a signaling theory of education suggests that educational attainment
A context that affects immigrant earnings trajectories is the structure of the host-country’s labor market. One conceptualization is based in human capital theory . Another, introduced by the economists Averitt (1968) and Doeringer and Piore (1971) , postulates that two types of demand determine the characteristics of jobs in the economy. Jobs in the primary sector (responding to the stable component of demand) are “good jobs” characterized by security, responsibility, and career lines; jobs in the secondary sector (responding to demand that is highly variable) are dead-end jobs. The dual labor market theory contends that it is not so much the human capital of individuals that determines their earnings trajectories, but the characteristics of the job a person is in. Jobs are, to some extent, “parceled out” with some groups benefiting from the employment fruits of the primary sector while others, primarily poorly educated minorities and immigrants, are shunted off to the secondary sector.
Figure 3 . Benefits and costs associated with alternative signals. Adapted from Spence, A. M. (1973). Job market signaling. Quarterly Journal of Economics 87, 355–374.
The signalling theory as developed by Spence ( 1973 ) starts from the following assumptions: (i) individuals differ in productivity and productivity is fully person-specific and not affected by schooling; (ii) more schooling entails more costs, and schooling costs are lower for the more productive (e.g., they may need to spend less time studying); (iii) individuals know their productivity; employers do not (asymmetrical information); and (iv) schooling levels can be observed without incurring a cost. As employers cannot observe potential workers’ actual productivity, they instead used educational qualifications to predict productivity, make hiring decisions, and set wages, based on the assumption that individuals who have more years of education are more productive. The model is based on the premise that individuals will invest in schooling as long as the benefits outweigh the cost. Since employers can observe total output for the entire workforce, they can use this as probabilistic information to check whether this assumption is correct in the aggregate. Thus, employers cannot directly observe the marginal product prior to hiring. What employers observe is a plethora of personal data in the form of observable characteristics and attributes of the individual (e.g., education, previous work, race, sex, criminal and service records, etc.).
If education only serves as a screen, the gross social returns to education will be zero because of its distributional effects. The net social returns will be negative, as investments in education imply social costs (Johnstone 2017 ) with are not matched by higher total output. However, the private returns to investment in education will be positive for those with more capabilities. In an economy without signalling, all employees would receive the same wage equal to the average marginal product. As a results of signalling, workers with more capabilities will receive more than average, while those with less capabilities will receive less than average.
I propose that government should rather decrease the prices of higher education, to make it more accessible to a much larger population. More population earning higher degrees will push the employers to engage in evaluating the human capital or skills possessed by an individual, than merely relying on signalling effect of higher education. Government should also simultaneously offer several vocational training courses — equivalent to a degree, to ensure that an average individual is equipped with necessary vocational and professional skills with regards to a certain field of work.
Further, it has to be understood that in India, a large section of the population is engaged in informal labour, which does not work on signalling. This is largely due to inaccessibility to formal training by a large section of the population. Unni and Sarkar (2012) found that returns on education are high, particularly for the disadvantaged social groups. Nevertheless, the education levels in these social groups remain low due to non-availability of schools in the local region, and a general perception that chances of obtaining regular employment are low even after obtaining the required education level. Hence, if the government lowers the overall cost of higher education, disadvantaged groups can gain in terms of upward economic and social mobility.
Subgraph 3.C in Fig. 3 shows that there is a high country-level correlation ( (r = 0.77) ) between the within-group standard deviations of literacy and numeracy skills for adults with intermediate and high levels of education. Correlations are lower for the comparisons involving the less-educated group: the correlation with the intermediate-educated group (Subgraph 3.A) is 0.44 and the one with the high-educated group 0.46 (Subgraph 3.B). Nevertheless, the overall picture emerging from Fig. 3 is one of rather strong interrelatedness: Countries where the less educated are very heterogeneous also tend to be countries where the intermediate and high educated are very heterogeneous. The relatively high degree of similarity across educational groups might partly reflect the impact of contextual factors that affect the different groups in similar ways. This possibility will be further pursued in next section where I take a closer look at the role of education systems. Another possible explanation is that some countries have more heterogeneous populations than others, and that these differences in population heterogeneity translate into more heterogeneous educational groups. While the within-group standard deviations in Fig. 3 are calculated after adjusting for differences in sex, age, and foreign-birth/foreign-language status, there clearly are many other individual-level characteristics that might influence the variance of literacy and numeracy skills within a country’s population. Potentially relevant factors include detailed adult training participation, immigration history, language proficiency, or childhood conditions. At least some of these factors are included in the more comprehensive set of covariates considered in the “Robustness checks” section below.
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I begin with two general observations that outline terms on which I agree with Caplan, given his analysis, and where I think that scholars interested in questions of educational justice and fairness in higher education should pay particular attention. In the final section, however, I argue that Caplan’s defense of “radical” cuts as the only way forward elides important questions about the justification of higher education.