Queen's University Belfast - NISRA BDR Programme (Propelling Growth in Northern Ireland: Measuring and Explaining Business Productivity)
Lead Research Organisation:
Queen's University Belfast
Department Name: Queen's Management School
Abstract
Our research will contribute to this existing literature by providing a more detailed picture of Northern Irish productivity. We identifying where opportunities exist for policy to raise productivity and efficiency across firms, sectors, and across geographies. We will do this by utilizing the firm level data from the NIABI, BESES and BRES, allowing us to address some of the long standing questions about NI's productivity problem, which so far have remained unanswered due to the limited availability of data (e.g. Mac Flynn, 2016; Goldrick-Kelly and Mac Flynn, 2018).
Our research will contribute in three main ways. First, in addition to calculating standard productivity metrics, we will use firm level data to construct and estimate production functions for a large number of firms across multiple sectors within NI's economy. This will constitute the largest analysis of a dataset of firms in NI's economy: by comparison, Driffield and Lavoratori's (2020) study was limited to under 300 firms from only the manufacturing sector, sourced from the FAME database.
Second, our approach will allow us to calculate measures of total factor productivity (TFP) and Pareto-Koopmans and Debreu-Farrell measures of technical efficiency (see Olley and Pakes, 1996; Levinsohn and Petrin, 2003; Färe and Primont, 1995; Simar and Wilson, 2002). This will provide a new insight into the interactions between labour and capital across the local economy. Exploiting the temporal and cross-sectional variation in productivity metrics will then allow us to understand where interventions are most needed to raise NI's productivity.
Third, our proposal will allow us to assess which policies -past, present and future - are most successful in stimulating employment, investment, innovation, and ultimately productivity. By exploiting cross-sectional variation across economic sectors, local government districts, we will be able to assess where firms are performing better and more efficiently. This will allow for more targeted government support in boosting regional productivity. Similarly, our approach with firm-level data allows us to identify best practices among firms and areas for other regions and entrepreneurs to learn from.
Our research will contribute in three main ways. First, in addition to calculating standard productivity metrics, we will use firm level data to construct and estimate production functions for a large number of firms across multiple sectors within NI's economy. This will constitute the largest analysis of a dataset of firms in NI's economy: by comparison, Driffield and Lavoratori's (2020) study was limited to under 300 firms from only the manufacturing sector, sourced from the FAME database.
Second, our approach will allow us to calculate measures of total factor productivity (TFP) and Pareto-Koopmans and Debreu-Farrell measures of technical efficiency (see Olley and Pakes, 1996; Levinsohn and Petrin, 2003; Färe and Primont, 1995; Simar and Wilson, 2002). This will provide a new insight into the interactions between labour and capital across the local economy. Exploiting the temporal and cross-sectional variation in productivity metrics will then allow us to understand where interventions are most needed to raise NI's productivity.
Third, our proposal will allow us to assess which policies -past, present and future - are most successful in stimulating employment, investment, innovation, and ultimately productivity. By exploiting cross-sectional variation across economic sectors, local government districts, we will be able to assess where firms are performing better and more efficiently. This will allow for more targeted government support in boosting regional productivity. Similarly, our approach with firm-level data allows us to identify best practices among firms and areas for other regions and entrepreneurs to learn from.
Organisations
People |
ORCID iD |
| Philip Fliers (Principal Investigator) | |
| John Turner (Co-Investigator) |