Skills, productivity and human capital

计划中的研究计划旨在解决四个问题：研发、创新和低技能职业工人的工资。理解创新和生产率的提高如何影响到社会不同群体的生活水平，应该是产业战略和技能政策设计的核心。我们计划针对不同技能水平的工人和从事不同任务的工人，研究工人的工资与他们所在公司的创新能力之间的关系。尤其令人感兴趣的是低技能工人的工资，不同公司之间的工资差异很大。我们的初步分析表明，对于低技能职业的工人来说，在创新型企业(相对于非创新型企业)工作的溢价高于高技能职业的工人。我们的研究旨在区分造成这种差异的主要可能原因。生产率的长期收益为了更全面地评估研发、创新和生产率提高带来的收益是如何分配的，我们不仅需要知道它们是如何转化为薪酬的，还需要知道它们对更全面的生活水平衡量标准(如终身收入和消费)有什么影响。这需要详细了解工资和生活水平之间的机制，如税收和转移支付制度、家庭拥有的资产，以及企业和家庭如何应对生产率冲击。我们的研究将使用详细的家庭和纵向数据来检验这些机制。企业家、业务增长和税收制度自2008年以来，公司所有者和经理的数量翻了一番，劳动力增长的40%来自通过自己的企业工作的人。对于旨在支持企业创业和成长的产业战略来说，越来越重要的是要了解初创企业的特点、这些最近趋势的驱动因素以及劳动力市场结构变化对生产率和增长的影响。特别令人感兴趣的是，正在创办新公司的个人在多大程度上是创业的(从他们投资的意义上讲)，而不是那些选择通过公司形式运营只是为了获得重大税收优惠的个人。一个关键问题是，税收制度在多大程度上帮助或阻碍了小企业的增长。我们建议通过探索业主经理如何应对税收，包括税收对公司规模和投资的影响，直接解决这个问题。高等教育对政府和学生来说，理解是什么推动了年轻人对高等教育的选择是重要的。科目和机构的选择可能会导致截然不同的终生收入，这对生活水平和社会流动性以及高等教育财务的长期成本具有重要影响。在这个项目中，我们试图了解哪些因素会影响个人的选择。我们使用的数据将告诉我们，当学生选择在哪里学习和学习什么时，哪些大学特征对他们来说最重要。我们的模型将使我们能够估计与(例如)某所大学的教学质量大幅提高相关的需求增长。这一点将使我们更好地了解大学是否选择将资源集中在对需求预测特别重要的领域，以及这种情况在高等教育制度改革后是否发生了变化。这项工作在当前的政策环境下尤其相关--卓越教学框架明确旨在通过改善现有信息来影响学生的选择，并可能对大学面临的激励措施产生重大影响。

The planned programme of research aims to address four issues:R&D, innovation and the wages of workers in low-skilled occupationsAn understanding of how increases in innovation and productivity feed through into the living standards of different groups in society should be central to an industrial strategy and the design of skills policy. We plan to examine the relationship between the wages of workers and the innovativeness of the firms for which they work, for workers of different skill levels and workers doing different kinds of tasks. Of particular interest are the wages of low-skill workers, which vary considerably between firms. Our preliminary analysis suggests that the premium from working in an innovative firm (as opposed to a non-innovative firm) is higher for workers in low-skilled occupations than for workers in high-skilled occupations. Our research aims to distinguish between the main possible reasons for the differences.The Longer Term Gains from productivityIn order to more fully assess how the gains from R&D, innovation and productivity improvements are distributed we need to know not just how they feed through into pay, but also what impact they have on more complete measures of living standards, such as lifetime incomes and consumption. This requires a detailed understanding of the mechanisms that stand between wages and living standards, such as the tax and transfer system, the assets that households have available, and how firms and households respond to productivity shocks. Our research will examine these mechanisms using detailed household and longitudinal data.Entrepreneurs, business growth and the tax systemThe number of company owner-managers has doubled since 2008, with 40% of the growth in the workforce coming from people working through their own business. It is increasingly important for an industrial strategy that aims to support businesses to start and grow to understand the characteristics of start-ups, the drivers of these recent trends and the effects that changing structure of the labour market is having on productivity and growth. Of particular interest is the extent to which the individuals that are starting new companies are entrepreneurial (in the sense that they invest) as opposed to individuals who choose to operate through an incorporated form simply to access significant tax benefits. A key question is to what extent the tax system helps or hinders small business growth. We propose to address this issue directly by exploring how owner-managers respond to taxes, including the effect of taxes on the size and investment of companies. Higher EducationUnderstanding what drives the choices that young people make over higher education is important for governments and students. The choice of subject and institution can result in very different lifetime earnings, which has important implications for living standards and social mobility, as well as for the long-run cost of higher education finance. In this project we seek to understand what factors affect individuals' choices. The data we use will tell us which university characteristics are most important for students when they make their choices about where and what to study. Our model will enable us to estimate the increase in demand associated with (for example) a large increase in teaching quality at a given university. This latter point will enable us to better understand whether universities choose to focus resources on areas which are particularly important predictors of demand, and whether this has changed following the reforms to the higher education system. This work is particularly relevant in the current policy environment - the Teaching Excellence Framework is explicitly targeted at influencing students' choices, through improving information available, and could have strong implications for the incentives universities face.