A metafrontier analysis of determinants technical efficiency in beef farm types: An application to Botswana
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Presented by Sirak Bahta at the International Conference of Agricultural Economists (ICAE), Milan, Italy, 9-14 August 2015
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A metafrontier analysis of determinants technical efficiency in beef farm types: An application to Botswana
- 1. A metafrontier analysis of determinants technical efficiency in beef farm types: An application to Botswana Sirak Bahta International Conference of Agricultural Economist (ICAE) Milan, Italy, 9-14 August 2015 •
- 2. Background Agriculture in Botswana: The main source of income and employment in Rural areas (42.6 percent of the total population) 30 percent of the country’s employment More than 80 percent of the sector’s GDP is from livestock production Cattle production is the only source of agricultural exports 1
- 3. Cont. Background (Cont…) Beef is dominant within the Botswana livestock sector 2
- 4. 3,060 1,788 2,247 0 500 1000 1500 2000 2500 3000 3500 '000 Commercial Traditional Dualistic structure of production, with communal dominating Background (Cont…) Cattle population 3
- 5. Background (Cont…) Despite the numerical dominance , productivity is low esp. in the communal/traditional sector 4 0 0.03 0.06 0.09 0.12 0.15 0.18 Sales Home Slaughter Deaths GivenAway Losses Eradication Commercial Traditional
- 6. Growing domestic beef demand and on-going shortage of beef for export: In recent years beef export has been declining sharply (e.g. from 86 percent of beef export quota in 2001 to 34 percent in 2007 (IFPRI, 2013 )) Background (Cont…) 5 0 30000 60000 90000 120000 150000 180000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Quantity (tonnes) Value (1000 $) US$
- 7. To derive a statistical measure of Technical efficiency and meta technology gap ratio (MTR) for different smallholder farm types . More specifically: • To measure farm-specific TE in different farm types • To measure technology-related variations in TE between different farm types • To analyze the determinants of farmers’ TE • Come up with policy recommendations to improve competitiveness of beef production Objective of the study 6
- 8. Measuring efficiency: potential input reduction or potential output increase relative to a reference (Latruffe, 2010). Technically defined by non-parametric and parametric methods The non-parametric approach uses mathematical programming techniques –Data envelope analysis (DEA) The parametrical analysis of efficiency uses econometric techniques to estimate a frontier function - Stochastic frontier analysis (SFA) Theoretical framework 7
- 9. Technological differences The stochastic frontier allows comparison of farms operating with similar technologies. However, farms in different environments (e.g., production systems) do not always have access to the same technology. Assuming similar technologies when they actually differ across farms might result in erroneous measurement of efficiency by mixing technological differences with technology-specific inefficiency (Tsionas, 2002). Various alternatives have been proposed to account for differences in technology and production environment. 8 Theoretical framework (cont…)
- 10. Metafrontier This technique is preferred in the present study because : - Enables estimation of technology gaps for different groups - Accommodates both cross-sectional and panel data The stochastic metafrontier estimation involves first fitting individual stochastic frontiers for separate groups and then optimising them jointly through an LP or QP approach. - It captures the highest output attainable, given input (x) and common technology. 9 Theoretical framework (cont…)
- 11. Source: Adapted from Battese et al. (2004). Figure 1: Metafrontier illustration 10 Theoretical framework (cont…)
- 12. • Household data, collected by survey • More than 600 observations (for this study classified by farm types) Data and Methodological Approach Study Area 11
- 13. SFA Accept hypothesis Reject hypothesis Linear programming/Shazam LR test TE effects/Tobit Bootstraping/ Standard dev. Data and Methodological Approach 12 Stop Technology Gaps
- 14. Results and discussion cont… A Technical efficiency and meta-technology ratios 13 35% 46% 57% 50%46% 84% 81% 76% 0% 20% 40% 60% 80% 100% Cattle farms Cattle and crop farms Mixed farms* All Farms TE w.r.t the meta-frontier Meta-technology ratio C B AB A A
- 15. Results and discussion Production function estimates Variable Metafrontier Constant (β0 ) 7.46*** 0.0001 Feed Equivalents(β1 ) 0.20*** 0.0001 Veterinary costs(β2 ) 0.21*** 0.0001 Divisia index (β3 ) 0.50*** 0.00029 Labour (β4 ) 0.10*** 0.0001 Log likelihood 456.66 Table1: Production function estimates 14
- 16. Results and discussion cont… Technical efficiency Beef herd size Sales to BMC Controlled breeding method Other agric- income Farmer age Distance to commonly Used market Indigenous breed Income/Ed ucation - Ve + Ve 15
- 17. • The majority of farmers use available technology sub-optimally and produce far less than the potential output; average MTR is 76% and TE is 50%. • Herd Size, Controlled cattle breeding method, market contract (BMC), other agricultural income and Farmer age and distance to market, all contribute positively to efficiency. Conclusion and policy implications 16
- 18. • On the contrary, proportion of indigenous cattle and interaction of income and formal education did not have a favorable influence on efficiency. • It is important to provide relevant livestock extension and other support services that would facilitate better use of available technology by the majority of farmers who currently produce sub-optimally. Conclusion and policy implications cont… 17
- 19. Conclusion and policy implications cont… 18 - Necessary interventions, for instance, would include improving farmers’ access to appropriate knowledge on cattle feeding methods and alternative feeds. - Provision of relatively better technology (e.g., locally adaptable and affordable cattle breeds and breeding programs). - Access to market services, including contract opportunities with BMC.
- 20. - Provide appropriate training/education services that enhance farmers’ management practices, and/or encourage them to employ skilled farm managers. - Policies that promote diversification of enterprises, would also contribute to improving efficiency among Botswana beef farmers. Conclusion and policy implications cont… 19
- 21. Next Steps • Latent class stochastic frontiers • System Dynamics
Notas del editor
- Not clear as to whether beef production is competitive Studies have relied on household budget analysis and limited household data Others have concentrated on productivity of agriculture
- Methods to address technology differences in efficiency estimation Continuous parameters method Bayesian stochastic frontiers that - assess the influence of exogenous factors on either the production function or inefficiency component (Van den Broeck et al. ,1994; and Koop et al. ,1997) Nonparametric stochastic frontier Nonparametric stochastic frontier based on local maximum likelihood approach (Kumbhakar et al. , 2007) ). Predetermined sample classification Classifying the data into various groups based on a priori information, and then separate frontiers are estimated for each group. Latent class stochastic frontier Uses of latent variable theory to classify the data into segments or groups, and then estimate a frontier for each group in one stage. Metafrontier Proposed by Battese et al. (2004) and estimated by specifying a single data generating process, which explains deviations between observed outputs and the maximum possible explained output levels in the group frontiers
- The metafrontier function captures the highest possible output level (y) attainable, given the input (x) and common technology in the industry (Figure 1). Output levels for producers who are efficient both in respective group frontiers (e.g., frontier 1) and in the entire industry lie on the metafrontier. Frontiers 2 and 3 fall below the metafrontier; this implies that they represent efficient production in the groups/production systems, but not so for the industry.
- Such analysis at the level of beef farm type is proposed as desirable because it is likely that these farms are operating with different technologies. It is also expected that differences in technology and organization, as well as asset ownership and human capital both within and between these beef farm types could cause or underlie significant differences in the technologies used by the farms. From the policy point of view, it is of interest for the study to distinguish the beef farm type differences in their mean efficiency levels, technology gaps and identify common determinants of technical efficiency. These assertions require statistical testing, as there would be no good reason for estimating the efficiency levels of beef farm types relative to a meta-frontier production function if these farmers are found out to operate under the same technology (Battese et al, 2004). A likelihood-ratio (LR)7 test of the null hypothesis, that the beef farm type stochastic frontier models are the same for all farms in Botswana, was calculated after estimating the stochastic frontier by pooling the data from all beef farm types. The value of the LR statistic was 76.2 which is highly significant (Kodde and Palm, 1986).
- The TE with respect to metafrontier show that those farms who have cattle, small stock and crop are relatively more efficient than the others and there is significant difference among the farm types in terms of efficiency. TE 45% for the pooled sample indicates This indicates that there is a considerable scope to improve beef farm technical efficiency under the prevailing input mix and production technology among beef cattle producers in Botswana. As you can see The mean MTR in the pooled sample is 0.77, implying that, on average beef farmers in Botswana produce 77 percent of the maximum potential output achievable from the available technology (crossbreed cattle). Further, 98 percent of farmers across the three production systems have MTR estimates below 1, indicating that they use the available technology suboptimally. Perhaps due to low adoption of technologies.