Nonlinearities in the Employment Response to Minimum Wages: Evidence from Industries in Adjacent States

Transcription

1 Nonlinearities in the Employment Response to Minimum Wages: Evidence from Industries in Adjacent States Kyle Schaul Under the Supervision of Professor Emeritus Joe Stone Presented to the Department of Economics, University of Oregon, in partial fulfillment of requirements for honors in Economics. June

2 Nonlinearities in the Employment Response to Minimum Wages: Evidence from Industries in Adjacent States Abstract: This thesis estimates the extent to which the employment response to an increased minimum wage depends on the gap between the initial mean and minimum wages. Generalized difference in differences estimates based on multiple industries and multiple changes in minimum wages in the adjacent states, Oregon and Washington, are consistent with the theoretical expectations that the employment response to an increased minimum wage is less positive the greater the gap between the industry wage and minimum wage. The estimated magnitudes indicate that the nonlinearities are an important source of variation in the employment responses to changes in the minimum wage. Approved: Professor Joe Stone Date 2

3 I. Summary In this paper, I explore the nonlinear impact that industry wages have on the minimum wage elasticity of employment. I assess how the gap between the average industry wage and the minimum wage influences the effects of the minimum wage on employment. This is the first study to incorporate, in an explicit way, the gap between the minimum and average industry wage in estimating effects of the minimum wage. I hypothesize that the larger the gap between the industry wage and the minimum wage, the less negative the effect of the minimum wage. I focus on adjacent states, Oregon and Washington, due to their close proximity and similar economies and climates. Paying attention to industries with relevant wage data, I observe the leisure and hospitality industry and the education and health services industry in the primary analysis and include the construction industry to evaluate the robustness of the results in a secondary analysis. The primary results are consistent with my hypothesis. Industries with a larger gap between their industry wage and the minimum wage are less negatively impacted by minimum wage increases, while industries with smaller gaps are more negatively impacted. Additionally, given the means of the regression, the average minimum wage elasticity of employment is about This effect roughly doubles if one-year lagged effects are included. I also find that if the nonlinear effect of the excess wage is not incorporated in the analysis, the minimum wage has no significant effect on employment. These results are robust to a series of alternative specifications in secondary analyses. 3

4 II. Introduction Under the Fair Labor Standards Act, a national minimum wage of 25 cents was established in 1938, the first minimum wage ever set in the United States (US Department of Labor). Its initial purpose was to provide low waged workers with a more livable wage, to ensure that they could afford basic resources. Since then it has increased numerous times at both the federal and state level, throughout the United States. When assessing a policy that is enacted throughout the country, it is of vital importance to understand exactly how it affects the living conditions of different types of workers. Conventional economic theory suggests that minimum wages lead to a decrease in employment. Minimum wages are a price floor in the labor market, and wages below this floor are pushed up. Employers offer fewer jobs to make up for the fact that labor costs more than it would otherwise while more workers demand jobs due to a higher wage offered. However, if employers have market power as monopsonists, a minimum wage can potentially increase employment. The debate regarding whether minimum wages lower employment has been going on since its inception, ultimately questioning whether the good the minimum wage brings outweighs the bad. Over the last 50 years or so, there have been numerous economic studies assessing minimum wage laws and their effect on employment. The results of these studies are mixed to say the least. Some find that the minimum wage affects employment negatively while others find that the minimum wage affects employment positively, and still others find that there is no effect at all. However, some meta-analyses suggest that there is more evidence for a negative employment effect. Neumark and Wascher conclude: 4

5 Our review indicates that there is a wide range of existing estimates and, accordingly, a lack of consensus about the overall effects among the papers we view as providing the most credible evidence, almost all point to negative employment effects, both for the United States as well as for many other countries. (NW, 2006) This conclusion conforms with conventional theory that in a competitive labor market, implementing a binding minimum wage will cause employment to fall, all else the same. There has been much progress in the statistical techniques and specifications used to come to these results. Studies have highlighted the importance of looking at the effect of the minimum wage on the percentage change in employment. Other studies have added that minimum wage should also be specified as a percentage change. These specifications have contributed greatly to the study of the minimum wage. Additionally, there has been a recent focus on isolating regions and industries to understand how the minimum wage affects different samples. Different regions and industries have different labor markets with different characteristics such as mean wages, which can lead to varying results. Some studies have isolated lower wage industries and higher wage industries to see if the effect is greater based on the industry wage, rather than examining the minimum wage effect on total employment. I pool across industries in an empirical specification designed to account for the varying responses in low and high wage industries. The average industry wage in each individual labor market plays a vital role in the effects of minimum wage laws. When a country or state increases the minimum wage, its effect is highly dependent on the existing industry wage. Some industries are affected more than others. For example, one would expect that the fast food industry would be affected more by a minimum 5

6 wage increase than the financial services industry. I examine how large of a role industry wages play by estimating how much the gap between the average industry wage and the minimum wage influences the effect of the minimum wage on employment across multiple industries. Even if the mean industry wage is well above the current minimum wage, some wages in the distribution of wages surrounding the mean industry wage may not be. I demonstrate theoretically how the gap between the minimum and average wage plays a nonlinear role in the effect of the minimum wage and incorporate that gap into an empirical specification. The wage gap is the central theme of this study. I estimate the effect the gap between the minimum and industry wage has on the employment effect of the minimum wage, using data for multiple industries in the adjacent states, Oregon and Washington. This is the first study to explicitly examine the influence of the industry and minimum wage gap, on the effect of the minimum wage. III. Prior Studies I focus here on only the most relevant, studies. Card and Krueger (CK, 1994) study the effect of a minimum wage increase in New Jersey in This paper focuses on the wage hikes effect on employment in the fast food industry as well as on teenage employment as a whole. They focus on low-wage workers to examine the effect of a price floor in the labor market on those most likely to be affected. CK note that fast food stores are the leading employer of low waged workers, and teenagers are the most inexperienced workers with the lowest pay (CK, 1194). 6

7 CK use survey data to assess the effect on the fast food industry. They compare New Jersey to nearby Pennsylvania to see whether employment fell relative to a constant. This approach helps to isolate the effects of the minimum wage increase. CK conclude that the minimum wage increase did not affect employment in the fast food industry and, if anything, lead to a slight increase in employment. They found the same result in overall teenage employment. Examining industries in two similar regions helps to isolate the effects of the minimum wage, and by looking at specific industries instead of more broadly at the larger economy, one can examine how a change in the minimum wage affects different industries with different characteristics. Recently, another study (Meer and West, 2013) argues that the method of assessing a minimum wage raise s effect on employment levels, is flawed. They claim that when faced with a wage hike, businesses are more likely to reduce the number of new hires rather than suddenly fire workers due to varying issues such as the costs of firing or rehiring and tenure. Instead, they suggest that it is best to look at job growth. They believe that by looking at the percentage of jobs created the real effects of minimum wage hikes on employment can be observed. They look at the entire labor market, using panel data, and assess the effect of the state minimum wage on employment levels as well as employment growth. They find that minimum wage increases have no effect on employment levels but do have a significant negative effect on employment growth. Examining employment growth has become a widely used approach when assessing the minimum wage. It is important to account for the fact that minimum wage hikes may not have an immediate impact on the current level of employment but may have an impact on future employment. 7

8 Another notable study, by Singell and Terborg (ST, 2007), estimates the effect of changes in minimum wages in both Oregon and Washington. Washington and Oregon are very close in location. People can easily travel between these states, which have similar climates and broadly similar economies. ST focus on low-wage industries, specifically the food and drinking places and hotel and lodging industries, to assess how multiple minimum wage hikes, in the late 1900s to early 2000s, affected low-wage jobs. ST assess the percentage rise in minimum wage s effect on the percentage change in employment, using log differences. This is comparable to MW s study, which uses percentage change in employment as well, but MW examine the effect of the logged minimum wage, while ST examine the effect of minimum wage growth. I employ the latter in this study. ST find that the minimum wage lead to slightly lower employment in the food industry but not in the lodging industry. This is an interesting finding since the food and drinking industry has a much lower average wage than hotel and lodging, which is evidence that the industry wage does have an impact on an industry s employment sensitivity to a minimum wage change. Looking at specific industries helps to isolate the effect of the minimum wage based on specific labor markets, which allows one to better assess how minimum wage hikes effect various types of workers. Different industries have different mean wages. If one industry has a higher wage then one would expect that industry to be less vulnerable to minimum wage hikes. I depart from the ST study in several ways. I observe a more recent period, 2009 to I examine two specific industries in a single pooled regression, controlling for differences between states and industries, and I incorporate an explicit role for the gap between the minimum wage and average industry wage, which I call the excess wage by including it as both a separate linear term and as a nonlinear interaction with the minimum wage. 8

9 In another study, Keil et al. (K, 2001) introduce the industry wage by simply deflating the minimum wage by the industry wage ( MinWage IndustryWage ) and separately adding the real industry wage variable as well to control for the changes in the industry wage. In this thesis, I demonstrate that it is the nonlinear role of the gap between the industry wage and the minimum wage that matters. By adding the excess wage variable and its interaction with the minimum wage directly into the regression, I am able to isolate the minimum wage effects on employment, and am able to estimate how the excess wage alters the minimum wage elasticity of employment. The results have important implications for minimum wage policy as well as for the direction of future research. IV. Theoretical Framework A minimum wage establishes a price floor in the labor market. No firm may offer, and no worker may take, a wage below the minimum wage. What is the implication? In the economy, there is usually a tradeoff. Looking again at conventional economics, I conclude that in the absence of employer monopsony, where employers do not face substantial competition from other employers in hiring workers, employers hire fewer workers when faced with higher labor costs. This can be thought of in terms of supply and demand, with many workers being paid wages in a distribution around this competitive wage. Setting a price floor above the market wage leads to a surplus of workers. Instead of the number of workers seeking work equaling the number of workers demanded, there are less workers demanded than supplied. Although in the economy not every worker is employed at one point in time, a minimum wage above the market wage decreases total 9

10 employment. According to this theory, one would expect minimum wage and employment to have a negative relationship. As minimum wage rises, employment falls. However, what if the competitive wage in the industry is above the minimum wage? How will the minimum wage affect these industries? In the past, many researchers have explored this idea by observing low wage industries and observing whether the effect of minimum wage has a significant effect on employment, while separately observing its effect in higher waged industries. These studies typically find a significant effect for minimum wage increases in low wage industries, but to what extent does the industry wage matter and how does it impact the minimum wage effect? In this study, I answer these questions by incorporating both the linear and nonlinear effects of the gap between the average industry and minimum wage in an empirical specification that pools industries with different levels of wages across time and states. Earlier I mentioned that by setting a minimum wage, the federal or state government is placing a wage floor in the labor market. The buyers in the market, or the firms, cannot offer a price, or wage, that is below the set minimum wage. If any wage is below this price floor, it gets artificially pushed up to a higher level, and is put in disequilibrium, with more labor being supplied by workers than labor being demanded by firms. Having a higher industry wage in the labor market makes a difference, though. Given that the average wage is not the only wage being offered in the industry, the higher the industry wage, the less of an impact the minimum wage has on that industry s employment. Figure 4.1 below illustrates how the size of the gap between the minimum and average wage influences the number of workers affected by an increase in a minimum wage. Along the horizontal axis, I is 10

11 the industry wage, M 1 is a wage that is close to the industry wage, and M 2 is a minimum wage that is farther away from the industry wage. With a normal distribution of wages, the relative heights of the frequency distribution at the respective minimum wages in the upper panel illustrate that fewer workers are affected by a minimum wage when the gap between the minimum and industry wage is large than when it is small. The highlighted portions of the lower panel illustrate the central hypothesis. That is, for a given increase in the minimum wage (d dollars in the figure), the increase affects a higher proportion of workers when the initial minimum is close to the average than when the minimum is farther away. Figure 4.1 Excess Wage and Industry Wage Distribution 11

12 The effect of the minimum wage is more positive (less negative) as the gap between the minimum and industry wage decreases, and vice versa. With both average and minimum wages varying, the effect of changes in the minimum wage depend on the interaction between the gap between the minimum and average wage. This is the reason that I look at two different industries, leisure and hospitality and education and health services. The former has a much lower industry wage while the latter has a higher wage. For the leisure and hospitality industry in Oregon, the average wage from 2009 to 2015 was $13.42 and in Washington was $ You can see for yourself how close these wages are to the minimum wage. For education and health services, the industry wage was $23.61 in Oregon and $26.28 in Washington. These wages are significantly higher than the minimum wage, which only reaches as high as $9.1 and $9.47 in each state respectively. By looking at both industries in a single pooled regression, I can identify the impact of the gap between the minimum and industry wage on employment. I incorporate the excess wage variable, the difference between the industry wage and the minimum wage, in both a simple linear form and in a nonlinear interaction with the minimum wage to capture its individual effect on employment as well as how it changes the minimum wage elasticity of employment. V. Data The Singell and Terborg (ST, 2007) study is the reference point for data and empirical specification. They use monthly employment for a single low-wage industry, along with state minimum wages. The analysis here requires data on multiple industries and monthly average wages for each industry. Not all industries have readily available monthly wage data, so I focus on three that do for the period of 2009 to The initial estimates focus on two of these, 12

13 education and health services, a higher wage industry and leisure and hospitality, a lower wage industry. The leisure and hospitality industry is the lowest wage industry with relevant data available (summary data can be found in the Appendix). I use the period of 2009 to 2015 because it is the most consistent period with wage data. All monthly wage data on the BLS website is only available as far back as However, I do not want to have the data include both the peak and crash of the most recent recession. By choosing 2009 as the initial year, I am able to observe the effect of the minimum wage in a period of stable and modest growth over years in which both Oregon and Washington simultaneously had or did not have an increase in the minimum wage. I use data from the states of Oregon and Washington due to their close proximity and broadly similar economies. Minimum Wage Table 5.1 Minimum Wage History Oregon Washington Date New Minimum Wage Date New Minimum Wage 7/1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/

14 1/1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ /1/ (Fed) 1/1/ /1/ *Data from the Oregon Bureau of Labor and Industries and Washington State Department of Labor and Industries Over the past 17 years both Oregon and Washington have had multiple minimum wage increases, some small, others large. On January 1, 2017 Washington s minimum wage jumped from 9.47 to 11 dollars. Over the period of 2009 to 2015, there were six minimum wage increases in Oregon and six in Washington, with increases occurring every year except for 2010 in both states. Washington s minimum wage is consistently above Oregon s over the period, reflecting the higher average wage and larger economy in that state. Leisure and Hospitality The leisure and hospitality industry is a super sector comprised of the arts, entertainment, and recreation (AER) sector and the accommodation and food services (AFS) sector. 14

15 sector is: The North American Industry Classification System definition (NAICS) of the AER The Arts, Entertainment, and Recreation sector includes a wide range of establishments that operate facilities or provide services to meet varied cultural, entertainment, and recreational interests of their patrons. This sector comprises (1) establishments that are involved in producing, promoting, or participating in live performances, events, or exhibits intended for public viewing; (2) establishments that preserve and exhibit objects and sites of historical, cultural, or educational interest; and (3) establishments that operate facilities or provide services that enable patrons to participate in recreational activities or pursue amusement, hobby, and leisure-time interests (Bureau of Labor Statistics, 2017). The NAICS defines the AFS sector: The Accommodation and Food Services sector comprises establishments providing customers with lodging and/or preparing meals, snacks, and beverages for immediate consumption. The sector includes both accommodation and food services establishments because the two activities are often combined at the same establishment (Bureau of Labor Statistics, 2017). In rough terms, one can think of the leisure and hospitality industry as firms operating motels and hotels, entertainment facilities, and restaurants and fast food places. Below is a comparison of this industry s employment during the 2009 to 2015 period across Washington and Oregon. 15

16 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jul-13 Oct-13 Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Jul-15 Oct-15 Figure 4.1 Leisure and Hospitality Employment Washington Oregon There is an explicitly similar trend in the two states, with employment higher in Washington. There is also a strong cyclical component in both Oregon and Washington. Education and Health Services The education and health services industry is a super sector comprised of the educational services (ES) sector and the health care and social assistance (HCSA) sector. The NAICS defines the ES sector: The Educational Services sector comprises establishments that provide instruction and training in a wide variety of subjects. These establishments include schools, colleges, universities, and training centers. These may be privately owned and operated for profit or not for profit, or they may be publicly owned and operated. They may also offer food and/or accommodation services to their students. 16

17 Educational services are usually delivered by teachers or instructors that explain, tell, demonstrate, supervise, and direct learning. Instruction is given in diverse settings, such as educational institutions, the workplace, or the home, and through diverse means, such as correspondence, television, the Internet, or other electronic and distance-learning methods. The training provided by these establishments may include the use of simulators and simulation methods. It can be adapted to the particular needs of the students, for example sign language can replace verbal language for teaching students with hearing impairments. All industries in the sector share this commonality of process, namely, labor inputs of instructors with the requisite subject matter expertise and teaching ability (Bureau of Labor Statistics, 2017). This industry can be thought of as all firms that are devoted to the creation and teaching of knowledge, whether for schools or for the workforce. Majority of this sector is made up of teachers and professors. As defined by the NAICS: The Health Care and Social Assistance sector comprises establishments providing health care and social assistance for individuals. The sector includes both health care and social assistance because it is sometimes difficult to distinguish between the boundaries of these two activities. The industries in this sector are on a continuum from those providing medical care exclusively to those providing health care and social assistance or only social assistance. The services provided by establishments in this sector are delivered by trained professionals. All industries in the sector share this commonality of process, namely, labor inputs of health practitioners or social workers with the requisite expertise. 17

18 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jul-13 Oct-13 Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Jul-15 Oct-15 Many of the industries in the sector are defined based on the educational degree held by the practitioners included in the industry (Bureau of Labor Statistics, 2017). To summarize this industry, it is comprised of health care firms, physicians and nurses, residential care firms, and firms that provide services to the mentally ill. Figure 5.2 Education and Health Services Employment Washington Oregon Looking at the employment during the period of 2009 to 2015, I can see that Washington and Oregon follow similar patterns. The employment level in Washington is consistently higher than in Oregon. The cyclical component in this industry is small, as reflected by the relatively flat patterns. Industry Wage The final data key to this study is the average industry wage because the difference between the industry wage and the minimum wage is key to understanding the effect of the minimum wage. 18

19 1/1/2009 4/1/2009 7/1/ /1/2009 1/1/2010 4/1/2010 7/1/ /1/2010 1/1/2011 4/1/2011 7/1/ /1/2011 1/1/2012 4/1/2012 7/1/ /1/2012 1/1/2013 4/1/2013 7/1/ /1/2013 1/1/2014 4/1/2014 7/1/ /1/2014 1/1/2015 4/1/2015 7/1/ /1/2015 The figure below charts average monthly wages by industry in Oregon, along with the minimum wage. Figure 5.3 Oregon Wages LH EHS Minimum Wage As you can see the wage for the leisure and hospitality industry increased slightly throughout the period of 2009 to 2015, maintaining a roughly five-dollar increment above the minimum wage, with a mean wage of $13.42 for that period. Imagine there being a distribution around each point on the LH line to better understand how the minimum wage affects the industry wage and thus employment. Although it would be ideal to know the actual distribution about the mean for these periods, this data is not readily available. The wage for the education and health services industry on the other hand rises at a faster rate than the leisure and hospitality industry. At the beginning of 2009, the average industry wage is about twelve dollars above the minimum wage and at the end of the period, it is around twenty-eight dollars above the minimum wage. With a mean wage of $23.57, this industry is likely to be less affected by minimum wage increases. 19

20 1/1/2009 4/1/2009 7/1/ /1/2009 1/1/2010 4/1/2010 7/1/ /1/2010 1/1/2011 4/1/2011 7/1/ /1/2011 1/1/2012 4/1/2012 7/1/ /1/2012 1/1/2013 4/1/2013 7/1/ /1/2013 1/1/2014 4/1/2014 7/1/ /1/2014 1/1/2015 4/1/2015 7/1/ /1/2015 The figure below charts similar data for Washington. Figure 5.4 Washington Wages LH EHS Minimum Wage The industry wage for the leisure and hospitality industry maintains a comparable five-dollar gap above the minimum wage, but reaches lower than 4 dollars at the end of However, the average industry wage in Washington over the 2009 to 2015 period is $14.75, but still within a range sufficiently close to expect a significant impact. The average wage for the education and health services industry in Washington does not follow as steep of a trend in Washington compared to Oregon, but is consistently higher, with a mean wage of $26.28 over the period. With the leisure and hospitality industry wage around the mid-teens and the educational and health services industry wage around the mid-twenties, I would expect a more negative employment impact on the leisure and hospitality industry relative to the education and health services industry, in both states. However, without the actual wage distribution, I cannot predict 20

21 exactly how each industry is affected, but given approximately similar distributions the leisure and hospitality industry is more likely to be affected. VI. Empirical Specification To adequately test whether the industry wage does influence the employment effect of the minimum wage, I build on the work done by Singell and Terborg (ST, 2007). I observe the same states as they do in their regression, looking at Oregon and Washington, but instead of looking at sectors hotel and lodging and food and drinking places, I observe at super sectors leisure and hospitality and education and health services. Note that the food and drinking places sector composes a part of the leisure and hospitality super sector. Instead of looking at the two industries in separate regressions like ST did, I analyze them in one large regression. This allows me to determine the effect of wages on the employment effect of minimum wage, across the two industries. Note that I observe all monetary data in real terms. To be able to adequately interpret the effect of the industry wage I have specified the following variable, that I call excess wage (X): X s,i,t = W s,i,t 1 M s,i,t Where W is the industry wage and M is the minimum wage. I focus on the gap between the industry wage a period prior to the minimum wage because it is expected that the current industry wage has already been influenced by the current minimum wage. Additionally, using specific parameterization of the gap is more useful then looking at the industry wage alone. It is not the industry wage specifically that influences the minimum wage effect, but how far away the average industry wage is from the minimum wage. 21

22 Focusing further on the excess wage s influence on the minimum wage effect, I specify this base equation: log (E s,i,t ) = β log (M s,i,t )X s,i,t Where E is employment in industry i and state s at time t. I would expect β to be a positive number. Given the competitive case for minimum wage, I would expect that as the industry wage gets further away from the minimum wage, the negative minimum wage effect would have a smaller magnitude. This is the main effect that I am trying to observe with this study. With specifications included, I hope to isolate this effect and determine what excess wage s impact on the minimum wage effect is. If this coefficient is positive and significant then the absence of a wage variable underestimates the magnitude of the minimum wage effect on low waged industries. To adequately control for the various confounding variables, I specify the model as such: log (E s,i,t ) = B1S + B2I + B3SI + B4 log(m s,i,t ) + B5W s,i,t 1 + B6X s,i,t + B7 log (M s,i,t )X s,i,t + s,i,t I include period, state, and industry dummies, as well as the interaction between state and industry. This allows employment to vary by period, state, industry, and industries between states. Additionally, I include the log difference in the minimum wage, and the lagged level of the industry wage. Finally, I include excess wage and its interaction with the log difference in the minimum wage, to find the true impact excess wage has on the minimum wage elasticity of employment. Furthermore, I include the percentage growth in personal income and population. 22

23 VII. Results Estimates of the primary specification are presented below in Table 7.1. Table 7.1 Main Results With an R 2 of.3648 this regression explains 36.48% of the variation in the log difference of employment. The minimum wage coefficient is negative but not significant at the 95% level with a p-value of.207. The excess wage variable also has a negative coefficient but is not significantly different from 0 with a p-value of.755. The excess wage interaction with the minimum wage is the only significant variable relative to the minimum wage. Its coefficient is significant at the 95% level with a p-value of.000 (variable descriptions can be found in Table A.1 in the Appendix). This means that the minimum wage only impacts employment growth through the gap between it and the industry wage. Due to the nonlinearity, the elasticity of employment with respect to the minimum wage cannot be inferred directly from the regression and must be evaluated for a specific value for the excess wage. The elasticity, or the partial derivative of the log of employment with respect to the log of the minimum wage is: 23

24 log(e s,i,t ) log(m s,i,t ) = B7((I s,i,t 1 M s,i,t ) M s,i,t log(m s,i,t ) Which can be simplified to: log(e s,i,t ) log(m s,i,t ) = B7(X s,i,t M s,i,t log(m s,i,t )) The sign on the coefficient B7 is positive which means that the larger the gap between the industry wage and the minimum wage the less negative the elasticity becomes. This is consistent with my hypothesis. Given the sample means, the average minimum-wage elasticity of employment is: log(e s,i,t ) = ( log( )) = log(m s,i,t ) A 1% increase in the minimum wage, decreases employment growth by.45363%, an elasticity of roughly -0.5 (mean data used to calculate the elasticity can be found in Table A.2 and A.3 in the Appendix). Examination of the elasticity raises the speculative possibility of a positive effect for industries with a sufficiently high excess wage (greater than $17.45 here), but I am hesitant to draw this conclusion based on the limitations of the present study, since no excess wage is that high in our data. Even so, this possibility may be a promising direction for future studies of the role of the excess wage. Robustness Next, I consider a series of specifications to assess the robustness of the base estimates model. First, I add a lagged excess wage interaction variable from a year back. I then add the log 24

25 change of employment over the previous year. Additionally, I remove the main interaction term, to observe the importance of including it in the regression. Finally, I add another higher wage industry, to observe how this changes the results in this regression. Adding a one-year lagged excess wage interaction variable to the regression, again yields a significant coefficient for the excess wage interaction with a p-value of.005, and the coefficient decreases slightly to The lagged term is also significant at the 95% level with a p-value of.000 and a coefficient of The total effect for the sum of the current and the lagged effect is.0955, roughly double the initial effect (results for this regression can be found in Table A.6 in the Appendix). Adding the percentage change in employment over the prior year as a check for the influence of important omitted trend factors leads to equivalent results, and the coefficient on the percentage change in employment over the prior year is not significant at the 95% level (results for this regression can be found in Table A.7 in the Appendix). After removing the excess wage minimum wage interaction, the R 2 drops from.3648 to Additionally, none of the variables in the regression are significant at the 95% level (results for this regression can be found in Table A.8 in the Appendix). By not including the excess wage minimum wage interaction it appears that the minimum wage has no effect on employment. As a final robustness check, I now extend the base analysis to include a third industry, construction. The construction industry offers two interrelated opportunities: 1) to apply the excess wage interaction hypothesis to an even higher wage industry (the average construction industry wage is five dollars higher than even the education and health services industry), and 2) 25

26 to gauge the role of the share of undocumented workers in moderating the effects of the minimum wage, since the construction industry has the highest share of undocumented workers out of the three observed industries (Statistica, 2012). It is possible that minimum wage compliance is poor for undocumented workers (Greenhouse, 2009). If so, the effect of a minimum wage may differ the higher the share of such workers. Adding the construction industry, along with the corresponding industry dummy and state industry interaction to the base specification, results for the excess wage interaction change significantly. The excess wage and minimum wage interaction is no longer significant at the 95% level with a p-value of.058 and the sign on the coefficient is now negative (results for this regression can be found in Table A.9 in the Appendix). To find an explanation for this reversal, I pursue the possible role of undocumented workers, since the construction industry employs an extraordinarily large share of undocumented immigrant workers. In 2012, the construction industry s workers were composed of 12.2% undocumented immigrants compared to a 5.6% average for the United States. (Statistica, 2012). Minimum wages may be only weakly enforced for these workers. (Greenhouse, 2009). In addition, undocumented workers wages may not be reported in the official data, making the industry wage seem higher and the industry employment seem lower than they are. Therefore, to attempt to control for this I add a series of control variables, namely a variable that measures the industry percentage of undocumented immigrants, relative to the national average: ii i = Industry% i National% i As well as an interaction between this variable and the excess wage interaction variable, ii s,i,t log (M s,i,t )X s,i,t. 26

27 Table 7.2 Undocumented Immigrant Results The results show that the relative share of undocumented workers weakens the wage gaps influence on the effect of the minimum wage and explains the reversal in results when the construction industry is added. The undocumented share variable s interaction with the main interaction term, ii s,i,t log (M s,i,t )X s,i,t, has a coefficient that is significantly negative at the 95% level. The excess wage-minimum wage interaction coefficient is significantly positive again and roughly equivalent in magnitude to the base results, with a p-value of.000. The updated elasticity for this sample is: log(e s,i,t ) log(m s,i,t ) = B7 (X s,i,t M s,i,t log(m s,i,t )) + B8(ii s,i,t )(X s,i,t M s,i,t log(m s,i,t )) The negative coefficient for the interaction term, ii s,i,t log (M s,i,t )X s,i,t, suggests that the higher the share of undocumented immigrants in each individual industry, the more negative the response of employment to the minimum wage, all else the same. The most logical explanation for this, would be that the incentive to replace documented workers with undocumented workers 27

28 increases as the minimum wage goes up, because employers can pay undocumented workers wages below the minimum wage. It is likely that only documented workers show up in official databases, and that I am only capturing the effect on documented workers, while more workers could be employed without any paperwork. However, I lack sufficient evidence to prove this. Weaknesses Potential shortcomings to this study include the small number of observed industries (especially the absence of one with an industry wage closer to the minimum wage), the absence of relevant data for the variance of wages within each industry, as well as the usual problem of possibly omitted variables. VIII. Conclusion Minimum wage increases accomplish their intended objective of increasing wages, but have adverse employment effects and these effects tend to affect some industries more than others in ways influenced by the gap between the minimum wage and the average wage. I find that the minimum wage does have a negative employment effect, but this effect depends on the industry and minimum wage gap. The greater the gap, the less negative the effect on employment, implying that lower higher waged industries are more negatively affected by minimum wage hikes than higher waged industries. This is the first time that a study has explicitly incorporated the industry and minimum wage gap in estimating the effects of the minimum wage, but it is of vital importance to do so. If the excess wage minimum wage interaction is not accounted for, the results show that the minimum wage has no effect. 28

29 Appendix Table A.1 Variable List Variable OR LH LHOR ldmin Lwage exwage1 exldmin ldpop ldinc CON COOR Description Oregon State Dummy Leisure and Hospitality Industry Dummy Interaction between OR and LH Monthly Percentage Change in Minimum Wage One Month Lagged Wage Variable Gap Between One Month Lagged Wage and Current Minimum Wage (Excess Wage) Interaction between ldmin and Excess Wage Monthly Percentage Change in US Population Monthly Percentage Change in National Personal Income Construction Industry Dummy Interaction between OR and CON Share of Undocumented Immigrant Workers in an Industry over National Average ii iiexld exldminly Yldemp Share Interaction between ii and exldmin One Year Lagged exldmin Yearly Percentage Change in Employment 29

30 Table A.2 Statistic Summary Primary Analysis Variable Mean Standard Dev. Min Max Employment Real Minimum Wage Real Industry Wage Real Personal Income Population 2.01E E E+08 Table A.3 Variable Summary Primary Analysis Variable Mean Standard Dev. Min Max ldem ldmin Lwage exwage exldmin ldpop ldinc

31 Table A.4 Statistics Summary 3 rd Industry Analysis Variable Mean Standard Dev. Min Max Employment Real Minimum Wage Real Industry Wage Real Personal Income Population 2.01E E E+08 Table A.5 Variable Summary 3 rd Industry Analysis Variable Mean Standard Dev. Min Max ldem ii ldmin Lwage exwage exldmin ldpop ldinc

32 Statistics by Industry Table A.I.1 - Leisure and Hospitality Variable Mean Standard Dev. Min Max Employment Minimum Wage Industry Wage Table A.I.2 - Education and Health Services Variable Mean Standard Dev. Min Max Employment Minimum Wage Industry Wage Table A.I.3 Construction Variable Mean Standard Dev. Min Max Employment Minimum Wage Industry Wage

33 Table A.6 Lagged exldmin Robustness Check Linear regression Number of obs = 336 F(91, 244) = 7.05 Prob > F = R-squared = Root MSE = Robust ldem Coef. Std. Err. t P> t [95% Conf. Interval] OR LH LHOR ldmin Lwage exwage exldmin exldminly ldpop ldinc Table A.7 Employment Change Over Previous Year Robustness Check Linear regression Number of obs = 336 F(91, 244) = 6.75 Prob > F = R-squared = Root MSE = Robust ldem Coef. Std. Err. t P> t [95% Conf. Interval] OR LH LHOR ldmin Lwage exwage exldmin Yldemp ldpop ldinc

34 Table A.8 Absent exldmin Term Robustness Check Linear regression Number of obs = 336 F(89, 246) = 8.82 Prob > F = R-squared = Root MSE = Robust ldem Coef. Std. Err. t P> t [95% Conf. Interval] OR LH LHOR ldmin Lwage exwage ldpop ldinc Table A.9 Basic Construction Included Robustness Check Linear regression Number of obs = 504 F(92, 411) = 8.40 Prob > F = R-squared = Root MSE = Robust ldem Coef. Std. Err. t P> t [95% Conf. Interval] OR LH CON COOR LHOR ldmin Lwage exwage exldmin ldpop ldinc

35 References Card, David, and Alan B. Krueger. "Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania." NBER Working Paper Series (1993) "Employed Undocumented Immigrants in the U.S. by Industry 2012." Statista. n.d. Retrieved from website: Greenhouse, Steven. "Low-Wage Workers Are Often Cheated, Study Says." The New York Times. The New York Times, 01 Sept "History of Federal Minimum Wage Rates Under the Fair Labor Standards Act, " United States Department of Labor. n.d. Retrieved from website: "History of Washington Minimum Wage." Washington State Department of Labor & Industries. n.d. Retrieved from website: "Industries in Alphabetical Order." U.S. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics, 07 June Retrieved from website Keil, Manfred, Donald Robertson, and James Symons. "Minimum Wages and Employment." Centre for Economic Performance (2001): 7-9 Meer, Jonathan, and Jeremy West. "Effects of the Minimum Wage on Employment Dynamics." (2013) Neumark, David, and William Wascher. "Minimum Wages and Employment: A Review of Evidence from the New Minimum Wage Research." NBER Working Paper Series (2006) Singell, Larry D., and James R. Terborg. "Employment Effects of Two Northwest Minimum Wage Initiatives." Economic Inquiry 45 (2007):