AQUACULTURE AND HAPPINESS IN VIETNAM

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AQUACULTURE AND HAPPINESS IN VIETNAM A
MICROECONOMETRIC ANALYSIS
DUC MINH NGUYEN
2
Introduction

• a growing awareness of the importance of food
  fish production on human nutrition, employment,
  poverty (Bailey and Skladany, 1991 Edwards,
  2000, Edwards, Little and Demaine, 2002) and even
  recreation in more developed societies (Jolly and
  Clonts, 1993).
• In Vietnam, aquaculture is a very important
  economic sector due to its rapid growth and
  contribution to total national fisheries
  production (FAO and NACA, 1997).
• aquaculture revenue constituted 4 of Vietnamese
  GDP in 2003 and exported a value of 2.35 billion
  in 2004 (FAO, 2005), or 10 of the countrys
  total export revenue
• total aquaculture area in Vietnam is 902,229
  hectares of two million potential water surface
  areas (FICEN, 2005), cover 3 of the total land
  area.
• the role of aquaculture in livelihood improvement
  of the poor farmers has not been considered
  rigorously.
• Lack of literature relating aquaculture adoption
  and happiness or life satisfaction of the
  adopters

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Happiness studies

• Frey and Stutzer (2002) since the 1990s, a
  number of studies of the determinants of
  happiness have been conducted by economists
  following a long history of happiness analysis by
  psychologists.
• However, Easterline (2001)
• The relationship between income and happiness is
  confounded by economists and social researchers
• because the terms of happiness, subjective
  well-being, satisfaction, utility or even welfare
  are usually used interchangeable
• Although each individual defines happiness in his
  or her terms, the factors cited as shaping
  happiness are much the same for most people.

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Money can buy happiness

• Frey and Stutzer (2002) Higher income persons
  are likely happier than the lower because they
  have more opportunities to get what they desire.
• Di Tella, MacCulloch and Oswald (2003) happiness
  increases with income, and have a similar
  structure in different countries.
• Lee, 2006 earning more money allows for life
  improvements, whether those improvements arise
  from more money or other desirable objects
• The important role of higher income in lasting
  happiness of human being is also supported by
  Andrews (1986), Argyle (1999), Diener (1984),
  Diener and Lucas (1999), Lykken and Tellegen
  (1996), Schwars and Strack (1999).

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or money reduces happiness?

• Frank (2004)
• the absolute income increases recently have
  failed to increase measured well-being.
• relative, not absolute, income affects happiness.
  
• Frey and Stutzer (2002) higher income
  aspirations reduce peoples satisfaction with
  life.

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Aquaculture raise happiness?

• contribution of fish production in livelihoods of
  small scale farmers is increasing (Edwards, 2000,
  Edwards, Little and Demaine, 2002)
• fish culture has increasingly contributed to
  household income of small scale farmers in
  southern Vietnam (Duc, 2001).
• But, does income from fish culture raise
  happiness of fish farmers?

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This study

• uses cumulative logistic models
• explore impacts of aquaculture on satisfaction
  and life improvement of fish farmers
• contains three parts.
• description of the methodology.
• investigates the determinants of pleasure to fish
  culture (job satisfaction)
• examines the role of earnings from fish culture
  in the farmers life satisfaction

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The cumulative logistic model

• Frey and Stutzer (2002) suggest a logistic
  function
• Wit a ?xit ?it
• - Wit is level of happiness
• - xit is a vector of explanatory variables of
  demographics and socioeconomics characteristics

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The cumulative logistic model

• the responses are ordinal variables,
• considering farmers utility has a following
  function
• Ui ? ?xi ??i (Equation 1)
• but Ui can not be observed directly.
• Allison (1999), Greene (2003) there exists a set
  of cut points or thresholds, p1, , pJ-1, that
  are used to transform Ui into the observed
  variable Y as following
• Yi 1 if p1 ? Ui
• Yi 2 if p2 lt Ui ? p1
• Yi 3 if p3 lt Ui ? p2
• .
• .
• Yi J if Ui? pJ-1

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The cumulative logistic model

• With ?i has a standard logistic distribution,
• the dependence of Y on x is given by the
  cumulative logit model.
• Log Fij/(1-Fij) ? ?xi j 1, , J -
  1 (Equation 2)
• Where
• (Equation 3)
• is cumulative probabilities

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The cumulative logistic model

• Agresti (2002) defines the cumulative
  probabilities
• P(Y jx) p1(x) pj(x) (j 1, ,
  J) (Equation 4)
• and the cumulative logits are
• j 1, . . . , J - 1 (Equation 5)
• A model that simultaneously using all cumulative
  logits is given by
• logitP(Yi j x) aij Xij? (Equation
  6)
• where - Yi is response level of ith respondents
• - j 1, , J - 1 and J is the number of
  categories of responses
• in our study, J 5, from strongly agree to
  strongly disagree
• - X is the vector of explanatory variables.

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The cumulative logistic model

• Each cumulative logit has its own intercept aj
  increasing in j, but the same coefficient ? for
  each explanatory variable,
• ? representing to the effect of explanatory
  variable x on the response Y.
• Agresti (2002) for fixed j, the response curve
  is a logistic regression curve for a binary
  response with outcomes Yj and Y gt j.

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Estimation methods

• The SAS logistic regression with backward
  selection
• In this study, the fixed threshold j2 can
  exhibit that the farmer is please with his (her)
  fish farming (in satisfaction model) or that he
  (she) is happy with his life (in happiness
  model).
• At j2, the response curve is a logistic
  regression curve for a binary response with
  outcomes Y 2 and Y gt 2, we can get the
  estimated cumulative probability p of farmers
  satisfaction or happiness to calculate marginal
  effects of continuous explanatory variables and
  then elasticity of the variables

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Data

• field survey in 2001 involving 120 fish farmers
  in-depth interviewed
• in 3 provinces Binh Phuoc, Tay Ninh and Long An
  in Southern Vietnam.
• The survey region is the target area of UAF-Aqua
  Outreach Program (UAF-AOP),
• implemented since 1994
• under cooperation of provincial extension
  agencies and Fisheries Faculty of University of
  Agriculture and Forestry (UAF1).
• Because of poor resources (dry soil, water
  deficiency, remote distances to urban),
  aquaculture was underdeveloped before the program
  began in 1994.
• Poor farmers live mainly on agriculture and
  irregular off-farm works.
• Aquaculture, therefore, is adopted as a good
  solution for improvement of farmers livelihoods.
  
• This enterprise has been continuously growing in
  both land area and production intensity
• In this study, the targets are limited to
  small-scale fish farmers when examining the
  relationship between their adoption of
  aquaculture technology and the improvement of
  their life quality
• 1 University of Agriculture and Forestry (UAF)
  is currently renamed Nong Lam University, locates
  in Thuduc District, Hochiminh City, Vietnam.

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• To get the levels of pleasure from fish culture,
  ask
• Do you feel to be completely satisfied or
  pleased by integrating fish culture into
  farming?
• To measure the farmers subjective well-being,
  ask
• Do you recognize generally a considerable
  improvement in quality of life in your household
  since adoption of fish culture?
• Farmers responses
• 1 strongly agree
• 2 agree
• 3 can not decide
• 4 disagree
• 5 strongly disagree

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Income definition

• household income
• farming income,
• off-farm income
• non-farm income
• wild fish catch income cash income from wild
  fish catch
• Farming income incomes from farming enterprises
• rice cultivation,
• livestock raising,
• fish culture,
• non-rice crop farming
• fruit trees,
• Fish income total income from fish culture
• cash income from selling fish harvest
• forgone income from amount of give-a-way and
  eaten fish
• per capita income total household income/
  household size

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Satisfaction with fish culture

• LogitP(pls_fishj) f(yield, income,
  fish-farmincome, fish_hhincome, fishincome, age,
  edulevel, men, land, pond-land, involve,
  expectation) Model (1)
• where
• pls_fish categorical variable for satisfaction
  to fish culture.
• j five levels of satisfaction from strongly
  satisfied to strongly dissatisfied, j 15.
• () yield the yield of fish culture, is fish
  production divided by pond area
• () income capita household income in USD.
• () fish-hhincome the ratio of fish income to
  total household income
• () fish-farmincome the ratio of fish income to
  farming income
• () age age of respondents,
• age 1 if the respondent is older 40, age 0
  otherwise
• () edulevel education level of respondents,
• edulevel 1 if the respondent has completed
  primary school, edulevel 0 otherwise
• () men number of men in household
• () land total land area the farmer occupied
• () pond-land the rate of pond area to total
  area of land
• () involve involvement to extension service,
• involve 1 if the farmer is involved with
  extension services (on-farm trial or technical
  training) involve 0 otherwise
• () expectation fish expectation measured by
  the difference between farmers estimation of
  fish-hhincome and the real value.calculated from
  collected field data.

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SATISFACTION WITH FISH CULTURE
  Regression Estimates Regression Estimates Regression Estimates Marginal effect Elasticity
Parameter Coef. Error Prob.
fish_farminc 1.8626 0.9848 0.0586 0.2176 0.2009
age 1.2231 0.6944 0.0782 0.15
edulevel 0.4164 0.5091 0.4135 0.0267
pond_land 0.0616 0.0272 0.0233 0.0072 0.0725
involve 2.3381 0.9999 0.0194 0.0203
expectation 0.1387 0.0335 0.0001 0.0162 0.1713
agepond_land 0.0642 0.0301 0.0329 0.0073 0.0514
ageexpectation -0.0795 0.0297 0.0074 -0.009 -0.0958
pond_landexpectation -0.003 0.00087 0.0005 -0.0004 -0.0036
edulevel1involve -2.1587 1.2277 0.0787 -0.2027  
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FISH CULTURE AND LIFE SATISACTION
LogitP( happy j) f(pls_fish, income,
fcash_total, nonfarm_total, catch_total,
fish_hhinc, age, edulevel, men, land) Model
(2) where happy categorical variable
improvement in framers life quality ()
pls_fish categorical farmers satisfaction to
fish culture, () income per capita income ()
fcash_total cash income from fish culture
relative to total household income ()
nonfarm_total income from non-farming activities
relative to total household income ()
catch_total income from wild fish capture
relative to total household income ()
fish_hhincome income from fish culture relative
to total household income () age age of
respondents age 1 if the age of respondent
higher than 40, age 0 otherwise () edulevel
education level of respondents edulevel 1 if
the respondent has completed primary school,
edulevel 0 otherwise () men Number of men in
respondents household () land total area of
land occupied by the respondents household
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fish farmers life satisfaction
Estimate Estimate Estimate Marginal effect Elasticity
Parameter Estimate Error Prob.
pls 3.1232 1.4613 0.0326 0.6172 0.9059
fcash_total 0.0226 0.013 0.0828 0.0045 0.0926
nonfarm_total -0.0415 0.013 0.0015 -0.0082 -1.1800
catch_total 0.057 0.0225 0.0115 0.0113 1.1093
fish_hhinc 0.4852 1.746 0.7811 - -
age 3.7583 1.4564 0.0099 0.0020 -
fish_hhincage -6.0363 2.0856 0.0038 -1.1659 -1.5656
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Determinants of Happiness
Parameter Logit Marginal Effect Elasticity
fcash_hhinc 0.0226 0.0045 0.0926
nonfarm_hhinc -0.0415 -0.0082 -1.1799
catch_hhinc 0.0570 0.0113 1.1093
age 7.5783 0.0946 -
fish_hhincage -6.0363 -1.1659 -1.5656
fish_farminc 5.8173 0.1343 0.1820
pond_land 0.1924 0.0044 0.0657
involve 7.3024 0.0125 -
expectation 0.4332 0.0100 0.1552
agepond_land 0.2005 0.0046 0.0466
ageexpectation -0.2483 -0.0491 -0.0868
pond_landexpectation -0.0095 -0.0002 -0.0033
fish_farmincinvolve -5.8248 -0.1345 -0.2167
edulevel1involve -6.7421 -0.1251 -