36

|**Variable Name**|**Variable Description**|
|---|---|
|multijt|A dummy variable = 1 if mill_j_owns more than one mill location in<br>time_t_, 0 otherwise|
|1974/75tto<br>1999/2000t|A series of dummy variables for each year between 1974/75 to<br>1999/2000, taking the value of 1 if it is the year and 0 if not|
|nyearsjt|A variable indicating the length of time (years) that mill_j_is in the<br>sample at time_t_|
|hbjt|the proportion of mill_j_’s harvest accounted for by HB in_t_|
|hbljt|the proportion of mill_j_’s harvest accounted for by HBL in_t_|
|shbjt|the proportionof mill_j_’sharvest accountedforby SHB in_t_|

Using a panel data set for the 157 mills that have been operational in Costa Rica over the 26 year period between 1974/75 and 1999/2000, the analysis of this paper attempts to identify the existence of mark-down behavior, market power, a ‘Fairtrade’ effect and to examine the determinants of any mark-down of producer prices observed. The panel is unbalanced as not all mills are present for each of the 26 years, although a full time series for many mills was possible due to fieldwork and interviews that tracked mills over time, even as they changed names.

# _The model_

The explanatory variables listed in Table 3 are included in reduced form regression equation (7) below<sup>38</sup> , estimated using a fixed effects formulation. The fixed effects model exploits the panel dimension of the data to control for any omitted factors in the model. Omitted factors may include the size of the mill, the degree of vertical integration with the exporter, the relationship of the mill with Icafé and any other mill characteristics on which the data to hand could not directly inform.<sup>39</sup> 38 The model of (7) was built up from a basic ‘core’ model, augmented to include the effects of geography and those of time. Earlier specifications of the reduced form equation (7) also included variables for which data and proxies were either unavailable, of insufficient quality or suffered from collinearity. For example, there was insufficient data to measure the intensity of contract monitoring by Icafé. Collinearity of inflation with time dummies led to inflation being dropped, etc. 39 In addition to estimating (7) with a pooled OLS and a ‘between estimator’ formulation, a random effects model was also estimated, but considered inferior to the fixed effect formulation. _A priori_ , the usefulness of the random effects model is questionable since the ultimate choice of estimation procedure should rest upon the appropriateness of the model for the application at hand (Hsiao and Sun 2000). In fact, there is reason to doubt the suitability of the random effects model in this application: In this analysis, the entire population of mills in Costa Rica is used, rather than a random sample drawn from a population of mills. It is therefore difficult to conceive, as one must in a random effects context, of omitted factors as randomly distributed across mills. In addition to this theoretical objection, the random effects estimators were found to be inconsistent. The Breusch-Pagan for unbalanced panels and Hausman tests were conducted to determine the presence of random effects and their independence from the explanatory variables, respectively. While the Breusch-Pagan test rejects the null of no random effects, the Hausman test informs on whether these random effects are independent of the explanatory variables. If they are not, then the random effects estimator is inconsistent and the model is misspecified. The Hausman Test rejects the null of independence of the random effects from the explanatory variables for (7) at the five and 10% levels, although it does not reject it at the one percent level. The evidence for the independence of the omitted random effects from other explanatory variables is, at best, inconclusive and at worst, negative. As the independence assumption is essential for the consistency of estimated coefficients, the failure of the Hausman test to provide a robust result in favor of independence provides a compelling reason to believe that fitting a random effects model to the data results in misspecification.