Supply and Demand Factors

4.4.1. Empirical Approach

To analyze the evolution of the STEM premium through the lens of supply and demand factors, I use a CES production function framework which allows for imperfect substitutabil-ity between STEM and non-STEM workers. The approach closely follows previous work by, e.g., Katz and Murphy (1992) and Goldin and Katz (2007) who apply this framework using low- and high-skilled workers as the two labor input factors.²⁸Formally, I assume that aggregate outputQt in each yeartis generated by a CES production function depending on STEM (LS_t)and non-STEM(LN_t)labor supplies²⁹:

Q_t = [α(a_tL_St)^ρ+ (1−α)(b_tL_Nt)^ρ]^ρ1, (4.2) whereα is a technology parameter indexing the share of work allocated to STEM labor (i.e., extensive margin),a_t andb_t represent the STEM and non-STEM labor augmenting technological change (i.e., intensive margin), andρdetermines the aggregate elasticity of substitution between STEM and non-STEM labor, whereσ=1/(1−ρ)∈(0,∞). Increases in(a_t/b_t)reflect STEM-biased technological progress. If the two labor inputs are gross substitutes (i.e.,σ≥1), a STEM-biased technological change will increase the STEM wage premium.

Under perfect competition, firms choose a level of each type of labor input such that marginal costs equal the marginal product. Consequently, I can relate the partial derivatives ofQt with respect to STEM and non-STEM labor supplies to the relative wages by the following equation:

Finally, under the assumption that there is a log linear increase in the demand for STEM workers over time coming from technology, the relationship between relative STEM/non-STEM wages and supplies in each yeartcan be expressed by the following linear regression

28Various versions of this modeling approach have been used to study the development and underlying drivers of skill premiums. Further studies focusing on the U.S. include Bound and Johnson (1992); Juhn et al.

(1993); Card and Lemieux (2001); Acemoglu and Autor (2011); for studies focusing on West Germany see, e.g., Dustmann et al. (2009); Glitz and Wissmann (2017).

29Following the common interpretation in the literature on skill premiums, the production function represents a one-good market that depends on two types of workers: STEM and non-STEM. Alternatively, the model may represent an economy where the consumer’s utility function is defined over two goods each produced by only one type of worker. Finally, the model may represent a mixture of the two former interpretations where different goods are produced in different sectors, and STEM and non-STEM workers are employed in both (Acemoglu and Autor 2011).

4.4. SUPPLY ANDDEMANDFACTORS

I estimate equation (4.4) by OLS. That is, I regress the log of relative wages on a constant, a time dummy, and the log of relative supplies. The aggregate elasticity of substitutionσ (which is equivalent to−1/β) is the parameter of interest and determines the relationship between changes in relative STEM/non-STEM supplies and STEM premiums.

I measure labor supplies L_St and L_Nt in efficiency units by determining productivity adjusted full-time equivalents of STEM and non-STEM workers. This is necessary because the model assumes that workers are perfect substitutes within each group of labor inputs (Glitz and Wissmann 2017). Moreover, I use composition constant wages to obtain the pure price for STEM and non-STEM workers net of any compositional differences.³¹

4.4.2. Results

Before I turn to the estimation results of model (4.4), I want to briefly discuss the identifi-cation issues of the model. As Glitz and Wissmann (2017) point out, the identifiidentifi-cation of the elasticity of substitutionσ relies on labor supplies to be predetermined. In the present case, this means that STEM and non-STEM labor supplies may not be correlated with any unobservables that also determine the STEM premium. Moreover, there may not be a contemporaneous correlation between the premiums and labor supplies. As opposed to the usual specification of the model along the skill dimension, the identification may be more problematic in my setting. In particular, I define STEM worker by the current job title, and thus occupational group changes allow for short-term adjustments in relative supplies. In contrast, the skill-specific labor supply is considered to be less elastic since education is viewed as a long-term investment into human capital (Glitz and Wissmann 2017). However, as Figure 4.8 shows, the mobility into and out of STEM jobs — defined as the number of movers between STEM and non-STEM jobs between two consecutive years divided by the total number of workers employed in yeart−1 andt— is very low. The annual non-STEM-to-STEM mobility rates (plotted with squares) fluctuate between 0.0030 and 0.0064 resulting in an average rate of 0.0044 in the years 1980 to 2010. Likewise, the STEM-to-non-STEM mobility rates (plotted with triangles) fluctuate between 0.0025 and 0.0051 resulting in an average rate of 0.0036 during the same period. In other words, 0.44% of workers move into STEM and 0.36% out of STEM jobs between two consecutive years.³²Consequently,

30Formally, the trend in the STEM-biased technological change is of the form: ln _1−α^α +^σ−1

31For details on the determination of labor supplies and composition constant wages, see Appendices C.1.2 and C.1.3.

32The average mobility rate for switches between skill groups during the same observation period is of a similar magnitude (i.e., 0.006). Note, however, that due to the imputation method for the education variable, skill changes can only be one-directional.

95

Figure 4.8.: Evolution of Mobility Rates Between non-STEM and STEM Occupations

Notes:Figure shows the annual mobility rates between the occupational group of non-STEM and STEM workers, defined as the number of movers from non-STEM to STEM jobs (plotted with squares) and movers from STEM to non-STEM jobs (plotted with triangles) divided by the total number of workers employed in two subsequent periods. Data source: SIAB-R 7510.

there is only limited scope for movements between occupational groups that would violate the identification of the model. In addition, I provide some evidence for the validity of the model’s identification by excluding workers in their 20s. I do so because young workers exhibit much higher mobility rates between STEM and non-STEM occupations (1.2% for workers aged≤31 vs. 0.4% for workers aged>30). Accordingly, there is more scope for contemporaneously adjustments of this age group to wage differentials. Finally, if anything, I expect estimates ofβ to be upwardly biased due to a simultaneous reaction of labor supplies to the STEM premiums; that is, I consider the estimated elasticity of substitutionσˆ defined as−1/ ˆβ as an upper bound.³³

Table 4.2 shows the OLS estimates for empirical model (4.4) for the combined sample of men and women.³⁴ Row 1 refers to the estimate on the relative labor supplies of STEM to non-STEM workers,βˆ =−1/ ˆσ. Row 2 captures the log linear trend in the STEM-biased technological change. Column 1 shows the results for the baseline sample while columns 2 to 6 provide further sensitivity checks. The statistically significant estimate of−0.588 in column 1 suggests that the evolution of the STEM premium can be characterized by an elasticity of substitution between the two labor inputs ofσˆ =1.70, meaning that the two occupational groups are gross substitutes. In addition, the positive estimate on the time trend in row 2 points to a STEM-biased technological change that increased the relative demand for STEM workers. Interestingly, Glitz and Wissmann (2017) find a similar elasticity of

33Glitz and Wissmann (2017) make the same argument with respect to the relationship between relative wages and supplies by skill groups.

34Except for column 4, all estimates in Table 4.2 are based on a sample that uses imputed employment and wages of occupation code 102 (fordoctors and pharmacists) at the level of skill-age-gender-year cells between 1996 and 1998.

4.4. SUPPLY ANDDEMANDFACTORS

Table 4.2.: Estimation Results for CES Regression Model

Med. Full &

Non- Full workers Part- Aged

Baseline Imputed Hetero. excl. Time ≥31

(1) (2) (3) (4) (5) (6)

STEM/non-STEM −0.588^∗∗∗ −0.489^∗∗ −0.429^∗∗∗ −0.412^∗∗ −0.450^∗∗∗ −0.244^∗∗∗

relative supply (0.196) (0.208) (0.184) (0.158) (0.132) (0.084) Time trend 0.012^∗∗∗ 0.011^∗∗∗ 0.010^∗∗∗ 0.009^∗∗∗ 0.008^∗∗∗ 0.005^∗∗∗

(0.003) (0.003) (0.003) (0.002) (0.002) (0.001)

Constant −0.860^∗ −0.744 −0.536^∗∗ −0.504^∗ −0.555 −0.032

(0.439) (0.468) (0.412) (0.363) (0.295) (0.171)

Observations 31 31 31 31 31 31

R-squared 0.739 0.833 0.853 0.815 0.736 0.669

Notes:Table shows results from OLS estimates of empirical model (4.4) for the pooled years 1980 to 2010. Employment and wages of workers with occupation code 102 (fordoctors and pharmacists) are imputed at the level of skill-age-gender-year cells between 1996 and 1998. Estimates in column 1 are based on all full-time workers without trainees and one observation by worker-year (baseline sample). Estimate in column 2 are based on the baseline sample but use right censored (non-imputed) wages. Estimates in column 3 are based on the baseline sample but use imputed wages following the ’normal, full heteroscedasticity’ imputation method (see Appendix C.1.1 for additional details).

Estimates in column 4 are based on the baseline sample but exclude workers with the occupation code 102 (fordoctors and pharmacists). Estimates in column 5 are based on all full- and part-time workers including trainees and multiple observations by worker-year weighted by days worked. Estimates in column 6 are based on the baseline sample but exclude workers aged≤30. Significance levels:

* 10%, ** 5%, and *** 1%. Data source: SIAB-R 7510.

substitution between college and non-college labor of 1.6 in West Germany between 1980 and 2008.³⁵ In contrast, Dustmann et al. (2009) find a somewhat larger estimate of 4.0 for the elasticity of substitution between high/medium-skilled to low-skilled men in West Germany between 1975 and 2004.

To visualize the relationship between relative wages and supplies, Figure 4.9 show the evolution of the STEM premium (plotted with squares) and relative STEM/non-STEM supplies (plotted with triangles) by year, each purged of the linear time trend. The negative relationship suggests that — within the boundaries of the model — decelerating (detrended) supplies of STEM workers were a driver for the growth in the STEM premium, in particular since the mid-1990s.

Columns 2 to 5 in Table 4.2 show that the main results are robust to a variety of sensitivity checks regarding the imputation of wages and the sample selection. Results in columns 2 and 3 are based on the same sample, but the estimation uses right censored (non-imputed) wages and wages based on the ’normal, full heteroscedasticity’ method by Dustmann et al. (2009), respectively, in the determination of efficiency units and composition constant wages.³⁶ In column 4, I estimate the model for a sample that excludes the occupation code 102 (for doctors and pharmacists) to show that the results do not depend upon the imputation of

35Note that Glitz and Wissmann (2017) use a nested CES framework which further allows for imperfect substitutability between young and old workers within skill groups.

36See Appendix C.1.1 for details on the alternative imputation method.

97

Figure 4.9.: Detrended Changes in STEM/non-STEM Relative Supplies and Relative Wages

Notes:Figure shows residuals from separate OLS regressions of the STEM/non-STEM relative wages and relative supplies on a constant and a linear time trend. Wages are adjusted holding the skill-, age-, and gender composition constant. Supplies are measured in efficiency units. Employment and wages of workers with occupation code 102 (fordoctors and pharmacists) are imputed at the level of skill-age-gender-year cells between 1996 and 1998. See Appendix C.1.2 and C.1.3 for additional details. Data source: SIAB-R 7510.

employment and wages for this occupation code.³⁷Results in column 5 are based on a sample that closely follows Glitz and Wissmann (2017). Notably, the sample includes part-time employment weighted by 1/2 and 2/3 (9.5% of total days worked), workers undergoing training (3.1% of total days worked) and multiple job spells by the same worker during a year (7.6% of total days worked) to determine supply measures and, in addition, weights job spells and wages by the spell-length measured in days worked per year. This enlarged sample could be considered as a more precise measure of supplies and wages. However, as columns 2 to 5 show, my main estimates (in column 1) are robust to these alternative specifications. Overall, the estimates for the negative inverse of the elasticity of substitution range between−0.412 and−0.489 and are always statistically significant.

Finally, to provide a test for the validity of the model’s identification, I exclude workers aged≤30 from the sample and re-estimate the model once more (see discussion above). As shown in column 6, I again obtain a statistically significant estimate ofβ which is, however, significantly higher than the estimates in columns 2 to 5. Nevertheless, the estimate is of reasonable size implying an elasticity of substitution of 4.10. Taken together, the framework provides a simple yet intuitive interpretation of how the demand and supply of STEM and non-STEM workers shaped the distribution of relative wages between 1980 and 2010.

37As described in section 4.2.1, there is an unusual decline in the number of workers with the occupation code 102 (fordoctors and pharmacists) between 1996 and 1998 compared to the neighboring years.