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Nicholas Rada and Constanza Valdes

Policy, Technology, 

and Effi ciency of 

Economic Brazilian Agriculture

Research 

Report 

Number 137

July 2012
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Rada, Nicholas, and Constanza Valdes. Policy, Technology, and Efficiency 

of Brazilian Agriculture, ERR-137, U.S. Department of Agriculture, 

Economic Research Service, July 2012.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

The Brazilian agricultural sector has been transformed from a traditional system of production with low use of modern technologies to a world agricultural leader. That transformation 

occurred as the country moved away from import-substitution policies—which nurtured 

domestic industrial development at the expense of agriculture—toward market-oriented 

policy reforms. These reforms included openness to foreign trade and foreign investment and the use of new technologies, which led to a new growth pattern. To evaluate that 

transformation, the authors use agricultural censuses spanning 1985-2006 to characterize 

Brazilian total factor productivity growth, decomposing that growth into technical and 

effi ciency changes. This report presents the fi ndings of a study that focuses on the effect of 

Brazil’s science and technology investments and other public policies on farm production. 

The fi ndings indicate that agricultural research benefi ts have been most rapidly adopted by 

the most effi cient farms, widening the productivity gap between these farms and average 

farms. That gap, however, has been narrowed through other public policies, such as rural 

credit and infrastructure investments, that favor average producers.

Keywords: Brazilian agriculture, total factor productivity (TFP) growth, technical change, 

technical effi ciency, Embrapa, agricultural research, distance function, stochastic frontier

Acknowledgments

Special thanks are given to José Garcia Gasques of Brazil’s Ministry of Livestock, 

Agriculture, and Food Supply, the Brazilian Institute of Applied Economic Research 

(IPEA), and Steve Buccola of Oregon State University. The authors are also grateful to 

Octavio Oliveira of the Brazilian Institute of Geography and Statistics (IBGE) and Keith 

Fuglie of USDA’s Economic Research Service (ERS). We appreciate the information 

supplied by Elisio Contini and Flavio Avila of Brazil’s Embrapa research corporation, 

Nienke Beintema of the International Food Policy Research Institute (IFPRI), Eduardo 

Magalhaes of Datalyze, Inc., and Chris Dicken of ERS. Thanks are also due to Courtney 

Knauth for editing the report and to Curtia Taylor for layout and design.

Nicholas Rada, nrada@ers.usda.gov

Constanza Valdes, cvaldes@ers.usda.gov

Policy, Technology, and 

Effi ciency of Brazilian 

Agriculture

Economic 

Research 

Report

Number 137

July 2012
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Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Policy Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

 Agricultural Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

 Rural Credit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Evaluating Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Total Factor Productivity Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 Assessing Total Factor Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Production Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Results

Policy Impacts on Productive Effi ciency . . . . . . . . . . . . . . . . . . . . . . . . 18

 Technical Change, Technical Effi ciency Change, 

 and TFP Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Appendix A. Understanding Total Factor Productivity 

(TFP) from a Stochastic Input Distance Frontier. . . . . . . . . . . . . . . . . . 25

Appendix B. Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix C. Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
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Summary

What Is the Issue?

Between 1985 and 2006, Brazilian agricultural production grew by 77 

percent and Brazil emerged as a major international agricultural exporter. 

The authors postulate that Brazil’s agricultural development was boosted by 

sustained public investments in science and technology, leading to a stream 

of new technologies. These investments, in an environment of improving 

economic liberalization (initiated in the late 1980s) and stability (post-Real 

plan), may have given farmers incentives to boost farm effi ciency and production. However, other policy drivers likely affected farm effi ciency, namely, 

public infrastructure and rural credit investments. To test their hypothesis 

that science and technology investments were the main impetus to Brazil’s 

agricultural productivity growth, the authors measure Brazil’s national total 

factor productivity (TFP—ratio of total output to total inputs employed in 

production) and analyze the impact of each of the policy drivers on that 

productivity growth. 

What Did the Study Find?

We hypothesize that Brazil’s agricultural development is a result of sustained 

investments in science and technology that led to a stream of new technologies. These policies, embedded in an environment of macroeconomic 

stability and economic liberalization, provided farmers with the incentives to 

boost farm effi ciency and production. The study fi nds:

• That Brazil’s national average farm TFP growth increased at an annual 

rate of 2.55 percent between 1985 and 2006. 

• That TFP growth was driven by factor productivity of the most effi cient 

farms, which progressed at an average of 4.4 percent each year. Total 

factor productivity of the most effi cient farms (4.4 percent) is composed 

of impressive productivity growth in the livestock (7.1 percent annually) 

and crops (2.9 percent) subsectors.

• The most effi cient producers achieved rapid TFP growth, enabling these 

farms to produce 138 percent more in 2006 than in 1985, while maintaining the earlier input levels.

• The TFP growth of average farms was slower, with mean technical effi -

ciency levels declining from 93 percent in 1985 to 84 percent in 1995/6 

and to 64 percent in 2006. These effi ciency levels imply that the average 

farm produced 93 percent of what the most effi cient farms produced in 

1985, but only 64 percent of what those most effi cient farms produced 

in 2006. 

• Despite an enlarging productivity gap between the most effi cient and 

average farms, the average farmer was able to produce 62 percent more 

in 2006 than in 1985, while maintaining the earlier input levels.

• Public agricultural research—provided through Embrapa, the agricultural research agency linked to the Ministry of Agriculture and Food 

Supply—appears to have had more infl uence on the most effi cient farms, 
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widening the TFP gap between those farms and average producers by 

0.2 percent for each 1-percent increase in Embrapa’s research stock.

• Embrapa’s national commodity research centers have been especially 

important in boosting TFP growth, while its regional resource research 

centers have not had the same measurable impact. Boosting the national 

commodity research stock by 1 percent widened the TFP gap by 0.23 

percent, while a similar boost to the regional resource stock did not 

change that TFP gap.

• Of the policies exerting a narrowing infl uence on the productivity gap 

between the most effi cient and the average producers, rural credit and 

transportation infrastructure investments have been factors, but primary 

school infrastructure investments exerted the greatest impact. 

How Was the Study Conducted?

To analyze Brazil’s TFP growth, the authors use decennial Brazilian farm 

census data obtained from the Brazilian Institute of Geography and Statistics 

(IBGE). These data are combined with data from Avila and Evenson (1995), 

Barros (1999), Brazil’s agricultural research agency, Embrapa, and Brazilian 

statistical yearbook information. The authors use these data, controlling for 

a number of factors, to evaluate Brazil’s national agricultural productivity 

growth and analyze policy impacts on productive effi ciency. The analysis 

entails estimating a stochastic distance frontier, which makes it possible to 

distinguish technical change among Brazil’s principal commodity groups. 

The method further allows estimation of the TFP growth of both the most 

effi cient farms and the average farms and of the TFP gap separating them. 

Technical effi ciency statistics are estimated for each microregion and year 

and are used in evaluating the effects of Government policy on Brazilian 

farm productivity.
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Introduction

Brazil is viewed by many as the latest exemplar for global agriculture, based 

on its agricultural growth, strong trade performance, and untapped potential. 

With the world’s largest arable land area of 76 million hectares (IBGE, 2010), 

fi fth largest population base (FAO, 2011), and strong record of agricultural 

production and exports, the attention focused on the country is not surprising. 

Economic reforms in the 1980s and early 1990s and agricultural technologies designed for tropical soil conditions have led to greater exploitation of 

Brazil’s considerable land availability, enabling the country to discover its 

agricultural potential.

Over the last quarter-century, Brazil’s agricultural production has grown 

signifi cantly. Using production and trade data from the Food and Agriculture 

Organization of the United Nations (FAO), we fi nd that the total value of the 

country’s agricultural production between 1985 and 2008 grew 3.79 percent 

each year, driving up its share of total global production from 3.9 percent in 

1985 to 5.7 percent in 2008. To provide context, that value-share growth is 

placed in contrast to that of the United States (fi g. 1). The robust production 

growth has increased Brazil’s agricultural exports, with the total value of its 

agricultural trade growing 7.70 percent annually between 1985 and 2008.

While crops, especially soybeans, have been the focal point of many reports 

on the increasing importance of Brazilian agriculture in global markets 

Source: Food and Agriculture Organization FAOSTAT Agricultural Databases. Available at: http://faostat.fao.org/.

Figure 1

Narrowing the agricultural production value-share gap between the United States and Brazil, 

1985-2008

Value shares of global gross production (percent)

0.0%

1985 87 89 91 93 95 97 99 2001 03 05 07 09

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

U.S. 

Brazil
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(Schnepf et al., 2001; Constantin et al., 2009; The Economist, 2010), one 

of the country’s most rapid gains in global trade has been in the livestock 

subsector. In light of the World Trade Organization’s Uruguay Round, which 

further liberalized meat trade in international markets, Brazilian livestock 

producers between 1985 and 2008 experienced an increase in their share 

of total volume of meat traded by 9.70 percent annually. That growth rate 

accelerated after 2000 by 17.42 percent each year until 2008. As depicted 

by fi gure 2, although the United States and Brazil were trading in separate 

global beef markets due to restrictions caused by the endemic presence of 

foot-and-mouth disease (FMD) in Brazil, by 2008, Brazil had caught up with 

the United States’ 17.6-percent share of the global meat trade. (U.S. beef 

exports did incur restrictions over the 2004-07 period due to bovine spongiform encephalopathy (BSE)). 

We hypothesize that Brazil’s agricultural development is a result of sustained 

investments in science and technology that led to a stream of new technologies. These policies, embedded in an environment of macroeconomic 

stability and economic liberalization, provided farmers with the incentives to 

boost farm effi ciency and production. We test our hypothesis by measuring 

Brazil’s national total factor productivity (TFP) growth using the three 

consecutive decennial farm censuses (1985, 1995/6, and 2006) conducted 

since its return to democracy in 1985. TFP, defi ned as total output per total 

measureable inputs, measures the effi ciency of production. We then examine 

Brazilian policies affecting farm effi ciency—namely, agricultural research, 

public infrastructure, and rural credit investments. 

Brazil has the potential to greatly expand its crop area, but maintaining its 

position as a top global supplier of agricultural products is likely to depend 

Source: Food and Agriculture Organization, FAOSTAT Agricultural Databases. Available at: http://faostat.fao.org/.

Figure 2

Rise in Brazil’s share of total global meat trade, 1985-2008

Volume shares of total global meat trade

1985 87 89 91 93 95 97 99 2001 03 05 07

U.S. 

Brazil

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%
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more on continued advances in effi ciency. Our results indicate that was the 

case between 1985 and 2006, as productivity among the most effi cient 

producers rapidly improved, particularly in the livestock subsector.1 Average 

farms were, however, unable to keep pace with the most effi cient farms 

defi ning the best-practice frontier. We further fi nd that agricultural research 

has widened the TFP gap between best-practice and average farms, while 

public infrastructure and credit investments have narrowed that gap. 

1The study’s level of observation is 

the microregion. We refer to farms or 

producers rather than microregions, 

however, when evaluating results. 

Aggregated, microregion-level data 

allow for imputations to farms, an 

imputation that is wrong only to the 

extent that the data suffer aggregation 

problems. Because of this, we refer 

often to farms to more clearly communicate the story to policymakers, the 

intended audience.
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Policy Review

In the post-World War II period, Brazil sought to establish an independent domestic industrial base through import substitution industrialization 

(ISI) policies that replaced imported inputs with domestic production. The 

ISI policies placed direct and indirect pressure on agriculture, shaping its 

performance. For example, export quotas, licenses, and taxes were accompanied by import controls on farm inputs, thereby raising fertilizer, chemical, 

and machinery prices above international prices (Schnepf et al., 2001). 

Agriculture benefi ted from these policies to the extent that the industrial base 

provided domestically produced agricultural inputs to modernize the sector, 

but the import and export controls explicitly and implicitly taxed farmers, 

lowering the returns to technology investment. The National System of 

Rural Credit was established in 1965 to promote agricultural modernization 

by quickening capital formation and increasing foreign exchange earnings 

(Schnepf et al., 2001). 

Agricultural export growth fl ourished in the 1970s, with soybeans holding 

a central position, and mini-devaluations and offsetting subsidies helped to 

reduce the implicit agricultural taxation caused by ISI policies (Graham, 

Gauthier, and Barros, 1987). But by 1979, an oil-price shock, rising international interest rates, and Mexican debt repayment problems propelled Brazil’s 

managed economy toward its own fi scal crisis. Policies affecting its agricultural performance and macroeconomic environment were at times contradictory. For instance, Helfand and Rezende (2004) note that the expansion of 

agricultural support price programs in the 1980s was intended to stimulate 

production and increase foreign exchange earnings in Brazil, but rural credit 

contractions obstructed that goal. Indeed, it appears that Brazil’s management 

of the macroenvironment led to a long transition period from ISI policies to 

the liberalized ones that eventually improved agriculture’s investment and 

production environment and expanded the private sector’s role. 

That transition period included a return of elected offi cials in 1985 who 

considered a variety of stabilization plans to deal with Brazil’s unstable 

macroeconomy.2 Various consecutive plans were aimed at harnessing infl ation through price stabilization, deregulation, fi scal tightening (tax increases, 

Government spending reductions), privatization, and removal of nontariff 

trade barriers and licensing. To boost competitiveness, Brazil joined the 

Southern Common Market (Mercosul) in 1991, which eliminated tariffs on 

most Argentinean and Uruguayan imports. 

Brazil’s last stabilization effort, the 1994 Real plan, introduced marketoriented reforms—including a reduction in the State’s role of setting prices, 

managing production, and regulating the trade of wheat, coffee, sugar, and 

milk—and decreased industrial protection that further contributed to agricultural modernization, particularly in the pork, poultry, and dairy sectors 

(Helfand and Rezende, 2004). The Real plan coincided with the development of grass-fed cattle production as a low-cost activity, allowing farmers to 

withstand rising input prices, tight credit conditions, and export taxes more 

easily than they could with crop production. During this stabilization effort, 

commercial hog and poultry production was initiated in Brazil’s Center-West 

2For a more comprehensive review of 

Brazil’s macroeconomic policy reforms, 

see Graham, Gauthier, and Barros 

(1987), Schnepf et al., (2001), and 

Helfand and Rezende (2004).
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region—sponsored by major international companies and targeted almost 

exclusively at international markets. 

The World Bank’s nominal rates of assistance to agriculture, which represent 

price distortions induced by policy interventions, indicate that the post-Real

plan period was the fi rst time Brazilian agricultural producers experienced 

sustained net subsidization rather than taxation (Anderson et al., 2008). This 

change in economic policy was likely an incentive for private investments in 

agricultural technology and effi ciency improvements, with private investors 

taking advantage of Brazil’s public investment in agricultural research and 

increased credit availability. Agricultural research plays the greatest role in 

raising productivity through technical innovations, while credit provides a 

primary means of accessing those new technologies.

Agricultural Research

Despite a proliferation of Brazilian research institutes and experiment stations 

in the fi rst half of the 1900s, it was not until the 1960s that national agricultural modernization became a priority. Indeed, apart from São Paulo State’s 

research efforts on exportables coffee and cotton, agricultural research was 

largely underfi nanced and poorly managed, and investment in human capital 

and rural extension services was defi cient (Graham et al., 1987). Following 

a comprehensive review of the agricultural research system, Embrapa 

(Brazilian Enterprise for Agricultural Research) was created in 1973 as a 

national agricultural research agency, organized along Federal lines and 

involving cooperation between Federal and State experiment stations. The 

agency employs a decentralized model of applied research, split between 

national commodity, regional resource, and “thematic” centers that allow for 

both a national and local focus (app. table C2).

Initially, Embrapa was tasked with providing extension services for the 

distribution of technological packages, including new seeds, soil correction techniques, and improved production practices. Embrapa’s most notable 

achievement, however, has been the development of technologies allowing 

agricultural expansion to the acidic soils of the Cerrado biome (fi g. 3). The 

Cerrado ecosystem is a savannah; its topography, climate, and soil characteristics have been conducive to large cattle-raising operations and have 

favored mechanized and technologically intensive cultivation of soybeans, 

corn, cotton, and sugarcane. The infertile and acidic soils of the Cerrado 

were originally used for extensive cattle production systems in the 1960s. 

Embrapa’s development in the 1970s of more productive perennial grasses—

such as adaptation of the African Brachiaria species, which has a high nutritional value, provides greater nitrogen fi xation and requires less phosphorus 

fertilization than native pastures—provided a signifi cant technological breakthrough in creating pastureland. This led to more intensive farming systems 

as cattle ranchers replaced extensive production schemes in pastures of low 

productivity with beef and milk production, resulting in increased livestock 

productivity and farmer income. Later, Embrapa expanded its efforts to developing high-yielding and disease-resistant crop varieties; seed for rice, beans, 

wheat, and potatoes; and more improved grass varieties to support new livestock breeding programs.
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Embrapa is fi nanced principally through Federal funds. The proportion of 

Government support rose from 78 percent in 1986 to 91 percent in 1999 and 

was maintained at that high level through 2006 (Beintema et al., 2001; Stads 

and Beintema, 2009). Those funds have been allocated largely to personnel 

expenses (fi g. 4). Over our sample period (1985-2006), the share of total 

Embrapa expenditures allocated to personnel increased from 68 to 80 percent 

(table 1). Within the Embrapa research structure, personnel expenditures have 

been allocated primarily to the national commodity and regional resource 

centers, but their combined shares fell from 84 percent in 1985 to 76 percent 

in 2006 (table 2). Thematic research accounts for the remaining research 

expenditures, with its share growing over each decade. 

Regional resource research differs from national commodity research in 

that it centers on technologies specifi c to States, biomes, and climates rather 

than on commodities with a national scope. Thematic research primarily 

supports national commodity and regional resource work in areas such as 

basic seed production, soil conservation, and genetics and biotechnology. 

These research centers, however, do not operate in isolation. For example, the 

more productive Brachiaria perennial grass was a joint development effort by 

international research centers, Embrapa’s Cerrado and West regional research 

centers, and Embrapa’s beef cattle national commodity research center. 

Source: Brazilian Institute of Geography and Statistics (IBGE), 2006.

Figure 3

Brazilian States and biomes

MATO GROSSO

(MT)

PARÁ

(PA)

AMAZONAS

(AM)

RONDÔNIA

(RO)

ACRE

(AC)

RORAIMA

(RR) AMAPÁ

(AP)

TOCANTINS

(TO)

GOIÁS

(GO)

MATO GROSSO

DO SUL

(MS) SÃO PAULO

(SP)

MINAS GERAIS

(MG)

PARANÁ

(PR)

SANTA CATARINA

(SC)

RIO GRANDE 

DO SUL

(RS)

BAHIA

(BA)

PIAUÍ

(PI)

MARANHÃO

(MA)

CEARÁ

(CE)

RIO GRANDE

DO NORTE

(RN)

PARAÍBA

(PB)

PERNAMBUCO

(PE)

ALAGOAS

(AL)

SERGIPE

(SE)

ESPÍRITO SANTO

(ES)

RIO DE JANEIRO

(RJ)

DISTRITO FEDERAL

(DF)

Brazilian Biomes

Amazônia

Caatinga

Cerrado

Mata Atlântica

Pampa

Pantanal
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Source: Embrapa (Brazilian Enterprise for Agricultural Research), 2010.

Figure 4

Embrapa expenditure allocations, 1974-2006

Millions of 2007 reais

1974 76 78 80 82 84 86 88 90 92 94 96 98 2000 02 04 06

Personnel

Operations

Capital

0

100

200

300

400

500

600

700

800

900

Table 1

Embrapa expenditure allocations, 1985-2006

Year Personnel Capital Operations

Percent

1985 68 6 26

1995 70 15 15

2006 80 6 14

Source: Embrapa (Brazilian Enterprise for Agricultural Research), 2010. 

Table 2

Embrapa expenditure allocations on research center personnel, 

1985-2006

Year National commodity Regional resource Thematic

Percent

1985 38 46 16

1995 34 44 22

2006 33 43 24

Source: Embrapa (Brazilian Enterprise for Agricultural Research), 2010. 
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By 2006, Brazil’s agricultural research intensity—that is, its research 

expenditure per dollar of agricultural gross domestic product (GDP)—had 

fallen from Latin America’s highest to second highest. Research intensity 

declined despite higher Government spending on research and research 

staff than in any other Latin American nation during the census periods we 

examined (Stads and Beintema, 2009). In 2006, Embrapa accounted for 57 

percent of spending on public agricultural research institutions. However, 

Government support for research increased during 2007−09, no doubt 

helping Brazil reemerge as Latin America’s leading public research entity 

(Beintema et al., 2010). 

Rural Credit

The National System of Rural Credit was created in 1965 to quicken capital 

formation in exportable farm products (Schnepf et al., 2001). The 1970s 

were a period of rapid rural credit growth—the most important lever for 

raising shortrun farm output at the time—which exacerbated infl ationary 

pressures (Graham et al., 1987). Graham and colleagues note that the proportion of rural credit to agricultural GDP rose from 58 percent in 1971 to a 

peak of 94 percent in 1976 and subsequently fell to 43 percent in 1981. By 

the 1990s, credit was funneled primarily to small farmers, leaving commercial producers to seek private credit sources. Rural credit volume declined 

throughout the 1980s and early 1990s, with reductions in the 1980s caused by 

international donor community pressure and in the 1990s through implementation of the various stabilization plans (Helfand and Rezende, 2004; Schnepf 

et al., 2001). 

Rural credit includes credit for investing in, producing, and marketing 

commodities. Government-subsidized rural credit represents about a third of 

the total credit needs of the agricultural sector in Brazil. The private sector 

(private banks, input providers, brokers, and farmers) provides the rest, a 

change from pre-1994 reforms when the Brazilian Government provided the 

bulk of the credit needs. 

Agricultural credit has been directed toward fi nancing crop and livestock 

production, as well as capital investments in agricultural infrastructure and 

equipment. Capital investments have included machinery for planting and 

harvesting and for the processing of livestock products and pastureland 

expansion. Over three-quarters of the investment credit disbursed between 

1985 and 2006 has been issued to the livestock sector, boosting livestock’s 

credit investments by 4.7 percent annually, more than double the investment credit allocations for crops during the same period (BACEN, 2009). 

The livestock sector’s greater growth in investment credit indicates Brazil’s 

priority of modernizing livestock production.
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Evaluating Productivity Growth

Total factor productivity (TFP) is generally defi ned as an accounting 

measure, taken as the ratio of an aggregate of total outputs to an aggregate of 

total inputs and measuring the effi ciency with which inputs are transformed 

into outputs. TFP is preferable to other partial-productivity measures, such as 

yield per hectare (land productivity) or output per worker (labor productivity), 

because those partial measures account for only a single factor of production, 

whereas TFP accounts for the contributions of all measurable inputs, such as 

land, labor, capital, and materials. 

While TFP accounting measures have the benefi t of providing statistics for 

each sample year, they are ratios that do not account for random—or 

stochastic—processes, nor do they permit the decomposition of TFP into 

technology and effi ciency changes, options that are available with some 

econometric or linear programming approaches. Modeling TFP in a 

stochastic framework is important because agriculture is inherently random, a 

phenomenon captured with an econometric error. Technical change is defi ned 

here as TFP growth of the most effi cient producers who form the technical 

frontier. Technical effi ciency change is a measure of the difference between 

TFP growth of the most effi cient producers and that of average producers 

(average-farm TFP) and is referred to as the TFP gap.3

Total Factor Productivity Measurement

One econometric method of measuring TFP is with a production function 

in which aggregate output is statistically correlated with measurable inputs. 

Shifts in the production function represent shifts in supply, and because a 

production function evaluates data at their mean, the function’s shifts represent average-farm TFP growth. An alternative measure is based on a production “frontier” that establishes the statistical relationship between inputs and 

outputs for the most effi cient—or frontier—producers. Thus, a shift in a 

production frontier represents TFP of the most effi cient farms. 

In light of Brazil’s recent substantial agricultural production growth, particularly in the livestock subsector (fi g. 2), we employ the stochastic distance 

frontier method to measure Brazil’s total factor productivity growth (see box, 

“Productivity Defi nitions”). The distance portion implies modeling more than 

one aggregate output grouping. The frontier component allows TFP growth 

measurement of the most effi cient farms and average farms, as well as of 

the TFP gap separating them. This stochastic distance frontier approach is 

important because it provides information on the roles of different subsectors 

and farms in productivity growth that can assist policymakers. 

Technical change is measured by the model’s time trend, allowing us to 

distinguish statistically technical change among multiple output groupings 

(app. B). But because the time trend provides just one parameter estimate 

over the entire sample period, only an annual average technical change rate 

is obtainable for each subsector. Technical effi ciency levels, however, are 

estimated by the model for each period. These information constraints imply 

that the annual average-farm TFP growth estimate is also only available as an 

average estimate for the entire 1985-2006 sample period because it is defi ned 

3To the extent that scale economies 

are present in Brazilian agriculture, 

the total factor productivity growth 

estimates capture that impact.
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as the summation of the aggregate technical change rate and the change in 

the mean technical effi ciency levels between censuses. 

Assessing Total Factor Productivity

In its present application to Brazilian agriculture, technical change is 

estimated in the crop and livestock subsectors using a stochastic distance 

frontier (app. B, equation B.8) and Brazil’s decennial agricultural census 

data (1985, 1995/6, and 2006). Technical change statistics for the crop and 

livestock subsectors are computed following the method provided in Rada 

Productivity Defi nitions

Accounting Measures

Total Factor Productivity (TFP): Ratio of an aggregate output to an aggregate 

input

Land Productivity (Yield per hectare): Ratio of an aggregate output to total land

Labor Productivity (Output per worker):Ratio of an aggregate output to total 

laborers

Stochastic Econometric Measures

Production function:An approach that correlates output variations with input 

variations, all data evaluated at their averages. Productivity growth is measured 

as the expansion of output (or shifts in the function over time), holding all inputs 

constant at their mean levels.

Production frontier: An approach that correlates output variations with input 

variations, all data evaluated relative to the best-practice (most effi cient) 

producers defi ning the technical frontier. Technical change is measured as the 

expansion of output (or shifts in the frontier over time), holding all inputs constant 

at their mean levels.

Distance function: An econometric approach that is similar to a production 

function but that allows for more than a single output. A distance function may 

be specifi ed as an output distance function, measuring the growth in output from 

a mean set of inputs, or as an input distance function, measuring the reductions in 

inputs given a set level of output.

Distance frontier: Combining the defi nitions of a production frontier and a distance 

function, a distance frontier allows for more than a single output, and all data 

are evaluated relative to the best-practice (most effi cient) producers who defi ne 

the technical frontier.

Technical change: TFP growth of the most effi cient producers.

Technical effi ciency change: Difference of TFP growth between the most effi cient 

and average farms, also referred to here as the TFP gap.

Average-farm TFP growth: Technical change plus technical effi ciency change.
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et al. (2011). This method entails successively differentiating parameter 

estimates presented in appendix D with respect to each output group and the 

time trend and then applying the implicit function theorem. Once obtaining 

the technical change estimates of the two subsectors (crops, livestock), we 

weight them by their respective mean revenue shares to obtain one aggregate (all-agriculture) technical change statistic. Summing the all-agriculture 

technical change statistic with the change in mean technical effi ciency—

obtained from equation B.9 in appendix B—provides Brazil’s average-farm 

TFP growth rate. (See appendix A for a diagrammatic and more technical 

understanding of measuring TFP by way of a stochastic distance frontier, 

and appendix B for the technical methodology employed.) 

After obtaining the average-farm TFP growth rate, decomposed into its technical change and technical effi ciency change components, we use the technical effi ciency levels to examine the impact of three public policy categories: 

(1) Public agricultural technology investment, represented by research 

stocks; 

(2) Public infrastructure investment, represented by road density, primary 

school education, and landline telephone connections; and 

(3) Credit investment, represented by rural credit volumes.

Agricultural research plays the strongest role in raising farm productivity. 

Roads are the primary conduit for supplying and accessing markets and are 

associated with economic development in middle-income and lower income 

nations (Calderón and Servén, 2004). Credit provides the liquidity for 

obtaining and modernizing farm inputs. Primary schools improve farmers’ 

human capital, enhancing their ability to employ new techniques and technologies, and telephones are important for transmitting information. 

Data

Critical to any productivity study is the quality of data employed. The farmlevel survey data collected in the Brazilian agricultural censuses are 

employed at two aggregation levels, microregion and State.4 Metric tons of 

each output commodity, hectares of agricultural land, and expenditures for 

fertilizer, feed, seed, pesticides, livestock vaccines, and electricity are all 

available in the census at the microregion level. In these data, we captured 

558 microregions distributed across the 27 States, each contributing directly 

to the cross-sectional variation we seek to exploit in our statistical analysis. 

Some data covering labor, livestock, and farm machinery are available at the 

microregion level; the remaining data are State aggregations. The policy data, 

recorded in the annual statistical yearbooks or obtained from Embrapa, are 

available at the State level or, in the case of agricultural research expenditures, at the level of the decentralized research unit (app. table C2). All data 

not available at the microregion level were imputed to the microregion, 

enabling their use in our microregion-level effi ciency evaluation. Each of 

Brazil’s 27 States is displayed in fi gure 3. Technical details of each variable’s 

construction are provided in appendix C, and sources of agricultural production and policy data are provided in appendix table C1. 

4Microregions are Brazilian political 

subdivisions encompassing several 

municipalities, or counties, and there 

are various microregions in each State. 
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The strength of the Brazilian agricultural census data lies in the stability of 

its structure across census years, with data on 20 outputs and 11 inputs for the 

558 microregions repeated across the 1985, 1995/6, and 2006 census years. 

Of the 20 commodities included in table 3, crops accounted for 72 percent 

of total revenue in 1985, with livestock making up the other 28 percent. By 

2006, the livestock sector had gained 6 percentage points, shifting the crop 

and livestock shares to 66 and 34 percent, respectively. Recorded inputs 

consist of agricultural land, labor, farm machinery, livestock, fertilizer, feed, 

seed, pesticide, animal vaccine, and electricity. 

Production Inputs

We converted agricultural labor counts, recorded in the censuses as either 

family or hired labor, to male-labor equivalents. Each has declined over the 

three census periods (fi g. 5). Between 1985 and 2006, hired labor counts 

declined by 26 percent, as did family labor, which is noteworthy given how 

many more family laborers exist in Brazilian agriculture than hired laborers 

and implies a substantially greater absolute decline in family laborers. In 

1985, family labor accounted for 16.7 million laborers and hired labor for 4.8 

Table 3

Commodity coverage of Brazilian agricultural total factor productivity 

growth analysis, 1985-2006

Crops

Beans, cotton, maize, manioc, onion, groundnuts, rice, 

soybeans, wheat, tomato, bananas, cocoa, coffee, oranges, 

and sugarcane

Livestock Cattle meat, eggs, cow milk, poultry meat, and pig meat

Source: IBGE. (Instituto Brasileiro de Geografi a e Estatística), 2010: Rio de Janeiro. 

Available at: http://ibge.gov.br/. 

Source: Authors’ estimates using data from IBGE (Instituto Brasileiro de Geografia 

e Estatística), 2010: Rio de Janeiro. Available at: http://ibge.gov.br/.

Figure 5

Decline in hired and family male-equivalent laborers, 1985-2006

Millions of laborers

0

2

4

6

8

10

12

14

16

18

1985 1995/6 2006

Family labor Hired labor
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million. By 2006, family labor had fallen to 12.4 million laborers and hired 

labor to 3.6 million. The Brazilian agricultural sector has shed a signifi cant 

portion of its labor over the previous 20 years, refl ecting a rural-to-urban 

migration acceleration following the 1994 stabilization program and echoing 

agricultural productivity’s contribution to the overall economic development 

process (discussed by Johnston and Mellor, 1961). 

Land

The agricultural census data provide hectares of land that are quality-differentiated into four groups: permanent cropland, temporary cropland, natural 

pasture, and planted pasture. Permanent croplands are those planted to perennials, and temporary croplands are planted to annuals, forages, and fl owers. 

Natural pastures may be partly cultivated; planted pasture may be degraded 

or improved. 

Aggregate land in production, assembled by simple summation across each 

land group, fell 6 percent between 1985 and 2006, from 241.7 million hectares to 226.8 million hectares. However, this aggregate summation masks 

shifts across the different land groups, as the majority of Brazil’s agricultural 

land (74 percent) is in pastures. Upon careful examination, we note that 

pastures indeed skew the aggregate land story. As natural pastures fell by 38 

percent, new planted pastures rose by 37 percent between 1985 and 2006. This 

presumed substitution of planted for natural pastures is consistent with greater 

applications of perennial grasses. There also has been a 17-percent expansion 

of permanent cropland and a 9-percent contraction of temporary cropland. 

In the present approach to measuring productivity growth, each land group 

was aggregated into a single aggregate land variable. Land quality-adjustment is important for measuring productivity growth because bias might 

arise if land changes occur unevenly among land groups (Fuglie, 2008), as 

was the case in Brazil (fi g. 6). Lacking rental rates for each land group—

price information that would account for uneven changes—we formed a 

temporary cropland-equivalent series by estimating land weights for each 

land group and for each census period, following the method described in 

Fuglie (2010). The aggregate land series is nearly 3.5 times as large as the 

temporary cropland-equivalent series; the adjusted land series fell from 76.8 

million hectares in 1985 to 65.2 million hectares in 2006, or 15 percent over 

the two decades (fi g. 7).

Capital and Materials

Annual farm service expenditures on capital (including tractors and livestock) and materials (including fertilizer, seed, pesticide, animal vaccine, 

feed, and electricity) were computed for each census year. Data defi ciencies 

in the 1985 census year bar the inclusion of other machinery capital. Material 

expenditures increased over the 1985-2006 period by 19 percent, while 

capital expenditures declined by 28 percent. Most growth in material expenditures occurred between 1995/6 and 2006 (table 4). Material growth was in 

fact negative for nearly all items except for electricity consumption, which 

showed extraordinary growth over the 1985-1995/6 period. Capital service 

expenditures for machinery and livestock declined across each census year, 

despite stocks of machinery (tractors) rising 23 percent and livestock rising 
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Source: IBGE (Instituto Brasileiro de Geografia e Estatística), 2010: Rio de Janeiro. 

Available at: http://ibge.gov.br/., and author’s estimates.

Figure 6

Comparison of raw and temporary cropland-equivalent land 

changes, 1985-2006

Million hectares

0

50

100

150

200

250

300

1985 1995/6 2006

Natural pastures Planted pastures

Permanent cropland Temporary cropland-equivalents

Temporary cropland

Source: Embrapa (Brazilian Enterprise for Agricultural Research), 2010. 

Figure 7

Embrapa’s national commodity, regional resource, and thematic 

personnel expenditures, 1974-2006

Millions of 2007 reais

0

50

100

150

200

250

300

350

400

1974 76 78 80 82 84 86 88 90 92 94 96 98 2000 02 04 06

National commodity

Regional resource

Thematic research
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26 percent between 1985 and 2006 (table 5). These growth estimates suggest 

a constriction of material expenditures between the fi rst two census periods 

and considerable expansion between the last two periods. The estimates also 

suggest that while capital service expenditures declined, machinery and livestock stocks increased, suggesting improved capital longevity.

Agricultural Research

Following Huffman and Evenson (1993), Embrapa’s personnel expenditures 

were used to estimate agricultural research knowledge stocks.5 Research 

stocks are preferred to using expenditures directly to account for research’s 

productivity impact because an expenditure series assumes each year’s expenditures affect productivity in that year only. However, any one year’s expenditures will generate knowledge and technologies that will benefi t future 

production. Research stocks represent agricultural research investments over 

multiple years, capturing research’s time-varying impacts. 

5Embrapa is employed in the analysis 

as a proxy for Brazil’s public agricultural research. And while Embrapa 

is the dominant public agricultural 

research institution in Brazil, it is not 

the only institution conducting research. 

For a review of Brazilian agricultural 

research institutions and funding, the 

reader is referred to Beintema et al. 

(2010), Stads and Beintema (2009), and 

Beintema et al. (2001).

Table 4

Change in material expenditures, 1985-2006

Census period Fertilizer Seed Pesticide Animal vaccine Feed Electricity Total

Percent

1985-2006 38.7 -56.1 119.6 -16.2 -44.0 486.3 18.6

1985-1995/6 -47.8 -49.0 -32.7 -41.2 -87.5 784.5 -73.0

1995/6-2006 165.9 -13.9 226.1 42.5 348.5 -33.7 339.5

Note: Expenditures are in real 2006 reais. Source: Authors’ estimates.

Table 5

Change in capital service expenditures and stocks, 1985-2006

Capital expenditures

Census period Machinery Livestock Total

Percent

1985-2006 -5.7 -56.7 -28.3

1985-1995/6 -4.9 -51.1 -25.5

1995/6-2006 -0.7 -11.3 -3.8

Capital stocks

Machinery Livestock Total

Percent

1985-2006 23.0 25.9 NA

1985-1995/6 20.5 17.8 NA

1995/6-2006 2.1 6.9 NA

Note: Expenditures are in real 2006 reais.

NA = Not applicable.

Source: Authors’ estimates.
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National commodity and thematic centers conduct research on specifi c 

commodities or themes that may have applications for all producers in Brazil, 

and each microregion in Brazil was assumed to benefi t from the technologies 

developed by these two kinds of research centers. Regional resource research, 

however, is constrained to specifi c States, biomes, or climates (app. table C2). 

Thus, the benefi ts from regional resource research are not national but are 

tailored to local conditions. Therefore, we assumed that the regional resource 

centers with a State focus develop technologies specifi c to the needs of that 

State’s producers. For regional resource centers that develop technologies 

related to specifi c biomes or climates, we employed geographic information 

systems (GIS) to determine which producers are assumed to benefi t from 

those specifi cally designed technologies. 

By using personnel expenditures to account for the impact of agricultural 

research on productivity (fi g. 4), we found that, within those expenditures, 

regional resource research has received the greatest proportion of total 

funding (fi g. 7). Given that these research centers sometimes collaborate, 

we accounted fi rst for the impact of Embrapa’s total personnel expenditures 

on farm effi ciency and then for the separate impacts of national commodity, 

regional resource, and thematic research. This approach gives a combined 

impact of all Embrapa research centers on productivity and then attributes the 

impact of each of the three types of research.

Rural Credit Investments

The credit variable, which accounts for production, investment, and 

marketing farm credit, is measured in terms of value per contract and is 

lagged 1 year to account for its potentially noninstantaneous impact. The 

credit measure increased by 104 percent in real terms between 1984 and 

1994, rising from 37,000 reais per contract in 1984 to 77,200 reais per 

contract in 1994 (fi g. 8).6 This value rose because credit volume declined at a 

greater rate than did the total value of credit. This trend was reversed by 2005 

as the number of contracts increased by 294 percent and the total value of all 

contracts rose by 25 percent, lowering the value per contract to 13,200 reais. 

The reversal in total value per contract indicates that credit was dispersed in 

greater quantities and at lower values, confi rming credit’s increasing dispersion to a greater number of farms. 

Infrastructure Investments

Three infrastructure investments were included in the analysis to assess their 

impact on productivity: (1) paved road density, (2) schools per 1,000 inhabitants, and (3) total landline telephones. Road density is defi ned as the sum 

of the length in kilometers (km) of asphalt road divided by land area (km2). 

In 1985, Brazil’s paved road density measured 0.008, rising to 0.017 in 1995 

and to 0.021 by 2006. By 1985, Brazil had constructed 69,105 km of roads 

amid its 8.5 million square hectares of total land area. By 2006, total paved 

roads had risen to 179,264 km, indicating that between 1985 and 1995, paved 

roads increased by 113 percent but then grew only 21 percent in the following 

decade.

School infrastructure, proxied by the number of primary schools per 1,000 

persons, declined across each of the census years, falling on average from 

6Credit data are presented in constant 

2006 reais.
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1.72 primary schools per 1,000 inhabitants in 1985 to 1.63 in 1995 and 1.10 

in 2006. Total school establishments fell by 30 percent between the initial 2 

census years, but recovered by 21 percent in 2006. Brazil’s population, which 

rose by 37.7 percent over the 1985-2006 period, led to the declining trend in 

the school infrastructure measure. The number of telephone lines in service 

expanded rapidly, rising from 7 million connections in 1985 to 14.8 million 

in 1995 and to 38.8 million landlines in 2006. However, the use of cell 

phones—information not recorded in the 1985 or 1995/96 censuses—likely 

dampens the total impact telephone communication may have because cell 

phones are often substitutes for landline connections. 

Source: Brazilian annual statistical yearbooks, various editions. Anuário Estatístico do Brasil. 

IBGE (Instituto Brasileiro de Geografia e Estatística), Rio de Janeiro, Brazil.

Figure 8

Fluctuating distribution of rural credit, 1984-2005 

Thousands of 2006 reais Thousands of credit contracts

Value per contract Credit volumes (right axis)

0

10

20

30

40

50

60

70

80

90

1984 1994 2005

0

20

40

60

80

100

120

140
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Results

Table 6 gives the estimated impact of each policy driver on every microregion’s technical effi ciency level. The technical change (TFP of the most effi -

cient farms), technical effi ciency change (TFP gap separating most effi cient 

from average farms), and TFP growth estimates for average farms are shown 

in table 7. Table 8 provides Brazil’s average-farm effi ciency levels relative 

to the most effi cient farms for each census year. Because of the differing 

personnel expenditures allocated by Embrapa to each of the three research 

centers (fi g. 7) and our interest in attributing Embrapa’s aggregate impact, we 

assess the effect of the aggregate Embrapa research stock and each research 

center’s stock separately. In doing so, however, we fi nd a very high correlation 

(0.99) between the national commodity and thematic research center stocks. 

We therefore omit the thematic center research stocks from the analysis given 

Table 7

Annual technical change, technical effi ciency change, and TFP growth 

rates, 1985-2006

Item Annual change

Percent

Crop technical change 2.93

Livestock technical change 7.13

All-agriculture technical change 4.44

Technical effi ciency change -1.89

Average-farm TFP growth rate 2.55

Source: Authors’ estimates.

Table 6

Policy impacts on productive effi ciency, 1985-2006

Dependent variable: Technical effi ciency Estimated coeffi cients

Percent

Specifi cation 1 Specifi cation 2

Total Embrapa research -0.20***

National commodity research -0.23***

Regional resource research 0.00***

Paved roads 0.08*** 0.08***

Rural credit 0.07*** 0.07***

Schooling 0.11*** 0.11***

Landline telephones 0.00 0.00

Constant 3.15*** 3.57***

Adjusted R2 0.40 0.39

N 1,617 1,617

*** = Statistical signifi cance at the 1-percent level.

Source: Authors’ estimates.
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the center’s role in supporting national commodity and regional resource 

research in areas such as soil conservation and biotechnology. 

Policy Impacts on Productive Effi ciency

To determine the impact of Brazil’s science and technology policy on its 

production effi ciency, we analyzed how a 1-percent increase in each policy 

variable impacts Brazil’s technical effi ciency or TFP gap (table 6). Our fi ndings indicate that research had a negative impact and rural credit and infrastructure investments had a positive impact on productive effi ciency. The 

fi ndings suggest that research has widened the TFP gap between average 

farms and the most effi cient ones that comprise the technical frontier, 

whereas rural credit and infrastructure investments have narrowed that gap. 

A 1-percent increase in the total Embrapa research stock pushes the TFP 

growth rate of the most effi cient farms 0.20 percent further ahead of the 

average farm. Thus, while research likely boosts the productivity of all farms,

Embrapa’s research appears focused toward—or has been primarily adopted 

by—the most effi cient farms. 

We fi nd that a 1-percent increase in the national commodity research stock 

enlarges the TFP gap by 0.23 percent (table 6). An equivalent increase in the 

regional resource research stock does not expand or narrow that gap. These 

estimates suggest a limited impact of the regional resource research that may 

require further investigation, particularly in light of the greater personnel 

funding to these centers (table 2 and fi g. 7). One possible explanation may 

be that the regional resource centers conduct predominately maintenance 

research (Plunket and Smith, 1986; Marasas, Smale, and Singh, 2003). 

Because regional resource research has not shifted the average farm closer 

to or further from the best-practice frontier established by the most effi cient 

farms, these centers may be conducting—among other things—research that 

adapts innovations from the national commodity centers to each regional 

resource’s research, focusing and sustaining the impact of those innovations 

despite changes in the production environment. 

Alternatively, the results consistently show that investments in roads, education, and rural credit tend to improve average-farm effi ciency and narrow 

the TFP gap (table 6). Of the policies evaluated, education exerts the largest 

positive impact, shifting the average farm toward the best-practice frontier. 

A 1-percent increase in the roads, rural credit, and schools per capita variables improves mean effi ciency relative to the frontier by 0.08, 0.07, and 0.11 

percent, respectively. That education has the largest impact is noteworthy, 

Table 8

National technical effi ciency, 1985-2006

Year National technical effi ciency 

1985 0.93

1995/6 0.84

2006 0.64

Note: Number of observations=539 for each year.

Source: Authors’ estimates.
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particularly given that the overall education variable has not risen over time. 

The result is more understandable if we interpret it in terms of the sample’s 

broad cross-section; that is, those areas with more schools per 1,000 inhabitants experienced greater average farm effi ciency. Landline telephones had a 

very small and insignifi cant impact on technical effi ciency. 

Technical Change, Technical Effi ciency Change, 

and TFP Growth

The technical change estimates show remarkable growth, particularly for 

the livestock subsector (table 7). Between 1985 and 2006, TFP of the most 

effi cient farms reached a strong 2.9 percent annual growth rate in crops and 

a formidable 7.1 percent growth rate in livestock. Weighting each subsector’s 

technical change by its respective revenue share, we obtain an all-agriculture 

annual technical change of 4.4 percent, which means that the most effi cient 

producers in 2006 were able to produce 138 percent more than in 1985 

without increasing input usage.

This high growth rate, however, was not achieved by all producers, as technical effi ciency declined by 1.9 percent each year. That is, between 1985 and 

2006, TFP growth of the most effi cient producers accelerated each year at a 

rate 1.9 percent faster than that of the average farm, annually widening the 

TFP gap between the two by nearly 2 percent. The average farm’s effi ciency 

levels relative to the most effi cient producers are presented in table 8 for each 

census year. The change in effi ciency levels or expanding TFP gap, combined 

with the all-agriculture annual technical change rate of 4.4 percent, equals 

the all-agriculture annual average-farm TFP growth estimate of 2.55 percent 

between 1985 and 2006 (table 7).7 This estimate is close to Gasques et al.’s 

(2010) index-number fi nding of 2.87 percent over the same period. Brazil’s 

average-farm TFP growth rate indicates that average farms in 2006 were able 

to produce 62 percent more than in 1985 with constant input levels. 

7The productivity estimates refl ect 

decennially recorded production data. 

When providing the results, we assume 

linear growth over each decade; that 

is, total factor productivity improved 

25.5 percent each decade between 1985 

and 2006. To allow comparisons with 

other productivity studies, the result is 

generalized to 2.55 percent.
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Conclusion

Brazil’s production growth has been driven by an increase in agricultural 

effi ciency, as evidenced by strong technical and TFP change over the 

1985–2006 period. Different types of research investments have different 

effects on agricultural production in Brazil. The signifi cantly greater 

impact from Embrapa’s national commodity research than from its regional 

resource research may be a cause for concern among Brazilian policymakers. 

One proposed explanation of the differing impacts is that the regional 

resource research centers focus on maintenance research while the national 

commodity research centers aim at innovation and the development of new 

agricultural technologies. These results suggest that Embrapa’s national 

commodity research programs are the ones most effective at boosting technological growth, providing the driving force behind Embrapa’s agricultural 

research success. 

Sustained investments in science and technology policies, supported by a 

stable, liberalized macroeconomic environment, have helped Brazil achieve 

signifi cant agricultural gains. Brazil could substantially boost its shares in 

global production and trade still further by raising its low 2006 average-farm 

effi ciency; that is, average farms produced only 64 percent of what the most 

effi cient producers achieved in 2006. The results suggest that new school 

infrastructure investments provide the largest boost to average-farm effi -

ciency, likely through raising human capital, which, in turn, contributes to 

a farmer’s ability to employ and manage new agricultural technologies and 

practices. Thus, despite its remarkable gains, Brazil has ample capacity for 

further productivity improvements.
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References

Aigner, D., C. Lovell, and P. Schmidt. 1977. “Formulation and Estimation 

of Stochastic Frontier Production Function Models,” Journal of 

Econometrics 6:21-37.

Anderson, K., M. Kurzweil, W. Martina, D. Sandri, and E. Valenzuela. 2008. 

“Measuring Distortions to Agricultural Incentives, Revisited.” WPS 4612, 

World Bank, Washington, DC. 

Annual Statistical Yearbooks. Various years. Anuário Estatístico do Brasil. 

Instituto Brasileiro de Geografi a e Estatística (IBGE). Rio de Janeiro, 

Brazil.

Avila, F., and R. Evenson. 1995. “Total Factor Productivity Growth in the 

Brazilian Agriculture and the Role of Agricultural Research.” Anais do 

XXXIII Congresso Brasileiro de Economia e Sociologia Rural, Vol. 1. 

Curitaba, Brazil, pp. 631-57. 

Baer, W. 2008. The Brazilian Economy. London: Lynne Rienner Publishers.

Banco Central do Brasil (BACEN) (2009). Anuário Estatístico do Crédito 

Rural. Available online: www.bcb.gov.br.

Barros, A. 1999. “Capital, Productividade e Crescimento da Agricultura: O 

Brasil de 1970 a 1995.” Doctoral Dissertation, University of São Paulo, 

January, 1999.

Battese, G., and T. Coelli. 1992. “Frontier Production Functions, Technical 

Effi ciency, and Panel Data: With Application to Paddy Farmers in India,” 

Journal of Productivity Analysis 3:153-69.

Beintema, N., F. Avila, and C. Fachini. 2010. Brazil: New Developments in 

the Organization and Funding of Public Agricultural Research. October 

2010, International Food Policy Research Institute (IFPRI), Country Note.

Beintema, N., F. Avila, and P. Pardey. 2001. Agricultural R&D in Brazil: 

Policy, Investments, and Institutional Profi le. August 2001, International 

Food Policy Research Institute (IFPRI), Embrapa, and FONTAGRO: 

Washington, DC.

Buccola, S., and B. McCarl. 1986. “Small-Sample Evaluation of MeanVariance Production Function Estimators,” American Journal of 

Agricultural Economics (68) no. 3:732-38.

Calderón, C., and L. Servén. 2004. “The Effects of Infrastructure 

Development on Growth and Income Distribution.” WPS 3400, World 

Bank, Washington, DC.

Constantin, P., D. Martin, and E. d. Rivera Y Rivera. 2009. Cobb-Douglas, 

Translog Stochastic Production Function and Data Envelopment Analysis 

in Total Factor Productivity in Brazilian Agribusiness (2) no. 2:20-34.
23

Policy, Technology, and Effi ciency of Brazilian Agriculture / ERR-137

Economic Research Service/USDA

Embrapa (Brazilian Enterprise for Agricultural Research), 2010. Research 

expenditures obtained through personal communication.

Evenson, R., and D. Alves. 1998. “Technology, Climate Change, Productivity 

and Land Use in Brazilian Agriculture,” Planejamento e Politicas 

Publicas, no. 18:223-60.

FAO. 2011. FAOSTAT Agricultural Databases. Food and Agricultural 

Organization (FAO), Rome. Available at: http://faostat.fao.org/. Accessed 

in January 2011.

Fuglie, K. 2008. “Is a Slowdown in Agricultural Productivity Growth 

Contributing to the Rise in Commodity Prices?” Agricultural Economics 

39:431-41.

Fuglie, K. 2010. “Total Factor Productivity in the Global Agricultural 

Economy: Evidence from FAO data.” In: Pardey, P., J. Alston, and B. 

Babcock (Eds.), The Shifting Patterns of Agricultural Production and 

Productivity Worldwide. The Midwest Agribusiness Trade Research and 

Information Center, Iowa State University, Ames. 

Gasques, J., E. Bastos, M. Bacchi, and C. Valdes. 2010. “Produtividade 

Total dos Fatores e Transformações da Agricultura Brasileira: Análise 

dos Dados dos Censos Agropecuários.” In: Gasques, J., Vieira Filho, J., 

Navarros, Z. (Eds.), Agricultura Brasileira: Desempenho, Desafi os, e 

Perspectivas. IPEA, Brasília, Brasil, 2010,

pp. 19-44.

Graham, D., H. Gauthier, and J. Barros. 1987. “Thirty Years of Agricultural 

Growth in Brazil: Crop Performance, Regional Profi le, and Recent Policy 

Review,” Economic Development and Cultural Change (36) no. 1:1-34.

Hayami, Y., and V. Ruttan. 1985. Agricultural Development. The Johns 

Hopkins University Press.

Helfand, S, and G. Rezende. 2004. “The Impact of Sector-Specifi c and 

Economy-wide Policy Reforms on the Agricultural Sector in Brazil: 1980-

98,” Contemporary Economic Policy (22) no. 2:194-212.

Huffman, W., and R. Evenson. 1993. “Science for Scarcity.” Ames: Iowa 

State University Press.

IBGE. 2006. Censo Agropecuário 2006. Instituto Brasileiro de Geografi a e 

Estatística (IBGE): Rio de Janeiro. Available at http://ibge.gov.br/.

IBGE. 2010. Instituto Brasileiro de Geografi a e Estatística (IBGE): Rio de 

Janeiro. Available at: http://ibge.gov.br/. 

IBRE. 2010. Instituto Brasileiro de Economía (IBRE), Fundação Getúlio 

Vargas (FGV): Rio de Janeiro. Available at: http://www.fgv.br/IBRE/. 

Accessed in June 2010. 
24

Policy, Technology, and Effi ciency of Brazilian Agriculture / ERR-137

Economic Research Service/USDA

ILO. 2010. LABORSTA internet, International Labor Organization (ILO): 

Geneva. Available at: http://laborsta.ilo.org/. Accessed in March 2010.

Johnston, B., and J. Mellor. 1961. “The Role of Agriculture in Economic 

Development,” The American Economic Review (51) no. 4:566-93.

Marasas, C., M. Smale, and R. Singh. 2003. “The Economic Impact of 

Productivity Maintenance Research: Breeding for Leaf Rust Resistance in 

Modern Wheat,” Agricultural Economics 29:253-63.

Matthey, H., J. Fabiosa, and F. Fuller. 2004. “Brazil: The Future of Modern 

Agriculture.” May, 2004, Midwest Agribusiness Trade Research and 

Information Center (MATRIC) Briefi ng Paper 04 – MBP 6: Ames, IA. 

Meeusen, W., and J. Van den Broeck. 1977. “Effi ciency Estimation from 

Cobb-Douglas Production Functions with Composed Error,” International 

Economic Review (18) no. 2: 435-44.

Mueller, C., and B. Mueller. 2006. “The Evolution of Agriculture and Land 

Reform in Brazil, 1960-2006.” Conference in honor of Werner Baer, 

University of Illinois. December 1-2, 2006.

Plunket, D., and N. Smith. 1986. “Sustaining Agricultural Yields.” BioScience

(36) no. 1:40-5. 

Rada, N., S. Buccola, and K. Fuglie. 2011. “Government Policy and 

Agricultural Productivity in Indonesia,” American Journal of Agricultural 

Economics (93) no. 3:867-84. 

Schnepf, R., E. Dohlman, and C. Bolling. 2001. “Agriculture in Brazil 

and Argentina: Developments and Prospects for Major Field Crops.” 

WRS-01-3, Economic Research Service, U.S. Department of Agriculture.

Shephard, R. 1970. Theory of Cost and Production Functions: Princeton 

University Press.

Stads, G., and N. Beintema. 2009. “Public Agricultural Research in Latin 

America and the Caribbean,” Agricultural Science and Technology 

Indicators (ASTI) Synthesis Report, March 2009, ASTI, International 

Food Policy Research Institute (IFPRI): Washington, DC.

The Economist. 2010. “The Miracle of the Cerrado.” Available at: 

http://economist.com/node/16886442. August 26, 2010.

World Bank. 2010. World Development Indicators On-Line. World Bank, 

Washington, DC. Accessed February 2011.
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Appendix A. Understanding Total Factor 

Productivity (TFP) from a Stochastic Input 

Distance Frontier

Stochastic distance frontiers are unique in that they allow productivity to be 

measured for more than one output and by defi ning the frontier as the most 

effi cient farms. We are thus able to determine how much more effi cient one 

farm is relative to the next. The fi gure above provides an example of a 

stochastic distance frontier at which, along the given ray, point A employs the 

fewest inputs needed to produce mean output y. Because TFP may be defi ned 

as an aggregate of total outputs per aggregate of total inputs, or y

x

, TFP at 

frontier point A then is A

y FP

OA =  , namely, mean output divided by inputs 

1 x and 2 x represented in distance OA



. Effi ciency of the average farm, point 

B, produces the same output at higher input levels. Thus, we can write 

average-farm TFP as , B

y FP

OB =  or mean output divided by distance 

OB. 

 

Technical effi ciency , TE, is the ratio of TFP at the average (FPB) and 

frontier (FPA ) farm: = B

A

FP OA TE

FP OB =



 . Solving for FPB and taking 

logs gives ln ln ln . FP FP TE B A = + In proportional terms, that is, TFP 

Source: Authors’ construction. 

Appendix figure 1

Understanding TFP from a Stochastic Input Distance Frontier

Pt. A: TFP of a most efficient farm

Pt. B: TFP of an 

 average farm

Stochastic

Distance

Frontier

x2

x1

0
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of the average farm is the sum of frontier TFP (or TFP of most effi cient 

farms) and mean technical effi ciency (or the average distance from the 

average farm to the frontier).
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Appendix B. Estimation Method

Our strategy is to characterize agricultural technology by way of its input 

requirement set ( ) { : ( , , ) }, o Ko Ly x y x t T jit kit jit kit =∈ ∈ + that is, the 

inputs kit x and technology T necessary to produce output set o

jit y , in which 

, 1... o M

jit y jM ∈ = + are scalar outputs; , K

kit x ∈+ 1... k K = are scalar 

inputs; t S =1... are technology indicators; and i N =1... are the observations on technology {( , , ) : T xyt = kit jit can produce } kit jit x y K+M

+ ∈ .

Our deterministic input distance function

(B.1) ( , , ) sup { 0 : ( , )} kit o M

I kit jit jit jit

x D x y t Ly t y λ λ = > ∈ ∀∈ λ +

is dual to input set ( , ) o Ly t jit and hence technology T. In particular, if 

inputs are weakly disposable, equation (B.1) implies () 1 DI ⋅ ≥ if and only 

if kit x ∈ ( , ) o Ly t jit . When () 1 DI ⋅ = , λ obtains its minimum at unity, and 

inputs kit x are located on the boundary of the input requirement set, maximizing technical effi ciency. Yet, stochastic frontiers differ from their deterministic counterparts in that maximized technical effi ciency is not constrained to unity. Central to releasing this restraint is the composite error 

term (Aigner, Lovell, and Schmidt, 1977; Meeusen and Van den Broeck, 

1977): 

(B.2) ( , , ; ) it it u Dx y t e I kit jit

ν − β =

in which β is a parameter vector to be estimated; 2 ~ ( , ) it u N μ σ + is a nonnegative, truncated normal error representing an observation’s distance from 

the frontier; and ν it is an independently and identically distributed (iid) 

random noise with mean zero and variance 2 σ v . Error terms ν it and it u are 

assumed to be distributed independently of one another: 0 σ vu = . 

Specifying the left-hand side of (B.2) as

(B.3) (ln , ln , ; ) ( , , ; ) Fx yt kit jit Dx y t e I kit jit

β β =

substituting it into (B.2), and employing Battese and Coelli’s (1992) timeeffect parameterization of ineffi ciency error it u —that is,

 exp[ ( )]; U U U tS it i it i i = = −− η η η being an iid random variable to be 
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estimated—allows a stochastic technical-effi ciency (TE) interpretation:

(B.4) ( ) (ln ln , ; ) , exp . kit jit i i

it

Fx yt u

it

t S

it D TE e e

e

η

ν

− −− ⎡ ⎤ ⎣ ⎦ = = = β

To impose linear homogeneity on (B.4), that is, () () D D I I ⋅= ⋅ ω for any 

ω > 0 (Shephard 1970), we let * / kit kit lit x xx = ≠ +∞ , where the th l

input is chosen as numeraire (Lovell et al., 1994). Rearranging terms in 

equation (B.4) and substituting 1 lit x for ω brings

(B.5) * (ln , ln , ; ) (ln , ln , ; ) 1 Fy xt Fy xt jit kit jit kit

lit e xe β β = ⋅

 exp ( )

.

it i i u t S

lit

e

x

ν − −− ⎡ ⎤ η ⎣ ⎦

=

Taking logs of (B.5) and rearranging terms gives:

(B.6) ( ) * ln (ln , ln , ; ) exp . lit jit kit it i i − = −+ −− x F y x t u tS ν η ⎡ ⎤ ⎣ ⎦ β

We express the Brazilian agricultural input distance frontier 

* (ln , ln , ; ) F y xt jit kit β as a generalized Cobb-Douglas, 

(B.7) * (ln , ln , ; ) F y xt kit kit β =

1 *

0

1 1

ln ln .

M K

j jit k kit t

j k

ββ β β y xt

−

= =

+++ ∑ ∑

Output subscript j indexes crops and livestock, and input subscript k indexes 

land, family labor, hired labor, and capital and materials; i indexes 558 Brazilian microregions, and t indexes the time trend (1985, 1995/6, 2006). Land 

is used as the numeraire input to facilitate parameter estimate interpretation. 

Rewriting the Cobb-Douglas terms, inclusive of State dummies 

, 1,..., , Mh H h = on the right side of (B.7) as * ( , ln , ln , ; ) FM y x t h jit kit β

and substituting into (B.6) gives 

(B.8) ( ) * ln ( , ln , ln , ; ) exp . lit h jit kit it i i − = − + −− x FM y x t u t S βν η ⎡ ⎤ ⎣ ⎦

Statistically differentiating technical progress among the multiple outputs 

requires successively differentiating appendix D’s distance frontier estimates 

with respect to the three product outputs and the time trend and then applying 

the implicit function theorem (Rada, Buccola, and Fuglie, 2011). Equation 

B.8 was estimated using Stata 11.2, command xtfrontier, and the time-varying 

decay (tvd) ineffi ciency error specifi cation. Estimating technology frontier 

(B.8) also provides observation-specifi c mean technical effi ciency levels:

(B.9) [ ] exp ( ) . i i u t S

it E TE E e

− −− ⎡ ⎤ η ⎡ ⎤ ⎣ ⎦ = ⎣ ⎦
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Effi ciency levels are then logged and regressed against Government research 

stocks Rit , road density Dit, one-period lagged rural credit Ci,t-1, education 

Eit, and landline phones Lit,

(B.10) ln ln , ln , ln , ln , ln ; , TE g R D C E L it it it i t it it it = + ( , 1− δ ε )

where δ is a vector of estimable parameters, and it ε is a normally distributed idiosyncratic error term with mean zero and variance 2 σ ε . 
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Appendix C. Data

As described below, some of the inputs are quantity indexes and others are 

expenditure indexes. Upon converting all output and input prices to reais, we 

normalize 1985 and 1995/6 prices to a 2006 basis via Brazil’s General Price 

Index-Domestic Availability (IGP-DI), which captures wholesale, consumer, 

and construction price changes (IBRE, 2010).

Labor

Labor inputs in the 1985 and 1995/6 periods are from Avila and Evenson 

(1995) and are available at the State level. 2006 labor data are available at the 

microregion level (IBGE, 2010). Two male-equivalent labor quantity indexes 

were constructed, representing hired and family labor. Proportionate femaleto-male average 1998-2002 International Labor Organization wage rates, 

specifi c to Brazilian agriculture, were used to quality-adjust female labor to 

male-labor equivalents (ILO, 2010). Female agricultural labor wages were, 

on average, 92 percent of male wages over that period.

Labor counts in the 1985 and 1995/6 data were distributed across type 

(family, permanent-hired, and temporary-hired labor) and agricultural 

subsector (crop, livestock, and forestry labor). We followed Avila and 

Evenson (1995) and interpolated crop-labor quantities to the microregion by 

weighting every labor type in the crop subsector by each microregion’s State 

share of cropland. Interpolating livestock-labor counts follows a similar 

approach, but the weight applied was each microregion’s State revenue share 

of livestock sold. Forestry labor weights were assumed equal for every 

microregion in each State.10 

Our hired labor variable was formed by summing permanent and temporary 

labor counts. Both hired and family labor were multiplied by each State’s 

agricultural labor gender share to obtain the proportion of male and female 

laborers in that State. All labor data were then reaggregated into male-equivalent quantity indexes using the International Labor Organization wage data. 

The 2006 census labor counts are available by gender and labor type at the 

microregion level and also were converted here to male-equivalent family and 

hired labor quantity indexes.

Land

Land size is available in the censuses at the microregion level and qualitydifferentiated into four groups: permanent cropland, temporary cropland, 

natural pasture, and planted pasture. Permanent croplands are those planted 

to perennials, and temporary croplands are those planted to annuals, forages, 

and fl owers. Natural pastures may be partly cultivated; planted pasture may 

be degraded or improved. Because adding 1 acre of natural pastures would 

likely have substantially less importance than adding 1 acre of temporary 

cropland that is regularly managed, we form a temporary cropland-equivalent 

quantity series. To obtain temporary cropland-equivalent land quantities, we used Fuglie’s (2010) method to estimate land weights for each land 

group and census period. Quality-adjusting land is important for measuring 
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productivity growth because bias might arise if land changes occur unevenly 

among land groups (Fuglie, 2008).

Capital and Materials

We formed a service expenditure index from capital and material inputs. The 

capital service index, due to a shortage of recorded data in the 1985 census, 

includes only tractors. State-level tractor service prices are from Barros 

(1999). Barros estimates 1985 and 1995/6 tractor service prices by using 

new and used 1997-98 prices of two Massey Fergusson tractor sizes, amortized over 21 years at a 7-percent depreciation rate and, after converting 1985 

prices to reais, defl ating them by the IGP-DI to a 2006 basis. Census data on 

numbers of total tractors in use were then multiplied by the service prices. 

The 2006 service expenditures were obtained by multiplying 1995/6 service 

prices by the IGP-DI, allowing a projection to 2006, then multiplying by 

tractors-in-use. 

Livestock capital accounts for on-farm stocks of bulls and steer, cattle, horses, 

asses, mules, pigs, goats, chickens, roosters, and hens. These stocks are available at the State level in 1985 and 1995/6, at the microregion level in 2006, 

and were aggregated to ‘bovine equivalents’ using cattle-normalized weights 

from Hayami and Ruttan (1985, p. 450). State bovine-equivalent animal 

stocks were imputed to each microregion by multiplying each State’s stock 

by every microregion’s State share of livestock sold. Bovine sale prices are 

available by State in 1985 and by microregion in 1995/6 and 2006. Those 

prices were amortized over 10 years at a 10-percent discount rate to obtain a 

bovine-equivalent capital service price. Multiplying the bovine-equivalent 

animal stocks by the service price provided the livestock capital service rate.

Materials include fertilizer, seed, pesticide, animal vaccine, feed, and electricity expenditures. These data are available at the microregion level for 

each census period. As with all other input data, 1985 expenditures are 

converted to reais, then the IGP-DI is employed to defl ate 1985 and 1995 

expenditures to a 2006 basis.

Agricultural Technology Investments

Despite Embrapa’s creation in 1973, research expenditures (PEit) were not 

allocated to Embrapa personnel until 1975. Because the fi rst census sample 

period is 1985, we had a very short time lag in creating our research stock 

(Rit). Accounting for this short lag we—like Evenson and Alves (1998)—did 

not construct a depreciation component into the research-stock lag structure, 

but, rather, assumed a geometric lag structure in which we had a 1-year lag 

before research expenditures began; then research’s productivity impacts 

started 10 years before the present and rose geometrically:

(C.1) ,1 ,2 ,3 0.000978* 0.001955* 0.00391* R PE PE PE it i t i t i t =++ − −− 

,4 ,5 ,6

,7 ,8 ,9

, 10

0.00782* 0.01564* 0.03128*

0.06256* 0.12512* 0.25024*

0.50048* .

it it it

it it it

i t

PE PE PE

PE PE PE

PE

−−−

−−−

−

++ +

++ +

+
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Equation (C.1) states that our research stock in time t and for microregion 

i is a weighted sum of the previous 10 years’ expenditures, with weights 

rising geometrically, placing greater emphasis on the later years. Because 

the national commodity and thematic centers conduct research on specifi c 

commodities or themes that may be applied by all producers across the 

entire nation, we assigned to each microregion the national commodity 

and thematic research expenditures. Regional-resource research, however, 

was constrained to specifi c States, biomes, or climates (app. table C2). For 

those centers with a State focus, we assigned the research expenditures to 

each microregion in that State’s geographic boundary. With respect to those 

centers focusing on specifi c biomes or climates, we used a unique approach of 

employing geographic information systems (GIS) in assigning each center’s 

research expenditures to every microregion in which the centroid of that 

observation lies within the biome or climate in question. Microregion-level 

research expenditures, accounting for national commodity, regional resource, 

and thematic research, were then summed, and equation (C.1) was used to 

compute each microregion’s aggregate Embrapa research stock. 

Rural Credit Investments

Rural credit is recorded in the annual statistical yearbooks by total value and 

contract numbers. A total credit value per contract variable was constructed 

from rural credit information to avoid any bias that may be introduced by 

employing strictly values or contract counts. Credit values per contract were 

lagged by 1 year under the assumption that credit does not impact production 

instantaneously. Between 1995/6 and 2006, a change in the level of aggregation occurred in which rural credit was recorded, with State-level data available in the former period but only national-level information available in the 

latter. For the 1985 and 1995/6 census periods, a State’s microregions were 

each assigned that State’s credit per contract volumes; in the 2006 contract 

period, every microregion was assigned the national total per contract 

volume. 

Infrastructure Investments

State-level road densities were measured as total kilometers of paved roads, 

expressed as a proportion of the State’s geographic area (km2) and assigned 

equally to each micro-region in that State. State-level school infrastructure was proxied by the number of primary schools per 1,000 persons and 

assigned equally to each microregion. Telecommunication was proxied by 

the number of landline telephones in service, and the data are available at the 

State level. Each microregion in every State was assigned the State values. 
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Appendix table C1

Data sources

Series Level of aggregation Source

Commodity production Microregion IBGE

Agricultural land use Microregion IBGE

Persons employed 

primarily in agriculture State & Microregion Avila and Evenson (1995) & IBGE

Tractors in use Microregion IBGE

Livestock capital Microregion IBGE

Fertilizer expenditures Microregion IBGE

Farm-level commodity 

prices Microregion IBGE

Tractor service prices State Barros (1999)

Farm animal prices State & microregion IBGE

R&D expenditures Decentralized unit Embrapa

Total primary schools 

per capita State Annual Statistical Yearbooksa

Road density (km/area) State Annual Statistical 

Yearbooksb

Rural credit State Annual Statistical 

Yearbooksc

SIBGE is the Brazilian Institute of Geography and Statistics.

a1987 – 88, 1997, and 2007 Statistical Yearbooks. b1985, 1986, 1990, 1995, 1997, 2006, and 2008 Statistical Yearbooks. c1987 – 88, 1992, 1997, and 2007 Statistical Yearbooks.
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Appendix table C2

Embrapa Research Centers

Embrapa Centers Decentralized Units Unit Focus

National Commodity Centers CNPSO Embrapa Soy National

CNPA Embrapa Cotton National

CNPGL Embrapa Dairy Cattle National

CNPGC Embrapa Beef Cattle National

CNPMF Embrapa Manioc and Fruit National

CNPMS Embrapa Corn and Sorghum National

CNPSA Embrapa Pig and Poultry National

CNPAF Embrapa Rice and Beans National

CNPH Embrapa Horticulture National

CNPT Embrapa Wheat National

Regional Resource Centers CPAMNa Embrapa Middle-North Piauí and Maranhão

CPATSA Embrapa Tropical Semi-Arid Caatinga biome

CPATC Embrapa Coastal Tablelands

Ceará, Rio Grande 

do Norte, Paraíba, 

Pernambuco, Alagoas, 

Sergípe, and Bahia

CPAP Embrapa Pantanal (Marshlands) Pantanal biome

CPACTb Embrapa Temperate Climate Temperate climate

CPAOc Embrapa West Paraná, Mato Grosso do 

Sul, Mato Grosso

CPAC Embrapa Cerrado (Savannah) Cerrado biome

CPPSE Embrapa Southeast Livestock

Minas Gerais, Espírito 

Santo, Rio de Janeiro, 

and São Paulo

CPPSUL Embrapa South Livestock Paraná, Santa Catarina, 

Rio Grande do Sul

CPAF-AC Embrapa Acre Acre

CPAF-RO Embrapa Rondônia Rondônia

CPAF-RR Embrapa Roraima Roraima

CPAF-AP Embrapa Amapá Amapá

CPAA Embrapa Western Amazon Amazonas

CPATU Embrapa Eastern Amazon Pará

--continued
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Appendix table C2

Embrapa Research Centers—continued

Embrapa Centers Decentralized Units Unit Focus

Thematic Centers CNPSd Embrapa Soils National

CNPAB Embrapa Agro-biology National

CNPMA Embrapa Environmental National

CENARGEN Embrapa Genetic Resources and 

Biotechnology National

TechTransfere Embrapa Technology Transfer National

CNPDIA Embrapa Agricultural Instrumentation National

CNPM Embrapa Satellite Monitoring National

CNPTIA Embrapa Agricultural Information National

CTAA Embrapa Agro-industrial Food 

Technology National

CNPAT Embrapa Tropical Agro-industry National

aCPAMN expenditures include UEPAE Teresina expenditures from 1974 to 1992.

bCPACT expenditures include CNPFT and CPATB expenditures from 1974 to 1992.

cCPAO expenditures include Uep-MT expenditures.

dCNPS expenditures include Uep Recife expenditures.

eTechTransfer expenditures include SNT and SCT expenditures.

Source: Embrapa (Brazilian Enterprise for Agricultural Research), 2010.
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Appendix table D

Technology Parameter Estimates

Dependent Variable: Land Coeffi cients Standard Error Z P>|Z|

Time trend 0.041 0.002 19.58 0

Livestock -0.058 0.010 -5.86 0

Crops -0.141 0.007 -19.92 0

Family Labor 0.078 0.017 4.64 0

Hired Labor 0.073 0.017 4.22 0

Capital & Materials 0.229 0.012 18.61 0

Rondônia -0.038 0.059 -0.65 0.52

Acre 0.017 0.071 0.24 0.81

Amazonas 0.178 0.049 3.65 0

Roraima -0.163 0.073 -2.23 0.03

Para 0.044 0.032 1.36 0.17

Amapa 0.124 0.079 1.55 0.12

Tocantins -0.023 0.051 -0.46 0.65

Maranhão 0.086 0.034 2.53 0.01

Piauí 0.039 0.038 1.02 0.31

Ceará 0.099 0.023 4.22 0

Rio Grande do Norte 0.052 0.031 1.67 0.10

Paraiba 0.083 0.028 2.95 0

Pernambuco 0.025 0.036 0.7 0.49

Alagoas 0.004 0.040 0.11 0.91

Sergípe -0.056 0.040 -1.4 0.16

Bahia -0.012 0.027 -0.44 0.66

Minas Gerais 0.022 0.019 1.2 0.23

Espírito Santo -0.058 0.038 -1.53 0.13

Rio de Janeiro 0.098 0.032 3.03 0

São Paulo 0.071 0.021 3.4 0

Parana 0.051 0.024 2.07 0.04

Santa Catarina -0.020 0.032 -0.62 0.53

Rio Grande do Sul -0.010 0.028 -0.35 0.73

Mato Grosso do Sul 0.070 0.043 1.61 0.11

Mato Grosso 0.025 0.035 0.72 0.47

Goiás 0.053 0.035 1.52 0.13

Federal District 0.058 0.156 0.37 0.71

/mu -0.321 0.577 -0.56 0.58

/eta -0.095 0.004 -24.36 0

--continued
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Appendix D

Technology Parameter Estimates—continued

Dependent Variable: Land Coeffi cients Standard Error Z P>|Z|

/lnsigma2 -0.461 0.409 -1.13 0.26

/ilgtgamma 3.091 0.415 7.45 0

sigma2 0.630 0.258

gamma 0.956 0.017

sigma_u2 0.603 0.258

sigma_v2 0.027 0.001

Note: The model’s log likelihood: 108.37; number of observations = 1,617. All production output and input variables except 

Time trend are logged.