Table B1 Probability of being employed in ICTC’s 25 ICT occupations
(1) (2) (3) (4) (5)
Female indicator –0.091*** –0.047*** –0.042*** –0.041*** –0.038***
(0.010) (0.009) (0.009) (0.009) (0.009)
Basic ICT scores 0.033*** 0.035*** 0.035*** 0.034*** 0.034***
(0.006) (0.006) (0.006) (0.006) (0.006)
Indicator for individuals with
a STEM degree 0.146*** 0.142*** 0.145*** 0.141***
(0.014) (0.014) (0.014) (0.014)
Working in the private sector 0.028** 0.027**
(0.009) (0.009)
Working full time 0.032*** 0.030***
(0.008) (0.008) Constant –0.253*** –0.278*** –0.308*** –0.276*** –0.305***
(0.070) (0.072) (0.075) (0.072) (0.075)
Controlling for other covariates Age and its square,
educational variables Y Y Y Y Y
Residential location N Y Y Y Y
Note: The linear probability model is used to obtain these estimates. Estimated coefficients are an equally weighted average of coefficients derived from each plausible value. All 80 replicate weights are used to account for the sample stratification. Estimated coefficients are interpreted as the percentage of being employed in the ICTC’s classification of ICT occupations. Standard errors are given in parentheses.
*p < 0.5
**p < .01
***p < .001.
Table B2 Women’s probability of working in ICTC’s 25 ICT occupations
(1) (2) (3) (4)
Female indicator –0.090*** –0.091*** –0.047*** –0.027**
(0.010) (0.010) (0.009) (0.008)
Basic ICT scores 0.057*** 0.052*** 0.051*** 0.050***
(0.008) (0.008) (0.008) (0.008)
Female x Basic ICT scores –0.040*** –0.039*** –0.035*** –0.033***
(0.008) (0.008) (0.008) (0.008)
Indicator for individuals with a STEM
degree 0.146*** 0.156***
(0.014) (0.018)
Female x STEM degree –0.053
(0.031)
Working in private sector 0.025**
(0.009) Female x Working in private sector
Working full time 0.033***
(0.008) Female x Working full time
Constant 0.126*** –0.255*** –0.281*** –0.315***
(0.008) (0.071) (0.073) (0.075)
Other covariates
Age and educational factors N Y Y Y
Immigration status N N Y Y
Residential location N N Y Y
Note: The linear probability model is used to obtain these estimates. Estimated coefficients are an equally weighted average of coefficients derived from each plausible value. All 80 replicate weights are used to account for the sample stratification. Estimated coefficients are interpreted as the percentage of being employed in the ICTC’s classification of ICT occupations. Standard errors are given in parentheses.
*p < 0.5
**p < .01
***p < .001
Table B3 Probability of being employed in ICTC’s 25 ICT occupations for the sub-sample of full-time employed individuals
(1) (2) (3)
Female indicator –0.093*** –0.047*** –0.044***
(0.011) (0.010) (0.011)
Basic ICT scores 0.038*** 0.040*** 0.040***
(0.007) (0.007) (0.007)
Indicator for individuals with a STEM degree 0.156*** 0.153***
(0.015) (0.015)
Working in the private sector 0.023*
(0.011)
Constant –0.241** –0.262** –0.291**
(0.087) (0.089) (0.091)
Controlling for other covariates
Age and its square, educational variables Y Y Y
Residential location N Y Y
Note: The linear probability model is used to obtain these estimates. Estimated coefficients are an equally weighted average of coefficients derived from each plausible value. All 80 replicate weights are used to account for the sample stratification. Estimated coefficients are interpreted as the percentage of being employed in the ICTC’s classification of ICT occupations. Standard errors are given in parentheses.
*p < 0.5
**p < .01
***p < .001
Table B4 Differences in basic ICT scores of women and men
Highest educational attainment (reference group = graduates with high-school diploma)
Below high-school diploma –0.723*** –0.717*** –0.720***
(0.095) (0.095) (0.094)
Postsecondary below a bachelor’s degree 0.137** 0.148** 0.149**
(0.050) (0.050) (0.050)
Bachelor’s degree 0.583*** 0.572*** 0.565***
(0.061) (0.061) (0.061)
Postsecondary above a bachelor’s degree 0.700*** 0.710*** 0.705***
(0.058) (0.058) (0.058)
Indicator for individuals who obtained education
abroad –0.289** –0.262** –0.262**
(0.088) (0.088) (0.088)
Indicator for individuals whose location of education
is unknown 0.057 0.107 0.096
(0.268) (0.280) (0.296)
Indicator for individuals with a STEM degree 0.158** 0.086 0.050
(0.054) (0.052) (0.051)
Female x STEM degree 0.049 0.091 0.111
(0.085) (0.085) (0.087)
Working in ICTC’s ICT occupations 0.429***
(0.068)
Female x ICTC’s ICT occupations –0.156
(0.113)
Working in ICT occupation (broad) 0.416***
(0.060)
Note: Ordinary linear regression is used to obtain these estimates. Estimated coefficients are an equally weighted average of coefficients derived from each plausible value. All 80 replicate weights are used to account for the sample stratification. Estimated coefficients are interpreted in terms of a standard deviation. Standard errors are given in parentheses.
*p < 0.5
**p < .01
Table B5 Differences between women and men in daily ICT usage at work
The probability of self-reported daily usage at work
Excel Word Coding/
programming All individuals in the sample
Female –0.0595** 0.0712*** –0.0450***
(0.0190) (0.0189) (0.0092)
Note: The linear probability model is used to obtain these estimated coefficients. These are weighted differences between women and men in the probability of self-reported daily usage of certain ICT applications at work. Excel indicates use of applications that are similar to Microsoft Excel. Word means use of word-processing applications similar to Microsoft Word. Coding/programming indicates use of applications that allow respondents to perform coding at work. In all regressions, standardized PS-TRE scores and levels of education are accounted for. The reference group is men with a bachelor’s degree as the highest educational attainment. Standard errors are given in parentheses.
*p < 0.5
**p < .01
***p < .001
Table B6 Differences between women and men in self-reported level of ICT skills used at work Probability of self-reported level of ICT skills used at work
Complex Moderate Straightforward
Note: The linear probability model is used to obtain these estimated coefficients. These are weighted differences between women and men in the probability of self-reported level of ICT skills used at work. Complex indicates individuals who use complex computer skills at work. Moderate indicates individuals who use moderate-level skills at work. Straightforward requires individuals to have basic computer skills to perform their work.
In all regressions, standardized PS-TRE scores and levels of education are accounted for. The reference group is men with a bachelor’s degree as the highest educational attainment. Standard errors are given in parentheses.
*p < 0.5
**p < .01
***p < .001
Table B7 Differences between men and women in self-perceived computer skills and job performance
Note: The linear probability model is used to obtain these estimated coefficients. These are weighted differences in how women perceive their computer skills, and how these skills influence their job performance at their current employment. The first column indicates individuals who self-reported having enough skills to perform well at work. The second column indicates individuals who think that lack of computer skills hurts their chance of promotion, a pay raise, and being hired at the current job. In all regressions, standardized PS-TRE scores and levels of education are accounted for. The reference group is men with a bachelor’s degree as the highest educational attainment. Standard errors given are in parentheses.
*p < 0.5
**p < .01
***p < .001.
References
Information and Communications Technology Council. (2016). A National Strategy To Develop Canada’s Talent In a Global Digital Economy. https://www.ictc-ctic.ca/
Information and Communications Technology Council. (2017a). Digital economy annual review 2017. Retrieved from https://www.ictc-ctic.ca/wp-content/uploads/2018/02/ICTC-Annual-Review-2017-EN.pdf
Information and Communications Technology Council. (2017b). The next talent wave: Navigating the digital shift – outlook 2021. Retrieved from https://www.ictc-ctic.ca/wp-content/uploads/2017/04/ICTC_Outlook-2021.
Kindsiko, E., & Türk, K. (2016). Detecting Major Misconceptions about Employment in ICT: A Study of the Myths about ICT Work among Females. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11(1), 107-114.
Mueller, R. E., Truong, K., & Smoke, W. (2018). The underrepresentation of women in Canada’s ICT sector: What can we learn from a Canadian survey of adult skills? Canadian Public Policy, 44(S1), S73–S90. doi:10.3138/
cpp.2017-073.
Organisation for Economic Co-operation and Development. (2016). Skills for a digital world (OECD Digital Economy Papers No. 250). Paris: OECD Publishing.
Tech Toronto. (2016). How technology is changing Toronto employment: 400,000 jobs and growing. Retrieved from https://www.techtoronto.org/Report2016.