Gender export gap in Scotland: research

3. Methodology

To provide greater insight into understanding the gender exporting gap in Scotland, we collected evidence in numerous ways and from different sources. Our data were collected during the month of May 2024 via quantitative and qualitative methods. To quantify the economic impact of women’s under-participation in exporting on Scotland’s economy we performed several calculations using a conditional difference-in-difference (CDiD) approach. This combined propensity score matching (PSM) and difference-in-difference (DiD) approaches to identify the direct impacts of entering an export market. To identify the mechanisms to support exporting and address the barriers that women-led SMEs face, in-depth interviews with women entrepreneurs (exporters and non-exporters) and ESOs were undertaken. Furthermore, we collated five case studies of successful women entrepreneurs exporting to give insights into their businesses and the support that they received to export.

3.1 Quantitative data collection

Our data came from the UK Longitudinal Small Business Survey, which provides annual information on current SME performance and the factors that affect this. The data covers all sizes of SMEs. The 2017-2020 period was selected for the main analysis as it gave us a pre-treatment and post-treatment period for matching and subsequent impact evaluation, with the treatment (exporting) selecting as occurring in 2018.

3.2 Qualitative data collection

It should be noted that the University of Strathclyde’s Code of Practice for Research Ethics was followed, and confidentiality and anonymity were maintained for all individuals who participated in the data collection.

The Research Ethics Committee at Strathclyde Business School (Hunter Centre for Entrepreneurship) approved the application for the data collection and for the participant information sheet and consent form. The participants were all informed that the study was on behalf of the Scottish Government. An email was sent to the participants inviting them to participate, highlighting the aims of the study and confirming the Scottish Government’s involvement (Appendix 3).

Stage 1: Semi-structured interviews

Semi-structured individual interviews were undertaken during May 2024 and there were three groups of participants: women who were exporting (Table 2 - 16 interviews), women entrepreneurs who were not exporting (Table 3 - 10 interviews), and ESOs, those agencies who provide support for exporting in Scotland (9 interviews). All interview protocols were discussed and confirmed with Scottish Government and are included in Appendix 4.

We employed a purposeful sampling method which is often used in qualitative research to select a specific group of individuals. In this study, we were conscious to ensure that the sampling of the participants represented various perspectives, characteristics and/or experiences related to the research aims. As such this allowed us to focus on specific areas of interest and gather in-depth data on the topic in question re. gender, exporting, SMEs, Scotland and cross-sector participation, with a focus on the service sector. Purposeful sampling was also appropriate because it is commonly used in small-scale studies with limited sample size.

The selection of businesses and other relevant participants was undertaken in consultation with the Scottish Government. We reached out to our sampling group through our personal and professional networks, through LinkedIn and various organisations.

Table 2 shows the women exporters who were interviewed. Numerous sectors are represented from retail to manufacturing to technology, and the women had a range of products/services on offer. However, not all sectors are represented as this would require a larger sample and there are sectors where women are not represented in Scotland e.g., construction. Furthermore, there are services that cannot be exported e.g., catering, where we intentionally did not reach out to women entrepreneurs in these sectors but despite these limitations, the sample provides relevant conclusions to inform the recommendations.

Each interviewee was asked to discuss their business, opportunities, and challenges in starting up their business and exporting, their support mechanisms and the challenges they faced when growing and exporting, and finally their future plans for their business. We did not ask the participants for their turnover information, as many SMEs prefer not to answer this question (substantiated from academic research limitations and the academic team’s experience) and often, this question can stall the interview or create a barrier between interviewer/interviewee.

Table 3 shows the women who participated in the study who were not exporting. They were asked about their business, opportunities, and challenges, if they wanted to export and if they did what support they would need. Again, like the women who are exporting, the women in Table 3 have their businesses in a range of sectors which included media, retail, technology and services. If they did not want to export, they were then probed as to why they did not want to export.

We also interviewed 9 individuals who were part of the wider Scottish entrepreneurial ecosystem and offered and delivered support to SMEs.

Enterprise support organisations: Pseudonym and Position

ESO1 Chief Operating Officer

ESO2 International Trade Director

ESO3 Project Manager

ESO4 Senior Development Manager

ESO5 Head of Innovation & Entrepreneurship

ESO6 CEO

ESO7 Chair

ESO8 Chair

ESO9 Trade Services

Stage 2: Case studies of successful women entrepreneurs exporting

In parallel with conducting the interviews with the women entrepreneurs and the ESOs, we also undertook 5 case studies of successful women entrepreneurs in Scotland who were exporting. Each interviewee was asked to discuss their business, opportunities and challenges in starting up their business and exporting, their support mechanisms and the challenges they faced when growing and exporting, and finally their future plans for their business. These case studies give insights into how women export and why. The case studies were undertaken because the interviews were a procedure of gathering data whereas the case studies were a research method which allowed us to examine and analyse the data in-depth from a small number of participants with much more detailed probing and responses. Appendix 5 details the case studies.

3.3 Data analysis

3.3.1 Quantitative data analysis

The data was analysed by first using descriptive statistics to identify the proportion of women-led and male-led SMEs who exported^[2] from the total number of SMEs in Scotland. We then conducted propensity score matching (PSM) and a difference-in-difference (DID) approach to ascertain the impact of exporting on our two dependent variables – turnover and number of employees.

The DID approach enables us to compare the changes in firm performance between exporting firms and their matched non-exporting counterparts. The PSM increases the reliability of the DID calculation by matching treated firms with a control firm using observable characteristics in the before-treatment period. We selected three sets of independent variables for our matching approach: location type, industry sector, and geographical context.

We chose 2018 as our exporting year to give us two years of data before and after to capture impact. We do not report on three-year impact of exporting as 2020 was affected by COVID-19.

See Appendix 6 for the full methodological note. Below highlights a summary of our methodological process.

1. Start with all SMEs in the UK…

• Select firms that export in 2018 and discard firms that exported before 2018

2. Split the groups into UK and Scotland…

• Scotland – treatment 182
• UK – treatment 3,333

3. Split the groups by gender ownership

• Women-led/equally led treatment (Scotland, 52; UK, 1,579)
• Male-led treatment (Scotland, 100; UK, 3,128)

4. Selected three sets of independent variables for matching…

• Urban/rural; sector; nation

5. Estimate propensity scores

• Estimated using a logistic regression model.
• Conditional probability that a firm exports based on firm characteristics.

6. Match each treated unit with multiple control units with different weights

• Kernel matching allowed all firms to be matched with control firms.
• Conduct PSTest to verify control and treatment are balanced.

7. Conducted the DiD to estimate treatment effect.

• Turnover, number of employees, 2018, 2019
• Women-led/equally led & male-led
• Scottish, UK

3.3.2 Qualitative data analysis

The interviews were analysed in a systematic and inductive manner. All interviews were recorded with permission and transcribed verbatim. We followed strict guidelines in analysing and reporting qualitative research (Gioia et al., 2013). The first was to ‘clean’ the transcripts – the real-time transcribing happened simultaneously when the interviews were being audio recorded. The second step involved manually coding the data and identifying preliminary ideas in the data and grouping them into categories, known as open coding (first order codes) - key words, phrases, sentences, and paragraphs from the transcripts and field notes were highlighted. The final step involved coding and interpreting all codes to ensure verification of the data, re-coding where necessary and linking key concepts to build a narrative. The analysis was done for each group – women entrepreneurs exporting, women entrepreneurs not exporting and the ESOs.

The following section highlights the findings from the data analysis – both from the quantitative and qualitative analysis.