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Graduate outcomes and earnings

Table of contents

  1. Introduction
  2. Executive Summary
  3. Methodology
  4. Exploratory Data Analysis

1. Introduction

Universities are increasingly concerned with low graduate outcomes, specifically graduate employment rates and earnings over time. The challenges graduates face in securing jobs quickly after graduation have raised questions about the alignment between academic programmes offerings and job market needs. Understanding the long-term impact on career growth has become a priority, as universities seek to reframe underperforming programmes and promote best practices. Universities are recognizing the importance of retaining graduates in their early careers and providing robust support for their professional development through increased industry partnerships, alumni networks and mentoring programmes.

Measuring graduate success is an important key performance indicator for the University that may support funding applications and reporting requirements from the government. By understanding the long-term success of graduates, the university can identify fields of study in their offered programmes that have weaker than usual employer demand and employment earnings. It is to the University's benefit to continually monitor and update their programme offerings to align it with labour market demands to improve their graduates employability and earning potential.

The insights below contains examples of work in my previous role, presented in a way that protects sensitive and proprietary information while demonstrating my technical skills and approach.

I have modified the data to ensure confidentiality and removed any information that could identify individuals or an organisation. This approach maintains the integrity of the analysis while upholding strict data privacy standards.

2. Executive Summary

I have been tasked with helping University management to improve their understanding of graduate outcomes and earnings in the New Zealand tertiary sector. I have access to public datasets obtained from the Tertiary Education Commision (TEC) from Statistics New Zealand’s Integrated Data Infrastructure (IDI).

My goal is to identify graduate destinations and earnings in the first 9 years after completing their studies. Specifically, I want to see whether there has been changes overtime in graduates decision to go into employment, overseas or return back to study. I would also like to see which fields of study have strong employer demand and good employment earnings.

To achieve these objectives, these are the top 3 questions:

  1. Identify the broad picture of graduate destination and earnings in the first 9 years after completing their university studies.
  2. Explore which fields of study gives the most robust employment rates and how these compare across the first 9 years.
  3. Analyse in detail graduate outcomes and earnings by field of study and other demographic factors.

Results

  1. The data reveals a broad picture of gradual decline in employment rates from 70% in Year 1 to 59% in Year 9, accompanied by a simultaneous decrease in full-time study and higher study percentages, while the proportion of graduates going overseas steadily increases from 6% in Year 1 to 24% in Year 9. When we compare between age groups, graduates under 25 years old show larger differences as compared to graduates 25 years and above. This trend suggests a potential shift in graduate priorities and opportunities over time, with international mobility becoming increasingly attractive as domestic employment and study engagement decrease.

    The earnings data demonstrates a consistent upward trend across over the nine-year period. However, this earnings growth occurs simultaneously with a significant decline in graduate numbers, suggesting potential career progression and salary advancement for a smaller, possibly more established group of graduates.

  2. Graduates who have studied nursing, medical studies and teacher education have the highest employment rates in each of the first nine years after study. Employment rates are lowest for Physics and Astronomy, and Language and Literature graduates. This is because many continue to do further study in the first few years after graduation. A large majority of Law graduates also do further study in the following year because they often complete their 'Profs' course in order to practise law.

  3. Health and Medical Studies demonstrate the highest median earnings and employment rates, with Medical Studies reaching a median of $109,596 and an employment rate of 83.5%, while Engineering and Information Technology fields also show strong performance with high employment percentages and competitive earnings. Conversely, Creative Arts and some Society and Culture subject areas exhibit lower median earnings and employment rates, with fields like Performing Arts and Language and Literature showing the most challenging graduate outcomes, indicating significant variations in career prospects across different academic disciplines.

3. Methodology

I can access the Excel datasets that contains graduate destinations and earnings information through the Tertiary Education Commission's public facing website. (source)

Dataset information

  1. Outcome year after graduation (1,3,5,7,9) -The year after graduation in which graduate destinations and earnings are measured ('Year 1' to 'Year 9')
  2. Graduation level - Level of completed qualification: 'Certificates L1-3', 'Certificates/Diplomas L4+', 'Degrees/Graduate diplomas' or 'Postgraduate'.
  3. Broad field of study
  4. Narrow field of study
  5. Headcount measure - The number of graduates in the cohort.

Demographics

  1. Age group - Graduate's age as at 1 July in year of graduation: 'Under 25', '25-39', '40 and over'
  2. Ethnicity - 'Māori', 'Pasifika', 'Non-Māori and non-Pasifika'.
  3. Gender - 'Male' or 'Female'

Outcome and earning measures to focus on:

  1. Median earnings (NZD) in the outcome year after graduation
  2. Employed % - Percentage of graduates who in the outcome year had income from employment sources above 50% of the minimum wage, measured over the 12 month period.
  3. Full-time study % - Percentage of graduates who in the outcome year were enrolled in a formal study of >=0.8 EFTS.
  4. Higher study % - Percentage of graduates who in the outcome year were enrolled in a formal study at a level higher than the completed qualification level.
  5. Went overseas % - Percentage of graduates who were out of NZ for more than 9 months in the year after graduation.

Other outcome measures:

  1. Non-Higher study % - Percentage of graduates who in the outcome year were enrolled in a study at a level same or lower than the graduated qualification level.
  2. Moved into employment: Percentage of graduates who were not qualified as employed 2 years prior to qualification completion and are employed in the outcome year.
  3. Jobseeker: Percentage of graduates who in the outcome year were in receipt of a jobseeker benefit for >183 days.
  4. Other: Percentage of graduates who did not qualify into any of the above outcome types in the outcome year.

Comments on the data

The dataset contains 419,047 rows and 35 columns, with no duplicate rows. Some earnings cell values were suppressed and given an S value due to low number of employed graduates.

Methods

My exploratory data analysis involved data inspection, data cleaning, and data visualisation. For data inspection, I imported the Excel dataset onto SQL Server Management Studio (SSMS) to understand the dataset structure, identify missing values, detect duplicates and ensure the variables are in the correct datatype format. To clean the data, I used Power Query to replaced S values with 10. Within Power BI's model view, I created an age group sort table and an ethnicity sort table that joins to the main dataset table which does the job of organising the order required for the user's ease of use. For visuaisation, line charts, ribbon chart and matrix table are incorporated to identify relationships between variables. I have also added slicers in each report page to allow users to filter data dynamically, updating visuals in real time based on the selected criteria.

4. Exploratory Data Analysis

4.1 Identify the broad picture of graduate destination and earnings in the first 9 years after completing their university studies.

One method to identify this broad picture is by creating two line chart tables; the first chart displaying the graduates' four most important outcome measures; Employed &, Full-time study %, Higher study % and Overseas % over as a legend over the first nine years after completion and the second chart displaying the graduates' earnings using a first quartile earning, the median earning and the third quartile earning as a legend over the first nine years. This approach provides an overview of how these four outcome measures and earnings are tracking over time and the patterns of increase or decrease it is showing.

4.2 Explore which fields of study gives the most robust employment rates and how these compare across the first 9 years.

I have chosen to use the ribbon chart visual in Power BI Desktop to showcase how graduates' field of study rank and to see the shifts of the relative order over the first nine years. For example, the employment rate is ranked highest for graduates in Education (orange ribbon) and Health (blue ribbon) and this order or rank is consistent throughout the first nine years. On the other hand, employment rate for graduates in Engineering and Related Technologies are ranked third highest in the first year but eventually dropped significantly over the following years.

In addition to the employment rate, users can select other outcomes such as full-time study %, higher study %, overseas % and median earnings (NZD). Engineering and Related Technologies graduates in this study area tend to go overseas by the ninth year after graduating. Not surprisingly, the median earnings for these graduates are also one of the highest, indicating that there be more prospects overseas.

4.3 Analyse in detail graduate outcomes and earnings by field of study and other demographic factors.

A matrix table enables side-by-side comparisons of median earnings, employment rate, and other outcome measures. This makes it easier to identify standout fields, such as high earnings in Medical Studies versus lower employment percentages in Human Welfare Studies.

This table also provides both aggregate-level insights (e.g., by broad study area) and detailed metrics for narrow study area for example Medical Studies having the highest median earnings but a relatively low higher study % compared to Nursing.