Gurmu, A T (2026) Econometric analysis of macroeconomic factors influencing construction labour productivity at industry level: Evidence from Australia. Construction Management and Economics, 44(1), pp. 23-40. ISSN 0144-6193
Abstract
Understanding the influence of macroeconomic factors on construction labour productivity is essential for developing strategies that mitigate the adverse effects of economic fluctuations. However, previous studies have rarely examined the dynamic lead-lag relationships between macroeconomic indicators and labour productivity in the construction industry using time-series forecasting models. This study addresses this gap by identifying key macroeconomic indicators, such as the Producer Price Index (PPI), Gross Domestic Product (GDP), and Consumer Price Index (CPI), that have a significant temporal influence on construction labour productivity (LP), and by developing a robust LP forecasting model. Time series data on labour productivity and economic indicators were sourced from the Australian Bureau of Statistics. Descriptive analysis, stationarity checks, breakpoint and Granger causality tests were performed to determine the leading indicators and select an appropriate multivariate model. Granger causality results identified PPIs for timber, plumbing, and appliances as significant predictors of construction labour productivity. Vector Autoregression (VAR) model was developed and validated using diagnostic tests, including Residual Serial Correlation and Heteroskedasticity tests, confirming model reliability. The results of impulse response functions showed that a one-standard-deviation shock to LP can lead to a substantial and immediate increase in LP. Furthermore, the variance decomposition test revealed that in the initial periods, nearly all the variance in LP is explained by its own shocks; however, over time, the influence of other variables, such as the PPI of appliances and timber, grows. Out-of-sample forecasting demonstrated high predictive accuracy, with RMSE of 1.36, MAE of 1.11, and MAPE of 1.07%. These findings demonstrate the model's robustness and practical utility. This research contributes to the existing body of knowledge by identifying key macroeconomic factors that influence construction labour productivity at the industry level and by offering a predictive tool to assist contractors, project managers, and policymakers in anticipating productivity trends.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction industry; labour productivity; macroeconomic indicators; vector autoregression |
| Index terms: | multivariate model, body of knowledge, variance, plumbing, statistics, Australia, productivity, accuracy, labour productivity, project manager, time series, economic indicator, deviation, construction labour, construction industry, strategy, Granger causality test, gross domestic product, econometric, evidence, forecasting |
| Subjects: | industry analysis, data science, economic analysis, prediction and forecasting, profession, mechanical systems, management, evaluation and assessment methods, financial and cost management, mathematical modelling, knowledge management, statistical analysis, Geography, measurement and scaling, data analysis and analytics, factor and component analysis, professional development |
| Topics: | Research Practice, Cost Management, Roles and Professions, Geographical Context, Business Strategy, Site Management, Design Practice, Information Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here