date of entering the program. This allows the effects of the interaction terms between the program indicator and time since participation to vary non-linearly with time in (some SME programs can last 2-3 years) and after the program.

The resulting estimates can be interpreted as the time effects of treatment if several assumptions hold. First, these effects are estimated holding constant all other factors that are time varying, including inflation and macroeconomic shocks. The model accounts for these factors by deflating all outcome measures into constant 1996 prices and including year dummy variables to capture year-specific stochastic shocks. A second assumption is that self-selection into treatment is not dependent upon time. The presence of cohort effects in treatment – firms that choose to participate early are different from those that join in later years – can introduce bias into these estimates. For example, if early cohorts were more entrepreneurial (or “go-getters”) than those that came later in the program, then this could bias the results towards finding treatment effects increasing with time since identification of interaction effects comes from comparing at a given point in time firms that had the treatment for different numbers of years.

We checked for, and found no, systematic time-varying differences between treatment cohorts. Two dimensions of treatment cohorts were considered – pre-treatment sales growth experiences and rankings of the importance of program(s) to the establishment.<sup>20</sup> The anticipated sales growth variable, first used to estimate the Cox proportional hazards model, could vary systematically by cohort and lead to bias in the estimated time effects if this ex-ante measure was realized in future growth in sales and other outcomes. Similarly, bias could arise if the value of the treatment varies systematically by cohort, as judged by ex-post rankings of the importance of programs to treated firms. Table 8 reports means of both measures by year of program entry. Sales growth by cohort varies for the most part between 5 and 7 percent, without any systematic trend over time.<sup>21</sup> Similarly, the ranking by cohorts shows no time trend, and its variation around 2.6 suggests that most cohorts judge program impacts to be important to very important.

Table 9 reports estimates of the time-path of program impacts on different final outcomes. As before, we estimated the regression models in levels and in differences, but only report the DID results since no significant time-effects were found for the levels regressions. Consistent with our priors, the results demonstrate that the treatment effects on final outcomes can take a long time to be realized. Compared to the results in Table 7, the coefficient on the participation variable is still positive, but now lower in magnitude and not statistically significant except for the wage outcome. None of the indicator variables for time since participation are statistically significant before 4 years. Beginning with 4-5 year after program entry, the estimated coefficients become positive and increase in value and statistical significance. Using the example of sales, the

> 20 The sales growth variable is the change in log(sales) between time t-2 and t-1, where t is the year of program entry, and it is calculated directly from the ENIA panel. The qualitative rankings of programs on a scale of 1 (minor) to 4 (crucially important) are taken from the 2004 ICS. Since treated firms can (and do) participate in multiple programs, we calculate the weighted mean ranking by year of treatment, using the frequency or each rank cited for a given treatment year as weights..

> 21 This was confirmed by a simple regression of the change in log(sales) on a time trend, with the time coefficient being indistinguishable from 0.

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