How Gender or Sex, Has a Significant Effect on Other Variables in the Life of Individual - Research Paper Example

SOCIOLOGY PAPER Introduction In this essay, I will examine how Gender or sex, has a significant effect on other variables in the life of individual. Given than, as their parents deliver a person, judgment, on how the child is received is fundamentally based on gender or sex differences. The kind of cloths, choice of toys all depends on the sex of the child. It can determine and shape the experiences of people. Social status, health and family activities and other demographic characteristics are highly dictated by the gender or sex of a person is. Differences in human behavior between the sexes are not established at the time of birth but are as a result of societal norms Some of the demographic characteristics that I will examine include characteristics by education, income, ethnicity, race, region, sex. The results received from this study are shown on the tables that accompany this essay. I will base my comparative analysis on the IPSUMS and other samples of surveys that are available. In this essay, I will show how the prevalence of the demographic and socioeconomic characteristics differs about occupation and education in both genders and sexes. I will start with a background to some of these demographic and socioeconomic characteristics. I will then look at the results of specific tables all of which are attached to this essay. Background Many countries including The United States, view women and men not equally at varied levels of social group. From the lower ebb of the socio-economic lives , through the various institutions of a society, to minority groups and as an identity, the two different genders are contrarily placed and rewarded with a huge disparity. More to say that gender disparities exist in our, social organization world over. Among some of the demographics that I will be comparing include education, income, ethnicity, race, region, and sex. According to the IPUMS, gender parity is seen to be more prevalent across all age groups studied. The studies however show that among the younger ages, sex differences and discrimination is more simply because parents are more guided by gender prejudices. (Pamuk) Education A lot of debate is going on as to whether public school classrooms are preferable for either boys or for girls. In fact, most studies have found that both girls and boys are not favored mainly because teachers do not understand gender or sex differences in the learning process.  Boys and girls differ so much in their learning styles, this is mostly seen during the primary school going age. Most studies has shown that boys have upper hand in most school activities, they are better in hands-on, while girls are more pensive. We cannot ignore this gap, as we will soon realize that it will not result in gender equality in children The study shows that those men and women who have management positions, have managed to pursue high levels of education, or are in professional occupations have a higher likelihood of gender differences. This group of people also tends to have higher number men compared to women in higher institution of learning, meanwhile higher number of girls go to school among the group who have not attained high levels of education and end up having proper professional occupations. Some professionals who are well educated also still have gender imbalances, with male counterparts taking a huge potion. Intelligence and skill in Age and sex The studies have also shown that there are basic behavioral differences among the various sexes, it is worth to note that these vary upwards with age because our childrens intellectual biases are blown out of proportion and emphasized by our cultures. Intellectual differences are inherited. They are acquired by learning. Thus boys develop better spatial skills because the society expect and encourage to be strong in various physical activities. Equally, it is projected that girls will be more emotionally oriented and verbal skills are well developed. This is what most teachers try to emphasis. this makes girls do start to speaking earlier than their counter part. Income differences I t is sad to note that women have continuously had lesser status than men, but the size of the gap among the sexes is varied across cultures and age groups. In 1980, the United Nations offered to help understand the burden of this disparity: since women take up half the worlds population, they form two thirds of the worlds workforce, but ends up earning only a tenth of the worlds income worse still they possess one hundredth of property across the world. More surprisingly it can be seen in the Bible, the book of Leviticus. Where God gave man a upper hand in terms of wealth and salary  Another demographic characteristic that has been analyzed in the IPSUM is that of ethnicity. The study shows that ethnic beliefs on gender difference and culture influence the income, health, and mortality of individuals. The level of education also influences that amount of income that either one of the genders receives. The more educated males are seen to earn more than their counterparts who are less educated. High educational attainments are seen to have strong correlations with better living standards especially in the females than in male adults, less diseases and mortality. Results TABLE2: Sex and income mean Many scholars have studied the manner in which income is distributed in the United States of America. There are various studies and surveys that have been conducted which shows that the disparities of income in terms of gender has been on the rise since the 70s. This is especially true in developed countries when compared to those countries that are less developed. For a long time, the gap between the earnings of females and males in America has been said to be on the rise. There are those scholars who believe that this disparity has been caused by the fact that there are differences in the level of education of men and women. Men have also been said to work longer hours than females. There are those scholars who disagree stating that surveys reveal that even in instances where demographic characteristics that are similar are used, same amount of time is put in and same work experience is considered, women are still seen to earn about 18.5% less than their male counterparts do. The United States Census Bureau, however, only accounts for the 18.5% gap and does not give reasons for the other percentage. There are instances where the disparity has been blamed on the existence of occupational segregation in the US and even in the rest of the world. Occupational segregation refers to the fact that there are those industries that are dominated by men. An example of such an industry is that of truck driving. This dominance by men makes it hard for women to gain employment in this industry. Even when women manage to enter this job market, they experience discrimination and are often paid less despite doing the same jobs for the same amount of time. The disparities between the earnings between men and women can also be explained by the fact that certain jobs are referred to as “pink collar” jobs. ‘Pink collar’ jobs refer to those jobs that are seen as fitting for women. Such jobs include nursing, house help jobs and in some places teaching. Researchers also argue that women are paid less because they do not normally negotiate their starting salaries even when they have the qualifications needed to perform those tasks. TABLE1: Age and employment Status The study shows that older people are less likely to gain employment when compared to the younger people. The table shows that more people who are older are reported to be without employment than younger people are. A study that was recently conducted showed that the elderly are exposed to discrimination in the workplace. The number of people who are over the age of fifty and are not gainfully employed has doubled over the years. More than a third of both men and women who are over fifty are not employed. More and more people are retiring earlier than is expected. Studies have shown that more than half of the people who retire at this age do not do so out of their own volition. This situation has been caused by the Economic restructuring which has taken place in the US. There has also been a change in the perception of the public with many employers insisting that older people do not have much to offer the society. This is because of the fact that there are prejudices, which are in most cases false. Regimes of occupational pension have also been implemented that are intended to act as incentives that are meant to encourage older employees to retire earlier. Older people are therefore not encouraged to keep working past their retirement age. In addition, other public policies have been formulated over the years. These policies have contributed to older people having to retire in order to make way for younger employees. The other factor that may have contributed to the fact that older people are not in employment is the fact that certain skills are seen as being obsolete because individuals have an uneven access to skills and knowledge. This has contributed to older job seekers being denied jobs due to the fact that they are not up to date with the latest technology also contributes to them not performing well. TABLE3: sex and grade level attending The table shows that despite numerous strides that have been made in education in the United States, there are still disparities about gender. Even though there are more women who have access to university education, a lot still needs to be done as is shown in the table. More women have managed to attain higher education. The statistics in the IPSUM shows that the number of males who attain high levels of education still exceed that of women. Women have also managed to get employment in industries that were previously regarded as a reserve for men. The data shows that inequality still exists in the Education system in the US despite the numerous sensitization campaigns that have been conducted. The table explores the correlation between the sex and level attending. The data provide an analysis which empirical, theoretical and critical in relationship with education. There is therefore need to formulate policies that will ensure that there is a balance of both sexes in education so that women also get equal opportunities to pursue higher education. According to the statistics available in the IPSUM, the authorities need to come up with policies that will ensure that this equality is achieved because gender equality does not occur by accident. This may call for affirmative action among other policies. A critical analysis of these facts reveals a need for more action to be taken by policy makers. There is a need for them to move away from just talking about it to actually doing something about it. The IPSUM can form a good basis for policy makers who wish to ensure that there occurs real change in the American education system. Table 7: Sex and feels discriminated because of gender (GSS) The question that many scholars have attempted to answer over the years is whether women are the only ones that experience discrimination based on their gender. The IPSUM however reveals that both men and women experience discrimination. The survey however shows that women experience higher levels of discrimination than their male counterparts do. This level of discrimination, according to the table shown below differs from one person to another. Some of the factors that influence discrimination include the age, level of education, race, and ethnicity and sometimes the religion of the person On average, however, research shows that women of all races, ages, ethnicity, educational backgrounds, and religious beliefs experience more discrimination. The truth is that when one hears about gender discrimination the one thing that comes to his or her mind are women. This is perhaps because female gender discrimination tends to get more press than male gender discrimination. Research reveals many men out there who have suffered merely because they happen to be male. Conclusion The IPSUM shows the various trends and age patterns and differences that exist with gender and sex , this in turn affects the demographic and socioeconomic characteristics by education, income, ethnicity, race, region of an individual. I have looked at how gender disparity are narrowed and how it affect various aspects of the lives of Americans. I have examined these aspects in the essay basing my comparison on the IPSUM survey. I started by providing a background analysis on these issues before analyzing the results. Studies still need to be conducted on these demographic and socioeconomic characteristics in order to determine the best ways of improving gender imbalances of Americans TABLE1: Employment status Gender Year Employed %age Unemployed %age Not in labor force %age ROW TOTAL %age Female 1960 26,362,596.0 15.4 21,159,522.0 7.8 1,220,217.0 6.0 15.4 42,478,808.0 15.4 1970 26,766,400.0 15.6 29,151,100.0 10.7 1,572,000.0 7.7 17.0 46,981,400.0 17.0 1980 27,379,600.0 16.0 41,765,860.0 15.3 2,883,980.0 14.2 16.3 44,869,440.0 16.3 1990 26,362,596.0 15.4 21,159,522.0 7.8 1,220,217.0 6.0 15.4 42,478,808.0 15.4 2000 26,766,400.0 15.6 29,151,100.0 10.7 1,572,000.0 7.7 17.0 46,981,400.0 17.0 2010 31,986,700.0 18.7 66,865,235.0 24.5 7,414,405.0 36.4 18.5 51,027,907.0 18.5 TOTAL 272,795,131.0 100.0 20,344,974.0 100.0 276,031,074.0 100.0 740,655,369.0 100.0 Male 1960 45,036,416.0 12.5 2,278,945.0 8.8 13,698,324.0 9.6 12.5 88,071,589.0 12.5 1970 49,551,200.0 13.8 1,957,400.0 7.5 19,360,800.0 13.5 13.9 98,495,700.0 13.9 1980 57,221,280.0 15.9 3,878,300.0 14.9 20,228,900.0 14.1 15.5 109,963,520.0 15.5 1990 64,092,504.0 17.8 4,281,947.0 16.4 23,454,210.0 16.4 17.1 120,895,364.0 17.1 2000 70,085,267.0 19.5 4,192,127.0 16.1 30,695,136.0 21.4 19.5 137,863,971.0 19.5 2010 73,296,592.0 20.4 9,455,874.0 36.3 35,786,161.0 25.0 21.5 152,055,442.0 21.5 TOTAL 359,283,259.0 100 26,044,593.0 100 143,223,531.0 100 707,345,586.0 100 TABLE2: Sex and income mean year Male Means Female mean Total SRS Male SRS Female Total Weighted N Male Weighted N Female Grand Total 1960 3,075,111.30 2,890,716.47 2,981,294.30 4,904.410 4,736.394 3,408.187 88,071,589.0 91,221,143.0 179,292,732.0 1970 2,809,543.66 2,563,598.39 2,682,950.79 4,523.560 4,270.093 3,107.585 98,495,700.0 104,470,900.0 202,966,600.0 1980 2,809,543.66 2,175,946.99 2,296,694.78 4,523.560 1,704.610 1,246.386 98,495,700.0 116,898,880.0 226,862,400.0 1990 2,423,270.25 2,183,004.27 2,300,078.63 1,731.961 1,622.994 1,185.565 120,895,364.0 127,212,264.0 248,107,628.0 2000 2,265,918.12 2,063,462.83 2,162,642.35 1,584.445 1,499.919 1,090.168 137,863,971.0 143,557,935.0 281,421,906.0 2010 2,093,029.39 1,924,537.72 2,007,356.86 3,301.747 3,123.527 2,270.796 152,055,442.0 157,294,247.0 309,349,689.0 Grand Total 2,456,837.20 2,244,676.56 2,348,316.61 917.843 866.942 630.759 707,345,586.0 740,655,369.0 1,448,000,955.0 TABLE3: 2010 Sex and income alone is enough Gender YES NO Total %age Weighted N %age Weighted N %age Total Weight Total Female 40.5 199.6 60.1 413.3 48.1 612.9 Male 59.5 293.2 39.9 274.0 51.9 567.2 Grand Total 100.0 492.8 100.0 687.3 100.0 1,180.1 TABLE4: sex and grade level attending Year Male Means Female mean Total SRS Male SRS Female Total Weighted N Male Weighted N Female Grand Total 1960 3.25 3.34 3.30 .003 .003 .002 88,071,589.0 91,221,143.0 179,292,732.0 1970 3.87 3.91 3.89 .003 .003 .002 98,495,700.0 104,470,900.0 202,966,600.0 1980 4.71 4.65 4.68 .001 .001 .001 109,963,520.0 116,898,880.0 226,862,400.0 1990 5.19 5.19 5.19 .001 .001 .001 120,895,364.0 127,212,264.0 248,107,628.0 2000 5.32 5.41 5.37 .001 .001 .001 137,863,971.0 143,557,935.0 281,421,906.0 2010 5.61 5.78 5.70 .003 .003 .002 152,055,442.0 157,294,247.0 309,349,689.0 Grand Total 4.81 4.87 4.84 .001 .001 .000 707,345,586.0 740,655,369.0 1,448,000,955.0 TABLE 5: Sex and Class of worker Gender Year N/A %age Self Employed %age Works for wages %age ROW TOTAL %age Female 2001 62,157,596.0 46.7 5,666,821.0 47.3 74,045,135.0 47.7 141,869,552.0 47.3 2011 70,850,136.0 53.3 6,303,722.0 52.7 81,190,073.0 52.3 158,343,931.0 52.7 TOTAL 133,007,732.0 100.0 11,970,543.0 100.0 155,235,208.0 100.0 300,213,483.0 100.0 Male 2001 49,623,464.0 45.5 10,407,586.0 48.6 75,175,190.0 47.6 135,206,240.0 46.9 2011 59,428,181.0 54.5 10,992,336.0 51.4 82,827,471.0 52.4 153,247,988.0 53.1 TOTAL 109,051,645.0 100 21,399,922.0 100 158,002,661.0 100 288,454,228.0 100.0 Table 6: sex and disability Gender Year N/A %age No disability that affect work %age Difficulty Working %age ROW TOTAL %age Male 2001 32,901,679.0 14.2 93,295,966.0 13.6 9,008,595.0 13.1 135,206,240.0 13.7 2002 32,997,659.0 14.2 94,671,138.0 13.9 9,410,471.0 13.7 137,079,268.0 13.8 2003 33,102,532.0 14.3 96,187,503.0 14.0 9,175,965.0 13.4 138,466,000.0 14.0 2004 33,153,203.0 14.3 97,370,701.0 14.2 9,272,530.0 13.5 139,796,434.0 14.1 2005 33,229,615.0 14.3 98,479,738.0 14.3 9,654,458.0 14.1 141,363,811.0 14.3 2006 33,367,568.0 14.4 103,023,002.0 15.0 11,032,806.0 16.1 147,423,376.0 14.9 2007 33,360,320.0 14.4 104,122,636.0 15.2 11,155,990.0 16.2 148,638,946.0 15.0 TOTAL 232,112,576.0 100 687,150,684.0 100 68,710,815.0 100 987,974,075.0 100 Female 2001 31,381,213.0 14.2 99,187,159.0 13.9 11,301,180.0 12.4 141,869,552.0 13.8 2002 31,617,024.0 14.3 100,272,261.0 14.0 11,748,817.0 12.9 143,638,102.0 14.0 2003 31,669,671.0 14.3 100,700,361.0 14.1 12,215,347.0 13.4 144,585,379.0 14.1 2004 31,700,789.0 14.3 101,597,594.0 14.2 12,580,176.0 13.8 145,878,559.0 14.2 2005 31,640,894.0 14.3 102,475,986.0 14.3 12,918,128.0 14.2 147,035,008.0 14.3 2006 31,805,030.0 14.3 105,257,731.0 14.7 14,912,348.0 16.4 151,975,109.0 14.8 2007 31,835,460.0 14.4 105,965,670.0 14.8 15,181,083.0 16.7 152,982,213.0 14.9 TOTAL 221,650,081.0 100.0 715,456,762.0 100.0 90,857,079.0 100.0 1,027,963,922.0 100.0 Age N/A %age No disability that affect work %age Difficulty Working %age ROW TOTAL %age 1: 16-20 .0 .0 64,893,871.0 9.1 1,590,918.0 1.8 66,484,789.0 8.2 2: 21-30 .0 .0 130,483,610.0 18.2 4,085,201.0 4.5 134,568,811.0 16.7 3: 31-40 .0 .0 140,303,903.0 19.6 7,079,384.0 7.8 147,383,287.0 18.3 4: 41-50 .0 .0 144,474,888.0 20.2 12,531,106.0 13.8 157,005,994.0 19.5 5: 51-60 .0 .0 108,447,515.0 15.2 15,465,519.0 17.0 123,913,034.0 15.4 6: 61-* 1,091.0 100.0 126,852,975.0 17.7 50,104,951.0 55.1 176,959,017.0 21.9 COL TOTAL 1,091.0 100.0 715,456,762.0 100.0 90,857,079.0 100.0 806,314,932.0 100.0 1: 16-20 .0 .0 68,374,741.0 10.0 2,057,763.0 3.0 70,432,504.0 9.3 2: 21-30 .0 .0 131,890,771.0 19.2 4,667,871.0 6.8 136,558,642.0 18.1 3: 31-40 .0 .0 138,193,300.0 20.1 6,537,928.0 9.5 144,731,228.0 19.1 4: 41-50 .0 .0 140,372,525.0 20.4 11,397,769.0 16.6 151,770,294.0 20.1 5: 51-60 .0 .0 102,352,557.0 14.9 14,108,899.0 20.5 116,461,456.0 15.4 6: 61-* 1,618.0 100.0 105,966,790.0 15.4 29,940,585.0 43.6 135,908,993.0 18.0 COL TOTAL 1,618.0 100.0 687,150,684.0 100.0 68,710,815.0 100.0 755,863,117.0 100.0 Table 7: Sex and feels discriminated because of gender (GSS) Age YES NO Total %age Weighted N %age Weighted N %age Total Weight Total 1: 16-20 .0 .0 2.2 524.6 49.5 2.1 2: 21-30 32.7 18.5 23.0 534.9 543.2 23.3 3: 31-40 17.9 10.2 23.5 558.9 545.1 23.3 4: 41-50 23.2 13.1 24.5 415.4 572.0 24.5 5: 51-60 24.3 13.8 18.2 196.0 429.1 18.4 6: 61-* 2.0 1.1 8.6 2,279.2 197.1 8.4 COL TOTAL 100.0 56.7 100.0 524.6 2,336.0 100.0 References Lee, Ronald D. Malthus and Boserup: A dynami. Oxford: Blackwell, 1986. Pamuk, E.R. Social class inequality in mortality from 1921 to 1972 in England and. England, 1985. Pappas, G., Queen,. "The increasing disparity in between socio-economic groups in the United States, 1960 and 1986." New England Journal of Medicine (1993): 103. Read More