# Introduction n 2012, there were 12.5 million unemployed persons in the United States, according to the Congressional Budget Office (CBO) (Elmendorf, 2012). At that time, the CBO projected that the unemployment rate would not return to the more favorable 5% mark until the year 2016 they offered no forecast of a return to the pre-2008 level of 4%. Despite unfavorable unemployment rates for all racial and age groups, some groups are impacted much more harshly than others. The social inequality underlying these disproportionate unemployment rates can only be discovered by further examination of the racial/ethnic groups affected most. The current situation is characterized by a hugely favored majority White population against highly disfavored minority groups, most notably African-Americans. Studying the racial, ethnic, and age groups that are more harshly affectedand controlling for level of education, and sex is one way to uniquely contribute the discourse on the problem of inequitable employment. The year 2005 marks the beginning of a prolonged period characterized with heightened economic and financial distress to most parts of the world, with the U.S. feeling the effects most immediately. This period followed the burst of the real estate bubble after years of flourishing business and preceded the collapse of the financial banking liquidity in late 2007 (Constance-Huggins, 2012; Jensen, 2011). Both events were of such magnitude the recession quickly spread to all states, and eventually to the international financial and real estate markets, a testament to the great significance of the US economy within the world economy. During the recession, many states experienced heightened unemployment rates and rising probabilities of unemployment. Sociologically, the crisis presented a critical period during which the risk factors for unemployment multiplied as a result of joblessness, hopelessness, financial instability among family members, a lack of social support, and loss of social class. Presented with an overwhelming situation with a hard to anticipate magnitude, the government failed to offer adequate protective measures to caution its population (Constance-Huggins, 2012). Collectively, all racial-ethnic groups were affected to a large extent by this situation, but data presented by the US Bureau of Labor Statistics (2009) shows that the effects had both racial-ethnic and age dimensions. An overview of literature on employment shows a relationship between race/ethnicity and age (e.g., the racial differences in unemployment between Whites, Blacks, and Hispanics). Studies show that Whites between the ages of 18 and 65 years have traditionally had more advantages, with approximately 2.3% of White college graduates registered as unemployed in 2008 (Bureau of Labor Statistics, 2009). Meanwhile, Black people between the ages of 18 and 65 years were the most disadvantaged, with a 7.3% unemployment rate among college graduates registered as unemployed in 2008 (Bureau of Labor Statistics, 2009). Although originally not as disadvantaged as other minority groups. Hispanic people between the ages of 18 and 65 years have become the group with the second highest unemployment rate: up to 11.4% in 2008 (Elmendorf, 2012). Persistent reasons for social discontent and social-structural evaluations include societal perceptions developed along racial-ethnic lines and age-related trends for employment and labor compensation. Bridging the interracial and inter-age employment and wage differentials stands out as America's longest outstanding problem. Minority groups majorly comprise immigrants who entered the country from the 15 th Century to the late 20 th Century (Farley, 1987). Upon review of the literature, I identified evidence of different unemployment rates among racial/ethnic and age groups. Ultimately, findings regarding unemployment rates may be causally related I Year ( ) A as barriers to labor market entry, premarket skills acquisition, wage differentials, and education levels either increase or reduce the impact of racial disparities. This helps narrow down the object of the current study to identifying occupational segregation while providing potential explanations for the disparities in jobs with lower levels of stability and job opportunities. This leads to the overarching research question of the current study: "How do race, ethnicity, and age (controlling for gender and education) affect access to employment?" Framed within life course theory, this research examines how the effects of race and ethnicity may vary at different periods of the life course in regards to employment. # II. # Methods # a) Data Source The data used for the current study was retrieved from the Current Population Survey(CPS) administered by the US Census Bureau and the Bureau of Labor and Statistics; surveys from December 2005, December 2008, and December 2011 were used. This allows the study to frame the start and decline of the recession. In 2003 the CPS modified their questions concerning classification of Hispanics; this included allowing respondents to choose more than one race and directly asking if the respondent was Hispanic (Bowler, Ilg, Robison, and Polivka, 2003). The CPS includes employment information by age, race, ethnicity, gender, level of education, and geographic location. The CPS is used by the US Department of Labor to examine employment patterns in the United States and is the primary source of labor statistics in the United States. Another benefit to the CPS is that county level information is provided for by the Current Population Survey using Federal Information Processing Standards (FIPS) codes The Current Population Survey uses a systematic probability sample designed to provide estimates of the labor force characteristics of the civilian non-institutional labor force. Data is gathered from a scientifically selected sample of 60,000 households. Households are in the survey for 4 months, removed for 8 months, and then reinserted for 4 months to ensure continuity. The survey does not account for institutionalized individuals, who would be considered out of the workforce. The CPS reports on the following information: ? How many people are unemployed; ? How they became unemployed; ? How long they have been unemployed; # b) Dependent Variable Use of "employment status" as the dependent variable shows how unemployment impacts a group within a specific period of time. Employed (at work) and employed (absent) will be recoded to 1 and represent the total number of people employed. Unemployed (laid off) and unemployed (looking) will be recoded to 0 and represent the total number of people unemployed. Retired, disabled (not in workforce), and other (not in workforce) were recoded to system missing, so that only individuals in the labor force were counted and this group was assigned no value. Discouraged worker is not a status and was therefore not used in this research # c) Independent Variable For purposes of the current study, the independent variable of "age groups" was recoded into the following age ranges: 25?34, 35?44, 45?54, 55?64 and over 65. The age groups are based on cohorts used by throughout various studies including the U.S, Census Bureau, Current Population Survey, and Annual Social and Economic Supplements, 2010. Each age group was assigned a dummy variable that is equal to 1. The age group of 24 and under was excluded and is used as the reference category. The race variable allowed me to identify the differences between Whites and Blacks in terms of unemployment. This variable was recoded to show White Non-Hispanics and Black Non-Hispanics as two individual dummy variables, where each race will be equal to 1 for its dummy variable. All other racial groups were removed from the study. The variable ethnicity allowed me to identify differences between Hispanics, White Non-Hispanics and Black Non-Hispanics in terms of unemployment. The Current Population Survey uses self-identification as either Hispanic or not Hispanic. This variable indicates if a respondent is Hispanic or not; Hispanic was set to 1 and not Hispanic was recoded to 0. Anyone classified as Hispanic was separately classified as White-Hispanic or Black-Hispanic in addition to the general categorization. This also informs us of whether Hispanic people's unemployment rates align with their race or ethnicity. # d) Control Variables Sex was used as a control variable to remove any effect of its association. Sex is being used instead of gender due to the fact that this is the terminology used by Current Population Survey. Males were recoded as 0, and females were recoded as 1. Level of education was used as a control variable to remove any effect of its association. Sixteen levels of education, ranging from kindergarten through doctorate, were coded. The original coding of 31?47 was recoded to 1?16, with 47 (unknown) being recoded to system missing. # e) Analysis The primary analysis method was binary logistic regression. This technique was chosen because the study uses a dichotomous dependent variable. Binary logistic regression enables use of significance tests and relationship strengths between variables. Data from each year was separately analyzed in models in relation to the dependent variable of unemployment, which makes "employment status" the dependent variable. Independent variables include age, race, and ethnicity, with control variables sex and level of education. This method allowed me to answer to the research question "How do race/ethnicity and age (controlling for gender and education) affect employment?" III. # Results # a) 2005 Binary Logistic Regression Results The binary logistic regression results for 2005 revealed existing differences in employment of individuals based on age. Employees below 25 years of age were excluded in the analysis and used as the reference category. This was done in order to avoid multi-collinearity issues, also known as the dummy variable trap. The probability of employment for a Non-Hispanic Whites in 2005 was 85.5%. Individuals in the age group 25 -34 were better positioned to be employed compared to Non-Hispanics below 25 years (92.4%). Persons between 35 and 44 years also had better chances of getting employed (94.3%), as well as those between 45 and 54 years (94.6%), and 55 -64 years (94.9%). Compared to Non-Hispanic Whites below 25 years, persons above 64 years had the greatest advantage in terms of probability of getting employed with a rate at 95.3%. Controlling for the dummy variables of the race groups in one model, Non-Hispanic Blacks, White-Hispanics and Black-Hispanics had significant negative impact on the model. When controlled individually, each ethnic group had significant negative impact on the model. Compared to Non-Hispanic Whites below 25 years, the 25 -34 age group was 2.1 times more likely to be employed, while the 35 -44 age group was 3.2 times better positioned to be employed. The 45 -54 age group was 3.2 times more likely to be employed, which placed them close to the 55 -64 age group, which was 3.15 times better positioned than the Non-Hispanic Whites aged below 25 years. The over 65 age group was the most likely to be employed (95.3%), and were 3.5 times more likely to get a job at the expense of the Non-Hispanic Whites below 25 years. When all dummy variables of ethnicity and race were controlled for alongside sex and education, Non-Hispanic Whites appeared 2.4 times more likely to be employed than Non-Hispanic Blacks. White-Hispanics were equally likely to be employed as Non-Hispanic Whites below 25 years, and Black-Hispanics were 0.75 times as likely to be hired compared to the reference group. Males were 2.2 times more likely to be hired for a job than females. Each educational level advanced secured the candidate a 2.8 better chance of getting employed. # b) 2008 Binary Logistic Regression Results The probability that a Non-Hispanic White aged below 25 years got employed in 2008 was 86.9%, lower than any other age group comprising the Hispanic, Non-Hispanic White, and Non-Hispanic Black racial-ethnic composition. Those between 35 and 44 years performed even better (94.3%), but slightly poorer than the age group 45 -54 years (94.7%). The highest probability of employment was observed among persons between 55 -64 years (95.5%), 0.3% higher than individuals aged over 64 years (95.2%). Compared to Non-Hispanic White job seekers below 25 years of age, those between 25 and 34 years were twice as likely to be employed, while the 35 -44 year age group was 2.5 times more likely to be employed. With the odds 17.94, the 45 -54 age group was 2.7 more likely to be employed while the 55 -64 age group was thrice as likely to be employed vis-à-vis the reference group. The most senior group (above 64 years) was 3 times more likely to be employed compared to the Non-Hispanic Whites below 25 years. Controlled for alone, Non-Hispanic Blacks, White-Hispanics affected the model significantly negatively, as were Black-Hispanics. When all the race and ethnicity variables were controlled for, Non-Hispanic Blacks, Black-Hispanics and White-Hispanics affected the employment model significantly negatively. Controlling for all race and ethnicity variables alongside age and gender, male candidates appeared 1.6 times more likely to be employed compared to the women. Each level of academic advancement was found to give a candidate 2.1 times better chances of getting employed than those in their previous qualifications. Non-Hispanic Whites were 2.3 times more likely to be employed compared to the least likely group (Blacks), and 1.2 times as likely compared to Black-Hispanics. Non-Hispanic Whites were 1.5 times more likely to be employed compared to White-Hispanics. # Global Journal of Management and Business Research Volume XVIII Issue II Version I # Year ( ) c) 2011 Binary Logistic Regression Results In 2011, Non-Hispanic Whites below 25 years of age were the least likely to be employed (85.1%), as observed in the two earlier periods (2005 and 2008). Members of the age group 25 -34 years were 91.4% likely to be employed, and those between 35 and 44 years were 93.6% as much. The group 45 -54 years was 93.8% likely to secure a job, while persons in the age groups 55 -64 and those above 64 years of age were equally likely to be employed at 94.4%. Over this period, persons aged 25 -34 years were 1.86 times better placed to secure employment slots than Non-Hispanic Whites below 25 years. The age group 35 -44 was 2.56 times more likely to be employed. Job seekers in the age group 45 -54 years were 2.65 times more likely to be employed, while those between 55 and 64 years were 2.96 times likely to be employed. Those over 64 years were also 2.96 times more likely to be employed compared to Non-Hispanic Whites below 25 years. Controlling for individual dummies of race and ethnicity, Non-Hispanic Blacks, White-Hispanics, and Black-Hispanics had a significantly negative impact on the model. When the variables were controlled for together, each group (Black-Hispanics, White-Hispanics and Non-Hispanic Blacks) had a significant negative impact on the model. When age and education variables were introduced alongside the race and ethnicity variables, Non-Hispanic Whites under 25 years were 2.3 times more likely to be employed than Non-Hispanic Blacks, and 1.2 and 1.45 times better positioned to secure employment than White-Hispanics and Black-Hispanics. Males were 1.4 times better positioned to acquire a new job while each new progressive level of education guaranteed the candidate a 1.9 times advantage at a job compared to those in their immediate, previous academic level. # IV. # Discussion I hypothesized that disparities in employment statuses were more evident among the younger cohorts, with specific bias towards Black males. Overall, individuals below 25 years of age were found to be least likely to be employed across the three study periods (2005, 2008, and 2011). The research presents evidence regarding on discriminatory unemployment of minority groups, particularly African Americans. From the 2005 data, the group aged below 25 years was least likely to be employed. Alongside White-Hispanics and Black-Hispanics, Non-Hispanic Blacks had a significant negative impact on the employment model. Non-Hispanic Whites aged below 25 years were more likely to be employed than both Black-Hispanics and Non-Hispanic African-Americans. However, they were equally likely to be employed as were White-Hispanics. Acknowledging that the reference group was Whites below 25 years of age, and that this age group has the largest percentage of unemployed individuals among Non-Hispanic Whites, these results show that two of the groups (Non-Hispanic African-Americans and Black-Hispanics) had lower employment rates than the worst-performing group of the Non-Hispanic Whites. Compared to other groups, Non-Hispanic Blacks are the group with minimal employment rates. Being 2.4 times less likely to be employed against Non-Hispanic Whites aged below 25 years, their performance compares more dismally vis-à-vis those of Black-Hispanics (who are 0.75 times less likely to be employed compared to Non-Hispanic Whites below 25 years), and White-Hispanics who were as likely to be employed as the Non-Hispanic Whites. Commenting on similar observations, Agyemang and Delorme (2010) remarked that being White is treasured in American society, and that White persons' ideals form the yardstick against which persons of all other racial-ethnic heritages are evaluated. By this argument, and in terms of the current study, it is fair to further suggest that mistakes committed by Whites easily pass for "normal, forgivable" errors, while those done by Blacks, Hispanics and persons of other racial backgrounds are overrated to represent a social-cultural weakness with members of the particular race/ethnicity. This amounts to stereotypical inequality, so referred by Asch (2001) because the source of the phenomenon is historic stereotyping that is not supported by any retrievable evidence. The concept of equity in light of life course theory suggests that there is more to inequality than genetic makeup and personal choices (US Department of Health and Human Sciences, 2010). In context of the current study, for Blacks failing to secure employment is seen both as a factor of fear among potential employers as hypothesized in this study, and entrenched tradition where this group has historically been alienated to a varying degree both in the provision of essential social privileges and accordance of fundamental human rights (Solorzano, Ceja &Yosso, 2010). This notion appears to also affect Black Hispanics, who in the 2005 data appeared second most unlikely to be employed. Solorzano et al. (2010) took on the issue of social branding and its implications where clear distinctions cannot be drawn against particular members or sections of the society marked for specific discriminate acts. In particular, Blacks are traditionally branded as having better physique and inferior mental capacity (Agyemang & Delorme, 2010). This has technically knocked out many aspiring Black contenders from rising to deserved positions, both in the world of sport management and general, mental-oriented roles (Asch, 2001). Despite the fact that Hispanics also bore the consequences of racial discrimination in appointments to employment positions, this appears to be much the product of the Black-Hispanics being seen to share a Black heritage. It is possible that with prior interrogation of the target employees, the employers would possibly employ more from this group after learning that they are partly Hispanic. This seems to be the case, especially when trying to make sense of the higher employment rates of White-Hispanics against the Black-Hispanics. As of 2005, the rising segregation is apparent and without proper warning to the system for reformation of this trend this appears to be a likely point of discontent amongst members of Hispanic heritage, majorly because of those among them with Black coloration. The year 2008 presented much the same findings, with a stepwise pattern emerging on the agegroups' employment statuses. Again, individuals below 25 years of age were least employed compared to all other age groups. Individuals from the age group 25 -34 years were twice as likely to be employed compared to the Whites below 25 years. Imitating a staircase arrangement, individuals in the age groups 35 -44 years, 45 -54 years, and 55 -64 years were 2.5, 2.7, and 3 times more likely to be employed compared to Non-Hispanic Whites aged below 25 years. At this point we draw an obvious comparison: having already observed that African-Americans and Hispanics of all age groups combined had significant negative impact on the employment model, it follows that in every age group, their employment rates are lower than those of Non-Hispanic Whites. Furthermore, Non-Hispanic Blacks are the overall least rated group in employment compared to the Non-Hispanic Whites below 25 years, implying that they have higher unemployment rates across all the age groups. As of 2008, a Non-Hispanic White was 2.3 times more likely to be employed compared to Non-Hispanic Blacks, 1.2 more than the Black-Hispanics and 1.5 times more than the White-Hispanics. This period presents lowered employment prospects almost similar to those of 2005. However, the advantages accorded the Non-Hispanic Whites remained. The dominance of Whites against the Blacks was again displayed, presenting a marginal drop from 2.4 to 2.3 times in advantage to employment. Meanwhile, their dominance over both White-Hispanics and Black-Hispanics intensified. Notably, towards 2008, White-Hispanics were worse position to be employed than Black-Hispanics. This is a reversal of their initial position in 2005 when the White-Hispanics enjoyed equal employment rates as Non-Hispanic Whites below 25 years. While the advantage of Non-Hispanic Whites over Non-Hispanic Blacks at employment did not increase over the three years, their advantage over both Black-Hispanics and White-Hispanics increased. This means that if the 2005 trend had remained, Non-Hispanic Whites took up some more of the employment slots supposedly due for Hispanics. Clearly, the developments between 2005 and 2008 represent a reversal of the doctrine of equality. Such factors as attitude and personality are important pillars of the doctrine of equality. Again, a higher percentage of African-Americans being unable to secure employment in equal proportions across the age groups implies that the bulk of those affected by unemployment was primarily young Blacks, particularly in the lowest age group examined. This stretches the trend observed in the previous study period (2005) and cements an observation either around inaction or ineffectiveness of the efforts to bridge the racial gap in employment statuses. In both situations, Non-Hispanic Whites are the benefactors of the ineffectiveness of government's programs to secure equal employment for persons of all racial groups. White supremacy is a doctrine that has long been perpetuated across generations in America, and is a leading tenet of the concept of critical race theory. Its exertion and sustenance is the result of the observed inequity in employment privileges. While pressing for equal treatment is often met with opposition and misunderstanding, many potential employers hide behind the veil of White superiority to wrongfully deny Black and Hispanic job seekers employment opportunities (Asch, 2001;Young, 2012). However, Blacks and other underrepresented races continue to press on, believing that the wide web of discrimination is not at play at every employment station (Wilson, 1979). Mentioned earlier, timing as a concept of life course theory appears as the top aspect in action at this point in time. Timing, as described in the introduction section of this research, is loosely the "oncoming of a new event in the life of an individual at a critical phase of their lives, such that it sufficiently alters their trajectories." Such an event could be in the form of a physical injury that affects their ability to cope with financial demands, entry into college increasing their employment prospects in future, or, among many other examples, a crisis like witnessed during the 2008 financial meltdown. Therefore, timing as explained here has a basis in the critical financial meltdown that occasioned the period immediately before and after 2008. It is against this background that employment opportunities were particularly narrowed to pave way for smoother managerial operations and effective productivity (Wilson, 1979;Kuehn, 2013). As is the case when Black persons present themselves for employment, a similar spectacle was observed when employers were presented with the inevitable option to lay off some of their staff. Around this time, the ratio of Black persons losing their positions to that of other races was remarkably higher (Kuehn, 2013). Inevitably, Non-Hispanic Blacks were more discriminated against, despite their academic or gender superiority; questioningly, the employment sphere has remained # Global Journal of Management and Business Research Volume XVIII Issue II Version I Year ( ) A more receptive of male employees compared to their female colleagues (Kuehn, 2013). While controlling for education and gender did not portray a significant decline in the employment rates of Non-Hispanic Blacks, it is worth noting that the group is still featured as the most discriminated against, achieving the lowest rates among the three racial groups examined. Despite the fact that the crisis affected employees across the board, the decline in employment rates for the Hispanics (both Black and White) represents a severe trend in the concept of timing. As Elder (1998) noted, the developmental impact of a life transition or event is contingent on when it occurs in a person's life. This means that the age at which a person is exposed to a social change, such as employment or unemployment, shapes their future. In this case, disproportional laying off of Hispanic employees represents a sudden ethnically-driven turn that breaks the social classes of the group, and effectively relegates them into lesser privileged social classes based on their emerging financial handicaps. This translates to more confounded problems, including an inability to join college, which effectively alters the life trajectories of affected members of the group. While the increased inability of the three ethnic groups was higher for the Blacks and Hispanics, the racial dimension to it is that Blacks' employment levels remained highest, even for the initially fair-rating Black-Hispanics. Equally, this implies that the life trajectories of the Non-Hispanic Blacks were hardly altered, and continued on their undeservedly poor path of fewer chances (e.g. in education slots and business setups) and little or no socio-economic improvement. However, a more cogent explanation as to why the younger groups were much more affected could rely on ability to pay for and acquire necessary knowledge and employment skills following the financial crisis. This inability to learn to high levels could have been due to long term deprivation of social equality, rendering the young Blacks as the least capable to save, and in effect least capable to respond to a crisis requiring deep reliance on previous savings (Sum, Khatiwada, McLaughlin &Tobar, 2007;Ladson-Billings, 2005). Unlike the trend in 2005, Non-Hispanic Whites and Non-Hispanic Blacks seemed to have been adequately cushioned against the shock effects and appeared unaffected by soaring unemployment. This sudden change for one target group and subsequent static displays by members of other groups is likely to have been the result of the much criticized front-loading policy (US Dept. of Health and Human Sciences, 2010). Front-loading is explained as a strategy by which society transforms to prepare for devastating occurrences/ events by putting in place effective measures (Pager, 2003;Esposito, 2012). The consequences were remarkably positive in light of employment status for the groups, but also appear to have widened the gap between persons of Non-Hispanic White and Hispanic origins, while closing the gap between Non-Hispanic Blacks and Hispanics. In solving the observed 2005 alienation of the Non-Hispanic Blacks, another problem was created through racial division. The whole period of study (2005 -2011) represents a phase still marked with unending White supremacy across employment windows. The period has presented little or no visible opportunities for convergence of interests of Blacks, Hispanics and Whites. Interest convergence is sometimes the product of misfortune in society, for those enjoying special privileges in society will hardly consider trading them for equality with other less privileged members of society (Rollock&Gillborn, 2011; Hylton, 2006). The initiatives put in place to counter this lack of desire by elitist White communities to retain undeserved privileges seem to be yielding quite slowly. At the rate witnessed between 2005 and 2011, the overall target envisaged is bound to take decades. When the dummy variables for the race groups were introduced in a single model, Non-Hispanic Blacks, White-Hispanics and Black-Hispanics had a significant negative effect on the 2005 employment model. Females had lower employment rates compared to males while higher education levels were a catalyst for higher employment probabilities. Combination of these factors shows that males could have certain advantages over females, advantages equally enjoyed by Whites against other races/ethnicities. This dominance of males over females represents a phase through which males acquired most essential characteristics that employers preferred more during this period, or a shift in the industries that were hiring the larger portions of the population. This could be based on one of the long-held beliefs that persons of opposite gender are at times more efficient in different types of jobs (Darity& Mason, 1998;Castano& Webster, 2012). For instance, nursing and hospitality have been traditionally viewed as women's preferred roles. Clearly, Hispanics, Whites and Blacks appear to lead significantly different employment lives in terms of selection to such positions in the work places. This is despite the fact that education had positive impact on employment. The effects of successive educational achievements influenced the success of employees. This could also have been an influential factor in the gender preference shift, indicating a possible improvement in educational attainments of males in society. Educational attainment can explain the lower employment rates among younger cohorts. Each succeeding education level improved individual's employment chances regardless of their age or race. This could hardly be achieved by employees within the youngest age group, most of who only have a first or second degree, as observed by the Bureau of Labor Statistics reports (2009). Controlling for education to determine the employment rates of persons of different racial/ethnic backgrounds brings out the bias in attainment of certain levels of education. There is also a degree of "natural influence" on the subject of educational levels vis-à-vis age. For instance, there is a certain highest level of education that a person of a certain age group could have attained. This is coupled with the properly-advised notion that a person with a certain educational level cannot perform job specifications up to a certain complexity only achievable by a member of a higher educational achievement. The basis of this observation is on issues of technical knowledge acquired at specific levels of education and accumulated experience at lower, preparatory levels of the system (Solorzano et al, 2010;Hammack, 2005). In life course theory, this assertion is fatalistic but irreversible. The developmental impact of a life transition or event is contingent on when it occurs in a person's life (Elder, 1998;Constance-Huggins, 2012). This implies that the age at which a person is exposed to a social change, such as employment or unemployment, shapes their future statuses (life trajectories). The fatalistic edge to it is that there does not appear to be a workable route around this barrier whenever any action is guided by the foundations of the educational system. It is in the educational system that the workforce is trained and sharpened for the future role of economic development, which translates into a high interdependence between the two (Miech& Shanahan, 2010; Borjas, Grogger& Hanson, 2010). Male dominance in the job market also benefits from the disadvantages accorded the female population. These results are a pointer to the existence of more barriers to a group's attainment of a higher social status. For instance, it can be seen that both Black and White Hispanics were highly affected by the introduction of the controlling factors. This could further point to a possible disparity between the distribution of academic achievements amongst the Hispanics themselves, or even the dummies within the group. These could include uneven distribution of educational opportunities among other factors of social suppression. Imminently, lower literacy rates among Blacks and other minorities in the US are easily interpreted in light of CRT explanation of White gains (Burton et al, 2010). The White gains stance posits that for every gain that did not trickle down to the deserved minority population, which largely comprises non-Whites, a proportional advantage is experienced by a section of majority groups, largely Whites. This is one centuries-old act of intent that has intentionally locked the minority groups out of employment. Naturally, the thinking is that when a people do not acquire the knowledge and skills that help them to step boldly into the employment sphere, they remain inferior and the majority can retain their superiority as intended (Carbado & Gulati, 2003). The White gains explanation, in this case, retraces a path of socioeconomic oppression perpetuated intentionally down generations. While the state of the situation looks grim from the analysis, with little possibility for betterment, CRT presents an avenue for a counter narrative for killing off this continued injustice by establishing common goals in society that will enhance convergence of interest of groups pulling in opposite directions. For many years, the main goal of minority groups in the US has been to gain equal leverage with their majority colleagues, while majority groups are consistently working for personal development, including creation of wealth, and enhancement of educational standards, among many other pursuits (Burton et al, 2010). When this balance is achieved, the extra benefits extended to the White majority will be equally enjoyed by the entire population, without the feeling of race-born inadequacy. V. # Conclusion Delving into the impact of race/ethnicity and age provided substantial insight into the exposure of each recognized working age-group to the total employment opportunities available. As hypothesized, race and age work simultaneously in influencing individuals' employment. For instance, individuals at a lower age group are less likely to be employed compared to older individuals. I investigated the claim that young Black males are more often likely to miss out on employment than any other persons. Recent research indicates that members of this group are more likely to be involved in crime, and that fewer from this group than any other group are in employment today. This research confirms the assertion that African-Americans, especially the younger ones, have the lowest employment rates in the country. Americans of all walks of life experienced a heightened risk of unemployment after the 2008 recession, which saw a massive rise in retrenchments so that entities could sustain profitable streaks. This study established that for the period occasioning the recession, employment rates among members of the different age groups studied decreased The results showed that older adults from the more disadvantaged racial ethnic groups were more likely to secure employment opportunities than their younger colleagues sharing similar racial backgrounds. In particular, Blacks and Hispanics compared more favorably with their White colleagues as age advanced. The rates of change were attributed to the number of individuals of advanced age groups seeking employment, and the Bureau of Labor Statistics' findings that there were more elderly individuals of non-White racial-ethnic background seeking employment. This corroborates the observations in studies by Devers (2011) and Abrams and Moio (2009) that explained the influence of the cultural competence model is at play when weighing factors that lead to racial discrimination. Partly, the competence of racial groups at work and in job search is attributable to the Life Course concepts of timeline and environmental setups, and the two played a vital role in explaining the results of this research, especially with reference to African-Americans and Non-Hispanic Whites. The environmental factors that an individual is exposed to eventually shape their personality and ability to steer through complex situations. The interaction of age and ethnicity in determining individual groups' employment rates can be attributed to the concept of timeline. This remark was prompted by Elder (1998), who separately explained the role of timeline as affected both by age and racial-ethnic affiliations of individuals. The position of Hispanics seemed to contrast at different study periods. The group was on average disadvantaged in terms of job acquisition, but seemed to fare relatively worse towards the height and end of the economic recession compared to the preceding periods. The group, however, had higher employment rates than Non-Hispanic Blacks across the study periods. The results also indicated that Hispanics of mixed origins experienced employment rates different from their respective ethnic backgrounds. For instance, White-Hispanics had significantly different employment rates than Non-Hispanic Whites, while Black-Hispanics had significantly different employment rates than Non-Hispanic Blacks. Remarkably, as the advantages enjoyed by Whites increased over time, Hispanics appear to be losing even further. The study period (2005 -2011) presented continued decline in the employment rates of Hispanics. The essence of these results is born in the earlier realization that for a particular group to gain, another is bound to lose, in line with the White gains explanation for social inequality (Solorzano et al, 2010). The study went beyond the levels of other studies to investigate the implications of age and race/ethnicity. Though not entirely new, this study went ahead to incorporate other controlling factors: sex and education. This improved the versatility of the models, on top of enabling a multi-level view of the various implications of introducing each dummy variable at a time and a combination of dummies and other variables at once. The response of the models enabled for varied views, including a tri-dimensional response by the dummy variables of race. For instance, the effects of introducing the controlling factors were clearly visible in a way many studies have not been able to do. The range of variables presented a view beyond what ordinary studies have been able to achieve, since the introduction of gender and age was relatively peripheral. The choice of the analysis approach was largely informed by the need to have step-by-step examinations of the introduction of different variables at different stages, and the binary logistic regression models were the most powerful tools especially since the result also provides an odds-ratio result from the SPSS output. Largely for the reason that the actual contributions of the controlling variables age and sex of individuals were not deeply explored on their own paves the way for future studies closely knit around these implications. 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