Fertility, relative wages, and labor market decisions: A case of female teachers

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Economics of Education Review 25 (2006) 591–604 www.elsevier.com/locate/econedurev

Fertility, relative wages, and labor market decisions: A case of female teachers Jaeun Shina,, Sangho Moonb a

KDI School of Public Policy and Management, 207-43 Cheongnyangri2-dong, Dongdaemoon-gu, Seoul 130-868, Korea SungKyunKwan University, Graduate School of Governance, 53 Myeongnyun-dong 3-ga Jongno-gu, Seoul 110-745, Korea

b

Received 18 November 2003; accepted 9 June 2005

Abstract This paper examines the effects of fertility and relative wages on occupational choice (teaching versus non-teaching) and labor force participation decisions of female college graduates using selectivity-corrected panel estimations. We find that the presence of a new born baby is not particularly important to the choice of occupation, but significantly discourages female labor force participation, especially among teachers. Higher relative wages are found to effectively attract female college graduates into teaching. College major in education is one of the most relevant determinants for female college graduates to become teachers. Though investing educational expenditures on teachers’ salary seems to be a valid policy, providing incentives for female college students to major in education will be an alterative way to secure teacher supply. r 2005 Elsevier Ltd. All rights reserved. JEL classification: I2; J13; J24; J31 Keywords: Educational economics; Human capital; Salary wage differentials; Teacher salaries

1. Introduction The primary issue discussed in this study is the relationship between fertility and relative earnings, and labor market decisions of females. In the labor market, females make decisions about whether to participate in the labor force and which occupation to work at, in particular, teaching or non-teaching. The effects of fertility on the labor force participation decisions and occupational choices of females Corresponding author. Tel.: +822 3299 1037;

fax: +822 3299 1240. E-mail addresses: [email protected] (J. Shin), [email protected] (S. Moon). 0272-7757/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2005.06.004

are estimated using a binary dependent variable model within a panel framework in which sample selection correction is incorporated. 1.1. Literature review and significance The strong negative correlation between fertility and female labor supply has been well established since 1960s (Olsen, 1994). Numerous studies find that with the presence of a new born child females are less likely to join in the labor force (Carrasco, 2001; O’Brien & Hawley, 1986). However, much less attention has been paid to the effect of fertility on occupational choices of female workers. Johnes (1999) estimates the effect of fertility on the joint

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decision of labor supply (out-of-labor force, parttime or full-time) and occupation (knowledge work or others). Females are found to prefer staying outof-labor force to working full time when they have children under age 16. This negative effect of fertility on female labor force participation is larger if the full-time occupation is related to knowledge. It suggests that there exist systematic differences in labor market status, both labor force participation and occupation of females depending on fertility conditions. We narrow down the occupational subgroups into teachers and non-teachers. The reason teaching is of our special interest is drawn from the fact that females take a dominant portion of total employment in the elementary and secondary schools (68% in 1990, Hanushek & Rivkin, 1997). According to Polachek (1981), female-dominated occupations are characterized by lower human capital depreciation rates than other occupations, which may allow females to take temporary leave from work due to fertility with small risk in reentry wages. Flyer and Rosen (1997) numerically find no reentering wage penalties for teachers who take such leave.1 Expecting the arrival of a child, females would choose occupations that impose little penalty on their reentry wages after taking a temporary leave for childbearing. Additionally, it is believed that teaching occupation provides several non-pecuniary advantages for females with children, such as location-related conveniences, regular holidays and vacations, which allow teachers to manage to keep working more easily than it would be in other occupations (Dolton, 1990). Little wage loss from the interrupted career path due to fertility and convenient working conditions for childrearing would explain the historical trend of female domination in the teaching profession. From these conjectures, the positive marginal effect of fertility on the occupational choice into teaching has been widely discussed, but rigorous evidence is extremely limited. In pursuit of obtaining direct marginal effect of fertility on occupational choices, we conduct panel estimations of the labor force participation and occupation choice decisions. From the results, we attempt to answer the following questions: when a female has a new born child, (1) how likely is she to be in the labor force?

(2) would she prefer teaching profession to nonteaching profession? and (3) how different would the tendency to stay in the labor force be among teachers as opposed to non-teachers? There are several distinct features of this paper. First, we employ the panel estimation method. The panel analysis has advantage in controlling for unobserved individual-specific effects which, unless properly treated, may cause biases in the crosssectional analysis. Second, we implement the sample selection correction method developed by Wooldridge (1995) who extends Heckman selection correction model (Heckman, 1979) to the framework of panel data. The sample selection takes place in our context for two reasons: wages are observed only for those who participate in the labor market, and wages are observed only for the occupation a female worker is actually involved in. It is possible that due to females’ endogenous selection into nonteaching jobs, the ‘observed’ non-teaching wages are different from the ‘true’ non-teaching wages that would be drawn if occupations are randomly assigned among females. By applying Wooldridge’s method, the resulting estimations suffer from neither unobserved heterogeneity bias due to individual fixed effects nor selection bias caused by individual endogenous labor market decisions. These are major advantages expected from using a selection-corrected panel estimation in preference to a conventional cross-sectional model employed by Dolton and Makepeace (1993). 1.2. Policy implications There has been little investigation on the relationship between fertility and labor market behaviors focusing on teacher–non-teacher comparisons using the US data. Dolton (1990) is one of the major contributors to the literature, but he analyzes the case of UK college graduates. Similar to the UK, in the US there have been many discussions and associated policy prescriptions about teachers’ labor supply. Policies implemented to reduce classroom size invoked an issue of teaching shortage. With enlarged job opportunities for females, the alarming tendency of luring high-quality females away from teaching occupations drew a national attention.2 As a starting effort to solve these issues, we attempt to 2

1

Noteworthy, the subsequent reentry wage loss after taking a leave due to fertility is found for females in nursing, another female-dominated occupation.

Stinebrickner (2002) shows that compared with non-teachers, teachers have stronger tendency to leave the job and stay at home longer once they take the leave. Stronger and longer attrition from job may exacerbate the shortage of teacher supply.

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understand the labor market behaviors of the female workers across teaching and non-teaching occupations, particularly focusing on what factors lead females to become teachers and how differently teachers behave responding to changes in these factors in comparison with non-teachers. This study provides evidence for the effects of fertility and relative wages on the female labor force participation and occupational choices. Results reveal that the presence of a new born child under age 2 has only insignificant and unstable (changing signs) marginal effects on the occupational choice for being teachers. The effect of having a new born child on the labor participation decision, however, is significantly negative. Moreover, the adverse marginal effect of fertility is substantially larger for teachers. These findings suggest that the occupational decisions are not directly affected by fertility condition while fertility yields a significant disincentive to be in the labor force to a different extent across occupations. Finally, we find that higher relative wages indeed attract females into teaching. The rest of this paper is organized as follows. Section 2 presents estimation models. Data description is documented in Section 3 and Section 4 reports estimation results. Section 5 concludes.

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explanatory variables including demographic characteristics, educational attainment, regional labor market conditions and family factors. The key explanatory variables are fertility, denoted by F, and relative log wages, ln Ytln Yn. We measure the fertility factor as the presence of a new born baby under age 2.3 We use a three-step procedure to estimate the model. In the first step (procedure 1), we derive and estimate the reduced form participation, and occupation equations by inserting ln Yt and ln Yn into T*. From the estimates of the reduced form equations, the selection correction terms are obtained. The second step (procedure 2) is to include these additional terms in estimating log wage for each occupation and obtain the predicted wages. Then, at last, the structural form equations of T  and P are estimated with the predicted relative log wages derived from the second step (procedure 3). First two steps are analogous to Willis and Rosen (1979) and Heckman (1979), but both are limited to the cross-sectional analysis. Wooldridge (1995) suggests a simple and practical application of the above procedures to the panel data with selection problems. We employ Wooldridge’s method in procedures 1 and 2 to obtain selection correction terms for selectivity-corrected fixed effects panel

2. Estimation methodology 3

2.1. Panel estimation of labor market decisions and wages The primary framework of this study is a binary probit estimation model of two limited dependent variables, labor force participation and occupation choice. This pooled model can be written as T  ¼ W d þ aF þ bðln Y t  ln Y n Þ þ u,

(1)

P ¼ Zg þ f1 F þ f2 LðY Þ þ n,

(2)

ln Y t ¼ X t jt þ t ,

(3)

ln Y n ¼ X n jn þ n ,

(4)

where T  and P are latent decision variables of being a teacher and of participating in the labor force, respectively. T ¼ 1 is observed if T  40 and P ¼ 1 is observed when P 40. ln Yj indicates logarithm hourly wage for an occupation j ¼ t (teaching), n (non-teaching). Labor force participation decision depends on wages in the previous year, denoted as L(Y). W, Z, and Xj, j ¼ t; n are the set of

Across the literature, various measures of ‘fertility’ are used (Browning, 1992, p. 1468). Any measure of the ‘fertility’ is found to be negatively correlated with any measure of female labor supply. Common measures of fertility are the presence of a new born child, the number of children under age 6, the number of children under age 16, total number of children in the household, the number of children ever born. Typically, the effect of fertility on female labor supply, often measured by labor force participation, is explored using any measure of young children such as the presence of preschool children or the presence of an infant. According to Nakamura and Nakamura (1990), the youngest child reaches age two, the employment rate of mothers recovers back and the rate of onleave drops to the level prior to childbearing. Similarly, Leibowitz, Klerman, and Waite (1991) tabulate changes in the participation rates for females who had a baby in the previous 12 months and reveals that over 89% of females in the labor force are back to work at 12 months since birth. Carrasco (2001) suggests that most of the effect of fertility on female labor participation depends more on very young children (aged under 2) than on the total number of children living in the household because the youngest children under age 2 are more time consuming than existing children aged 2 or older (also see Browning, 1992, p. 1458, Fig. 3). These findings imply that older children have relatively modest influence on female labor force participation decisions compared to a new-born child. It may be that older children can be in the child care facility or attend preschool allowing their mothers to be in the workplace.

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by the fixed effects method. After Step 1 and Step 2, procedure 3 is conducted to complete the estimation. We have two selection equations, T  and P , and two wage equations, ln Yt and ln Yn. In the pooled model, we repeat Step 1 for each probit equation and repeat Step 2 for each wage regression and obtain selection bias corrected relative log wages for the structural form probit estimations of T  and P . In the switching model, the labor force participation behaviors are allowed to be heterogeneous across occupational subgroups.4 Then, we rewrite the model as follows:

groups: P ¼ 1 if Pt 40 (for teachers), and P ¼ 1 if Pn 40 (for non-teachers), respectively. Note that the participation decision Pj is observed only when an occupation j is chosen. In the longitudinal analysis, the occupation group is not defined as clear as in the cross-sectional analysis, because females may change their occupations over lifetime. There is no obligation or prior belief that once females become teachers they would stay at teaching profession thereafter. After spending the early years of their careers at home or in non-teaching jobs, females might choose a teaching occupation in the later period. Accordingly, females report teaching as their current occupation in one year and non-teaching in another year over the survey years. Thus, it is unreasonably restrictive to define ‘teachers’ as those who are teachers once and for all. Instead, we consider teachers group in two ways: those who have ever worked as teachers (experience-based) or those who are currently teaching at a given survey year. For comparison purposes, descriptive characteristics between teachers and non-teachers are documented based on the experience-based definition of teachers (see Table 1). In estimating Eqs. (1) and (5), we regard ‘teachers’ those who are teachers in the current/last job at a given survey year and then examine whether the current fertility status has a direct effect on the occupational choice. For estimating the switching labor force participation, Eqs. (6) and (7), a female’s status in the labor force is considered within each occupation group.5 Specifically, for each occupation group, the participation decisions are regarded unobservable if the alternative occupation is chosen. In this way, between two occupation subgroups, we compare the response of labor market participation decision and occupation choice to fertility and relative wages.

T  ¼ W d þ aF þ bðln Y t  ln Y n Þ þ u,

(5)

3. Data

Pt

¼ Zgt þ f1 F þ f2 LðY Þ þ nt ,

(6)

Pn

¼ Zgn þ f1 F þ f2 LðY Þ þ nn ,

(7)

regressions of wages. Procedure 3 is implemented within a panel probit estimation. 2.2. Selection correction fixed effects estimation Wooldridge (1995) develops a tractable way to correct the selection bias within the panel framework of a binary latent selection equation and a main equation. The estimation steps are well illustrated in Vella (1998) and Baltagi (2001). Define a latent dependent variable, hit as hit ¼ X it d þ it . A dummy variable sit takes a value of 1 if hit 40. The main equation is log wage equation, ln Y it ¼ X it j þ uit . Step 1: For each period t ¼ 1; 2; :::; T, estimate the binary selection equation Prðs ¼ 1jX Þ as a standard probit model. Then, the selection correction term can be obtained as an inverse Mill’s ratio: ^ fðX dÞ ; l^ it ¼ ^ FðX dÞ

t ¼ 1; . . . ; T

for sit ¼ 1.

Step 2: Include this additional term and estimate the following wage equation: ln Y it ¼ X it j þ gl^ it þ uit



Pt 40,

where T ¼ 1 if T 40 and while T ¼ 0 if T  o0 and Pn 40. The labor force participation equations are written separately by occupation 4 Eq. (5) is estimated using the whole sample of females, both teachers and non-teachers. The labor force participation equation for teachers uses the sample of females whose current/last occupation is teaching. For non-teachers, we use the sample of females whose current/last occupation is other than teaching.

3.1. The sample We use data from the National Longitudinal Survey of the Young Women. Several criteria are 5 For example, the labor force participation status of a female is included in the teachers’ decision (Eq. (6)) if she is currently or most recently working as a teacher at a given year when the participation status is reported. In another survey year when she is not a teacher at her current occupation, her labor force participation status is included in the non-teachers’ decision (Eq. (7)).

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Table 1 Descriptive statistics Variables

Age Overall Between Within Education (completed by 1988)

Years of schooling 16 17 18

Have been teachers

Have never been teachers

Mean

Std. dev.

Mean

Std. dev.

32.53

5.56 3.69 4.72

32.00

5.38 3.72 4.50

16.73

0.85 0.87 0

16.53

0.77 0.83 0

38.6% 24.2% 37.3%

57.2% 19.7% 23.1%

Married

0.78

0.41 0.38 0.24

0.62

0.49 0.42 0.28

White

0.86

0.85

Presence of a new born baby under age 2

0.13

0.35 0.37 0 0.34 0.13 0.31

0.35 0.36 0 0.32 0.13 0.29

College major Education Business Social science Health, medicine Other

50.0% 0.98% 10.1% 0.65% 38.2%

0.12

10.4% 12.0% 18.4% 14.7% 44.5%

Labor force participation

0.77

0.42 0.28 0.32

0.84

0.37 0.27 0.25

Log hourly wages

6.28

0.89 0.67 0.65

6.43

0.91 0.67 0.72

Currently teaching Currently not teaching

6.22 6.35

Mother’s job ¼ teaching

0.10

0.30 0.31 0

0.06

0.24 0.24 0

12.17

2.85 2.84 0 4.60 3.84 3.05

12.65

2.96 2.86 0 4.99 4.15 3.18

Mother’s education in 1968

Husband income

N (individuals)

6.86

306

5.52

299

Note: Earnings are measured as log hourly rate of pay in the current/last job. The annual average Consumer Price Index of all items (seasonally adjusted) released by the Federal Reserve Bank at St. Louis is used to normalize hourly rate of pay (base year ¼ 1982–1984 as 100).

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used to limit the sample for the analysis. By excluding the respondents who are discontinuously interviewed during 1968–1988,6 we obtain a balanced panel. The constructed balanced panel might be at the risk of loss of information and, more importantly, attrition bias, which is a consequence of selective non-response in a longitudinal survey. The impact of attrition bias on estimates has been a controversial issue in the empirical literature.7 However, numerous studies on the impact of selective attrition find no or little evidence for the significance of the bias (Becketti, Gould, Lillard, & Welch, 1988; Falaris & Peters, 1998; Fitzgerald, Gottschalk, & Moffitt, 1998; Hausman & Wise, 1979; Lillard & Panis, 1998; MaCurdy, Mroz, & Gritz, 1998; Ridder, 1992; van den Berg & Lindeboom, 1998; Zabel, 1998). Noteworthy, Ziliak and Kniesner (1998) show that non-random attrition is of little concern because the effect of attrition is absorbed into fixed effects in labor supply. These studies conclude that major labor market decisions are the same regardless of whether we estimate them using the entire sample, or using a constructed subsample of individuals who never attrit from the survey. Furthermore, our balanced panel still maintains large sample size (2712 individuals for 15 waves, N ¼ 40680). We presume that loss of information and attrition bias will not undermine the validity of our results. Additionally, we limit the education level of females to be 16 or more years in order to have teachers group and non-teachers group comparable in terms of education qualification in line with Manski (1985), Dolton (1990), Dolton and

6

Though the survey data are available up to 2001, the key variables regarding fertility, education, occupation and wages are dropped or changed a lot since 1991. Since the prime ages of childbearing among females are their 20s and 30s, the survey period of 1968–1988 is long enough to collect most of the information regarding fertility by surveying the respondents during their ages from 21 to 44. In 1988, the youngest is of age 31 and the oldest is of age 47. Since the focus of this paper is on the effect of fertility conditions on the labor supply behaviors of young females, the limited sample period seems not to cause a problem. 7 Nijman and Verbeek (1992) run three alternative tests for attrition bias and find that the numerical size of attrition bias depends strongly on a precise way in which a test is carried out. Their test results on the difference between estimates of the balanced panel and unbalanced panel are not conclusive: Hausman test rejects the equivalence while Heckman (1979) correction term for attrition bias does not show any significance of attrition bias.

Makepeace (1993), and Stinebrickner (2002).8 The morale behind this sample limitation criterion is to make valid assessment of the importance of fertility and relative wages in the labor market decisions. It is necessary to have a control group consisting of individuals who could have become teachers, but chose not to. In our sample, only 2.13% of females with high-school graduation (12 years of schooling) have experiences in teaching occupation. This ratio is much higher for females with college education (16 years of schooling, 37.7%) and females with 18 years of schooling (60.2%). Approximately, 86.3% of females who have ever been teachers have 16 or more years of education. These statistics indicate that the decision to become (or not to become) a teacher is the decision mostly of females with at least 16 years of education. Based on this observation, we presume that the majority of teachers or potential teachers are college graduates. As Stinebrickner (2002) points out, having teaching and non-teaching samples of females with similar educational backgrounds allows this study to avoid sample selection problems due to endogenous decisions on educational attainment. Without the educational restriction imposed on our sample, study results would be contaminated by improper comparison of teachers and irrelevant control group from different educational backgrounds (Dolton, 1990). Among 5159 females of ages 13–26 initially interviewed in 1968, 2712 are continuously followed up until 1988. With the education criterion applied, 605 females remain in the final sample. Among them, 306 have teaching experience and 299 not. 3.2. Sample description The descriptive statistics documented in Table 1 show the differences in characteristics between teachers and non-teachers. Females with teaching experience are slightly older and more educated on average though the difference is less than a year. Teachers are under a bit heavier pressure from family commitment due to marriage and fertility, 8

These studies use the sample of college graduates. In particular, Manski (1985), in analyzing the decision to become a teacher, use the subsample of the National Longitudinal Study of the High School Class 1972 (NLS72) who reported that they had received a bachelor’s degree in 1976 or 1977. Similarly, the sample used in Stinebrickner (2001, 2002) includes the respondents of NLS72 only who became certified to teach during 1975–1985.

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and have stronger tendency to be out of the labor force. Among females who are currently out of the labor force, those with teaching experience are more likely to have a new born baby (29.3%) than those without teaching experience (28.9%). Mothers of teachers are more likely to be teachers and less educated relative to mothers of non-teachers. Half of teachers receive their Bachelors degrees in education. This tendency does not appear for nonteachers. It indicates that among teachers, college major seems to be a predetermining signal for their future occupation after graduation. The real wages for teachers (6.28) are lower than for non-teachers (6.43). More specifically, if females with teaching experience choose teaching as their current jobs, their wages are on average lower (6.22) than the wages of those who have teaching experience but are not in the teaching career currently (6.35). This raises a question why females would choose to be in the teaching profession where they receive lower wages. We note that additional to monetary wages, teaching profession offers ‘nonpecuniary benefits’ to attract females with children either currently or potentially. In the theory of compensating wage differentials and equalizing differences, Rosen (1986) postulates that individuals weigh such non-pecuniary rewards as well as pecuniary compensations in making labor market decisions. This leads Dolton (1990) to formulate an individual college graduate’s decision-making model to account for the effect of non-pecuniary aspects of teaching profession compared to other types of jobs. Non-pecuniary rewards are found to have an important effect on females’ occupational choices. The ‘actual’ wages must be considered in female decision-making process for occupational choices to contain both pecuniary and non-pecuniary rewards in jobs (Dolton, 1990). In this regard, Table 1 compares only pecuniary factors of the actual wages between teachers and non-teachers. The idea of ‘comparative advantage’ explains the split between teachers and non-teachers in a way that everyone is placed where she can make the best. Provided that a female has a rational expectation of what the salary or rewards will be before making her educational choice and subsequent occupational choice, the reason she would choose teaching profession in spite of lower monetary wages may be that the loss in pecuniary wages are compensated by non-pecuniary benefits. Consequently, the actual wages for teachers are equivalent to non-teachers given other factors

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controlled (Rosen, 1986). Since non-pecuniary benefits are not easily measured in terms of monetary value, we presume that teachers are those who have higher subjective evaluation of the relative non-pecuniary rewards of the teaching profession. 4. Results 4.1. Wage equations and self-selection Table 2 presents the results from the fixed effects estimation of wages for teachers and non-teachers, respectively. The first thing to notice is the difference in age effects. The linear effect of age is significant for both occupation groups but much larger for non-teachers. The negative effect of the quadratic term of age is significantly negative and much larger in magnitude for non-teachers. Fig. 1 illustrates the predicted log wages-age profiles of both occupation subgroups. Though both earnings profiles are nonlinear and quadratic in age, one for teachers is much flatter than one for non-teachers, consistent with Flyer and Rosen (1997). Teachers start with higher initial wages but wages rise at a constant rate. Relative to teachers, non-teachers start their careers with lower initial wages but owing to higher growth rates, at age 25, earnings of non-teachers catch up with earnings of teachers and this pattern continues until age 43. Once they reach to age 35, the wage growth rates for non-teachers start decreasing and as a consequence, their wages fall below the level of teachers’ wages after age 43. The observed pattern of the wage rigidity for teachers over time may be attributed partly to the government regulations to maintain the quality of education and to retain sufficient number of qualified teachers. In the prospective of the theory of human capital, the flat age-earnings profile of teachers may represent the fact that in the teaching profession, the amount of experience and the rate of technology adoption have relatively mild effects on wages. In occupations where technology changes are prevalent, workers benefit from wage gains if they are capable of catching up new skills. These wage gains may be relatively small in occupations where the required skills are stable with little technology evolution. However, as workers get old, workers become slow in learning new skills and tend to stay behind the ongoing technology advance in their workplaces. In occupations with rapid technological development, the wage gains

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Table 2 Selection correction fixed effects estimation of wages Variables

Pooled model

Switching model

Teachers Constant Age Age2 Married Region of residence Unemployment rate Lamda (participation) Lamda (occupation)

Non-teachers

0.434 (0.307) 0.221*** (0.020) 0.0013*** (0.0003) 0.007 (0.033) 0.058 (0.047) 0.012 (0.008) 0.153** (0.075) 0.032 (0.069)

R2 Overall Between Within

1.700*** (0.318) 0.350*** (0.021) 0.0032*** (0.0003) 0.018 (0.030) 0.110** (0.046) 0.004 (0.009) 0.079 (0.076) 0.213*** (0.054)

0.729 0.696 0.916

F N (n) Fraction of variance contributed by fixed effects F (no fixed individual effect)

0.617 0.491 0.857

905.95 800 (214) 0.863

1038.17 1592 (374) 0.802

11.02

8.75

Teachers

Non-teachers

0.246 (0.385) 0.223*** (0.024) 0.0014*** (0.0004) 0.020 (0.055) 0.210*** (0.056) 0.022* (0.011) 0.020 (0.036) 0.104* (0.059)

1.824*** (0.348) 0.359*** (0.021) 0.0033*** (0.0003) 0.018 (0.034) 0.130** (0.052) 0.009 (0.010) 0.048 (0.061) 0.141*** (0.050)

0.711 0.656 0.926

0.572 0.467 0.835

559.92 502 (183) 0.883

726.71 1385 (373) 0.803

12.72

9.51

Note: (1) *, **, and *** indicate 10%, 5% and 1% significance, respectively. (2) Standard deviations are in the parenthesis. (3) The dependent variables are log hourly wages at any survey year. Teachers’ wages are observed for females who are in the labor force as elementary or secondary school teachers. Non-teachers’ wages are measured for females who are in the labor force as non-teachers.

8

Predicted average log(wage)

7.5 7 6.5 6 5.5 5 4.5 teaching

nonteaching

4 20

22

24

26

28

30

32 34 Age

36

38

40

42

44

46

Fig. 1. Age-earnings profiles by occupation.

from experience disappear more quickly than in occupations with slow skill advance, such as teaching. As a result, the gap in earnings between teachers and non-teachers starts to decrease at age

35, and eventually teachers’ wages take over nonteachers’ wages at age 43. Different earnings profiles across occupation subgroups imply heterogeneity of their behaviors in the labor market.

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Our pooled estimation of wages finds a negative selection bias due to endogenous labor force participation decision for both teachers and nonteachers, but statistically insignificant for nonteachers. As unobserved factors that lead females to be in the labor force are negatively correlated with teachers’ wages, teachers’ wages are below average among all females. Selectivity associated with occupational decisions is positive but insignificant for teachers while it is statistically significant and negative for non-teachers. Non-teachers are adversely self-selected among all females who are in the labor force. When the selection correction is implemented separately for teachers and non-teachers (Table 2, columns 3 and 4), the negative selection bias found in the pooled model for teachers becomes insignificant. Instead, the positive selectivity in occupational choices is found for teachers though it is statistically weak. Negative selection in occupational choices for non-teachers stays significant and even more substantial. This confirms that nonteachers are negatively selected into non-teaching occupation among all female college graduates who participate in the labor force. Also, importance of collecting selectivity bias in wages in revealing the uncontaminated estimate of the effect of relative wages on occupational choices is established. Local labor market conditions seem to have a positive effect on teachers’ wages but this impact is adverse in terms of non-teachers’ wages. Living in the South have a positive effect on wages for teachers and this effect is significant in the switching model. In contrast, non-teachers receive lower wages if they live in the South. We find that the unemployment rate9 is an insignificant determinant 9

The variable of ‘unemployment rate of the respondent’s labor market of current residence’ is defined using finer divisions of geographical area than ‘regions of residence’ is defined (NLS Young Women User’s Guide, Chapter 4, pp. 84–89). The unemployment rate is drawn from the 1970 Census of Population and varying years of the Current Population Surveys (CPS) and ‘residence’ is defined using the 1970 Primary Sampling unit (PSU). Each PSU is made up of one or more contiguous counties within States not crossing State boundaries. Total 2007 PSUs compose the entire area of the United States. Unemployment rates are calculated for each CPS PSU by summing the total number of unemployed for the 12-month period and dividing by the total number in the labor force. This variable is coded as integers from 1 (0–1.7%) to 10 (10.7% or higher). The variable of ‘region of residence’ represents much broader geographical information of respondents for confidentiality, that is, whether the residence is located in the South or non-South (the Northeast, North Central, or West). (See Appendix 2 in the NLS

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of wages for both teachers and non-teachers. In the switching model, however, a positive and weakly significant coefficient of the unemployment rate is found in teachers’ wages. This finding looks contradictory to Blanchflower and Oswald (1994) who show a negative correlation between earnings and unemployment rates. There are several factors accounting for the difference. First, as Blanchflower and Oswald use pooled cross-sectional data, their results may be susceptible to the omitted variable bias if unobserved fixed characteristics of individuals are correlated with their locational placement of residence. Our panel method has advantage in avoiding such bias, which produces estimates different from Blanchflower and Oswald (1994). Secondly, they perform a pooled estimation including individuals in all occupations, while we run separate regressions for teachers and nonteachers. Thus, our finding of the positive correlation between unemployment rates and wages reflects an occupation-specific relationship, not a marketwide relationship. It is possible that the unemployment rates are negatively correlated with earnings across occupations, but they may be positively associated with earnings within teachers group. Indeed, Rosen (1986) and many other followers of Hall (1970) suggest that wages and unemployment rates must be positively correlated across regions in the US. The possible heterogeneity between industries may fill out the gap between our finding and the results of Blanchflower and Oswald (1994). Lastly, Blanchflower and Oswald use nominal wages while our measure of wages is converted to real terms in adjustment to aggregate price levels. Higher unemployment rates are typically associated with lower price levels.10 Lower price levels lead to lower nominal wages, producing a negative correlation between nominal wages and unemployment rates. Once the nominal wages are adjusted to changes in the price level, real wages are maintained unchanged, leaving no significant relationship between wages and unemployment rates.

(footnote continued) Codebook Supplement for the listing of states constituting each division). Since the ‘regions of residence’ is defined too crude to sufficiently control the effect of local labor market conditions on wages, we add a more refined variable of ‘unemployment rate’. By including both variables as explanatory variables, we control for the regional variation in wages due to region-specific labor market factors. 10 This relationship is well established as the Phillips Curve.

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Table 3 Probit estimation of occupation choice decision Variables

Pooled model Marginal effect

Switching model Marginal effect

Age Years of schooling Married White Baby Region of residence Mother’s occupation in 1968 College major ¼ education College major ¼ business College major ¼ social science College major ¼ health, medical science Relative log wages N (n) Log-likelihood Variance of fixed effects Fraction of variance contributed by fixed effects LR test (H0: no fixed effects)

0.008*** (0.002) 0.144*** (0.023) 0.049** (0.020) 0.033 (0.028) 0.018 (0.026) 0.0005 (0.033) 0.046** (0.029) 0.290*** (0.045) 0.111*** (0.022) 0.121*** (0.023) 0.094*** (0.019) 0.379*** (0.119) 2345 (513) 784.00 2.45 (0.112) 0.857 (0.014) 1046.45

0.008** (0.004) 0.168*** (0.034) 0.049 (0.049) 0.036 (0.068) 0.035 (0.054) 0.013 (0.094) 0.048 (0.073) 0.355*** (0.064) 0.139*** (0.028) 0.161*** (0.034) 0.129*** (0.026) 0.558** (0.243) 1413 (467) 546.93 2.43 (0.206) 0.855 (0.021) 583.92

Note: *, **, and *** indicate 10%, 5% and 1% significance, respectively.

4.2. Fertility, college major and occupational choice Table 3 documents the probit results11 of occupational decision estimations. As the conventional marginal effect interpretation of coefficients is inappropriate in the nonlinear model, we report the marginal effect of each explanatory variable on the predicted probability of being teachers. Younger and highly educated females are more likely to choose teaching. Noteworthy, the presence of a new born baby has a negative but insignificant effect on occupational choice. It implies that fertility is not a relevant factor for females to decide to work in teaching occupations. Though it is believed that pecuniary and non-pecuniary benefits are offered in teaching jobs for females with a young child, these rewards seem not to be critical in their occupational choices. The large significant marginal effect on the probability of being teachers is found for college majors. Majoring in education has the strong impact (0.290) on being teachers among females. With other college majors such as business, social science, and health/medical science, females are very 11

The panel logit estimations both in the fixed effects and in the random effects are implemented. The results are mostly consistent with the findings from the probit estimations reported here, but weak in the significance.

unlikely to be in the teaching profession. It suggests that whether or not to be teachers is a decision made during college years, not after graduation. In the switching model, majoring in education has even larger effect (0.355) on the decision into teaching. A policy designed to persuade female college students into majoring in education may be powerful as college major is considerably important in the occupational choices into teaching after graduation. 4.3. Relative wages and occupational choice The effect of relative wages on occupational decisions is widely discussed in the literature. Hypothetically, a rational worker compares wage offers from potential employers and makes her choice of the best offer. Our finding provides strong empirical evidence for this hypothesis. In Table 3, column 1 (the pooled model), relative log wages have the largest positive and significant effect (0.379) among all other explanatory variables. Even larger marginal effect of relative wages on occupational choice (0.558) is found in a switching participation model (Table 3, column 2). It implies that with slight increase in wages relative to alternative occupations, females will be attracted into the teaching occupation. The recent increases in educational expenditures intend mostly to raise

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Table 4 Probit estimation of labor force participation decision Variables

Age Years of schooling Married White Baby Husband income Mother’s education in 1968 Region of residence Unemployment rate Last year log hourly wage N (n) Log-likelihood Variance of fixed effects Fraction of variance contributed by fixed effects LR test (H0: no fixed effects)

Pooled model

Switching model

Marginal effect

Teachers Marginal effect

Non-teachers Marginal effect

0.002* (0.001) 0.020*** (0.007) 0.069*** (0.015) 0.053*** (0.010) 0.159*** (0.028) 0.0006 (0.001) 0.0001 (0.002) 0.003(0.010) 0.001(0.002) 0.019*** (0.007) 3063 (561) 922.62 1.000 (0.095) 0.500 (0.048) 155.71

0.003 (0.002) 0.015 (0.013) 0.076*** (0.022) 0.050*** (0.018) 0.212*** (0.058) 0.002 (0.004) 0.0003 (0.004) 0.012(0.019) 0.002(0.004) 0.038*** (0.014) 916 (238 ) 261.59 0.908 (0.182) 0 .452 (0.061) 23.22

0.001 (0.0009) 0.008 (0.006) 0.054*** (0.016) 0.029*** (0.009) 0.125*** (0.030) 0.0003 (0.001) 0.002 (0.002) 0.007 (0.009) 0.00002 (0.002) 0.018*** (0.006) 2121 (447) 546.54 0.870 (0.112) 0.431 (0.063) 57.16

Note: *, **, and *** indicate 10%, 5% and 1% significance, respectively.

teachers’ salary, hoping more and better teachers to join the teaching profession. Our finding supports that such wage incentives will be an effective policy tool for resolving the shortage of teachers. Furthermore, when we control for individual fixed-effects along with selection correction in wages, the response of females to the increase in relative wages is statistically significant but to a less extent than what has been found previously in the cross-sectional analysis (Dolton & Makepeace, 1993; Johnes, 1999). It gives a caution that any education policy aimed at increasing teacher supply with improved pecuniary compensation needs careful assessment on its effectiveness. 4.4. Fertility and participation decision Table 4 presents the estimation results of labor force participation decisions among female college graduates. The education level has a significantly positive effect on the participation probability. It seems that more education equips females with better qualification and offers greater opportunity in the labor market, leading them to join in the labor force. Husbands’ income is not a factor significantly affecting wives’ labor force participation decisions. Previous studies mostly find the disincentive effect of husbands’ earnings on wives’ labor force

participation.12 It has been a concern in empirical studies on the role of husband earnings in wives labor market status that positive assortative mating behavior, if not properly controlled for, produces upward bias in the estimate of the effect of husband earnings on wives labor force participation (Liu, Hammitt, & Lin, 2000). Obviously, without knowing the precise way of assotative mating, it is hard to determine what the estimated coefficient of husband income in wives labor force participation equations measures. In principle, the estimate of the effect of husband income must be the combination of ‘true’ effect and ‘contamination’ from assortative mating. Unfortunately, the direction of assotative mating is yet controversial in the literature. Becker (1973, 1974) predicts a negative assortative mating on wages in marriage market equilibrium, but most of empirical studies13 find it positive. To fill in this gap, Lam (1988) notes that the realized magnitude and direction of assortative mating on wages depends on both positive tendency from joint consumption of household public goods and negative tendency from

12 See Eckstein and Wolpin (1989), Ermisch and Wright (1993), and van der Klaauw (1996). 13 See Bruce (1999), Liu et al. (2000), Dalmia and Lawrence (2001), Jepsen and Jepsen, (2002), and Nakosteen, Westerlund, and Zimmer (2004).

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gains from specialization. Indeed, Zhang and Liu (2003) find evidence for negative assortative mating. Our panel analysis allows us to control for fixed individual effects so that marginal labor supply responses of wives to changes in husband income would be disentangled from permanent factor like assortative mating (Lundberg, 1988). In this way, we may expect to acquire the uncontaminated measure of the effect of husband income on wives’ labor force participation. Females who receive higher wages in the previous year are more likely to be in the labor force in the current year. The level of wages earned in the past may represent the proxy of the potential opportunity cost that females would bear if they decide to be out-of-labor force in the present. Therefore, it is not surprising that females who have earned high wages have stronger incentive to stay in the labor force than those who have earned low wages. This tendency is more obvious in the case of teachers than non-teachers. Family variables such as marriage and fertility are found to be substantially important for females to decide whether or not to be in the labor force.14 Among all other explanatory factors, the presence of a new born child shows the largest negative 14

It is important to keep in mind that individuals are likely to make labor decisions and family decisions jointly. As a result, the family variables are not truly exogenous. Nonetheless, our results strongly suggest that the family variables are very important in understanding why females leave the labor force. In particular, our finding of the negative relationship between fertility and female labor force participation is consistent with the vast majority of empirical studies (for example, Jacobsen, Pearce, & Rosenbloom, 1999). However, due to strong theoretical reasons to believe that fertility and female labor supply of females are jointly, rather than sequentially, determined (Browning, 1992; Goldin, 1990), the causal interpretation of this association has been under skepticism. This endogeneity problem is often resolved using the instrumental variables method. Though it is widely accepted that allowing for the endogeneity of fertility makes a difference to the estimated effect of fertility on female labor force participation, no simple relationship between the results ignoring and allowing for endogeneity has been established. The early attempt of IV estimation by Rosenzweig and Wolpin (1980) shows that without instrumenting fertility, the true impact of fertility on female labor force participation is largely underestimated. Similarly, Carrasco’s (2001) application of IV method to panel analysis finds that OLS estimates of the fertility effect are smaller than IV estimates and that for all other regressors except fertility variables, the coefficients are not much changed. In contrast, Angrist and Evans (1998) find OLS estimates larger than IV estimates. In spite of variations in results, these studies uniformly suggest a statistically significant negative impact of fertility on the female labor force participation.

significant marginal effect on participation (0.159 in the pooled model). This result is compatible with the description of Olsen (1994). Similarly, being married plays a discouraging role for females in participating in the labor market. The results from the switching model estimations show that teachers respond to family variables to a different extent compared with non-teachers. When we allow heterogeneity in their labor market behaviors, negative effects of the marital status are larger for teachers relative to nonteachers. The significant and negative effect of the presence of a new born baby is almost twice larger for teachers. Both teachers and non-teachers, family commitments such as marriage and childbearing retract them from the labor force in a significant way. Furthermore, teachers are more sensitive to adjust their labor market status according to marriage and fertility conditions. Teachers have stronger tendency to be out-of-labor force if they have a new born baby. 5. Conclusion In this paper, using the panel data of young females, we investigate the relationship between fertility and relative wages, labor force participation, and occupation decisions. Several conclusions emerge from the analysis. First, the presence of a new born baby seems not to affect the occupational decisions of whether to be in teaching jobs. On the other hand, the fertility condition is an important disincentive for females’ labor force participation. This effect varies across occupations: upon a new birth, teachers have stronger tendency to leave outof-labor force. That may be explained by the fact that wage loss for the temporary leave due to fertility and skill obsolescence are relatively modest in teaching occupations than in non-teaching occupations. Without such flexibility in leave-andreenter options, non-teachers are less likely to take off the jobs even when they have a new baby and, instead, adhere to the jobs, although staying out-oflabor force is a preferred option for non-teachers as well upon a new born child arrival. Second, the direct effect of relative wages on occupational choice has been studied. There is strong evidence that higher relative wages for teachers provide a proper incentive for females to be teaching. This result supports the effectiveness of the recent policy of raising teachers’ salary in order to secure teacher supply. Other than relative wages,

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college major is an important factor in occupational decisions among female college graduates. Therefore, further analysis on the choice of college majors and its relationship with post-graduation occupational choice will be useful to understand what determines supply and quality of teachers and what is the most efficient way to invest educational resources to provide more and better teachers for the next generation. Additionally, we find that ageearnings profiles are substantially different between teachers and non-teachers. It suggests that teachers and non-teachers are heterogeneous in their labor market decisions, which demonstrates the importance of properly correcting selection bias in our wage estimations.

Acknowledgements We are deeply indebted to Qi Li and Donald Deere for insightful suggestions. We thank Manuela Ureta for data references. Finis Welch and Badi Baltagi kindly support our research with useful comments.

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