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DETERMINANTS OF THE MULTINOMIAL CHOICE FOR THE ENVIRONMENTAL ATTRIBUTE IMPROVEMENT IN THE NATIONAL PARKS IN MALAWI. APPLICATION OF MULTINOMIAL LOGIT CHOICE MODEL TO THEORETICAL DATA PATRICK MAZICK ENVIRONMENTAL ECONOMICS SERIES JANUARY, 2012 UNIVERSITY OF MALAWI, BUNDA COLLEGE, DEPARTMENT OF AGRICULTURAL AND APPLIED ECONOMICS, LILONGWE, MALAWI Page 1 of 20 THE AUTHOR Patrick Mazick is a Resource economist, who is currently undertaking his research for the Collaborative Masters in Agricultural and Applied Economics (CMAAE) and expected to graduate in September, 2012. A part from school, Mr Mazick is business person; he produces potatoes, beans, supplies ground nuts, rice and runs salon and cosmetics shop but enjoys writing papers. He may be contacted at: University of Malawi Bunda College Department of Agricultural and Applied Economics Box 219 Lilongwe Malawi Mobile: (00265) 999 365 952 pat_mazick@rocketmail.com or mazickpatrick@gmail.com Page 2 of 20 TABLE OF CONTENTS TABLE OF CONTENTS ................................................................................................................ 3 ABSTRACT .................................................................................................................................... 4 1.0 INTRODUCTION .................................................................................................................... 5 2.0 LITERATURE REVIEW ......................................................................................................... 7 3.0 METHODOLOGY ................................................................................................................... 8 3.1 THE EMPIRICAL MODEL ..................................................................................................... 8 4.0 RESULTS AND DISCUSSION ............................................................................................. 10 Table 4.1 BASE CATEGORY 3 ............................................................................................... 10 Table 4.2 base category 1 .......................................................................................................... 11 Table 4.3 base category 2 .......................................................................................................... 12 Table 4.4: base category 4 ......................................................................................................... 12 Table 4.5 marginal effects base category 1 ........................................................................... 13 Table 4.6 1V3 ........................................................................................................................ 14 Table 4.7 1V4 ........................................................................................................................ 14 Table 4.8 1V2 ........................................................................................................................ 15 Table 4.9 2V3 ........................................................................................................................ 15 Table 5.0 2V4 ........................................................................................................................ 16 Table 5.1 3V4 ........................................................................................................................ 16 Table 5.2 relative risk ratio ....................................................................................................... 17 5.0 CONCLUSIONS AND RECOMMENDATION ................................................................... 18 6.0 REFERENCES ....................................................................................................................... 19 Page 3 of 20 ABSTRACT In this paper, we are demonstrating how individuals would choose their preference of the environmental attribute given their socioeconomic characteristics and we can conclude that level of awareness of an individual has positive contribution to somebody‟s choice of an environmental attribute improvement. This is so not only In theoretical applications of the model but also in real life situations, if an individual has knowledge or information about something, the decisions that are made are well informed unlike somebody who is not informed of some attributes about the park. No wonder, it is only information/knowledge about the park that apparently influences the probability of opting for an environmental attribute improvement not others because, even if you are educated, well advanced in years and have a lot of income but you are not informed about a particular situation, your choice can not be rational. So, it makes a lot of sense to see that these other three regressors (income, education and age) are insignificant statistically even though to extent may help to make decisions. Due to this finding, this paper recommends that through awareness campaigns, trainings, outreaches inform the community members be about the park( what it offers, how to participate, charges) and ensure that all the categories of individuals poor, rich, educated or not be informed to belong to the park Page 4 of 20 1.0 INTRODUCTION For several decades, social scientists have undertaken research on the motivations of individuals who engage in pro-environmental behavior. In other words, when we see an individual trying or wanting to promote environmental improvement, what factors are behind that person‟s motivation? Gaining a detailed understanding of why individuals undertake in environmental improvement options is important for policy makers and researchers seeking solutions to environmental problems that require behavioral change. Many research efforts thus far, however, tend to polarize around predominant themes in specific disciplines, (Moore et al, 2002) Economists, for example, examine the influence of external circumstances, such as income, price, and socio-economic characteristics, on human behavior. This implies that when as economists, we observe a consumer in a market, how she/he behaves in that particular market is hypothesized to be influenced by several factors of which income, prices and so forth are some of them. Our approach is based on the assumptions of neoclassical economic theory, which presupposes that individual decisions are based on a specific definition of rational self-interest. Solutions to environmental problems that reward, penalize, or regulate behavior result from this mode of analysis. Psychologists, on the other hand, concentrate on linking internal or psychological variables to behavior. Their literature suggests that pro-environmental behavior originates from values, beliefs, and attitudes that orient individuals toward particular actions. Consequently, psychologists recognize awareness, education, guilt, and persuasion as tools for invoking behavioral change. In trying to bring conformity between economists and psychologists, we need to identify the link between the economists‟ external influences and the psychologists‟ internal influences. We know that psychological variables like values, beliefs, morals, obedience, are based on individuals‟ background of religion, culture and exposure. The two disciplines are just interdependent in the sense that the increase in knowledge is done through education (informal or formal) which affects our religious knowledge, cultural values and so on. But to have education, we need income for us to access better education. Therefore, the understanding of these factors between economists and psychologists are just the same, the difference comes when it comes to measurements. Since economists like working with figures, we just focus on those factors that are observable and can be objectively measured to make scientific conclusions. To reconcile with psychological variables, the explanations of those figures are linked to some immeasurable issues like those ones suggested by psychologists. Despite the dominant role psychologists attribute to internal factors for motivating pro- environmental behavior, a handful of researchers identify the need to formulate an Page 5 of 20 interdisciplinary perspective. Van Liere and Dunlap (1980) argue that researchers should pay equal attention to cognitive variables and demographic determinants that underlie environmental concern. They assert that ‘‘the most powerful analyses of the social bases of environmental concern will likely be those which consider both its demographic and cognitive determinants’’. Considering pro-environmental behavior in particular, Guagnano, Stern, and Dietz (1995) argue that „„science and policy require a socioeconomic theory of behavior that incorporates both external conditions and internal processes‟‟. They note that reluctance of applied researchers to merge insights from economics and psychology has led to narrowly defined policies that often fall short of objectives. In this paper, we are going to consider both sides of the coin; economic and psychological considerations, so that we can have concrete understanding of why do people choose the environmental improvement options they choose. Hypothesized Internal variables according to psychology consist of a newly developed scale for altruistic attitudes and external variables according to economic theory consist of household income and standard socio- demographic characteristics. In this paper, we will focus on only participants in the park and analyze their specific socioeconomic attributes for choosing the environmental attribute improvement. From theory we know that individuals go for the environmental attribute improvement for different motives. These include motives relating to several concerns: ecosystem health, personal health, environmental quality for residents and even to upgrade the recreational value of the park. The main hallmark of this paper is examine the determinants of the peoples‟ choice for the environmental attribute improvement (water quality in the lake in the park, preservation of elephants, introduction of the white rhinos, building of a guest house near the park). After asking different respondents to the questionnaires, individuals indicated their desires in wanting these four different environmental improvement attributes done. Page 6 of 20 2.0 LITERATURE REVIEW Despite research attempts devoted to theory building with regards to the factors influencing firms‟ environmental behaviors (e.g. Alvarez Gil et al., 2001; Andersson and Bateman, 2000; Henriques and Sadorsky, 1996, 1999; Hoffman, 1999; Moon and de Leon, 2007; Rivera, 2002; 2004; Sharma, 2000) to date theories are contested and empirical findings are inconclusive. This conclusion comes after considering the fact that the framework within which firms or individuals make decisions they make about environmental management is difficult to be easily disclosed without their honest openness. The choice of firms on their contribution to better environmental management depends in part on the knowledge of the environmental benefits or costs of our actions towards the environment and at the same time looking at how important the environment is to the overall performance of the firm. Traditionally, one theoretical approach or a single level of analysis has been used to explain firms‟ adoption of environmental management attributes, providing an incomplete picture. Interaction between multiple theoretical perspectives and various levels of analyses is, however, argued to be essential to provide a better explanation of such a complex phenomenon (Bansal and Roth, 2000). Although multiple studies had addressed this issue in manufacturing, few examine tourism businesses (Bohdanowicz, 2006, Chan and Wong, 2006). The analysis starts at the individual-level arguing that environmental management attributes are not only driven by organizational-level determinants but also they may be outcomes of managers‟ environmental paradigms or belief systems. This is consistent with theories that emphasize the importance of organizational actors holding eco-centric values to be able to help their companies in the move towards sustainability (Gladwin et al., 1995; Shrivastava, 1995a, Stead and Stead, 1992; Starik and Rands, 1995). Empirical research has shown also that eco- oriented managers may play a role in corporate greening, although more empirical analyses are still needed in this area. Andersson and Bateman (2000), for example, have demonstrated the critical role that a “strong environmental paradigm” plays in a firm‟s decision to adopt environmental management attributes. Applying the Ajzen theory of planned behavior, Cordano and Frieze (2000) and Flannery and May (2000) have also identified managers‟ attitudes as an important antecedent to preferences for source reduction activity. In this light, hotel or tourism businesses in general are expected to vary in terms of their level of environmental commitment according to how strongly their executives embrace eco-centric values inherent in their beliefs systems. Besides, the understanding of the community members surrounding the tourism business about the environment may also help to have an influence on how much of the Page 7 of 20 environmental attributes would they want to change to improve the recreational value and increase the welfare of the community from the national park. 3.0 METHODOLOGY In this paper, we are using the data given about the socioeconomic characteristics of the respondents on various questions concerning their preference on the environmental attribute they wanted to see it improved. The environmental attributes were water quality in the lake in the park, preservation of elephants, introduction of the white rhinos, and building of a guest house near the park which were coded 1, 2, 3 and 4 respectively. The socioeconomic attributes of respondents were income, education level, age and awareness measuring the level of knowledge. To estimate how each of the independent variables affect the probability of opting for a particular environmental attribute, the multinomial logit was employed as an analytical technique. 3.1 THE EMPIRICAL MODEL The Multinomial Logit model used in this paper is basically represented as follows; Where; Environattribute is the dependent variable (where 1, 2, 3, 4 represent water quality in the lake in the park, preservation of elephants, introduction of white rhinos and building of a guest house near the park respectively) The betas are the parameters to be estimated. is the stochastic disturbance error term assumed iid Age, income, awareness (where 1 implies an individual knows about the park and 0 means otherwise) and education (where 1, 2, 3 and 4 mean no school, primary, secondary and tertiary education respectively) are hypothesized to affect the individual preference of the environmental attribute improvement. In the multinomial logit model, the numbers 1 up to 4 do not indicate order of importance. In other words 1 is not any better to all other attributes. In computing the probabilities of each of the options, the formula for option one for instance is as follows; Page 8 of 20 In all the four probabilities, the base category is option four. Page 9 of 20 4.0 RESULTS AND DISCUSSION The table 4.1 below provides the summary for the Multinomial Logit output. In this table base category is 3. The overall model when the base is 3, is statistically adequate and reliable as indicated by probability of the chi-square value. The p-value of 0.0222 shows that the model is reliable at 5% level of significance. However, except awareness under outcome 2, all other coefficients are statistically insignificant, meaning that relative to base category 3, most factors do not affect the individual‟s probability of opting for a particular environmental attribute improvement. Table 4.1 BASE CATEGORY 3 Multinomial logistic regression Number of obs = 250 LR chi2(12) = 23.71 Prob > chi2 = 0.0222 Log likelihood = -304.50686 Pseudo R2 = 0.0375 env Coef. Std. Err. z P>|z| [95% Conf. Interval] 1 age -.0454051 .0555393 -0.82 0.414 -.1542602 .0634501 levelofedu~n -.0209394 .210021 -0.10 0.921 -.4325731 .3906942 awareness .011212 .3603275 0.03 0.975 -.6950169 .7174409 inc .0004403 .0008119 0.54 0.588 -.0011509 .0020315 _cons .6533387 1.307279 0.50 0.617 -1.90888 3.215558 2 age .1106172 .0870123 1.27 0.204 -.0599237 .2811581 levelofedu~n .0261653 .2908959 0.09 0.928 -.5439802 .5963109 awareness 20.48798 2.160454 9.48 0.000 16.25357 24.72239 inc .0007475 .0011985 0.62 0.533 -.0016015 .0030965 _cons -24.53341 . . . . . 4 age .0313629 .067926 0.46 0.644 -.1017697 .1644954 levelofedu~n .3842928 .2806613 1.37 0.171 -.1657932 .9343789 awareness -.4411668 .4423297 -1.00 0.319 -1.308117 .4257835 inc .0001335 .0010565 0.13 0.899 -.0019372 .0022042 _cons -2.619674 1.671624 -1.57 0.117 -5.895998 .6566491 (env==3 is the base outcome) In all outcomes relative to the same base as in table 4.1 above, age, level of education in outcome 1 and awareness in outcome 4 have negative direction which means that any changes in the such regressors, may reduce the probability while the rest of the regressors in all outcomes have positive direction implying increase in probability if the regressors change. Table 4.2 below provides a summary of the multinomial logit when the base category is changed from 3 as in above case to 1. As it is the case in table 4.1 above, the chi-square p-value (0.0222) is also statistically significant at 5% level of significance. This means that the overall model is reliable for policy implications. In other words, the model results are reliable enough to make decisions on. By changing the base category to 1, it is only awareness variable in outcome 2 relative to base 1 which is statistically significant while all other variables in all outcomes are statistically insignificant. At the same time, all coefficients have positive direction in outcome 2, and coefficients for income and awareness have negative direction in outcomes 3 and 4. Page 10 of 20 Table 4.2 base category 1 Multinomial logistic regression Number of obs = 250 LR chi2(12) = 23.71 Prob > chi2 = 0.0222 Log likelihood = -304.50686 Pseudo R2 = 0.0375 env Coef. Std. Err. z P>|z| [95% Conf. Interval] 2 age .1560223 .0894742 1.74 0.081 -.0193439 .3313885 levelofedu~n .0471048 .3004365 0.16 0.875 -.54174 .6359495 awareness 20.47677 2.207063 9.28 0.000 16.151 24.80253 inc .0003072 .0012115 0.25 0.800 -.0020673 .0026818 _cons -25.18675 . . . . . 3 age .0454051 .0555393 0.82 0.414 -.0634501 .1542602 levelofedu~n .0209394 .210021 0.10 0.921 -.3906942 .4325731 awareness -.011212 .3603275 -0.03 0.975 -.7174409 .6950169 inc -.0004403 .0008119 -0.54 0.588 -.0020315 .0011509 _cons -.6533387 1.307279 -0.50 0.617 -3.215558 1.90888 4 age .0767679 .0715182 1.07 0.283 -.0634052 .216941 levelofedu~n .4052323 .2904159 1.40 0.163 -.1639724 .9744369 awareness -.4523788 .4598547 -0.98 0.325 -1.353677 .4489198 inc -.0003068 .0010825 -0.28 0.777 -.0024284 .0018148 _cons -3.273013 1.73302 -1.89 0.059 -6.669669 .1236434 (env==1 is the base outcome) Regardless of the signs of the coefficients, except for awareness coefficient in outcome 2, all other coefficients are statistically insignificant, meaning that the changes in the magnitudes of the regressors do not have any influence of the probability. Table 4.3 below provides the summary of the results for the multinomial logit output with base category of 2. In the table below, we can see gain that the overall model is statistically significant (p=0.0222) implying that it is a reliable model for policy implications. Page 11 of 20 Table 4.3 base category 2 Multinomial logistic regression Number of obs = 250 LR chi2(12) = 23.71 Prob > chi2 = 0.0222 Log likelihood = -304.50686 Pseudo R2 = 0.0375 env Coef. Std. Err. z P>|z| [95% Conf. Interval] 1 age -.1560223 .0894742 -1.74 0.081 -.3313885 .0193439 levelofedu~n -.0471048 .3004365 -0.16 0.875 -.6359495 .54174 awareness -20.47677 2.450439 -8.36 0.000 -25.27954 -15.674 inc -.0003072 .0012115 -0.25 0.800 -.0026818 .0020673 _cons 25.18675 1.73302 14.53 0.000 21.7901 28.58341 3 age -.1106172 .0870123 -1.27 0.204 -.2811582 .0599237 levelofedu~n -.0261653 .2908959 -0.09 0.928 -.5963109 .5439802 awareness -20.48798 2.450977 -8.36 0.000 -25.29181 -15.68415 inc -.0007475 .0011985 -0.62 0.533 -.0030965 .0016015 _cons 24.53341 1.671624 14.68 0.000 21.25709 27.80974 4 age -.0792544 .0985223 -0.80 0.421 -.2723545 .1138458 levelofedu~n .3581275 .3557628 1.01 0.314 -.3391548 1.05541 awareness -20.92915 2.47944 -8.44 0.000 -25.78876 -16.06953 inc -.000614 .0014059 -0.44 0.662 -.0033694 .0021414 _cons 21.91374 . . . . . (env==2 is the base outcome) . In the table 4.3 above, it is only coefficient for awareness that is statistically significant (p=0.0000) in all outcomes. It is also worth noting that all the coefficients have negative directions in all outcomes apart from level of education in outcome 4 which has positive direction. Table 4.4 below gives a summary of the results for multinomial logit with the base category of 4. The overall model is statistically significant (p=0.0222) and we can use the model results to make policy decisions. Table 4.4: base category 4 Multinomial logistic regression Number of obs = 250 LR chi2(12) = 23.71 Prob > chi2 = 0.0222 Log likelihood = -304.50686 Pseudo R2 = 0.0375 env Coef. Std. Err. z P>|z| [95% Conf. Interval] 1 age -.0767679 .0715182 -1.07 0.283 -.216941 .0634052 levelofedu~n -.4052323 .2904159 -1.40 0.163 -.9744369 .1639724 awareness .4523788 .4598547 0.98 0.325 -.4489198 1.353677 inc .0003068 .0010825 0.28 0.777 -.0018148 .0024284 _cons 3.273013 1.73302 1.89 0.059 -.1236433 6.669669 2 age .0792544 .0985223 0.80 0.421 -.1138458 .2723545 levelofedu~n -.3581275 .3557628 -1.01 0.314 -1.05541 .3391548 awareness 20.92915 2.47944 8.44 0.000 16.06953 25.78876 inc .000614 .0014059 0.44 0.662 -.0021414 .0033694 _cons -21.91374 . . . . . 3 age -.0313629 .067926 -0.46 0.644 -.1644954 .1017697 levelofedu~n -.3842928 .2806613 -1.37 0.171 -.9343789 .1657932 awareness .4411668 .4423297 1.00 0.319 -.4257835 1.308117 inc -.0001335 .0010565 -0.13 0.899 -.0022042 .0019372 _cons 2.619674 1.671624 1.57 0.117 -.656649 5.895998 (env==4 is the base outcome) . In the table 4.4 above, the coefficient of awareness under outcome 2 is the only coefficient which is statistically significant while all other coefficients are statistically insignificant. At the same Page 12 of 20 time, level of education coefficient under all outcomes has negative direction whereas age coefficient under outcomes 1 and 3 has negative direction and income coefficient is negative only in outcome 3 while the coefficient for awareness has positive direction in all outcomes. Now, since we have an understanding about the significance of the coefficients of the various factors and their direction of influence, it is important to interpret the coefficients corresponding to changes in the independent variables. Interpreting the coefficients for multinomial logit requires the marginal effects so that we may know by how much will the probability change when there is a unit change in the magnitude of the independent variable (except the dummy variable). Table 4.5 below provides the summary of the marginal effects when the base category is 1. In this table, it is only awareness marginal effect that is significant while all others are insignificant, the probability when the environmental attribute is preservation of elephants relative to water quality is computed to be 0.00101113(1v2). Table 4.5 marginal effects base category 1 Marginal effects after mlogit y = Pr(env==2) (predict, outcome(2)) = .00101113 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0001224 .00014 0.90 0.368 -.000144 .000389 22.76 levelo~n -.0000368 .00027 -0.14 0.891 -.000564 .000491 3.272 awaren~s* .1325244 .026 5.10 0.000 .081573 .183476 .756 inc 5.71e-07 .00000 0.51 0.613 -1.6e-06 2.8e-06 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 The interpretation for the marginal effect for awareness for instance, if the individual knows or as information about the park, the probability to opt for the preservation of elephants will increase by about 13.3%. In other words, if the individual has knowledge about the park, there is 13.3% chance that that individual will choose preservation of elephants relative to water quality in the lake in the park. Table 4.6 below gives a summary of the marginal effects of base category 1 but in relation to outcome 3. In the table, all the marginal effects for all the regressors are insignificant meaning that changes in the regressors do not have an influence on the probability of opting for a particular environmental attribute. However, we still need o give an interpretation. The probability of opting for introduction of white rhinos (envi=3) is computed as 0.45916495. Page 13 of 20 Table 4.6 1V3 Marginal effects after mlogit y = Pr(env==3) (predict, outcome(3)) = .45916495 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0047807 .01227 0.39 0.697 -.019259 .028821 22.76 levelo~n -.0287236 .04748 -0.61 0.545 -.121774 .064326 3.272 awaren~s* -.0240158 .07777 -0.31 0.757 -.176435 .128404 .756 inc -.0000838 .00018 -0.46 0.649 -.000445 .000277 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 If we take age for example, it means that if somebody‟s age increases from 23 years to 24 years, the probability of that individual opting for introduction of white rhinos relative to water quality will increase by about 0.04%. But the probabilities for level of education, income will go down. For awareness, If the person has knowledge about the park, the probability of opting for introduction of the white rhinos will decline by almost 2.4%. Table 4.7 below provides a summary of the marginal effects of the multinomial logit when the base category is one but with respect to outcome 4. The predicted probability that an individual will opt for building of a guest house relative to water quality in the park is p(env==4) =0.18220019. Again, all the marginal effects are insignificant. Table 4.7 1V4 Marginal effects after mlogit y = Pr(env==4) (predict, outcome(4)) = .18220019 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0076113 .00949 0.80 0.422 -.010985 .026208 22.76 levelo~n .0586205 .03884 1.51 0.131 -.017498 .134738 3.272 awaren~s* -.0934957 .06786 -1.38 0.168 -.226503 .039511 .756 inc -8.93e-06 .00015 -0.06 0.952 -.000298 .00028 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 Taking level of education as example, it means that if the education of an individual increases from secondary to tertiary, the probability that that individual will opt for building of a guest house relative to water quality will increase by about 5.86%. if the person has knowledge about the park, the probability of opting building the guest house will decline by 9% where as income increases the probability of opting for the building guest house will decline with negligible component. Let us see what can happen to the direction and significance of the marginal effects if we change the base from1 to 2. Page 14 of 20 Table 4.8 gives a summary of the marginal effects when the base category is 2 with respect to outcome 1 (water quality). The probability p (env==1) that an individual will opt for water quality relative to preservation of elephants is 0.35762372. All the marginal effects are insignificant. Table 4.8 1V2 Marginal effects after mlogit y = Pr(env==1) (predict, outcome(1)) = .35762372 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age -.0125144 .01195 -1.05 0.295 -.035938 .010909 22.76 levelo~n -.02986 .0455 -0.66 0.512 -.119039 .059319 3.272 awaren~s* -.0150128 .0734 -0.20 0.838 -.158866 .12884 .756 inc .0000922 .00017 0.53 0.597 -.00025 .000434 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 The marginal effects mean that for instance, if the income increases by a unit, the probability that an individual will opt for water quality relative to preservation of elephants will increase by 0.009%. For age, the probability will decrease by 1.25%, level of education will decrease the probability by about 3% and if an individual has knowledge about the park will decrease the probability by 1.5%. Table 4.9 gives a summary of the marginal effects when the base category is 2 with respect to outcome 3 (introduction of rhinos). The probability p (env==3) that an individual will opt for introduction of rhinos relative to preservation of elephants is 0.45916495. All the marginal effects are statistically insignificant. Table 4.9 2V3 Marginal effects after mlogit y = Pr(env==3) (predict, outcome(3)) = .45916495 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0047807 .01227 0.39 0.697 -.01926 .028821 22.76 levelo~n -.0287236 .04748 -0.61 0.545 -.121775 .064328 3.272 awaren~s* -.0240158 .07777 -0.31 0.757 -.176435 .128404 .756 inc -.0000838 .00018 -0.46 0.649 -.000445 .000277 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 These marginal effects mean that for example if age increases from 23 to 24 the probability that an individual will opt for introduction of rhinos relative to preservation of elephants will increase 0.47% and decrease by 0.008% if income increases by unit, decrease by 2.9% if education goes up to tertiary and decrease by 2.4% if an individual has knowledge about the park. Table 5.0 below provides a summary of the marginal effects when the base category is 2 with respect to outcome 4 (building guest house). The probability p (env==4) that an individual will Page 15 of 20 opt for building guest house relative to preservation of elephants is 0.18220019. All the marginal effects are statistically insignificant. Table 5.0 2V4 Marginal effects after mlogit y = Pr(env==4) (predict, outcome(4)) = .18220019 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0076113 .0095 0.80 0.423 -.011003 .026226 22.76 levelo~n .0586205 .03885 1.51 0.131 -.017517 .134758 3.272 awaren~s* -.0934957 .06786 -1.38 0.168 -.226503 .039511 .756 inc -8.93e-06 .00015 -0.06 0.952 -.000298 .00028 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 In the table 5.0 above, if age increases by a unit, the probability of opting building guest house relative to preservation of elephants will increase by 0.7%, level of education will decrease the probability by 5.9%, knowledge about the park will decrease the probability by 9% and increase in income will decrease the probability by a negligible component of 0.000893%. Table 5.1 below gives a summary of the marginal effects of multinomial logit when the base category is 3 (introduction of white rhinos) relative to option 4 (building guest house). The probability of an individual opting for building guest house relative to introduction of white rhinos [p (envi==4)] is. The marginal effects of all the regressors are statistically insignificant meaning that their marginal effect to the probability is equal to or close to zero. Table 5.1 3V4 Marginal effects after mlogit y = Pr(env==4) (predict, outcome(4)) = .18220019 variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X age .0076113 .00949 0.80 0.422 -.010985 .026208 22.76 levelo~n .0586205 .03884 1.51 0.131 -.017497 .134738 3.272 awaren~s* -.0934957 .06786 -1.38 0.168 -.226503 .039511 .756 inc -8.93e-06 .00015 -0.06 0.952 -.000298 .00028 431.96 (*) dy/dx is for discrete change of dummy variable from 0 to 1 If for example income increases by a unit from 432 to 433, the probability of an individual opting for building guest house relative to introduction of white rhinos will decrease by 0.00000893%, if an individual has knowledge about the park, the probability will decrease by 9.3%, education level will increase the probability by 5.86% and age will increase it by 0.078%. The table 5.2 below provides the summary of the relative odds ratio which gives the relative risk ratio of the change in the regressors. Page 16 of 20 Table 5.2 relative risk ratio Multinomial logistic regression Number of obs = 250 LR chi2(12) = 23.71 Prob > chi2 = 0.0222 Log likelihood = -304.50686 Pseudo R2 = 0.0375 env RRR Std. Err. z P>|z| [95% Conf. Interval] 1 age .9556103 .053074 -0.82 0.414 .857049 1.065506 levelofedu~n .9792783 .205669 -0.10 0.921 .6488374 1.478006 awareness 1.011275 .3643902 0.03 0.975 .499066 2.049183 inc 1.00044 .0008122 0.54 0.588 .9988497 1.002034 2 age 1.116967 .0971899 1.27 0.204 .9418364 1.324663 levelofedu~n 1.026511 .2986078 0.09 0.928 .5804334 1.815409 awareness 7.90e+08 1.71e+09 9.48 0.000 1.15e+07 5.46e+10 inc 1.000748 .0011994 0.62 0.533 .9983998 1.003101 4 age 1.03186 .0700901 0.46 0.644 .9032376 1.178798 levelofedu~n 1.468575 .4121723 1.37 0.171 .8472214 2.545632 awareness .6432854 .2845442 -1.00 0.319 .2703286 1.530789 inc 1.000134 .0010567 0.13 0.899 .9980646 1.002207 (env==3 is the base outcome) These relative risk ratios may change subject to the base category. As seen from the table 5.2, it is only awareness relative risk ratio in outcome 2 that is statistically significant and it actually means that the relative risk ratio of a unit change in awareness (if the person has knowledge about the park) relative to the introduction of the white rhinos is 790000000 units. In other words, the risk of outcome 2 (preservation of elephants) relative to base category 3 (introduction of rhinos) is 790000000. The interpretation of all other outcomes is the same. For example, if the income level in outcome 2 (preservation of elephants) changes by a unit, the risk ratio relative to introduction of rhinos is 1.000748, for outcome 4 risk ratio is 1.000134 and so forth. Page 17 of 20 5.0 CONCLUSIONS AND RECOMMENDATION After going through the nitty gritty of the multinomial logit model on the choice of the preference of the environmental attribute improvement, we can conclude that level of awareness of an individual has positive contribution to somebody‟s choice of an environmental attribute improvement. This is so not only In theoretical applications of the model but also in real life situations, if an individual has knowledge or information about something, the decisions that are made are well informed unlike somebody who is not informed of some attributes about the park. No wonder, it is only information/knowledge about the park that apparently influences the probability of opting for an environmental attribute improvement not others because, even if you are educated, well advanced in years and have a lot of income but you are not informed about a particular situation, your choice can not be rational. So, it makes a lot of sense to see that these other three regressors (income, education and age) are insignificant statistically even though to extent may help to make decisions. Due to this finding, this paper makes the following recommendations; Through awareness campaigns, trainings, outreaches inform the community members be about the park( what it offers, how to participate, charges) Ensure that all the categories of individuals poor, rich, educated or not be informed to belong to the park Page 18 of 20 6.0 REFERENCES Alvarez Gil, M. J., J. Burgos Jimenez, and J.J. Céspedes Lorente. (2001). An analysis of environmental management, organizational context and performance of Spanish hotels. Omega 29 (6), 457-471. Andersson, L. M. and T.S. Bateman. (2000). Individual Environmental Initiative, Championing Natural Environmental Issues in U.S. Business Organizations. Academy of Management Journal 43 (4), 548-570. Bansal, P. and K. Roth. (2000). 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