The document summarizes a study that analyzed the impact of information and communication technology (ICT) skills on women's earnings in the United States using data from the Program for International Assessment of Adult Competencies (PIAAC). The study found that: 1) Both men and women saw similar average earnings increases (0.3%) from acquiring more ICT skills, however women still earned 36% less than men on average. 2) ICT skills impacted men's and women's earnings at the same rate when controlling for factors like education, age, and family status. 3) While the study provided preliminary results, further research is needed to more fully understand the overlaps between gender, discrimination, and technology

1. Drew University
Impact of ICT Skills on Women’s Earnings in the United States
Milla Schneider
Introductory Econometrics ECON 303
Professor Miao Chi
13 December 2019

2. Schneider 1
Impact of ICT skills on Women’s Earnings in the United States
This paper provides a preliminary quantitative analysis on the overlaps of gender,
discrimination and technology skills and how they impact adult earnings in the United States.
Such topic has not been the main target of previous econometrics research. The US Household
sample of the Program for International Assessment of Adult Competencies (PIAAC) data from
2012-2014 is used for this analysis. OLS models are built in order to determine whether women
who possess higher information and communication technologies skills (ICT) earn more than
women who do not, and whether the gender pay gap decreases as individuals achieve higher ICT
skill levels. The results indicate that men and women see a similar rate of increase in their
average earnings as they acquire more aptitude with using technologies. However, as a
preliminary study, the results explored are still limited and further research is needed.
Keywords:
ICT Skills, Gender Inequality, Earnings
JEL: C4, Z0
Introduction
In the United States and other developed countries, employers are increasingly placing
higher value on employees’ skills on computer and technology, for example, rather than
traditional academic skills (Gale et al., 2002). This shift in skills demanded by employers is
possibly associated with increasing levels of wage inequalities in the country (Borghans and ter
Weel, 2005). It is also possible that increasing wage inequality affects women differently than

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men in the contemporary labor market. At the same time that more digital skills are demanded
from workers, there is still a significant gap in the number of positions held by women in high
paying technology-oriented positions (Ashcraft et al., 2016). Looking only at skill level
differences in mobile and computer technology across the country, the fact that women are still
minority in positions that require higher skills levels put them in a disadvantage when compared
to their male counterparts in being able to find jobs in currently highly valued positions that
require the use of new technologies.
Nonetheless, little is still understood on the overlaps between gender, discrimination and
technology skills on individual earnings. The topic has not been the main target of previous
research and data collection. In this context, this paper provides a preliminary analysis as it
investigates whether women with higher information and communication technologies skills
(ICT) – which are related to the use of internet and computer applications – present higher
earnings when compared to women with lower or none of such skills in the United States.
Additionally, a comparison is also presented on the impact of ICT skills in earnings between
women and men in order to examine if the wage gap shortens as individuals develop these skills.
Both a literature review and an empirical analysis are presented in the following sections.
The PIAAC – Program for International Assessment of Adult Competencies – data from 2012-
2014 is used, and a focus is given on the United States. It is hypothesized that women who
possess ICT skills and use them at their jobs earn relatively more compared to the ones who do
not. It is also hypothesized that the gender gap in wages does not decrease significantly when
comparing levels of aptitude with computer and communications applications between groups.
The first section of the paper provides a review of the previous academic literature on the
topic, signaling the results and limitations of previous research. This section is followed by a

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description of the PIAAC dataset and the methods used for this analysis. The last section
explores the results obtained and provides a discussion of these results.
1. Literature Review
There is a lack in academic literature that analyzes the impacts of gendered differences in
ICT on adult earnings in the United States. However, a number of studies have been made on
similar topics. Focusing on the academic literature produced in the XXI century, three main
categories appear. (1) Authors have focused on the importance of ICT related skills in the labor
market, without including a gendered perspective. (2) Studies have looked at gendered
differences in both ICT skill levels and access to computer and mobile technologies across
developed and developing countries. (3) Lastly, empirical research has been conducted to
analyze skill differences between men and women and impacts on earnings, without mention to
computer, technology or ICT skills. In this section, I discuss some of the main findings of
contemporary literature and this paper’s contribution to the debate.
(1) Importance of ICT in the Labor Market
Studies have focused on the importance of ICT related skills in the labor market, without
a focus on gendered differences on possession of these skills. For example, in the United States
Gale et al. analyze a sample of over 3000 rural and urban establishments and show that
technology use at work across the country is strongly linked to employers’ computer
requirements (2002). This study also shows that job requirements in traditional academic skills -
such as numeracy and literacy - do not increase at the same fast-rate as requirements for
technology, interpersonal and problem-solving skills. In complement, Borghans and Ter Weel

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note that there are links between rising wage inequality and the computerization of labor markets
(2005).
Using the PIAAC data, another study concludes that, across the OECD countries, there is
evidence of significant benefits of ICT skills on wages for individuals with tertiary education.
Increase in these skills can lead to an approximate 15% wage premium for college graduates in
several countries, including the United States (Conlon and Lane, 2016). The same patterns are
noticed for most of the countries under investigation.
However, the three studies present a limited perspective on the gendered difference in use
and skill level of use of ICTs between men and women. The first study does not include an
analysis of gendered differences in how employers demand technology skills and who fulfills the
job positions. Borghans and Ter Weel present data showing that, in 2000, more women used
computer at work than men in the United States. However, the authors do not analyze the types
of positions these women fulfilled and how their skill levels in computers and their earnings
compared to men’s in jobs that required the use of computer technologies. Conlon and Lane do
not evaluate if there are differences in wage increase for ICT skilled between male and female
college graduates.
(2) Gendered differences in ICT skill levels and access to ICTs across developed and
developing countries.
Furthermore, authors have noted gendered differences in access and use of technology.
According to the literature, women are worse off than men in access to and aptitude with
computer and communication technologies in developing countries. Women in developing
countries face specific challenges that serve as barriers to physical internet access, such as

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difficulty of entry in the labor market (Mariscal et al., 2019; Antonio and Tuffley, 2014). At the
same time, cultural aspects of society, such as preconceived ideas of gender behavior, prevent
them from using the internet even when access is provided (Antonio and Tuffley, 2014).
However, similar inequality patterns are also prevalent in developed countries, in which physical
access to technology is less limited for women.
A study conducted on data from 39 countries from the Program for International Student
Assessment (PISA) data found that gender inequality continues to favor adolescent boys across
developing and developed nations, showing that inequalities in incentives to use and physical
access to ICTs exist even before adulthood. Boys in all countries in this study reported using
ICTs for educational purposes more often than girls; results did not change significantly when
controlling for GDP per capita (Drabowicz, 2014). Along these lines, Judith et al. notes that
although affordability and access to digital technologies are closer to equal to both men and
women in many developed countries, women are still minorities in tech positions in these
countries (2019).
(3) Empirical research on skill differences between men and women and impacts on earnings
Several empirical studies have aimed to analyze factors that influence adult earnings.
Although these studies lack a thorough analysis of the importance of technology skills for
earnings, they generally include an analysis on skill levels in numeracy and literacy. Both Sonja
(2016) and Paas and Tverdostup (2017) use the PIAAC data in their analysis. Sonja creates OLS
models that estimate the importance of numeracy skills and years of schooling for earnings in
OECD countries, controlling for gender, experience, experience squared and immigrant status.
Although Sonja controls for gender, the author does not make an analysis of the gendered
differences in earnings.

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Differently from Sonja, Paas and Tverdostup do not directly compare women’s and
men’s skill profiles, rather differentiating sets of female-unique and male-unique human
competencies in an effort to explain unobservable characteristics in wage gaps in Estonia. The
author’s divide their sample in four gender-skill groups: two groups of men and women with
gender-unique human capital profiles, and two groups of men and women with similar human
capital profiles. The authors apply a quantile regression approach in order to dissect the effect of
gendered skills on a full-wage distribution, finding evidence that men’s higher earnings in
Estonia are linked to their higher numeracy and problem-solving skills despite general lower
levels of formal education. Considering the full distribution of wages, the authors also find
evidence that women who have gender-specific skills experience higher “glass ceiling effect” on
earnings when compared to other groups.
Lastly, in the United Kingdom, Cheng and Furnham make overlapping points to Paas and
Tverdostup in that males’ and females’ unique characteristics affect their wages differently.
Cheng and Furnham use longitudinal data from a representational sample of UK residents,
finding that earnings is not only a function of occupational prestige and educational
qualifications, but also dependent on childhood intelligence, parental social status and
personality traits (2013). Both parents’ class and personality traits are found to influence
earnings differently across gender. On personality traits, the authors claim that hard-work and
prudence influence men’s earnings, but the same cannot be said for women; on the other hand,
emotional stability is seen as more important for women’s earnings (p. 124).
***
This paper aims to fill in some gaps in the recent literature, providing evidence on the
importance of ICT skills for earnings across genders in the United States. Similarly to other

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recent studies, the PIAAC data is used in this paper as a basis to empirical models. However,
gender unique profiles are not traced and accounted for as in Paas and Tverdostup (2017).
Further research is still needed to complement and validate the analysis and results presented in
the following sections in a broader global perspective.
2. Data and Method
The data chosen is the 2014 revised public use file of the Program for the International
Assessment of Adult Competencies (PIAAC), more specifically the U.S. Household Sample. The
dataset contains measurements of adult numeracy, literacy and problem solving skills in
technology rich environments skills (PSL), as well as information on participants’ family,
educational and career background. In total, the data contains 8,670 observations, representing
individual participants of the survey in the United Sates, of which 53.74% are female and
46.26% male, aged between 16 and 74 years old.
The PIAAC provides 10 plausible scores of problem solving in technology rich
environments for each participant, and a choice was made to use the average of the scores
provided as a measurement of ICT skills level. Other variables of interest for this paper are
monthly income of wage, salary and self-employed participants (including bonuses), gender
(flagged as female), whether or not a participant has a college degree, urban status, region, age
(flagged as young for participants between 16 and 34 years old), and whether or not the
participant has children or if he or her cohabits with a spouse (refer to Table 1 for descriptive
statistics for some selected variables).
In accordance with Jovicic, the paper used a OLS model in the form of Mincer’s earnings
equation (2016):

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ln(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 + 1)𝑖 = 𝛽0 + 𝛽1 𝑃𝑆𝐿𝑖 + 𝛽2 𝐹𝑒𝑚𝑎𝑙𝑒𝑖+ 𝛽3 𝐴𝑔𝑒3565𝑖 + 𝛽4 𝑆𝑝𝑜𝑢𝑠𝑒𝑖 +
𝛽5 𝐶ℎ𝑖𝑙𝑑𝑟𝑒𝑛𝑖 + 𝛽6 𝐶𝑜𝑙𝑙𝑒𝑔𝑒𝑖 + 𝛽7 𝑁𝑜𝑟𝑡ℎ𝑒𝑎𝑠𝑡𝑖 + 𝛽8 𝑆𝑜𝑢𝑡ℎ𝑖 + 𝛽9 𝑊𝑒𝑠𝑡𝑖 + 𝛽10 𝐶𝑖𝑡𝑦𝑖 +
𝛽11 𝑆𝑢𝑏𝑢𝑟𝑏𝑎𝑛𝑖
Only participants with ages up to 65 years old were considered for the analysis. The independent
variable was set as the natural logarithm transformation of the translated variable of monthly
earnings of wage, salary and self-employed participants, ln(Earnings + 1). Numeracy and
Literature skills levels were not considered in the analysis as they were found to be strongly
positively correlated with ICT skills – respectively, r(6878) = 81.2, p < 0.0005 and r(6878) =
0.92, p < 0.0005.
Table 1
Descriptive Statistics
Scale variables
Mean SD Range N. of Observations
Earnings (salary,
wage and self-
employed
7.65 1.34 (0, 11.13) 4,498
PSL (Measurement of
ICT skill level)
270.47 40.88 (90.56, 393.38) 6,880
Categorical Variables
N. of Observations Valid Percentage
female (1 = female,
0 = Male)
4,659 8,670 47.73
age3565 (1 = Age
between 35-65, 0 =
Age between 16-34)
3,783 8,670 43.63
age16-34 (1 = Age
between 16-34, 0 =
Age between 16-34)
4,138 8,670 52.27

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Spouse (1 =
cohabiting with
Partner, 0 = Not)
4,069 7,103 57.29
Children (1 = Have
children, 0 = No
children)
5,119 8,474 60.41
College (1 =
Completed tertiary
education, 0 = Not)
2,118 8,471 25.00
3. Results and Discussion
Models were built to identify not only if ICT skills impact earnings for women, but if the
impacts are different for them than they are for men. More specifically, three types of models
were built, with male and female participants ages 15-65 (Table 2), only female (Table 3) and
only male (Table 4). Across models, PSL (measurement of ICT skill competencies) and controls
for gender, age, cohabitation status and children were found to be statistically significant at the
1% level. Jointly, region controls were found not to be statistically significant; similarly, urban
status was found not to statistically impact earnings. Equations from the second column of
Tables 2, 3 and 4 were therefore picked for a comparative analysis.
Controlling for spouse (cohabitation status), children, college education, and age (young
or middle age), PSL was found to impact earnings for all participants at the same rate, on
average. The coefficient of the impact of PSL on earnings, β = 0.003, was found to be the same
for the three types of models run. In other words, independently of the gender of the participant,
each additional point scored on the problem solving in technology rich environments test (PSL)
was predicted to increase the participant’s earnings by an average rate of 0.3%. This analysis
thus shows that ICT skills impact men and women at similar rates. However, it is important to

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note that, as shown in Table 2 and using the same control variables as previously, women earn
36% less than men do, on average. A 0.3% increase in women’s average earnings is still lower,
in absolute values, than a 0.3% increase in men’s average earnings.
Table 2
OLS Estimates, Male and Female Participants 16-65 years-old; Dependent Variable: ln(Monthly
Earnings of wage, salary earnings and self-employed)
(1) (2) (3) (4) (5) (6)
PSL (Measurement of ICT skill level 0.005***
(0.0005)
0.003***
(0.0005)
0.003***
(0.0005)
0.003***
(0.0006)
0.003***
(0.0006)
0.003***
(0.0006)
Female (1 = Female, 0 = Male) -0.29***
(0.04)
-0.36***
(0.04)
-0.36***
(0.04)
-0.36***
(0.04)
-0.36***
(0.04)
-0.36***
(0.04)
Age3565 (1 = Age between 35-65, 0 = Age
between 16-34
0.55***
(0.45)
0.32***
(0.05)
0.33***
(0.05)
0.33***
(0.05)
0.31***
(0.05)
0.32***
(0.05)
Spouse (1 = cohabiting with Partner, 0 = Not) 0.66***
(0.05)
0.47***
(0.05)
0.47***
(0.05)
0.48***
(0.05)
0.47***
(0.05)
0.48***
(0.05)
Children (1 = Have children, 0 = No children) 0.31***
(0.05)
0.31***
(0.05)
0.31***
(0.05)
0.31***
(0.05)
0.32***
(0.05)
College (1 = Completed tertiary education, 0
= Not)
0.73***
(0.05)
0.72***
(0.05)
0.71***
(0.05)
0.72***
(0.05)
0.70***
(0.05)
Northeast 0.15*
(0.06)
0.15*
(0.06)
South 0.10
(0.05)
0.10
(0.05)
West 0.06
(0.06)
0.04
(0.06)
City 0.08
(0.04)
0.11*
(0.05)
0.11
(0.06)
Suburban 0.05
(0.05)
0.04
(0.05)
Constant 5.71***
(0.15)
38.91***
(0.16)
6.16***
(0.16)
6.13***
(0.16)
6.10***
(0.17)
6.03***
(0.17)
N 3158 3157 3157 3157 3157 3157
F 171.42 166.37 143.23 125.42 111.82 91.93
R2 0.18 0.24 0.24 0.24 0.24 0.24
Standard errors in parentheses, *p < 0.05, **p < 0.01, ***p < 0.001
Table 3

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OLS Estimates, Female Participants 16-65 years-old; Dependent Variable: ln(Monthly Earnings
of wage and salary earnings and self-employed)
(1) (2) (3) (4) (5) (6)
PSL (Measurement of ICT skill level 0.005***
(0.0008)
0.003***
(0.0008)
0.003***
(0.0009)
0.003***
(0.0008)
0.003***
(0.0006)
0.003***
(0.0008)
Age3565 (1 = Age between 35-65, 0 = Age
between 16-34
0.53***
(0.06)
0.32***
(0.07)
0.33***
(0.07)
0.33***
(0.07)
0.32***
(0.07)
0.32***
(0.07)
Spouse (1 = Lcohabiting with Partner, 0 =
Not)
0.41***
(0.06)
0.27***
(0.06)
0.28***
(0.07)
0.28***
(0.06)
0.28***
(0.06)
0.29***
(0.06)
Children (1 = Have children, 0 = No children) 0.24***
(0.07)
0.31**
(0.05)
0.24**
(0.07)
0.24**
(0.07)
0.24**
(0.07)
College (1 = Completed tertiary education, 0
= Not)
0.75***
(0.07)
0.74***
(0.07)
0.73***
(0.07)
0.74***
(0.07)
0.72***
(0.07)
Northeast 0.16
(0.08)
0.16
(0.09)
South 0.15*
(0.07)
0.15*
(0.07)
West 0.09
(0.09)
0.08
(0.09)
City 0.08
(0.06)
0.11
(0.08)
0.10
(0.08)
Suburban 0.04
(0.07)
0.03
(0.08)
Constant 5.54***
(0.22)
6.02***
(0.23)
5.96***
(0.24)
5.93***
(0.24)
5.87***
(0.24)
5.80***
(0.25)
N 1691 1690 1690 1690 1690 1690
F 66.06 69.77 58.47 50.14 44.32 95.64
R2 0.11 0.17 0.17 0.17 0.17 0.18
Standard errors in parentheses, *p < 0.05, **p < 0.01, ***p < 0.001
Table 4
OLS Estimates, Male Participants 16-65 years-old; Dependent Variable: ln(Monthly Earnings of
wage and salary earnings and self-employed)
(1) (2) (3) (4) (5) (6)
PSL (Measurement of ICT skill level 0.005*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003***

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(0.0007) (0.0007) (0.0007) (0.0007) (0.0007) (0.0007)
Age3565 (1 = Age between 35-65, 0 = Age
between 16-34
0.53***
(0.06)
0.32***
(0.07)
0.33***
(0.07)
0.33***
(0.07)
0.32***
(0.07)
0.32***
(0.07)
Spouse (1 = Lcohabiting with Partner, 0 =
Not)
0.98***
(0.07)
0.75***
(0.07)
0.75***
(0.07)
0.75***
(0.07)
0.75***
(0.07)
0.75***
(0.07)
Children (1 = Have children, 0 = No children) 0.31***
(0.07)
0.32***
(0.07)
0.32***
(0.07)
0.31***
(0.07)
0.32***
(0.07)
College (1 = Completed tertiary education, 0
= Not)
0.68***
(0.07)
0.67***
(0.07)
0.66***
(0.07)
0.72***
(0.05)
0.65***
(0.07)
Northeast 0.15
(0.09)
0.15
(0.09)
South 0.04
(0.07)
0.03
(0.07)
West 0.03
(0.09)
0.01
(0.09)
City 0.07
(0.06)
0.11
(0.08)
0.10
(0.08)
Suburban 0.05
(0.07)
0.04
(0.08)
Constant 5.46***
(0.20)
5.98***
(0.21)
5.94***
(0.21)
5.92***
(0.21)
5.92***
(0.22)
5.86***
(0.23)
N 1467 1467 1467 1467 1467 1467
F 167.22 130.20 108.78 93.28 81.82 65.64
R2 0.26 0.31 0.31 0.31 0.31 0.31
Standard errors in parentheses, *p < 0.05, **p < 0.01, ***p < 0.001
Furthermore, the individual importance of ICT skills (measured through PIAAC
participants’ scores on problem solving in technology rich environments) is also analyzed. The
model described on the second column of Table 2 is considered for this analysis. Comparing the
absolute values of the standardized coefficients of the OLS model described, it is noticeable that
ICT skills are almost as impactful to adult earnings as other commonly used indicators, such as
children or age group (Table 5).
Table 5
OLS Estimates and Standardized Coefficients, Male and Female Participants 16-65 years-old;
Dependent Variable: ln(Monthly Earnings of wage, salary earnings and self-employed).

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In short, the results of the preliminary OLS models contained in this paper show that
higher ICT skill levels for men and women in the United States are predicted to positively impact
their earnings. The impacts are lower than formal college education, gender, and marital status
(considered as status of cohabitation with a partner in the PIAAC data), and lower but more
comparable to children and middle age status. This paper thus confirms the hypothesis that
women with higher ICT skill levels earn significantly more than women with lower of these
skills. This preliminary analysis does not confirm, however, the hypothesis that higher levels of
ICT skills have little to no to the gender gap in wages in the United States.
Conclusion
This paper looked at adult earnings in the United States. Differently from the bulk of
previous research, a specific focus was given to gender and to the importance of ICT skills. OLS
models were built on the PIAAC U.S. Household 2012-2014 data. It was found that women and

15. Schneider 14
men see their average earnings increase at the same rate as their skill levels with technology
increase when controlling for spouse (cohabitation status), children, college education, and age
(young or middle age).
This paper constitutes a preliminary study and thus presents limitations on the analysis of
such a complex interdisciplinary topic. The models used were OLS, and no gender specific ICT
skill profiles were considered. Additionally, a focus was given on selecting appropriate controls
for the models and on comparing models with female only individuals, male only individuals and
individuals from both genders. It was beyond the scope of this analysis to use of more complex
techniques, such as polynomials, interaction terms, and Oaxaca decomposition. Further research
is needed in order to deepen the understanding on the relationship between gender,
discrimination and technology skills and how they impact individual earnings.

16. Schneider 15
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