39 ICAs in 1988/89 or natural disasters, makes it difficult for Icafé to compare reported prices to an international benchmark and mills/exporters may add an extra deviation to their imperfectly competitive input price with less fear of detection. The plausibility of this information failure at Icafé is supported in interviews of key informants in Costa Rica, who consistently hinted at the limitations of contract price monitoring at Icafé.

Whereas unpicking the time effects may be an issue of separate interest, the data requirements to do so in a rigorous fashion are heavy. While data are clearly available on natural disasters, production and ICA policy variables, if these do not account for the whole of the variation observed over time in the Costa Rican data, a clean interpretation of their estimated coefficients is rendered difficult. Second, if they do not account for the whole of the variation observed over time, then they do not even serve the primary objective of controlling effectively for time to estimate clean Fairtrade and cooperative effects necessary for the identification of market power and ‘Fairtrade effects’.

# _Estimating time-invariant factors_

Fixed effects estimators are also called ‘within’ estimators because it transforms the data into deviations from its mean over time. As such, time-invariant variables drop out of a fixed effects regression. This includes time-invariant mill characteristics such as geographical location and cooperative status.<sup>41</sup> The explanatory value of time invariant variables must be explored in a second stage weighted least squares (WLS) regression model (8): ∧ _fe j_ = α + δ 1coopj + φ 1zcarj + φ 2zturj + φ 3atenj + φ 4cotj + φ 5sanj + φ 6zgenj + φ 7guaj

> 41  With the exception of two out of 26 cooperative mills, cooperatives in Costa Rica did not change their cooperative status in the time period under analysis. The estimation of a coefficient for the cooperative dummy variable (coop) in (7) would therefore be driven entirely by the two cooperatives that did change status in the data set. It would be difficult to generalize about cooperatives based on two observations. If, however, the two mills are dropped from the sample, then the remaining cooperatives are all time invariant and their explanatory effect can only be examined in a second stage equation, along with the geographic variables. To ensure the results are not sensitive to the removal of the two status-changing cooperatives, the full model (7) was re-estimated, this time including the two cooperatives in the data set. No difference in size or significance of the independent variables was found.  This satisfies any concern that estimates of (7) are sensitive to the presence or removal of these two mills. Their removal does permit a ‘clean’ estimation of cooperative effects with the other time invariant (geographic) explanatory variables in a second stage weighted least squares regression (8). Although the inability of a fixed effects model to estimate time invariant coefficients in a first stage is often cited as a drawback of the procedure, it is not sensible to force that first stage estimation of the cooperative variable on the strength of two potentially anomalous observations.