The Impact of Home Ownership on Life Satisfaction in Urban China: A Propensity Score Matching Analysis

China has implemented a series of socioeconomic reforms since 1978. One of the reforms allows urban residents to purchase their own houses rather than renting houses from state institutions which has resulted in a rapid increase in home ownership. This paper estimates the impact of home ownership on life satisfaction in urban China on the basis of the 2010 wave of the China General Social Survey. Special attention is paid to the methodological problem of confoundedness between the determinants of home ownership and life satisfaction. Propensity score matching (PSM) is applied to control it. The results show that PSM reduces upward estimation bias caused by confoundedness and that it is more appropriate to control confoundedness than ordered probit regression. The estimates furthermore indicate that home ownership has a significant positive impact on life satisfaction of medium- and high income urban residents. For low income urban residents, the impact is also positive, though insignificant. The outcomes connect to the objectives of national development policy and thus have several important policy implications. First, the central and local governments, especially in provinces where it is still low, may want to continue stimulating home ownership as it enhances life satisfaction. Secondly, specific programs may be designed to make home ownership financially affordable for low income groups. Thirdly, local governments may want to initiate or intensify urban (renewal) programs to improve poor public facilities including public transportation, green space and sports accommodations in the immediate vicinity of depressing low income neighborhoods.     

Tuning down the hedonic brain: Cognitive load reduces neural responses to high-calorie food pictures in the nucleus accumbens

The present research examined whether cognitive load modulates the neural processing of appetitive, high-calorie food stimuli. In a functional magnetic resonance imaging (fMRI) study, participants quickly categorized high-calorie and low-calorie food pictures versus object pictures as edible or inedible while they concurrently performed a digit-span task that varied between low and high cognitive load (memorizing six digits vs. one digit). In line with predictions, the digit-span task engaged the dorsolateral prefrontal cortex (DLPFC) when cognitive load was high compared to low. Moreover, exposure to high-calorie compared to low-calorie food pictures led to increased activation in the nucleus accumbens (NAcc), but only when cognitive load was low and not when it was high. In addition, connectivity analyses showed that load altered the functional coupling between NAcc and right DLPFC during presentation of the high-calorie versus low-calorie food pictures. Together, these findings indicate that loading the cognitive system moderates hedonic brain responses to high-calorie food pictures via interactions between NAcc and DLPFC. Our findings are consistent with the putative cognitive nature of food motivation. Implications for future research are discussed.     

Prime and probability: causal knowledge affects inferential and predictive effects on self-agency experiences

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their (combined) contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence (50% or 80%) of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired.     

The Longitudinal Relationship Between Satisfaction with Transitional Care and Social and Emotional Quality of Life Among Chronically Ill Adolescents

This study aimed to identify the relationship between satisfaction with transitional care and quality of life of chronically ill adolescents over time. This longitudinal study included adolescents with type I diabetes, juvenile idiopathic arthritis (JIA), and neuromuscular disorders (NMD). At baseline 138 respondents (response rate 31 %) filled in a questionnaire and 188 about 1 year later (response rate 43 %). Analysis of variance showed that adolescents with diabetes reported the highest physical quality of life, followed in order by those with NMD and JIA (p?≤?0.01). Adolescents with diabetes reported the highest social quality of life, followed in order by those with JIA and NMD (both at p?≤?0.001). Univariate analyses showed that satisfaction with transitional care at T0 was significantly related to emotional and physical quality of life at T1 (both at p?≤?0.05). At T1, satisfaction with transitional care was significantly related to the emotional, physical, and social domains of quality of life (all at p?≤?0.001). Multiple regression analyses revealed that satisfaction with transitional care at T1 was related to emotional (β -0.20; p?≤?0.05) and social (β -0.35; p?≤?0.01) quality of life domains over time. This indicates that lower gap scores, which measured differences between ‘best care’ and ‘current care,’ are associated with better social and emotional quality of life in this sample of adolescents. Satisfaction with transitional care and social and emotional quality of life are related over time.     

Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

On the inference of personal authorship: enhancing experienced agency by priming effect information

Three experiments examined whether the mere priming of potential action effects enhances people's feeling of causing these effects when they occur. In a computer task, participants and the computer independently moved a rapidly moving square on a display. Participants had to press a key, thereby stopping the movement. However, the participant or the computer could have caused the square to stop on the observed position, and accordingly, the stopped position of the square could be conceived of as the potential effect resulting from participants' action of pressing the stop key. The location of this position was primed or not just before participants had to stop the movement. Results showed that (subliminal as well as supraliminal) priming of the position enhanced experienced authorship of stopping the square. Additional experimentation demonstrated that this priming of agency was not mediated by the goal or intention to produce the effect.     

The role of figure-ground segregation in change blindness

Partial report methods have shown that a large-capacity representation exists for a few hundred milliseconds after a picture has disappeared. However,change blindness studies indicate that very limited information remains available when a changed version of the image is presented subsequently. What happens to the large-capacity representation? New input after the first image may interfere, but this is likely to depend on the characteristics of the new input. In our first experiment, we show that a display containing homogeneous image elements between changing images does not render the largecapacity representation unavailable. Interference occurs when these new elements define objects. On that basis we introduce a new method to produce change blindness: The second experiment shows that change blindness can be induced by redefining figure and background, without an interval between the displays. The local features (line segments) that defined figures and background were swapped, while the contours of the figures remained where they were. Normally, changes are easily detected when there is no interval. However, our paradigm results in massive change blindness. We propose that in a change blindness experiment, there is a large-capacity representation of the original image when it is followed by a homogeneous interval display, but that change blindness occurs whenever the changed image forces resegregation of figures from the background.     

Retrieving the correlation matrix from a truncated PCA solution: The inverse principal component problem

Whenr Principal Components are available fork variables, the correlation matrix is approximated in the least squares sense by the loading matrix times its transpose. The approximation is generally not perfect unlessr =k. In the present paper it is shown that, whenr is at or above the Ledermann bound,r principal components are enough to perfectly reconstruct the correlation matrix, albeit in a way more involved than taking the loading matrix times its transpose. In certain cases just below the Ledermann bound, recovery of the correlation matrix is still possible when the set of all eigenvalues of the correlation matrix is available as additional information.