Skills mismatch Policy scenarios and applications

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2. Policy scenarios and applications

This section presents applications of EPMs to inform employment and labour policies. Specifically, it illustrates the use of the models to project and analyse the issue of skills mismatch and to simulate potential labour market impacts of a change in fiscal policy. Even though the issue of skill mismatch has received renewed attention in the advanced economies due to the economic crisis ILO, 2013, the formulation and implementation of effective education and training policies, including responsive education and training systems, is a continuous challenge for all countries. Meeting this challenge requires not only linking skills development with employment and economic development, and involving social partners and key stakeholders in skills development systems, but also effective labour market information and analysis systems. One of the outputs of such systems can be projections of skills mismatch which inform education and skills policies. Similarly, employment projections models can be used to produce a credible projection of the impact of macroeconomic policies, within the constraints set by the model at hand.

2.1. Skills mismatch

Confronted with consistently high unemployment, despite growing employment opportunities, policymakers often face the issue of skills mismatch. Skills mismatch – when skills supply does not correspond to skills demand in an industry or in the economy as a whole – may indeed cause unemployment rates to remain high, as firms are reluctant to hire workers without adequate skills or qualifications, preferring to leave certain positions vacant or else hire workers from abroad. 35 Skills and qualifications mismatch does not only lead to high unemployment however, but may also exist in the form of workers employed in occupations that underutilise their skills set overqualified workers or in occupations normally requiring skills that they do not possess under-qualified workers. In both cases, this skill mismatch affects job satisfaction and wages of individual workers, as well as the productivity of firms. It may also lead to increases in turnover of staff Quintini, 2011. In line with much of the literature, in this paper education level of schooling is used as a proxy for skills, and three levels of educational attainment are distinguished: less than secondary, secondary and tertiary. Therefore, an important and highly relevant factor with respect to the skills mismatch issue is the quality of education. However, available data do not reflect the quality of education. An Asian Development Bank ADB study on education and structural change in four Asian economies, including the Philippines, highlighted the need for labour force surveys to collect data on school attributes in order to allow for an analysis of the impact of education quality on labour market outcomes ADB, 2007b. Nevertheless, available LFS data over the historical periods and PEPM projections over the forecast period allow significant insights and analysis of the skills mismatch issue. The so-called ‘normative’ approach to skills mismatch has been adopted in this paper to measure skill mismatch. In this approach, mismatch is determined based on the presumed correspondence between education levels and major occupational groups see 35 A situation in which the demand and supply for skills do not match is often characterized by a ‘skill shortage’ or ‘skill surplus’, while ‘skill mismatch’ is used with reference to job characteristics e.g. Quintini, 2011, Table 1. As discussed in the main text, skill mismatch is used in a broad sense in this paper, but nevertheless in relation to the requirements of jobs. 35 e.g. ILO, 2011b, box 5a. Alternative approaches such as the ‘statistical’ and ‘self-declared’ method seem less appropriate in the context of employment projection models. 36 In addition, a distinction is made between ‘actual’ mismatch and ‘potential’ mismatch. Actual mismatch is a measure of the number or share of workers employed in occupations requiring a different skill level than they possess, resulting in three categories of workers ‘no mismatch’; ‘overqualified’; and ‘underqualified’. 37 The shares of these three categories have been calculated over the historical period, and the actual mismatch distribution over the forecast period is obtained by linearly projecting these shares in accordance with the sectoral employment projections. Potential mismatch also refers to the number of workers who are either employed in occupations for which they have the adequate skills level, are under-qualified, or are overqualified for their positions, but in this case mismatch is calculated on the basis of trends in supply and demand. The skills distribution of workers skills supply is determined for the historical period and compared with the skills distribution that is required by the occupational distribution skills demand, 38 resulting again in three categories of workers ‘no mismatch’; ‘overqualified’; and ‘underqualified’. Similarly, potential mismatch is obtained over the forecast period by subtracting demand obtained from translating the forecasted employment by occupation from the PEPM into their associated levels of educational attainment from supply obtained by linearly projecting the skills distribution of workers for each industry. 39 36 The ‘statistical’ method is based on the actual distribution of education of workers in each occupation, while the ‘self-declared’ method is based on workers’ views regarding the match between their education and their job Hartog, 2000. Both methods rely on information that is not readily available for future years. 37 For each industry j, actual mismatch is calculated as: where and represent the number of workers with skills level , who are over- and under- qualified, respectively. 38 For each industry j, potential mismatch is calculated as: where and represent the supply and demand of workers at skills level i. Note that when demand exceeds supply for a skill level, potential mismatch is set to zero. 39 Potential mismatch comes closer to the concept of skill shortage as usually discussed in the literature. 36