Determinants of Cigarette Consumption - Research Paper Example

Research Paper, Macro & Micro economics Determinants of cigarette consumption Determinants of cigarette consumption I. Introduction Many researchers have determined the purchasing behaviour of cigarette in the past but not much work is done on the “Determinants of cigarette consumption”. It is very important to determine the effect as it leads to many economic (i.e. Micro and Macro environment) issues related to health (Macro environment) and consumption and saving behaviours of individuals (Micro environment). The consumption and saving behaviour of an individual or a group have an impact on the economy. There are factors that have an impact on the consumption of people but there are many products that usually do not have any impact on such factors. The consumption of cigarettes has a big impact on the economy as many people purchase it. The government must take possible steps to bring awareness in people about the negative impact of cigarettes. This research will help to determine some possible factors that can have an impact on the consumption of cigarettes. The result of the past researches will be observed to find the extent to which any of the factors can have an impact on the consumption of cigarettes. II. Background Cigarettes are believed to be very dangerous drugs, which can badly affect the health of a human. A smoker can suffer from various diseases if they continue smoking for a long time. It is even believed that young children (teenagers) who are involved in smoking are the ones that do not care about the consequences of the cigarettes and continue smoking for a longer period of time. The factors of age, gender, pay of a person, health, etc. are some variable, which will be determined in the research. A survey will be conducted using such variables, on the basis of which a result will be interpreted related to the present study. Scholarly articles will also be used to determine the result of such factors and develop learning from them. Smoking is practiced as route of administration for recreational drug use. The blood stream and bodily tissues absorbs the combusted substances that are extracted due to the smoking and provides the user a relief. Some people carry out smoking as a part of their culture. They believe that this will provide them the “ability to induce trance-like states that will eventually lead them to spiritual enlightenment”. Studies found out that in the world more than 4.9 million people die from smoking. The effects are well known to people all over the world but still they use tobacco. The use of tobacco affects the consumers as they spend a lot of their incomes on buying “Cigarette” and as more amount of money they spend the less they have to spend on other essential of life. The study will reveal the important factors that contribute to the consumption and saving behaviour of the users and also provide the recommendation to stop the use of tobacco. III. Hypothesis H0: There is a significant relationship between the cigarette consumption and income behaviour. H1: There is no significant relationship between the cigarette consumption and income behaviour. IV. Literature Review Consumption and saving behaviour has a big impact on the economy of a country. Consumption and saving behaviour of an individual makes a lot of difference in the economy. If a person saves money rather than consuming it on a product, which is not socially accepted, then that person is contributing in the economy. On the contrary, products like cigarettes have an impact as those who consumes it contributes more to the economy of a country because such products have high tax rates. There are certain factors, which have an impact on the consumption of cigarettes. Theoretical Components Consumption Behaviour The process to chose, use and dispose products and services by an individual or a group is called consumption behaviour. Saving Behaviour The amount of money earned but not used for consumption of product and services by an individual or a group is called saving behaviour. Impact of Age on Consumption of Cigarette Warner et al. (2014), in their article observed through their studies the impact of adults to control the cigarette. The government to bring awareness of the health issues related to smoking has conducted various programmes. The control of tobacco has had a positive impact on the adults who consumed cigarettes. If such cigarette control programmes had not been conducted by the government then there would have been a lot of people addicted to cigarettes. The adults are focused more on this because the minors are strictly prohibited to purchase cigarettes. Though the teenagers are prohibited from purchase and smoking of cigarettes still they are involved in smoking cigarettes. It is an alarming situation for any country where the teenagers are smoking cigarettes as they must be properly educated about the negative impact of the cigarettes. From a research conducted amongst the teen aged groups in Spain it was found that a large percentage of people are aware of the negative consequences of the cigarettes but they still do not refrain themselves from smoking. Most of the adolescents do not even consider cigarettes as drugs and totally avoid the negative impacts of it. These are the results of the research conducted by Ruiz-Moral et al. (2015). Reidpath et al. (2013) conducted a research to find the relationship between the age and cigarette consumption. In their research they believed that the cigarette initiators at a young age (school going age) continue to smoke cigarettes. As they conducted a survey of 3 countries they have an unclear result if this research. The results of one country show positive relation between age and consumption but the other result of other two countries was not very satisfying. The results are determined to be unclear as there were some factors, which did not support the research. Impact of Usual pay and price on Consumption of Cigarette The pay of a person does not have an impact on the consumption of cigarettes. If the pay of a person increases their consumption of cigarette will not necessarily increase. Adams et al. (2012) stated in their article that there is no certain relationship between pay and consumption of cigarette. From their survey, it was observed that if a person earns fewer amounts of money it will not negatively impact the consumption of cigarette and if a person earns more they will only consume their desired amount of cigarettes. It can be said that there is a negative relationship between consumption of cigarette and the pay of a person. The government in most of the countries increases the tax on cigarettes so that they can stop its citizen from consuming more cigarettes. Cebula et al. (2014) in their studies researched about the impact of increasing tax on cigarettes on the consumption of cigarettes. From the survey it was found that the result of this research was not very successful as a negative relation was found between hike in tax and consumption of cigarettes. Even if the government increases tax the people will buy cigarettes in the same amount and will not stop purchasing them. The similar results were found in the research conducted by Pawar et al. (2014). It is proved that there is no or negative impact of increase in the price of cigarettes ion the consumption of cigarettes and similarly the pay of a person does influence their decision of consumption of cigarette. Impact of Health on Consumption of Cigarette Everybody is aware of the impact of smoking cigarettes on the health of a human being. The government spends its revenues to bring awareness to people about the side effects of cigarette so that they consume fewer cigarettes. The consequences of consuming cigarettes are well known to the people but they still consume cigarettes. Various diseases like lung cancer, mouth cancer, high blood pressure, etc. are caused because of consuming cigarettes. After being aware of the consequences and being warned by the government, people still consume cigarettes ignoring their health safety. White et al. (2014) in their study also determined the same outcome as from the survey conducted for their research stated similar results. After the completion of their survey, it was observed that the there are many negative impact on health of consuming cigarettes but still people consume a lot of cigarettes. There is a very high rate of people who die every year because of consuming cigarettes but these awareness facts does not put an end or have a positive relation on consumption of cigarettes. The steps taken by the government does not have a big impact on the consumption of cigarettes and health awareness activities also do not stop from consuming cigarettes. V. Empirical Analysis Model The models were used in order to support the conclusions as the ordinary least square is not a reliable test to rely on. Ordinary least square (OLS) is used to find out the variable that is likely to explain approximates of the given data. The core objective is to identify the approach that is likely to fit a model in the research methodology (observed data). The model is fairly basic and helps to minimize the potential errors. The basic issue with the OLS is that it is not useful for the determination of binary forms. The other issues include the assumption of no linear relationship between the variables as the results are ambiguous. The ordinary least square shows errors in the significance of the variables in case the variables how high variations among the values. For these reasons the OLS is not the only model implemented in the research; “Logit” and “Probit” models are also used. This is because the variables both dependent and independent are qualitative in nature and are binary in form. For these reasons the OLS is not the only model implemented in the research; “Logit” and “Probit” models are also used. This is because the variables both dependent and independent are qualitative in nature and are binary in form. The Logit and Probit models are used to counter the problems of the OLS model. The logit model is a representation derived from the following equation: Pi = E [Y = 1 | Xi] = 1 / 1 + e –Zi (equation: i) This is a representation of the cumulative logistic distribution, where: Zi = βo + β1 X1 + β2 X2 + …+ βk Xk The Logit model is obtained by the taking the Log of the 1st equation: Li = Ln [Pi / 1 – pi] = Zi The use of exponential function eradicates the negative signs for the values in the data and the – sign for Zi forces the values of all the variables to be between 0 and 1. The Probit Model is similar to Logit Model the only difference is that it is derived from a cumulative standardized normal distribution function. It can be presented as: Ooprlg2 = βo + β1 (variable1) + β2 (variable2) + β3 (variable3) + … + β∞ (variable∞) + ∑ Here, ∑ represents the “normally distributed random error”. Data The variables that are changed include: the dependent variable “Smoke Cigarette now”, Total monthly income – gross, current job: permanent or temporary, sex, Urban or rural area, derived, Number of own children in household, Legal marital status and the ethnic group. The rest of the variables are analysed using a Likert scale of 5-7 response options. These variables are used in the research to support the main independent variables that is “Total monthly income – gross”. The missing values were deleted from the original variables. This was done to make sure the data is set in accordance to the tests and tools used. The missing values ranged from -1 to -9. The data run without deleting these missing variables showed negative relationships with the dependent variable. Therefore the need to change the variables in accordance with requirements was necessary. The final change was made to the type of the variables. All were set as numeric previously but the type is changed to Scale and Ordinal type. The reason is to make the research a mix of both qualitative and quantitative approaches and to use the tools that will help in the reliability of the research. The Variables Smoke cigarette now and the Total monthly labour income – gross are the core variables used to identify the relationship of Smoking and Income behaviour. The other variables are supportive to the independent variable “Total monthly labour income – gross”. The data provided is used to identify the variables for the research. The hypothesis is also designed in such a manner that it reflects the impact of all the variables that are chosen for the research. The dependent variable is “Smoke cigarettes now (b_smnow) i.e. the person is still consuming cigarettes. The independent variables are: Dependent Variable Smoke cigarettes now (b_smnow) If the person is still smoking or not, this variable is selected as the dependent variable and all the other independent variables are used to identify the effect on the dependent variable “smoke cigarette now”. Independent Variables Total monthly labour income – gross (b_fimnlabrgs_dv) The variable is the core independent variable that is used to identify the relationship that exists between the spending on cigarettes and the effect on the income of the person. Current job: permanent or temporary (b_jbterm1_1) The variable is used to identify the effect of a person’s job on the cigarette consumption. General health (b_sf1) General health conditions of the person are also used to find the relationship between the health of the person and cigarette consumption. Sex (b_sex_1) The amount of males and females that use cigarettes were identified by the use of this variable and running the test on SPSS (Regression). Urban or rural area, derived (b_urban_dv_1) The demographics of the person whether he is living in the urban or rural area of the region and his/her cigarette consumption. Number of own children in household (b_nchild_dv_1) The regression was done to find out the relationship of the amount of children a person has and the effect that can be noted on the cigarette consumption. Age (computed) (b_dvage_1) The relationship of the age of the person related to the cigarette consumption was identified by using this variable. Legal marital status (b_marstat_1) The legal marital status of the person and whether it has significance with the cigarette consumption, the variable showed a weak relationship. Satisfaction with life overall (b_sclfsato_1) The person’s overall satisfaction with his/her life and the relationship that exists between the independent variable and the dependent variable was identified. Ethnic group (b_racel_1) Whether the ethnic group have any relationship with the cigarette consumption, the independent variable would have a relationship. Variables Used for the Analysis Pos Variable Coding Missing Value Type 11 Smoke cigarettes now (b_smnow) 1 = Yes 0= No -9 - 1 Ordinal 73 Total monthly labour income – gross (b_fimnlabrgs_dv) ln (x)+1 No Missing Value Scale 13 Current job: permanent or temporary (b_jbterm1_1) 1 = Permanent Job 0 = Not Permanent Job -9 - 1 Ordinal 7 General health (b_sf1) 1 = Excellent 2 = Very Good 3 = Good 4 = Fair 5 = Poor -9 - 1 Ordinal 48 Sex (b_sex_1) 1 = Male 0 = Female -9 - 1 Nominal 69 Urban or rural area, derived (b_urban_dv_1) 1 = Urban 0 = Rural -9 Nominal 61 Number of own children in household (b_nchild_dv_1) 1 = More than 1 0 = No Children 49 Age (computed) (b_dvage_1) Squared 50 Legal marital status (b_marstat_1) 1 = Married 0 = Otherwise No Missing Value 57 Satisfaction with life overall (b_sclfsato_1) 1 = Completely dissatisfied 2 = Mostly dissatisfied 3 = Somewhat Dissatisfied 4 = Neither Satisfied Nor Dissatisfied 5 = Somewhat Satisfied 6 = Mostly Satisfied 7 = Completely Satisfied -9 - 1 6 Ethnic group (b_racel_1) 1 = British 0 = Others Descriptive Statistics   N Mean Std. Deviation smoke cigarettes now 28048 1.62 .486 b_sex_1 54597 .4587 .49830 b_jbterm1_1 30752 .9126 .28249 b_urban_dv_1 54490 .7591 .42763 b_nchild_dv_1 54597 .2848 .45130 b_dvage_1 54597 6.6858 1.39102 b_marstat_s_1 54597 .4834 .49973 b_racel_1 54597 .0499 .21765 b_fimnlabrgs_dv_1 51948 8.0199 1.08550 general health 54561 2.61 1.133 satisfaction with life overall 43424 5.22 1.490 Valid N (list wise) 13486     The descriptive analysis describes the coefficients that are helpful for describing the data set. This summary can be a representation of the population or the sample. The changes in number of response have been changed because the “Missing Values” are deleted from the data set. The descriptive analysis show that the values are not dispersed and is reliable. The mean value of dependent variable is 1.62 this means that most of the respondents (that is total number of respondents was 28,048) have chosen the option that they are not smoking in the recent time. The value of standard deviation is .486 this means that the values are not dispersed and is consistent. The value of the independent variable “Total monthly labour income – gross” has a mean value of 8.0199. Taking the log of the value and then adding 1 to it determined the value of the variable. The standard deviation is 1.08550, which means that the values in the data set are dispersed and are not consistent. Regression  Explanatory Variables OLS Regression Logit Probit b_fimnlabrgs_dv_1 -.017 .000 -.102 0.000 -.015 0.000 b_jbterm1_1 .025 .005 .160 0.006 -.028 0.000 b_urban_dv_1 .007 .226 .054 0.161 -.018 0.000 b_sex_1 .041 .000 .264 0.000 -.001 0.000 b_racel_1 .008 .475 .038 0.577 .019 0.000 b_nchild_dv_1 .034 .000 .228 0.000 -.025 0.000 b_dvage_1 -.014 .000 -.086 0.000 -.012 0.000 b_marstat_s_1 -.111 .000 -.693 0.000 .045 0.000 general health .051 .000 .314 0.000 -.008 0.000 satisfaction with life overall -.013 .000 -.073 0.000 -.004 0.000 R-Squared .045   .045   .045   The OLS represents that the independent variable Monthly labour income – gross is negatively related to the dependent variable as the value is less than .05. This means that an increase in smoke consumption will cause a decrease in the total monthly labour income. The other variables that shows a negative relationship with the dependent variable is the age, satisfaction with life and legal marital status. The rest of the variables are directly related to the dependent variable, which means that the increase in smoking will cause an increase in the values of the variables. The results of the Logit show that same results as the OLS Regression whereas the Probit model shows that general health, number of own children in household, sex, Urban or rural area, derived and current job: permanent or temporary have a negative relationship with Smoke Cigarette now. The variations in the results of regression are due to the use of different models that had different assumptions regarding the qualitative variables that were selected for the research. VI. Conclusion The factors that influence the consumption of cigarette are the ones that have a significant relationship (value less than 0.05) identified in the detailed discussion of the analysis techniques. Three types of regression analysis were used to determine the relationship first and afterwards the degree of relationship respectively. The results show that there is a significant relationship between the cigarette consumption and income behaviour. Therefore these factors and elements must be considered and well understood so that the economic issues related to cigarette consumption (Micro level) and income and the consumption behaviours (Micro level) can be well determined and mitigated in the future. VII. Limitations There are certain limitations that were faced while conducting this study. 1. The consistency of data is weak. The scale used for gathering data does not allow the researcher to include all data in the results. 2. The coding of data has resulted in certain values of data to be ignored. This has resulted in high number of missing values. 3. The output of regression analysis indicates weakness of the model measured by R-squared and insignificant values of coefficients of slope. It also implies that there are other variables that may affect the consumption of cigarettes. List of References Adams, S., Blackburn, M.L. & Cotti, C.D., 2012. Minimum wages and Alcohol-related traffic facilities among teens. Review of Economics & Statistics, 94(3), pp.828-40. Cebula, R., Foley, M. & Houmes, R., 2014. Empirical analysis of the impact of cigarette excise taxes on cigarette consumption: estimates from recent state-level data. Journal of Economics & Finance, 38(1), pp.164-80. Huang, J. et al., 2014. Chinese smokers cigarette purchase behaviours, cigarette prices and consumption: findings from the ITC China Survey. Tobacco Control, 23(1), pp.i67-72. Ketchoo, C. et al., 2013. Smoking behaviour and associated factors of illicit cigarette consumption in a border province of southern Thailand. Tobacco Control, 22(4), pp.255-60. Pawar, P.S. et al., 2014. The relation between price and daily consumption of cigarettes and bidis: Findings from the Tobacco Control Policy Evaluation Wave 1 Survey. Indian Journal of Cancer, 51(5), pp.83-87. Reidpath, D.D. et al., 2013. The Relationship Between Age of Smoking Initiation and Current Smoking: An Analysis of School Surveys in Three European Countries. Nicotine & Tobacco Research, 15(3), pp.729-33. Ruiz-Moral, R. et al., 2015. Opinions and beliefs held by Spanish teenagers regarding tobacco and alcohol consumption: a descriptive study. BMC Public Health, 15(1), pp.922-34. Sloan, F.A. & Wang, Y., 2008. Economic theory and evidence on smoking behaviour of adults. Addiction, 103(11), pp.1777-85. Warner, K.E. et al., 2014. Impact of Tobacco Control on Adult per Capita Cigarette Consumption in the United States. American Journal of Public Health, 104(1), pp.83-89. White, C., Oliffe, J.L. & Bottorff, J.L., 2014. 828-840. Health Sociology Review, 23(2), pp.113-24. Appendix Coefficients Model Un standardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -3.095 .936 -3.306 .001 total monthly personal income - gross .000 .000 -.032 -6.696 .000 monthly amount saved -.001 .000 -.031 -7.129 .000 part-time or full-time: current job .007 .115 .000 .063 .950 job satisfaction .002 .007 .002 .332 .740 sex -1.157 .078 -.064 -14.799 .000 legal marital status .381 .015 .118 25.154 .000 ethnic group -.042 .008 -.021 -5.021 .000 general health 1.314 .035 .167 37.099 .000 Age group: 13 categories -.354 .013 -.131 -26.330 .000 Number of own children in household .191 .042 .020 4.532 .000 Read More