EL APRENDIZAJE DE LA TECNICA DEFENSIVA EN EL VÓLEIBOL

As mentioned earlier in section 1.1, many analyses had been carried out in order to examine the productivity growth of Thailand in the pre-crisis period. However, the results of these analyses were far from unison. This section is devoted to the review of these works, and also, to suggest possible explanations for the divergent in these findings.

The empirical studies on the sources of growth during the period from 1985 to 1995 have created a conflicting view between factor accumulation and productivity growth. While some studies (i.e. Sarel (1997) and Dollar et al. (1998)) recognized high productivity growth associated with openness as part of the explanations for Thailand’s rapid growth, others (including Tinakorn and Sussangkarn (1996, 1998)) argued that it was the capital accumulation that played the significant role. Table 2.1 shows some results from the previous studies. Tinakorn and Sussangkarn (1996) found that after adjusting for the quality of labour, during the period from 1978 to 1990, only 16 percent of the economic growth came from the growth in total factor productivity, while the remaining 84 percent came

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Nukul’s Commission Tasked with Making Recommendations to Improve the Efficiency and Management of Thailand’s Financial System, (1998)

Background to Research

Table 2.1: Total Factor Productivity Growth in Thailand

Whole Economy Manufacturing Source Time

Period TFP Contribution to

Growth TFP Contribution Growth to

Methodology and

Type of Data Remarks

World Bank (1993) 1960-1990 2.5 N/A N/A N/A Parametric,

Time Series Full Sample

1960-1990 0.5 N/A N/A N/A Parametric,

Time Series High-Income

Marti (1996) 1970-1990 1.6 42.5 N/A N/A Parametric,

Panel Data Tinakorn & Sussangkarn (1996) 1978-1990 2.7 36 0.4 4.4 Growth Accounting, Time Series 1972 Prices (1.2) (16) (-0.4) (-4.1) Adjusted* 1981-1990 3.1 39 1.2 13.1 Growth Accounting, Time Series 1972 Prices (2.5) (32) (0.9) (9.1) Adjusted* 1981-1990 2.8 37 1.9 19.0 Growth Accounting,

Time Series 1988 Prices

(2.2) (29) (1.6) (15) Adjusted*

Collins and

Bosworth (1997) 1960-1994 1.8 36.0 N/A N/A Parametric, Panel Data

Tinakorn &

Sussangkarn (1998) 1981-1995 2.1 26 1.1 10.5 Unadjusted

(1.3) (16) (-0.1) (-1.2) Adjusted*

1986-1990 N/A N/A 3.8 N/A Unadjusted

(4.0) Adjusted*

1991-1995 N/A N/A -0.6 N/A Unadjusted

(-3.1) Adjusted*

Sarel (1997) 1978-1996 2.0 39 N/A N/A Econometric/

Growth Accounting, Panel Data

1991-1996 2.3 35 N/A N/A Econometric/

Growth Accounting, Panel Data Source: Tinakorn and Sussankarn (1998) and SME Technical Working Paper Series, No. 8

from the increase in factor inputs used. Repeating this exercise with the revised data for the period from 1980 to 1995, Tinakorn and Sussangkarn (1998) found that the unadjusted total factor productivity growth contributed around 20 percent to the overall GDP growth, and declined to just 10 percent in the manufacturing sector. When adjusted for the increase in labour quality, this figure turned into a negative value for the manufacturing sector, indicating the worsening of productivity. In contrast, Sarel (1997) indicated a much more respectable productivity growth for the period from 1978 to 1996. He estimated that productivity was growing at the rate of 2 percent per annum, and accounted for 39 percent of the aggregate economic GDP growth. Re-estimating for the period from 1991 to 1996, the productivity growth rose to 2.3 percent, and explained 35 percent of the total economic growth. Another study by Dollar et al. (1998) found that the total factor productivity among the manufacturing establishments grew by 25 percent between 1994 and 1996.

There are several reasons that could explain such divergent findings of the pre- crisis productivity level, including approaches in the estimation, sources of data, periods of study, and assumptions assumed. The first problem to be mentioned is concerning the methodology of the estimation. For total factor productivity, unlike in the case of GDP and GNP calculations, there is not yet an international standard, guideline, or methodology in which researchers can follow. Therefore, depending on the specification adopted, TFP often measures different things in different cases7_{. For example, the TFP calculated from gross}

output data could give an entirely different set of results from the one calculated using value added data. Details regarding this issue will be mentioned in Chapter 5, Section 5.2.3.

Second, in most cases, the TFP computation demand a rather rich set of time series data on capital stock (preferably by sector) which are often lacking in developing countries,

including Thailand8_{. Therefore, these studies on Thai productivity growth employed}

different sets of data from difference sources, and hence, leading to divergent outcomes. As

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Dhanani and Scholtrs, (2002) 8

a result, the comparability between these studies suffers greatly from the problem generated by data sensitivity. Moreover, the TFP estimates are also very sensitive to the time periods of study. As seen from Table 2.1, these studies all estimated TFP for different time periods, and therefore, diverging results are not unexpected.

Finally, many TFP specifications assume constant returns to scale and perfect competition, two neoclassical assumptions that do not apply in many developing countries. Dhanani and Scholtes (2002) alleged that, in fact, economies of scale occurred in the modern and large-scale production facilities were a major source of productivity growth. In addition, market power has also been found to be a fairly important determinant of the productivity estimations, as Kee (2002) suggested that when adjusted for the factor concerning the market power, the estimates of the average productivity growth in Singapore was doubled. These problems could, nevertheless, be solved by the use of the econometric approach in productivity measurement, in which the assumptions of constant return to scales and perfect competition are not necessary.

Even with the assumptions of constant return to scales and perfect competition being relaxed, this still cannot guarantee an unbiased measurement of productivity. The traditional econometric approach is, nonetheless, subjected to a limitation of not being able to include the technical inefficiency components in the model used for estimations. The conventional estimation techniques associated with the traditional econometric approach (i.e. ordinary least square, generalized least square) usually assumed ‘zero’ mean error component9_{. Therefore, this implies that the only source of deviation from the estimated}

production function is due to the statistical noises10_{. However, when considering the case of}

Thailand, in particular in the pre-crisis period in which the technical inefficiency is expected to be high and persistence, this assumption might be considered too strong. Hence, an alternate approach, namely a stochastic production frontier approach (Pitt and Lee (1981),

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Green, (1993b) 10

Battese, Coelli, and Colby (1989), Battese and Coelli (1992), is proposed for the estimations in this thesis (details for such approach can be found in Chapter 7).