to have relatively higher pre-treatment employment levels as compared to the control group, and keeping employment levels constant may have permitted gains elsewhere in wages and labor productivity.

In Panel B, differences across programs in treatment effects emerged from the analysis by program types. Participation in technical assistance programs (FAT) is associated with the largest and most consistent gains in most outcomes, ranging between 18 and 20 percent for sales and output, 16 percent in labor productivity and 8 percent in wages. Participation in cluster programs (PROFO and PDP) produced gains of 7 to 8 percent in sales, output and wages, while technology development programs yielded gains of about 5 percent in wages and exports as a share of sales. However, use of credit and loan programs was not associated with any improvements in outcomes; if anything, these treated firms had marginally lower sales growth than the control group. It appears that improved access to finance by itself is insufficient to spur firms to make the necessary organizational and technological changes to improve future performance.

From these results, we conclude that unobserved firm heterogeneity motivating the selfselection of less productive firms into treatment can bias downwards estimates of program impacts. We observe this in the case of Chile, and speculate that similar behavioral processes are in operation in other countries and may be responsible in part for the mixed findings of program impact evaluations.<sup>19</sup>

# **B. Time-paths of Treatment Effects**

Thus far, we have estimated the average treatment effect of program participation without consideration for whether these effects vary over time. While useful, these estimates of average treatment effects leave unanswered the question of whether impacts are realized immediately or only slowly over time. In the program logic model, program interventions impact final outcomes only indirectly; they target a range of business development and credit services to address and resolve identified enterprise shortcomings. The resulting impacts on intermediate outcomes – such as new management, marketing and quality systems, worker training, technology development, and greater linkages with other enterprises – in turn give rise (at least in theory) to improvements in final outcomes such as sales, job creation, wages and labor productivity, and exports. How time dependent these impacts are makes a difference for how long the time horizon of impact evaluations should be to reasonably expect measurable impacts on final outcomes.

We test for time-effects of participation in any program by including interactions terms between the program participation measure and time since entering a program, as in equation (9). Rather than forcing a functional form on these time effects (for example, with a quadratic specification of time and time squared), we define a set of indicator variables for different intervals – 1, 2, 3, 4-5, 6-7, 8-10 and over 11 years – following the

> key results are essentially unchanged when a random effects tobit model was estimated.

> 19 Tan and Lopez-Acevedo (2007) came to a similar conclusion in their impact evaluation study of SME programs in Mexico, where they also found sign reversals of treatment effects estimated in levels and in difference-in-differences (DID).

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