In Nigeria, male-founded startups showed consistent growth each year (with the exception of 2022). In contrast, startups founded by women and mixed teams have a lower growth rate, starting from 2019.

Nigeria: startups growth dynamic

27%
70%
21%

2018

26%
12%
22%

2019

26%
24%
7%

2020

29%
23%
17%

2021

8%
5%
3%

2022

male founded
mixed teams
female founded

Reasons for choice of educational direction

Students in Nigeria make educational choices driven by practical considerations rather than conforming to gender stereotypes.


Nevertheless, non-STEM students acknowledge a lack of information in school and feel that teachers have not cultivated an interest in STEM subjects. This situation may be related to the practice of gender-segregated schooling at the primary or secondary education levels.

STEM subjects
n=118 (Total)
39%
  • STEM subjects are relevant to the
    career I plan to choose
    51%
  • For me STEM subjects are interesting
    32%
  • I’m better in STEM subjects
    28%
  • There was a lot of information about
    possible careers at school
    21%
  • STEM subjects are for everyone, there is
    no gender connection
    10%
  • In school teachers make STEM
    subjects appealing for me
    8%
  • I am okay with a primarily male
    environment
    1%
non-STEM subjects
n=183 (Total)
61%
  • I’m better in humanities or other
    subjects
    54%
  • There was a lack of information about
    possible STEM careers at school
    31%
  • In school teachers didn’t make STEM
    subjects appealing for me
    19%
  • STEM subjects are not relevant
    to the career I plan to choose
    16%
  • For me STEM subjects are not interesting
    8%
  • I didn’t want to be active
    in a male dominated environment
    2%
  • STEM subjects are for boys only,
    
there is no place for women
    2%
  • Girls studying IT experience bullying/
    special attention
    1%
option was shown only for women

Job offerings from companies

There is no statistically significant difference in job offerings between genders. As for Malaysia and Brazil, Nigerian companies don’t prioritize men above women in the technology sector.

n=165 (Male)
Yes
79%
No
21%
n=136 (Female)
Yes
70%
No
29%
Significantly higher/lower than opposite gender at 95% significance level: >M,F/<M,F

Information channels about tech careers

At present, there is no gender disparity among technology students, favoring either gender. The majority of information is sought through proactive searches. In contrast to Brazil and Malaysia, individuals in Nigeria's technology sector come across information about tech careers during their time in school. This observation might be linked to the Nigerian educational system, which mandates STEM subjects as part of the secondary-level curriculum.

Information channels about tech careers

  • Personal research

    33%
  • Online forums

    32%
  • Professional websites

    31%
Details
  • I conduct research
    
about STEM careers by myself
    33%
  • Found information
    
in online forums
    32%
  • Found information on professional
    websites
    31%
  • I was told about it in school
    29%
  • Saw information on social media
    27%
  • Visited conferences
    
and got knowledge there
    22%
  • My friends/relatives worked
    
in STEM companies
    19%
  • Read publications
    18%
  • Official information
    
from the authorities
    11%
Significantly higher/lower than opposite gender at 95% significance level: >M,F/<M,F

Barriers to work in technology. Gender aspect

There is no statistically significant evidence of difference in barriers facing men and women in Nigeria.

TOTAL

  • Lack of sufficient training opportunities
    40%
  • The market is very competitive
    33%
  • Excessive competition for job opportunities
    33%
42%
34%
34%
36%
33%
32%
Details
  • Lack of sufficient training
    opportunities
    40%
  • Excessive competition
    
for job opportunities
    33%
  • There is big competition
    on the market
    33%
  • High pressure to meet deadlines
    29%
  • Difficulty keeping up with the latest
    technological advances
    28%
  • Limited opportunities
    
for career progression
    20%
  • Fast-paced work environment
    16%
  • Difficulty building and maintaining
    customer relations
    11%
  • Lack of job security
    10%
  • Unclear job roles and responsibilities
    10%
Significantly higher/lower than opposite gender at 95% significance level: >M,F/<M,F

Reasons to work in technology. Gender aspect

In general there is no statistically significant difference between males and females regarding reasons to work in technology.

TOTAL

  • Opportunity for career growth
    48%
  • Desire to make a positive impact in the world
    47%
  • Interest in technology and its possibilities
    44%
43%
44%
44%
54%
50%
44%
Details
  • Opportunity for career growth
    48%
  • Desire to make a positive impact
    in the world
    47%
  • Interest in technology
    and its possibilities
    44%
  • Access to the latest technology
    
and tools
    37%
  • Personal interest
    37%
  • Desire to work with an innovative
    and modern team
    36%
  • Possibility to work
    
from home or remotely
    33%
  • Attractive salary and benefits
    31%
  • Strong sense of purpose
    27%
  • Job stability
    25%
  • A challenge and the creative
    
freedom it offers
    22%
Significantly higher/lower than opposite gender at 95% significance level: >M,F/<M,F

Role models awareness

STEM workers are well aware of tech sector role models, especially female ones. Non-STEM workers, however, are less so, with approximately a quarter unaware of female role models. It's interesting that in all three countries, female role models are more widely known than males, especially among STEM workers.

  • STEM workers

    58%
    42%
  • non-STEM workers

    46%
    54%

Most common role models by gender

Male role models

  • Elon Musk
    30%
  • Mark Zuckerberg
    16%
  • Bill Gates
    12%

Female role models

  • Ada Lovelace
    8%
  • Grace Hopper
    7%
  • Melinda Gates
    3%

Male role models

  • Elon Musk
    21%
  • Mark Zuckerberg
    20%
  • Bill Gates
    11%

Female role models

  • Ada Lovelace
    17%
  • Grace Hopper
    7%
  • Susan Wojcicki
    5%

Local names

STEM workers

  • Umar Abdullahi
  • Aliyu Shuaibu
Fisayo Fosudo
  • Bankole Oluwafemi
  • Mike Adenuga
  • Philip Emeagwali
  • Dr Charles Awuzie
  • Sani Dangote
  • Don Jazzy
  • Innocent Chukwuma
  • Tony O. Elumelu
  • Igho Sanomi
  • Isa Ali Pantami
  • Engr. Danjuma I Isah
  • Ishola Williams
  • Dr Jibril Gabriel Solomon
  • Mitchell Elegbe
  • Olugbenga Agboola
  • Hakeem Belo-Osagie
  • Tayo Oviosu
  • Muhammad Auwai
  • Albani Zaria
  • Juliana Rotich
  • Ada Nduka Oyom
  • Victoria Popoola
  • Nenne Adaora Nwodo
  • Adeshola Awosika
  • Adora Nwodo
  • Mercy Johnson
  • Grace Oyelude
  • Bukky Wright
  • Chimamanda Ngozi Adichie
  • Blessing Chukwuka
  • Confidence Stavelay
  • Damilola Olokesusi
  • Tope Omotolani
  • Dr Chinwe Nwokolo Nwokolo
  • Hafsat Abdullahi
  • Ms Olorunfemi Oluwatoyin Bereola
  • Folorunsho Alakija
  • Funke Opeke
  • Charity Wanjiku
  • Nnenna Nwakanma
  • Abiola Eniola Aminu
  • Honey Ogundeyi
  • Igwe Chioma
  • Kimberly Bryant
  • Ire Aderinokun
  • Chioma Uzodimma
  • Mary-Jane Okolie
  • Ngozi Okonjo-Iweala
  • Professor Lilian Chibuike
  • Funke Opeke
  • Seun Runsewe
  • Aniekan Inyang
  • Shakirat Oluwatosin Raji
  • Sadiya Umar Farouq
  • Odunayo Eweniyi
  • Titi Alakija

non-STEM workers

  • Bola Tijani
  • Mike Adenuga
  • Nelson Mandela
  • Philip Emeagwali
  • Tony Elumelu
  • Juliana Rotich
  • Bukky Wright
  • Chimamanda Ngozi Adichie
  • Dr Okonjo Iweala
  • Eunice Olopade
  • Ibukun Akinnawo
  • Kiki Oniwinde
  • Mrs Adebowale Okonji-Umukoro
  • Theodosia Okoh
  • Ngozi Okonjo
  • Okonjo Owoela
  • Olamide Adeyinka
  • Odunayo Eweniyi
  • Ursula M Burns
  • Yewande Otedola