Using administrative data to gain insights into microdrivers of productivity
type: CreativeWork
description: Since the late 1990s, the information technology revolution has improved our ability to process, store and analyse large administrative datasets. The idea of making better use of administrative data for social and economic research is not new. While researchers recognise that there is rich information contained in administrative data, they also acknowledge the need to address the methodological challenges of analysing it for research. This research explores methods to address some of these challenges and harness the opportunities from analysing administrative data to understand micro-drivers of productivity. We start by providing the rationale for this research. We describe how we measure productivity, define firm participation in business networks and handle missing data for this research. Missing data is a common problem in data analysis, but the problem is exacerbated when we analyse an integrated administrative dataset. We need to address this problem because it can affect the conclusions that we draw from analysing the dataset. While there is no single method to address missing data, we prefer using approaches that minimise information loss. We then address the computational challenge while studying the contributions from firm dynamics - that is, firm entry and exit, within-firm growth and reallocation - to aggregate productivity. Our experimental integrated administrative dataset contains more than 10 million workers across 1.5 million firms. We estimate unique worker- and firm-specific effects using a scalable approach. We use the estimated labour component to study firm dynamics' contributions to Australia's productivity growth in the 2002-03 to 2012-13 period across 18 industries. In general, firm exit contributes positively to productivity growth, whereas firm entry generally contributes negatively. Firm dynamics are not the only micro-drivers of productivity. Therefore, we analyse another experimental dataset, integrating data from the Australian Bureau o…
abstract: Since the late 1990s, the information technology revolution has improved our ability to process, store and analyse large administrative datasets. The idea of making better use of administrative data for social and economic research is not new. While researchers recognise that there is rich information contained in administrative data, they also acknowledge the need to address the methodological challenges of analysing it for research. This research explores methods to address some of these challenges and harness the opportunities from analysing administrative data to understand micro-drivers of productivity. We start by providing the rationale for this research. We describe how we measure productivity, define firm participation in business networks and handle missing data for this research. Missing data is a common problem in data analysis, but the problem is exacerbated when we analyse an integrated administrative dataset. We need to address this problem because it can affect the conclusions that we draw from analysing the dataset. While there is no single method to address missing data, we prefer using approaches that minimise information loss. We then address the computational challenge while studying the contributions from firm dynamics - that is, firm entry and exit, within-firm growth and reallocation - to aggregate productivity. Our experimental integrated administrative dataset contains more than 10 million workers across 1.5 million firms. We estimate unique worker- and firm-specific effects using a scalable approach. We use the estimated labour component to study firm dynamics' contributions to Australia's productivity growth in the 2002-03 to 2012-13 period across 18 industries. In general, firm exit contributes positively to productivity growth, whereas firm entry generally contributes negatively. Firm dynamics are not the only micro-drivers of productivity. Therefore, we analyse another experimental dataset, integrating data from the Australian Bureau o…
author: Chien, Chien-Hung
language: en_AU
date published: 2021
date modified: 2026-07-24T00:54:33Z
provider: Australian National University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian National University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1885/219651