Elephants and Mammoths:

Can Ice Ivory Save Blood Ivory?∗

Naima Farah† and John R. Boyce‡

University of Calgary

This Draft: April, 2015

Abstract

We study how the presence of a substitute alters the exploitation path of an open access resource.

We model elephants, poached for ivory, as the endangered species, with mammoth ivory as a

non-renewable substitute. Our theory shows that the presence of the mammoth ivory substitute

reduces the elephant poaching rate. We find that if the initial elephant population is sufficiently

high, the mammoth ivory substitute ensures that the elephant population avoids extinction.

But for low initial elephant stocks, the mammoth ivory substitute only causes extinction to be

delayed, not avoided. Our empirical analysis concludes that the 84 tonnes of Russian mammoth

ivory exports per annum 2010-2012 reduced poaching from 85,000 elephants per year to current

poaching of about 34,000 elephants per year, and reduced elephant ivory prices by about $100 per

kilogram. Thus policies promoting mammoth ivory trade contribute to elephant conservation.

JEL Classification: Q21; Q28; Q38.

Keywords: endangered species, conservation, extinction, elephant ivory, economic substitutes.

∗We have benefited from comments by Julian Blanc, Herb Emery, Esmond Martin, Lucija Muehlenbachs, HoratiuA. Rus, M. Scott Taylor, Trevor Tombe, Lucy Vigne and from seminar participants at the University of Calgary,Department of Economics Summer Seminar Series, and at the 2014 Canadian Resource and Environmental EconomicsAnnual Conference, Saskatoon, Saskatchwan. All remaining errors are our own.†PhD Candidate, Department of Economics, University of Calgary, 2500 University Drive NW, Calgary, Alberta,

Canada T2N 1N4, +1(403)926-8286, nfarah@ucalgary.ca‡Professor of Economics, Department of Economics, University of Calgary, +1(403)220-5860, boyce@ucalgary.ca.

1 Introduction

While elephants have been harvested for their ivory since antiquity, during the 1980s, the poaching

rate became so severe that the African Elephant (Loxodonta africana) population more than halved

from about 1,300,000 to 600,000 animals between 1979 and 1989 (Barbier et al. 1990). This rapid

decline led to trade in African elephant ivory being declared illegal in 1989 under the Convention on

International Trade in Endangered Species (CITES).1 Yet continued poaching threatens elephants

with extinction. Between 2010 and 2014, Wittemyer et al. (2014) estimate that nearly 34,000

African elephants per annum were slaughtered for ivory out of a population believed to be between

430,000 to 680,000 (Elephant Database 2013). Currently, elephants are listed as “vulnerable” under

the International Organization for Conservation of Nature (IUCN) “Red List” of threatened species

(Blanc 2008). Swanson (1994) has characterized the main factors leading to extinction as: (i) open

access to resource, (ii) high price relative to cost of harvesting and (iii) low growth rate relative

to harvesting of the resource; the African elephant fulfils all of these characteristics. Since several

species have become extinct by human actions in the past, there is much concern for the plight of

the elephants.2

This paper examines the effect that the presence of a substitute has upon open access harvesting

of a renewable resource. Following the 1991 collapse of the Soviet Union, Russian mammoth

(Mammuthus primigenius) ivory has arisen as an important substitute for elephant ivory (Martin

et al. 2010, 2011). A large stock of mammoth tusks, perhaps as many as ten million carcasses,

lies beneath the Arctic tundra (Lister and Bahn 2007). Thus, mammoth ivory has the potential to

be an important substitute for elephant ivory. Substitutes have long played an important role in

elephant ivory. In the mid-19th century, an important use of ivory was to produce billiard balls.

Since one elephant tusk was required to make a single set of billiard balls, two New York suppliers

offered a $10,000 reward to whomever could find a viable substitute. This led John Wesley Hyatt in

1869 to invent nitrocellulose, the first industrial plastic, to substitute for ivory (Miodownik 2014).3

1Trade in Asian elephant (Elephas maximus) ivory was banned in 1975. The Asian elephant population is be-tween 41,410 to 52,345 (Sukumar 2003). The major threats to Asian elephants are habitat loss, degradation, andfragmentation (Leimgruber et al., 2003; Sukumar, 2003; Hedges, 2006). Poaching is less severe for the Asian elephantbecause some males and all female Asian elephants lack tusks (Dawson and Blackburn, 1991). However, like Africanelephants, Asian elephants are poached for other products as well, such as meat and leather, and poaching is a threatfor the long term survival of some Asian elephant populations (Kemf and Santiapillai, 2000; Menon, 2002).

2The population of passenger pigeon (Ectopistes migratorius), a wild North American bird, was 3 to 5 billion atthe time of arrival of the Europeans in North America, constituting 25 to 40 percent of the total bird population ofthe United States. With mass deforestation due to European settlement and commercial exploitation of pigeon meat,by the mid 1880s, the passenger pigeon completely disappeared (Yeoman, 2014; Bucher, 1999). Other examples ofextinction due to over-exploitation include the Dodo bird (Raphus cucullatus) (extinct in 1755), the near-extinctionof the American bison in the 1870s (Taylor 2011) and the Eastern Bowhead whale (1911) (Allen and Keay 2001,2004), and the extinction of the Tasmanian tiger (Thylacinus cynocephalus) (1933).

3Although Hyatt’s invention of nitrocellulose billiard ball was inspired by the prize, no evidence exists that he was

1

Most bioeconomic models for open access renewable resources, however, implicitly relegate

substitutes to the background by simply postulating a downward sloping demand curve for the

good being investigated (e.g., Gordon 1954, Clark 1973, Cropper 1988, Swanson 1994, and Kremer

and Morcom 2000). But these partial equilibrium approaches are inappropriate when substitutes

might play a transformative role in resource exploitation. The extinction of the sperm whale, for

example, whose oil provided bright, odourless illumination – in contrast to tallow candles and other

types of whale oil – was probably averted due to the development of kerosene (Fouquet and Pearson

2006). Since there often exists close substitutes for a renewable resource, kerosene for whale oil,

Viagra for Rhinoceros horn and other poultry for passenger pigeons, the question is what role

mammoth ivory may play in preventing the extinction of elephants.

Because mammoths are extinct, mammoth ivory is a non-renewable resource. Elephant ivory, in

contrast, is a renewable but exhaustible resource, produced by poachers. Using a general equilibrium

bioeconomic model, we explicitly analyze how poaching of elephants is affected when mammoth

ivory is consumed as a close substitute to elephant ivory. We then calculate how the elephant

population evolves with and without the mammoth ivory substitute. Not surprisingly, we find

that the presence of a mammoth ivory substitute lowers the demand for elephant ivory. This is

important, however, because extinction occurs only if the elephant population is reduced below a

certain minimum viable population level. We show that the presence of a substitute causes this

minimum viable population level to occur at a lower number of elephants. Thus a substitute can

save an endangered species from extinction.

In addition, we estimate the magnitude of the effect that the mammoth ivory substitute may

have upon elephant poaching over the post-ban period. We measure the effect of mammoth ivory

has had upon elephant poaching by controlling for the demand shocks from rising Asian income,

and controlling for institutional changes in Africa. Because mammoth ivory is often discovered

while mining for other resources, our analysis of the post-ban era uses measures of the mining

boom in Russia to instrument the mammoth ivory in a regression on elephant ivory interdiction

seizures.

We find that a one tonne (1000 kilograms) increase in Russian mammoth ivory exports causes

the seizures of illegal African elephant ivory to decrease by as much as 0.8 tonnes. Since an average

African elephant produces about 10 kilograms of ivory (Barbier et al. 1990), and since based on

kill estimates and seizures data, we estimate that less than 12% of poached ivory is seized, the

84 tonnes of Russian mammoth ivory exports produced on average per annum between 2010-2012

may have reduced elephant ivory harvesting by over 500 tonnes per year, which means it has saved

awarded it. This might be because nitrocellulose balls often exploded when they collided (Miodownik 2014). Billiardballs are now made from a strong crack-resistant plastic called phenolic resin.

2

over 50,000 elephants from being poached each year. Thus, absent mammoth ivory, the elephant

poaching may have been as high as 85,000 animals per year, more than two and a half times current

rates, and nearly as high as the rate just before the CITES ban on elephant ivory trade.4 We also

find that elephant ivory prices may have been reduced by 80$ to 120$ per kilogram as a result of

mammoth ivory trade. In addition, we examine the effect upon elephant ivory seizures and prices

of rising Chinese income, African institutional quality, and of permitted elephant ivory sales.

This paper contributes to the renewable resource literature (e.g., Gordon 1954, Clark 1973,

Cropper 1988, Swanson 1994, Kremer and Morocom’s 2000) by explicitly considering the role a

substitute plays on the extraction of an open access renewable resource both theoretically and

empirically. The paper also extends a growing literature on the ivory trade and the effect of the

CITES ban (e.g., Barbier et al. 1990, Dawson and Blackburn 1991, Bulte et al. 1999, Kremer and

Morcom 2000, Kemf and Santiapillai 2000, Menon 2002, Leimgruber et al. 2003, Sukumar 2003,

Hedges 2006, Martin et al. 2010, 2011, Wittemyer et al. 2014), by providing empirical evidence as

to the magnitude of the substitution effect mammoth ivory has had upon elephant poaching.

The remainder of the paper is organized as follows. Section 2 provides a description of the

events leading up to the CITES ban on elephant ivory trade and of the events subsequent to the

ban. Section 3 presents the general equilibrium theoretical model, which is used to motivate the

empirical analysis. Section 4 presents the empirical analysis of the effect of mammoth ivory upon

elephant poaching and provides estimates of the magnitude of the substitution effect mammoth

ivory has had upon elephant poaching and prices. Section 5 concludes.

2 Background

This section provides background on elephant and mammoth ivory developments post-World War

II, with the discussion divided between the pre-ban and post-ban eras. Detailed descriptions of the

data sources, summary statistics and correlations, are contained in the Appendix.

Pre-Ban Ivory Trade

Prior to the CITES ban on trade in elephant ivory in 1989, the 30 African countries with elephant

populations all exported elephant ivory. The CITES ban was the third step in a series of tightening

restrictions on elephant ivory trade (Barbier et al. 1990). The first CITES action was the 1976

listing of elephants under CITES Appendix II, which required exporters to issue certificates for

exports and importers to demonstrate that their imports had certificates. This, however, had the

4In the pre-ban period, 1950 to 1988, approximately 55,000 African elephants were killed for ivory per year onaverage and around 100,000 animals were killed per year at the peak during the 1980s.

3

Figure 1: Pre-Ban African Elephant Ivory Exports, Average African Polity Index, and Number ofIndependent African Countries, 1950-1988.

important loophole that only raw ivory required certificates, so exporters began to work the ivory

sufficiently to avoid the need for a certificate. In response, in 1986, CITES further tightened control

over ivory trade by forcing the exporting countries to submit a Management Quota System before

they were issued export certificates. This system collapsed largely because the exporting countries

were unwilling to restrict their own exports when the verification of other’s exports was not possible.

With the African elephant population diminishing from between three and five million in the 1930s

and 1940s (World Wildlife Fund, African elephant) and then more than halving from 1,343,100

in 1979 to 622,700, in 1989, all commercial trade in elephant ivory was banned by listing African

elephant ivory in Appendix I of CITES (Barbier et al. 1990).

Estimates of the total African elephant ivory exports from are presented in Fig. 1. African

ivory exports per year averaged about 300 tonnes in the 1950s, 420 tonnes in the 1960s, 700 tonnes

in the 1970s, and nearly 800 tonnes by the 1980s, reaching its peak of 1,162 tonnes in 1980. Barbier

4

et al. (1990) note that because average tusk size was decreasing as the large male elephants were

eliminated by poachers, the decline in the late 1980s in ivory production may not have corresponded

to a decline in animals killed by poachers.5 In addition, the decline in exports after 1986 is in part

due to exporting countries switching to illegal trade rather than submitting management quotas

from CITES.

While part of the rise in ivory exports was due to the post-war boom driving up demand, there

were also important supply side effects. As late as 1950, only four African countries had achieved

independence from their colonial rulers (Egypt, South Africa, Ethiopia, and part of Morrocco),

and of these only South Africa had sizeable ivory exports. But by 1960 there were twenty seven

independent countries, and by 1975 the last of the colonial powers had relinquished its control.

Thus, the four decades preceding the CITES ban corresponded to a time of great political upheaval

in Africa. This is also shown in Figure 1, which, in addition to ivory exports, plots the number

of independent African countries and the African average an index of government quality, “Polity

2.” The Polity 2 index ranges between -10 and +10 in value, with positive values corresponding

to political systems that are competitive in their democratic selection of government and negative

values corresponding to more autocratic systems. Throughout the pre-ban period, the average

African country was more autocratic than democratic in nature, and the degree to which this was

true was increasing, as can be seen by a generally decreasing trend in the Polity 2 index. From

1960 forward, the number of independent countries rose gradually, and the average African Polity 2

index gradually fell. This may have played an important role for the rise in elephant ivory exports

during the pre-ban period.

Post-Ban Ivory Trade

Following the 1989 CITES ban on elephant ivory trade, the Elephant Trade Information System

(ETIS), a sub-organization of CITES, began monitoring illegal trade in elephant ivory and ivory

related products. In spite of the elephant ivory trade ban, ETIS data show that ivory seizures

averaged over twenty tonnes per year since 1989, and seizures 2009-2012 have increased to 39.4

tonnes per year on average. However, since not all illegal ivory trades are seized, the ETIS ivory

seizure data represents only a fraction of poaching. For example, using the estimate by Wittemyer

et al. (2014) that 33,630 elephants were killed per year during 2010-12, and comparing that with

the average of 39.4 tonnes of ETIS seizures, suggests that only about 11.7% of the poached ivory

was seized.

Figure 2 shows the post-ban African elephant ivory seizures for the period 1989-2013. The solid

5According to statistics from Shoshani (1992, p. 73), average tusk weight of the African elephant was 26 lbs. 7oz. (12 kg.) in 1970, but only 6 lbs. 10 oz. (3 kg.) in 1990.

5

Figure 2: Post-Ban Elephant Ivory Seizures, Average Seizures Weight, and Large Seziures Weightas Percent of Total Seizures, 1989-2013.

line is ETIS seizures. The dashed line from 1996-2011 is the Underwood et al. (2013) reconstruction

of seizures.6 The long-dashed line is average seizure weight per elephant ivory seizure, and the short-

dashed line is the percentage of seizures from seizures in which 500 kilograms or more was seized.

The seizures data suggests that elephant poaching is on rise. This could be the result of a rise in

ivory demand, stricter law enforcement, or both.7 In the empirical section below, we attempt to

sort out these different effects. Peaks in average seizure size and in proportion of large seizures

correspond to the peaks in total weight seized.

6Underwood et al. (2013), examined how the ETIS seizures varied with factors such as rule of law across Africancountries. Their data series, which excludes seizures for which they could not determine the weight of seizures, isalmost perfectly correlated with the raw ETIS seizures data (r = 0.97, p < 0.01), as can be see in Figure 2.

7Milliken et al. (2004, 2012) and Milliken (2014) caution against reading too much into the trend in ETISseizures, since the number of countries reporting seizures and the quality of seizures reporting have changed overtime. However, the trend in the Underwood et al. (2013) reconstruction, which attempts to extract the true trend,is highly correlated with the ETIS seizures.

6

Figure 3: Post-Ban African Elephant Population and Range Estimates, 1995-2013, and Proportionof Illegally Killed Elephants, 2002-2013.

Figure 3 shows several estimates of the elephant population in Africa, the effective range of

elephants, and the median proportion of elephant carcasses found to have been killed illegally across

a number of sites in Africa. The elephant population numbers range from definitely observed, to

probably exist, to possibly exist, to the highest possible speculated population. This data is based

on surveys conducted in 1995, 1998, 2002, 2007, and 2013. All estimates concur that the population

was rising in the decade prior to 2007, but has been declining since. Interestingly, the decade of

rising population was associated with a declining range. In addition, the Proportion of Illegally

Killed Elephants (PIKE), a carcass survey data by Monitoring The Illegal Killing of Elephants

(MIKE), shows that the median proportion (across a number of sites throughout Africa) of elephant

carcasses which were killed illegally has been sharply increasing since 2009.8

8The number of illegally killed elephants reported in PIKE are downward biased as not all illegally killed elephantscarcasses are found, not all illegally killed elephants are reported, and the PIKE surveys cover only part of Africa.

7

Figure 4: Post-Ban Russian Mammoth Ivory Exports and Hong Kong & China Mammoth IvoryImports, 1988-2013.

Mammoth ivory, from the large woolly elephant which became extinct around 10,000 years ago,

has become an important source of ivory in the post-ban era. It is estimated that nearly 50,000

mammoths have been excavated in the 250 years since Siberia became a part of Russia in the

17th century (Lister and Bahn 2007). Mammoth ivory can be crafted in the same way as elephant

ivory, competes with the elephant ivory-crafted artefacts and is demanded by ivory customers. A

stock of around 10 million mammoth carcasses are thought to lie beneath the permafrost in the

Arctic tundra (Lister and Bahn 2007). These are exploited by the mammoth tusk hunters every

summer. Although mammoth hunters are required to have permit from the Russian government to

sell mammoth ivory, many mammoth hunters operate without a valid permit (Larmer 2013); thus,

mammoth ivory can be considered as an open access resource.

According to Wittemyer et al. (2014) around 33,600 elephants were illegally killed in Africa each year for 2011-2013,whereas PIKE data reports from 2011-2013, each year on average counted around 1,000 elephants illegally killed.

8

In Figure 4, the solid line shows Russian mammoth ivory exports for the period 1988 to 2013.

Data on mammoth ivory trade is collected from UN COMTRADE database as the sum of the rest

of the worlds’ ivory imports from Russia.9 Russian mammoth ivory exports have been increasing

steadily, averaging approximately 17 tonnes per year for 1991-2000 and averaging sixty tonnes per

year for 2001-2013.10 The dashed line shows Hong Kong and China’s combined mammoth ivory

imports.11 Hong Kong and China’s combined average annual mammoth ivory imports account for

over 95% of total Russian exports since the mid 1990s. Mammoth ivory trade suffered a more than

50% decline in the Great Recession.

In the post-ban era, some elephant ivory trade has been permitted. This includes hunting

trophies, privately owned ivory crafts, government confiscated stockpiles, and ivory from elephants

killed before the 1989 ban. In addition, there were two CITES approved African elephant ivory

auctions. In 1999, 50 tonnes of elephant ivory was auctioned to Japanese dealers at an average

price of 103$/kg. In 2008, 101.8 tonnes ivory was auctioned at an average price of 157$/kg,

where Chinese dealers bought 62 tonnes and Japanese dealers bought the rest (Stiles 2009, CITES

press release). Figure 5 shows permitted elephant ivory trade in the post-ban era. The solid line

shows permitted trade from the UN COMTRADE data, which equals the sum of the non-African

countries imports from Africa.12 The dashed line shows permitted trade data from the CITES

Trade database. On average legal exports of elephant ivory to the rest of the world since 1989 have

averaged approximately 31 tonnes per year. These variables equal the sum of the African countries

exports to the non-African countries. Based on the data from CITES Trade database, permitted

trade in African elephant ivory averaged approximately 30 tonnes per annum, which is very close

to UN COMTRADE average. The two spikes in the permitted trade in African elephant ivory

represents CITES approved ivory sales in 1999 and 2008.

Figure 6 compares (nominal) elephant and mammoth ivory prices, both pre- and post-ban,

collected from several sources. The vertical line corresponds to the 1989 CITES ban on elephant

ivory trade. Pre-ban prices from Barbier et al. (1990) (left-scale) approximately doubled in the

decade leading up to the ban on ivory trade. Post-ban data from UN COMTRADE are export

prices (left-scale) based upon dividing total value of exports by the total quantity of exports. These

9We used the rest of the world’s imports from Russia rather of Russian exports because Russian export statisticswere not available for all years and partner countries.

10Given that an average mammoth tusk weighs between nine and 45 kg (Lister and Bahn 2007), 60 tonnes of mam-moth ivory is equivalent to between 1,300 and 6,600 mammoth carcasses. If Russia exports sixty tonnes mammothivory every year, it would take approximately 150 to 750 year to exhaust the ten million mammoth carcass stock.

11As Hong Kong’s mammoth ivory import statistics is available after 1995 and China’s import statistics is availableafter 1991, therefore for 1992-95 the summed Hong Kong and Chinese mammoth ivory imports equals Chinesemammoth ivory imports from Russia.

12We also obtained this data as African countries legal exports of elephant ivory, but that data shows unusuallylow trend with high volatility.

9

Figure 5: Post-Ban Permitted African Elephant Ivory Exports (Tonnes), 1989-2013.

show that elephant ivory prices were very high right after the ban, much lower in the mid 1990s,

and have started to rise again in the late 2000s. These prices, however, are based upon imputed

values from small quantities of sales. Mammoth ivory prices, which are based upon larger and

more continuous quantities of sales, were very volatile until the mid 1990s, when they settled down

to around $50 per kilogram. Like elephant ivory prices, mammoth ivory prices started to rise in

the late 2000s. A second source of data is from market surveys conducted by Martin et al. (2006,

2011, 2014). These are the average from market surveys (see Table 7 in the Appendix) done in

1987, 2002, 2004, 2006, 2009, 2010, and 2014. These averages (right-scale) are substantially higher

than the UN COMTRADE prices. But both sets of series show that elephant and mammoth ivory

prices have sharply increased in the late 2000s.

An important implication of the variation in prices and production of mammoth and elephant

ivory in the post-ban era is that mammoth and elephant ivory are imperfect but close substitutes,

since both were being consumed in positive quantities even as prices varied. This implication is

10

Figure 6: Elephant and Mammoth Ivory Prices.

explored in the theoretical analysis to which we now turn.

3 Theoretical Model

We assume mammoth ivory is an imperfect substitute to elephant ivory. Both poachers and mam-

moth hunters are assumed to compete under conditions of open access. But as mammoths are

extinct, it is a non-renewable resource. We ignore the possibility of storage of elephant ivory (e.g.,

Kremer and Morocom 2000), since the illegal nature of elephant ivory means that the holder of

elephant ivory stores would need to be compensated not just for the interest foregone, but also for

the added interdiction risk that holding the ivory would entail.

11

Model Assumptions

The elephant population growth rate is the difference between the net birth function G(x) and

harvest rate h:dx(t)

dt= G[x(t)]− h(t), x(0) = x0, (1)

The net birth function G(x) has the properties that G(x) > 0 for 0 < x(t) < K, that G(x) < 0

for x(t) > K, and that G(0) = G(K) = 0, where K is the carrying capacity of the population.

Therefore, absent poaching, the elephant population is viable for all x(t) > 0.13

The stock of mammoth ivory at time t is S(t). Since the stock is exhaustible with mammoth

tusk collected at rate y, the stock S declines according to

dS(t)

dt= −y(t), S(0) = S0. (2)

Production

Assume that there are two goods in the economy, ivory I, and all other goods z. There are, however,

two types of ivory, elephant ivory h, and mammoth ivory y. For each good, there is only one factor

of production, labor. The total labor endowment L is allocated among the three sectors. So the

labor market clearing condition is:

L = Lh(t) + Ly(t) + Lz(t). (3)

The production functions for the harvest rate h, mammoth tusk collection rate y, and all other

goods production z are each constant returns to scale. These production functions are:

h(t) =Lh(t)

c[x(t)], y(t) =

Ly(t)

m, z(t) = Lz(t). (4)

One unit of h requires c(x) units of labor; one unit of y requires m units of labor; and one unit of

z requires 1 unit of labor. The unit labor requirement for elephant ivory harvesting, c(x), has two

sorts of cost embedded in it, the production cost and the cost of bearing the risk of extracting a

resource illegally. Both of these costs are assumed to be decreasing with x. A larger stock makes

the prevention of poaching more difficult, and makes it easier to find animals to poach. Thus

c′(x) < 0. In addition, c(x) is bounded so that the maximum marginal cost of poaching elephant

is c(0) = c <∞. This assumption is crucial to the determination of whether extinction is possible,

since the demand functions must be such that willingness to pay (WTP) exceeds harvesting costs

13Alternatively, if the population exhibits critical depensation, then G(x) < 0 for some 0 < x(t) < x, then xrepresents the minimum viable population.

12

as the population approaches zero. For simplicity, we assume finding one mammoth tusk does not

affect the probability of finding another tusk. Thus the labour requirement for mammoth tusk

collection rate m is constant.

Firms pay their workers wage w per unit of labor and earn ph for elephant ivory, pm for mammoth

ivory, and pz for all other goods. Thus profits for each sector are given by:

Πh = phh− wLh = phLhc(x)

− wLh,

Πy = pyy − wLy = pyLym− wLy,

Πz = pzz − wLz = pzLz − wLz,

(5)

where the values of h, y, and z are substituted from equation (4). Firms take their prices as given.

Free entry implies that for wage w = 1, the equilibrium prices are,

ph = c(x), py = m, and pz = 1. (6)

Consumption

Each representative consumer’s utility is given by

Ui =

[(h

L

)σ−1σ

+( yL

)σ−1σ

+( zL

)σ−1σ

] σσ−1

, σ > 1. (7)

The parameter σ is the elasticity of substitution among the goods. We assume that σ > 1, so that

these goods are each-others substitute.14

Each L identical individuals, supplies one unit of labor to the firms and earns wage, w = 1 for

each unit of labor. Each individual’s budget constraint is therefore

1 = c(x)h

L+m

y

L+z

L(8)

Each individual chooses their consumption of elephant and mammoth ivory and the numeraire good

to maximize (7) subject to (8). Solving the optimization problem yields the equilibrium aggregate

demand functions for elephant ivory, mammoth ivory and other goods, as function of the prices

14If the goods were instead complements, σ < 1, then either extinction of elephants or exhaustion of mammothivory would cause utility to go to zero, since no amount of the numeraire good z could compensate for the lost ivory.Since extinctions have occurred in the past for animals consumed by humans, this seems implausible.

13

c(x) and m:15

h∗(x) =Lc(x)−σ

1 + c(x)1−σ +m1−σ , y∗(x) =Lm−σ

1 + c(x)1−σ +m1−σ , z∗(x) =L

1 + c(x)1−σ +m1−σ .

(9)

Absent a substitute, i.e. when m→∞, the aggregate demand for elephant ivory becomes:

h0(x) =Lc(x)−σ

1 + c(x)1−σfor σ > 1. (10)

Characterization of Equilibrium Harvest

In this section we discuss some characteristics of the equilibrium demand functions in the presence of

a substitute. First, both types of ivory demands are decreasing in their own prices. Differentiating

h∗(x) with respect to c(x) yields

dh∗(x)

dc(x)=

−Lc(x)−σ

(1 + c(x)1−σ +m1−σ)2[σc(x)−1(1 +m1−σ) + c(x)−σ

]< 0.

Similarly, differentiating the mammoth ivory collection rate with respect to collection cost m yields:

dy∗(x)

dm=

−Lm−σ

[1 + c(x)1−σ +m1−σ]2[σm−1(1 + c(x)1−σ) +m−σ

]< 0.

Second, both types of ivory demands are increasing in other ivory’s price when they are sub-

stitutes (σ > 1). Differentiating the elephant ivory demand with respect to the cost of mammoth

tusk collection rate gives, dh∗(x)dm = − (1−σ)m−σLc(x)−σ

[1+c(x)1−σ+m1−σ ]2 T 0 as σ T 1, which says if σ > 1, so

that mammoth and elephant ivory are substitutes, elephant harvesting rises when mammoth ivory

collection costs rise. Similarly, the effect of the change in the cost of harvesting elephants, c(x), on

equilibrium mammoth ivory collection rate is dy∗(x)dc(x) = − (1−σ)c(x)−σLm−σ

[1+c(x)1−σ+m1−σ ]2 T 0 as σ T 1, which

says mammoth ivory demand is increasing in the elephant harvesting cost. This implies that the

harvest rate of a resource increases when the cost of producing the substitute good rises.

Next, we show the effect having a substitute has upon demand:

Proposition 1. Equilibrium demand for elephant ivory is lower when the mammoth ivory substitute

exists.

15The equilibrium amount of labor used in each sector can be calculated from the production functions from (4):

L∗h = Lc(x)(1−σ)

1+c(x)(1−σ)+m(1−σ) , L∗y = Lm(1−σ)

1+c(x)(1−σ)+m(1−σ) , and L∗z = L

1+c(x)(1−σ)+m(1−σ) . Summing L∗h, L∗y, and L∗z leads to

L which shows that all individuals are employed and each sector uses a portion of the total labor, L.

14

Proof.

h∗(x)

h0(x)=

Lc(x)−σ

1+c(x)1−σ+m1−σ

Lc(x)−σ

1+c(x)1−σ

=1 + c(x)1−σ

1 + c(x)1−σ +m1−σ < 1 for all 0 < m <∞.

Thus in the presence of a substitute, the harvest rate h∗(x) of a resource is lower than the

harvest rate h0(x) when there is no substitute. This is true even as elephants approach extinction,

as the following shows:

limx→0

h∗(x)

h0(x)=

1 + c1−σ

1 + c1−σ +m1−σ < 1 for all 0 < m <∞.

Effect of Population Size on h∗(x) and y∗(x)

Next, we consider the effect of the elephant population size has upon the elephant ivory harvest

rate and the mammoth tusk collection rate.

The elephant harvest rate is increasing in the elephant population stock. Differentiating the

equilibrium harvest rate from (9) with respect to population stock x(t) gives:

dh∗(x)

dx=dh∗(x)

dc(x)︸ ︷︷ ︸<0

c′(x)︸︷︷︸<0

> 0, wheredh∗(x)

dc(x)< 0.

This result says the larger is the stock, the higher is the poaching rate, all else constant. In

contrast, dy∗(x)dx = dy∗

dc(x)c′(x) < 0, which means mammoth ivory collection rate is a decreasing

function of elephant population stock, since dy∗(x)dc(x) > 0 when the goods are substitutes.

Elephant Population Dynamics

Now, we can turn to an analysis of how the elephant population dynamics are affected by the

presence of a substitute. In Figure 7, G(x) is the net birth function and h∗(x) and h0(x) are the

harvest rates with and without the substitute, respectively. A steady-state, dx/dt = 0, occurs

when demand equals net births. The values x∗u (x0u) and x∗s (x0s) correspond to the unstable and

stable steady states for demand h∗(x) (h0(x)), respectively.16 In the absence of mammoth tusk

collection, the elephant harvest rate is h0(x) and the system evolves as described in Kremer and

Morcom (2000)’s poaching-without-storage scenario. When there is no mammoth tusk collection,

16If there is a critical depensation level of stock x such that for xε[0, x), that G(x) < 0, then x < x∗u < x0u, so theanalysis below holds for this case as well.

15

Figure 7: Elephant Population Growth and Equilibrium Harvest Rates.

the demand for ivory is satisfied only by elephant harvesting. With no storage of elephant ivory,

the elephant population growth rate is the difference between net growth function G(x) and the

harvest rate h0(x), x = G(x) − h0(x). There are thus three possible steady-state levels of the

elephant population. If the initial level of population x0 is less than x0u, then the population will

become extinct since h0(x) > G(x) in this interval causing x < 0. If x0 is in the interval (x0u, x0s],

the growth rate of the elephant population is positive (x > 0) and when x > x0s, the growth rate of

the elephant population is negative (x < 0). Thus for x0 > x0u the elephant population will always

move towards the stable steady state x0s. Finally, if x0 is exactly at x0u, the elephant population

remains there forever since at x = x0u, x = 0.

In the presence the of mammoth ivory substitute, the equilibrium level of harvest rate of ele-

phants is h∗(x) and the equilibrium level of mammoth tusk collecting rate is y∗(x). So long as

both stocks are positive, h∗(x) and y∗ are functions of c(x) and m. Once the mammoth ivory is

exhausted, however, the demand for elephant ivory switches to h0(x), since S → 0 is equivalent to

m → ∞. At time t = 0, the initial elephant population is x0 and mammoth ivory stock is S0. If

the mammoth ivory stock is exhausted at time t = Ts, then x(Ts) represents the elephant stock

16

from where the elephant population starts to approach it’s final equilibrium. The steady state level

of x after the mammoth tusk stock is exhausted depends on the position of x(Ts) relative to x0u: if

x(Ts) > x0u, then x0s is the steady state; if x(Ts) = x0u then x0u is the steady state; and if x(Ts) < x0u,

then the elephant population goes extinct.

Now consider how the equilibrium evolves for different initial elephant stock x0 given a positive

stock S0 for mammoth ivory.

Proposition 2. When there exists a mammoth ivory substitute to elephants, then for initial ele-

phant population stock x0 ∈ (x∗u, x0u] and for sufficiently high substitute mammoth stock S0, the

elephant population will avoid extinction. However, if x0 ≤ x∗u < x0u, extinction occurs even when

there is a substitute, and if x0 > x0u > x∗u, the elephant population converges to the stable steady

state x0s for all mammoth stocks.

Proof. When x0 < x∗u:

If x0 < x∗u, then h∗(x) > G(x), which implies that x < 0. With this level of initial elephant popu-

lation extinction will occur absent a policy intervention. It may occur even before the mammoth

ivory stock is exhausted.

When x0 = x∗u:

If x0 = x∗u, then h∗(x) = G(x). But at t = Ts, when S(Ts) = 0, the harvest rate becomes h0(xu)

and thus for t ≥ Ts, x < 0 and the elephant population eventually goes extinct.

When x0 ∈ (x∗u, x0u):

For x0 ∈ (x∗u, x0u], x = G(x) − h∗(x) > 0. If S0 is large enough so that x(Ts) > x0u, where

S0 =∫ Ts0 y∗(x)dt implicitly defines Ts, then since x > 0, dc(x)

dt < 0 implying that dy∗(x)dt > 0. For

a given x ∈ (x∗u, x0u], a higher S0 implies a larger time t, which implies a larger x(Ts). When

x0 < x∗u < x0u, x = G(x) − h∗(x) < 0 ∀t and when x0 > x0u > x∗u, x = G(x) − h∗(x) < 0 until

x = xs.

When x0 > x0u:

If x0 > x0u, then h∗(x) < G(x), which implies that x > 0. Thus, for all initial mammoth ivory

stocks, the elephant population will eventually converge to x0s.

To summarize, the theoretical model shows that the presence of a substitute causes demand to

shift down to h∗(x) from h0(x), and this causes the unstable steady-state x∗u to be at a lower level

17

than x0u, which means that for sufficiently large stock of mammoth, the presence of a substitute

may save elephants from extinction if x0 ∈ (x∗u, x0u].

4 Empirical Analysis

Now we turn to an empirical analysis of the post-ban ivory market. Our objective is to evaluate the

effect of mammoth ivory production on African elephant ivory harvest and elephant ivory prices.

Methodolgy

In principle, having both price and production data, we would like to estimate the equilibrium

demand functions (9). However, the problems of doing this are insurmountable since both mammoth

and elephant prices are endogenously determined. Therefore, we instead approach this problem by

writing down reduced form equations in which elephant ivory seizures and elephant ivory prices

are a function of mammoth ivory production. This allows us to reduce the potential endogeneity

problems to only mammoth ivory being an endogenous right-hand-side regressor.

For the post-ban period, we observe African elephant ivory seizures. The reduced form regres-

sion for the post ban period elephant ivory seizures is

African Elephant Ivory Seizurest = α0 + α1 Russian Mammoth Ivory Productiont

+ α2 Institutional Qualityt + α3 China Per Capita GDPt + α4 Legal Harvestt + εt,

t = 1989, . . . , 2013. (11)

These regressions include as explanatory variables Russian mammoth ivory exports, measures of the

institutional quality of the African countries, China GDP per capita, and the tonnage of permitted

trade in African elephant ivory.

The second regression we run attempts to determine the effect of Russian mammoth ivory upon

African elephant ivory prices. The reduced form equation is

African Elephant Ivory Pricet = β0 + β1 Russian Mammoth Ivory Productiont

+ β2 Institutional Qualityt + β3 China Per Capita GDPt + β4 Legal Harvestt + εt,

t = 1989, . . . , 2013, (12)

and the right-hand-side variables are the same as in the seizures equation. In each case, we assume

that all other factors which might explain elephant seizures or prices are left in the error terms,

where we assume that E(εt) = 0.

18

Mammoth Ivory

Since our objective is to determine the effect that Russian mammoth ivory exports have had upon

elephant ivory poaching, Russian mammoth ivory exports is the key variable of interest in the

post-ban era. But because mammoth and elephant ivory are substitutes, elephant ivory harvests

may also affect mammoth ivory exports, generating the potential for reverse causality. To avoid

this problem we use an instrumental variable approach, where we find a variable which is correlated

with mammoth ivory exports, but not with elephant ivory poaching, to instrument mammoth ivory

exports. Two instruments, the real gold price and the Russian mining share of GDP, were suggested

in a CBC News interview with Canada Fossils president Pierre Parre, who noted that “the global

mining boom. . . is feeding the mammoth boom,” and that ivory prices were inversely correlated with

the gold price.17 As miners search for gold and other minerals in Siberia, they discover mammoth

tusks, thereby increasing Russian mammoth ivory exports. Figure 8 shows how Russian mammoth

ivory exports correlate with Russian mineral rents and the real gold price. Russian mammoth ivory

exports are highly correlated both with Russian mineral rents (r = 0.896, p < 0.01) and with the

real gold price (r = 0.625, p < 0.01).18

Control Variables

The first control variable used in our analysis is the China GDP per capita variable. While China

is not the only consumer of ivory, the rapid rise in China GDP per capita has been identified (e.g.,

Martin et al. 2006, 2010) as a contributor to the continued poaching of elephants. Furthermore,

we noted in Figure 4 that over 95% of mammoth ivory exports have gone to China and to Honk

Kong, indicating that this is where mammoth and elephant ivory most interact.

Second, we control for institutional quality in our empirical analysis in two ways. The “Polity

2” index measures the average level of democracy across African countries, where each countries’

index is a weighted average of its electoral competitiveness and openness, the nature of political

participation, and the extent of checks on executive authority. Polity 2 index values range from

−10 to +10, with more autocratic countries having lower numbers and more democratic countries

having higher numbers. The second measure, which is only available from 1996 forward, is the

“Rule of Law” index, measured as the minimum rule of law index across African countries.19 The

rule of law index probably most directly measures institutional quality, since the worst parts of

17See Peter Evans, “Mammoth ivory trade raises fears for elephants,” CBC News, 29 September, 2010.18See Table 4 in the Appendix for the complete correlation matrix for post-ban variables.19The average rule of law across African countries has less variation than does the minimum rule of law. The

minimum rule of law also is more plausible than the average rule of law as it is likely that the channels throughwhich illegal exports occur are through areas with the weakest institutions. We could not use the minimum Polity 2variable, however, as it had almost no variation, equalling −9 or −10 (the lowest possible values) in every year.

19

Figure 8: Russian Mammoth Ivory Exports, and the Instruments, Russian Mineral Rents and theReal Gold Price, 1989-2013.

Africa are likely to offer the channels through which poachers export their products.

To see more clearly how the post-ban seizures data is correlated with the different explanatory

variables, we plot in Figure 9 the OLS residuals from regressing the seizures data upon China GDP

per capita.20 The unexplained portion is the variation in elephant ivory seizures which cannot

be explained by demand-side conditions. We see that there exists a strong negative correlation

between the seizures residuals and Russian mammoth ivory exports. There is a weaker positive

correlation between seizures residuals and the Polity 2 and minimum rule of Law measures.

Variables not included in the regressions include the elephant population and range, and the

20The regression is

ETIS Seizures =5.75

(3.19)+

3.49(0.92)

China GDP per Capita, R2 = 0.48,

with robust standard errors in parentheses.

20

Figure 9: Seizures, Mammoth Ivory Exports, and Institutional Quality.

proportion of illegally killed elephants. We also could not use the price series from the Martin

surveys. Each of these variables were only available for a few years.

Now, let us turn to the econometric results.

Post-Ban Elephant Ivory Seizures Results

First, let us turn to the post-ban regression in which the central focus is the effect of Russian

mammoth ivory exports upon seizures of elephant ivory. We estimate this effect using both ordinary

least squares and instrumental regressions of elephant ivory seizures on Russian mammoth ivory

exports. Control variables include China GDP per capita, two measures of institutional quality

(Poliy 2 and Rule of Law), and legal elephant ivory exports.21 The instrumental regressions use

the real gold price and Russian mineral rents to instrument Russian mammoth ivory exports. The

21Since legal elephant ivory trade is a substitute to elephant seizures, higher elephant ivory seizures can increaselegal elephant ivory trade. This endogeneity is not solved in our analysis.

21

F -statistic for the test of the hypothesis that the instruments are jointly equal to zero in the first

stage regression and the Sargan J-Statistic for the test of the hypothesis that the instruments are

uncorrelated with the error terms are also presented.

The regression results are reported in Table 1 panels A and B, for the OLS and instrumental

variable regression results, respectively. Since theory does not guide us as to which institutional

quality variables or which of the two sources for legal ivory trade should be included, we report

several specifications. All specifications include China GDP per capita and Russian mammoth

ivory exports. Column (1) contains just these two variables; column (2) adds Polity 2; columns

(3) and (4) include the Polity 2 index and African permitted trade in elephant ivory from CITES

trade database and UN COMTRADE database respectively; columns (5) through (7) repeat this,

using the Rule of Law instead of Polity 2 to control for institutional quality.

In both OLS and IV regressions, Russian mammoth ivory exports reduce elephant ivory seizures.

In the OLS regressions, the estimated coefficients of Russian mammoth ivory range between -0.17

and -0.35. In the IV regressions, the negative correlation between elephant ivory seizures and

Russian mammoth ivory exports is larger in magnitude, though not when the Rule of Law variable

is included, with elephant ivory seizures decreasing by between -0.59 to -0.77 tonnes per tonne of

mammoth ivory.

The first stage IV regressions of the instrumental regressions are shown in Table 5 in the

Appendix. These regressions show that the real gold price is negatively correlated with Russian

mammoth ivory exports and the Russian mining rents share of GDP are positively correlated.

Jointly, we may reject the null hypothesis that Russian mammoth ivory exports are unrelated to

these two instruments (except in column (2)), though the instruments are “weak” in the sense

that the F -statistic for their joint significance is less than 10 in value.22 The Sargan test that the

instruments are uncorrelated with the error terms cannot be rejected at the 95% confidence level

in any specification. Other notable first stage results are that Russian mammoth ivory exports are

positively correlated with China GDP per capita, and are negatively correlated with legal elephant

ivory sales.

22When only one of the instruments is used in the regression, the coefficients are statistically significant in all buttwo cases, indicating that the high correlation (r = 0.705, p < 0.01) between the real gold price and Russian miningshare may contribute to the lower levels of significance of individual coefficients. Also, the real gold price is highlycorrelated with China GDP per capita (r = 0.769, p < 0.01), which may account for the negative coefficient on realgold price.

22

Table 1: Post-Ban Elephant Ivory Seizures Regression Results, 1989-2013

A. Ordinary Least Squares Regression Estimates

(1) (2) (3) (4) (5) (6) (7)

Russian Mammoth Ivory (Tonnes) *-0.301 *-0.250 **-0.301 *-0.285 *-0.223 *-0.346 -0.167(0.109) (0.114) (0.1) (0.134) (0.087) (0.115) (0.146)

China GDP per Capita (1000s of 2005 USD) ***6.733 ***7.124 ***8.645 **7.525 ***8.316 ***9.339 ***7.906(1.722) (1.766) (1.739) (2.093) (1.317) (1.429) (1.639)

Polity2 Index (Mean of African Countries) -1.486 -3.015 -1.437(0.841) (1.857) (0.924)

Rule of Law (Minimum of African Countries) *37.53 27.45 *39.63(14.09) (13.54) (15.96)

Legal Elephant Ivory Trade (Tonnes, CITES) -0.076 -0.09(0.055) (0.065)

Legal Elephant Ivory Trade (Tonnes, Comtrade) -0.039 0.047(0.059) (0.087)

Constant 4.251 0.394 -1.794 1.362 *78.66 58.64 *81.06(3.036) (4.545) (5.089) (4.549) (29.45) (28.62) (31.39)

Observations 25 25 25 25 18 18 18F -Statistic ***9.464 ***6.825 ***8.561 ***7.168 ***15.477 ***14.753 ***11.001R2 0.547 0.544 0.592 0.528 0.699 0.723 0.682

B. Instrumental Variables Regression Estimates

(1) (2) (3) (4) (5) (6) (7)

Russian Mammoth Ivory (Tonnes) *-0.597 -0.768 **-0.772 *-0.755 -0.194 -0.362 -0.225(0.239) (0.433) (0.225) (0.344) (0.132) (0.218) (0.193)

China GDP per Capita (1000s of 2005 USD) **9.601 *9.851 ***12.236 **10.014 **6.665 **8.767 **6.969(3.017) (3.975) (2.829) (2.782) (1.661) (2.587) (2.215)

Polity2 Index (Mean of African Countries) 2.66 0.901 3.173(3.181) (2.178) (3.104)

Rule of Law (Minimum of African Countries) 28.142 23.634 25.029(13.994) (15.302) (16.034)

Legal Elephant Ivory Trade (Tonnes, CITES) *-0.207 -0.089(0.075) (0.081)

Legal Elephant Ivory Trade (Tonnes, Comtrade) -0.188 0.012(0.096) (0.075)

Constant 3.54 9.279 4.548 13.581 62.342 52.151 54.868(3.695) (7.633) (6.426) (7.991) (29.217) (32.115) (32.495)

Observations 23 23 23 23 17 17 17F -Statistic ***6.445 *2.853 ***5.797 ***8.808 ***6.908 **5.085 ***9.248R2 0.166 -0.109 0.337 0.096 0.561 0.56 0.513

F -Statistic (Instruments=0) **4.73 2.26 **4.77 ***6.75 **4.04 **4.43 **4.24Sargan J-Statistic, χ2(1) 0.051 0.707 0.008 0.040 *2.756 1.875 *3.544

Notes: Dependent variable: Elephant Ivory Seizures (tonnes). Robust Standard Errors in parentheses.The IV regressions use 1990-2012 data (23 observations), as the instrument Russian Mining Share of GDPis not available for 1989 or for 2013. The OLS regressions include these two extra years. The Rule ofLaw variable is only available from 1996, 1998, 2000, and 2002-2013. For the years 1997, 1999, and 2001the Rule of Law is interpolated as the average of the year before and year after. Significance levels: ***p < 0.01, ** p < 0.05, * p < 0.1.

23

Table 2: Post-Ban Elephant Ivory Price Instrumental Variable Regression Results, 1989-2013

(1) (2) (3) (4) (5) (6) (7)

Russian Mammoth Ivory (Tonnes) *-1.532 -0.74 -0.727 -0.926 *-0.958 -0.809 -0.529(0.659) (0.489) (0.532) (0.61) (0.407) (0.542) (0.401)

China GDP per Capita (1000s of 2005 USD) **24.372 ***22.087 *20.731* **22.804 **21.104 *18.897 **15.973(7.171) (5.127) (7.233) (6.168) (5.182) (7.427) (4.973)

Polity2 Index (Mean of African Countries) **-10.531 **-9.561 *-10.351(3.349) (2.973) (4.319)

Rule of Law (Minimum of African Countries) 11.705 20.177 20.779(24.544) (19.311) (15.943)

Legal Elephant Ivory Trade (Tonnes, CITES) 0.123 0.151(0.275) (0.282)

Legal Elephant Ivory Trade (Tonnes, Comtrade) 0.32 **0.577(0.206) (0.135)

Constant 3.405 -17.181 -14.616 *-22.555 17.352 36.574 20.02(9.075) (10.929) (10.267) (10.699) (51.216) (41.289) (35.289)

Observations 23 23 23 23 17 17 17F -Statistic ***11.09 ***13.298 ***10.4 ***49.393 ***9.159 ***7.554 ***86.471R2 0.443 0.609 0.612 0.651 0.601 0.608 0.788F -Statistic (Instruments=0) **4.73 2.26 **4.77 ***6.75 **4.04 **4.43 **4.24Sargan J-Statistic, χ2(1) 0.513 0.136 0.017 0.743 0.245 0.000 1.452

Notes: Dependent variable: Elephant Ivory Prices in nominal U.S. dollars per kilogram. Robust StandardErrors in parentheses. The IV regressions use 1990-2012 data (23 observations), as the instrument RussianMining Share of GDP is not available for 1989 or for 2013. The Rule of Law variable is only availablefrom 1996, 1998, 2000, and 2002-2013. For the years 1997, 1999, and 2001 the Rule of Law is interpolatedas the average of the year before and year after. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1.

In all OLS and second stage IV specifications in Table 1, Chinese GDP per capita is strongly

positively correlated with elephant ivory seizures, with an increase of $1000 USD of Chinese per

capita income causing between a six to twelve tonnes increase in elephant ivory seizures, depending

upon the specification. The Polity 2 variable is statistically insignificant in all specifications, and

with positive coefficients in the OLS equations but negative coefficients in the IV regressions.

The Rule of Law variable is positive in sign and statistically not different from zero in all IV

specifications and in column (6) of the OLS specifications. The estimated coefficients imply that a

one unit increase in the minimum African Rule of Law index corresponds to seizures of between 24

to 40 tonnes of additional poached elephant ivory. Finally, the two measures of legal elephant ivory

trade are mostly statistically insignificant, but are negative in sign in all but one specification [OLS

column (7)]. Nevertheless, the negative sign suggests that legal sales may have reduced poaching.

Post-Ban Elephant Ivory Price Results

Table 2 presents the instrumental variables results for the post-ban elephant ivory price regres-

sions.23 Since the right-hand-side regressors are identical to those in Table 1, the instrumental

23The OLS results find that the coefficient on Russian Mammoth Ivory is much weaker, with only one significantresult, and with many coefficients for Russian Mammoth Ivory estimated to be positive in sign. See Table 6 in the

24

variable first-stage regressions are the same as those given in Table 5 in the Appendix. For the

most part, the results of the effect of Russian mammoth ivory exports on post-ban elephant ivory

prices is much weaker than the effect upon seizures. The effect of Russian mammoth ivory is esti-

mated to be negative in every specification, and is weakly significant in two specifications. Other

notable results are that the effect of China GDP per capita is positive in all specifications, which is

consistent with demand from China driving the market, and the effect of an increase in the Polity

2 variable, which corresponds to a increase in the political competitiveness of the average African

country, has a negative effect upon prices. Finally, the legal sales in elephant ivory appear to have

had a positive effect upon elephant ivory prices.24

Thus, in summary, for each tonne of Russian mammoth ivory exports, we find that the price of

elephant ivory decreases by about a dollar to a dollar and a half per ton. At the rate of 84 tonnes

per year observed over the past couple of years in the data, this implies that elephant ivory prices

would be between 80 and 120 dollars per kilogram higher, had mammoth ivory not been available.

Again, this is broadly supportive of the hypothesis that mammoth ivory production has reduced

the incentive for poaching of elephants.

Interpretation of the Estimation Results

How economically meaningful is the observed negative correlation between elephant ivory seizures

and Russian mammoth ivory harvests? From the Table 1, the IV results suggest that one tonne of

Russian mammoth ivory exports decreases elephant ivory seizures by between -0.59 and -0.77 tonne.

Take this as -0.7. Since not all illegal African elephant ivory harvests are being reported to ETIS,

elephant ivory ETIS seizures data are inherently downward biased (Milliken 2014). Based on the

analysis of Wittemyer et al. (2014), on average around 33,600 elephants were illegally being poached

every year between 2010 and 2012. Assuming an average tusk weight of 10 kg. per elephant killed,

this corresponds to roughly 330 tonnes of elephant ivory being harvested illegally every year.25 The

ETIS seizures, in contrast, averaged 39.4 tonnes of elephant ivory per year during these three years.

Thus, ETIS seizures represented on average only 11.7% of illegal elephant ivory poaching in Africa.

Russian mammoth ivory production during this period averaged nearly 84 tonnes per year. Given

the coefficient of -0.7 tonnes of seizure reduced per tonne of mammoth ivory, Russian mammoth

Appendix.24Elephant ivory prices affect the legal elephant ivory trade as well. This reverse causality is not resolved in our

analysis.25The average tusk size may be smaller than this. Shoshani (1992) estimates that tusk size had diminished to

about 3 kg per elephant by the 1990s. At these rates, the 33,600 elephants killed would amount to about 100 tonnesof elephant ivory being harvested, which means that the ETIS seizures would amount to about 40% of harvesting.At these numbers, the effect of mammoth ivory drops to about 15,200 elephants per year being saved by mammothivory, which corresponds to a total of 48,000 elephants per year poached.

25

ivory exports have reduced seizures by approximately 59 tonnes per year. Given an interdiction rate

of 11.7%, this implies that mammoth ivory production during this period reduced ivory poaching

by 508 tonnes of elephant ivory harvest per year or saved slightly more than 50,800 elephants from

being poached. Thus, rather than 33,600 elephants per year being poached for ivory, had there

been no mammoth ivory exports during this period, over 84,400 elephants per year would have

been slaughtered for their ivory. Given African elephant population estimates of around 500,000

elephants, these would have translated to poaching rates of around 17% per year, or about 10%

higher than the estimates in Wittemyer et al. (2014). A final way to look at this coefficient is to

note that the estimated 10 million mammoth ivory carcases in Siberia, whose combined weight (at

18 to 90 kg. per animal) may be between 18,000 to 90,000 tonnes, would save anywhere from 12

to 63 million African elephants from being poached.

Of course, these are all-else-constant results. A worsening of the rule of law in Africa, an

increase in income per capita in China, or a reduction in legal ivory sales, either from elephants or

from mammoth, could undo the positive effects mammoth ivory has had upon reducing elephant

poaching in Africa.

5 Concluding Remarks and Policy Suggestions

This paper studies the effect that the development of Russian exports of mammoth ivory has had

upon the poaching of African elephants. Our theory shows that the presence of a substitute lowers

the rate of poaching relative to what it would be absent the substitute. This lowers the minimum

viable population of elephants, which makes it possible that the lower harvest rate allows the

population to recover sufficiently to eventually be sustainable. Our empirical analysis shows that

theoretical conclusion that the harvest rate of elephants is reduced by the presence of a mammoth

ivory substitute holds. We show that the current production levels of over 80 tonnes mammoth

ivory per year may save approximately 50,000 African elephants from being killed. Since current

poaching rates are about 34,000 elephants per year, prohibiting mammoth ivory trade could cause

the poaching rate to more than double. We also find that mammoth ivory has reduced elephant

ivory prices by between 80$ to 120$ per kilogram. Our results suggests that policies which make

the substitutes readily available for renewable resource will lower demand for that resource, and

may mitigate against over-exploitation of the species. In particular, this research suggests that

efforts to promote mammoth ivory consumption reduce the demand for elephant ivory, and might

help to prevent elephant extinction.

26

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Appendix

Data and Sources

African elephant population: Source: Population estimates for the years 1995, 1998, 2002,

2007, and 2012 are from African Elephant Specialist Group (AfSEG). The “definite” number is

the number of elephants reported as definitely counted. The “probable” is the sum of definitely

counted plus probably exists. The “possible” is the sum of definitely counted, plus probably exists,

plus possibly exists. The “speculative” is the sum of of definitely counted, plus probably exists,

plus possibly exists, plus speculative. Elephant population estimates for 1981 and 1989 are from

Barbier et al. (1990), and elephant population estimates for 1979 and 1987 are from Stiles (2004).

Units: thousands of animals. (http://www.elephantdatabase.org/report/Loxodonta africana).

Range: Source: Range estimates for the years 1995, 1998, 2002, 2007, and 2012 are from African

Elephant Specialist Group (AfSEG). Units: Millions of square kilometers of land area thought to be

the existing range of wild elephants. (http://www.elephantdatabase.org/report/Loxodonta africana).

Pre-Ban African ivory production: Pre-ban African ivory production data is from Barbier

et al. (1990). Units: tonnes (1000 kilograms).

Pre-Ban African ivory prices: Pre-ban African ivory price data is from Barbier et al. (1990).

Units: nominal dollars per kilogram.

31

Polity 2: Source: Centre for Systemic Peace An index of a country’s election’s competitiveness

and openness, the nature of political participation, and the extent of checks on executive authority.

Polity score ranges from−10 to +10, where−10 to−6 correspond to autocracies, −5 to 5 correspond

to anocracies, and 6 to 10 correspond to democracies (Marshall et al. 2014). The number used in

the analysis is the average over all African countries (i.e., those with country codes (CCODE) 400-

626, and plus Egypt (651) for each year. The number of countries in the average varies according

to Number of Independent African Countries. Territories which were parts of European colonies

are excluded. Units: -10 to +10 in value. (http://www.systemicpeace.org/polityproject.html).

Rule of Law: Source: World Bank.(http://info.worldbank.org/governance/wgi/index.asp)

Regime Durability: Source: Centre for Systemic Peace. The average number of years a

current governmental system has been in place. A change in governmental system is defined as

a change in the Polity 2 index by an absolute value of three or greater. The number used in the

analysis is the average over all African countries (i.e., those with country codes (CCODE) 400-626,

and plus Egypt (651) for each year. The number of countries in the average varies according to

Number of Independent African Countries. Territories which were parts of European colonies are

excluded. Units: Years. (http://www.systemicpeace.org/polityproject.html).

Number of Independent African Countries: Source: Centre for Systemic Peace. The

number used in the analysis is the count of the African countries (i.e., those with country codes

(CCODE) 400-626, and plus Egypt (651) for each year. Territories which were parts of European

colonies are excluded. Units: number of countries. (http://www.systemicpeace.org/polityproject.html).

ETIS Seizures Source:1989-1995 data is from Milliken et al. (2004, Table 5, pp. 17-18) 1996-

2010 data is from Milliken et al. (2012, Table 1, p. 4). 2011-2013 data is from Milliken (2014,

Figure 1, p.2). The 2011-2013 data is imputed from his Figure 1, as Mr. Milliken did not respond

to requests for the underlying data. The combined weight of all elephant ivory seizures made by

authorities throughout the world. The data Units: tonnes (1000 kilograms).

ETIS Average Seizure Weight The average seizure weight is calculated from dividing ETIS

Seizures (in kilograms) by the “Number of elephant product seizures” summed over all countries.

Source: Number of seizures data 1989-2004 is from Milliken et al. (2004, Table 2, pp. 9-12).

Number of seizures data 2005-2013 is from Milliken (2014, Figure 1, p.2). The 2005-2013 data is

imputed from his Figure 1 (in increments of 25), as Mr. Milliken did not respond to requests for

the underlying data. Units: kilograms per seizure.

ETIS Large (> 500 kilogram) Seizures This variable is calculated as the total weight

of seizures where greater than 500 kilograms was seized, divided by the weight of total seizures.

Source: Total large seizures 2009-2013 is from Milliken (2014, Table 1, p. 8). Total large seizures

2000-2008 is from CITES Standing Committee Report (2012, Table 2, p. 17). Units: Percent of

32

total seizures.

Russian Mammoth Ivory Exports: Source: UN Comtrade. Product code HS 050710, series

”Ivory, its powder & waste, unworked”. Calculated as the sum over all countries of imports from

Russia. Units: tonnes (1000 kilograms).

Russian Mammoth Ivory Price: Source: UN Comtrade. Series number: Calculated as the

sum to the total value mammoth ivory over all countries of imports from Russia. Units: nominal

dollars per kilogram.

China GDP per Capita: Source: Penn World Table 7.1. Units: thousands of constant 2005

U.S. Dollars.

Russian Mineral Rents (% of GDP): Source: World Bank Development Indicators, series

code: NY.GDP.MINR.RT.ZS. Data description: “Mineral rents are the difference between the

value of production for a stock of minerals at world prices and their total costs of production.

Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite,

and phosphate.” Units: Percent of GDP.

Real Gold Price: Source: U.S. Geological Survey, Historical Statistics (data series 140). Aver-

age of U.S. import and export price. Units: 1998 U.S. dollars per kilogram. (http://minerals.usgs.gov/min-

erals/pubs/historical-statistics/).

Legal Ivory Trade, CITES: Source: UNEP World Conservation Monitoring Centre. CITES

Trade Database. Cambridge, UK. http://www.cites.org/eng/resources/trade.shtml. Units: tonnes

(1000 kilograms).

Legal Ivory Trade, WITS: Source: UN Comtrade. Product code HS 050710, series ”Ivory,

its powder & waste, unworked”. Calculated as the sum, excluding re-exports, of exports from all

African countries. Units: tonnes (1000 kilograms).

Legal Ivory Trade Price, WITS: Source: UN Comtrade. Product code HS 050710, series

”Ivory, its powder & waste, unworked”. Calculated as the sum, excluding re-exports, of the average

value of exports from all African countries. Units: nominal dollars per kilogram.

PIKE: Source: MIKE reports to CITES (2013, 2014). Mean proportion of illegal killed ele-

phants over all sites. Units: Proportion of kills which were illegal (0 to 1 in value).

33

Table 3: Summary Statistics

Post-Ban Period Summary Statistics, 1989-2013

Variable Observations Mean Std. Dev. Min Max

Elephant Ivory Seizures (Tonnes, ETIS) 25 21.9 13.1 6.9 58.0Russian Mammoth Ivory (Tonnes) 25 41.0 31.3 0.7 99.8Real Gold Price (1998 USD per Kilogram) 25 16.3 9.1 7.8 38.4Russian Mining Share (Percent of GDP) 23 0.68 0.60 0.00 1.84China GDP per Capita (2005 USD) 25 4,279 2,605 1,288 9,798Polity2 Index (Mean of African Countries) 25 -0.05 2.07 -5.76 2.25Rule of Law (Minimum of African Countries) 18 -2.34 0.19 -2.67 -1.91Legal Elephant Ivory Trade (Tonnes, CITES) 25 30.1 51.5 1.4 205.9Legal Elephant Ivory Trade (Tonnes, Comtrade) 25 31.4 27.2 8.8 132.6Raw Elephant Ivory Export Value (Nominal USD per Kilogram) 25 53.51 57.8 19.87 295.55Raw Mammoth Ivory Export Value (Nominal USD per Kilogram) 25 77.82 62.88 15.33 253.14

Table 4: Post-Ban Correlation Matrix

Seizures Mammoth Ivory China GDP Polity2 Rule of Law Legal CITES Legal WITS Gold Price

Mammoth Ivory 0.478(0.015)

China GDP 0.694 0.895(0.000) (0.000)

Polity2 0.409 0.820 0.800(0.043) (0.000) (0.000)

Rule of Law -0.017 -0.483 -0.567 -0.455(0.747) (0.043) (0.014) (0.058)

Legal Cites -0.021 -0.168 -0.002 -0.335 -0.457(0.916) (0.423) (0.993) (0.102) (0.057)

Legal WITS 0.158 -0.090 0.123 0.023 -0.180 0.578(0.449) (0.669) (0.560) (0.914) (0.475) (0.003)

Gold Price 0.800 0.625 0.838 0.422 -0.511 0.127 0.0330.000 (0.001) (0.000) (0.036) (0.030) (0.545) (0.875)

Russian Mining -0.123 0.896 0.899 0.787 -0.740 0.234 0.033 0.705(0.567) (0.000) (0.000) (0.000) (0.001) (0.283) (0.881) (0.000)

Note: Pearsons r correlation coefficient. (p-values in parentheses.)

34

Table 5: First Stage Regression Results

(1) (2) (3) (4) (5) (6) (7)

Real Gold Price *-1.234 -1.115 **-1.504 **-1.838 -0.634 *-1.303 -1.336(0.445) (0.723) (0.512) (0.511) (0.827) (0.692) (0.744)

Russian Mineral Rents (% of GDP) *22.481 22.282 *18.303 13.639 **40.266 20.807 22.131(12.643) (13.042) (9.264) (11.657) (18.048) (15.641) (17.938)

China GDP per Capita (1000s of 2005 USD) *9.795 9.015 ***13.892 ***14.236 5.276 *11.921 *10.709(3.512) (5.209) (3.234) (2.978) (6.26) (5.669) (5.546)

Polity2 Index (Mean of African Countries) 0.812 -2.11 -0.273(3.405) (2.305) (2.381)

Rule of Law (Minimum of African Countries) **46.428 7.615 16.107(19.51) (19.427) (26.353)

Legal Elephant Ivory Trade (Tonnes, CITES) **-0.206 **-0.190(0.062) (0.081)

Legal Elephant Ivory Trade (Tonnes, Comtrade) ***-0.297 **-0.249(0.047) (0.102)

Constant 3.819 5.298 -1.195 *9.254 **110.367 18.943 47.667(6.797) (9.68) (7.325) (5.272) (49.874) (48.692) (59.478)

Observations 23 23 23 23 17 17 17F -Statistic ***39.173 ***32.08 ***49.166 ***49.575 ***33.984 ***19.886 ***22.602R2 0.855 0.848 0.906 0.91 0.784 0.832 0.834

F -Statistic (Instruments=0) **4.73 2.26 **4.77 ***6.75 **4.04 **4.43 **4.24

Note: Dependent variable: Russian Mammoth Ivory Exports (Tonnes). Robust Standard Errors inparentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

Table 6: Post-Ban Elephant Ivory Price OLS Regression Results, 1989-2013

(1) (2) (3) (4) (5) (6) (7)

Russian Mammoth Ivory (Tonnes) -1.297 0.23 0.316 *1.137 -0.635 -0.067 0.133(0.63) (0.697) (0.52) (0.515) (0.545) (0.455) (0.237)

China GDP per Capita (1000s of 2005 USD) 11.769 *21.391 12.562 9.588 12.828 8.125 7.174(6.624) (8.951) (7.101) (7.513) (7.764) (6.758) (6.803)

Polity2 Index (Mean of African Countries) **-37.732 -23.138 ***-35.748(10.976) (12.451) (7.885)

Rule of Law (Minimum of African Countries) -13.782 32.608 15.035(31.015) (24.094) (22.239)

Legal Elephant Ivory Trade (Tonnes, CITES) *0.326 0.412(0.143) (0.274)

Legal Elephant Ivory Trade (Tonnes, Comtrade) ***1.052 **0.647(0.252) (0.188)

Constant 61.949 *-48.518 -26.211 **-69.376 -20.67 71.433 12.377(31.367) (22.405) (22.152) (19.927) (56.796) (51.467) (36.084)

Observations 26 26 26 26 18 18 18F -Statistic 2.272 ***5.155 ***5.857 ***12.424 2.261 *3.143 ***12.789R2 0.004 0.597 0.718 0.765 0.288 0.444 0.479

Notes: Dependent variable: Elephant Ivory Prices in nominal U.S. dollars per kilogram. Robust StandardErrors in parentheses. The Rule of Law variable is only available from 1996, 1998, 2000, and 2002-2013.For the years 1997, 1999, and 2001 the Rule of Law is interpolated as the average of the year before andyear after. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1.

35

Table 7: Wholesale Prices of Raw Elephant and Mammoth Ivory

A. Wholesale Prices of Raw Elephant Ivory

Year Price Ivory Size Site Source(USD/kg)

1987 190 High quality tusk China Vigne & Martin (2014)1987 60-80 3 kg tusk China Vigne & Martin (2014)2002 120-170 2.5 kg tusk China Martin & Stiles (2004)2004 200 5 kg tusk China Martin (2006)2004 320 10 kg tusk China Martin (2006)2004 200 small tusk Hong Kong Martin (2006)2004 250 small tusk China Martin (2006)2004 320 large tusk Hong Kong Martin (2006)2004 316 Large tusk China Martin (2006)2009 346 1-5 kg tusk USA Stiles & Martin (2009)2009 185 5-10 kg tusk USA Stiles & Martin (2009)2009 264 10-20 kg tusk USA Stiles & Martin (2009)2010 750 1-5 kg tusk China Martin & Vigne (2011)2010 900 5-9 kg tusk China Martin & Vigne (2011)2014 2100 1-5 kg tusk China Vigne & Martin (2014)

B. Wholesale Prices of Raw Mammoth Ivory

Year Price Ivory Type Site Source(USD/kg)

2004 275 Grade A Hong Kong Martin (2006)2004 225 Grade B Hong Kong Martin (2006)2004 364 Grade A China Martin (2006)2004 243 Grade B China Martin (2006)2004 250 High quality Russia Martin & Martin (2010)2006 350-400 High quality Russia Martin & Martin (2010)2009 500 High quality Russia Martin & Martin (2010)2009 350 Average quality Russia Martin & Martin (2010)2009 100 Low quality Russia Martin & Martin (2010)2010 400 Grade A China Martin & Vigne (2011)2010 300 Grade B China Martin & Vigne (2011)2010 260 Grade C China Martin & Vigne (2011)2010 120 Grade D China Martin & Vigne (2011)2010 600 Grade A Hong Kong Martin & Martin (2011)2010 400 Grade B Hong Kong Martin & Martin (2011)2010 300 Grade C Hong Kong Martin & Martin (2011)2010 200 Grade D Hong Kong Martin & Martin (2011)2014 50 Small,poor quality China Vigne & Martin (2014)2014 1300-2500 5-10 kg China Vigne & Martin (2014)2014 1639-3229 Large, high quality China Vigne & Martin (2014)

36