Financing tourism entrepreneurship: a gender perspective on the reproduction of inequalities

Figures & data

Table 1. Variables, content analysis.

Variable	Type	Options
Application success	Categorial. Binary	Yes/No
Subprogramme	Categorical. Nominal	R&D; Innovative product; Young Entrepreneur; Internationalisation
Gender	Categorial. Binary	Women / Men
Requested loan	Categorial. Ordinal	Transformed from a continuous variable (euros), with the exact amount requested
Size of the project	Categorial. Ordinal.	Transformed from a continuous variable (euros), with the exact amount requested
CNAE	Categorical. Nominal	List of options
Geographical location	Categorical. Nominal	Spain’s provinces

Source: authors.

Table 2. EMPRENDETUR’s main aims.

PNIT focus	Aims / Goals
Energy and sustainability	Promoting R&D in transport technologies, climatisation, lighting and energy generation.
Information and communication technologies (ICTs)	Mobile technologies, content management, geo-referencing, tourism 2.0, virtual reality and augmented reality, biometrics, smart tourist destinations, emotional computing, natural language processing, 3D avatar modelling, contextual marketing, responsive web design, geofencing.
Materials and construction	Minimising environmental impacts in the design and construction of buildings and infrastructure.
Transport and associated services	Quality communication networks, border flows and the accessibility of tourists with disabilities.
Business management	Improvements in productivity through technologies and innovations in marketing, energy efficiency and renewable energies, among others.
Humanities, society and legal sciences:	New sociodemographic and leisure trends in legislative framework in the business and social fields.
Accessibility	Standardisation and certification of accessibility on tourism services and products.

Source: authors.

Table 3. Total budget and loan.

Budget (Euro)	Project size			Loan requested
	Male	Female	Female/Male percentage point difference	Male	Female	Female/Male percentage point difference
1–100.000	10.52%	23.08%	12.56%	29.02%	48.95%	19.93%
100.001–300.000	35.36%	37.76%	2.40%	41.44%	34.27%	−7.18%
300.001–600.000	28.14%	16.78%	−11.35%	20.28%	10.49%	−9.79%
600.001–900.000	8.49%	6.99%	−1.50%	3.80%	4.20%	0.39%
More 900.0000	17.49%	15.38%	−2.11%	5.45%	2.10%	−3.35%

Source: authors. The colour has no significance

Table 4. OR according to size and legal classification.

Variables	B	Sig.	Exp(B)	95% C.I. para EXP(B)
				Inferior	Superior
Big and medium	1.504	.001	4.501	1.876	10.801
Small	1.386	.000	3.997	1.930	8.279
Micro small	1.518	.000	4.563	2.230	9.337

Source: authors.

Table 5. OR according to size and legal classification.

Variables	B	Sig.	Exp(B)	95% C.I. para EXP(B)
				Inferior	Superior
Constitution date		.000
Before 2000	−1.553	.000	.212	.117	.384
2000–2009	−.395	.049	.674	.455	.998

Source: authors.

Figure 1. DT. Source: authors

Table

Hypothesis / Relationship	Results	Statistical significance
Gender perspective on applications analysis
H1a. Women apply less to funding schemes—HYPOTHESIS CONFIRMED	Women only made 15.4% of applications	Chi-square 8.424, p value 0.077
H1b. Women apply for funds for smaller projects—HYPOTHESIS CONFIRMED	Most women applicant’s projects (60.84%) had a total budget under €300,000	Chi-square 22.087, p value 0.000
	Women concentrate in micro-small enterprises (53.2% versus 48.5% of men) and individual entrepreneurship forms (16.1% versus 8.2% men)	Chi-square 13,190, p value 0.01
	Men were 72.4% more likely to be in big and medium enterprises	B: 0.966; OR: 2.629; Sig. 0.040; CI: 1.046–6.604
	21.7% of women-led projects had no employees compared to only 12.3% of the men-led projects	Chi-square 41.125, p value 0.000
H1c. Women that apply in funding schemes, apply for less money—HYPOTHESIS CONFIRMED	19,93% more women than men requested loans under €100,000	Chi-square 25.513, p value 0.000
	Men were 67.5% more likely than women to have submitted larger loans, above €300,000	B: 0.731; OR: 2.078; Sig. 0.02; CI: 1.306–3.306
H1d. Women are more likely to engage in entrepreneurship projects related to consumer services—HYPOTHESIS CONFIRMED	Women-led enterprises were significantly more likely to be in hospitality (15.4% versus 7.0%), and men were more likely to lead ICT projects (44.1% vs. 32.9%	Chi-square 22.885, p value 0.002
	Men have 61.7% more probability than women of being entrepreneurs in ICT-related organisations	B: 0.477; OR: 1.612; Sig. 0.013; CI: 1.106–2.348
Relationship 1. Women tend to present applications for more established businesses—CONFIRMED	18.8% of women’s businesses were created before 1990, compared to only 8.5% of men’s	Chi-square 20.696, p value 0.00
	Men had 54% less probability of being the applicant of organisations created before 2000	B: −0.616; OR: 0.540; Sig. 0.010; CI: 0.338–0.864
Analysis of successful applications
H3. Women are generally less successful than men when applying for funding from gender-neutral funding schemes—HYPOTHESIS CONFIRMED	33.0% of men′s applications were successful and 19.6% of women’s	Chi-square 10.139, p value 0.001
	Men had 66.9% more probability of being successful than women	B: 0.702; OR: 2.019; Sig. 0.002; CI: 1.302–3.131
H2a. The size of the project has an influence on success—HYPOTHESIS CONFIRMED	Success rate increased when the total budget was over €300,000 and under €900,000	Chi-square 16.882, p value 0.002
	Projects with a total budget between €100,001 and 300,000 had 2.168 times greater probability of being successful	B: 0.774; OR: 2.168; Sig. 0.01; CI: 1.391–3.378
	Projects with total budgets ranging between €300,001 and 600,000 were 2.069 times more likely to be successful	B: 0.727; OR: 2.069; Sig. 0.02; CI: 1.301–3.290
	Individual entrepreneurs are less successful than enterprises, with a success rate of 10.2% compared to micro-small enterprises (34.0%), small (32.3%) and big or medium enterprises (33.3%)	Chi-square 20.483, p value 0.00
H2b. The amount of requested money has an influence in success—HYPOTHESIS CONFIRMED	Success rate increased when the loan was over €300,000 and under €900,000	Chi-square 15.356, p value 0.004
H2c. The economic activity has an impact on the success rates—HYPOTHESIS CONFIRMED	Applications from ICT businesses, professional, scientific, and technical activities, and management activities were more successful (40.5%, 30.1%, 28.1% rate of success respectively). Hospitality applications were the least successful (5.19%).	Chi-square 49.494, p value 0.00
	Non-ICT projects were 2.17 times less probable of being successful	B: −0.777; OR: 0,460; Sig. 0.00; CI: 0.347–0.610
Relationship 2. Year of business’s creation and success—CONFIRMED	The highest success rates were in companies created after 2009, especially between 2010 and 2014 (36.3%) and after 2015 (32.1%), compared to those created 1991–1999 (5.4%)	Chi-square 37.367, p value 0.00
Relationships between gender, size, and economic activity
Relationship 3. Projects over €300,001 (total budget) and women’s success—CONFIRMED	In project over €300,001 of total budget, women were almost half as likely as men to be successful (16.1% compared to 30.7%)	Chi-square 5.132, p value 0.023
	Men were 2.311 times more successful than women (69.8% probability) in projects above €300,000	B: 0.838; OR: 2.311; Sig. 0.027; CI: 1.100–4.855

Source: authors.