Family Business in Spain: towards an empirical definition and

classificationRojo Ramírez, Alfonso. University of Almeria (arojo@ual.es)

Diéguez Soto, Julio. University of Málaga (jdieguez@uma.es)López Delgado, Pilar. University of Málaga (dlp@uma.es)

One of the main issues in FB research have consisted in knowing if FB perform better than NFB. To do this, researchers choose a specific definition of FB and then, they test if companies behave differently according to different approaches or theories.

The results are mix (regardless of the theory it is applied): some suggest that FBs behaves better than NFBs. Others suggest the opposite.

Motivation (Why?)

Why results are contradictory? Dealing with data:

◦ The volatility of the financial information which have been used, (Dyer, 2006) or to the existence of a great number of outliers, which distort the results in parametric test.

◦ The variables selected: there are no differences among considered variables, although they can be found in other ones.

FB definition itself, do not permit a good classification of FBs (Chrisman et al., 2003).

The main problem is the selected definition used by researchers

Authors point out the importance to define adequately FB (Westhead & Cowling, 1997;Villalonga & Amit, 2006) because it is difficult to have knowledge about them without identifying (Chrisman, Chua, & Sharma, 2005).

¿It is possible to improve FB definition?

Our view and objective

It’s difficult to find consensus on the exact definition of FB (Miller et al., 2007). ◦ They found more than 28 definitions in previous

studies worldwide; ◦ In the EU, up to 90 definitions have been

suggested (Mandl, 2008). Different criteria have been used:

◦ Objective: % of family ownership; managers family members or board positions (Dyer, 2006)

◦ Subjective: respondent believed the firm is a FB (Smith, 2007) or intention to transfer the firm (Litz, 1995)

Theoretical bacground (I-1)

Theoretical background (I-2)

Literature review shows us that many questions remain unsolved related to defining the FB.

Methods for distinguish FBs from NFBs is still in its infancy (Chrisman et al.,2003).

Our view: if FBs are different from the NFBs, it must be possible to form a cluster of companies that enjoy similar characteristics based on different economic and financial variables, which behave different from NFBs

RQ: Should be possible to form a cluster of companies with similar characteristics according to different economic and financial variables which can be call FBs?.

H 1. FBs have to show a similar behavior regarding performance, and different from NFBs.

H 2. FBs have to a show similar behavior regarding debt, and different from NFBs

Research questions and hipothesis

Research method-1 (How?)

Defining Family

Business

Observing differences

in perfomance

and debt

Previous research

This study

Research Method-2 (How?)

Data processing1. Firms classification2. Obtaining 12 samples3. Create 8companies groups

Exploratory analysis1. Outliers management2. Descriptive statistics3. Test of homogeneity (K-W )

Confirmatory analysis1. Univariate tests ( T & M-W)2. Multivariate Logistic Regression

Theorical & empiriral

Family firms

Spanish SABI dat. 4,958 companiesPeriod: 2006-2007

Clasif: size & industr.

Differences in performance and debt between groups, in

order to obtain a superior cluster of firms with family ties

Firm clasification scheme

research.

¿Is the firm a general or

limited partnership

or is a company the last and main

owner?

No

no

Is there coincidence

among surnames of

internal group?

No

Yes

YesDoes the internal group** consist of two people with

different gender?

Yes

No Does the internal group consist of just one person?

Yes

Has it A* independence

indicator? No

Yes

Type 6Independent run

FB

No

Type 7Professional run

FB

Type 8Solely run

FB

Type 5Coprenurial firm

CFB

Type 4Entrepreneurial firm

EF

Type 3NFBType 1-2

NFB

Is there surnames

coincidence with CEO?

Yes

taking advantage of the Spanish’ two-surnames

Results:K-W t-test (outliers included)

    Ageln

Size Ln asset

Asset turnover (ln) Debt ROE EBITDA/Equity

  type 2007 2007 2006 2007 2006 2007 2006 2007 2006ServSmall

1-8 .000 .000 .000 .176 .283 .500 .177 .549 .0564-8 .000 .000 .001 .087 .399 .513 .260 .341 .0455-8 .050 .085 .092 .397 .417 .344 .255 .242 .042

Serv.Medium

1-8 .001 .000 .000 .055 .227 .413 .199 .180 .2314-8 .000 .000 .000 .707 .843 .234 .089 .128 .1245-8 .006 .003 .009 .613 .755 .140 .471 .072 .130

Serv.Big

1-8 .020 .000 .000 .050 .174 .005 .000 .596 .0024-8 .005 .000 .000 .018 .073 .062 .002 .384 .0005-8 .508 .152 .229 .803 .713 .035 .028 .775 .066

ConsSmall

1-8 .007 .000 .000 .022 .035 .007 .024 .006 .0004-8 .005 .001 .000 .019 .024 .022 .046 .121 .0035-8 .890 .692 .869 .711 .669 .366 .240 .189 .691

ConsMedium

1-8 .000 .000 .000 .009 .112 .396 .147 .007 .0004-8 .000 .000 .000 .061 .486 .189 .063 .117 .0295-8 .062 .122 .166 .952 .683 .910 .376 .947 .592

Cons.Big

1-8 .000 .002 .006 .000 .024 .085 .185 .004 .0044-8 .000 .002 .005 .001 .012 .035 .080 .005 .0015-8 .466 .851 .859 .077 .270 .450 .413 .358 .199

ManufSmall

1-8 .000 .000 .000 .163 .672 .097 .073 .059 .038  4-8 .000 .000 .000 .283 .493 .080 .042 .144 .107  5-8 .170 .113 .133 .853 .685 .381 .314 .236 .801  

ManufMedium

1-8 .055 .000 .000 .901 .577 .009 .039 .000 .000  4-8 .009 .337 .132 .834 .321 .008 .041 .003 .001  5-8 .180 .896 .861 .696 .319 .125 .339 .433 .201  

ManufBig

1-8 .117 .000 .000 .869 .782 .806 .357 .855 .861  4-8 .052 .008 .048 .838 .938 .725 .367 .800 .505  5-8 .142 .205 .766 .979 .958 .741 .345 .831 .355  

AgricSmall

1-8 .016 .111 .131 .012 .372 .001 .003 .206 .196  4-8 .006 .085 .099 .015 .391 .001 .003 .108 .075  5-8 .561 .447 .577 .972 .980 .051 .024 .942 .235  

AgricMe-ium

1-8 .011 .001 .004 .034 .215 .312 .174 .492 .838  4-8 .007 .007 .021 .036 .186 .314 .253 .363 .690  5-8 .038 .045 .116 .124 .293 .322 .807 .842 .621  

AgricBig

1-8 .270 .275 .169 .421 .769 .270 .359 .332 .848  4-8 .234 .181 .083 .163 .522 .807 .866 .750 .917  5-8 .139 .755 .938 .212 .755 .755 .815 .640 .697  

Include entreprenurship

2007 Mean Median Standarddeviation Max. Min.

Full SampleSize (m €) 52408,8482 6260 229569,4069 6203778,00 19,00Age (years) 18,6290 15,9712 13,22342 107,08 1,00Assets Turnover 1,9181 1,4597 2,06368 44,86 0,01ROE 0,4353 0,3258 0,46673 3,67 -2,35Leverage 0,6713 0,7076 0,22637 1,63 0,00

FB (types 5, 6, 7 & 8)  (1.228 - 26,78%)Size (m €) 38828,3637 4811 172796,8869 3853308,01 19,00Age (years) 19,7469 18,0164 11,43877 72,04 1,00Assets Turnover 1,7301 1,3613 2,1633 44,86 0,01ROE 0,4043 0,3087 0,43947 3,44 -1,22Leverage 0,6527 0,6837 0,22126 1,37 0,00

NFB (types 1, 2, 3 & 4) (2.005- 46,46% + 1.227 - 26,76%)Size (m €) 58711,2200 6964 250898,9180 6203778,00 24,00Age (years) 18,2845 14,8753 13,9109 107,08 1,00Assets Turnover 1,9771 1,4947 1,99855 30,24 0,01ROE 0,4475 0,3329 0,47324 3,32 -2,35Leverage 0,6793 0,7147 0,22713 1,63 0,00

Descriptive Data (2007)(n=4.460)

Results:Two Univariate tests    Age

(n)Size

Ln assetAsset

turnover (ln )

Debt ROE EBITDA/Equity

    2007 2007 2006 2007 2006 2007 2006 2007 2006ServSmall

t .000 .005 .022 .043 .465 .414 .121 .407 .947M-W .000 .002 .010 .021 .364 .334 .160 .215 .373

Serv.Medium

t .038 .888 .945 .501 .085 .474 .094 .666 .482M-W .079 .875 .926 .434 .141 .519 .086 .965 .170

Serv.Big

t .003 .413 .419 .095 .232 .000 .000 .029 .001M-W .005 .286 .259 .081 .109 .000 .000 .056 .009

Const.Small

t .008 .005 .000 .041 .027 .064 .037 .814 .054M-W .002 .002 .000 .031 .070 .044 .053 .450 .106

Const.Medium

t .001 .008 .028 .048 .095 .017 .082 .090 .034M-W .000 .012 .027 .031 .065 .005 .022 .038 .099

Const.Big

t .007 .017 .006 .013 .037 .124 .014 .469 .512M-W .013 .027 .022 .003 .004 .093 .006 .707 .123

Manuf.Small

t .000 .022 .014 .368 .195 .037 .067 .173 .202M-W .000 .008 .005 .288 .152 .061 .106 .410 .401

Manuf.Medium

t .018 .205 .203 .646 .388 .678 .666 .119 .263M-W .025 .102 .090 .401 .218 .805 .778 .147 .300

Manuf.Big

t .057 .060 .045 .629 .557 .072 .068 .016 .878M-W .151 .070 .092 .558 .196 .094 .047 .119 .918

Agric.Small

t .001 .041 .013 .010 .019 .009 .020 .032 .000M-W .001 .047 .023 .003 .047 .011 .077 .007 .002

Agric.Medium

t .242 .920 .863 .404 .530 .964 .647 .516 .504M-W .330 .998 .987 .347 .509 .926 .674 .240 .934

Agric.Big

t .830 .094 .099 .650 .793 .015 .010 +.526 .545M-W .713 .045 .061 .511 .554 .035 .014 .090 .265

Results:Logistic Regression (1: FB, 0: NFB). Outliers omitted.

Year 2007 ALL INDUSTRIES

INDUSTRYManufacturing Construction Services

VARIABLES β E(β) β E(β) β E(β) β E(β)

Debt Asset turnover (lnAge (ln)Size (ln asset) Profitability

-0.408(.001) 0.665-0.304(.000) 0.738 0.375(.000) 1.455-0.185(.000) 0.831

 NO

NO-0.306 (.006) 0.736 0.535 (.000) 1.708-0.334 (.000) 0.716

 0.446 (.005) 1.5610.497 (.004) 1.644

 -1.236 (.000) 0.291-0.200 (.000) 0.818

NONO 

NO 

-0.518 (.021) 0.595-0.211 (.006) 0.809 0.363 (.000) 1.438-0.173 (.000) 0.841

 NO 

Classification table 72.6% 75% 72.3% 73.2%Hosmer-Lemeshow 0.729 0.308 0.627 0.128

-2 lg likelihood 5052.182 1400.648 1481.598 1498.172Pseudo R2 Nagelkerke 0.283 0.395 0.273 0.295LR all coefficients 0.000 0.000 0.000 0.000

Conclusions (1) H1 is accepted.

◦Almost all variables used to measure performance (Size, Age and Assets utilization) show significant differences between a group, identified as FBs, and the rest of firms, classified as NFBs

◦Profitability are not as significant as in the rest of the variables used

Conclusions (2) H2 is accepted.

◦Debt ratio confirms significant differences between FBs and NFBs: FBs are less indebted than NFBs.

◦FBs have a different financial behavior than NFBs

◦FBs show aversion to debt, since it is considered a loss of control (Allouche et al, 2008)

Conclusions (3) Main H (RQ) is accepted.

◦Copreneur, Independent, Professionally and Solely FBs (types 5-8), are an homogeneous group of firms in relation to performance and debt and different from NFBs (types 1-4).

◦Entrepreneurial Firm (solely proprietor) are excluded (Miller et al, 2007)

◦Copreneur FB are included (Barnett& Barnett, 1988)

A new empirical approach to research that can help to a more in-depth comprehension of the FB and its dynamic.

It is identified 4 types of FBs: Partner, Independent, Professional and Run FB, all of then with homogeneous behavior.

A FB, whatever its form or structure, should have older, smaller, with a smaller utilization of assets and low leverage in order to set up a database for research.

Contributions

Typology have been made according with SABI database

The two-Spanish surname◦ Nevertheless, there are an important number of

Spanish-speaking countries◦ The conclusion can help as a benchmark in

another countries

Limitations

Inductive methodology could be applied in other contexts or countries.

Analyze if different cluster of FBs perform differently

Try to understand what are differences between FBs and Entrepreneurial firms

Future research

That’s allThanks