State Subsidies to Film and Their Effects at the Box Office: Theorizing and Measuring Why Some Genres Do Better than Others

Abstract

This chapter explores several theoretical approaches that can be employed to measure the performance of the movies and to assess the main factors that may influence their performance. In particular, we focus on evaluating the impact of public intervention on the movie performance and its effects on genre success at the box office. From an empirical perspective, we provide some examples to highlight the impact of public subsidies on the movie performance in Italy. To this aim, we consider quantity (box office revenues) and quality (film festival awards) as two separate indicators. Specifically, public subsidies and movie genres are employed as explanatory variables to investigate the impact of public intervention and film genre on the movie performance. In this respect, we use specific methods, such as fixed-effects approach and count models, in accordance with the type of the dependent variable under investigation. The findings show that although public funding has an overall negative impact on quantity and quality, there are some differences when considering public subsidies by genre. On balance, there is statistical evidence that dramas and thrillers are the genres that should be primarily financed by public agents.

Appendices

From Forte and Mantovani (2013).

Appendix 1: Trend of State Subsidies in the Italian System

From Forte and Mantovani (2013).

The revenue market share of the Italian movies on the aggregate revenue was 39.00% in 1983, 33.12% in 1984, and 30.06% in 1985 when the FUS was issued for the first time. From 1986 to 2010, it oscillated in the range of 20.65–27.84%, with two exceptions slightly above in 1987 and in 1997 and two slightly below in 1993 and 2000. Basically, the market share of the Italian movies, in the entire period after FUS, remained at a slightly lower level than that of the first year of the FUS, with a limited recovery on the last decade of the considered period. Meanwhile, FUS funds for movies declined from 0.026% of GDP to 0.005% of GDP in 2010.

Other types of public finance aids to movies were issued from the end of 1990 onwards. The trend of the share of the Italian movies in terms of the number of new movies presented in the Italian cinemas, after FUS, was similar to that of the revenue market share until the end of 1990, although higher in the last decade. The number of new Italian movies as a share of the total number of new movies released in Italian cinemas, that was between 33.5 and 31.8% in the 3 years before 1985, went down to an average level of 25.8% in the first 5 years of the FUS. Then, it declined with a certain volatility to <25.0% until 1996 and reached the maximum level of 40.96% in 2008, after the new law was issued, which provided tax incentives. The share of the market in terms of revenue of the Italian movies was smaller than that of the foreign movies, but still they had a recovery because of the new ways of financing other than the FUS.

Appendix 2: Dataset

Data providers:

• www.cinemaitaliano.info (movie characteristics and revenues)

• www.comingsoon.it (movie characteristics and revenues);

• http://www.cinema.beniculturali.it/ (public subsidization data)

The dataset consists of 754 movies produced in Italy and exhibited during the period 2002–2011.

For each movie, the following variables were collected:

• box-office: amount of money earned by each movie, expressed in euros and adjusted for inflation;

• subsidization: amount of public subsidization granted from MiBACT (Ministero dei Beni delle Attività Culturali e del Turismo), expressed in euros and adjusted for inflation;

• festivals: variable that accounts participation at film festivals when a movie is eligible for awards. Out of competition appearances are not recorded;

• prizes: prizes won at film festivals;

• comedy: factor variable which takes value 1 if a movie belongs to comedy, romantic comedy, family movies genres or if it is an animation movie (and 0 otherwise);

• drama: factor variable which takes value 1 if a movie is of dramatic genre and 0 otherwise;

• documentary: factor variable which takes value 1 if a movie is a documentary and 0 otherwise.

• thriller: factor variable which takes value 1 if a movie belongs to thriller or horror genres and 0 otherwise.

Appendix 3: Syntax

iis year

The seldom used iis command declares the time dimension of the dataset without the need of declaring also the panel variable as in xtset.

foreach var of varlist comedy-documentary { qui gen subs_‘var’=‘var’*log_subs qui replace subs_‘var’=0 if subs_‘var’=. } foreach var of varlist comedy-documentary { qui gen nosubs_‘var’=‘var’ qui replace nosubs_‘var’=0 if subs_‘var’!=0 }

The first loop generates iteration variables between genre and subsidization. The command foreach calls variables from the list comedy, drama, thriller, documentary. The second loop is then used to generate a dummy variable that takes value 1 if a movie belongs to a given genre but did not received public funding and 0 otherwise.

xtreg log_box log_subs drama comedy thriller, fe est store fe_reg xtreg log_box log_subs drama comedy thriller, re est store re_reg

xtreg command fits regression models to panel data. The fe option fits fixed-effects models (by using the within regression estimator), while the re option fits random-effects models by using the GLS estimator (producing a matrix-weighted average of the between and within results).

hausman fe_reg re_reg

To discriminate between random and fixed effects, the Hausman test is performed.

xtreg log_box subs_comedy nosubs_comedy subs_drama nosubs_drama subs_thriller nosubs_thriller subs_documentary nosubs_documentary, fe est store re_iter_reg xtreg log_box subs_comedy nosubs_comedy subs_drama nosubs_drama subs_thriller nosubs_thriller subs_documentary nosubs_documentary, re est store fe_iter_reg hausman fe_iter_reg re_iter_reg poisson prizes festivals log_subs comedy drama thriller documentary if festivals>0, irr est store prizes_pois

Poisson regression fits count models, that is, the number of occurrences of an event. Here, the condition if festivals > 0 limits the estimation to those movies that competed at film festivals. The irr option reports estimated coefficients transformed into incidence-rate ratios, that is, β _r rather than β _i . Standard errors and confidence intervals are similarly transformed.

nbreg prizes festivals log_subs comedy drama thriller documentary if festivals>0, irr est store prizes_nbreg

With the same restriction as above, the model is estimated with a negative binomial. In this model, the count variable is believed to be generated by a Poisson-like process, except that the variation is greater than that of a true Poisson.

poisson prizes festivals nosubs_comedy subs_comedy nosubs_drama subs_drama nosubs_thriller subs_thriller nosubs_documentary subs_documentary if festivals>0, irr est store prizes_poiss_iter nbreg prizes festivals subs_comedy subs_drama subs_thriller subs_documentary nosubs_comedy nosubs_drama nosubs_thriller nosubs_documentary if festivals>0, irr est store prizes_nbreg_iter