Productivity spillovers from multinational corporations (MNCs) to local firms have been an area of keen research interest in developing economics. Claims of positive spillovers in the form of technology transfers have been questionable, in part because of the many ambiguous conclusions obtained. The paper argues that the lack of focus in the mechanisms underpinning spillovers may be one of the reasons for the ambiguity. Using local input–output linkages as the mechanism for technology transfer, this study examines the presence and the enabling conditions for spillovers. Accounting for the variations in firms' characteristics, the findings show that skills‐oriented MNCs participating in international production networks transmit horizontal spillovers to local establishments. Vertical spillovers from MNCs are mostly relevant only to lower‐skilled establishments. For skilled and export‐oriented local establishments, technologies learned from producing for international production networks are more significant than forming linkages with MNCs in the domestic market. 相似文献
This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight S&P500 index and a newly constructed index, Asian Tigers Malaysia Index (ATMI). 11 years historical data of stock indexes futures and the economic statistics are studied; 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.