Productividad en las cooperativas de Mondragon: estudio de un caso econométrico

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Efficiency in the Mondragon Cooperatives: Evidence from an Econometric case study

For Presentation at the CONGRESO INTERNACIONAL DE ECONOMIA SOCIAL (EOI)

Saioa Arando (MIK, S.Coop. & MU-Enpresagintza)Monica Gago (MIK, S.Coop. & MU-Enpresagintza)

Derek C. Jones (Hamilton College)Takao Kato (Colgate University)

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Mondragon Group The case: EROSKI Data Insider econometric evidence Conclusions

INDEX

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MONDRAGON GROUP

MONDRAGONHUMANITY AT WORK

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Mondragon Group

The Mondragon Group: often considered the most successful example of employee-owned enterprise in the world.

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Mondragon Group

250+ organizations, 92,773 employees 3 BUSINESS GROUPS:

FINANCIAL INDUSTRIAL – 12 DIVISIONS RETAIL & ALLIED

KNOWLEGDE AREA UNIVERSITY – 3 Faculties / Schools... Engineering –

Business – Humanities & Ed R&D CENTERS (11) MANAGEMENT & COOPERATIVE TRAINING CENTER

.

Group Structure

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Group Structure

Mondragon Group

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Sales, 2008

Industrial Group6,511

Retail Group9,073

TOTAL SALES15,584 M€

Mondragon Group

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Work force Geographic Distribution, 2008

Mondragon Group

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Work force Industrial Distribution, 2008

Mondragon Group

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Mondragon in the world, 2008

Mondragon Group

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Cooperative Structure

Mondragon Group

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The case: EROSKI

EROSKI GROUP

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Group Structure

Mondragon Group

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Distribution Area

Agro-food

Eroski

Retail

Hypermarkets

Supermarkets

Dapargel

The case: EROSKI

Forum Sport

Eroski Travel

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Why Eroski?

Retail & Allied Group, Sales History, 1988-2008

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The Case: Core Businesses

Eroski Chain

One of the largest and rapidly growing members of Mondragon Group

Core businesses=supermarkets (705) and hypermarkets (109)=the focus of our investigation.

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The Case: Third largest retail chain in Spain

Eroski Chain

Total employment ~ 50, 600 Eroski the third largest retail chain in Spain. Eroski is among the ten best spanish

brands (Branding Global y Brand Finance).

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Research Questions

RQ1: Is the legal structure important to explain firm productivity?

H1: Cooperatives are more productive than others.

RQ2: Which legal structure is nearest to the HPWS?

H2: Cooperatives are more likely to perform as a HPWS

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The Case: THREE OWNERSHIP STRUCTURES

COOP stores GESPA stores Capitalist stores

Members COOP Members GESPA Members

None.

Non-members

COOP non-members (prospective members on probation and temporary contract workers)

GESPA non-members (regular workers opting not to join and temporary contract workers)

Regular and temporary contract workers.

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The Case: THREE OWNERSHIP STRUCTURES

COOP GESPA CONVENTIONAL

Ownership Participation High Moderate Null

Decision-making Participation High Moderate Null

Job securityYes for

membersYes for

members No

Wage premiaYes for

members Minimum No

% members among workers very high reduced Null

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COOP as High Performance Work System

Ability/skillIncentive

Goal AlignmentJob Security

Opportunity

High Performance Work System

•for teamwork;•to produce and share valuable local

knowledge;•to respond to local shocks quickly;•to accumulate firm-specific human

capital;

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COOP vs. GESPA Much more limited participation in decision

making in GESPA than in COOP. GESPA membership widely regarded as a

“second class” form of membership. More limited opportunities in GESPA than in

COOP.

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COOP vs. GESPA Membership in GESPA involves a capital

stake that is about half as large as in a COOP (3,000 vs. 6,000 euros).

Average stake of GESPA members: less than one tenth of that of COOP members

%members: 61% in GESPA vs. 76% in COOP

Weaker incentives in GESPA than in COOP. In sum, COOP more likely to be HPWS than

GESPA.

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COOP vs. Capitalist Capitalist lacks:

1. Whatever GESPA lacks as compared to COOP;

2. Job security;

3. Efficiency wage enjoyed by COOP members (about 20%);

COOP more likely to be HPWS than Capitalist.

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GESPA vs. Capitalist As compared to GESPA, Capitalist lacks:

1. Job security;

2. Efficiency wage enjoyed by GESPA members (about 20%);

GESPA’s advantages in opportunities and incentives over Capitalist are much more modest.

Neither GESPA nor Capitalist is close to HPWS.

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Key performance & financial panel data for:

435 supermarkets (142 coop, 26 Gespa, 267 conventional) and 80 hypermarkets (25 Coop, 55 Gespa).

Monthly data (feb-06/may-08)

10.000 observations for supermarkets and 2.150 observations for hypermarkets.

Data

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it

it6

it5it4it3

it2it1it

Udummies monthdummies year

Yearopened

MarketGespaCoop

KlnLlnAlnQln

First difference model

Insider econometrics evidence

(2)

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Δ indicates the first difference between month t and t-1;

Qit = output (real sales) in store i in month t;

Lit = employment (measured by the number of full-time equivalent workers) in store i in month t;

COOPi = 1 if store i is a coop store, 0 otherwise;

GESPAi = 1 if store i is a GESPA store, 0 otherwise.

itit6it5it4it3it2it1it Udummies monthdummies yearYearopenedMarketGespaCoopKlnLlnAlnQln

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Hypermarket

Supermarket

All stores City

COOP GESPA COOP GESPA Conventional COOP Conventional

lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590)(0.1692

) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974)(0.0615

) (0.0874) (0.1390) (0.1024)

N 675 1420 4747 703 8001 967 321

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In addition to labor (L), store space often considered crucial capital input (K) in retail service production.

For all Eroski stores during the time period under study, however, month to month variations of store space are zero and hence in our first-difference model,

lnKit = 0.


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Control variables A store located in a rapidly growing market

with rising population and average household income will naturally grow its sales faster.

To control for such differences in each store’s market condition,

MARKETit where MARKETit = monthly market index in month t for the area which store i serves.


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Hypermarket

Supermarket

All stores City


lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590) (0.1692) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974) (0.0615) (0.0874) (0.1390) (0.1024)

MARKETit 0.0034 0.0035 0.0029 0.0036 0.0038 0.0029 0.0012

(0.1152) (0.1071) (0.1129)(0.0967

) (0.1048) (0.1156) (0.0999)

N 675 1420 4747 703 8001 967 321

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Due to the standard lifecycle model of retail stores, younger stores tend to grow faster than older stores. To control for such a lifecycle effect, we also include

YEAROPENEDi = the year store i was opened.


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Hypermarket

Supermarket

All stores City


lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590) (0.1692) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974) (0.0615) (0.0874) (0.1390) (0.1024)

MARKETit 0.0034 0.0035 0.0029 0.0036 0.0038 0.0029 0.0012

(0.1152) (0.1071) (0.1129) (0.0967) (0.1048) (0.1156) (0.0999)

YEAROPENEDi 1995.48 1999.90 1998.41 2000.63 1999.36 2000.18 2002.05

(5.4675) (4.4902) (4.7485)(2.7747

) (4.9424) (2.5270) (1.8423)

N 675 1420 4747 703 8001 967 321

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constant (to capture an Eroski-wide time trend which is common to all Eroski stores regardless of its ownership types),

monthly dummy variables (to capture seasonality of retail sales), and

year dummy variables (to control for year time effects)


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The first-difference model adopted for two reasons.

1. Field research at Eroski sales growth a primary business goal,

2. First-difference models control for all time-invariant unobserved heterogeneity of stores that affects the level of sales.


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Sales Growth and Ownership Types: Insider Econometric Evidence Dependent variable=lnQit

Hypermarket Supermarket SupermarketCity only

lnLit 0.552***[6.57]

0.265***[4.96]

0.292**[2.03]

MARKETit 0.645***[9.92]

0.815***[19.39]

1.165***[5.90]

YEAROPENEDi 0.00016*[1.87]

0.0004**[2.36]

0.0002[0.29]

COOPi 0.0022**[2.94]

-0.0003[-0.32]

0.0074**[2.63]

GESPAi -0.0001[-0.10]

N 2070 10994 1195

R-squared 0.852 0.404 0.311

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Insider econometrics evidence: additional analysis

1. The extent of “Opportunity” measured by: INVOLVEi = proportion of scheduled work

hours spent on joint labor-management meetings (monthly average of store i during the time period under study).

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Hypermarket

Supermarket

All stores City


lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590) (0.1692) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974) (0.0615) (0.0874) (0.1390) (0.1024)

MARKETit 0.0034 0.0035 0.0029 0.0036 0.0038 0.0029 0.0012

(0.1152) (0.1071) (0.1129) (0.0967) (0.1048) (0.1156) (0.0999)

YEAROPENEDi 1995.48 1999.90 1998.41 2000.63 1999.36 2000.18 2002.05

(5.4675) (4.4902) (4.7485) (2.7747) (4.9424) (2.5270) (1.8423)

INVOLVEi 0.0024 0.0002 0.0033 0.0012 0.0000 0.0044 0.0000

(0.0048) (0.0007) (0.0057)(0.0034

) (0.0001) (0.0084) (0.0003)

N 675 1420 4747 703 8001 967 321

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2. The strength of “Incentive” gauged by:a) STAKEi = average stake of employee

owners (monthly average of store i during the time period under study).

b) MEMBERi= proportion of workers who are COOP or GESPA members (monthly average of store i during the time period under study).

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Hypermarket

Supermarket

All stores City


lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590) (0.1692) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974) (0.0615) (0.0874) (0.1390) (0.1024)

MARKETit 0.0034 0.0035 0.0029 0.0036 0.0038 0.0029 0.0012

(0.1152) (0.1071) (0.1129) (0.0967) (0.1048) (0.1156) (0.0999)

YEAROPENEDi 1995.48 1999.90 1998.41 2000.63 1999.36 2000.18 2002.05

(5.4675) (4.4902) (4.7485) (2.7747) (4.9424) (2.5270) (1.8423)

INVOLVEi 0.0024 0.0002 0.0033 0.0012 0.0000 0.0044 0.0000

(0.0048) (0.0007) (0.0057) (0.0034) (0.0001) (0.0084) (0.0003)

STAKEi 33295.79 2511.33 26270.68 865.63 1.40 23030.07 0.00

(8847.05)

(1010.40)

(8175.98)

(201.35) (23.56)

(10545.04) 0.00

MEMBERi 0.7590 0.6076 0.7289 0.5181 0.0000 0.6443 0.0000

(0.0739) (0.1352) (0.1186)(0.1532

) 0.0000 (0.1549) 0.0000

N 675 1420 4747 703 8001 967 321

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3. The extent of “skill/ability” measured by:• TRAININGi = proportion of scheduled hours

spent on training in general (crude).

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Hypermarket

Supermarket

All stores City


lnQit 0.0021 0.0004 0.0042 0.0061 0.0053 0.0105 0.0020

(0.1663) (0.2345) (0.1590) (0.1692) (0.1749) (0.1826) (0.1162)

lnLit 0.0004 0.0016 0.0024 0.0039 0.0025 0.0069 0.0022

(0.0434) (0.0669) (0.0974) (0.0615) (0.0874) (0.1390) (0.1024)

MARKETit 0.0034 0.0035 0.0029 0.0036 0.0038 0.0029 0.0012

(0.1152) (0.1071) (0.1129) (0.0967) (0.1048) (0.1156) (0.0999)

YEAROPENEDi 1995.48 1999.90 1998.41 2000.63 1999.36 2000.18 2002.05

(5.4675) (4.4902) (4.7485) (2.7747) (4.9424) (2.5270) (1.8423)

INVOLVEi 0.0024 0.0002 0.0033 0.0012 0.0000 0.0044 0.0000

(0.0048) (0.0007) (0.0057) (0.0034) (0.0001) (0.0084) (0.0003)

STAKEi 33295.79 2511.33 26270.68 865.63 1.40 23030.07 0.00

(8847.05) (1010.40) (8175.98) (201.35) (23.56) (10545.04) 0.00

MEMBERi 0.7590 0.6076 0.7289 0.5181 0.0000 0.6443 0.0000

(0.0739) (0.1352) (0.1186) (0.1532) 0.0000 (0.1549) 0.0000

TRAININGi 0.0074 0.0081 0.0139 0.0103 0.0062 0.0108 0.0059

(0.0130) (0.0152) (0.0386)(0.0215

) (0.0534) (0.0415) (0.0129)

N 675 1420 4747 703 8001 967 321

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it

it5

it4it3

it2it1it

Udummies monthdummies year

HPWS

YearopenedMarket

KlnLlnAlnQln

New model:


(3)

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Sales Growth and HRM for Hypermarket:

Dependent variable=lnQit

(i) (iii) (v) (ii)

lnLit 0.552***[6.57]

0.576***[6.53]

0.552***[6.57]

0.552***[6.57]

MARKETit 0.645***[9.92]

0.653***[9.51]

0.645***[9.92]

0.645***[9.92]

YEAROPENEDi 0.00014[1.61]

0.0002***[2.71]

0.0001[1.16]

0.00007[0.87]

INVOLVEi 0.558***[2.84]

STAKEi 6.2x10-8***[2.74]

MEMBERi 0.0037[1.01]

TRAININGi 0.255[1.15]

N 2070 1889 2070 2070

R-squared 0.852 0.847 0.852 0.852

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Sales Growth and HRM for Supermarket (City only)

Dependent variable=lnQit

(i) (iii) (v) (ii)

lnLit 0.292**[2.03]

0.292**[2.03]

0.292**[2.03]

0.292**[2.03]

MARKETit 1.165***[5.90]

1.165***[5.90]

1.165***[5.90]

1.165***[5.90]

YEAROPENEDi -0.0002[-0.36]

-0.0002[-0.27]

0.00003[0.004]

-0.0002[-0.29]

INVOLVEi 0.151[0.48]

STAKEi 2.26x10-8

[0.29]

MEMBERi 0.0069[1.51]

TRAININGi -0.047[-0.67]

N 1195 1195 1195 1195

R-squared 0.311 0.311 0.311 0.311

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We also estimated a fully nested version of Eq. (3) with all four HPWP variables considered simultaneously.

The results turned out to be quite robust to the use of such a fully nested specification although the estimates are slightly less precise due to multicollinearily as expected.

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RQ1: Is the legal structure important to explain firm productivity? H1: Cooperatives are more productive than others.

Hypermarket stores with cooperative ownership grow sales significantly faster than do Gespa stores.

City supermarket: coop ownership stores are more productive than conventionally owned stores.

However for Center supermarkets we find that conventional owned stores grow faster than both coops and Gespa.

Conclusions

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RQ2: Which legal structure is nearest to the HPWS? H2: Cooperatives are more likely to perform as a

HPWS Consistence with those who argue for the existence

of powerful incentive mechanisms for coop members who work under institutional arrangements that differ from those facing workers in other firms: a large financial stake in the firm; substantial employee involvement; unusual job security; and working in firms with earnings differences that are

substantially more compressed.

Conclusions

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Thank you!

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