USE OF POLYCHORIC INDEXES TO MEASURE THE IMPACT OF SEVEN SUSTAINABILITY PROGRAMS ON COFFEE GROWERS LIVELIHOOD IN COLOMBIA

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1 USE OF POLYCHORIC INDEXES TO MEASURE THE IMPACT OF SEVEN SUSTAINABILITY PROGRAMS ON COFFEE GROWERS LIVELIHOOD IN COLOMBIA GARCIA, Carlos; OCHOA, Gustavo; GARCIA, Julián; MORA, Juan; CASTELLANOS, Juan. Centre for Regional Coffee and Entrepreneurial Research, Manizales, COL. SUMMARY By promoting standards of better social, environmental and economic practices at the farm, sustainability initiatives expect to get an improvement in farmer s livelihood over time. Until now a lack of tools and methodologies to measure this impact has made it difficult to demonstrate the impact of these programs and available studies are commonly focused on analyzing separated simple indicators. The multidimensional nature of sustainability is not still represented in impact assessment. This study is the first to analyze the effects of coffee sustainability programs -Voluntary Sustainability Standards VSS- on coffee farmers livelihood in Colombia by incorporating the multidimensional scope of the initiatives. The work is based on COSA approach, adapted by CRECE to Colombia, which consists of the development and application of an internationally-recognized methodology and data gathering process to measure the outcomes of the adoption of sustainability initiatives. A set of sustainable practices in the social, environmental and economic dimensions is used to test for aggregate effects of the programs on farmers livelihood. To consider differential effects, contrasts by farm size and multi-certification status are also considered. The data come from a longitudinal and panel data set on a probabilistic sample of coffee farmers participating in seven VSS. Four certifications (Fairtrade, Organic, Rainforest Alliance and UTZ Certified) and three codes of conduct (Nespresso AAA, 4C and Starbuck s C.A.F.E. Practices) as well as a group of conventional farmers operating as a control were followed-up during three years. Principal Component Analysis (PCA) for ordered categories -polychoric correlations-, was implemented to summarize the performance of the farms in sustainability dimensions. The method guarantees comparability since the rating obtained by one specific condition will be the same for all the certifications, all the locations and all the years. The performance of aggregated indexes is positive during the years of observation for the seven sustainability initiatives analyzed. There is an average improvement in the social, environmental and economic conditions of farmers participating in sustainable schemes. On average, farmers have better socioeconomic and environmental conditions compared with their conventional counterparts. However, the difference of scores between sustainability initiatives and control groups tend to decrease over time and the aggregate indexes decrease for smaller farms.

2 METHODS This study examines the use of polychoric principal component analysis for the measure of farms sustainability. This method allows to synthetize multiple variables in one aggregate index that ranges from 0 to 100. Through the polychoric analysis, three indexes are calculated: the Economic, Social and Environmental Indexes. At the same time, the sustainability index is calculated as the average of a linear function of the three indexes, as it is shown in the Function 1. SU = 1 E + 1 S + 1 A (1) Function 1 results in an index that summarizes the three sustainability sub-indexes by assigning them the same weight, considering that each one of these categories has the same relevance inside the sustainability concept. Each one of the sub-indexes are calculated by using scores that result from performing polychoric principal component analysis over a series of variables that are theoretically considered to be related with economic, social and environmental conditions of the farms. The polychoric procedure uses discrete variables and calculates what would be their correlation as if they were on a continuous scale (Uebersax, 2006). In the case in which there are two variables (X 1 and X 2 ) that represent the binary discretized form (this discretization is defined by thresholds) of two continuous variables (Y 1 and Y 2 ), the polychoric correlation of both variables would suppose that there exists a common continuous latent trait (T) defined by the interaction of both variables. This relation is explained by Uebersax (2006) as follows: Y 1 = β 1 T + u 1 + e 1 (2) Y 2 = β 2 T + u 2 + e 2 (3) In which u represents unique components of each variable and e represents the errors. As β 1 and β 2 are the correlations between the variables and the latent trait, they can be considered as one (β). The polychoric correlation between both variables would be: r = β 2 (4) The polychoric correlations can be used for the calculus of a set of vectors (equal to the number of variables), in which each vector is a lineal combination of the variables 1. The first vector is the one that represents the higher variance, so it is the one that is used for calculating the indexes (Kolenikov & Angeles, 2004). 1 This formulation explains the tetrachoric correlations. For a further explanation that includes the underlying distribution assumptions see Ekström (2008).

3 The variables used for the indexes are dichotomous, categorical and discrete. Some of them are transformations of continuous numerical variables into categorical variables. The Economic sub-index includes yield of the farm, net income that comes from the coffee production (the variables were categorized in an ascending order), the level of knowledge that the producer has on the market and the access to it, technical use of inputs, perception of the economic situation of the farm, affection by diseases and pests and the percentage of coffee sold as low quality. The Social sub-index includes indicators of farm s resilience, working conditions inside the farm, producer s perception of their household quality of life and their relationships with their employees. The Environmental sub-index includes information about the recycling programs in the farm, conservation practices, training in environmental topics and subjective perception of the environment conditions of the farm and the village. Previous to the polychoric analysis, the association of the variables inside each index was tested by the non-parametric test Kendall s τ b. This test showed that almost all the variables were positively and significantly correlated which suggests that they can be represented by a single latent variable. For the Polychoric Principal Component Analysis, the Stata module polychoric.ado was used. By using the polychoric correlations, the variables of each index can be transformed into a new linear combination of components in which each component represents a proportion of the total variance. The first component is the one that captures the highest variance so the weights of each variable given by this component can be used as a proxy for the common information contained by the variables that correspond to each one of the subindices (OECD, 2012). A score is estimated by every single farm according to the weights that resulted from this analysis. The scores were standardized so they could be represented in a scale from 0 to 100. The sample was divided into certified and non-certified farms. Then Target and Control farms were selected by using Propensity Score Matching and Difference in Differences method was added in order to control for factors other than the program over time. Despite the methods used, it has to be said that part of the difference could be due to unobservable factors coming from the selection criteria used by the organizations. The group of certified farms is composed by Organic, UTZ Certified, FLO and Rainforest Alliance; the group of verified farms is made up of farms Nespresso AAA, 4C and C.A.F.E. Practices in 2008, 2009 and For Nespresso AAA the first and the fourth years correspond to 2009 and 2012, while for Organic they correspond to 2009 and 2011.

4 RESULTS The upward trend for the calculated indexes between the first and the fourth year (Figure 1) indicates an improvement over time in the social, environmental and economic conditions of the producers in both groups -except for the economic in conventional producers-. The scores show that producers 3 participating in a sustainability initiative tend to have better overall conditions in the three dimensions of sustainability. The difference between target and control groups in the first year around ten points in every dimension- reveals certain selection bias originated in the way the organizations chose the participating farmers. In fact, in most cases it was known that farmers that had participated in other programs were preferably engaged. Figure 1. Sustainability indexes by year. Target and Control Social Environmental Economic In order to control this bias as much as possible to quantify the impact of the VSS programs, PSM + DID methods were implemented, making clear that some bias could even persist after due to the potential influence of unobservable factors. In line with the methods, the projection line in every graph depicts the trend that the target group should have had if the baseline difference target to control were imposed during the period of evaluation. In other words, it is a counterfactual situation that shows what would be the average scores of the producers if they had not been part of a sustainability program. Therefore, the impact of the initiatives over the corresponding index is the difference between the score of the target group and the projection line. As a result, the impact of VSS is positive for the three dimensions. The highest score corresponds to the environmental dimension, which shows that 8.1 points in average of the index (70-62) can be attributed to the effect of the VSS as a group 4, compared to the 3 The mean showed values that were very similar to the median. 4 This result corresponds to the seven programs grouped, so the significance is not presented. The programs differ in the amount of the scores and not all of them have achieved statistically significant impacts.

5 situation in absence of the program. The impact for the social and the economic indexes were also positive and equal to 4.3 and 5.4 respectively. In the social index, the variable worker s living conditions drove much of the positive effect of the VSS. A share of around 30% of the producers were found in the highest category of the variable in the first year, while in the last year this proportion was near 60%. However, three variables remained almost constant during the period: Farm crop production for family consumption, Revenues from sales of other farm crops and Possession of household assets. All the environmental variables improved over time for the group of VSS, being the most representative Soil conservation practices: in the first year around 35% of the producers under environmental-focused initiatives used more than five soil conservation practices, while this achieved around 65% in the last year. The component variables of the economic index that explain its increase for the group of economic-focused initiatives are mainly training in marketing topics and perception of better business opportunities. However, variables such as yield and percentage of the coffee sold as low quality decreased or keep stable. This impeded the initiatives to have a larger impact on the economic index. On the first year around 55% of the producers had a yield higher than 5, while in the last year, this proportion was around 50%. This result is highly influenced by the conditions of the context in the country, mainly characterized by weather variability, presence of plagues and diseases such as coffee rust and Coffee Berry Borer, as well as high coffee trees renovation rates that kept some of the crops temporarily without production. To face the question if VSS have differential effects on small producers, we opened the indexes in three farm sizes: less than one hectare, one to five and greater than five hectares. When compared by coffee area sizes it can be seen that, as expected, the higher the size of the farm the higher the score of the index in the dimensions of sustainability (Figure 2). At the baseline, the status of sustainability differs among the farm sizes: the difference between the biggest and the smallest farms scored 20 points for the social index, 9 for the environmental and 7 for the economic. Looking at the differences achieved over time, it is observed that the impacts of the VSS on sustainability tend to be concentrated on the small and medium size farms and in the environmental conditions. However, the average differences in the impact are quite low among the groups, scoring no more than two points. 5 The is equivalent to 12.5 kg.

6 Figure 2. Sustainability indexes by farm size. Social Environmental Economic Impact To evaluate the effect of participating in one VSS against two or more at the same time, the sample was divided into these two groups. It was expected to find a higher effect when the farmer is multi-certified, mainly under the assumption that this condition allows them access to better market opportunities and by this way getting a better price. In that sense, the economic index would perform better for multi-certified farmers. As can be seen from the next figure, the sustainability index shows better results when the farmers participate in more programs. Figure 3. Economic index by Single-certified and Multi-certified However, the initial difference of six points for the economic index between multi-certified and single-certified farmers has been slightly reduced over time to just four points, both in the second and in the fourth year. This suggests that the effect of multi-certification could be positive but decreasing. DISCUSSION The Polychoric PCA method allows score the aggregated impact of sustainability programs through synthetic indexes of the social, environmental and economic dimensions. The indexes show to be a proper tool for measuring sustainability since they can integrate a large number of variables for representing a multidimensional concept. They also consent comparing groups of producers over time under different conditions which also allows performing impact assessment of sustainability initiatives.

7 The results show that the producers who participate in sustainability initiatives have higher indexes than conventional coffee producers. All the indexes also showed to be related to the coffee area of the farms. It was also found that for every coffee area range, the producers under sustainable initiatives presented higher indexes. Producers with a larger number of initiatives tend to obtain higher scores, which suggest that the fact of having multiple certifications can contribute to sustainability conditions. Even if the indexes showed to represent the sustainability conditions of the farms, there are some aspects that can be taken into account for improving their performance. Namely, variables that were included such as the different perceptions of the producers on their conditions could be reconsidered in order to obtain a more objective measure and improve the formulation. Some influencing factors make complex the interpretation of the results: the starting point of the farmers and the regional conditions, farm size, multi-certification and number of years in the certification program. More investigation is needed to gain better understanding of the complexity of the impact of sustainable initiatives in coffee. REFERENCES Ekström, J A generalized definition of the polychoric correlation coefficient. Ph. D. thesis, Upsala: Acta Universitatis Upsaliensis. Kolenikov, S. y Angeles, G The use of discrete data in PCA: Theory, simulations and Applications to Socioeconomic Indices. Chapel Hill: Carolina Population Center, University of North Carolina. OECD (2012). Social Institutions and Gender Index: A methodological and technical background paper. Uebersax, J Introduction to the Tetrachoric and Polychoric Correlation Coefficients.