Directorate General of Budget, Accounting and Statistics (DGBAS) of Taiwan.
Among the 23 two-digit industries, we select two of them, i.e., electrical machinery and electronics industry, and food products industry as our targets. The former is known as a high-tech and highly capital-intensive industry with swift technological advance, while the latter is characterized as a traditional industry and carries the opposite traits of the former.
We define the output variable (
y
) in the production function as the value-added that is equal to the sales revenue minus the sum of expenses on raw materials and electricity. The capital input ( k ) is defined as the net amounts of operating fixed assets. The labor input ( l ) is measured as the number of employee. The electricity expenses (e
) is identified as the intermediate input. Note that all of the dollar-valued variables are deflated by Taiwan’s consumer price index (CPI) with the base year of 2006 and all variables are further transformed by taking the natural logarithm.The dummy variable
I
it 1
if firm i stays in the market;I
it 0
, otherwise. The determinants of exit used in this study are classified into three parts. The first is the threshold of unobserved productivity
it1 that is a function ofk
it1 ande
it1. Following OP,
it1 is substituted by the fourth order polynomial series expansion in1 1
( k
it, e
it)
. The second consists of a set of macroeconomic variables such as EXCHANGEt (annual percentage change in the exchange rate of New Taiwan’s5 The survey is not conducted in 2001.
‧
former intends to examine whether a depreciation or appreciation in domestic currency influence the probability of exit, while the latter wants to examine whether the macroeconomic condition affect firms’ current decision on exit. Variable EXCHANGEt is taken from Taiwan Economic Journal and GDPGrowtht fromQuarterly National Economic Trends published by the DGBAS. The third type of
determinants are firm-/industry-specific variables. Variable CPTL2OUTPUTit is calculated as the ratio of capital to output, representing a firm’s sunk cost; AGEit the sample of electrical machinery and electronics industry consists of 5,512 plants.Among them, 3,404 plants are classified as continuing plants with a total of 11,891 plant-year observations and the rest of 2,108 plants belong to exit firms with a total of 4,453 plant-year observations. Food products industry consists of 1,976 continuing plants with a total of 7,104 plant-year observations and 559 exit plants with a total of 1,199 plant-year observations.
As far as the whole sample is concerned, the mean values of
y
, l , k , ande
in both industries are much larger than their medians, reflecting the distributions of these variables are skewed to the right. This implies that most of Taiwanese electronic and food products plants are small and medium enterprises (SMEs). The exit rate of electronics industry is 38.24% that is much higher than that of food product industry (22.05%). This is because the electronics industry is facing a highly uncertain‧
atmosphere and keen competition relative to the food products industry. According to the indices of PCM and CPT2OUTPUT, the electronics industry is more profitable than the food products industry, but the former incurs higher initial sunk costs than the latter.
The continuing plants are inclined to be larger than the exit ones, as the former has higher average values of output and inputs than the latter. This indicates that smaller plants tend to have higher probability of leaving the market. In addition, plants with greater sunk costs (CPT2OUTPUT) have higher probability of exiting the market in both industries.
[Table 3 and Table 4 here]
Tables 3 and 4 summarize the correlation coefficient matrices of all variables for the two industries. Panel (A) reports correlation coefficients for variables in the production function and Panel (B) for variables in the selection equation. These tables reveal that all of the variables in the production function are significantly and positively correlated with each other. The magnitudes of the correlation coefficients in the selection equation are relatively smaller and their signs vary substantially. It is noteworthy that the correlation coefficient between EXCHANGE and PCM is positive in the electronics industry, while the reverse is true in the food products industry, implying that the electronics (food products) firms can earn higher profit from the depreciation (appreciation) of domestic currency. This may be attributed to the fact that most of the electronics firms in Taiwan devote themselves to export their products and the depreciation of New Taiwan’s dollar stimulates their sales revenue.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
‧
Tables 5 and 6 present parameter estimates of the production and selection equations for the two chosen industries. Columns 1-4 of the upper panel, respectively, list estimates of OLS, Heckman’s sample selection model, the conventional SFA, and the SFA with sample selection (SFAS) of Lai et al. (2009).6 These four models do not take the simultaneity problem into account, since the unobserved productivity
is precluded from the production function. Columns 5-6 of the upper panel summarize estimates of OP/LP and our proposed SFSS model, respectively. The latter two models consider both simultaneity and selection problems, but the SFSS further generalizes the OP/LP model to a stochastic frontier framework.[Table 5 and Table 6 here]
The OLS estimates of labor and capital are 1.000 (1.081) and 0.153 (0.170) for the case of electronics (food products) industry. The sum of the two coefficients is equal to 1.153 (1.251), suggesting that these plants in both industries are operating under technology of increasing returns to scale. The coefficient of labor is found to be more than six times as large as that of capital in both industries. One may suspect that labor coefficient is inclined to be overestimated, while capital coefficient
The estimation procedure is similar to (16). Note that their model ignores the simultaneity problem.