Purpose This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms. Design/methodology/approach The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity. Findings The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC. Originality/value The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.

PurposeThe paper aims to investigate the effect of firm age and size on profitability and productivity of construction firms in India. It also attempts to understand the indirect effect of firm age and size on profitability mediated through firm's productivity.Design/methodology/approachData of 64 construction firms, for a period of 12 years (2006–2017), were collected. In order to measure the direct and indirect effect of size and age on profitability and productivity, a structural equation model was developed. In the structural models, productivity is a latent variable measured through proxies of material productivity (MP), labor productivity (LP) and equipment productivity (EP). The profitability is measured using three financial ratios: return on asset (ROA), return on capital employed (ROCE) and return on net worth (RONW). Then the direct and indirect effect of age and size is measured on ROA, ROCE, RONW and productivity.FindingsThe findings of the study suggest that age has a direct negative effect on profitability; however, it has an indirect positive effect on profitability, which is mediated by firm's productivity. This positive indirect effect compensates the direct negative effect and leads to an overall positive effect of firm age on profitability. However, firm size shows no effect on profitability and productivity.Originality/valueTo the best of authors’ knowledge, the study is the first attempt to measure the indirect effect of age and size on profitability, mediated through productivity. The study also examines the interrelationship among firms’ profitability and productivity and bridges an important research gap. The study proposes an integrated theoretical framework with a clear view of the interrelationships among age, size, profitability and productivity for construction firms in India, which can be further tested and validated for generalization.

The purpose of this study is to measure the total factor productivity (TFP) of industrial sector in India at an aggregate level and find the impact of technical inefficiency and other input variables on TFP using stochastic frontier analysis approach. Based on the aggregated data for a period of 29 years, the output productivity is measured as net sales revenue of an industry in a particular year, whereas input is measured as the raw material cost, labor cost, capital employed and research and development (R&D) investment of an industry in a particular year. The TFP is measured based on the functional form of Cobb-Douglas model. The results of the study indicate that material, labor and R&D are the prime drivers of TFP for industrial sector and the industrial sector is suffering from poor productivity due to technical efficiency that is decreasing over time.