1. Introduction
Market access to the national film industry is one of the more controversial issues in international trade. There is concern that free trade in films or media programming can have a negative influence on the diversity of public opinion and the cultural sovereignty of importing countries. Also, foreign films with a strong competitive edge (most often US films) have been argued to cause harm to local film production.
As a consequence, protective commercial policies have been implemented in many countries, including screen quotas requiring theaters to screen national films at least for a certain number of days per year, import quotas, and subsidies for local film production. Audiovisual services, including films, are frequently excluded from members’ schedules of concessions in the World Trade Organization's GATS (General Agreement on Trade in Services).
However, the US film industry claims that cultural exceptions are practically a protectionist measure restricting the free flow of foreign artistic works, and it demands that partner countries remove trade barriers (such as screen quotas), reduce subsidies, and strengthen the protection of intellectual property rights.
Considering the importance of this issue, numerous economic studies have been conducted to address the determinants of US dominance in international trade in films and other media programming. They focus on market size, cultural distance, and trade policies (such as screen quotas) as variables that influence the pattern of trade. However, the results of existing empirical studies have not been satisfactory. Almost all studies indicate that market size is a significant predictor of international trade, but cultural distance and trade policies turn out to be either insignificant or have an inconsistent sign in many studies. Both market size and cultural distance being two pillars for an explanation of the observed dominance of US films and media programming, further studies on this issue are warranted. We also need more precise empirical evidence on the efficacy of trade policies, which has not been properly quantified in the existing literature.
The objective of this study is to investigate the effects of trade barriers and cultural distance on the domestic market share in the film industry. To do so, we analyze panel data on 30 countries importing US films for the period from 2001 to 2013. The main contributions of this paper are threefold. First, we confirm that cultural distance and market size both have significant positive effects on the share of the market taken by domestic suppliers (the domestic share), which is consistent with theoretical studies. In estimating the panel data models, we employ methods which can estimate the effects of time-invariant variables separately from the significant country-specific effects. Second, we utilize the OECD Services Trade Restrictiveness Index (STRI) for motion pictures as a continuous measure of trade barriers, and show that the STRI has positive effects on domestic market share. On the other hand, the use of screen quotas, measured using a 0–1 discrete variable, turns out to be insignificant, implying that the screen quota system does not fully represent the extent of trade barriers of a country, and/or it may have not been enforced with rigor, as noted in previous studies. Third, the magnitude of the STRI impact on the domestic market share is much smaller than that of the market size.
The remainder of this paper is organized as follows. In Section 2, we review the existing literature on international trade in film and media programming. Section 3 presents econometric models and panel data. Section 4 explains the estimation methods and the empirical results. The concluding remarks are made in Section 5.
2. Literature Survey
2.1 Theoretical Research
Economic studies on international trade in films and media programming have begun to explain why Hollywood dominates the international market. Focusing on product differentiation and the public good nature of films,Footnote 1 Wildman and Siwek (Reference Wildman and Siwek1988) argue that the film industry is characterized by economies of scale and that market size is a key factor determining the competitiveness of a film. Filmmakers in a larger market produce a greater variety of high-budget films, which tend to have a higher quality.
Wildman and Siwek (Reference Wildman and Siwek1988) also consider the cultural discount and argue that the value of a film decreases when it is shown in a foreign country. Movie-goers prefer a film that is produced in their native language or reflects their own culture. A foreign film may be dubbed or subtitled, but subtle expressions of emotion cannot be easily delivered to a foreign movie-goer, and thus satisfaction with the film would be reduced. Therefore, all other things being equal, a country tends to import fewer films when the films are produced in a country that is more culturally distant from the importing country.
Waterman (Reference Waterman1988) also emphasizes market size and cultural discount as determinants of domestic market share. A large audience base enables producers to invest more in their programs, which leads to higher quality and thus to greater competitive advantage for their programs in the world market. Viewers in foreign countries prefer their local programs to ones imported from the US, but the competitive advantage of the US programs compensates for this effect.
Hoskins and Mirus (Reference Hoskin and Mirus1988) argue that the US programs are more competitive in the international market because they receive less cultural discount than those of other countries. Due to the diversity of the US population with immigrants from many countries, the US producers could make programs which are well received in foreign countries. They also note that the cultural discount explains why trade occurs predominantly in some genres, such as entertainment and drama that are less culture-specific than news and public affairs programming. Hoskins and McFadyen (Reference Hoskins and McFadyen1991) argue that the US competitive advantage in the global television market comes from economies of scale, first-mover advantage,Footnote 2 and the characteristics of the market environment, such as private broadcasters seeking to maximize their audience, the heterogeneous nature of the US population, and the lower tolerance by US viewers to foreign programming.
2.2 Empirical Research
Based on the theoretical arguments above, empirical research has been conducted to test a hypothesis that a country with a larger market tends to have a greater market share of local films or media programming. This hypothesis was generally accepted by all research that we reviewed (Dupagne and Waterman, Reference Dupagne and Waterman1998; Jayakar and Waterman, Reference Jayakar and Waterman2000; Choi, Reference Choi2011; Oh, Reference Oh2001; Lee and Bae, Reference Lee and Bae2004; Lee and Waterman, Reference Lee and Waterman2007; Fu and Lee, Reference Fu and Lee2008). Market size is often measured by GDP or box office revenue.
The other key variable, cultural discount, represented by cultural distance (for example, between the US and a sample country) or English proficiency turned out to be insignificant or to have an inconsistent sign (Dupagne and Waterman, Reference Dupagne and Waterman1998; Jayakar and Waterman, Reference Jayakar and Waterman2000; Oh, Reference Oh2001;Footnote 3 Lee and Bae, Reference Lee and Bae2004; Chan-Olmsted et al., Reference Chan-Olmsted, Cha and Oba2008Footnote 4). Jayakar and Waterman, (Reference Jayakar and Waterman2000) interpret this result as implying that a foreign film can overcome differences in culture and/or language through dubbing.Footnote 5 Meanwhile, Hoskins et al. (Reference Hoskins, Mirus and Rozeboom1989) show that English-speaking countries tend to have greater willingness to pay for US television programming, which suggests linguistic differences as barriers to entry into foreign markets.
In contrast, some studies support the significance of the cultural discount in explaining international flow of films. For example, Fu and Lee (Reference Fu and Lee2008) analyze imported films in Singapore from various source countries during February 2002 to January 2004. They find that films from culturally more distant countries experience less box-office success in Singapore than films from countries with more similar cultures. Fu and Sim (Reference Fu and Sim2010) examine the international flow of films of nine major exporting countries and report that cultural distance reduces the flow of films between two countries while a common language increases such flow.
Dupagne and Waterman (Reference Dupagne and Waterman1998) and Lee and Bae (Reference Lee and Bae2004) have considered an import quota and a screen quota, respectively, as policy variables. Contrary to expectations, neither study lends support to the alleged protective effects of restrictive policies. Chan-Olmsted et al. (Reference Chan-Olmsted, Cha and Oba2008) consider the protection of intellectual property rights and show that it is significant in one model but not in another. Dupagne and Waterman (Reference Dupagne and Waterman1998) state that a quota as a dummy variable is a crude measure of regulations, and the results may have been different with a more refined measure. Lee and Bae (Reference Lee and Bae2004) point out that screen quota requirements in some countries are often ignored by local exhibitors who seek to maximize profits.
3. Econometric Models and Data
We investigate the effects of trade barriers and cultural distance on the domestic market share in the film industry. In line with previous studies, we also include the market size in our econometric model.Footnote 6 Thus, the domestic market share of country i in year t (
$MS\_dom_{it}^{} $) is regressed on measures of trade barriers (STRI), cultural distance (CD) and market size.
$$MS\_dom_{it}^{} = \alpha _1STRI_i + \alpha _2CD_i + \alpha _3\log (market{\rm} \; size)_{it} + f_i + \delta _t + u_{it}^{}$$where f i represents the unobserved time-invariant differences between countries after allowing for the explanatory variables; δ t the unobserved year-specific effects; and 
$u_{it}^{} $ the random disturbance with mean 0 and variance 
$\sigma _u^2 $. If the effects f i and δ t are correlated with an explanatory variable (market size)it, they have to be accounted for in estimating the model even though they are not observable. Otherwise, omitted effects f i and δ t will cause an endogeneity problem. To control for f i and δ t, fixed-effects models treat them as unknown parameters and use dummy variables for f i and δ t. However, due to the time-invariant variables included in equation (1), STRI i and CD i, this dummy-variable method cannot separate their effects from f i. In the next section, we explain the estimation methods employed in this study which can isolate the net effects of the explanatory variables from the country-specific effects.
The dependent variable 
$MS\_dom_{it}^{} $ is the market share of the domestic films, expressed as a percentage. These data were collected from the Korean Film Biz Zone provided by the Korean Film Council.Footnote 7
The trade barriers are measured by the services trade restrictiveness index for motion pictures (STRI), published by the Organization for Economic Cooperation and Development (OECD) in 2014. It measures the extent of trade policy restrictiveness covering 18 sectors, including motion picture servicesFootnote 8 for the 34 OECD countries and six major emerging economies. The STRI is a weighted average of the scores of restrictive measures (laws and regulations) in five policy areas: limitations on foreign entry, limitations on the movement of people, barriers to competition, regulatory transparency, and other discriminatory measures (OECD, 2014). The individual policy measures in each policy area are assigned a value of 0 (not restrictive) or 1 (restrictive) and a score of each policy area is the sum of the assigned values. To calculate the STRI, the five policy areas are weighted according to the relative importance determined by experts. The STRI ranges from 0 (completely open) to 1 (completely closed). Like a tariff on imported goods, a high STRI is expected to restrict trade in film industry. This variable is time-invariant as its value is constant during the data period from 2001 to 2013 for each country. The data for this STRI variable were obtained from the OECD database.Footnote 9
Another measure of trade barriers is the 0–1 binary variable of the existence of screen quotas requiring theaters to screen national films at least for a certain number of days per year. In our study, use of the screen quota variable did not produce any meaningful results, as in Dupagne and Waterman (Reference Dupagne and Waterman1998) and Lee and Bae (Reference Lee and Bae2004). Thus, we use the continuous index STRI which is expected to better represent the extent of trade barriers in general.
The cultural distance (CD) for country i is represented by the following composite measure (Kogut and Singh, Reference Kogut and Singh1988; Hofstede, Reference Hofstede2011).
$$\eqalign{ CD_i &= \displaystyle{1 \over 6} \times \left\{ {{\left( {\displaystyle{{\Delta PDI_i} \over {\sigma_{PDI}}}} \right)}^2 + {\left( {\displaystyle{{\Delta IDV_i} \over {\sigma_{IDV}}}} \right)}^2 + {\left( {\displaystyle{{\Delta MAS_i} \over {\sigma_{MAS}}}} \right)}^2 + {\left( {\displaystyle{{\Delta UAI_i} \over {\sigma_{UAI}}}} \right)}^2} \right. {\rm} \cr&\left. {\quad\;+\; {\left( {\displaystyle{{\Delta LTO_i} \over {\sigma_{LTO}}}} \right)}^2 + {\left( {\displaystyle{{\Delta IVR_i} \over {\sigma_{IVR}}}} \right)}^2} \right\}} $$where PDI i is the power distance index, IDV i the individualism versus collectivism index, MAS i the masculinity versus femininity index, UAI i the uncertainty avoidance index, LTO i the long-term versus short-term orientation index, and IVR i the indulgence versus restraint index.
Hofstede (Reference Hofstede1980, Reference Hofstede2011) has identified the above six dimensions based on factor analyses of questionnaires. It is found that national cultures vary substantially along these dimensions. Each country is positioned relative to other countries through a score on each dimension. By combining the six indices, this CD variable measures the cultural distance of each country from the US in the six cultural aspects. Large values of CD indicate a large cultural gap and movie-goers in a country with high CD tend to have lower willingness-to-pay for the US films, thus restricting import of the US films. The functional form of the index implies that positive and negative distances are weighted equally and that the marginal effect of distance increases as the distance increases.
The data for these six indices were originally constructed in Hofstede et al. (Reference Hofstede, Hofstede and Minkov2010) and were downloaded from Hofstede Insights (www.hofstede-insights.com). A symbol Δ denotes the difference of the index between country i and the US, and σ is the standard deviation of each index among the countries involved. This CD variable is also time-invariant.
This CD composite measure has been used to account for the cultural distance between the US and other countries by numerous researchers to analyze the international trade in films, including Oh (Reference Oh2001), Lee and Bae (Reference Lee and Bae2004), Chan-Olmsted et al. (Reference Chan-Olmsted, Cha and Oba2008), Fu and Lee (Reference Fu and Lee2008) and Fu and Sim (Reference Fu and Sim2010). We also use this CD measure to estimate the effect of the aggregate cultural distance on movie consumption. In addition, we use each of the six indices of the CD measure to compare their individual effects.
The market size of the film industry is measured with three variables based on previous empirical studies. These include box office revenue (BOR), the number of screens (SCR), and the real gross domestic product (GDP); BOR and GDP are real values expressed in 2005 US dollars. Among the three variables, BOR and SCR are considered to represent the realized market size and to be relevant variables to explain the domestic market share. In contrast, GDP is considered to represent the potential market size and would be less relevant than BOR and SCR (Oh, Reference Oh2001; Lee and Bae, Reference Lee and Bae2004). Data on BOR and SCR were collected from the Korean Film Biz Zone provided by the Korean Film Council, and the data on GDP were obtained from the World Development Indicators database at the World Bank.Footnote 10 We use the logarithmic values of the market size to obtain a meaningful interpretation of its coefficient; 
$\alpha _3^{} /100$ measures the percentage-point change (%p) in 
$MS\_dom_{it}^{} $ for a 1% change in the market size.
Table 1 reports the within-country averages over the sample period from 2001 to 2013 for each country of the total 30 countries. The bottom panel reports the order statistics (minimum, maximum, median, and quartiles) of the total observations pooled across countries and years, with the minimum and maximum values noted according to country and year. The domestic market share in India is the highest, with a 13-year average of 93.86% and a maximum of 96.7% in year 2001. The lowest average is 2.48% in Portugal.
Table 1. Within-country averages during the sample period from 2001 to 2013 and the order statistics of the total observations pooled across countries and years
Note: a We multiplied the original index by 100.
Table 2 shows that the three variables of market size are highly correlated, and their correlation coefficients (shown in bold face) are 0.868, 0.919, and 0.858. To avoid a multicollinearity problem, in the next section we include the three variables one by one in estimating equation (1).
Table 2. Correlation coefficients
Note: *** and ** denote a significant correlation at the 1 and 5% level, respectively.
4. Estimation Results
In estimating the panel data models, we have to account for the unobserved differences across cross-sectional units and between time periods. In equation (1), they are represented by country- and year-specific effects, f i and δ t, respectively. As shown below, f i is significant and correlated with (market size)it. Thus, ignoring f i will cause an endogeneity problem leading to biased results.
The fixed-effects models treat f i as unknown parameters and include dummy variables to account for the f i. However, since country-specific effects (f i) are not separable from the time-invariant variables in equation (1), this dummy-variable approach can estimate only the combined effects of STRI i, CD i, and f i, but not their individual effects.Footnote 11 In this section, we estimate equation (1) using a two-stage least squares method (subsection 4.1) and an instrumental-variable method (subsection 4.2). In subsection 4.1, we also test whether country-specific effects exist and whether they are correlated with (market size)it.
In previous studies, the country-specific effects were not properly addressed. Using cross-sectional data across countries, Jayakar and Waterman (Reference Jayakar and Waterman2000) and Lee and Bae (Reference Lee and Bae2004) were not able to account for country-specific effects because there is only one observation for each country. In an analysis of panel data for 14 countries during a seven-year period, Oh (Reference Oh2001) simply assumed that the country-specific effects were uncorrelated with the explanatory variables and just accounted for the error components in calculating the standard errors. This is the random-effects model which rules out the possibility of estimation bias due to country heterogeneity. Lee and Waterman (Reference Lee and Waterman2007) tested for country-specific effects using panel data collected from six countries for the period from 1950 to 2003.Footnote 12 Based on the test results that the country-specific effects were significant but uncorrelated with the explanatory variables, they employed random-effects models. In contrast, our test results in subsection 4.1 show that country-specific effects were significantly correlated with explanatory variables in our panel data.
4.1 Two-Stage Least-Squares (LS) Estimation
This two-stage LS estimation method can isolate the effects of time-invariant variables from the country-specific effects if an orthogonality condition is satisfied (Breusch et al., Reference Breusch, Ward, Nguyen and Kompas2011; Greene, Reference Greene2011). The orthogonality condition is satisfied in our model, as explained below.
At the first-stage, we use dummy variables to represent the combined effects of the time-invariant variables and the country heterogeneity in equation (1); for country i (=1,···, 30) and year t (=2001,···, 2013)
$$MS\_dom_{it}^{} = \theta _i + \alpha _3\log (market{\rm} \;size)_{it} + \delta _t + u_{it}^{} $$where 
$\theta _i^{} = \alpha _1STRI_i + \alpha _2CD_i + f_i$, the sum of three terms. Since the country-specific effects (f i) are captured by 
$\theta _i^{} $, we can obtain consistent LS estimates for α 3 and δ t in equation (3). These estimates are equivalent to the LS estimates obtained after the within-transformation approach is applied. Conditional on the consistent estimates of 
$\hat{\alpha} _3$ and 
$\hat{\delta} _t$, we calculate residuals which contain the combined effects of the time-invariant variables and the country-specific heterogeneity.
$$\eqalign{ {\hat{\eta}} _{it} &= MS\_dom_{it}^{} -{\hat{\alpha}} _3\log (market{\rm} \; size)_{it}-{\hat{\delta}} _t \cr & {\rm} = \alpha _1STRI_i + \alpha _2CD_i + f_i + \psi _{it}^{}} $$where 
$\psi _{it}^{} = u_{it} + e_{it}$ with e it representing the sampling errors of 
$\alpha _3-\hat{\alpha} _3$ and 
$\delta _t-\hat{\delta} _t$. Since consistent estimates of 
$\hat{\alpha} _3$ and 
$\hat{\delta} _t$ are used, these sampling errors converge to zero as the number of cross-sectional units approaches to infinity.
At the second stage, we regress the residuals on the observed time-invariant variables, STRI i and CD i. Since the trade barriers and the cultural distance were determined before the sample period and remained the same afterwards, they are exogenous in the determination of 
$MS\_dom_{it}^{} $ and thus are uncorrelated with f i.Footnote 13 Therefore, this second-stage LS estimation for equation (4) yields consistent estimates for α 1 and α 2 although f i is not used as a regressor (Hausman and Taylor, Reference Hausman and Taylor1981; Breusch et al., Reference Breusch, Ward, Nguyen and Kompas2011; Greene, Reference Greene2011). To account for possible heteroscedasticity caused by the use of an estimated dependent variable and by the fact that the estimation error of (
$\alpha _3-\hat{\alpha} _3$) is multiplied by log (market size)it, we calculate White's heteroscedasticity-consistent standard errors (Lewis and Linzer, Reference Lewis and Linzer2005; Greene, Reference Greene2011).Footnote 14
According to the results in Table 3, log(BOR) and log(SCR) representing the realized market size are positively significant at the 1% and 5% levels, respectively. In contrast, the proxy for the potential market size, log(GDP), is not significant even at the 10% level. As expected, the realized market size is more relevant to explain the differences of the domestic market shares across countries. The cultural distance (CD) also turns out to be a positively significant factor for the domestic market share, indicating that cultural distance reduces the inflow of foreign films.
Table 3. Estimates and test results by the two-stage LS estimation method
Notes: a This R 2 is calculated without the variation explained by the country-specific effects.
b This is to test whether there exist country-specific effects (i.e., differences between countries). Small p-values indicate that there exist significant country-specific effects in the dependent variable (MS_dom), given barriers, CD, and a market-size variable.
c This is to test whether the country-specific effects are correlated with the market-size variable included in each regression. Small p-values indicate that the country-specific effects are significantly correlated with the market-size variable in each regression.
Year dummies are included in all regression models.
The standard errors are in the parentheses below their coefficient estimates.
***, **, and * denote a significant coefficient at the 1, 5, and 10% level, respectively.
To test whether the country-specific effects exist and are correlated with (market size)it, we calculate the residuals using consistent estimates of 
$\hat{\alpha} _1$ and 
$\hat{\alpha} _2$ in equation (4).
$$\hat{\hat{\eta}} _{it} = \hat{\eta} _{it}-\hat{\alpha} _1STRI_i-\hat{\alpha} _2CD_i = f_i + \hat{\psi} _{it}$$where 
$\hat{\psi} _{it} = \psi _{it} + v_{it}$ with v it representing the sampling errors of 
$\alpha _1-\hat{\alpha} _1$ and 
$\alpha _2-\hat{\alpha} _2$. Since consistent estimates of 
$\hat{\alpha} _1$ and 
$\hat{\alpha} _2$ are used, these sampling errors converge to zero as the number of cross-sectional units approaches to infinity. Thus, the country-specific effects (f i) can be consistently estimated by the within-country averages of the residuals 
$\hat{\hat{\eta}} _{it}$.
We now test the null hypothesis of H 0 : f i = constant for all i, which means that there exist no differences across countries, i.e., no country-specific effects. The bottom panel in Table 3 supports the existence of significant differences across countries in the domestic market share, given the variables for trade barriers, cultural distance, and market size. The correlation coefficients indicate significant positive correlations between f i and the variables for market size. Therefore, these test results suggest that country-specific effects have to be accounted for to avoid the endogeneity problem.
To evaluate the effectiveness of trade barriers (measured by STRI) in promoting the domestic market share, we focus on regression (i) which uses log(BOR) to proxy the market size. Its coefficient estimate of 5.456 indicates that the domestic market share (
$MS\_dom$) increases by 0.05456 percentage point (%p) for a 1% increase in BOR. The impact of STRI is estimated as 0.340, indicating an increase in 
$MS\_dom$ by 0.340%p for an increase of 1 in STRI.Footnote 15 We now calculate the impact when each variable changes across its interquartile range in the data. To obtain this impact, we multiply the coefficient estimate by the interquartile range of each variable. A change by 569% in BOR across its interquartile range (from 111 to 743, in Table 1) is expected to promote 
$MS\_dom$ by 31.1%p (= 569 × 0.05456), while an interquartile change by 6 in STRI (from 13 to 19) is to promote 
$MS\_dom$ by 2.0%p (= 6 × 0.340). These estimates imply that the magnitude of the impact on the domestic market share is much larger when the film market grows than when the trade barriers become higher.
4.2 Instrumental-Variable (IV) Estimation
Hausman and Taylor (Reference Hausman and Taylor1981) suggest an IV estimation method for panel data models which include time-invariant variables and individual effects. We rewrite equation (1) as follows
$$\eqalign{ MS\_dom_{it}^{} &= \alpha _1STRI_i + \alpha _2CD_i + \alpha _3\log (market{\rm} \; size)_{it} + \delta _t + \varepsilon _{it}^{} \cr {\rm} \varepsilon _{it}& = f_i + u_{it}} $$where f i is treated as a random variable and thus included in a new error term. To isolate the effects of the time-invariant variables from the unobserved individual effects (f i), it is required that the model has to include a sufficient number of explanatory variables uncorrelated with f i (and thus ɛ it). As explained in subsection 4.1, the trade barriers (STRI) and the cultural distance (CD) are considered to be exogenous and uncorrelated with f i, thus satisfying the requirement. However, the proxy variables for the market size are shown to be correlated with f i in subsection 4.1. For such endogenous proxy variables, we use the deviations from their within-country means as instrumental variables, following the suggestions in Hausman and Taylor (Reference Hausman and Taylor1981) and Breusch et al. (Reference Breusch, Mizon and Schmidt1989).
Hausman and Taylor (Reference Hausman and Taylor1981) treat f i as a random variable but allow f i to be correlated with some explanatory variables. This is the key difference from the random-effects models which assume f i to be uncorrelated with all explanatory variables. Treating f i as a random variable, Hausman and Taylor (Reference Hausman and Taylor1981) apply the generalized method of moments (GMM) with the aforementioned instrumental variables. This GMM can account for disturbance covariances containing f i and u it, and it is thus expected to produce consistent and efficient IV estimators.Footnote 16
Table 4 reports the IV estimation results, which are consistent with the two-stage LS ones. The 95% confidence intervals of the coefficient estimates in Table 4 overlap with the two-stage LS ones in Table 3, indicating that they are not different at the 5% significance level. However, the IV estimation method produced larger standard errors and lower significance levels than the two-stage LS estimation. One reason for this is that the IV estimation method uses only the exogenous parts of the endogenous variables to identify its effects, thereby reducing the explanatory power. So, the R 2s in Table 4 are lower than the ones in Table 3. Another reason is that the two-stage LS method conditions the second-stage estimation on the first-stage estimates of 
$\hat{\alpha} _3$ and 
$\hat{\delta} _t$. Since the uncertainty associated with the estimates is ignored, the two-stage LS standard errors could be underestimated. In sum, we can say that the significance levels in Table 3 are the upper bounds, and the ones in Table 4 are the lower bounds.
Table 4. Estimation results by the Hausman and Taylor (Reference Hausman and Taylor1981) instrumental-variable (IV) method
Notes: Year dummies are included in all of the regression models.
The standard errors are in parentheses below their coefficient estimates.
***, **, and * denote a significant coefficient at the 1, 5, and 10% level, respectively.
In the above estimation, we used the CD composite measure which is an average of the six indices weighted by their respective variances. We also include all of the six indices in the same regression model. Table 5 reports the estimation results when the market size is measured by log BOR it and the model is estimated by the Hausman and Taylor (Reference Hausman and Taylor1981) instrumental-variable method.Footnote 17 The null hypothesis of the equality of their coefficients is rejected with a p-value of 0.001. In this regression shown in the second column (ALL), the coefficient for each index represents its net effect when the other five indices are held constant. Since the six indices are correlated with each other, their net effects could be positive or negative depending on their correlations. With the six indices included, the effect of log BOR it is significant at the 10% level but the one of STRI is not.
Table 5. Estimation results by the Hausman and Taylor (Reference Hausman and Taylor1981) IV method when all and each of the six indices in the CD composite measure are used
Notes: When all of the six indices are included (ALL), the null hypothesis of equal coefficients is rejected with a p-value of 0.001.
Year dummies are included in all of the regression models.
The standard errors are in the parentheses below their coefficient estimates.
***, **, and * denote a significant coefficient at the 1, 5, and 10% level, respectively.
We also include the six indices one by one. In these regressions (PDI ~ IVR), the coefficient for each index represents the total effect, i.e., the sum of its net effect and the indirect effect through the correlations with the other indices. Thus, the coefficient for each index captures the effect of cultural distance in all of the six aspects. Five indices of PDI, IDV, UAI, LTO, and IVR have significant total effects on the domestic market share at the 1% or 5% levels; only MAS is not significant. Their estimates are within the 95% confidence intervals. In the regressions (PDI ~ IVR), the effect of log BOR it is significant at the 1% or 5% levels but the one of STRI is not.
4.3 Robustness Checks: Lagged Effects of the Market Size
It might take time for the market size to influence the domestic market share in each country. To consider such lagged effects of the market size, we use its lag-one variable.
$$MS\_dom_{it}^{} = \alpha _1STRI_i + \alpha _2CD_i + \alpha _3\log (market{\rm} \; size)_{i,t-1} + f_i + \delta _t + u_{it}^{} $$ The estimation results for equation (7) are summarized in Table 6. These results are qualitatively the same as the ones with the contemporaneous market size in Tables 3 and 4. For regression (i) which uses log(BOR) to proxy market size, the two-stage LS estimates in Table 6 are 0.461 for STRI, 7.000 for CD and 1.971 for log (BOR) while the estimates in Table 3 are 0.340, 6.906, and 5.456, respectively. The corresponding estimates and their significance levels are qualitatively the same, although the lagged 
$\log (BOR_{i,t-1}^{} )$ has a weaker impact than the contemporaneous 
$\log (BOR_{it}^{} )$. When the Hausman and Taylor IV method is employed, the estimation results in Table 6 are also qualitatively the same as the ones in Table 4.
Table 6. Estimation results when the lagged values of the market size are used
Notes: Year dummies are included in all of the regression models.
The standard errors are in the parentheses below their coefficient estimates.
***, **, and * denote a significant coefficient at the 1, 5, and 10% level, respectively.
In addition, use of the lagged values could reduce a possible simultaneity problem. For example, the box office revenue 
$(BOR_{it}^{} )$ as a measure of market size in equation (1) is assumed to have a causal effect on the domestic market share (
$MS\_dom_{it}^{} $) during the same period. However, their relation could be simultaneous in that having a strong domestic film industry leads to higher box office revenue due to higher quality of domestic films. If there exists such two-way causation, the estimation results obtained with the contemporaneous 
$BOR_{it}^{} $ could be biased. Since the lagged values of box office revenue have been determined in the previous period t–1, the relation is one-way from 
$BOR_{i,t-1}^{} $ to 
$MS\_dom_{it}^{} $ in equation (7). As explained above, the results in Table 6 with the lagged market size are qualitative the same as the ones in Tables 3 and 4 with the contemporaneous market size. It appears that our main results about the determinants of the domestic market share and the effectiveness of trade barriers are not sensitive to a possible simultaneity.Footnote 18
5. Concluding Remarks
Using panel data from 30 countries for the period from 2001 to 2013, we have examined the determinants for the domestic market share in the film industry and evaluated the effectiveness of trade barriers. When estimating the panel data models with the time-invariant variables, we paid special attention to the country-specific effects that could cause an endogeneity bias. The empirical results reveal that the cultural distance as well as the market size is a significant factor for the domestic market share in the film industry, which is consistent with theoretical studies. For a measure of the trade barriers, we have utilized STRI published by OECD and have shown that STRI has a positive impact on the domestic market share. However, the magnitude of the STRI impact is much smaller than the one of the market size.
The empirical analysis has shown that significant differences in the domestic market share still exist across countries given trade barriers, cultural distance, and market size. Instead of investigating the sources of the differences, this study has focused on consistent estimation with controlling for the unobserved country-specific effects. To better understand the determinants of the domestic market share, it would be worth identifying additional factors.
Author ORCIDs
Chung-ki Min, 0000-0002-0109-7890, Chanyul Park, 0000-0001-7919-1388
Acknowledgement
This work was supported by the Hankuk University of Foreign Studies Research Fund.
