Fishing intensity, Technological Gap and Technical Efficiency in Lake Victoria Fisheries

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Fishing intensity, Technological Gap and
Technical Efficiency in Lake Victoria Fisheries

• Wisdom Akpalu, Razack Lokina and Moses Ikiara
• Monkey Valley
• Cape Town

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(No Transcript)
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Introduction

• Wild fish promotes food security and enhance
  sustainable livelihoods in developing countries
• The poor, mostly in SSA countries depend largely
  on such resources for their survival
• A typical example is the Lake Victoria fisheries
  in East Africa
• Lake produces over 800,000 tonnes of fish
  annually
• support about 2 million people in terms of
  household earnings
• the fish harvested is consumed by almost
  22 million people in the riparian states annually
  
• LV is the largest in Africa, with a surface area
  of 68,870 km2
• shared by three countries Tanzania (49), Uganda
  (45) and Kenya (6).
• Policies towards sustainable fisheries management
  are fairly synchronised in broader terms through
  the LVEMP

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Introduction

• These effort-targeting policies include
• mesh size regulation,
• licensing of fishing fleets,
• prohibition of the use of destructive fishing
  techniques
• Competition for the stock leads to over-havesting
  
• Kenya (6) harvested about 33 of the total Nile
  perch
• Tanzania (49) harvested 38 and
• Uganda (45) harvested 29
• Evidence of illegal fishing e.g. In Kenya about
  34 of the Nile perch caught juvenile within a
  particular period
• This research seeks to investigate
• how the extent of inadequate fisheries management
  practices, affect technological usage and
  technical efficiency across the three riparian
  countries for three species (Nile perch, dagaa
  and tilapia)
• How does improving the design and implementation
  of sustainable fisheries policy increase resource
  rents

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Introduction

• We intend to use the stochastic metafrontier
  production function approach
• Procedure for estimations
• The stochastic frontier model will be estimated
  for each fishery (i.e. Nile perch, Dagaa, and
  Tilapia) in each of the three countries (Using
  STATA).
• The parameters of the metafrontier function for
  each fishery will be computed (using GAMS).
• The technological gap ratios and technical
  efficiency ratios will then be calculated for
  each fishery and each country.
• The figures for the TGR and TER will be
  correlated with fishing intensity in each of the
  fisheries
• We will simulate to show how improvement in the
  management practices, could improve the use of
  the existing fishing technology and subsequently
  improve economic rents

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Methodology

• The model is the stochastic metafrontier
  production function of Battesse and Rao (2002)
  and Battese, Rao and ODonnelle (2004)
• The stochastic production function for each
  fishery in each country can be specified as
• v is a random error term which captures the
  random events (e.g. luck and weather conditions)
• ui is a one sided error which measure technical
  in-efficiency
• The metafrontier production function model for
  each fishery (but for all countries) is

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Methodology

• But we know that
• Furthermore
• If equation (1) is divided by equation (2), it
  gives
• Where the three ratios at the right hand side
  are

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The model

• The empirical translog stochastic production
  frontier

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Data Requirements

• Collect data on fishing effort, harvest and
  socio-economic characteristics of the skippers
  on
• 3 fisheries (Nile perch, dagaa and Tilapia)
• 3 countries (Tanzania, Kenya and Uganda)

Table 3 Time Table of the project
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Some contraints

• Biological information on stock assessment across
  the riparian states (yet to find data that exist)
• Panel data ?