Analytical Studies Branch Research Paper Series
Survival and Performance of Start-ups by Gender of Ownership: A Canadian Cohort Analysis

by Lydia Couture and Stephanie Houle
11F0019M No. 450
Release date: September 10, 2020

Acknowledgements

This study was funded by Women and Gender Equality Canada.

Abstract

This paper provides an overview of women-owned start-ups in Canada. The recently developed data on the gender of business owners, as well as longitudinal data on firm entry by cohort for Canadian private corporations, make it possible MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ for the first time MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ to examine the entry and performance of women-owned start-ups from 2005 to 2013. In this paper, the annual number, size and industry concentration of new majority women-owned firms are examined and compared with majority men-owned and equally owned entrants. Subsequently, the survival rate and performance of surviving new private corporations by gender are examined.

Executive summary

Issues pertaining to gender diversity in business ownership are of interest to policy makers and researchers. This paper uses newly developed data identifying the gender of business owners and longitudinal cohorts of firms from 2005 to 2013 to investigate differences in firm survival and labour productivity between majority men-owned, majority women-owned and equally owned start-ups with less than 100 employees. These are first examined at an aggregate level and then broken down by industry.

This paper outlines a number of features of women-owned start-ups. First, less than one-fifth of new entrants were women-owned, similar to what was observed in the overall firm population. Women-owned start-ups were not concentrated in a particular business size category and tended to have a stronger presence in the service industries. Second, women-owned start-ups had an overall lower survival rate than men-owned and equally owned start-ups, with equally owned start-ups having the highest survival rate when controlling for firm size but not industry. Third, women-owned start-ups had lower labour productivity than equally owned and men-owned start-ups, with men-owned start-ups having the highest overall labour productivity when controlling for capital but not industry. Fourth, when industry differences were accounted for, women-owned start-ups were more productive and more likely to survive than men-owned start-ups in industries in which they had a large presence (i.e., retail trade, and accommodation and food services, but not health care and social assistance).

These results highlight the importance of identifying industry-specific barriers that may still remain for women-owned start-ups.

1 Introduction

The entry of new firms into the market is an important mechanism for new ideas and innovations to be brought into the economy (Schumpeter 1912, 1943). Therefore, the entry and exit dynamics of new businesses (Ciobanu and Wang 2012; Macdonald 2014) and their life patterns (Macdonald 2012), productivity performance relative to incumbents (Baldwin and Lafrance 2011; Gu and Lafrance 2014; Liu and Tang 2017), and contribution to aggregate employment (Rollin 2012) have been tracked in numerous studies in Canada.

There is a large body of literature on self-employment and business ownership among women (e.g., Jeon and Ostrovsky 2016; Moyser 2017). These studies often examine the individual motivations for becoming self-employed or a business owner, the personal remuneration earned from becoming self-employed or a business owner, and the transition between and duration of different labour market states. These studies are based primarily on data where the unit of observation MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ or survey unit MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ is the individual. The incorporation of business data into these types of analyses makes it possible to study the economic performance of women-owned firms.

In Budget 2018, the Government of Canada emphasized the importance of women’s economic participation and developed the Women Entrepreneurship Strategy. To better understand the status of women entrepreneurs in Canada, this paper used innovative data on cohorts of new firms to examine the characteristics of women-owned start-ups, their survival and their labour productivity, compared with men-owned and equally owned start-ups in Canada.

Previous studies using survey data have looked at business performance differences between women-owned and men-owned firms and found that women-owned firms tended to underperform or be less likely to survive (Robb 2002; Du Rietz and Henrekson 2000; Brusch 1992; Rosa, Carter and Hamilton 1996; Fisher 1992; Fairlie and Robb 2009). However, another set of papers argued that this underperformance is only because of differences in the size of the business and the risk aversion of the business owner being improperly accounted for (Kalleberg and Leicht 1991; Marlow and McAdam 2013; Watson 2002; Watson and Robinson 2003; Robb and Watson 2012).

There are also barriers to entry that women-owned firms have been found to face MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ women tend to have greater difficulty accessing capital and obtaining bank loans to start a business. Bates (2002) found that, when seeking to operate in a new market, women-owned enterprises in the manufacturing sector have less access to business clients than men-owned firms. Using the 2011 Survey on Financing and Growth of Small and Medium Enterprises, Rosa and Sylla (2016) found that majority women-owned small and medium enterprises (SMEs) were less likely to receive the full amount of financing they requested, and the amount they received was at a higher interest rate, than majority men-owned SMEs.

Controlling for firm size and age, the current study found that women-owned businesses have lower survival rates and labour productivity overall than men-owned start-ups, indicating that women-owned start-ups do not fare as well in the Canadian market as men-owned start-ups.Note  For this paper, labour productivity was measured as the firm’s value added divided by the number of workers it employed.

However, this is only one part of the story. Bates (1995) and Anna et al. (2000) demonstrated how important it is to account for industry in measuring self-employment outcomes for women-owned firms. Kalnins and Williams (2014) measured women-owned firm survival, controlling for industry-specific effects, and found that women-owned businesses were more likely to survive in the retail industry in particular. The current paper found that, after industry was controlled for, women-owned start-ups were relatively more productive and more likely to survive than men-owned start-ups in industries in which they were more concentrated, with the exception of health care and social assistance. This new evidence of how the majority gender of ownership and industry intersect on firm performance measures may help policy makers support women as entrepreneurs.

Start of text box

Key concepts

Labour productivity: A measure of output per unit of labour, here defined as value added per employee.

Value added: Total output minus the value of intermediate inputs (e.g., goods used in the production process). In this study, value added is approximated by the sum of the compensation of employees (total payroll, a measure of return to labour) and capital income (a measure of return to capital).

End of text box

The remainder of the paper is organized as follows. Section 2 describes the firm data and new data source for the determination of gender of ownership. Section 3 presents tabulations of entrants by size and industry. Section 4 compares the survival of firms and Section 5 compares the performance of new firms by majority gender of ownership. Section 6 provides a discussion of the results in greater detail, and Section 7 concludes.

2 Data

The National Accounts Longitudinal Microdata File (NALMF) developed by the Economic Analysis Division at Statistics Canada was used to identify cohorts of start-up firms in Canada from 2005 to 2013. This firm-level dataset was produced by linking various administrative datasets from the Canada Revenue Agency (CRA) using Statistics Canada’s Business Register. In each annual cross-section, the universe of the NALMF is defined as all enterprises in Canada that filed either a T2 Corporate Income Tax Return, a T4 Statement of Remuneration Paid or a PD7 payroll deduction form. As a result, the NALMF is a rich firm microdata set comprising firm characteristics, such as detailed industry classification, employment, payroll and revenue.

The NALMF also includes a set of predecessor MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=nbiaaa@37A3@ successor tables that were constructed using T4 slips and labour tracking for each pair of consecutive years. Labour tracking was first used in Statistics Canada’s Longitudinal Employment Analysis Program to create a longitudinal history of firms in which spurious births and deaths (that could be the result of structural or administrative changes) were removed from the dataset.Note 

In this paper, for a given year, a firm was classified as a birth if it (1) had a new business identifier, (2) had one or more employees for the first time, and (3) was not identified as a successor in labour tracking. To ensure that the files created provide a comprehensive picture of start-up firms, entrants with more than 100 employees were excluded. A start-up was considered as a death if its business identifier no longer existed in the NALMF and had not been flagged as a predecessor in labour tracking.

Data from the Canadian Employer MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=nbiaaa@37A3@ Employee Dynamics Database (CEEDD) were used to assign the gender of ownership of firms to the NALMF data (see Grekou, Li and Liu 2018a). This module used Schedule 50 of the T2 Corporate Income Tax Return form. This schedule provides key information on the type of shareholders (i.e., company, individual and income trust) and owned shares for all shareholders with at least 10% ownership. Demographic characteristics of these shareholders, including sex, were then obtained by linking the T1 individual tax files to the T2 corporate tax files. In this paper, the sex (i.e., male or female) of the individual was recorded by the CRA as a proxy for gender.

A firm owner’s gender was identified by gender majority MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ the gender with the highest share of ownership. If 51% or more of the shares were owned by women, the firm was defined as majority women-owned. A similar logic was used to identify majority men-owned firms. Firms were identified as equally owned when men and women each owned 50% of the firm.

2.1 Coverage and limitations

While the use of newly linked administrative files such as the NALMF and CEEDD allows for the measurement of Canadian statistics on the entry of women-owned firms for the first time, the data present various challenges and limitations.

First, only Canadian-controlled private corporations (CCPCs) are required to file a Schedule 50 as part of their corporate tax filings. As a result, gender of ownership information is available only for private corporations (i.e., non-publicly traded firms). A CCPC is an incorporated firm that is not controlled MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ directly or indirectly in any manner MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ by public corporations, non-residents or a combination of the two. It also excludes corporations such as Crown corporations and general insurers. Therefore, this study excludes public corporations and is limited to the sample of incorporated CCPCs with non-zero employment in the NALMF.

Second, since only shareholders of private corporations with at least 10% of shares are required to file a Schedule 50 and since, in some cases, the quality of reporting is poor, the majority gender of ownership cannot be established for all private corporations. CCPCs without an assigned gender can be divided into two categories: (1) firms for which the information is missing and no Schedule 50 was filed and (2) firms for which the information provided by the schedule is insufficient to establish the majority gender of ownership. The former category could include CCPCs owned by companies rather than individuals. Overall, the sample used for this analysis MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ CCPCs for which a majority gender of ownership can be attributed MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ covers roughly three-quarters of CCPCs in the NALMF (see Appendix Table A.1).

The evolution of cohorts of start-up firms from 2005 to 2013 was examined in this analysis. Although entrants were identified from 2005 to 2013 only, at the time of this analysis, the information on these entrants (e.g., balance sheet, employees, survival status) could be tracked up to 2015. The gender of ownership is the gender defined during the firm’s first year of existence, unless it is missing. If it is missing but defined within the next two years, it is used to define the majority gender of ownership of that firm.

While majority gender of ownership for a start-up is defined in the data as it is described above, in reality, the shareholders and, as a result, the majority gender of business ownership, may change over time. However, this impact of potential bias is limited, as only 0.2% of surviving firms switched to becoming women-owned firms from being either men-owned or equally owned firms, and only 0.3% went from being women-owned to either men-owned or equally owned.

3 Overview of entry

Similar to the findings of Grekou, Li and Liu (2018b), men-owned firms represented approximately two-thirds (67.4%) of all active private enterprises in Canada for which a majority gender of ownership could be attributed from 2005 to 2013.Note  Women-owned firms represented 16.8% of firms, while 15.8% were equally owned (see Appendix Table A.2). Over this period, the number of women-owned firms increased at a faster pace than the number of men-owned firms, with the share of women-owned firms growing.

Using data from entry cohorts 2005 to 2013, Chart 1 shows that the distribution of entrants by gender of ownership was comparable to what was observed in the overall firm population. Less than one-fifth of new private corporations for which a gender of ownership could be identified were majority owned by women. However, the share of women-owned start-ups increased over the period, while the share of equally owned start-ups declined over the same period. Together, women-owned and equally owned firms represented roughly one-third of entrants, while the majority (two-thirds) of new firms were majority owned by men.

Chart 1 Breakdown of new firms by majority gender of ownership, 2005 and 2013

Data table for Chart 1 
Chart 1 Breakdown of new firms by majority gender of ownership, 2005 and 2013
Table summary
This table displays the results of Chart 1 Breakdown of new firms by majority gender of ownership, 2005 and 2013. Men-owned, Women-owned and Equally owned, calculated using percent units of measure (appearing as column headers).
Men-owned Women-owned Equally owned
percent
2005 62.63 17.64 19.73
Average 62.98 18.56 18.47
2013 63.32 19.47 17.21

To examine the concentration of start-ups by firm size (Chart 2), three size categories were defined: micro (fewer than five employees), small (5 to 19 employees) and medium (20 to 99 employees).Note  Women-owned entrants were not concentrated in a particular size category MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ they represented roughly one-fifth of start-ups in each category from 2005 to 2013. Men-owned start-ups represented about two-thirds of entrants in all size categories and represented a larger share of medium-sized entrants. Equally owned start-ups were more likely to be micro or small firms rather than medium-sized entrants with 20 employees or more. Grekou (2020) found that women who start a business were more likely than men to go into business with a partner or have a partner who owns their own business.

Overall, the data show that the number of women-owned micro entrants increased more than that of men-owned micro entrants, and it decreased less than men-owned firms in the count of small entrants. As a result, the women-owned share of micro and small entrants increased over the period. The total number of medium entrants decreased, but the number of men-owned start-ups decreased less than that of women-owned start-ups. Therefore, men-owned entrants’ share of medium-sized firms increased. Overall, the share of micro and small women-owned start-ups increased from 2005 to 2013, while their share of medium-sized start-ups decreased. The share of medium-sized men-owned start-ups increased significantly. The share and count of equally owned start-ups decreased in all size categories over the period.

Chart 2 Breakdown of new enterprises by majority gender of ownership and firm size, 2005 and 2013

Data table for Chart 2 
Chart 2 Breakdown of new enterprises by majority gender of ownership and firm size, 2005 and 2013
Table summary
This table displays the results of Chart 2 Breakdown of new enterprises by majority gender of ownership and firm size, 2005 and 2013. Micro, Small, Medium, Men-owned, Women-owned, Equally owned and Percent of total, calculated using percent units of measure (appearing as column headers).
Micro Small Medium
Men-owned Women-owned Equally owned Percent of total Men-owned Women-owned Equally owned Percent of total Men-owned Women-owned Equally owned Percent of total
percent
2005 62.46 17.59 19.95 87.06 63.16 18.03 18.81 11.21 67.43 17.88 14.69 1.73
2013 63.23 19.40 17.37 90.27 62.54 20.96 16.50 9.83 74.66 14.86 10.49 1.55

According to several studies,Note  women-owned firms tend to have a stronger presence in the service industries. By disaggregating entrant cohorts based on the North American Industry Classification System (NAICS), a similar trend can be established for women-owned start-ups. The largest industry concentrations of women-owned entrants were observed in educational services; health care and social assistance; and arts, entertainment and recreation (Table 1).Note  Also consistent with the literature is the finding that men-owned entrants were more represented in the transportation and warehousing, and construction industries. Equally owned start-ups were most represented in mining, quarrying, and oil and gas extraction; agriculture, forestry, fishing and hunting; real estate and rental and leasing; and management of companies and enterprises.

This distribution of entrants across industries remained mostly constant over time. From 2005 to 2013, only two industries experienced changes: transportation and warehousing, and health care and social assistance. In the case of the former, the share of men-owned entrants increased at the expense of both women-owned and equally owned entrants, while for the latter an increase in women-owned and equally owned entrants greater than that in men-owned entrants resulted in them representing a larger share of the industry in 2013 than in 2005.

This study found that less than one-fifth of new entrants were women-owned. Combined, women-owned and equally owned start-ups represented only about one-third of entrants. In addition, there were fewer women-owned start-ups, although the rate of entry is rising.


Table 1
Distribution of new firms across industries, 2005 to 2013, by majority gender of ownership
Table summary
This table displays the results of Distribution of new firms across industries. The information is grouped by Industry (appearing as row headers), Men-owned, Women-owned and Equally owned (appearing as column headers).
Industry Men-owned Women-owned Equally owned
Number of firms Share of industry (percent) Number of firms Share of industry (percent) Number of firms Share of industry (percent)
11 – Agriculture, forestry, fishing and hunting 8,162 64.4 1,169 9.2 3,342 26.4
21 – Mining, quarrying, and oil and gas extraction 3,951 65.8 407 6.8 1,645 27.4
22 – Utilities 187 71.6 28 10.7 46 17.6
23 – Construction 53,666 75.7 4,946 7.0 12,265 17.3
31-33 – Manufacturing 10,530 69.5 2,179 14.4 2,448 16.2
41 – Wholesale trade 10,504 67.3 2,466 15.8 2,637 16.9
44-45 – Retail trade 24,184 55.7 10,507 24.2 8,741 20.1
48-49 – Transportation and warehousing 29,161 78.4 2,886 7.8 5,138 13.8
51 – Information and cultural industries 5,042 69.2 1,351 18.6 890 12.2
52 – Finance and insurance 5,972 60.1 2,001 20.2 1,957 19.7
53 – Real estate and rental and leasing 9,937 56.9 3,559 20.4 3,983 22.8
54 – Professional, scientific and technical services 53,696 59.4 17,884 19.8 18,781 20.8
55 – Management of companies and enterprises 3,498 61.0 979 17.1 1,261 22.0
56 – Administrative and support, waste management and remediation services 12,817 60.3 4,491 21.1 3,958 18.6
61 – Educational services 2,120 43.3 1,746 35.7 1,029 21.0
62 – Health care and social assistance 17,262 47.3 11,992 32.9 7,238 19.8
71 – Arts, entertainment and recreation 3,054 54.0 1,632 28.8 974 17.2
72 – Accommodation and food services 19,737 55.7 8,193 23.1 7,515 21.2
81 – Other services (except public administration) 15,203 56.6 6509 24.2 5141 19.1
Unclassified 44,702 62.2 14,403 20.0 12,792 21.2
Total 333,385 62.4 99,328 18.6 101,781 19.0

4 Survival analysis

4.1 Survival rates

This next section examines firms’ likelihood of survival given their entry into the market. First, a Kaplan-Meier survival curve was estimated for each majority gender of ownership, then two Cox proportional hazards models were run, first estimating hazard rates by majority gender of ownership only, and then by majority gender of ownership and industry.

The survival rate was measured for the 2005 cohort of start-up firms, which was the cohort that could be followed over the longest period in the data. It was estimated using the Kaplan-Meier hazard rate by majority gender of ownership.

The Kaplan-Meier estimator for the survival function S ( t ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGtbWaaeWaa8aabaWdbiaadshaaiaawIcacaGLPaaaaaa@3990@ was defined as

  S ( t ) =   t i t   ( 1   d i n i ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGtbWaaeWaa8aabaWdbiaadshaaiaawIcacaGLPaaacqGH9aqp caGGGcWaaybuaeqal8aabaWdbiaadshapaWaaSbaaWqaa8qacaWGPb aapaqabaWcpeGaeyizImQaamiDaaqab0WdaeaapeGaey4dIunaaOGa aiiOamaabmaapaqaa8qacaaIXaGaeyOeI0IaaiiOamaalaaapaqaa8 qacaWGKbWdamaaBaaaleaapeGaamyAaaWdaeqaaaGcbaWdbiaad6ga paWaaSbaaSqaa8qacaWGPbaapaqabaaaaaGcpeGaayjkaiaawMcaaa aa@4D8D@

where d i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGKbWdamaaBaaaleaapeGaamyAaaWdaeqaaaaa@3848@ is the number of start-ups that exited and n i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGUbWdamaaBaaaleaapeGaamyAaaWdaeqaaaaa@3852@ is the number of start-ups that survived.

Chart 3 shows that for the first four years of the start-up’s existence, the survival rates of both men-owned and women-owned start-ups were somewhat similar (though they were statistically significantly different), then they diverged after. Interestingly, firms that were equally owned had a higher survival rate than men-owned or women-owned firms. Ten years after entry, 67.0% of the equally owned entrants from the 2005 cohorts were still alive, while 61.3% and 57.1% of the men-owned and women-owned entrants had survived, respectively. This indicates that in addition to lower entry counts for women-owned firms relative to men-owned and equally owned entrants, women-owned firms also faced lower survival rates than their counterparts. As a result, the proportion of women-owned firms decreased over time for that cohort.Note 

Chart 3 Kaplan-Meier survival probability for 2005 cohort, by majority gender of ownership

Data table for Chart 3 
Chart 3 Kaplan-Meier survival probability for 2005 cohort, by majority gender of ownershipChart 3 Note 1
Table summary
This table displays the results of Chart 3 Kaplan-Meier survival probability for 2005 cohort, by majority gender of ownership. The information is grouped by Year (appearing as row headers), Men-owned survival rate, Women-owned survival rate and Equally owned survival rate, calculated using percent units of measure (appearing as column headers).
Year Men-owned survival rate Women-owned survival rate Equally owned survival rate
percent
1 99.10 98.86 99.52
2 95.83 95.49 97.35
3 91.20 90.58 93.94
4 86.06 84.94 89.63
5 81.60 79.72 85.34
6 77.27 75.07 81.85
7 73.09 70.58 78.44
8 69.13 66.11 74.47
9 65.35 61.78 71.16
10 61.30 57.06 67.00

4.2 Model

To further assess how the gender of ownership and industry relate to entrants’ survival time, a baseline Cox proportional hazards model was estimated using the following specifications of the hazard function   h ( t ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaGGGcGaamiAamaabmaapaqaa8qacaWG0baacaGLOaGaayzkaaaa aa@3AC9@ , the first one (Model 1) restricted and the second one (Model 2) unrestricted:Note 

Model 1

h ( t ) = h 0 ( t ) × exp ( β 1   g e n d e r i   + β 2 l a b o u r i + β 3 c o h o r t i ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGObWaaeWaa8aabaWdbiaadshaaiaawIcacaGLPaaacqGH9aqp caWGObWdamaaBaaaleaapeGaaGimaaWdaeqaaOWdbmaabmaapaqaa8 qacaWG0baacaGLOaGaayzkaaGaey41aqRaaeyzaiaabIhacaqGWbWa aeWaa8aabaWdbiabek7aI9aadaWgaaWcbaWdbiaaigdaa8aabeaak8 qacaGGGcGaam4zaiaadwgacaWGUbGaamizaiaadwgacaWGYbWdamaa BaaaleaapeGaamyAaiaacckaa8aabeaak8qacqGHRaWkcqaHYoGypa WaaSbaaSqaa8qacaaIYaaapaqabaGcpeGaamiBaiaadggacaWGIbGa am4BaiaadwhacaWGYbWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbi abgUcaRiabek7aI9aadaWgaaWcbaWdbiaaiodaa8aabeaak8qacaWG JbGaam4BaiaadIgacaWGVbGaamOCaiaadshapaWaaSbaaSqaa8qaca WGPbaapaqabaaak8qacaGLOaGaayzkaaaaaa@6784@

h 0 ( t )   MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGObWdamaaBaaaleaapeGaaGimaaWdaeqaaOWdbmaabmaapaqa a8qacaWG0baacaGLOaGaayzkaaGaaiiOaaaa@3BF7@ is the baseline hazard rate. Since a firm’s size is generally positively linked to its survival rate (Macdonald 2012; Grekou and Liu 2018), the variable labourNote  was added to the model. It is defined as the natural logarithm of the initial number of employees in the firm. The variable cohort controls for the cohort to which the firm belongs.

The categorical variable industry (industry was classified at the two-digit NAICS level) was then introduced in the model and estimated in the unrestricted specification:

Model 2

h ( t ) = h 0 ( t ) × exp ( β 1 g e n d e r i + β 2 i n d u s t r y i + β 3 g e n d e r i × i n d u s t r y i + β 4 l a b o u r i +   β 3 c o h o r t i ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGObWaaeWaa8aabaWdbiaadshaaiaawIcacaGLPaaacqGH9aqp caWGObWdamaaBaaaleaapeGaaGimaaWdaeqaaOWdbmaabmaapaqaa8 qacaWG0baacaGLOaGaayzkaaGaey41aqRaaeyzaiaabIhacaqGWbWa aeWaa8aabaWdbiabek7aI9aadaWgaaWcbaWdbiaaigdaa8aabeaak8 qacaWGNbGaamyzaiaad6gacaWGKbGaamyzaiaadkhapaWaaSbaaSqa a8qacaWGPbaapaqabaGcpeGaey4kaSIaeqOSdi2damaaBaaaleaape GaaGOmaaWdaeqaaOWdbiaadMgacaWGUbGaamizaiaadwhacaWGZbGa amiDaiaadkhacaWG5bWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbi abgUcaRiabek7aI9aadaWgaaWcbaWdbiaaiodaa8aabeaak8qacaWG NbGaamyzaiaad6gacaWGKbGaamyzaiaadkhapaWaaSbaaSqaa8qaca WGPbaapaqabaGcpeGaey41aqRaamyAaiaad6gacaWGKbGaamyDaiaa dohacaWG0bGaamOCaiaadMhapaWaaSbaaSqaa8qacaWGPbaapaqaba GcpeGaey4kaSIaeqOSdi2damaaBaaaleaapeGaaGinaaWdaeqaaOWd biaadYgacaWGHbGaamOyaiaad+gacaWG1bGaamOCa8aadaWgaaWcba WdbiaadMgaa8aabeaak8qacqGHRaWkcaGGGcGaeqOSdi2damaaBaaa leaapeGaaG4maaWdaeqaaOWdbiaadogacaWGVbGaamiAaiaad+gaca WGYbGaamiDa8aadaWgaaWcbaWdbiaadMgaa8aabeaaaOWdbiaawIca caGLPaaaaaa@88ED@

Controlling for industry differences is important, as some industries may have an inherently higher turnover rate for firms, and differences in industry composition across start-up gender lead to misleading results at the aggregate level.

4.3 Results

The results for Model 1 (restricted model) are summarized in Table 2. The hazard rates show that women-owned firms were 14.9% more likely to exit than men-owned firms, whereas equally owned firms were 24.5% less likely to exit than men-owned firms. As expected, the size of the firm was positively correlated with a higher probability of survival.Note 

Interestingly, equally owned start-ups had a lower probability of exit than men-owned and women-owned firms. Not only are there fewer women-owned start-ups entering the market, but those that enter are also more likely to exit.


Table 2
Cox proportional hazards model result for all cohorts, by majority gender of ownershipTable 2 Note 1
Table summary
This table displays the results of Cox proportional hazards model result for all cohorts. The information is grouped by Variables (appearing as row headers), Coefficient, Hazard rate and p-value (appearing as column headers).
Variables Coefficient Hazard rate p-value
Women-owned 0.139 1.149 0Note ***
Equally owned -0.281 0.755 0Note ***

The results of Model 2 (unrestricted Cox proportional hazards model specification) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ within-industry differences between men-owned and women-owned start-ups and men-owned and equally owned start-ups MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ are shown in charts 4 and 5, respectively.Note  Women-owned start-ups had a lower and statistically significant (at the 5% level) hazard rate of exit relative to men-owned firms in retail trade and accommodation and food services. These service industries typically have a higher concentration of women-owned firms, whereas women-owned firms in industries with a lower concentration of women-owned start-ups were more likely to exit. These industries include construction; agriculture, forestry, fishing and hunting; administrative and support, waste management and remediation services; and transportation and warehousing. However, they were much more likely to exit in health care and social assistance, an industry in which they do have a large presence.Note 

Chart 4 Relative hazard rate of exit results between men-owned and women-owned start-ups

Data table for Chart 4 
Chart 4 Relative hazard rate of exit results between men-owned and women-owned start-upsChart 4 Note 1
Table summary
This table displays the results of Chart 4 Relative hazard rate of exit results between men-owned and women-owned start-ups Percent (appearing as column headers).
Industry Percent
62–Health care and social assistance 321.9Note ***
11–Agriculture, forestry, fishing and hunting 153.2Note ***
55–Management of companies and enterprises 42.2Chart 4 Note 
56–Administrative & support, waste management & remediation services 26.9Note ***
48-49–Transportation and warehousing 25.6Note *
52–Finance and insurance 23.9
81–Other services (except public administration) 21.3Chart 4 Note 
23–Construction 19.7Note *
54–Professional, scientific and technical services 13.3
71–Arts, entertainment and recreation 10.2
53–Real estate and rental and leasing 10.1
22–Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act
41–Wholesale trade -2.1
31-33–Manufacturing -13.4
61–Educational services -18.6
44-45 – Retail trade -19.1Note *
21–Mining, quarrying, and oil and gas extraction -20.4
51–Information and cultural industries -32.4Note *
72–Accommodation and food services -49.3Note ***
unclassified -62.3Note ***

Equally owned start-ups were more likely to exit than men-owned start-ups in health care and social assistance; agriculture, forestry, fishing and hunting; finance and insurance; management of companies and enterprises; and professional, scientific and technical services. However, in administrative and support, waste management and remediation services, as well as in accommodation and food services, equally owned firms were less likely to exit than men-owned firms in their respective industries.

Chart 5 Relative hazard rate of exit results between men-owned and equally-owned start-ups

Data table for Chart 5 
Chart 5 Relative hazard rate of exit results between men-owned and equally-owned start-upsChart 5 Note 1
Table summary
This table displays the results of Chart 5 Relative hazard rate of exit results between men-owned and equally-owned start-ups. The information is grouped by Industry (appearing as row headers), Percent (appearing as column headers).
Industry Percent
62–Health care and social assistance 316.52Note ***
11–Agriculture, forestry, fishing and hunting 95.73Note ***
52–Finance and insurance 77.46Note **
55–Management of companies and enterprises 66.57Note *
53–Real estate and rental and leasing 40.00Note *
54–Professional, scientific and technical services 35.55Note **
71–Arts, entertainment and recreation 29.34
81–Other services (except public administration) 19.48
48-49–Transportation and warehousing 19.26Chart 5 Note 
61–Educational services 3.12
41–Wholesale trade 0.42
22–Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act
31-33–Manufacturing -7.58
23–Construction -11.38
44-45–Retail trade -14.65
51–Information and cultural industries -16.41
56–Administrative & support, waste management & remediation services -20.51Note ***
21–Mining, quarrying, and oil and gas extraction -22.38
72–Accommodation and food services -44.41Note ***
Unclassified -62.30Note ***

5 Performance of entrant cohorts by gender of ownership

The previous sections of this paper established that, in addition to being fewer, majority women-owned entrants tended to have a lower probability of survival than their men-owned and equally owned counterparts from 2005 to 2013. To better understand the underlying differences between entrants by majority ownership, the following section examines the average labour productivity for the first five years of all entrant cohorts with positive employment from 2005 to 2010, by gender of ownership.

Labour productivity is an indicator that reflects the effectiveness of labour use. This measure was derived by taking the firm’s value added in nominal terms and dividing it by the number of employees it has.Note  Firms that are more productive tend to be larger and survive longer and are more likely to export (Melitz 2003; Bernard et al. 2003; Scarpetta et al. 2002; Esteve-Pérez, Máñez-Castillejo and Sanchis-Llopis 2008). Chart 6 shows that men-owned and equally owned start-ups had significantly higher labour productivity than women-owned start-ups initially. However, the labour productivity of equally owned start-ups trended lower than men-owned start-ups and diverged in later years.

Chart 6 Average labour productivity of new firms, by majority gender of ownership

Data table for Chart 6 
Chart 6 Average labour productivity of new firms, by majority gender of ownership
Table summary
This table displays the results of Chart 6 Average labour productivity of new firms, by majority gender of ownership. The information is grouped by Years since birth (appearing as row headers), Men-owned, Women-owned and Equally owned, calculated using average value added per worker ($/worker) units of measure (appearing as column headers).
Years since birth Men-owned Women-owned Equally owned
average value added per worker ($/worker)
1 40,937.72 34,520.69 40,059.05
2 44,226.49 36,499.04 43,167.26
3 45,717.51 38,098.92 44,893.54
4 48,721.06 39,232.26 46,747.93
5 50,845.29 40,969.66 46,605.17
Grand total 230,448.06 189,320.57 221,472.95

This time evolution graph shows that women-owned entrants appeared less productive overall. As a result, they are less competitive and would require more resources to produce the same amount of output as men-owned or equally owned start-ups.

5.1 Regression analysis

5.1.1 Model

To further quantify the sources of differences across start-up genders, two models were estimated MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ one restricted and one unrestricted. First, the restricted model controlled only for gender, capital per worker, cohort and a time trend. The unrestricted model also controlled for industry and interaction effects between industry and gender. The unrestricted model was used to measure within-industry differences in labour productivity between the different majority genders of ownership. This made it possible to isolate differences previously attributed to gender in the aggregate numbers, rather than differences stemming from different majority genders of ownership having different industry compositions.

The restricted and unrestricted models are the following:

L P i , t =   α +   β 1 g e n d e r i +   β 2 c a p i t a l i , t + β 3 t i m e   + β 4 c o h o r t i +   ε i , t MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGmbGaamiua8aadaWgaaWcbaWdbiaadMgacaGGSaGaamiDaaWd aeqaaOWdbiabg2da9iaacckacqaHXoqycqGHRaWkcaGGGcGaeqOSdi 2damaaBaaaleaapeGaaGymaaWdaeqaaOWdbiaadEgacaWGLbGaamOB aiaadsgacaWGLbGaamOCa8aadaWgaaWcbaWdbiaadMgaa8aabeaak8 qacqGHRaWkcaGGGcGaeqOSdi2damaaBaaaleaapeGaaGOmaaWdaeqa aOWdbiaadogacaWGHbGaamiCaiaadMgacaWG0bGaamyyaiaadYgapa WaaSbaaSqaa8qacaWGPbGaaiilaiaadshaa8aabeaak8qacqGHRaWk cqaHYoGypaWaaSbaaSqaa8qacaaIZaaapaqabaGcpeGaamiDaiaadM gacaWGTbGaamyzaiaacckacqGHRaWkcqaHYoGypaWaaSbaaSqaa8qa caaI0aaapaqabaGcpeGaam4yaiaad+gacaWGObGaam4Baiaadkhaca WG0bWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgUcaRiaaccka cqaH1oqzpaWaaSbaaSqaa8qacaWGPbGaaiilaiaadshaa8aabeaaaa a@72BC@

L P i , t =   α + β 1 g e n d e r i + β 2 i n d u s t r y i + β 4 g e n d e r i × i n d u s t r y i   +   β 5 c a p i t a l i , t + β 6 t i m e + β 7 c o h o r t i + ε i , t MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGmbGaamiua8aadaWgaaWcbaWdbiaadMgacaGGSaGaamiDaaWd aeqaaOWdbiabg2da9iaacckacqaHXoqycqGHRaWkcqaHYoGypaWaaS baaSqaa8qacaaIXaaapaqabaGcpeGaam4zaiaadwgacaWGUbGaamiz aiaadwgacaWGYbWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgU caRiabek7aI9aadaWgaaWcbaWdbiaaikdaa8aabeaak8qacaWGPbGa amOBaiaadsgacaWG1bGaam4CaiaadshacaWGYbGaamyEa8aadaWgaa WcbaWdbiaadMgaa8aabeaak8qacqGHRaWkcqaHYoGypaWaaSbaaSqa a8qacaaI0aaapaqabaGcpeGaam4zaiaadwgacaWGUbGaamizaiaadw gacaWGYbWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgEna0kaa dMgacaWGUbGaamizaiaadwhacaWGZbGaamiDaiaadkhacaWG5bWdam aaBaaaleaapeGaamyAaaWdaeqaaOWdbiaacckacqGHRaWkcaGGGcGa eqOSdi2damaaBaaaleaapeGaaGynaaWdaeqaaOWdbiaadogacaWGHb GaamiCaiaadMgacaWG0bGaamyyaiaadYgapaWaaSbaaSqaa8qacaWG PbGaaiilaiaadshaa8aabeaak8qacqGHRaWkcqaHYoGypaWaaSbaaS qaa8qacaaI2aaapaqabaGcpeGaamiDaiaadMgacaWGTbGaamyzaiab gUcaRiabek7aI9aadaWgaaWcbaWdbiaaiEdaa8aabeaak8qacaWGJb Gaam4BaiaadIgacaWGVbGaamOCaiaadshapaWaaSbaaSqaa8qacaWG PbaapaqabaGcpeGaey4kaSIaeqyTdu2damaaBaaaleaapeGaamyAai aacYcacaWG0baapaqabaaaaa@9309@

The variables gender and industry are categorical. The breakdown used for industry was the same one used in Table 1 (NAICS two-digit sector level). The variable capital is the log amount of tangible assets per worker the firm has, and t is a time trend. It is important to control for capital when measuring labour productivity, as capital can influence the production process and because MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ as discussed in the introduction MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ women-owned firms tend to have greater difficulty accessing financing. All cohorts from 2005 to 2013 were used, and cohort dummies were included.Note 

5.1.2 Results

Table 3 shows labour productivity for the restricted specification used in this study. Women-owned and equally owned start-ups were 14.1% and 2.3% less productive on average than men-owned start-ups, respectively. As expected, capital per worker and start-up age were both positively related to labour productivity. These aggregate differences between genders were similar to those found in Chart 6.


Table 3
Labour productivity, restricted model results
Table summary
This table displays the results of Labour productivity. The information is grouped by Variables (appearing as row headers), Coefficient and p-value (appearing as column headers).
Variables Coefficient p-value
(Intercept) 8.964 0Note ***
Women-owned -0.141 0Note ***
Equally owned -0.023 0Note ***
Capital 0.145 0Note ***
Time 0.046 0Note ***

In the previous section, it was found that equally owned entrants had a lower probability of exit overall than men-owned or women-owned start-ups. However, labour productivity points to men-owned start-ups performing better overall than equally owned or women-owned start-ups. This could be because many equally owned firms are family-owned firms,Note  which may benefit from risk sharing and do not follow similar profit-maximizing goals. A more detailed study on the characteristics of these owners and the number of owners per firm could be an opportunity for further examination.

Chart 7 shows the results of the unrestricted specification, where the within-industry differences between majority men-owned and women-owned start-ups are highlighted. When these results are contrasted with industry concentration, women-owned start-ups were more productive in industries with a higher concentration of women-owned businesses (see Table 1), particularly in retail trade, and accommodation and food services, but not in health care and social assistance. Health care and social assistance may not share the same production optimization as other industries because of the large number of doctors who are permitted by law to incorporate, following government-regulated costing schemes that vary by province. As was shown in the previous section, not only were women-owned start-ups more likely to survive in industries in which they were more concentrated, but this section shows that they were also more productive in these industries.

Chart 7 Labour productivity unrestricted model results, within-industry differences between men-owned and women-owned start-ups

Data table for Chart 7 
Chart 7 Labour productivity unrestricted model results, within-industry differences between men-owned and women-owned start-ups
Table summary
This table displays the results of Chart 7 Labour productivity unrestricted model results, within-industry differences between men-owned and women-owned start-ups. The information is grouped by Industry (appearing as row headers), Percent (appearing as column headers).
Industry Percent
72–Accommodation and food services 0.933Note ***
44-45–Retail trade 0.097Note ***
61–Educational services 0.035
71–Arts, entertainment and recreation 0.026
22–Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act
56–Administrative & support, waste management & remediation services -0.044Note ***
48-49–Transportation and warehousing -0.051Note **
23–Construction -0.169Note ***
31-33–Manufacturing -0.180Note ***
Unclassified -0.265Note ***
51–Information and cultural industries -0.303Note ***
41–Wholesale trade -0.364Note ***
81–Other services (except public administration) -0.443Note ***
53–Real estate and rental and leasing -0.446Note ***
11–Agriculture, forestry, fishing and hunting -0.579Note ***
21–Mining, quarrying, and oil and gas extraction -0.765Note ***
54–Professional, scientific and technical services -0.825Note ***
52–Finance and insurance -0.943Note ***
55–Management of companies and enterprises -1.090Note ***
62–Health care and social assistance -1.709Note ***

Chart 8 shows the results of the unrestricted specification but for within-industry differences between majority men-owned and equally owned start-ups. These results, in combination with the industry concentration, show that equally owned start-ups were also more productive in some of the industries in which they were more highly represented, such as in accommodation and food services and in retail trade.

Chart 8 Labour productivity unrestricted model results, within-industry differences between men-owned and equally owned start-ups

Data table for Chart 8 
Chart 8 Labour productivity unrestricted model results, within-industry differences between men-owned and equally owned start-upsChart 8 Note 1
Table summary
This table displays the results of Chart 8 Labour productivity unrestricted model results, within-industry differences between men-owned and equally owned start-ups. The information is grouped by Industry (appearing as row headers), Percent (appearing as column headers).
Industry Percent
72–Accommodation and food services 0.926Note ***
44-45–Retail trade 0.104Note ***
48-49–Transportation and warehousing 0.074Note ***
61–Educational services 0.048
56–Administrative & support, waste management & remediation services 0.033Note ***
22–Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act
31-33–Manufacturing -0.061Note ***
81–Other services (except public administration) -0.079Note ***
23–Construction -0.094Note ***
71–Arts, entertainment and recreation -0.191Note ***
Unclassified -0.196Note ***
41–Wholesale trade -0.322Note ***
11–Agriculture, forestry, fishing and hunting -0.367Note ***
51–Information and cultural industries -0.448Note ***
53–Real estate and rental and leasing -0.578Note ***
54–Professional, scientific and technical services -0.811Note ***
21–Mining, quarrying, and oil and gas extraction -0.819Note ***
52–Finance and insurance -0.958Note ***
55–Management of companies and enterprises -1.259Note ***
62–Health care and social assistance -1.526Note ***

The following predominant result was derived from this analysis: women-owned start-ups tended to have a higher survival rate and better performance than men-owned start-ups in industries in which they were more concentrated MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbaqcLbwaqa aaaaaaaaWdbiaa=rbiaaa@37A4@ namely, retail trade, and accommodation and food services, but not health care and social assistance. There is some question as to whether the health care and social assistance industry follows typical profit maximizing behaviours, as it is largely populated by health care practitioners who may incorporate in some provinces. Provinces follow different regulated costing schemes. Furthermore, the results for this industry may reflect differences in the composition of physician types between men and women. Women doctors also tended to be family physicians (Richards 2019) rather than specialists.

This paper acknowledges that there are more aspects to firm performance that were not observed or controlled for. Some of the literature suggests that women tend to enter entrepreneurship for non-financial reasons, such as flexible work hours (Moyser and Burlock 2018).

6 Conclusion

This paper uses recently developed administrative data on the gender of firm owners, as well as data on firm entry cohorts for CCPCs, to examine the evolution and performance of these cohorts of start-up firms from 2005 to 2013. In aggregate, men-owned firms represented the largest share of these start-ups (just under two-thirds). However, while the number of women entrants represented only one-fifth of the start-ups, their number and share of entrants increased throughout the period. In terms of size at entry, women’s share of micro and small entrants increased over the period, while men’s share of medium-sized entrants rose. Furthermore, the share of equally owned start-ups decreased across all size categories over the period.

When examining the survival and performance of these start-ups, some salient differences were observed across the three majority genders of ownership. Women-owned start-ups had an overall lower survival rate and lower labour productivity than equally owned and men-owned start-ups. When industry was controlled for, women-owned start-ups were more productive and more likely to survive in industries with a higher concentration of women-owned start-ups (i.e., retail trade, and accommodation and food services), relative to men-owned start-ups in the same industries. However, the women-owned start-ups in health care and social assistance, of which there was also a high concentration, were less productive and less likely to survive than men-owned start-ups in that industry.

While the aggregate results leave room for improvement in terms of performance and survival for women-owned start-ups, within-industry results show some promise.

More work is needed to establish the source of the barriers that remain in some industries and identify any industry-specific barriers to women-owned start-ups that may still remain. This paper shows that future research on the topic needs to account for these industry differences and that policies may need to target particular industries to be more efficient. In the future, the CEEDD data used in this paper may make it possible to further disaggregate and study additional characteristics of business owners that might also impact their survival (e.g., age or immigration status).

7 Appendix

7.1 Tables


Table A.1
Canadian-controlled private corporations and Canadian-controlled private corporations with gender in the National Accounts Longitudinal Microdata File
Table summary
This table displays the results of Canadian-controlled private corporations and Canadian-controlled private corporations with gender in the National Accounts Longitudinal Microdata File. The information is grouped by Year (appearing as row headers), Incorporated enterprises , CCPCs, CCPCs with assigned gender and Share of CCPCs with assigned gender , calculated using number and percent units of measure (appearing as column headers).
Year Incorporated enterprises CCPCs CCPCs with assigned gender Share of CCPCs with assigned gender
number percent
2005 1,698,522 1,625,297 1,170,707 72.0
2006 1,772,471 1,689,556 1,234,781 73.1
2007 1,852,656 1,762,830 1,300,655 73.8
2008 1,919,120 1,825,054 1,354,804 74.2
2009 1,951,750 1,858,202 1,390,136 74.8
2010 2,009,045 1,912,407 1,438,204 75.2
2011 2,066,152 1,965,429 1,485,997 75.6
2012 2,115,365 2,006,809 1,528,681 76.2
2013 2,152,136 2,038,808 1,560,880 76.6

Table A.2
Total number of firms, 2005 to 2013, by majority gender of ownership and firm sizeTable A.2 Note 1
Table summary
This table displays the results of Total number of firms. The information is grouped by Year (appearing as row headers), Majority gender of ownership, Men, Women, Equal and Total (appearing as column headers).
Year Majority gender of ownership
Men Women Equal Total
Number of firms Share (percent) Number of firms Share (percent) Number of firms Share (percent) Number of firms
2005 786,131 68.1 187,474 16.2 180,250 15.6 1,153,855
2006 827,865 68.0 198,560 16.3 190,426 15.6 1,216,851
2007 873,445 67.9 211,431 16.4 201,610 15.7 1,286,486
2008 905,667 67.6 222,191 16.6 211,145 15.8 1,339,003
2009 924,818 67.4 230,195 16.8 216,189 15.8 1,371,202
2010 953,320 67.3 240,837 17.0 223,273 15.8 1,417,430
2011 979,962 67.0 251,188 17.2 231,464 15.8 1,462,614
2012 1,007,332 66.8 261,019 17.3 240,758 16.0 1,509,109
2013 1,026,296 66.6 268,800 17.4 246,552 16.0 1,541,648
Average 920,537 67.4 230,188 16.8 215,741 15.8 1,366,466

Table A.3
Kaplan-Meier survival probabilities for the 2005 cohorts, by majority gender of ownership
Table summary
This table displays the results of Kaplan-Meier survival probabilities for the 2005 cohorts. The information is grouped by Year (appearing as row headers), Men-owned survival rate, Women-owned survival rate and Equally owned survival rate, calculated using percent units of measure (appearing as column headers).
Year Men-owned survival rate Women-owned survival rate Equally owned survival rate
percent
1 99.1 98.9 99.5
2 95.8 95.5 97.4
3 91.2 90.6 93.9
4 86.1 84.9 89.6
5 81.6 79.7 85.3
6 77.3 75.1 81.9
7 73.1 70.6 78.4
8 69.1 66.1 74.5
9 65.3 61.8 71.2
10 61.3 57.1 67.0

Table A.4
Cox proportional hazards model coefficient results, by majority gender of ownership differences and industryTable A.4 Note 1
Table summary
This table displays the results of Cox proportional hazards model coefficient results. The information is grouped by Industry (appearing as row headers), Men-owned and women-owned coefficient, p-value and Men-owned and equally owned coefficient (appearing as column headers).
Industry Men-owned and women-owned coefficient p-value Men-owned and equally owned coefficient p-value
11 – Agriculture, forestry, fishing and hunting 0.929 0.000Note *** 0.672 0.000Note ***
21 – Mining, quarrying, and oil and gas extraction -0.228 0.278 -0.253 0.119
22 – Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act
23 – Construction 0.180 0.048Note * -0.121 0.196
31-33 – Manufacturing -0.144 0.243 -0.079 0.545
41 – Wholesale trade -0.021 0.859 0.004 0.974
44-45 – Retail trade -0.212 0.017Note * -0.158 0.101
48-49 – Transportation and warehousing 0.228 0.028Note * 0.176 0.093Table A.4 Note 
51 – Information and cultural industries -0.392 0.017Note * -0.179 0.326
52 – Finance and insurance 0.214 0.231 0.574 0.002Note **
53 – Real estate and rental and leasing 0.096 0.471 0.337 0.016Note *
54 – Professional, scientific and technical services 0.125 0.154 0.304 0.001Note **
55 – Management of companies and enterprises 0.352 0.097Table A.4 Note  0.510 0.015Note *
56 – Administrative and support, waste management and remediation services 0.238 0.000Note *** -0.229 0.000Note ***
61 – Educational services -0.206 0.290 0.031 0.888
62 – Health care and social assistance 1.440 0.000Note *** 1.427 0.000Note ***
71 – Arts, entertainment and recreation 0.098 0.534 0.257 0.147
72 – Accommodation and food services -0.678 0.000Note *** -0.587 0.000Note ***
81 – Other services (except public administration) 0.193 0.052Table A.4 Note  0.178 0.103
Unclassified -0.976 0.000Note *** -0.975 0.000Note ***

Table A.5
Labour productivity coefficient results, by majority gender of ownership differences and industry
Table summary
This table displays the results of Labour productivity coefficient results. The information is grouped by Industry (appearing as row headers), Men-owned and women-owned percentage difference, p-value and Men-owned and equally owned percentage difference (appearing as column headers).
Industry Men-owned and women-owned percentage difference p-value Men-owned and equally owned percentage difference p-value
11 – Agriculture, forestry, fishing and hunting -0.579 0.000Note *** -0.367 0.000Note ***
21 – Mining, quarrying, and oil and gas extraction -0.765 0.000Note *** -0.819 0.000Note ***
22 – Utilities Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act Note x: suppressed to meet the confidentiality requirements of the Statistics Act
23 – Construction -0.169 0.000Note *** -0.094 0.000Note ***
31-33 – Manufacturing -0.180 0.000Note *** -0.061 0.000Note ***
41 – Wholesale trade -0.364 0.000Note *** -0.322 0.000Note ***
44-45 – Retail trade 0.097 0.000Note *** 0.104 0.000Note ***
48-49 – Transportation and warehousing -0.051 0.002Note ** 0.074 0.000Note ***
51 – Information and cultural industries -0.303 0.000Note *** -0.448 0.000Note ***
52 – Finance and insurance -0.943 0.000Note *** -0.958 0.000Note ***
53 – Real estate and rental and leasing -0.446 0.000Note *** -0.578 0.000Note ***
54 – Professional, scientific and technical services -0.825 0.000Note *** -0.811 0.000Note ***
55 – Management of companies and enterprises -1.090 0.000Note *** -1.259 0.000Note ***
56 – Administrative and support, waste management and remediation services -0.044 0.000Note *** 0.033 0.000Note ***
61 – Educational services 0.035 0.261 0.048 0.138
62 – Health care and social assistance -1.709 0.000Note *** -1.526 0.000Note ***
71 – Arts, entertainment and recreation 0.026 0.396 -0.191 0.000Note ***
72 – Accommodation and food services 0.933 0.000Note *** 0.926 0.000Note ***
81 – Other services (except public administration) -0.443 0.000Note *** -0.079 0.000Note ***
Unclassified -0.265 0.000Note *** -0.196 0.000Note ***

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