Fairtrade consumption and social milieu

Data: Umweltbewusstsein in Deutschland 2016 (Welle 1) - Version 1.0.0 (12.04.2017)

Data access: https://doi.org/10.4232/1.12764 (Gesis-Archiv - SPSS Data - SAV Format)

R Version 4.0.2 (22.06.2020) - RStudio Version 1.3.1056

Clear workspace

rm(list = ls())

Load packages

Packages <- c("tidyverse",   ## 1.3.0
            "naniar",        ## 0.5.0
            "haven",         ## 2.2.0
            "survey",        ## 3.37
            "car",           ## 3.0-6
            "gmodels",       ## 2.18.1
            "psych",         ## 1.9.12.31
            "mfx",           ## 1.2-2
            "epiDisplay",    ## 3.5.0.1
            "effects",       ## 4.1-4
            "DescTools",     ## 0.99.37
            "margins"        ## 0.3.23
            )

lapply(Packages, require, character.only = T)

Data import and preparation (preparation of the analysis)

Read in data set

Data <- read_sav("Umweltbewusstsein_DE_2016_Welle1_v1-0-0.sav")

Create variable subset

Data <- Data %>%
  dplyr::select(f624_3, ## If possible, only buy products that have been produced under fair working conditions.
                f821,   ## gender (m = 1 / w = 2) 
                f8210,  ## Total monthly net income of a household (in 11 income groups).
                nf822,  ## Age of respondents in years.
                f823,   ## Educational attainment (highest school degree or university degree). 
                QCL_1,  ## Social milieus (six social segments, 17 items for identification).
                weight  ## Weighting variable (weighting by region, gender, age and education).
                )

Define missing data or unusable values as “NA”

Data <- Data %>%
  mutate(f624_3 = na_if(f624_3, 5), ## "don't know"
         f8210  = na_if(f8210, 12), ## "not specified"
         f823   = na_if(f823, 1),   ## "still student"
         f823   = na_if(f823, 7),   ## "Other school diploma"
         f823   = na_if(f823, 8)    ## "not specified"
         )

Recode / dichotomize variables for analysis

Dichotomize target variable purchase probability as new variable “buy”

Recode categories 1 and 2 to 1 = Yes / categories 3 and 4 to 0 = No

Data <- Data %>%
  mutate(buy = car::recode(Data$f624_3,
                            "1:2 = 1; 3:4 = 0; else = NA", as.factor = T))

Gender from m = 1 / w = 2 to m = 0 / w = 1

New variable “female” (female = 1)

Data$female <- dummy.code(Data$f821, group = 2)

Define milieu as factor variable and thus automatically create a dummy variable

New variable “milieu” (milieu 1 automatically set as reference category)

Means: The first milieu is included in the calculation with 0 and the other categories with 1 each.

Data <- Data %>%
  mutate(milieu = car::recode(Data$QCL_1,
                            "1 = 1;
                             2 = 2;
                             3 = 3;
                             4 = 4; 
                             5 = 5;
                             6 = 6", 
                             as.factor = T))

Distribution of milieus rounded to one decimal place

round(prop.table(table(Data$milieu))*100, digits = 1)

## 
##    1    2    3    4    5    6 
## 12.7 20.2 25.2  8.4 15.2 18.3

How exactly is the variable “milieu” structured as a factor. Important for later model interpretation.

contrasts(Data$milieu)

##   2 3 4 5 6
## 1 0 0 0 0 0
## 2 1 0 0 0 0
## 3 0 1 0 0 0
## 4 0 0 1 0 0
## 5 0 0 0 1 0
## 6 0 0 0 0 1

Change reference category of milieu variable to 4 (preparation for logistic regression).

Data$milieu <- relevel (Data$milieu, ref = "4")

Structure of the variable after changing the reference category

Fourth milieu (precarious) set as reference and assigned value 0

contrasts(Data$milieu)

##   1 2 3 5 6
## 4 0 0 0 0 0
## 1 1 0 0 0 0
## 2 0 1 0 0 0
## 3 0 0 1 0 0
## 5 0 0 0 1 0
## 6 0 0 0 0 1

Define education as factor variable and thus automatically create a dummy variable

New variable “education” (“without degree” automatically set as reference category)

Data$education <- factor(Data$f823)

Add age variable as “age” for clarity

Data$age <- Data$nf822

Add income variable as “income” for clarity

Data$income <- Data$f8210

Missing values analysis

summary(Data)

##      f624_3           f821           f8210            nf822      
##  Min.   :1.000   Min.   :1.000   Min.   : 1.000   Min.   :14.00  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.: 5.000   1st Qu.:34.00  
##  Median :2.000   Median :2.000   Median : 6.000   Median :51.00  
##  Mean   :1.971   Mean   :1.511   Mean   : 6.465   Mean   :49.26  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.: 9.000   3rd Qu.:64.00  
##  Max.   :4.000   Max.   :2.000   Max.   :11.000   Max.   :98.00  
##  NA's   :79                      NA's   :173                     
##       f823           QCL_1           weight        buy           female      
##  Min.   :2.000   Min.   :1.000   Min.   :0.103   0   : 373   Min.   :0.0000  
##  1st Qu.:4.000   1st Qu.:2.000   1st Qu.:0.496   1   :1578   1st Qu.:0.0000  
##  Median :5.000   Median :3.000   Median :0.797   NA's:  79   Median :1.0000  
##  Mean   :4.597   Mean   :3.482   Mean   :1.000               Mean   :0.5113  
##  3rd Qu.:6.000   3rd Qu.:5.000   3rd Qu.:1.303               3rd Qu.:1.0000  
##  Max.   :6.000   Max.   :6.000   Max.   :7.831               Max.   :1.0000  
##  NA's   :72                                                                  
##  milieu  education       age            income      
##  4:171   2   :  7   Min.   :14.00   Min.   : 1.000  
##  1:257   3   :411   1st Qu.:34.00   1st Qu.: 5.000  
##  2:410   4   :532   Median :51.00   Median : 6.000  
##  3:512   5   :422   Mean   :49.26   Mean   : 6.465  
##  5:308   6   :586   3rd Qu.:64.00   3rd Qu.: 9.000  
##  6:372   NA's: 72   Max.   :98.00   Max.   :11.000  
##                                     NA's   :173

Visualization of missing values

variables <-data.frame(Data$female, Data$education, Data$income, Data$age, Data$milieu, Data$buy)
gg_miss_var(variables)

Check application requirements of binary logistic regression.

1. No extreme unequal distribution of the dependent variable.

prop.table(table(Data$buy))*100

## 
##       0       1 
## 19.1184 80.8816

2. Sufficient cases in predictor variable categories associated with outcome variable.

CrossTable(Data$milieu, Data$buy, prop.r=T, prop.c=F, prop.t=F, prop.chisq=F, digits = 2) 

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |           N / Row Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  1951 
## 
##  
##              | Data$buy 
##  Data$milieu |         0 |         1 | Row Total | 
## -------------|-----------|-----------|-----------|
##            4 |        44 |       113 |       157 | 
##              |      0.28 |      0.72 |      0.08 | 
## -------------|-----------|-----------|-----------|
##            1 |        33 |       215 |       248 | 
##              |      0.13 |      0.87 |      0.13 | 
## -------------|-----------|-----------|-----------|
##            2 |        69 |       329 |       398 | 
##              |      0.17 |      0.83 |      0.20 | 
## -------------|-----------|-----------|-----------|
##            3 |       124 |       368 |       492 | 
##              |      0.25 |      0.75 |      0.25 | 
## -------------|-----------|-----------|-----------|
##            5 |        15 |       288 |       303 | 
##              |      0.05 |      0.95 |      0.16 | 
## -------------|-----------|-----------|-----------|
##            6 |        88 |       265 |       353 | 
##              |      0.25 |      0.75 |      0.18 | 
## -------------|-----------|-----------|-----------|
## Column Total |       373 |      1578 |      1951 | 
## -------------|-----------|-----------|-----------|
## 
## 

3. Low multicollinearity (Checked for overall model).

variance inflation factor (VIF) (should not exceed 10).

vif(m6.w)

##               GVIF Df GVIF^(1/(2*Df))
## female    1.029018  1        1.014405
## education 2.166764  4        1.101480
## income    1.347658  1        1.160887
## age       2.791546  1        1.670792
## milieu    5.255834  5        1.180495

Binary logistic regression model

Dependent variable: buy from Fairtrade Yes (1) / No (0)

Independent variables: Gender, education, income, age, milieu

Exclusion of cases with missing values (“na.action=na.omit” set as default)

Models (each separately and at the end together in one model)

Error message “non-integer #successes in a binomial glm!” comes up in each case.

This is due to the weighting, since there are no integer values left

The error message is therefore hidden in the following output

Zero model (unweighted)

m.null <- glm(buy ~ 1,
              family = binomial,
              data = Data)
summary(m.null)

## 
## Call:
## glm(formula = buy ~ 1, family = binomial, data = Data)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8191   0.6514   0.6514   0.6514   0.6514  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.44234    0.05757   25.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1903.9  on 1950  degrees of freedom
## Residual deviance: 1903.9  on 1950  degrees of freedom
##   (79 observations deleted due to missingness)
## AIC: 1905.9
## 
## Number of Fisher Scoring iterations: 4

gender (weighted)

m1.w <- glm(buy ~ female,
            family = binomial,
            data = Data,
            weights = weight)
summary(m1.w)

## 
## Call:
## glm(formula = buy ~ female, family = binomial, data = Data, weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.6809   0.3601   0.4868   0.6847   1.7152  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  1.11550    0.07526  14.822  < 2e-16 ***
## female       0.71523    0.11872   6.024  1.7e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1898.9  on 1950  degrees of freedom
## Residual deviance: 1861.5  on 1949  degrees of freedom
##   (79 observations deleted due to missingness)
## AIC: 1863.4
## 
## Number of Fisher Scoring iterations: 4

education (weighted)

m2.w <- glm(buy ~ education,
            family = binomial,
            data = Data,
            weights = weight)
summary(m2.w)

## 
## Call:
## glm(formula = buy ~ education, family = binomial, data = Data, 
##     weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.3546   0.3908   0.5673   0.6777   1.5206  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  1.18090    0.78401   1.506    0.132
## education3   0.47507    0.79071   0.601    0.548
## education4   0.20211    0.79102   0.256    0.798
## education5  -0.04284    0.79812  -0.054    0.957
## education6   0.18826    0.79671   0.236    0.813
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1785.1  on 1884  degrees of freedom
## Residual deviance: 1775.9  on 1880  degrees of freedom
##   (145 observations deleted due to missingness)
## AIC: 1787
## 
## Number of Fisher Scoring iterations: 4

income (weighted)

m3.w <- glm(buy ~ income,
            family = binomial,
            data = Data,
            weights = weight)
summary(m3.w)

## 
## Call:
## glm(formula = buy ~ income, family = binomial, data = Data, weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.0557   0.3708   0.5374   0.6933   1.5501  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 1.375431   0.162140   8.483   <2e-16 ***
## income      0.009733   0.024672   0.394    0.693    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1748.4  on 1791  degrees of freedom
## Residual deviance: 1748.2  on 1790  degrees of freedom
##   (238 observations deleted due to missingness)
## AIC: 1742.8
## 
## Number of Fisher Scoring iterations: 4

age (weighted)

m4.w <- glm(buy ~ age,
            family = binomial,
            data = Data,
            weights = weight)
summary(m4.w)

## 
## Call:
## glm(formula = buy ~ age, family = binomial, data = Data, weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.4675   0.3262   0.5236   0.6855   1.6565  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 0.509342   0.155982   3.265  0.00109 ** 
## age         0.019863   0.003218   6.172 6.74e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1898.9  on 1950  degrees of freedom
## Residual deviance: 1859.8  on 1949  degrees of freedom
##   (79 observations deleted due to missingness)
## AIC: 1857.1
## 
## Number of Fisher Scoring iterations: 4

milieu (weighted)

m5.w <- glm(buy ~ milieu,
            family = binomial,
            data = Data,
            weights = weight)
summary(m5.w)

## 
## Call:
## glm(formula = buy ~ milieu, family = binomial, data = Data, weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.0709   0.2436   0.4673   0.6691   1.7382  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.9743     0.1402   6.949 3.69e-12 ***
## milieu1       1.2934     0.2460   5.257 1.47e-07 ***
## milieu2       0.4523     0.2040   2.217   0.0266 *  
## milieu3       0.1717     0.1752   0.980   0.3272    
## milieu5       1.9195     0.3091   6.210 5.29e-10 ***
## milieu6       0.1624     0.1875   0.866   0.3864    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1898.9  on 1950  degrees of freedom
## Residual deviance: 1810.9  on 1945  degrees of freedom
##   (79 observations deleted due to missingness)
## AIC: 1822.8
## 
## Number of Fisher Scoring iterations: 5

Overall model (weighted) - socio-demographics as control variables.

m6.w <- glm(buy ~ female + education + income + age + milieu,
            family = binomial,
            data = Data,
            weights = weight)
summary(m6.w)

## 
## Call:
## glm(formula = buy ~ female + education + income + age + milieu, 
##     family = binomial, data = Data, weights = weight)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.0779   0.1917   0.4612   0.6463   2.0388  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -1.277187   0.914926  -1.396 0.162731    
## female       0.667700   0.134049   4.981 6.32e-07 ***
## education3   0.513920   0.850617   0.604 0.545729    
## education4   0.205692   0.859776   0.239 0.810920    
## education5   0.031382   0.878798   0.036 0.971514    
## education6   0.118313   0.875149   0.135 0.892460    
## income       0.007816   0.030581   0.256 0.798278    
## age          0.029095   0.006581   4.421 9.82e-06 ***
## milieu1      0.618234   0.310695   1.990 0.046608 *  
## milieu2      0.729261   0.270061   2.700 0.006927 ** 
## milieu3      0.321030   0.220737   1.454 0.145848    
## milieu5      2.549934   0.390853   6.524 6.84e-11 ***
## milieu6      1.130821   0.305692   3.699 0.000216 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1654.6  on 1737  degrees of freedom
## Residual deviance: 1503.2  on 1725  degrees of freedom
##   (292 observations deleted due to missingness)
## AIC: 1516.3
## 
## Number of Fisher Scoring iterations: 5

Model comparison and model quality

Log-Likelihood

logLik(m.null)

## 'log Lik.' -951.9617 (df=1)

logLik(m5.w) 

## 'log Lik.' -905.3995 (df=6)

logLik(m6.w)

## 'log Lik.' -745.1535 (df=13)

Likelihood-Ratio-Test

lrtest(m.null, m5.w)

## Likelihood ratio test for MLE method 
## Chi-squared 5 d.f. =  93.12431 , P value =  1.481293e-18

AIC - Akaike Information Criterion

AIC(m.null, m5.w, m6.w)

##        df      AIC
## m.null  1 1905.923
## m5.w    6 1822.799
## m6.w   13 1516.307

BIC - Bayesian Information Criterion

BIC(m.null, m5.w, m6.w)

##        df      BIC
## m.null  1 1911.499
## m5.w    6 1856.256
## m6.w   13 1587.293

Pseudo R2 (McKelvey & Zavoina)

Directly in percent and rounded to one decimal place

round((PseudoR2(m1.w, "McKelveyZavoina")*100), digits = 1)   ## gender

## McKelveyZavoina 
##             3.7

round((PseudoR2(m2.w, "McKelveyZavoina")*100), digits = 1)   ## education

## McKelveyZavoina 
##             0.9

round((PseudoR2(m3.w, "McKelveyZavoina")*100), digits = 2)   ## income

## McKelveyZavoina 
##            0.02

round((PseudoR2(m4.w, "McKelveyZavoina")*100), digits = 1)   ## age

## McKelveyZavoina 
##             3.7

round((PseudoR2(m5.w, "McKelveyZavoina")*100), digits = 1)   ## milieu

## McKelveyZavoina 
##            11.9

Overall model - R2 (McKelvey & Zavoina)

round((PseudoR2(m6.w, "McKelveyZavoina")*100), digits = 1) ## Overall model 

## McKelveyZavoina 
##            21.2

Proportion of explained variance by milieu in total explained variance.

round(11.9/21.2*100, digits = 1)

## [1] 56.1

Advanced analysis of the models

Odds Ratio

logistic.display(m5.w)

## 
## Logistic regression predicting buy : 1 vs 0 
##  
##                OR(95%CI)         P(Wald's test) P(LR-test)
## milieu: ref.=4                                  < 0.001   
##    1           3.65 (2.25,5.9)   < 0.001                  
##    2           1.57 (1.05,2.34)  0.027                    
##    3           1.19 (0.84,1.67)  0.327                    
##    5           6.82 (3.72,12.5)  < 0.001                  
##    6           1.18 (0.81,1.7)   0.386                    
##                                                           
## Log-likelihood = -905.3995
## No. of observations = 1951
## AIC value = 1822.799

logistic.display(m6.w)

## 
## Logistic regression predicting buy : 1 vs 0 
##  
##                     crude OR(95%CI)         adj. OR(95%CI)         
## female: 1 vs 0      2.07 (1.61,2.67)        1.95 (1.5,2.54)        
##                                                                    
## education: ref.=2                                                  
##    3                1.56 (0.33,7.36)        1.67 (0.32,8.86)       
##    4                1.22 (0.26,5.74)        1.23 (0.23,6.62)       
##    5                0.93 (0.19,4.47)        1.03 (0.18,5.78)       
##    6                1.26 (0.26,6.03)        1.13 (0.2,6.26)        
##                                                                    
## income (cont. var.) 0.9995 (0.9505,1.0511)  1.0078 (0.9492,1.0701) 
##                                                                    
## age (cont. var.)    1.02 (1.02,1.03)        1.03 (1.02,1.04)       
##                                                                    
## milieu: ref.=4                                                     
##    1                3.81 (2.31,6.31)        1.86 (1.01,3.41)       
##    2                1.65 (1.08,2.5)         2.07 (1.22,3.52)       
##    3                1.16 (0.81,1.65)        1.38 (0.89,2.12)       
##    5                9.09 (4.48,18.43)       12.81 (5.95,27.55)     
##    6                1.03 (0.69,1.54)        3.1 (1.7,5.64)         
##                                                                    
##                     P(Wald's test) P(LR-test)
## female: 1 vs 0      < 0.001        < 0.001   
##                                              
## education: ref.=2                  0.626     
##    3                0.546                    
##    4                0.811                    
##    5                0.972                    
##    6                0.892                    
##                                              
## income (cont. var.) 0.798          0.81      
##                                              
## age (cont. var.)    < 0.001        < 0.001   
##                                              
## milieu: ref.=4                     < 0.001   
##    1                0.047                    
##    2                0.007                    
##    3                0.146                    
##    5                < 0.001                  
##    6                < 0.001                  
##                                              
## Log-likelihood = -745.1535
## No. of observations = 1738
## AIC value = 1516.307

AME (Average Marginal Effects)

m5.w.marginal <- margins(m5.w)
summary(m5.w.marginal)

##   factor    AME     SE      z      p   lower  upper
##  milieu1 0.1802 0.0328 5.4994 0.0000  0.1160 0.2444
##  milieu2 0.0804 0.0362 2.2184 0.0265  0.0094 0.1514
##  milieu3 0.0328 0.0339 0.9680 0.3330 -0.0336 0.0992
##  milieu5 0.2216 0.0311 7.1305 0.0000  0.1607 0.2825
##  milieu6 0.0311 0.0361 0.8617 0.3888 -0.0396 0.1018

m6.w.marginal <- margins(m6.w) 
summary(m6.w.marginal)

##      factor    AME     SE      z      p   lower  upper
##         age 0.0041 0.0009 4.5808 0.0000  0.0024 0.0059
##  education3 0.0713 0.1339 0.5326 0.5943 -0.1911 0.3337
##  education4 0.0309 0.1353 0.2282 0.8195 -0.2344 0.2961
##  education5 0.0049 0.1385 0.0355 0.9717 -0.2665 0.2763
##  education6 0.0181 0.1377 0.1318 0.8951 -0.2517 0.2880
##      female 0.0951 0.0189 5.0241 0.0000  0.0580 0.1322
##      income 0.0011 0.0044 0.2557 0.7982 -0.0074 0.0096
##     milieu1 0.1125 0.0549 2.0495 0.0404  0.0049 0.2201
##     milieu2 0.1297 0.0492 2.6347 0.0084  0.0332 0.2262
##     milieu3 0.0618 0.0436 1.4183 0.1561 -0.0236 0.1472
##     milieu5 0.2892 0.0433 6.6834 0.0000  0.2044 0.3740
##     milieu6 0.1838 0.0488 3.7691 0.0002  0.0882 0.2793

Visualization of the AME

m5.w.marginal.sum <- summary(m5.w.marginal)

ggplot(data = m5.w.marginal.sum) +
  geom_point(aes(factor, AME)) +
  geom_errorbar(aes(x = factor, ymin = lower, ymax = upper)) +
  geom_hline(yintercept = 0) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))

m6.w.marginal.sum <- summary(m6.w.marginal)

ggplot(data = m6.w.marginal.sum) +
  geom_point(aes(factor, AME)) +
  geom_errorbar(aes(x = factor, ymin = lower, ymax = upper)) +
  geom_hline(yintercept = 0) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))