TECHNICAL EFFICIENCY ANALYSIS OF ARTISANAL FISHERIES IN THE SOUTHERN SECTOR OF GHANA

Transcription

1 TECHNICAL EFFICIENCY ANALYSIS OF ARTISANAL FISHERIES IN THE SOUTHERN SECTOR OF GHANA By Justin Tetteh Owusu Otoo 1 Edward Ebo Onumah 1 (PhD) Yaw Bonsu Osei Asare 1 (PhD) Department of Agricultural Economics and Agribusiness, University Of Ghana

2 Order of presentation Background and problem Objective Methodology Results and discussion Conclusion IIFET Conference 2014 Brisbane, Australia 2

3 Background The marine sector is categorized into four subsectors namely 1. Small scale (artisanal) 2. Semi-industrial 3. Industrial 4. Tuna The artisanal fishery sub sector in Ghana is of great importance because it accounts for percent of national marine fish production ( Quaatey, 1997 ; Amador et al., 2006) IIFET Conference 2014 Brisbane, Australia 3

4 Problem statement Demand for fish is estimated to be over 600,000 mt per annum whilst traditional inland and marine fisheries records an average of 435,000 mt of harvest per annum (Atta-Mills et al., 2004). The difference of about 165,000 mt has to be compensated by the government spending about 125 million US dollars of the country s scarce resources on fish imports (Atta-Mills et al., 2004). IIFET Conference 2014 Brisbane, Australia 4

5 Problem statement In an economy where resources are scarce and opportunities for new technologies are lacking, efficiency studies are important to boost production output without increasing the resource base or developing technologies (Onumah and Acquah, 2010). Technical efficiency studies in the artisanal fisheries in Ghana is lacking. IIFET Conference 2014 Brisbane, Australia 5

6 Objectives The primary objective of this study is to analyze the determinants of farm level technical efficiency of artisanal fisheries in the southern sector of Ghana. Specifically, the study seeks; To identify and rank the major constraints affecting the artisanal fisheries. To determine the level of productivity with respect to the various inputs To estimate the level of technical efficiency. To assess the determinants of technical efficiency levels. IIFET Conference 2014 Brisbane, Australia 6

7 Methodology The Kendall s coefficient of concordance was adopted to test for the agreements between the rankings of the constraints. The constraints identified and presented to the fishers to rank included availability of canoe, lack of insurance, depleted fish stock, premix distribution, availability of storage facilities, access to credit, activities of trawlers or large fishing vessels, bad weather and high cost of inputs. IIFET Conference 2014 Brisbane, Australia 7

8 Technical efficiency measures (Farrell 1957) Efficiency measures Technical component Allocative component Maximize output given fixed inputs Optimal use of inputs given prices Economically efficient level IIFET Conference 2014 Brisbane, Australia 8

9 Methodology Two techniques for efficiency and productivity analysis: Non-parametric frontier technique(dea) Parametric frontier approach. Deterministic frontier- deviations are entirely assumed to be due to inefficiency effect. Stochastic frontier - noise and inefficiency effects. The SFP model is specified as: Y f ( X ; ).exp( v u ), i 1,2,..., N i i i i IIFET Conference 2014 Brisbane, Australia 9

10 Methodology cont. The maximum potential output ( value of catch); Y f ( X ; ) exp( v ) * i i i (2) Measure of TE; TE i Y f ( X ; ) exp( v u ) exp( ui) Y f X v i i i i * i ( i; ) exp( i) (3) Difference between the maximum potential output ( value of output) and the actual output (value of output) is embodied in the u i. IIFET Conference 2014 Brisbane, Australia 10

11 Methodology cont. The translog function was preferred to the cobb- Douglass after statistical tests. lny i = β j=1 β j lnx ji j=1 4 k=1 β jk lnx ji lnx ki + (v i u i ) where X represents the specified inputs in the model IIFET Conference 2014 Brisbane, Australia 11

12 Methodology cont. For 0 1output variability is characterized by technical inefficiency and stochastic errors. One-step MLE procedure developed by Coelli (1995) was used, and this was done by incorporating the technical inefficiency effects model into the translog frontier production function. IIFET Conference 2014 Brisbane, Australia 12

13 Sampling technique The Central and Greater Accra regions of Ghana were purposively selected because these two regions jointly account for the majority of canoes (64.49%) and fishermen (63.98%) along the coastal belt (Amador et al., 2006). 280 fishers were randomly selected from across the above mentioned regions. IIFET Conference 2014 Brisbane, Australia 13

14 Methodology continued The fisher-specific variables hypothesized to influence technical inefficiencies in artisanal fisheries are defined by the model: µ i = δ 0 +δ 1 (household size) + δ 2 (level of education) + δ 3 (use of credit) + δ 4 (association) + δ 5 (primary occupation) + δ 6 (canoe ownership) + δ 7 (power of OBM) + δ 8 (boat training) + δ 9 (Age) + δ 10 (Experience) + δ 11 (age*experience) + W i IIFET Conference 2014 Brisbane, Australia 14

15 Variable description Variable Description Units Apriori expectation Output Value of catch Ghana cedis Cost of fuel Amount spent on premix fuel Ghana cedis + Hours at sea Hours spent at sea hours + Canoe size Length of canoe Meters + Crew size Number of crew Number of members on board + IIFET Conference 2014 Brisbane, Australia 15

16 Likelihood ratio test of hypothesis for the production frontier and technical inefficiency Null hypothesis description λ* df Critical value Inference β ij = 0 The frontier is a cobb-douglas form *** Rejected γ=δ 0 =δ 1 =δ 2 = = δ 11 Technical inefficiency effect is absent *** Rejected δ 0 =δ 1 =δ 2 = = δ 11 Half-normal * Rejected δ 1= δ 2 = δ 3 = = δ 11 Vessel and skipper specifications do not influence the technical inefficiency * Rejected γ = 0 βi = 1 Technical efficiency effect is non stochastic The production function exhibits constant returns to scale *** Rejected *** Rejected LR= -2[ln L R lnl U ] Critical value in Kodde and Palm(1986) is used when test involves a γ. IIFET Conference 2014 Brisbane, Australia 16

17 Results of Constraint Ranking Kendall s W Test Constraints Mean ranks 1. High cost of inputs Bad weather Access to credit Availability of storage facilities Depleted fish stock 4.55 Test statistic Kendall s W a Chi-square df 4 N 280 Asymp. Sig IIFET Conference 2014 Brisbane, Australia 17

18 Output Elasticities Input variables Elasticity t-ratio Cost of fuel 0.183** Hours at sea Crew size 0.719*** Canoe size 1.080** Returns to scale IIFET Conference 2014 Brisbane, Australia 18

19 Technical Efficiency scores PERCENTAGE Minimum TE 3.93 Maximum TE Mean technical efficiency IIFET Conference 2014 Brisbane, Australia 19

20 Technical inefficiency Model Estimates Variable Description Measure Coefficient t-stat Apriori expectation δ 0 constant δ 1 Household size Number of members ** /- δ 2 Level of education Years * δ 3 Use of credit Dummy ( 1= yes, 0 = No) 0.682** δ 4 Association Dummy ( 1= yes, 0 = No) * δ 5 Primary occupation Dummy ( 1= yes, 0 = No) δ 6 Canoe ownership Dummy ( 1= yes, 0 = No) 0.560* δ 7 Power of OBM Horse power ** δ 8 Boat Training Dummy ( 1= yes, 0 = No) 0.639* δ 9 Age Years * δ 10 Experience Years δ 11 Age Experience Years IIFET Conference 2014 Brisbane, Australia 20

21 Conclusion Fishers ranked high cost of inputs, bad weather, access to credit, availability of storage facility and depleted fish stock as their most pressing constraint in this order. All inputs positively and significantly influenced production except hours at sea which was not significant The study demonstrates that the fishers are only able to obtain 51% of their frontier output whilst 49% are lost due to inefficiency The joint effect of exogenous variables were able to explain variation in efficiency IIFET Conference 2014 Brisbane, Australia 21

22 Recommendations The study recommends policy to subsidize inputs used in the fishing industry. The importance of fishermen association for information flow to enhance technical efficiency. Outboard motors with 40 hp or higher horse power is recommended to artisanal fishers. Ghana Navy should intensify the monitoring of the activities of large vessels or trawlers to check destructive fishing. IIFET Conference 2014 Brisbane, Australia 22

23 References Aigner, D. J., C. A. K. Lovell, and P. Schmidt Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6: Amador, K., Bannerman, P., Quartey, R. and Ashong, R Ghana Canoe Frame Survey. Information report number 34. Marine Fisheries Research Division. Ministry of Fisheries. Ghana. 43p Atta-Mills, J., J. Alder, and U. R. Sumaila The decline of a regional fishing nation: the case of Ghana and West Africa. Natural Resources Forum 28:13 21 Battese, G. E. and G. S. Corra Estimation of production frontier model: with application to the pastoral zone of Eastern Australia. Australian Journal of Agricultural Economics 21: Coelli, T. J. 1995a. Recent development in frontier modelling and efficiency measurement. Australian Journal of Agricultural Economics 39: Farrel, M.J. (1957). The Measurement of Productive Efficiency. Journal of Statistics Society Vol. 120, No.3, pp Kodde, D.A, and Palm, F.C Wald criteria for jointly testing equality and inequality restrictions. Econometrica, 54 : Onumah E.E., Acquah D.H Frontier Analysis of Aquaculture farms in the southern sector world Applied Science Journal 9(7): IIFET Conference 2014 Brisbane, Australia 23

24 Thank you IIFET Conference 2014 Brisbane, Australia 24