5. Econometric Modeling
6.3. Explanatory variables
We define here the explanatory variables that will be used in the Logit analysis presented in Section 5. These variables try to account for both the theoretical determinants of the decision to innovate (cf. Section 3) and for the factors that are specific to Taiwan (cf. Section 4). From a theoretical point of view, market structure, firm size, and technological opportunities are said to be the main determinants of the decision to innovate. From the viewpoint of Taiwan’s innovation history, three main types of factors could lead Taiwanese firms to innovate: (1) the increase in labor costs, (2) the fluctuations of Taiwan’s currency and (3) growth of the export market.
Table 9 gives summary statistics for all our explanatory variables.
The MOEA panel data did not keep track of the economic context of the 1990’s, however. In our effort to build reliable proxy variables for the stylized facts highlighted above, we had to use the information provided from the Directorate General of Budget, Accounting and Statistics (DGBAS) of Taiwan’s Executive Yuan.
The DGBAS data was presented to some extent in Section 4 (Tables 5 and 6): it comes from a large survey conducted every five years by the DGBAS, and available at the 4-digit industry level. This data records nominal wages and the monetary value of Taiwan’s exportations (by industry) in 1986, 1991 and 1996; the value of the real wages can be computed by dividing the DGBAS nominal wages by the Consumer Price Index7. The DGBAS data can be used to compute industry-level indicators;
these indicators can then be added to the MOEA panel. The matching is made
7 Available from the DGBAS, 2001, Commodity-Price Statistics Monthly in Taiwan, Taiwan's Executive Yuan. The Consumer Price Index is normalized to 100 for year 2001.
possible because the MOEA panel data precisely records the industry (4-digit) to which each firm belongs.
Our initial intention was to build two indicators, one for the changes in labor costs and another one for the changes in exportations. Unfortunately, only the latter was meaningful, as the DGBAS data does not allow us to distinguish between high-skill and low-high-skill labor. This lack of information on the quality of the labor force makes it irrelevant to include (for instance) the growth of real wages (at the industry-level) in our model : because we don’t know if it is driven by a demand of high-skill or low-skill labor, it is impossible to interpret the effect of a rise in labor costs. A higher demand for high-skilled workers may be correlated with more R&D activity;
on the contrary, a higher demand for low-skilled workers may come from firms that either rely only on the importation of technology, or that do not innovate.
Table 9: summary statistics
Variable Description Mean (Std Error)
Grexp91-86 Rate of growth of exportations between 1986 and 1991 0.22 (1.01) Grexp96-91 Rate of growth of exportations between 1991 and 1996 1.44 (6.89) dEt-86 Variation of NT$/ US$ exchange rate from 1986 to year t
(t = 1992, 1993, 1994, 1995).
-9.50 (0.47) CR4 Concentration Ratio, Level 4 (%) 26.89 (16.24) Size50 Firm size (number of employees) small than 50 0.83 (0.38) Size100 Firm size (number of employees) between 50 and 100 0.087 (0.28) Size500 Firm size (number of employees) between 100 and 500 0.063 (0.24) Size1000 Firm size (number of employees) between 500 and 1000 0.006 (0.08) Size1001 Firm size (number of employees) bigger 1000 0.017 (0.13)
Age Firm’s age in years 13.19 (6.48)
D1 1 if firm belong to (11) Food Industry, 0 otherwise 0.11 (0.32) D2 1 if firm belong to (13) Textile Industry, 0 otherwise 0.07 (0.25) D3 1 if firm belong to (14), (16), or (17) industries (Wearing Apparel,
Leather, Wood, Furniture), 0 otherwise 0.08 (0.27) D4 1 if firm belong to (15), (18) or (19) industries (Paper, Printing)
and 0 otherwise 0.06 (0.25)
D5 1 if firm belong to (21) Chemical Industry, 0 otherwise 0.02 (0.15) D6 1 if firm belong to (22) or (23) industry (Chemical Products, Oil
and Coal Products), 0 otherwise 0.04 (0.20) D7 1 if firm belong to (24) Rubber Industry, 0 otherwise 0.01 (0.11) D8 1 if firm belong to (25) Plastic Industry, 0 otherwise 0.08 (0.29) D9 1 if firm belong to (26) Non-Metal Mineral Products Industry, 0
otherwise 0.06 (0.23)
D10 1 if firm belong to (27) Basic Metal Industry, 0 otherwise 0.05 (0.23) D11 1 if firm belong to (28) Fabricated Metal Products Industry, 0
otherwise 0.12 (0.32)
D12 1 if firm belong to (29) Machinery Industry, 0 otherwise 0.08 (0.28) D13 1 if firm belong to (31) Electronic Industry, 0 otherwise 0.07 (0.25) D14 1 if firm belong to (32) Transportation Industry, 0 otherwise 0.07 (0.25) D15 1 if firm belong to (33) Precision Instruments Industry, 0 otherwise 0.02 (0.14) D16 1 if firm belong to (39) Miscellaneous Industry, 0 otherwise 0.04 (0.20)
Thus, our model will not include any information about changes in the labor costs; this may lead to more unobserved heterogeneity between firms, but we are confident that this heterogeneity can be captured either by our industry fixed effect or by the individual random effect. Even if the DGBAS data had provided us with information on the quality of the labor force, this information would only have controlled for inter-industry differences anyway.
Nonetheless, the DGBAS data proved useful in that it allowed us to represent the effect of changes on the export market on firms’ probability to innovate. For that, we calculate the growth of exportations in industry j over two subsequent periods (1986-1991 and 1991-1996):
where expjt is the export shipment in industry j at year t. As stated in Sections 3 and 4, the growth of exportations is expected to have a positive impact on the probability to innovate, through a higher technology requirement. By using these indicators, we want to test for the possibility of a simultaneous impact (growth of exportations between 1991 and 1996, which roughly correspond to the observation period of the MOEA panel) and of lagged impact (growth of exportations between 1986 and 1991, i.e. just before the observation period of the MOEA panel).
A similar approach was adopted in order to represent the fluctuations of the New Taiwan Dollar, which is an even broader environment variable. The exchange rate of NT dollar to US dollar is available in Taiwan Statistical Yearbook8, which allowed us to compute the difference in the exchange rate between each year t in the observation period (t = 1992, 1993, 1994, 1995) and a year of reference (1986):
(11) dEt-86=exchange ratet - exchange rate86
Again, this indicator was matched, for each year, to the MOEA panel data.
If the MOEA panel was lacking of some contextual/environmental variables, it provided us, however, with several explanatory variables that are directed related to the classical theoretical framework of industrial organization theory. The most
8Council for Economic Planning and Development (CEPD), 1999, Taiwan Statistical Yearbook, ROC Taiwan.
important of these variables, from a theoretical perspective, may be our indicator of market structure, the concentration ratio (CR4).
The CR4 measures the market share of the 4 largest firms in industry j at time t:
(12) CR4jt =
∑
has a maximal value of 100% (which corresponds to the case of a pure monopoly).In order to control for possible simultaneity biases, we will consider several alternative specifications for our Logit model: the first one is “simultaneous”, and involves the CR4 at year t as an explanatory variable for the probability to innovate in that year. The others are “lagged” specifications using CR4t-1 or CR4t-2 (rather than CR4t) to explains the probability to innovate in year t.
Another important explanatory variable is firm size (Sizeit), which is represented by a 5-categories variable10 based on Nit, the number of employees of the
firm in year t:
By taking Category 2 as the reference, it is possible to control for the presence of non-linearity in the size-innovation relationship. Finally, we also control for firms’ age (Ageit), computed in years, and for industry, thanks to 16 industry dummies (with dummy n° 15 being the reference category). These dummies are presented in Appendix II.
9 In this chapter, following classical IO literature, we use sales to compute the CR. In some cases, alternatives have been used, such as: the number of employees, the value added, and the output.
10 We initially used a continuous variable (the number of employees) to represent firm size, but, as this