Off-farm work and technical efficiency of wheat production: An analysis accounting for multiple dimensions of off-farm work

ABSTRACT

This study analyzes the impacts of off-farm work on the technical efficiency (TE) of wheat production, using data collected from 549 farming households in China. Unlike previous studies that only capture one dimension of off-farm work, in this study, we consider multiple dimensions, including off-farm work participation status of household heads, location choices (local or migrated off-farm work), and off-farm work intensity. We employ the stochastic frontier production model to estimate the TE of wheat production and a two-stage residual inclusion (2SRI) approach to address the endogeneity of the off-farm work variables. We find that: (1) household heads’ off-farm work participation significantly increases TE of wheat production; (2) local (rather than migrated) off-farm work participation significantly increases TE; (3) off-farm work intensity significantly increases TE when household heads work off the farm for more than 9 months. Additional analysis reveals that off-farm work participation of household heads, rather than other members, plays a significant role in improving the TE of wheat production.

KEYWORDS:

• Off-farm work
• technical efficiency
• two-stage residual inclusion
• wheat production
• China

Acknowledgments

Hongyun Zheng gratefully acknowledges the financial support from the Fundamental Research Funds for the Central Universities (2662022JGQD006).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from Hongyun Zheng upon request.

Notes

1. FAO Data: http://www.fao.org/faostat/en/#data/QC

2. Data envelope analysis has also been employed to estimate the technical efficiency of farm production (Stokes et al. 2007), but it cannot estimate the effects of inputs on output due to its non-parametric nature. Therefore, we estimate the SPF model in this study.

3. For reference, we present the SPF results of the Cobb-Douglas (CD) function in Table A1 of the Appendix.

4. Equation (6) can be estimated by fractional regression Logit, Probit, log-logistic (Loglog), and complementary log-logistic (Cloglog) models. The tests based on the approach of Ramalho et al. (2010) suggest that Cloglog generates the best results.