Economic transition as skill-biased technical change

The education sector is a critical factor in the economy’s technological advancement. Rapid technological changes require the education sector to retrain labour for optimal capital-labour complementarity. The robustly observed consequence of this process is an increase in inequality. For example, computers (or steam engines) are only useful if workers know how to use them. However, those workers who do will be rewarded better than the rest. This skill-biased technical change (SBTC) applies to organisational technologies too. 

A recent paper shows that the economic transition of the 1990s, which is viewed as a separate field of economics, can be parsimoniously reinterpreted as a particular case of SBTC. This view uniformly explains two actively researched areas of the Russian labour market: (1) the expansion of education and (2) the drop in the college wage premium. Using reduced form and structural models applied to the Russian household survey, the work shows that the reason for both is a massive demand-side shock for law and business (LB) skills driven by the reorganisation of the Russian economy. 

Figure 1: Russian economic output and college returns. Notes: Red dotted line is GDP per capita (right axis). Blue circles are the return on the college degree (left axis). The dashed vertical line separates periods: the Soviet, transition, and nowadays. Source: Alexeev (2022).

The paper starts by replicating the drop in the Russian college wage premium (Figure 1). Then, using novel data techniques, it scrutinises the supply side of the Russian labour market. Figure 2 shows the share of all employed workers with higher education from 1985 until 2015. A pronounced tendency can be seen: in 1985, LB graduates occupied approximately 3% of the employed workers, whereas, in 2015, it was 12%. Thus, the reason for the expansion of education was to saturate the market economy with the skills of LB graduates, which had neither demand nor supply in the command economy.

Figure 2: Share of graduates with higher education in the labour force. Source: Alexeev (2022).

A measurable price signal must accompany a change of this magnitude. Therefore, I reestimate the college return, allowing LB to have a separate value. Figure 3 confirms that there has been a massive increase in the return on LB graduates’ skills, with a subsequent drop. The overall wage premium in Figure 1 (a weighted average across all specialisations) mimics this massive transitory between-majors differential, explaining the puzzling decline in college returns that started in 1998.

Figure 3: Return of LB and other college graduates. Notes: The figure shows the estimate of the wage equation. Vertical grey dashed lines separate the periods, as in Figure 1. Dotted lines are locally weighted scatterplot smoothers. Source: Alexeev (2022).

Then the paper formulates and estimates a novel structural model to explicitly establish that the demand for LB skills is behind these returns. The structural model also shows that an economywide deficiency of LB skills can reduce economic performance, linking the demand shock with the GDP drop shown in Figure 1. 

To test this implication, the work shows that the size of the between-majors differential positively correlates with the recession in a cross-sectional sample of transitional economies. To proxy for the differential, the work uses the share of LB students in all enrolled students (easily accessible statistic). The logic for this proxy is that, as labour markets are competitive, an unusually high proportion of enrollments into the specialisation implies the desired wage differential.

Figure 4: GDP loss and recomposition of skills during the transition. Notes: The GDP loss is a percentage decrease of real GDP during the transitional recession. The share of LB graduates is their percentage in tertiary institutions after the transitional recession. Source: Alexeev (2022).

Apart from offering a uniform theory that links the transformation recension, college expansion and changes in college returns, this paper shows the importance of studying the technology-induced differentials within skilled labour. These differentials are known to be present in modern economies today due to a new wave of adaptation of high-return technologies (e.g., AI).

These differentials deserve further scrutiny on empirical and conceptual levels, as they are known to cause unexpected and (as the Russian case shows) severe drops in economic performance and income equality. These two effects in isolation are temporal, but if they are incorrectly interpreted, and the policy response is suboptimal, severe adverse consequences will follow.

(Lacking the skill to navigate the contractual and informational imperfections of the market economy, Russian businesses of the early 1990s executed bargaining powers using `crimson jackets.’ One of these is shown in the feature image at the top.)