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Minimum Wage Increases and Low Income FamiliesThomas PayeaDepartment of Economics, State University of New Paltz, New Paltz, NY 12561

IntroductionThis paper proposes that the rising cost of labor through federal wage increases hurt the real wealth position of low income workers. Low income workers are defined as workers earning, on aggregate, between one and one and a half times the federal minimum wage given a 40 hour work week annually. The population for the quantitative research discussed by this paper will be restricted to workers over the age of 18 as minor workers portray an incomplete and biased assessment of the whole labor equation for both workers and employers. Including workers that typically rely on their household income rather than contribute dilutes the measure of poverty since their income is supplemental rather than primary (Sabia, 2010). Refining the study to adult workers, ages 25 54, limits research to workers whose earnings contribute a larger portion of total household income (Campolieti, 2012). And that older employees earning the minimum wage are more likely to earn that wage indefinitely. Sabia argues that the inclusion of minor workers may instill a false positive impact of wage legislation in the studys results where non - poor teenagers are benefited but a large portion of adult primary household earners with families are financially harmed. The working population included in the study may be further divided in order to describe trends among specific worker groups, such as single earner households, marital or parental status, level of healthcare coverage, or level of education. Data used for this study will be restricted to the United States and Canada, two developed OECD nations with similar industrial and commercial economic structures, political values, and very interdependent trade economies. The exchange rate between these two nations is also nearly equivalent and Canada, being the United States largest trade partner, accounts for a significant portion of the day to day and long run exchange rate volatility. The null hypothesis being tested in this report will be defined as those successive increases in the minimum wage will reduce the aggregate net income position of the population between 1 and 1.5 times minimum wage full time employees. Low income workers are more sensitive to price fluctuations and shocks to labor costs than higher income working populations. Thusly, low income families, the focus of this report, are far more likely to be negatively offset by minimum wage increases while the positive spillover benefits middle class workers. Aggregate net income position will represent a collective value accounting for real weekly income, hours worked, and worker benefits for employees. The low income working population will be defined as employees earning between one and one and one half times the federal minimum wage given a 40 hour work week including all federally mandated benefits of a full time employee. Post inflation accounted income is impacted by rising cost of living, reactive economic factors by businesses in response to rising labor costs, such as reduced hiring or average hours, and increases in the consumer price index through reactive Consumer Price Index increases (Fraja, 1998). Defining direct change in the level of real income as a result of a minimum wage increase requires the use of time, at least one year according to Neumark, to account for individual firms microeconomic reactions to shocks in the price of labor. For this reason author Neumark and his colleagues, in their article Minimum Wage Effects through the Wage Distribution, 2004, describe how within one year microeconomic equilibrium will be reestablished following a federal wage. This year delay, known as a time lag effect, is used as an allowance period for firms to adjust their business platforms to account for the higher cost of labor. Neumark utilizes an algorithm that combines government labor statistics from the Census Population Survey and an artificial time scalar to calculate effects one year following an individual employees work pattern being affected by a minimum wage increase. Results from this study indicated that all immediate benefits to worker populations following a year of minimum wage increase would be negated in the following year by which real wages, average worker hours, and total worker benefits would be redacted amongst the low income working population. The high income working population would be far less directly hurt, and would actually see benefits from the minimum wage increase, in terms of immediately quantifiable wages, work hours, and benefits while the impoverished class bears the brunt of higher labor costs. A limitation to this assessment is that the test did not incorporate aggregate increases in consumer price indexes, food prices, transportation costs, or housing prices which are more difficult to directly link to a single policy change over time. Higher labor costs are transmuted, over time (one year as described by Neumark) into increased business expenses for companies who are encouraged to increase prices to recoup lost profits.

Review of LiteratureAuthor Sabia, in the article Do Minimum Wages Fight Poverty, 2010, describes a recurring state of declining marginal utility effect for a continually increasing rising minimum wage in terms of real worker income weighed against the value of the dollar. Sabia and her colleagues argue that as the minimum wage continually rises proportional to the dollar or faster than the nominal rate of inflation, a declining percentage of workers between the one and one and half times minimum wage earning populations are assisted. This means that as the legal minimum wage is pushed higher the political measure of increasing the price of labor for unskilled workers will become increasingly ineffective in adding real income to poor households. Impacts from a non-effective subsidy to poor workers can only occur in the form of a net deterioration in the welfare of the national economy or a benefit to non poor workers equal to or less than that of the aggregate federal labor price increase. The basis for this hypothesis is that much of the low income households primary earners are earning more than the minimum wage already and that the predominant occupiers of minimum wage positions are earning supplemental household incomes rather than primary (Sabia, 2010). A political move to raise federal minimum wages merely dilutes the wage pool, encouraging youth workers to take on less desirable part time positions while diminishing the availability of more desirable full time or higher paying positions previously worked by head of households or primary household earners. This effect is compounded by the fact that firms are most likely to eliminate higher paying and supervisory positions first in an effort to maintain solvency. These employers attempt to pass down the supervisory work to the less paid employees and eliminate the higher paying positions as a response to the added labor cost. The only economic inevitability from the mathematical function Sabia and her colleagues describe is a rise in labor costs which will either be absorbed or recouped by staff or hour reductions given the time lapse effect of one year described by Neumark. In either event though output will not be increased as would be the case in a pure free market structure and labor costs will either remain constant or rise given the particular course of action of the firm. Should a firm absorb the costs, its efficiency will drop and if a firm chose to eliminate supervisory positions, it would be assumed, that output would also be decreased as a result of less effective or reduced management structure.

Labor Model and AssumptionsAnother journal article written by author Michele Campolieti, 2012, and two others describes the effectiveness of wage legislation within the other developed nation in this report, Canada, data which will make up a large portion of the empirical evaluation of this paper. A similar story to Sabias previously mentioned paper, a paper also cited within Campolieti own report, describes a state of declining utility associated with rising federal mandates on labor costs. Campolieti reports that among the 25 64 year old working population in Canada, the group that should be of the highest concern for poverty risk as the majority of these individuals are primary household earners, only 3% earn minimum wage. A stark 60% of all Canadian minimum wage earners are teenagers or youths, 25% are couples whose spouse earn a wage higher than minimum, 11% are economically unattached, and only 4% are primary household earners (Campolieti, 2012). Data this overtly pronounced should be of great concern to any legislator considering an uninformed and widespread federal wage increase. In addition to the necessary focus towards scaling the economic benefits to head of households the choice to exclude youth workers becomes an increasingly obvious choice for the empirical analysis. As discussed earlier in the works of Sabia in order to depict a complete case study of the economic impact of wage legislation on the working poor one must focus on primary earners who are non youths. A new measureable factor is also introduced in order to describe the fiscal effects on the microeconomic level following a legislated minimum wage increase. This effect is defined as spill over, a sort of inverse to the trickle down economics of Reagan, where only a small portion of the intended economic subsidy reaches the working poor and the remainder is absorbed upward into the economy by higher class non poor workers (Campolieti, 2012). Absorption by the non-poor occurs in a variety of inflationary price responses where basic goods and services rise in price to opportunize on the economic surplus while the job market remains almost the same or moves into a state of decline hurting only the working poor.An article published by Gianna De Fraja entitled, Minimum Wage Legislation, Productivity and Employment, 1998, defines a two key assumptions that must be maintained in order for accurate and practical assessment of the complete economic function of minimum wage increase. The first of these, and arguably the most important, is microeconomic flexibility on the part of the individual firm to adapt the characteristics of employees labor scheduling in response to a federally mandated wage increase (Fraja, 1998). The second assumption, which is less directly quantifiable, is that employees have differing preferences on the quality, or personal value, of their labor schedules. Differing personal values affect an employees understood value their work schedule in the way that frequent night, or weekend hours are less desirable to the employee since they are less sociable and limit the employee from partaking in personal leisure expenditure (Fraja, 1998). This assumption is critical to the empirical analysis of the article where a formula is developed to incorporate individual firm flexibility and provide a more accurate assessment of what truly happens to worker hours following an exogenous labor price shock. Fraja uses this algorithm in order to refute a previous analysis by authors Card and Krueger in regards to previous United Kingdom wage increases. These two authors assessment of the U.K.s wage increases utilizes the textbook model of competitive labor markets, a model that Fraja finds serious weakness in. The competitive labor market model provides a skewed and misleading analysis that overemphasizes the positive effects of a wage hike because of the unidimensionality of the model. Competitive labor market theory provides only employee dismissal as a response to a federal wage hike where firms may only eliminate jobs and cannot adapt the scheduling of its workers. This, Fraja states, is overtly false and non practical, a model that could and should not be utilized for real world analysis for it lacks the previously mentioned schedule quality dimension which depicts the true macroeconomic impact of a minimum wage hike. Price Effects and SpilloverOne of the most prominent of such impacts is the occurrence of a bunching effect that occurs as minimum wage rises closing the gap between minimum wage earners and previously slightly higher than minimum wage earners. The slightly higher earners, wage equal to between one and one half times minimum wage with a forty hour work week, are drawn into a group that would be aptly defined as impoverished or, the alternative, wages of higher wage earners are pushed up in response to the rising minimum wage legislation (Fraja, 1998). Though, according to the author, the majority of these low income workers are not receiving financial benefit and only the significantly higher earners find any real wage increase. In this way, Frajas article compounds the hypothesis of Campolieti in regards to the presence of a spillover effect where the true benefit of poverty alleviating wage hikes are found in the pockets of higher wage earners. The data Fraja presents shows very limited effect on the working poor with a federal wage hike and a far more pronounced positive effect on higher wage employees by way of wage inflation and labor market dilution. Wage inflation reflects a distortion in the most basic value of labor, the federal minimum wage. Workers produce no additional units of production and only the price, not the quality of labor has increased. There are now more Year 0, dollars in the hands of the working poor that is spent on goods and services of the more wealthy, this defines the nature of the spillover problem. Year 0 being defined as the year prior to a federally mandated minimum wage increase and Year 1 defined as the year of a minimum wage increase. Year 2 would be the post year following an increase that would allow the time lapse effect to take place allowing microeconomic adaption for firms to adapt employment and for macroeconomic price increase to occur (Neumark, 2004). This postulate will become critical in the calculation of real wage effects as opposed to the nominal which becomes a more complex issue than a basic characterization of inflation. In Year 1 an immediate influx of Year 0 dollars has occurred as firms have not had the necessary time to cut hours or dismiss any of their workforce. Minimum wage earners are now facing a Year 0 pricing structure with a Year 1 budget and thusly have more disposable income in the current time frame (Neumark, 2004). Free market structure thusly dictates that companies will be incurring increased demand from this group with no added supply or output as the quality of labor has not increased with the labor cost increase. Ceteris parabis microeconomic theory will mandate a rise in the price of consumer goods, food, housing, and transportation. This fundamental equation lays the foundation for the inevitable trickle up effect that will lead to the non impact on or corrosion of the net income position of the working poor. Those who profit from this additional disposable income and increased revenue in the immediate will be the business owners, those who earn commission, and managers or supervisors of companies that are reporting increased profits as a result of the added income (Fraja, 1998). This defines the core of the spillover effect and absolute counter intuitiveness of political efforts to alleviate poverty by simply mandating a non specific and sweeping cost of labor increase. In addition to the spillover which already directly adds to wage disparity of high income earners to low income workers there also occurs the bunching effect near the minimum labor price. A microcosm of socialism, slightly higher than minimum wage earners are now earning a wage nearer to or equivalent to the Year 1 minimum wage in the immediate and only the equivalent to minimum wage earners have higher disposable income. The slightly higher than minimum wage earners, who were already deemed more productive workers by their employers, are now earning a smaller percentage of the minimum wage which in a free market would indicate that the quality of their labor had declined which is clearly not the case. Raising the base rate of minimum wage also raises the legal rate of poverty according to the 2013 Federal Poverty Guidelines and this process explains how the bunching affect harms the income position of low income workers in basic proportion to an employees labor earnings against the rate of poverty (Fraja, 1998). The low income workers focused on by this paper is the one and one half times minimum wage working population and in the immediate a rise in the minimum wage will actually increase the population of this group by expanding the upper limit of the population. To effectively scale the true impacts of federal wage increase while discounting masking variables a secondary set of empirical data for use in this paper were developed by author Neumark and his colleagues, in their work Minimum Wage Effects throughout the Wage Distribution, 2004, by the way of the time lag algorithm previously mentioned. This methodology used data from the Census Population Survey to scale individual impacts on workers on a base year, wage increase year, and post increase stabilization year. Using such methodology the authors and researchers were able to use metadata from the CPS and refine the data in a way that would eliminate many of the false positives that may imply a beneficial financial impact that is unsustainable. Neumark and his colleagues argue that though some financial windfall may occur in the very short run but that these gains are in no way permanent and are actually a toxic attempt at subsidizing of the working poor. By artificially propping up the cost of labor without giving the necessary time, defined as one year by the article, for individual firms to react to the exogenous shock the labor market is poised for future Consumer Price Index inflation. Neumark found in the American data that nearly all of the short term positive gains were eliminated completely or counteracted and reversed within one year leaving workers in an equivalent Year 0 income position in Year 2. This effect also does not account for the inevitable consumer price inflation function as described by Campolieti or other deterioration in real value of the USD that may occur during that time period. In effect the real wages had fallen and the net income position was damaged for all but some of the one and one half times minimum wage working population. Time Lag EffectsNeumark describes this phenomenon as the time lag effect that, in his and his coauthors hypothesis, is unrepresented by other studies leading to misleading outcomes. This time lag effect, as he describes, reflects a macroeconomic adaption to the effects of previous minimum wage hikes that effectively neutralize positive financial gains that minimum wage workers may have incurred in the very short run. The basis for such an effect is derived from the market constraints employers are faced with in the immediate but not the long term where firms can better cope with shocks to labor supply, such as a minimum wage hike. The incurred differential between the cost of labor and the output of existing employees will be readjusted by employers over time when they are not bound by current labor needs and can resize, or reformat, their workforce. A downsizing or reformatting, a move away from full time to part time employment, will undoubtedly hurt employees and must be accounted for to gain a complete sense of the economic effects of a wage increase. An additional element of this is the real value of wages once inflation and rising cost of living is accounted for. It is argued that minimum wage increases do not serve as a means to cope with inflation, but rather, one of the driving factors causing higher costs of living. Actual changes in the macro economy following a wage hike are unlikely to be immediately recognizable, a primary critique made by the article towards previous studies that did not include a time dimension in their models. Rather these models were limited to an immediate cross sectional quantification of worker employment and income which does not allow for any market adaption following a wage hike. As a response to this limitation, Neumark develops artificially applies the effect time lag has on worker wages using an algorithm that weighs worker wages on a sliding scale against a previous year of minimum wage increase. Using this method short term or immediate gains that are not macroeconomically feasible or stable in the long run are negated and only lasting benefits that improve a workers real wealth are retained. U.S. Correlation Test DataThe econometric analysis this paper includes will be ran through the Gretl software and includes two tests for correlation. One tests for correlation between the minimum wage increases and changes over time in the Consumer Price Index and the other is a test for minimum wage increases and raw poverty numbers. Both tests use data from 1970 through present, a period of accelerated increases in the minimum wage. The CPI, poverty, and minimum wage data comes from the Bureau of Labor and Statistics government website. U.S. Correlation Test MethodologyThe annual minimum wage will serve as the independent variable in both tests while the CPI and total poverty numbers will be dependent variables in two tests respectively. Logarithmic functions for all variables will be used to better account for differing variances and an ordinary least squares test will be utilized. This test will set the increasing minimum wage against the changes in CPI over the same period of time in order to determine if there is any statistical correlation. The same type of regression will be run to test for correlation between poverty rates and the rising minimum wage. Results will be tested for normality, heteroskedacity, functional forms, and auto correlation. If the data is found to be permissible then the null hypothesis that these dependent variables, CPI and poverty, are impacted by the independent variable, minimum wage, will be tested and accepted or rejected. U.S. Correlation Test ResultsThe first test for correlation of CPI to minimum wage increases came back as highly significant with a p-value of less than 0.01. This regression was found to have a normal statistical distribution and did not test positive for a significant influence through heteroskedacity. A null hypothesis for the absence of autocorrelation among the data was also tested and accepted proving that autocorrelation did not influence the dataset results. However, the CUSUM test was not entirely within the parameters for acceptance, the only test that was not entirely passable, and peaked outside of the range of acceptance in the 1980s. The CUSUM SQ test, though, was entirely passable with the full spectrum of results within the desired parameters.In the actual analysis of the data from the CPI test the regression had an R-squared value of 0.96 furthering proof of a correlation between minimum wage increases and a rising Consumer Price Index. The beta value associated with the minimum wage log was 1.26162 indicating a one unit change in the logarithmic minimum wage resulted in a 1.26162 change in the logarithmic value of Consumer Price Index. The entire logarithmic range for the logarithmic function for the CPI was between 3.65 in 1970 and 5.43 in 2013, a range of 1.78 and mean of 4.78. Minimum wage logarithmic range was between 0.47 in 1970 and 1.98 in 2009, a range of 1.51. The beta of 1.26 means that the minimum wage had a 26% higher impact on the change in consumer price index than a one unit change of itself. Through this analysis it is apparent that the minimum wage was a significant factor in promoting increase in the consumer price index.The second regression performed, minimum wage as independent and poverty as dependent, also came back as significant with a p-value of less than 0.01. The normality plot for the minimum wage and poverty regression also came back as acceptable and heteroskedacity did not test positive for a significant factor. A null hypothesis for autocorrelation not being present was also accepted verifying that autocorrelation did not pose a significant effect on the data results either. CUSUM test results, though, had the same issue as the data in the first regression and peaked out of the acceptable limits near 1990. Similarly, as well, the CUSUMQ test came back with the entirety of the data plot within the acceptable limits. For the minimum wage affecting poverty regression an R-Squared value of 0.968 was found furthering the hypothesis that minimum wage had a significant impact on poverty. This regression had a beta value of 1.27 implying a 1.27 change in the logarithmic function of poverty for every 1 unit change in the logarithmic of minimum wage. The entire logarithmic range for poverty ran from 7.57 to 9.369, a range of 1.79, and had a mean of 8.64. Minimum wage logarithmic range was the same as before, between 0.47 in 1970 and 1.98 in 2009, a range of 1.51. The beta of 1.27 implied that a rising minimum wage had a 27% increased impact on rising poverty than a one unit change of increasing minimum wage indicating that a rising minimum wage a significant impact on rising poverty.

U.S. Time Lag Test DataThe empirics of this study will be based on findings by the Current Population Survey conducted by the Bureau of Labor Statistics and will use a graded scale of state and federal minimum wages over the period of 1979 1997. As described by Neumark in his article, Minimum Wage Effects through the Wage Distribution, 2004, a dummy variable of Year 1 to denote a year of increase in the minimum wage while the correspondent Year 2 will indicate an effect known as time lag in which the market has readjusted to equilibrium afterwards. The time lag effect measured by the study takes place over a one year period in which businesses are not constrained to their current workforce and there is some flexibility in the macroeconomy to account for any inflation or other change in real wages that may have occurred. Thusly, when a census survey is conducted in a year of a minimum wage spike an individuals household income, working conditions, education, and race are added to the data set. This will serve as the initial observation instance for the hypothesis testing Year 1 data point. The subsequent year, if data is available, the individual will be also added into the Year 2 data set, if the individuals characteristic information is confirmed to be a match; mathematically the same individual studied in the previous year.Year 2 data for the individual is then compared to the economic state of that person in Year 1 in order to determine changes in wage, usual working hours per week, employment, and total income based on changes in the minimum wage. While CPS records show comprehensive household statistics, results for individuals are not clearly defined and this could potentially become an issue if steps are not taken to properly ensure a correct match is made. In order to accurately match an individual from the Year 1 to the Year 2 Neumark and his colleagues use specific attributes to vet the observations, beginning with age and sex. For a successful match to occur household characteristics must coincide with individual attributes over a two year period, such as a 31 year old female being at a particular residence and then the following year a 32 year old female is a resident at the same location. If multiple matches were to occur for the same individual subsequent, more refined characteristics, such as education, would be applied to arrive at a singular match. The study found that roughly 20% of individuals could not be matched and was likely due to a change in residence in which case the observation was not used in the study. Descriptive StatisticsThe data used by Neumark and his colleagues for the Minimum Wage Effects through the Wage Distribution, 2004, study was gathered from a random sample of 847, 175 observations in the dummy variable Year 1, the base year of the study. A one year interval in observation would define the second dummy variable Year 2 of the study in order to allow for the test for a time lapse effect and the hypothesized negation of minimum wage hike benefits. Year 2 results would be supported by 749,510 observations that were able to be accurately matched with previous correspondents to CPS inquiries. The proportions and demographics of the study are listed in the table on the following page.

Table 1: Employment Effects

For the time lapse test to effectively isolate causative factors the independent variables are partitioned according to like attributes specific to each population group and subgroup, and a grand mean for each variable is identified. Variables included in the study and test for a time lapse effect and long term negative outcome of a wage hike were the proportion of populations within the sample group, weekly income, age, gender, and race. Neumark states that the low income population is disproportionately Hispanic despite the fact that the un-weighted majority for each class is neither black nor Hispanic, making up 83% of the sample population. There is also a heavy bias towards age where a majority of the sub, equal, or slightly above minimum wage working populations is younger. Age represents somewhat of a weakness to most analyses of the minimum wage effect because the political focus and primary supporting argument for hikes in federally mandated wages is aimed at the alleviation of poverty. However, as stated earlier in this study, the vast majority of young workers earning minimum wage salaries are not the head earner of households and are members of wealthy families. From the data set it can be seen that only 6% of the total sample population are youth workers, yet youth workers make up 36% of the total minimum wage earners. Neumark will discount this population from the hypothesis testing for this reason, focusing the analysis instead on primary earners predominantly living just above or above the poverty line. Specifying this population as the focal point of the test will more accurately identify the hypothesized equalization effect and drawing of higher paid workers to the poverty line. Macroeconomic inflation and increased employer costs, it is argued, will only weaken the dollar and economy as a whole rather than push lower paid workers into a higher standard of living.

Figure 1: Wage Distributionw = Actual WageMW = Minimum WageTo further examine the influence of the more affected higher salaried workers the sample population is arranged into hourly wage brackets derived from the minimum wage. There is a near normal distribution centering on the lower side of the2 < w/MW 3 bracket, or between 2 and three times the minimum wage in hourly pay. There are two obvious groupings of wages seen in the bar graph from the data set, one near the true mean of two times the minimum wage per hour and the other centered between $.10 above the minimum wage and 1.1 times the minimum wage. It is also notable that only 10.3% of the total sample population, youth included, are earning less than or equal 110% of the minimum wage, already signifying a potential disproportionate adverse affect to higher earners in the event of a minimum wage hike. This is the first indication that a heavy handed approach to the manipulation of labor costs will have nearly guaranteed adverse affects on higher wage earners founded on the very basic reality that wealth cannot simply be created arbitrarily but must be generated through productive labor.An expansion on the marginal utility of a minimum wage hike are the mean weekly wages of the sample population and mean worked hours. The initial data set includes begins with the incident year of the minimum wage increase and the test will weigh the reaction of these figures to the following year so that the time lapse factor can be established and accounted for. In Year 1 the mean weekly wage for the total population is $377.30 with a standard deviation of $337.10. Mean of weekly hours worked in the following year was 38.8 with a standard deviation of 4.944 hours per week. There is an obvious grade upwards as income bracket rises where the highest earners are working 40 or more hours per week. This hourly labor report takes place one year following the minimum wage hike and implies correlation between higher costs of labor for low income workers pushing employers to cut hours on average for these workers. Such a shift away from full time allows companies to skip out on benefits they would otherwise have to provide for their full time employees and signifies a forced cost saving tactic by the firms to compensate for higher hourly labor costs. In addition a rising supply of workers due to the higher minimum wage means low wage workers will be willing to do more for less and a company will be able to hire more part time workers much easier than before. U.S. MethodologyIn order to prove the existence of a time lag effect and the negative impact of minimum wage increases during the time period of 1979 1996 a comprehensive regression must be established to calculate the correlation of minimum wage increases against a workers multidimensional real wealth. The real wealth figure is derived from wage, usual weekly work hours, and income. To test the dual hypothesis of a negative monetary impact on worker welfare as well as the masking effect of time lag a theoretical one time minimum wage increase is applied to a sample of normal workers from the population group in order to measure expected response. In simply proving the negative fiscal impact the presence of time lag is also verified as the results of minimum wage increases in the short run always appear positive. Investment in technology and management in order to reformat a workforce takes what is estimated in Neumarks study as at least one year in which case all previous studies that do not include a time element for the reaction to minimum wage hikes cannot be held true.Hypothesis TestingIn testing for the existence of a negative Neumark has developed an equation using the Year 1, Year 2 model as follows.Equation 1:

In this equation the expected change of the dependant variables is calculated using the individuals actual wage (w) in Year 2 minus the actual wage in Year 1 divided by actual wage in Year 1. This is then multiplied by the minimum wage (MW) in Year 2 minus the previous Year 1 minimum wage divided by the Year 2 MW and set equal to an artificial rate of change. This invented rate of change (C) is multiplied by the differential of the Year 1 minimum wage where the increase occurred subtracted from the previous minimum wage prior to Year 1 divided by the new MW in Year 1. This can be applied to the various variables and calculated independently to determine what significant changes, if any, have taken place over the course of one year from the minimum wage hike. Results are listed in the data set below.

In addition to the algorithm ran by Neumark I compiled U.S. Department of Labor data in order to provide supplemental evidence of the minimum wage effect over the same time period. Three variables, consumer price index, year to year percent change of CPI, and annual unemployment rates were tested for their correlation to the minimum wage (U.S. Department of Labor). This test used data over the time period of 1979 through 1996, a time of successive minimum wage increases, in order to determine what correlation existed within these variables. Minimum wage was set equal to Y, the independent variable, and CPI, percent change of CPI, and unemployment served as the independent X variables.

U.S. ResultsThe hypothesis poised by Neumark was validated by the mathematical model as there were short term fiscal benefits in the short term but they were reversed within one year. Most affected adversely in fact were the low income populations, particularly those already earning the minimum wage, where the short term gains sought by a minimum wage hike were more potently reversed in labor hours, weekly earnings, and wages within a year. Surprisingly, though, individuals earning wages within $.10 per hour greater than the minimum wage and 110% did actually benefit in terms employment. Within one year employment rates had risen 10% but were first adversely hurt roughly 70% of the gain. This can likely be explained by the fact that, earning slightly higher than the minimum wage prior to the increase, these workers probably had more experience or skill at their professions. Responding to higher forced labor costs, employers would desire the most efficient and effective workers they could get and these workers were already making higher wages earlier. The initial 70% decline and employment supply shock would have come from a general increase in the cost of labor to all employers dealing with lower income workers. This is paralleled as the income brackets ascend in Table 2revealing how higher income worker population were less affected by the legal wage increase. This too is not overtly surprising given the nature of much higher income and professional businesses which are only reliant on minimum wage workers for tasks such as building and grounds maintenance. Most troubling from the regression data is the drawing effect illustrated in the lag period towards those living just out of poverty that may now be forced to care for their families at a sub-par rate of compensation. As real wages fall and cost of living rises due to the minimum wage hike low income families now are considered impoverished and in the very long term poverty rates would likely rise. Evidence for this reaction is visible in the first class of worker above minimum wage labor but beneath 110% of the minimum rate hourly. They experience an immediate 74% increase in income but in the lag period wages fell 24%, still a gain but the other factors are also necessary for a complete analysis of the situation. Discounting even the potential loss of benefits from a shift to part time labor workers in this income bracket also incurred an immediate 5% drop in employment. Within one year another 23% decrease would occur, coupled with a net loss of 4.5% drop in average working hours and an 11% drop in average weekly wages. As well the risk of inflation eroding the real value of what income these workers are still receiving is dictating a disturbing trend. The poverty line is being pushed up; people are not being pushed out of poverty. Test for Correlation of VariablesThe results of the SPSS linear regression showed a high correlation between the three independent variables and the minimum wage. An R value of 0.944 and an R squared value of 0.891 verify a correlation between minimum wage and the unemployment rate and CPI, and CPI annual percent change. CPI had a beta value of 0.31 meaning that the minimum wage rate was increased 31 cents for every dollar increase in the aggregate CPI, denominated in 1983 dollars. CPI had a significance of 0.00 meaning there was a very high level of correlation to minimum wage and defines a very direct impact of CPI on cost of labor. CPI percent change annually had a beta value of 0.51 showing a 51 cent increase in minimum wage resulting from a 1 percent increase in consumer price index annually. The significance level for this variable was lower however, at 0.069, where the data is less reliable than the basic CPI figure. This data collectively shows correlation between the rising price of goods and the rising minimum wage showing a mutually causation affect between the rising cost of labor and the rising cost of living. Compounding Neumarks research, this regression function helps to verify that there is a direct effect on inflation and cost of living that results from and contributes to a rising minimum wage, defining an unsustainable inflation of the dollar. The third independent variable, unemployment, had a beta value of 0.138 and a significance of 0.029. This factor is slightly more significant than the CPI percent change figure and is still relevant, just not to the level that basic CPI was. A beta of 0.138 in this case implies that for every additional percent of unemployment to the workforce the minimum wage will rise 0.138 USD. There is correlation here but it is not as strong as the CPI in proving that there is a cost of living adjustment that will be made in response to an increase in minimum wage. Collectively this data does show a high level of correlation between cost of living, CPI change over time, and unemployment with the federal minimum wage and that a change in one of these factors will have a significant effect on the other. This validates the hypothesis that the increase of minimum wage has an inflationary impact on cost of living and in fact hurts the poor working class of Americans living just above the minimum wage but still living in a state of poverty. Canadian DataA portion of the empirical and statistical evidence presented in this report deals with the economy of Canada. Reasons for this focus arenoted earlier in the paper and cover the similarity between the two nations commercial and industrial structures, a near equivalent exchange rate, and similar worker population distributions. It can be reasonably assumed that there would be a similar cause and effect relationship between a minimum wage increase and post factor labor market adaptation in both Canada and America. Campolietis data, the core statistical analysis to be analyzed will discuss the relationship between the minimum wage labor price and the relief of poverty. This data is reported during the period of 1997 through 2007, during the 11 years there were 78 minimum wage increases across 10 provinces (Campolieti, 2012). Raw data for the statistical analysis comes from the governments Survey of Labor and Income Dynamics (SLID) which aggregates confidential microeconomic records for workers both before and after taxation. In addition data to be used for an economic simulation for a minimum wage increase comes from a March 2008 Labour Force Survey (LFS) which is the Canadian counterpart of the American Census Population Survey (CPS). This data provides unemployment and income level data arranged by significant characteristic delimiters, such as age group. The Canadian measure of poverty is measured by a statistic know as the Low Income Cut Off (LICO), below which families are likely to spend at least 20% more of their incomes on food, clothing, and housing than the average family in a community. LICO establishes a comprehensive poverty measure by which the actual economic cost of living can be accurately scaled and effects of changes to the minimum wage policy can be quantified for statistical assessment.CN Methodology Compolieti is testing the hypothesis that there is an aggregate decline in the real wealth position of the low income working populations and all benefit from the wage increase is either temporary poverty alleviation or received by the upper class. What benefit the poor do receive is mitigated over time by job loss and decline in hours and benefits. Evidence for the non effectiveness of federal wage hikes towards impoverished populations and the mitigation effect is derived through the use of an OLS regression presented by Campolieti. Equation 2:Equation 1.sets the dependant Y value,, as the natural log of the rate of poverty in Canada. Supscripts i and t define province and time respectively meaning the effects of a wage increase will be delimited locally and scaled proportionally for a dummy time variable. The first independent variable is which defines the natural log function of the minimum wage given the i subscript of province and at t time. Factor represents a vector that can be artificially manipulated to observe the impact of a wage hike on individual population characteristics such as worker age (Campoleti, 2012). Represents a time trend variable, accounted for as a quadratic function of subscript t as time and variable T as time squared. Factor represents a dummy variable that will reflect the provincial, subscript i, minimum wage law. The X vector testable characteristics can be used to define: 25 -54 year old men (the population with the highest propensity to increase poverty); the natural log of the average adult wage (an increase here would typically be understood as a reduction in poverty); the percentage age 54 64 and the percentage age 16 24 (to restrict false readings by age grouping effects) (Campolieti, 2012). Whether the coefficients sign is positive or negative does not determine the effectiveness of a wage increase since a higher minimum wage can either relieve or intensify poverty depending on the importance of wage gains, primarily through 25 54 year old males, and net employment losses (Campolieti, 2012). Campiolieti also cites authors Sabia and Neumarks work in the field in identifying that the majority of primary household income earners fall in the 25 54 year old population distribution. For this reason the focus of the analysis will be in determining the effectiveness of minimum wage legislation on alleviating poverty among this population. The population income brackets used for Campolietis study include income from 1 to 1.5 times the LICO level in order to include not only the immediately poor, but, near poor populations as well. These near poor populations, as mentioned early, are also of great interest to a study measuring minimum wage effectiveness and are also more volatile to changes than wealthier populations. CN ResultsCampolietis minimum wage effects results for low income families are measured by the three poverty indicators described before. The equivalent, 1 to 1.25 times, and 1.25 to 1.5 times LICO rate. This encompasses the mathematically understood poor, and the near poor by community economic standards centered on the basic cost of living and provides a comprehensive approach to scaling real poverty based on prices and not nominal wages. Statistical results from Campolietis equation are provided in the following table.Table 2:Effect of Minimum Wages and Controls on Log Poverty Rates (Before-Tax LICO's)

(standard errors in parenthesis)

Income < 1.0 LICOIncome < 1.25 LICOIncome