INCENTIVES TO INVEST IN STUDYING THE NATIVE LANGUAGE OF THE HOST COUNTRY Erez Siniver Department of Economics College of Management, Israel

2 ABSTRACT Cross-sectional analyses show that immigrant earnings tend to rise faster than those of natives. One reason for this phenomenon is that immigrants' wages rise as they acquire the host country's native language. Immigrants can improve their knowledge of the native language simply by interacting with native speakers or by taking formal language courses. The present study inquires whether immigrants with the highest expected benefits from studying Hebrew will tend more to invest in learning the language by taking the basic Hebrew course.

3 INTRODUCTION The economic literature indicates a positive relationship between immigrants‘ knowledge of the native language of the host country and their earnings.Chiswick and Repetto (2001) and Chiswick (1998), using the 1972 and 1983 census of Israel, respectively, found that Hebrew speaking skills and Hebrew literacy increase with the level of schooling and duration in Israel and that earnings increase with the acquisition of both writing and speaking Hebrew skills. Other studies [e.g, Beenstock (1996), Berman, Lang and Siniver, (2003), Beenstock, Chiswick, Repetto (2001), Beenstock, Chiswick, Paltiel, (2005)] also found that earnings of immigrants to Israel increase with being more proficient in Hebrew. Studies conducted by Carliner (1981) and Lazear (1995) found that immigrants are most likely to learn English when they live in communities having small proportions of individuals from their home country. Immigrants living in communities with large proportions of compatriots will tend to learn English more slowly. This finding is explained by the fact that immigrants who live in ethnic enclaves obtain lower returns for knowing the native language than do immigrants who live in communities having small proportions of compatriots.

4 METHODS The lifetime earnings of Russian immigrants who have taken the course in Hebrew and of Russian immigrants who have not taken the course were compared. In each period t there is a probability of employment that depends, among other factors, on Hebrew skills, and a wage that depends on Hebrew skills and multiplicatively on experience (t-s), where s is either 0 or 12 months depending on whether the immigrant did not take the course or did take it, respectively. Adding discounting, I have that the immigrant will take the course if the sum from 12 months to retirement with Hebrew set at its highest level is greater than the sum from 0 to retirement with Hebrew adjusting with time in the labor market. In order to arrive at lifetime earnings, I estimate the following parameters: a.How quickly immigrants learn Hebrew without taking the course in Hebrew. b.The employment probability as a function of Hebrew knowledge, labor market experience and other factors. c.Earnings as a function of Hebrew knowledge, potential experience and other factors. d.Demographic characteristics of Russian immigrants who have taken the course in Hebrew. e.The present value of earnings with and without taking the course for each immigrant.

5 DATA The data were obtained from the Survey of Recent Immigrants (SRI) [1]. The data were based on a sample of nearly 1,200 households, migrants from the former Soviet Union. These households contain 2715 individuals aged The information I derived from the survey is: [1] (1) Personal details such as: Gender, coded as 1 for male and 0 for female; marital status coded as 1 for married and 0 for single; age, years of education and year of immigration to Israel. (2) Details about employment and current earnings. Respondents were asked whether they were employed and if they were employed what were their current earnings. (3) Details regarding the immigrants' ability to speak and write Hebrew. Respondents were asked to classify their ability to speak Hebrew as "fluently”, "with difficultly" or "cannot speak Hebrew at all", which were coded also as 1, 2 and 3, respectively. Respondents were asked to classify their ability to write Hebrew as "fluently," "with difficultly" or "cannot write Hebrew at all" coded also as 1, 2 and 3, respectively. [1] [1] Israel Central Bureau of Statistics, Monthly Bulletin of Statistics,April Jerusalem: ICBS. (Hebrew).

6 Russian Immigrants Aged UnemployedEmployedAll Samples (0.38) (0.33) (0.25) Age (0.08) (0.07) (0.05) Years of education (0.37) (0.33) (0.25) Experience (0.13) (0.06) (0.07) Year since Migration 0.79 (0.01) 0.85 (0.01) 0.82 (0.01) Currently married 0.54 (0.01) 0.52 (0.01) 0.53 (0.01) Male 1.48 (0.02) 1.40 (0.02) 1.44 (0.01) Speak Hebrew 1.79 (0.02) 1.79 (0.02) 1.79 (0.02) Write Hebrew (33.74) -Wage %Employed %Unemployed 17.92%13.39%15.80%Studied Course 82.08%86.61%84.35%Did not studied Course No. of cases Table 1 – Descriptive Statistics

7 Russian Immigrants Aged with 13+ years of education UnemployedEmployedAll Samples (0.41) (0.35) (0.27) Age (0.06) (0.05) (0.04) Years of education (0.40) (0.34) (0.26) Experience (0.17) (0.08) (0.09) Year since Migration 0.90 (0.01) 0.92 (0.01) 0.91 (0.01) Currently married 0.57 (0.02) 0.51 (0.02) 0.54 (0.01) Male 1.44 (0.02) 1.36 (0.02) 1.40 (0.01) Speak Hebrew 1.74 (0.03) 1.72 (0.02) 1.73 (0.02) Write Hebrew (42.10) -Wage %Employed %Unemployed 22.53%16.10%19.20%Studied Course 77.47%83.90%80.82%Did not studied Course No. of cases Table 1a – Descriptive Statistics

8 THE PROBABILITY OF ACHIEVING ROFICIENCY IN HEBREW WITHOUT A FORMAL COURSE To estimate the probabilities of achieving moderate or fluent proficiency in Hebrew within any given period of time for immigrants of different characteristics, two ordered probit estimations were run. The dependent variables for the first and second order probit stimation are the ability to speak and to write Hebrew, respectively. Immigrants were asked to classify their ability to speak/write Hebrew as "fluently," "with difficultly" or "cannot speak/write Hebrew at all", which were coded as 1, 2 and 3, respectively. The independent variables were: marital status (a dichotomous variable, where 1 = married and 0 = single), gender (a dichotomous variable, where 1 = male and 0 = female), education (in number of schooling years), duration in Israel (in months of residence), and age.

9 Table 2 – The Probabilities of Achieving Fluent Proficiency in Hebrew Without Taking a Formal Course. Dependent variable – ability to write Hebrew Dependent variable – ability to speak Hebrew (2)(1) 0.109* (0.041) * (0.022) Gender 1.033* (0.147) 0.389* (0.192) Marital status * (0.017) * (0.017) Education 0.096* (0.004) 0.113* (0.005) Age * (0.034) * (0.032) Duration in Israel (month) 0.003* (0.0008) 0.002* (0.0008) Duration in Israel^ (0.471) (0.463) Cutoff (0.464) (0.453) Cutoff # of observation Log likelihood

10 To calculate the probability that an immigrant will be employed, the following probit regression was run, using employment as the dependent variable (a dichotomous variable, where 1 = employed and 0 = unemployed). The independent variables entered into the equation were gender, marital status, education, experience, experience^2, residence in Israel, ability to speak Hebrew and ability to write Hebrew. THE PROBABILITY OF EMPLOYMENT

11 Table 3: Probabilities of Employment Dependent variable – Employment (0.078) Gender 0.379* (0.140) Marital status (0.016) Education 0.023* (0.010) Experience (0.0003) Experience^ * (0.0009) Duration in Israel (month) (0.107) Ability to Write (0.147) Ability to Write * (0.065) Ability to Speak * (0.206) Ability to Speak (0.365) Constant Number of Observations Log Likelihood

12 THE EARNINGS ESTIMATION There is a vast international evidence that speaking the language of the host country fluently has a positive effect on the immigrants' earnings. Indeed, Table 3 shows that immigrants who improve their ability to speak Hebrew also improve their probability of finding a job. This implies that the OLS estimates might be biased. In our case, it might be that those with low potential wage chose not to participate in the workforce, which creates an upward bias in the OLS equation for wage. To estimate the earnings equation controlling for self-selection I use (1) The inverse Mill's ratios in a standard two-stage Heckman model; (2) The Maximum Likelihood estimation, Newton-Raphson maximization.

13 Maximum Likelihood estimation Newton-Raphson maximisation (2) 2-step Heckman (1) Dependent variable – Employment Probit selection equation (0.049) (0.049) Gender 0.236* (0.087) 0.235* (0.087) Marital status (0.010) (0.010) Education 0.014* (0.009) 0.018* (0.009) Experience * (0.0002) * (0.0002) Experience^ * (0.0006) 0.002* (0.0006) Duration in Israel (month) (0.067) (0.067) Ability to Write (0.092) (0.092) Ability to Write * (0.032) * (0.042) Ability to Speak * (0.128) * (0.128) Ability to Speak3 Table 4 – Earnings – Equation Estimates.

14 Maximum Likelihood estimation Newton-Raphson maximisation (2) 2-step Heckman (1) Table 4 – Earnings – Equation Estimates. Dependent variable – Ln Wage Outcome equation (0.032) (0.237) Gender (0.059) (0.772) Marital status (0.006) (0.008) Education 0.009* (0.004) 0.009* (0.003) Experience (0.0001) (0.001) Experience^ * (0.006) (0.065) Duration in Israel (month) (0.043) (0.317) Ability to Write (0.058) (0.442) Ability to Write * (0.004) * (0.003) Ability to Speak * (0.057) * (0.050) Ability to Speak (5.032) InvMillsRatio 0.608* (0.036) 0.619Sigma 0.623* (0.106) 0.655rho Number of Observations Log Likelihood

15 DEMOGRAPHIC CHARACTERISTICS OF IMMIGRANTS WHO INVEST IN THE FORMAL COURSE In this section, I discuss the relationship between the demographic characteristics (gender, years of education, age, marital status) of the Russian immigrants and the incentives for them to invest in studying Hebrew. The dependent variable is study of the native language (a dichotomous variable, where 1 = taking the course and 0 = not taking the course). The independent variables are: gender (1 = male, 0 = female), marital status (1 = married, 0 = single), education (years of schooling), age, and age^2.

16 Dependent variable – Study in Ulpan Probability of taking a formal course in Hebrew (2) Probability of taking a formal course in Hebrew (1) 0.130* (0.060) * (0.050) Gender * (0.206) * (0.201) Marital status 0.845* (0.141) 0.141* (0.022) Education 0.022* (0.0033) 0.018* (0.0033) Age * ( ) * ( ) Age^2 --Benefit * (0.565) * (0.557) Constant Number of Observations Log Likelihood Table 5 – Demographic Characteristics of Immigrants Who Invest in Formal Course.

17 BENEFITS GAINED WHEN TAKING THE COURSE IN HEBREW I estimate the PV of lifetime earnings for Russian immigrants who have taken a course in Hebrew and for those who have not taken the course. If the difference in earnings is higher than the foregone earnings, the immigrants are better off if they take the course. The PV of lifetime earnings for immigrants who have not taken the course in Hebrew is calculated as: Where S is the ability to speak Hebrew, W is the ability to write Hebrew and P t (e) is the probability that the immigrant is employed. After each specified number of months, P t (S=i, W=j) is the probability that a Russian immigrant will have achieved S(i) and W(j); E t (S=i, W=j) is the earnings given that the immigrants' ability to speak Hebrew is level i, and the immigrants' ability to write Hebrew is level j. The PV of lifetime earnings for immigrants who have taken the course in Hebrew (which extends 12 months) is calculated as: The data show that immigrants who had taken the course in Hebrew could speak and write Hebrew fluently (i.e, S=1, W=1).

18 BENEFITS GAINED WHEN TAKING THE COURSE IN HEBREW In order to test whether the decision to take the course in Hebrew is driven by the benefit that each immigrant gains when taking the course, I added to the probit regression the independent variable benefit (a dichotomous variable, where 1 = immigrants whose PV2/PV1 is in the top 15.8 percent of all the immigrants and 0 = otherwise). If the decision to take the course is driven by the benefit each immigrant gains when taking the course in Hebrew, I expect to find that only the coefficient for the independent variable benefit is significant and the coefficients for the other independent variables are not.

19 Table 6 – Benefit Gained When Taking the Course in Hebrew Test for Benefit as a Single Motivation Benefit Gained When Taking the Course in Hebrew Dependent variable – Study in Ulpan Dependent variable – Benefit r = 4% (8) r = 10% (7) r = 6% (6) r = 4% (5) r = 2% (4) r = 1% (3) (0.100) 3.643* (1.013) 3.728* (1.071) 3.543* (1.013) 3.528* (1.071) 3.526* (1.040) Gender (0.428) * (5.261) * (5.545) * (5.161) * (5.545) * (5.321) Marital status 0.135* (0.022) * (5.011) * (5.616) * (5.001) * (5.516) * (5.211) Education (0.038) * (2.252) * (2.499) * (2.152) * (2.399) * (2.252) Age (0.0004) * (0.015) * (0.014) * (0.012) * (0.014) * (0.013) Age^ * (0.068) -----Benefit * (0.595) * (20.765) * (21.550) * (19.765) * (21.497) * (20.487) Constant Number of Observations Log Likelihood

20 BENEFITS GAINED WHEN TAKING THE COURSE IN HEBREW Uneducated immigrants benefit more from taking the course than educated immigrants; however, taking the course is more common among educated immigrants than among uneducated immigrants. It might be that the greater tendency of educated immigrants to take the course may reflect lower psychic cost of education for this group or it might be that the coefficients for the independent variables ability to speak Hebrew and ability to write Hebrew are probably biased upward for less educated immigrants (Berman, E., Lang, K., Siniver, E. 2003). To deal with this problem, I have redone the entire analysis only for immigrants with 13+ years of education, the results of which are shown in Table 7.

21 Test for Benefit as a Single Motivation Benefit Gained When Taking the Course in Hebrew Demographic Characteristics of Immigrants Who Invest in Formal Course Dependent variable – Study in Ulpan Dependent variable – Benefit Dependent variable – Study in Ulpan r = 4% (3) r = 4% (2) (1) (0.124) 0.354* (0.100) 0.093* (0.011) Gender (0.348) * (1.429) * (0.248) Marital status (0.048) * (10.639) 0.063* (0.032) Education (0.045) * (2.872) 0.033* (0.004) Age (0.0005) * (0.282) * (0.0002) Age^ * (0.214) --Benefit * (0.932) * (0.902) * (0.931) Constant Number of Observations Log Likelihood Table 7 – Immigrants with 13+ Years of Education.

22 SUMMARY AND ONCLUSIONS 1.The length of time it takes immigrants who do not take a formal course to attain moderate or fluent ability to speak Hebrew is longer for male, uneducated, older and married immigrants than for female, educated, younger and single immigrants. 2.Immigrants who improve their ability to speak Hebrew also improve their probability to be employed as well as their earnings. 3.Male, uneducated, older and single immigrant workers benefit more from taking the formal course in Hebrew than do female, educated, younger and married immigrants. 4.Taking the course in Hebrew is more common among male, educated, older and single immigrant workers than among female, uneducated, younger and married immigrant workers. 5.When including only immigrants with 13+ years of education, the results show that the decision to take the course is driven only by the benefit each immigrant gains when taking the course in Hebrew. The conclusion is that the earnings maximization model does a good job of predicting who will take the Hebrew course, especially when including immigrants with 13+ years of education.