Consumer behaviour and the welfare eects of gambling in

Transcription

1 Consumer behaviour and the welfare eects of gambling in Finland Preliminary results University of Turku September 16, 2016

2 1 Introduction Gambling as utility creating activity Problem gambling Gambling in Finland Purpose of the research 2 Methodology and data Methods to analyse consumer behaviour Data 3 Econometric analysis of consumer behaviour Threshold regression Beta coecient for dierent levels of gambling Estimating the persistence of gambling expenditures Quantile estimation for dierent types of games 4 Summary and discussion of the welfare eects

3 Gambling as utility creating activity Clotfelter & Cook (1987) have summarized four reasons why people might participate in gambling activities: First, there can be circumstances where the expected value of e.g. lottery ticket is positive (because of rollovers) and gambling might be seen as an investment. (However, the law of large numbers tells us that anyone gambling sucient number of times will end up losing money) Friedman & Savage (1948) were the rst suggesting that a rational individual having a particular type of utility of wealth function will participate in gambling activity and buy an insurance at the same time, that is, individuals unsatised with their current wealth, usually having low income, play more games of chance, games that can change persons economic status

4 Third reason is that gamblers may have illusion of control, e.g. having a list of "lucky numbers" or some kind of rituals when participating gambling activity and related to this, people often have incomplete information about the probabilities and prize structures of the games Finally, individuals may also just enjoy the social and other non-monetary aspects of gambling and participate to gambling activities because of that

5 Problem gambling In most cases individuals are satised in gambling with decent stakes and get utility from excitement, dreaming of dierent economic status, entertainment etc. as mentioned previously However, small portion of population don't get satisfaction similarly, in worst case leading to a compulsive and an excessive gambling This in turn causes costs to the society e.g. in form of lower productivity and implicates decrease in the overall welfare

6 Gambling in Finland Finnish gambling market has been operated by three monopoly companies Veikkaus (lotteries, daily games and sports betting), RAY (casino and slot machines) and Fintoto (horse betting) Consumption of gambling products per adult has been about 400 euros per year in the previous years, totaling 1.7 billion euros in 2014 and 0.8% relative to GDP, one of the highest gures in the world Also the share of problem gambling has been quite high in Finland, estimated being 3,3% in the latest prevalence study (Salonen Raisamo 2015)

7 Purpose of the research The purpose of this research is to study gambling behaviour, especially if there are signs of increased risk behaviour The signs of risk behaviour include high gambling expenditures, large number of games and times played and the persistence of gambling expenditures (especially in the higher end of the expenditure distribution) By analysing customer behaviour we can then evaluate the possible welfare eects of gambling in Finland If there are signs of increasing share of risk behaviour (and problem gambling), we can say that the welfare is decreasing as the social costs of gambling are increasing faster than well-being

8 Methods used to analyse the consumer behaviour in gambling markets With threshold regressions one can nd some ranges of values where the estimated coecients of interest vary in some important way As in the capital asset pricing model (CAPM), by estimating beta coecient one can see if there are dierences in "portfolios" of gamblers at some specic level of gambling relative to the aggregate market By estimating an AR(1)-model and using dummy variables for dierent levels of gambling, we can see if there are dierences in the persistence of gambling expenditures between dierent level gamblers Finally, using panel quantile regression we can study how some of the characteristics of interest change over the whole conditional distribution of gambling expenditures

9 Data The data is weekly data from 3 years span ( ) of randomly sampled 2000 individuals from the Veikkaus' customer registry, total of observations Veikkaus has in total almost 2 million registered customers However, it must keep in mind that the data consists only customers that have been veried on the counter or have played via digital channel

10 Description of the data Table: Data by selected quantiles Quantile Amount gambled EUR Products palyed N Times played N 20% % % % % % %

11 Table: Aggregated expenditures with specic level of gambling expenditures Played euros individuals Avg. share of total sum Share of total obs Avg.< % 79.2% 17 Avg. < % 16.5% Avg % 4.3% 100< 497* 52% 2.5% 200< 215* 40% 1% 500< 65* 28% 0.33% *No. of individuals that have played the amount concerned at least in one week

12 Figure: Aggregate time series for dierent average gambling expenditures

13 Threshold regression The Threshold Regression model describes a simple form of nonlinear regression and features piecewise linear specications and regime switching that occurs when an observed variable crosses unknown thresholds We use individual gambling expenditure as dependent and also as threshold variable The total sum of all gambling expenditures and lagged values of individual gambling expenditures are used as threshold varying independent variables With this specication we can analyse if there are certain values or ranges of gambling expenditures where the reaction to market uctuations and past values of gambling (persistence) are dierent

14 Threshold regression results Dependent Variable: GAMBLING EXPENDITURE Included observations: after adjustments Variable Coecient Std. Error t-statistic Prob. GAMBLING EXPENDITURE < obs C TOTAL SUM -2.04E E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE < obs C TOTAL SUM 6.86E E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE obs C TOTAL SUM GAMBLING EXPENDITURE(-1) R-squared Mean dependent var F-statistic Durbin-Watson stat Prob(F-statistic)

15 Threshold regression results Dependent Variable: GAMBLING EXPENDITURE Included observations: after adjustments Variable Coecient Std. Error t-statistic Prob. GAMBLING EXPENDITURE < obs C TOTAL SUM -3.40E E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE < obs C TOTAL SUM 1.98E E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE < obs C TOTAL SUM -5.56E E GAMBLING EXPENDITURE(-1)

16 24 <= GAMBLING EXPENDITURE < obs C TOTAL SUM 2.12E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE < obs C TOTAL SUM -1.61E GAMBLING EXPENDITURE(-1) <= GAMBLING EXPENDITURE obs C TOTAL SUM GAMBLING EXPENDITURE(-1) R-squared Mean dependent var F-statistic Durbin-Watson stat Prob(F-statistic)

17 Discussion of the threshold regression results Threshold regression results show us that there are identied certain groups respect to the threshold variable: At the lower level of gambling expenditures only constant is statistically signicant covariate, which can be interpreted as individuals belonging in specic group just play some specic amount and their behaviour is not aected by their previous or overall level of gambling However, at the higher level of gambling expenditures (>64eur) the overall market level and the lagged dependent variables are also signicant covariates, which suggests that there is more persistence and dependency of the overall market at the higher gambling volumes

18 Estimating beta coecient for dierent levels of gambling Estimated with least squares (LS) and in logarithms, the beta coecient tells us the elasticity of how the group with specic volume of gambling is aected by the changes in overall market With the elasticity estimate we can make conclusions about the behaviour of gamblers at dierent levels, e.g. is there some group that reacts dierently to the uctuations of the whole market It also tells us about the variation relative to the whole market and gives us a prediction for the future evolution

19 Estimation results Table: Beta coecients for dierent level gamblers Dependent variable: log(low gamblers) log(medium gamblers) log(high gamblers) Constant (α) * * (0.826) (0.517) (0.574) log(total sum) (β) 0.800* 0.729* 1.221* (0.079) (0.049) (0.055) No. of obs R-squared Durbin-Watson stat * statistically signicant at 1% level

20 Table: Estimated beta coecients for specic amounts Dependent variable: log(sum over 100) log(sum over 200) log(sum over 500) Constant (α) * * * (0.460) (0.795) (1.448) log(total sum) (β) 1.492* 1.694* 2.011* (0.044) (0.076) (0.138) No. of obs R-squared Durbin-Watson stat * statistically signicant at 1% level

21 Interpreting estimation results of beta coecient From the estimates of beta coecient we can conclude that the higher level gamblers overreact to the increase in the overall level of gambling as the elasticity (the beta coecient) is statistically signicantly greater than one (Wald-test) Thus, the Veikkaus' registry data suggests that as the overall level of gambling rises this might increase the incidence of problem gambling as the sum of heavy gamblers' expenditures increase relatively more than the overall market in the sample used However, while the high expenditures are not necessary a sucient indicator of problem gambling, the results are in line with the ndings of comprehensive population surveys of the evolution of problem gambling (Salonen & Raisanen, 2015; Turja et al, 2012)

22 Estimating the persistence of gambling expenditures Estimating an AR(1)-model gives us more information about the persistence of gambling We introduce dummy variables indicating the level of 3 years weekly average gambling expenditures for an individual: Individuals that have gambled less than 17 euros on average per week are dened to be in the group having low gambling expenditures individuals having mean gambling expenditures between 17 and 64 euros per week are classied as medium level gamblers Over 64 euros per week on average gambled individuals are in the high expenditure group The values are based on the previous threshold regression results and descriptives of the data

23 Estimation results for the AR(1)-model Dependent Variable: GAMBLING EXPENDITURE Periods included: 155 Cross-sections included: 2000 Total panel (balanced) observations: Variable Coecient Std. Error t-statistic Prob. C GAMBLING EXPENDITURE(-1) GAMBLING EXPENDITURE(-1)*MEDIUM DUMMY GAMBLING EXPENDITURE(-1)*HIGH DUMMY Cross-section xed (dummy variables) Period xed (dummy variables) Eects Specication R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid 1.98E+09 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

24 Discussion about the AR(1)-model results The results show us that the persistence is stronger at the medium and high levels of gambling relative to the low level However, the coecients for the medium and high groups do not dier statistically signicantly (Wald-test) This on the other hand also rises the incidence of problem gambling As already mentioned before, it would be crucial to know about the total balance of individual's gambling success (expenditures relative to wins) In addition, it might also be that the lower level gamblers do not use their customer card in transaction as often as more and often gambling individuals

25 Estimation of conditional quantile functions In the quantile regression model the quantiles of the conditional distribution of the dependent variable are expressed as functions of observed covariates We t a panel data quantile regression model with individual xed-eects, proposed by Koenker (2004), to study how the eect of number of dierent types of games played to gambling expenditures changes over the whole conditional distribution This tells us if there is tendency at a certain level of gambling to concentrate on some specic type of games and if dierent types of games are related to gambling with higher amounts

26 Figure: Coecients for dierent types of games

27 Quantile regression results From the gure of the estimated eect of dierent types of games played on various quantiles of gambling expenditure we can see that at the higher levels of gambling, individuals tend to play more and more betting and daily games and less lottery games There might be some extra information about the betting targets among individuals playing large volume of sports games, alternatively the illusion of control might be stronger among betting games (however, without the data of wins we cannot tells if this is the thing) If the eect of all of the games had increased similarly in higher quantiles, this would have been a denite sign of increased risk for gambling problems

28 Summary and discussion about the welfare eetcs The results indicate that there is some evidence of increasing risk for problem gambling as the share of high expenditure gamblers seems to grow faster than the whole market Also the persistence at the higher level of gambling expenditures rise some concern regards the risks At the highest percentiles gamblers do not although play "just anything", but rather concentrate to especially in betting games The heaviest gamblers might have some information rent that we are not aware of without the data of possible wins All in all, these preliminary results suggest that as the overall gambling volume in Finland keeps increasing it will abate welfare to some extent as the risks for problem gambling will rise relatively more

29 Thank you for listening! Questions?