list of chapters - washington state university€¦ · invasive species - the case of the apple...

Yeon A Hong Preliminary Exam

1. The Effect of Religion on Meat Consumption and

Greenhouse Gas Emissions

2. The Effect of a Government Planner’s Educational

Background on Environmental Outcomes in Autocratic

Regimes

3. Modeling the Economic Impact of Trade Regulation of

Invasive Species - The Case of the Apple Maggot

Quarantine Program in Washington State

List of Chapters


The Effect of Religion on Meat Consumption and

Greenhouse Gas Emissions

Yeon A Hong

Washington State University

[email protected]

August 11, 2017

mailto:[email protected]


Motivation

Livestock sector accounts for 14.5 % of human-induced global green

house gas emissions (GHG) (FAO, 2013)

Direct factors causing an individual‘s dietary habit – income and

urbanization (Steinfeld, 2007; York an Gossard, 2004)


Religion - Meat Consumption

Religion – a critical factor which influences an individual’s dietary

habits

Religion restricts an individual’s dietary choice – Hinduism and

Islam

India – Consumption of meat is not accompanied by income growth

as apposed to China and Brazil because of strong vegetarian

tradition and religious practices (Mohanty et al. 1998, Steinfeld,

2007)


Research Question

Research Question

• How does religion affect meat consumption and subsequently GHG

emissions?

Objective

• Estimate religion elasticity of GHG emissions from meat

consumption

Contribution

• First study to theoretically link and empirically measure the

impact of religion on GHG emissions through meat consumption

• Unique cross-country panel data to measure the effect of religion

from multiple sources


Incorporate an aspect of identity

• Each religion has its own prescriptions that indicate different ideal

behaviors for an individual in religious belief.

• An individual follows the prescriptions to keep identity shaped in

one’s religious belief (Akerlof and Kranton 2000).

The extent of concern with identity is consistent across all

individuals within the same religious belief?

• Homogenous society in religion vs. Heterogeneous society

Theoretical Framework



ReligionIdentity

Concern Consumption

decision

Socially Accepted

Good

Socially Discouraged

Good

Production Level

– Endow. & Tech.

Religious

Homogeneity

Figure 1. Conceptual framework linking religion to the equilibrium level of socially accept

ed and discouraged goods under static general equilibrium model

Production Level

– Endow. & Tech.


ReligionIdentity

Concern Consumption

decision

Socially Accepted

Good

Socially Discouraged

GoodReligious

Homogeneity

Household weighs a socially accepted good and a discouraged good

based on the degree of religious homogeneity within a society

• Homogenous society – magnitude of weight increases

• Heterogeneous society – magnitude of weight decreases



Equilibrium

Aggregate emissions

= Pollution emissions generated to produce one unit of the good

* Equilibrium quantity of the final good

Equilibrium of the

Final Good

Weight by

Household AWeight by

Household B

Production

Factors

Endowment,

Land & Labor

Socially Accepted Socially Discouraged


Testable Implication

An increase in the religious homogeneity raises the equilibrium

level of a socially accepted good, but lowers the equilibrium level of

a socially discouraged good.

Because religions have their own distinctive prescriptions, the

influence of religious homogeneity on consumption of goods will

significantly vary from religion to religion.


Empirical Model

𝑀𝑗,𝑛,𝑡 = 𝛼 + 𝑹𝒉′𝒊,𝒏,𝒕𝜷𝑹 + 𝛽𝐴𝐴𝑛,𝑡 + 𝛽𝑆𝑆𝑛,𝑡 + 𝛽𝐿𝐿𝑛,𝑡 + 𝜃𝑛 + 𝜌𝑡 + 𝜀𝑛𝑡

𝑀𝑗,𝑛,𝑡 indicates the equilibrium level of good 𝑗 for 𝑗 =

{𝑠𝑜𝑐𝑖𝑎𝑙𝑙𝑦 𝑎𝑐𝑐𝑒𝑝𝑡𝑒𝑑 𝑔𝑜𝑜𝑑, 𝑑𝑖𝑠𝑐𝑜𝑢𝑟𝑎𝑔𝑒𝑑 𝑔𝑜𝑜𝑑} in country 𝑛 for year 𝑡.

𝑹𝒉′𝒊,𝒏,𝒕 is a vector of measures of religious homogeneity for all

religions in category 𝑖 in country 𝑛 for year 𝑡.

𝐴𝑛,𝑡 is a measure of the factor productivity in country 𝑛 for year 𝑡

𝑆𝑛,𝑡 and 𝐿𝑛,𝑡 measure the endowed land and labor in country 𝑛 for

year 𝑡.

What are the expected signs of 𝜷𝑹 ?


Data

We use country-level panel data including 44 developing countries

spanning from 2000 to 2011.

• In developing countries, the dietary transition actively occurs, and agricultural

emissions are seriously taking place due to limited access to modern technologies.

The measure of socially accepted and discouraged good

consumption is four types of meat consumption: cattle meat, pig

meat, chicken meat, and goat meat.

As the proxy of religious homogeneity, the religious prevalence

data are used, indicating the proportion of population believing in

four major religions, Christianity, Buddhism, Islam, and Hinduism

(ex. In Indonesia for the year 2005, 8.9% Christians, 88.6% Muslims, 0.6%

Buddhists, and 1.7% Hinduists of the total population)


Cattle 47.61

Goat 50.93

Pig 8.56

Chicken 2.06

Variables Mean

Cattle meat (kg/capita) 6.76

Pig meat 2.25

Chicken meat 9.93

Goat meat 1.06

Christianity (%) 46.62

Buddhism 6.08

Islam 32.88

Hinduism 5.48

Table 1. Summary Statistics

Table 2. Emission Intensity (CO2-eq kg /kg product)

The measure of pollution emissions

per unit is GHG emissions intensity

of 1kg meat product.

Data


Table 3. Effect of Religion on Meat Consumption, Two-Way Fixed Effects

(1) (2) (3) (4)

logcattle logpig logchicken loggoat

Christianity 3.180*** 6.660*** -2.231** 2.458**

(0.998) (1.769) (1.117) (1.110)

Islam 1.561 -8.510*** -4.942*** 6.884

(1.615) (2.877) (1.895) (5.032)

Buddhism 8.683*** -1.481 -3.948 5.452*

(1.589) (3.996) (2.769) (2.897)

Hinduism -0.106* -0.023 -0.234 -0.101

(0.064) (0.096) (0.223) (0.068)

logmeadows&pastures -0.275** -0.189 -0.139 -0.216

(0.114) (0.449) (0.210) (0.181)

employment 0.004 0.017** -0.010 0.004

(0.006) (0.008) (0.006) (0.009)

logagri-researchers -0.044 0.053 0.007 -0.096

(0.036) (0.138) (0.043) (0.149)

trade 0.0008 0.0007 -0.0003 -0.003

(0.0009) (0.002) (0.001) (0.001)

urb -0.026** 0.051*** 0.051*** 0.005

(0.013) (0.019) (0.018) (0.021)

N 352 265 372 130

adj. R-sq 0.984 0.987 0.978 0.997

Robust standard errors in parentheses. * p<0.1 ** p<0.05 *** p<0.01


Religion Elasticity of GHG Emissions

Extra carbon emissions per capita from meat consumption for 1%

increase in the proportion of population believing in religion

evaluated at the sample mean

𝜕𝐺𝐻𝐺

𝜕𝑅𝑒𝑙𝑖𝑔𝑖𝑜𝑛

𝑅𝑒𝑙𝑖𝑔𝑖𝑜𝑛

𝐺𝐻𝐺=

𝜕𝑀𝑒𝑎𝑡

𝜕𝑅𝑒𝑙𝑖𝑔𝑖𝑜𝑛

𝑅𝑒𝑙𝑖𝑔𝑖𝑜𝑛

𝑀𝑒𝑎𝑡

𝜕𝐺𝐻𝐺

𝜕𝑀𝑒𝑎𝑡

𝑀𝑒𝑎𝑡

𝐺𝐻𝐺

where 𝑅𝑒𝑙𝑖𝑔𝑖𝑜𝑛 , 𝑀𝑒𝑎𝑡, and 𝐺𝐻𝐺 are mean values reported in summary statistics.


Religion Elasticity of GHG Emissions

Cattle Pig Chicken Goat

0.58 -3.87*** -3.81*** 2.21

(0.98) (1.94) (1.66) (2.86)


1.68*** 4.43*** -2.44*** 1.12*

(0.73) (1.42) (1.17) (0.75)


0.60 -0.12 -0.56 0.32

(0.42) (1.16) (1.05) (0.71)


-0.007 -0.002 -0.03 -0.005

(0.02) (0.03) (0.08) (0.02)

Christianity

Islam

Buddhism

Hinduism

Standard errors in parentheses. * p<0.15 ** p<0.1 *** p<0.05


Future works

We first plan to modify the theoretical model using a different

utility function proposed by Benjamin et al. (2010).

We then intend to create a new time-series index that indicates a

change in the degree of homogeneity within a country.

We plan to add an assessment of how changes in religious

landscape would affect GHG emissions from the livestock sector


The Effect of a Government Planner’s Educational

Background on Environmental Outcomes in

Autocratic Regimes

Yeon A Hong


[email protected]

August 11, 2017



Motivation

A rise in the educational level of individuals along with economic

development provides an underlying mechanism for explaining

the environmental Kuznets curve (Panayotou, 1997, Stern, 2006).

The educated are more aware or environmental issues and are

likely to have a stronger preference for environmental amenities

(Farzin and Bond, 2006).

They organize advocacy groups to express their demands through

existing political channels along with votes (Fredrikssons et al.

2005)


Motivation

Access to such political participation is restricted autocratic

countries.

Performance of autocratic countries on environmental issues

• Half of top 10 worst performers in autocracy

• China and Russia- the largest contributors to greenhouse gas emissions

Who makes the influence on the process of environmental policy

formation in autocratic regimes?

Education Planner’s

Attitude

Environmental

Policy

GOVERNMENT PLANNER


Source: World Health Organization, 2016

Air Quality Levels


Research Question

Research Question

• How does the educational background of a government planner

impact environmental outcomes in autocratic regimes?

Contribution

• Novel mechanism of environmental policy making process in

autocratic regimes

• Unique dataset to measure the effect of education of a planner

• Rigorous estimation using an instrument to fix potential

endogeneity of the main variable


Definition

Regimes are considered autocratic when any one of the following

occurs:

• A political leader achieves power through undemocratic means.

• A leader achieves power through competitive elections but changes

a rule to limit competition afterwards.

• Despite existence of competitive elections, the military dictates

policy choice in critical policy areas.


Literature Review

Level of Education

• Individuals with higher levels of education express greater concern

about the environment and more likely to engage in

environmentally responsible behaviors (Mobly et al, 2010, Xiao and

McCright, 2007, etc.)

Field of Education

• University students’ attitudes towards environmental issues

significantly vary depending on their academic major.

• Economics and business students are less concerned with the

environment (Benton, 1994, Synodios, 1990, Tikka et al. 2000).



Industry

Sector 1 Sector 2Consumer

Dirty Good Numeraire Good

Government

Externalities

(-) Consumer welfare

Pollution Tax (-) output

(+) investment on pollution control

Optimal tax rate to maximize aggregate welfare

• Aggregate profits + Consumer surplus net of disutility from externality + Pollution

tax revenues


ProfitsConsumer

SurplusDisutility from

Pollution

Weight

The weight is dependent on the planner’s academic background.

• Academic discipline which influences an individual to be more aware – Magnitude

of weight increases

• Academic discipline which influences an individual to be less aware – Magnitude of

weight decreases


Tax Revenues

Social welfare function capturing the extent of a planner’s

awareness about environmental damages


Equilibrium

Aggregate pollutions

= Pollution emissions generated to produce one unit of dirty good

* Output level of dirty good

Equilibrium tax rateWeight by

the planner Price of dirty good

# of firms Consumer income


Testable Implication

If a government planner was educated in an academic discipline

which shapes one to be less conscious of environmental damages, a

less-stringent regulation would be implemented and thus more

pollution would be generated.

On the contrary, if the planner was educated in an academic

discipline which affects them otherwise, a more-stringent

regulation would be implemented and thus less pollution would be

generated.


Empirical Model

𝑃𝑜𝑙𝑙𝑢𝑛,𝑡 = 𝛽𝐸𝐸𝑑𝑢𝑛,𝑡 + 𝛽𝐹𝐹𝑖𝑟𝑚𝑛,𝑡 + 𝛽𝐼𝐼𝑛𝑐𝑜𝑚𝑒𝑛,𝑡 + 𝛽𝑃𝑃𝑟𝑖𝑐𝑒𝑛,𝑡 + 𝜌𝑡 + 𝜀𝑛𝑡

• 𝑃𝑜𝑙𝑙𝑢𝑛,𝑡: measure of aggregate pollution in country 𝑛 at year 𝑡

• 𝐸𝑑𝑢𝑛,𝑡: measure of the government planner’s academic background

• 𝐹𝑖𝑟𝑚𝑛,𝑡: measure of the number of firms

• 𝐼𝑛𝑐𝑜𝑚𝑒𝑛,𝑡: measure of the consumer income

• 𝑃𝑟𝑖𝑐𝑒𝑛,𝑡: measure of the price of the dirty good

We separately run regressions for two distinct types of regimes,

autocracy, and democracy, for comparison.

We expect a negative sign for the estimate of the academic discipline

turning a planner to be brown.


Issues

Our main explanatory variable, the government planner’s

educational background, barely varies over time.

• OLS, a time fixed effects model, and control variables measuring

geography and inheritance

Omitted variable bias: country-level characteristics such as

measures of institutional quality, openness to trade, and socio-

demographic characteristics


Issues

Endogeneity of the planner’s academic background (their

university major)

• We expect the direction of the bias to be positive since the planner’s interest could

be developed earlier, so that the effect of university education could be

overestimated.

● Instrumental variable reflecting educational condition during

planners’ age of adolescence when academic interests are

developed and planning for the future begins, the adolescent

fertility rate

• The higher adolescent fertility rate could indicate that the young are less likely to

participate in secondary education or more preferably attend military academies

which are tuition free and guarantee the future job.


Data

To measure the effects of the government planner’s educational

background, we collect unique data set on a university major for

heads of state from Barcelona Center for International Affairs.

• Academic disciplines into five categories: STEM & Health, Business & Economics,

Law & Social Sciences, Humanities, and Military Academy

● Business & Economics – measure of the academic disciplines

which influence the planner to be relatively less aware of

environmental issues

Country-level panel data on sulfur dioxide emissions from

Smith et al. (2011)

The dataset of autocratic regimes: 23 countries, 1981-2005 /

democratic regimes: 45 countries, 1994-2005


Table 1. Effects of Business & Economics on Sulfur Dioxide Emissions –Autocracy vs.

Democracy, OLS and Time fixed effects

----------Autocracy---------- ----------Democracy----------

(1) (2) (3) (4)

Business & Economics 0.015** 0.011** -0.00006 0.0005

(0.007) (0.005) (0.003) (0.003)

Birth year 0.0003*** 0.0005*** -0.0003** -0.0002

(0.00008) (0.00008) (0.0001) (0.0002)

Interest rate -0.00003 0.00002 -0.000004 -0.00006

(0.00003) (0.00003) (0.0001) (0.0001)

GDP growth -0.0002 -0.00003 0.0008** 0.0009***

(0.0002) (0.0003) (0.0003) (0.0004)

Industry -0.0002*** -0.0002** 0.0002 0.0001

(0.00008) (0.00008) (0.0002) (0.0002)

Polity -0.001*** -0.001*** 0.005*** 0.006***

(0.0004) (0.0004) (0.001) (0.001)

Year dummy No Yes No Yes

N 172 172 272 272

Adj. R-sq. 0.711 0.752 0.285 0.281



Table 2. Effect of Business & Economics on Adolescent Fertility Fate - First Stage – Au

tocracy vs. Democracy, OLS and Time fixed effects



(1) (2) (3) (4)

Adolescent fertility -0.001*** -0.002*** -0.001 -0.0009

(0.0004) (0.0004) (0.001) (0.001)

Birth year -0.006* -0.006* -0.018*** -0.018***

(0.003) (0.003) (0.005) (0.005)

GDP growth 0.002 0.001 -0.006 0.0003

(0.003) (0.003) (0.006) (0.008)

Interest rate 0.0009 0.001 -0.004 -0.004

(0.001) (0.001) (0.003) (0.003)

Industry -0.006* -0.006 0.005 0.006

(0.004) (0.004) (0.005) (0.005)

Polity 0.003 0.002 0.033 0.043

(0.010) (0.011) (0.037) (0.038)


N 194 194 345 345

Adj. R-sq. 0.386 0.348 0.116 0.104


Table 3. Effect of Business & Economics on Sulfur Dioxide Emissions – IV Instrumenti

ng Business & Economics– Autocracy vs. Democracy


The maximal IV sizes of 10%, 15%, 20%, and 25% reported by the Stock and Yogo test are 16.38, 8.9

6, 6.66, and 5.53, respectively.


(1) (2) (3) (4)

Business & Economics 0.021** 0.006 -0.009 -0.012

(0.008) (0.005) (0.009) (0.009)

Birth year 0.001*** 0.0008*** -0.001* -0.001**

(0.0003) (0.0002) (0.0006) (0.0006)

GDP growth -0.0001 0.0001 0.0009** 0.001**

(0.0001) (0.00007) (0.0004) (0.0004)

Interest rate 0.00003 -0.00005*** 0.0001 -0.000005

(0.00003) (0.00001) (0.0003) (0.0003)

Industry 0.0005*** -0.000007 0.0001 0.00007

(0.0002) (0.0001) (0.0003) (0.0003)

Polity -0.001 -0.001*** 0.007*** 0.007***

(0.0007) (0.0003) (0.002) (0.002)


N 62 62 175 175

Adj. R-sq. 0.955 0.986 0.121 -0.012

KP Wald F statistic 15.946 25.525 13.71 14.619

Significant rejection rate 15% 10% 15% 15%


Future works

We plan to build a model for democratic regimes.

We need to validate the assumption for our instrument use

referring reliable sources.

Lastly, we consider adding more control variables that capture the

planner’s individual characteristic such as a birth place and the

highest degree earned.


Modeling the Economic Impact of Trade Regulation

of Invasive Species - The Case of the Apple Maggot

Quarantine Program in Washington State

Yeon A Hong and R. Karina Gallardo


[email protected]

August 11, 2017



● Export dependency of the United States

(US) apple industry has risen over the

past decade along with rapid growth of

global agricultural markets.

• The State of Washington exported fresh apples by

more than 30% of its total production (WSTFA,

2016).

● The State of Washington has

implemented the quarantine program

to prevent infestation of an invasive

species, apple maggot (Rhagoletis pomonella).

Motivation

Source: WSDA, 2015


Motivation

The apple maggot quarantine program is designed to facilitate the

movement of commercial fruits to domestic and international

markets by providing shippers with certification that their fruits

are apple maggot free (Sansford et al. 2016).

Apple growers having an orchard in the quarantined areas should

comply with regulations on the apple pest to ship fresh apples

(Kalus, 2015)

• For example, Canada requires all apples from the quarantined areas shipped to

British Columbia to be in storage at 1℃ for 40 days.


● The apple maggot quarantine areas consist of the areas where

the pest is considered established but officially controlled.

2014 Apple Maggot Quarantine Status

Source: WSDA, 2016

In recent, industry concerns potential

apple maggot introduction to pest free

areas because of transporting yard (gr

een) waste from Seattle area to the Ea

st area.


Research Question

Objective of Study

• We investigate how the trade regulation of the invasive species

affects the optimal choice for an apple grower under the pest

quarantine program and assess its economic impact on the

grower’s economic profits.

Contribution

• Provide tailored information of economic profits at the individual

level in terms of the level of infestation of a grower’s orchard and

the existence of the trade regulation


The objective of a fruit grower is to maximize one’s economic profit

by choosing an optimal strategy of chemical use.

The model specifies the characteristics of two regions.


Quarantine

Level of Pest Infestation

Production Costs

Pest-Free

Level of Pest Infestation

Production Costs

Differ

The control variable is the rate of insecticide application and

the state variable is the insect stock.



High price for

intact fruitYield

Prob. of receiving

high price

YieldProb. of receiving

low price

Low price for

infested fruit

Storage costsOther production

related costs

Insecticide

application costs

+

-

Revenues

Objective Function

The insecticide applications detain the growth of the insect

population.

Insect population

Costs


Maximum principle condition

• Marginal cost of insect control = Marginal benefits from decreasing

a unit of the insect population

Comparative statics of the storage cost and the insecticide

application in a steady – state

• The sign is indeterminate, conditioned on assumptions.



Data

We calibrate the model using data on yield, cost, and price of ‘Red

Delicious’ spanning from 2009 to 2015.

• Red Delicious contributes 50% of total export in the state.

• Processed juice prices are applied for infested portion of annual yield.

Cost

• Unit cost of insecticide application – Average cost of chemicals

commonly used to control apple maggots (Assail, Imidan, etc.)

• Unit storage cost – Two export markets, BC and China, $0.56 per

40lb box

• Other production related cost – Establishment, maintenance, etc.

Note: The initial investment years are counted : no yield in the first two years, six-

years to reach full production.


• The WSDA traps apple

maggots from June to

September in thirteen

Washington State counties,

and reports the trap results.

• Number of detections /

orchard acreage, quarantine

vs. pest – free

Data

0

100

200

300

400

500

600

700

800

900

1000

2002 2004 2006 2008 2010 2012 2014 2016

Number of Apple Maggot Caught

Total detections Quarantine Pest free


Scenario

Base

Pest-free

Quarantined

Non-Regulation

Trade Regulation

Export to BC & China

10% of Yield

Export to BC & China

20% of Yield

We separately estimate a grower’s economic profits considering

four different scenarios.


Results

Profits/acre Loss/acre Loss/380 acres

$ -26,740 $490 1.9% $186,200

Profits/acre

$ -26,250Baseline

Scenario 2

Scenario 3

Scenario 4


$ -27,280 $1,030 3.9% $391,400


$ -27,750 $1,500 5.7% $570,000

Estimated profits by Galinato S. et

al. (2014) : $ -25,040

Note: The values are calculated for 7 establishment years.

The average size of a fam in Washington State is approximately 380

acres.


Future Works

We plan to consider only a year of full production to have the

effects to be more clear rather than accounting for the initial

investment year.

We will see if the simulation result follow our story by changing

the marginal storage cost more continuously.

The same simulations will be conducted for other varieties.


Thank you!

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