Silviase/jgov_v1.4_full_ann huggingface dataset:
greatly facilitate us in constructing relevant explanatory variables and clarifying the implications of regression results in later sections.

#### 4.1. Patent Applications by Technology Classes

For the sake of convenience, we classified 19 technology fields into the following three broader categories: basic technologies (Figure 1), post-genome technologies (Figure 2), and other technologies (Figure 3). Figures 1 to 3 illustrate the increased trend of patenting in many technology classes. “Genetic engineering” and “gene analysis” are the most active fields in which patents are applied for in Japan. On the other hand, although the rate of increase in patenting on post-genome technologies is remarkable, particularly since the latter half of 1990s, the number of patents on these technologies is much smaller than that on basic technologies. With regard to other technologies, patenting on “micro-organisms and enzymes” exploded since 1997, whereas the number of patents on biomedical products, such as “biopharmaceuticals” and “biochemical products,” did not show a significant increase within our observation period.

## [INSERT FIGURES 1, 2 & 3 AROUND HERE]

It is worth noting that the compositional change in the technology fields may reflect the Japanese backwardness in biomedical research. Post-genome technologies are essential to perform translational research, i.e., the combination of basic and applied research to produce clinically effective biomedical products or gene therapy/diagnoses. However, the number of patents of this kind is not yet impressive in Japan. Inventing biomedical products would be one of the ultimate goals of biomedical research, and it may be even more difficult to obtain important patents on biomedical products. On the other hand, “genetic engineering” and “gene analysis” are rather upstream technologies in the long-term process of biomedical research, and these are, if anything, mature research fields. However, these technologies are still the most active fields of patenting in Japan $ ^{12} $ .

#### 4.2. Patent Applications by Assignee Types

Figures 4 and 5 present the number of patents that were filed by a single assignee. As can be seen from Figure 4, the trend of corporate patents is quite similar to that of total patents: the trend was almost flat or rather decreased through the mid-90s and then increased steadily from 1998. On the other hand, government and university patenting is rising sharply, although the
latter is somewhat lagging behind the former.

## [INSERT FIGURES 4 & 5 AROUND HERE]

Figure 6 plots the number of patents with no less than two co-assignees; this number fluctuated to some extent, up to the mid-1990s. In the succeeding years, however, especially since 1998, we can observe a more or less upward trend in patenting by multiple co-assignees. In particular, industry-government patents (for simplicity, corp_gov in the figures denotes the sum of corp1_gov and gov1_corp) and industry-university patents (similarly, corp_univ in the figures denotes the sum of corp1_univ and univ1_corp) rose nearly twofold in the latter half of the 1990s.

In general, cooperative research between the public and private sectors seems to have always been quite active in Japan, while it was not so common for public sector researchers to file patents until recently (Odagiri and Goto, 1993; Goto, 1997; Odagiri, 1999; Kneller, 2003). However, there has been an increasing trend of patenting by the public sector in biomedical research. Figure 7 depicts the share of patents by various assignee types. The share of patents that were filed by the public sector almost doubled in the 1990s and accounted for more than 30% of the total patents in 2002.

## [INSERT FIGURES 6 & 7 AROUND HERE]

Government patenting is highly concentrated with the following top five government research institutes: the Japan Science and Technology Agency (JST), the National Institute of Advanced Industrial Science and Technology (AIST), the Institute of Physical and Chemical Research (RIKEN), the National Agriculture and Bio-oriented Research Organization (NARO), and the National Institute of Agrobiological Sciences (NIAS) $ ^{13} $ . These government institutes are defined by the order of the total patent applications from 1991 through 2001 in biomedical research $ ^{14} $ . As shown in Table 3, they account for approximately 70% of the total patent applications by the government, and the top three government institutions (i.e., JST, AIST, and RIKEN) account for the majority of the government patents. This may partly reflect the fact that
government subsidies are concentrated with these research institutes $ ^{15} $ .

## [INSERT TABLE 3 AROUND HERE]

#### 4.3. Biomedical Patents by Industry

Figure 8 depicts the number of patent applications by industry over time. Industry classification is based on the Security Identification Committee of Japan. The underlying dataset in Figure 8 thereby consists of listed companies alone. It is necessary to mention three points here. First, chemical and food companies have been the main patentees in Japan. Traditionally, industrial biotechnology research was mainly carried out by chemical and food companies with strong capabilities in fermentation $ ^{16} $ . Corporate patenting is not dominated by pharmaceutical companies; rather, it is dispersed across many industrial sectors. Patenting by pharmaceutical companies increased only after 1999. This is in stark contrast with the situation in the U.S. and possibly in most countries in the E.U.

Second, an increasing number of patents by electronics companies—that have been the main actors in the Japanese industrial innovation—have been filed since the latter half of the 1990s. This is probably due to the fact that biomedical research has increasingly come to rely on information technologies such as “bioinformatics.”

Finally, the large companies on the list still comprise the main patentees in biomedical research. These companies account for the majority (55.4%) of the total biomedical patents for the years 1991–2002 in Japan. According to the JBA, as of March 2004, there were only twelve listed new bio-venture firms (NBFs) $ ^{17} $ . All of these are listed in emerging equity markets such as JASDAQ, Mothers (Tokyo Stock Exchange), and Hercules (Osaka Stock Exchange). The share of patenting among NBFs accounts for approximately 3% in Japan.

## [INSERT FIGURE 8 AROUND HERE]

### 5. Variable Constructions

#### 5.1. Dependent Variable: Normalized Citation Intensity

This sub-section explains our patent value measure, which is constructed using forward
citations $ ^{18} $ . Following Jaffe and Lerner (2001) and Hall, Jaffe, and Trajtenberg (2002), we adjust the heterogeneity concerning the propensity to cite proceeding patents in the technology field $ k \left( k = 1, 2, \ldots, K \right) $ in year t ( $ t = 1, 2, \ldots, T $ ). In other words, we construct a weighted citation count (normalized forward-citation intensity) that is defined by the difference between the actual number of citations received per patent and the reference citation intensity for each technology field k in year t.

As mentioned in Section 2, the patent data at hand are classified by 19 technology fields non-exclusively. Therefore, our normalization procedure on patent values will be slightly complicated. The total number of forward citations received by patent i is denoted by $ C_{i} $ (the time subscript is suppressed for simplicity). We also define $ f_{ik} $ , which assumes the value of unity ( $ f_{ik}=1 $ ) if this patent corresponds to a technology field k, and zero otherwise. Then, we define the proxy for “patent scope” of patent i as

$$ p a t\_{s} c o p e_{i}=\sum_{k=1}^{K}f_{i k}, $$

which represents the total number of technology fields. Then, we define the weight for the citation count for each patent i on technology field k as follows:

$$ g_{ik}=\frac{f_{ik}}{pat\_{s}cope_{i}}\ (1/K\leq g_{ik}\leq1). $$

Next, we construct the weighted total citation count on technology field k as

$$ C_{k}=\sum_{i}^{n}C_{i k}g_{i k}, $$

where n is the total number of patents filed in year t. Thus, the total citation counts in year t are equal to the total citation counts of $ C_{k} $ across all the technology fields in year t.

It is possible that each patent has multiple technology-field flags. Using this, we calculate the expected citation count for each technology field as follows:

$$ C_{k}^{e}=\frac{C_{k}}{\sum\limits_{i=1}^{n}f_{ik}}. $$

Then, we define the expected citation count for each patent in technology field k as follows:
$$ C_{i}^{e}=\sum_{k=1}^{K}\left(f_{i k}C_{k}^{e}\right). $$

Finally, we normalize the citation counts for each patent that is defined by the difference between the actual number of citations received per patent and the reference citation count. In other words,

$$ d c i t i n g_{i}=C_{i}-C_{i}^{e}. $$

This normalization adjusts the heterogeneity of technology characteristics: in addition, since the mean of $ dciting_{i} $ is zero in any year t, there is no cohort (or “age”) effect of a patent $ ^{19} $ .

5.2. Independent Variables: Assignee Types and Other Patent Characteristics

Variable names and definitions are summarized in Table 4. There are three sets of independent variables that we will use in regressions: assignee types, characteristics of patents, and other characteristics of assignees, as listed in Table 4. We already explain assignee type dummies in Section 3.

## [INSERT TABLE 4 AROUND HERE]

Regarding patent characteristics, $ pat\_scope $ has already been defined in equation (5). This is a proxy for patent scope that is defined by the number of technology fields of a patent. Note that technology-field flags are assigned to each patent non-exclusively. Quite a large number of observations have multiple technology flags (the mean is 2.1 and the maximum is 14). There is no theoretical basis for the relationship between patent scope and patent value. As pointed out by Merges and Nelson (1990), patent scope would have a complex impact on innovation. This is an empirical issue that remains to be explored. Lerner (1994) relates the market value of biotechnology firms in the U.S. to the number of IPCs in a patent, which is regarded as the patent scope. Lerner also indicates that the firm's value is positively correlated with the patent scope. On the other hand, Harhoff et al. (2003) utilize a similar patent scope measure as an explanatory variable in patent value equations and find no significant impact on German patents.

Next, we define a dummy variable $ jp_{only} $ that denotes whether the JPO is the sole jurisdiction for which a patent protection was sought. This is a control variable for the fact that there are very few citations received by patents that are filed to the JPO alone. In Japan,
citations of prior arts were not obligatory for patent examiners and applicants, which would greatly decrease the likelihood of a patent being cited by subsequent patents $ ^{20} $ . Therefore, patents in which protection for an invention was sought solely in Japan (and written in Japanese) are not very likely to be cited by subsequent patents.

In addition, we define $ science\_ratio $ as a proxy for the extent of science-linkage for each patent. We define $ science\_ratio $ as “the number of backward citations of non-patent articles” divided by “the total number of backward citations + 1.” Since the number of equivalent patents has a significant positive correlation with the number of backward citations, as shown in the next section, $ science\_ratio $ would have a strong positive correlation with the size of the patent family, even though we normalized this variable by the total number of backward citations. Accordingly, although a science-linkage indicator is frequently used in the literature to emphasize the role of academic research in industrial R&D $ ^{21} $ , an estimated coefficient of this variable should be interpreted with caution in the present study.

With regard to the other characteristics of assignees, we define $ pat\_size $ as the total number of biomedical patents filed by the first assignee of a patent in each year $ ^{22} $ . We examine whether there is a scale effect whereby prolific patentees file more valuable patents on average (this may also be called the dilution effect if there is a negative impact of patent size on the average patent value) $ ^{23} $ . Moreover, we define an indicator variable as listed; this denotes whether or not a patent is filed by a listed company. As mentioned earlier, industrial biomedical research has been performed primarily by listed companies in Japan and there are quite a few listed new bio-venture firms in Japan. However, it is yet uncertain whether the listed companies are producing more valuable patents in biomedical research.

#### 5.3. Other Patent Value Measures

There are other patent value indicators that are extensively used in the literature $ ^{24} $ . We construct
three patent value indexes here for the sole purpose of comparison with the normalized citation intensity. First, we define $ bwd\_cites $ , which is the total number of backward citations on both patent and non-patent articles.

Second, we define $ fam\_size $ , which is the total number of “equivalent patents.” It is possible that the family size of a patent affects the likelihood of forward citations by subsequent patents that are filed in other countries. It should be noted that, according to the definition of the DWPI, patents in the same family do not necessarily share the same priority date. DWPI defines a patent family as patents sharing the same “invention” as well as relevant inventions that are scrutinized by experts with relevant scientific knowledge. Therefore $ fam\_size $ is not necessarily equivalent to the number of countries in which protection was sought.

Finally, we define claim as the number of claims for a patent that is filed to the JPO. The number of claims is somewhat less utilized in the literature as a patent value measure $ ^{25} $ . Numeric data on claims was retrieved from the IIP Patent Database. The sample size of claim is somewhat smaller than that of our basic dataset due to missing data in the IIP Patent Database. A patentee would have the incentive to claim as much as possible but patent examiners may require that claims be narrowed before granting. Thus, the number of claims at the time of patent filing tends to be larger than that at the time at which the patent is granted. We use the number of claims at the date of patent filing because it is almost complete in the available dataset.

### 6. Summary Statistics

#### 6.1. Patent Values by Assignee Type

The summary statistics are shown in Tables 5 and 6. Since the standard deviations are quite large in many variables, correlations between patent values and assignee types should be carefully confirmed in panel regressions with a series of control variables. However, it is worth noting that there seem to be somewhat consistent differences among assignee types in patent value measures.

Table 6 presents the summary statistics on the selected variables by assignee types. This table shows that the average normalized citation intensity (dciting) is positive only for corporate patents (corp and corp_corp). Another salient point is the large number of patent applications that were filed by the first assignee belonging to government research institutes. It
should be noted that $ pat\_size $ of government patents is, on average, particularly large (50.9). This reflects the fact that propensity to patents by government research institutes was very high, especially in the latter half of the 1990s.

## [INSERT TABLES 5 & 6 AROUND HERE]

#### 6.2. Science-linkage, Patent Size, and Patent Scope

As shown in Table 6, university patents have the highest value of science-linkage (science_ratio). Although this fact may not be surprising, given the importance of the open science culture in university research, it should be interpreted with caution. This may be due to the differences in technology fields wherein more non-patent articles tend to be cited. Alternatively, it may be due to the university researchers' higher propensity to cite academic articles. Thus, the relationship between science-linkage and patent values is quite uncertain.

Apart from universities, there appear to be no noticeable differences in $ science\_ratio $ among assignee types. However, if anything, it is a stylized fact in the literature that science-linkage is a beneficial source of information about the importance of a patent, particularly biotechnology patents (Narin et al., 1997; McMillan et al., 2000; Harhoff et al., 2003; Tamada et al., 2004; Branstetter and Kwon, 2004).

In addition, the average value of $ pat\_scope $ of government patents as well as university patents tends to be a bit higher than those of other types of assignees. On the other hand, patents filed by the private sector (corp and corp\_corp) have relatively lower values of $ pat\_scope $ . Further, it should be noted that on average, corporate patents have relatively fewer claims (claim). In other words, compared to public sector patents, the private sector is likely to file more technologically-focused patents with fewer claims. Thus, $ pat\_scope $ may have some salient effects on patent values.

#### 6.3. Correlation Matrix for Selected Variables

Table 7 provides a correlation matrix for the selected variables. It should be noted that there is a highly negative correlation between $ jp_{only} $ and $ science\_ratio $ (-0.66). We suspect that backward citations of non-patent articles by subsequent patents occurred mainly by patents that were filed in jurisdictional patent offices other than the JPO.

Regarding the alternative patent value measures, significant positive correlations are detected among dciting, fwd_cites, fam_size, and bwd_cites. These alternative patent value measures have been frequently used in the literature, and there is no certain guiding principle that specifies the best patent value measure $ ^{26} $ . Furthermore, incorporating these alternative
patent value measures in regressions as independent variables would have provoked a number of problems regarding multicollinearity and endogeneity. Therefore, we exclude $ fam\_size $ , $ bwd\_cites $ , and claim from a list of explanatory variables in regressions.

## [INSERT TABLE 7 AROUND HERE]

### 7. Panel Regressions

#### 7.1. Empirical Specification

We utilize the following sets of variables as explanatory variables in equation (1):

$$ X_{i(t),j}=(pat_{-}scope_{i(t),j},jp_{-}only_{i(t),j},science_{-}ratio_{i(t),j}). $$

These variables are estimated by exploiting cross-sectional variations of the pooled observations of patents filed by the first assignee j of patent i across several years. We also employ regressions with the following set of variables:

$$ X_{j(t)}=(p a t_{-}s i z e_{j(t)},l i s t e d_{j(t)}). $$

The variables in (8) are in fact invariant across i within j. However, they have within-year variations since the number of patents as well as the listing status of the first assignee j is not constant across the years. Therefore, it may be possible to estimate coefficients for them even under the fixed effect specification of the first assignee j.

The previous sections have already defined these variables and discussed their possible impacts on patent values. By using the normalized citation intensity $ (dciting_{i(t),j}) $ as a dependent variable, which is defined by equation (6) in Section 4, and introducing three sets of dummy variables—(2) to (4)—and the other explanatory variables in (7) and (8) into (1), we specify a “fixed effect” regression model as follows:
$$ \begin{aligned}dciting_{i(t),j}=&c_{j}+\alpha_{1}pat\_{s}cope_{i(t),j}+\alpha_{2}jp\_{o}nly_{i(t),j}+\alpha_{3}science\_{r}atio_{i(t),j}\\&+\alpha_{4}pat\_{s}ize_{j(t)}+\alpha_{5}listed_{j(t)}\\&+\beta_{1}corp_{i(t)}+\beta_{2}corp\_{c}orp_{i(t)}\\&+\gamma_{1}gov_{i(t)}+\gamma_{2}corp1\_{g}ov_{i(t)}+\gamma_{3}gov1\_{c}orp_{i(t)}\\&+\delta_{1}univ_{i(t)}+\delta_{2}corp1\_{u}niv_{i(t)}+\delta_{3}univ1\_{c}orp_{i(t)}\\&+D99_{i(t)}\times(\varsigma_{1}gov_{i(t)}+\varsigma_{2}corp1\_{g}ov_{i(t)}+\varsigma_{3}gov1\_{c}orp_{i(t)}\\&\quad+\xi_{1}univ_{i(t)}+\xi_{2}corp1\_{u}niv_{i(t)}+\xi_{3}univ1\_{c}orp_{i(t)})+\varepsilon_{i(t),j}.\end{aligned} $$

It should be noted that both the heterogeneity of year effect (i.e., cohort effect on forward citations) as well as the technological heterogeneity are fully adjusted, as discussed in Section 5. Therefore, we do not add a series of year dummies and technology field dummies to the present specification. Regarding policy effect dummies, we use either $ D99_{i(t)} $ or $ D98_{i(t)} $ , alternatively, in regressions for the sole purpose of a robustness check.

The normalized citation intensity $ (dciting_{i(t),j}) $ is not a count variable, and the error term is assumed to be independently and identically distributed. Therefore, we apply an ordinary least square (OLS) method to the panel regressions $ ^{27} $ . However, regression disturbances may not be constant across observations. In order to alleviate the heteroscedasticity that may be related to the explanatory variables, we use heteroscedasticity robust standard errors in inferences for estimation results. The total number of observations is 30,350 and the number of first assignees is 3577 $ ^{28} $ .

#### 7.2. Estimation Results

The estimation results are summarized in Table 8. The observation period is for the years 1991–2002. All the equations are employed by using the fixed effect model for the first assignee. This model is supported in all specifications according to the conventional Hausman test statistics. Heteroscedasticity robust standard errors are given in parentheses.

## [INSERT TABLE 8 AROUND HERE]

The estimation results in Table 8 show that the corporate dummy (corp) is statistically