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make sense of and present within 10 minutes. I need your help to extract the essence of the paper, with explanation examples to drive these core ideas in succinct way. I need some graphical representation of data derived from this paper, to put into my presentation slides... especially Section III of this paper with the equations, etc... The point of this presentation is to explain complex concepts in lament terms to audience that is learning about international economics at MBA level (but not majoring in Economics... this is the only economics class). Goal here is for me to clearly explain these complex concepts in easy to understand terms to students that focuses in the consumer goods sector. If this can take on a "fun" tone that would be great for speeches at this dry level. Immigration and Domestic Wage: An Empirical Study on Competition among Immigrants DRAFT – DO NOT QUOTE Chong-Uk Kim Department of Economics Sonoma State University 1801 E. Cotati Avenue Rohnert Park, CA 94928-3609 USA Email: [email protected] I. Introduction The dropping of the Mayflower’s anchor at Cape Cod in 1620 marks the beginning of the United States’ long history of immigration. Following this event, immigration, through its massive and successive waves, has carved the image of the United States. Due to its concordance with domestic economic issues, immigration policy continues to be a center of social debate. This is evident in the frequent headlining of the President’s actions on immigration in newspapers all over the country. The main concerns regarding immigration reform center around the expectation, or fear, that new immigrants entering a domestic labor market will replace and take opportunity away from native workers while decreasing domestic wages. These concerns have spurred many research efforts that provide a wide range of empirical findings. For example, some studies such as Borjas (2003) and Mishra (2007) find that immigration has a significant impact on wages in both receiving and sending countries while other studies such as Card (2005) and Ottaviano and Peri (2012) show that there is no meaningful impact of immigration on domestic labor markets. While studies on the wage effects of immigration focus on native workers, there is significantly less information on the wage effects of immigration on domestic foreignborn workers. In addition to analyzing the impact of immigration on wages of native workers, in this paper, we estimate the internal competition amongst foreign-born workers in the United States. Firstly, using data from the Current Population Survey (CPS), we find no empirical evidence supporting the substitutability of native workers immigrants. Secondly, there is no statistical difference between skilled and unskilled immigrants on the influence of the domestic labor market outcomes. Lastly, there is no internal competition among immigrants. The income of non-citizen workers mainly depends on state and national levels of economic situations, not the number of noncitizen workers available in the labor market. The paper proceeds as follows. Section II describes the literature on the effects of immigration on domestic wages. Section III provides the empirical methodology and the data used in this analysis. Section IV discusses empirical results and Section V concludes. II. Literature Review The main focus of the majority of immigration literature is to empirically prove if immigration suppresses domestic wages. In other words, primary to these studies is the degree of substitutability of immigrants for domestic native workers. The general conclusion has been that a 10 percent increase in the population due to immigration decreases wages of native workers by 1 to 4 percent.1 Using US census data, Altonji and Card (1991) measure the impact of immigration on wages of unskilled native workers; their findings indicate that a 10 percent increase in the population due to immigration lowers wages of unskilled native workers by 1.2 percent even though the effects of immigration on wages heavily depend on native subgroup. Immigration supporters most frequently cite Card’s famous 1990 paper on Cuban boat people. In 1980, more than 125,000 unskilled Cubans immigrated to Miami. Card finds no empirical evidence to support the theory that these immigrants lower domestic wages or increase unemployment rates of either Cubans or non-Cubans. Butcher and Card (1991), Card and DiNardo (2000), Card (2001, 2005) and more recently Henrickson and Kim (2012) also find no empirical evidence supporting a strong degree of substitutability of immigrants for native workers. In contrast, a series of publications by Borjas (1995, 2003, and 2006), report empirical evidence that an influx of immigrants decreases wages of native workers and affects domestic labor market outcomes. Immigration critics frequently cite Borjas’s famous 2003 paper that treats immigration as an increase in national labor supply. This study finds that a 10 percent increase in the population due to immigration suppresses wages of native workers by 3 to 4 percent. While the majority of studies on immigration have focused on the supply side of labor market, Lach (2007) suggests that immigrants not only increase domestic labor 1 Examples of such results are numerous, but include: Friedberg and Hunt (1995), Borjas (2003), Borjas, Grogger, and Hanson (2010). supply, but also increase the demand for domestic output. Companies must hire more labor to supply more output; therefore, immigrants eventually shift both labor supply and demand curves outward to the right. More recently, without perfect substitutability, Peri (2012) and Ottaviano and Peri (2012) show that immigrants do not replace native workers and there is no short-run effect on wages of native workers. III. Empirical Model and Data 1. Empirical Model Our empirical model which we use to test the main hypothesis is based on the standard Mincer earning equation. Since Jacob Mincer published his seminal book Schooling, Experience, and Earnings in 1974, many labor economists have used his earning equation as a key empirical framework. In the standard Mincer equation, your earnings depend on your years of schooling and working experience. (1) ln(Y) = a + ß1S + ß2E + ß3E2 + u where Y is earnings, S is years of schooling, and E is years of working experience.2 This equation (1) has been used as a starting point of many empirical economic papers on income determination. Based on the individual research purposes, this standard equation has been modified and tested in many academic papers.3 To test our main hypothesis, we modify Equation (1) to implement our model. Since the Current Population Survey (CPS) does not report information on the working experience, first, we use an age variable as a proxy for the working experience. Second, instead of using a years of schooling variable, we use a ratio of high-­-skilled and low-­-skilled workers to capture the impact of education on income.4 Third, we 2 a is a constant term. It is the logarithm of the income level with no years of schooling and no working experience. u is a Gaussian white noise error term. 3 Lemieux (2006) provides a nice summary of works using Mincer equations. 4 Details on this variable will be given in the next section. include a state-­-level unemployment rate variable to reflect each of US states economic situation. Similarly, we add a national-­-level real Gross Domestic Product (GDP) variable to consider the US national economy. Finally, we include the total number of foreign workers which is our key variable to see the effects of immigration on domestic wages. (2) Yit = a(FOREIGNit)ß(GDPt)?exp(dRATIOit+?UNEMit+?AGEit+?AGE2it+uit) where Yit denotes the real income of US citizens of state i in period t; FOREIGNit denotes the total number of foreign workers of state i in period t; GDPt denotes the real GDP of US in period t; RATIOit denotes the ratio of high-­-skilled and low-­-skilled US citizens of state i in period t; UNEMit denotes the unemployment rate of state i in period t; AGEit denotes the median age of US citizens of state i in period t; AGE2it denotes the square of AGEit variable; and finally uit is a Gaussian white noise error term. We take natural logs on both sides of equation (2) to implement our income equation and it provides us the following estimation equation: (3) ln(Yit) = C + ß ln(FOREIGNit) + ? ln(GDPt) + d (RATIOit) + ? (UNEMit) + ? (AGEit) (+/-­-) (+) (+) (-­-) (+) + ? (AGE2it) + uit 5 (-) 5 C = ln(a) which is a constant term. Based on findings from previous research efforts, we expect that the coefficients of GDPt, RATIOit, and AGEit variables are positive while the coefficients of UNEMit and AGE2it variables are negative. A positive coefficient of our key variable, FOREIGNit, will support the complementarity idea between immigrants and citizen. Similarly, a negative coefficient of FOREIGNit will uphold the idea that immigrants and citizens are substitutes. 2. Data This research uses data mainly drawn from the Current Population Survey.6 The CPS provides a variety of labor force statistics for the population of the United States. We extract our main variables such as income, educational attainment, and citizenship status from CPS. Information on the consumer price index (CPI) and gross domestic product (GDP) come from the World Bank.7 We also use the StateData.info website to collect data on U.S. state level unemployment rate.8 Since we are testing the impacts of immigration on the US state-­-level income, the final sample for our empirical tests consists of data on 51 US states including the District of Columbia (DC). Our data set is strongly balanced and comprises 16 years from 1995 to 2010. Table I presents descriptive statistics for our data set. Since we want to investigate the impact of immigration on the US citizens’ income at the state level, we modify the CPS data set to fit our research purposes. To attain the data set including information on US citizens only, first, we separate the CPS data set into two groups, US citizens and non-­-citizens. From the individual observations, second, we get the average income per each state including DC for both US citizens and non-­-citizens.9 Similarly, the average age variable per each state is obtained in the same way. 6 http://www.census.gov/cps/ 7 http://data.worldbank.org/ 8 http://www.statedata.info/ 9 The average incomes are measured in constant 2005 dollars. Table I. Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Y (Real Income) 816 41,845.56 6376.732 28393.09 64327.52 FOREIGN 816 218.8517 410.5372 5 3,320 GDP (M) 816 223,094 270,254.3 14,211 1,768,607 RATIO 816 .4360832 .1544683 .1972789 1.683784 UNEM 816 5.363603 1.857371 2.2 14.9 AGE 816 40.53957 .9966217 37.29797 43.22313 AGE2 816 1644.449 80.60823 1391.139 1868.239 Like our income variable (Y), The total number of foreign workers per state also comes from the CPS data set. We count the number of non-­-citizen individuals who are working in the US by states and year. Table II shows our sample number of non-­-citizen workers at the state level. Table II. The Number of Foreign Worker in 2010 State Foreign State Foreign State Foreign State Foreign AK 131 ID 75 MT 23 RI 271 AL 50 IL 509 NC 202 SC 68 AR 57 IN 71 ND 21 SD 58 AZ 204 KS 103 NE 141 TN 89 CA 3,320 KY 93 NH 149 TX 1,185 CO 259 LA 46 NJ 645 UT 131 CT 384 MA 234 NM 106 VA 292 DC 278 MD 488 NV 386 VT 56 DE 169 ME 66 NY 1,092 WA 293 FL 938 MI 174 OH 107 WI 124 GA 268 MN 221 OK 84 WV 16 HI 353 MO 64 OR 173 WY 47 IA 166 MS 25 PA 157 The CPS data set contains information on personal educational attainment. However it does not include information on the years of schooling. Educational attainments are originally classified by academic degrees such as high school diploma, associate degree, professional degree, and etc. Therefore, to quantify this information, we generate the RATIO variable which represents the quality of labor force for each states. Equation 4 displays how we generate the RATIO variable. (4) ?????????? = !h! !"#$%& !" !"#$%" !h! !"# ! !"#h!" !"#$"" !h!" !"#h !"h!!" !"#$%&' (!h! !"#$%& !" !"#$%" !h! !"# ! !"#h !"h!!" !"#$"" !" !"#$%) According to our calculation, the average value of the RATIO variable is around .44 over the sample time period. It means that there are 44 high-­-skilled workers available per each 100 low-­-skilled workers. Table III shows the ratio in 2010. Table III. The Ratio of High-­-Skilled and Low-­-Skilled US Citizens in 2010 State Ratio State Ratio State Ratio State Ratio AK .4 ID .40 MT .51 RI .57 AL .34 IL .59 NC .49 SC .36 AR .29 IN .35 ND .47 SD .44 AZ .44 KS .54 NE .56 TN .43 CA .61 KY .39 NH .65 TX .43 CO .80 LA .42 NJ .68 UT .45 CT .70 MA .89 NM .45 VA .71 DC 1.68 MD .75 NV .33 VT .60 DE .46 ME .52 NY .65 WA .56 FL .51 MI .47 OH .37 WI .50 GA .55 MN .54 OK .38 WV .38 HI .46 MO .36 OR .54 WY .34 IA .39 MS .28 PA .47 IV. Empirical Results The main purpose of this empirical study is to quantify the impact of non-­-citizen workers on US citizens’ income. Our empirical results satisfy standard Mincer equation expectation. The empirical results are shown in Table IV. First three results are from the ordinary least squares (OLS) method. Regression IV.4 shows the results of our model including state fixed effects and regression IV.5 presents the results from random effects estimation. Regression IV.1 contains only basic Mincer variables such as educational attainment of population (RATIO) and working experiences (AGE and AGE2). In addition to these variables, regression IV.2 includes information on each states economy (UNEM) and national economy (GDP). Regression IV.3, IV.4, and IV.5 contain all six independent variables, which accords with our equation (3). All coefficients on the RATIO variable are positive and statistically significant, which confirms that educational attainment is positively correlated with US citizens’ income. Coefficients on AGE and AGE2 variables also satisfy the theoretical prediction and coefficients on UNEM and the logarithm of the GDP variables empirically suggest that US citizens’ income depends on the state and national economic situation. The coefficients on our key variable, the logarithm of the FOREIGN variable, turn out positive and statistically significant. It suggests that the number of non-­-citizen workers is positively correlated with US citizens’ income. Since the coefficients on the logarithm of the FOREIGN variable are elasticities, we need to calculate the marginal effects of non-­-citizen workers on citizens’ income to see the economic significance. In 2010, there are approximately 31.3 million non-­- citizens living in the US and the nominal GDP per capita in the US is $48,387.10 The coefficients on the logarithm of the FOREIGN variable have a range from 0.011 to 0.046. Therefore if we increase the number of non-­-citizen workers by 313,000 which is 1% point of the total number of non-­-citizen workers in 2010, then, according to our model prediction, the nominal GDP per capita will increase by from 10 According to the Department of Homeland Security, there are 19.7 million legal residents and approximately 11.6 million illegal immigrants in 2010. $5.32 to $22.26. Since the number of foreign born living in the US increases approximately 500,000 annually, these additional non-­-citizen workers raise the nominal income of US citizens by from $8.5 to $35.6 in 2010, which is negligible. Table IV. Immigration and Real Income ln(Real Income) IV.1 IV.2 IV.3 IV.4 IV.5 RATIO 0.675 0.619 0.51 0.304 0.414 (29.06)*** (31.05)*** (26.47)*** (6.10)*** (14.58)*** AGE 0.416 0.085 0.155 0.304 0.547 (2.06)** (0.49) (1.01) (2.34)** (4.95)*** AGE2 -0.005 -0.001 -0.002 -0.004 -0.006 (-1.94)* (-0.34) (-0.85) (-2.25)** (-4.72)*** UNEM -0.006 -0.008 -0.007 -0.009 (-3.66)*** (-5.51)*** (-6.91)*** (-7.84)*** ln(GDP) 0.052 0.017 0.21 0.062 (18.07)*** (4.94)*** (10.31)*** (7.60)*** ln(FOREIGN) 0.046 0.011 0.026 (14.65)** (2.06)** (6.41)*** Constant 1.443 7.532 6.407 1.617 -1.925 (0.35) (2.16)** (2.07)** (0.64) (-0.87) Observations 816 816 816 816 816 R-squared 0.57 0.69 0.76 0.68 F-statistic 358.77 366.62 421.83 271.10 Wald ?2 1525.14 Note: Figures in parentheses are t-statistics. ***Significant at 1%; **significant at 5%; *significant at 10%. To see the role of educational attainments of non-citizen workers in affecting citizens’ income, we separate non-citizen workers by two different groups: Skilled and Unskilled. Table V and VI display our empirical results. The L-FOREIGN variable represents the number of non-citizen workers who has a high school diploma or less while the H-FOREIGN variable indicates the number of non-citizen workers who has higher educational attainments than a high school diploma. The empirical results from OLS do not differ. Compare to regression V.1 which is the same regression as regression IV.3 in Table IV, the coefficients on the logarithm of the L-­-FOREIGN and H-FOREIGN variables do not show any significant differences. These results support that both skilled and unskilled non-citizen workers are complements to US citizens even though the economic impact is negligible. Regression V.5 contains F-RATIO variable representing the educational attainments of non-citizen workers. The mean value of the F-RATIO variable is .30, which means that there are 30 high-­-skilled workers available per each 100 low-­-skilled workers. The coefficient on the F-RATIO variable is positive but statistically not different from zero. In short, our empirical results from OLS suggest that there is no heterogeneity between skilled and unskilled non-citizen workers in affecting US citizens’ income. Table V. Non-­-Citizen Workers’ Educational Attainments (OLS) ln(Real Income) V.1 V.2 V.3 V.4 V.5 RATIO 0.51 0.531 0.489 0.497 0.502 (26.47)*** (28.02)*** (23.47)*** (24.50)*** (24.71)*** AGE 0.155 0.128 0.192 0.165 0.166 (1.01) (0.83) (1.22) (1.08) (1.08) AGE2 -­-0.002 -­-0.001 -­-0.002 -­-0.002 -­-0.002 (-­-0.85) (-­-0.67) (-­-1.08) (-­-0.92) -­-(0.93) UNEM -­-0.008 -­-0.008 -­-0.008 -­-0.008 -­-0.008 (-­-5.51)*** (-­-5.33)*** (-­-5.09)*** (-­-5.51)*** (-­-5.53)*** ln(GDP) 0.017 0.021 0.02 0.016 0.016 (4.94)*** (6.22)*** (5.48)*** (4.34)*** (4.53)*** ln(FOREIGN) 0.046 0.047 (14.65)** (13.90)*** ln(L-­-FOREIGN) 0.04 0.027 (14.07)*** (6.94)*** ln(H-­-FOREIGN) 0.043 0.02 (12.79)*** (4.33)*** F-­-RATIO 0.028 (1.22) Constant 6.407 6.922 5.767 6.283 6.197 (2.07)** (2.22)** (1.81)* (2.03)** (2.00)** Observations 816 816 816 816 816 R-squared 0.76 0.75 0.75 0.76 0.76 Note: Figures in parentheses are t-statistics. ***Significant at 1%; **significant at 5%; *significant at 10%. Table VI contains results from the fixed effects estimator. Regression VI.1 is our benchmark in Table VI. Regression VI.1 is the same regression as IV.4 in Table IV, which is empirically most preferred result.11 Our empirical results from the fixed effects estimator do not support the heterogeneity between skilled and unskilled non-citizen workers neither. The coefficients on the logarithm of the L-­-FOREIGN variable are positive and statistically significant. Once again, however, the magnitude is way to small to consider. Meanwhile, The coefficients on the logarithm of the H-­-FOREIGN variable are also positive but, surprisingly, statistically not significant. In addition, the coefficient on the F-­-RATIO variable is statistically insignificant, too. These empirical results provide evidence, in contrast to previous findings, that it is not skilled immigrants but unskilled immigrants who are complements to US citizens even though the economic magnitude is small. Table VI. Non-­-Citizen Workers’ Educational Attainments (Fixed Effects) ln(Real Income) VI.1 VI.2 VI.3 VI.4 VI.5 RATIO 0.304 0.308 0.307 0.307 0.304 (6.10)*** (6.12)*** (6.21)*** (6.10)*** (6.09)*** 11 The Lagrange Multiplier (LM) and the Hausman tests suggest that results from the fixed effects estimator are the most preferred results over OLS and the random effects estimator. AGE 0.304 0.294 0.295 0.296 0.303 (2.34)** (2.27)** (2.27)** (2.27)** (2.34)** AGE2 -­-0.004 -­-0.003 -­-0.003 -­-0.004 -­-0.004 (-­-2.25)** (-­-2.17)** (-­-2.18)** (-­-2.18)** (-­-2.24)** UNEM -­-0.007 -­-0.007 -­-0.006 -­-0.007 -­-0.007 (-­-6.91)*** (-­-6.84)*** (-­-6.62)*** (-­-6.91)*** (-­-6.91)*** ln(GDP) 0.21 0.212 0.223 0.212 0.21 (10.31)*** (10.34)*** (11.14)*** (10.38)*** (10.27)*** ln(FOREIGN) 0.011 0.011 (2.06)** (2.06)** ln(L-­-FOREIGN) 0.01 0.009 (1.94)* (1.73)* ln(H-­-FOREIGN) 0.004 0.001 (1.07) (0.17) F-­-RATIO -­-0.001 (-­-0.07) Constant 1.617 1.788 1.657 1.762 1.629 (0.64) (0.71) (0.66) (0.7) (0.65) Observations 816 816 816 816 816 R-­-squared 0.68 0.68 0.68 0.68 0.68 Finally, we also test for the internal competition among non-citizen workers in the US. The dependent variable is now the state-level real income of non-citizen workers and we include the US-­-WORKERS variable indicating the number of US citizen workers in the model. The empirical results are shown in Table VII. Regression VII.1 and VII.2 from OLS and regression VII.3, VII.4, and VII.5 are from the fixed effects estimator. The empirical results suggest that, first, there is no empirical evidence of the internal competition among non-citizen workers.12 Second, the coefficients on the logarithm of the US-­-WORKERS variable are positive and statistically significant, which supports, again, the complementarity between non-citizen workers and US citizens. Third, the real income of non-citizen workers mainly depends on state and national level of economic situations not their personal attributes. Table VII. Non-­-Citizen Workers’ Income ln(F-­-Real Income) VII.1 VII.2 VII.3 VII.4 VII.5 F-­-RATIO 0.071 0.067 -­-0.173 -­-0.168 -­-0.173 (1.26) (1.2) (-­-1.23) (-­-1.2) (-­-1.23) F-­-AGE 0.107 0.097 0.028 0.028 0.028 (4.99)*** (4.51)*** (0.6) (0.58) (0.59) F-­-AGE2 -­-0.001 -­-0.001 0 0 0 (-­-4.13)*** (-­-3.72)*** (0.44) (0.41) (0.42) UNEM -­-0.018 -­-0.019 -­-0.019 -­-0.019 -­-0.019 (-­-4.68)*** (-­-4.91)*** (-­-3.96)*** (-­-4.03)*** (-­-3.95)*** ln(GDP) 0.032 0.009 0.202 0.191 0.196 (4.68)*** (0.97) (4.23)*** (3.76)*** (3.86)*** ln(US-­-WORKERS) 0.072 0.051 0.046 (3.80)*** (2.29)** (1.09) ln(FOREIGN) 0.027 0.005 (1.26) (0.13) Constant 7.349 7.348 6.825 7.18 6.911 (17.72)*** (17.87)*** (7.64)*** (7.15)*** (6.66)*** Observations 816 816 816 816 816 R-­-squared 0.15 0.17 0.1 0.1 0.1 12 The coefficients on the logarithm of the FOREIGN variable are statistically significant. V. Conclusion Using the CPS data set, in this paper, we investigate the impact of non-citizen workers on US citizens’ state-level income. Our empirical findings suggest that first, noncitizen workers, immigrants, are complements to US citizens but their contribution on US citizens’ income is too small to consider. Second, there is no heterogeneity between skilled and unskilled non-citizen workers in affecting US citizens’ income. Third, there is no empirical evidence that there is internal competition among non-citizen workers in the US. References Altonji, J. G., and D. Card. (1991) “The Effects of Immigration on the Labor Market Outcomes of Less-Skilled Natives.” in J.M. Abowd and R.B. Freeman (ed.) Immigration, Trade, and the Labor Market, University of Chicago Press: Chicago. Borjas, G. (1995) “The Economic Benefits from Immigration.” Journal of Economic Perspectives 9 (2): 3-22. Borjas, G. 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(2012) “Rethinking the Effects of Immigration on Wages.” Journal of the European Economic Association 10 (1): 152-197. Peri, G. (2012) “The Effect of Immigration on Productivity: Evidence from U.S. States.” Review of Economics and Statistics 94: 348-58.