The Complementary Role of Exports and R&D Investments as Sources of Productivity Growth

This paper examines two potential channels of knowledge acquisition that underlie firm productivity growth in the Taiwanese electronics industry: participation in the export market and investments in R&D and/or worker training. We focus on the argument that a firm's own investments in R&D are necessary for the firm to assimilate knowledge or expertise gained from foreign contacts and thus are an important component of the process of learning-by-exporting. Firm-level panel data from 1986, 1991, and 1996 is used to investigate a firm's decision to invest in these two activities and to assess the effects of these investments on the firm's future total factor productivity. The empirical model consists of four equations. The firm's decisions to export and invest in R&D and/or worker training are modeled with a bivariate probit model that recognizes the interdependence of the decisions. We then estimate how participation in these investment activities alters the firm's future productivity trajectory while controlling for the potential selection bias introduced by endogenous firm exit. The primary empirical findings are that, on average, firms that export but do not invest in R&D and/or worker training have significantly higher future productivity than firms that do not participate in either activity. In addition, firms that export and invest in R&D and/or worker training have significantly higher future productivity than firms that only export. These findings are consistent with the hypothesis that export experience is an important source of productivity growth for Taiwanese firms and that firm investments in R&D and worker training facilitate their ability to benefit from their exposure to the export market.

Productivity and the Decision to Export: Micro Evidence from Taiwan and South Korea

While there is widespread empirical evidence indicating exporting producers have higher productivity than nonexporters, the mechanisms that generate this pattern are less clear. One view is that exporters acquire knowledge of new production methods, inputs, and product designs from their international contacts, and this learning results in higher productivity for exporters relative to their more insulated domestic counterparts. Alternatively, the higher productivity of exporters may simply reflect the self-selection of more efficient producers into a highly competitive export market. In this paper we use micro data collected in the manufacturing censuses in South Korea and Taiwan to study the linkages between a producer's total factor productivity and choice to participate in the export market. We find differences between the countries in the importance of selection and learning forces. In Taiwan, transitions of firms in and out of the export market reflect systematic variations in productivity as predicted by self-selection models. Firms with higher productivity, ex ante, tend to enter the export market and exporters with low productivity tend to exit. Moreover, in several industries, entry into the export market is followed by relative productivity improvements, a result consistent with learning-by-exporting forces. In South Korea, the evidence of self-selection on the basis of productivity is much weaker. In addition, unlike Taiwan, we find no significant productivity changes following entry or exit from the export market that are consistent with learning from exporting. Comparison of the two countries suggests that in Korea factors other than production efficiency play a more prominent role as determinants determinants of the export decision.

Productivity, output, and failure

노트 : The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research

R&D Investment, Exporting, and Productivity Dynamics

A positive correlation between productivity and export market participation has been well documented in producer micro data. Recent empirical studies and theoretical analyses have emphasized that this may reflect the producer's other investment activities, particularly investments in R&D or new technology, that both raise productivity and increase the payoff to exporting. In this paper we develop a dynamic structural model of a producer's decision to invest in R&D and participate in the export market. The investment decisions depend on the expected future profitability and the fixed and sunk costs incurred with each activity. We estimate the model using plant-level data from the Taiwanese electronics industry and find a complex set of interactions between R&D, exporting, and productivity. The self- selection of high productivity plants is the dominant channel driving participation in the export market and R&D investment. Both R&D and exporting have a positive direct effect on the plant's future productivity which reinforces the selection effect. When modeled as discrete decisions, the productivity effect of R&D is larger, but, because of its higher cost, is undertaken by fewer plants than exporting. The impact of each activity on the net returns to the other are quantitatively unimportant. In model simulations, the endogenous choice of R&D and exporting generates average productivity that is 22.0 percent higher after 10 years than an environment where productivity evolution is not affected by plant investments.

Role of the Perigenual Anterior Cingulate and Orbitofrontal Cortex in Contingency Learning in the Marmoset.

Two learning mechanisms contribute to decision-making: goal-directed actions and the "habit" system, by which action-outcome and stimulus-response associations are formed, respectively. Rodent lesion studies and human neuroimaging have implicated both the medial prefrontal cortex (mPFC) and the orbitofrontal cortex (OFC) in the neural basis of contingency learning, a critical component of goal-directed actions, though some published findings are conflicting. We sought to reconcile the existing literature by comparing the effects of excitotoxic lesions of the perigenual anterior cingulate cortex (pgACC), a region of the mPFC, and OFC on contingency learning in the marmoset monkey using a touchscreen-based paradigm, in which the contingent relationship between one of a pair of actions and its outcome was degraded selectively. Both the pgACC and OFC lesion groups were insensitive to the contingency degradation, whereas the control group demonstrated selectively higher performance of the nondegraded action when compared with the degraded action. These findings suggest the pgACC and OFC are both necessary for normal contingency learning and therefore goal-directed behavior.This research was supported by a Programme Grant [G0901884] from the Medical Research Council UK (MRC) to ACR, and a Wellcome Trust Senior Investigator Award [104631 /Z/14/Z] to TWR. SAWJ was supported by a BCNI-MRC studentship. The authors wish to thank C. H Parkinson and R. Underwood for preparation of the histological material. T.W.R. consults for Cambridge Cognition, Lilly, Lundbeck, Teva, Shire Pharmaceuticals and Merck, Sharp and Dohme. He has received research grants from Lilly, Lundbeck and GSK. The remaining authors have no potential competing financial interests to disclose.This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/cercor/bhw06

Best-Practice Criteria for Practical Security of Self-Differencing Avalanche Photodiode Detectors in Quantum Key Distribution

Fast-gated avalanche photodiodes (APDs) are the most commonly used single photon detectors for high-bit-rate quantum key distribution (QKD). Their robustness against external attacks is crucial to the overall security of a QKD system, or even an entire QKD network. We investigate the behavior of a gigahertz-gated, self-differencing (In,Ga)As APD under strong illumination, a tactic Eve often uses to bring detectors under her control. Our experiment and modeling reveal that the negative feedback by the photocurrent safeguards the detector from being blinded through reducing its avalanche probability and/or strengthening the capacitive response. Based on this finding, we propose a set of best-practice criteria for designing and operating fast-gated APD detectors to ensure their practical security in QKD

Best-Practice Criteria for Practical Security of Self-Differencing Avalanche Photodiode Detectors in Quantum Key Distribution

Fast gated avalanche photodiodes (APDs) are the most commonly used single photon detectors for high bit rate quantum key distribution (QKD). Their robustness against external attacks is crucial to the overall security of a QKD system or even an entire QKD network. Here, we investigate the behavior of a gigahertz-gated, self-differencing InGaAs APD under strong illumination, a tactic Eve often uses to bring detectors under her control. Our experiment and modelling reveal that the negative feedback by the photocurrent safeguards the detector from being blinded through reducing its avalanche probability and/or strengthening the capacitive response. Based on this finding, we propose a set of best-practice criteria for designing and operating fast-gated APD detectors to ensure their practical security in QKD

Intensity modulation as a preemptive measure against blinding of single-photon detectors based on self-differencing cancellation

Quantum key distribution is rising as an important cryptographic primitive for protecting the communication infrastructure in the digital era. However, its implementation security is often weakened by components whose behavior deviates from what is expected. Here we analyze the response of a self-differencing avalanche photodiode, a key enabler for high speed quantum key distribution, to intense light shone from a continuous-wave laser. Under incorrect settings, the cancellation entailed by the self-differencing circuitry can make the detector insensitive to single photons. However, we experimentally demonstrate that even in such cases intensity modulation can be used as an effective measure to restore the detector's expected response to the input light.A.K.-S. gratefully acknowledges financial support from Toshiba Research Europe Ltd. and the Engineering and Physical Sciences Research Council (EPSRC) through an Industrial CASE studentship Grant No. NMZE\187 (RG84410)