Labour Economics - Assignment Example

Name : xxxxxxxxxxx Institution : xxxxxxxxxxx Course : xxxxxxxxxxx Title : Labour Economic Tutor : xxxxxxxxxxx @2010 Labour Economic Q 1 Compensating wage differentials False This is because in wage systems, the comparative prices of labour are used in allocation of labour to its most productive use and in encouragement of human capital development, for instance education into areas of yielding the greatest return (Steven 2005). Moreover, higher wage rate, for instance in private schools are used in compensating the teachers for undesirable job characteristics and in making sure that the equality of supply and demand for such undesirable employment. This is because in private schools there is extra workload and there more strict regulations when compared to public schools. Even if private schools have better job characteristics, in compensating wage differentials, varying wages are paid to different employees or employees within different markets that regulate for differences within their jobs or within the productivity of the employees. And in this regard, wage differentiation take place for several reasons. In most cases, they occur because of individual preferences of the workers. For example, the teachers at public schools are willing to “buy” free time or other kinds of household production by taking lesser wages. Variations within job risks, education as well as location are among the reasons for the constant wage differentials (Steven 2005). Wage differentials within the labour market are regularly compensating wage differentials. Even though, the private schools have better working conditions, they relatively pay more because of the undesirable working conditions, for instance the higher workload in private schools as compared to lesser workload in public schools. Teachers in public schools find the time to attend their household activities and other productive activities because of the free time. The main reason as to why the above statement is false is; the differential wages do not come as a result of only working conditions. Moreover, higher wages in private schools are mostly used in compensation of higher productivity as well as offer returns on investment in education since private schools main aim is to attract more student for higher returns. Q2. Schooling and signalling model False Both schooling and signalling model assume a positive correlation between the years of schooling and earnings. The signalling model acknowledges this by the fact that it presumes that, even though education did not make any contributions to the productivity of an employee, it still have some worth to the employer as well as the employee. If the suitable cost/benefit system is present or developed, excellent employees will buy education for them to signal higher productivity (Germano 2006). This is because the signalling model the potential workers send a signal regarding their capability to the employer through getting some education credentials. The information worth of the credential is from the fact that the one, who is employing makes an assumption that the information is positively correlated with having a higher capability (Germano 2006). Q3. Effects of immigration on wages The wages at South region will decrease since the workers within South will be competing against immigrants who will be more willing to accept lesser wages. There is a likelihood that the immigrants will be willing to work for a low pay. Still, more immigrants will raise labour supply at South and hence reduce wages that will be paid because more individuals will be competing for the similar jobs (Thomas 2005). The wages at North region will remain constant in short run since the labour force will remain constant and there will be no increased competition for jobs at the region. The ratio would be 1:1 since the equilibrium will be balance by the displaced workers at South who will be seeking for labour at North. Q4. Education path Education path three where Peter will receive a doctorate degree maximises his net present value of his lifetime earnings. This is because the total amount he will earn in the last option supersedes the 1st and the 2nd options. This is as a result of on education acquired by the employees having an important effect on labour market experience. Education affects labour market experience of employees is through increasing the productivity and hence increasing the earnings. Therefore, the more education Peter acquires, the better he is in a position to absorb new information, obtain new skills and familiarize himself new technologies. Through increasing the human capital, the employees are able to improve their productivity of their labour as well as of the other capital they utilize while working (Levine 2007) As a result, the elevated level of productivity indicate levels of human capital, which are consecutively mainly a result of increased education, and therefore a positive relationship will take place between educational achievement and earnings. This is the reason why Peter has a high lifetime earnings when he follows the last option where he invests highest in education as compared to the 1st and 2nd option. Q5. Education as an effective screening tool The K must range between $37 and $45 to make education an effective screening tool. This is because the workers with low-ability can be offered $37 while the workers with a high-ability can be offered $45. This is to compensate for the cost of education they incurred while at school to gain the skills which will be applied while working. In this case, this screening device will be used to establish the applicant’s capability to learn and hence preference will be given to the workers who have earned the required diploma in order to lower the employer’s risk of recruiting a worker with a diminished ability to learn. Consequently, the education as well as the ability of the potential worker to perform his or her work will be the determining factors in whether the individual will be recruited. The more educated and the person with the higher learning capability will be offered the highest pay and this is as a result of the investment the potential worker invested in education (Levine 2007). Q6. Rate of return to education The main difficulty when estimating the rate of return to education is the selection bias. Selection bias comes in because workers choose the jobs that they suit best. The bias also comes as a result of flaw while selecting the sample since s subset of the information is systematically exempted because of a certain attribute, for instance, non-participating men and women as well as weak labour market attachment. In such instances, regression results that consist of only wage earners can give results that are distorted and hence experience from sample selection bias (Wayne 2006). Exemption of the subset can affect the statistical meaning and result into altered results. Truncated regression refers to a conventional linear regression form for cross-section data whereby the sampling process utterly exempts part of the population based on the results of the dependant variable. On the other hand, there is a general problem in estimating the rate of return called non-random sample selection. Incidental truncation is a sample selection difficulty where one variable, normally the dependant variable, is only observed for specific results of another variable. A good illustration of incidental truncation is the deterioration of wages on education to compute rate of returns on a supplementary schooling year. The problem is that only the dependent variable is observed; Y is the subset population. To ruling to establish if Y is observed is not directly dependant on the result of Y (Wayne 2006). If the individual is in reality working during the survey, the wage offer is observed. In contrast, it is not possible to observe any wage offer for the individuals not in the labour force. Consequently, the truncation of wage offer is incidental since it is dependant on varying variable, which is labour force participation. The observable and unobservable aspects, for example education, experience, capability, luck, living location among others, that determine if an individual had a job are related to the aspects that determine how much such an individual earns once employed. This implies that the error term within the wage equation for the individual who has a job is positive, or even equal. This means that, irrespective of if a person has a job is in some measure determined by the exempted variable within the error term of the wage regression. The plain fact that a person has a job, and hence appears within the selected sample, offers information that the error within the regression has some positivity on average, and could be compared with independent variables, and this could result into bias as well as irregularity of the OLS estimator (Jorgenson 2004). Several statistical techniques have been developed to tackle this difficulty. Such techniques essentially engross estimating wage equations that are inclusive of not only education, job experience in addition to family background as independent variables, but also calculated probability that an individual is working (Jorgenson 2004). Q7. Employment differences Such differences within earning and employment can take place in such instances due to race, gender, country origin, sexual orientation as well as other seemingly irrelevant features. Such differences are often associated with labour market discrimination (Jorgenson 2004). Discrimination coefficient Finances, which are mostly used while measuring such differences, also serve as an assessment of discrimination. For instance, if an individual has a taste of discrimination, he or she acts as if he/she is willing to pay something either openly or in form of decreased income, to be connected with some people instead of others. Through using the model of a discrimination coefficient (DC), a definition of taste for discrimination can be obtained. It is parallel for varying factors of production, workers as well as clients. A DC stands for a non pecuniary factor in some types of transactions, and it is positive or negative, depending on whether the non pecuniary aspect is viewed as “good” or “bad”. Discrimination is mostly associated with disutility as a result contract with people.     Money cost (return)                            net cost (return) Employer                            π                                                                      π(1+di) Employee                            πj                                                                      πj(1-dj) Consumer                            P                                                                      P (1+dk)   Discrimation: di , dj ,dk > 0 Nepotism   : di , dj ,dk < 0 (Mokyr 2002). The above quantitative illustration of a taste for discrimination demonstrates the means for empirically computing the quantitative significance of discrimination (Jorgenson 2004). Consumer discrimination In case the consumers happen to have taste for discrimination, the purchasing decisions of the clients are not based on the cost of the good, P, but on the utility-altered price, P (I + d). The d is the discrimination coefficient. As long as the firm is able to assign a certain worker one of the numerous different positions in the firm, consumer discrimination will not have a big effect. In case the firm is not able to hide black workers from the public perception, consumer discrimination will have an unpleasant effect on the wages of the black workers. A firm that employs a black worker within the sales will be forced to reduce the cost of the product to recompense white buyers for their disutility (Mokyr 2002).  Statistical discrimination Both racial and gender differences can occur even when there is no prejudice when membership in a certain group has information regarding an individual’s expertise and productivity. Statistical discrimination takes place due to the information gotten from the curriculum vitae as well as the interview does not forecast faultlessly the applicant’s real productivity. The uncertainty motivates the employer to make use of the statistics regarding the standard performance of the group (thus statistical discrimination) to foretell a certain applicant’s productivity. Consequently, applicants from high productivity group gain from their membership within those groups, whereas applicants from low productivity membership do not (Baker 2005). Such discriminations are likely to lower the revenue within an organization since the equally skilled employees who are paid lower wages will be demoralized and hence this will in turn lower their productivity. This will in the long run affect the profitability of the firm especially if a large and influential group is being segregated (Levine 2007). Bibliography Baker, D., 2005, Global trends in educational policy, Emerald Group Publishing, London. Germano, M., 2006, Education returns across quantizes of the wage function: alternative explanations for returns to education by race in South Africa, Yale University, New York. Jorgenson, D., 2004, “The Explanation of Productivity Change." Review of Economic Studies, vol. 34/3. Levine, L., 2007, "The Education/Skill Distribution of Jobs, Congressional Research Service, Washington, DC. Mokyr, J., 2002, Differences within earning and employment, Oxford University Press, New York. Steven J., 2005, Flexible Funding Supports State and Local Education Reform, Yale University Press, New Haven, CT. Thomas, S., 2005, Educational Knowledge and Decisions, Basic Books, New York, NY. Wayne, J., 2006, “The Effect of Higher Education on Unemployment Rates." In Higher Education and Economic Growth, Kluwer Academic Publishers, Boston, MA. Read More