EFFICIENCY AND EFFICACY MODEL APPLICATION FOR THE BRAZILIAN LIVESTOCK FARMING ACTIVITY: MAPPING WITH PANEL DATA

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1 Academy of Agriculture Journal 1: 3 May (2016) Contents lists available at ACADEMY OF AGRICULTURE JOURNAL Available online at EFFICIENCY AND EFFICACY MODEL APPLICATION FOR THE BRAZILIAN LIVESTOCK FARMING ACTIVITY: MAPPING WITH PANEL DATA Nelson Guilherme Machado Pinto¹, Daniel Arruda Coronel² 1, 2 Adjunct Professor, Department of Administrative Sciences, Federal University of Santa Maria, Brazil. ARTICLE INFO Corresponding Author: Nelson Guilherme Machado Pinto Federal University of Santa Maria, Department of Administration - Campus Palmeira das Missões. Avenida Independência, Bairro Vista Alegre. CEP State of Rio Grande do Sul Brazil. nelguimachado@hotmail.com Keywords: Efficiency; Efficacy; Data Envelopment Analysis; Ratio Analysis. ABSTRACT The aim of this study was to analyze the performance of the Brazilian livestock farming activity through the application of models of efficiency and efficacy in the regions of Brazil through the use of panel data in the decades of 1990 and Thus, regarding the efficiency, it is noteworthy the high levels of agricultural efficiency, especially among the states of São Paulo, Santa Catarina, Goiás, Rio Grande do Sul and Minas Gerais. The most concerning regions regarding the aspect of agricultural efficiency are the states of Rio Grande do Norte, Piauí and Tocantins. In relation to the efficacy aspect, the states of Minas Gerais, Sao Paulo, Espirito Santo, Rio Grande do Sul, Mato Grosso do Sul and Mato Grosso are prominent, highlighting the importance of this activity for the Southeast, South and Midwest regions. The lowest efficacy values in the analyzed periods are for the states of Rio Grande do Norte, Piauí and Paraíba, reflecting the difficulties of the North and Northeast regions in relation to agricultural efficacy Finally, the model of panel data analysis supported the hypothesis of the study, demonstrating that the livestock farming efficiency is a path to achieve efficacy in this activity. 1 INTRODUCTION Organizations are social units or human groups that aim to achieve specific objectives of any society, being the responsible for all of its activities. In this context, the agricultural activity is essential for the sustainability of any population, since it provides us with basic products, and it has vital importance to the social and economic perspective of a society. The agricultural sector, especially livestock farming, is very relevant in the Brazilian economy since the beginning of the country's colonization to the present day, because it has a great capacity of generating jobs, income and foreign exchanges (CERDÁ, 2003). Given the importance of livestock farming to the economy of many regions, it is essential, as claimed by Costa et al. (2013), to study aspects related to this activity. In most countries of Latin America, especially in Brazil, livestock farming is an important source of income and employment, and it is one of the main factors that contribute to the generation of foreign exchanges. Therefore, this activity, in general, aims to generate economic growth and development for the country. From this, the evaluations and discussions regarding livestock farming, in this context, are relevant in determining aspects of the dynamics of local society. When discussing performance, one of the central issues to be considered is with respect to the environment, which supplies the human beings with energy, products and other aspects that have leveraged and still provide our development. From the exploitation of natural resources, 2016, AAJ, All Right Reserved man transforms the natural environment, reducing and making scarce resources from nature. One of the human activities that represent this dynamic is livestock farming; therefore it is essential to verify its performance aspects. The performance is, in fact, what ensures the survival and the success of a process, activity or organization. Thus, the performance is linked to the implementation and fulfillment of a task or activity. However, there are two words to indicate and identify the good performance of an organization. Actually, there are two ways of analyzing the same issues, which are related to efficiency and efficacy. Thus, while the first concept focuses on the means of utilization, the second concentrates on the results (McAULEY; DUBERLEY; JOHNSON, 2007). The concepts of efficiency and efficacy are applicable in any human and labor activity, as stated by Mouzas (2006). However, according to the same author, these two concepts are rarely treated empirically, and there are serious constraints on the ability to discuss and understand issues related to them. The adoption of indicators to consider the efficiency and efficacy needs to be widespread throughout the academic literature and in a practical context, since this discussion allows comparisons, qualification, development and measurements of aspects related to the performance of any industry or activity (GAETANI, 1997). Understanding the concepts of efficiency and efficacy starts from the analysis of Administration as open Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Page 42

2 systems, and the fact that its relationship is a part altogether, because in many cases efficiency is a necessary condition to achieve efficacy (MIHAIU; OPREANA; CRISTESCU, 2010). Thus, efficiency is more closely linked to the means used to achieve the results, that is, carrying out the activity itself. In the other hand, efficacy is related to the results and, that is, what is obtained from the activity. This discussion has beenpart of the Brazilian reality in the last two decades, because aspects related to agriculture and livestock farming in Brazil have been updated steeply during this period. This implies that studies from previous decades are still valuable, but more because of its historical contribution than due to its practical applicability nowadays. From this, there is the need to increasingly renew studies related to the agricultural context, in order to contribute to a greater critical thinking in this field of study (BENGOA, 2003). Thus, this paper seeks to expand and renew this discussion, under a new approach that involves the efficiency and efficacy of aspects related to livestock farming in regions of Brazilover the last two decades, when the sector hassuffered several transformations. In this sense, it is known that the agricultural activity transforms the place of its activities, causing impacts on the environment and society, so it is noteworthy that studies that aim to create indicators which can support decision making, regarding measures in this scenario, are extremely relevant (BRAGA et al., 2004). In this sense, to deepen the discussion about issues of livestock farming in Brazil, the research problem of this study aims to answer the following question: What is the performance of livestock farming in the regions of Brazil from the perspective of efficiency and efficacy during the 1990s and 2000s? In order to characterize agricultural efficiency and efficacyin Brazil and contribute to the progress of performance studies in agricultural aspects, the objective of this study is to analyze the performance of Brazilian livestock farming activity from models of efficiency and efficacy in regions of Brazil through the use of panel data for the 1990s and 2000s. This study is justified by three axes: theoretical, practical and empirical. The theoretical axis aims to promote and strengthen the discussions, relationships and dilemmas on efficiency and efficacy in administrative studies and its function also involves contributing to further discussions of aspects of efficiency and efficacy in the agricultural context. The practical axis of this study has the function of being a source of information for the regions and states of Brazil regarding matters of farming performance in these territories to support possible future action plans. Finally, the empirical axis of this study is to provide a work plan and a study tool for any future work attempting to study efficiency and efficacy together. This article is structured, in addition to this introduction, into three sections. In the second section, the applied methodological procedures are presented. In the third section, there are the results and discussion. Finally, the main conclusions are presented. 2. METHODOLOGICAL PROCEDURES The methodological procedures regarding efficiency and efficacy are directly interconnected, because the measurement of organizational efficacy involves the assessment of efficiency, that is, the evaluation of the relationship between the resources used and the results achieved. Moreover, since efficiency is related to the results achieved, meaning the outputs as one of its parameters, its measurement also includes efficacy, as this concept is responsible for verifying the results originated from the system being analyzed (FERNANDES, 2008). From this, the present study quantitatively analyzed the issues of efficiency and efficacy within the Brazilian livestock farming reality. The model of efficiency was conducted using the data envelopment analysis efficiency technique (DEA). In the other hand, the efficacy model was carried out by the preparation of an index to measure this aspect. Because of the methodology used to calculate the DEA and the index and the consequent quantitative analyses performed to check the results, the work can be classified as quantitative. Moreover, it is also characterized as being of a descriptive nature, since observations and analyses were made in order to record and correlate phenomena without manipulating them (RAMPAZZO, 2002). The aspects of efficiency and efficacy were discussed within the Brazilian agricultural reality. Even when efficiency and efficacy are not achieved at the same time, its results can be interconnected. Thus, it is possible, by means of a unique methodology to measure these two aspects together, as shown in some evidences from the literature (BILOSLAVO; BAGNOLI; FIGELJ, 2013). In this context, two different techniques have been developed to separately evaluate these issues. However, there is evidence that efficiency and efficacy are related and, especially, that efficiency is a path (and not a necessary condition) to achieve efficacy (MOUZAS, 2006; MIHAIU; OPREANA; CRISTESCU, 2010; OZCAN, 2014). To this end, econometric regression analyses were performed to verify the degree of influence of efficiency on efficacy in the studied reality. 2.1 Universe of Study, Sample, Variables and Data Source From the analysis of matters regarding efficiency and efficacy, this study verified this phenomenon in the Brazilian reality, analyzing the country s municipalities. Currently, the country has 5570 municipalities, divided into 26 states and the Federal District. However, for the purposes of this study, 4647 municipalities were considered for the two study periods. The elimination of some municipalities were due to the lack of data for the first period surveyed (the 1990s), the absence of agricultural activity according to the classification of research institutions and also due to the need of reconciling the different sources of researched data (KAGEYAMA, 2004). In order to have a better characterization of the phenomenon studied in Brazil, the analyses were performed from the grouping of results in 26 states and the Federal District and its five major regions, that is, the Midwest, North, Northeast, South and Southeast (MELO; PARRE, 2007). For the construction of the models, 22 variables were used, based on the availability of data sources and on the determinants of livestock farming indicated by the academic literature, in particular those related to labor, business conditions, environment, economic development and infrastructure (COSTA et al., 2013). Since the results were analyzed from the perspective of efficiency and efficacy, it is necessary to distinguish data inputs and outputs. 43

3 The input variables used were: livestock farming production area, number of livestock farming establishments, number of men working in this agricultural activity, number of women working in this agricultural activity, number of tractors, machinery for planting and harvest used in this agricultural activity, number of establishments that have technical assistance available, number of establishments that use fertilizers and correctives, number of establishments that have a control on pest and diseases, number of establishments with soil conservation, number of establishments that use irrigation, number of establishments with electricity available, the amount of investments made and the amount of funding of this activity. The output variables used are: the value of crop and animal production, and the total production value, the value spent on the activity, the amount of revenue obtained from the activity, gross domestic product of municipalities and the undegraded production area. Data were collected from the Agricultural Censuses of Brazil organized by the Brazilian Institute of Geography and Statistics (IBGE) conducted in 1996 and 2006, reflecting data from the decades of 1990 and 2000, respectively, and it was also obtained from several surveys conducted by the Institute of Applied Economic Research (IPEA) for the periods of analysis. It was raised 15 input variables and 7 output variables that are used for the efficacy analysis. From this, it is worth noting that for the efficiency analysis, the variables are already relativized by the ratio input/output, but for the efficacy analysis, since only output variables are used, all variables are relativized by the production area of the agricultural activity of the municipality. The software used included the Statistical Package for Social Sciences (SPSS) 20.0, DEAP 2.1 and Stata 14.0, which performed the procedures of index analysis, DEA and panel data analysis, respectively. 2.2 Data envelopment analysis DEA is a linear programming technique that emerged in the 1950s; however, this method has only been operationalized later, between 1970 and 1980, when there was a proposal to measure the efficiency of processes through the analysis of nonparametric inputs and outputs (products) of the process (BANKER; CHARNES; COOPER, 1984). Briefly, the DEA method establishes boundaries of efficiency by comparing the performance of many groups of decision makers (Decision Making Unit or DMU), establishing those that are references to others (benchmarks). Unlike other methodologies, such as econometrics, DEA is not directed to a central tendency, but rather to the borders. Thus, according to Lins et al. (2007), the DEA optimization problem for each DMU analyzed can be expressed as follows: jj uu jj YY jjjj ii vv ii XX iiii = uuuu kk vvvv kk (1) where u and v are weights or multipliers; XX kk are inputs; YY kk are outputs; and by convention, uuuu kk vvvv kk 1, which generates efficiency indices between 0 and 1. The operationalization of Data Envelopment Analysis can generally follow three steps: 1) definition of DMUs; 2) choice of DEA method; and 3) selection of inputs and outputs that have an importance to establish the relative efficiency of DMUs (FERREIRA; GOMES, 2009). As this work aims to study Brazil, each municipality was delimited as a DMU, because these units are autonomous decision makers along with their states of origin in relation to aspects of livestock farming. Thus, 4647 DMUswere studied for both decades. There are two basic methods used for the construction of DEA, the Constant Returns to Scale (CRS) and the Variable Returns to Scale (VRS) (BANKER; CHARNES; COOPER, 1984). In this study, the VSR method was used because, as stated by some authors who discuss efficiency and efficacy, efficiency is a path to achieve efficacy, that is, to achieve the results (MOUZAS, 2006; MIHALU; OPREANA; CRISTESCU 2010; OZCAN, 2014). The VSR is intended to increase production, keeping the input levels, and therefore, it is guided to the product. According to Coelli et al. (1998), the VRS is expressed by: Maxθ, sujeito a : θy + Yλ 0, x Xλ 0, N1' λ = 1 e λ λθ i i (2) where N1 is a vector (N x 1) of unit numbers. 1 <θ < corresponds to the score of gross technical efficiency of DMUs; y is the product of the DMU; x is the input; X is the matrix of inputs (n x k); Y is the matrix of products (n x m); e λis the vector of constants that multiplies the matrix of inputs and products. As a result, this model was oriented to the outputs of the efficiency model. Moreover, it is worth noting that for the model developed, all variables weighted the same. 2.3 Construction of the Efficacy Index Regarding efficacy, which verifies the results obtained through an object of analysis, the indices that aim to quantify these results are one of the most used methods to address this aspect. Efficacy can be calculated through an index that shows issues related to the result of an action (BILOSLAVO; BAGNOLI; FIGELJ, 2013). There is not a standard established when the indices are developed and the methodology to be used varies depending on who is in charge of the development of the index. In this study, taking into account the reality studied and the lack of empirical evidence structured regarding the analysis of efficacy using indices, the Efficacy Index (EI) used was an adaptation of rural development indices used by Kageyama (2004), Conterato Schneider and Waquil (2007) and Melo and Parré (2007) For the construction of the Efficacy Index, all the results should be taken into consideration, that is, only the outputs and not the inputs and the processing. When calculating efficiency, the weighting of results is carried out in the analysis of the model, but for the construction of the EI, there should be a standardization of variables, since they are handled all in the same way, i.e. as a result, but in different scales. The procedure of standardization of variables is necessary because of the problems that can occur in researches, if data are presented in different ways or processed incorrectly (GREENE, 2008). Thus, it is desirable to turn the objects of study comparable, reducing the effects of different scales (BASSAB; MIAZAKI; ANDRADE, 1990). From the standardization of variables, the construction of the EI can be performed. The Efficacy Index is obtained by following three steps: 1) Treatment of, 0 44

4 output variables; 2) Construction of the GrossIndex of Efficacy (GIE); and 3) GEI transformation into the EI. Since the present study dealt with the efficacy of thelivestock farming activity, the standardized output variables were analyzed individually and transformed into partial indexes, according to Equation 3 (KAGEYAMA, 2004; CONTERATO; SCHNEIDER; WAQUIL, 2007). IIII jj = (ZZ ZZ jj mmmmmm ) (ZZ jj mmmmmm ZZjj mmmmmm ) (3) where IIII jj are the partialindices of each output variable ZZ jj mmmmmm is the maximum value observed for the j-th municipality studied; and ZZ jj mmmmmm is the minimum value observed for the j-th municipality studied; With this procedure, each output variable will be a partial indicator. The sum of these partial indexes will result in the Gross Index of Efficacy (GIE) (KAGEYAMA, 2004; MELO; PARRÉ, 2007). IBE j = i= IV ji 1 (4) Where IBE j Corresponds to the Gross Index of Efficacy of the j-th municipality studied; i refers to the number of variables of efficacy included in the model; IV ji is the partial index of each variable of the j-th municipality studied for the i-th number of efficacy variables included in the model; By the interpolation of the GIE, considering the highest value as 100 and 0 as the lowest, the Efficacy Index (EI) is obtained. Therefore, as for efficiency, efficacy in this study is analyzed in relative terms, since the scale used has its values based on the average. 2.4 Panel Data: regression between efficiency and efficacy In order to better visualize the efficiency and efficacy values found for the different objects of analysis, they were classified in a scale, using similar procedures according to the classifications ofmelo and Parré (2007) and an adaptation of Pinto, Coronel and Bender Filho (2015). Table 1 -Score and performance scales in order to classify the efficiency and efficacy of livestock farming in the Brazilian states Values of efficiency and efficacy Criteria Score scales Performance Lower than the average minus 1 standard deviation B (M - 1δδ) Low Average plus or minus 1 standard deviation (M - 1δδ) < M < (M + 1δδ) Average Higher than the average plus 1 standard deviation (M + 1δδ) A High Source: Adapted from Melo and Parré (2007) and Pinto, Coronel and Bender Filho (2015). Accordingly, the scale used is relative, its values are based on the average and they were divided into three degrees of performance. It is worth noting that, despite having values from 0 to 1, the results of efficiency and efficacycannot be compared with each other by their numeric value, due to the different methodologies used to measure this issue. Based on this, the relationship between efficiency and efficacy supports the hypothesis of this study. H1: Greater efficiency levels lead to higher levels of efficacy in the Brazilian livestock farming activity. That is because it is believed that efficiency is a path to achieve efficacy (MOUZAS, 2006; MIHALU; OPREANA; CRISTESCU, 2010; OZCAN, 2014). Therefore, the sign expected for the relationship of the variables is positive, i.e., that efficiency will positively impact the efficacy of the agricultural reality. Thus, to investigate the relationship between efficiency and efficacy, panel data analyses were performed to verify the degree of influence that efficiency has on the efficacy of the studied reality. The panel data consists in observing n entities or objects of analysis for two or more periods of time. Due to the characteristics and the size of the sample, the panel methodology corroborates the scope of the research, because it allows analyzing dynamic relationships in time and space by combining the dimensions of time series and cross sections (GREENE, 2008). Moreover, using panel data has some advantages. The first is related to the heterogeneity that different study units, for example, regions and countries may have when considered together over a period of time, and the panel estimation takes this heterogeneity into account. In addition, regression models with panel data provide greater degrees of freedom for the analysis, capturing aspects that are not seen when an analysis is made only taking into account a series in time or a specific unit (BALTAGI, 2005). According to Baltagi (2005), the overall panel regression equation is expressed as: y jk = αα + XX jjjj ββ + μμ jjjj,, j = 1,..., N; k = 1,..., T (5) where j = denotes units of measure researched as individuals, companies and countries; k = represents the period of time; αα = specific individual effect; ββ = represents the angular coefficient; XX iiii = matrix of regressor variables of unit i at time t; μμ = represents the random error term. Regarding the characteristics of data, the panel model can be classified as balanced and unbalanced. The balanced panel occurs when data are available for all cross-sectional units in all periods of time. The nonbalanced panel is characterized by the situation in which the data set does not have some years in the studied cross cutting units (Greene, 2008). In this study, the panel used is considered as balanced. By calculating the DEA for efficiency and the EI for efficacy, there is the possibility to check how the efficacy of Brazilian livestock farming is impacted by the efficiency in this sector. Therefore, the results of the Data Envelopment Analysis are used as a proxy for efficiency and the results of the Efficacy Index are used as a proxy for the efficacy of livestock farming. The decades of 1990 and 2000 were used as the two time periods and the municipalities were used as cross-sectional of the panel analysis performed, resulting in a total of

5 observations, that is, 4647 for the decade of 1990 and 4647 for the 2000s. The impact of efficiency on the efficacy of the agricultural reality can be seen in the following regression analysis: IIII kj = αα + ββ 1 EEEEEEccccênnnnnnnn kkkk + μμ kkkk (6) Where IIII kkkk is the efficacy index of the j-th municipality studied for the k-thperiod of time; EEEEEEEEEEênnnnnnnn kkkk Level of Efficiency of the j-th municipality studied for the k-thperiod of time; αα Specific individual effect; ββ 1 é angular coefficient ratio of the regression variables; μμ kkkk is the random error term. Therefore, the efficiency is used as a dependent variable and the efficacy as an independent variable. It is expected, based on empirical evidence, that efficiency will lead to efficacy and that the relationship of the equation will be positive, confirming the hypothesis of this study. From Equation 6 it was possible to verify the model estimation by a panel estimation method. The most used models are the Fixed Effects (FE) and the Random Effects (RE). In this sense, the FE model considers the fact that there may be correlations between the intercept and the explanatory variables at any time. In the other hand, the RE model has the same assumptions as the FE model, varying only in the treatment of the intercept, which shall be considered as a random variable and not as a fixed parameter (BALTAGI, 2005; DUARTE; LAMOUNIER; TAKAMATSU, 2008; GREENE, 2008). However, the uses of these models are excluding and, according to the characteristics of the data, one of them will be more recommended. In this context, one way to decide between which models to use is by comparing the effects of the methods by checking whether there is a correlation between the unobserved factors and the explanatory variables after applying the Hausman test. The null hypothesis of this test verifies if the difference between the coefficients of the Fixed and Random Effects are not systematic. So, if there is significant difference between the two models, there is evidence in favor of using the FE model, rejecting the null hypothesis of the test. Furthermore, the RE method ends 46 up consuming too many degrees of freedom, and because of that, it is necessary to have a greater number of periods in relation to the number of regression coefficients, and when it is not possible to estimate because of this need, the FE model should be used (GREENE, 2008). In order to verify some aspects and assumptions of the regressive models to avoid achieving biased results, some econometric tests were conducted. To check for the presence of heterocedasticity, that is, if the error terms have differing variance, the Wald test was performed. In order to confirm whether the errors are random or uncorrelated, i.e., if there is the presence of autocorrelation, the Cumby-Huizing test was carried out. 3. Discussion and Analysis of Results 3.1 Efficiency of Brazil s Livestock farming activity Based on data of the twenty two variable inputs and outputs used for the calculation of livestock farming efficiency in the 1990s and 2000s, the analysis was developed for the 9294 objects and it was grouped according to the states and regions of Brazil, respectively, in order to obtain the average value of the municipalities in each of its subdivisions. Thus, the Brazilian states were analyzed in terms of agricultural efficiency, through Table 2, for the 1990s. From Table 2, it appears that Brazil s average livestock farming efficiencywas 53,96% for the decade of In addition, there is a high agricultural efficiency range for the municipalities of Brazil as a whole, since there is a difference of more than 80% in the values ofagricultural efficiency between thebenchmark municipalities and the municipalities with the lowest efficiency values. Regarding the Brazilian states, only Santa Catarina and Goiás were placed under the high-performance range, with livestock farming efficiency of 84,62% and 75,82%, respectively. It is worth noting that after these two states, those classified under theaverage performance range that showed higher values of agricultural efficiency were São Paulo, the Federal District, and Rio de Janeiro, which presented an efficiency of 58,61%, 57,34% and 57,15 %, respectively. Table 2 - Mean, number of cases, standard deviation, maximum and minimum efficiency values for the Brazilian states for the 1990s State Mean Ranking Performance Numberof Standard Maximum Minimum Cases Deviation Santa Catarina º High Goiás º High São Paulo º Average Distrito Federal º Average Rio de Janeiro º Average Rio Grande do Sul º Average Paraná º Average Pernambuco º Average Minas Gerais º Average Espírito Santo º Average Alagoas º Average Amazonas º Average Sergipe º Average Paraíba º Average Bahia º Average Maranhão º Average Ceara º Average Mato Grosso do Sul º Average Amapá º Average Para º Average Mato Grosso º Average

6 Acre º Average Rondônia º Average Piauí º Average Roraima º Average Tocantins º Average Rio Grande do Norte º Low Brazil Average Source: Elaborated by the author. The high value of efficiency found for the state of Santa Catarina during this review period can be justified by the adoption of many public policies in the state in order to recover the agricultural production environment, and the means that are involved in the process. Thus, high efficiency results are only a consequence of measures taken in this state, so that agriculture is aligned with environmental and socially sustainable development (THEIS; FERNANDES, 2002; STUKER, 2003). As for the state ofgoiás, it is noteworthy that it provides a favorable environment for the practice of this activity, since the development of the state is directly related to agriculture, especially in the occupation of new agricultural frontiers and the reduction of production costs (BEZERRA; CLEPS JR, 2004; SILVA, JR FERREIRA, 2010). Rio Grande do Norte was the only state that showed a low performance range for the 1990s, and its average agricultural efficiency was 39.81%. This state has many production needs, such as shortage of land, water, education, technology and technical assistance, as well as lack of funds for investments in the agricultural activity in the region. This generates a low performance in the agricultural activity for several regions of this state (AQUINO, LACERDA, 2014). In the average performance range, the states with the lowest efficiency values were Tocantins and Roraima, with agricultural efficiency of 44.19% and 44.25%, respectively. In order topicture Brazil in a macro context, the analysis for the decade of 1990 was grouped into five major regions of the country. The results demonstrate that the South had the highest average (65.50), followed by the Midwest, Southeast, Northeast and North, that presented average efficiencies of 56.40; 55.12; 47.87; 45.93, respectively, for the 1990s. From this, it appears that the five major regions of the country have an average performance value. In the 1990s, the region with the highest value was the South, and it demonstrates that the South Region of the country has a prominent position in aspects related to the agricultural activity nationwide (PINTO; CORONEL, 2013). Thus, part of this position can be considered a result of the higher levels of efficiency regarding the livestock farmingactivities in this region. After the South Region, the highest efficiency levels obtained are from the Midwest, Southeast and Northeast. The North showed the lowest value of livestock farming efficiency for that period. From this, it is possible to attest that this concentration, especially in the Southeast (mainly in the state of São Paulo), South and in the Midwest, reveals a historical phenomenon of greater development of the means of production in these regions when compared to other regions of the country (ALMEIDA; AMIM; SOUZA FILHO, 2009; IMORI, 2011). Furthermore, the North and Northeast regions form a group with high concentration of lands, low levels of government investments and historical differences regarding the agricultural means of production when compared to other regions, justifying the lowest averages, and it also has the municipalities and mesoregions with the worst ranking positions (SILVA; FERNANDES, 2005; COSTA et al, 2012). In order to analyze the twenty-six Brazilian states and the Federal District, Table 3 shows the results for the 2000s. In Table 3, it appears thatthe average efficiency of Brazilian livestock farming was 78.66% for the 2000s, a figure that represents an increase of 24.70% over the previous decade. In addition, it is possible to see an increase in the amplitude of the agricultural efficiency of the municipalities in Brazil as a whole, for that period, since there is a difference of almost 85% in the values of livestock farming efficiency between the benchmark municipalities and the municipalities with the lowest efficiency values. Regarding the Brazilian states, none of them were classified in the high-performance range. This demonstrates that despite the greater amplitude between the maximum and the minimum values at the municipal level, an overall average shows that the values are with a smaller standard deviation from each other. In the average performance range, the states that had higher livestock farming efficiency in the 2000s were São Paulo, Santa Catarina and Rio Grande do Sul, with efficiency values of 85.31%, 85.04% and 82.85%, respectively. This corroborates the results of the previous decade, which showed greater efficiency in the states from the South and Southeast of the country in relation to other regions. Three states were placed under the low performance range and thislogically corroborates the lowest values found for the municipalities and mesoregions of these states. In this perspective, the Federal District, Piauí and Tocantins presented livestock farming efficiency of 15.49%, 70.58% and 70.66%, respectively, for the 2000s. The low value obtained for the Federal District was because the data did not have variability, due to the fact that its classification was compared to a municipality, mesoregion and state. In addition, the low efficiency of this region regarding the agricultural activity for this period only reflects the importance given to other economic activities; especially those related to the administrative role that this region plays for the country. Moreover, Piauí and Tocantins, despite having relevance to the livestock farming activity, do not make the best use of the land, mainly because the land dedicated to this activity in these states have a high level of environmental degradation (PEREIRA; NASCIMENTO, 2014). 47

7 Table 3 - Mean, number of cases, standard deviation, maximum and minimum efficiency values for the Brazilian states for the 2000s State Mean Ranking Performance Numberof Cases Standard Deviation Maximum Minimum São Paulo º Average Santa Catarina º Average Rio Grande do Sul º Average Paraná º Average Espírito Santo º Average Alagoas º Average Pernambuco º Average Rio de Janeiro º Average Minas Gerais º Average Sergipe º Average Ceara º Average Goiás º Average Rio Grande do Norte º Average Paraíba º Average Amazonas º Average Para º Average Mato Grosso do Sul º Average Mato Grosso º Average Bahia º Average Amapá º Average Maranhão º Average Roraima º Average Rondônia º Average Acre º Average Tocantins º Low Piauí º Low Distrito Federal º Low Brazil Low Source: Elaborated by the author. In order to picture Brazil in a macro context for the 2000s, the analysis was grouped into five major regions of the country and the results showed that the South was the region with the highest average (83.44), followed by the Southeast, Northeast, North and Midwest, which presented an efficiency average of 81.28; 75.89; 72.98; 59.74, respectively, for the 2000s. Thus, it appears that four of the five major areas of the country have average performance values. In the 2000s, the region with the highest value was again the South, justifying the prominent position of this region in relation to agricultural issues within the Brazilian reality (PINTO; CORONEL, 2013). The highest values of efficiency, apart from the South, are for the Southeast, Northeast and North regions. The Midwest was the only region that showed a low performance value of livestock farming efficiency for that period. Regarding the analysis developed so far, in terms of agricultural efficiency, it is clear that despite the importance that agriculture has to the country as a whole, its development does not occur homogeneously. This is because some regions, especially the Southeast, South and Midwest, have, since the colonization period until the 48 recent public policies, a concentration of high standards of production means of this activity, which consequently lead to a higher efficiency compared to other regions of the country (ALMEIDA; AMIM; SOUZA FILHO, 2009; IMORI 2011;. COSTA et al, 2012). 3.2 Efficacy of Brazil s Livestock Farming Activity Based on data of the seven output variables used for the calculation of the livestock farming efficacy in the 1990s and in the 2000s, the analysis was developed for the 9294 objects, and it was grouped according to the states and regions of Brazil, respectively, in order to obtain the average value of the municipalities in each of its subdivisions. Thus, the Brazilian states were analyzed in terms of agricultural efficacy, through Table 4, for the 1990s. From Table 4, it appears that Brazil s average agricultural efficacy was 19.00% for the decade of In addition, there is a high livestock farming efficacy amplitudefor the municipalities of Brazil as a whole, since there are a difference of more than 80% in the values of livestock farming efficacybetween the municipalities with greater and lesser amount of efficacy. Table 4 - Mean, number of cases, standard deviation, maximum and minimum values of efficacy for the Brazilian states for the 1990s State Mean Ranking Performance Numberof Standard Deviation Maximum Minimum Cases Distrito Federal º High Mato Grosso do Sul º High Espírito Santo º High Mato Grosso º Average Paraná º Average Rio Grande do Sul º Average São Paulo º Average Santa Catarina º Average Goiás º Average

8 Para º Average Rondônia º Average Roraima º Average Minas Gerais º Average Amazonas º Average Rio de Janeiro º Average Maranhão º Average Pernambuco º Average Acre º Average Bahia º Average Alagoas º Average Ceara º Average Amapá º Average Sergipe º Average Tocantins º Average Piauí º Average Paraíba º Average Rio Grande do Norte º Low Brazil Average Source: Elaborated by the author. Regarding the Brazilian states, only the Federal District, Mato Grosso do Sul and Espírito Santo were classified in the high-performance range, with an agricultural efficacy of 84.28%, 45.52% and 35.22%, respectively. It is worth noting that other states had average performance ranges, with the exception of Rio Grande do Norte, which was the only state placed under the low performance scale in the 1990s. The state s average value of livestock farming efficacy was 3.89 %, reflecting the low value of agricultural production generated by this state, due to several structural problems and also because many livestock farmers in this region have other sources of revenues, including rural retirement incomes and social government programs (AQUINO, DUARTE, 2014). In the average performance range, the states of Paraiba and Piauí had the lowest efficacy values, 5.97 %% and 6.42% respectively. In order to picture Brazil in a macro context, the analysis for the decade of 1990 was grouped into five major regions. Thus, the results demonstrate that the Midwest region showed the highest value (44.99). The South, Southeast, North and Northeast, presented an average efficacy value of 25.01; 23.40; and 10.04, respectively, for the 1990s. From this, it appears that four of the five major regions of the country have average performance value. In the 1990s, the region with the highest value was the Midwest and it was the only one that had a high performance value. This is because this region had elevated property area and high technical capacity in these properties, which enabled it to achieve elevated results in the livestock farming activity (IMORI, 2011; STEGE; PARRE, 2011). The Midwest region is followed by the South, Southeast and North. The Northeast showed the lowest value of agricultural efficacy for this period due to some gaps in its agricultural structure, which cause lower levels 49 of development in the regions of this state (STEGE; PARRE, 2011). In order to analyze the twenty-six Brazilian states and the Federal District, Table 5 shows the results for efficacy in the 2000s. In Table 5, it appears that the average agricultural efficacy in Brazil was 28.10% for the decade of 2000, which represents an increase of 9.10% over the previous decade. In addition, it is possible to see a decrease in the amplitude of livestock farming efficacy for the municipalities in Brazil as a whole for this period, since there is a smaller difference between the municipality with the highest and the lowest agricultural efficacy. Regarding the Brazilian states, all of them were classified in the average performance range, with the exception of the Federal District, which presented a performance of 75.72%, and was placed under the high-performance scale. In the average performance range, the states that presented the highest livestock farming efficacy in the 2000s were Mato Grosso and Mato Grosso do Sul, with values of 49.55% and 48.97%, respectively. Since no state showed a low a performance, the lowest values of the average performance scale were for the states of Rio Grande do Norte, Paraíba and Piauí, which showed an efficacy of 8.94%, 9.69% and 10.10%, respectively. These results highlight the situation of Rio Grande do Norte, previously mentioned, which showed the lowest values due to the region s production needs and low agricultural efficacy (AQUINO, LACERDA, 2014). In order to picture Brazil in a macro context for the 2000s, the analysis was grouped into five major regions of the country. Thus, the results demonstrate that the Midwest region showed the highest value (50.80). The South, Southeast, North and Northeast, presented average efficacy values of 37.92; 29.68; and 17.85, respectively, for the 2000s. Table 5 - Mean, number of cases, standard deviation, maximum and minimum values of efficacy for the Brazilian states for the 2000s State Mean Ranking Performance Numberof Standard Deviation Maximum Minimum Cases Distrito Federal º High Mato Grosso º Average Mato Grosso do Sul º Average Rondônia º Average Espírito Santo º Average Paraná º Average Rio Grande do Sul º Average

9 São Paulo º Average Santa Catarina º Average Para º Average Goiás º Average Acre º Average Pernambuco º Average Minas Gerais º Average Ceara º Average Alagoas º Average Maranhão º Average Amazonas º Average Bahia º Average Rio de Janeiro º Average Sergipe º Average Roraima º Average Tocantins º Average Amapá º Average Piauí º Average Paraíba º Average Rio Grande do Norte º Average Brazil Average Source: Elaborated by the authors It is possible to see, on this context, that the five major regions of the country have an average performance value. In the 2000s, the region with the highest value was the Midwest, followed by the South, Southeast and North. These regions, particularly the Midwest, South and the Southeast, have some aspects that make the results of agricultural activity to be higher in relation to other areas of the country. This happens because these regions have higher productivity, better infrastructure, rural credit, agricultural modernization and improved quality of life of rural households, which help to enhance the results for the agricultural activity in these regions (IMORI, 2011; STEGE; PARRE 2011; COSTA et al, 2012). The Northeast showed the lowest value of agricultural efficacy for the period. Some of the reasons for this situation involve historical and cultural gaps regarding agribusiness, which result in less developed aspects, leading to a difficulty to achieve results related to the agricultural activity (STEGE; PARRE, 2011). 3.3 Effects of Efficiency on the Efficacy of Livestock Farming in Brazil 50 With the results of efficiency and efficacy in the Brazilian livestock farming reality, it is necessary, at first, before the interpretations of the panel regression, to check the tests in order to meet the assumptions of the regression model used. The analysis of the study model, which used the livestock farming efficacy as the dependent variable and the livestock farming efficiency as the independent variable, demonstrated, from the cumby- Huizinga test, the absence of autocorrelation between variables. The Wald tests howed the presence of hetero scedasticity in the variables, and to correct this problem, the estimates were made from robust standard errors (RSE) i. Due to the limited degrees of freedom associated with the study equation, and because the number of periods of analysis would not be greater than the number of coefficients and regression constant, it was not necessary to perform the Hausman test, since it was not possible to estimate with Random Effects (Greene, 2008). From this, the estimation of the regression analysis by the method of Fixed Effects, as shown in Table 6, confirms the hypothesis of the study. Table 6 - Results of the estimation of the regression model studied by the Fixed Effects method by robust standard errors (RSE) with the dependent variable as the livestock farming efficacy in the 1990s and the 2000s Variable Coefficient tstatistic Significance const Agriculturalefficiency ** R² adjested = Source: Elaborated by the author. Note: Values with two asterisks (**) denote significant coefficients at 5% Analyzing the adjusted R² of the regression model, that is, its power of explanation, the value obtained was This value appears to be satisfactory, because it shows that 56.28% of agricultural efficiency captures the relationship studied related to agricultural efficacy Verifying aspects of the relationship between variables, it is possible to see that there is statistical significance, since the independent variable showed a significance level lower than 0.05 with respect to the dependent variable, allowing, therefore, the analysis of the relationship coefficient. The coefficient has a positive value and it is statistically significant. Therefore, the relationship between efficiency and efficacy support the hypothesis of this study. Even if it is not a necessary condition, within the reality studied in the Brazilian agricultural context, efficiency is one of the paths to achieve efficacy. Therefore, the higher the efficiency of an activity, the greater its efficacy will be (MOUZAS, 2006; MIHAIU; OPREANA; CRISTESCU, 2010; OZCAN, 2014). Analyzing the value of the coefficient, it is noteworthy that higher levels of efficiency will lead to higher levels of efficacy in the Brazilian livestock farming activity. From this, the positive sign of this relationship is in accordance to the sign expected by the study. Thus, it is possible to infer that when there is a 1% increase in the Brazilian livestock farming efficiency, there is an increase of 0.74% in the efficacy of Brazil s livestock farming. Based on this result, it is possible to infer that any improvements in the efficacy of the Brazilian agricultural activity can be achieved if the means and the inputs of the