For this thesis, I will use a database constructed by the Wharton Social Impact Initiative of the University of Pennsylvania, plus data about the cultural and social dimensions identified by Hofstede and information from Social Institutions and Gender Index (SIGI) and the World Economic Forum (WEF) about the advance on the closure of the gender gap.

Descriptive patterns

Funds per Continent

continent frequency percentage
America 85 62%
Asia 19 14%
Europe 18 13%
Africa 16 12%

Insights:

  1. There are more funds allocated in America.

America’s funds break down

country frequency percentage
United States 74 87
Canada 5 6
Colombia 3 4
Guatemala 1 1
Mexico 1 1
Peru 1 1

Insights:

  1. The United States is the country with the majority of funds.

Funds per investment vehicle and per country/investment vehicle

asset_class frequency percentage
Venture Capital 75 54%
Private Equity 27 20%
Debt 17 12%
Evergreen/ Holding Company 5 4%
Mezzanine Debt (Structured Exits) 2 1%
Angel Fund 1 1%
Collaborative Angel Fund 1 1%
Debt - Equity Growth Fund 1 1%
Evergreen + angel structure 1 1%
Evergreen Blended Finance Structure 1 1%
Fund of Funds PE/VC 1 1%
GP Led Investment Vehicle 1 1%
Impact Fund 1 1%
Other 1 1%
Private Equity Real Estate 1 1%
Private Equity, Growth, Venture (Fund of Funds) 1 1%
Social Impact Real Estate Fund 1 1%
investment_vehicle frequency percentage
Equity 77 56%
Debt and Equity 23 17%
Debt 14 10%
PE Fund 11 8%
Other 9 7%
Mezzanine Debt (Structured Exits) 2 1%
Convertible Debt and Equity 1 1%
Invoice Discounting 1 1%
country investment_vehicle frequency percentage
Belgium Equity 1 1%
Canada Debt and Equity 3 2%
Canada Equity 2 1%
Colombia Equity 2 1%
Colombia PE Fund 1 1%
Czech Republic Equity 1 1%
Denmark Debt and Equity 1 1%
France PE Fund 1 1%
Germany Debt 1 1%
Guatemala Other 1 1%
India Debt 1 1%
India Equity 6 4%
Israel Equity 1 1%
Ivory Coast Equity 1 1%
Japan Equity 2 1%
Kenya Debt and Equity 1 1%
Kenya Mezzanine Debt (Structured Exits) 1 1%
Kenya Other 1 1%
Kenya PE Fund 1 1%
Luxembourg Equity 1 1%
Mauritius Debt 2 1%
Mauritius PE Fund 2 1%
Mexico Debt 1 1%
Netherlands Debt and Equity 1 1%
Netherlands Equity 2 1%
Netherlands PE Fund 1 1%
Nigeria Debt and Equity 1 1%
Nigeria Equity 1 1%
Pakistan Equity 1 1%
Peru Mezzanine Debt (Structured Exits) 1 1%
Senegal Equity 1 1%
Singapore Debt and Equity 2 1%
Singapore Equity 4 3%
South Africa Debt and Equity 1 1%
South Africa Other 1 1%
South Africa PE Fund 1 1%
South Korea Equity 1 1%
Spain Debt 1 1%
Switzerland Other 1 1%
Uganda Debt and Equity 1 1%
United Arab Emirates Equity 1 1%
United Kingdom Equity 3 2%
United Kingdom Invoice Discounting 1 1%
United Kingdom PE Fund 2 1%
United States Convertible Debt and Equity 1 1%
United States Debt 8 6%
United States Debt and Equity 12 9%
United States Equity 46 33%
United States Other 5 4%
United States PE Fund 2 1%

Insights:

  1. The most used asset class is venture capital and private equity accounting for 74%.

  2. 56% of funds use equity as their investment vehicle. The US funds accounts for a third (33%) of it.

Average fund size per year

fund_inception_date number_of_funds percentage.x total_fund_size percentage.y avg_fund_size
1995 1 1% 750000000 8% 750000000
1999 1 1% 80000000 1% 80000000
2002 1 1% 30000000 0% 30000000
2006 2 1% 5500000 0% 2750000
2008 1 1% 52500000 1% 52500000
2009 1 1% 80000000 1% 80000000
2012 6 4% 435385000 4% 72564167
2013 2 1% 50000000 1% 25000000
2014 6 4% 456700000 5% 76116667
2015 7 5% 1191000000 12% 170142857
2016 10 7% 251600000 3% 25160000
2017 14 10% 376400000 4% 26885714
2018 22 16% 1766900000 18% 80313636
2019 64 46% 4159100000 43% 64985938

Insights:

  1. The number of funds created per year present a positive tendency; however, the average fund size has been declining over time.

Correlation between variables

I will start with a correlation analysis in order to find what are the variables that are more related.

encoded_percentages %>% 
  select(-priv_inv, -inst_inv, -meet_criteria) %>% 
  summary()
##    fund_size             fem_gp           rac_gp          lgtb_gp       
##  Min.   :        0   Min.   :  0.00   Min.   :  0.00   Min.   :  0.000  
##  1st Qu.: 15000000   1st Qu.: 30.75   1st Qu.:  0.00   1st Qu.:  0.000  
##  Median : 32500000   Median : 62.50   Median : 33.00   Median :  0.000  
##  Mean   : 70181775   Mean   : 62.66   Mean   : 39.47   Mean   :  4.457  
##  3rd Qu.: 70000000   3rd Qu.:100.00   3rd Qu.: 66.00   3rd Qu.:  0.750  
##  Max.   :800000000   Max.   :100.00   Max.   :100.00   Max.   :100.000  
##      fem_ic           fem_lp      
##  Min.   :  0.00   Min.   :  0.00  
##  1st Qu.: 33.00   1st Qu.: 17.00  
##  Median : 50.00   Median : 46.10  
##  Mean   : 56.62   Mean   : 42.32  
##  3rd Qu.: 80.00   3rd Qu.: 67.75  
##  Max.   :100.00   Max.   :100.00

Correlation between fund size and the participation of women in the investment process

fund_size fem_gp fem_ic fem_lp
fund_size 1.00 -0.20 -0.28 -0.17
fem_gp -0.20 1.00 0.60 0.34
fem_ic -0.28 0.60 1.00 0.44
fem_lp -0.17 0.34 0.44 1.00

Insights:

  1. Fund size has a small positive correlation with the ticket sizes of both private and institutional investors.

  2. Fund size has a negative correlation with the percentage of female members present in the investment committee.

  3. Fund size has a negative correlation with the percentage of limited partners that are women.

Correlation between fund size and the stage of the targeted ventures

fund_size seed early series_a_b series_b_c growth
fund_size 1.00 -0.36 -0.11 -0.01 0.19 0.42
seed -0.36 1.00 0.25 -0.29 -0.22 -0.35
early -0.11 0.25 1.00 0.45 -0.03 -0.24
series_a_b -0.01 -0.29 0.45 1.00 0.24 -0.06
series_b_c 0.19 -0.22 -0.03 0.24 1.00 0.43
growth 0.42 -0.35 -0.24 -0.06 0.43 1.00

Insights:

  1. Fund size increases as the needs of companies does.

Correlation between fund size and the targeted geography

fund_size north_america latin_america europe australia asia_pacific sub_saharan_africa global east_asia south_asia middle_east north_africa eastern_europe
fund_size 1.00 -0.15 0.04 -0.03 0.02 0.02 0.12 0.20 0.07 0.02 0.07 0.29 0.09
north_america -0.15 1.00 -0.19 0.16 0.22 -0.11 -0.30 0.03 0.03 -0.11 -0.07 -0.09 0.04
latin_america 0.04 -0.19 1.00 0.05 0.27 0.20 0.22 0.04 0.20 0.15 0.22 0.12 0.27
europe -0.03 0.16 0.05 1.00 0.43 0.13 -0.05 0.07 0.24 0.13 0.17 0.06 0.18
australia 0.02 0.22 0.27 0.43 1.00 0.30 0.19 0.37 0.65 0.30 0.33 0.30 0.49
asia_pacific 0.02 -0.11 0.20 0.13 0.30 1.00 0.13 0.07 0.39 0.47 0.25 0.14 0.30
sub_saharan_africa 0.12 -0.30 0.22 -0.05 0.19 0.13 1.00 -0.04 0.11 0.04 0.14 0.31 0.19
global 0.20 0.03 0.04 0.07 0.37 0.07 -0.04 1.00 0.20 0.07 0.22 0.11 0.23
east_asia 0.07 0.03 0.20 0.24 0.65 0.39 0.11 0.20 1.00 0.31 0.31 0.29 0.48
south_asia 0.02 -0.11 0.15 0.13 0.30 0.47 0.04 0.07 0.31 1.00 0.25 0.14 0.30
middle_east 0.07 -0.07 0.22 0.17 0.33 0.25 0.14 0.22 0.31 0.25 1.00 0.31 0.51
north_africa 0.29 -0.09 0.12 0.06 0.30 0.14 0.31 0.11 0.29 0.14 0.31 1.00 0.48
eastern_europe 0.09 0.04 0.27 0.18 0.49 0.30 0.19 0.23 0.48 0.30 0.51 0.48 1.00

Insights:

  1. Fund size has a small positive correlation when the targeted geography is North Africa.

Analysis per country

country number_funds avg_gp_fem avg_ic_fem avg_lp_fem
United States 74 63 62 46
India 7 78 48 53
Singapore 6 41 52 35
United Kingdom 6 63 50 30
Canada 5 80 65 61
Kenya 4 75 44 50
Mauritius 4 43 36 34
Netherlands 4 84 38 45
Colombia 3 8 7 21
South Africa 3 85 55 2
Japan 2 88 75 0
Nigeria 2 75 75 34
Belgium 1 100 57 88
Czech Republic 1 100 75 33
Denmark 1 33 50 10
France 1 43 14 50
Germany 1 100 40 0
Guatemala 1 0 50 0
Israel 1 30 30 20
Ivory Coast 1 50 67 25
Luxembourg 1 100 100 100
Mexico 1 25 28 25
Pakistan 1 100 67 17
Peru 1 33 40 0
Senegal 1 100 80 97
South Korea 1 100 100 30
Spain 1 10 0 50
Switzerland 1 10 66 50
Uganda 1 0 25 25
United Arab Emirates 1 100 100 90

Correlation per country

number_funds avg_gp_fem avg_ic_fem avg_lp_fem
number_funds 1.0000000 0.0238926 0.0474986 0.0618183
avg_gp_fem 0.0238926 1.0000000 0.6759499 0.3691284
avg_ic_fem 0.0474986 0.6759499 1.0000000 0.3533709
avg_lp_fem 0.0618183 0.3691284 0.3533709 1.0000000

Insights:

  1. Using all data available, there is no significant correlation between the number of funds per country and the presence of minorities in the investment process.

Correlation per country: countries with at least 2 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem
number_funds 1.0000000 -0.0350392 0.1794939 0.2395849
avg_gp_fem -0.0350392 1.0000000 0.7185768 0.0602688
avg_ic_fem 0.1794939 0.7185768 1.0000000 -0.0427471
avg_lp_fem 0.2395849 0.0602688 -0.0427471 1.0000000

Insights:

  1. Selecting all countries with at least 2 funds, it is possible to see that the number of funds have a positive correlation with the presence of women as limited partners or in the investment committee.

Correlation per country: countries with at least 3 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem
number_funds 1.0000000 0.0260297 0.3739636 0.1997067
avg_gp_fem 0.0260297 1.0000000 0.7021225 0.3110080
avg_ic_fem 0.3739636 0.7021225 1.0000000 0.3293048
avg_lp_fem 0.1997067 0.3110080 0.3293048 1.0000000

Insights:

  1. Selecting all countries with at least 3 funds, there is a higher correlation between the percentage of female members in the investment committee and the number of funds.

Correlation per country: countries with at least 4 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem
number_funds 1.00 -0.07 0.51 0.07
avg_gp_fem -0.07 1.00 0.13 0.74
avg_ic_fem 0.51 0.13 1.00 0.44
avg_lp_fem 0.07 0.74 0.44 1.00

Insights:

  1. Considering the countries with at least 4 funds, the correlation between the number of funds and the presence of women in the investment committee gets higher.

  2. As the presence of women in the investment committee gets higher, the average of investments that meet the GLI criteria also increases.

  3. There is a high correlation between the female venture partners and the female limited partners.

Correlation between number of funds per country and WEF Gender Gap

country number_funds avg_gp_fem avg_ic_fem avg_lp_fem score eco edu hea pol
United States 74 63 62 46 76.3 75.4 100.0 97.0 32.9
India 7 78 48 53 62.5 32.6 96.2 93.7 27.6
Singapore 6 41 52 35 72.7 74.9 99.0 96.3 20.8
United Kingdom 6 63 50 30 77.5 71.6 99.9 96.6 41.9
Canada 5 80 65 61 77.2 74.1 100.0 96.8 38.1
Kenya 4 75 44 50 69.2 67.2 92.9 97.5 19.3
Mauritius 4 43 36 34 67.9 60.0 99.2 98.0 14.4
Netherlands 4 84 38 45 76.2 71.3 100.0 96.2 37.5
Colombia 3 8 7 21 72.5 70.8 100.0 97.5 21.6
South Africa 3 85 55 2 78.1 65.8 99.4 97.9 49.3
Japan 2 88 75 0 65.6 60.4 98.3 97.3 6.1
Nigeria 2 75 75 34 62.7 68.7 80.6 96.7 4.7
Belgium 1 100 57 88 78.9 70.9 NA 96.8 48.0
Czech Republic 1 100 75 33 71.1 66.2 100.0 97.8 20.3
Denmark 1 33 50 10 76.8 73.6 100.0 96.4 37.1
France 1 43 14 50 78.4 71.0 100.0 97.0 45.7
Germany 1 100 40 0 79.6 70.6 99.7 97.2 50.9
Guatemala 1 0 50 0 65.5 56.0 96.9 97.9 11.2
Israel 1 30 30 20 72.4 70.5 100.0 96.4 22.7
Ivory Coast 1 50 67 25 63.7 66.4 82.8 97.9 7.6
Luxembourg 1 100 100 100 72.6 69.1 100.0 96.5 24.7
Mexico 1 25 28 25 75.7 59.0 99.7 97.5 46.8
Pakistan 1 100 67 17 55.6 31.6 81.1 94.4 15.4
Peru 1 33 40 0 72.1 62.9 98.1 96.4 31.0
Senegal 1 100 80 97 68.4 55.4 88.8 96.7 32.7
South Korea 1 100 100 30 68.7 58.6 97.3 97.6 21.4
Spain 1 10 0 50 78.8 69.9 99.8 96.5 49.1
Switzerland 1 10 66 50 79.8 74.3 99.2 96.4 49.4
Uganda 1 0 25 25 71.7 69.2 89.8 98.0 29.6
United Arab Emirates 1 100 100 90 71.6 51.0 98.7 96.3 40.3
number_funds avg_gp_fem avg_ic_fem avg_lp_fem score eco edu hea pol
number_funds 1.00 0.04 0.05 0.08 0.14 0.19 0.14 -0.01 0.04
avg_gp_fem 0.04 1.00 0.69 0.32 -0.24 -0.32 -0.15 -0.23 -0.08
avg_ic_fem 0.05 0.69 1.00 0.37 -0.34 -0.24 -0.24 -0.08 -0.29
avg_lp_fem 0.08 0.32 0.37 1.00 0.07 -0.04 0.01 -0.25 0.17
score 0.14 -0.24 -0.34 0.07 1.00 0.72 0.73 0.26 0.82
eco 0.19 -0.32 -0.24 -0.04 0.72 1.00 0.39 0.53 0.26
edu 0.14 -0.15 -0.24 0.01 0.73 0.39 1.00 0.14 0.52
hea -0.01 -0.23 -0.08 -0.25 0.26 0.53 0.14 1.00 -0.09
pol 0.04 -0.08 -0.29 0.17 0.82 0.26 0.52 -0.09 1.00

Insights:

  1. The number of funds is positive correlated with economic participation and opportunity.

Correlation between number of funds per country and WEF Gender Gap: countries with at least 2 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score eco edu hea pol
number_funds 1.00 -0.04 0.18 0.24 0.27 0.23 0.19 0.01 0.18
avg_gp_fem -0.04 1.00 0.72 0.06 -0.06 -0.23 -0.21 -0.22 0.20
avg_ic_fem 0.18 0.72 1.00 -0.04 -0.17 0.02 -0.39 -0.09 -0.14
avg_lp_fem 0.24 0.06 -0.04 1.00 0.01 -0.04 -0.09 -0.48 0.13
score 0.27 -0.06 -0.17 0.01 1.00 0.66 0.64 0.33 0.82
eco 0.23 -0.23 0.02 -0.04 0.66 1.00 0.10 0.62 0.17
edu 0.19 -0.21 -0.39 -0.09 0.64 0.10 1.00 0.09 0.57
hea 0.01 -0.22 -0.09 -0.48 0.33 0.62 0.09 1.00 -0.08
pol 0.18 0.20 -0.14 0.13 0.82 0.17 0.57 -0.08 1.00

Insights:

  1. The number of funds is positive correlated with economic participation and opportunity.

Correlation between number of funds per country and WEF Gender Gap: countries with at least 3 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score eco edu hea pol
number_funds 1.00 0.03 0.37 0.20 0.20 0.22 0.19 0.02 0.08
avg_gp_fem 0.03 1.00 0.70 0.31 0.16 -0.20 -0.25 -0.28 0.61
avg_ic_fem 0.37 0.70 1.00 0.33 0.30 0.07 0.00 -0.18 0.48
avg_lp_fem 0.20 0.31 0.33 1.00 -0.31 -0.16 -0.32 -0.49 -0.26
score 0.20 0.16 0.30 -0.31 1.00 0.81 0.63 0.47 0.71
eco 0.22 -0.20 0.07 -0.16 0.81 1.00 0.44 0.66 0.18
edu 0.19 -0.25 0.00 -0.32 0.63 0.44 1.00 0.19 0.41
hea 0.02 -0.28 -0.18 -0.49 0.47 0.66 0.19 1.00 -0.04
pol 0.08 0.61 0.48 -0.26 0.71 0.18 0.41 -0.04 1.00

Insights:

  1. The number of funds is positive correlated with economic participation and opportunity.

Correlation between number of funds per country and WEF Gender Gap: countries with at least 4 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score eco edu hea pol
number_funds 1.00 -0.07 0.51 0.07 0.28 0.25 0.25 0.12 0.16
avg_gp_fem -0.07 1.00 0.13 0.74 0.09 -0.19 -0.23 -0.38 0.57
avg_ic_fem 0.51 0.13 1.00 0.44 0.45 0.32 0.28 -0.08 0.45
avg_lp_fem 0.07 0.74 0.44 1.00 -0.11 -0.22 -0.29 -0.29 0.19
score 0.28 0.09 0.45 -0.11 1.00 0.88 0.65 0.42 0.69
eco 0.25 -0.19 0.32 -0.22 0.88 1.00 0.44 0.70 0.27
edu 0.25 -0.23 0.28 -0.29 0.65 0.44 1.00 0.12 0.52
hea 0.12 -0.38 -0.08 -0.29 0.42 0.70 0.12 1.00 -0.25
pol 0.16 0.57 0.45 0.19 0.69 0.27 0.52 -0.25 1.00

Insights:

  1. The number of funds is positive correlated with economic participation and opportunity.

Correlation between number of funds per country and SIGI Index

country number_funds avg_gp_fem avg_ic_fem avg_lp_fem score dis_fam phy_int fin_res civ_lib
United States 74 63 62 46 18 27 11 11 23
India 7 78 48 53 34 47 29 37 21
Singapore 6 41 52 35 27 27 15 12 49
United Kingdom 6 63 50 30 17 28 24 10 7
Canada 5 80 65 61 18 27 4 17 23
Kenya 4 75 44 50 35 50 29 42 17
Mauritius 4 43 36 34 NA 53 NA 19 41
Netherlands 4 84 38 45 16 24 13 5 21
Colombia 3 8 7 21 15 10 15 14 21
South Africa 3 85 55 2 22 33 15 20 21
Japan 2 88 75 0 24 20 21 30 25
Nigeria 2 75 75 34 46 55 32 41 54
Belgium 1 100 57 88 11 22 8 3 10
Czech Republic 1 100 75 33 20 27 13 12 26
Denmark 1 33 50 10 10 15 10 5 11
France 1 43 14 50 11 28 6 4 5
Germany 1 100 40 0 15 18 15 13 14
Guatemala 1 0 50 0 29 26 24 18 43
Israel 1 30 30 20 NA 47 NA 28 38
Ivory Coast 1 50 67 25 43 30 36 76 20
Luxembourg 1 100 100 100 NA 22 NA 7 8
Mexico 1 25 28 25 29 60 16 17 15
Pakistan 1 100 67 17 59 80 37 60 53
Peru 1 33 40 0 24 48 27 5 13
Senegal 1 100 80 97 37 65 42 28 4
South Korea 1 100 100 30 23 22 18 33 20
Spain 1 10 0 50 14 28 12 11 6
Switzerland 1 10 66 50 8 0 13 12 7
Uganda 1 0 25 25 45 54 34 61 27
United Arab Emirates 1 100 100 90 NA 87 NA 28 NA
number_funds avg_gp_fem avg_ic_fem avg_lp_fem score dis_fam phy_int fin_res civ_lib
number_funds 1.00 0.04 0.09 0.12 -0.11 -0.08 -0.18 -0.13 0.04
avg_gp_fem 0.04 1.00 0.62 0.24 0.13 0.19 0.08 0.09 0.07
avg_ic_fem 0.09 0.62 1.00 0.10 0.26 0.07 0.25 0.28 0.28
avg_lp_fem 0.12 0.24 0.10 1.00 -0.09 0.10 -0.05 -0.10 -0.33
score -0.11 0.13 0.26 -0.09 1.00 0.82 0.86 0.86 0.62
dis_fam -0.08 0.19 0.07 0.10 0.82 1.00 0.70 0.53 0.33
phy_int -0.18 0.08 0.25 -0.05 0.86 0.70 1.00 0.76 0.29
fin_res -0.13 0.09 0.28 -0.10 0.86 0.53 0.76 1.00 0.40
civ_lib 0.04 0.07 0.28 -0.33 0.62 0.33 0.29 0.40 1.00

Insights:

  1. The number of funds is negative correlated with restricted access to productive and financial resources.

Correlation between number of funds per country and SIGI Index: countries with at least 2 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score dis_fam phy_int fin_res civ_lib
number_funds 1.00 -0.07 0.16 0.24 -0.23 -0.11 -0.30 -0.28 -0.08
avg_gp_fem -0.07 1.00 0.70 0.06 0.27 0.47 0.15 0.34 -0.11
avg_ic_fem 0.16 0.70 1.00 -0.05 0.41 0.40 0.11 0.33 0.37
avg_lp_fem 0.24 0.06 -0.05 1.00 0.13 0.35 -0.07 0.03 -0.02
score -0.23 0.27 0.41 0.13 1.00 0.88 0.78 0.87 0.59
dis_fam -0.11 0.47 0.40 0.35 0.88 1.00 0.71 0.77 0.30
phy_int -0.30 0.15 0.11 -0.07 0.78 0.71 1.00 0.76 0.14
fin_res -0.28 0.34 0.33 0.03 0.87 0.77 0.76 1.00 0.24
civ_lib -0.08 -0.11 0.37 -0.02 0.59 0.30 0.14 0.24 1.00

Insights:

  1. As there is less discrimination for women to access productive and financial assets, the number of funds increase.

Correlation between number of funds per country and SIGI Index: countries with at least 3 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score dis_fam phy_int fin_res civ_lib
number_funds 1.00 -0.01 0.36 0.19 -0.19 -0.08 -0.26 -0.22 0.03
avg_gp_fem -0.01 1.00 0.69 0.30 0.26 0.64 0.07 0.26 -0.29
avg_ic_fem 0.36 0.69 1.00 0.32 0.18 0.43 -0.21 0.04 0.12
avg_lp_fem 0.19 0.30 0.32 1.00 0.28 0.35 0.00 0.26 0.02
score -0.19 0.26 0.18 0.28 1.00 0.88 0.71 0.88 0.20
dis_fam -0.08 0.64 0.43 0.35 0.88 1.00 0.68 0.84 -0.14
phy_int -0.26 0.07 -0.21 0.00 0.71 0.68 1.00 0.68 -0.33
fin_res -0.22 0.26 0.04 0.26 0.88 0.84 0.68 1.00 -0.16
civ_lib 0.03 -0.29 0.12 0.02 0.20 -0.14 -0.33 -0.16 1.00

Insights:

  1. As there is less discrimination for women to access productive and financial assets, the number of funds increase.

Correlation between number of funds per country and SIGI Index: countries with at least 4 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem score dis_fam phy_int fin_res civ_lib
number_funds 1.00 -0.20 0.51 0.00 -0.29 -0.23 -0.30 -0.25 0.01
avg_gp_fem -0.20 1.00 -0.23 0.68 -0.05 0.27 0.03 0.29 -0.63
avg_ic_fem 0.51 -0.23 1.00 0.30 -0.30 -0.32 -0.59 -0.18 0.16
avg_lp_fem 0.00 0.68 0.30 1.00 0.21 0.34 -0.24 0.46 -0.08
score -0.29 -0.05 -0.30 0.21 1.00 0.91 0.72 0.90 0.19
dis_fam -0.23 0.27 -0.32 0.34 0.91 1.00 0.80 0.98 -0.23
phy_int -0.30 0.03 -0.59 -0.24 0.72 0.80 1.00 0.69 -0.34
fin_res -0.25 0.29 -0.18 0.46 0.90 0.98 0.69 1.00 -0.17
civ_lib 0.01 -0.63 0.16 -0.08 0.19 -0.23 -0.34 -0.17 1.00

Insights:

  1. As there is less discrimination for women to access productive and financial assets, the number of funds increase.

Correlation between number of funds per country and Hofstede Index

country number_funds avg_gp_fem avg_ic_fem avg_lp_fem pow_dis indi mas unc_avo lt_ori indu
United States 74 63 62 46 40 91 62 46 26 68
India 7 78 48 53 77 48 56 40 51 26
Singapore 6 41 52 35 74 20 48 8 72 46
United Kingdom 6 63 50 30 35 89 66 35 51 69
Canada 5 80 65 61 39 80 52 48 36 68
Kenya 4 75 44 50 70 25 60 50 NA NA
Mauritius 4 43 36 34 NA NA NA NA NA NA
Netherlands 4 84 38 45 38 80 14 53 67 68
Colombia 3 8 7 21 67 13 64 80 13 83
South Africa 3 85 55 2 49 65 63 49 34 63
Japan 2 88 75 0 54 46 95 92 88 42
Nigeria 2 75 75 34 80 30 60 55 13 84
Belgium 1 100 57 88 65 75 54 94 82 57
Czech Republic 1 100 75 33 57 58 57 74 70 29
Denmark 1 33 50 10 18 74 16 23 35 70
France 1 43 14 50 68 71 43 86 63 48
Germany 1 100 40 0 35 67 66 65 83 40
Guatemala 1 0 50 0 95 6 37 98 NA NA
Israel 1 30 30 20 13 54 47 81 38 NA
Ivory Coast 1 50 67 25 NA NA NA NA NA NA
Luxembourg 1 100 100 100 40 60 50 70 64 56
Mexico 1 25 28 25 81 30 69 82 24 97
Pakistan 1 100 67 17 55 14 50 70 50 0
Peru 1 33 40 0 64 16 42 87 25 46
Senegal 1 100 80 97 70 25 45 55 25 NA
South Korea 1 100 100 30 60 18 39 85 100 29
Spain 1 10 0 50 57 51 42 86 48 44
Switzerland 1 10 66 50 34 68 70 58 74 66
Uganda 1 0 25 25 NA NA NA NA NA NA
United Arab Emirates 1 100 100 90 90 25 50 80 NA NA
number_funds avg_gp_fem avg_ic_fem avg_lp_fem pow_dis indi mas unc_avo lt_ori indu
number_funds 1.00 -0.01 0.07 0.10 -0.17 0.34 0.11 -0.24 -0.25 0.14
avg_gp_fem -0.01 1.00 0.66 0.13 -0.10 0.17 0.06 0.07 0.46 -0.46
avg_ic_fem 0.07 0.66 1.00 0.19 -0.20 0.01 0.14 -0.10 0.37 -0.25
avg_lp_fem 0.10 0.13 0.19 1.00 0.03 0.35 -0.17 0.05 0.18 0.09
pow_dis -0.17 -0.10 -0.20 0.03 1.00 -0.66 0.22 0.31 -0.16 -0.06
indi 0.34 0.17 0.01 0.35 -0.66 1.00 -0.08 -0.26 0.13 0.25
mas 0.11 0.06 0.14 -0.17 0.22 -0.08 1.00 0.23 0.03 0.07
unc_avo -0.24 0.07 -0.10 0.05 0.31 -0.26 0.23 1.00 0.18 -0.16
lt_ori -0.25 0.46 0.37 0.18 -0.16 0.13 0.03 0.18 1.00 -0.54
indu 0.14 -0.46 -0.25 0.09 -0.06 0.25 0.07 -0.16 -0.54 1.00

Insights:

  1. The number of funds is positive correlated with individualism. This implies that as a culture gives more importance to personal goals, the number of funds are higher.

Correlation between number of funds per country and Hofstede Index: countries with at least 2 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem pow_dis indi mas unc_avo lt_ori indu
number_funds 1.00 -0.06 0.15 0.28 -0.31 0.44 0.04 -0.13 -0.25 0.08
avg_gp_fem -0.06 1.00 0.72 0.04 -0.31 0.55 -0.02 0.04 0.35 -0.32
avg_ic_fem 0.15 0.72 1.00 -0.01 -0.06 0.27 0.34 -0.08 0.22 -0.22
avg_lp_fem 0.28 0.04 -0.01 1.00 -0.06 0.31 -0.60 -0.48 -0.17 -0.02
pow_dis -0.31 -0.31 -0.06 -0.06 1.00 -0.88 0.14 -0.06 -0.14 -0.24
indi 0.44 0.55 0.27 0.31 -0.88 1.00 -0.20 -0.14 0.08 0.07
mas 0.04 -0.02 0.34 -0.60 0.14 -0.20 1.00 0.46 0.01 -0.17
unc_avo -0.13 0.04 -0.08 -0.48 -0.06 -0.14 0.46 1.00 -0.08 0.21
lt_ori -0.25 0.35 0.22 -0.17 -0.14 0.08 0.01 -0.08 1.00 -0.69
indu 0.08 -0.32 -0.22 -0.02 -0.24 0.07 -0.17 0.21 -0.69 1.00

Insights:

  1. A high score in the individualism dimension implies that a society cares more for achieving personal goals than societal ones. In this case, we can see there is a positive correlation between the individualism score and the number of funds and the percentage of female partners.

  2. A high score in the indulgence dimension allows relatively free gratification of basic and natural human drives related to enjoying life and having fun. There is a positive correlation between the indulgence score and the percentage of investments that meet the gender lens criteria.

Correlation between number of funds per country and Hofstede Index: countries with at least 3 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem pow_dis indi mas unc_avo lt_ori indu
number_funds 1.00 0.02 0.35 0.24 -0.27 0.40 0.21 -0.02 -0.32 0.11
avg_gp_fem 0.02 1.00 0.71 0.28 -0.47 0.73 -0.34 -0.24 0.35 -0.34
avg_ic_fem 0.35 0.71 1.00 0.35 -0.37 0.62 0.06 -0.62 0.27 -0.35
avg_lp_fem 0.24 0.28 0.35 1.00 -0.08 0.29 -0.36 -0.19 0.25 -0.31
pow_dis -0.27 -0.47 -0.37 -0.08 1.00 -0.88 0.16 -0.17 0.10 -0.60
indi 0.40 0.73 0.62 0.29 -0.88 1.00 -0.12 -0.03 0.03 0.19
mas 0.21 -0.34 0.06 -0.36 0.16 -0.12 1.00 0.07 -0.63 0.06
unc_avo -0.02 -0.24 -0.62 -0.19 -0.17 -0.03 0.07 1.00 -0.75 0.62
lt_ori -0.32 0.35 0.27 0.25 0.10 0.03 -0.63 -0.75 1.00 -0.54
indu 0.11 -0.34 -0.35 -0.31 -0.60 0.19 0.06 0.62 -0.54 1.00

Insights:

  1. A high score in the individualism dimension implies that a society cares more for achieving personal goals than societal ones. In this case, we can see there is a positive correlation between the individualism score and the number of funds, the percentage of female partners, and the proportion of women memebers in the investment committee.

Correlation between number of funds per country and Hofstede Index: countries with at least 4 funds

number_funds avg_gp_fem avg_ic_fem avg_lp_fem pow_dis indi mas unc_avo lt_ori indu
number_funds 1.00 -0.17 0.48 0.04 -0.24 0.38 0.35 0.21 -0.69 0.26
avg_gp_fem -0.17 1.00 -0.19 0.67 -0.37 0.54 -0.39 0.90 -0.26 0.13
avg_ic_fem 0.48 -0.19 1.00 0.39 -0.16 0.18 0.65 0.00 -0.77 0.23
avg_lp_fem 0.04 0.67 0.39 1.00 0.01 0.14 -0.12 0.58 -0.48 -0.13
pow_dis -0.24 -0.37 -0.16 0.01 1.00 -0.92 0.09 -0.63 0.43 -0.95
indi 0.38 0.54 0.18 0.14 -0.92 1.00 0.07 0.80 -0.63 0.76
mas 0.35 -0.39 0.65 -0.12 0.09 0.07 1.00 -0.27 -0.58 -0.13
unc_avo 0.21 0.90 0.00 0.58 -0.63 0.80 -0.27 1.00 -0.52 0.41
lt_ori -0.69 -0.26 -0.77 -0.48 0.43 -0.63 -0.58 -0.52 1.00 -0.32
indu 0.26 0.13 0.23 -0.13 -0.95 0.76 -0.13 0.41 -0.32 1.00

Insights:

  1. A high score in the individualism dimension implies that a society cares more for achieving personal goals than societal ones. In this case, we can see there is a positive correlation between the individualism score and the percentage of female partners.

  2. A high score in the masculinity dimension indicates distinct gender roles, assertive, and concentrated on material achievements and wealth-building. It has a positive relation with the presence of female members in the investment committee.

  3. A low score in the long term orientation dimension involves delivering short-term success or gratification, and places a stronger emphasis on the present than the future. Short-term orientation emphasizes quick results and respect for tradition. It has a negative correlation with the number of funds.