In this first draft of the paper, we demonstrate hiring behavior of female CEOs compared to their male counterpart. We investigate the hiring behavior relying on Norwegian register data. The results show that women are more likely to hire, but whenever men hire they hire more people for on average more hours.

1. Female CEOs and Hiring Behavior
Astrid Kunze
NHH Norwegian School of Economics, IZA, CESIfo
Bram Timmermans
NHH Norwegian School of Economics, Aalborg University Business School
This research is made possible through the ” Equal in Reaching Aspirations” (EARHART) project funded by
EEA and Norway Grants

2. EARHART PROJECT
• Overall research question: How does the presence of women in the upper-
echelon affect the performance of new ventures
• Founders
• CEO
• Board members
• A challenge in this literature is to identify start-ups, have large samples of start-
ups, observe these roles including role and demographics, and recruitment.
• Incomplete ownership data
• Incomplete role information
• Incomplete board information

3. Existing research
• Large body of literature that addresses that women are poorly positioned when it
comes to starting firms compared to their male counterpart.
• Less likely to start a firm (Ruef et al 2003)
• Less likely to outperform male counterparts (Kim et al 2006; Yang and Aldrich 2014)
• Less likely to obtain external finance (Greene et al 2003; Guzman and Kacperczyk 2019)
• Gender differences in:
• Growth ambitions (even within industries) (Darnihamedani & Terjesen)
• Networks
• Risk preference
• Industry choices

4. Female Entrepreneurs and Early hires
• There have been several studies that investigate early hiring decision of
(female) entrepreneurs.
• Fairlie and Miranda 2018 (women are less likely to hire)
• Coad et al 2017 (women are less likely to hire)
• Bublitz et al 2018 (women are less likely to hire in Denmark)
• Devine et al 2019 (women demonstrate lower employee growth ambitions)
• Impact of hiring women
• Increase changes for survival (Weber and Zulehner 2010 AER)
• Role models (Rocha and Van Praag 2020)

5. Goal of this paper
• We use population register data for Norway for the period 2004-2014 in which we
can identify start-ups and the demographics of the owners and CEOs of startups
• Emphasize on gender dimension among owner CEO and non-owner CEOs of
these startups
• We will exploit a timing-of-events approach to trace the direct effect demographi
characteristic of CEOs on recruitment during the first year.
• Whether they hire
• How many people they hire
• How many FTEs they hire.

6. EMPIRICS

7. DATA AND SAMPLE
• Norwegian register data:
• Firm register
• Ownership register
• «Role» registerEmployee register
• Newly registered firms (2004-2013)
• Limited corporations (akjseselskap)
• Non-primary and private sector enterprises
• Remove new registered that are majority owned by a foreign or corporate entity
• Remove industries with high share of holding structures and developing of propperty.
• Remove with missing ownership information
• Activiy requirements
• Secure that there is no trace of the new venture prior to year of founding
• Remove newly registered venture with high comobility rates
Total of 59,910 newly registered firm

8. OWNERS, CEOs, BOARD DIRECTORS AND EMPLOYEES
• Owners: all individuals with at least
a 10 percent ownership share
• CEO:
• 7513 cases where we cannot identify
a CEO removed
• 114 cases where we identify several
CEOs removed
• Total observations: 52,283
• All board members
• All individuals that are registered as an
employee but who are not owner, CEO
or board member
• Hired in the first 12 month after founding
• Including the fte of employment
• 14,278 that hire
• (-1 with 322 employees)
• Totally hired: 46,511
Owners, CEOs and Board Directors Employees
Demographics of all individuals

9. 0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
new ventures that hire new ventures that do not hire
Distribution of new ventures over the years and hiring
Lower capital
requirements to register

10. RESULTS

11. Firm level analysis descriptives
Variable
New Ventures
all (n=52,282) that hire (n=14,277)
Mean SD Mean SD
Hiring 0.273 0.446
Total # hires 0.890 2.575 3.258 4.070
Totel fte hires 0.699 2.161 2.561 3.511
CEO owner female 0.164 0.370 0.200 0.400
CEO non-owner female 0.018 0.135 0.027 0.163
CEO non-owner male 0.060 0.237 0.065 0.247
CEO owner male 0.758 0.428 0.708 0.455
Founding Team size 1.780 0.868 1.844 0.884
Family firm 0.292 0.455 0.293 0.455

12. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Hiring Hiring total # hires totel fte hires
total # hires
(hiring =1)
totel fte hires
(hiring=1)
Probit Probit (me) OLS OLS OLS OLS
CEO owner female 0.076*** 0.022*** -0.069* -0.100*** -0.456*** -0.487***
(0.020) (0.006) (0.033) (0.024) (0.091) (0.067)
CEO non-owner female 0.294*** 0.084*** 0.302** 0.138* -0.056 -0.277+
(0.046) (0.013) (0.100) (0.068) (0.228) (0.162)
CEO non-owner male 0.100*** 0.028*** 0.280*** 0.288*** 0.695*** 0.754***
(0.028) (0.008) (0.068) (0.061) (0.202) (0.184)
Founding team size 0.096*** 0.027*** 0.186*** 0.173*** 0.367*** 0.352***
(0.008) (0.002) (0.017) (0.015) (0.048) (0.042)
Family firm -0.129*** -0.037*** -0.179*** -0.163*** -0.260** -0.248***
(0.016) (0.004) (0.027) (0.022) (0.082) (0.070)
Constant -0.366 1.244** 1.097* 3.308*** 2.983**
(0.278) (0.447) (0.436) (0.957) (0.989)
N 51,650 51,974 51,974 14,185 14,185
Adjusted R2 0.116 0.089 0.104 0.095
Pseudo R2 0.140
Log Likelihood -26119.482
Standard errors in parentheses
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001
Year, region (oekreg) and industry (4-digit) controls
Firm level analysis, who hires, how many and for how many hours

13. All hires Male hires Female hires
Variable Obs Mean SD Obs Mean SD Obs Mean SD
Female hire 46511 0.397 0.489
Full time equivalent (fte) 46511 0.786 0.308 28063 0.877 0.257 18448 0.648 0.327
Age 46488 31.986 11.981 28045 33.298 11.830 18443 29.991 11.935
# of children 46511 0.549 0.932 28063 0.532 0.918 18448 0.574 0.954
Child under 6 (binary) 46511 0.196 0.397 28063 0.195 0.396 18448 0.196 0.397
Married 43030 0.274 0.446 25238 0.284 0.451 17792 0.259 0.438
Immigrant 46487 0.261 0.439 28044 0.312 0.463 18443 0.184 0.388
Family with founding team 46511 0.071 0.257 28063 0.052 0.222 18448 0.100 0.300
CEO owner female 46511 0.175 0.380 28063 0.077 0.266 18448 0.325 0.468
CEO non-owner female 46511 0.030 0.169 28063 0.017 0.130 18448 0.049 0.215
CEO non-owner male 46511 0.086 0.281 28063 0.098 0.298 18448 0.068 0.252
CEO owner male 46511 0.709 0.454 28063 0.808 0.394 18448 0.558 0.497
Founding Team size 46511 1.932 0.920 28063 1.961 0.931 18448 1.888 0.901
Family firm 46511 0.293 0.455 28063 0.267 0.442 18448 0.334 0.472
Individual level analysis: descriptives

14. Model 7 Model 8
Probit Probit marginal effects
Female hire Female hire
CEO owner female 0.373*** 0.097***
(0.021) (0.005)
CEO non-owner female 0.308*** 0.080***
(0.042) (0.011)
CEO non-owner male -0.050+ -0.013+
(0.026) (0.007)
Founding team size -0.030*** -0.008***
(0.009) (0.002)
Family firm -0.003 -0.001
(0.018) (0.005)
Family with founding team 0.669*** 0.173***
(0.031) (0.008)
Constant -0.448
(0.293)
N 45,925
Pseudo R2 0.312
Log Likelihood -21230.232
Standard errors in parentheses
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001
Year, region (oekreg) and industry (4-digit) controls
Individual level analysis: who hires female workers

15. Female hire -0.093*** -0.095***
(0.004) (0.004)
Family with founding team member -0.062*** -0.061***
(0.006) (0.006)
CEO owner female -0.044*** -0.042***
(0.004) (0.007)
CEO non-owner female -0.039*** -0.033*
(0.009) (0.014)
CEO non-owner male 0.035*** 0.024***

16. Age 0.003*** 0.003***
(0.000) (0.000)
# children under 18 0.021*** 0.021***
(0.002) (0.002)
Child under 6 year (binary) 0.045*** 0.045***
(0.004) (0.004)
Married -0.018*** -0.018***
(0.003) (0.003)
Immigrant 0.054*** 0.054***
(0.003) (0.003)

17. (CEO owner female)*(Female hire) -0.001
(0.009)
(CEO non-owner female)*(Female hire) -0.008
(0.017)
(CEO non-owner male)*(Female hire) 0.035***
(0.010)

18. Summary
• Women are more likely to hire
• But conditional on hiring,
• Non-owner CEO hire more
• Male CEO hire more (total and FTE)
• Women work on average less hours
• women with children work more hours (unreported tables)
• Women work less hours for female CEOs