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The Impact of Trade Liberalization on Brazilian Agriculture

By

Dragan Miljkovic, Professor

North Dakota State University, USA

Silvia Helena Galvao de Miranda, Professor

University of Sao Paulo – ESALQ, Brazil

Saleem Shaik, Assistant Professor

North Dakota State University, USA

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The Impact of Trade Liberalization on Brazilian Agriculture

The relationship between free trade and productivity and technical efficiency gains has

been controversial among general public and politicians alike. The sentiment often echoed by

promoters of trade liberalization is one of substantial expectations of productivity gains due to

technical efficiency improvements following trade liberalization. This position is universally and

often utilized by politicians all around the globe when trying to gain support by their

constituency, especially by various producer groups, to promote trade liberalization. This point

may be nicely summarized with a couple of examples from different countries. Daniella

Markheim (2007) of the Heritage Foundation in the US wrote: “Free trade allows a country to

compete in the global market according to its fundamental economic strengths and to reap the

productivity and efficiency gains that promote long-run wealth and prosperity.” (p.3) Similarly,

Pereira (2006) conveyed the position of various Brazilian governments: “An open trade regime

would oblige domestic firms to adopt more efficient productive techniques; moreover, it would

reveal comparative advantages, leading to a better allocation of national resources, and, thereby,

resulting in greater international competitiveness.” (p. 124)

Economists are more cautious concerning this issue and contend how the relationship

between trade openness and productivity and efficiency is more complex than what appears on

the surface. Productivity growth is comprised of two mutually exclusive and exhaustive

components, technological change (TC) and technical efficiency change (TEC). TC represents a

shift of the production possibility frontier (PPF) (i.e., TC represents changes to potential output).

TEC indicates a country’s movement towards or away from the PPF (i.e., TEC measures the gap

between a country’s actual and potential outputs). It has been determined that trade openness

may not have the same effect on both TC and TEC: trade typically does not lead to negative TC,

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but it can give rise to either positive or negative TEC (e.g., Iyer, Rambaldi, and Tang, 2008).

This in turn makes the impact of trade on overall productivity (TE and TEC) uncertain and the

relationship between trade openness and technical efficiency an empirical question.

According to Rodrik (1992), this lack of consensus among economists arises because

there are no systematic theories linking trade policy to technical efficiency. The reasons for the

absence of systematic, comprehensive theories are multifold. First, it may be due to conflict

between the longstanding prevalence of Ricardian doctrine of comparative costs which relies on

allocative efficiency (i.e., the allocation of domestic resources into sectors where they are most

productive) and the scale economies argument. The original case for the gains from trade was not

developed by Ricardo but by Adam Smith (1937) and relied on scale economies via an expanded

division of labor within a larger market to lead to overall gains in productivity: “By means of

(foreign trade), the narrowness of the home market does not hinder the division of labour in any

particular branch of art or manufacture from being carried to the highest perfection. By opening a

more extensive market for whatever part of the produce of their labour may exceed the home

consumption, it encourages them to improve its productive powers …” (Book IV, Ch. I, p. 415).

Scale economies were rediscovered by new trade theorists (Krugman, 1979; 1980) as a

rationale for trade. They limited it, however, only to cases of imperfect competition. Under this

assumption, “The range of possible outcomes of trade policy then becomes limited only by the

analyst’s imagination” (Rodrik, 1992, p. 156). Many contributions that followed the original

seminal works by Krugman (1979, 1980) strongly support Rodrik’s statement (e.g., Helpman and

Krugman, 1985; Bernard et al., 2003; Melitz, 2003).

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Scale economies are not the only argument for trade liberalization made by those

favoring increasing trade. Protection is known to lead to higher concentration in the domestic

markets. The rise of non-competitive market structures under border protection is presumed to

discourage improvements in productivity and technical efficiency. On the other hand,

liberalization reverses the incentives to concentration by creating a more competitive

environment. However, this relationship between market structure and innovation is hotly

debated and disputed in industrial organization. The Schumpeterian perspective, for instance,

strongly disagrees with the view that competition is conducive to either innovation or cost

reducing investment.

Another argument used by proponents of trade liberalization is that inward-oriented

regimes and macroeconomic instability go hand-in-hand. Macroeconomic instability often leads

output falling below the full-capacity level, further discouraging productivity growth. In

addition, the overvaluation of domestic currency and shortages of imported inputs discourage

domestic firms from trying to benefit from scale economies via foreign markets. However, these

arguments have nothing to do with trade policy per se (Sachs, 1987). Sachs maintains countries

should change their exchange rate and fiscal policies when technological performance suffers

due to mismanagement of macroeconomic policy. Promotion of trade liberalization likely is

driven by ideology rather than economics. Indeed, once attention is focused on trade policy, it

becomes extremely difficult to argue that liberalization, as a general rule, must have a positive

impact on technical efficiency.

The above theoretical uncertainties resulted in empirical studies identifying the

relationship between trade liberalization and, in turn, trade openness and technical efficiency.

The introduction of linear programming and frontier approaches enabled researchers to isolate

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TEC from TC in a comparison of countries. A number of studies examined the effect of outward

orientation (trade liberalization) on technical efficiency at the industry or national economy level

(e.g., Iyer, Rambaldi, and Tang, 2008; Amiti and Konings, 2007; Fernandes, 2007; Shafaeddin,

2005; Milner and Weyman-Jones, 2003; Pavcnik, 2002; Lall, Featherstone, and Norman, 2000).

Unfortunately these findings did not conclusively lend credibility to either proposition: that trade

openness does or does not improve technical efficiency.

The overall results for the Brazilian economy after the trade liberalization process of

1990s have provoked an intense debate about its benefits. As Bonelli (2002) points out, there is

no overall agreement concerning the magnitude of the gains, as methodologies and data sets

differ. However, all the studies show Brazil’s productivity growth following the introduction of

trade liberalization policies of 1990. For instance, Bonelli and Fonseca (1998) estimated the

increase in the growth rate of total factor productivity to be 3.4 percent in the 1990s, while

Muendler, Servén and Sepúlveda (2001) estimated the increase to be in the range of 0.2–0.3

percent. López-Córdova and Moreira (2003) found that total factor productivity grew at a rate of

2.7 percent annually during the period of 1996–2000. According to Bonelli (2002), labor

productivity measured in terms of value added rose at an average annual rate of 1.8 per cent

during the period 1991–2000. All the studies show that productivity growth was not uniform

among sectors. Bonelli concluded that productivity growth has been the answer to increased

competition in only a limited number of sectors’ cases.

The only study to date to measure the impact on trade openness on technical efficiency in

agriculture was done by Shaik and Miljkovic (2011). They use the stochastic frontier analysis

(SFA) and apply it to US data. The results of their study indicate that overall trade openness

does not have an impact on technical efficiency in US agriculture. For Brazil, Coura, Figueiredo

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and Santos (2006) analyzed and found the positive impact of trade openness on total factors

productivity in agriculture in the state of Sao Paulo. These authors used a Data Envelopment

Analysis (DEA) and their results pointed also to gains in both technical efficiency and in

technological change, and showed that indicators for technological change were higher after the

opening of the economy’s, particularly for exports’ products.

The goal of this study is to examine the impact of trade liberalization on technical

efficiency in Brazilian agriculture using state level data, and hence to further contribute to the

debate in the literature regarding the success of Brazilian trade liberalization strategy and the

general relationship between trade openness and technical efficiency.

Brazilian Agricultural Sector

Brazilian agriculture sector has increased significantly in the last three decades quickly becoming

one of the largest and fastest growing in global economy today. Grains production, for example,

increased from 50.9 million tons in 1979/1980 to 162.7 million tons in 2010/2011 (CONAB,

2011). This growth has come mostly from yield gains (increases in productivity) rather than

farmland increasing. Federal government has also started trade liberalization process in 1988, a

process which has been further consolidated later with a significant import tariff reduction both

unilateral and due to participation in various global and regional free trade agreements (Bonelli,

Brito and Veiga, 1997).

Figure 1a highlights that the cultivated area increased from 37.3 to 51.5 million hectares

(ha) or by about 38.1 percent in grains and cereals (e.g., corn, soybean, cotton, rice, beans,

wheat) between 1976/77 – 2011/12. During the same 36-year period, production (Figure 1b)

increased by almost 247 percent. The basic explanation can be seen from Figure 1c, which shows

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the yield gains during these years. Brazilian average yield for these crops rose from 1,258 kg/ha

to 3,049 kg/ha. Cultivated area had a lesser but still an important role in production increases,

particularly in Center-West region (Cerrado areas).

(Insert Figures 1a, 1b, and 1c Here)

There are a few papers discussing the gains in productivity in Brazilian agriculture.

Notably, Gasquez, Bastos and Bacchi (2008) estimated the total factors productivity and growth

sources for Brazilian agriculture, for the period between 1975 and 2005. These authors compare

the growth pattern of Brazilian agricultural product with that in the United States, highlighting

that in the former inputs use contributes to agriculture product growth, while in the latter inputs

use is being reduced in recent years. They also explain that the growing inputs use in Brazil

comes from an increase in capital use (machinery) and fertilizers application. Quantity of labor

use has been decreasing in the analyzed period, as the Index for labor dropped from 100 in 1975

to 98 in 2005. The positive impact of labor use on productivity can be explained by the changing

nature of agricultural labor in recent years. The average years of schooling is increasing for labor

in agriculture, despite the fact that those employees are still the lowest qualified people among

all sectors in Brazilian economy (De Negri et al., 2006, p. 34). Land quantity index rose from

100 to 143 and capital quantity index from 100 to 171 in the same years. Gasquez, Bastos and

Bacchi (2008) also found that the total factor productivity (TFP) growth has been more stable

since 1996. Between 1975 and 2005, they found the 2.51 percent of growth in TFP resulted from

3.5 percent growth in product and 0.96 percent from inputs use.

Gasquez, Bastos and Bacchi (2008) point to three factors to help explain the evolution of

inputs use and productivity improvements. First element is the change in agriculture and

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livestock composition in its sector gross production, as animal production showed a huge

increase between the 70’s and 2005. Partially, this increase in participation and in absolute

production value can be explained by the boost in beef, poultry and pork exports. Annual crops

were also diversified and had an area expansion in that same period, contributing to changes in

productivity. Second factor is an increase in rural credit availability which has been noted

especially between 2000 and 2006. Finally, they point out the relevance of creating Embrapa

(Empresa Brasileira de Pesquisa Agropecuária) in 1973, a federal research institute that has been

contributing significantly to agriculture and livestock growth in the country. They also mention

the importance of other state institutes, universities and private companies to explain agricultural

sector productivity increase.

Trade liberalization policy of the early- and mid-1990s led to an improved balance of

trade in agribusiness sector in the subsequent period. Figure 2a, 2b, and 2c show that the

agribusiness exports increased faster than the agribusiness imports during the last fifteen years,

even though Brazil has faced some periods of domestic currency appreciation. The only

remarkable strike affecting drastically, though temporarily, this trend has been the financial

crises in 2008. In general, all regions improved their trade balance of agriculture and livestock

products, although Southeast and South still account for the more significant increase and the

largest share. Center-West region also increased participation in agribusiness exports during the

last decade. During the whole period, on average, soybean (22%) and sugarcane (11%)

complexes accounted for around 33 percent of the total exported value from agribusiness sector.

On the other hand, one can note that agribusiness imports remained below US$ 10 billion during

the same period, and only started reacting since 2008, mostly because of exchange rate

appreciation effects.

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(Insert Figures 2a, 2b, and 2c Here)

Method and Data

The technical efficiency concept was introduced by Farrell (1957). It is defined as the distance of

the observation from the production frontier and measured by the observed output of a firm. In

other words, technical efficiency of a firm can be defined as a measure of how well the firm

transforms inputs into outputs given technology. Technical efficiency can be estimated by (a)

two-step non-parametric linear programming approach or (b) parametric stochastic frontier

analysis (SFA). In the first step of the two-step procedure, the efficiency measures are estimated

using non-parametric linear programming approach. This is followed by a Tobit model to

evaluate the factors affecting the efficiency in the second step. However, the two-step procedure

has been the subject of criticism by some researchers since it might be biased due to omitted or

left out variables (see Wang & Schmidt, 2002, and Greene, 2004). The SFA has recently

become a popular tool to estimate the relationship between input and output quantities and has

been primarily used to estimate the technical efficiency of firms. This method, first proposed by

Aigner et al. (1977) and Meeusen and van den Broeck (1977), has seen a surge with extensions

to estimate technical change, efficiency change, and productivity change measures (e.g., Greene,

2003; Kumbhakar and Lovell, 2000).

Here SFA, used to estimate technical efficiency, is extended to examine the importance

of trade openness on technical efficiency in Brazilian agricultural sector. The Battese and Coelli

(1993) SFA model accounts for heterogeneity in the efficiency measures. This methodology has

been extended to evaluate half-normal, truncated normal and gamma efficiency distributions

(Green, 2004; Shaik et al., 2009). Following Iyer, Rambaldi, and Tang (2008), a stochastic

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frontier production function equation and trade equation are estimated with panel data using a

state output and technical inefficiency measure, respectively, as endogenous variables. The

stochastic frontier model may be represented as:

(1) yit = f(xit ,Dit; β) ∙ vit - uit

u it = f (z; γ) ∙ εit

where xit is a vector of input variables affecting output yit; Dit is a vector of regional and time

dummy variables; β and γ are the input and trade openness parameter coefficients respectively, zit

is a vector of trade openness variables hypothesized to affect technical inefficiency u; vit is a

random error assumed to be iid and normally distributed with mean zero and variance σ2ν; uit is

the technical inefficiency of the state i at time t constrained to be positive and hence is a

truncated normally distributed variable with mean zero and variance σ2u; and εit is a random error

which is normally distributed with mean zero and variance σ2ε. Finally, i and t represent cross-

sections (states) and time-series (years).

Equation (1) is used to econometrically estimate the impact of trade openness on

technical inefficiency. In order to differentiate the effect of different inputs on agricultural

output, the Hicks-neutral production function containing three independent inputs: capital, land,

and labor, along with trade openness in the technical inefficiency equation, is estimated. Hicks-

neutral assumption implies a common technology change is associated with the production

function. Given the data being used and estimation of one-way fixed effects model (see in the

estimation section), Hicks-neutral technology was assumed. Further, the non-neutral technical

change that implies technology is independently associated with each input variable could not be

estimated due to non-convergence.

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Time trend is typically used to proxy the Hicks-neutral change. In this case, we deemed

using time dummy variables instead, given the length of the time series (Antle and Capalbo,

1988). Cross-sectional dummies are included to differentiate for the regional efficiency

differences. The panel stochastic frontier production function can be represented by a Cobb-

Douglas functional form. A more flexible functional form, Translog production function, could

also be estimated. Due to convenience of interpreting the input elasticities and the returns to

scale, Cobb-Douglas production function seems to be most appropriate. The empirical stochastic

one-way fixed effect panel stochastic frontier model can then be formulated as following:

(2) yit= α1 + β1,1Capitalit + β1,2Landit + β1,3Laborit + β1,4 DTimei + β1,5DRegionalt + vit – uit

uit = α2 + β2,1TOpenit + β2,2Time + εit

Cross-section time-series data are used in the analysis comprised of a cross section for 26

Brazilian states and a four year time-series – 1990, 1995, 2000 and 2005. The states were

combined in five relatively homogeneous regions: South (Region 1), Southeast (Region 2), North

(Region 3), Northeast (Region 4) and Center-West (Region 5), which are shown in Figure 3. This

time selection is explained by trade data restrictions as disaggregated trade data by state are only

available for more recent years. Also, trade liberalization was in its infancy in 1990 and trade

flows were largely unaffected by the change in trade policy. Hence this year seems to be a

reasonable starting and reference point for the analysis. There are no time series of inputs

(capital, labor, land) for Brazilian states, except by those obtained through the Census, which has

been organized each every 5 years. So, in terms of aggregation, official trade data were collected

from Sistema Alice/Secex-MDIC (www.midc.gov.br), and summed up for HS products of

chapters 1 to 24 and also 1 to 99, for each one of the 27 states from Brazil. The choice of

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aggregation of chapters 1 to 24 includes a vast majority of agribusiness products and is

consistent with the definition of agribusiness products and with the World Trade Organization

(WTO) classification in terms of the Agreement on Agriculture. The aggregation of chapters 1 to

99 combines total trade, i.e., all agricultural and non-agricultural goods. Both imports and

exports were collected to analyze trade openness.

Inputs and outputs information have been taken from the Brazilian Census data and also

from other IBGE – Instituto Brasileiro de Geografia e Estatística [Brazilian Institute of

Geography and Statistics] documentation1. Census data also corresponded to the above-

mentioned four years between 1990 and 2005. The following data have been gathered, per year,

per state: employed people, capital stock, cultivated area (in 1000 ha), total GDP and the

Agriculture and Livestock GDP. Interpolation has been necessary to fill out some blanks in the

time-series for some states. All value data were corrected by IGP-DI (General Index of Prices) to

real values of base-year 2005, in Brazilian currency (Reais).

Trade statistics are originally in US$ and were converted to Reais using the average

government official exchange rate (buying), collected from Ipeadata2. Trade openness was

calculated summing total imports plus exports and dividing the total by agriculture GDP (Alcala

and Ciccone, 2004). In particular fast growing non-agricultural imports of chemicals or

machinery may have had an impact on productivity and in turn on technical efficiency in

Brazilian agriculture. Alternatively, and to check the robustness of the results, agricultural

exports divided by agriculture GDP were used as another measure of trade openness. All these

data were also converted to Reais of the base year 2005.

1 Available at http://www.ibge.gov.br/english/ . January 2011.2 Available at http://www.ipeadata.gov.br/. June 2011

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Results

Equation (2) is estimated using LIMDEP software that estimates the SFA using maximum

likelihood estimation techniques (for details see LIMDEP, 2007). ). All the variables are in

logarthims, so the parameters can be interpreted as elasticity. Both variations of the model

presented in equation (2), i.e., efficiency equations containing different representations of the

trade openness, have been estimated using log-log specification. Hence results are provided in

the form of elasticities. The results are presented in Table 1 and 2 respectively for total trade

openness and agriculture export/agriculture GDP models.

(Insert Table 1 Here)

The results for the total trade openness model are as following. The only significant time

dummy is for 2005, and it has positive sign. It suggests higher output than in the omitted year of

1990. However, this result does not lead with confidence to a conclusion of the presence of

Hicks neutral change. Considering major improvements in global agriculture from 1970s

onward, our prior belief has been that it is likely to find stronger evidence than a mere possibility

of the presence of Hicks neutral change.

The factor input coefficients contribute very differently to the increase of output. Single

most important factor input has been capital: for every 10 percent increase in capital stock,

agricultural output would increase by 5.2 percent. Considering major investments in agriculture

by the governments of Brazil since 1970s as well as by major international agribusiness

companies since 1990s, this result comes hardly as a surprise. An increase in labor use by 10

percent leads subsequently to an increase in agricultural output by almost 4.1 percent. There are

two equally important reasons for this still very important role that labor has in Brazilian

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agriculture. First, it is still relatively cheap compared to available technology, and second,

production of many commodities including citrus and other fruits, vegetables and coffee is

relatively labor intensive. Moreover, in southeast, south, and northeast, there is a large share of

family production in fruits, tobacco, vegetables, and poultry. Although the production of grains

and cereals is mechanized in Center-West and South, a high aggregated value is coming from

those smaller properties, with family labor. According to Guilhoto et al. (2007), the total

agribusiness has accounted for 30 percent of total Brazilian GPD between 1995 and 2005, while

the family segment of agribusiness has accounted for 10 percent. Finally, additional agricultural

land would not lead to further increase in agricultural output, but to its small decrease. The best

farm land has already been in use for many decades. Newly introduced agricultural land is in

need for costly additional preparation and fertilization. Moreover, during the last decade, there

were new public policies limiting the expansion of agricultural land in some regions, particularly

in northern states.

Regional dummies indicate significantly larger increase in output in more temperate

South and Center-West Regions relative to the omitted Northeast. More favorable weather, land

availability and better quality soil and the presence of farming tradition and know-how in more

southern regions contributes to this disparity.

The efficiency equation of this model specification, i.e., the total exports plus imports

divided by agriculture GDP, reveals that trade openness has no impact on technical efficiency.

We need to clarify here again that we do not measure the effects of trade openness on overall

outward shifts in the production possibility frontier as supported by the time dummy coefficients,

but only the relative distance of individual state aggregates from an expanding production

possibility frontier. While this result simply states that, for the given data set, a change in

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agricultural trade openness does not impact technical efficiency in agriculture, the implications,

which are discussed in the subsequent section, are more significant for many participants in

domestic and global agricultural markets and policy arena.

(Insert Table 2 Here)

The robustness of the previous results is tested with an alternative formulation of trade

openness as a ratio of agriculture exports and agriculture GDP. While the trade liberalization in

Brazil as a whole may have been initiated primarily to introduce more (foreign) competition into

a traditional and undeveloped economy, a large and potentially powerful agricultural sector saw

it as an opportunity for the world-wide expansion. Hence while this measure of trade openness

may not be a textbook example of the definition, its relevancy is surely underscored in the

ambition of the agriculture sector.

The only significant and positive time dummy coefficient remains to be for 2005. Single

most important factor input remains capital: for every 10 percent increase in capital stock,

agricultural output would increase by almost 5.4 percent. An increase in labor use by 10 percent

leads subsequently to an increase in agricultural output by almost 4.6 percent, while additional

agricultural land would lead to a small decrease in agricultural output. Regional dummies

indicate now larger area benefitting from the agricultural exports expansion: there is an increase

in output in South, Southeast and Center-West Regions relative to the omitted Northeast. Most

importantly, trade openness as measured here also does not impact technical efficiency in

Brazilian agriculture confirming our previous finding.

Policy Implications

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The results of this paper are consistent with findings of Shaik and Miljkovic (2011) for

the United States. This similarity suggests the emerging of global patterns of the (lack of) impact

of trade liberalization on technical efficiency in agriculture. Both Brazil and the United States

are major producers and traders of agricultural products in the world markets. The impacts of

trade policies on their agriculture are likely to be similar in many other countries. Here are some

reasons why that may be the case.

One of the key policy and political arguments among promoters of trade liberalization is

its potential to increase productivity and technical efficiency in the economy overall as well as in

individual industries. Benefits from free trade to consumers have been long recognized; it is

producers who are typically skeptical about potential benefits. Agricultural producers are

generally well organized in relatively small size commodity groups/organizations and are

successful in rent seeking through policy intervention with rare instances of free riding

(Miljkovic, 2004). Hence there is a pressure on policy makers to make appealing arguments to

producers and to convince them how trade liberalization is “good business” for them. However,

agricultural producers remain to be successful and maintain relatively high level of protection

through various domestic non-trade policies and non-tariff barriers which often are in conflict

with trade liberalization policies. Although this argument has been made and analyzed for the

United States (Miljkovic, 2004), it could easily be applied to most countries given historic and

strategic importance of agriculture. Once trade liberalization loses its main purpose of increasing

the efficiency in production through the exposure to global market competition, it is easy to see

how the whole free trade idea becomes less marketable. As producers are to choose between high

risk market competition and safety of domestic protection, they surely will choose the second

one. And even if trade liberalization is forced on them, they are able to maintain high level of

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protection via non-trade policy instruments. In turn, the incentive to increase efficiency becomes

lessened substantially since the (international) competition becomes largely irrelevant.

An additional argument which may explain the lack of the impact of trade openness on

agriculture is the relatively large size of domestic market. Both Brazil and the United States

agriculture are reliant on domestic markets foremost. Even if they both are among world’s

largest exporters of agricultural commodities, they still export a relatively small share of

domestic production since most of it is being marketed domestically. Particularly in the case of

Brazil, as it was a closed country until the end of 1980’s, there was a scarcity of food and

consumer goods in general. As the liberalization started, there was a potential or repressed

consumption to be reached (“consume reprimido”), and little by little, after 1995 and 2003, the

real per capita income improved and people could consume more goods, including food

products. Thus, except for some typical/traditional exporting sectors such as soybean grains and

meal and coffee, there was an increase in consumption of beef, poultry, sugar, fruits and other

commodities.

An obvious remedy for this situation would be to have domestic policies and trade

policies consistent and directed towards increased productivity and efficiency. However, the

political power of strong commodity interest groups is unlikely to exchange the safety of

government protection for uncertainty of market competition.

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Figure 1a – Cultivated area for main crops (grains and cereals) in Brazil. 1976/77 – 2011/12 (1000 ha). Source: CONAB (2012).

Figure 1b – Production of major grains and cereals in Brazil. Harvest years 1976/77 – 2011/12 (1000 tons). Source: CONAB (2012).

Figure 1c – Average yield for crops in Brazilian regions. 1976/77 – 2011/12 (kg/ ha). Source: CONAB (2012).

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Figure 2a – Brazilian Agribusiness Trade Balance (Current US$): 1997-2011.

Figure 2b – Brazilian Agribusiness Exports by Region (Current US$): 1997 – 2011

Figure 2c – Brazilian Agribusiness Imports by Region (Current US$): 1997-2011

Source: AGROSTAT. Available at: www.agricultura.gov.br

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Figure 3 - Map of Brazilian regions

Center West North Northeast Southeast South

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Table 1. Parameter coefficients of production function and (total imports + total exports)/agricultural GDP as the trade openness measure

Stochastic Frontier Production Function Equation

Parameter Standard Error (SE) Z-value P[|Z|>z]Intercept 1.07125 .70373 1.52 .1279Capital .52263*** .07075 7.39 .0000Land -.04477* .08902 1.674 0.0941Labor .40611*** .10503 3.87 .0001DRegion1 .67974** .30422 2.23 .0255

DRegion2 .37354 .23871 1.56 .1176

DRegion3 -.05493 .13127 -.42 .6756

DRegion5 .49859* .26627 1.87 .0611

DUM95 -.13377 .08538 -1.57 .1172

DUM00 .02630 .09154 .29 .7739

DUM05 .21955** .09852 2.23 .0259

TradeOpennessIntercept -.30003 .5316 -.55 .5565YEAR .00250 .00443 .57 .5715TOpen -.76279 1.13540 -.67 .5017

Variance parameters for compound errorLambda -1.53493 1.5316 1.040 0.2818Sigma(u) -1.42067*** .10402 -13.66 .0000

Note: *, **, and *** indicate statistical significance at 10, 5 and 1 percent respectively.

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Table 2. Parameter coefficients of production function and agricultural exports/agricultural GDP as the trade openness measure

Stochastic Frontier Production Function Equation

Parameter Standard Error (SE) Z-value P[|Z|>z]Intercept .93447 .71815 1.30 .1932Capital .53824*** .06758 7.96 .0000Land -.10973* .06595 -1.66 .0961Labor .45767*** .07734 5.92 .0000DRegion1 .78413*** .25021 3.13 .0017

DRegion2 .50251*** .13579 3.70 .0002

DRegion3 -.05506 .10741 -.51 .6082

DRegion5 .60862*** .19110 3.18 .0014

DUM95 -.09674 .07936 -1.22 .2229

DUM00 .17697 .12787 1.38 .1664

DUM05 .52329** .20860 2.51 .0121

TradeOpennessIntercept -.35503 .5887 -.55 .5589YEAR .13659 .24587 .56 .5785TOpen -.19602 .35907 -.55 .5851

Variance parameters for compound errorLambda -1.3624 1.8987 .946 0.3416Sigma(u) -1.50989*** .26725 -5.65 .0000

Note: *, **, and *** indicate statistical significance at 10, 5 and 1 percent respectively.