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This paper explores the link between skill and qualification mismatch and labor productivity using cross-country industry data for 19 OECD countries. Utilizing mismatch indicators aggregated from micro-data sourced from the recent OECD Survey of Adult Skills (PIAAC), the main results suggest that higher skill and qualification mismatch is associated with lower labor productivity, with over-skilling and under-qualification accounting for most of these impacts. A novel result is that higher skill mismatch is associated with lower labor productivity through a less efficient allocation of resources, presumably because when the share of over-skilled workers is higher, more productive firms find it more difficult to attract skilled labor and gain market shares at the expense of less productive firms. At the same time, a higher share of under-qualified workers is associated with both lower allocative efficiency and within-firm productivity – that is, a lower ratio of high productivity to low productivity firms. While differences in managerial quality can potentially account for the relationship between mismatch and within-firm productivity, the paper offers some preliminary insights into the policy factors that might explain the link between skill mismatch and resource allocation.

The aim of this chapter is to determine the relationship between labor productivity and economic development. In this context, the annual data of Turkey on a range of 1970–2017 are included in the study period. On the other hand, these data are tested with the help of Toda Yamamoto causality analysis. Thus, it will be possible to determine whether there is a strong relationship between the two variables. According to the obtained results of the analysis, it is defined that there is a causal relationship from labor productivity to economic growth in Turkey. Based on these results, it can be said that labor productivity should rise in order to increase economic development in Turkey. For this purpose, educational programs in Turkey can be revised with the help of a detailed study. In this process, cooperation with companies to understand the needs in the market plays a key role. Additionally, regulations should also be prepared related to the salaries of the employees. If it can be possible to prevent employees from receiving wages below a certain amount by placing a minimum legal limit on salaries, it will be possible to increase the motivation of the employees. This situation has a positive and significant contribution to the labor productivity.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

Board size has received significant attention among researchers and regulators. However, the advisory role of boards has not been studied much. In this study I examine the notion that investors value larger boards for their advisory capabilities. Prior studies examine board size in the context of monitoring role of corporate boards and find opposite effects on debt holders and equity holders. Using market-based measures of total firm performance, which take both equity and debt into account; I find that larger boards are associated with greater economic value added (EVA). Using a sample of S&P 1500 firms from 2000 to 2003 and controlling for various firm and industry characteristics, I also find that the board size is positively associated with firm productivity and various other efficiency measures such as return on assets (ROA), return on equity (ROE) and Sales-Turnover ratio. I argue that firms with larger boards, valuing the advisory role of directors offer greater compensation to the directors. Overall the results indicate that large board size has a positive impact on firm's performance. The results are robust to alternative measures of firm performance and other key variables.

Recent literature on firm-level heterogeneity and trade has emphasized a self-selection mechanism: only the most productive firms can recover the transaction (sunk) costs for serving foreign markets and become exporters. The role of trade integration is that a productivity gap between exporters and nonexporters becomes lower when the market becomes more integrated due to a fall in trade costs. The focus of this chapter is the role of EU harmonization of food regulations in explaining the intra-EU export-productivity premium. The food industry is an interesting case to examine because many directives and regulations of the Single Market Program concern this important economic sector and have the potential to affect trade and productivity. We use data on Dutch food processing firms for the 1979–2005 period, which we link with a dataset that codes food products subject to EU harmonization. The chapter confirms that more productive firms are more likely to enter the EU export market. The result of EU harmonization is that this probability increases. Second, we find a positive and significant export-productivity premium: that is, firms that export to other EU markets are more productive than nonexporting firms. This finding is robust to the estimation technique and the way we measured TFP growth. Third, when we test whether the export-productivity premium is affected by EU harmonization, we find weak evidence that is the case for Dutch food processing firms: much depends on the estimation method, the way we measure TFP growth, and the population of exporting firms.