A. The Agreement on Trade-Related Aspects of Intellectual Property Rights

Negotiated at the Uruguay Round of the General Agreement on Tariffs and Trade between 1989 and 1990, the TRIPS agreement sets minimum standards for national governments’ protection of IP. Widely acknowledged as the most important global agreement on international IP rights, TRIPS requires that national governments grant patents for inventions across all fields of technology, provided the inventions meet certain requirements (Article 27.1). The patents must be enforceable for at least 20 years (Article 33). With a few exceptions, no unreasonable prejudice to the patent holders’ legitimate interests is allowed. Moreover, unlike other IP agreements, TRIPS has a powerful enforcement system: non-compliant countries can be disciplined through the World Trade Organization’s (WTO) dispute settlement mechanism. Between 1995 and 2019, 41 consultations were initiated under this mechanism, including 18 by the United States and 8 by the European Union (Van den Bossche (Reference Van den Bossche2020)).Footnote ⁵

TRIPS went into effect on Jan. 1, 1995, though several countries were granted extensions. Countries that self-identified as “developing” were given an initial 5-year extension until Jan. 1, 2000, which was then extended to Jan. 1, 2005. Interestingly, 69 countries, including high-income countries such as Israel and South Korea, identified themselves as “developing” for this purpose. Countries that were categorized as “least developed” by the United Nations were given until 2006; this deadline was subsequently extended to Jan. 1, 2013. As a result, TRIPS’s global adoption is staggered. Moreover, because adoption occurs at the national level, it is not within the control of a single firm and is therefore an exogenous event at the firm level.

To the extent that TRIPS meets its intended objective of strengthening IP rights, we expect it to enhance both the demand for and the supply of affected firms’ firm-level information. Prior literature finds that innovation outputs (e.g., patents) are significant sources of firm value (Griliches (Reference Griliches1981), Bloom and Van Reenen (Reference Bloom and Van Reenen2002), and Hall et al. (Reference Hall, Jaffe and Trajtenberg2005)). If TRIPS strengthens IP rights, the agreement should also enhance the value of innovations accrued to the creator by mitigating the likelihood that imitators will infringe on the creator’s IP rights. The value gap between innovators and imitators then widens, incentivizing investors to search for and process information about innovative firms. In addition, investors have enhanced incentives to actively seek information that allows them to distinguish between innovations with more versus less commercial potential. TRIPS may therefore increase the demand for information about firms and their innovation activities. Moreover, because TRIPS ensures the stronger protection of IP rights, firms’ proprietary cost concerns should diminish after adoption. Proprietary costs are a major deterrent to firms’ voluntary disclosure (Verrecchia (Reference Verrecchia1983), Kim and Valentine (Reference Kim and Valentine2021)). If TRIPS ameliorates these concerns, then after adoption, firms should increase their supply of information. Investors’ increased searching for firm-level information and firms’ increased dissemination of that information should result in firms’ stock price reflecting more firm-specific information, leading to decreased stock price synchronicity.

However, TRIPS enforcement varies across countries (Helpman (Reference Helpman1993), Grossman and Lai (Reference Grossman and Lai2004)), which raises concerns about the agreement’s universal effectiveness as the associated cost–benefit tradeoffs differ from country to country (Duggan et al. (Reference Duggan, Garthwaite and Goyal2016)). Moreover, the dispute settlement process could be long and tedious (Van den Bossche (Reference Van den Bossche2020)). Hence, if TRIPS’s overall efficacy is weaker than initially intended, these conjectured effects could be attenuated or vary systematically with enforcement strength.

B. Measure of Price Synchronicity

To empirically capture the incorporation of firm-specific news into the stock price, we construct a measure of stock price synchronicity. Following Jin and Myers (Reference Jin and Myers2006) and Eun et al. (Reference Eun, Wang and Xiao2015), we first obtain R ² from firm-level regressions on an expanded market model as follows:

(1) $$ {\displaystyle \begin{array}{c}{r}_{i,c,t}={\beta}_0+{\beta}_1{r}_{m,c,t}+{\beta}_2\left[{r}_{US,t}+{EX}_{c,t}\right]\\ {}+{\beta}_3{r}_{m,c,t-1}+{\beta}_4\left[{r}_{US,t-1}+{EX}_{c,t-1}\right]\\ {}+{\beta}_5{r}_{m,c,t-2}+{\beta}_6\left[{r}_{US,t-2}+{EX}_{c,t-2}\right]\\ {}+{\beta}_7{r}_{m,c,t+1}+{\beta}_8\left[{r}_{US,t+1}+{EX}_{c,t+1}\right]\\ {}+{\beta}_9{r}_{m,c,t+2}+{\beta}_{10}\left[{r}_{US,t+2}+{EX}_{c,t+2}\right]+{\varepsilon}_{i,c,t},\end{array}} $$

where $ {r}_{i,c,t} $ is the weekly return of stock i in country c for week t of a given year, $ {r}_{m,c,t} $ is country c’s weekly market return for week t of a given year, and $ {r}_{US,t}+{EX}_{c,t} $ is the U.S. market return adjusted for the change in country c’s exchange rate to U.S. dollars. As Eun et al. (Reference Eun, Wang and Xiao2015) explain, the inclusion of lead and lag market returns accounts for non-synchronous trading in each market, and the inclusion of U.S. market returns proxies for the world stock market performance.

Following prior literature, we impose several filters to mitigate data errors in estimating equation (1) (e.g., Dang et al. (Reference Dang, Moshirian and Zhang2015), Eun et al. (Reference Eun, Wang and Xiao2015)). We remove firm observations with weekly stock returns that exceed 300% and that reverse in the subsequent week. We also require each firm-year to have at least 30 weekly observations for calculating the stock price synchronicity. After obtaining the firm-year-level R ², we transform the value of R ² and obtain our synchronicity measure using the following equation:

(2) $$ {Synch}_{i,t}=\mathrm{Ln}\left(\frac{R_{i,t}^2}{1-{R}_{i,t}^2}\right). $$

This measure of synchronicity is widely used in prior studies, including Morck et al. (Reference Morck, Yeung and Yu2000), Dang, Dang, Hoang, Nguyen, and Phan (Reference Dang, Dang, Hoang, Nguyen and Phan2020), and Kim et al. (Reference Kim, Su, Wang and Wu2021).

C. Regression Specification

To examine the impact of TRIPS adoption on the incorporation of firm-specific news into the stock price, we estimate the following staggered DID regression using an international sample:

(3) $$ {Synch}_{i,t}={\beta}_0+{\beta}_1{POSTTRIPS}_{c,t}+\gamma {Controls}_{i,t}+{\alpha}_i+{\delta}_t+{\varepsilon}_{i,t}. $$

Synch is the firm-level measure of stock price synchronicity for firm i in year t. Footnote ⁶ POSTTRIPS equals 1 for post-TRIPS firm-year observations in country c, and 0 otherwise. If, as we conjecture, firms’ stock prices become less synchronous (i.e., they impound more firm-specific information) after the country-level adoption of TRIPS, we expect the coefficient on POSTTRIPS (β₁) to be significantly negative. We include firm and year fixed effects to, respectively, mitigate concerns about the effects of time-invariant, firm-specific characteristics and time trends. With both firm and year fixed effects in place, the coefficient β₁ in equation (3) is a DID estimator that captures the effect of TRIPS adoption on firms’ stock price synchronicity. Throughout this article, we report t-statistics based on standard errors clustered at the country level.

The model includes a series of control variables. For the firm-level controls, we follow prior literature (e.g., Dang et al. (Reference Dang, Dang, Hoang, Nguyen and Phan2020), Kim et al. (Reference Kim, Su, Wang and Wu2021)) and include the following variables: firm size, measured as the natural logarithm of total assets (Size); growth opportunity, measured as the book-to-market ratio (BM); financial leverage, measured as the ratio of total debt to total assets (Leverage); profitability, measured as the return on assets (ROA); performance volatility, measured as the standard deviation of ROA over the previous 5 years (SDROA); return volatility, measured as the standard deviation of the firm-specific weekly stock returns during a year (Sigma); mean returns, measured as the mean value of the firm-specific weekly stock returns during a year (MeanRet); and analyst coverage, measured as the natural logarithm of 1 plus the number of analysts following the firm (Analyst). We also control for innovation inputs, as captured by the R&D intensity (RD). We calculate RD as a firm’s R&D expenditure scaled by its lagged total assets. If the firm’s R&D expenditure is missing, we set RD to 0. Because it is unclear whether the missing R&D information is due to a lack of R&D activity or the firm’s reluctance to disclose it, we control for RDMissing, a dummy variable that equals 1 if a firm’s R&D expenditure is missing in the Compustat database, and 0 otherwise (Koh and Reeb (Reference Koh and Reeb2015)).

We also control for firms’ financial reporting opacity (AcctOpacity) because prior literature, such as Hutton, Marcus, and Tehranian (Reference Hutton, Marcus and Tehranian2009), suggests that opaque financial reports are associated with less revelation of firm-specific information. The model also includes an indicator of a firm’s cross-listing status (CrossListed) and the correlation between its earnings and industry-level earnings (FundaCorr). Finally, we control for stock illiquidity. Gassen, Skaife, and Veenman (Reference Gassen, Skaife and Veenman2020) emphasize the importance of controlling for stock illiquidity when examining synchronicity due to the strong negative and nonlinear relationship between illiquidity and the market model R ². Following Gassen et al.’s (Reference Gassen, Skaife and Veenman2020) example, we first measure stock illiquidity as the fraction of trading days with a zero return during a year. We then create 100 dummies based on the percentile rank of the illiquidity measure with a zero return and include them as control variables.Footnote ⁷

For the country-level control variables, we follow Jin and Myers (Reference Jin and Myers2006) and include GDP growth (GDPGrowth) and the natural logarithm of per-capita GDP (GDPPC). We also control for equity market development (EquityMktDev), as captured by the ratio of a country’s total stock market capitalization to its annual GDP, and whether the country adopted International Financial Reporting Standards (IFRS). Further, we follow the prior stock price synchronicity literature (e.g., Dang et al. (Reference Dang, Moshirian and Zhang2015), Eun et al. (Reference Eun, Wang and Xiao2015)) and control for the number of public firms in a country (NumFirm); the firms’ Herfindahl index (FirmHerf), calculated as the summed square of the ratio of firm i’s sales to the total sales within each country and year; and the industry Herfindahl index (IndHerf), calculated as the summed square of the ratio of industry j’s sales to the total sales within each country and year. We summarize all variable definitions in the Appendix.

D. Data and Sample

Our study employs several data sources. We obtain data on the TRIPS adoption year primarily from Kyle and Qian (Reference Kyle and Qian2017), supplemented with further information from Kyle and McGahan (Reference Kyle and McGahan2012). We then double-check these data using information from the WTO. The stock returns data used to construct the synchronicity measure come from the Compustat Global database. Data for the firm-level measures are from the Compustat Global & North America databases. For the other control variables and the partitioning variables, we obtain country-level data from the World Bank’s World Development Indicators, International Financial Reporting Standards (IFRS) adoption data from the IFRS website, analyst coverage data from IBES, and corporate innovation data from the World Patent Statistical Database (PATSTAT), which are maintained by the European Patent Office.Footnote ⁸ For the mechanism tests, we also use media coverage data from RavenPack and management forecast data from Capital IQ.

To construct our sample, we merge all necessary data from their respective sources and take the intersection as the starting point for our sample selection. Given that most countries adopted TRIPS and almost all adoptions occurred between 1995 and 2005, we focus on these adoption events by excluding non-adopting countries.Footnote ⁹ Accordingly, we limit our sample period to 1990–2010, which covers the 5 years before the earliest adoption to the 5 years after the latest one.

After dropping observations with missing baseline regression variables, we further require each sample firm to have at least one observation in both the pre- and post-TRIPS adoption periods. Finally, we exclude from the regression analyses countries that have fewer than 50 firm-year observations. Our final sample consists of 84,844 firm-year observations from the period of 1990–2010 for 6,161 unique firms from 29 economies.

Table 1 presents the sample distribution by economy and the corresponding year of TRIPS adoption. Due to the adoption’s staggered nature, the sample exhibits significant variation with respect to the year of adoption. Specifically, 13 countries adopted the TRIPS agreement in 1995, 9 in 2000, 1 in 2001, 1 in 2002, and 5 in 2005. The highest number of observations is from the United States (38,351), followed by Japan (18,634) and the United Kingdom (6,785). The smallest number of observations is from Chile (83), Israel (87), and Argentina (93).Footnote ¹⁰

Table 1Sample Distribution

Table 1 Long description

The table consists of six columns divided into two identical groups of three. Each group contains Economy, No. of Firm-Years, and T R I P S Adoption Year.

Left side data:

* Argentina: 93 firm-years, 2005.

* Australia: 1,020 firm-years, 1995.

* Brazil: 195 firm-years, 2005.

* Canada: 3,880 firm-years, 1995.

* Chile: 83 firm-years, 2000.

* China: 1,244 firm-years, 2001.

* Denmark: 327 firm-years, 1995.

* Germany: 1,871 firm-years, 1995.

* Hong Kong: 559 firm-years, 1995.

* India: 1,987 firm-years, 2005.

* Indonesia: 504 firm-years, 2000.

* Israel: 87 firm-years, 2000.

* Japan: 18,634 firm-years, 1995.

* Korea, Rep.: 1,656 firm-years, 2000.

* Malaysia: 2,688 firm-years, 2000.

Right side data:

* Mexico: 258 firm-years, 2000.

* Netherlands: 271 firm-years, 1995.

* Norway: 243 firm-years, 1995.

* Pakistan: 363 firm-years, 2005.

* Philippines: 166 firm-years, 2000.

* Singapore: 995 firm-years, 2000.

* South Africa: 154 firm-years, 1995.

* Spain: 298 firm-years, 1995.

* Sweden: 255 firm-years, 1995.

* Taiwan: 1,026 firm-years, 2002.

* Thailand: 597 firm-years, 2000.

* Turkey: 254 firm-years, 2005.

* United Kingdom: 6,785 firm-years, 1995.

* United States: 38,351 firm-years, 1995.

* Total: 84,844 firm-years, N / A.

Table 2 presents the descriptive statistics for the regression variables in our baseline model. All continuous variables are winsorized at the 1st and 99th percentiles. The mean (median) stock price synchronicity is −0.976 (−0.935). These statistics are consistent with prior studies that use the same measure (e.g., Jin and Myers (Reference Jin and Myers2006), Eun et al. (Reference Eun, Wang and Xiao2015)). The mean value of POSTTRIPS is 0.741, suggesting that 74.1% of firm-year observations are in the post-TRIPS period. The descriptive statistics for the control variables are largely consistent with prior international studies (e.g., Zhong (Reference Zhong2018), Khurana and Wang (Reference Khurana and Wang2019)). For example, the mean (median) firm size, as measured by the natural logarithm of total assets, is 5.734 (5.698). On average, the sample firms have a book-to-market ratio of 0.805, a financial leverage of 0.245, and a return on assets of 3.2%.

Table 2Summary Statistics (N = 84,844)

Table 2 Long description

The table contains summary statistics for N equals 84,844. The columns are Variable, Mean, S D, P 25, P 50, and P 75. Key rows include:

* Synch: Mean -0.976, S D 0.981, P 25 -1.618, P 50 -0.935, P 75 -0.283.

* P O S T T R I P S: Mean 0.741, S D 0.438, P 25 0.000, P 50 1.000, P 75 1.000.

* Size: Mean 5.734, S D 2.092, P 25 4.275, P 50 5.698, P 75 7.175.

* B M: Mean 0.805, S D 0.791, P 25 0.348, P 50 0.591, P 75 0.973.

* Leverage: Mean 0.245, S D 0.191, P 25 0.084, P 50 0.227, P 75 0.367.

* R O A: Mean 0.032, S D 0.107, P 25 0.005, P 50 0.033, P 75 0.075.

* S D R O A: Mean 0.067, S D 0.138, P 25 0.014, P 50 0.029, P 75 0.063.

* Sigma: Mean 0.061, S D 0.033, P 25 0.038, P 50 0.053, P 75 0.075.

* MeanRet: Mean 0.003, S D 0.009, P 25 -0.002, P 50 0.002, P 75 0.007.

* Analyst: Mean 1.063, S D 1.082, P 25 0.000, P 50 0.693, P 75 1.946.

* R D: Mean 0.023, S D 0.055, P 25 0.000, P 50 0.000, P 75 0.018.

* R D Missing: Mean 0.542, S D 0.498, P 25 0.000, P 50 1.000, P 75 1.000.

* AcctOpacity: Mean 0.169, S D 0.152, P 25 0.071, P 50 0.122, P 75 0.210.

* CrossListed: Mean 0.089, S D 0.284, P 25 0.000, P 50 0.000, P 75 0.000.

* FundaCorr: Mean -1.393, S D 2.433, P 25 -2.698, P 50 -1.116, P 75 0.206.

* G D P Growth: Mean 2.734, S D 2.586, P 25 1.528, P 50 2.746, P 75 4.038.

* G D P P C: Mean 10.098, S D 0.876, P 25 10.103, P 50 10.360, P 75 10.550.

* EquityMktDev: Mean 0.933, S D 0.374, P 25 0.667, P 50 0.888, P 75 1.225.

* I F R S: Mean 0.052, S D 0.222, P 25 0.000, P 50 0.000, P 75 0.000.

* NumFirm: Mean 8.013, S D 1.219, P 25 7.253, P 50 8.011, P 75 9.137.

* FirmHerf: Mean 0.012, S D 0.013, P 25 0.003, P 50 0.006, P 75 0.015.

* IndHerf: Mean 0.063, S D 0.029, P 25 0.040, P 50 0.055, P 75 0.077.

A. Main Findings

Table 3 presents our main findings. In column 1, we estimate our baseline model as specified in equation (3). We find a significantly negative coefficient on POSTTRIPS, suggesting that firms’ stock prices become less synchronous after a country adopts the agreement. That is, we find that after TRIPS adoption, the stock prices of the firms in the adopting country impound more firm-specific information. This finding is consistent with our argument that the enhanced IP rights protection resulting from TRIPS adoption enhances both the demand for and the supply of firm-level information, leading to more firm-specific information being incorporated into the firm’s stock price.

Table 3Effect of TRIPS on Stock Price Synchronicity and the Parallel Trend Test

Table 3 Long description

The table presents regression results for the dependent variable Synch.

* Column 1 focuses on the P O S T T R I P S variable, showing a coefficient of minus 0.274 with a t-statistic of minus 2.53, significant at the 5 percent level.

* Column 2 presents the parallel trend test with time-specific T R I P S indicators:

- T R I P S (t minus 3): minus 0.119 (t = minus 0.70)

- T R I P S (t minus 2): minus 0.008 (t = minus 0.08)

- T R I P S (t minus 1): 0.021 (t = 0.20)

- T R I P S (t = 0): minus 0.138 (t = minus 1.26)

- T R I P S (t plus 1): minus 0.017 (t = minus 0.11)

- T R I P S (t plus 2): minus 0.575 (t = minus 6.58), significant at the 1 percent level.

- T R I P S (t plus 3): minus 0.333 (t = minus 2.99), significant at the 1 percent level.

- T R I P S (t plus 4): minus 0.551 (t = minus 3.52), significant at the 1 percent level.

- T R I P S (t greater than or equal to 5): minus 0.576 (t = minus 4.55), significant at the 1 percent level.

Control variables include Size, B M, Leverage, R O A, S D R O A, Sigma, Mean Ret, Analyst, R D, R D Missing, Acct Opacity, Cross Listed, Funda Corr, G D P Growth, G D P P C, Equity Mkt Dev, I F R S, Num Firm, Firm Herf, and Ind Herf. Both models include Illiquidity rank dummies, Firm F E, and Year F E. The sample size N is 84,844 for both. Adjusted R-squared is 0.577 for Column 1 and 0.580 for Column 2.

The estimated coefficient on POSTTRIPS in column 1 suggests that after adoption, stock price synchronicity (Synch) decreases by 0.274. This outcome translates to a shift in the average firm’s stock price synchronicity measure from −0.976 (Table 2) to −1.250, which corresponds to a decrease in the proportion of stock price variation explained by industry and market returns from 27.37% to 22.27%.Footnote ¹¹ Therefore, TRIPS adoption reduces the R ² by 5.1 percentage points (= 27.37%−22.27%), which is equivalent to 18.63% (= 5.10%/27.37%) of our sample mean. This magnitude is economically meaningful and comparable to prior international studies on stock price synchronicity. For example, Kim and Shi (Reference Kim and Shi2012) document that IFRS adoption reduces synchronicity by 32.3% (roughly 59% of their sample mean), Eun et al. (Reference Eun, Wang and Xiao2015) show that a 1-standard-deviation increase in individualism culture is associated with a 18.2% decrease in synchronicity, and Fernandes and Ferreira (Reference Fernandes and Ferreira2008) show that cross-listing in a developed market increases firms’ synchronicity by 10.8% (roughly 10% of the sample mean), whereas cross-listing in an emerging market decreases firms’ synchronicity by 29.2% (about 27% of the sample mean).

B. Parallel Trends Analysis

To further establish causality and to ensure that our results are not due to a diverging, pre-adoption trend in the affected and unaffected firms’ synchronicity, we also conduct a parallel trends analysis. Specifically, we re-estimate equation (3) by replacing the POSTTRIPS dummy with separate year indicators, each of which denotes the year relative to the TRIPS adoption year (t = 0). TRIPS (t − n) is an indicator variable that equals 1 if the firm-year observation is in the nth year before the TRIPS adoption year, and 0 otherwise. TRIPS (t + n) is an indicator variable that equals 1 if the firm-year observation is in the nth year after the TRIPS adoption year, and 0 otherwise. We omit the indicator variables for the years before t − 3, which serve as the benchmark period.

Column 2 of Table 3 presents the results of the parallel trend test, and Figure 1 depicts the coefficients on the time dummies in graphical form. The coefficients on TRIPS (t − n) are not significant, indicating that the difference in stock price synchronicity for the treatment and control firms remains unchanged in the years leading up to TRIPS adoption. This finding supports the parallel trend assumption inherent to a DID research design, allowing us to draw causal inferences from the results. Focusing on TRIPS (t + n), we find that the treatment and control firms diverge significantly in terms of their stock price synchronicity from year t + 2 onward. In other words, firms in TRIPS-adopting countries begin to experience reduced stock price synchronicity from the second year after TRIPS adoption. This outcome suggests that changes in the information environment that lead to reduced stock price synchronicity require some time to manifest, as countries gradually implement their IP protection rules and establish adequate and effective enforcement mechanisms after the adoption of TRIPS (Yu (Reference Yu2001), Deere (Reference Deere2008), Stoianoff (Reference Stoianoff2012)).Footnote ¹²

Figure 1Parallel Trend

Figure 1 reports the coefficients produced by the regression that examines the effect of the TRIPS on stock price synchronicity in event time and that corresponds to column 2 of Table 3. In this parallel trend test, we estimate baseline model (1) but replace the POSTTRIPS dummy with separate year indicators, each of which marks a year relative to the TRIPS adoption year (t = 0). We omit the indicators for the years before t − 3, which serve as the benchmark period. The vertical bands represent the 95% confidence intervals for each point estimate.

Figure 1 Long description

The Y-axis is labeled Coefficients and ranges from negative 0.8 to 0.2. The X-axis is labeled Year relative to T R I P S adoption and includes time markers from t minus 3 to t plus 5. A horizontal dashed red line marks the zero coefficient level, and a vertical dashed red line marks the event year t.

From left to right, the data points and their 95% confidence interval bands show:

* t minus 3: Point estimate at approximately negative 0.1 with a wide interval crossing zero.

* t minus 2: Point estimate near zero.

* t minus 1: Point estimate slightly above zero.

* t: Point estimate at approximately negative 0.15.

* t plus 1: Point estimate near zero.

* t plus 2: A sharp decline to approximately negative 0.58, with the entire confidence interval falling below zero.

* t plus 3: A slight recovery to negative 0.35.

* t plus 4: A drop back to approximately negative 0.55.

* t greater than or equal to 5: A final point at approximately negative 0.58.

The trend remains relatively flat and near zero before the event, followed by a significant and sustained shift into negative territory starting at t plus 2.

C. Robustness Tests

In Section III.C, we perform a battery of robustness tests to confirm our main findings. We present these results in Table 4.

Table 4Robustness Tests

Table 4 Long description

The table is divided into seven panels, A through G, all using Synch as the dependent variable.

* Panel A: Stacked D I D Designs. Column 1 (Event Window -5, +5) shows a T R E A T times P O S T coefficient of -0.276. Column 2 (Event Window -10, +10) shows -0.301. Both are significant at the 1 percent level.

* Panel B: Alternative Fixed Effects. Across four columns with varying combinations of Country, Industry, Year, and Firm F E, the P O S T T R I P S coefficient ranges from -0.253 to -0.278, all significant at the 5 percent level.

* Panel C: Alternative Clustering. Across five columns with different clustering (Country and Year, Firm, Firm and Year, Industry, Industry and Year), the P O S T T R I P S coefficient is constant at -0.274, with varying significance levels.

* Panel D: Weighted Regressions. Column 1 (weighted by 1 over observations) shows -0.252. Column 2 (weighted by 1 over S E squared) shows -0.357.

* Panel E: Ruling Out the Tech Bubble. Three columns excluding specific S I C codes or the 1998 to 2002 period show P O S T T R I P S coefficients of -0.280, -0.307, and -0.204.

* Panel F: Excluding the Pharmaceutical Industry. Column 1 (excluding S I C 3 = 283) and Column 2 (excluding S I C 2 = 28) both show a coefficient of -0.277.

* Panel G: Ruling Out Increased Post-T R I P S Innovation. Four columns controlling for patents and citations show P O S T T R I P S coefficients between -0.269 and -0.274. The interaction P O S T T R I P S times H I G H is statistically insignificant.

First, in Panel A, we investigate the sensitivity of our results to a stacked DID design. Because TRIPS adoption is staggered across countries, our baseline model is a conventional staggered DID design with 2-way fixed effects. Recent literature in econometrics suggests that staggered DID research designs may result in the “bad comparisons” problem, which arises from using earlier-treated observations as controls for later-treated ones (see Callaway and Sant’Anna (Reference Callaway and Sant’Anna2021), Goodman-Bacon (Reference Goodman-Bacon2021), and Baker et al. (Reference Baker, Larcker and Wang2022)). We follow Baker et al.’s (Reference Baker, Larcker and Wang2022) recommendation and examine whether our results are robust to a stacked DID specification in which we construct cohorts for the treatment and control groups around each batch of TRIPS adoption. The adoption batches are determined by year, and the earliest batch includes countries that adopted TRIPS in 1995. For each adoption batch, the treatment group (TREAT = 1) comprises firms in the adopting countries, and the control group (TREAT = 0) comprises firms in countries that have not yet adopted TRIPS. The event window in column 1 in Panel A is [−5, +5]; it is [−10, +10] in column 2. For both the treatment and control groups in each cohort, POST equals 1 for the years after TRIPS adoption, and 0 otherwise. We then combine all the constructed cohorts as the testing sample. Following standard practice for implementing a stacked DID design (e.g., Gormley, Matsa, and Milbourn (Reference Gormley, Matsa and Milbourn2013), Cengiz, Dube, Lindner, and Zipperer (Reference Cengiz, Dube, Lindner and Zipperer2019)), we include firm-cohort and year-cohort fixed effects, and we cluster standard errors at the country-cohort level. Panel A reports the results for the stacked DID specifications. In both columns, we observe that the coefficient of interest on the interaction term, TREAT × POST, is significantly negative. The estimated coefficient magnitudes are also comparable to those from our baseline model. Therefore, our finding that TRIPS adoption has a negative effect on price synchronicity remains robust when we employ a stacked DID design.

Second, we consider the sensitivity of our results to the use of alternative fixed effects in the regression model. Note that our main analyses include firm and year fixed effects. Our dependent variable captures the extent to which firm-specific information is impounded into the stock price. To mitigate the concern that cross-country differences in market efficiency or industry factors might systematically affect this measure, we re-examine our model after including these fixed effects. Column 1 in Panel B reports the results with country and year fixed effects, and column 2 reports the results with country, industry, and year fixed effects. Furthermore, in column 3, we include country and industry-year fixed effects, and in column 4, we include firm and industry-year fixed effects. The results show that our findings remain robust to these different fixed effects combinations.

Third, we consider alternative clustering methods. In our baseline regression, we cluster standard errors at the country level. In column 1 in Panel C, we investigate the sensitivity of our findings to 2-way clustered standard errors at the country and year level. In column 2, we cluster standard errors at the firm level, and in column 3, we use 2-way clustered standard errors at the firm and year level. We cluster standard errors at the industry level in column 4 and employ 2-way clustering by industry and year in column 5. The use of these alternative clustering methods does not alter our inferences; we continue to observe significantly negative coefficients on POSTTRIPS in each column.

Our fourth set of robustness tests addresses the issue of the uneven representation of firm-year observations from different countries. We follow the prior literature and perform weighted regressions, ensuring that each country is given a similar weight, regardless of the number of observations it has. Panel D reports the estimation results. Following Akins, Dou, and Ng (Reference Akins, Dou and Ng2017), column 1 uses the weighting of 1/the total number of firm-year observations per country. In column 2, we construct the weights to improve the efficiency of the regression model by following Ball, Robin, and Sadka’s (Reference Ball, Robin and Sadka2008) approach, where each country’s observations are weighted by the inverse of the square of the standard error of its beta estimate on the variable of interest (POSTTRIPS) when regressing the baseline model in the country.Footnote ¹³ As reported in Panel D, our results remain robust across both columns.

Our sample period includes the tech bubble. Moreover, 13 of the sample countries adopted TRIPS in 1995, around the time the tech bubble began. Hence, a potential concern arises that our observation of a post-TRIPS decline in stock price synchronicity could be related to the tech bubble. To allay this concern, Panel E considers whether our results are robust to the exclusion of technology industries or to the tech bubble period. Following Griffin, Harris, Shu, and Topaloglu (Reference Griffin, Harris, Shu and Topaloglu2011), we remove tech companies, specifically those firms denoted by the 3-digit SIC code 737. The results in column 1 show that excluding technology firms does not change our inference. In column 2, we follow Campello and Graham (Reference Campello and Graham2013) and expand the definition of tech firms (i.e., those excluded from the sample) to include the additional 3-digit SIC codes 481, 355, 357, 366, 367, 369, 381, 382, and 384. In column 3, we further exclude firm-year observations from 1998 to 2002, which are the 2 years before and after the tech bubble burst. All these robustness tests continue to show significantly negative coefficients on POSTTRIPS, suggesting that the tech bubble is unlikely to affect our results.

TRIPS made several exceptions with respect to IP protection for pharmaceutical products because low-cost access to life-saving drugs and essential medicines was deemed a higher public policy priority (see Chaudhuri et al. (Reference Chaudhuri, Goldberg and Gia2006), Qian (Reference Qian2007), and Kyle and McGahan (Reference Kyle and McGahan2012)). Accordingly, our next set of robustness tests considers the sensitivity of our findings when the sample excludes pharmaceutical firms (SIC 3-digit = 283). As reported in column 1 in Panel F, our coefficient of interest remains essentially the same. In column 2, we broaden the definition of pharmaceuticals to include all chemical industries (SIC 2-digit = 28). We continue to find similar outcomes, suggesting that our results are not driven by the special exceptions granted to the pharmaceutical industry under TRIPS.

Promoting innovation is an intended objective of TRIPS’s strengthening of IP rights (Abrams (Reference Abrams2008), Qiu and Yu (Reference Qiu and Yu2010)). Prior literature suggests that innovation activities increase information demand (Huang et al. (Reference Huang, Ng, Ranasinghe and Zhang2021)). Consequently, another channel through which TRIPS could impact stock price synchronicity is via the regulation’s role in promoting innovation. Our article’s intent is not to rule out the role of innovation, but to focus squarely on the role that stronger IP rights play in improving stock price informativeness. To isolate TRIPS’s role in strengthening IP rights, as opposed to its impact on innovation, we control for innovation input (i.e., R&D expenditure) in our baseline model. To ensure that our findings are not confounded by greater innovation in the post-TRIPS period, we control for innovation output in Panel G. Columns 1 and 2, respectively, employ the number of patents and patent citations to measure innovation output. In both columns, we continue to find that TRIPS has a significantly negative effect on stock price synchronicity.

Columns 3 and 4 report the results of an alternative test designed to establish that our findings cannot be attributed to increased innovation activity. Specifically, we split the sample firms into 2 groups based on whether they experienced a post-TRIPS increase in innovation. Columns 3 and 4, respectively, capture the change in innovation activity in terms of the number of patents and the number of patent citations. HIGH equals 1 for firms that have more patents (or patent citations) during the 5 years after TRIPS adoption, compared with the 5 years before it, and 0 otherwise. Suppose that the post-TRIPS decline in stock price synchronicity is solely driven by increased innovation. In that case, we should observe significant results for firms that experience an increase in innovation after TRIPS, but not for firms that do not. However, as reported in columns 3 and 4, we find that the coefficient on the interaction term, POSTTRIPS × HIGH, is insignificant, while the main effect on POSTTRIPS remains significantly negative. These results strongly suggest that our finding of lower stock price synchronicity after TRIPS adoption is likely independent of any effect due to heightened post-TRIPS innovation activities.

A. The Increase in IP Protection

If, as we argue, the post-TRIPS reduction in stock price synchronicity can be attributed to the TRIPS-induced enhancement of IP protection, then the effect should be more pronounced for firms in countries where TRIPS leads to greater changes to IP rights. Results that are consistent with this conjecture would further enhance the validity of our assertion. To test the conjecture, we employ Park’s (Reference Park2008) country-level index of patent protection and partition the firms affected by TRIPS into 2 groups based on whether they are domiciled in countries with a high or low increase in patent protection after TRIPS adoption. Specifically, we create a dummy variable, HIGH, that equals 1 for firms in countries that experience an above-median increase in Park’s (Reference Park2008) country-level index of patent protection in the wake of TRIPS adoption, and 0 otherwise.Footnote ¹⁴ We then interact POSTRIPS with HIGH. We expect a significantly negative coefficient on this interaction term. In Panel A of Table 5, we find a negative coefficient on POSTTRIPS, which is either significant (in column 1) or marginally significant (in column 2), depending on how we measure the change in IP protection. More importantly, we find that the coefficient on POSTTRIPS × HIGH is significantly negative, which is consistent with our expectations. This finding suggests a greater decline in stock price synchronicity after TRIPS for firms in countries where TRIPS led to a more significant increase in IP protection. These results support our argument that TRIPS enhances IP protection and, by this means, affects stock price synchronicity. This finding serves as an important validation test of our primary inferences.

Table 5Validation Tests

Table 5 Long description

Panel A: Increase in I P Protection Around T R I P S Adoption. The dependent variable is Synch. Column 1 uses the Change in the I P Protection Index, showing a P O S T T R I P S times H I G H coefficient of minus 0.180 and a P O S T T R I P S coefficient of minus 0.186. Column 2 uses the Change in the I P Protection Index Scaled by the Pre-T R I P S I P Protection Index, showing a P O S T T R I P S times H I G H coefficient of minus 0.186 and a P O S T T R I P S coefficient of minus 0.175. Both columns include Controls, Firm F E, and Year F E with N equals 84,844 and Adjusted R-squared of 0.577.

Panel B: Effect of T R I P S on the Association Between Patents and Firm Value. The dependent variable is Tobin Q. The interaction Patent times P O S T T R I P S has a coefficient of 0.040. Other variables include Patent at 0.008, P O S T T R I P S at minus 0.429, Size at minus 0.608, R O A at minus 0.972, R D at 3.976, R D Missing at 0.194, G D P Growth at 0.009, G D P P C at 0.000, Equity Mkt Dev at 0.705, and I F R S at 0.227. N equals 209,957 with Adjusted R-squared of 0.566.

Panel C: Increase in the Sensitivity of Firm Value to Patents. The dependent variable is Synch. Column 1 covers countries with a significant increase in firm value sensitivity to patents, showing a P O S T T R I P S coefficient of minus 0.504. Column 2 covers countries without a significant increase, showing a P O S T T R I P S coefficient of minus 0.067. The coefficient difference p-value is 0.000.

B. The Increase in Patent Value

We also validate whether, as we argue, patents become more economically valuable after TRIPS adoption. If so, it is quite natural that the agreement should increase information demand, because distinguishing between more and less innovative firms, as well as between innovations with more and less economic promise, becomes more important after adoption. We employ the following regression model to investigate TRIPS’s effect on patents’ value:

(4) $$ {\displaystyle \begin{array}{c}{TobinQ}_{i,t}={\beta}_0+{\beta}_1{Patent}_{i,t}\times {POSTTRIPS}_{c,t}+{\beta}_2{Patent}_{i,t}\\ {}+{\beta}_3{POSTTRIPS}_{c,t}+\gamma {Controls}_{i,t}+{\alpha}_i+{\delta}_t+{\varepsilon}_{i,t}.\end{array}} $$

The dependent variable TobinQ is a firm’s Tobin’s Q, calculated as the market value of equity plus the book value of debt divided by total assets. Patent is the natural logarithm of 1 plus the number of patents that the firm applies for in a year that are eventually granted. POSTTRIPS equals 1 for post-TRIPS and 0 for pre-TRIPS observations. The coefficient of interest is on the interaction term Patent × POSTTRIPS. If the greater IP protection afforded by the agreement makes patents more economically valuable after TRIPS, we would expect the association between firm value and patents to become stronger following adoption, yielding a positive coefficient on Patent × POSTTRIPS. Footnote ¹⁵

Panel B of Table 5 reports the results. Consistent with our expectation, we find that the coefficient on the interaction term Patent × POSTTRIPS is significantly positive. These results validate our argument that the contribution of patents to firm value improves after TRIPS.

In Panel C, we further examine whether the effect of TRIPS on stock price synchronicity is concentrated among countries that experience a significant increase in the sensitivity of the firm value to patents. According to our argument, investors’ information demand increases due to innovations becoming more valuable after TRIPS. Consequently, we should expect TRIPS’s effect on stock price synchronicity to be more pronounced for countries where the agreement’s adoption effectively enhances patent value. To validate this, we divide our sample countries into 2 subsamples based on the change in the sensitivity of firm value to patents around the TRIPS adoption. Specifically, we estimate TRIPS’s effect on the association between patents and firm value in each country.Footnote ¹⁶ The subsample in column 1 consists of countries where the sensitivity of firm value to patents significantly increases, as suggested by the coefficient on Patent × POSTTRIPS, which is positive and statistically significant (at the 5% level). The remaining countries are grouped into the subsample in column 2.

Consistent with our expectation, we find that in column 1, the coefficient on POSTTRIPS is negative and statistically significant; it is insignificant in column 2. We also formally test the difference between the magnitudes of the coefficients in these 2 columns; we confirm that the effect magnitude of TRIPS on stock price synchronicity is significantly larger in column 1. These findings indicate that the effect of TRIPS on stock price synchronicity is concentrated in countries where TRIPS adoption effectively enhances the value of patents.

Collectively, the results reported in Table 5 show that the post-TRIPS reduction on stock price synchronicity is stronger for firms in countries where TRIPS more substantially enhances IP protection, patents become more economically valuable in the wake of TRIPS, and the agreement’s effect on stock price synchronicity is stronger for firms in countries with higher patent-firm value sensitivity.

A. The Moderating Role of Innovativeness

Naturally, TRIPS adoption should be of greater relevance to more innovative firms. However, in our baseline analyses, we do not distinguish between more and less innovative firms. Instead, we investigate the average effect of TRIPS adoption on firms’ price informativeness. In this section, we examine the conjecture that a post-TRIPS increase in stock price informativeness (i.e., a decline in synchronicity) should be greater for more innovative firms.

To test this conjecture, we employ three measures of innovativeness: The first two are at the country level, and the third is at the firm level. Specifically, we first use country-level innovation culture to proxy for local firms’ innovativeness. The data are from the 2008–2009 Global Innovation Index (GII) report published by the World Intellectual Property Organization.Footnote ¹⁷ A country’s innovation culture is captured in terms of the response to the survey question: “To what extent do you feel that companies in your own country have fostered a culture that expects everyone to contribute to innovation?” (1: Not at all, 5: Definitely).Footnote ¹⁸ The report aggregates survey data at the country level and creates a global ranking of how effectively cultures foster innovation. Second, we directly use the overall ranking of innovativeness in the 2007 GII report, which is the first and most comprehensive assessment of the innovation capabilities for countries around the globe. Firms in countries with a culture that effectively fosters innovation or in countries with a higher global innovation ranking are likely to be more innovative. Third, we follow the innovation literature and construct a patent-based firm-level measure of innovativeness, calculated as the number of patents the firm applies for (and is eventually granted) during the 5-year pre-TRIPS period.

Table 6 presents the results. In columns 1 and 2, we construct the subsamples based on the country-level measure of innovation culture. In columns 3 and 4, we split our sample based on the global innovation ranking at the country level. For each of these two country-level measures, HIGH equals 1 for countries with a value equal to or above the median, and it equals 0 for all other countries. We find that the effect of TRIPS adoption on stock price informativeness is only pronounced for countries with a culture that can better foster innovation and for those with a higher overall innovativeness ranking. In columns 5 and 6, we split the sample using a patent-based firm-level measure of innovativeness. HIGH equals 1 for firms for which the number of pre-TRIPS patents is equal to or above the median, and 0 otherwise. We find significant negative coefficients on POSTTRIPS in both columns, but the coefficient magnitude is significantly larger in column 6, suggesting a stronger effect for more innovative firms. Taken together, these results are consistent with our expectation that the effect of TRIPS on stock price informativeness should be stronger for firms that are likely to be more innovative.

Table 6Cross-Sectional Analyses: Innovativeness

Table 6 Long description

The table presents regression results for the dependent variable Synch across three main categories of innovativeness, each split into HIGH equals 0 and HIGH equals 1 subsamples.

1. Header Structure:

* Subsamples 1 and 2: Country-Level Innovation Culture.

* Subsamples 3 and 4: Global Innovation Ranking.

* Subsamples 5 and 6: Patent-Based Firm-Level Innovativeness.

2. Data Rows:

* P O S T T R I P S: Subsample 1 is minus 0.039. Subsample 2 is minus 0.482 with three asterisks. Subsample 3 is minus 0.054. Subsample 4 is minus 0.312 with one asterisk. Subsample 5 is minus 0.229 with two asterisks. Subsample 6 is minus 0.444 with two asterisks.

* T-statistics are provided in parentheses below each coefficient.

* Controls, Firm F E, and Year F E: All subsamples are marked Yes.

* N (Observations): Ranges from 6,755 in subsample 1 to 78,089 in subsample 2.

* Adjusted R-squared: Ranges from 0.433 to 0.579.

* Coefficient difference p-value: Reported between pairs. Subsamples 1 and 2 is 0.000 with three asterisks. Subsamples 3 and 4 is 0.005 with three asterisks. Subsamples 5 and 6 is 0.000 with three asterisks.

B. The Moderating Role of Enforcement

A key feature of supranational agreements such as TRIPS is that although they apply equally to all signatories, their enforcement varies significantly between countries. For example, in their examination of investor protection laws in 49 countries, La Porta et al. (Reference La Porta, Lopez-de-Silanes, Shleifer and Vishny1998) find the quality of enforcement to be highest in Scandinavian and German civil law countries and lowest in French civil law countries, with common law countries in the middle. Similarly, Djankov et al. (Reference Djankov, La Porta, Lopez-de-Silanes and Shleifer2003) observe significant country-level variation in judicial efficiency.

In theory, TRIPS strengthens all firms’ IP rights. However, its practical efficacy for firms is likely to vary depending on the strength with which the country legally enforces it. As with any law, TRIPS is likely to be more enforced effectively in a country where the legal system is generally strong; enforcement is likely weaker in a country with a weak legal system. Moreover, although the WTO’s dispute settlement mechanism ensures that TRIPS’s global enforcement is stronger than that of many other international agreements, this mechanism is unlikely to be a perfect substitute for country-level enforcement. Therefore, we expect that any effects of TRIPS, including facilitating the incorporation of more firm-level information into firms’ stock price, will be stronger for firms in countries with strong law enforcement than it is for those in weak-enforcement countries. If so, the agreement’s impact on stock price synchronicity should be stronger (weaker) for firms in countries with strong (weak) enforcement.

To test this argument, we capture the strength of country-level legal enforcement using two alternative proxies. First, we utilize the Rule of Law Index from the World Justice Project to split our sample countries into two.Footnote ¹⁹ The World Justice Project’s Rule of Law Index is an aggregated score for the following eight factors: i) Constraints on Government Powers; ii) Absence of Corruption; iii) Open Government; iv) Fundamental Rights; v) Order and Security; vi) Regulatory Enforcement; vii) Civil Justice; and viii) Criminal Justice. A higher value on the Rule of Law Index indicates stronger law enforcement. Second, we use La Porta et al.’s (Reference La Porta, Lopez-de-Silanes, Shleifer and Vishny1998) judicial efficiency measure, which captures the efficiency and integrity of the legal environment as it affects business, particularly foreign firms.

Irrespective of enforcement ability, enforcement incentives also vary at the country level. Especially in the short term, stronger IP protection is likely detrimental to developing countries because these countries lack the resources to produce valuable innovations, and IP rights limit their ability to produce imitations based on innovations generated in developed countries (e.g., Helpman (Reference Helpman1993), Grossman and Lai (Reference Grossman and Lai2004), and Santacreu (Reference Santacreu2025)). Accordingly, the stronger IP rights afforded by TRIPS are less advantageous to developing countries relative to their developed counterparts, which limits the former’s incentives to effectively enforce the agreement (McCalman (Reference McCalman2001), (Reference McCalman2005), Chaudhuri et al. (Reference Chaudhuri, Goldberg and Gia2006), Duggan et al. (Reference Duggan, Garthwaite and Goyal2016), and Brandl, Darendeli, and Mudambi (Reference Brandl, Darendeli and Mudambi2019)). For this reason, we predict that the post-TRIPS reduction in firms’ stock price synchronicity will be stronger for firms in developed countries relative to those in developing countries. We examine this by partitioning the sample into developed and developing countries based on the World Bank’s classification of countries by gross national income per capita.

Table 7 reports the results of the tests that examine how these country-level factors moderate TRIPS’s effect on price informativeness. In columns 1 and 2, we split our sample based on the Rule of Law Index. We find that the effect of TRIPS is only pronounced for the subsample with a higher Rule of Law Index, indicating that law enforcement plays an important moderating role. In columns 3 and 4, we partition our sample based on judicial efficiency. The results indicate that the post-TRIPS increase in stock price informativeness (i.e., a decline in synchronicity) is significant for firms in countries with high judicial efficiency, but not for those in low-judicial-efficiency countries. The subsample in column 5 consists of developing countries, and that in column 6 includes developed countries. Consistent with our expectations, we find the impact of TRIPS on stock price synchronicity to be significant for developed, but not developing, countries. Taken together, these results suggest that the extent to which the agreement is likely to be effectively enforced at the country level moderates the effect of TRIPS on stock price informativeness.

Table 7Cross-Sectional Analyses: Enforcement

Table 7 Long description

The table presents regression results for the dependent variable Synch across three main categories: Rule of Law Index, Judicial Efficiency, and Developed Country. Each category is split into two subsamples labeled HIGH equals 0 and HIGH equals 1.

* Subsamples 1 and 2 (Rule of Law Index): For HIGH equals 0, the P O S T T R I P S coefficient is 0.061 with a t-statistic of 0.72. For HIGH equals 1, the coefficient is minus 0.550 with a t-statistic of minus 6.36, significant at the 1 percent level.

* Subsamples 3 and 4 (Judicial Efficiency): For HIGH equals 0, the coefficient is minus 0.068 with a t-statistic of minus 0.93. For HIGH equals 1, the coefficient is minus 0.589 with a t-statistic of minus 6.79, significant at the 1 percent level.

* Subsamples 5 and 6 (Developed Country): For HIGH equals 0, the coefficient is 0.143 with a t-statistic of 1.47. For HIGH equals 1, the coefficient is minus 0.453 with a t-statistic of minus 4.41, significant at the 1 percent level.

All models include Controls, Firm F E, and Year F E. The number of observations N ranges from 5,056 to 79,788. Adjusted R-squared values range from 0.417 to 0.579. The coefficient difference p-value is 0.000 for all three category comparisons.

A. The Moderating Role of Information Intermediation

Our main finding is consistent with the argument that strengthening IP rights through TRIPS facilitates the greater incorporation of firm-specific information into the stock price. The validation tests and cross-sectional analyses reported in prior sections offer corroborative evidence in support of this argument. As a supplementary analysis, we delve more deeply into information intermediation’s role by focusing on financial analysts. Financial analysts are pivotal to the stock market because of the information that they process and produce (e.g., Bradshaw, Ertimur, and O’Brien (Reference Bradshaw, Ertimur and O’Brien2017)). For example, analysts could examine patent filings along with firm disclosures and other pertinent information and create reports that enable investors to better assess fundamental firm value. If so, analyst coverage should enhance the informativeness of stock prices, and TRIPS’s impact on stock price informativeness should be greater for firms with greater analyst coverage.

We examine this by splitting the sample based on the pre-TRIPS analyst coverage. In the first (second) column in Panel A of Table 8, the subsample consists of firms with analyst coverage that is below (equal to or above) the median. We find that while the negative effect of TRIPS adoption on stock price synchronicity is statistically significant for both subsamples, the magnitude of the effect is significantly larger for the subsample of firms with high analyst coverage. These results substantiate our argument by showing that when there is greater information flow into the market, as proxied by analyst following, the effect of TRIPS on stock price informativeness is stronger.

Table 8Information Intermediation and Information Supply

Table 8 Long description

The table is divided into two sections.

Panel A, titled The Role of Information Intermediation, uses Synch as the dependent variable across two subsamples.

Column 1 (Lower Analyst Coverage): P O S T T R I P S coefficient is minus 0.254 with a t-stat of minus 2.30.

Column 2 (Higher Analyst Coverage): P O S T T R I P S coefficient is minus 0.341 with a t-stat of minus 2.74.

Both columns include Controls, Firm F E, and Year F E.

Sample size N is 34,994 for column 1 and 41,856 for column 2.

Adjusted R-squared values are 0.591 and 0.567 respectively.

The coefficient difference p-value is 0.019.

Panel B, titled Firms’ Information Supply, features two dependent variables.

Column 1 (M F Frequency): P O S T T R I P S coefficient is 0.722 (t-stat 2.65). Size is 0.402, B M is minus 0.294, and I F R S is 0.442.

Column 2 (Media Cov): P O S T T R I P S coefficient is 0.390 (t-stat 2.90). Size is 0.328, B M is minus 0.062, and R D is 2.975.

Both columns include Firm F E and Year F E.

Sample size N is 53,152 for column 1 and 36,951 for column 2.

Pseudo R-squared values are 0.378 and 0.708 respectively.

B. The Effect of TRIPS on the Supply of Information by Firms and the Media

Finally, we test our conjecture that one reason for the greater incorporation of firm-specific information into a firm’s stock price after TRIPS adoption is the greater supply of information about the firm. We use management earnings forecasts to capture firms’ proclivity to provide more information.Footnote ²⁰ We also use media coverage to capture the media’s supply of firm-specific information. We then run the following Poisson regression:

(5) $$ {Information}_{i,t}={\beta}_0+{\beta}_1{POSTTRIPS}_{c,t}+\gamma {Controls}_{i,t}+{\alpha}_i+{\delta}_t+{\varepsilon}_{i,t}. $$

Information is measured using either MFFrequency or MediaCov. MFFrequency is the number of management forecasts issued by a firm in a given year. MediaCov is the number of media articles covering news that pertains to a firm in a given year. We obtain management forecast data from Capital IQ and media coverage data from RavenPack.Footnote ²¹ The coefficient of interest is that on POSTTRIPS. A significant post-TRIPS increase in firm-level information via the information channels above would yield a positive coefficient.

Panel B of Table 8 presents the results. In column 1, we use the management forecast frequency (MFFrequency) as the dependent variable. We find a significantly positive coefficient on POSTTRIPS, suggesting that firms increase their supply of information after TRIPS adoption. In column 2, we report the results that pertain to media coverage. Again, we find a significantly positive coefficient on POSTTRIPS, indicating an increase in media coverage in the wake of the agreement. These results support our argument that following TRIPS adoption, both the amount of information that firms supply and the firm-level information that the media disseminates increase, potentially contributing to the greater incorporation of firm-specific information into a firm’s stock price (i.e., lower stock price synchronicity).