In their analysis Anirban Mukherjee and Sourabh Bikas Paul Community identity and skill mismatch: A study on Indian labour market “characterize skill mismatch in Indian labor market” and assess “the role of community identity in explaining the existence of skill mismatch measured by the difference between a laborer’s education level and the educational requirement of a job (s)he is in”. (Adapted choosen excerpts by Job Market Monitor to follow) Such mismatch leads to inefficient allocation of resources asking for policy reorientation in both the education and labor sectors.
Network plays an important role in getting a job or being discriminated in the job market. Therefore if a community identity acts as an adverse (favorable) signal, people from that community should acquire more (less) education than the educational requirement for a job to compensate for the signal coming out of their community identities.This may lead to over or under education depending whether the community identity transmits an adverse or favorable signal.
The question of skill mismatch in the Indian labor market becomes even more important after the economic reforms in 1991 which led India to the path of skill-biased growth. Changes in the Indian labour market over recent decades has raised concern over misallocation of skill.
On the supply side, the proportion of high skilled workers increased substantially between 1983 and 2004-05. In 1983 the proportion of illiterate workers in working age population (not enrolled in any educational institution) was around 50.6 percent and proportion of graduate workers was around 3.75 percent. The corresponding figures are 29.6 and 8.4 in 2004-05. Since independence, the number of universities has increased by 18 times, the number of colleges by 35 times and gross enrollment ratio more than 10 times.
There is signicantly greater number of people with higher skill level left out of active labour force in 2003-04 compared to twenty years back. What may cause this declining supply (not in absolute sense) of talent pool? One reason could be higher educational attainment of women but less participation due to several social constraints, especially after marriage exit from labour force. To understand the dynamics, the autors also calculate the labour force participation rates by sex (not reported). It is found that labour force participation rate by skill level remained constant over time for male population.
Skills Mismatch
The estimated proportions of over-educated and under educated workers are given in Figure 4(a) and 4(b) respectively. The author group three digit levels into seven broad occupation categories. On the other hand, there is a clear trend in convergence of under-education rates across occupation.
groups over time.
The rates of over-education are signicantly high for all occupation groups ranging from 13 percent to 19 percent. During 1983, the rate was very similar across all occupations. Over time, the rates diversied.
The authors find that both Muslim, Scheduled Castes (SCs) and the Scheduled Tribes (STs) (SC/ST) identity have positive signicant impact on the probability of over education. They also find that in case of under education Muslim identity is positive signicant while SC/ST is not. They calculate the extent of over and under education for different industries and the wage effect of over education which is found to be positive.
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