revisiting the Minimum Wage-Employment Debate: Throwing out the Baby with the Bathwater?”, Industrial and Labor Relations Review, 67, Supplement, 608-48.
Neumark, D, J M Ian Salas, and W Wascher (2014b), “More on Recent Evidence on the Effects of Minimum Wages in the United States”, IZA Journal of Labor Policy, 3:24 (online).
Neumark, D, and W Wascher (2017), “Reply to Credible Research Designs for Minimum Wage Studies”, Industrial and Labor Relations Review, 70 (3), 593-609.
Neumark, D, and W Wascher (2011), “Does a Higher Minimum Wage Enhance the Effectiveness of the Earned Income Tax Credit?”, Industrial and Labor Relations Review, 64 (4), 712-46.
Neumark, D, and W Wascher (2008), Minimum Wages Cambridge, MA: MIT Press.
Neumark, D, and W L Wascher (2007), “Minimum Wages and Employment”, Foundations and Trends in Microeconomics, 3 (1-2), 1-182.
Neumark, D, and W Wascher (2003), “Minimum Wages and Skill Acquisition”, Economics of Education Review, 22 (1), 1-10.
Neumark, D, and W Wascher (2002), “State-Level Estimates of Minimum Wage Effects: New Evidence and Interpretations from Disequilibrium Methods”, Journal of Human Resources, 37 (1), 35-62.
Neumark, D, and W Wascher (2000), “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania: Comment”, American Economic Review, 90 (5), 1362-96.
D (2016), “Synthetic Control Estimation Beyond Case Studies: Does the Minimum Wage Reduce Employment?”, RAND Labor & Population Working Paper WR-1142.
Slichter, D (2016), “The Employment Effects of the Minimum Wage: A Selection Ratio Approach to Measuring Treatment Effects”, Unpublished paper.
Thompson, J P (2009), “Using Local Labor Market Data to Re-examine the Employment Effects of the Minimum Wage”, Industrial and Labor Relations Review, 62 (3), 343-66.
Totty, E (2015), “The Effect of Minimum Wages on Employment: A Factor Model Approach”, Institute for Research on Labor and Employment Working Paper #110-15.
 For broader reviews of the minimum wage literature, see Brown et al. (1982), Card and Krueger (1995), and Neumark and Wascher (2007, 2008).
 For example, California, New York State, Seattle, and Washington, DC have scheduled (or have already reached) a $15 minimum wage.
 Their instrument isolates the variation due to the historical propensity of each state to let the federal minimum wage bind. Their finding that the instrumental variables estimates are larger than the OLS estimates is consistent with policymakers raising minimum wages when youth labor market conditions are strong (in contrast to the direction of bias implied by the results from the close-controls approach).
 See Allegretto et al. (2011, 2017), Neumark et al. (2014a), and Neumark and Wascher (2017).
 Monras (2015) allows for separate trends pre- and post-treatment, in an event-study design
 Meer and West (2016) demonstrate this clearly in the context of estimating effects of minimum wages on employment growth.
 However, Baskaya and Rubinstein (2015) suggest that a fraction affected variable is particularly prone to endogeneity with respect to local labor market shocks, and is procyclical and hence leads to bias against finding a disemployment effect.
 Indeed, Neumark and Wascher (2011) find evidence of these more “positive” distributional effects from a combination of a higher minimum wage and a higher Earned Income Tax Credit.
 See, e.g., Dickens et al. (1999).
 The incidence of large positive effects in the literature is actually very rare. The main case is Card and Krueger (1994). However, re-evaluations of that evidence fail to replicate those findings (Neumark and Wascher, 2000; Card and Krueger, 2000).
 I do not think meta-analysis helps. Averaging estimates from studies of minimum wage effects, as meta-analyses do, is problematic. Variation in the economic factors that can influence minimum wage effects, as well as in the population studies, can render an average across studies of “the” minimum wage effect virtually meaningless. Moreover, why include more reliable and less reliable studies on an equal footing? And it may be worse than this. Meta-analyses often assign more weight to estimates that are more statistically precise (e.g., Belman and Wolfson, 2014), even though the most rigorous empirical methods are likely to be less precise. Yet it is precisely the studies using the most rigorous methods – if valid – that that should receive the most weight – if not all the weight.