苏格兰生产力与学徒就业强度:企业层面的纵向研究

Alma Sobrevilla, Zoe Mackay, Martyna Walczak, Malcolm Greig
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 MethodsThe Office for National Statistics (ONS) identified multiple matches for many Company IDs, so a cleaning process was required to identify a single match for each record. This involved matching records based primarily on company name and address.
 We carried out Random Effects, Fixed Effects and System Generalised Method of Moments (GMM) regressions to analyse the relationship between productivity (real GVA per worker) and apprenticeship employment intensity (number of in-training apprentices as a proportion of total employment).
 ResultsWhen we summarise the final dataset by enterprise, 42,486 company IDs were matched to 19,180 unique enterprises. We were able to link our SDS MA employer dataset to the following ONS datasets: Annual Business Survey, Business Register and Employment Survey, Business Structure Dataset, Business Enterprise Research and Development, Labour Force Survey, Employer Skills Survey and data on Producer Price Index.
 Using this matched dataset we found a significant positive relationship between productivity and apprenticeship employment, which is robust to the inclusion of enterprise-level fixed effects (factors that are specific to each enterprise that could affect productivity but that do not change over time) and the use of a System GMM framework.
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 MethodsThe Office for National Statistics (ONS) identified multiple matches for many Company IDs, so a cleaning process was required to identify a single match for each record. This involved matching records based primarily on company name and address.
 We carried out Random Effects, Fixed Effects and System Generalised Method of Moments (GMM) regressions to analyse the relationship between productivity (real GVA per worker) and apprenticeship employment intensity (number of in-training apprentices as a proportion of total employment).
 ResultsWhen we summarise the final dataset by enterprise, 42,486 company IDs were matched to 19,180 unique enterprises. We were able to link our SDS MA employer dataset to the following ONS datasets: Annual Business Survey, Business Register and Employment Survey, Business Structure Dataset, Business Enterprise Research and Development, Labour Force Survey, Employer Skills Survey and data on Producer Price Index.
 Using this matched dataset we found a significant positive relationship between productivity and apprenticeship employment, which is robust to the inclusion of enterprise-level fixed effects (factors that are specific to each enterprise that could affect productivity but that do not change over time) and the use of a System GMM framework.
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摘要

目的探讨学徒就业与生产力的关系。创建一个新的关联数据集使我们能够第一次为苏格兰探索这个研究问题。苏格兰技能发展(SDS)拥有现代学徒(MAs)雇主的数据,但不收集行业、规模或经济绩效指标,如总增加值(GVA)。因此,有必要使用企业参考编号(ERN)将我们的雇主记录与部门间商业登记簿(IDBR)相匹配。方法英国国家统计局(ONS)为许多公司id识别了多个匹配项,因此需要一个清理过程来为每个记录识别单个匹配项。这涉及到主要基于公司名称和地址的匹配记录。 我们采用随机效应、固定效应和系统广义矩量法(GMM)回归来分析生产率(每个工人的实际GVA)与学徒就业强度(在训学徒人数占总就业人数的比例)之间的关系。 当我们按企业汇总最终数据集时,42,486个公司id与19,180个唯一的企业相匹配。我们能够将SDS MA雇主数据集与以下国家统计局数据集联系起来:年度商业调查、商业登记和就业调查、商业结构数据集、企业研究与发展、劳动力调查、雇主技能调查和生产者价格指数数据。 使用这个匹配的数据集,我们发现生产率与学徒就业之间存在显著的正相关关系,这对于包含企业级固定效应(可能影响生产率但不随时间变化的特定于每个企业的因素)和系统GMM框架的使用是稳健的。 结论即使在控制了企业和行业层面的特征后,徒弟比例高的企业生产率也更高。为了研究这种关系,构建一个包含不同来源信息的匹配数据集(SDS和ONS数据集)至关重要。
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Productivity and apprenticeship employment intensity in Scotland: A longitudinal study at the enterprise level
ObjectivesWe investigate the relationship between apprenticeship employment and productivity. Creating a new linked dataset allowed us to explore this research question for Scotland for the first time. Skills Development Scotland (SDS) holds data on employers of Modern Apprentices (MAs), but does not collect industry, size or economic performance measures such as Gross Value Added (GVA). Therefore, it was necessary to match our employer records to the Inter Departmental Business Register (IDBR) using Enterprise Reference Number (ERN). MethodsThe Office for National Statistics (ONS) identified multiple matches for many Company IDs, so a cleaning process was required to identify a single match for each record. This involved matching records based primarily on company name and address. We carried out Random Effects, Fixed Effects and System Generalised Method of Moments (GMM) regressions to analyse the relationship between productivity (real GVA per worker) and apprenticeship employment intensity (number of in-training apprentices as a proportion of total employment). ResultsWhen we summarise the final dataset by enterprise, 42,486 company IDs were matched to 19,180 unique enterprises. We were able to link our SDS MA employer dataset to the following ONS datasets: Annual Business Survey, Business Register and Employment Survey, Business Structure Dataset, Business Enterprise Research and Development, Labour Force Survey, Employer Skills Survey and data on Producer Price Index. Using this matched dataset we found a significant positive relationship between productivity and apprenticeship employment, which is robust to the inclusion of enterprise-level fixed effects (factors that are specific to each enterprise that could affect productivity but that do not change over time) and the use of a System GMM framework. ConclusionOur results suggest that enterprises with a high proportion of apprentices are more productive, even after controlling for enterprise and industry-level characteristics. In order to study this relationship, it was crucial to construct a matched dataset containing information from different sources (SDS and ONS datasets).
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