Case study This paper is a study on self-employment, which we define as the situation in which
an individual works for him/herself directly providing services and does not have a
contract of employment.
Our analysis is based on the British Household Panel Survey, which collects vital
information on a selected panel of 10,264 individuals from households over various
regions of Great Britain. For our dependent variable ‘jbsemp’, 4112 observations that
were reported as “missing” or “inapplicable” were dropped, leaving us with 6,152
individuals that reported themselves as either employed or self-employed. This
allowed for a more direct measure of the effect of other explanatory variables on our
dependent variable. Based on our literature review, we will be analysing those
variables in further detail in our report.
From Table 1, we can see the means of specific variables based on one’s employment
status (employed or self-employed.) The data from the table shows that 26% of the
respondents who were self-employed were females compared to the 49% of the
females that were employed. Furthermore, white respondents comprised of more than
90% of the dataset for both employed and self-employed personnel. While this large
statistic is justified by the fact that the dataset is a British Household survey, it
displays an early sign that the dataset may not permit a more detailed ethnic
breakdown of self-employment rates.
Age:
Looking at our first key explanatory variable, we see from Graph 1 that, for both
males and females, there is an increase in the rate of self-employment as one’s age
increases (average age 42). This leads us to believe that the likelihood of a person
being self-employed at an older age may be reflected by his/her past employment
experience and the capital accumulated over the years (Simoes, Moreira, & Crespo,
2013). Graph 2 displays this increasing trend, which is also suggested by the mean
working experience of 23.8 years for those self-employed compared to 17.4 for those
employed.
Region:
Our initial interest in region spurred from ‘Skills for Self-employment, Annex LFS
Analysis’ (Meager & Carta, 2011) which suggested that self-employment may be
higher in more metropolitan areas as people would be more likely to take
entrepreneurial roles in more dynamic local economies. However, by dividing the
geographic regions of UK into metropolitan, unitary authority, non-metropolitan and
greater London, we observe that this is not the case (Graph 3). The average rate of
self-employment for metropolitan areas (0.11) is in fact lower than that for nonmetropolitan
areas (0.15), although the average rate for Greater London remains high
(0.15).

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