Maori Acting Workforce Breakdown

Toi Puaki Workforce Overview

Figure 1 The dataset provides insight into the workforce size across various years, spanning from 2015 to 2022. The data exhibits fluctuations in the workforce size over this period, with some years experiencing increases while others show decreases. For instance, the workforce size grew steadily from 2015 to 2019, reaching its peak in 2019 with 13,710 employees. However, in 2020, there was a notable decline in the workforce size, followed by a slight recovery in 2021. The data for 2022 is incomplete, due to the availability of relevant government agency data within the Statistic New Zealand’s Integrated Data Infrastructure environment.

Code
bar_plot <- tribble( ~"Year", ~"Workforce Size"
         ,2015        ,11855
         ,2016        ,12190
         ,2017        ,12130
         ,2018        ,13065
         ,2019        ,13710
         ,2020        ,12040
         ,2021        ,12850
         ,2022        ,9600) %>% 
    ggplot(aes(Year, `Workforce Size`))+
    geom_col(fill="#007282")+
    geom_text(aes(label = `Workforce Size`),color="black",vjust=5)+
    theme_void()+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro')+
    theme(
        legend.position = 'top',
        strip.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank()
    )+
    labs(title = "The size of Toi Puaki Workforce By Reporting Year",
         subtitle = "2015 - 2022",
         caption = " Datasource: Infometrics")

ggplotly(bar_plot)
Figure 1: Workforce Size By Reporting Year

Toi Puaki Sectors Split

Figure 2 presents the percentage distribution of workforce size across different subsectors within the performing arts industry from 2015 to 2021.

For “Creative Artists, Musicians, Writers and Performers,” the percentage ranges from approximately 47.1% in 2017 to 51.3% in 2020, indicating fluctuations in workforce distribution within this subsector over the years.

“Performing Arts Operation” exhibits a relatively stable percentage distribution, ranging from approximately 33.5% in 2020 to 37.8% in 2017.

Similarly, “Performing Arts Venue Operation” maintains a consistent proportion, with percentages ranging from around 13.9% in 2015 to 16.5% in 2019.

Code
line_chart <- tribble( ~"Year",    ~"Subsector",                                                    ~"Workforce Size"
         ,2015       ,"Creative Artists, Musicians, Writers and Performers"                             ,6060
         ,2016       ,"Creative Artists, Musicians, Writers and Performers"                             ,6250
         ,2017       ,"Creative Artists, Musicians, Writers and Performers"                             ,5710
         ,2018       ,"Creative Artists, Musicians, Writers and Performers"                             ,6390
         ,2019       ,"Creative Artists, Musicians, Writers and Performers"                             ,6620
         ,2020       ,"Creative Artists, Musicians, Writers and Performers"                             ,6180
         ,2021       ,"Creative Artists, Musicians, Writers and Performers"                             ,6510
         ,2022       ,"Creative Artists, Musicians, Writers and Performers"                             ,3370
         ,2015       ,"Performing Arts Operation"                                                       ,4150
         ,2016       ,"Performing Arts Operation"                                                       ,4180
         ,2017       ,"Performing Arts Operation"                                                       ,4590
         ,2018       ,"Performing Arts Operation"                                                       ,4540
         ,2019       ,"Performing Arts Operation"                                                       ,4830
         ,2020       ,"Performing Arts Operation"                                                       ,3990
         ,2021       ,"Performing Arts Operation"                                                       ,4300
         ,2022       ,"Performing Arts Operation"                                                       ,4145
         ,2015       ,"Performing Arts Venue Operation"                                                 ,1645
         ,2016       ,"Performing Arts Venue Operation"                                                 ,1760
         ,2017       ,"Performing Arts Venue Operation"                                                 ,1830
         ,2018       ,"Performing Arts Venue Operation"                                                 ,2135
         ,2019       ,"Performing Arts Venue Operation"                                                 ,2260
         ,2020       ,"Performing Arts Venue Operation"                                                 ,1870
         ,2021       ,"Performing Arts Venue Operation"                                                 ,2040
         ,2022       ,"Performing Arts Venue Operation"                                                 ,2085
) %>% 
    group_by(Year) %>% 
    mutate(Total_Size=sum(`Workforce Size`)) %>% 
    mutate(Percent=`Workforce Size`/Total_Size) %>%
    #select(Year, Subsector, Percent)
    ungroup() %>% 
    ggplot(aes(Year,Percent,group=Subsector,color=Subsector))+
    geom_point()+
    geom_line()+
    scale_color_brewer(type = "qual",palette = "Set2")+
    scale_y_continuous(labels = scales::percent_format())+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro')+
    theme(
        legend.position = 'top',
        strip.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank()
    )+
    labs(title = "Proportions of Toi Puaki subsectors By Reporting Year",
         subtitle = "2015 - 2022",
         caption = " Datasource: Statistic New Zealand Integrated Data Infrastructure")

ggplotly(line_chart)
Figure 2: Linechart By Industry

Breakdown of Composition of Ethnic Groups within Creative Artists, Musicians, Writers and Performers Subsector

Figure 3 and Figure 4 provided data spanning from 2015 to 2021, it is evident that there are notable trends in ethnic composition across the years. European ethnicity consistently appears to be the predominant group, with percentages hovering around 75% across most years, albeit showing a slight decline towards 2022. Conversely, Maori representation remains relatively stable, with proportions consistently around 14-15% throughout the years. Meanwhile, Asian ethnicity displays a slight increase in representation over time, starting around 5% in 2015 and rising to over 6% by 2021. Similarly, Pacific ethnicity shows a modest upward trend, increasing from around 3-4% in 2015 to approximately 5-6% by 2021. Interestingly, the MELAA (Middle Eastern, Latin American, and African) group demonstrates a consistent but smaller proportion compared to other ethnicities, ranging from approximately 1.5% to 2%. The “Other” category, representing ethnicities not explicitly mentioned, maintains a relatively marginal presence, consistently below 1%. At the moment, the ethnic group composition for 2022 is incomplete, due to the availability of information in the data source.

Code
tribble( ~"Year", ~"Ethnicity", ~"n"
    ,2019   ,'European' ,5040
,2018   ,'European' ,4830
,2021   ,'European' ,4730
,2015   ,'European' ,4610
,2016   ,'European' ,4540
,2020   ,'European' ,4530
,2017   ,'European' ,4250
,2022   ,'European' ,2090
,2019   ,'Maori'    ,970
,2021   ,'Maori'    ,960
,2018   ,'Maori'    ,910
,2020   ,'Maori'    ,900
,2015   ,'Maori'    ,850
,2016   ,'Maori'    ,850
,2017   ,'Maori'    ,830
,2022   ,'Maori'    ,615
,2021   ,'Asian'    ,425
,2019   ,'Asian'    ,360
,2020   ,'Asian'    ,355
,2021   ,'Pacific'  ,350
,2016   ,'Asian'    ,325
,2018   ,'Asian'    ,310
,2015   ,'Asian'    ,300
,2020   ,'Pacific'  ,290
,2022   ,'Asian'    ,290
,2022   ,'Pacific'  ,271
,2017   ,'Asian'    ,270
,2019   ,'Pacific'  ,258
,2018   ,'Pacific'  ,240
,2016   ,'Pacific'  ,226
,2017   ,'Pacific'  ,210
,2015   ,'Pacific'  ,206
,2021   ,'MELAA'    ,129
,2018   ,'MELAA'    ,109
,2020   ,'MELAA'    ,106
,2019   ,'MELAA'    ,103
,2017   ,'MELAA'    ,96
,2022   ,'MELAA'    ,95
,2016   ,'MELAA'    ,93
,2015   ,'MELAA'    ,87
,2015   ,'Other'    ,34
,2016   ,'Other'    ,27
,2017   ,'Other'    ,27
,2018   ,'Other'    ,26
,2019   ,'Other'    ,24
,2021   ,'Other'    ,20
,2020   ,'Other'    ,18
,2022   ,'Other'    ,15
) %>% 
    group_by(Year) %>%
    mutate(Total_Workforce = sum(n)) %>% 
    mutate(Percent = n/Total_Workforce) %>% 
     mutate(Percent = scales::percent(Percent, accuracy =0.01)) %>% 
    select(-c(Total_Workforce,n)) %>% 
    arrange(Year) %>% 
    pivot_wider(names_from = Year, values_from = Percent) %>% 
    kbl(caption = "The Proportion of Ethnic Group By Reporting Year") %>% 
    kable_classic(full_width = F, html_font = "Cambria")
The Proportion of Ethnic Group By Reporting Year
Ethnicity 2015 2016 2017 2018 2019 2020 2021 2022
European 75.74% 74.91% 74.78% 75.18% 74.61% 73.08% 71.51% 61.91%
Maori 13.96% 14.02% 14.60% 14.16% 14.36% 14.52% 14.51% 18.22%
Asian 4.93% 5.36% 4.75% 4.82% 5.33% 5.73% 6.43% 8.59%
Pacific 3.38% 3.73% 3.70% 3.74% 3.82% 4.68% 5.29% 8.03%
MELAA 1.43% 1.53% 1.69% 1.70% 1.52% 1.71% 1.95% 2.81%
Other 0.56% 0.45% 0.48% 0.40% 0.36% 0.29% 0.30% 0.44%
Figure 3: Breakdown By Ethnicity
Code
column_chart <- tribble( ~"Year", ~"Ethnicity", ~"n"
    ,2019   ,'European' ,5040
,2018   ,'European' ,4830
,2021   ,'European' ,4730
,2015   ,'European' ,4610
,2016   ,'European' ,4540
,2020   ,'European' ,4530
,2017   ,'European' ,4250
,2022   ,'European' ,2090
,2019   ,'Maori'    ,970
,2021   ,'Maori'    ,960
,2018   ,'Maori'    ,910
,2020   ,'Maori'    ,900
,2015   ,'Maori'    ,850
,2016   ,'Maori'    ,850
,2017   ,'Maori'    ,830
,2022   ,'Maori'    ,615
,2021   ,'Asian'    ,425
,2019   ,'Asian'    ,360
,2020   ,'Asian'    ,355
,2021   ,'Pacific'  ,350
,2016   ,'Asian'    ,325
,2018   ,'Asian'    ,310
,2015   ,'Asian'    ,300
,2020   ,'Pacific'  ,290
,2022   ,'Asian'    ,290
,2022   ,'Pacific'  ,271
,2017   ,'Asian'    ,270
,2019   ,'Pacific'  ,258
,2018   ,'Pacific'  ,240
,2016   ,'Pacific'  ,226
,2017   ,'Pacific'  ,210
,2015   ,'Pacific'  ,206
,2021   ,'MELAA'    ,129
,2018   ,'MELAA'    ,109
,2020   ,'MELAA'    ,106
,2019   ,'MELAA'    ,103
,2017   ,'MELAA'    ,96
,2022   ,'MELAA'    ,95
,2016   ,'MELAA'    ,93
,2015   ,'MELAA'    ,87
,2015   ,'Other'    ,34
,2016   ,'Other'    ,27
,2017   ,'Other'    ,27
,2018   ,'Other'    ,26
,2019   ,'Other'    ,24
,2021   ,'Other'    ,20
,2020   ,'Other'    ,18
,2022   ,'Other'    ,15
) %>% 
    group_by(Year) %>%
    mutate(Total_Workforce = sum(n)) %>% 
    mutate(Percent = n/Total_Workforce) %>% 
    select(-Total_Workforce) %>% 
    ggplot(aes(Year, Percent, fill=Ethnicity))+
    geom_col()+
   # geom_text(mapping=aes(label=scales::percent(round(Percent,3))))+
    scale_fill_brewer(type="qual",palette = "Set1")+
    scale_y_continuous(labels = scales::percent_format())+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro')+
    labs(x=element_blank(),
         y=element_blank())+
    theme(
        strip.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank()
    )+
    labs(title = "The proportions of different ethnic groups for Creative Artists, \nMusicians, Writers and Performers Subsector By Reporting Year",
         subtitle = "2015 - 2022",
         caption = " Datasource: Statistic New Zealand Integrated Data Infrastructure")+
    theme(legend.position = "top")

ggplotly(column_chart)
Figure 4: Breakdown By Ethnicity

Breakdown of Actor Workforce

Figure 5 represents the workforce size in two categories, “Toi Puaki Only” and “Total Economy”. Over the years 2015 to 2023. Across this period, both categories exhibit an upward trend in workforce size. “Toi Puaki Only” shows a more modest increase, starting at 158 in 2015 and reaching 194 in 2023. Conversely, “Total Economy” demonstrates a more pronounced growth trajectory, beginning at 602 in 2015 and culminating at 865 in 2023. The difference in workforce sizes between the two categories suggests that the “Total Economy” sector encompasses more employment opportunities than “Toi Puaki Only”. This divergence may reflect factors such as industry expansion, economic fluctuations, and policy interventions influencing workforce dynamics over time.

Code
actor_workforce <- tribble(~"Year",      ~"Category",    ~"Workforce_Size"
                            ,2015     ,"Toi Puaki Only"        ,158                    
                            ,2016     ,"Toi Puaki Only"        ,164                    
                            ,2017     ,"Toi Puaki Only"        ,172                    
                            ,2018     ,"Toi Puaki Only"        ,184                    
                            ,2019     ,"Toi Puaki Only"        ,192                    
                            ,2020     ,"Toi Puaki Only"        ,197                    
                            ,2021     ,"Toi Puaki Only"        ,187                    
                            ,2022     ,"Toi Puaki Only"        ,182                    
                            ,2023     ,"Toi Puaki Only"        ,194                    
                            ,2015     ,"Total Economy"         ,602
                            ,2016     ,"Total Economy"         ,631
                            ,2017     ,"Total Economy"         ,665
                            ,2018     ,"Total Economy"         ,708
                            ,2019     ,"Total Economy"         ,738
                            ,2020     ,"Total Economy"         ,778
                            ,2021     ,"Total Economy"         ,788
                            ,2022     ,"Total Economy"         ,824
                            ,2023     ,"Total Economy"         ,865) 

actor_workforce %>%
  ggplot(aes(x = Year)) +
  geom_point(data = actor_workforce %>% filter(Category == "Total Economy"),
             aes(y = Workforce_Size, color = "Total Economy"),
             size = 3, show.legend = TRUE) +
  geom_line(data = actor_workforce %>% filter(Category == "Total Economy"),
            aes(y = Workforce_Size, linetype = "Total Economy"),
            size = 1, color = "#007282") +
  geom_col(data = actor_workforce %>% filter(Category == "Toi Puaki Only"),
           aes(y = Workforce_Size, fill = "Toi Puaki Only"),
           color = "black") +
  geom_text(data = actor_workforce %>% filter(Category == "Toi Puaki Only"),
            aes(y = Workforce_Size, label = Workforce_Size), vjust = -1) +
  geom_text(data = actor_workforce %>% filter(Category == "Total Economy"),
            aes(y = Workforce_Size, label = Workforce_Size), size = 3, vjust = 2) +
  scale_color_manual(values = "#007282", name = "Category",
                     labels = c("Total Economy")) +
  scale_linetype_manual(values = "dashed", name = "Category",
                        labels = c("Total Economy")) +
  scale_fill_manual(values = "steelblue", name = "Category",
                    labels = c("Toi Puaki Only")) +
  theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
  labs(x = NULL, y = NULL,
       title = "Acting workforce (Toi Puaki VS. Total Economy)",
       subtitle = "2015 - 2023",
       caption = "Datasource: Infometrics") +
  theme(legend.position = "top",
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())
Figure 5: Breakdown By Ethnicity

Breakdown of Maori Actor Workforce

When this file was drafted, the official result of 2023 CENSUS has yet to be released into Statistic New Zealand’s Integrated Data Infrastructure. Therefore, the information on Figure 6 below was generated and aggregated with information from different sources for projecting an indicative view of what Maori acting workforce within Toi Puaki sector might look like from 2015 to 2022.

Code
tribble(~'Year',  ~'Maori Workforce %', ~'Total Acting Workforce'
         ,2015         ,0.1396                ,158
         ,2016         ,0.1402                ,164
         ,2017         ,0.1460                ,172
         ,2018         ,0.1416                ,184
         ,2019         ,0.1436                ,192
         ,2020         ,0.1452                ,197
         ,2021         ,0.1451                ,187
         ,2022         ,0.1822                ,182) %>% 
    mutate(Total_Maori_Actor= `Maori Workforce %` * `Total Acting Workforce`) %>% 
    ggplot(aes(Year,Total_Maori_Actor))+
    geom_col(fill="#007282")+
    geom_label(mapping=aes(label=round(Total_Maori_Actor,0)))+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Potential Maori Actor Workforce Size By reporting year",
       subtitle = "2015 - 2021",
       caption = "Datasource: Infometrics") +
  theme(legend.position = "top",
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())
Figure 6: Maori Actor Workforce Size

Programme Enrolment And Completion

Following are relevant qualifications that Toi Mai Workforce Development Council currently looks after:

                      NZC in Drama (Level 4) ref. 3415
                      NZC in Drama (Level 5) ref. 3428
                      NZC in Drama (Level 6) ref. 3429

Enrolment

Figure 7 shows the enrolment trend for above qualifications. Due to TEC’s suppression rule, the information relates to both New Zealand Certificate in Drama (Level 4) and New Zealand Diploma in Drama (Level 5) looks intermittent, with some years showing no enrolment while others record a modest number. On the other hand, New Zealand Diploma in Drama (Level 6) showcases a more consistent pattern, with a steady increase in enrolment from 2019 to 2022, indicating sustained student engagement and potentially enhanced programme offerings or market demand for higher-level drama qualifications.

Code
enrolment <- tribble(~'NZQA Qualification Code',    ~'NZQA Qualification Title',    ~'NZQA Qualification Status',   ~'TEC Qualification Code',  ~'NZQF Level Degree Split',  ~'2017', ~'2018',  ~'2019',    ~'2020',    ~'2021',    ~'2022',    ~'2023',
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   0,  5,  0,  0,  5,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   0,  0,  30, 0,  15, 15, 0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   0,  0,  20, 70, 90, 90, 55
)

enrolment %>% 
    pivot_longer(!c(`NZQA Qualification Code`,
                    `NZQA Qualification Title`,
                    `NZQA Qualification Status`,
                    `TEC Qualification Code`,
                    `NZQF Level Degree Split`),names_to = 'Year', values_to = 'Enrolment') %>% 
    ggplot(aes(Year, Enrolment, fill=`NZQA Qualification Title`))+
    geom_col()+
    geom_text(aes(label=Enrolment))+
    scale_fill_brewer(type = "qual", palette = "Set2")+
    facet_wrap(~`NZQA Qualification Title`,nrow = 2)+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Enrolment By reporting year",
       subtitle = "2017 - 2023(August)",
       caption = "Datasource: Tertiary Education Commission (The information was impacted by the suppression rule)",
       fill = "Legend") +
  theme(
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())+
    theme(legend.position = "top")
Figure 7: Enrolment By Year

Enrolment By Gender

Figure 8 shows enrolment patterns by gender for relevant programmes. Despite potential limitations imposed by data suppression rules, a clear trend emerges: these programmes exhibit a stronger appeal among female students.

Code
Enrolment_By_Gender <- tribble(~'NZQA Qualification Code',  ~'NZQA Qualification Title',    ~'NZQA Qualification Status',   ~'TEC Qualification Code',  ~'NZQF Level Degree Split', ~'Gender',  ~'2017',    ~'2018',    ~'2019',    ~'2020',    ~'2021',    ~'2022',    ~'2023',
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Another Gender',   0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Another Gender',   0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Another Gender',   0,  0,  0,  0,  0,  0,  50,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Female',   0,  0,  0,  0,  5,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Female',   0,  0,  20, 0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Female',   0,  0,  10, 35, 50, 35, 25,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Male', 0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Male', 0,  0,  10, 0,  10, 10, 0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Male', 0,  0,  10, 30, 40, 50, 25)

Enrolment_By_Gender %>% 
    pivot_longer(!c(`NZQA Qualification Code`,
                    `NZQA Qualification Title`,
                    `NZQA Qualification Status`,
                    `TEC Qualification Code`,
                    `NZQF Level Degree Split`,
                    `Gender`),names_to = "Year",values_to = "Enrolment") %>% 
    ggplot(aes(Year,Enrolment,group=Gender,color=Gender))+
    geom_point()+
    geom_line()+
    scale_color_brewer(type="qual",palette = "Set1")+
    facet_wrap(~`NZQA Qualification Title`,nrow = 2)+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Enrolment By gender and reporting year",
       subtitle = "2017 - 2023(August)",
       caption = "Datasource: Tertiary Education Commission (The information was impacted by the suppression rule)",
       fill = "Legend") +
  theme(
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())+
    theme(legend.position = "top")
Figure 8: Enrolment By Year

Enrolment By Ethnicity

The data visualization, represented by Figure 9, delineates the enrollment trends across different ethnic groups for three distinct qualifications. However, the efficacy of insights gleaned from the data is hampered by the TEC’s suppression rule. Nonetheless, a prevailing trend emerges wherein the majority of enrolled students across relevant programs are of European descent. Notably, the graph pertaining to the New Zealand Diploma in Drama (Level 6) prominently showcases the involvement of Māori students. Their participation exhibits a discernible rise starting from 2020, reaching its zenith in 2021, followed by a subsequent decline. This trajectory underscores the fluctuating but notable engagement of Māori students within the specified qualification, offering valuable insights into enrollment dynamics and potential areas for further examination and support.

Code
Enrolment_By_Ethnicity <- tribble(~'NZQA Qualification Code',   ~'NZQA Qualification Title',    ~'NZQA Qualification Status',   ~'TEC Qualification Code',  ~'NZQF Level Degree Split', ~'Ethnicity',   ~'2017',    ~'2018',    ~'2019',    ~'2020',    ~'2021',    ~'2022',    ~'2023',
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Asian',    0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Asian',    0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Asian',    0,  0,  0,  0,  0,  0,  0,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'European', 0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'European', 0,  0,  15, 0,  10, 10, 0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'European', 0,  0,  20, 50, 65, 60, 40,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Māori',    0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Māori',    0,  0,  5,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Māori',    0,  0,  0,  15, 20, 15, 10,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'PacificPeople',    0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'PacificPeople',    0,  0,  5,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'PacificPeople',    0,  0,  0,  0,  0,  5,  0,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'MELAA',    0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'MELAA',    0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'MELAA',    0,  0,  0,  0,  5,  5,  0,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'OtherEthnicity',   0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'OtherEthnicity',   0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'OtherEthnicity',   0,  0,  0,  0,  0,  0,  5
)

Enrolment_By_Ethnicity %>% 
    pivot_longer(!c(`NZQA Qualification Code`,
                    `NZQA Qualification Title`,
                    `NZQA Qualification Status`,
                    `TEC Qualification Code`,
                    `NZQF Level Degree Split`,
                    `Ethnicity`),names_to = "Year",values_to = "Enrolment") %>% 
    ggplot(aes(Year,Enrolment,group=Ethnicity,color=Ethnicity))+
    geom_point()+
    geom_line()+
    scale_color_brewer(type="qual",palette = "Set1")+
    facet_wrap(~`NZQA Qualification Title`,nrow = 2)+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Enrolment By ethnicity and reporting year",
       subtitle = "2017 - 2023(August)",
       caption = "Datasource: Tertiary Education Commission (The information was impacted by the suppression rule)",
       fill = "Legend") +
  theme(
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())+
    theme(legend.position = "top")
Figure 9: Enrolment By Year

Enrolment By Age-Band

The data visualization, represented by Figure 10, illustrates the enrollment patterns categorized by age bands. Despite the challenges posed by the TEC’s suppression rule on the interpretability of insights, a discernible trend emerges: relevant programs tend to attract students predominantly under the age of 25. This observation underscores the appeal of these programs to younger demographics.

Code
Enrolment_By_Age <- 
tribble(~'NZQA Qualification Code', ~'NZQA Qualification Title',    ~'NZQA Qualification Status',   ~'TEC Qualification Code',  ~'NZQF Level Degree Split', ~'Age Band',    ~'2017',    ~'2018',    ~'2019',    ~'2020',    ~'2021',    ~'2022',    ~'2023',
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   'Under 25', 0,  5,  0,  0,  5,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   'Under 25', 0,  0,  25, 0,  15, 10, 0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   'Under 25', 0,  0,  20, 55, 75, 75, 50,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   '25 - 39',  0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   '25 - 39',  0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   '25 - 39',  0,  0,  0,  10, 10, 10, 0,
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   '40 and Over',  0,  0,  0,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   '40 and Over',  0,  0,  0,  0,  0,  0,  0,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   '40 and Over',  0,  0,  0,  0,  0,  0,  0)

Enrolment_By_Age %>% 
    pivot_longer(!c(`NZQA Qualification Code`,
                    `NZQA Qualification Title`,
                    `NZQA Qualification Status`,
                    `TEC Qualification Code`,
                    `NZQF Level Degree Split`,
                    `Age Band`),names_to = "Year",values_to = "Enrolment") %>% 
    ggplot(aes(Year,Enrolment,group=`Age Band`,color=`Age Band`))+
    geom_point()+
    geom_line()+
    scale_color_brewer(type="qual",palette = "Set1")+
    facet_wrap(~`NZQA Qualification Title`,nrow = 2)+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Enrolment By Age Band and reporting year",
       subtitle = "2017 - 2023(August)",
       caption = "Datasource: Tertiary Education Commission (The information was impacted by the suppression rule)",
       fill = "Legend") +
  theme(
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())+
    theme(legend.position = "top")
Figure 10: Enrolment By Year

Completion

Figure 11 demonstrates completion counts by programme and reporting year. Due to the impact of TEC’s suppression rule, completion patterns for New Zealand Certificate in Drama (Level 4) look intermittent, with some years showing minimal or no completions. New Zealand Diploma in Drama (Level 5) shows a similar intermittent pattern, peaking in 2019 and showing minimal completions in 2021 and 2022. Conversely, the Level 6 diploma showcases a progressive increase in completions from 2020 to 2022.

Code
completion <- tribble(~'NZQA Qualification Code',   ~'NZQA Qualification Title',    ~'NZQA Qualification Status',   ~'TEC Qualification Code',  ~'NZQF Level Degree Split', ~'2017',    ~'2018',    ~'2019',    ~'2020',    ~'2021',    ~'2022',
'3415', 'New Zealand Certificate in Drama (Level 4)',   'Current',  'NZ3415',   '04',   0,  5,  0,  0,  0,  0,
'3428', 'New Zealand Diploma in Drama (Level 5)',   'Current',  'NZ3428',   '05',   0,  0,  25, 0,  5,  10,
'3429', 'New Zealand Diploma in Drama (Level 6)',   'Current',  'NZ3429',   '06',   0,  0,  0,  20, 25, 45
) 

completion %>% 
    pivot_longer(!c(`NZQA Qualification Code`,
                    `NZQA Qualification Title`,
                    `NZQA Qualification Status`,
                    `TEC Qualification Code`,
                    `NZQF Level Degree Split`),names_to = 'Year', values_to = 'Completion') %>% 
    ggplot(aes(Year, Completion, fill=`NZQA Qualification Title`))+
    geom_col()+
    geom_text(aes(label=Completion))+
    scale_fill_brewer(type = "qual", palette = "Set2")+
    facet_wrap(~`NZQA Qualification Title`,nrow = 2)+
    theme_minimal(base_size = 12, base_family = 'Source Sans Pro') +
    labs(x = NULL, y = NULL,
       title = "Completion By reporting year",
       subtitle = "2017 - 2023(August)",
       caption = "Datasource: Tertiary Education Commission (The information was impacted by the suppression rule)",
       fill = "Legend") +
  theme(
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 10),
        axis.title = element_blank(),
        panel.grid = element_blank())+
    theme(legend.position = "top")
Figure 11: Completion By Year