Table 1: Results from partial likelihood estimation for COX proportional Hazard model (processed meat agro-industry).

Table 2: Results from partial likelihood estimation for COX proportional Hazard

model (wine agro-industry)

In both sector, results demonstrates that factors which increase the likelihood of exit are: leverage and positive extraordinary results as a results of disinvestment decisions.

Factors that decrease the hazard of abandoning market are: age (Date of setting-up), Current Ratio, Technical Efficiency, firm size and Technical efficiency. However there are small difference between sectors.

Dummy variables (2000 for meat and 1990 for wine are relevant in explaining the hazard of market exit as a result of the PAC reforms and regulations.

Results allow policy makers to focus on agro-industries with higher hazard.

Table 1: Results from partial likelihood estimation for COX proportional Hazard model (processed meat agro-industry).

Table 2: Results from partial likelihood estimation for COX proportional Hazard

model (wine agro-industry)

In both sector, results demonstrates that factors which increase the likelihood of exit are: leverage and positive extraordinary results as a results of disinvestment decisions.

Factors that decrease the hazard of abandoning market are: age (Date of setting-up), Current Ratio, Technical Efficiency, firm size and Technical efficiency. However there are small difference between sectors.

Dummy variables (2000 for meat and 1990 for wine are relevant in explaining the hazard of market exit as a result of the PAC reforms and regulations.

Results allow policy makers to focus on agro-industries with higher hazard.

Duration analysis to determine factors that lead wine and processed meat agro-

industries to abandon their activities: Policy implications

Zein KALLAS and Fatima LAMBARRAAzein.kallas@upc.edu and fatima.lambarraa@upc.edu

1. INTRODUCTION1. INTRODUCTION

This Work focuses on assessing the determinants factors that drive agro-industries to abandon their activities as well the timing of their decision. Moreover, we analyze agro-industry exit and Technical Efficiency.

We analyze two different agro-industry sector in Catalonia (Spain); the wine and processed meat sectors.

The selection of both sectors has been motivated by the strategic position they occupy within the Catalonian agro-industry sector:

Both have a relevant economic function representing 43% of the total sales.

The input-output tables show a strong interrelationship with other economic sectors. such as agricultural input suppliers (50% of total input), logistic, banking, technological, etc.

They play an important social and territorial role employing the equivalent of 31,534 full-time workers, representing 42% of the labor force in the Catalonian agro-industry sector.

Results allow policy makers to obtain information about agro-industries with high hazard to abandon market. Thus, decision could be taken to maintain the social fabric associated to these agro-industries.

This Work focuses on assessing the determinants factors that drive agro-industries to abandon their activities as well the timing of their decision. Moreover, we analyze agro-industry exit and Technical Efficiency.

We analyze two different agro-industry sector in Catalonia (Spain); the wine and processed meat sectors.

The selection of both sectors has been motivated by the strategic position they occupy within the Catalonian agro-industry sector:

Both have a relevant economic function representing 43% of the total sales.

The input-output tables show a strong interrelationship with other economic sectors. such as agricultural input suppliers (50% of total input), logistic, banking, technological, etc.

They play an important social and territorial role employing the equivalent of 31,534 full-time workers, representing 42% of the labor force in the Catalonian agro-industry sector.

Results allow policy makers to obtain information about agro-industries with high hazard to abandon market. Thus, decision could be taken to maintain the social fabric associated to these agro-industries.

Center for Agro-food Economy and Development (CREDA)- Polytechnic University of Catalonia (UPC)- Food and Agriculture Investigation and Technology Institute (IRTA).Department of Agricultural Engineering and Biotechnology. Edifici ESAB- Avinguda del Canal Olímpic s/n, 08860-Castelldefels (Barcelona)- Spain.

The XXVII conference of the International Association of Agricultural Economists, 16-22 August 2009, Beijing, China.

Several factors can influence the decision to market exit and the likelihood of firm dissolution: Manager characteristics and

risk behavior; Age, gender, education, experience, attitudes, risk aversion, etc.

Firm characteristics; firm size (Capital, employee number), firm age, legal form, location, etc.

Firm management; number of brand, Total Assets Turnover, diversification of activities and products, economies of scale, Technical Efficiency, etc.

Economic and financial results; profitability, leverage, solvency, current ratio,, etc.

Exogenous factors: market size and concentration, industry growth, input prices, subsidies, information access, reforms, etc.

Several factors can influence the decision to market exit and the likelihood of firm dissolution: Manager characteristics and

risk behavior; Age, gender, education, experience, attitudes, risk aversion, etc.

Firm characteristics; firm size (Capital, employee number), firm age, legal form, location, etc.

Firm management; number of brand, Total Assets Turnover, diversification of activities and products, economies of scale, Technical Efficiency, etc.

Economic and financial results; profitability, leverage, solvency, current ratio,, etc.

Exogenous factors: market size and concentration, industry growth, input prices, subsidies, information access, reforms, etc.

2. DETERMINANTS OF EXIT2. DETERMINANTS OF EXIT

3. METHODOLOGY3. METHODOLOGY

First step we used the stochastic frontier methodology to measure firm’s technical efficiency. (TE)

Second step we apply The Duration Analysis in order to determine not only why firms exit but also the timing of dissolution and the factors that influence the observed time patterns.

First step we used the stochastic frontier methodology to measure firm’s technical efficiency. (TE)

Second step we apply The Duration Analysis in order to determine not only why firms exit but also the timing of dissolution and the factors that influence the observed time patterns.

3.1. STOCHASTIC FRONTIER

METHODOLOGY

3.1. STOCHASTIC FRONTIER

METHODOLOGY

The stochastic frontier production function can be expressed as follows:

where yit is the output of the i-th firm (i=1...N) in period (t=1...T), where f(xit,t,) represents the production technology, xit is a (1K) vector of inputs and other factors influencing production associated with the i-th firm in period t, and is a (K1) vector of unknown parameters to be estimated.

Disturbance term is composed of two parts: vit and uit . The former captures the effects of statistical noise outside the firm's control. The latter is associated with output oriented technical inefficiencies.

The distributions of the two error terms are independent:

Maximum likelihood techniques are used for estimation.

The stochastic frontier production function can be expressed as follows:

where yit is the output of the i-th firm (i=1...N) in period (t=1...T), where f(xit,t,) represents the production technology, xit is a (1K) vector of inputs and other factors influencing production associated with the i-th firm in period t, and is a (K1) vector of unknown parameters to be estimated.

Disturbance term is composed of two parts: vit and uit . The former captures the effects of statistical noise outside the firm's control. The latter is associated with output oriented technical inefficiencies.

The distributions of the two error terms are independent:

Maximum likelihood techniques are used for estimation.

-( , ; ) it itv uit ity f x t e

exp it iu t T u

Duration Analysis (DA) models the time length of a spell or “event”. The spell starts at the time of entry into a specific state (entry decision) and ends at a point when a new state is entered (exit decision).

The conceptual foundations of DA rely on probability theory. We define the hazard function that specifies the rate at which a spell is completed at time t=T , given it survives until time t . in our analysis, the hazard function represents the probability that a farmer exit at time t , given he has not adopted before t :

Duration Analysis (DA) models the time length of a spell or “event”. The spell starts at the time of entry into a specific state (entry decision) and ends at a point when a new state is entered (exit decision).

The conceptual foundations of DA rely on probability theory. We define the hazard function that specifies the rate at which a spell is completed at time t=T , given it survives until time t . in our analysis, the hazard function represents the probability that a farmer exit at time t , given he has not adopted before t :

3.2 . DURATION ANALYSIS3.2 . DURATION ANALYSIS

0

0

Pr ( )( ) lim

( ) ( )lim

( )

( )

( )

t

t

t T t t T th t

tF t t F t

t S t

f t

S t

A set of explanatory variables of economic and non-economic nature influence and alter the distribution of the duration:

where is a vector of unknown parameters of x , the vector of explanatory variables and is a vector of parameters that characterize the distribution function of the hazard rate.

To estimate the duration model we use the semiparametric Cox proportional hazards model. Under this model the duration of each member of a population is assumed to follow its own hazard function:

The estimation procedure is based on the partial likelihood function introduced by Cox (1972, 1975)

A set of explanatory variables of economic and non-economic nature influence and alter the distribution of the duration:

where is a vector of unknown parameters of x , the vector of explanatory variables and is a vector of parameters that characterize the distribution function of the hazard rate.

To estimate the duration model we use the semiparametric Cox proportional hazards model. Under this model the duration of each member of a population is assumed to follow its own hazard function:

The estimation procedure is based on the partial likelihood function introduced by Cox (1972, 1975)

0

Pr ( )( , ) limx,θ,β

t T t T th t

'0 0 1 1( ) ( ; ) ( ) exp( ) ( ) exp( )x x βi i i i k ikh t h t h t h t x x

βx

βx1

1

i

i

j

n

ni

ijj

ePL

Y e

Data used in this analysis were obtained from the ‘Sistema Anual de Balances Ibéricos’ (SABI) database. The SABI collects extensive economic and non economic information on the firms.

The sample size for the wine and processed meat sectors is formed by 231 and 288 active firms. While the inactive industries are 20 and 47 respectively.

For the stochastic frontier production yit is defined as the deflated total sales in meat and wine product. xit is a (13) vector that contains three inputs: a) wage expenses, b) intermediate inputs and c) capital employed in the production process.

For the DA, the dependent variable is the time firms waited before exit market. from the set of explanatory variables, we use: a) age of firm represented by the Date of firms’ setting–up, c) leverage (ratio of debt to total assets), d) current ratio (is an indication of a company's ability to meet short-term debt), e) Technical efficiency, f) firm size (employee) g) extraordinary results, h) Profit growth and i) Return on capital employed .

Data used in this analysis were obtained from the ‘Sistema Anual de Balances Ibéricos’ (SABI) database. The SABI collects extensive economic and non economic information on the firms.

The sample size for the wine and processed meat sectors is formed by 231 and 288 active firms. While the inactive industries are 20 and 47 respectively.

For the stochastic frontier production yit is defined as the deflated total sales in meat and wine product. xit is a (13) vector that contains three inputs: a) wage expenses, b) intermediate inputs and c) capital employed in the production process.

For the DA, the dependent variable is the time firms waited before exit market. from the set of explanatory variables, we use: a) age of firm represented by the Date of firms’ setting–up, c) leverage (ratio of debt to total assets), d) current ratio (is an indication of a company's ability to meet short-term debt), e) Technical efficiency, f) firm size (employee) g) extraordinary results, h) Profit growth and i) Return on capital employed .

4 . EMPIRICAL APPLICATION4 . EMPIRICAL APPLICATION

5 RESULTS AND DISCUSSION5 RESULTS AND DISCUSSION

Variable ParameterStd.

ErrorP-value

Hazard Ratio

Date of firms’ setting -up 0.0012*** 0.0003 0.0001 1.0010

Leverage 2.4526** 0.9936 0.0136 11.6180

Current Ratio -1.5904*** 0.5173 0.0021 0.2040

Technical Efficiency -2.1821* 1.1754 0.0634 0.1130Employee number -0.0076 0.0062 0.2176 0.9920Business extraordinary results 0.4579** 0.2325 0.0488 1.5810Profit growth -0.0011 0.0009 0.2319 0.9990Return on capital employed (%) 0.3269 0.2613 0.2109 1.3870

Dummy year 2000 2.0176* 1.2008 0.0929 7.5200Likelihood Ratio: 96.20 (0.000)

Wald test: 45.11 (0.000)

Lagrange Multiplier Test: 75.76 (0.000)

Variable ParameterStd.

ErrorP-value

Hazard Ratio

Date of firms’ setting -up 0.0009*** 0.0003 0.0010 1.0010

Leverage 3.4227*** 1.1542 0.0030 30.6530

Current Ratio -0.4604 0.3476 0.1853 0.6310

Technical Efficiency -2.8863* 1.7368 0.0965 0.0560Employee number -0.1406** 0.0630 0.0257 0.8690Business extraordinary results 0.8460*** 0.3087 0.0061 2.3300Profit growth -0.0005 0.0011 0.6596 1.0000Return on capital employed (%) 1.6610 1.4263 0.2442 5.2640

Dummy year 1990 -2.1924* 1.2155 0.0713 0.1120

Likelihood Ratio: 44.91 (0.000)Wald test: 22.29 (0.003)Lagrange Multiplier Test: 24.70 (0.008)

6. REFERENCES6. REFERENCES

1. Agarwal R.; Gort M. 1996. The evolution of markets and entry, exit and survival of firms. The review of economics and statistics, 78: 489-498.

2. Aigner, D.J.; Lovell, C.A.; Schmidt, P.J. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6: 21-37.

3. Allison, P.D. 2001. Survival Analysis Using the SAS System: A Practical Guide. SAS publishing.

4. Audretsch, D.B., Houweling, P.; Thurik, A.R. 2000. Firm survival in the Netherlands. Review of Industrial Organisation, 16: 1-11.

5. Audretsch, D.B. 1994. Business survival and the decision to exit. Journal of Economics and Business, 1: 125-137.

6. Battese, G.E.; Coelli, T.J. 1992. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3: 153-69.

7. Dimara, E.; Skuras, D.; Tsekouras, K.; Tzelepis, D. 2003. Firm efficiency and survival. Paper presented at the 2nd Hellenic Workshop on Productivity and Efficiency Measurement. University of Patras, Greece.

8. Doi N (1999) The determinants of firm exit in Japanese manufacturing industries. Small Business Economics,13:331–337.

9. Efron, B. 1977. The efficiency of Cox's likelihood function for censored data. Journal of the American Statistical Association, 76: 312-319.

10. Kumbhakar, S.C.; Lovell, C.A. 2000. Stochastic Frontier Analysis, New York, Cambridge University Press.

11. Lambarraa, F.; Serra, T.; Gil, J.M. 2007. Technical efficiency and decomposition of productivity growth of Spanish olive farms. Spanish Journal of Agricultural Research, 5(3). 259-270.

12. Tsionas, E.G.; Papadpgpnas, A.T. 2006. Firm exit and technical efficiency. Empirical economics, 31:535-548.

1. Agarwal R.; Gort M. 1996. The evolution of markets and entry, exit and survival of firms. The review of economics and statistics, 78: 489-498.

2. Aigner, D.J.; Lovell, C.A.; Schmidt, P.J. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6: 21-37.

3. Allison, P.D. 2001. Survival Analysis Using the SAS System: A Practical Guide. SAS publishing.

4. Audretsch, D.B., Houweling, P.; Thurik, A.R. 2000. Firm survival in the Netherlands. Review of Industrial Organisation, 16: 1-11.

5. Audretsch, D.B. 1994. Business survival and the decision to exit. Journal of Economics and Business, 1: 125-137.

6. Battese, G.E.; Coelli, T.J. 1992. Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3: 153-69.

7. Dimara, E.; Skuras, D.; Tsekouras, K.; Tzelepis, D. 2003. Firm efficiency and survival. Paper presented at the 2nd Hellenic Workshop on Productivity and Efficiency Measurement. University of Patras, Greece.

8. Doi N (1999) The determinants of firm exit in Japanese manufacturing industries. Small Business Economics,13:331–337.

9. Efron, B. 1977. The efficiency of Cox's likelihood function for censored data. Journal of the American Statistical Association, 76: 312-319.

10. Kumbhakar, S.C.; Lovell, C.A. 2000. Stochastic Frontier Analysis, New York, Cambridge University Press.

11. Lambarraa, F.; Serra, T.; Gil, J.M. 2007. Technical efficiency and decomposition of productivity growth of Spanish olive farms. Spanish Journal of Agricultural Research, 5(3). 259-270.

12. Tsionas, E.G.; Papadpgpnas, A.T. 2006. Firm exit and technical efficiency. Empirical economics, 31:535-548.