We Eat First with Our (Digital) Eyes: Enhancing Mental Simulation of Eating Experiences via Visual-Enabling Technologies

This research examines how consumers’ intentions to purchase food change depending on the visualisation mode (3D vs. AR) and product format (served vs. packaged). In three studies, we demonstrate that mental simulation of eating experiences (process and outcome) mediate these effects. Study 1 shows that AR visualisation of a served food improves simulation of the eating process over 3D visualisation, with a positive effect on purchase intention. Study 2 reveals that 3D visualisation improves purchase intention for packaged products (high instrumental properties) over served products (low instrumental properties) while the opposite is true for AR visualisation. In addition, interactivity and immersion mediate the effects of 3D (vs. AR) on mental simulation of the eating process for packaged products. Study 3 extends these results by showing that 3D increases purchase intention by eliciting mental simulation of the eating outcome, when the food is visible due to transparent (vs. opaque) packaging (displaying both sensory and instrumental properties), but that no such differences emerge for AR. This research highlights the importance of using different visualisation modes to promote food depending on the product format. The findings have important implications for both offline and online retailers.     

Personal Income Tax Reform in China in 2018 and Its Impact on Income Distribution

In the context of personal income tax (PIT) reform in China in 2018, this paper examines some of the major issues of concern regarding the reform and income distribution. Using the China Personal Income Tax Micro‐simulation model, the paper compares the differences between the 2011 and 2018 PIT systems, and finds that residents relying on different income sources may face a large degree of real tax rate change. Once the tax system is altered to PIT 2018, the coverage of PIT for wage earners will decrease from 46.9 to 23.4 percent, the income redistributive effect will drop from 1.95 to 1.22 percent and the PIT's role in fiscal revenue will also be negatively affected. Nevertheless, if individual income continues to grow, the share of PIT in fiscal revenue is expected to return to the 2018 level in 2022, but its income redistribution function is difficult to recover in the short term. The paper finds that the effect of PIT on income distribution depends on the tax structure. Gradual transition to an “entirely comprehensive” tax system when conditions are appropriate will achieve better income redistribution results at a lower average tax rate.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

Impact of modern communication infrastructure on productivity,production structure and factor demands of US industries: Impact revisited

This study investigates the contribution of modern communication infrastructure characterized by high speed broadband access network on the productivity growth, production structure and factor demands for US industries and for the aggregate economy. To evaluate such contribution, we modify the traditional cost function by incorporating communication infrastructure as input in production process in conjunction with other public infrastructures. The network externality and spillover effect of broadband access technology are captured by introducing broadband penetration rate as a shift factor in industry level production function. Empirical results show that the increased use of modern communications infrastructure increases the productivity of all industries with wide variations across industries. Estimated impacts on input demands show that increase in use of communications infrastructure service saves labor and materials and increase the demand for private capital. Finally, aggregate social rate of return on such investment has been estimated for policy implications.     

R&D Investments and Tax Incentives: The Role of Intra-Firm Cross-Border Collaboration*

U.S. multinational corporations increasingly use intra-firm, cross-border research collaboration to disperse R&D across different countries. This paper investigates the implications of such collaboration on the abilities of firms to garner benefits from R&D tax incentives. We find that the association between R&D intensity and tax incentives is three to five times larger when firms have extensive cross-border collaboration connected to a country. We also find that the effect is stronger when local intellectual property protection is weaker and when local innovation resources are higher. Our results suggest that cross-border collaboration helps firms achieve more tax-efficient R&D investments both by reducing the nontax frictions posed by weak intellectual property protection and by increasing the nontax benefits of foreign R&D.     

Does corruption slow down innovation? Evidence from a cointegrated panel of U.S. states

I investigate the long-run relationship between corruption and innovative activity using annual data from 48 contiguous U.S. states between 1977 and 2006. Using U.S. data allows me to work with a panel long enough to exploit time series properties of the data. I use two different measures of innovative activity: one measuring the quantity and the other measuring the quality of the patents granted. I also use two different measures of corruption: one based on the number of corruption convictions, the other based on number of corruption stories covered in Associated Press news wires. Following Pedroni (1999, 2000), I estimate the cointegrating relationship between corruption and innovative activity with Fully Modified Ordinary Least Squares (FMOLS). The results indicate that corruption indeed slows down innovation in the long-run.     

Increasing linkages among European regions. The role of sectoral composition

This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.     

Periodic strategies in optimal execution with multiplicative price impact

We study the optimal execution problem with multiplicative price impact in algorithmic trading, when an agent holds an initial position of shares of a financial asset. The interselling decision times are modeled by the arrival times of a Poisson process. The criterion to be optimized consists in maximizing the expected net present value of the gains of the agent, and it is proved that an optimal strategy has a barrier form, depending only on the number of shares left and the level of the asset price.