Understanding and Explaining Management Practices to Promote Higher Productivity in UK Businesses

我们的管理和期望调查（MES），在ESRC电话会议中引用，源于ONS和ESCoE之间的合作伙伴关系：这是英国管理能力有史以来最大的调查，对不同行业，地区，公司规模和年龄的25,000家公司进行了调查，记录了英国企业管理实践的不同质量。我们的分析发现，英国公司的管理实践与劳动生产率之间存在显著关系，并研究了某些类型的公司是否存在管理实践不佳和生产率停滞不前的问题，得出了它们之间联系的结论，ONS（2018）。这个团队，有两位对管理实践和绩效有重大贡献的人（布鲁姆，斯坦福大学和麻省理工学院的范·雷南），他们发起了世界管理调查，来自国家统计局的合作伙伴（Awano, Dolby, Vyas, Wales），以及NIESR的ESCoE主任和研究员（Riley, Mizen, Senga, Sleeman），将调查五个问题：1。管理实践和绩效的纵向变化最初的MES提供了公司之间管理实践和期望变化的横截面，但由于缺少数据的纵向维度，它没有探索企业内部随时间的变化。第二波市场经济地位将扩大我们的分析范围，使我们能够解释英国的管理实践是如何随时间变化的。这个扩展解决了最近ESRC-ONS研讨会的“广泛共识”，即“没有足够的纵向数据围绕生产力，允许一致的，持续的分析，特别是数据，使研究人员能够识别，隔离和准确测量随时间的变化。”“2。通过Bloom和Van Reenen与美国人口普查局的美国管理和组织实践调查（MOPS）的联系，我们将能够i)使用他们的方法和变量来检验相同的假设，以得出有助于确定企业层面生产率因果驱动因素的研究见解，并比较和对比英国和美国的数据；ii)组织一个独特的联合国家统计局和人口普查局的方法论坛，收集最有用的微观数据，以衡量英国和美国公司的管理、投资和招聘意向。其他国家也在进行类似的数据收集工作。我们已经与德国和日本的团队建立了联系，我们打算讨论主要的差异，例如美国和欧洲的商业环境之间的差异，以及相似之处，例如日本的低生产率经验。3. 学术研究人员与国家统计局之间的合作关系促进了来自其他来源的数据的匹配，以回答以下关键问题：a)管理和企业应对不确定性的能力，通过将MES响应与贸易数据、增值税管理数据、研发支出和专利数据联系起来，并通过供应链和货物出口目的地利用企业在欧盟市场上的差异；b)卓越的创新、研发和出口绩效的证据，来自企业之间的商业创新和出口如何随着管理实践和文化的不同而随时间变化的证据。这将直接为英国企业提供实践经验。我们将使用自然语言处理和机器学习来调查来自求职公司的大数据，客观地确定影响英国员工满意度和绩效的因素。根据MES和其他微观数据集，我们将审查心理健康与管理实践之间的联系。随机对照试验：MES中近9000家做出回应的企业寻求对其管理评分的“反馈”。通过对受访者的不同反馈，我们将与BIT（“推动单位”）和CMI合作，观察对公司后续适应和绩效的影响。

Our Management and Expectations Survey (MES), cited in the ESRC call, arose from a partnership between the ONS and ESCoE: it is the largest ever survey of UK management capabilities, executed on a population of 25,000 firms across industries, regions, firm sizes and ages documenting the variable quality of management practices across UK businesses. Our analysis found a significant relationship between management practices and labour productivity amongst UK firms, and examined whether certain types of firms have poor management practices and stagnant productivity, drawing conclusions about the links between them, ONS (2018). This team, with two seminal contributors to management practice and performance (Bloom, Stanford, and Van Reenen, MIT) who initiated the World Management Survey, partners from the ONS (Awano, Dolby, Vyas, Wales), and the Director and Fellows of the ESCoE (Riley, Mizen, Senga, Sleeman) at the NIESR, will investigate five issues:1. Longitudinal changes in management practices and performanceThe initial MES offers a cross section of variation in management practices and expectations between firms, but it does not explore variations within businesses through time due to the missing longitudinal dimension to the data. A second wave of the MES will expand our scope of analysis so that we can interpret how management practices in the UK have varied over time. This extension addresses the 'broad consensus' from the recent ESRC-ONS workshop that 'there is not enough longitudinal data around productivity that allows for consistent, ongoing analysis, and in particular data that enables researchers to identify, isolate and accurately measure changes over time.' 2. International comparisonsDrawing on our links through Bloom and Van Reenen with the US Management and Organizational Practices Survey (MOPS) at the US Census Bureau will enable us to i) test identical hypotheses using their methods and variables to draw research insights that help identify causal drivers of productivity at the firm level, and compare and contrast the UK and US data; ii) draw together a unique joint ONS-Census Bureau methodological forum for collecting the most useful micro-data for measuring management, investment and hiring intentions for UK and US firms. Similar data collection exercises have been taking place across other countries. We have established links with German and Japanese teams and we intend to discuss key differences, e.g. between the US and European business environments, and similarities, e.g. the Japanese experience of low productivity. 3. Analysis of linked business surveys and administrative data Partnership between academic researchers and ONS facilitates the matching of data from other sources to answer key questions around: a) management and firms' ability to cope with uncertainty by linking MES responses to trade data, administrative data on VAT, R&D expenditure, and patenting data, and exploiting variation across firms in exposure to EU markets through supply chains and export destination of goods; b) evidence of superior innovation, R&D and export performance from evidence of how business innovation and exporting varies across firms and over time in response to management practices and cultures. This will directly inform practical lessons for UK businesses.4. Experimental analysis using big dataWe will use natural language processing and machine learning to investigate big data from job-search companies to objectively identify the factors that affect staff satisfaction and performance in the UK. Matching to the MES and other micro datasets we will examine links between mental health and management practices.5. Randomised control trialsNearly 9,000 responding businesses in the MES sought 'feedback' on their management score. By varying feedback to respondents we will observe in collaboration with BIT (the 'Nudge Unit') and CMI the impact on firm's subsequent adaptation and performance.