Untitled

Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model

“…Moreover, the coefficient for CO (cows) is the largest among the partial elasticities, which implies that a percent change in the number of cows has a larger influence on milk production than the same relative change on any other input. This result is consistent with several other dairy studies including Kumbhakar et al (1991), Heshmati and Kumbhakar (1994), Ahmad and Bravo-Ureta (1996), Cuesta (2000) and Lawson et al (2004a, b).…”

“…4% between 1999/2000 and 2005/ 2006, reaching a total level of milk output equal to 1,620 million liters in 2005/2006 followed by a small reduction to 1,576 million liters in 2006/2007. On average, during the period 1999/2000to 2003 the export of dairy products generated around US $130 million in revenues per year. These figures imply an opening of the Uruguayan dairy sector to international trade and that farmers have been able to benefit from foreign markets.…”

“…The definition of t, a smooth time trend to capture TC (technological change), varies with each data set as follows: for Argentina t is 1 = 1997/1998, 3 = 1999/2000, and 5 = 2001/2002; for Chile it is 1 =/1997, 3 = 1998/1999, 4 = 1999/2000, 5 = 2000/2001, and 6 = 2001 for Uruguay it is 1 = 1999 for Uruguay it is 1 = /2000 for Uruguay it is 1 = , 2 = 2000 for Uruguay it is 1 = /2001 for Uruguay it is 1 = , 3 = 2001 for Uruguay it is 1 = / 2002 for Uruguay it is 1 = and 4 = 2002 for Uruguay it is 1 = /2003. For the pooled data, t is 1 =…”

A Stochastic Frontier Model to Assess Agricultural Eco-efficiency of European Countries in 1990–2019

“…Although the use of the half Normal distribution has become prominent in empirical applications, several different specifications have been proposed to make technical efficiencies depend on time. For instance, Cornwell et al (1990) proposed the specification u i,t = a + bt + ct 2 + u i , where a, b and c are parameters to be estimated and u i ∼ N + (0, σ 2 ); Battese and Coelli (1992) assumed u i,t = u i exp[η(t − T )], where η is a parameter to be estimated and u i follows the truncated Normal distribution u i ∼ N [0,∞) (µ, σ 2 ); Lee and Schmidt (1993) proposed u i,t = u i b t , i.e., parameter b varies across time points; Battese and Coelli (1995) assumed a specification similar to the one of Cornwell et al (1990), with the difference that only the linear component of the trend is considered and several determinants of technical efficiency are included; Cuesta (2000) allowed parameter η in the specification of Battese and Coelli (1992) to vary across production units, i.e., u i,t = u i exp[η i (t − T )]. All these specifications for random errors u i,t have the common limitation of making production units share the same trend of technical efficiency, which is an unreliable assumption preventing production units to change their rank in time (parallel trends).…”

“…Tonini and Pede (2011) Translog with fixed effects. Cuesta (2000). Tonini (2012) Translog with fixed effects.…”