aquaculture and happiness in vietnam a microeconometric analysis n.
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DUC MINH NGUYEN. AQUACULTURE AND HAPPINESS IN VIETNAM – A MICROECONOMETRIC ANALYSIS.

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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 country’s 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

Introduction
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.
Happiness studies
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).
Money can buy happiness…
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 people’s satisfaction with life.
or money reduces happiness?
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?
Aquaculture raise happiness?
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
This study
the cumulative logistic model

Frey and Stutzer (2002) suggest a logistic function

Wit= α + xit + it

- Wit is level of happiness

- xit is a vector of explanatory variables of demographics and socioeconomics characteristics

The cumulative logistic model
the cumulative logistic model1

the responses are ordinal variables,

  • considering farmer’s 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, π1, …, πJ−1, that are used to transform Ui into the observed variable Y as following

Yi = 1 if π1 Ui

Yi = 2 if π2 < Ui π1

Yi = 3 if π3 < Ui π2

.

.

Yi = J if Ui πJ−1

The cumulative logistic model
the cumulative logistic model2

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

The cumulative logistic model
the cumulative logistic model3

Agresti (2002) defines the cumulative probabilities

P(Y ≤ j|x) = 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

logit[P(Yi ≤ j | x)] = αij + 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.

The cumulative logistic model
the cumulative logistic model4

Each cumulative logit has its own intercept αj 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 Y≤j and Y > j.

The cumulative logistic model
estimation methods

The SAS logistic regression with backward selection

In this study, the fixed threshold j=2 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 j=2, the response curve is a logistic regression curve for a binary response with outcomes Y ≤ 2 and Y > 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

Estimation methods
slide14

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 (UAF[1]).
  • 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.

Data
slide15

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
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
Income definition
satisfaction with fish culture

Logit[P(pls_fish≤j)] = 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 = 1…5.

(+) 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.

Satisfaction with fish culture
slide19

FISH CULTURE AND LIFE SATISACTION

Logit[P( 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 framer’s life quality

(+) pls_fish: categorical farmer’s 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 respondent’s household

(+)land: total area of land occupied by the respondent’s household