Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfactual impact and test the robustness of our results, we employ several evaluation methods, including inverse probability weighting (IPW) and multiple variants of augmented inverse probability weighting (AIPW). We also consider various modifications, including alternative modeling architectures and different assignment procedures. The impact estimates remain consistent in magnitude in all scenarios as well as statistically significant in most cases. Furthermore, the estimates are also consistent with the results originally presented in Bansak et al. (2018).

Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving a complex life cycle model requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. To analyze results, we use a selection of statistics and also compare the resulting optimal employment choices at various states. The statistics demonstrate that ACKTR yields almost as good results as dynamic programming. Qualitatively, dynamic programming yields more spiked aggregate employment profiles than ACKTR. The results obtained with ACKTR provide a good, yet not perfect, approximation to the results of dynamic programming. In addition to the baseline case, we analyze two social security reforms: (1) an increase of retirement age, and (2) universal basic income. Our results suggest that reinforced learning algorithms can be of significant value in developing social security reforms.

This paper presents a novel framework for prioritizing urban green space development in Tehran using diverse socio-economic, environmental, and sensitivity indices. The indices were derived from various sources including Google Earth Engine, air pollution measurements, municipal reports and the Weather Research & Forecasting (WRF) model. The WRF model was used to estimate the air temperature at a 1 km resolution due to insufficient meteorological stations, yielding RMSE and MAE values of 0.96°C and 0.92°C, respectively. After data preparation, several machine learning models were used for binary vegetation cover classification including XGBoost, LightGBM, Random Forest (RF) and Extra Trees. RF achieved the highest performance, exceeding 94% in Overall Accuracy, Recall, and F1-score. Then, the probability of areas lacking vegetation cover was assessed using socio-economic, environmental and sensitivity indices. This resulted in the RF generating an urban green space development prioritization map. Feature Importance Analysis revealed that the most significant indices were nightly land surface temperature (LST) and sensitive population. Finally, the framework performance was validated through microclimate simulation to assess the critical areas after and before the green space development by green roofs. The simulation demonstrated reducing air temperature by up to 0.67°C after utilizing the green roof technology in critical areas. As a result, this framework provides a valuable tool for urban planners to develop green spaces.

Where did all the jobs go? Well, we're finally starting to find some satisfactory answers to the granddaddy of all economic questions. The share of Americans with jobs dropped 4.5 percentage points from 1999 to 2016 -- amounting to about 11.4 million fewer workers in 2016. At least half of that decline probably was due to an aging population. Explaining the remainder has been the inspiration for much of the economic research published after the Great Recession.

In 2016, an estimated 65.6 million people across the globe were forced from their homes by everything from war to human rights violations. Climate change and global warming are exacerbating the problem of displaced persons, with millions of people expected to be forced to relocate to other -- often cooler -- countries. The problem is becoming so widespread that New Zealand is even considering creating a new visa specifically for those displaced by climate change. Once they make the difficult decision to leave their home, refugees face a slew of other questions: To which country do they flee? Where in that country should they go?

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

John Kelleher is a partner at McKinsey & Co. and the co-chair of Next Canada. Laura McGee is an engagement manager at McKinsey & Co. and co-founder of #GoSponsorHer. There's no doubt that Canada could lead the planet in artificial intelligence (AI). Canadian academics such as Geoffrey Hinton and Yoshua Bengio essentially created the field of deep learning and put Canada on the map; today, Edmonton, Toronto and Montreal are globally important centres of AI research. The best AI talent in the world is also increasingly coming to Canada to launch AI businesses such as integrate.ai

The rise in artificial intelligence is here and has brought with it many advantages. However, there are also disadvantages that come with it. One of those is in people that are finding it difficult to hang on to their current roles and are being pushed out by more effective machines. The threat that automation will steal jobs is a worldwide issue that's affecting millions of people already, and it's not over yet. According to the results of a recent study carried out by Accenture, more than 1,000 large companies worldwide that are already using or in the process of testing AI and machine learning systems were, in fact, creating entirely new, unique categories of jobs for humans.

More than 10 million UK workers are at high risk of being replaced by robots within 15 years as the automation of routine tasks gathers pace in a new machine age. A report by the consultancy firm PwC found that 30% of jobs in Britain were potentially under threat from breakthroughs in artificial intelligence (AI). In some sectors half the jobs could go. The report predicted that automation would boost productivity and create fresh job opportunities, but it said action was needed to prevent the widening of inequality that would result from robots increasingly being used for low-skill tasks. PwC said 2.25 million jobs were at high risk in wholesale and retailing – the sector that employs most people in the UK – and 1.2 million were under threat in manufacturing, 1.1 million in administrative and support services and 950,000 in transport and storage.