# **5.2 Labor market effects**

To recover the labor market effects of SC programs for compliers (relative to the nextbest alternative), we use TSLS to estimate the model described by equations (1) and (2). Table 6 reports estimates for the two outcomes of interest (employment and monthly wages in the formal labor market) for all our specifications and samples. Panel A contains point estimates of _βSC_ for the full sample. It shows that the effects of SC programs vs. the next-best alternative are generally larger, in absolute value, when interactions are included than when they are not. For the full sample, SC effects on formal employment are not significant, but the SC effects on (log) wages are negative and significant. For instance, the interaction model indicates that a SC program lowers average monthly wages by about 23 percent relative to the next-best alternative.<sup>20</sup> For all outcomes and specifications we include diagnostic tests for weak instruments (Kleibergen-Paap and Cragg-Donald F-statistics), which are all well above the corresponding critical values.

We find heterogeneous effects by gender. Female compliers benefit from SC programs, at least in terms of employment opportunities (panel B of Table 6). Their probability of formal employment rises by 31 pp (in the interaction model) while effects on their wages are not significant. For males, in contrast, effects are negative for both outcomes (panel C). Choosing an SC lowers their formal employment probability by 24 percentage points and their average monthly salaries by about 23 percent. Based on these estimated effects as well as the large diversion share for males and females, we conclude that female compliers might experience employment gains from choosing an SC rather than a bachelor’s program, but male compliers might experience employment and wage losses.

These stark differences might be driven by pre-higher education differences between male and female compliers. Thus, we estimate average pre-higher education enrollment characteristics by gender. Figure 2 displays the estimated share of compliers with an above-median _Saber 11_ score. We compare five different groups: all bachelor’s program students, all SC program students, all compliers, male compliers, and female compliers. In general, compliers outperform the average SCP student but not the average bachelor’s program student. Male compliers outperform female compliers in all subjects except Reading and, notably, the share of male compliers scoring above the median in mathematics is almost as high as among bachelor’s students. While compliers are more advantaged than the average SC program student in terms of household income and mother’s level

> 20Table A4 presents point estimates obtained via OLS (regressing _Y_ on _DSC_ and X) and via a “standard” TSLS, where both _Zj_ and _Hj_ are excluded from the outcome equation). First, as expected, OLS estimates are substantially different from TSLS estimates. Second, TSLS estimates are similar to those in Table 6, which might be expected if the IV weights are positive when both IVs were excluded. We confirm that this is indeed the case using the test for positive IV weights in Mogstad et al. (2021) (results available upon request). The largest differences between the TSLS estimates and those in Table 6 are in the interacted model, but the results are largely consistent in terms of magnitude and significance.

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