PCA factor loadings from the CCES 2016 survey.a
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
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Malignant entities, soft tissue sarcomas, are rare tumors that account for1% of all malignancies. These are predominantly tumors of adults, but 15% arise in children and adolescents. The wide biological diversity of soft tissue tumors, combined with their high incidence and potential morbidity and mortality represent challenges to contemporary researches, both at the level of basic and clinical science. Determining whether a soft tissue mass is benign or malignant is vital for appropriate management. \nThis book is the result of collaboration between several authors, experts in their fields; they succeeded in translating the complexity of soft tissue tumors and the diversity in the diagnosis and management of these tumors.",isbn:null,printIsbn:"978-953-307-862-5",pdfIsbn:"978-953-51-6730-3",doi:"10.5772/1319",price:119,priceEur:129,priceUsd:155,slug:"soft-tissue-tumors",numberOfPages:284,isOpenForSubmission:!1,hash:"375dc4018f82027f6258134d0b422082",bookSignature:"Fethi Derbel",publishedDate:"November 16th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/810.jpg",keywords:null,numberOfDownloads:34170,numberOfWosCitations:10,numberOfCrossrefCitations:9,numberOfDimensionsCitations:13,numberOfTotalCitations:32,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 20th 2011",dateEndSecondStepPublish:"February 17th 2011",dateEndThirdStepPublish:"June 24th 2011",dateEndFourthStepPublish:"July 24th 2011",dateEndFifthStepPublish:"November 21st 2011",remainingDaysToSecondStep:"10 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"4",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1088",title:"Physical Oncology",slug:"physical-oncology"}],chapters:[{id:"23546",title:"Considerations for Treatment Development in Rhabdomyosarcoma: In Vitro Assessment of Novel DNA Binding Drugs",slug:"considerations-for-treatment-development-in-rhabdomyosarcoma-in-vitro-assessment-of-novel-dna-bindin",totalDownloads:1644,totalCrossrefCites:0,authors:[{id:"63157",title:"Dr.",name:"Daniel",surname:"Catchpoole",slug:"daniel-catchpoole",fullName:"Daniel Catchpoole"},{id:"93018",title:"Dr.",name:"Steven",surname:"Wolf",slug:"steven-wolf",fullName:"Steven 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The 2016 US Presidential election stands out as an anomaly in election history. A candidate with no prior political experience used his advantage on social media and, in particular, on Twitter to reach the oval office. The election took over the media in extensive news coverage, TV ads, and social media trending. The awareness this election generated due to explicit and implicit advertising may have had a large impact on the outcome. Obama was the first candidate to utilize Twitter and other social media platforms in order to communicate directly with voters during the 2012 election (see Ref. [1]). Trump stormed the media and constantly trended on Facebook and Twitter throughout the campaign and used Twitter to speak freely about his platform (see Ref. [2]).
We examine whether voter awareness of the electoral campaign affects voting decisions. Following Schofield et al [3], we model voters’ utility functions as depending on their preferences on an economic and a social policy dimension and sociodemographic characteristics (age, race, gender, education, income, and home state). Voters’ utility is also influenced by their awareness of the campaign through TV news, radio, social media trending, their social media activity, the reported ideologies of themselves, and perception of candidates’ ideology. We also include in voters’ utilities other policy dimensions (stance on state spending on law enforcement, approval of the military, increasing the number of police officers, harsher punishments for previous offenders, and environmental policies) and a random shock. We derive the probability of voting for the candidates using voters’ utilities assuming voters vote for the candidate that maximizes their utility.
Using responses to the 2016 Cooperative Congressional Election Survey and voters’ utility functions, we estimate the probability that a voter with certain characteristics votes for Trump relative to Clinton. Our findings indicate that a voter who is more aware of media outlets (TV and radio) and that has a higher level of social media activity is, respectively, less and more likely to vote for Trump (relative to Clinton). We also find that voters’ awareness of the campaign affected their voting decisions, though this impact is less strong than the effect of voters’ sociodemographic characteristics. Advertising and awareness, in the form of active use of social media, influenced the election. Trump raised and spent significantly less than Clinton did an indication that campaign advertising is not just a matter of dollars but that voters’ awareness of the campaign also affects their voting decisions (see Ref. [4]).
Section 2 summarizes the findings in the literature on the effect that media has on US elections. Section 3 models the utility voters derived from each candidate, then using this utility we derive the probability that a voter votes for Trump relative to Clinton. Section 4 gives the descriptive statistics of our data with results presented in Section 5. Final comments are given in Section 6 with the Appendix containing tables that support the analysis carried out in Section 5.
We first review the literature on the effects of campaign advertising, the impact of Twitter on elections, and on modeling voters’ choices using their preferences.
Huber and Arceneaux [5] study whether advertising mobilizes, informs, or persuades citizens in non-battleground states in the 2000 Presidential election, as candidates’ advertising campaigns did not target these voters. Using the overlapping nature of media markets (TV) across states, they examine if campaign advertising aimed at swing states, also airing in non-battleground states, affects voting in non-battleground states. They argue that the volume and partisan balance of advertising in swing states is uncorrelated with voter behavior in non-battleground states. They find advertising campaigns did not mobilize or inform citizens but had a strong persuasive effect with moderately aware individuals being the most susceptible to advertising-induced changes in opinion.
Gordon and Hartmann [6] use the 2000 and 2004 US elections to analyze the effect of market-level advertising on county-level vote shares. They use gross ratings points (GRP) from the Campaign Media Analysis Group as their advertising variable measuring the number of exposures to ads per capita. After controlling for other factors, they find that an increase of 1000 GRPs increases the probability of voting for the Republican and Democratic candidates by 1.5 and 1.7%.
Hong [7] uses a sample of the 112th US House of Representatives’ activity on social media to study the impact of Twitter on the politicians’ campaign finances from June 8 to 22, 2011. He finds that politicians’ adoption of social media increases donations from outside their constituencies, that politicians with extreme ideologies benefit more from social media, and that social media tends to react to salient ideas more easily and is thus more likely to benefit political extremists. He finds that an increase in out-of-state donations allows candidates to become more ideologically extreme concluding that social media bridges the gap between politicians and citizens, which may lead to increased inequality and polarization of candidate platforms.
Schofield et al [3] builds stochastic models of the 2000 and 2004 US Presidential elections with valences1 that affect voter’s decisions. After placing voters in an economic and social policy space using factor analysis, they estimate a cleavage line, from a binomial logit model, dividing likely Democratic and Republican voters, and find voters’ valence judgments and policy preferences significantly influence candidates’ policy choices.
For the 2008 Presidential election, Clarke et al [8] models voters’ choices using a valence model (including stance on social, economic, and education issues), partisanship, and party leader images. They find that McCain had a positive image as voters viewed him as more experienced, patriotic, and trustworthy than Obama but that voters’ believed Obama would improve America’s standing. Despite the presence of racial resentment, meaning that those with it had a negative view of Obama and a positive view of McCain, they find that Obama inspired hope with a “yes we can” attitude, typical in valence politics, and attributed Obama’s higher valence to the belief that he could tackle the nation’s issues and get the job done.
The literature finds that campaign advertising has an impact and a persuasive effect on voter’s decisions. The large discrepancy between Clinton’s and Trump’s Twitter followers and number of tweets indicates that Trump had an advantage on social media as Trump more effectively used social media to connect directly with voters (see Ref. [9]). We study the effect that voters’ awareness of the campaign had on their choice of candidate after taking into account the effect of differences between voters’ and candidates’ economic and social policy preferences, voters’ sociodemographic characteristics, and their stance on other policy dimensions.
In this section, we first model voters’ electoral choices using the utility they derive from each candidate, then using the assumptions made on the shock affecting their utility derive the probability that the voter votes for Trump relative to Clinton.
We model the utility voter
We assume voters have preferences over the economic and social policies they would like candidates to implement if elected. Voter
Voters’ individual sociodemographic characteristics (age, education, gender, income, and race) affect their voting behavior independent of their policy positions, through
We allow voters’ awareness of the electoral campaign to affect their utility function to examine if their awareness of the campaign influences their choice of candidate. Aware measures the number of media-related things the voter did in the past 24 hours (watch TV news, listen to the radio, read the newspaper, read a blog) with higher values measuring higher engagement by the voter in these events. A higher socialmedia value indicates
Voter
Voters’ ideology and their perception of candidates’ ideology may affect their voting decisions. The self-reported ideology is rated on a scale of very conservative (7) to very liberal (1), whereas voters’ perception of candidate’s ideology is rated from very liberal (1) to very conservative (7). These variables capture voters’ beliefs of where they stand relative to their perception of candidates’ ideology. The ideology valence
We also added other policy variables that may affect the utility voters derive from candidates. We included voter’s opinions on increasing state spending on law enforcement and their approval of the military ensuring the supply of oil. We also incorporated voters’ opinions on increasing the number of police officers (crime a), their support for harsher prison sentences for individuals with prior offenses (crime b), and their stance on environmental policies. We grouped these variables in what we call the other policy valence,
As in Schofeld et al [3], we model
Voter
We assume that only Clinton and Trump run in the election and code the vote of
Voter
where
The marginal impact of an explanatory variable on the probability that
where
We now provide the descriptive statistics of the variables used in the analysis. Our data comes from the 2016 Cooperative Congressional Election Survey (CCES), a nationally representative sample of the voting age population, interviewing 64,600 pre- and post-election respondents. We exclude those not voting for Clinton or Trump from our sample. Since the post-election follow-up survey asked the same individuals “For whom did you vote for President of the United States?,” we know whom each individual voted for assuming truthful revelation.
The 2016 CCES survey includes a wide range of responses to related questions essentially conveying similar though different information on voters’ preferences. Given the high correlation among these questions, these variables should not be simultaneously included in the regressions to avoid multicollinearity effects that may render the regression coefficient estimates unstable and that lead to the interpretation of the effect of these variables on the probability of voting for Trump, relative to Clinton, difficult. Rather than including a large number of highly correlated variables, we use the principal component analysis (PCA) to reduce the number of correlated variables included in the regression. The PCA performs orthogonal transformations to convert correlated variables into a smaller set of linearly uncorrelated variables called principal components.5 The PCA gives the factor loading6 of each principal component variable and identifies a smaller set of latent dimensions along which voters make their decisions.
Schofield et al [3],7 we perform a PCA on 12 survey questions relating to voters’ stances on the military, welfare spending, condition of the economy, approval of Obama, gun control, immigration, abortion, gay marriage, budget cuts, personal ideology, tax increases, and racism. Table A.1 in the Appendix contains the questions used in the analysis and the coding of possible responses. We use the PCA factor loadings for each question and each voter’s response to each question to derive each voter’s preferences along the dimensions identified in the PCA.
The PCA revealed two latent dimensions, labeled as the social and economic dimensions. Table 1 shows the PCA factor loadings for each survey question. The first component has two heavy loadings, racism (consistent with Schofield et al [3]) and military, on the economic dimension. We anticipated that voters’ opinion on economic problems and spending would load strongly in the economic dimension, as found in the literature; however this was not the case in our sample. Perhaps the 2016 election was too different from previous elections. The loadings indicate which component is associated with our social dimension and are consistent with previous literature (such as abortion and gay marriage).
Question | Economic policy | Social policy |
---|---|---|
1. Military approval | −0.4058 | −0.1564 |
2. Tax increases | 0.0934 | −0.3249 |
3. Racism | 0.8535 | −0.0633 |
4. State welfare spending | 0.1545 | 0.3255 |
5. Economic problems | −0.0671 | −0.3456 |
6. Approve of Obama | 0.0635 | 0.4131 |
7. Gun control | −0.1236 | 0.2907 |
8. Immigration | −0.1593 | 0.2906 |
9. Abortion | 0.1295 | 0.3091 |
10. Gays | −0.0855 | 0.3027 |
11. Federal spending | −0.0317 | 0.3373 |
PCA factor loadings from the CCES 2016 survey.a
Bolded factor loadings are statistically significantly different from zero.
We multiplied the significantly different from zero factor loading of each variable, given in Table 1, with the corresponding response of the voter to that question, and then aggregated these products according to their identification in the economic or social dimensions to find voters’ locations along these two latent dimensions and assume these locations represent their preferences in these dimensions. Using the factor loadings in Table 1, voter
Right on the economic axis (horizontal) in Figure 1 represents an individual that approves of the military and is fearful of people of other races. We interpret north on the social axis (vertical) as liberal concerning, for example, civil rights issues. Figure 1 shows that, while the Clinton and Trump voters are clearly divided along the social dimension, there is no strong divide among them in the economic axis. Table 2 echoes Figure 1 indicating that, on average, Trump voters are more conservative (−1.757) in their social values and Clinton’s more liberal (1.753). The statistics of the two candidates along the economic dimension are relatively similar in mean, median, and standard deviations.
Voter policy positions in 2016 US Presidential election.
Variable | Mean | Median | Std. dev. | Min | Max |
---|---|---|---|---|---|
Clinton voters (15,264) | |||||
Social | 1.753 | 1.925 | 1.148 | −3.994 | 4.194 |
Economic | −0.024 | −0.218 | 0.986 | −1.972 | 3.909 |
Trump voters (12,314) | |||||
Social | −1.757 | −1.934 | 1.299 | −4.103 | 3.478 |
Economic | −0.032 | −0.150 | 0.991 | −2.039 | 3.806 |
Descriptive statistics: economic and social policy dimensions.
Schofield et al [3], candidates’ platforms,
Since voter’s decisions depend on more than their economic and social stances, we control for voters’ sociodemographic characteristics, reported ideologies, “other” policy variables, and our awareness valence. Table 3 shows that, while a larger proportion of women voted for Clinton, a larger proportion of men voted for Trump (see Figure 2a where 0 = male and 1 = female). Clinton had a higher proportion of nonwhite voters as 70% of her voters were white than Trump’s 88% (see Figure 2b where 0 = nonwhite and 1 = white) with a higher proportion of educated and young individuals voting for Clinton (Figures 2c and d).
Variable | Mean | Median | Std. dev. | Min | Max |
---|---|---|---|---|---|
Clinton voters (15,264 voters) | |||||
Birth year | 1965.9 | 1964 | 16.008 | 1923 | 1998 |
Female | 0.554 | 1 | 0.497 | 0 | 1 |
Education | 4.176 | 5 | 1.426 | 1 | 6 |
White | 0.699 | 1 | 0.459 | 0 | 1 |
Family income | 7.124 | 7 | 3.575 | 1 | 31 |
Trump voters (12,314 voters) | |||||
Birth year | 1960.7 | 1959 | 14.69 | 1923 | 1998 |
Female | 0.451 | 0 | 0.498 | 0 | 1 |
Education | 3.647 | 3 | 1.414 | 1 | 6 |
White | 0.880 | 1 | 0.325 | 0 | 1 |
Family income | 7.026 | 7 | 3.390 | 1 | 31 |
Descriptive statistics: sociodemographic characteristics.
(a) Female and male voters by candidate. (b) White and nonwhite voters by candidate. (c) Voter education by candidate. (d) Voter birth year by candidate.
In Table 4, the self-reported ideologies and voters’ perceived ideologies of each candidate show that Trump voters, on average, identify themselves as “conservative” and perceive Clinton as “very liberal” and Trump as “somewhat conservative.” Clinton voters, on average, identify themselves as “somewhat liberal” and perceive Clinton as “somewhat liberal” and Trump as “very conservative.” Clinton voters are more pro-environmental than Trump’s, and Trump (Clinton) voters prefer to increase (maintain) state spending on law enforcement and support (oppose) increasing the number of police officers.
Variables | Mean | Median | Std. dev. | Min | Max |
---|---|---|---|---|---|
Clinton voters (15,264 voters) | |||||
Aware | 2.126 | 2 | 0.932 | 1 | 4 |
Social media | 0.163 | 0 | 0.370 | 0 | 1 |
Attend | 1.654 | 1 | 1.674 | 0 | 5 |
Sign | 0.204 | 0 | 0.403 | 0 | 1 |
Work | 0.099 | 0 | 0.298 | 0 | 1 |
Donate | 0.346 | 0 | 0.476 | 0 | 1 |
News interest | 1.513 | 1 | 0.806 | 1 | 7 |
Ideology (self) | 5.141 | 5 | 1.425 | 1 | 7 |
Ideology (Clinton) | 3.210 | 3 | 1.443 | 1 | 8 |
Ideology (Trump) | 6.156 | 7 | 1.876 | 1 | 8 |
Military | 1.114 | 1 | 0.318 | 1 | 2 |
Environment | 1.984 | 2 | 0.124 | 1 | 2 |
Law enforcement | 2.559 | 3 | 0.944 | 1 | 5 |
Crime policy a | 1.564 | 2 | 0.496 | 1 | 2 |
Crime policy b | 1.249 | 1 | 0.432 | 1 | 2 |
Trump voters (12,314 voters) | |||||
Aware | 2.020 | 2 | 0.902 | 1 | 4 |
Social media | 0.132 | 0 | 0.338 | 0 | 1 |
Attend | 1.379 | 1 | 1.637 | 0 | 5 |
Sign | 0.209 | 0 | 0.407 | 0 | 1 |
Work | 0.051 | 0 | 0.220 | 0 | 1 |
Donate | 0.251 | 0 | 0.434 | 0 | 1 |
News interest | 1.460 | 1 | 0.762 | 1 | 7 |
Ideology (self) | 2.567 | 2 | 1.251 | 1 | 7 |
Ideology (Clinton) | 1.767 | 1 | 1.721 | 1 | 8 |
Ideology (Trump) | 4.947 | 5 | 1.434 | 1 | 8 |
Military | 1.295 | 1 | 0.456 | 1 | 2 |
Environment | 1.596 | 2 | 0.491 | 1 | 2 |
Law enforcement | 2.042 | 2 | 0.886 | 1 | 5 |
Crime policy a | 1.278 | 1 | 0.448 | 1 | 2 |
Crime policy b | 1.051 | 1 | 0.221 | 1 | 2 |
Descriptive statistics: all other variables.
It is well known that voters’ choice of candidate may depend on their state of residence (see also Refs. [10, 11]) and that candidates carry out their campaign mostly in swing states. To control for differences across states, we create Democratic, Republican, and Swing dummy variables for voters living in Democratic, Republican, and swing states, coded using Politico’s June 2016 list of swing states (see Table A.2 in the Appendix and [12]) called the state swingness variable.
We examine the effect that the various components in voters’ utility function in Eq. (1) have on the probability that voters choose a particular candidate in Eq. (3). We estimate a set of logit models sequentially adding groups of variables to show the effect these variables, as a group, have on the models’ decision criteria and later discuss their marginal effects on the probability of voting for Trump.
Table 5 shows the results for the basic policy model (column 1), then we add voters’ sociodemographic characteristics (column 2). The decision criteria (highest log likelihood ratio and the lowest AIC and BIC statistics) show that voters’ sociodemographic characteristics are an important determinant of their voting decisions. Adding the awareness variables (column 3) and the ideology variables (column 4) also improves the model’s specification. The full model (column 6), our preferred model specification, includes all of the previous variables plus the “other” policy variables and gives the best fit according to the decision criteria statistics. The sign and significance of the coefficients of all variables except the economic dimension, attend, and social media are significant and stable across model specifications. The economic dimension becomes insignificant in column (6) after introducing other policy variables, which improves the model fit as seen in the decision criteria statistics.
Variables | (1) | (2) | (3) | (4) | (5) | (6) Full |
---|---|---|---|---|---|---|
Social2 | 0.110*** (34.300) | 0.100*** (28.730) | 0.131*** (34.160) | 0.086*** (16.850) | 0.098*** (15.160) | 0.075*** (10.350) |
Economic2 | 0.043*** (4.920) | 0.080*** (8.470) | 0.089*** (9.130) | 0.049*** (4.490) | 0.037*** (2.790) | 0.021 (1.620) |
Military | 0.878*** (21.0) | 0.577*** (11.000) | ||||
Environment | −3.599*** (47.910) | −2.196*** (25.400) | ||||
Law enforcement | −0.375*** (19.330) | −0.243*** (9.880) | ||||
Crime policy a | −0.753*** (20.690) | −0.412*** (8.930) | ||||
Crime policy b | −1.204*** (20.770) | −0.649*** (9.030) | ||||
Birth year | −0.012*** (13.560) | −0.015*** (16.140) | −0.001 (0.780) | 0.000 (0.040) | 0.006*** (3.770) | |
Female | −0.332*** (12.290) | −0.366*** (13.110) | −0.322*** (9.570) | −0.222*** (5.480) | −0.235*** (5.500) | |
Education | −0.284*** (28.290) | −0.239*** (23.040) | −0.164*** (13.060) | −0.191*** (12.590) | −0.160*** (9.940) | |
White | 0.927*** (26.340) | 0.971*** (27.020) | 1.143*** (26.540) | 1.248*** (24.670) | 1.282*** (24.300) | |
State “swingness” | 0.485*** (14.460) | 0.485*** (14.190) | 0.377*** (9.180) | 0.122*** (2.420) | 0.121** (2.290) | |
Family income | 0.022*** (5.400) | 0.034*** (8.050 | 0.020*** (3.960) | 0.012* (1.900) | 0.009 (1.320) | |
Social2 | 0.110*** (34.300) | 0.100*** (28.730) | 0.131*** (34.160) | 0.086*** (16.850) | 0.098*** (15.160) | 0.075*** (10.350) |
Aware | −0.046*** (2.840) | −0.075*** (3.840) | −0.068*** (2.870) | −0.086*** (3.460) | ||
Attend | 0.083** (1.970) | 0.014 (0.270) | 0.058 (0.930) | 0.004 (0.050) | ||
Social media | −0.057*** (6.340) | −0.037*** (3.270) | −0.002 (0.180) | 0.004 (0.260) | ||
Signs | 0.251*** (6.680) | 0.255*** (5.510) | 0.294*** (5.100) | 0.279*** (4.560) | ||
Work | −0.582*** (9.850) | −0.392*** (5.260) | −0.256*** (2.850) | −0.175* (1.800) | ||
Donate | −0.756*** (21.810) | −0.648*** (14.960) | −0.471*** (8.920) | −0.460*** (8.030) | ||
News interest | 0.109*** (5.680) | 0.128*** (5.960) | 0.113*** (4.240) | 0.118*** (4.440) | ||
Ideology (self) | −1.083*** (70.830) | −0.930*** (59.000) | ||||
Ideology (Clinton) | −0.413*** (34.900) | −0.340*** (28.910) | ||||
Ideology (Trump) | −0.253*** (24.410) | −0.209*** (19.760) | ||||
−0.759*** (34.960) | 22.805*** (13.240) | 29.578*** (15.840) | 9.879*** (4.340) | 5.697** (2.060) | −1.120 (0.380) | |
Log likelihood | −18333.82 | −16487.57 | −16012.94 | −11753.72 | −8529.503 | −7795.902 |
AIC | 36673.64 | 32993.14 | 32057.88 | 23549.43 | 17097.01 | 15639.8 |
BIC | 36698.32 | 33066.89 | 32188.99 | 23721.42 | 17252.58 | 15836.21 |
Obs = N | 27,578 | 26,767 | 26,758 | 26,639 | 26,580 | 26,462 |
Logit model specifications (base = Clinton).
|z-score| in parentheses.***prob <0.001, **prob <0.05, *prob <0.1.
We take an alternative approach to the fixed effects used in the literature by incorporating the real swingness of each state as reported by Politico in June 2016 prior to the election. Tables 5 and 6 show that the full model with the state swingness variable gives a better fit to the data.
Model 1 | Model 2 | |||
---|---|---|---|---|
Variables | Coefficient | Z-score | Coefficient | Z-score |
Social2 | 0.076*** | 10.580 | 0.075*** | 10.350 |
Economic2 | 0.022 | 1.650 | 0.021 | 1.620 |
Military b | 0.581*** | 11.210 | 0.577*** | 11.000 |
Environment | −2.219*** | 25.820 | −2.196*** | 25.400 |
Law enforcement | −0.243*** | 10.000 | −0.243*** | 9.880 |
Crime policy a | −0.419*** | 9.200 | −0.412*** | 8.930 |
Crime policy b | −0.651*** | 9.150 | −0.649*** | 9.030 |
Birth year | 0.006*** | 3.860 | 0.006*** | 3.770 |
Female | −0.238*** | 5.630 | −0.235*** | 5.500 |
Education | −0.159*** | 10.020 | −0.160*** | 9.940 |
White | 1.317*** | 25.280 | 1.282*** | 24.300 |
State | −0.001 | 0.640 | 0.121** | 2.290 |
Family income | 0.005 | 0.850 | 0.009 | 1.320 |
Aware | −0.087*** | 3.570 | −0.086*** | 3.460 |
Attend | −0.007 | 0.110 | 0.004 | 0.050 |
Social media | 0.004 | 0.290 | 0.004 | 0.260 |
Signs | 0.264*** | 4.370 | 0.279*** | 4.560 |
Work | −0.201** | 2.090 | −0.175* | 1.800 |
Donate | −0.456*** | 8.080 | −0.460*** | 8.030 |
News interest | 0.127*** | 4.880 | 0.118*** | 4.440 |
Ideology (self) | −0.933*** | 60.040 | −0.930*** | 59.000 |
Ideology (Clinton) | −0.343*** | 29.600 | −0.340*** | 28.910 |
Ideology (Trump) | −0.212*** | 20.360 | −0.209*** | 19.760 |
−1.113 | 0.380 | −1.120 | 0.380 | |
State “swingness” | 0.121** | 2.290 | ||
Log likelihood | −7990.705 | −7795.902 | ||
AIC | 16029.41 | 15639.8 | ||
BIC | 16226.53 | 15836.21 | ||
Obs = N | 27,264 | 26,462 |
State “swingness” models (base = Clinton).
***prob. <0.001, **prob. <0.05, *prob. <0.1.
The logit coefficients given in Tables 5 and 6 do not measure the marginal effect that a variable has on the probability of voting for Trump. As shown in Eqs. (3)–(5), this probability varies in a nonlinear manner with changes in variable
Table 7 shows the marginal effects on the probability of voting for Trump of each variable and their significant levels holding all other variables at their mean (given in column 2). A white voter that has the mean characteristics in all the other variables is 31.6% (which is significantly different from zero) more likely to vote for Trump relative to Clinton. A mean voter who approves of using the military for securing the oil supply is 14.2% more likely to vote for Trump, who cares about the environment is 54.1% less likely to vote for Trump, and who views themselves as very liberal on the ideology scale is 22.9% less likely to vote for Trump. An increase in the level of education, from say high school to some college, decreases the probability of voting for Trump by almost 4%.
Variable | Meana | Marginal effect | Z-scoresb |
---|---|---|---|
Social2 | 4.532 | 0.019*** | 10.240 |
Economic2 | 0.974 | 0.005 | 1.620 |
Militarya | 1.193 | 0.142*** | 10.980 |
Environment | 1.811 | −0.541*** | 24.620 |
Law enforcement | 2.331 | −0.060*** | 9.880 |
Crime policy a | 1.437 | −0.101*** | 8.930 |
Crime policy b | 1.162 | −0.160*** | 9.060 |
Birth year | 1963.6 | 0.001*** | 3.760 |
Female | 0.508 | −0.058*** | 5.500 |
Education | 3.940 | −0.039*** | 9.930 |
White | 0.782 | 0.316*** | 24.310 |
State “swingness” | 0.447 | 0.030** | 2.290 |
Family income | 7.080 | 0.002 | 1.320 |
Aware | 2.078 | −0.021*** | 3.460 |
Attend | 0.149 | 0.001 | 0.050 |
Social media | 1.531 | 0.001 | 0.260 |
Signs | 0.207 | 0.069*** | 4.560 |
Work | 0.077 | −0.043* | 1.800 |
Donate | 0.304 | −0.113*** | 8.040 |
News interest | 1.489 | 0.029*** | 4.440 |
Ideology (self) | 3.998 | −0.229*** | 58.870 |
Ideology (Clinton) | 2.567 | −0.084*** | 28.780 |
Ideology (Trump) | 5.619 | −0.051*** | 19.810 |
Marginal effects (base = Clinton).
Marginal effects are calculated holding all other variables at the mean of all voters given in this column.
***prob. <0.001, **prob. <0.05, *prob. <0.1.
In this paper, we examine which factors influence the probability that an individual votes for Trump relative to Clinton in the 2016 US Presidential election. Our major contribution is the addition of variables that measure voters’ awareness of the electoral campaign after controlling for other factors that the literature finds significantly affect voters’ choice of US Presidential candidate. Others in the literature include the number of advertising per capita or the amounts spent on advertising. We opt for a different approach by looking at the effect of the media on voters’ choices by using data at the voter level. That is, our awareness variables measure voters’ direct interest in the news, their use of social media, and their interest in the electoral campaign. By measuring variables at the voter level, we capture the impact that voters’ media awareness had on their voting decisions. We also estimate voters’ position along the economic and social dimensions to study the influence the disutility voters derive from candidates adopting positions that differ from their ideal policies had on their voting decisions.
We estimate a set of binomial logit regressions to examine the probability that a voter with certain characteristics votes for Trump relative to Clinton. Our results indicate that the more active a voter with mean characteristics is on social media, the more likely she/he was to vote for Trump. We also find that the more aware the mean voter was of the media (TV, radio, reading newspaper) and the more interested she/he was in the news, the less likely she/he was to vote for Trump. Even though the mean voter’s awareness of the campaign impacted the mean voter’s decision, the stances on social and economic issues, perceived ideologies, and voter sociodemographic had a greater impact on her/his voting decision.
The 2016 US election was the first election in which a candidate adopted a Twitter platform to communicate directly with voters. Our results indicate that future candidates should capitalize on this low-cost approach to bridging the gap between themselves and voters. Hong [7] argues that social media allows the voter to self-select in the form of a “follow” or “friend” to reinforce their ideological positions. Trump sent almost four times more tweets than Clinton did, finding many supporters along the way (see Ref. [9]). Furthermore, social media tends to react to salient ideas more easily and faster and therefore is more likely to benefit political extremists (see Ref. [7]). Although voter awareness has a lower impact than say, race, in the 2016 election, it still influenced the mean voter’s choice of candidate. Future candidates can learn from the 2016 election as it may have changed the political campaign battleground forever.
We wish to acknowledge the Department of Economics at Wilfrid Laurier University who provided funding to Lauren Dique for this project.
Variable | Question | Coding |
---|---|---|
Economic problems | Over the past year the nation’s economy has…? |
|
Approve of Obama | Do you approve of the way each is doing their job? |
|
Gun control | On the issue of gun regulation, do you support or oppose each of the following proposals? Make it easier for people to obtain concealed-carry permit |
|
Immigration | What do you think the US government should do about immigration? Select all that apply. Grant legal status to all illegal immigrants who have held jobs and paid taxes for at least 3 years and not been convicted of any felony crimes |
|
Abortion | Do you support or oppose each of the following proposals? Always allow a woman to obtain an abortion as a matter of choice |
|
Economic problems | Over the past year the nation’s economy has…? |
|
Gun control | On the issue of gun regulation, do you support or oppose each of the following proposals? Make it easier for people to obtain concealed-carry permit |
|
Gay marriage | Do you favor or oppose allowing gays and lesbians to marry legally? |
|
Federal spending | The federal budget deficit is approximately 1 trillion dollar this year. If the Congress were to balance the budget, it would have to consider cutting defense spending, cutting domestic spending (such as Medicare and Social Security), or raising taxes to cover the deficit. Please rank the options below from what would you most prefer that the Congress do to what you would least prefer they do: (1) cut defense spending, (2) cut domestic spending, and (3) raise taxes |
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Ideology (self) | How would you rate each of the following individuals and groups? Yourself |
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Military a | Would you approve of the use of US military troops in ensuring the supply of oil? |
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Military b | Would you approve of the use of US military troops in order to destroy a terrorist camp? |
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State spending on welfare | State legislatures must make choices when making spending decisions on important state programs. Would you like your legislature to increase or decrease spending on the five areas below? Welfare |
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Tax increases | If your state were to have a budget deficit this year, it would have to raise taxes on income and sales or cut spending, such as on education, health care, welfare, and road construction. What would you prefer more, raising taxes or cutting spending? Choose a point along the scale from 100% tax increases (and no spending cuts) to 100% spending cuts (and no tax increases). The point in the middle means that the budget should be balanced with equal amounts of spending cuts and tax increases | The values range between 0 and 100. The closer to 0 the more they prefer increases in taxes The closer to 100 the more they prefer spending cuts |
Racism | Question: I often find myself fearful of people of other races |
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Aware | In the past 24 hours have you (check all that apply) (1) read a blog, (2) watched TV news, (3) read a newspaper in print or online, (4) listened to a radio news program or talk radio, or (5) used social media (such as Facebook or YouTube)? | This variable is taken as an aggregate. The higher the coded number the greater their awareness |
Social media | Did you do any of the following on social media (such as Facebook, YouTube, or Twitter)? Posted a story, photo, video, or link about politics; Posted a comment about politics; Read a story or watched a video about politics; Followed a political event; Forwarded a story, photo, video, or link about politics to friends | This variable is taken as an aggregate. The higher the social media variable the more active the voter was on social media |
Attend | During the past year, did you attend local political meetings (such as school board or city council)? |
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Signs | During the past year, did you put up a political sign (such as a lawn sign or bumper sticker)? |
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Work | During the past year, did you work for a candidate or campaign? |
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Donate | During the past year, did you donate money to a candidate, campaign, or political organization? |
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Ideology (Clinton) | Question: How would you rate each of the following individuals and groups? Hillary Clinton |
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Ideology (Trump) | Question: How would you rate each of the following individuals and groups? Donald Trump |
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State spending on law enforcement | State legislatures must make choices when making spending decisions on important state programs. Would you like your legislature to increase or decrease spending on the five areas below? Law enforcement |
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Crime policy a | Do you support or oppose each of the following proposals? Increase the number of police on the street by 10 percent, even if it means fewer funds for other public services |
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Crime policy b | Do you support or oppose each of the following proposals? Increase prison sentences for felons who have already committed two or more serious or violent crimes |
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Interest in the news | Some people seem to follow what’s going on in government and public affairs most of the time, whether there’s an election going on or not. Others are not that interested. Would you say you follow what’s going on in government and public affairs? |
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Environmental policy | Do you support or oppose each of the following proposals? Give Environmental Protection Agency power to regulate carbon dioxide emissions; Raise required fuel efficiency for the average automobile from 25 mpg to 35 mpg; Require a minimum amount of renewable fuels (wind, solar, and hydroelectric) in the generation of electricity even if electricity prices increase somewhat CC16_333d; Strengthen enforcement of the Clean Air Act and Clean Water Act even if it costs US jobs | This variable is taken as an aggregate, if individual said support to one or more of the environmental policies they are coded as a higher value (2) |
Variable description and coding.
Republican | Democratic | Swing | ||
---|---|---|---|---|
Alabama | Nebraska | California | New York | Colorado |
Alaska | North Dakota | Connecticut | Oregon | Florida |
Arizona | Oklahoma | Delaware | Rhode Island | Michigan |
Arkansas | South Carolina | District | Vermont | Iowa |
Indiana | South Dakota | Hawaii | Washington | Nevada |
Idaho | Tennessee | Illinois | New Hampshire | |
Kansas | Texas | Maine | Pennsylvania | |
Kentucky | Utah | Maryland | North Carolina | |
Louisiana | West Virginia | Massachusetts | Ohio | |
Missouri | Wyoming | Minnesota | Virginia | |
Montana | New Jersey | Wisconsin |
We wish to thank Prof. Ryan Merlin Yonk and members of the Department of Economics at Wilfrid Laurier University for their useful comments on how to improve this paper.
In recent years, the value that society places on reducing greenhouse gas (GHG) emissions and better stewardship of soil, water and wildlife is leading to consumer demand for climate-friendly production methods and supply chain. In addition there has become a greater level of the world’s appreciation of the impact of climate change and environmental sustainability. There is also society’s general consciousness of personal and public health in relation to products that they use. And then there is an increase awareness of the Sustainable Development Goals (SDGs) especially the SDG12 – sustainable consumption and production. Day in and out people are becoming more concern of environmental issues, consumers thus ask a lot of questions these days about the authenticity of products that they buy. And we question how green producers manufacturing processes are, how big their carbon footprints are and how they recycle [1]. Various supply activities can have a significant threat to the environment. These threats vary but in terms of carbon monoxide emissions, discarded packaging materials, scrapped toxic materials, traffic congestion and other forms of industrial pollution [2, 3].
\nThis means that we are heading towards a near future where, people are becoming so conscious of their health and the ecology that, if your production or ecological service does not have anything to do with protection of the earth’s resource, along its supply chain, you will not survive on both international and local markets. Undoubtedly, across all sectors of the economy, we see that companies are being influence to reformulate their environmental and supply chain models due to increases in environmental problems caused by products. Therefore, the requirement to extend companies’ environmental activities to include the whole supply chain has been instrumental in the concept of green supply chain management (GSCM) [4]. It is therefore, reasonable to say that the requirement to extend companies’ environmental activities to include the whole supply chain has been driving force of the development of the GSCM concept.
\nGreen supply chain management is basically the integration of environmental thinking into various supply chain management (SCM) models. In the broader sense, GSCM can be considered an environmental innovation [2]. This is because, it’s ultimate goal is to eliminate wastages including hazardous chemical, emissions, energy and solid waste along supply chain such as product design, material resourcing and selection, manufacturing process, delivery of final product and end-of-life management of the product [5, 6]. Also, because GSCM has become a multidisciplinary concept by constructing environmental management practices in the context of various supply chains [7], whether they directly provided environmental services or not. And finally, because GSCM plays a vital role in influencing the total environment impact of any firm involved in supply chain activities and thus contributing to sustainability performance enhancement [2]. Therefore, with the environmental issues becoming an integral part of the supply chain management concept, we see the context being extended so that each step from organization’s materials management and transportation functions to the end customer can be structured to include environmental awareness [8].
\nIt is against this backdrop that green supply chain becomes very important when designing environmental impact initiatives especially within environment organization such as waste management companies, or any social enterprise that works at the national, regional or community level to impact the triple bottom line from a green economy context. And this disposition spurs the authors of this chapter to do a review of literature in relation to the theme while reflecting on their work on waste, at their organizational level, to establish or reestablish the gains in waste value chains to inform policy makers to rethink the current waste management practices to accelerate the socioeconomic development of the country.
\nThis chapter therefore, analyses the waste management services as key green initiatives in Ghana; siting the case of how EcoCare Waste Initiative an environmental startup in Goaso, Ghana, achieves green supply chain in its attempt to offer community waste collection service, the lessons that could be drawn.
\nIt is noted that poor water, sanitation and hygiene, and indoor air pollution from burning wood for cooking and heating were as well as unconventional burning of wastes have been the cause of pneumonia and other common environmental related disease over and over again. In order to prevent these diseases, the environment needed to change [9]. The World Health Organization (WHO) estimates that, 24% of all global deaths – roughly 13.7 million deaths a year – are linked to environment risks such as air, water and soil pollution, chemical exposures, climate change and ultraviolet radiation. Beyond pneumonia and diarrheal diseases, these risks may contribute to more than 100 diseases and injuries [9, 10]. In sub-Saharan African region the 2.5 million deaths a year are attributable to environmental risks. Generally, the fraction of the global burden of disease due to the environment is 22% if we are accounting for both death and disability. And in children under 5 years especially, up to 26% of all deaths could be prevented, if environmental risks were removed [10]. These facts, show us the importance of ensuring that whatever product or service that impacts the environment is sustainable to give us healthy environments which will help prevent the greatest percentage if not all environmental related diseases.
\n\nFigure 1 below, adopted from [10] gives a nice patristic illustration of (i) the ratio of deaths attributable to environment to deaths by other facts, and (ii) ration of disability-adjusted life year (DALY) attributable to environment to DALY non-attributable to environment; globally in 2012.
\nImagery illustration of the global deaths attributed to environmental factors (adopted from [10]).
In the developing world, the private sector and various actors have been keenly involved in solid waste management over the past decades, yet there are still problems with solid waste delivery services [11]. The problems with solid waste in most urban cities of some countries have really become burdensome despite efforts being made by city authorities and governments. The problems of solid waste common in developing countries include the following but not limited to:
inadequate service coverage,
irregular waste collection,
waste spill over from bins and storage containers, and
lax attitude of people towards indiscriminate disposal at unauthorized places
waste littering on streets and in gutters/drains
indiscriminate burning of waste and
pressure on dump sites (locally created landfills).
These solid waste problems are what consequently lead to environmental health problems, such as discussed in the previous section above. And not only do they impact environmental health, they also lead to esthetic nuisance, and environmental pollution in general [12, 13].
\nOne major challenge in handling solid waste problems is that these problems impact on each other and on the environment in a circular manner – which makes it difficult to only tackle only a single aspect of the solid waste value chain. For example, in Ghana, due to inadequate service coverage in many cities, rural settlements and slums, insufficient service coverage; the public dump uncollected solid waste into drains, rivers and surrounding areas, or it is locally burnt or buried. These practices lead to considerable environmental pollution and degradation, and pose serious health risk to the population. These problems impair, in the long run, not only the quality of life of the urban poorer communities but also affect the welfare of the entire urban population. These complexities are what make cities still faced with urban environmental health issues related to solid waste management [14].
\nOn the other hand the main causes of these waste problems in developing parts are not feasible to tackle in isolation because they are enormous, and complex interconnected social, environmental and economic factors. First, rapid urbanization poses a big problem to urban solid waste management. The rapid and unregulated urban growth and development in urban areas lead to a situation where solid waste services infrastructure lag behind the growth in population, or simply overpowers government strengths to fund municipal solid waste management.
\nSecond, low cost of solid waste recovery as well as limited funds from central government is another constraint to solving the challenges in the waste management value chaing in developing economies. Local authorities have difficulties in keeping pace with solid waste facilities development and in meeting the growing demand for solid waste services due to financial constraints.
\nThird, there is very low political will and priority given to solid waste service, Meanwhile most governments acknowledge immediacy and seriousness of solid waste problems to their countries. The lack of priority, political will, and public sector commitment limit rapid and sustainable improvements in the solid waste sector in developing countries like Ghana.
\nFourth, there is also the factor of the enforcement solid waste management. This contributes to the lax attitude of the people towards indiscriminate disposal at unauthorized places, waste littering, and burning in open space. The enforcement of regulation by government officials appears to be weak and this may be due to lack of capacity, lack of resources and political will, and problems with the institutional set-up.
\nFinally, we have also noticed that there are problems with physical and human capacity for solid waste collection and technology operations in some countries. The human capacity of the public and private sector organizations may not be adequate and most of the few waste collection businesses that step in to contribute are usually not designed to be green, scalable and sustainable enough [14].
\nThe root causes of solid waste problems have causal relationships with the various environmental and socio-economic factors that are already impacting the lives of populations living in these cities that are the most affected by the solid waste management challenge. That is why for our solid waste collection services to be sustainable enough, it must integrate social, environmental and economic impact models. If a waste collection service provider is able to consider the impact of their services on the triple bottom line – that is addressing social, environmental and economic variables – then we can say that the service is sustainable. Figure 2 illustrates the intersessions of the social, environmental and economic variables that affect populations which when address makes a service sustainable.
\nThe interconnection of the elements of the triple bottom line concept (adopted from [15]).
First of all, the service must have measurable impacts on the social variables faced by the affected people and communities – that is to cause social change and improvement. This means for a waste collection services to be green it must address some social variables that deals with the community, education (e.g. increased awareness about effects of waste etc.), equity (e.g. reduction in child labor, increase in girl child education etc.), health, well-being and quality of life of the people.
\nSecondly, a green service must bring economic benefits to the populations involved. This means, not only must the service make some profits for its sustainable cash flow (to remain in business), the service provider must also factor the economic dynamics of the users when pricing its services, and the activities in the service must create jobs and employment opportunities. Thus they must offer a service that is available and affordable to all classes of the population.
\nFinally a green waste collection service must integrate protection of the environment at the core of serving the people and the economy. It must have considerations for saving natural resources, water, air quality, energy conservation and land use across all of its supply chains. If all aspects of a solid waste collection service – from procurement of logistics, collection of wastes and transport to disposal, are able to respect protection of the environments, reduce pollution and carbon emission, then we can say that such a waste collection service is green – it respects green supply chain management (GSCM).
\nWithin the waste management industry a generalized broader way of defining waste would be: wastes are materials that are not prime products for which the generator has no further use in terms of his or her own purposes of production, transformation, or consumption and of which he or she wants to dispose [16]. And from national to local contexts we identify that:
the extraction of raw materials,
processing of raw materials into intermediate and final products,
the consumption of final products, and
other human activities are ways through which waste may be generated [17].
In Ghana particularly it is estimated that an average city generates about four thousand (4000) tons waste daily. To this end, the rate of waste generation in Ghana stands at 0.47 kg/person/day, which translates into about 12,710 tons of waste per day averagely [5].
\nThe conventional methods of dealing with these waste in Ghana have included throwing into open dumps, wetlands, landfills, and uncontrolled dumps or in some cases are incinerated in the open air. Due to our current conventional approaches to dealing with waste, it is difficult to get a significant quantity of waste generated to go into recycling, composting or reuse to tap the potential end use value of solid wastes. However, if waste is properly managed – from generation to disposal – there are enormous untapped potentials that could be fully exploited. And this also have a consequential potential of improving greatly the quality of life for Ghanaians through energy generation, employment generation, income acquisition, and resource for goods production among others.
\nTherefore, the municipal solid waste (MSW) collection model ran by the government and city authorities where waste is collected and simply dumped at landfills by only one government agent or contractor, could be remodeled to make it more green and sustainable. We can decentralize the entire municipal waste management value chain – using a zonation approach. Government in an effort to rid the respective metropolis of the mountain of filths, these metropolises contract private waste management companies in a joint venture termed public-private partnerships (PPP) which over the years have witnessed a considerable increase particularly as witnessed in the Accra Metropolitan Assembly, to assist in the collection and disposal of MSW at landfill sites [16, 17]. This PPP for waste management could be replicated at local levels in peri-urban centers where we are witnessing an exponential growth of startups and youth organizations committed to social entrepreneurship [18, 19, 20].
\nWhen the waste management value chain is decentralized, what it also means is that we will have many actors – even SMEs and startups – on the various points of the waste value chain. Some will specialize on the collection to ensure that communities achieve 100 percentage recovery of waste from homes and the public. Which will also make waste collection service relatively affordable for every household to subscribe. More people subscribed to waste collection means little or no more indiscriminate disposal of waste at unauthorized places like drains, bushes and water bodies; and no burying or burning of wastes by households. Secondly, at local (zonal) levels, waste management companies could partner, together with government initiative, and build one major community owned state-of-the-art landfill system or incinerator. This will ensure that waste disposal in each community is not scattered to claim more arable lands that could be used for farming. Finally, while some waste service providers focus on collection and disposal, some others could only invest in treatment and recycling. The waste collected by company A from the same city could be sold to company B as raw material to be converted into various end-user resources like upcycling plastic wastes into bricks, tiles and ceramics or processing to produce energy like combustion chambers, gasification systems, liquefaction etc. [16, 21, 22].
\nHere, the authors propose a conceptual framework for turning wastes back into production chains in a circular economy manner – to make national and local wastes supply chains green. From the triple bottom line modeling, we considered EcoCare activities that result in economic gains, those that impact the environment and those that lead to social change. For a given community as an entity if a waste initiative is able to link all of its solutions to economic and environment holistically, that service will indirectly take care of its social impact variables (Figure 3).
\nConceptual framework for green waste supply chains (from EcoCare pitch deck [23]).
Solid Waste Management (SWM) could be defined as managing the processes involved in solid waste collection, treatment and disposal of waste generated in households, commercial and business establishments, institutions, and non-hazardous industrial process wastes. Solid waste collection has evolved over the years from collection of unsegregated waste and disposal on dumping grounds to collection of source separated waste streams through formal and informal service providers. The management of the processes involved which was traditionally public has now become public-private-community provision and partnerships arrangements [24].
\nThe direct activities in solid waste management could be grouped into six functional elements basically:
waste generation and characterization,
on-site storage and handling,
collection,
transfer and transport,
separation processing, treatment and resource recovery, and
final disposal.
These functional elements require planning and management in order to achieve high quality of service. Unfortunately, however, the waste management value chain has not contributed to greening cities because most cities’ agencies responsible for SWM often pay too little attention to integrated management approaches based on adequate information systems, management approaches, methods, and techniques. The activities of service provision may be concentrated in one organization or fragmented over multiple organizations.
\nAs mentioned in Section 5 above, if solid waste management in cities is decentralized whereby various actors of waste management initiatives (private and public) were contracted to work on different activities of the waste value chains, it would be easy for cities to achieve green waste management supply chains. It is noted by [11] that these activities of the waste supply chain could best be regrouped into four fragmented stages, and it is best if different actors worked on different fragments in the process of making solid waste management green. A diagrammatic representation of the four fragmented process of waste management is shown by Figure 4 below.
\nElements and actors in the solid waste management supply chain (adopted from [11]).
From Figure 4, we discover that at least 4 groups of elements and 4 groups of actors run through 4 stages of the waste management supply chain. Again we can see that a waste management supply chain can take a linear pathway (as is being done in most cities) and a circular pathway (which has only been promoted recently to be the most eco-friendly and green approach).
\nThe linear waste management supply chain will take a path in which waste moves straight away from stage 1 (primary storage points) through stage 2 (collection and transport) to stage 4 (final disposal). In some typical rural communities in Ghana, the linear model could even involve just stage 1 (waste generators store long piles of waste in their houses for a long time) and then move it straight away to stage 4 (a local community dump site, call “borla so” in local dialect). This means that in the linear model, most actors may be left out. In the Stage 1–4 linear approach for example, you notice that households, institutions etc. who generate their waste at stage 1, store the waste, and then later send it to dump site by themselves – cutting off public and private waste collectors in stage 2, and even recyclers in stage 3.
\nOn the other hand, the circular waste management supply chain is one that progress from Stage 1 through 2 and 3 to 4, then back to 3. What happens here is that, after the waste reaches the final stage they are not disposed completely into permanent landfills, but all or some percentage of the waste is recovered back for reuse, recycling and other purposes of getting the waste material back into a production cycle again. These waste recovered could be repurpose into other end user goods and products through various means of treatment and processing – such as organic compositing, plastic recycling and upcycling, plastic extrusion, reuse etc. With this circular model of solid waste management, all the actors across the 4 stages on the waste supply chain get some work to do, and contribute to making the city’s waste management green and sustainable.
\n\nTable 1 gives an idea of how different types of solid waste could be repurposed in the circular economy waste management model.
\nTypes of solid waste | \nTypical sources | \nPotential products | \nMethods (processes) | \n
---|---|---|---|
Agro waste (organic) | \nRice, wheat, straw and husk, cotton stalk, saw mill waste, vegetable residues, nut, shells etc. | \nOrganic soils and fertilizer, particle boards, insulation boards, wall panels, printing paper, roofing sheet, fuel, bricks, acid proof, polymer composite, cement board | \nCompositing, recycling, combustion, incineration, gasification | \n
Industrial waste (inorganic) | \nCoal combustion residues, steel slag, bauxite red mud, construction debris | \nCement, bricks, blocks, tiles, paint, concrete, ceramic products, wood substitute product | \nCombustion, liquefaction | \n
Mining waste (mineral) | \nCoal washer waste, mining waste, tailing from iron, copper, zinc, gold and aluminum industries | \nBricks, tiles, lightweight aggregate, fuel etc. | \nCombustion, liquefaction, biochemical esterification | \n
Non-hazardous and other process waste | \nWaste gypsum, lime sludge, drains sludge, limestone waste, marble processing residues, broken glass and ceramics, kiln/oven dust (waste) | \nCement clinker, super sulphate cement, hydraulic binder, blocks, cement, fibrous gypsum boards, gypsum plaster | \nLiquefaction, purification decontamination, recycling | \n
Hazardous waste | \nMetallurgical residues, galvanizing waste, tannery waste | \nCement, bricks, ceramic tiles and boards | \nRecycling, upcycling | \n
Plastic waste* (various class of recyclable plastics) | \nPackaging, used plastic containers, oceans, | \nReinforced fiber plastic, pellets, plastic beams, plastic artifacts, plastic jerseys, cement, plastic tiles and blocks, fuel, etc. | \nRecycling, upcycling, reuse, biochemical extraction | \n
Types of waste and their repurpose potential in a circular waste supply chain.
We could not separate recycle and non-recyclable plastics as it is done commonly, so take these to be only plastics that could directly be recycled/or repurposed easily.
[Source: authors’ construct based on synthesis of various prototypes and recycled products in literature].
According to 11 we can identify four modes of solid waste collection service1 depending on the income levels of the people, housing types and the level of service required. They include:
\nCommunal\n2\ncollection mode is rendered in low income areas. The householders discharge their waste into communal storage containers at transfer stations or designated locations and collection vehicles pick up the containers full of waste at frequent intervals.\n
\nBlock collection mode is used where there are large apartment residential buildings. For this service, collection vehicle travels along a predetermined route at specific intervals (every 2–3 days) and stops at selected locations. The householders bring their waste bins upon hearing a bell sound and hand them to the crew who empties the bins and gives them back to the householders [\n\n11\n\n].\n
\nKerbside collection mode is rendered in middle and high income areas. The collection crew collects bins and bags of waste which are deposited at the kerbside on fixed days (e.g. 2 specific days in a week) when collection takes place. The householders leave their bins at the kerbside and collect them later in the day [\n\n11\n\n].\n
\nDoor-to-door or house-to-house\n3\ncollection mode is rendered in middle and high income areas. The collection crew enters each premise, takes out the bin and sends it back after emptying the waste into collection vehicles. No bins are left out-side household premises.
The residents served by the kerbside and house-to-house collection use standard bins to store waste. And this is what is practiced in the EcoCare H2H Waste Collection model.
\nA brief capture of the story of the EcoCare team and initiative could help paint a picture of how an environmental company could be born out of a passion to protect lives by cleaning and greening our environments. We capture this from an unpublished executive document of the EcoCare Waste Initiative [25]:
\nJackson Nyarko the visionary of EcoCare had a friend who was part of the victims that lost their lives to the June 3, 2015 flood disaster in Accra, Ghana. One of the major cause of this disaster was later linked to a major drain that was choked by filth (plastic wastes especially) due to indiscriminate waste disposal. A year later, 2016, Jackson had the opportunity to participate in an Eco-enterprises development incubator. He was touched by his lost due to the indiscriminate problem to build a concept around Waste Management. In search of his first team members, Jackson first brought on board his Friend, Frank Yeboah, who also related to the loss of lives in that June 3rd disaster. So the 2 united their passion, loss and expertise to design EcoCare Waste Initiative. With the mission to challenge Ghanaians wasteful paradigm and throwaway culture. Then from there, their need to fill up the expertise gap brought in Felix Ankamah Yeboah (an environmental engineer), then later Isaac Sarfo Larbi, Maxwell Osei-Bonsu and the late Acheamponmaa Duffour.
\nEcoCare Waste Initiative was set up with the sustainable development goal (SDG) 12 in mind first, then later partnerships (SDG 17) with other actors to integrate other goals – SDG 6 and 13 started running through the model. Figure 5 below shows a non-updated business model of EcoCare developed by the co-founders which is used in modeling all the services and programs initiated by EcoCare since 2017.
\nBusiness model of EcoCare waste initiative (from EcoCare pitch deck for TEDxAccra, 2017).
Since the conception in 2016, the EcoCare team has been challenged by the daunting vision “to become a world class environmental company solving global sustainability and ecological problems and inspiring African excellence.” And driven by the mission “develop innovative technologies for managing wastes and protecting environments for people and communities,” EcoCare has moved through 3 growth stages:
\nThrough 2017–2018: EcoCare first focused on social campaigns against plastics pollution and indiscriminate waste disposal – mainly on social media (virtual) and talk shows in and around Goaso. At this point it was quite difficult to identify on which of the four stages (illustrated in Figure 4) that the EcoCare was acting. But it served as a foundation process.
\nThrough 2019: EcoCare added EcoEvents Cleanup to its business model – where we did waste Collection and segregation at source (people’s events like wedding receptions and funerals) - for revenue generation. Now it becomes clear to identify EcoCare as an actor at the Stage 2 and 4 – in a linear model of waste management.
In May 2020, we rolled out the House to House Waste Collection Service (called H2H Waste Collection) in 4 “elite communities” in Goaso. This business model though currently on a small scale, has elements of primary storage (with segregation at source), collection, and recovery of recyclables before disposal at authorized landfill sites. Now you can see EcoCare acting across the 4 major stages of green waste supply green chain – in a circular mode. The next subsection (7.3) describes some details of how the H2H Waste Collection makes EcoCare contribute to greening the waste management supply chain of Goaso.
The EcoCare House to House Waste Collection was designed to achieve six (6) goals, but which will cut across the social, economic and environment triple bottom line and have a tremendous impact in setting a new pace for green waste management supply chain models in Goaso. The six goals are:
support municipal waste collection to reach all neighborhoods in Goaso – Social\n
help every household to adopt the behavior of segregating their wastes – Social\n
ease families of monthly sanitation bills by cheap and easy payment plans – Economic\n
create direct and indirect jobs for waste supply chain in Goaso – Economic\n
ensure prompt collection and odor free neighborhoods – Environment\n
make Goaso the cleanest district capital in Ghana – Environment\n
So moving from the conventional linear approach to solid waste management, the EcoCare H2H Waste Collection has introduced a circular model by:
supplying waste bins for storage of waste in households,
supplying recycling bags for segregation of recyclable wastes at the homes (source),
prompt collection of wastes on weekly basis,
recovering and storing recyclables waste (e.g. plastics, glass, paper etc.) at our warehouse,
transporting disposable waste to authorized community landfills and
Distributing recovered recyclable wastes to partner recycling companies.
Without capacity to recycle, EcoCare is currently creating a distribution model for connecting with plastic recycling companies within Ahafo who will receive our recovered wastes as raw materials for the recycling plants. This will ensure that even while we do not have the capacity to complete our circular model, by partnership with other actors in the sector we could still make a complete green waste supply chain.
\nMost cities in Ghana continue to spend on solid waste management whereas others in other parts of the world persistently generate clean energy, income and raw materials and social development through the solid waste value chains. The common practice of waste management in Ghana since independence, which has been the linear supply chain approach – where we just collect and dispose – has not been very much beneficial until the last decade.
\nIn recent years, some actors in the waste management industry have made efforts to transform Ghana’s sanitation services into a more sustainable development (circular economy) model – where different actors partner to work on different aspects of the waste value chain. By so doing, today, not only do we see collect and dump waste management services, but also there are being a crop of recycling initiatives coming in.
\nThere are large scale companies like Zoomlion, Surfisana, and CleanTeam etc. contributing to this new revolution to transform Ghana’s linear waste management supply chain into a more circular sector. And interestingly, the efforts of environmental SMEs which are even at startup stages but are more passionate about circular economy, are inspiring green initiatives in Ghana, and EcoCare Waste Initiative is the number on of such inspirations for the Asunafo North Municipality and almost the entire Ahafo region of Ghana.
\nSince 2017, EcoCare has been into advocacy and contributed a voice to speaking out for a change in Ghanaians behavior towards indiscriminate disposal of waste, plastic pollution, environmental depletion and a call for circular economy in the environment industry. And EcoCare Waste Initiative was the first the startup to inspire the small scale local green waste supply chain model in Goaso, which few other startups and youth groups are to replicating today in the Asunafo North Municipality.
\n\nTable 2 below shows the four (4) main initiatives that had been rolled out by EcoCare since incorporation in 2017; and the impacts that these activities have made across the social, environmental and economic development variables of Goaso and other.
\nInitiative/service | \nStatus | \nImpact***\n | \n||
---|---|---|---|---|
Social | \nEnvironmental | \nEconomic | \n||
EcoCare plastic free campaigns*\n | \nActive in 2017–2019; No current active challenge in 2020 | \nImpacting and influencing climate action and responsible consumption and production in 70 members | \nSaved 21,900 L of water in 2018–2019 by our community of 70 environmental lovers through the challenge | \nSeveral indirect jobs and revenue created for partners and suppliers – e.g. media houses, graphic designers, web/app designers | \n
EcoEvent cleanup service | \nOperated in 2019; Not operated yet in 2020 | \nInfluenced the way event organizers manage their waste in Goaso | \nReduced amount of waste that ended up at dump site in Goaso during weekends; Expected to save up to 6516 tons of CO2-eq emission from waste burning by 2022 | \nCreated 11 direct jobs within, 2 outside; several indirect jobs | \n
EcoCare DIY recycling training | \nActive 2018–2019 | \nInfluenced 600+ youth and students to consider waste as a resource for artifact creation | \nPrevented indiscriminate throwing of plastic wastes; | \nProvided skills for youth and women in plastic craft work | \n
EcoCare H2H waste collection**\n | \nRolled out in May 2020; Operated for about 10 weeks now | \nServed and impacted 16 households to choose paid waste collection service, learn to practice segregation of waste and control their waste generation | \n1800 kg of wastes collected and properly disposed in 8 weeks of active operation; 11.25 kg waste per household per week; Generation is fairly constant | \nCreated 3 different direct jobs for transporters, collectors and recyclers; 1/3 of waste generated go into recycling for revenue creation etc. | \n
Social, environmental and economic impacts of EcoCare’s green initiatives.
Plastic Free Campaign is global remote community (across 7 African countries) so impact are more global than local.
Find highlights of impacts discussed in Sec 9 below, since H2H Collection is the main model discussed for this chapter.
Some values are estimates from our unpublished Impact Assessment.
From a careful review of the results and lessons learned from EcoCare and other solid waste management companies that incorporate circularity into their services, we found that incorporation of a recovery and recycling channel at some point makes them contribute greatly to greening their environments. And such services are setting a pace for understanding, adoption and replication of green supply chain management (GSCM) into waste management in the country.
\nWaste generation among the user communities of EcoCare’s H2H Waste Collection, has been fairly constant. Meaning users are influenced to keep their waste generate at a moderation (in check) since the bin provided them is not the usual larger bins that public services provide. Also, the weekly collection and pay per pickup plan of the H2H Collection service influence users to not exceed their thresholds between weekly pickups.
\nCustomer feedback tells us that the people prefer this service to the conventional collect and dump model. Feedback also suggests that a flexible and affordable payment plan has a potential to motivate more households to subscribe to paid waste collection services, rather than resort to their indiscriminate disposal of wastes.
\nA green supply chain model like the H2H Waste Collection also makes users (the people) feel a part of contributing to keeping their cities clean and green. This could be true because customers generally accepted the responsibility for the environmental pollution in their neighborhoods caused by the dumping of wastes at unauthorized places or long term storage of piles of wastes in their backyards.
\nThe H2H Waste Collection model is highly commended for the fact that it connects multiple actors in the waste service, and opens door for more jobs and economic activities to be created in the city if the public municipal solid waste department adopts this model too.
\nOver all, if successful, we are optimistic that these four (4) green initiatives provided by EcoCare Waste Initiative could create 1000+ decent direct employment activities and 1000 change agents for the global goals on sustainable consumption and production (SDG 12) by 2022 from recycling model alone.4\n
\nThe authors recommend that private-public-partnerships for waste management in Goaso and other cities would be greener if they replicated the circular economy model implemented by EcoCare Waste Initiative and its H2H Waste Collection model.
\nOne key factor that affects green supply chain management (GSCM) is the integration of environmental value proposition. Environmental companies thus have little to do to achieve a green supply chain model, since their business models start with providing solutions to environmental problems, with consequential impacts on social and economic variables. EcoCare, a waste management startup in Goaso, Ghana, has proved that incorporating the triple bottom line, in municipal solid waste management – according to a circular economy model – is very possible, and that more cities have the potential to respecting the green supply chain management. Four main activities of EcoCare Waste Initiative – Plastic Free Campaigs, EcoEvent CleanUps Service, DIY Recycling Training and H2H Was Collection service – have been found to contribute to the greening the waste collection supply chain in Goaso, the capital of Asunafo North Municipality and the Ahafo region of Ghana. The House-to-house waste collection service (EcoCare H2H) ran by EcoCare is setting a pace for green supply chain management in Goaso.
\nThe authors declare no conflict of interest.
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