EffectStars2 (version 0.1-3)

insolvency: Insolvency data

Description

The data set originates from the Munich founder study. The data were collected on business founders who registered their new companies at the local chambers of commerce in Munich and surrounding administrative districts. The focus was on survival of firms measured in 7 categories, the first six represent failure in intervals of six months, the last category represents survival time beyond 36 months.

Format

A data frame with 1224 observations on the following 16 variables.

Insolvency

Survival of firms in ordered categories with levels 1 < 2 < 3 < 4 < 5 < 6 < 7

Sector

Economic Sector with levels industry, commerce and service industry

Legal

Legal form with levels small trade, one man business, GmBH and GbR, KG, OHG

Location

Location with levels residential area and business area

New_Foundation

New Foundation or take-over with levels new foundation and take-over

Pecuniary_Reward

Pecuniary reward with levels main and additional

Seed_Capital

Seed capital with levels < 25000 and > 25000

Equity_Capital

Equity capital with levels no and yes

Debt_Capital

Debt capital with levels no and yes

Market

Market with levels local and national

Clientele

Clientele with levels wide spread and small

Degree

Educational level with levels no A-levels and A-Levels

Gender

Gender with levels female and male

Experience

Professional experience with levels < 10 years and > 10 years

Employees

Number of employees with levels 0 or 1 and > 2

Age

Age of the founder at formation of the company

References

Bruederl, J. and Preisendoerfer, P. and Ziegler, R. (1996): Der Erfolg neugegruendeter Betriebe: eine empirische Studie zu den Chancen und Risiken von Unternehmensgruendungen, Duncker & Humblot.

Examples

# NOT RUN {
# }
# NOT RUN {
data(insolvency)
insolvency$Age <- scale(insolvency$Age)

my_formula <- Insolvency ~ Age + Gender

m_acat <- vglm(my_formula, data = insolvency,family = acat())
m_cratio <- vglm(my_formula, data = insolvency,family = cratio())
m_sratio <- vglm(my_formula, data = insolvency,family = sratio())
m_cumulative <- vglm(my_formula, data = insolvency,family = cumulative())

summary(m_acat)
effectstars(m_acat, p.values = TRUE)

summary(m_cratio)
effectstars(m_cratio, p.values = TRUE)

summary(m_sratio)
effectstars(m_sratio, p.values = TRUE)

summary(m_cumulative)
effectstars(m_cumulative, p.values = TRUE)
# }
# NOT RUN {
# }