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Lottery economics

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I.

INTRODUCTIONIn more than half of this world, one can see people playing lottery games (Garrett 2001). In United Kingdom (U.K.), more than half of the adult population plays lotto every week.

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Lottery’s profound effect in U.K. was inflicted immediately after its first launch in 1994.This paper is a study focusing on international comparison of lotteries.

Covered are the lottery sales of 80 countries in five continents. Inspired by Thomas Garrett’s study in 2001 (1997 data used), a similar study is done using the 2004 data from the same sources.

It is this study’s assumption that sometimes and in some situations, people are not totally rational.The main purpose of this work is to analyse and/or compare the expenditures in lottery tickets of different consumers (signified by the area of distribution discussed in here).

This study tries to find out the factors influencing people’s aptitude for gambling across countries. The wealth and development status of the country, geographical distribution, and corruption are among the factors this study will try to find correlation with people’s aptitude for gambling.

The result’s reflection in stock market will also be looked at.II.

REVIEW OF RELATED LITERATUREAccording to the World Economic Outlook survey of International Monetary Fund (2004), the rate of global recovery apparently surged from the third quarter of 2003 up to second quarter of 2004. Industrial production, strong global trade, consumer confidence, and growth of investment topped the list of factors affecting the positive recovery of world economy during this phase. The highest growth average of Gross Domestic Product (GDP) since 1999 (6 per cent), at an annual rate, had been recorded to happen in mid 2003.The noticeable strength of domestic demand, with respect to both consumption and investment, happened in 2004.

The pace and mode of recovery differ, considerably, per country and per region.  Among the contributing factors are degree of policy stimulus, development of exchange rates, corporate restructuring, developments specific to a region or country, and the readiness and ability to embrace rising global trade, particularly, in the line of Information Technology.Per the data of International Monetary Fund in its World Economic Outlook (2004), with the GDP growth rebounding to 4.6 per cent in 2004, the United States of America still proves itself to be the strongest among industrial countries; that is, in regard to GDP.

Japan, with strong external demand, continues to exceed level of expectations. In mid 2004, the projected GDP at the end of the year is 3.4 per cent and, to note, this is the highest this country attained in the past eight years.After the Asia crisis in 1997 and 1998, the projected GDP of 7.

2 per cent in 2004 marked as the highest ever attained by its emerging and developing countries. The macroeconomic policies, competitive exchange rates, and the recovery in the field of Information technology paved the way for the boosting GDP in the region’s emerging and developing countries.In Latin America, notably in Brazil, the expected GDP for 2004 may be a way better than that of 2003 which is considerably weak. The surging oil production in Nigeria rendered a big helping hand in boosting the GDP of some of the considered poorest countries, sub-Saharan Africa (South Africa excluded), with the GDP of 4.

4 per cent in 2003 with expected significant increase in 2004. This is seen to be made possible by macroeconomic fundamentals, better weather conditions, higher commodity prices, and the rising oil and gas production in several member countries.People’s confidence in government drives many things. It has the so-called domino effect of defining impact of things in the people, in the society, and in the country in general.

Corruption is just one of the significant factors defining people’s confidence in its government.Due to the increasing number of people engaging in gambling around the globe, studies about gambling-related problems consequently increased in the scientific community (Ladouceur & Walker 1996).  This same study made mention that there was a previous study that revealed that pathological gambling is not restricted to adults, but that teenagers and even children can be faced with such problem. Gambling, when considered a problem, is seen to have been significantly influenced by parental behavior in which parental attitude plays a vital role.

  There were studies showing that there seemed to be misconceptions parents have towards gambling back in 1996. Years later, popular attention was given to gambling-related issues. This comes along with the increase in prevalence of gambling-related problems in adults, brought about by the accessibility of gambling activities. It may be worth noting that when internet phenomenon came in the picture, there came the massive opportunity of making money through short selling (Pruden, et al, 2005).

This study likewise reveals that parents, in year 2000, were more aware of the risk of early gambling experiences for the development of later and bigger gambling problems. Lotteries possess odd and unique attributes that made it interesting for researchers to study. It is a combination of being an investment, as stated by Selinger (1993) and entertainment per Wagman (1986). These facts make lottery play significant part in lives of consumers nowadays (Miyazaki, A.

, Langerderfer J. & Sprott, D. 1999). Lotteries are the only risk-laden products in the state being sponsored and marketed by the government agencies for government gain; and the rapid growth of this type of consumer product in the United States gave room for some disputes in regard to the government-sponsored gambling and the rules and policy that should be imposed by the state (Clotfelter & Cook 1989).

The fact that there were very few researches made on the purchasing behavior underlying the growing consumption on government-sponsored lotteries – the rapidly growing consumer products – inspired Miyazaki, A., Langerderfer J., and Sprott, D. (1999) to explore people’s purchase and non-purchase  motivations on lotteries.

This study tried to investigate the motives both for playing and for not playing lottery games. In trying to explain the purchasing and non-purchasing behaviors people in the same location have toward lotteries, it is important to note that the psychological differences within each and every individual may be accountable to the differences in purchasing behaviors (Miyazaki, A., Langerderfer J. & Sprott, D.

1999).There were previous studies trying to find out the possible correlation between lottery purchasing behavior and demographics such as age, sex, race, income, and educational attainment; but to little corollary. The study of Clotfelter & Cook (1989) suggested that lottery players who have relatively low income play for money while those who have higher income play lottery for fun; thus, giving low income earners stronger motivation to win. This study was supported by the findings of the study of Kallick-Kaufmann (1979) that say, unlike in other types of gambling, it is making money that drives people to play lottery games over the fun and excitement that can be drawn from taking risks.

In this same study, the general reasons why there are individuals who do not participate in lottery games were also cited, such as moral convictions, monetary considerations, lack of involvement, and general mistrust of the gambling enterprise. Slightly contrary to this, according to Barber & Odean (1999), engagement in risk-seeking activities, such as buying lottery tickets, is done by people to possibly do away with unpleasant experiences.The study of Miyazaki, A., Langerderfer J.

& Sprott, D. (1999) found the enjoyment, feeling of being contributor to government-sponsored activities, and feeling of luckiness to be of no significant bearing in the lottery purchasing behavior of people.  These purchasing and non-purchasing behaviors, although found not to act as the direct stimuli to the behavior, add to the tendency that the behavior will occur or not. The non-purchasing behavior, on the other hand, is strongly driven by the recognition of poor economic value of lottery, belief against the lottery, and the lack of money and time to play.

Behavioral finance is a descriptive theory of choice under uncertainty (Statman & Caldwell 1987). It is concerned not just with how people should act but how people do act – not often rational but the deviation from rationality is often systematic (Svenson 1981). Supporting this is the study of Thaler in 1993 which made use of the term, ‘open-minded finance’ to describe behavioral finance. It acknowledges the fact that people, sometime in their lives, tend to act irrationally.

As said, deviation from the usual rationality is, more often than not, random and equitable to being systematic.Overconfidence is one aspect of Behavioral Finance. Control illusion is one kind of overconfidence that makes people believe they are in control of the situation where, in fact, they are not. This control illusion can be very evident in cases where investment is likely to be considered a failure (Fromlet 2001).

Shriller (2000) described it as people’s notion of thinking they more than they really do.Lottery games involve concept of randomness which simply suggests that the outcome of an event is unknown prior to the actual occurrence of the event (Draper & Lawrence 1970).  As put by Miyazaki, Brumbaugh & Sprott (2001) in his study of consumer misconceptions about random events, if consumers hold this mistaken belief about random nature of lotteries, i.e.

, they are in control of outcomes of random events, there is the tendency that these misconceptions influence the decision to play lottery games.This goes in parallel with what Ladouceur & Walker (1996) says in their study that people (majority, if not really all) behave and think irrationally when gambling. This erroneous thought make people fail to consider the concept of randomness and uncertainty in the event. These reigning misconceptions in each of the gamblers in the world determine the development and maintenance of gambling habit in them.

As noted by Kahneman and Trevsky (1979), people do not adopt easily to losses and that people tend to display risk-seeking behavior when faced with a situation of surely losing an investment and a gamble. This supports the claim that the likelihood of a person to be entrapped into continuing pouring investment prevails for as long as he has not adapted his asset position to losses – and this happens only when the losses are realized. This relates to the study of Clotfelter & Cook (1993) which states the thing about gambler’s fallacy that says a player has a better chance of winning an independent random event after experiencing a series of losses.In any wager, the possibility of ending up with regret can never be taken off the picture.

As quoted by Kahneman and Trevsky (1979), “Regret is felt if one can readily imagine having taken an action that would have led to more desirable outcome. This interpretation explains the close link between the experience of regret and the availability of choice: actions taken under duress generate little regret. The reluctance to violate standard procedures and to act innovatively can also be an effective defense against subsequent regret because it is easy to imagine doing more conventional thing and more difficult to imaging doing non-conventional one.” And as supported by the study of Thaler (1980), regret will be acute where an individual must take responsibility for the final outcome.

Thus, when faced with gamble, it is absolutely important for a person to possess the ability to control oneself. The study of Miyazaki, Brumbaugh & Sprott (2001) supports the claim that control and perceptions of control can significantly influence how people behave.The study of Balabanis (2002) identified the positive correlation between lottery ticket and scratch-card buying behavior and compulsiveness. As corollary, it suggested that the factors that affect lottery ticket buying also influence scratch-card buying.

Gender and age factors were not found to be related with buying compulsiveness in lottery tickets and scratch-cards. This study was so profound to even draw the findings about the three types of personality that can be related with buying compulsiveness in both lottery tickets and scratch cards – extraverts who are more prone to compulsive buying of lottery tickets or scratch-cards while individuals who score high in agreeableness and intellect are less prone to the said compulsive act. III. DATA PRESENTATIONIt is evident in a study made by Garrett (1997) that by dividing the countries per level of their 1997 GDP (4 quartiles) we can observe an increase of lottery sales as GDP increases until the third quartile.

In the fourth quartile, where developed and industrialized countries are classified, the absolute value of lottery sales continues to increase, but if we are to compare this with their GDP, lottery sales decrease. A sound explanation to this can be answered by behavioral finance theories where the “object” of the risk-taking tendency of those participating in lotteries is not present anymore: why spend money with lottery tickets that promise wealth if you already have it?There were 80 countries taken into consideration in this study. A complete list can be found in the appendix (the list will include some important data also sued in this study — GDP, per capita income, geographic grouping membership, etc.).

The data that were used here were obtained La Fleur`s 2005 World Lottery Almanac (like that of Garret, for total lottery sales), World Bank data (for an overview economic standing of the world as well as some groupings made therein – geographic distribution, income grouping; see www.worldbank.org), Corruption Perception Index Report (World Bank Institute Governance & Anti-Corruption and Internet Center for Corruption Research, data are available online), and Human Development Index.To ensure that significant change in technology and financial changes that happened did not affect the above-mentioned conclusion of Garrett, his study will be replicated here to include up to 2004 available data.

The following questions will be addressed:Is it possible that the more developed a country is, less money (relative to the GDP) is spent on lottery tickets?Is it a cultural matter (Latino Countries spend more than Nordic ones)?Does governance matter? Does a country with more corruption and with less confidence from citizens in its governance spend more on lottery tickets or less?What other factors affect the lottery sales of an economy?Does the stock market reflect the tendencies questioned above?A.        REVALIDATION OF GARRETT’S STUDYIn Garrett’s paper, a comparison between total lottery sales and GDP was made in the continent level. The topmost groups in this 1997 paper were Europe and Australia/New Zealand, and this remained the same in the case of the current study (refer to Table 1 for distribution of other data). Except for South/Central America group, all other groups incur increases in total lottery sales as well as total GDP; but the total sales as a percentage of GDP did not increase for South and Central America, North America, and Australia/New Zealand.

 Table 1. Classification of countries by geographic distributionContinentTotal Sales (US$M)Total GDP (in millions)Total Sales as a % of GDPNumber of countriesSouth/Central America3,346.30985,500.360.

349North America53,743.9013,323,776.100.403Europe93,753.

9013,820,389.720.6834Australia/New Zealand3,498.40730,942.

630.482Asia/Middle East28,845.908,460,559.230.

3413Africa1,178.40466,770.050.2519In the same study, theory was proposed: Total lottery sales increase together with increase in GDP up to a point where a country has reached a certain level where the GDP is high enough and lottery sales (or expenses on the side of consumer) become an inferior good.

In the 80 countries studied, an equal distribution of 4 quartiles was done with each quartile containing 20 countries each. Same with the results of Garrett’s study, 2004 data for the countries included now reflected a continued increase in total lottery sales as a percentage of GDP from Quartile 1 to Quartile 3 (0.28%, 0.42%, and 0.

82% for Quartiles 1, 2, and 3, respectively), but eventually showed a decrease in Quartile 4 (0.47). The absolute value of lottery sales did not decrease. In millions of dollars, the actual sales were actually 7,156 for Quartile 1 countries, 8,806 for Quartile 2, 30,556.

90 for Quartile 3, and 137,847.90 for Quartile 4. However, it should be noted that these increases are nominal in nature and once set against the GDP, the real value of these figures (specifically, for Quartile 4 countries) would actually decrease. Table 2 shows the quartile distribution as well as pertinent data on this.

;Table 2. Countries by income class (countries divided into quartiles)Quartile 1Quartile 2Quartile 3Quartile 4Number of Countries20202020Total GDP (in millions US$)2,593,014.452,090,361.743,735,651.

4029,368,910.50Total Lottery Sales (in millions US$)7,156.008,806.0030,556.

90137,847.90Average Lottery Sales (in millions US$)357.80440.301,527.

856,892.40Average Per Capita GDP627.863,617.8213,305.

4138,955.33Average Per Capita Lottery Sales1.5420.36106.

48224.58Total Sales as a % of GDP0.280.420.

820.47*For list actual countries in each quartile, refer to the Appendix.;B.        WEALTH EFFECTThe table below shows, in general, the financial standing of the world in 2004, and distributed per distribution and income group.

;Table 3. GDP and GNI per capita of World Bank member-countriesGDP (current US$) (billions)GNI per capita, Atlas method (current US$)World41,365.86,338Country groupingEuropean Monetary Union9,500.927,921East Asia and Pacific2,653.

51,417Europe and Central Asia1,795.93,307Latin America and Caribbean2,031.13,584Middle East and North America550.01,995South Asia881.

7598Sub-Saharan Africa529.1607Income group*High Income32,927.732,132Middle Income7227.22,265Low Income1216.

3507From the above available data (details per country can be found in the site), the following variables were included in analyzing the wealth effect with (1) Total Sales, (2) Per Capita (PC) Sales, and (3) Sales/GDP as the dependent variables:GDP and PC GDP (current US$),Agriculture, value added (% of GDP),Exports of goods and services (% of GDP), andGNI and PC GNI (Gross National Income, previously GNP) per capita, Atlas method (current US$).Using Statistical Package for the Social Sciences (SPSS), some variables were excluded (inflation, GDP deflator (annual %) and gross capital formation (% of GDP)).;Table 4. Included variables, with dependent variable: total sales (US$M)Unstandardized CoefficientsStandardized CoefficientsBStd.

ErrorBetatSig.(Constant)-6137.019699.183-8.

777.000PC GDP-1.024.140-2.

646-7.312.000GDP (current US$)6.18E-008.

00014.24712.987.000Agriculture, value added (% of GDP)99.

74522.754.1794.384.

000Exports of goods and services (% of GDP)107.32710.431.37410.

289.000GNI per capita, Atlas method (current US$)1.072.1502.

5277.160.000GNI, Atlas method (current US$)-5.58E-008.

000-13.335-12.138.000Table 5.

Included variables, with dependent variable: total PC salesUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.(Constant)-1476.988131.

268-11.252.000PC GDP-.141.

013-18.029-11.198.000GDP (current US$)3.

24E-009.00037.08911.361.

000Inflation, GDP deflator (annual %)26.5042.989.8568.

866.000Agriculture, value added (% of GDP)20.1161.7861.

79211.260.000GNI per capita, Atlas method (current US$).176.

01520.58611.831.000GNI, Atlas method (current US$)-3.

30E-009.000-39.162-11.474.

000Gross capital formation (% of GDP)37.8423.6541.62610.

357.000Table 6. Included variables, with dependent variable: Sales/GDPUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.

(Constant)0.0004097.002.187.

853PC GDP-1.563E-06.000-4.748-2.

438.020GDP (current US$)7.331E-14.00019.

8743.375.002Inflation, GDP deflator (annual %)-0.0004214.

000-.322-1.950.060Exports of goods and services (% of GDP)0.

0001817.000.7443.716.

001GNI per capita, Atlas method (current US$)1.5308E-06.0004.2412.

238.032GNI, Atlas method (current US$)-7.0875E-14.000-19.

901-3.376.002C.        GOVERNANCE EFFECTGarrett has already proven that the lottery sales of any economy is positively correlated in its wealth, i.

e., economic standing, to a certain degree. Other than validating this finding to adapt to the changes in technology and financial cases, this study also aims to look at factors wherein lottery sales is positively or negatively correlated with. A slowly growing measure of a nation’s development is its political systems.

Many international institutions, in fact, conducted study to assess nation’s governance indicators. In a paper written by Kaufmann et al. (2005, The World Bank), Governance Matters IV: Governance Indicators for 1996-2004, they reinforced 6 governance indicators:Voice and Accountability – measuring political, civil and human rightsPolitical Instability and Violence – measuring the likelihood of violent threats to, or changes in, government, including terrorismGovernment Effectiveness – measuring the competence of the bureaucracy and the quality of public service deliveryRegulatory Burden – measuring the incidence of market-unfriendly policiesRule of Law – measuring the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violenceControl of Corruption – measuring the exercise of public power for private gain, including both petty and grand corruption and state capture (page 6, of the paper).Together with economic indicators, these are being used to assess a country’s development.

The data on governance indicators found in this World Bank study (Kaufmann et al., 2005) were used as independent variables with total lottery sales, PC sales, and sales/GDP as dependent variables. The result of regression analysis done in this study can be found in Tables 7–9.Table 7.

Included variables, with dependent variable: total sales (US$M)Unstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.(Constant)42271.5086310.

4156.699.000Corporate Legal Corruption Index Component-197.19977.

136-.593-2.557.013Public Sector Ethics Index-866.

527168.342-3.374-5.147.

000Voice and Accountability-14814.5122702.741-2.005-5.

481.000Political Stability-4310.4771502.138-.

607-2.870.006Government Effectiveness11629.1873771.

7571.8563.083.003Regulatory Quality-8750.

2022158.494-1.291-4.054.

000Rule of Law12606.8044674.9132.0092.

697.009Control of Corruption19994.9383894.5283.

4855.134.000Table 8. Included variables, with dependent variable: total PC salesUnstandardized CoefficientsStandardized CoefficientsBStd.

ErrorBetatSig.(Constant)994.57628.97834.

321.000Corporate Illegal Corruption Index Component-8.885.483-1.

742-18.382.000Public Sector Ethics Index-19.984.

640-3.860-31.222.000Judicial / Legal Effectiveness2.

721.362.5697.521.

000Corporate Governance Index-3.480.230-.622-15.

138.000Corruption Index score*77.3326.5301.

49711.842.000Voice and Accountability-378.38910.

172-2.540-37.198.000Political Stability-82.

2655.915-.574-13.909.

000Government Effectiveness342.17614.7212.70923.

244.000Regulatory Quality-172.4038.505-1.

263-20.271.000Rule of Law74.04020.

162.5853.672.001Control of Corruption570.

71017.2164.93533.150.

000Table 9. Included variables, with dependent variable: sales/GDPUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.(Constant)0.

045683.0058.450.000Corporate Legal Corruption Index Component-0.

00014.000-.482-2.191.

032Public Sector Ethics Index-0.0008.000-3.675-5.

809.000Judicial / Legal Effectiveness0.00013.000.

6422.230.030Corporate Governance Index-0.00023.

000-.966-4.822.000Voice and Accountability-0.

01452.002-2.310-6.978.

000Government Effectiveness0.015686.0032.9425.

201.000Regulatory Quality-0.00705.002-1.

222-3.968.000Control of Corruption0.021798.

0034.4646.801.000D.

        DEVELOPMENT EFFECTAnother aspect of this study is to look at other developmental factors as possible independent variables that may be correlated with total lottery sales, PC sales, or sales/GDP. Human development index (HDI) is usually described as the comparative measure of poverty, literacy, education, life expectancy, childbirth, and other factors for nations and economies around the world (UN Human Development Index Report). It is fast becoming a standard measure for any country’s well-being. It is used by many people to distinguish whether the country is a first, second, or third world country.

This was developed by Pakistani economist Mahbub ul Haq (1990), and was now widely used by many institutions, specifically, the United Nations Development Programme in its annual Human Development Report.The HDI measures the average achievements in a country in three basic dimensions of human development:A long and healthy life, as measured by life expectancy at birth.Knowledge, as measured by the adult literacy rate (with two-thirds weight) and the combined primary, secondary, and tertiary gross enrollment ratio (with one-third weight).A decent standard of living, as measured by gross domestic product (GDP) per capita at purchasing power parity (PPP) in USD.

The 2004 report of UN entitled Human Development Report 2004: Cultural Liberty in Today’s Diverse World (July 15 2004, United Nations Development Programme) data were compared against the topic of this study. Regression analysis was done to compare these independent variables against total lottery sales, PC sales, and sales/GDP. Some variables were excluded (such as Internet users (per 1,000 people) for year 2001, cellular subscribers for 2003, personal computers (per 1,000 people) for 2001, etc.) The variables that were included as well as the result of regression analysis can be found in Tables 10–12.

Table 10. Included variables, with dependent variable: total sales (US$M)Unstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.(Constant)98289.

38932490.1453.025.005Internet users (per 1,000 people) 200268.

50715.8412.0024.325.

000Gov’t enterprises and investment as a share of gross investment-802.539380.673-.388-2.

108.043Freedom of citizens to own foreign currency bank accounts (domestically and abroad)-753.238326.640-.

468-2.306.028Sound Money3120.564916.

430.7543.405.002Standard deviation of tariff rates-9168.

3773351.604-2.977-2.736.

010Foreign ownership restrictions (GCR)3582.2931080.629.7973.

315.002Ownership of banks1230.398493.289.

5092.494.018Burden of Regulations2614.9961336.

003.5201.957.059Starting a new business-2603.

2841114.014-.658-2.337.

026Business Regulations-5816.2752424.032-1.054-2.

399.022Internet Users 19902925.874598.210.

8274.891.000Gender Empowerment measure (GEM Value)-71983.86716343.

483-1.580-4.404.000Table 11.

Included variables, with dependent variable: total PC salesUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.(Constant)2692.888475.

9785.658.000Internet users (per 1,000 people) 20021.618.

2402.3466.753.000General gov’t consumption as share of total consumption-59.

76310.690-.863-5.591.

000Transfers and subsidies as a share of GDP27.2339.131.4392.

982.006Gov’t enterprises and investment as a share of gross investment-24.0295.580-.

577-4.306.000Judiciary independence27.7039.

702.5072.855.009Military in Politics45.

4467.788.9545.835.

000Freedom of citizens to own foreign currency bank accounts (domestically and abroad)-18.8095.095-.580-3.

691.001Sound Money71.17014.061.

8545.061.000Standard deviation of tariff rates-225.15547.

812-3.627-4.709.000Foreign ownership restrictions (GCR)79.

52817.978.8784.424.

000Freedom to Trade Internationally70.46527.859.5982.

529.018Ownership of banks60.6669.4451.

2466.423.000Credit Market Regulation60.69326.

297.5822.308.030Burden of Regulations191.

44123.5081.8888.144.

000Starting a new business-100.08317.989-1.256-5.

564.000Business Regulations-161.74836.950-1.

454-4.377.000Regulation-198.11334.

742-1.372-5.702.000Internet Users 199076.

34110.6131.0717.193.

000General Empowerment measure (GEM Value)-2777.665350.488-3.025-7.

925.000Table 12. Included variables, with dependent variable: sales/GDPUnstandardized CoefficientsStandardized CoefficientsBStd. ErrorBetatSig.

(Constant)0.161087.0207.876.

000Internet users (per 1,000 people) 20010.000040480.0001.2493.

054.006Internet users (per 1,000 people) 20020.000055004.0001.

8894.033.001Personal computers (per 1,000 people) 2001-0.000024108.

000-.849-3.434.002General gov’t consumption as share of total consumption-0.

0034340.000-1.175-7.857.

000Gov’t enterprises and investment as a share of gross investment-0.00087699.000-.499-4.

264.000Military in Politics0.0026970.0001.

3417.954.000Freedom of citizens to own foreign currency bank accounts (domestically and abroad)-0.0013981.

000-1.021-6.952.000Sound Money0.

0053345.0011.5168.350.

000Standard deviation of tariff rates-0.010409.002-3.972-5.

394.000Foreign ownership restrictions (GCR)0.0065083.0011.

7029.255.000Freedom to Trade Internationally-0.0021776.

001-.437-1.912.068Ownership of banks0.

0036318.0001.76710.013.

000Interest rate regulations (leading to neg. rates)0.0012581.000.

4233.059.006Impact of minimum wage0.0019513.

001.2752.251.034Burden of Regulations0.

010738.0012.50911.817.

000Time with government bureaucracy-0.0022148.001-.388-3.

143.005Starting a new business-0.0048315.001-1.

436-7.109.000Business Regulations-0.0080284.

001-1.710-5.567.000Regulation-0.

0049916.001-.819-4.793.

000Internet Users 19900.0049901.0011.6589.

070.000General Empowerment measure (GEM Value)-0.18452.014-4.

760-13.056.000IV.      RESULTS AND DISCUSSIONA.

        INTERPRETATION OF THE WEALTH, GOVERNANCE, AND DEVELOPMENT EFFECTSThe data presented in this study cover until the year 2004 only. The wealth effect is clearly a validation of Garrett’s study. GDP and GNI are the two topmost independent variables that are positively correlated with total sales. It is worth noting how strongly correlated the two variables are with the dependent variable total sales (R2 = 0.

8598 for GDP, 0.845 for GNI). Clearly, absolute total sales would continue to increase together with an increase in GDP of an economy (see Figs. 1 and 2).

The same results can be observed in the case of having total per capita sales as the independent variable. The positive relation has been retained. However, a significant change has to be noted: the strength of the correlation between GDP and total PC sales significantly decreased (from 0.8598 to 0.

0236). This could be attributed to a dominating trend of positive relation of increase in GDP to increase in population. Hence, no matter how high your total lottery sales are, a large population would bring down its value if applied on a per capita level (see Figs. 3 and 4).

An interesting finding here is when we use the total sales/GDP as the dependent variable. While all of the independent variables are positively correlated to the dependent variable, the strength of correlation is significantly weak, with the GNI per capita having the strongest correlation (R2 = 0.0372).Fig.

1. GDP (current US$) versus total sales (US$)Fig. 2. GNI (Atlas method, current US$) versus total sales (US$)Fig.

3. GND (current US$) versus total per capita sales (US$);Fig. 4. GNI per capita (Atlas method, current US$) versus total per capita sales (US$)The way the government handles its affairs seems to have no effect to the total lottery sales as well as sales/GDP.

They are all positively correlated but with a very weak correlation. However, all these correlation increased dramatically if the dependent variable is per capita sales. This is the same scenario in the development effect. This will be addressed below.

Fig. 5. Comparison of correlation effects of the three dependent variablesThe development effect from this analysis resulted in the same scenario as the corruption effect. All independent variables did not strongly correlate with both the total lottery sales and sales/GDP.

A minor exception to this is the mild correlation between Internet users (per 1000 population, year 2002) with R2 = 0.1446 and for year 1990, R2 = 0.2011. There are two possible explanations to this: one, Internet availability makes online gaming/lottery available to the population, thus, increasing the sales.

Another possible explanation is that availability of Internet denotes, in a way, increased individual income. One cannot afford Internet without a personal computer. We could refer to the theory stated herein that an increase in per capita income is positively and strongly correlated with increase in total lottery sales.As mentioned above, there is an interesting observation where there is a sudden increase in correlation when independent variables are correlated with per capita sales – this is true to both to governance and development effects.

The possible explanation in the case of governance is the fact that both governance and developmental aspects both have independent variables that can be hypothetically be related to increase in income. This can be deduced from the fact that any increase in ‘development‘ variables can only be made possible with increase in income. Internet use, for instance, can only increase if there would be increase in income of that economy. Other variables in the development aspect are the following:·         Gov’t enterprises and investment as a share of gross investment·         Freedom of citizens to own foreign currency bank accounts (domestically and abroad)·         Sound Money·         Ownership of banks·         and others.

As for the case of governance effect, all the factors mentioned can be equated to increased awareness, and thus, knowledge. While the factors are mostly based on perception, such assessment of the government would definitely entail what is happening in the society/government, or even more appropriate in this particular case, such assessment can actually be equated to hope of a better system – whether they have good or bad assessment. Theories in behavioral finance support this claim.B.

        MANIFESTATION OF THESE TENDENCIES IN THE STOCK MARKETIn this effect (stock market) there are a great number of variables with few observations and, also a lot of countries with few observations. This will restrict the possibility of obtaining a “good” model. In the date gathered, the following independent variables were considered (Table 13):Table 13. Included variables, with dependent variable: total salesUnstandardized CoefficientsStandardized CoefficientsBStd.

ErrorBetatSig.(Constant)991.266225.0744.

404.000Domestic market capitalization0.00578663.000.

67717.168.000Total value of share trading-0.002463479.

000-.533-9.820.000SMC average2002-2003-33.

4611.827-.297-18.317.

000Domestic firms /population04-463.47238.948-.168-11.

900.000no_banks4.096.1161.

04335.372.000ownership93.7953.

843.37224.408.000Running a correlation test resulted in the validation/manifestation of the above tendencies.

The variable domestic market capitalization has an R2 = 0.5914, total value of share trading = 0.7332, no banks = 0.7603.

These figures equate to two things: the desire to move to a wealthier class through risk-taking, as in the case of lotteries.  C.                 Summary of correlation effects for wealth, governance, and developmentTotal Sales (US$M)Total PC Sales (US$)Sales / GDPWealthR Square0.9780.

910.378Adjusted R Square0.9750.8910.

265Std. Error of the Estimate1004.491$41.973780.

004594GovernanceR Square.542.985.579Adjusted R Square.

481.983.523Std. Error of the Estimate4538.

1741$16.75.003702DevelopmentR Square.606.

880.908Adjusted R Square.433.779.

816Std. Error of the Estimate4741.2622$59.69.

002296V.        SUMMARY AND CONCLUSION This study has revalidated Garrett’s study that there is a strong correlation between the increase in total lottery sales and country’s GDP, i.e., as GDP increases, total lottery sales increase, too.

In this study, a strong correlation was observed in the continuing increase in GDP and total lottery sales.A few questions were posed:•          Is it possible that the more developed a country is, less money (relative to the GDP) is spent on lottery tickets? This is a possibility. However, the data in this study showed how the percentage of lottery sales against country’s GDP would decrease as soon as the country has reached a certain peak of financial stability or maturity. While the absolute value of total lottery sales would still increase, this is still less than the real value of the amount when compared with the GDP.

•          Is it a cultural matter (Latino Countries spend more than Nordic ones)? There are no enough data to prove any relation between culture or geographical distribution and total lottery sales.•          Does governance matter? Does a country with more corruption and with less confidence from citizens in its governance spend more on lottery tickets or less? What other factors affect the lottery sales of an economy? Governance and development effects were observed to have positive correlation to total lottery sales as well as sales relative to GDP, but only with a weak correlation. The per capita sales assessment would be the one to give the strong correlation. As mentioned above, this per capita sales assessment simply validates the initial claim that increase in total lottery sales equates to increase in GDP, or in this case, increase in wealth.

It somehow appears that while better governance and increase in development aspects are both positive contributors in the increase of lottery sales, it is only true insofar as this betterment in government and increase in development would translate to increase national wealth.VII. APPENDIXAppendix 1.   Geographic distribution of the 80 subject countries;South/Central AmericaArgentinaBoliviaBrazilChileCosta RicaPanamaPeruTrinidadUruguay;North AmericaCanadaMexicoUnited States;EuropeAustriaBelgiumBulgariaCyprusCzech Rep.

DenmarkEstoniaFinlandFranceGermanyGreeceHungaryIcelandIrelandItalyKazakhstanLatviaLithuaniaLuxembourgMaltaMoldovaNetherlandsNorwayPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenSwitzerlandTurkeyU.K.Ukraine;Australia/New ZealandAustraliaNew Zealand;Asia/Middle EastChinaCroatiaHong KongIndiaIsraelJapanKorea, Rep.LebanonMacedoniaMalaysiaPhilippinesSingaporeThailand;AfricaAlgeriaBeninBurkina FasoCongoCote d’IvoireEthiopiaGambiaGhanaKenyaMadagascarMaliMauritiusMoroccoMozambiqueNigerSenegalSouthTogoZimbabweAppendix 2.

List of countries distributed into 4 quartiles basing on per capita GDP Total Sales (US$M)Total PC SalesSales/GDPPC GDPGDP (current US$)Quartile 1Ethiopia8$0.110.10%$1158,076,860,928Madagascar1.6$0.

090.04%$2524,363,989,504Niger12.9$1.070.

42%$2553,081,292,544Gambia1.2$0.830.29%$287415,083,040Mozambique2.

1$0.110.04%$2905,547,729,408Burkina Faso22.00$1.

780.46%$3894,823,723,008Mali0.2$0.020.

00%$4074,862,884,352Ghana33.70$1.600.39%$4098,620,011,520Togo13.

7$2.760.66%$4152,061,009,536Kenya8.6$0.

270.06%$48115,600,289,792Benin12.2$1.770.

30%$5914,075,074,560Moldova1$0.240.04%$6152,594,980,608India2131.50$1.

970.31%$641691,876,265,984Senegal35.60$3.410.

46%$7337,665,048,064Cote d’Ivoire67.20$3.920.44%$89215,285,883,904Bolivia1.

7$0.190.02%$9768,773,135,360Phillippine175.40$2.

110.20%$1,04186,428,598,272Congo17.70$4.590.

40%$1,1374,383,809,536China4592.10$3.540.28%$1,2721,649,329,438,720Ukraine17.

60$0.370.03%$1,35765,149,341,696Quartile 2Morocco153.60$5.

020.31%$1,63750,054,918,144Zimbabwe0.9$0.070.

00%$1,85324,370,000,000Peru39.90$1.450.06%$2,48368,394,958,848Macedonia11.

2$5.430.21%$2,5445,246,011,000Algeria12.50$0.

390.01%$2,61584,649,009,152Thailand1972.00$31.611.

21%$2,621163,491,463,168Kazakhstan13.8$0.920.03%$2,72440,743,190,528Bulgaria118.

70$15.260.49%$3,10224,130,615,296Romania216.50$9.

900.30%$3,34773,166,831,616Brazil737.20$4.120.

12%$3,384604,855,074,816Uruguay55.50$16.330.42%$3,86513,138,378,752Argentina1677.

80$43.891.11%$3,963151,501,176,832Turkey873.30$12.

180.29%$4,210301,949,845,504Costa Rica133.10$32.770.

72%$4,52918,395,269,120Panama343.10$113.322.49%$4,55513,793,000,448South Africa759.

40$16.660.36%$4,668212,777,304,064Malaysia1373.30$54.

481.17%$4,672117,775,785,984Lebanon78.10$17.150.

36%$4,78021,767,827,456Mauritius15.30$12.400.25%$4,9076,056,131,584Chile220.

80$13.840.23%$5,89894,104,944,640Quartile 3Latvia7$3.040.

05%$5,91813,628,607,488Poland867.10$22.720.36%$6,337241,832,542,208Lithuania37.

20$10.820.17%$6,47422,262,693,888Mexico831$8.010.

12%$6,518676,497,326,080Croatia111.70$24.780.33%$7,58734,199,977,984Slovakia101.

00$18.740.25%$7,62341,091,854,336Estonia14.00$10.

410.13%$8,03610,807,917,568Trinidad137.20$103.671.

09%$9,47912,544,420,864Hungary613.30$60.890.62%$9,90099,712,024,576Czech Rep.

296.00$29.070.28%$10,512107,046,756,352Malta71.

30$177.811.32%$13,4395,388,840,448Korea, Rep.3356.

7$69.720.49%$14,118679,674,314,752Portugal1382.70$132.

490.82%$16,125168,281,374,720Slovenia33.50$16.790.

10%$16,13132,181,755,904Israel908.90$133.710.77%$17,292117,548,417,024Greece4813.

50$434.642.37%$18,366203,401,003,008Cyprus211.20$272.

301.37%$19,87915,418,346,496Hong Kong816.40$119.270.

50%$23,814163,004,743,680Spain15521.7$375.951.57%$24,014991,441,649,664New Zealand425.

50$104.780.43%$24,54799,686,834,176Quartile 4Singapore3005.00$693.

192.81%$24,641106,818,306,048Italy20339.6$353.281.

22%$29,0471,672,301,903,872Canada5468.80$171.420.56%$30,711979,764,183,040Australia3072.

90$152.730.49%$31,375631,255,793,664Germany13350.00$161.

560.49%$32,8502,714,417,758,208France11666.00$194.460.

58%$33,3822,002,582,110,208Belgium1471.40$141.410.42%$33,621349,829,824,512Netherlands1299.

30$79.960.23%$35,524577,259,569,152Austria2121.60$261.

440.73%$35,750290,109,489,152Finland1719.70$329.760.

92%$35,781186,597,015,552U.K.9120.20$153.

530.43%$36,0392,140,898,066,432Japan10313.60$80.720.

22%$36,1874,623,398,076,416Sweden2110$234.840.61%$38,554346,404,061,184United States47444.1$161.

650.41%$39,75211,667,514,589,184Iceland47.90$165.170.

39%$42,69112,380,297,216Denmark1439.80$266.770.59%$45,031243,043,254,272Ireland800.

10$199.080.44%$45,673183,559,618,560Switzerland1400.3$189.

690.39%$48,695359,465,287,680Norway1588.20$346.620.

63%$54,598250,168,049,664Luxembourg69.40$154.220.22%$69,20731,143,241,728;;;;;;;;;;;;;;;;;;;;References;Barber, B.

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Cite this Lottery economics

Lottery economics. (2017, Mar 26). Retrieved from https://graduateway.com/lottery-economics/

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