Essays into Firms, Innovation and Productivity

This empirical research aims to understand firm-level innovation and productivity in the context of firm’s innovative activities or international activities such as offshoring and exporting. The PhD dissertation consists of three chapters that use the Survey of Business Activities annually provided by the Statistics Korea to gain insights into the link between such activities and firm performances such as markup or productivity. Chapter 1 investigates the link between R&D and firm-level markups. A detailed data on R&D expenditures is used to examine whether a firm’s innovation activities have any impact on its markups rather than productivity, whose relationship is already well-established in the literature. R&D is likely to help to differentiate a firm’s products from those of other competitors, thus boosting its demand. However, this demand-enhancing aspect of R&D has not been thoroughly examined in the literature. With the consistent estimation of firm-level markups using a dataset of the Korean manufacturing firms, it is found that R&D increases firm-level markups, despite its size being small. To account for the fact that R&D is simply a measure of the innovation input, patent counts have been used as an alternative measure of innovation, but no evidence of positive effect was found. This may indicate that demand-enhancing innovations are not necessarily all patented, which may be due to various reasons. Chapter 2 investigates the learning-by-exporting hypothesis. This has been widely researched in the current literature. However, this chapter places more focus on the estimation of productivity, which has been given little attention. In the learning-by-exporting hypothesis, it is theoretically suggested that productivity increases as a result of increase in efficiency. However, the conventional total factor productivity (TFP) measure contains information not only on efficiency but also on measurement errors and temporary shocks, the latter of which is hardly related to the theoretical link between exports and productivity. In this chapter, a real total factor productivity that is derived as part of the semi-parametric estimation (denoted RTFP) is suggested as an alternative measure in which the latter elements above can be minimised. The findings show that, when RTFP is employed, the-learning-by-exporting effect is not only positive but also significant and long-lasting. However, this effect becomes short-lived and insignificant when the TFP is used. This does not necessarily suggest that the learning-by-exporting effect is better captured with the RTFP, but a certain measure, despite being not so relevant for productivity, can end up providing a misleading result. Moreover, the markups measure obtained from the first chapter are used to reconcile the fact that the productivity measure is measured by revenue data rather than quantity. Chapter 3 examines the firm-level productivity, with much focus on offshoring. This chapter suggests modification to the Levinsohn-Petrin (LP) method to ensure alignment with the context of offshoring. This chapter suggests that value-added be used in place of gross sales when estimating a production function using the LP method to avoid inconsistent estimation in the second stage. The results show that offshoring has a positive impact on productivity, but the effect is not long-lasting. This suggests that offshoring can enhance productivity in the short-term by a composition effect in which resources are reallocated towards more productive activities or departments, whilst offshoring less productive ones to foreign vendors. The results also show that the modifications generate a significant difference in the estimators, suggesting the possibility of misleading results if no modification is made. RTFP, introduced in the second chapter, is also employed as a dependent variable and the results again display a highly significant effect of offshoring on productivity. As will be explained in the second chapter, RTFP is more fitting in measuring the trend of productivity, as it is designed to be less influenced by transitory shocks or measurement errors. Thus, the change in results indicates that the productivity-enhancing effect is clearer when using RTFP, however becomes less so when the conventional measure is employed. Moreover, the same estimates of markups from the first chapter are employed again to mitigate any bias arising from the demand-side

Tracking and Estimation of Multiple Cross-Over Targets in Clutter

Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations