Since total factor productivity growth plays an essential role in China’s economic growth, the source of this growth has been a critical issue over the past decades. Hence, this paper applies an input slack-based productivity (ISP) index to investigate the contributors (i.e., labor and capital inputs) to China’s total factor productivity growth. The ISP index, combining the features of the directional distance function and Luenberger productivity index, can calculate the productivity change of each input factor under the total factor framework. According to the decomposition analyses, we find that China is confronting a dual challenge in total factor productivity growth: first, capital productivity growth exhibits a remarkable slowdown after the mid-1990s; second, although labor productivity continually expands, the relative labor efficiency among provinces has deteriorated since the 2000s. The results imply that the government should not only advocate upgrading industrial structure, but also consider balanced regional development policies for China’s sustainable growth.

This research examines whether Google search volume index (GSVI), a proxy of investor attention, can predict the excess returns and abnormal trading volumes of TPEx 50 index constituents. It also explores the motive underlying GSVI based on positive or negative shocks to stock prices. The empirical data include 48 companies from TPEx 50 index constituents and cover a period from 1 September 2016 to 31 August 2019. The empirical results present that (1) lagged GSVI negatively affects current excess returns, perhaps due to the characteristics of TPEx, in which there are a higher proportion of retail investors, smaller listed companies, and a higher information asymmetry problem. (2) Lagged GSVI can positively affect abnormal current trading volumes. (3) If GSVI is driven by positive shocks, then it can predict excess returns and abnormal trading volumes positively.

This paper applies the context-dependent total-factor energy efficiency (CD-TFEE) to determine the multi-layer disaggregate energy efficiency frontiers of twenty administrative regions in Taiwan for the year of 2016. The CD-TFEE overcomes the shortcoming of conventional TFEE index that TFEE is not able to find the “closest target” for each inefficient region in the short run. Furthermore, the CD-TFEE scores here deal with four types of energy inputs (electricity for production, electricity for household and non-household lighting, diesel sales, and gasoline sales), illustrating that multi-layer TFEE frontiers for each energy input in the case of Taiwan can be computed. Empirical results indicate that there are three levels of TFEE frontiers for electricity for production and four levels for other types of energy inputs. In addition, New Taipei City, Taipei City, Keelung City, and Penghu County are at the top level of TFEE frontier for all four energy inputs. This paper also demonstrates that the CD-TFEE procedure generates results different from the CD-DEA introduced by Seiford and Zhu (2003).

Purpose: This research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.Design/methodology/approach: This paper adopts a two-state Markov switching model to characterize the bull and bear markets spanning from 1994 to 2019 and then conduct a bear stock market predictability test by running regressions between the filtered probabilities of bear markets and a series of macroeconomic variables in turn at different horizons of 1, 3, 6, 12 and 24 months.Findings: This paper shows that inflation rates, changes in real exchange rates, and foreign currency reserve growth are key predictors of bear markets in China, while term spreads, unemployment rates and foreign reserve growth are major factors that can predict bear markets in Taiwan. Remarkably, industrial production growth does not have predictive power for bear markets, which may suggest emerging markets are driven by fund flows rather than real economic activities. Besides, the impact directions of foreign currency reserve growth are opposite, which may be due to different proportions of the financial accounts in their balance of payments.Practical implications: In practical respect, this paper provides market participants the usefulness, impact direction and implications of bear market predictors when building their market-timing strategies in China and Taiwan stock markets. The government institutions may also thereby make appropriate policies to prevent huge stock market downturns and serious drawbacks.Originality/value: It highlights the “fund-driven market hypothesis” and “foreign currency reserve effects” that commonly dominate Taiwan and China stock markets since both are highly affected by international funds.

This study aims to examine how the social networks of top management affect the recovery of their companies when facing a financial crisis. We mainly use the logit and Cox regression models to investigate whether social networks help overcome the financial distress and shorten the crisis duration. The empirical findings suggest that companies with characteristics of low degree centrality of the chairman’s bank networks and high closeness centrality of the general manager’s general networks and bank networks are more likely to overcome financial distress and get back to normal status. Furthermore, for companies with characteristics of low degree centrality of the chairman’s personal general networks, low closeness centrality of the financial executive’s personal general networks, and high degree centrality of the financial executive’s personal bank networks, it was easier to shorten the crisis duration. The practical implication is that companies need to prioritize quality over quantity in order to survive or shorten the crisis. All company top managers should not look only at the size of the company but consider how the social network is configured.