Linking Smallholders to the New Agricultural Economy: the case of the Plataformas de Concertación in Ecuador

Keywords: Impact evaluation, New Agricultural Economy, smallholder potato farmers, Food security < Health, agrobiodiversity, use of pesticides .manuscriptcentral.com/fjds

This paper examines the challenges of linking smallholders to high-value food markets by looking at the experience of the Plataformas program in the Ecuadorian Sierra. Multiple evaluation methods are employed to ensure identification of program impact. The findings suggest that the program successfully improved the welfare of beneficiary farmers, as measured by yields and gross margins. These benefits are achieved through improving the efficiency of agricultural production and through selling at higher prices. No significant secondary health or environmental effects were found. Overall, the program provides clear evidence that combining production support with facilitating market access can be successful.

3 has used this broader approach in the Andes is the Plataformas de concertación (multistakeholder platforms, or Plataformas) which seeks to link smallholders to high-value agricultural markets [START_REF] Devaux | Collective Action for Market Chain Innovation in the Andes[END_REF]. The Plataformas are alliances between small scale farmers and a range of agricultural support service providers. 1 The main objectives of the Plataformas are to increase yields and profits of potato-producing smallholders in order to reduce poverty and improve food security [START_REF] Pico | La cadena agroalimentaria de la papa a través de la metodología de plataformas de concertación y proyectos compartidos[END_REF]. The program provides participants with new technologies and high quality seeds in addition to facilitating access to high-value potato markets. Through the Plataformas, smallholder potato producers are directly linked to restaurants, supermarkets and processors who are willing to pay a premium for potatoes that meet their grades and standards. By establishing direct linkages between farmer organizations and purchasers, the Plataformas have reduced the number of intermediaries within the value chain, providing smallholders with the opportunity to benefit from the changes in agricultural marketing systems.

The objective of this paper is to understand whether, and to what extent, participating in the Plataformas impacts farmers' wellbeing through enhancing the earnings from potato production in poor areas of Ecuador where potatoes are a key staple crop. The mechanisms by which program objectives have been achieved and secondary environmental and health effects are also analyzed. The results, although context specific, provide insights into the challenges of linking smallholders to high-value markets and of the possibility of meeting these challenges. The remainder of the paper is organized as follows. Section II presents the logic of the Plataformas intervention. The methodological approach used is described in section III, whilst section IV provides a description of the context and the data. Section V presents the results followed by a discussion of lessons learned and section VI concludes. 5 meet its quality and timing needs, intermediaries are likely to play the role of obtaining the necessary product and providing it to the agribusiness. While these intermediaries may purchase the crop from smallholders, it will be at going market rates and provide no price premium or cost benefit to smallholders unless they are large enough suppliers that they can influence overall price.

The motivation for linking smallholders to agribusinesses is the presumed price premium for selling in these markets and thus overall income gains. When smallholders have no apparent comparative advantage in production, the challenge is to create that advantage or to reduce the transaction costs associated with purchasing from large numbers of farmers producing small quantities. Linking smallholders to high-value purchasers is likely to require organizing smallholders to overcome transaction costs as well as providing them with the necessary information to meet market requirements. While this adds costs for smallholders since they must take the time to organize and obtain information, it lowers the costs to industry. This is exactly the logic of the intervention undertaken through the creation of the Plataformas; namely, reducing transaction and production costs so smallholders can be a low cost option for high-value purchasers, and providing smallholders with the necessary tools to meet quality and quantity demanded. The primary mechanism by which the Plataformas reduce transaction costs is through providing support for smallholders from a range of agricultural support service providers including the National Autonomous Institute for Agricultural Research (INIAP), nongovernmental organizations, researchers, universities, local governments and international donors, and through fostering organization among smallholders. This support network comprises the Plataformas. The support and organization enables smallholders to generally improve production and meet the needs of high-value markets allowing them to sell directly to More specially, the Plataformas ensure seed provision and seed inventories are matched to detailed production plans established during regular meetings held among farmers, coordinating NGOs, and other stakeholders in order to achieve monthly quotas for delivery to clients. Further, the Plataformas provide training through Farmer Field Schools (FFS) to enhance productivity and promote Integrated Pest Management (IPM) techniques with the aim of improving quality and quantity of production through reduced use of pesticides (or at least limited increases).

Farmers are also trained to oversee quality control during harvesting and commercialization, and to identify potential clients who can make a verbal commitment to buy their produce as long as the required standards are met.

Our main interest in evaluating the Plataformas project is to determine the feasibility of linking smallholders to the new agricultural economy in a context in which they have little obvious comparative advantage. The approach seeks to lower transaction costs and to improve overall cost effectiveness through creating a support system to facilitate smallholder entry into this market. The three hypotheses we wish to test are: 1) participating in the Plataformas has increased farmers' welfare as measured by potato yields and gross margins; 2) greater potato sales and higher prices are the primary mechanism through which the program has improved welfare; 3) although high-value markets require high product quality, participation has not led to health or environmental degradation as measured by levels of agrochemicals used, their toxicity, precautions taken in their applications and changes in varietal use. The methods for testing these hypotheses are discussed in the next section.

III. Empirical approach and the search for a counterfactual

The key to identifying and measuring the impact of Plataformas participation is to have a proper counterfactual-that is, a comparison (control) group that is similar to the intervention (treatment) group in all ways except that it did not receive the intervention. The empirical problem faced in this analysis is thus the typical one of missing data to fill in the counterfactual; that is, it is not known what the outcomes for participants would have been had they not participated. In experimental studies, households are randomly assigned to treatment and control ex ante and, given a sufficiently large sample size, it is reasonable to assume that the treatment and control are alike in all ways except in receiving the intervention. When assessment studies are set up ex post (after project implementation) and not as part of project design, experiments are not possible and non-experimental methods must be used to identify impact. This section describes the steps taken to ensure quality data to construct a proper counterfactual was collected, followed by a description of the empirical approach used in the analysis.

a. The data collection

The data used in this analysis comes from household and community level surveys that were administered from June to August of 2007 in the Ecuadorian provinces of Chimborazo and Tungurahua. Prior to administering the surveys, a series of steps were taken to facilitate an evaluation of the program. First, participating communities (treatment communities) were identified in each province and information on these communities was obtained. Second, using the 2001 Ecuador census data (INEC, 2001), the treatment communities and a set of potential control communities with similar geographic, agroecological and socio-demographic characteristics were identified. This provided a list of all possible treatment and control communities to be included in the survey. Third, using propensity score matching (PSM), (described more fully below), control communities that were most comparable to treatment communities were identified-that is, control communities with similar propensity scores to the treated communities were kept as the potential set of communities for the sample. Fourth, the resulting list of potential control communities was discussed with key local organizations that had a central role in the Plataformas to determine if they were indeed comparable to the treatment communities. Some of the key characteristics considered were similarities in agricultural production, agroecological traits and levels of community and farmer organization.

Thus, the PSM selection was fine tuned by local agronomists and leaders of organizations that had local knowledge. Through this process, the best control communities were identified.

Further, treatment communities with distinct characteristics with no comparable control communities were excluded from the sample. The final community list contained 35 communities (18 treatment and 17 controls).

Within each treated community, there are community members who participate in the program and others that do not (non-participants). There are two concerns about including nonparticipants in the treatment communities as part of the counterfactual. First, they may have chosen not to participate and therefore may be fundamentally different from the participants. The fact that participant and non-participants self select can lead to a potential bias in estimates of impact since the estimates may reflect fundamental differences between the two groups rather than the impact of the program. Secondly, since they live in close proximity to beneficiaries they may obtain indirect benefits from the program (spillover effects). For both these reasons, using solely these households as a control group is potentially problematic. Yet, this is a potentially useful group because their observable characteristics are likely similar to participants and so they were included in the sample. The final sample, therefore, includes three sets of households: i) 9 beneficiaries of the program, ii) non-beneficiaries in the treatment communities (referred to as non-participants), and iii) non-beneficiary households in the control communities (referred to as non-eligible). Lists of households from each of these subgroups were provided by Plataformas coordinators and community leaders. Households were randomly selected to be included in the sample. The final sample includes a total of 1007 households of which 683 reside in treatment communities (324 beneficiaries and 359 non-participants) and 325 in control communities (noneligible). Of those, full information on the potato production cycle is available for 660 households. 2 This sampling strategy allows for different comparison groups, each offering interesting insights.

The ideal comparison group partly depends on whether there are spillover effects on nonparticipants. If there are such effects, including non-participants in the counterfactual would lead to an underestimation of program impact [START_REF] Angelucci | Estimating ATT effects with nonexperimental data and low compliance[END_REF]. If spillover effects are substantial it may be desirable to include non-participants as treated households (Intent to Treat group: ITT) to get the total effect (direct and spillover effect) of the program and use only noneligible households as a counterfactual. These different options are considered below.

b. Empirical approach

With the available data, four methods are used to identify impact: ordinary least squares (OLS), propensity score matching (PSM), propensity score weighted least squares (WLS) and instrumental variable (IV) regression. The reason for these multiple methods is to ensure a reasonable level of confidence in our impact estimates. The methods and underlying assumptions are presented below. The approach also includes exploring alternative counterfactual groupings to determine the role of spillover effects. Ultimately, we argue that results are consistent when using approaches based on selection on observables (PSM and WLS) as well as when using an 10 approach that deals with unobservables (IV). Further, we argue that spillover effects are minimal and that the main source of potential bias is related to program selection of beneficiaries.

The first approach is a standard OLS regression framework where the program impact on outcome variable Y i can be determined by:

Y i =βX i +αd i +ε i (1)
where d i =1 if households participate, 0 otherwise, X i is a set of exogenous variables including socio-economic characteristics of the households, agroecological conditions, geographic and location effects, and so forth, α measures the treatment effect for household i, β defines the relationship between X i variables and Y i , and

ε i is the error term.
This formulation assumes that the outcomes are linear in parameters and that the error term is uncorrelated with the exogenous variables X i and with treatment. Conditional on these X variables, if the control group is like the treatment group in all characteristics except for having received the program, α, the measure of treatment's effects provides an unbiased estimate of the program effect. However, d i may be correlated with the error term ε i leading to a biased estimate of the treatment effect α since it may capture not just the impact of the program but differences between treated and control households [START_REF] Ravallion | The Rapid Rise of Supermarkets in Latin America: Challenges and Opportunities for Development[END_REF]. If the source of the problem is program placement bias-differences due to characteristics of the household the program deemed desirable-the differences are more likely to be observable. If self-selection bias is the issue-certain types of households chose to enter into the program-the differences are more likely to be unobservable. Assuming the source of bias is observable, a way to obviate the problems outlined above is offered by our second method, the PSM approach. The main contribution of PSM 3 is to construct a control group that has similar observable characteristics (X i ) to the treated group, through a predicted probability of group membership calculated through a logit or probit regression, and then compare the outcomes. Given the unconfoundness assumption [START_REF] Rosenbaum | The Central Role of the Propensity Score in Observational Studies for Causal Effects[END_REF] or selection on observables assumption (Heckmann and Robb, 1985), if we call Y Ti, the value of the outcome for the treated household and Y Ci, the value of the outcome for the control, these are independent of the treatment (d i ) but conditional on a set of observable characteristics

X i . (Y Ti , Y Ci ┴ d i ) | X i (2)
Since matching on X i is the same as matching on the probability of being treated P(X i ) [START_REF] Rosenbaum | The Central Role of the Propensity Score in Observational Studies for Causal Effects[END_REF]), all dimensions of X i can be summarized into a predicted probability of being treated:

(Rosebaum
P(X i )= Pr(d i =1 | X i ) = h (x' i b ) (3)
where h is the standard normal distribution function.

Households in the untreated group that have a very similar probability of participating would be used as controls for their treated counterparts. So the effect of the treatment on the treated α can be defined as:

α=E (Y Ti -Y Ci | P(X), d=1) (4)
Conditioning on the propensity score, results in the balancing of covariates across treatment and control groups, thus focuses the analysis on the area of common support by dropping those observations without a clear match. Further, PSM evades the arbitrary linear-in-parameters form of an OLS approach [START_REF] Ravallion | The Rapid Rise of Supermarkets in Latin America: Challenges and Opportunities for Development[END_REF]. [START_REF] Heckman | Matching as an Econometric Evaluation Estimator[END_REF], [START_REF] Heckman | Sources of selection bias in evaluation social programs: An interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method[END_REF] and Dehejia andWahba (1999, 2002) show that PSM does well in replicating experimental results provided researchers have access to a rich set of covariates or control variables and use the same survey instruments. These two requirements are fulfilled in this case since the collected data, as described in the next section, are rich in information, and were obtained using the same survey for treatment and control households. In the PSM approach, a common method of determining statistical significance of results is to use bootstrapped standard errors since it provides reliable standard errors for all of the matching estimators and also accounts for the fact that the balancing score is estimated [START_REF] Diaz | An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator, Evidence from Mexico's PROGRESA Program[END_REF]. Bootstrapped standard errors are therefore used to test the significance of the PSM estimates of impact.

An alternative to PSM, particularly when control and treatment, although not randomly assigned, are reasonably comparable, is a weighted least squares method using weights calculated by the inverse of the propensity score (Sacerdote, 2004;[START_REF] Todd | Conditional Cash Transfer and Agricultural Production: Lessons from the Oportunidades Experience in Mexico[END_REF]. Weighting by the inverse of the estimated propensity score has demonstrated to achieve covariate balance and, in contrast to matching and stratification/blocking, uses all observations in the sample (Sacerdote, 2004).

Following [START_REF] Hirano | Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization[END_REF], weights are calculated as follows:

ω( T, C )=       - - - ) ( 1 ) 1 ( ) ( i i i i X p d X p d (5)
where p(X i ) are the estimated propensity scores calculated as in equation ( 3), above.

Intuitively, the weights imply a greater emphasis on those treated households with lower scores and control households with higher scores-that is, the area of greatest common support. Using equations ( 5) the weights created can be used in a regression framework where X i is included as a set of covariates and where standard tests of significance can be used [START_REF] Robins | Semiparametric Efficiency in Multivariate Regression Models with Missing Data[END_REF][START_REF] Hirano | Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization[END_REF]. Further, the approach retains full information from all households. Using weights ensures no correlation between treatment and covariates leading to a 13 consistent estimate of the average treatment effect [START_REF] Imbens | Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review[END_REF]. Impacts are thus measured as follows:

Y i =βX i +αd i +ω i +ε i (6)
where:

ω i are the weights used in the regression and calculated as per equation ( 5), above, α, β,X i, d i and ε i are defined as in equation ( 1), above.

Each of these three approaches relies on an assumption of exogeneity, namely that program participation is exogenous to outcomes given a rich set of observable covariates X i . When this assumption holds, treatment effects can be estimated without bias using observed estimands.

Although we are reasonably confident that this assumption holds, to explore the possibility of estimates being biased by unobservable differences between treatment and control groups an IV approach is also used. An IV approach allows relaxing the exogeneity assumption, but requires identifying an instrument, Z i , which is correlated with program participation but uncorrelated with the error term (that is, would not capture the bias associated with unobservable differences between treatment and control). In an IV approach, two stages are estimated as follows:

Stage 1: i i i i X Z d ν ϕ δ + + = Stage 2: Y i =βX i +αd_hat i +ε i (7)
where δ defines the relationship between instrument Z i and Plataformas participation, φ defines the relationship between instrument X i and Plataformas participation, The first stage is estimated as a linear probability model. [START_REF] Angrist | Estimation of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice[END_REF] suggests this approach when the first stage is a limited dependent variable model and argues that it is consistent and safer since predicting using a probit in the first stage is only consistent if the model is exactly correct. The main advantage of using an IV approach, when a valid instrument can be found, is that it deals with potential bias from observable and unobservable differences in control and treatment. In addition, the method can be used to test the exogeneity assumption used in PSM and OLS [START_REF] Ravallion | The Rapid Rise of Supermarkets in Latin America: Challenges and Opportunities for Development[END_REF].

To summarize, for the indicators analyzed (Y i ) that tests the hypotheses noted in section II, these four empirical approaches are employed. This allows for a clear assessment of the impact of the program. The next section presents the data used to conduct these analyses.

IV. Data

Two survey instruments (household and community) administered in the field were developed using qualitative information gathered by means of value chain analysis, stakeholder consultations and focus group discussions. Several revisions of the survey instruments were done based on field testing and conversations with key informants from the two study regions. The household survey included demographic information, economic and financial conditions of the households, social capital information and agricultural production data, including detailed information on potato production. The community survey included information on the overall community population characteristics, access to infrastructure and community organization. in the analysis for which full information on an entire production cycle is available. 4 In the interest of space, the details of the descriptive statistics are not discussed and we focus only on a

A. Household characteristics

few key characteristics and overall on the evidence regarding whether the survey design and data collection created a reasonable counterfactual. The exception is the social capital variables which played a key role in the formation of the Plataformas and are therefore discussed in more detail.

Examining the first three sections of the table, the results suggest that households in the sample have many of the characteristics of smallholders in the Andes. They have limited amounts of land (2.58 hectares of land with less than half dedicated to potato cultivation), which tend to be spread across a few (about 3), often steep plots. Household heads tend to be indigenous (62%)

and have limited levels of education (around five years) with an average family size of nearly five members. Asset ownership is generally limited and diverse so a principal component analysis has been conducted to construct variables for assets ownership, grouped as durable assets, agricultural assets and livestock. Although households tend to own their own homes and have access to a water system (95%), many have limited sewage access (7%) and modern methods of cooking (54% cook with electricity or gas). Among the land, socio-demographic and welfare variables, most do not show statistically significant differences between the beneficiary group and any of the non-beneficiary groupings. The few variables that are significantly different have similar magnitudes and could potentially be controlled for in the analysis. In general, the first part of Table 1 shows that the most similar possible control group would be the group of non-participants, since they have the fewest differences with beneficiaries. However, even the non-eligible group seem to be reasonably comparable to the beneficiaries. The entire group of non beneficiaries thus is a reasonable counterfactual and it offers a higher number of farmers highly comparable to the beneficiaries.

Moving to the social capital section of Table 1, a broad set of variables is presented since social capital was a key element in the Plataformas program. These show that participation in nonagricultural community associations is quite high (83%) and over three times the membership in agricultural community associations. While membership in non-agricultural associations is not different across the groupings, the membership in an agricultural association does show statistically significant differences: while 43 per cent of beneficiaries belong to an agricultural association, the percentage adds up to 14 per cent for both non-participants and non-eligibles. At first glance, these results would indicate that there is something fundamentally different about the group of beneficiaries who participate in an agricultural association at higher rates than the possible control groups. However, while the Plataformas allowed all individuals and households to participate in the program, the program gave preference to those in associations. Thus, prior to joining the Plataformas, farmers may have been members in existing associations, may have joined existing ones or may have formed new groups. This may explain the differences in the percentages of those that belong to an agricultural association across the three groups compared in Table 1.

A way to corroborate this hypothesis is to use data on the number of years that farmers have belonged to an agricultural association. If beneficiaries joined, or formed an agricultural association to qualify for the Plataformas, the maximum number of years belonging to such an association would be expected to be less than five years prior to the implementation of the surveys, which is when the Plataformas were introduced in Tungurahua and Chimborazo. We would expect then that beyond five years prior the survey, the levels of social capital would be very similar across groups.

To this end, the bottom part of Table 1 presents an additional set of social capital variables. First, there are no statistically significant differences in the number of years of membership and frequency of meetings for participation in non-agricultural associations. However, for agricultural associations, whilst the number of meetings per year is not significantly different, membership is a relatively new event for beneficiaries who have been members for 3.96 years on average, as opposed to 10.03 for non-participants, and 11.06 years for non-eligibles. This seems to confirm that many beneficiaries recently joined an agricultural association. Another way to corroborate this is by looking at the rate of participation for those that have been part of an agricultural association for more than five years. The next set of variables confirms this as 7 per cent of beneficiaries belonged to an agricultural association for more than five years versus 8 per cent for non-participants and for non-eligible with all differences being statistically insignificant.

Looking at the maximum number of years of membership for this subgroup, the data show that there are no differences across groups. Lastly, the final set of variables show no statistically significant differences between beneficiaries and possible control groups in the rate of participation with outside agricultural and or non-agricultural associations. Based on this information it is reasonable to assume that the differences that exist today across the groups are likely due to the join the Plataformas and to the willingness to create or strengthen social capital to do so. Hence, potential unobservable differences, if existing, are likely to be captured by the social capital variables that best proxy this selection criterion. (ii) mechanisms through which primary objectives were reached, or why they were not reached;

B. Indicator variables

and (iii) secondary indicators arising from participation, particularly related to use, knowledge and practice of precautionary measures in agrochemical applications, and other environmental impacts. Table 2 presents these indicators.

Among the primary indicators, the amount of potato produce harvested per hectare is the most direct indicator of productivity. The log of the quantity harvested is used and analyzed due to the expectation the data is log normal. On average, the harvest per hectare is 7,006 kg or 7.94 in logarithms. Gross margins express returns to fixed factors of production, which provide a good indication of profitability, and are calculated as the total value of harvest minus the total variable costs incurred for their production. On average farmers earn $112 per hectare of potatoes harvested. 5

There are multiple mechanisms through which farmers could increase yields and the income they generate from potato production. One key mechanism is through improved returns to potato production that can be obtained through selling more potatoes, getting a higher price for those 19 input costs while changes in yields could be due to additional input use and, or, better farming practices. Four cost indicators are used to explore this mechanism. The average total input cost for households is $650 per hectare, of which $97 is paid labour costs per hectare, and $49 purchased seeds per hectare. The average value of seeds planted, however, is over three times that amount at $181 per hectare suggesting much of the seed is not purchased.

The secondary indicators capture the possible side effects of participation. The first set, which incorporates both health and environmental impacts, is the use of agrochemicals. To avoid increased agrochemical use and minimize their negative effects, FFS introduced an integrated pest management (IPM) approach that combines good management practices, including the use of insect traps for the Andean weevil, with the use of low-toxicity pesticides. Nevertheless, in order to comply with standards required, farmers might be inclined to use more pesticides and chemical fertilisers to make sure harvested output is of a required physical quality [START_REF] Orozco | Relationships among production systems, preschool nutritional status, and pesticiderelated toxicity in seven Ecuadorian communities: A multi-case study approach[END_REF]. To explore these possibilities, the amount of preventive and curative fungicides, the amount of insecticides and the costs of chemical fertilisers are considered. Further, alternatives to chemical inputs, namely the cost of organic fertiliser and use of traps, are also examined.

FFSs teach the different risks associated with the toxicity of agrochemicals, how to recognize toxicity levels of a product and what precautions to use. The expectation is that participants use less toxic pesticides, that farmers recognize toxicity levels and take more precautions when applying agrochemicals. To assess pesticide toxicity, the Environmental Impact Quotient (EIQ) is used, which accounts for the toxicity level of the active ingredients of each agrochemical (Kovach et al., 1992). EIQs for each active ingredient were gathered and aggregated according to the rate and concentration of each. The average value of the EIQ is 95. An indicator of knowledge of toxicity level is also included, and on average 34 per cent of farmers can identify reported. Data shows that the percentage of households that use protective measures is in general very low, with 13 per cent of farmers interviewed using plastic ponchos and only 6 per cent using masks.

The final secondary indicators are related to the level of agrobiodiversity maintained at the household level-that is, how the composition and share of potato varieties changes due to market participation. The Plataformas focus on commercial varieties and theory suggests that as farmers shift to market varieties and begin to specialize, the overall number of varieties cultivated is reduced [START_REF] Pingali | Agricultural commercialization and diversification: processes and polices[END_REF][START_REF] Pingali | Environmental consequences of agricultural commercialization in Asia[END_REF] even though this does not necessarily imply genetic erosion [START_REF] Smale | The Green Revolution and wheat genetic diversity: some unfounded assumption[END_REF]. The Berger-Parker index of inverse dominance, which expresses the relative abundance of the most common species [START_REF] Magurran | Ecological diversity and its measurement[END_REF][START_REF] Baumgärtner | Measuring the diversity of what? And for what purpose? A conceptual comparison of ecological and economic measures of biodiversity[END_REF] is reported. 6 Also included is the share of potato area planted with the Fripapa variety, a key variety promoted through the Plataformas, which at the time of the survey was the dominant one in 29 per cent of cases.

V. Analysis and results

As noted, the approach used to select communities for inclusion in the sample focused on establishing a good counterfactual. To avoid remaining biases requires controlling for any further differences between treatment and control groups. Discussions with key informants and program leaders suggest that social capital is the key factor of program participation and the data presented earlier supports this. In particular, whether a household participated in an agricultural association for more than one year appears to capture the differences between treatment and control households. Since this is closely related to participation in the Plataforma, controlling for this variable in the regression model or using it in PSM should ensure controlling for those The assumption is that this variable is correlated with unobservables related to being an "organization joiner", which compels households to join the program, and thus any bias associated with self-selection should be eliminated. This variable is included in each of the regressions.

Since there remains the possibility of potential unobservable differences and, therefore, biased impact estimates, an IV approach is also employed as per equations ( 7). Finding a suitable and valid instrument is often a challenge, but a common solution used in impact evaluation is to use the intent-to-treat (ITT) since all households in the treated communities had the option to enter the program but not everybody participated [START_REF] Galasso | Assisting the Transition from Workfare to Work: Argentina's Proempleo Experiment[END_REF][START_REF] Ravallion | The Rapid Rise of Supermarkets in Latin America: Challenges and Opportunities for Development[END_REF][START_REF] Oosterbeek | The impact of cash transfers on school enrollment : evidence from Ecuador[END_REF]. Provided that we control for location-specific effects which might have a direct effect on outcomes, this should be a good predictor of participation. The eligibility criteria are shown to be, indeed, a valid instrument in our case being the instrument (ITT) highly significant in the first stage and the instrumented variable highly significant in the second stage. We also checked that the null hypothesis that the instrument is weak and reject this hypothesis as it passes the rule of thumb that the F statistics for excluded instruments is higher than 10. Lastly, the endogeneity test accepts the null hypothesis that Plataformas can be treated as exogenous to our specification thus supporting the exogeneity assumption needed in the PSM and WLS. 7

For each of the four specifications presented, all non-beneficiaries are used as the potential counterfactual group and results are reported in Table 4. In general, the four approaches provide robust results suggesting impact estimates are accurate. Since all non-beneficiaries are used for this first set of results, they may be lower bound estimates due to the possibility of spillover effects of the program on non-participants in the treatment communities. Even if there are spillover effects, they are likely to be small since non-participants would not have obtained the Nonetheless, to make sure no spillover effects are found we consider additional counterfactual groups within the WLS framework. These include non-eligibles, non-participants as well as the ITT group (beneficiaries and non-participants) contrasted to the non-eligibles. The benefit of this last approach is that it potentially captures both direct and spillover effects. These results are presented in Table 5. Before proceeding with a discussion of these two sets of results, the probit on participation is first examined.

A. Participation in the Plataformas

Table 3 reports the results of the probit on Plataformas participation with marginal effects calculated at the sample mean. The model accurately predicts 71.8 per cent of outcomes and

shows the importance of a number of variables. The differences are as expected and reflect those reported in Table 1. Membership in an agricultural association within the community for more than a year is significant and has the expected sign.

Using the probit results, propensity scores are calculated for the treatment and control group.

Figure 1 shows the kernel density estimates of the distribution of estimated propensity scores for each group. The scores obtained are almost entirely in the area of common support suggesting that non-beneficiaries represent a reasonable counterfactual to the treated population. 8

Furthermore, Annex A1 reports the punctual test of means reporting a drastic reduction of significant differences across the two groups and demonstrating the capability of the method to balance the baseline covariates and to make the two groups highly comparable. Nevertheless, the difference in mean propensity score across the treatment and control groups (mean of 0.37 in the treatment group versus 0.29 in the control group, p < 0.000) implies that simply conditioning on

B. Assessing Results

Table 4 presents the results of the analysis using the OLS, PSM, WLS and IV approaches reporting the impact estimate of Plataformas participation (α) on the indicator of interest (Y i ).

Table 5 reports results using the WLS, which we think best represents and approximates impacts, for the alternative counterfactual groups. The results are remarkably consistent across specifications (Table 4) and make sense for the different counterfactual groupings (Table 5)

indicating that the program effects are well identified.

Table 4 shows that both primary indicators, log of yields and gross margins, are positively and significantly influenced by participation in the program with the estimated differences being very similar and significant across specifications. Gross margins per hectare are around $200 higher for participants which are substantial given average margins are only around $100 per hectare (see Table 2). The findings in Table 5 suggest results are similar even when using different counterfactual groupings. The results using the non-participants suggests there are little or no spillover effects and indicates that participating in the Plataformas program is associated with a successful welfare improvement for beneficiary farmers.

The mechanisms leading to these results show that beneficiaries sell more of their harvest compared to non-beneficiaries and at a significantly higher price thus obtaining a greater value.

Prices obtained are indeed about three USD per metric quintal more than non beneficiaries, corresponding approximately to 30 per cent higher price if looking at the differences in prices (Table 2). The results on the time taken for the transaction are mostly insignificant although the 4 shows that, overall, total input costs do not appear to be significantly higher for the beneficiaries, however, seeds purchased and used are significantly higher for treated households and for most specifications so are labour costs (the exception being the IV results).

Moving to the secondary indicators of Table 4, the increased use of some inputs suggest possible environmental and health problems if it is linked to increased use of agrochemicals. The evidence is somewhat mixed, but does not seem to imply a widespread problem. Beneficiaries do not use significantly more fungicides, but do use more insecticides (although not according to the IV results) and chemical fertilizers. Findings suggest, however, that farmers are using less toxic chemical mixes given that they are using more chemicals and the EIQ ratio is not significantly different from zero in any of the specifications except for the IV where it is negative and moderately significant. The finding is also supported by the evidence that beneficiaries can identify toxic products better than non-beneficiaries. This is most likely due to the training participants received in FFS. Additionally, traps for the Andean weevil are more commonly used by beneficiaries than non-beneficiaries. Lastly, program participants are generally more likely to use protective gear as evidenced by a greater use of a plastic ponchos and masks (this result, however, does not hold for the IV results which is insignificant).

With respect to the potential losses of agricultural biodiversity as market demand pressures farmers to abandon traditional varieties, the evidence does not support this hypothesis as indicated by the insignificant impact on the agrobiodiversity indicator reported. Participants do seem to have switched to the Fripapa variety. Thus, Plataformas farmers seem to maintain the same diversity level although changing the primary market variety grown. Chimborazo and Tungurahua, however all trainers used the same methodology and curriculum.

C. Linking different farmers to market

Likewise the process of incorporating farmers to the Plataformas was the same in both regions.

Although Chimborazo and Tungurahua are both relatively poor areas, it is important to note that there are significant differences between the two. Data from the Ecuadorian National Institute of Statistics and Census shows that about 54.1 per cent of the population in Chimborazo lived in consumption poverty in 2006, while only 36.2 per cent lived in poverty in Tungurahua (INEC, 2005(INEC, -2006)). 9 These differences are reflected in our own data where land variables as well as socio-demographic indicators suggest that, although both provinces are rather poor, farmers in Tungurahua are, on average, better off than their counterparts in Chimborazo owning more land and generally having higher socioeconomic indicators. It is reasonable to assume that these differences may be reflected in divergent results in the two regions.

To determine how well the Plataformas perform in each area, the analysis is done for each region. Table 6 shows results for the two provinces and seems to suggest that the effects of the Plataformas participation are stronger for farmers in Chimborazo who have clearer direct impacts: larger and strongly significant gross margins and a higher impact on harvest. In Tungurahua, on the other hand, while the signs for these indicators are positive, only the log of harvest per hectare is significantly (at 10% level of confidence) larger for participants. However, this difference does not translate into significantly higher gross margins. This is likely due to a combination of factors led by a smaller difference in productivity between beneficiaries and nonbeneficiaries but also by smaller differences in price of potato sold, in the percentage of produce sold and in the value of produce harvested, although for both the former indicators differences are significantly higher for beneficiaries in both regions. It is interesting to note that beneficiary farmers in Tungurahua, purchased a greater amount of seeds spending more than the control group, while the remaining input costs are not significantly different as opposed to Chimborazo where participant farmers spent significantly higher amounts for inputs particularly in terms of hired labour. For the secondary indicators, the differences between the two groups are similar in both regions with the only exception of costs of chemical fertilizers that are significantly greater for participants in Chimborazo. Overall, Plataformas farmers are successfully adopting the new production approach in both regions, even though participation seems to be having a greater effect on participants in Chimborazo. These differences may suggest that poverty levels and/or financial constraints are more of an issue for farmers in Chimborazo. If this is the case, we might conclude that program participation is more effective for less endowed and more financially constrained farmers. However, it may be that other regional factors are playing a role.

To explore better whether the differences in results are due to greater benefits going to smallholders and less endowed participants, additional analyses by land holding size is included.

Keeping in mind that generally all farmers have relatively small land holdings, we divide land holdings into small (less than 1 hectare), medium (1 to 5 hectares) and large (more than 5 hectares) landholdings. The results presented in Table 7 show that medium farms have been able

to gain the largest benefits of the program obtaining significantly higher yields and productivity which translates into higher gross margins. These have been achieved through a larger percentage of potato sold as well as through higher price gains of the produce sold, even though higher input costs, both for seeds and fertilizers have been afforded. Beneficiaries with very small farms managed to harvest more than their control group and sold a significantly higher amount and share of potatoes, however these did not translate into higher gross margins. This is due to significantly higher input costs which did not lead to a high enough productivity increase suggesting that land holding, and thus smaller total amounts harvested and sold, are insufficient to compensate the sunk costs participant farmers incur in production. To achieve higher benefits they would need to either further increase productivity or to cut costs. Importantly, it should be noted that small farmers experienced a significantly shorter time to sell their produce. Looking at relatively larger farmers significantly higher gross margins seem to be due mostly to economies of scale. What seems to have played a major role for larger farms are the reduced per unit costs supported for each type of input and particularly for significantly smaller labour costs. Larger farmers are also not increasing other costs compared to those with smaller landholdings. This may be due to the fact larger farmers are already relatively efficient and do not get the level gains that medium farmers experience. In sum while for larger farmers, economies of scale are sufficient to outweigh the costs and guarantee higher gross margins, in the case of smallholders an intensification of technology adoption combined with a reduction of direct and transaction costs would be needed to guarantee that higher productivity translates into higher gross margins.

VI. Conclusions

In this paper, the challenges of linking smallholder potato farmers to high-value markets is examined by looking at the experience of the multistakeholder Plataformas program in the provinces of Chimborazo and Tungurahua in the Ecuadorian Sierra. An empirical analysis to assess whether the program has been successful in increasing yields and profits of potato producing smallholders while protecting farmers' health and the environment has been conducted. Mechanisms by which these objectives have been achieved were also analyzed.

To ensure a proper and sound empirical analysis the data was collected in a way that it was possible to create a reasonable counterfactual for comparing Plataformas participants.

Additionally, multiple econometric methods were employed to ensure results were not driven by a specific methodology. Spillover effects are also considered using different counterfactual groupings. The results are strongly consistent across the different specifications and the use of different types of counterfactuals suggesting that the success of the Plataformas is well identified. Our findings show that the Plataformas program successfully improved the welfare of beneficiary farmers and that the benefits were limited to farmers that directly participated since there appear to be little spillover effects on non-participants.

Both primary indicators, namely yields and gross margins, are positive and significant for beneficiaries with estimated differences very similar across specifications. The mechanisms through which the Plataformas achieve these primary benefits are through selling higher percentages and amounts of potato harvest than non-beneficiaries in addition to selling at a 30 per cent higher price. Although participant farmers incur higher input costs, particularly for seeds but also for hired labour and fertilizers, benefits are enough to outweigh these added costs. Clear benefits are achieved by medium farmers while large farmers achieve benefits mainly due to economies of scale. On the other hand, smallholders need to intensify technology and reduce direct as well as transaction costs to be able to achieve higher returns. The regional analysis has shown that farmers in Chimborazo, which are on average poorer than farmers in Tungurahua, have achieved higher and better results through participating in the Plataformas.

Results for secondary indicators related are somewhat mixed. With respect to the use of agrochemicals, beneficiaries do use slightly more insecticides and chemical fertilizers, but most of the other indicators are not significantly different and products utilised are likely to be less toxic given the EIQ is not significantly different from non-beneficiaries and in general has a negative sign. The Plataformas is clearly having an impact on the utilization of traps and in diffusing knowledge: a significantly higher percentage of participant farmers apply traps while a While this paper has, overall, found important positive and significant impacts of the Plataformas on the welfare of farmers and no negative effects on farmers' health and the environment, there still remains a question of cost-effectiveness and the potential effect on efficiency. For example,

  and supermarkets. The Plataformas, therefore, reduces costs for two types of transactions: a) between farmers and final purchasers; and b) between farmers and suppliers of services (inputs, seeds, and technical assistance).

  d_hat i is predicted participation in the Plataformas as estimated from the first stage, i ν is the error term in the first stage, and remaining variables are as previously defined.

  hypotheses noted in section II, the following three sets of indicators are analyzed: (i) primary indicators, expressed by log of total harvest per hectare and gross margins per hectare;

  potatoes or requiring less time to sell. Four indicators for this mechanism are presented: (i) percentage of potato sold per hectare, (ii) value of potato production, (iii) price of sale, and (iv) time required for sales transactions. Households on average sell almost half of their potato harvest (45%) which has a total value of $763 per hectare and sells at a price of about $0.11 per kg. On average, it takes 1.29 hours to sell their potatoes. The Plataformas also worked on the input side of the supply chain introducing and supplying the most market-demanded varieties of which Fripapa represents the main variety. Changes in gross margins could reflect a change in

  products. A selected set of indicators for the use of protective gears is also

  have driven certain households to participate.

  access, which appear substantial, and instead are only likely to receive indirect benefits from improved access to seed and transmission of new production technologies.

X

  through an OLS specification might not yield the correct average treatment effect if this effect is in fact heterogeneous. Given these results, PSM, WLS and IV estimates are considered to ensure an unbiased estimate of impacts.

  they are lower. Table

  the field training in the FFS in the two regions of

  of farmers are able to recognise the toxicity of agrochemicals. This latter translates into a higher utilization of protective gears although percentages are generally relatively low.Concerns related to potentially negative impacts on agricultural biodiversity are unfounded given the insignificant effect on the agrobiodiversity indicator considered. Results suggest that participants and non-beneficiaries maintain the same level of diversity and that genetic erosion, if any, happened in the past due to a combination of natural causes (El Niño), agroindustrialization and farmers' preferences in response to changing market opportunities. Most of the varieties cultivated are modern.Overall, participation in the Plataformas suggests a successful way of linking smallholder potato farmers to the markets. The success of the Plataformas can be first explained by its intervention along the value chain. On the output side, this led to reduced transaction costs that resulted from circumventing intermediaries and making sure farmers obtain a greater share of the returns from their production. Value chain interventions on the input side led to the introduction and supplying of market-demanded varieties, provided high quality seeds and taught efficient farming techniques. Secondly, the success of the Plataformas highlights the importance of social capital in identifying and organizing beneficiaries in a manner that effectively overcomes entrance barriers.

  Table1presents descriptive statistics of household characteristics along with t-test of difference for equality of means for the various counterfactual groups. Beneficiaries are contrasted to non-is non-participants plus non-eligibles). The t-test of difference for equality of means provides evidence of significant differences among the groups offering an initial assessment of which group may represent a better counterfactual. The table presents statistics for 660 households used

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participants and non-eligible households as well as to the whole group of non-beneficiaries (that

Before Plataformas (5 yrs. Prior to surveys)

  [START_REF] Thiele | Multi-stakeholder platforms for pro-poor innovation and governance in value chains[END_REF] note that one question that has not been addressed is whether there is sufficient value added in the new market opportunities to cover the costs of the Plataformas and

	Variable name		Whole Sample	Benef.	Non-part.	Pr(|T| > |t|)	Non-elig.	Pr(|T| > |t|)	All non-benef.	Pr(|T| > |t|)
	Land
	Altitud (m.a.s.l)		3458	3448 3461		0.701	3466		0.617	3463	0.613
	Land Owned (ha)		2.58	2.55	2.04		0.106	3.14		0.115	2.59	0.891
	Owned Plots (#)		2.97	3.25	2.55		0.001 ***	3.11		0.502	2.83	0.016 **
	Black Soil (%)		79%	77% 80%		0.407	81%		0.240	81%	0.242
	Flat Land (%)		39%	38% 40%		0.446	40%		0.516	40%	0.420
	Irrigated Land (%)		57%	54% 57%		0.499	61%		0.135	59%	0.214
	Socio-Demographic
	F o F Average Educ. Of Head Family Size Indigenous Head o Female Head	4.71 4.96 62% 12%	4.79 5.24 58% 59% 4.77 4.91 12% 12%		0.905 0.342 0.766 0.766	4.57 4.74 68% 13%		0.241 0.169 0.020 ** 0.827	4.67 4.82 64% 12%	0.448 0.176 0.133 0.939
	Age of Head Dependency Share	r 42.3 r 29%	42.2 40.33 29% 31%		0.143 0.332	44.38 27%		0.105 0.399	42.35 29%	0.901 0.929
	Welfare Durable assets Agricultural Assets Livestock Own House Concrete/brick House Access to Water System Sewage	P e e r 0.013 0.040 -0.025 P -0.005 0.129 -0.095 0.067 0.063 -0.036 e 86% 84% 88% 87% 83% 90% e 95% 92% 94% r 7% 6% 7%	0.474 0.033 ** 0.297 0.234 0.041 ** 0.413 0.743	0.025 -0.048 0.174 87% 90% 97% 7%		0.874 0.125 0.300 0.374 0.043 ** 0.016 ** 0.600	0.00 -0.07 0.07 87% 90% 96% 7%	0.623 0.014 ** 0.950 0.223 0.015 ** 0.060 * 0.627
	Cook with Electricity/Gas Dist. to Closest City (km) Social Capital Participate in Non-Ag. Ass. in comm. Participate in Ag. Ass. in comm. Non-Ag. Associations in Comm. Membership (Max # of yrs.) Meetings (#/yr) Agricultural Ass. in Comm. Membership (Max # of yrs.) Meetings (#/yr)	R e v i e w 57% 54% 0.518 52% 29.38 27.13 25.46 54% 0.171 35.53 R 83% 82% 83% 0.815 84% e 23% 43% 14% 0.000 *** 14% v 9.54 9.97 8.60 0.129 10.06 i 32.46 32.32 33.18 0.808 31.88 e 6.57 3.96 10.03 0.000 *** 11.06 w 16.56 16.82 12.77 0.189 19.45	0.285 0.000 *** 0.639 0.000 *** 0.921 0.892 0.000 *** 0.433	53% 30.49 84% 14% 9.33 32.53 10.56 16.16	0.323 0.025 ** 0.684 0.000 *** 0.405 0.944 0.000 *** 0.794
	Agricultural Ass. in Comm. Membership (Max # of yrs.) Meetings (#/yr) Outside Associations Non-Ag.Associations Agricultural Associations Observations	8% 17.29 14.74 17% 7% 660	7% 15.20 17.00 8% 21.30 12.69 17% 18% 4% 5% 217 222		0.938 0.585 0.144 0.887 0.512	8% 18.88 12.69 16% 7% 221	O n l y 0.918 0.311 17.94 8% O 0.167 12.69 n 0.782 17% l 0.231 6% y 443	0.920 0.404 0.084 * 0.969 0.773
	Source: authors' calculation using Linking smallfarmers to the new agricultural economy data set

Table 1 :

 1 Descriptive statistics

	* Significant at the 10% level, ** = 5% ; and *** = 1%

Table 2 : Program Impact Indicators

 2 Significant at the 10% level, ** = 5% ; and *** = 1% Source: authors' calculation using Linking smallfarmers to the new agricultural economy data set

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Table 4 : Impact of Plataformas

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	OLS	PSM, Kernel	PS Weighted LS	IV

Table 6 : Impact by Region (Using PS Weighted LS)

 6

	Tungurahua	Chimborazo
	Primary Indicators
	Mechanisms
	Secondary Indicators

Kernel distribution and common support area across the 2 groups.

  Notes: the common supp ort area is marked within the black vertical lines. Tests are for differences in means * Significant at the 10% level, ** = 5% ; and *** = 1% Source: authors' calculation using Linking smallfarmers to the new agricultural economy data set

			Mean	Mean	% reduction
	Variable		Treated	Control	|bias|	p>|t|
	Land Owned (ha)		2.55	2.41	-230.7 0.622
	4 Owned Plots (#)		3.25	3.11	68.2 0.617
	Black Soil (%)		0.77	0.78	60.3 0.884
	Flat Land (%)		0.38	0.36	48.6 0.857
	Irrigated Land (%)		0.54	0.52	49.1 0.659
	3 Family Size		4.79	4.82	75 0.930
	2 Average Educ. Of Head Indigenous Head F Female Head o Age of Head r Dependency Share	5.24 0.58 0.12 42.20 0.29	4.96 0.61 0.11 42.38 0.29	32.3 0.462 43.6 0.532 -155.5 0.913 -22.7 0.953 64 0.958
	1 Livestock Agricultural Assets Durable Assets	P	0.06 0.13 0.04	0.05 0.00 0.01	-113.1 0.893 33.6 0.788 30.5 0.870
	0 House Concrete/brick House 0 Access to Water System Sewage Cook with Electricity/Gas .2 Dist. to Closest City (km) Altitude Chimborazo Ag. Association (>1 year) External non ag. Association External ag. Association Non Agricultural ass. in Community e .4 e r 0.84 0.83 0.92 0.06 0.57 Estimated propensity score .6 0.86 0.85 0.93 0.06 0.55 program control 27.13 26.14 3447.50 3446.00 R 0.50 0.50 0.34 0.33 e 0.17 0.17 v 0.07 0.06 i w .8 27.8 0.570 73.6 0.732 70.1 0.759 72.5 0.954 60.5 0.751 70.4 0.362 90.4 0.918 -20.8 0.849 98.7 0.943 -221.9 0.930 3 0.763 0.82 0.85 -93.5 0.595 e Notes:	1
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Annex I: Punctual Test of Means comparing beneficiaries to all non-beneficiaries

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URL: http://mc.manuscriptcentral.com/fjds Journal of Development Studies

still provide farmers with a sufficient income increment to justify program participation. The authors also observe that while the program received substantial subsidies through project funding, this was probably a reasonable investment given the positive results. In the long run and for scaling up the program, however, other funding mechanisms would need to be explored to achieve financial sustainability [START_REF] Thiele | Multi-stakeholder platforms for pro-poor innovation and governance in value chains[END_REF]. Although we recognise the importance of assessing costs and shedding light on the sustainability of the Plataformas, it is not possible with the current available data. The total investments in the program have not been sufficiently identified since they came from multiple sources. Further, sustainability would need to be assessed with a new round of data collection that would examine how the program is currently operating now that much of the external support has been withdrawn. New initiatives are underway to gather the necessary information to arrive at a more accurate answer to these important questions, presenting a clear direction for future research. 7 With regard to the identification strategy, no tests for over-identification can be run since given one instrument, the equation is exactly identified. To verify the endogeneity assumption a test under the null hypothesis that that the specified endogenous regressors (participation to the Plataforma) can actually be treated as exogenous has been run. The test statistic is distributed as chi-squared with degrees of freedom equal to the number of regressors tested and defined as the difference of two Sargan-Hansen statistics: one for the equation with the smaller set of instruments, where Plataforma is treated as endogenous, and one for the equation with the larger set of instruments, where Palataforma is treated as exogenous. 8 Figures assessing the common support for all possible counterfactual options were also constructed but are not reported as they all consistently suggested a similar area of common support indicating high similarity across groups. For simplicity, only one figure is presented. The consistency of the common support across potential control groups is corroborated in the results of the various analyses presented in this section. 9 Using INEC, Base de Datos de la Encuesta Condiciones de Vida ECV, Quinta ronda (2005)(2006), by DISUR.