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FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS -*** ECONOMETRICS REPORT EFFECT OF EDUCATION ON WAGE-EARNING IN THE USA IN 1980 Name Student ID Đồng Huyền Anh 1815520148 Ngô Bảo Huy 1815520178 Trịnh Thị Yến Linh 1815520197 Lecturer: Dr Từ Thuý Anh Ha Noi, 2019 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT A.Introduction About the topic…………………………………………………………………………….4 Literature review………………………………………………………………………… Theoretical background…………………………………………………………………….7 B Content I Describe the variables…………………………………………………………………………8 II Describe the data Data overview………………………………………………………………………………8 Data collection…………………………………………………………………………… III Describe the correlation between variables………………………………………………10 IV Econometrics model……………………………………………………………………… 11 V Estimation of econometric model………………………………………………………… 11 VI. Hypothesis testing 1. Testing the overall significance………………………………………………………… 13 Testing model’s problem 2.1 Testing Omit variable……………………………………………………………….13 2.2 Testing multicollinearity…………………………………………………………….14 2.3 Testing Heteroskedasticity………………………………………………………… 16 C Summary…………………………………………………………….……… 18 D References…………………………………………………………………………………19 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Abstract Nowadays, education is one of the most important issues in the individual development of people in society Therefore, the attention on the development of education is increasing During the past years, several economic theorists have studied education and training concepts Econometrics has been identified as the most common technique in previous studies In this research, the income values in different levels of education are examined Moreover, in this study, the meaningfulness of coefficient and regression equations is examined Finally, the reformed equations of income are developed at different levels of education Our research includes parts: Part 1: INTRODUCTION Firstly, to make sure that everyone has an overview of this topic, we prepare an introduction to the Effect of Education on Wage Earning and literature review Besides, a theoretical background, which illustrates the theories used in this research will be discussed Part 2: CONTENT This is the most important part of this research that points to capture and process data based on analytical methods of econometrics before creating the regression model of factors affecting the Wage Earning of America in… Also, the robustness check will be added to clarify the accuracy of this model in the following chapter Part 3: SUMMARY In the end, we will summarize and evaluate the research According to this evaluation, we will suggest some solutions to discuss Finally, we hope that we can receive more feedback and distributions to complete this research. 1. RESEARCH OBJECTIVES LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 1.1 GENERAL OBJECTIVES - The factors of education that affects wage-earning - Find out how these factors affect the wage-earning 1.2 DETAIL OBJECTIVES - Determine what these factors are - Analyze whether they are good or bad factors - Suggest some solutions to improve the situation if necessary and how to use the result of the research effectively 2. RESEARCH TARGET - Range research: Based on data of education on wages in the United States that collected by many sources - Time research: 1980 - Research subjects: 935 Employees 3. RESEARCH METHODS - Find information on the internet - Use econometric models and analyze data in Gretel and Excel 4. SAMPLE SIZE AND SAMPLE-CHOOSING METHOD - Sample size: 935 observations Sample-choosing method: Collect data from researches that were carried out by many sources LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com A Introduction About the topic This paper examines the effect of education and experience on the wages of workers in America, especially the differences in urban and rural labor markets This paper proposes that both education and experience significantly impacts wages The study estimates are based on the Mincerian wage equation with a large cross-section data of American individuals The results suggest that education and experience are positively correlated with the wages of labor in both urban and rural labor markets This relationship is significant and evident across all the estimation In the past, we witnessed an increased interest in the level of wage and level of education for workers across the world According to David Autor, during the 1950’s and 1970’s in the U.S., a rising level of educational attainment kept up with rising demand for skilled labor However, in the late 1970’s and 1980’s, the rising level of educational attainment has not kept up with the rising demand for skilled labor, resulting in a sharp rise in the inequality of wages Within the last few years, it is clear that the gap in wage between more highly skilled and less-skilled workers in America has been increasing Thailand, as an developed country, has been in a period of highly rapid growth, mostly in urban areas As there has been a dramatic difference between the rural area and the urban areas, the wage distribution is not uniform across regions of the country; poverty is more concentrated in the rural areas Therefore, wage inequality was also not uniform across the country; the wider income differentials between households in different locations accounted for the increase in overall inequality Regional differences in wages also reflect different degree of urbanization and participation in international trade When a modern industrial sector is introduced into an agricultural economy during the rapid economic growth of America, the wage gap between these two sectors will rise However, when the modern sector absorbs labor from rural areas, the wage gap will narrow again (Ikemoto and Uehara 2000) This means that educational attainment also plays an important role for labor to make a transition from the old sector to the new sector Literature review Here, this paper is talking about the effect of education and work LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com experience on wages, which has been an important topic to our society By looking at and studying the literatures and data, the goal of this paper is to learn and understand more profoundly about the effects of education and work experience in particular on the difference between the rural and urban areas of America By studying this, we can have a better understanding of the relationships and be able to implement and support the investment in education and policy in the right direction However, looking at previous studies, the relationship between education and work experience in terms of wage is not yet fully explored and well established, as there is no previous study that makes this direct correlation to US Many economic theorists such as Mincer (1984) explain that economic growth and wage are contributed from the role of human capital in the form of education In 1974, Mincer agreed with Becker (1964) that the upward sloping wage profile occurs as human capital, or skills, increase with education and experience Therefore, as a worker acquires more training, the individual’s productivity and earning should increase Mincer (1991) found that since labor market skills are acquired by learning at school and by learning on the job, changes in demand for skill should affect both wage differentials by education as well as those by labor market experience, according to human capital theory He also stated that the slope of the cross-sectional profile is steeper in one period than another because more training takes place, or because the acquired learning has become more profitable He suggested, with limited evidence, that there might be a substitution between (college) school education and work experience or training of high school graduates Using wage regression, Constantine and Neumark (1996) showed that shifts in the incidence of various types of training over the 1980s favored more-educated, more-experienced workers Giving that training is associated with higher wages, and that training is more prevalent among more-educated, more-experienced workers, changes in the distribution of and returns to both education and the effects strengthened by the interaction between education and job training may have contributed to the growth of wage inequality in this period Pereira and Martins (2004) used the Mincer wage equation to find the returns to education in Portugal They support that using the Mincer equation in its simpler form seems to give an approximate value for the total return to education Newell and Socha (2007) also found the relationship between experience and education toward wage determination in Poland, using Ordinary least squares of Mincerian hourly earnings equations They illustrated that there were sharp increases in the returns to professional and managerial work, as well as an increase in the wage penalty imposed on primary-educated workers, after controlling for other characteristics Lynch (1992) showed that the private sector training plays a significant role in the determination of wages As a consequence, the experience-wage differentials should move relatively in the same direction as the effect of education Evidently, Brown (1983) LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com showed that wages grew slowly before the training period, rapidly during the training period, and leveled off after it An additional year of training raised wage growth in the firm by 4-5% over the year, in both cross sections and over time. However, Alexander (1974) seems to disagree with this idea, as he found that the relationship between income and experience does not vary across structure within income classes Oosterbeek and Webink (2007) evaluated the effect of an extension of three years basic vocational programs with one year of general education on later wage of graduate However, they fail to find any significant effect of this extension They suggest that individuals attending basic vocational programs not benefit from additional general education (in term of later wages) Fengliang, Xiaohao, and Morgan (2009) show that in China, class rank and status of job matching have no significant effect on the starting wages of graduates in most educational specializations The labor market for graduates with higher education in China is characterized by the signaling effect, although some graduates also benefit from the human capital accumulated in higher education Many literatures also focus on wage gap by examining the relationship between wage, work experience, and education For example, Katz noted huge growth during the 1980’s in the wage gap between those with college education and those without, between those with non-manual jobs and those with manual jobs, between those with experience and those without In United Kingdom, the wage gap expanded dramatically in 1980’s, however with a different cause In Japan, the wage gap occurred more moderately, on account of strength of the Japanese manufacturing sector In France, the causes are from a high and pervasive minimum wage, and also the union contract extensions prevented wages of unskilled workers from falling significantly However, the overall reason is the increase in demand for well-educated workers Hotchkiss and Shiferaw (2010) agree with Katz by demonstrating that changes in endowments of workers with college degrees were largely responsible for the increasing wage gap in the 1980’s and 1990’s Michelaci and Pijoan-Mas (2007) provide a model in which they specify the channel whereby wage inequality affects the return to working longer hours A rise in the dispersion of job offers, which translates into higher within-skill wage inequality, raises the gains from obtaining better jobs and gives workers greater incentives to work longer hours; the effect is stronger as the labor market becomes tighter Wheeler (2005) suggests that the vast majority of the rise in U.S wage inequality over the past two decades is the product of increasing gaps between workers within the same industry rather than between workers across different industries His finding suggests that the variance of wages among workers with the same level of education has also grown However, Mishel and Bernstein (2003) believed that the returns to education and experience, frequently account for less than half of the growth of wage inequality Pereira and Martins (2000) used the standard OLS from quantile LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com regression of the Mincer wage equation from fifteen European countries across a fifteen-year period (1980-1995), which points out that education is a risky and unpredictable investment They believed that the marginal reward some individuals reap from their schooling is very low or even negative Bedard (2001) stated that as constraints decline or higher education becomes more accessible, wage would more closely reflect productivity. However, increased university access is often touted as part of the prescription to improve the lives of the“less” fortunate, and his results suggest that increased university access might result in lower earning power for the less able By looking on the supply side, Psacharopoulos (1977) suggests that a policy of more equal access to education might have the desired impact of making income distribution more equal Theoretical background Effects of education on wage-earning Education is the wealth of knowledge acquired by an individual after studying particular subject matters or experiencing life lessons that provide an understanding of something Wage-earning is the total of money you can get after working or doing any tasks The amount of money you would gain depends on how successfully you finish the job, which requires lots of knowledge and experience about that tasks linking closely to your educational background Hence, education and wage-earning have intimate correlation and education is one of the main effects on how much money you can earn through the jobs LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com B Content I Describe the variables Function we have in this report will include these following variables: Dependent variable: lwage (natural log of wage – monthly earning) Independent variables: educ: years of education exper: years of work experience married: =1 if married black: =1 if black south: =1 if live in south urban: =1 if live in SMSA II Describe the data Data overview This set of data is a secondary one, as they are collected from a given source Data source: Wooldridge Source: M Blackburn and D Neumark (1992), Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials, Quarterly Journal of Economics 107, 1421-1436 The structure of Economic data: cross-sectional data LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Data collection Exhibit 1: Statistic indicators of variables in the Housing Price model Summary Statistics, using the observations - 935 Variable Mean Median S.D Min lwage 6.78 6.81 0.421 4.74 educ 13.5 12.0 2.20 9.00 exper 11.6 11.0 4.37 1.00 hours 43.9 40.0 7.22 20.0 married 0.893 1.00 0.309 0.00 south 0.341 0.00 0.474 0.00 urban 0.718 1.00 0.450 0.00 Max 8.03 18.0 23.0 80.0 1.00 1.00 1.00 where: S.D is the standard deviation of the variable Min is minimum value of the variable Max is maximum value of the variable LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com III Describe the correlation between variables Exhibit 2: Correlation matrix Correlation Coefficients, using the observations - 935 5% critical value (two-tailed) = 0.0641 for n = 935 lwage educ exper hours married south lwage 1.0000 educ 0.3121 1.0000 exper 0.0206 -0.4556 1.0000 hours -0.0472 0.0910 -0.0621 1.0000 married 0.1500 -0.0586 0.1063 0.0326 1.0000 south -0.1948 0.0970 0.0213 -0.0295 0.0228 1.0000 urban 0.2038 0.0722 -0.0474 0.0166 -0.0402 -0.1099 Urban 1.0000 From the matrix, it can be inferred that the correlation between lwage and each of the independent variable is decent enough to run the regression model Specifically: - lwage and educ has a moderate uphill relationship lwage and exper have a weak uphill relationship lwage and hours have a weak downhill relationship lwage and married have a weak uphill relationship lwage and south have a weak downhill relationship lwage and urban have aweak uphill relationship 10 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com IV Econometrics model To demonstrate the relationship between wage and other factors, the regression function can be constructed as follows: (SRF): lwage = 0 + 1 educ + 2 exper + 3 hours + 4 married + 5 south + 6 urban + i Where: 0 is the intercept of the regression model i is the slope coefficient of the independent variable xi is the disturbance of the regression model From this model, this report is interested in explaining lwage in terms of each of the six independent variables (educ, exper, hours, married, south, urban) V Estimation of econometric model Having checked the required condition of correlation among variables, the regression model is ready to run The result is shown in Exhibit Exhibit 3: Regression model Model 1: OLS, using observations 1-935 Dependent variable: lwage const educ exper hours married Coefficient 5.51207 0.0741690 0.0178577 −0.0048739 0.226223 Std Error 0.133331 0.00630296 0.00315432 0.00170139 t-ratio 41.34 11.77 5.661 −2.865 p-value α = 0,05=> We don’t have enough evidence to reject H0 => The model does not omit variables 2.2 Testing multicollinearity Using the following command vif regression to examine multicollinearity “VIF” commands specific to the variance inflation factor, if a variable value vif > 10, the model has the possibility of multicollinearity Using “VIF” command in Gretl, we have following result in Exhibit Exhibit 5: Multicollinearity test Variance Inflation Factors Minimum possible value = 1.0 Values > 10.0 may indicate a collinearity problem educ 1.283 exper 1.274 hours 1.011 married 1.015 14 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com south 1.022 urban 1.018 VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient between variable j and the other independent variables Belsley-Kuh-Welsch collinearity diagnostics: variance proportions lambda cond const educ exper hours married south urban 5.880 1.000 0.000 0.001 0.003 0.001 0.003 0.008 0.006 0.641 3.029 0.000 0.000 0.001 0.000 0.001 0.912 0.026 0.249 4.860 0.001 0.001 0.034 0.002 0.030 0.059 0.868 0.117 7.076 0.001 0.025 0.607 0.009 0.055 0.000 0.037 0.084 8.352 0.005 0.030 0.002 0.030 0.880 0.001 0.053 0.022 16.326 0.004 0.328 0.046 0.730 0.000 0.000 0.002 0.006 30.720 0.989 0.615 0.309 0.228 0.030 0.020 0.008 lambda = eigenvalues of inverse covariance matrix (smallest is 0.00623083) cond = condition index note: variance proportions columns sum to 1.0 15 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com We see: VIF (educ) = 1.283 < 10 VIF (exper) = 1.274 < 10 VIF (hours) = 1.011 < 10 VIF (married) = 1.015 < 10 VIF (south) = 1.022 < 10 VIF (urban) = 1.018 < 10 => Multicollinearity is not too worrisome a problem for this set of data 2.3 Testing Heteroskedasticity Given that the hypothesis is: 𝑯𝟎: 𝑻𝒉𝒆 𝒎𝒐𝒅𝒆𝒍 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐡𝐚𝐯𝐞 𝐡𝐞𝐭𝐞𝐫𝐨𝐬𝐤𝐞𝐝𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 𝐩𝐫𝐨blem 𝑯𝟏: 𝑻𝒉𝒆 𝒎𝒐𝒅𝒆𝒍 has 𝐡𝐞𝐭𝐞𝐫𝐨𝐬𝐤𝐞𝐝𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 WHITE’S TEST Exhibit shows the result Exhibit 6: Heteroskedasticity test White's test for heteroskedasticity OLS, using observations 1-935 Dependent variable: uhat^2 coefficient std error t-ratio p-value const −0.0877885 0.769505 −0.1141 0.9092 educ 0.0320147 0.0699414 0.4577 0.6473 exper 0.0146575 0.0279941 0.5236 0.6007 hours −0.00794067 0.0143561 −0.5531 0.5803 16 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com married −0.0426806 0.278322 −0.1533 0.8782 south −0.336264 0.194019 −1.733 0.0834 * urban 0.345694 0.215808 1.602 0.1095 sq_educ −0.000713493 0.00205823 −0.3467 0.7289 X2_X3 0.000303844 0.00129642 0.2344 0.8148 X2_X4 0.000182309 0.000605227 0.3012 0.7633 X2_X5 −0.0117139 0.0134886 −0.8684 0.3854 X2_X6 0.00188109 0.00949100 0.1982 0.8429 X2_X7 −0.0179262 0.00982812 −1.824 0.0685 * sq_exper −0.000115076 0.000427208 −0.2694 0.7877 X3_X4 −0.000277079 0.000309777 −0.8944 0.3713 X3_X5 −0.00251847 0.00815887 −0.3087 0.7576 X3_X6 0.00756464 0.00459377 1.647 0.1000 * X3_X7 −0.00882870 0.00482169 −1.831 0.0674 * sq_hours 6.48590e-05 8.53655e-05 0.7598 0.4476 X4_X5 0.00455469 0.00392745 1.160 0.2465 X4_X6 0.00474647 0.00242633 1.956 0.0507 * X4_X7 −0.000399422 0.00267717 −0.1492 0.8814 X5_X6 0.0468276 0.0599674 0.7809 0.4351 X5_X7 0.00828918 0.0635983 0.1303 0.8963 X6_X7 0.0252714 0.0386367 0.6541 0.5132 Unadjusted R-squared = 0.038769 Test statistic: TR^2 = 36.248964, with p-value = P(Chi-square(24) > 36.248964) = 0.051908 We see p-value = P(Chi-square(24) > 36.248964) = 0.051908 > α = 0.05 => We don’t have enough evidence to reject H0 => The model does not have heteroskedasticity problem 17 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com C Summary Conclusion In debating whether or not a person’s years of schooling would have an effect on their yearly income, we found the results to clearly dictate the significance that education has on income We can see how only one extra year of education can potentially increase a person’s income by over 7.4196% showing the importance of earning a good education However, it is not as clear cut as it may seem, and as one would expect a person's education and hours worked per week also play a role in income earnings These help confirm many common and well known assumptions that are seen in today’s society Another common assumption that is a major talking point in many circles is the accusation of female wage earnings being lower than men’s With the inclusion of our dummy variables we can see that in both the cases of married and unmarried employees in the urban area, they tend to have a higher income Although, even with all others factors (such as urban, south, ) a person’s education still seems to have the most significant effect on income. This relationship does lead to one major issue, the problem of opportunity cost Even though one could earn 7.4196% more of their current wage by going to school for another year, that is potentially one year without wages, or working only part time There then arises the cost of schooling where people will spend well 7.4196% exceed their wage to go to school for one more year This dilemma can cause many issues both on the personal level, and the federal level when debating what the best choice to make is, choose one more year of work, or one more year of school 18 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com D References Jamison, E A., Jamison, D T., & Hanushek, E A (2006) The Ef fects of Education Quality on Income Growth and Mortality Decline Retrieved from http.//www.nber.org/papers/w12652 Yang, J., Qiu, M (2015) The impact of education on income inequality and intergenerational mobility China Economic Review, 37, 110-125 Nuno, A (2012). The impact of education on household income and expenditure inequality Applied Economics Letters, 19(10), 915-919 doi: 10 1080/13504851.2011.607125 Turčínková, J., Stávková, J (2012). Does the Attained Level of Education Affect the Income Situation of Households? Procedia-Social and Behavioral Sciences, 55, 1036-1042 19 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 20 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ... urban) jointly explain 21.8236% of the variation in the dependent variable (lwage); other factors that are not mentioned explain the remaining 78.1764% of the variation in the lwage Based on the. .. your educational background Hence, education and wage- earning have intimate correlation and education is one of the main effects on how much money you can earn through the jobs LUAN VAN CHAT LUONG... policy of more equal access to education might have the desired impact of making income distribution more equal Theoretical background Effects of education on wage- earning Education is the
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