biodiversity and economic development

8/9/2019 Biodiversity and Economic Development

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Biodiversity, Demography and Economy: An Exploration of Linkages

Amitabha Sinha

Reader

Department of Analytical Applied Economics

Tripura University

Abstract: A text book definition of biodiversity is variation of life at all levels of biological

organisation. This paper investigates the inter-linkages between biodiversity, population

density and economic prosperity in the context of 112 countries of the world A production

function approach is adopted. However, the analysis allows for technical inefficiency .World

Bank classification is adopted in classification of the countries as high, medium and lowincome. 112 countries are taken up for study on the basis of availability of relevant data. The

results of the analysis do not support the hypothesis that high income countries are

technically more efficient in maintaining biodiversity, even when allowance is made for

differences in population density.

Keywords: Biodiversity, Technical Efficiency, Deforestation.

I. Introduction

Wikipedia observes that the current text book definition of biodiversity is variation of life

at all levels of biological organisation, that is, genes, species and habitations existing on

land, water and air. According to the HIPPO hypothesis of Edward O. Wilson (1988), the

threat factors for biodiversity are: Habitat destruction (H), Invasive species (I), Pollution (P),

Human over population (P) and Over harvesting (O). One of the aspects of habitat destruction

.Availability of flora and fauna, on the other hand, can be related to availability of forest area

This can measured in terms of total geographical area of the country as percentage of forest

area in total geographical area, It can be also looked upon from the context of total population

.Dividing the forest area by total population, per capita forest area can be calculated, Per

capita GDP is a measure of economic productivity of a country. In economics , production is

value addition or prevention of value loss .Here , not only exchange value but also use value

and non use value have to be considered . Similarly, per capita forest is a measure of

biodiversity productivity of a country (Bhattacharya, R, N. (2001). Taking this approach, one


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can employ production function and efficiency frontier method naturally .This is elaborated

in the next section .

II. The Model

The model proposed in the present study has two layers. Habitat is captured by the proxy

variable per capita forests of a country. Economic development is measured by per capita

GDP. In the traditional analysis of the relationship between biodiversity and economic

development, it is implicitly assumed that each country is operates at the margin of

technologically determined production possibility frontier. This assumption is replaced by the

possibility that the country may operate inside the production possibility frontier. Michael

Farrell (1957) introduced a methodology to measure economic, technical (TE), and input and

output allocative efficiency (AE). According to Farrell, TE is associated with the ability to

produce on the frontier iso-quant, while AE refers to the ability to produce at a given level of

output using the cost-minimizing input ratios. Alternatively, technical inefficiency is related

to deviations from the frontier iso-quant, and allocative inefficiency refers to deviations from

the minimum cost input ratios. Thus, EE is defined as the capacity to produce a

predetermined quantity of output at minimum cost for a given level of technology (Farrell

and Michael, 1957; Dewatt and Billie R. 1979).

Therefore, the production function is notwritten as follows:

Yi = 0 + 1Xi + vi (1)

Here i represents the country and vi is white noise. We write the production function as:

Yi = 0 + 1Xi + vi ui (2)

Here ui represents the gap between the actual production possibility frontier and the efficient

production possibility frontier. Obviously, ui is a negative term. Only when ui = 0, the country

operates at efficient level. We assume that vi is a stochastic variable which has a half normal

distribution because ui cannot have positive values. The maximum value it can have is zero.

Obviously, ui is a measure of technical efficiency. In the first layer of analysis, we estimate

the technical efficiency of rich and poor countries. Then we introduce the demographic factor

in the second layer. Population density represents the demographic dimension of population.

Here two types of questions are addressed. Firstly, what is the impact of population density

on the technical efficiency of the countries? Secondly, is there any difference in this impact

between the rich and the poor countries? For testing this hypothesis, we construct the

following econometric model.

Yi = 0 + 1X i + 2d1 + 3d2+ vi (3)


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Here Y represents technical efficiency, X represents population density, d1 is a dummy

variable which takes the value of 0 for poor countries and 1 for high income countries.

Similarly , d2 is 1 for middle income countries . Here , vi is the white noise. The

interpretation of the coefficients in the case of dummy variable model can be briefly

discussed here. Let us take the case when d = 0. Then, we have

Yi = 0 + 1X i + vi (4)

However, if d2 = 1 and d1 = 0 ,which is the middle income country case ,we shall have

Yi = (0 + 3) + 1X i + vi (5)

Therefore, 3 measures the difference in technical efficiency of middle income and poor

countries caused by population density. We expect that 3 is positive and statistically

significant. Similarly, for 2. We expect that 1 is negative and statistically significant.

III. Data

We obtained data from http://www.mongabay.com/deforestation_pcover.htm.

These data, based on FAO for forests, have been criticised for their uneven

quality across nations andinconsistencies in dentitions. But despite this .they remain

important source of cross-national information on forests and the total amount of

forest cover. Per-capita GDP is in US $ for the year 1997. Forest data are for the year

2000. Population densities data refer to 1999.Based on availability of data, 112 countries are

selected for analysis. The countries have average per capita forests less than or equal to 2

hectares. Major three categories of Wold Bank, namely, High, Middle and Poor income

classification is adopted (World Bank, 2003) in classifying the countries .

IV. Results It is argued in this paper that the capacity of the high income countries to protect

their forest resources can be analysed assuming that these economies are technically at the

efficiency frontier. If this possibility is not taken for granted, then one may try to relate per

capita GDP in terms of TE to maintain average per capita forest area of a country. This can

be done using Stochastic Frontier Model. The results are summarised in Table 1.
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Table 1: Efficiency Analysis

Stochastic Frontier Normal/half-normal Model

Log likelihood = -85.277955Number of observations = 112Wald chi2(1) = 0.17

Prob>chi2 = 0.6811Per CapitaForests(Dependent)

Coefficient Std. Error z P>z [95% Conf. Interval]

Per Capita GDP -0.0000021 0.0000052 -0.41 0.681 -0.000012 0.0000080

Constant 0.5446 0.2482 2.19 0.028 0.0582 1.0310

/lnsig2v -1.3151 0.1337 -9.84 0 -1.5771 -1.0531

/lnsig2u -10.5422 117.9498 -0.09 0.929 -241.72 220.6352

sigma_v 0.5181 0.0346 - - 0.4545 0.5907

sigma_u 0.0051 0.3030 - - 3.25E-53 8.13E+47

sigma2 0.2685 0.0359 - - 0.1981 0.3389

lambda 0.0099 0.3061 - - -0.5899 0.6098Note: Half normal distribution

The sign of the income coefficient is negative. This implies that per capita forest is

lower in those countries which have higher per capita GDP on the average. However, the

statistical significance of the estimate is very low as shown by the z value which is -0.41.

For the estimated log likelihood value we obtain average per capita forest of 1.09 hectare.

Among log likelihood ratio test, the lagrangian multiplier test and the Wald test, the Wald

statistic is considered to be more applicable. The p value for the Wald statistic is 0.68.

This means that the goodness of fit of the model is not very high. These limitations of the

data have to be kept in mind while studying Table 2.

Ten countries (including highest and lowest ranking in terms ofper capita income,

population density and technical efficiency) as major countries among 112 selected

countries are reported in Table 2. These 10 countries are classified in terms of World

Bank classification by income as high, low and low (medium),per capita income ranking,

population density ranking and technical efficiency ranking. If the countries are ranked

according to technical efficiency and classified as: high technical efficiency (rank 1 to 37),

medium technical efficiency (rank 38 to 74) and lo technical efficiency (rank 75 to 112), but

considers World Bank categories of high, medium and low income countries, then one finds

that only 4 countries out of the 37 countries of high technical efficiency belong to high

income group. However, if the countries are ranked according to the per capita GDP and one

considers the countries with rank 1 to 37 as high income countries then one finds that 10

countries out of the high income countries belong to high technical efficiency group. In other

words, the general impression that high income countries are always better in managing there


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environment in biodiversity is not supported by the present set of data. Table 2 focuses on 10

countries which includes apart from India, China and U.S.A. the countries which have the

highest and lowest rank in per capita GDP, population density and technical efficiency. It

may be noted that the country with the highest per capita income has rank 1 and the country

with the lowest per capita income has rank 112. Similarly, the country with the highest

technical efficiency has rank 1 and the lowest has the rank 112. In the case of population

density, the country with the highest population density has the rank 1 and the lowest 112.

India and China are low in per capita income rank and very low in technical efficiency rank.

U.S.A. fares better in technical efficiency with a rank of 27 as shown in Table 2.

Table 2: A Few Major Countries Location Pattern

Name of theCountry

World BankClassification

Per CapitaIncome Rank

PopulationDensity Rank

TechnicalEfficiency Rank

1. Switzerland High 1 15 64

2. USA High 7 92 27

3. India Low 87 3 106

4. China Low 76 22 102

5. Japan High 2 2 65

6. Germany High 5 13 84

7. Ethiopia Low 112 64 112

8. New Zealand High 12 102 1

9. Republic ofKorea

High 16 1 86

10. Mauritania Low 84 112 105

Note: Rank according to descending order.

At the second layer of analysis, we ask two questions: (a) how does population

density impact on technical efficiency of the countries and (b) is there any difference between


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the impacts among the rich and the poor countries. Table 3 shows that the coefficient of

population density is negative and statistically significant. However, the coefficient of the

dummy variable is positive in the case of high income and negative in the case of medium

income countries. However, the coefficients are not statistically significant. The positive sign

of dummy of high income countries implies that given population density technical efficiency

is higher in high income countries compared to the low income countries which can be

thought of as the control group. But the dummy has a negative sign for medium income

countries implying a poorer performance in efficiency compared to the poor income

countries. However, these results are not statistically significant. This means that no

significant difference is found in technical efficiency given population density among high,

medium and poor countries.

Table 3: Regression Results

Source SS df MSNumber of observations = 112F(3, 108) = 13.26Prob > F = 0R-squared = 0.2691Adj R-squared = 0.2488

Root MSE = 0.000045

Model 0.000000080 3 0.000000027

Residual 0.00000022 108 0.0000000020

Total 0.00000030 111 0.0000000027

Efficiency Coefficient Standard Error t P>t [95% conf. Interval]

Population Density -0.00000032 0.000000053 -6.03 0 -0.00000042 -0.00000021

Dummy High 0.0000145 0.000014 1.06 0.292 -0.0000127 0.0000417

Dummy Medium -0.0000086 0.0000094 -0.92 0.361 -0.0000273 0.00001

_Constant 0.9966236 0.0000080 - 0 0.9966077 0.9966394

[NOTE: LINEAR REGRESSION MODEL

V. Discussion

The results obtained in the present study do not support the proposition that an economically

advanced country manages its environment and biodiversity more efficiently. In spite of the

limitations of data this major finding seems to be a reasonable finding, the vast literature

which has emerged in environmental economy (Bhattacharya, 2001) show that environmental

goods exhibit the properties of non-rivalry and non-excludability. In the case of such goods

there is market failure. This means that market driven solutions are not pareto- efficient.

Pareto efficiency is the sum total of technical efficiency and allocative efficiency. The

relationship between public good, property rights and technical efficiency is not analysed in


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the literature as thoroughly as the relationship between pareto- efficiency and public good.

But the essential economic logic may not be so difficult to understand. Due to unclear

ownership pattern of natural resources like forest their economic value which may be

considered to be the sum total of used value and non-used value may not be properly

recognised by the producers. This may lead to over exploitation of natural resources often

called Tragedy of the Commons (Hanley, Nick, Jason F. Shogren and Ben White, 1997).

Therefore, technical efficiency may not be exhibited by countries which are mainly market

driven economies.

VI. Conclusions

Economic development has led to industrialisation, greater use of fossil fuel, population

explosion and a high degree of regional inequality. A deeper analysis will show that it is not

economic development as such but market led economic development and market failures

which are at the root of the environmental crisis of the present world. Interventions by

government and national and international level have thus become important. The United

Nations Environmental Agency has been organising conferences to create a common

platform of nations from 1972 onwards. The Copenhagen Summit held in the period (7 to 18)

December, 2009 on climate change fail to bring about a comprehensive global commitment

indicating the fact that national policies of advanced countries often serve the interest of big

business. The profit interests obstruct the corporate decision making process from adopting

initiatives which are perceived to have negative implications for profitability. The long term

point of view may sometimes be sacrificed at the altar of immediate gains. The civil society

throughout the world must adopt a more clear stance to countervail the short-run perspective

for a more sustainable human development process not only at regional and national level but

also at global level. This conclusion emanates from the premise that the macro always

dominates the micro in the socio-economic spheres.

References

1. Bhattacharya, Rabindra N. (2001). Environmental Economics: An Indian Perspective,

Oxford University Press.

2. Chakravarty, Satya R., Dipankar Coondoo, Robin Mukherjee (1998). Quantitative

Economics: Theory and Practice, Allied Publishers Limited, New Delhi.


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3. Dewatt, Billie R. (1979). Modernization in a Mexican Ejido: A Study in Economic

Adaptation, New York, Cambridge University Press.

4. Farrell, Michael (1957). The Measurement of Productivity Efficiency, Journal of the Royal

Statistics Society, Series A, 120, Part 3: 253-90.

5. FAO, (2003). The State of the World's Forests 2003,

http://www.mongabay.com/deforestation_pcover.htm.

6. Hanley, Nick, Jason F. Shogren and Ben White (1997). Environmental Economics: In

Theory and Practice, Macmillan Press Limited, Delhi.

7. Wilson, Edward O. (ed.) Frances M.Peter (associate editor) (1988). Biodiversity, National

Academy Press, online edition.

8. World Bank (2003). World Development Report 2004: Making Services Work for Poor

People, The World Bank, USA.

http://apps.fao.org/faostat/forestry/
http://darwin.nap.edu/books/0309037395/html/R2.htmlhttp://darwin.nap.edu/books/0309037395/html/R2.html

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