The Impacts of Tariff Rate Import Quotas on Market Access 2

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THE IMPACTS OF TARIFF-RATE IMPORT QUOTAS ON MARKET ACCESS

Xianghong Li and Colin A. Carter

Department of Agricultural Economics, Kansas State University, KS, USA

University of California, Davis, CA, USA

March 2009

ABSTRACT

The utilization of tariff-rate quotas (TRQs) for enhancing market access is a key component of

global agricultural trade negotiations. We identify factors affecting the performance of TRQs in

terms of improving market access. The analysis covers individual TRQs notified by 28 WTO

member countries from 1995 through 2000. Our results show that reducing in-quota tariffs will

significantly improve market access while the market access effect of any reduction in over-

quota tariffs is marginal. We also find that the empirical ranking of the efficiency of alternative

TRQ administration methods differs from the theoretical ranking.

JEL no. F13, Q17, Q18.

Key words: agricultural trade, market access, tariff-rate quotas, quota administration.

Please address correspondence to Colin A. Carter, Department of Agricultural And ResourceEconomics, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA. E-

mail: [email protected]. Phone:+1 530 752 6054. Fax: +1 530 752 5614.



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THE IMPACTS OF TARIFF-RATE IMPORT QUOTAS ON MARKET ACCESS

Agriculture remains a holdout when it comes to global trade liberalization. The 1994 Uruguay

Round Agreement on Agriculture (URAA) set new rules for trade in agricultural products but

this agreement was a modest reduction in import protection (Anania et al 2004). A key

systematic change regarding market access was the replacement of quantitative trade restrictions

and other non-tariff barriers with tariffs known as tariffication. To ensure current and minimum

market access in the process of tariffication, a system of tariff rate quotas (TRQs) was instituted.

TRQs are two-tiered tariffs in which a limited volume of imports (i.e., the quota) is imported at a

lower tariff rate and all additional “above quota” imports are subject to a higher tariff. As a result

of the URAA, TRQs were broadly adopted in agricultural trade. For many importers, non-tariff

trade barriers were converted to TRQs as a way to manage trade and at the same time comply

with the URAA. These instruments now affect international trade in most agricultural

commodities, ranging from imports of wheat into the EU, pork into Japan, cotton into China,

milk powder into India, and sugar into the United States, for example. As of May 2005, there

were 1,434 different agricultural TRQs notified to the World Trade Organization (WTO), with a

total of 45 countries employing TRQs (WTO 2005).

TRQs were a compromise between exporters and importers. The introduction of TRQs

ensures one thing—that agricultural trade policies continue to be very complex. In principle,

TRQs can improve market access for exporters compared to simple quantitative restrictions

because under TRQs the import quantity is not limited and over-quota imports are permitted at

the higher tariff. However, TRQs can still be used as a significant barrier to trade and they have a

number of undesirable features. For instance, TRQs generate quota rents, legitimize a role for

state trading agencies, and allow importers to blatantly discriminate among exporting countries.



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Hence, from a theoretical point of view, it is questionable whether the implementation of TRQs

improves market access and global welfare.

So far, experience with agricultural TRQs suggests that the TRQ regime has failed as a

market access instrument (Abbott 2002) because agricultural TRQs are underutilized by a

significant margin (Podbury and Roberts 1999; WTO 2000 and 2001). For the implementation

period 1995-2002, the fill rates of more than one-quarter of all agricultural TRQs were below

20% (WTO 2005). The average yearly unweighted TRQ fill rate was less than 70%. In addition,

TRQ fill rates, on average, dropped from 66% in 1995 to 58% in 2002 (WTO 2005). The

relatively low average fill rates suggest that the agricultural TRQ regime has not led to a

significant improvement in market access.

The most effective way to further reform agricultural TRQs may be to change those

factors that reduce TRQ fill rates the most. Several studies have identified factors that impede

market access under specific TRQs and have singled out ideas for further TRQ reform. The

recommendations for the best way to liberalize agricultural TRQs vary across the different

studies. Abbott and Paarlberg (1998) argued that a reduction of the above-quota tariff on pork

would lead to more pork imports into the Philippines. Alternatively, it has been argued that

increasing quota volumes would result in greater welfare gains than would tariff reductions in the

EU (Bureau and Tangermann 2000). The combination of quota expansion and in-quota and over-

quota tariff reductions would achieve maximum liberalization results for the OECD countries

(OECD 2002). On the other hand, one study (IATRC 2001) advocated that further WTO

negotiations should focus on developing better rules for the administration of quota licenses.

The maximum gain from reforming TRQs would presumably be realized through

liberalizing those factors that influence the performance of TRQs the most. Trade theory offers



3

several possible explanations for the poor performance of TRQs. Theoretical analysis can

provide a possible ranking of such factors, but cannot identify which factors are empirically the

most important. However, there is a lack of empirical evidence on the impacts of TRQ

implementation practices on market access. There are some case studies in the literature that are

country and commodity specific, but this previous work has failed to uncover general factors

governing implementation of TRQs. The commonly used descriptive approach in the literature

can provide general conclusions regarding the overall performance of agricultural TRQs, but

may exaggerate the effect of a single factor because it ignores complex interrelationships.

Only Monnich (2003) has attempted to empirically untangle explanations of TRQ fill

rates. However, her study only covered agricultural TRQs in the EU, which account for less than

7% of total global agricultural TRQs. Moreover, her model specification failed to capture the

double-censored nature of TRQ fill rates.

To expand the literature, we conduct a comprehensive study of agricultural TRQs. We

identify factors that impede market access under TRQs and draw out implications for reforming

current agricultural TRQs. Our analyses are conducted at the individual TRQ level in order to

avoid aggregation problems. Each TRQ notified to the WTO during the 1995-2000 time period

is included in this study. An empirical model is carefully specified to best capture trading

behavior under the TRQ regime and a two-limit random-effects Tobit model is used to deal with

the double censored nature of TRQ fill rates.

In policy circles, there is a difference of opinion whether or not institutional regulation is

a major factor hindering trade reform, without explicit measurement of its effects. For instance,

Brazil complained to the WTO that the EU import licensing procedures for poultry products

nullified or impaired any benefits from the TRQ regime. The U.S. has pressured China to reform



4

its TRQs. Many economists have expressed concern that the benefits of TRQ access have been

reduced by administration methods (Abbott 2002; IATRC 2001; Skully 2001). In contrast, the

WTO (2001) and OECD (2002) have stated that quota administration methods have had only a

limited influence on fill rates. A major contribution of this paper is that we explicitly examine

whether administration methods affect market access of agricultural TRQs, and to what degree

these methods matter. We find that administration methods do matter in terms of granting market

access to exporters.

The Economics of TRQs

A TRQ scheme contains a low in-quota tariff (T1), a quota (Q0) that defines the maximum

volume of imports that are subject to the low in-quota tariff, and a relatively high over-quota

tariff (T2). Figure 1 shows the basic analytics of a TRQ mechanism. Here, we assume that the

importing country is small.1 For imports within quota, importers face the price Pw+T1. For

imports over quota, the import cost is Pw+T2. Hence, the excess supply curve is a step function

consisting of three sections: a horizontal section (Pw+T1)A for the in-quota tariff, a vertical

section AB for the quota, and another horizontal section BC for the over-quota tariff. Imports

under TRQs are determined where the excess demand curve in the home country intersects the

world excess supply curve.

A main quantifiable indicator of market penetration under a TRQ regime is the TRQ fill

rate. It is often constructed to assess the performance of TRQs relative to market access goals.

The TRQ fill rate is a simple descriptive statistic defined as the actual import volume through

TRQs relative to the scheduled quota volume (i.e., Q0 in Figure 1) and can range from 0 to +.

1The small country assumption is reasonable in the context of agricultural TRQs. For instance, the total TRQ quota

volume of a typical agricultural product in 1995 accounted only for 3% to 7% of the total world trade in that product

(FAO 2000). The quota volume of each individual TRQ for an individual country is even smaller. For instance, the

U.S. sugar TRQs accounts for less than 5% of world imports.



5

Figure 1. Imports under TRQs

Four categories of TRQ fill rates can be identified: underfilled, just filled, overfilled, and

zero fill, respectively. If the excess demand curve lies between ED0 and ED4 in Figure 1(e.g.

ED1), the 1st

tier tariff is the binding instrument. The TRQ is said to be underfilled with a fill rate

(M1/Q0) between 0 and 1 and a portion of the quota is not utilized. If the quota regime is the

effective instrument (i.e., the excess demand curve falls within the range of ED4 to ED5 in Figure

1), then the quota fill rate will equal 1 as the quota is just filled. In the case of a strong import

demand, the 2nd tier tariff becomes effective. The fill rate (M3/Q0) is then greater than 1, and the

TRQ is said to be overfilled. It is also possible that the TRQ is not filled at all. If the excess

demand curve lies to the left of ED0 (including ED0), the fill rate equals zero and there are no

imports at the current in-quota tariff level.

Various administration methods have been adopted to implement TRQs, and TRQ

administration fundamentally is a rationing problem (Skully 2001). It determines the volume and

distribution of trade as well as the distribution of quota rents. Alternative administration methods

Pw

Pd

P

Q

ED2

ES

M0 M2=Q0 0

Pw+T1

Pw+T2

ED1

M1

A

B

M3

ED3

ED4

ED5

C

ED0



6

typically result in different hidden costs and fill rates. The introduction of TRQs in the EU

banana market, for instance, induced a high degree of rent seeking (Herrmann et. al. 2001).

Moreover, it is possible for the government to assign a large number of small quotas so that any

one quota is commercially infeasible to import. Furthermore, administration methods may

allocate TRQs to high-cost third country producers and thereby cause inefficiency (Herrmann et.

a.l 2001; Boughner et. al. 2000) .

The impact of administration methods can be depicted in Figure 1 as a leftward shift of

ED by the amount of the transactions costs. The weakening of import demand reduces the import

quantity. The magnitude of the shift in the excess demand curve determines how the fill rate is

affected. If the transactions costs are relatively high, they could even change the binding

instrument. For instance, if ED2 in Figure 1 shifts to ED1 due to administration costs, the quota is

no longer effective, and the 1st-tier tariff will instead become binding. As a result, the TRQ

moves from being just filled to being underfilled.

Ten principal TRQ administration methods have been identified by the WTO and are

defined in the Appendix below. Each method has its own advantages and disadvantages, incurs

transactions costs of different magnitudes, and affects imports differently. The “applied tariffs”

method is the most commonly employed and accounts for almost 50% of the total TRQs. The

“licenses on demand” method serves as the second most popular approach, accounting for about

one-quarter of the total TRQs. “Producer groups” and “other” methods are seldom used The

“non-specified” method was used only once and was dropped from our regression analysis.

Four “additional conditions” and certain combinations of these conditions are used to

help administer TRQs, in addition to the ten principal administration methods. These “additional

requirements” are applied to more than 22% of the TRQs each year. Among these four methods,



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“limits on tariff quota shares per allocation (LA)” is the most intensively used, and is applied to

more than 45% of the TRQs that have additional conditions. In addition, the “domestic purchase

requirement” and “past trading performance requirement” together account for more than 40% of

those TRQs with additional conditions.

The Model

We define the TRQ fill rate of a commodity i in year t , *

it y , as

0

*

*

Q

Y y it

it = . (1)

where

*

it Y

is imports of commodity i in year t , and 0Q is the scheduled or notified quota quantity

for this commodity in year t .

The TRQ scheduled (notified) quota for a specific commodity is set in multilateral

negotiations and predetermined before implementation of the TRQ. In practice, importers have

rarely changed the notified quota quantity during our study period. Hence, the only random

component in a TRQ fill rate is actual imports. Consequently, we start the model construction by

modeling a country’s importing behavior. If the domestic price deviates from the world price,

arbitrage opportunities should exist and result in imports. Hence, import demand, Y , is modeled

as a function of the domestic price, Pd , the world price, Pw, and a vector of other exogenous

demand shifters, XD,2

d wY = f(P , P , XD) . (2)

Four sets of variables are included in the exogenous demand shifters, XD, and these

variables are defined in Table 1. The first two sets of demand shifters include both principal

administration methods and “additional” conditions as per the above discussion. Three policy

2 Both the world price and the domestic price are included in the model because the included policy instruments can

not fully account for the imperfect transmission between these two prices.



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instruments—the 1st-tier tariff (

1 ), the 2

nd -tier tariff (

2 ), and quota volume ( 0Q )—are included

in the model as the third set of demand shifters. The last set of variables includes each country’s

income (Inc) and population (Pop).

Because prices and import quantities are simultaneously determined, it is quite possible

that some factors embodied in the random error term, eit , are also correlated with those

explanatory variables representing prices. Assuming that the international market is perfectly

competitive and that every importing country is simply a price taker in the world market, the

world price can be treated as an exogenous variable. However, the domestic price is likely

serially correlated with the random error term. To deal with this endogenous variable problem, a

one-period lagged production variable is used to proxy the domestic price. Thus, net imports are

estimated as a function of world price, lagged domestic supply, trade policies, and administration

method.

The effects of principal administration methods and additional conditions are represented

by two groups of dummy variables in our specification. To avoid perfect multicollinearity, the

“applied tariffs method” dummy is dropped. This administration method does not impose

quantitative restrictions on imports and requires the least administrative effort. Therefore, this

method is considered the most efficient way to administer TRQs. Choosing this dummy as the

benchmark facilitates the interpretation of the results. The alternative “additional” administration

methods variables do not cause a multicollinearity problem because more than one-half of the

global agricultural TRQs are not subject to these additional requirements.



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Table 1. Regressors in the Import Equation

Regressors

Principal Administration Methods In the form of dummy variables

AT Applied tariffs

FC First-come, first-served

LD Licenses on demand

AU Auctioning HI Historical importers

ST Imports by state trading enterprises

PG Producer groups/associationsOT Other

MX Mixed allocation methods

Additional Conditions In the form of dummy variables

DPR Domestic purchase requirements

LA Limits on quota shares per allocation EC Export certificates

PT Past trading performance

Policy Instruments

1 1st

– tier tariffs

2 2nd

– tier tariffs

Q0 Quota quantity

Other Regressors

Pd Domestic price in terms of domestic currency

Pw World reference price in domestic currency

Inc IncomePop Population level

C Individual-specific effects

E Random error term

Plag One-period lagged production

We also add individual specific effect variables to control for the unobserved individual

heterogeneity and measurement error. This gives the following specification for TRQ fill rates:3

*

0 1 2 3 4 5 6 7 8

9 1 11 12 13 14 15

16 17 18 1 19 2

'

,

it

it wit it it it it it it

it it it it it it it

it it it i it it

y X c ePlag P AU FC LD AU HI ST

PG OT MX DPR LA EC PT

Pop Inc c e

= + +

= + + + + + + + +

+ + + + + + +

+ + + + + +

(3)

3 The quota quantity is not explicitly included in the model, but its effect on the fill rate is embodied in the constant

term.



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where ci is the unobserved individual specific effect.

The dependent variable, the TRQ fill rate, is censored. It is left-censored at 0 because no

imports occur for some TRQs during some years either due to insufficient demand or a

prohibitive in-quota tariff. It is also censored from the right because the quota is binding for

some TRQs with fill rates equal to 1. As in Figure 2, the observed fill rate equals 1 for any

demand curve lying from ED1 to ED3. Hypothetically, if there was no quota, a fill rate of 1

would be observed only with demand curve ED1. Excess demand curves other than ED1 would

lead to imports higher than Q0 and the quota fill rate greater than 1. For instance, imports for

demand ED2 in Figure 2 would occur at M2 and would result in a fill rate higher than 1,0

2

Q M , if

the quota instrument was not contained in the TRQ regime.

Figure 2. Imports under TRQs When the Quota is Binding

Hence, the TRQ fill rate, *

it y , is a latent variable. Theoretically, the observed fill rate, yit ,

can be defined as:

Pw

Pd

P

Q

ED2

ES

M=Q0 0

Pw+1

Pw+2

A

B

M2

ED1

ED3

C

M3



11

3

0

19 2 3

0 0

0 * 0

* 0 * 1

1 1 *

* * ,

it

it it

it it

it it

if y

y if y

M if y y

Q

M y if yQ Q

< <

=

+ >

(4)

Empirically, TRQ fill rates greater than 1 are censored to 1 to assure consistency among

countries. All WTO members are obligated to notify their TRQ import quantities. However,

notification procedures are not uniform across countries. Some countries report all imports

governed by a TRQ regime, while many countries notify only imports up to the quota level, and

do not report over-quota imports. Hence, TRQ fill rates are censored from both sides and

confined between 0 and 1 (i.e., it y [0,1]). To account for this critical factor, we specify a

double-censored Tobit model suggested by Maddala (1983) as follows:

0 0

0 1

1 1

it

it it it

it

i f y *

y = y * if < y * <

if y *

(5)

and *

it y is defined in equation (3).

In this specification, the true values are observed only when TRQs are underfilled. The

observed TRQ fill rates are 0 when no imports are reported. If agricultural TRQs are just filled or

overfilled, we observe fill rates of 1.

Data Description

A panel data set including scheduled quota quantities, notified quantities imported, relevant in-

and over-quota tariffs, production, and world prices, were primarily derived from the

Agricultural Market Access Database (AMAD) as updated on July 5, 2004. The information on

administration methods was from a background paper by the WTO (2002). The exchange rates



12

were obtained from the International Financial Statistical Yearbook by the International

Monetary Fund (2005). Other data, including population and national income, were obtained

from the 2004 World Development Indicator published by the World Bank. Harmonized system

(HS) codes were used as the concordance tool to map the information from different sources. In

the case of changing HS codes, descriptions of HS codes were carefully inspected and the

concordance was drawn based on similarities between descriptions.

Our data set includes every TRQ notified by twenty-eight member countries that

scheduled TRQs from 1995 to 2000.4

This time period coincides with the TRQ implementation

period as part of the URAA for developed countries. To some extent, this period can be viewed

as the trial period for agricultural TRQs, providing valuable information for possible reform of

this trade regime. The sample descriptive statistics are reported in Tables 2 and 3. Table 2 shows

that the dependent variable, the TRQ fill rate, is censored from both sides. No imports were

reported for 10.2% of the 4,201 agricultural TRQs covered in this paper. At the same time,

41.8% of the covered agricultural TRQs were just filled or overfilled. This double-censored

feature suggests that a two-limit Tobit model specification rather than a simple linear

specification is appropriate.

The majority of agricultural TRQs are employed by developed countries that can afford

to protect their agricultural sector. TRQs provide rich countries with considerable flexibility in

terms of managing imports. Moreover, agricultural TRQs are heavily used in the trade of

politically sensitive commodities. A large number of the TRQs apply to trade in fruits and

4Five additional countries also scheduled TRQs beginning in 1995 but were not included in this study. El Salvador

and Nicaragua did not notify any TRQs because they did not offer quotas for any of the products on their schedules.

Malaysia, Mexico and Romania only notified a few of their scheduled TRQs, for one or two years. Hence, these five

countries were dropped from this study.



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Table 2. Distribution of Agricultural TRQ Fill Rates, 1995-2000 Fill Rate(y) Frequency Percent Cumulative Percent

0 430 10.24 10.24

0<y0.1 454 10.81 21.040.1<y0.2 202 4.81 25.85

0.2<y0.3 155 3.69 29.540.3<y0.4 125 2.98 32.52

0.4<y0.5 138 3.28 35.800.5<y0.6 126 3.00 38.800.6<y0.7 111 2.64 41.44

0.7<y0.8 147 3.50 44.94

0.8<y0.9 167 3.98 48.920.9<y<1.0 389 9.26 58.18

1 1757 41.82 100.00

Total 4201 100.00

Source: Complied from AMAD.

Table 3. Basic Descriptive Statistics for Key Variables, 1995-2000

Variable Measurement Unit Mean Standard Deviation

Fill ratea

0-1 0.65 Overall 0.41Between 0.36Within 0.20

1st-tier tariff

a% 61.69 Overall 108.81

Between 108.30Within 23.14

2nd -tier tariff a % 147.42 Overall 359.15

Between 218.58

Within 283.09

Production

a

Million metric tons 15.14 Overall 246.00Between 250.43Within 5.59

World pricea US$/kg 1.70 Overall 3.71

Between 3.52

Within 0.93

Exchange rate b

Domestic currency/US$ 230.00 Overall 534.20Between 481.66Within 210.55

Income per capitac

US$ 16628.83 Overall 14637.38Between 14536.58

Within 1067.14

Population

c

Millions 67.49 Overall 112.65Between 113.45

Within 1.57

Number of Observations 4201

Source: a. compiled from AMAD; b. International Financial Statistical Yearbook; c. 2204 World

Development Indicator.



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vegetables, dairy products, meat products, and cereal crops. Historically, these products have

received more protection than other agricultural commodities. Furthermore, agricultural TRQs

are subject to high tariff protection, with an average in-quota tariff of 62%. The average over-

quota tariff is 147%. Such high tariff protection is not surprising if we consider why these TRQs

are in place.

Summary statistics for the important regressors are reported in Table 3. The standard

deviation is decomposed into between and within components (see the last column of Table 3).

Clearly, variation of the regressors mainly arises from variation between individual TRQs, as any

variation of an individual TRQ over the years is relatively small.

Estimation

The pooled Tobit model without individual specific effects can be estimated by maximizing the

log-likelihood function. When the unobserved effects are included, either a fixed-effects

approach or a random-effects approach can be used. As the cross section dimension increases

and the time dimension is fixed, the estimators of limited dependent variable models with fixed

effects using panel data are, in general, inconsistent due to the “incidental parameters problem”

(Greene 2004). The problems are most acute when the time dimension of the panel is short.5

In contrast, the estimates of random-effects limited dependent variable models are

consistent even when the length of the panel is short, if the model is specified correctly.

Therefore, the individual specific variable ci enters the fill rate model (3) as a random-effects

variable in this study. Assume that the random-effects variable ci is not correlated with other

5 Honore (1992) developed trimmed least squares estimators, a semi-parametric method, to obtain consistent

estimates of a fixed-effects Tobit model in a short panel,. However, this method cannot be used to estimate the

consistent coefficients of variables that lack variation over time. Hence, this method is not appropriate for our model

because, principal and additional administration methods do not change over time once they are first specified.



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regressors, and has a normal distribution and the disturbance term, eit , is also normally

distributed. The traditional two-limit random-effects Tobit model is specified as:

0 0

0 1

1 1

it

it it it

it

i f y *

y = y * if < y * <

if y *

(6)

where *

it y is defined in equation (3), and in which

),,0(~| 2cii

N X c

and

),0(~,| 2eiit

N c X e .

This standard random-effects Tobit model strictly assumes the individual specific effects

are exogenous from the regressor X . A more general Chamberlain-like random-effects Tobit

model allows the random-effects variable ci and other regressors, X , to be correlated in a certain

way (Wooldridge 2002). We assume ci is related to X in the following manner:

iiia X c ++= , (7)

wherei

X is a vector of the average of each regressor X in the group i over the time dimension

and is time-invariant.

Under this assumption, the Chamberlain-like model can be specified as

0 0

0 1

1 1

it

it it it

it

i f y *

y = y * if < y * <

if y *

(8)

where it iiit it ea X X y ++++= * , and in which

),0(~,| 2eiiit

N c X e

and



16

),0(~| 2aii

N X a .

Moreover, we assume that the set of error terms, {eit }, is serially independent conditional

on ( X i , ai). Hence, the Chamberlain-like random-effects two-limit Tobit model is the traditional

random-effects Tobit model with an additional set of time-constant explanatory variables.

Results

Three models—a simple pooled two-limit Tobit model, a standard random-effects two-limit

Tobit model, and a Chamberlain-like random-effects two-limit Tobit model—were estimated.

The results are presented in Table 4. A likelihood ratio test was performed to compare the

standard random-effects model with the pooled model. A Chi-square test statistic of 2,547.70

with 1 degree of freedom clearly rejects the null hypothesis that individual specific effects are

not present. Therefore, the random-effects model is preferred to the simple pooled model. In the

Chamberlain-like model, the time-invariant means of the continuous variables are added to the

standard random-effects model. The Chi-square test statistic of 56.13 with 7 degrees of freedom

indicates that the null hypothesis is rejected and that the time-invariant variables are jointly

significant. Individual significance tests of the time-invariant variables show that some

coefficient estimates are statistically different from zero. The results of these tests therefore

indicate that the Chamberlain-like random-effects model best fits the data. Hence, the following

discussion is based on that model.

The estimate of the world price coefficient has a negative sign as expected but is not

significantly different from zero, suggesting that the world price is not an important determinant

of fill rates. This result seems a bit surprising. One possible explanation is that variation in the

world commodity prices over the six year study period was relatively small and this results in the

insignificant coefficient. A higher exchange rate makes imports more expensive in terms of the



17

domestic currency, discouraging imports and leading to lower fill rates.6

This negative estimate

helps explain the import price story because world transactions prices of most commodities are

in U.S. dollars.

A one-period lagged production variable is included as the instrument for the domestic

price. Its estimated coefficient has the correct sign. When domestic supply is higher, the demand

for imports is lower and the fill rate decreases. But this estimated coefficient is statistically

insignificant, which suggests a weak link between the domestic and the international market,

implying that other factors may play a more important role in determining imports. However,

production data is more aggregate than trade data for many TRQ commodities, and the

aggregated data may also reduce the estimated production effect.

The estimated impacts of the population and per capita GDP variables on fill rates are

found to be trivial. The coefficient estimate of population is statistically different from zero only

at the 10% significance level while that of income is insignificant. This result is not surprising

given the short panel nature of the data. During the period under study, the standard deviations of

these two variables over time are quite small (Table 3) and hence their influence on the fill rate

may not be fully picked up in such a short time period. At the same time, most TRQs are used by

rich countries, where population and income growth are not strong drivers of food imports.

Two policy instruments, the in-quota and the over-quota tariffs, are both subject to

pressure for reduction as part of the WTO trade negotiations. These two tariffs increase the

import price and should reduce fill rates. Surprisingly, the coefficient estimates seem to show

that neither of these two tariff rates play an important role in determining fill rates and that the

6 The exchange rates used in the study are defined as the amount of domestic currency that can be exchanged for one

U.S. dollar. Hence, a higher exchange rate means a weaker domestic currency.



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pass-through effect of these tariffs is weak, which implies that further tariff reductions would not

sharply improve market access.

Table 4. Regression Results for TRQ Fill Rate Models

Regressors Pooled Tobit Random-Effects

Tobit

Chamberlain-like

Random-Effects Tobit

AU -0.844 -0.589 -1.096

(12.11)** (9.72)** (17.21)**

FC -0.243 -0.061 -0.315

(5.06)** (1.09) (5.31)**

HI -0.188 -0.246 -0.167

(3.80)** (5.62)** (3.46)**

LD -0.487 -0.328 -0.492

(10.72)** (5.66)** (9.86)**

MX -0.362 -0.308 -0.162

(6.32)** (5.71)** (3.13)**OT 0.103 -0.247 -0.221

(0.65) (2.00)* (1.73)

PG -0.132 -0.371 -0.395

(0.97) (4.07)** (4.58)**

ST -0.099 -0.359 -0.806

(1.12) (3.10)** (9.43)**

DPR 0.602 0.457 0.116

(8.35)** (5.50)** (1.66)

PT -0.136 -0.175 -0.057

(2.34)* (2.86)** (1.07)

LA -0.022 -0.148 0.032

(0.47) (2.68)** (0.69)EC 0.248 0.102 -0.173

(2.90)** (1.23) (2.69)**

World price 0.002 -0.005 -0.003

(0.58) (1.88) (0.26)

Exchange rate 1.37e-04 -4.10e-06 -2.69e-04

(4.00)** (0.12) (4.34)**

Production 1.89e-05 4.33e-05 -0.001

(0.37) (1.26) (1.13)

1st-tier tariff -0.001 -4.13e-04 0.001

(5.36)** (2.57)* (1.45)

2nd

-tier tariff 2.65e-04 1.75e-04 5.12e-05

(3.49)** (3.20)** (0.64)

Income 6.31e-06 3.51e-06 -4.68e-07

(5.44)** (2.45)* (0.06)

Population 2.73e-04 -2.90e-04 0.008

(1.80) (1.96)* (1.69)

Note: Continued on next page.



19

Table 4. Regression Results for TRQ Fill Rate Models (Continued) Regressors Pooled Tobit Random-Effects

Tobit

Chamberlain-like

Random-Effects Tobit

1st-tier tariff -0.002

(average) (3.54)**

2nd

-tier tariff 0.001(average) (5.82)**

World Price -0.002

(average) (0.20)

Exchange rate 0.001

(average) (9.16)**

Production 0.001

(average) (1.16)

Income 9.18e-06

(average) (1.25)

Population -0.007

(average) (1.51)

Constant 0.903 0.936 0.792(28.09)** (23.07)** (21.56)**

Log likelihood -3897.99 -2624.14 -2595.07

Observations 4201 4201 4201

Note: 1. Absolute value of t statistics in parentheses

2. *significant at 5%; ** significant at 1%

3. AU = Auctioning, FC = First-come, first-served, HI = Historical importers, OT = Other,

LD = Licenses on demand, MX = Mixed allocation methods, PG = Producer groups,

ST = Imports by state trading enterprises, DPR = Domestic purchase requirement,

LA = Limits on quota shares per allocation, PT = Past trading performance,

EC = Export certificates.

The results from the corresponding time-invariant estimated coefficients for these two

tariffs, however, prevent us from reaching such a strong conclusion. The coefficient estimate for

the time-invariant average of each TRQ’s 1st-tier tariff is statistically significant and negative.

However, changes in the in-quota tariff of each TRQ over time do not strongly influence TRQ

fill rates. The dominant effect of average in-quota tariff is plausible because the annual changes

in each in-quota tariffs are small since the 1

st

-tier tariffs were not subject to reduction during the

URAA implementation period. The significance of the average in-quota tariff also indicates that

some unobserved factors related to the in-quota tariff serve to reduce fill rates. For instance,

strong domestic pressure for protecting a commodity can lead governments to deliberately set the



20

in-quota tariff so high that the fill rate is significantly lowered. In summary, the regression

results regarding the in-quota tariff imply that further reductions of the 1st-tier tariffs will

increase agricultural TRQ fill rates.

On the other hand, the coefficient estimates for the time-invariant average of each TRQ’s

over-quota tariff are positive, which at first seems counterintuitive but is plausible. For instance,

an important factor, the quota rent, is closely linked to the over-quota tariffs but is not

observable. A higher 2nd -tier tariff can generate a higher per unit quota rent, which tends to

encourage importers to use the quota to gain windfall profits. As a result, TRQs are more likely

to be filled and fill rates may go up. This result does not imply that the 2

nd

-tier tariff rate should

be increased, however.

In interpreting the impacts of the in-quota and over-quota tariffs on market access, it is

worth noting that the estimated effects of tariffs could be underestimated due to the possible

positive impact of fill rates on tariff levels. Tariffs and fill rates could mutually affect each other

rather than one-way causation where tariffs only affect fill rates. It is well known that higher

tariff rates raise import costs and thereby result in lower fill rates. However, an expected high

TRQ fill rate may motivate the government to intentionally set high tariffs in order to prevent the

fill rate from becoming too high. Hence, two factors may be present-the negative effect of tariffs

on fill rates and the positive effect of fill rates on tariffs. The signs of the estimated impacts of

tariffs on fill rates depend on the relative magnitude of these two effects.

The potential presence of the positive effect has two implications for the results. First, the

actual fill-rate-reducing effect of the first-tier tariff should be higher than the estimated

coefficient, if fill rates also positively affect tariffs. Moreover, this possible positive effect of fill

rates provides another plausible explanation for the positive estimated coefficient of the over-



21

quota tariff. The data show that the average over-quota tariff for TRQs that are just filled or

overfilled is higher than that of TRQs that are underfilled, which suggests that positive causation

is very possible. Hence, in addition to the quota rent story, it is possible that the positive effect of

fill rates on over-quota tariffs is stronger than the negative effect, so that we observe the

surprising result that a higher over-quota tariff may lead to a higher fill rate.

Regarding the principal administration methods, the regression results show that

“administration” affects the implementation of TRQs and plays an important role in determining

fill rates. The coefficient estimates on all of the principal administration method dummy

variables with the exception of the “other” method are statistically significant and have a

negative sign. These results suggest that, in practice, the costs generated by administration

methods are high enough to reduce fill rates.

The estimation results indicate that the benchmark method, “applied tariffs”, has the least

negative effect on market access. Under the “applied tariffs” method, imports are allowed in

unlimited quantities at the in-quota tariffs. No quantitative component is specified under this

method, so that no foreign market shares are allocated and no quota rents are generated. TRQs

managed by this method are actually equivalent to simple tariffs. Hence, this administration

method is transparent and the most efficient.

It is surprising that the “auctioning” method has the largest negative (estimated) effect on

market access, given that it is the second most efficient administration method in theory.

However, in practice the capacity of the “auctioning” method to function as a price-discovery

mechanism is hindered if the market is not sufficiently liquid (Skully 2001). If markets are thin,

transactions costs for this method can be very high, which in turn leads to low fill rates. The

different bidding systems designed by governments adopting “auctioning” methods can affect



22

the competitiveness of bidding and thereby reduce imports. For example, Switzerland’s auctions

are based on a principle of “maximum bid, maximum share”. This auctioning system allows one

group to purchase the entire import quota, and that group can then limit imports to maximize its

revenue, which in turn often results in relatively low fill rates (Khorana 2004).

The “first come, first served (FC)”, “license on demand (LD)”, and “historical importer

(HI)” methods all belong to the “quasi-market” allocation group. However, their estimated

impacts on market access differ because each method allocates import rights in different ways.

Of the three methods, our estimates show that LD has the strongest negative influence on fill

rates. Under this method, licenses are issued among applicants based on quantities requested. It

is the uncertainty surrounding the actual share that a firm will realize that probably leads to low

fill rates. To try to obtain a larger share, importers have an incentive to exaggerate their quota

requests. Moreover, the eventual market shares from the pro rata allocation can be so small that

individual importers choose not to import in order to avoid high transportation costs per unit.

Hence, some quota may not be filled. The estimated negative effect of the FC method is slightly lower than that of the LD

method. The FC method places a premium on timing because the timing of imports determines

the applicable tariff. If an importer is caught importing over the quota, then he will incur huge

costs through a prohibitive tariff, storing the product outside the border, or trans-shipping the

product to another country. This inherent risk of being caught over quota is probably the main

reason for the low fill rates for this method.

The “historical importers” method has the least negative impact on fill rates of the three

methods. Under this method, quotas are allocated to firms primarily based on their past market

shares. The main concern over this method is its discriminatory nature. Historical suppliers are



23

given market share irrespective of whether or not they have a cost advantage and whether they

even have surplus production for export. On the other hand, this method has the advantage of

predictability and brings certainty into the exporters’ planning processes. Licensed exporters

have an incentive to fill the quota to secure future market shares, which helps raise the fill rate.

Compared with the “applied tariffs” method, the “other” method does not run much risk

of lowering fill rates, given that the coefficient estimate for this method is only statistically

different from 0 at the 10% significance level. Any discussion of the effects of this method is

difficult because the definition of this method is ambiguous and detailed information is lacking.

One known “other” method is a scheme that allocates import rights through some form of lottery.

The lottery mechanism is similar to other “quasi-market” methods. However, it does not involve

the risk of being caught over quota the way the “first come first served” method does. Moreover,

every competitor has the opportunity of obtaining an import license, which is an improvement

over the rigidity of the “historical importers” method. Hence, it is not too surprising that this

mechanism actually performs better than “quasi-market” allocation methods.

The negative effect of the “mixed” allocation method is mild. Similarly, it is difficult to

provide a convincing explanation for this result, because of a lack of information. This method

involves more than one administration method and no one single method is dominant. It is

possible that none of the individual methods employed under this administration procedure are

too restrictive, so that the combined effect on the fill rate is relatively small.

Under the last two methods, the “producer groups (PG)” method and the “state trading

(ST)” method, the import-sourcing decisions are made by a specific domestic group. Many

factors that may have nothing to do with economic considerations are involved in import

decisions in these cases and they could easily bias imports away from low-cost suppliers. The



24

estimation results reveal that state trading has a large negative effect on fill rates, while the

“producer groups” method only moderately impedes imports.

Although all of the principal administration methods have negative impacts on TRQ fill

rates, the magnitudes of their impacts differ. We tentatively rank the effects of each method on

fill rates according to the size of the empirically estimated coefficients (see Table 5).

Surprisingly, the empirical ranking is quite different from the theoretical ranking by

Skully(2001). It is the “auctioning” method, the second best method in theory that results in the

lowest fill rates of all the principal methods. The “producer groups” method has a medium

influence on fill rates. Moreover, the most discriminatory method, the “historical importers”

method, has a rather small impact on TRQ fill rates.

Table 5. Rank of the Impacts on Fill Rates across Administration Methods Impacts on Fill Rates Theoretical Ranking Empirical Results

Low Market allocation AT

methods AU

AT

OT

MX

HI

Medium Quasi-market FC

methods LD

HI

FC

PG

LD

High Discretionary PG

methods ST

ST

AU

Note: AT = Applied tariffs, AU = Auctioning, FC = First-come, firs- served,

HI = Historical importers, LD = Licenses on demand, PG = Producer groups,

ST = Imports by state trading enterprises.

Fulfilling additional conditions places additional costs on imports and can potentially

decrease TRQ fill rates. Hence, it is not surprising that “export certificates (EC)” and “past

trading performance (PT)” conditions are negatively related to quota fill rates. The former

condition requires export certificates in addition to import licenses so it significantly increases



25

import costs and reduces fill rates. The “past trading performance” condition is quite similar to

the “historical importers” method. Under this condition, import licenses are allocated only to

established importers. Given the small magnitude of the estimated impact of the “historical

importers” method, it is reasonable that the effect of this additional condition is also

insignificant. The “limits on quota shares per allocation (LA)” condition does not impose

additional significant effects on the fill rates.

The coefficient estimate on the “domestic purchase requirement (DPR)” condition is

significant at the 10% level. This additional condition requires the purchase or absorption of

domestic production of the imported product in order to gain eligibility for a share of the import

quota. This requirement obviously increases costs for some firms. However, the estimation

results show that this additional condition can actually promote higher fill rates, which is a bit

puzzling. One possible reason for this result is that firms are perhaps more likely to commit to

imports once they obtain their quota share because they are involved in the domestic industry,

which leads to higher overall fill rates.

Robustness of Results

We estimated several additional models to test the sensitivity of our results and found our results

to be rather robust.7 In the first set of these models, applied bound tariffs rather than applied base

tariffs were used. In the second set of models, dummy variables for country or product that

represent characteristics of each TRQ were included. The main conclusions remain valid and the

results are statistically reliable. Greene’s results (2004) also provide support for the robustness of

our results. His results showed that, even in the extreme case in which traditional random-effects

models are clearly inappropriate, they do not perform too badly. In particular, the dummy

variable coefficients are essentially correctly estimated.

7 The results of these additional models are not reported here because they are similar to those in Table 4.



26

Other issues could potentially weaken the robustness of our estimation results. First, the

empirical analysis pertains to calendar years that may differ from crop years for some

agricultural products. The discrepancy between crop and calendar years could lead to a scenario

in which calendar-year imports and fill rates do not reflect domestic demand cycles. However, if

the harvest is normal, the seasonality disguised by calendar-year annual data will not cause the

calendar-year fill rates to differ from the crop-year fill rates, because the bias is averaged out

given the time series nature of the data. But if the harvest in a particular crop year is poor,

calendar-year fill rates will deviate from crop-year fill rates and may not reflect true market

access in that year. In that case, dramatic fluctuations in fill rates for a specific TRQ could be

observed over time. However, significant systematic fluctuations in fill rate patterns do not

appear in our data set, suggesting that discrepancies between crop years and calendar years do

not bias our empirical results in any significant way.

In addition, this study covers only the 1995-2000 time period due to data availability. For

several reasons, we believe that the estimation results based on these six years would not change

much even if data for the years after 2000 were added. First, no significant trends in agricultural

TRQ fill rates were observed during the study period. Moreover, the estimated coefficient for a

time trend variable is statistically insignificant if it is included in the model. Furthermore,

implementation of TRQs since 2000 has not changed much for developed countries because no

consensus has been reached about TRQ reform in the Doha round. At the same time,

implementation of TRQs in developing countries has not changed much either because the

URAA implementation period for developing countries ended in 2004 and no new agreement has

been reached.

Summary and Conclusion



27

The 1994 Uruguay Round Agreement on Agriculture (URAA) marked a historic turning point in

reform of the global trading system. As part of the URAA process of tariffication, over 1,400

tariff-rate quotas (TRQs) emerged as a mechanism to manage agricultural imports. TRQs

combine elements of both tariffs and quotas in a complex way. An examination of the

implementation of agricultural TRQs from 1995 to 2000 confirms the a priori belief that TRQs

effectively protect agricultural commodities from foreign competition. The relatively low fill

rates raise an interesting question. What factors limit market access the most under TRQs and

what is the best way to reform the TRQ regime to achieve greater market access?

We conduct a systematic analysis to find explanations for the relatively low TRQ fill

rates. Our results show that under the general TRQ regime, the linkage between the domestic and

international market is weak due to many non-market factors. Imports of TRQ protected

commodities do not respond well to changes in domestic production. Our results suggest that

high in-quota tariffs serve to reduce fill rates, which means that further reducing in-quota tariffs

will improve market access. It is surprising that we find the current over-quota tariffs have a

positive effect on TRQ fill rates. But this result is most likely explained by the existence of

unobserved quota rents. This does not imply that the over-quota tariffs should be left untouched.

It simply means that the over-quota tariffs are not currently as relevant as the in-quota tariffs in

terms of affecting market access.

Reforming TRQ administration methods is a key issue in future WTO trade negotiations.

Participants in the negotiations generally accept that there is no single best method for

administering TRQs. However, the negotiations could potentially sort out which allocation

methods should be encouraged and which should not (WTO 2004). Our empirical results suggest

that all administration methods reduce market access compared to the “applied tariffs” method of



28

quota allocation. We find that the effects of alternative administration methods on fill rates differ

in practice, implying that the choice of the TRQ administration method is important. Moreover,

the empirical ranking of their impacts on fill rates deviates from the theoretical ranking.

We conclude that WTO negotiation efforts devoted to making agricultural TRQ

administration simpler and more transparent will surely improve market access. The result that

the “applied tariff” method is superior to other administration methods and has the least impact

on market access suggests that the sooner the transitional TRQ regime is phased out and replaced

by a tariff-only regime, the greater will be market access.

References

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Appendix. WTO’s Definition of Principal Administration Methods

Administration Methods Description

Applied Tariffs (AT) No shares are allocated to importers. Imports of the products

concerned are allowed into the territory of the Member in

unlimited quantities at the in-quota tariff rate or below.

Auctioning (AU) Importers' shares are allocated, or licenses issued, largely on

the basis of an auctioning or competitive bid system.

First-come, First-served (FC) No shares are allocated to importers. Imports are permitted

entry at the in-quota tariff rates until the tariff quota is f illed;

then the higher tariff automatically applies. The physical

importation of the good d etermines the order and hence the

applicable tariff.

Historical Importers (HI) Importers' shares are allocated, or licenses issued, principally

in relation to past imports of the product concerned.

Licenses on Demand (LD) Importers' shares are generally allocated, or licenses issued,

in relation to quantities demanded and often prior to the

commencement of the period during which the physicalimportation is to take place. This includes methods involving

licenses issued on a first-come, first-served basis and those

systems where license requests are reduced pro rata where

they exceed available quantities.

Producer Groups (PG) Import shares are allocated entirely or mainly to a producer

group or association which imports (or has direct control of

imports undertaken by the relevant Member) the product

concerned.

State Trading Entities (ST) Import shares are allocated entirely or mainly to a state

trading entity which imports (or has direct control of imports

undertaken by intermediaries) the product concerned.

Other (OT) Administration methods which do not clearly fall within any

of the above categories.

Mixed Allocation (MX) Administration methods involving a combination of the

methods as set out above with no one method being

dominant.

Non-specified (NS) Tariff quotas for which no administration method has been

notified.

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