likelihood of program participation for the sample of treatment and control groups followed over the 1994 to 2004 period.<sup>12</sup> The Cox proportional hazard model relates the likelihood of entry into a program, conditional upon survival (non-entry) up to that point in time, to a baseline hazard function and a set of independent covariates. The underlying hazard function _h_ ( _t_ ,..) may be written as follows: where _Z_ is a vector of _m_ covariates, and _h_ 0 (<sup>_t_</sup> ) is the baseline hazard when the values of all the covariates are set to 0. This model can be made linear by dividing both sides of equation (6) by _h_ 0 (<sup>_t_</sup> ) and taking natural logarithms: This leaves an equation (7) that is readily estimable, and from which the predicted value of  . _Z_ can be calculated.  . _Z_ is the relative hazard of program entry for firms with attributes _Z_ , and we use it as the propensity score for defining the region of common support for matching successive cohorts of treated firms and their control group.

The multitude of treatment cohorts also poses challenges for traditional PSM methods of estimating impacts. This will become evident when we use cross sectional data from the 2004 ICS to investigate the impact of program participation on levels of intermediate and final outcomes measured at one point in time.<sup>13</sup> In 2004, measured outcomes from the different treatment cohorts reflect wide variations in time since program participation, some as long as 10 years ago, as well as the cumulative effects of macroeconomic shocks occurring over this period. PSM methods cannot readily accommodate variations in time following program participation by the different treatment cohorts, or separate treatment effects from broader time-related effects affecting both groups. This is compounded if treatment effects are only realized over time. We flag this caveat but defer the issue to analyses using the panel data.

# **B.  Conditional Likelihood of Program Participation**

We begin by estimating a Cox proportional hazards model to calculate the relative hazard rate of program participation of firms in our panel based on their pre-participation attributes. The sample included treated and control group firms in the 1992 to 2004 panel, and firms that entered the panel sometime over this period. Entry into a program

> 12 An alternative approach is to estimate separate logit models of program participation for different crosssections (or year intervals) to derive propensity scores for each treatment cohort (or groups of cohorts). This did not prove feasible because of small sample sizes which led to very imprecise estimates of the logit model.  The Cox proportional hazards model was preferred not only because of sample size considerations but also for its unified treatment of the underlying process of selection into programs over time.

> 13 Here, the intermediate outcomes are measured in levels, and not first differences, since they were elicited for just one point in time by the 2004 Investment Climate Survey (ICS).  The subsequent analysis of final outcomes compares the level and DID estimates of treatment effects using the linked ICS-ENIA panel data.

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