First, we present the results obtained from application of X-STATIS in each year in order to find underlying data patterns for the characteristics of gender, age group, and socioeconomic status of Colombians in each year of the ENUT survey. Afterwards, we present the results obtained from the comparative analysis of the two years of the study by means of co-inertia analysis of the compromises of the series of tables from each year. The graphic representations and tables presented below were obtained using the R statistical package and the ade4 function.
3.1. Results Using X-STATIS
3.1.1. Interstructure Analysis
The first step of the X-STATIS is the interstructure analysis to compare the overall structures of the matrices that summarize the age groups, in order to observe which age groups are similar to each other. The information provided by the vector correlation matrices (RV) shown in Table 3
and the representations in Figure 5
indicate that in both years the greatest vector correlations are perceived in the older adult groups, whereas the lowest correlations are found between the latter and minors. Moreover, in ENUT 2017, all vector correlations were lower than those found in 2013.
shows that in both years the interstructure is almost similar showing that the largest angle is formed between the vector that represents pre-teens and over-60 group. Therefore, these are the less similar groups. From this pattern it can be interpreted that there is a much clearer common pattern between adults and older adults in the most recent ENUT survey than in the first, in terms of how Colombians use their time according to their socioeconomic status (SES). In 2013, the order of the vectors is more evident, starting with the youngest group (at the top of the graph), to the over-65 age group (at the bottom).
Before interpreting the graphic results of the compromise table, it should be noted that based on the eigenvalues obtained, which are shown in Table 4
, in the 2013 ENUT, the first two axes account for 45.6% of the variability of the information, whereas in the second survey this percentage decreases to 35.9%.
3.1.2. Compromise Analysis
displays the covariance structure of the time variables for the first 2 dimensions of the axes of the principal components of the compromise, and also presents the positions of the gender–SES (socioeconomic status) combinations in that compromise. By observing the first two axes of the compromise, we can interpret three characteristics that the two years have in common: the behavior of the gender–SES combinations, the associations between the variables and the interpretation that can be given in combination to the positions of gender by socioeconomic status in the compromise space.
The first interesting pattern we can observe in Figure 6
is the sharp difference between gender and socioeconomic status. In both years, the first axis of the compromise makes it possible to differentiate by gender (women on the left of the graph and men on the right), while the second axis reflects the ordering by socioeconomic level.
The sharp differences by gender displayed by both compromises are basically explained by the variables WOR, INT, and SPO (time devoted to work, the Internet, and sports). It can be seen how the vectors that represent these variables characterize the right end of axis 1, and consequently it is the men (especially in 2013, and with greater intensity in the low SES) who spend more time in these activities compared to women (located on the left size of the axis).
An analogous interpretation, but in the opposite direction, is that women (especially in low and medium socioeconomic levels) devote much more time than men to DHA, BAC, HAI, and FHC (domestic and household activities, basic activities, hairdressing, and family and home care), because the vectors that display such activities are on the negative portion of axis 1, which displays the points that represent women.
Regarding the differences in time use by socioeconomic status, the graph shows that higher socioeconomic levels spend more time in leisure activities than lower levels. See how in 2013, resting or listening to music (vectors RES and LMU located in the direction of the second axis) characterize men and women from higher levels, whereas in 2017, they display more time in the direction of activities such as reading, speaking on the phone and visiting friends (vectors REA, PHO, and FRI).
According to the above, regarding the structure of variance and covariance of the variables in the compromise space, we can summarize by saying that in both years, we can see four sets of variables that are differentiated from each other. The first involves household activities, home care, going to the hairdresser, and, to a lesser extent, basic activities, and which characterize the women who are located towards the left of the representations of Figure 6
(the vectors DHA, FHC, HAI, and BAC). On the opposite side of this group of variables are paid work, practicing sports, and agricultural activities, which are associated with men (the vectors WOR, SPO, and AGR).
On the second axis, which sets the differences in terms of socioeconomic status, two other groups of variables stand out, which are: reading, speaking on the phone, and visiting friends, and to a lesser extent commuting time and studying (vectors REA, PHO, FRI, RES, and STU), all of which are on the opposite side of watching television or attending sports events (vectors VTV and EVE). This latter group of variables displays differences between the years: in 2013, the vectors that represent just resting and listening to music (RES and LMU) are also directly correlated with the variables VTV and SPE (small angle between the vectors).
3.1.3. Intrastructure Analysis
Intrastructure analysis enables projecting all the variables from each studied matrix over the compromise, i.e., to analyze each age group matrix to assess the degree of similarity or difference between the covariance structures of the various age groups, between the groups and compared to the compromise of time use activities. This is represented in Figure 7
, which specifically presents the results of the latest ENUT. The graphics from 2013 are not included because, in terms of interpretation, the conclusions that can be obtained from that year are, in general, similar to those described below.
In Figure 7
, in the representations on the left side, for all age groups, the distance between men and women is large, and they are ordered by socioeconomic status; i.e., the differences in time use between Colombian men and women are found throughout every age group. Specifically for men, the distances between low and medium socioeconomic levels are always smaller compared to the higher socioeconomic level. The greatest difference between age groups is in the structure of variance and covariance of the vectors, because different variables form subgroups of greater association depend on the specific age group.
According to Figure 7
, among preteens and teens, the subgroup of variables related to studying, listening to music, reading, and commuting times (STU, LMU, REA, and MJO) is differentiated from the other variables and they are associated to a greater extent with women and men from higher socioeconomic levels. In contrast, in the older adult group, the variables with the closest associations with each other (with less correlation compared to other vectors) are looking for a job or creating an own business, activities related to agriculture, listening to music, and resting (vectors LJO, AGR, LMU, and RES). Such activities are more characteristic among older men from medium and low socioeconomic levels, who are therefore the ones who devote most time to these activities in this age group.
In the case of age groups of the economically active population, a clear difference is observed in terms of the activities carried out by men from high socioeconomic levels. Young adult men spend more time being with friends, going to bars, studying, and listening of music (FRI, BAR, STU, and LMU), whereas men in the 35 to 59 age group spend more time working, reading, speaking on the phone, and with friends (variables WOR, REA, PHO, and FRI). Regarding women in all age groups, it can be observed that the vectors that represent the variables of household and home care activities (FHC and DHA) are always located on the left of the graph. This means that in Colombia, independently from their age, women report that they devote a great amount of time to unpaid activities related to household chores and caring for the family.
3.2. Results of the Co-Inertia Analysis
Below we present the results of the simultaneous comparative analysis of the compromises from the X-STATIS analyses of the two ENUT Colombia surveys, to inspect objectively the obtained patterns. Co-inertia analysis was performed to describe patterns that are not visible at first sight by means of indirect comparisons made in explaining the partial triadic analyses between years (the results displayed previously in Table 3
and Table 4
and Figure 6
and Figure 7
presents a table that summarizes the explained variability of each co-inertia axis and their eigenvalues, along with the correlations between them and the axes of the principal component analysis of the individual compromises for each ENUT survey. In this figure, we can see that the first two co-inertia axes summarize 81.2% of the variability of information, and for this reason, in the eigenvalue graph, only two axes are highlighted as necessary to explain the data’s behavior. This is also confirmed by observing that the high correlations (greater than 0.91 in absolute value) between the PCAs of each compromise table and the co-inertia axes arise in the first two dimensions (these are highlighted as important in brown).
The RV coefficient obtained from the co-inertia analysis was 0.7334, which indicates that the two structures of the compromises of ENUT 2013 and 2017 vary simultaneously with high co-inertia. This, in terms of interpretation, implies that the times reported in both surveys are consistent with each other; however, some behaviors in specific variables and groups of sociodemographic characteristics are worth mentioning based on the interpretation of Figure 9
, which presents the projection of the first two co-inertia axes, both of the new sets of standardized coordinates of the sex, socioeconomic status, and age groups, and of the canonical weights of the variables of each compromise table.
The co-structure graph on the top of Figure 9
projects the new standardized coordinates of the sociodemographic characteristics on the Co-inertia axes of the two datasets. Each pair of points are linked by an arrow. The origin of each arrow indicates the position according to the ordering of the first compromise matrix (from the ENUT 2013 survey), and the arrow indicates the position according to the ordering of the second matrix (of ENUT 2017).
It can be observed that most combinations of gender, SES, and age display short arrows, which means that the times reported in both ENUT surveys were similar and consistent. The exceptions are for the older adult groups, independently from gender, in the medium socioeconomic level (MM60+ and WM60+), and for teens of medium and high status (MH10-17, WH10-17, MM10-17, and WM10-17), whose vectors are longer.
Regarding this finding, specifically for older adults of medium SES, it can be said that the direction of the vectors indicates a shift in the reported times from watching videos and television and simply resting (vectors VTV and RES in ENUT 2013), toward studying (direction of the STU vector in ENUT 2017). This behavior is because in the first ENUT survey this group did not report time related to studying, but in the second survey, both men and women over the age of 60 reported that they spent on average over two hours studying, and they reported a slight decrease in the time they use to watch TV or simply rest.
On the other hand, the graphs in Figure 9
show the weights of the variables from the co-inertia analysis. Here we can see differences in the size of the vectors and changes in their direction, that is, changes in the structure of co-variation in each year. For example, the vectors that represent religious activities and reading (REL and REA), which are located in quadrant I of the representations in both years, are longer in ENUT 2013, which reflects a greater variability in the information of that year compared to 2017.
In contrast, the vector that can be seen to change direction between 2013 and 2017 is CUA, which represents the cultural events or activities. This change in CUA position implies that it covariates positively or negatively with different variables in the two years compared, and characterizes different age groups. In the first ENUT Colombia survey, attending cultural events was observed as a feature of young adults, whereas in the latest ENUT, it is included in the group of variables that characterize older adults.
A quadrant change was also observed in LJO (looking for a job and establishing an own business), which in 2017 is located in the third quadrant. It directly covariates with FHC, MJO, WOR, and PHO (family and home care activities, time spent in movements and journeys, work time, and speaking on the phone). Nevertheless, in 2013, LJO was located in the second quadrant directly related to REL, MUS, and REA (time to attend or organize religious activities, time spending in practice a musical instrument, paint, etc., and time to read).