White Paper

This report considers the current situation of the UK construction sector, the challenges it faces over the medium-term and the implications for skills. The Importance of the Sector • Construction is a vital component of the UK economy, not only directly due to its contribution to output and employment, but also strategically in its contribution to the built environment and as part of various initiatives designed to stimulate growth. • It also has a critical functional role in meeting the low-carbon agenda either through retro-fitting buildings and ensuring that all new buildings meet energy saving standards. • The recent recession has adversely affected construction employment and performance with the sector experiencing a sharp drop in the total value of contracts. With cuts to public spending and low consumer confidence, the recession has presented the sector with a number of challenges. Construction has been one of the most affected sectors in terms of the relative decrease in employment with a decline of around 8 per cent resulting from the 2008/09 recession. • The sector rebounded faster than other sectors in the first three quarters of 2010, but there have been contractions in construction output since. While construction is often the sector which helps to pull the entire economy out of recession, it is slow in doing so in the present global downturn. • Employment in construction accounted for around 8.5 per cent of total employment in the UK in 2010 and the sector contributed more than 9 per cent of the economy’s output in the same year.

In this report, we will explain why collaboration is essential to innovation, identify the major barriers that exist within Canada and make recommendations for government action to encourage open innovation

Having a decent job is necessary for Canadians’ financial security and to be a part of the community. The purpose of skills training support is to help Canadians, within their ability, remain consistently part of the labour force and have control over their career. Skills training programs exist to help Canadians get good jobs, overcome barriers to entering the labour market, and recover from setbacks such as layoffs. Two target populations are most in need of support from federally backed skills programs: new labour market entrants and those facing long-term unemployment or underemployment. Those new to the labour market or returning after a long absence (such as youth, newcomers to Canada, Aboriginal Canadians, and those living with disabilities) may need support to overcome challenges to finding their first, stable employment and to establishing careers. Workers who have had their careers upended by largescale, disruptive economic forces may need training to help them shift careers.

The report is framed around three broad questions: 1. What do we know now about equity of postsecondary access in Ontario and what are the key data sources that contribute to that understanding? 2. What do we need to know in order to design policies and programs that effectively address access‐related issues? 3. What are the relevant criteria for effective data infrastructure and models to which Ontario can look in order to expand policy‐relevant knowledge about equity of access? This project was based on 35 in‐depth interviews with leading researchers, institutional stakeholders, and policy actors, as well as an extensive literature review.

In the past twenty years, globalization has had both observable and intangible impacts on business and labor markets at the local level, that are of critical importance to communities and the people who inhabit them. While impacts of global economic change on local labor markets have been anticipated, there is little insight in the research literature into the empirical dynamics of the interrelationship between global economic change and local labor markets. This study examined the impacts of globalization on local labor markets through three lenses: (1) quantitative analysis of employment change in the Denver Metropolitan Region local labor market, (2) quantitative comparison of six other metropolitan regional labor markets across different geographies, and (3) a qualitative analysis of explicit reports by participants in the Denver Metropolitan local labor market (people in business, consulting, the public sector, and education). The main hypothesis of this study is that, in metropolitan areas where the forces of global economic change are at work, two proxy measures for globalization, foreign direct investment (FDI) and export trade, have a statistically significant relationship to changes in industry employment in local labor markets. Quantitative analysis used multiple regression to identify correlation between industry employment and FDI and export trade. Results indicate that there is a correlation in selected industries where the geographic factors of location provided an explanatory basis for the results. Qualitative analysis revealed that respondents have cautious optimism regarding the economic promise of globalization and still acknowledge the challenges that globalization brings into focus for the region's business, education and government stakeholders. It also demonstrated the differences in perspectives of the respondents from different roles: business owner, enterprise employee, educator, and government official. The study concludes that the data support the hypothesis in select industries where there are geographic advantages, but they do not support the generalization of the hypothesis to all cases where FDI or international export trade affect industry employment. The study further finds that each of the metros examined have unique regional economic development entities that partner to attract FDI and encourage international export trade.

This paper examines the distributional impact of capital account liberalization. Using panel data for 149 countries from 1970 to 2010, we find that, on average, capital account liberalization reforms increase inequality and reduce the labor share of income in the short and medium term. We also find that the level of financial development and the occurrence of crises play a key role in shaping the response of inequality to capital account liberalization reforms.

Apprenticeships are one of the few bipartisan ideas in the realm of workforce development, promising an alternative way of training skilled workers without requiring higher education. Instead of accumulating debt, students get to “earn while they learn.” President Obama offered tens of millions of dollars in grants to expand innovative apprenticeship programs,1 and President Trump recently issued an executive order making it easier for businesses to apply for apprenticeship approvals.

Business and civic leaders, educators, and policymakers of all stripes share concerns over the relentless erosion of America’s middle class and growing polarization of incomes. Most decry the loss of middle-class jobs and fear the corrosive effects such trends might wreak on the nation if left unchecked. At the heart of the issue is an oft-discussed anomaly: while millions of aspiring workers remain unemployed and an unprecedented percentage of the workforce report being underemployed, employers across industries and regions find it hard to fill open positions. The market for middle-skills jobs—those that require more education and training than a high school diploma but less than a four-year college degree—is consistently failing to clear. That failure is inflicting a grievous cost on the competitiveness of American firms and on the standard of living of American workers.

A defining question of our age is how AI will influence the workplace of the future and, thereby, the human condition. The dominant perspective is that the competition between AI and humans will be won by either humans or machines. We argue that the future workplace may not belong exclusively to humans or machines. Instead, it is better to use AI together with humans by combining their unique characteristics and abilities. In three experimental studies, we let humans and a state-of-the-art AI classify images alone and together. As expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was inversion, where the AI delegated to a human when it was uncertain. Humans could in theory outperform all other configurations if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well; the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI. This result is novel and important for human-AI collaboration at the workplace. We believe it has broad implications for the future of work, the design of decision support systems, and management education in the age of AI.