The minimum wage and regional wage structure: implications for income distribution.
by Oren M. Levin-Waldman The issue of the minimum wage in the United States is often couched as a debate between those arguing the youth disemployment effects on one hand and those arguing the potential benefits to those in poverty on the other. Because most minimum wage earners are teenagers, the argument goes, increasing the minimum wage to assist the poor would be badly targeted. Opponents of minimum wage increases are often quick to point out that only a small segment of the labor market, generally about 6 percent, earns the minimum wage. Therefore, the minimum wage should be considered nothing more than an insignificant issue not terribly relevant to the public debate. And yet, the minimum wage often engenders considerable political antagonism. The focus on the small portion of workers who earn the minimum wage, however, has obscured some critical issues. One of the most important of these issues is that the minimum wage as a labor market institution may have an important effect on the larger number of workers who earn from some point below the statutory minimum to some point above and hence on the community's wage structure and distribution of income. My purpose in this paper is to show that as a labor market institution the minimum wage does affect different regions of the country differently and that this is all the more true when the minimum wage population is broadened to include those earning a wage within a range around the statutory minimum wage. When legislation for the 1938 Fair Labor Standards Act (FLSA) was being debated, the fiercest opposition came from the South, where wages were considerably lower than in the industrial North (Nordund 1997, 39-40). Opposition to the minimum wage today is not restricted just to the South but often emanates from states with right to work laws--laws generally favorable to open shops and otherwise hostile to union activity. In 1996, for instance, when Congress last raised the minimum wage, 17.5 percent of representatives from right-to-work states compared with 11.5 percent of all representatives in the House voted against the increase. Similarly in the Senate, 37.5 percent of members from right-to-work states voted against the increase compared with 25.5 percent of all members who voted against the increase. On the other hand, 90.5 percent of representatives in the House from high-union-density states voted for the increase compared with 88.5 percent of all House members. In the Senate, however, the difference was much larger: 90 percent of members from high-union-density states voted for the increase compared with 74.5 percent of all Senate members who voted for the increase (Levin-Waldman 2001, 161). In this paper I examine the influence the minimum wage has on a region's wage structure and how variations in wage structure from one region to another may affect the general distribution of income by looking at the effects of region on who earns around the minimum wage, defined as those earning from some point below the statutory minimum to some point above. My analysis is based on census data from the Integrated Public Use Microdata Series (IPUMS) for the years 1940 through 1990. The data will show that when controlling for educational attainment and industry type, the location of workers--the regions of the country where they work--will play a role in determining who is more likely to earn around the minimum wage, largely because of the types of institutions in them. Ultimately, this paper argues that labor market institutions like the minimum wage and unions have a role to play in ensuring a more equitable distribution and that the decline of these institutions in recent years has played an important role in increasing wage inequality (Craypo and Cormier 2000; Wallerstein 1999; Lemieux 1998; Galbraith 1998, 142-144; Palley 1998, 127-132; DiNardo and Lemieux 1997; Machin 1997; Lee 1997). A federal minimum wage in a low-wage region can effectively remove regional disparities by forcing up the bottom of the wage structure. As this paper will suggest a correlation between these labor institutions and the likelihood of earning around the minimum wage, it then stands to reason that other institutions like the minimum wage could similarly impact on workers whose wages hover around the statutory minimum wage. Because certain regions have a vested interest in maintaining a low wage structure, they seek to weaken institutions that could affect their respective wage structures. Oth erwise, an issue affecting only a small segment of the labor market would not elicit the level of opposition that it does. The minimum wage by itself is not nearly as important as what it actually represents: an institution with the potential to drive up the wages of those at the bottom of the income distribution with potential ripple effects in wage intervals above the minimum. Were this issue to be postulated in the form of a couple of propositions, it might look as follows: The closer a wage floor is to either the average or median wages of a particular community, the more of a threat it will be to the overall wage structure of that community. Also, the more people there are earning around that wage floor in a given community, the more politically charged that issue is likely to be. I am unable to test these propositions directly in this paper, but I am able to show the effects of institutions like right-to-work laws and unions on the probability of earning around the minimum wage by comparing those earning around the minimum wage in right-to-work states to those earning around the minimum wage in high-union-density states. By showing that there are differences between these respective wage structures and that these differences are largely derived from the presence of a respective set of institutions, a sufficient basis exists to infer that the above propositions are true. But it also provides a basis upon which to conclude that there is ultimately a role for labor market institutions in reducing income inequality. Problems with Minimum Wage Theory The empirical literature on the minimum wage does not always support the theoretical literature. According to the predominant theory of perfect competition, a minimum wage is harmful because it results in less employment. Each worker receives the value of his or her marginal revenue product of labor (the amount of increased revenue that results from an additional unit of labor in the absence of a minimum wage). A wage floor, such as a mandated minimum wage, prevents the cost of labor from dropping below a set rate. A minimum wage higher than the equilibrium wage results in fewer workers being hired than are willing to work; in other words, there is unemployment caused either by layoffs of workers whose value is less than the minimum or a decrease in hiring of low-productivity workers (Stigler 1946; Ehrenberg and Smith 1997). Empirically, the effects of the minimum wage are not clear. The more conventional wisdom has always maintained that increases in the minimum wage do cause disemployment effects, primarily among teenagers; the effects among adults are considered to be less severe (Kosters and Welch 1972; Welch 1974, 1978; Meyer and Wise 1983; Neumark and Wascher 1992). But with the findings of David Card and Alan Krueger that in a couple of states where minimum wage increases, particularly in the fast food industry, did not have any disemployment effects, this conventional wisdom has been called into question (1995, 25-40, 82-95; 1998). Further studies have suggested, particularly among small businesses paying entry level wages around the minimum, that in addition to recent increases not having any employment consequences, additional increases would also have little effect (Levin-Waldman 2000a, 2000b). Another controversy surrounding the minimum wage is just who earns it. Opponents of increases often point out that because most minimum wage earners are teenagers, it really isn't an important issue affecting the overall labor market and thus is an insignificant issue. They say that teenagers and other minimum wage earners are not the primary earners but secondary earners (Burkhauser and Finegan 1989; Kosters 1996). When the Minimum Wage Study Commission (MWSC) issued its 1981 report, which for many formed the basis of the prevailing consensus on the minimum wage's employment consequences, it noted that most minimum wage workers--68 percent--were in families headed by married couples. Of these, 1.5 million were the only earners in their families, making up 14 percent of low income workers. 54 percent of all minimum wage workers were in families with two or more earners. Although 17 percent of families had minimum wage workers, they were not the primary earners in their families (MWSC 1981, 13). A relatively h igh percentage of minimum wage workers in middle income families were spouses, mostly wives, whereas in higher income families teenagers usually constituted more than 50 percent of minimum wage workers (MWSC 1981, 18-19). But it also suggests that our construction of the minimum wage population is simply too narrow. Others, however, argue that the MWSC figures are misleading. Were it even true that most minimum wage earners are secondary earners in their households, it doesn't follow that their income is any less necessary to the maintenance of those households. And most minimum wage workers are not teenagers. For the period from October 1995 to September 1996, Lawrence Mishel, Jared Bernstein, and John Schmitt found that only 28.6 percent of the affected 9,866,158 minimum wage workers were teenagers. To them this means that most minimum wage workers are adults with economic responsibilities (1999, 189-190). That there is this discrepancy at the empirical level only raises questions about the reliability of the theoretical construct. Who is actually earning the statutory minimum wage may be the wrong question. Rather the question that should be asked is, Who earns around the minimum wage--from, say, 50 percent below to 50 percent above? At issue is how many are earning at various wage intervals, or wage contours, above the minimum wage. The claim that the minimum wage is irrelevant because most people earn above it obscures the potential impact the minimum wage may have on wage contours--the various wage intervals directly above the statutory minimum wage. An increase in the minimum wage does impact those earning wages within one or two of the contours above (Gordon 1996, 214-215). Mainstream economic theory implicitly recognizes the potential wage contour effect but conceives of it in negative terms by couching it in terms of inflationary pressures. Wage contour theory, first developed by John Dunlap, suggests that the internal wage structure of a firm is affected as much by external forces as internal ones. Specifically, if an economy's overall wage structure could be thought of in terms of wage contours (defined as a group of workers with similar characteristics working in similar industries and earning similar wages) and in each case there is a group of rates surrounding the key rate, change in the key rate would have an effect on those surrounding it (Dunlop 1957). Dunlop did not specifically define a key rate, but there is reason to believe that any rate serving as a reference point within an industry could constitute a key rate. Moreover, the key rate could vary from industry to industry. Wage contour theory, in essence, makes it clear that wage rates are not established by a natural marketplace but by institutions. The minimum wage and union wages may be key rates of change in particular wage contours. If this is true, a strong argument could be m ade for defining the minimum wage population in terms of those earning around the statutory minimum wage, which would include a range from some point below the wage to some point above. In their study of the minimum wage, William Spriggs and Bruce Klein appeared to reinforce Dunlop's theory of wage contours. They suggested that the minimum wage's greatest import was that it served as a reference point for wages around it. More important than its impact on employment levels is the role it plays in determining the wages of America's overall workforce--especially workers with only a high school education and those living in rural areas. Despite changes in minimum wages, however, firms merely maintain their internal wage structures. Although minimum wage increases may result in some employment consequences, their effects for the most part are not significant. Firms simply view the minimum wage as a reference point for what starting wages ought to be. Instead of conceiving of costs in terms of reduced employment opportunities, costs should be seen in terms of maintaining low value for the minimum wage, which diminished opportunities for young adult workers during the 1980s (Spriggs and Klein 1994 ). This was because minimum wage jobs did not afford their occupants the opportunity to earn a wage above the poverty line. If minimum wages, as they suggested, are a cultural artifact--that is, a socially defined reference point--the implication might be enormous. Presumably that reference point could be altered, and when it is altered it will have an impact on wages around it. And to the extent that reference point impacts the wages around it, its impact will be even greater in regions where wage rates are generally lower. To the extent that this is true, it may also explain some of the historic opposition displayed by some regions, for that opposition may also be implicit recognition of the potential wage contour effects. Regional Conflicts over the Minimum Wage Congressional opposition to the minimum wage has tended to be stronger in the South and "right to work" states--even among Democrats, who otherwise vote for increases--than in high-union-density states where wage rates on average are considerably higher (Levin-Waldman 1998; 2001, 146-158). Southern and right-to-work states also tend to have lower wages, lower standards of living, and more workers earning around the minimum wage. It has long been assumed that wage rates are lower in those states because industries there have been more labor intensive and highly inefficient and because where wage rates are lower so too is the cost of living (Coelho and Gladi 1971). Such an assumption no doubt accords with the theory of competitive markets, but it isn't entirely supported by the record. In his comparative studies of industries in the North and South during the 1940s, Richard Lester found that Southern industries were not less efficient than Northern industries. On the contrary, wage differentials persisted becau se of the absence in the South of institutions like unions. He also found that when the minimum wage came into being, wage differentials, especially in textiles, were reduced (1946, 1947). The fact that these wage differentials exist begs the question of whether the regional differences are merely a matter of simple economic differences or other cultural, demographic, political, and even ideological factors (such as unionization, and political opposition to it) that need to be accounted for. Much of the history of the minimum wage assumes that the minimum wage indeed does have the potential to impact a region's wage structure. Initially when the FLSA was adopted, the principal opposition came from the southern states--most of which have since passed right to work laws. Southern states specifically viewed the federal minimum wage as an attempt by northern industrialized states to impose their values on them. For national policy makers, the low wage structure in the South, especially in industry, was a classic example of why the minimum wage was needed, not just to end poverty but also to bring about the modernization of the South. The minimum wage was actually viewed as a tool for the economic development of the region. Bruce Schulman observed that the South's most vexing problem was how to organize its labor force after the end of slavery. Primarily an agrarian economy, the southern labor market had for centuries been supplied by slaves. Its limited industrial capacity was concentrated in low wag e, low productivity industries. Low wage manufacturing, specifically textiles, hosiery, and lumber formed the backbone of southern industry, and it relied heavily on unskilled labor, which often included women and children (1991, 5). Moreover, the nature of southern industry militated against the development of a more skilled labor force. A minimum wage, following the logic of Sidney Webb, would force employers to modernize and effectively invest in human capital, which ultimately would increase productivity (1912). The South, however, was reluctant to invest heavily in schooling and higher education because this was likely to increase out-migration from the region (Wright 1986, 14). The South opposed the minimum wage for two basic reasons. First arid foremost, southerners feared that it would drive up wages in the South, thereby forcing employers to lay off workers. Second, it represented a federal intrusion on a paternalistic tradition deeply embedded in southern political culture. Programs initiated and implemented by the federal government were seen as a threat to the patron-client relationships that dominated employer-employee relations, especially in agriculture (Alston and Ferrie 1999, 49-59). The North, on the other hand, favored the minimum wage because it would exert an upward pressure on the southern wage structure, thereby forcing the South to modernize its otherwise backward economy. And yet, the fact that there was this North-South divide on the issue only reinforces the understanding at the time that the minimum wage was a policy with a potential impact on regional wage structure. That northern states viewed it as an option for boosting wages in a region with historically l ow wages and southern elites viewed it as something that would alter their regional wage structure suggests a well-founded understanding that wage structure could be as much a product of public policy as so-called natural market forces. At the same time, the literature focusing on regional differences only focuses on this North-South difference. This literature does not necessarily address itself to the types of institutions that would affect their respective wage structures. Since the federal minimum wage was initially passed, many states have passed right-to-work laws. All the southern states that were initially opposed to the minimum wage passed right to work laws, but not all right-to-work states are in the South. To the extent that states that passed such laws have a particular culture that also found expression in the earlier debates, these states essentially created institutions designed to affect their wage structure. Similarly, states in the North also had institutions that affect the wage structure that found expression in a more positive stance. Most states in the North tended to be heavily unionized, even though not all highly unionized states were necessarily located in the North. While it may be easy to conduct an analysis comp aring those earning around the minimum wage in northern states to those earning around the minimum wage in southern states, it would not capture the effects of specific types of institutions. It is true that culture does make a difference, but there is no way to easily measure the concept. To the extent that the presence of certain institutions reflects certain cultural predispositions, one might view them as proxies for their states' respective cultural underpinnings. But this is more the case with right-to-work laws than high union density. Though law can certainly impact the level of unionization, it is by no means the sole determinant. Institutions with differing effects on wage structures in different regions also have implications for income inequality. Just as the presence of certain institutions can result in less income inequality, so too can the presence of other types of institutions result in perhaps greater inequality, with the result being greater inequality between regions. While there is a literature on the effects of institutions on income inequality, there is very little on the effects of institutions on inter-regional wage structures. Recent studies have documented the declining fortunes of the middle class and stagnant wages for years now (Phillips 1990; Newman 1993; Hungerford 1993; Wolf 1994; Danziger and Gottschalk 1995). Perfect competitive market theory blames rising inequality on structural changes in the economy which have resulted in a mismatch between good-paying jobs and the skills available to workers. The main culprit, according to this theory, is technological change biased toward those with higher levels of edu cation and skills (Juhn, Murphy, and Pierce 1993). This school of thought suggests that the labor market is divided into a primary market where high premiums are placed on skilled workers and a secondary market where unskilled workers are trapped in the lowest wage service sector of the economy. The growth in wage inequality between the primary and secondary labor markets has been caused by increasing skills differentials between the two (cf. Katz and Murphy 1992; Krueger 1993). More than a story about human capital, it is a story about individual responsibility. Individuals, after all, can pass from the secondary labor market into the primary one if they upgrade their skills through education and training. Consequently, labor market institutions like the minimum wage and unions are either irrelevant or counter-productive. They only inflate wages beyond the worth of workers in the secondary labor market. Instead, it is incumbent upon workers themselves to upgrade their skills. Another school of thought suggests that rising wage inequality is due to a shift in public policy and a corresponding decline in labor market institutions like unions and the minimum wage (Craypo and Cormier 2000; Lemieux 1998; Galbraith 1998, 142-144; Palley 1998, 127-131; DiNardo and Lemieux 1997; Piore 1995; Gordon 1996). Michael Wallerstein, for instance, suggested that the more centralized wage setting is--with minimum wage setting as the most nominal form of centralized wage setting--the more equal will be the wage distribution (1999). The corollary would be that when no such mechanism exists, chances are greater that the wage distribution will be unequal. Moreover, once such institutions begin to deteriorate, rising income inequality is by no means an unexpected outcome. Stephen Machin found that the weakening of labor market institutions like unions and minimum wage councils in Britain played an important role in rising income inequality there (1997). David Lee too has shown that decreases in the mini mum wage tend to increase measured wage inequality and that the minimum wage, or the failure of it to keep up with inflation, may account for as much as 80 percent of "within group" wage inequality during the 1990s. Specifically, during the 1980s the falling relative level of the minimum wage can explain 70 percent to 100 percent of increased inequality in the lower tail (Lee 1997). Nicole Fortin and Thomas Lemieux (1997) also found that whereas the decline in unions contributed to increased wage inequality among men, the decline in the minimum wage contributed to increased wage inequality specifically among women. Institutional structures, however, vary by state in terms of the minimum wage, union density, and the friendliness or hostility of the legal structure toward unionization. As some wages are more likely to be stagnant in some regions than in others, it may also be the case that in those regions workers are more likely to earn the minimum wage than others. Isolating the causal effects of regional institutions and market structures could help determine the causes of rising inequality and could help to suggest possible remedies. Moreover, it would demonstrate how institutions like the minimum wage are bound to affect different regions differently. Labor Institutions, Regional Wage Structures, and Rising inequality In the pages that follow I present demographic profiles of who is more likely to earn around the minimum wage from IPUMS for the years 1940-1990, largely because that is the period in which census data encompassing the minimum wage period from its inception in 1938 until the present are available. The IPUMS are decennial census files that contain a uniform set of variables throughout each file. Because my purpose was to track changes over time, I specifically looked at census data from the year following the first federal minimum wage law until the most recently available. Sample sizes in the IPUMS data set differ for each census year, ranging from 1.5 million to 3 million individuals. The data sets are composed of two files, one a household and one a personal. I have pulled out of these files individuals, primarily key income earners in a household, who are employed and who specifically work for wages. Although this reduced the sample sizes, they still ranged from 225,000 in 1940 to more than 660,000 in 1990 . My purpose was to draw inferences for the role of labor market institutions in determining regional wage structure, by testing the effects of region--as defined by the type of institutions they had--on who was earning around the minimum wage after controlling for certain demographic characteristics like education and industry type. While it is a given in much of the literature that unions and minimum wage laws are institutions designed to prop up wages, right to work laws are not often thought of in the same way because their function is negative in that their intent is to suppress wages. Right to work laws are often regarded as favorable to business and hostile to unions because they specifically prohibit closed union shops, thereby making it more difficult to unionize workers. They are institutions affecting wage rates and therefore must be regarded as negative labor market institutions. By negative labor market institutions, I mean any laws, customs, policies, and so on whose intent and effect are for wage s to be held down. The easiest way to construct a model for analyzing regional differences is to divide the states between those that are specifically "right to work" and those that are "high union density." Although some right-to-work states might have relatively high union densities, most have relatively low ones because unionization is considerably more difficult. Today union density in most cases doesn't exceed 30 percent and often hovers around 15 percent to 25 percent in the more heavily unionized states. Table 1 shows the breakdown of states according to union density. On one level, it is designed to provide a snapshot in time, but it is also designed to illustrate that as a general rule states with right-to-work laws have on average lower union densities than states without such laws. At the same time, there are states that are neither right-to-work nor have high union densities. With a couple of exceptions, union density in right-to-work states rarely exceeds 13 percent. With the exception of Nevada, all the right-to-w ork states have union densities below 14 percent, and the majority have union densities below 9 percent. High-union-density states have union densities in excess of 15 percent. States that neither are right to work nor have union densities higher than 15 percent fall into the other (or middle) category. Some of these also have union densities below 9 percent. Nevertheless, the snapshot that table 1 captures provides a reference point for the division of states into three types of categories: right to work, high union density, and other, which will form the basis for subsequent regional comparisons and regression analyses. One possible methodological objection might be that instead of dividing the states into "right to work" versus "high union density" they could simply be divided into "low union density" versus "high union density." It is certainly true that right-to-work states will for the most part have low union densities because of laws that are hostile to unionizing activities. But a low union density state by itself doesn't necessarily capture the spirit of a political, legal, and economic structure that is hostile to unions. The point was to demonstrate that because certain states have a particular hostility to unions and have institutional structures specifically designed to boost the interests of business by creating favorable business climates, there is perhaps a greater likelihood of wages being lower. The goal of the analysis, then, was specifically to compare those earning around the minimum wage in right-to-work states to those earning around the minimum wage in high-union-density states. On the basis of this co mparison, I then constructed logistical regression models to test whether certain institutions in particular regions will result in a greater probability of earning around the minimum wage. And to the extent that they may show this to be true, they also show the converse to be true--that because other institutions in other regions result in a lower probability of earning around the minimum wage, a sufficient basis then exists for inferring why the minimum wage appears to have greater significance in some places compared with others. Table 2 shows the relative percentages in descriptive terms of those earning around the minimum wage based on a construction of a uniform range from 50 percent below to 50 percent above. And table 3 presents other basic demographic data that call into question the so-called conventional wisdom about how large the minimum wage labor market is and whether most of those earning around the minimum wage are really teenagers. When states are divided among these categories, it becomes clear that there are regional wage differences. Wages are clearly lower in right-to-work states than in high-union-density states. For each of the census years there is a greater percentage of workers in right-to-work states earning around the minimum wage than in the country as a whole. Wage differences in right-to-work states are even larger than in high-union-density states. Even though the overall number of workers earning around the minimum wage decreases over time, it is still the case that by 1990 more workers in right-to-work t han both high-union-density states and the country as a whole are earning around the minimum wage. Despite the overall decrease in the number of workers earning around the minimum wage, the critical point is that when the minimum wage is conceived of more broadly, a not-so-insignificant segment of the labor force earns around the minimum wage. Runs were done for each year to see who was earning exactly the statutory minimum wage. With the exception of 1940, with .4 percent earning the minimum wage, each year had nobody earning the statutory minimum wage. The data I looked at were specifically heads of households who were employed. That there are no employed heads of households earning the statutory minimum wage for most census years would appear to reinforce the views of competitive market theorists that the minimum wage is a non-issue because only secondary earners and teenagers earn it. But that there is a significant percentage of workers earning around the minimum wage while nobody is earning the statutory minimum wage only strengthens the argument that our construction of the minimum wage has simply b een too narrow. It does not, however, control for the effects of other variables that would, independent of region, also predispose one to earning around the minimum wage. Table 2 makes it clear that right-to-work states, particularly at the low end, have an overall lower wage structure than high-union-density states and the nation as a whole. Such differences could conceivably be accounted for by other demographic factors like age of the labor force, education, and types of industry. As already mentioned, contemporary critics of the minimum wage often claim that most of those earning the minimum wage are teenagers. But, as table 3 makes clear, the highest percentage of those earning around the minimum wage are in the 25-34 age group, and the percentages of those earning around the minimum wage in that group are higher in the right-to-work states than they are in the high-union-density states. It is true that as time passes, the percentage of those in the 18-24 age group increases, but most workers earning around the minimum wage are clearly adults. In 1940, the largest age cohort was 25-34, with even more of that cohort earning around the minimum wage in right-to-work states t han in high-union-density states. By 1990, that cohort was still the largest in both right-to-work and high-union-density states. Although within that cohort the gap between right-to-work and high-union-density states declined, there were still more in that cohort in right-to-work states than in high-union-density states. Generally speaking, there are more workers between the ages 18 and 54 in right-to-work states. Still, as to be expected, a greater percentage of those with lower levels of education appear to be more likely to earn around the minimum wage. Demographics on education could best be described as showing increasing educational attainment. More importantly, they suggest an inverse relationship: the lower one's education, the more likely one is to earn around the minimum wage, and that person is even more likely to earn around the minimum wage in right-to-work states. And yet it also suggests a basis for some concern. In 1940, those with no more than an eleventh grade education constituted the largest group of workers earning around the minimum wage. By 1990, however, the largest group of workers earning around the minimum wage were actually high school graduates. Among workers with no more than an eleventh grade education earning around the minimum wage, a greater number were in right-to-work states in both 1940 and 1990, with the difference between the two being even greater in 1990 than 1940. On the other hand, of the largest group earning around the minimum wage in 1990 (high school graduates), more of them were in high-union-density states than in right-to-work states, which might be reflective of the fact that during this period so-called high-union-density states lost good-paying unionized jobs in manufacturing to low-wage jobs in services and retail sales. During the 50-year period, there was also an increase of those with one to four years of college earning around the minimum wage. Despite general changes, the critical point remains: workers in right-to-work states appear to have lower educational attainment than workers in high-union-density states. Based on the figures in table 3, it could be inferred that a worker in a right-to-work state is more likely to or has a higher probability of earning around the minimum wage than one in a high-union-density state. Basic demographics on education in table 3 illustrate some basic trends in right-to-work states relative to high-union-density states. It is not clear from these figures alone that more people earning around the minimum wage in right-to-work states than high-union-density states is a function of institutions per se or other factors like industry type, or that there are more people in right-to-work states with lower levels of education. To test whether this proposition is true, I conducted a logistical regression analysis to determine the probabilities of both these regional variables on earning around the minimum wage, while also controlling for the effects of industry and education. A logistical regression analysis can demonstrate what variables that are statistically significant, relative to other s, have stronger effects for earning around the minimum wage. Strength was measured in terms of probability, with a high coefficient signifying a greater probability of earning around the minimum wage and a lower coefficient signifying a lower probability. With earning around the minimum wage (MNWAGE) as the dependent variable, I have set up four different models for each year. Those who earn around the minimum wage, that is, within the range of 50 percent below to 50 percent above, have a value of 1 while those who do not have a value of zero. Because wage rates are likely to be affected by other variables such as industry and educational attainment, I included those variables in the model along with region. I present these four different logistical regression models in the form of a taxonomy, ranging from the more general in table 4 to the most specific in table 5. The first set of coefficients in table 4 is merely a general test for the effects of region when controlling for the other variables of education and industry. It basically looks at the effects of each variable on the probability of earning around the minimum wage irrespective of where industries are necessarily located or irrespective of the region in which one is working with a low educational attainment. What are tested in the second set of coefficients are the differences between industries and low educational attainment in right-to-work states and industries and low educational attainment in high-union-density states. The independent variables are as follows: LOEDUCAT = Those with an educational level between first and eleventh grade, or no more than an eleventh grade education. HU = High-union-density states--states listed in table 1 with union densities greater than 15 percent. RTW = Right-to-work states, as designated according to table 1. MANUFACT = Those working in manufacturing according to census industry classifications. RETRADE = Those working in retail trade. WLTRADE = Those working in wholesale trade. CONSTRUC = Those working in construction. HULOED = Those with no more than an eleventh grade education in high-union-density states. RTWLOED = Those with no more than an eleventh grade education in right-to-work states. RTWMANUF = Those working in manufacturing in right-to-work states. HUMANUF = Those working in manufacturing in high-union-density states. RTWRETRD = Those working in retail trade in right-to-work states. HURETRD = Those working in retail trade in high-union-density states. RTWWLTRD = Those working in wholesale trade in right-to-work states. HUWLTRD = Those working in wholesale trade in high-union-density states. All equations are based on each variable being set to a value of 1. Both the right-to-work and high-union-density variables are dummy variables and are based on the classifications from table 1. Regression coefficients in the first set above the line suggest that workers with no more than an eleventh grade education are more likely to earn around the minimum wage and that this probability is greater than being in a right-to-work state. Moreover, not only does this remain true over time, this is true irrespective of state type. Still, industry type also appears to have an effect. Workers in manufacturing and wholesale trade, as to be expected, are less likely to earn around the minimum wage. Retail, however, is more interesting. Whereas in 1990 those in retail were more likely to earn around the minimum wage, in 1940 they were less likely to earn around the minimum wage. Moreover, this change is also consistent with economic transformations that have occurred during the period, namely the post-industrial shift from manufacturing to services. On the basis of the first set of coefficients, a worker with a low educational attainment has a greater probability of earning around the minimum wage than one fr om a high-union-density state, although it is clear that the probability of earning around the minimum wage is greater in right-to-work states than in high-union-density states. The second set of coefficients, however, gets even more specific by comparing the effects of having a low educational attainment in right-to-work states with having a low educational attainment in high-union-density states. Those with no more than an eleventh grade education are still more likely to earn around the minimum wage in right-to-work states than their counterparts in high-union-density states, and this is consistent throughout the fifty-year time period. In fact, they were at least two to three times more likely to earn around the minimum wage in right-to-work states than in high-union-density states. Still, the question remains as to just how the location of these industries affects the likelihood of one earning around the minimum wage. Industry demographics showed no evidence of a saturation of any specific industries that might result in the depression of wages. Rather, industries were evenly distributed between right-to-work and high-union-density states. But wages within industries, as shown in table 5, are affected by location. Industries typically not associated with earning around the minimum wage, like manufacturing, for instance, were more likely to be paying their workers around the minimum wage in right-to-work states than were these same industries in high-union-density states. Even though wage differences narrowed between 1940 and 1990, workers in these same industries in 1990 still had a greater probability of earning around the minimum wage than their counterparts in high-union-density states. Table 5 presents essentially two models for the manufacturing, retail, and wholesale trade industr ies, which are shown in six sets of coefficients. The first model tests for the effects of working in a specific industry when located in a particular region. Or stated another way, it compares the effects of working in a particular industry, whether it be manufacturing, retail, or wholesale, when located in right-to-work states to working in the same industries in high-union-density states. The second model builds on the first by controlling also for the effects of a low education in both right-to-work and high-union-density states. One, of course, might respond that this may be accounted for by differences in the type of manufacturing. The diminishing probability of earning around the minimum wage has declined over this period and may be a testament to the growth of high-wage and high-technology industries in many right-to-work states, especially those in the South where much of the New Economy was a product of "military Keynesianism" (Schulman 1991, 107). Much of the Southern economic growth was due to huge defense spending following World War II, with the result being that there were fewer low-wage industries in 1990 than in 1940. But this cannot fully explain the differences between right-to-work and high-union-density states. Despite the decline in unionism, and capital flight from the Northeastern and Midwestern industrial belts, which resulted primarily in the loss of union jobs, there was little change in the negative effects of manufacturing in high-union-density states from 1940 to 1990. The sum total of the taxonomy of regression models, then, can be stated as follows: In general terms, workers with low educational attainment are still the most likely to earn around the minimum wage, but when these workers with no more than an eleventh grade education are placed within their respective regions those with less than an eleventh grade education in right-to-work states are more likely to earn around the minimum wage than those with less than an eleventh grade education in high-union-density states. So when controlling for the effects of low educational attainment, regional institutions then become important determinants of who has a greater probability of earning around the minimum wage. And when controlling for industry type, workers in industries like manufacturing, for instance, which standing by itself would not typically be associated with earning around the minimum wage, are still more likely to earn around the minimum wage when they are in right-to-work states than in high-union-density s tates. In the end, when controlling for the effects of educational attainment and industry type, one has a greater probability of earning around the minimum wage in right-to-work states than in high-union-density states. This then suggests that states with right-to-work laws have an overall lower wage structure than states with high-union-densities, which leads to the inevitable conclusion that institutions such as these do affect a region's wage structure. To the extent that this is true, it would also imply that institutions aimed at boosting wages, such as unions and by extension the minimum wage, have a role to play in wage policy as it effects income distribution and goals of achieving more equitable income distributions. Implications The results presented in this study, at a minimum, suggest why the minimum wage engenders the type of antagonism it often does and why more so in some regions of the country than others. The minimum wage itself isn't as important as what it represents as a tangible symbol for wages around it. And this is even more true in regions with lower wage structures, like the right-to-work states. The fact that political opposition to the minimum wage has always been greater in the South and other right-to-work states, in which more workers earn around the minimum wage, implies that the minimum wage does have a significant impact on the distribution of income in those states and that political concerns have an interest in maintaining the existing wage structure. Labor market institutions, whether positive or negative, obviously matter. That there is such a strong correlation between these states and lower wages suggests that the minimum wage, as but one labor market institution, has a role to play in giving workers voice in those parts of the country and achieving a more equitable wage structure. Just as institutions have the power to suppress wages, as they do in right-to-work states, they also have the power to boost wages, especially for those at the bottom rungs of the income distribution. As the minimum wage appears to affect a wider segment of the market than those who simply work for the actual statutory minimum wage, it then becomes a tool critical to affecting the overall distribution of income. And with the general decline of labor unions, especially during the 1980s, the minimum wage assumes greater importance to the goal of narrowing the wage gap. As Galbraith suggested, minimum wages and unions effectively give workers a degree of monopoly power they otherwise would not have: "Minimum wage laws can move people en masse from the crowded first floor toward the second or third in our wage building (1998, 61)." That states actually passed laws aimed at making unionization more difficult would imply that they sought to maintain the monopoly power of employers at the expense of workers. These same states routinely oppose minimum wage increases for the same reasons they oppose unionizing efforts (Levin-Waldman 1998, 2001). And that they oppose both is perhaps revealing of their understanding that both do in fact have the potential to boost wages and raise the overall wage structure. One implication of this study, of course, is that the minimum wage could be used to equalize disparities between the states, but to do so would require minimum wage differentials with even higher minimum wages in states with the lowest wage structures. Though an option that policy makers may want to consider, that was not the point of this study. Rather it was to show that by looking at those disparities it can be seen that labor market institutions like the minimum wage and unions do affect wage structure and that if they can potentially force wages up--through various contours--they should have the effect of achieving a more equitable income distribution. To the extent that there are wage contours, this should have the effect of boosting wages in contours above because raising the minimum wage will raise the wages of those earning around it. To the extent that it does this, the effect will be to narrow the wage gap. This no doubt will have little effect on the wages at the top of the income distribution-as they no doubt will continue to rise--but by increasing the wages of those at the bottom the top-to-bottom ratios--as one method of measuring income inequality--will be reduced. Just as policies like right-to-work laws can have the effect of maintaining low wages, with the effect of perhaps widening the earnings gap, policies like the minimum wage can have the effect of raising those wages, with the effect of perhaps narrowing that gap. | |
Table 1
Percentage of Unionized Workers
Right-to-work Middle High Union Density
Alabama (13.6) Colorado (9.9) Alaska (24.1)
Arizona (8.0) Delaware (13.0) California (17.7)
Arkansas (7.8) Kentucky (12.6) Connecticut (20.2)
Florida (7.3) Maryland (14.9) D.C. (15.1)
Georgia (6.8) Missouri (14.6) Hawaii (24.6)
Idaho (8.1) New Hampshire (12.6) Illinois (20.2)
Iowa (12.1) New Mexico (9.4) Indiana (16.5)
Kansas (10.2) Oklahoma (9.3) Maine (15.6)
Louisiana (7.0) Vermont (9.3) Massachusetts (16.2)
Mississippi (5.2) Michigan (23.7)
Nebraska (9.1) Minnesota (20.3)
Nevada (20.2) Montana (15.8)
North Carolina (4.2) New Jersey (21.9)
North Dakota (10.0) New York (27.7)
South Carolina (3.3) Ohio (18.5)
South Dakota (7.7) Oregon (20.1)
Tennessee (9.5) Pennsylvania (18.9)
Texas (6.5) Rhode Island (19.4)
Utah (9.0) Washington (21.0)
Virginia (6.7) West Virginia (16.3)
Wyoming (11.2) Wisconsin (17.7)
Source: Drawn from table 8 in Barry T. Hirsch and David A. Macpherson,
Union Membership and Earnings Data Book: Compilations from the Current
Population Survey (1996 Edition) (Washington: The Bureau of National
Affairs, Inc., 1996, 22-23).
Table 2
Percentage of Workers Earning around the Minimum Wage by State Type
Year Workers in Workers in Difference Workers in
All of USA Right-to-Work between RTW High Union
States and all of USA Density States
1940 29.9 37.0 +7.1 26.7
1950 28.4 36.2 +7.8 24.6
1960 19.3 26.5 +7.2 15.7
1970 17.0 22.8 +5.8 13.8
1980 19.7 23.7 +4.0 17.3
1990 14.2 16.8 +2.6 12.4
Year Difference Difference
between High between High
Union Density Union Density
and All of USA and RTW
1940 -3.2 10.3
1950 -3.8 11.6
1960 -3.6 10.8
1970 -3.2 9.0
1980 -2.4 6.4
1990 -1.8 4.2
Note: On the basis of chi-square tests, all are significant at the 95
percent confidence level.
Table 3
Comparative Demographics of Those Earning around the Minimum Wage on
Age, Education, and Industry in Right-to-Work (RTW) States and High
Union (HU) States
1940 1950 1960
RTW HU RTW HU RTW
Age
0-17 .1 .1 .2 .1 .3
18-24 12.0 11.0 15.8 12.5 19.7
25-34 33.2 25.0 26.7 23.1 22.1
35-44 25.6 23.9 25.3 20.1 19.2
45-54 17.6 23.2 17.2 19.5 19.6
55-64 8.8 14.8 10.9 17.4 13.9
65-72 2.3 3.6 3.3 6.2 4.1
73+ .4 .7 .6 1.2 1.1
Education
1st-11th grade 84.2 82.9 78.8 72.1 70.3
12th grade 10.0 11.4 13.5 18.5 19.3
1-4 years college 3.7 3.5 4.7 5.7 6.8
4 years college 1.6 1.4 2.1 2.4 2.4
More than 4 .6 .7 .9 1.4 1.3
years
1960 1970 1980
HU RTW HU RTW HU
Age
0-17 .3 .2 .2 .2 .1
18-24 18.6 24.4 25.4 24.5 23.5
25-34 18.3 19.0 16.9 27.8 27.9
35-44 16.3 15.4 12.9 14.1 13.9
45-54 18.0 17.4 18.9 13.2 12.4
55-64 17.8 4.6 7.0 13.3 14.1
65-72 8.3 1.5 2.7 5.4 6.1
73+ 2.5 .6 1.3 1.6 1.9
Education
1st-11th grade 63.8 57.5 47.4 37.9 30.1
12th grade 21.9 25.9 30.1 36.7 38.8
1-4 years college 8.7 10.9 13.8 16.4 18.8
4 years college 2.9 3.7 4.6 5.3 6.6
More than 4 2.6 2.1 4.1 1.5 2.9
years
1990
RTW HU
Age
0-17 .1 .1
18-24 18.4 19.2
25-34 25.3 23.7
35-44 18.0 17.4
45-54 12.7 11.7
55-64 13.3 13.3
65-72 8.4 10.2
73+ 3.7 4.3
Education
1st-11th grade 36.9 28.7
12th grade 45.4 47.9
1-4 years college 5.4 7.0
4 years college 8.8 11.0
More than 4 3.4 5.4
years
Note: All are statistically significant at the 95 percent confidence
level.
Table 4
Logistical Regression Results by Industry, Education and State Type
Variable 1940 1950 1960 1970 1980 1990
LOEDUCAT .7312 .7664 .5648 .4883 .4675 .7559
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HU -.2548 -.3230 -.3039 -.3222 -.1599 -.1820
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .1902 .1693 .3029 .2340 .1894 .1181
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
MANUFACT -.0214 -.1743 -.4131 -.4048 -.4116 -.4782
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RETRADE -.4179 -.0648 .2668 .1830 .2953 .5417
(.0000) (.1623) (.0000) (.0000) (.0000) (.0000)
WLTRADE .0317 .1009 -.3601 -.4233 -.3226 -.4112
(.0343) (.0000) (.0000) (.0000) (.0000) (.0000)
CONSTRUC .7495 - -.0411 -.3030 -.1625 -.1858
(.0000) - (.0051) (.0000) (.0000) (.0000)
Constant -1.3911 -1.2457 -1.5877 -1.5855 -1.4345 -1.8546
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HULOED .4270 .4176 .2361 .1615 .2849 .5635
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTWLOED .8568 .9639 .9240 .8440 .6960 .9377
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
WLTRADE -.1786 .0835 -.3659 -.3886 -.3049 -.3914
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RETRADE -.6670 -.1335 .2736 .2279 .3183 .5700
(.0000) (.0037) (.0000) (.0000) (.0000) (.0000)
MANUFACT -.2321 -.1927 -.4267 -.3803 -.4015 -.4622
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
Constant -1.1253 -1.2320 -1.5938 -1.6816 -1.4530 -1.9076
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
Table 5
Logistical Regression Results Within Industries
Variable 1940 1950 1960 1970 1980 1990
RTW .0615 .0994 .2380 .1855 .1629 .1266
(.0010) (.0010) (.0000) (.0000) (.0000) (.0000)
RTWMANUF .6305 .4459 .3736 .3135 .2223 .1172
(.0000) (.0000) (.0000) (.0000) (.0000) (.0004)
MANUFACT -.1405 -.1391 -.4531 -.3841 -.4179 -.4812
(.0001) (.0115) (.0000) (.0000) (.0000) (.0000)
HU -.2051 -.2839 -.2767 -.2959 -.1469 -.1719
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HUMANUF -.1778 -.1436 -.0876 -.1050 -.0754 -.1140
(.0000) (.0147) (.0073) (.0008) (.0031) (.0000)
Constant -.7047 -.7359 -1.2298 -1.3892 -1.2877 -1.6583
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .0501 .0776 .1035 .0751 .1220 .0808
(.1843) (.1320) (.0000) (.0000) (.0000) (.0000)
RTWMANUF .6313 .4429 .3629 .2950 .2106 .0874
(.0000) (.0000) (.0000) (.0000) (.0000) (.0081)
MANUFACT -.1888 -.2325 -.5040 -.4231 -.4572 -.5181
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HU -.2280 -.1643 -.2661 -.2663 -.1384 -.1669
(.0000) (.0005) (.0000) (.0000) (.0000) (.0000)
HUMANUF -.1915 -.1212 -.0781 -.1052 -.0838 -.1258
(.0000) (.0433) (.0175) (.0008) (.0012) (.0001)
RTWLOED .0003 -.0182 .1891 .1822 .0728 .1059
(.9934) (.7621) (.0000) (.0000) (.0014) (.0002)
HULOED .0422 -.1519 -.0120 -.0489 .0090 .0362
(.2702) (.0041) (.6583) (.0492) (.6853) (.1949)
LOEDUCAT .8006 .0776 .5097 .4323 .4413 .6968
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
Constant -1.3040 -1.2886 -1.5180 -1.5728 -1.4020 -1.7990
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .1963 .1986 .3351 .2693 .2281 .1632
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTWRETRAD -.0455 .0526 .1525 -.1599 -.1544 -.1559
(.6799) (.1747) (.0002) (.0000) (.0000) (.0000)
RETRADE -.5141 -.4901 .4526 .3569 .4495 .7493
(.0000) (.0008) (.0000) (.0000) (.0000) (.0000)
HU -.2536 -.3618 -.3510 -.3785 -.2055 -.2176
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HURETRD -.1803 .4211 .0267 .1240 .1010 .0236
(.0001) (.0069) (.4890) (.0004) (.0004) (.4140)
Constant -.7291 -2.2219 -1.3807 -1.5149 -1.4340 -1.8566
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .1553 .1446 .1765 .1435 .1780 .1082
(.0000) (.0040) (.0000) (.0000) (.0000) (.0000)
RTWRETRD .0801 .0883 -.1317 -.1519 -.1437 -.1409
(.4756) (.6201) (.0012) (.0000) (.0000) (.0000)
RETRADE -.3621 -.2925 .4407 .3413 .4397 .7326
(.0001) (.0492) (.0000) (.0000) (.0000) (.0000)
HU -.2777 -.2420 -.3421 .3454 -.1964 -.2132
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HURETRD -.2427 .3619 .0375 .1317 .1044 .0253
(.0144) (.0225) (.3328) (.0002) (.0003) (.3832)
RTWLOED .0357 .0251 .2213 .2040 .0935 .1157
(.3921) (.6752) (.0000) (.0000) (.0000) (.0000)
HULOED .0117 -.1640 -.0158 -.0686 -.0125 .0135
(.7598) (.0030) (.5585) (.0056) (.5735) (.6301)
LOEDUCAT .7798 .8383 .4700 .4008 .4019 .6547
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
Constant -1.3159 -1.3141 -1.6551 -1.5728 -1.5441 -1.9729
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .1964 .2439 .3133 .2441 .2083 .1414
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTWWLTRD .0165 -.2575 .1616 .1463 .0107 -.0555
(.7350) (.0003) (.0482) (.0416) (.8467) (.3965)
WLTRADE -.0274 .2795 -.3766 -.4439 -.2807 -.3991
(.5058) (.0000) (.0000) (.0000) (.0000) (.0000)
HU -.2536 -.3269 -.3495 -.3659 -.1924 -.2145
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HUWLTRD -.1803 -.1418 -.0192 .1116 .0379 .0384
(.0001) (.0338) (.8123) (.1116) (.4802) (.5471)
Constant -.7291 -.8122 -1.3152 -1.4518 -1.3638 -1.7224
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
RTW .1447 .1880 .1595 .1210 .1614 .0916
(.0002) (.0003) (.0000) (.0000) (.0000) (.0000)
RTWWLTRD .0626 -.2199 .1968 .1600 .0120 -.0445
(.2064) (.0025) (.0167) (.0264) (.8278) (.4986)
WLTRADE .0438 .3159 -.3458 -.4300 -.2744 -.3902
(.2965) (.0000) (.0000) (.0000) (.0000) (.0000)
HU .2592 -.1998 -.3363 -.3314 -.1827 -.2096
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
HUWLTRD -.1992 -.1391 -.0015 .1101 .0453 .0374
(.0000) (.0409) (.8871) (.1189) (.3992) (.5588)
RTWLOED .0440 .0197 .2152 .1990 .0871 .0986
(.2908) (.7420) (.0000) (.0000) (.0001) (.0004)
HULOED .0100 -.1769 -.0212 -.0692 -.0141 .0127
(.7945) (.0013) (.4311) (.0051) (.5248) (.6485)
LOEDUCAT .7931 .8551 .4721 .4050 .4073 .6702
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
Constant -1.3421 -1.3843 -1.5932 -1.6318 -1.4769 -1.8445
(.0000) (.0000) (.0000) (.0000) (.0000) (.0000)
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