treatment effect is 10 percent at 4-5 years, rising to 15 percent at 6-7 years and to 30 percent from 8 years on since program entry. At 6-7 years, the impacts on wages and labor productivity are 10 and 17 percent, growing to 15 and 28 percent respectively by the 11<sup>th</sup> year. While there are no discernable time effects on employment, both existing workers and their employers benefit from these support programs through improved wages and gains in labor productivity exceeding wage costs.

These results can be used to compare the post-program time paths of final outcomes for both groups of firms. Assuming that the treated firms enter SME programs in 1994, we can compute the values of each outcome for each year between 1994 and 2006 using the estimated coefficients of the constant term, program participation variable, indicator variables for time since program entry, and year dummies, interpolating where necessary using the mid-points of each time interval. For comparison, we calculate the corresponding time paths for the control group excluding all the coefficients associated with program participation.

Figure 3 graphs the time paths of output, employment, wages and labor productivity predicted for the treatment and control groups. The treatment effects at any point in time are represented by the gap between the time profiles of the two groups. Take the case of the value of output. Between 1994 and 1997, the time profiles of this variable for the treatment and control groups are barely distinguishable from each other. Subsequently, the gap between them – the treatment effect – widens up to 2004 after which both profiles turn downwards. In the case of employment, the time profiles of labor for both groups decline slightly between 1994 and 2000; after 2001, employment in the treatment group actually rises above that of the control group (which remains roughly constant) yielding positive treatment effects. The treatment effects on labor productivity resemble those of the output variable, not surprisingly since labor productivity is defined as output per worker, and treated firms saw gains in output without increases in employment up until 2001. The treatment effects on wages are initially small but become larger at about 6-7 years after program entry, that is, from 2000 on.

# **C. Bounding Estimates of Program Impacts**

As a final exercise, we investigate the sensitivity of our estimates of treatment effects to a peculiar feature of our data, namely that only ENIA firms that responded to (survived until) the 2004 ICS were included in our panel data. As such, there is firm entry as new firms join the treatment and control groups sometime over this 1992 to 2004 period, but no firm exit from the panel. One implication is that the firms in our panel may not be representative of the firms in the manufacturing sector, being a selected group that had survived (in some cases) up to 14 years in the panel.

How serious is the potential bias from not accounting for firm exit? Using time series ENIA data from the 1980s and 1990s, Alvarez and Vergara (2007) estimated 5-year firm survival rates of about 69 percent, ranging from 67 percent for small firms to 82 percent for large firms. This implies an average annual exit rate of between 7 and 8 percent for the manufacturing sector as a whole. However, the relevant figure here is the differential 24