A Bayesian Stochastic Frontier Model with Endogenous Regressors: An Application to the Effect of Division of Labor in Japanese Water Supply Organizations

Eri Nakamura, Takuya Urakami and Kazuhiko Kakamu

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, 2019, vol. 40B, pp 29-46 from Emerald Group Publishing Limited

Abstract: This chapter examines the effect of the division of labor from a Bayesian viewpoint. While organizational reforms are crucial for cost reduction in the Japanese water supply industry, the effect of labor division in intra-organizational units on total costs has, to the best of our knowledge, not been examined empirically. Fortunately, a one-time survey of 79 Japanese water suppliers conducted in 2010 enables us to examine the effect. To examine this problem, a cost stochastic frontier model with endogenous regressors is considered in a cross-sectional setting, because the cost and the division of labor are regarded as simultaneously determined factors. From the empirical analysis, we obtain the following results: (1) total costs rise when the level of labor division becomes high; (2) ignoring the endogeneity leads to the underestimation of the impact of labor division on total costs; and (3) the estimation bias on inefficiency can be mitigated for relatively efficient organizations by including the labor division variable in the model, while the bias for relatively inefficient organizations needs to be controlled by considering its endogeneity. In summary, our results indicate that integration of internal sections is better than specialization in terms of costs for Japanese water supply organizations.

Keywords: Division of labor; instrumental variable model; Markov chain Monte Carlo (MCMC); stochastic frontier model; water supply organizations; Gibbs sampler; endogeneity; inefficiency; C11; C21; C26; L32 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040b003

DOI: 10.1108/S0731-90532019000040B003

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