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Austin Naples, Hannah Emigh, Ryan Danley
This section of “Wage Differentials,” will primarily focus on how geographical location plays a role in wage differentials. This will be done by analyzing several variables that may influence or cause wage differentials to occur throughout the United States. Particularly, how various locations differ in wage, such as; Urban versus Rural areas, the impacts of pollution on health and productivity and the risks associated, differences in right to work states versus non-right to work, and how each of these variables plays a role in the dynamics of wage differentials across the United States.
In analyzing the various wage differentials across the United States we will elaborate by conducting a thorough examination of historical data. This will help to better understand the overall trend and relationship of each variable in which each variation influences these outcomes, year over year, and the trends of growth within the economy. While also analyzing how severe each location may vary in comparison to an individual’s income. Evidence to support this research and data was found from, “The National Longitudinal Study of 1979,” which will be the basis for the conducted research and empirical data study.
Urban Versus Rural
Geographical differences have been the subject of wage differential studies since the mid-20th century. During this early testing period, economists primarily looked at the effects of moving and how it could potentially impact earnings. It is because of these early studies which found that when accounting for differences in experience or ability that there was little to no impact on earnings from moving (Lansing, 1967.) There are very few scenarios where there was an impact, which could be measurable, on earnings from moving. For example, those who move from rural areas to urban areas and those who move from the “deep south” to more northern states (Lansing, 1967). Those who move from poorer areas do not see any effects from moving to high income areas. This phenomenon may be occuring due to individuals in poorer areas having disadvantages such as lower education quality which hinders their wage earning potential. This was still true even after leaving that low income area (Lansing, 1967). This discovery put into perspective the dangers of low income areas like, the ghetto or places ravaged by outsourcing, become traps for those living there. This could lead some to believe that the best course of action is increasing labor laws in a way that would bring back some of these dying areas.
By making it harder to decrease wages or downscale employment, companies will be forced to decrease labor cost by investing in new and more efficient technologies (Rothstein, 1989). The idea here is if you increase the level of technology in a firm, this raises productivity and therefore increases one’s earnings potential. The relationship between productivity and wages is one of the strongest contributors to income inequality. As found from Lansing’s work there is a positive income effect on moving from rural to urban areas we can attribute this difference to differences in productivity that come from living in a city or a rural area. This is because high skilled workers will go work in urban areas this means that the area will have benefits such as a higher quality education and the ability to share ideas allows for higher income in areas like cities and other urban areas (Raunch,1993).
Another aspect that plays in wage differentials in areas that are urban versus rural is regional price parity. Regional price parity or RPP is the measure of various price levels across the United States. Regional price parity pairs well with the analysis of urban versus rural in discussion of wage differentials because it estimates the average cost of goods across the United States and shows how it affects income distribution (Bishop 8). RPP can be broken down even further by metro status, sex, race, and family income (Bishop 3). By incorporating RPP with wage differential, it can give a better picture of income inequality, vertical equity, and tax progressivity. In past research, it appears compared to other countries, like India and Brazil, showed that rural advantages diminished as they became richer (Bishop 2). However, due to lower housing prices and fuel prices, the United States’ rural advantage doesn’t diminish. Bishop’s finding in his research for his article, “U.S. Income Comparisons with Regional Price Parity Adjustments”, found that housing prices highly correlated with the regional price parity, however there should be adjustments made for spatial differences. By including spatial differences, it would help make a more comprehensive welfare comparison for individuals within a geographic area.
As we moved further into the 90’s we saw the expansion of tech industries and the rise of Silicon Valley. This led economists to wonder how the concentration of industries would impact wages. This trend seems to only impact highly specialized industries, like software development or wine (Ellison, 1994). This distinction between urban and rural areas became more pronounced as we made our way into the 2000’s. This made economists curious about the differences they could find between the economies. One approach to this was observing urban cities and regional areas to see if the density in cities causes some differences in how its economy develops. Many of the regional differences observed can be attributed to decisions made prior to the industrial age for example the Northeast adopted industrialization early on while southern regions relied more on slavery which slowed their development down (Kim, 2004). The Urban cities successes can be attributed to “Inventive activity, productivity, nominal wages, immigrants, government, and entertainment” all favoring urban cities economies.(Kim 2004).
“This research suggests that the United States economy is veering toward a less broadly dynamic, less entrepreneurial, and more geographically concentrated equilibrium. With the United States mostly reliant on a few high-performing geographies…. abundant in talent and capital to carry national growth. Even in the short period of time analyzed, patterns have reversed. Large urban counties dominate where they once lagged, while small counties have nearly disappeared from the map of recovery altogether (EIG.org, May 2016).”
Another feature of industry and income inequality, is how labor laws play into effect. Corporations certainly contribute to the large economic disparity (Fishman 1). Some corporations tend to care more about their upper-level employees than their lower-level employees. Corporations that choose to not follow strictly follow labor laws underpay employees and take away certain rights (Fishman 1). The solution to this problem would be for corporations to do a better job at following labor laws and to develop family-sustaining jobs (Fishman 1). If corporations were to pursue these solutions, they can assist in decreasing the size of the economic disparity within their own company. By corporations spreading their care to lower-level employees and following labor laws, it would help close the wide wage/income gap within their business.
Health Impacts on Productivity Due to Pollution
As industries expand and technology advances, the rate of pollution increases. The expansion of industry, not only affects pollution, but affects economic growth. In Baek’s article, he looked into the connection between income inequality, carbon emissions, and economic growth. Baek had found that as income inequality increases carbon emissions increases (Baek 1436). If his research is true then the connection between income inequality and carbon emissions is positive in the long run and short run. Another discovery is that there is a negative relationship between income and carbon emissions, meaning that economic growth contributes to a better environment (Baek 1436).The reason behind this relationship is that people who care more about the environment as it gets cleaner, they are more active and productive in their communities.
There are two main effects that play into working time and ecological output. The two effects are scale and composition(Fitzgerald 1857). The scale effect is the response of higher working hours leading to higher levels of economic output, income, and consumption (Fitzgerald 1857). This is referred to as the “work and spend cycle” (Fitzgerald 1857). Due to this effect, those who work less hours consume less and have a lower ecological footprint. However, those who would work more and consume more, would generate higher ecological footprints. The consumption effect is the influence of working time on GDP (Fitzgerald 1857). Fitzgerald stated that the consumption effect highly relied on the house-hold decision. Households who work less are likely to be more ecologically intensive compared to those who work more (Fitzgerald 1857). Although the relationship between pollution and productivity is positive, over time the amount of productivity could pay off leading to lower levels of pollution in the future. If the economy grows due to high levels of productivity, then the amount of labor hours may eventually decrease because of a prosperous economy (Fitzgerald 1856). This example shows growth that is sustainable, for both the economy and the environment. It wouldn’t necessarily mean there are layoffs, just fewer working hours. This helps the economy grow consistently and sustainably, without majorly interrupting the environment.
The Environmental Protection Agency in the United States has taken serious action to help ensure safer air since 1970. Although the United States air pollution continues to harm people’s health and the environment still today. “Under the Clean Air Act, EPA continues to work with state and local government including other federal agencies to help reduce and mitigate the harmful air pollution. Pollution can cause various health and environmental impacts.An extensive body of scientific evidence shows the long-term and short-term exposures to fine particle pollution, also known as fine particulate matter (PM2.5), can cause premature death and harmful effects on the cardiovascular system, including increased hospital admissions and emergency department visits for heart attacks and strokes. Scientific evidence also links PM to harmful respiratory effects, including asthma attacks (EPA.gov, September 2019)”
“Ozone is a colorless gas, created when emissions of nitrogen oxides and volatile organic compounds react with one another. Ozone can increase the frequency of asthma attacks, cause shortness of breath, aggravate lung diseases, and cause permanent damage to the lungs through long-term exposure. Elevated ozone levels are linked to increases in hospitalizations, emergency room visits and premature death. Pollution also causes environmental damage and can impair visibility. Fine particles can be emitted or formed from gaseous emissions such as sulfur dioxide or nitrogen oxides (EPA.gov, September 2019).”
After analyzing and studying the risks associated with different geographical locations such as; North v.s. South, and Urban v.s. Rural within the United States there are some areas, particularly cities, in which have experienced rapid economic industrialization. Rapid industrialization; such as the building of factories, and various evolutionary technological advances or the use of devices which emit dangerous levels of greenhouse gasses were found to exist in more modernized areas. These areas that had the worst pollution were mostly urban areas instead of rural areas which are producing pollution at higher levels.
In turn, this exposure poses a significantly higher risk to an individual’s health. Living in a more polluted area, which does cause harm to your health, has correlated a higher earning potential. As mentioned earlier, corporations have to spend more money on protecting their employees’ health. With a higher risk associated, there is a higher return on earnings which means the lower the risk the lesser the return on earnings will be, opposed to an individual taking a higher level of risk.
O-Ring Model Effect
“There are several implications one can derive from this model.
- Workers performing the same task earn higher wages in a high-skill firm than in a low-skill firm;
- Wages will be more than proportionately higher in developed countries than would be assumed by measurements of skill levels;
- Workers will consider human capital investments in light of similar investments by those around them;
- This model magnifies the effect of local bottlenecks which also reduce the expected returns to skill;
- O-ring effects across firms can create national low-production traps. (Kremer,1993)”
Kremer’s O-ring Model helps explain brain drain and help better understand international economic disparity. As Kremer puts it, “If strategic complementarity is sufficiently strong, macroeconomically identical nations or groups within nations could settle into equilibria with different levels of human capital (Kremer, 1993).” The model also explains how quantity of a good cannot be substituted for a better quality of another good. Workers of similar skill will be matched together with the schedule of wages as a function of that workers skill level. Small differences in skill lead to large differences in wages and output, so wage and productivity differentials between countries with different skill levels are enormous. Now try to imagine it happening on a lower level within the United States. Firms will offer jobs to only some workers rather than paying all workers their estimated marginal product. If tasks are performed sequentially, high-skill workers will be allocated to later stages of production. “If firms can choose among technologies with different numbers of tasks, the highest skill workers will use the highest technology (Kremer, 1993).” These predictions of the model correlate to a collection of factual and proven evidence of how employees are grouped together around the world today. “Although these facts may be due to a variety of causes, together they suggest that O-ring production functions are empirically relevant. Small differences between countries in such subsidies or in exogenous factors such as geography or the quality of the educational system lead to multiplier effects that create large differences in worker skill (Kremer, 1993).”
Ultimately, Kremer’s O-Ring Model helps to explain the variations in wage differentials due to high-skilled versus low-skilled workers by geographical location as it causes a multiplier effect. This methodology is quite simple: high-skilled workers are grouped with other high-skilled workers and low-skilled workers are placed with other low-skill workers. When this grouping occurs it causes income inequality which stunts growth from surpassing a given threshold, eventually stunting the overall growth of the economy. Which in turn, then causes low-skilled workers who are unable to earn higher potential wages from advancing to higher paying and higher skilled jobs.
Right to Work States
Federal law enforces the right of employees to not be forced by joining a union even if it is a requirement for employment. Right to work (RTW) laws allow employees access to the same benefits of a union contract. “This includes; the right to have the union take up their grievance if their employer abuses them without paying any of the cost. This means that if an employer mistreats a worker who does not pay a union fee, the union must prosecute that worker’s grievance just as it would a dues-paying member, even if it costs tens of thousands of dollars. Non-dues paying workers would also receive higher wages and benefits that their dues paying co-workers enjoy. RTW laws have nothing to do with whether people can be forced to join a union or contribute to a political cause they do not support; that is already illegal. Nor do RTW laws have anything to do with the right to have a job or be provided a job (EPI.org, 2019).”
Right to work laws were designed to help a unions’ ability in assisting an employee with more bargaining power, with their employers for improved benefits, wages, and safer working conditions. Although unionization raises that of the workers wage both for an individual union member, as well for non-union workers. This research shows that both union and non-union workers in right to work states have lower wages and fewer benefits, than that of many comparable workers in other areas of the United States where they are considered to live in a non-right to work state.
“In an EPI paper from 2011, Elise Gould and Heidi Shierholz estimate that wages in RTW states are 3.2 percent lower on average than wages in non-RTW states, even after controlling for a full set of worker characteristics and state labor market conditions. Gould and Shierholz (2011) also find that workers in RTW states are less likely to have employer-sponsored health insurance and pension coverage. (Shierholz and Gould, 2011)”
The difference between certain workers in a right to work state and workers in a non-right to work state is that; workers in non-right to work states are more likely to be in a union and paid more. It was estimated that non-right to work states were 2.4 times more likely to be protected under a union contract than right to work state workers are. “Average hourly wages, the primary variable of interest, are 15.8 percent higher in non-RTW states ($23.93 in non-RTW states versus $20.66 in RTW states). Median wages are 16.6 percent higher in non-RTW states ($18.40 vs. $15.79) (Gould and Kimball, 2015).”
This being said, workers in non-right to work states end up making more in potential and realized earnings than right to work states do. Workers may want to migrate to those states depending on their current socio-economic standpoint and if the trade off of moving would be more beneficial than staying at their current place of employment.
Empirical Research
| Model Female: OLS, using observations 1-2367 (n = 2281) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Missing or incomplete observations dropped: 86 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Dependent variable: l_Income | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Heteroskedasticity-robust standard errors, variant HC1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Model Male: OLS, using observations 1-2465 (n = 2389) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Missing or incomplete observations dropped: 76 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Dependent variable: l_Income | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Heteroskedasticity-robust standard errors, variant HC1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Empirical Research Overview
Using the National Longitudinal Survey 1979 data set the model above was created to find the effects on wage differentials by geographical location. The 79 data was chosen over the 97 data due to the fact the people involved would be seen at the end of their income potential as they enter into their mid 50’s. Also,it allows evaluation of data for the entire amount of people who have graduated from universities.This data will be used as general theory that predicts those who go to college would see much larger gains in their careers. However, due to this general theory, data was lost in this category because it went two years back to 2014 data.
Using roughly 5000 observations from the NLSY, the model was able to include key variables to help separate effects from geographical impact. These variables included Work Experience, Ability (Measured through AFQT score), Race, and Educational attainment. The variables chosen act as proxies for what in theory impacts wage. For example, work experience is considered a desirable trait that can boost productivity and lead to higher wages. The dummy variables for education play a similar role as they measure skills as many going to college go with the objective of procuring the skills to enter into a desired job market. The variables we chose to include outside of the geography-related data were based on what economic theory predicts to have an impact on wage differentials. We then ran two versions of the model one with all male participants and the other female to help identify differences in genders.
Summary of Variables
The NLSY 79 study allowed us to include a wide array of variables of which we only included a small number from 2014. The variables that were chosen were backed by theory and past studies and whose impact if unaccounted for we believe would taint the rest of the study. Educational attainment is a great proxy for skill, the reason being that those seeking educational attainment are forgoing wages today as an investment in skills. This investment has a return based on the difference in wages compared to an individual who did not make such an investment. The relationship between the two is well documented as the link can be traced to “1890 to 1910 where earnings in occupations that required greater levels of schooling were far higher than those that require little education”(Goldin 2007). After education comes job experience and that will allow individuals to obtain even more skills which can improve efficiency or quality of the work. By improving in ways that increase the value of your work you will see increases to wages. For example consider that you have 2 economists working and one has 10 years of experience and the other has 1 year. The one with 10 is more familiar with programs, office procedures, better office relationships, ect could lead to the experienced economist receiving higher wages.
Using AFQT scores is much like using an I.Q test only if it is administered through the military. Using I.Q measures give a good indication of the person’s ability to learn and adapt which is why they are being used as a proxy for ability differences. Ability is a difficult measure to have and even I.Q isn’t a perfect predictor of ability if you had 2 people in the same blue collar profession for example that job may require something other than I.Q to succeed.
While much of the difference people see in wages come from ability or skills that give those workers with high wages at an advantage but some workers experience discrimination. Discrimination is when workers of one group may be viewed as less diserable by employers so even when these group are offering to be paid less employers will pay a premium to hire their preferred groups. This could affect the regional measure as attitudes and demographics differ by region for example their may be more discrimination in one area due to a cultural difference meaning the impact to moving to that area would be overstated. To account for discrimination we put in dummy variables for race and sex as these 2 catagories are seen as wide sources of discrimination. For race we measured the effect of being black or hispanic against that of non black or hispanics this will show the impact from being black and hispanic compared to other races. Gender differences can be seen by the differences in the male and female models constant. By running two models we are able to see the differences each factor has on wage differential given gender as well.
After accounting for the variables that we believed would hinder our ability to measure the true impact of geography we broke geography down into 2 variables. Urban and rural environments were used to measure the impact on living in a city with much more job opportunity would impact wages. The model ran rural areas against the impact of urban areas using dummy variables to see the difference between the 2. The model also captures regional differences using Southern, Northeastern, North central, and Western regions of the U.S to capture the effect we ran the South, Northeast, and North central regions against the West. This will let us see the impact of large cities like L.A and Seattle in the west against the northeast cities like NYC and Boston. At the same time the model will show the impact of communities in middle america who have been hit hard by poor economic strategies, outsourcing, and trade policies.
Test
Using the variables from the NLSY study we decided to run a OLS regression model against the log of income earned from wages and salary. Before we conduct the OLS regression we first manipulated our variables so they would function within the Gretl stats program. Before running the OLS we first had to do some basic manipulation of the variables such as creating the log of income and adding a squared work experience to help more accurately capture the % impact our variables have. We then dummified the variables Region, Urban/Rural, Race, and Highest grade completed so we can test the effects of the qualitative variables by testing their effect against the variable left out. We then ran the test with robust standard errors to help deal with any Heteroskedasticity that the variables present. Because all the data was gathered in 2014 the data is cross sectional and isn’t faced with an autocorrelation problem.
Results
The OLS test gives an estimate of the impact each factor has on the % change in income and the statistical significance of the result is measured using t-ratios. For reference, we are looking for t-ratios above 1.97 to say that the result is statistically different from 0. By looking at the two models we see that the impact of the variables varies significantly by gender. While initially there are bad signs for the data mostly the low r2 this can be explained by our use of cross sectional data which is known for producing results with a low r2 .
The Male model found significant impacts from some regions, race, IQ, work experience, and education. The only region to show a statistical impact was the southern region which held a t-ratio of 2.63 and provided a 37% increase to wages compared with those in the west. The other regions showed similar effects but both t-ratios showed the results were not statistically different from 0. Race was shown to only be a significant factor for black males as the result for hispanics was not statistically different from 0. Being black led to an estimated .63% decline in income compared to non-black individuals and had one of the largest t-ratios in the data set at -4.5. IQ was a strange result showing a minimal impact of 8.69506e-06 but had a t-ratio of 4 which suggests that its impact is higher than 0. Part of the reason for this could possibly be because those with high I.Q attended university and the benefits of their I.Q are captured in the measure for college returns. Education seemed to have the largest impact on wage differentials amongst men with returns to a bachelor’s degree seeing an estimated .615% greater return to wages than an associate or some college has and this is supported by a 4.03 t-ratio. Work experience seemed the most off result in the study as most would believe that wage would increase as you accrue more workplace knowledge which would lead to increases in productivity and wages. This doesn’t occur in our study which estimates a negative return to work experience and shows statistically insignificant results for work experience^2. Rural versus urban environments also showed to have no impact to male earning differentials.
The Female model differs in many ways from the male but the first difference of note is the lower constant to female earning which shows an earning disparity between the genders. Many of the variables changed most notable black for females showed to have almost no impact but along with the low estimation the OLS prediction showed that it was statistically insignificant from 0. Even stranger, the model estimates hispanic women to experience wages roughly .2% greater than non-hispanics with a t-ratio of 3.33. The effect from the south was also erased and all regions now show no impact on the female wage differential and the effect from rural vs urban areas remained statistically insignificant.The effects from schooling also seem to be more unfair to women as they experience a harsher decrease to wages than male for a much lower benefit for a bachelors. A bachelors for a female versus an associates is a .26% increase to wages versus the male .615% increase. The effects from IQ and work experience seem to be the same as the males showing unexpectedly low values but females’ work experience was statistically different from 0.
Interpreting Results
Before conducting this study we had a hypothesis that the impact would be low or statistically indifferent from 0. Our initial reasoning was because of the lack of restriction to movement in the U.S. This trait would make it difficult for regional differences to exist as if there were wage differences attributable to just location then people would move to the high wage location.This movement would be a shock to the supply side on labor shifting the supply curve up which decrease price of labor in the region. This hypothesis seems to hold true from our data as all impacts regarding regional differences are indistinguishable from 0 which indicates our prediction could be correct. We see that the majority of the impact for differences in wage differentials is attributable to race, education, and gender.
Summary/Conclusion
This paper focused on wage differentials in relation to geographic location. Wage differentials are the differences in wages between people in the same industry with different skills or people with similar skills in different industries. This paper took a closer look into how geographic location correlated with wage differentials by looking into different factors like urban v. rural, health impacts on productivity due to pollution, and empirical data from two different models.
The findings of this paper related to urban v. rural were that, overall, it seemed over time it had more of an impact on wage differentials. As the United States entered the 2000’s and became more industrialized, the larger the wage differential was seen between areas that are urban versus areas that are rural. However, after looking into the potential of impact on income after moving from rural to urban or poorer areas to high-income areas, there wasn’t much difference found in income. This is likely because those who start out living in rural areas, are affected in the long-run by the disadvantages they experienced living in poorer areas. However, one potential benefit found from moving from rural to urban is that the change in technology and industrialization in urban areas leads to higher earning potential. Another way to analyze the difference between urban and rural is looking into regional price parity. This helps look into the average cost of goods and evaluate income distribution. Also, looking into density within an area to evaluate how exactly it affects the economy’s development. Despite one moving from a rural area to an urban area, there seemed to be a positive impact on earning potential when living in an urban area. This is due to the larger presence of industry and technology that helps increase the earning potential. With this being said, wage differentials across the U.S. become more present as the economy develops more in urban areas than rural areas.
There were interesting findings for health impacts on productivity due to pollution. There were definite connections, both positive and negative relationships, between income inequality, carbon emissions, and economic growth. What was found is that the relationship between income inequality and carbon emissions is positive. This means that as carbon emissions increase, income inequality also increases. The relationship was found to be positive in both the short and long run. However, there is a negative relationship between economic growth and carbon emissions. As carbon emissions decrease, economic growth rises. This is likely linked to behavioral economics and that people who strive for a better environment are more active and productive within their community, contributing to greater economic growth. This then links to geographic location in areas that are more industrialized and technology heavy, like urban areas, have higher levels of pollution. In the long-term this could impact production whether companies need to reduce working hours to reduce the level of pollution emitted. However, there is the potential if the economy develops enough from the current high level of production, that people would be able to work less, leading to lower levels of pollution emitted. In a way, it could be seen as pay off from the current level of production in an area, that only temporarily affects the amount of pollution in an area.
The O-Ring Model helped analyze the varying wage differentials that were seen between high-skilled workers and low skilled workers within different geographic locations. This ultimately caused a multiplier effect. In other words, high-skilled workers were likely to be grouped with similarly high-skilled workers and low-skilled workers were paired with low-skilled workers. This particular grouping led to income inequality and ultimately hinder growth of the workers and economy.
Another factor that impacted wage differentials across the U.S. was the difference between right to work states and non right to work states. Right to work states allow workers more bargaining power and union benefits in comparison to non-right to work states. However, when the two are compared, it seems that overall workers in non-right to work states benefit more with higher wages and benefits versus workers in right to work states. Across the U.S, this would cause greater income inequality due to different states that adopt the right to work or don’t.
Lastly, there were several discoveries from the empirical data. The data chosen was from NLS79. This was chosen over 97 because those who were at the end of their income earning potential would be analyzed. The empirical data evaluated different geographic location, race, ethnicity, AFQT score, work experience, and gender. Previous to conducting the study, the hypothesis was that the impact of geographic location would be low or statistically indifferent from zero. This was due to the lack of restriction of movement within the U.S. that could greatly change and impact one’s earning potential. If one didn’t earn much in one area, they could easily move to a high-income area to increase their level of earning. After conducting the study, the hypothesis held true. The variables that had greater impact were race, education, and gender.
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