(or program of study), we can see from Table 4 that the effect of FPE on student achievement is bounded between zero effect to 19.36 percentage points increase on students achievement. The upper bound slightly increases in this case, but not by a significant margin. More importantly, there are no noticeable heterogeneities by gender. This implies that FPE policy has affected academic performance of females and males equally. However, the effect of FPE varies by program of study. For instance, looking at column (2) results, we notice that, post-FPE, academic performance of students doing science and technology programs is 4 percentage points (i.e. 19 _._ 36 _−_ 15 _._ 13) lower than that of a typical student, this effect is significant at 5 percent. Further, we can see from column (4) results that the program decreased academic performance of those in the faculty of agriculture.

The faculties of agriculture, health sciences, and science and technology have exactly the same entry requirements, the most important being a good COSC pass in mathematics and sciences. Therefore, while these results seem to suggest that FPE-treated students are less prepared on mathematics and science subjects, they likely reflect the effect of discontinuing the bridging program for students into these faculties (or programs), known as the pre-entry science program (PESP), in 2012.<sup>15</sup> In the next section, I test the sensitivity of the results against this potential confounder.

# **6.3 Robustness checks**

In this section, I present a number of robustness checks’ results. First, I show that the results are less likely to have been driven by selection bias. Second, I present the placebo test results, using the pre-FPE data, assuming that FPE was introduced in 1999, such that the first FPE-treated cohort entered university in 2011. Third, I narrow the age window to include only students aged between 18 and 22 years old. Fourth, I present the semiparametric DID results controlling for some confounding factors, particularly the discontinuity of PESP at NUL, which affected all students doing physical science-based programs. Lastly, I present the doubly robust DID estimates that are robust to misspecification in either the propensity score ( _π_ 0( **X** _i_ ) _≡ P_ ( _D_ = 1 _|_ **X** _i_ )) or the outcome regression models for _E_ [ _Yit|D_ = _d, T_ = _t,_ **X** _i_ ],<sup>16</sup> following Sant’Anna and Zhao (2020).

## **6.3.1 Who entered the university post FPE?**

Given that I use university administrative data, which are not representative of the population, one basic concern could be that the students who got admitted into university post FPE program are different from those admitted pre-FPE in a number of ways. First, it could be that post FPE program, the number of those completing secondary school and qualifying for university entrance

> 15I explain the PESP program in detail below.

> 16Sant’Anna and Zhao (2020) shows that the outcome regression DID estimator is given as _δ_ ˆ _DID_ = _Y_ ̄1 _,_ 1 _−_ ̄ � _Y_ 1 _,_ 0 + _n_<sup>_−_</sup> _treat_<sup>1</sup> � _i|Di_ =1<sup>(ˆ</sup><sup>_μ_0</sup><sup>_,_1(</sup><sup>**X**</sup><sup>_i_)</sup><sup>_−μ_ˆ0</sup><sup>_,_0(</sup><sup>**X**</sup><sup>_i_))</sup> �, where _Y_<sup> ̄</sup> _d,t_ = _n_<sup>_−_</sup> _d,t_<sup>1</sup><sup>_·_�</sup> _i|Di_ = _d,Ti_ = _t_<sup>_Yit_is the sample average outcome for</sup> ˆ treatment group _d_ units in time _t_ , and _μd,t_ ( **X** _i_ ) is the estimator for _md,t_ = _E_ [ _Yit|D_ = _d, T_ = _t,_ **X** _i_ ].

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