This article examines the trends in the economic advantage that highly educated immigrants hold over less educated immigrants in Canada, focusing on the differences between short-run and longer-run outcomes. Using data from the Longitudinal Immigration Database covering the period from the 1980s to the 2000s, this study finds that the relative entry earnings advantage that higher education provides to new immigrants has decreased dramatically over the last 30 years. However, university-educated immigrants had a much steeper earnings trajectory than immigrants with trades or a high school education. The earnings advantage among highly educated immigrants increases significantly with time spent in Canada. This pattern is observed for virtually all immigrant classes and arrival cohorts. The results suggest that shortrun economic outcomes of immigrants are not good predictors of longer-run results, at least by educational attainment. The implications of these findings for immigration selection policy are discussed in the conclusion.

We assess the risk and cost of worker displacement in Canada over the last three decades. We show that neither the risk of job loss nor the short‐term earnings losses of displaced workers trended upwards during that period. However, short‐term earnings losses of workers displaced from manufacturing increased in recent years, as a smaller proportion found a post‐displacement job in that sector. In line with Stevens and Couch and Placzek, we find that high‐seniority workers and individuals with stable labour market attachment experienced, five years after displacement, earnings losses that ranged between 10% and 18%.

The determinants of the velocity of money have been examined based on life-cycle hypothesis. The velocity of money can be expressed by reciprocal of the average value of holding time which is defined as interval between participating exchanges for one unit of money. This expression indicates that the velocity is governed by behavior patterns of economic agents and open a way to constructing micro-foundation of it. It is found that time pattern of income and expense for a representative individual can be obtained from a simple version of life-cycle model, and average holding time of money resulted from the individual's optimal choice depends on the expected length of relevant planning periods.

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment.