This paper examines the incidence and the effect of overskilling on wages by taking individuals\u2019 unobserved heterogeneity in ability using quantile regression (QR) method. Using data from the second Malaysia Productivity and Investment Climate Survey (PICS-2), the incidence of overskilling was reported around 31 percent – for which moderately overskilled accounted for 23 percent and severely overskilled accounted for 8 percent. Preliminary analysis revealed that overskilling was found to be heavily concentrated within low-ability segments of the workers\u2019 conditional wage distributions. Using quantile regression (QR) method, the results revealed that although being overskilled resulted in wage penalty, the penalty, however, was heterogeneous across the entire workers\u2019 conditional wages distribution. Indeed, the penalty for moderately overskilled was greater at the lower deciles and became smaller or even disappears as one moved up the wages distribution. This may be consistent with the view that the overskilled workers are likely amongst the lowability workers. By contrast, the penalty for severely overskilled, in particular women was evident all the way through the conditional wage distribution. This perhaps suggests that unobserved heterogeneity unable to explain the wages penalty for mismatched women. Nevertheless, this study may suggest the importance of including explicit controls for individuals\u2019 unobserved ability where possible, as a mean to avoid bias estimation of the wage impacts of the overskilling.<\/p>\n","protected":false},"template":"","tags":[1000,659,1001,346],"jel-code":[],"article-type":[1329],"class_list":["entry","author-jem","post-5338","article","type-article","status-publish","tag-overskilling","tag-quantile-regression","tag-unobserved-ability","tag-wages","article-type-normal-article"],"yoast_head":"\n