No study has been conducted linking Chinese migrants’ subjective well-being (SWB) with urban inequality. This paper presents the effects of income and inequality on their SWB using a total of 128,000 answers to a survey question about “happiness”. We find evidence for a satiation point above which higher income is no longer associated with greater well-being. Income inequality is detrimental to well-being. Migrants report lower SWB levels where income inequality is higher, even after controlling for personal income, a large set of individual characteristics, and province dummies. We also find striking differences across socio-economic and geographic groups. The positive effect of income is more pronounced for rural and western migrants, and is shown to be significantly correlated with the poor’s SWB but not for the well-being of more affluent respondents. Interestingly, high-income earners are more hurt by income inequality than low-income respondents. Moreover, compared with migrants in other regions, those in less developed Western China are found to be more averse to income inequality. Our results are quite robust to different specifications. We provide novel explanations for these findings by delving into psychological channels, including egalitarian preferences, social comparison concerns, expectations, perceived fairness concerns and perceived social mobility.

The sharp changes in oil prices since 2004 featured a nonlinear data-generating mechanism which displayed bubble-like behavior. A popular view is that such a salient pattern cannot be explained by shifts in economic fundamentals, but was driven by speculative bubbles as a consequence of the increased financialization of oil future markets. Testing this hypothesis, however, is challenging since the fundamental component of the oil price is unobservable. This paper attempts to isolate the contribution of speculative bubbles and fundamentals to the evolution of oil prices by providing a stylized model of commodity pricing. Motivated by our theoretical model, we adopt a continuous-time model with a random and time-varying persistence parameter to empirically investigate the presence of speculative bubbles in daily oil future prices over the period April 1983 to June 2020. We do not find any evidence in favor of speculative bubbles, although we indeed find that oil prices exhibit episodes of unstable behavior after 2004.

Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping heterogeneous fuzzy data to a unified distribution space by adjusting the mean and variance of data from each information source. As a result of combining the normalized descriptions from various sources for an object, we can obtain a fused representation of that object. We then derive an adaptive Gaussian-shaped membership function based on the addition law of the Gaussian distribution. GFIS uses it to dynamically granulate fusion inputs and to design inference rules. This proposed membership function has the advantage of being able to adapt to changing information sources. Finally, we integrate the normalization method and adaptive membership function to the Takagi–Sugeno (T–S) model and present a modified fuzzy inference framework. Applying our methodology to four datasets, we confirm that the data do lend support to the theory implying the improved performance and effectiveness.