Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the U.S. construction sector

2020 ◽  
Vol 25 (3) ◽  
pp. 313-330
Author(s):  
Navid Ahmadi Esfahani ◽  
Mohsen Shahandashti
Keyword(s):  
Large Scale ◽  
Civil Engineering ◽  
Labor Cost ◽  
Industrial Building ◽  
County Level ◽  
Construction Sector ◽  
Location Quotient ◽  
Content Type ◽  
Engineering Construction ◽  
Industry Group

Purpose The primary objectives of this study are to (1) highlight subsectors and industry groups of the construction sector that are most vulnerable to weather-related disasters (with highest labor cost escalation) and (2) analyze how immediate this labor wage escalation happens in different subsector of the construction sector. Design/methodology/approach The research methodology consists of three steps: (i) integrating various data sources to enable measurement of the county-level labor wage changes following large-scale weather-related disasters; (ii) measuring postdisaster labor wage changes at the county level; and (iii) comparing amount and timing of postdisaster labor wage changes among all sub-sectors (and industry groups) of the construction sector. Findings The results show that among the three construction subsectors (Heavy and Civil Engineering Construction subsector, Construction of Buildings subsector, and Specialty Trade Contractors sub-sector), Heavy and Civil Engineering Construction subsector is the most vulnerable to weather-related disasters. The industry groups under the Heavy and Civil Engineering Construction subsector showed the same vulnerability level; however, under the Construction of Buildings subsector, Industrial Building Construction industry group showed to be the most vulnerable; and under the Specialty Trade Contractors subsector, the Building Foundation and Exterior Contractors industry group is the most vulnerable. The results also showed that in approximately 75% of the damaged counties, there were increases in wages of all construction labors, over the following three quarter after the disasters. In average, labor wages in Construction of Buildings subsector and the Specialty Trade Contractors subsector decreased by 0.6% and 0.8%, respectively, in the quarter of disaster and gradually increased by 4.4% and 4.6%, respectively, in the following three quarters. On the other hand, Heavy and Civil Engineering Construction’s labor wages did not experience this decrease right after the disasters; wages increased immediately after disasters hit the counties and continually increased by 8.6% in three quarters after the disasters. It is expected that the results of this study will help policy makers, cost estimators and insurers to have a better understanding of the post-disaster construction labor wage fluctuations. Originality/value This study is unique in the way it used construction labor wage data. All data are location quotient, which makes the comparison among the affected counties (with different construction size) feasible.

scholarly journals Embedding ensemble learning into simulation-based optimisation: a learning-based optimisation approach for construction planning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kailun Feng ◽  
Shiwei Chen ◽  
Weizhuo Lu ◽  
Shuo Wang ◽  
Bin Yang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Large Scale ◽  
Quality Measurement ◽  
Construction Planning ◽  
Content Type ◽  
Engineering Construction ◽  
Alternative Approach ◽  
Simulation Based ◽  
Computing Platforms ◽  
Searching Ability

PurposeSimulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.Design/methodology/approachThis study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.FindingsA large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.Originality/valueThe core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Chinese regional productivity and urbanization: a county-level study in 2007-2010

2015 ◽  
Vol 8 (2) ◽  
pp. 82-105 ◽  
Author(s):  
Fei Jin ◽  
Qi Zhang
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
County Level ◽  
Change Analysis ◽  
Content Type ◽  
Tfp Growth ◽  
Visual Spatial ◽  
Level Data ◽  
Short Period ◽  
The Impact

Purpose – This study aims to analyze the total factor productivity (TFP) performance of Chinese counties and cities over the period from 2007 to 2010. Chinese regional and urban–rural TFP performance are investigated by using county-level data, and the impact of the urbanization policy on TFP is discussed. Design/methodology/approach – The data envelopment analysis (DEA)-Malmquist technique and Kumbhakar–Sun’s semi-parametric model are used for TFP change measurement and comparison. The county-level TFP performances are summarized and studied by statistical methods. Their spatial distribution is exhibited in a geographical thematic map. Findings – The county-level analysis proves that China underwent a large-scale TFP decline over the period from 2007 to 2010. Statistically speaking, cities’ TFP growth is more positive than counties’; however, different provinces also have their regional characteristics. In addition, the Chinese Hukou (household registration) institution divides Chinese urbanization into halves, which have the opposite correlation on TFP growth. Research limitations/implications – Because the collection of county-level data is enormous and costly, this study only focuses on a very short period (2007-2010) with estimated data. This TFP change analysis is limited to the short-term phenomenon around the 2008 international financial crisis. Practical implications – This study provides a visual spatial distribution for county-level TFP change in China over the period 2007-2010. Results of the analysis demonstrate that the Chinese Hukou system is among the policy factors that can influence productivity in the course of urbanization. Originality/value – The achievement of the first nationwide county-level TFP change study for economic growth in China is innovative. This study provides a unique perspective for understanding productivity performance at the regional level over the period investigated, which provides invaluable data for investigating the impact of urbanization and the rural–urban gap on TFP growth.

scholarly journals Systematic analysis of driverless technologies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
David John Edwards ◽  
Jahangir Akhtar ◽  
Iain Rillie ◽  
Nicholas Chileshe ◽  
Joseph H.K. Lai ◽  
...  
Keyword(s):  
Conceptual Model ◽  
Large Scale ◽  
Civil Engineering ◽  
Social Scientist ◽  
Future Research ◽  
Multidisciplinary Teams ◽  
Supporting Evidence ◽  
Content Type ◽  
Practical Applications ◽  
Extant Literature

Purpose The advent of Industry 4.0 has engendered opportunities for a coalescence of digital technologies that collectively enable driverless vehicles to operate during the construction and use of a highway. Yet, hitherto scant research has been conducted to review these collective developments and/or sample construction practitioner opinion on them. This study aims to present a systematic review of extant literature on the application of driverless technologies in civil engineering and in particular, the highways infrastructure sector and offers insight into the limitations of associated barriers to full adoption, namely, current technological development processes, legal deficiencies and societal concerns. In so doing, this work presents a vignette of contemporary developments augmented by a critical analysis from practitioners’ perceptions. Design/methodology/approach A mixed philosophical methodological approach is adopted for this inductive research study. Interpretivism is used to critically analyse the literature and post-positivism to perform content analysis of the literature and synthesis of the discourse with practitioners. A total of 44 related papers published between 1998 and 2019 have been included in this study. Emergent themes identified from literature are then discussed in some further detail, namely, 1) automation and robotics; 2) case studies and simulations; and 3) safety and ergonomics). A focus group is then held with leading industrialists to discuss their experiences of advanced driverless technology applications in practice. Based upon a culmination of emergent evidence, a conceptual model of prevailing barriers is then developed to further elucidate upon the challenges facing the highways infrastructure sector. Findings Research into driverless technologies within the highways infrastructure sector has received relatively scant academic attention. Hitherto, most advancements made have stemmed from multidisciplinary teams consisting of engineering, information technology and social scientist researchers. There is insufficient supporting evidence of civil engineering and construction academics input into developments made – suggesting that prototype products often fail to adequately consider practical applications in the highways infrastructure sector at the design and use case stage. This view is substantiated by feedback from leading industry experts who participated in unstructured telephone interviews. Their feedback suggests that practical applications of products have been beset with problems, thus creating a perception that advanced technologies are largely “unusable” within the highways infrastructure sector and so are unsuitable for large-scale (and particularly bespoke) industrial applications. Originality/value This research critically synthesises the prevailing scientific discourse within extant literature on driverless technologies implemented but also garners practitioner feedback from leading UK industrialists on their applications in practice. Hitherto, this combined analysis approach has been rarely used in spite of it having significant advantages of tacit knowledge reflection on technologies used, where such can be used as a basis for further informed discourse and/or development. Moreover, this work culminates in a conceptual model that acts as a catalyst for future research investigations.

scholarly journals ANALISIS SEKTOR UNGGULAN DI KABUPATEN NAGAN RAYA TAHUN 2012 - 2017(Analysis of Leading Sectors in Nagan Raya Regency, in 2012 – 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 40
Author(s):  
Safarul Aufa
Keyword(s):  
Large Scale ◽  
Animal Husbandry ◽  
Construction Sector ◽  
Economic Sectors ◽  
Location Quotient ◽  
Industrial Processing ◽  
Retail Business ◽  
Information And Communication ◽  
Water Provision ◽  
Regional Product

This study was aimed at highlighting the leading sectors of economy in Nagan Raya Regency. The data were gained from Gross Domestic Regional Product (GDRP) on the basis of diverse  economic sectors in Nagan Raya Regency from 2012 to 2017. This research utilized two types of calculation method, namely Location Quotient (LQ) and Klassen’s Typology method. When overlay was carried out between the method of LQ and Klassen’s Typology method, it could be concluded that sub-sectors of Agriculture, Animal Husbandry, Hunting  and Agricultural service were could classified as more leading sectors in Nagan Raya because these sectors suited the criteria of those two methods. Sub-sectors  of Forestry and Logging, and sectors of Mining and Excavation merely matched the criteria of LQ method.  Accordingly, construction sector was in line with Klassen’s Typology. However, the classifications of Klassen Typology method which encompassed the progressing and growing sector categories in Nagan Raya Regency were as follows 1) Sub-Sector of Agriculture, Animal Husbandry, Hunting and Agricultural Service and  2) Construction Sector. The potential sectors were  1) Industrial Processing Sector 2) Electricity Power Provision Sector 3) Accommodation, Drink and Food Sector 4) Finance and Insurance Service. However, the categories of saturated sectors included 1) Mining and Excavation and 2) and a Large Scale Business and a Retail Business, Car and Motorcycle Reparation. Meanwhile, the categories of lagging sectors were 1) Forestry  and Logging, 2) Sub-Sector of Fisheries 3) Water Provision, Garbage Management, Wastes and Recycling 4) Transportation and Warehousing; 5) Information and Communication; 6) Real Estate 7) Company Service 8) Government Administration; Defense and Required Social Security 9) Educational Service 10) Health Service; Social Activities and 11) Other sorts of services.

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