University Students’ Preferences for Labor Conditions of a Mining Site:
Evidence from Australia〇 Shuto Mikami i , Yutaka Ito ii , Hyongdoo Jang iii , Abbas Taheri iv , Kenta Tanaka v ,and Youhei Kawamura i

1. Introduction
The mining industry makes up 10.2% of the gross domesti c product (GDP) of Australia.
Although the mining industry is one of the largest industries in Australia, securing humanresources remains a problem in that field. Specifically, skilled labor shortages are concentrated inremote areas, which is where minin g sites are generally located in Australia. A previous study
reported that mining companies in mining states such as Western Australia and Queensland aremuch more likely to have increase in benefits such as pay rates for current staff due to skills
shortage than other states in Australia. (59.5% vs 38.0%) and to have offered financial incentives(Bankwest, 2012). Therefore, the aim of this paper is to identify Australian university mining
students’ preferences for labor conditions at mining sites by means of a discrete choice
experiment to promote efficient improvements in labor conditions in the mining industry.

2. Method
Data collection was conducted through a paper -based questionnaire survey based on amethod of conjoint analysis on October 26, 2017. Th e respondents were students majoring infields relating to mining engineering at Curtin University and The University of Adelaide inAustralia. The number of respondents was 95, including males and females aged 19 -44 years. The
number of valid responses wa s 93. The attributes and levels used for the analysis are shown in
Table 1. A conditional logit model was used for data analysis. First, to estimate the value of each
attribute as monetary numbers, marginal willingness to accept (MWTA) was estimated from theresults of the model. Second, this study examines how much each individual characteristic affects
job selection by logit estimation results. Finally, we demonstrate that the factors include
individual characteristics.

i

Department of Earth Resource Engineering and Environmental Science, Akita University, 010 -8502, Japan
TEL: 018 -889-2207 E-mail:m6019219@s.akita-u.ac.jp
ii Graduate School of International Resource Sciences, Akita University
iii Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University
iv School of Civil, Environmental and Mining Engineering, The University of Adelaidev Faculty of Economics, Musashi University

Table 1: Attributes and levels in the conditional logit model
Attributes

LevelsWage (AUS Dollars){70,000, 90,000, 110,000, 130,000, 150,000}

Fatality rate (Per 100,000 employees){3/100,000, 5/100,000, 7/100,000}

Working position{Mine planning, Blasting, Driller, Geotechnical engineer}

Style{Living near site, Fly in fly out}

Method{Underground mining, Surface mining}

Company{BHP, AngloGold Ashanti, Barrick, Rio Tinto, Paddington}

3. Estimation ResultA total of 7 variables were deemed statistically significant: wage, fatality rate, positions(e.g. mine planning, blasting, and drilling), method of employment, and choice of company. The
marginal willingness to accept (MWTA) each attribute is shown in Table 2. The estimation resultsof interactions between individual characteristics and main variables are shown in Table 3.
Table 2: Estimation Result of MWTA
Attributes

Marginal willingness to accept: MWTA (Australian Dollar)

Fatality rate
Working postion(Referencing Geotech engineer)
Style (Referencing Fly in Fly out)

3719.457
Mine planning-17149.321

Blasting

14524.887

Driller

22986.425

Living near site14932.127

Method (Referencing Surface mining)
Underground mining

Company(Referencing Paddington)

5746.606

BHP-13574.661

AngloGold Asahnti-2411.765

Barrik-5882.353

Rio Tinto-7963.801

Table 3: Estimation Result of L ogit Model
Wage × Adelaide-6

Mine planning × Female

6.510×10 **0.535*

3.29×100.3010

Blasting × Female

0.538*

0.3210

Living near site × Adelaide-0.654***0.1770Underground mining × Adelaide-0.423**0.1750

Underground mining × Female-0.534**0.2170

BHP × Adelaide0.663**

0.2760

AngloGold Ashanti × Adelaide0.504*

0.2800-64.Discussion and Conclusion
The results of this study show that students prefer to work in conditions where fatality
rates are lower as, generally expected. Furthermore, the interaction between individual
characteristics and the main variables have a significant correlation with job selection, indicatingthat working conditions or contracts could be improved by adjusting these variables for potentialworkers who have specific individual characteristics.