A Practitioner's Guide to Stochastic Frontier Analysis Using Stata

Kumbhakar, Subal C.; Wang, Hung‐Jen; Horncastle, Alan P.

doi:10.1017/cbo9781139342070

Cited by 439 publications

(479 citation statements)

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“…A negative coefficient implies a positive impact on technical efficiency and vice versa. The maximum likelihood coefficients of the technical effects model are not marginal effects due to the non-linearity in the relationship between E(ui) and the Zis [35]. Given the model, = ′ , the marginal effect of the kth element of Zi on E(ui) is given by the formula:…”

Section: Technical Efficiencymentioning

confidence: 99%

Resource Use Efficiency as a Climate Smart Approach: Case of Smallholder Maize Farmers in Nyando, Kenya

Salat

Swallow

2018

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Abstract:To simultaneously enhance agricultural productivity and lower negative impacts on the environment, food systems need to be much more efficient in using resources such as land, water, and fertilizer. This study examines resource use efficiency of maize production among smallholder farmers in Nyando, Kenya. The main objective is to assess the degree of technical efficiency of smallholder farmers and identify the impact of so-called "climate smart practices" on technical efficiency. The method of Stochastic Frontier Analysis is used to simultaneously estimate a stochastic production frontier and a technical inefficiency effect model. Data for 324 subplots farmed by 170 households were available for this analysis. The study reveals that maize production in Nyando is associated with mean technical efficiency of 45% and that soil conservation practices such as residue management, legume intercropping, and improved varieties significantly increase farmers' technical efficiency. Soil carbon is found to be a critical factor of production. These results imply that there is potential to more than double production using the same resources and that soil conservation practices can be very "climate smart," at once increasing soil carbon, production, climate resilience, and technical efficiency.

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Section: Technical Efficiencymentioning

confidence: 99%

Resource Use Efficiency as a Climate Smart Approach: Case of Smallholder Maize Farmers in Nyando, Kenya

Salat

Swallow

2018

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“…We estimate the stochastic frontier function with a maximum likelihood approach using the package developed by Kumbhakar et al (2015) in STATA. We derive the technical efficiency that has been used as output in the PSM component of our analysis.…”

Section: Stochastic Production Frontier Modelmentioning

confidence: 99%

Impact of Weather Index Insurance on Groundnut Farmers’ Technical Efficient in Senegal: A Propensity Score Matching Approach

Atozou¹,

Lawin²,

Niang³

2017

JSD

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Irregular and low rainfall levels and drought have become important sources of low agricultural yields and agricultural incomes in sub-Saharan Africa. Weather index insurance is a financial product for climate risk management aimed at securing farmers' incomes. This paper aims to evaluate the impact of a weather index insurance project piloted with groundnut farmers in Senegal in 2015-2016 agricultural season on farmer's technical efficiency (TE). A Stochastic Production Frontier model was used to estimate the TE scores. A matched group of beneficiaries and control farmers was determined using propensity score matching techniques to mitigate biases stemming from observed variables. The results showed that average TE is consistently higher for control farmers than the beneficiary group. Age, gender and education were found to be significantly related to technical efficiency, while membership in farmers' association, credit, improved seeds and extension contact were not significantly related to technical efficiency. From a policy perspective, we suggest that weather index insurance programs targeting smallholder farmers in developing countries, and particularly in sub-Saharan Africa, be accompanied with education services, provision of new technologies such as high yield seeds and other best farm management practices and credit to help farmers better adapt to weather shocks and secure their production and income.

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“…All other variables are as described before. We assume that vit are random variables and distributed as ) , 0 ( heteroscedasticity (see Kumbhakar and Lovell (2000) and Kumbhakar et al (2015) for details).…”

Section: Stochastic Frontier Modelmentioning

confidence: 99%

“…The firm-specific estimate of the technical efficiency and the marginal effects of determinants of inefficiency are obtained from the conditional mean of u and derivatives of E(u) (see Kumbhakar et al (2015) for details). Table 6 reports the results.…”

Section: Stochastic Frontier Modelmentioning

confidence: 99%

Estimation of firm performance from a MIMIC model

Chaudhuri

Kumbhakar

Sundaram

2016

European Journal of Operational Research

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In this paper we propose a new approach (based on the Multiple Indicator Multiple Cause (MIMIC) model of Joreskog and Goldberger (1975)) to assess the performance of firms assuming that the 'true' firm performance is latent but there are many observable indicators of it. In our MIMIC model, the latent firm performance variable is linked with some observed explanatory variables (determinants) like age, size, advertising expenses, debt equity ratio, etc. Since there are many observed indicators (ROE, ROA, Tobin's Q, etc.) of the unobserved latent firm performance, the measurement equations in the MIMIC model link these observed indicators to the latent performance measure. We use firm level data from India during the period 2001 to 2008 to estimate the latent firm performance using the predicted factor scores and rank the firms according to the proposed measure. Finally, we estimate two stochastic frontier models and compute Pearson's correlation between pairs of performance measures. We find high rank correlation between the two measures of firm performance/efficiency, which justifies the use of the MIMIC model as a complementary method of performance measures.

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A Practitioner's Guide to Stochastic Frontier Analysis Using Stata

Cited by 439 publications

References 0 publications

Resource Use Efficiency as a Climate Smart Approach: Case of Smallholder Maize Farmers in Nyando, Kenya

Resource Use Efficiency as a Climate Smart Approach: Case of Smallholder Maize Farmers in Nyando, Kenya

Impact of Weather Index Insurance on Groundnut Farmers’ Technical Efficient in Senegal: A Propensity Score Matching Approach

Estimation of firm performance from a MIMIC model

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