Distribution and Determinants of Technical Efficiency

Technical efficiency is of interest not only in the context of total factor productivity, but also as an important economic factor in its own right. As lined out before, technical efficiency can be described as the degree to which the potential of the available production technology is used (Kalirajan et al., 1996). In order to be able to draw sensible conclusions with respect to technical efficiency in Chinese inland aquaculture and to obtain a foundation for developing additional policy advice, three aspects of the time-varying technical efficiency scores will be analyzed: (1) Their development over time, (2) their geographical distribution and (3) their determinants.

The development over time and the geographical distribution of the efficiency scores can be directly analyzed employing the estimation results of the stochastic frontier model. For the purpose of analyzing the determinants of technical efficiency, a two-stage approach is chosen, which involves regressing the technical efficiency scores obtained from the stochastic frontier production model (Stage 1) on a set of environmental and other explanatory variables (Stage 2). Such two-stage approaches are particularly common in the DEA literature (see Simar and Wilson (2007) for an extensive list of applied studies), but have also been used in the context of stochastic frontier models (e.g. Pitt and Lee, 1981; Kalirajan, 1984). Various factors have been found in the previous literature to affect the level of technical efficiency, including environmental conditions (Simar and Wilson, 2007), farm-level characteristics like management and ownership (Alam et al. 2012), input intensities (Pitt and Lee, 1981) as well as the education and experience of producers and their access to extension services (Bravo-Ureta and Pinheiro, 1993).

In the present study, environmental variables will be represented by the annual average climate conditions as climate has been found to influence aquaculture production (see Section 2). Moreover, the provincial number of aquaculture technical extension staff per unit of labor in aquaculture production will be included as a measure of the capacity of the extension system to offer services to producers. Despite their likely relevance for technical efficiency, farm-level characteristics, however, cannot easily be represented in the present study given its province-level nature and the poor data availability regarding aquaculture producers in China. Instead, the input levels will be considered as additional explanatory variables on Stage 2 because they carry important information

about the characteristics of production, which in turn might be relevant for technical efficiency. The second-stage regression model then takes the following form:

where represents the total number of aquaculture technical extension staff per unit of labor in aquaculture production in province and year , and stands for climate variable in the respective province and year. represents the coefficients to be estimated and all other variables are defined as before.

The two-stage approach to analyzing the determinants of technical efficiency, however, has an econometric drawback. It has been criticized on the ground that regressing the technical efficiency scores on a set of explanatory variables in an auxiliary regression violates the assumption that the error term component in the stochastic production frontier model is independent and identiacally distributed (Battese and Coelli, 1995). To remedy this issue, several authors (e.g. Reifschneider and Stevenson (1991); Battese and Coelli (1995)) have proposed procedures that involve incorporating a model determining technical inefficiency into the SFA model and allow for simultaneous estimation of both models. Pitt and Lee (1981), however, warn that methods aimed at avoiding biased estimation in an SFA context usually involve imposing further restrictions on the technical inefficiency term. Particularly given the aforementioned data limitations regarding farm-level characteristics of aquaculture producers in China, it would be unclear how to specify the technical inefficiency model in a way that would not be unduly restrictive. Hence, despite the above drawback, the two-stage approach will be employed in the present study in order to analyze which factors have a significant influence on technical efficiency.

4.3 Data

The panel data set employed for the present analyses contains data on inland aquaculture production and climate in 24 Chinese provinces over the period 1993-2009.

The individual data were originally published in the corresponding issues of the Chinese

Fishery Yearbook (Chinese Ministry of Agriculture, 1994-2010) and the China Statistical Yearbook (National Bureau of Statistics of China, 1994-2010).

The provinces included in the analysis are Anhui, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Inner Mongolia, Shaanxi, Shandong, Shanxi, Sichuan, Xinjiang, Yunnan and Zhejiang. The remaining provinces, province-level municipalities and special administrative regions were not considered due to their exceptional economic structures or due to insufficient data.

With respect to inland aquaculture production, the data set contains information on the aggregate provincial aquaculture output as well as on the aggregate use of input factors, including the water area, the amount of fry used for production, the number of professional laborers and the total capacity (i.e. weight) of boats. Moreover, it comprises information on the total number of aquaculture technical extension staff.

Table 6: Descriptive statistics of Chinese inland aquaculture production

Variable Unit Mean Std. Dev. Min Max Obs

Aggregate aquaculture output tons 620,047.4 720,759.5 5,314.0 3,076,742.0 408

Water area 1,000 ha 207.6 174.4 6.8 683.7 408

Total annual precipitation mm/m^2 944.3 510.7 159.8 2,678.9 408 Data: Chinese Ministry of Agriculture 2010); National Bureau of Statistics of China

(1994-2010)

Except for the total weight of boats used in aquaculture production, all data were either directly available or could be accurately calculated from the yearbooks. The total weight of boats in inland aquaculture (BIA) for province in year has been approximated by where stands for the average share of the total weight of boats used for aquaculture (inland and marine production) in the total weight of boats used across all sub-sectors of fishery (capture fishery and aquaculture in inland and marine waters), BF²⁹. BM in turn refers to the total weight of boats employed in marine fishery (marine capture fishery and marine aquaculture). Even though

29 Due to poor data availability regarding the use of boats in aquaculture production, could not be calculated for each year but had to be calculated as the average over the period 2003-2007.

approximations such as the above potentially introduce some degree of measurement error, they are not uncommon in the literature in cases where exact data are not available (e.g. Lin, 1992; Zhang and Carter, 1997). For the analysis of the determinants of technical efficiency, the total number of aquaculture technical extension staff in each province is normalized by the respective province’s total number of laborers in inland and marine³⁰ aquaculture because the extension system offers services to both types of aquaculture.

The provincial average climate conditions in each year are represented by the annual average air temperatures and the total annual precipitation levels of the respective capital cities. The approach of representing the climate of a wider geographical area by that of a single point in that area, like the capital city, is also followed in other studies (e.g. Horowitz, 2009). The above climate variables are chosen for this analysis because they can be considered to be key indicators of the environmental conditions under which inland aquaculture operates. Air temperatures critically determine the temperature of inland surface waters (Boyd and Tucker, 1998) and precipitation is an important source of those waters (Yoo and Boyd, 1993)³¹.