servable variables but also unobservable variables. This solves the self- selection issue by construction. Formally we have, E[Y
\ T = 1] = E[Y \ T = 0] = E[Y
] and E[Y
1
\ T
= 1] = E[Y
1
\ T = 0] = E[Y
1
]. In our social experiment we defi ned two different treatments: an ACS voucher in-
crease for the fi rst treatment group and an information meeting proposal in addition to the voucher increase for the second treatment group. As these treatments were ran-
domly assigned, the impact of the treatments can then be estimated by difference in the means between the treated and untreated groups. The effect of the voucher increase
will then be estimated by the difference in the means between Treatment Group 1 and the control group, and that of the meeting proposal by the difference in the means of
the outcome variable between Treatment Group 2 and Treatment Group 1. Signifi cant differences between groups are evaluated via Chi- squared tests.
B. Outcomes variables
We focus on two outcome variables to evaluate treatment effectiveness. We assess the demand for or the interest in the ACS by the number of returned application forms
subsequent to the letter received from the CPAM. The fi rst outcome variable is then the rate of returned application forms.
Beyond the rate of completed application forms, and within the experimental frame- work, we can also calculate the percentage of individuals effectively entitled to ACS,
since a number of the applications were refused. The second outcome variable is de- fi ned as the rate of ACS notifi ed—that is, the proportion of experienced individuals
who effectively received an ACS voucher after eligibility reassessment by the CPAM. There are two cases where applications were refused; when individual’s resources
were below the eligibility threshold, they could benefi t from the free CMU- C plan and when their resources were above the ACS cutoff point, their application were refused.
IV. Results
A. Impact of the voucher increase
Of the 4,209 individuals involved, only 701 returned an application form for a takeup rate of 16.7 percent Table 4. Table 4 also compares the application return rates by
group. 15.9 percent of the control group returned an ACS application form 222 ap- plications. The takeup rate in Treatment Group 1, which benefi tted from the increased
voucher amount, is higher at the 5 percent signifi cance level than that in the control group with 18.6 percent of applications. Increasing fi nancial aid thus appears to have
a positive, though limited, impact on the probability of takeup.
This impact can be measured by the elasticity of the probability of returning a com- pleted application form to the fi nancial aid proposed. This elasticity is calculated in
average for all age- groups by the ratio of the rise between treatment groups in the probability of returned forms to the rise in the voucher amount,
10
and is equal to 0.22
10. By using a growth rate of 75 percent for the voucher amount although this fi gure is 62.5 percent for the over- 60s, we choose to underestimate the aggregate elasticity rather than estimate the elasticity separately
by age group with limited accuracy due to the small number of individuals over 60 years old.
Table 5. Surprisingly, the results are quite similar for individuals already covered by CHI 66.2 percent of the sample and those who are not 33.8 percent. The rate
of returned applications among individuals already covered by CHI is 16.4 percent in the control group and 19 percent in Treatment Group 1, as against 15 percent and
17.6 percent respectively among individuals without CHI coverage: These differences are not signifi cant. Similarly, we observe no difference in the elasticity of takeup rate
to voucher amount according to CHI coverage 0.23 for individuals initially covered by CHI as against 0.21 for those not covered.
Looking at the ACS agreements and the case of refusals, we see that in total, 55.2 percent of returned applications were in fact eligible for ACS Table 6, 10.1 per-
cent were eligible for CMU- C but not ACS in the cases where income was below the minimum ACS threshold and 34.7 percent were refused because their income levels
were too high. Among the 4,209 individuals included in the experiment, 9.2 percent were effectively eligible for ACS, 1.7 percent for CMU- C, 5.8 percent was refused
both ACS and CMU- C, and 83.3 percent failed to apply Table 6 and Table 7.
A comparison of the number of ACS agreements by group yields similar results to those obtained for the comparison between returned applications. However, the gap
between the control group and Treatment Group 1 is accentuated. The rate of ACS agreements relative to the number of participants is 10.8 percent in Treatment Group 1
as against 7.9 percent in the control group. The voucher amount elasticity of the prob- ability of ACS notifi cation is 0.49 Table 5, and is signifi cantly higher compared to
the one calculated on the basis of the rate of returned forms. This increase in elasticity is explained by a much lower proportion of returned forms in the control group than
in the Treatment Group 1. In fact, the proportion of ACS notifi cation among returned application forms is only 49.6 percent in the control group and 58 percent in the fi rst
treatment group Table 6.
Table 4 Returned ACS Application Forms
Completed Forms
95 Percent Confi dence
Interval in Number of
Individuals 1
2 Percentages
1 2
Control 222
15.9 14.0; 17.8
1,394 100.0
Treatment 1 262
18.6 16.5; 20.6
1,412 100.0
Treatment 2 217
15.5 13.6; 1.4
1,403 100.0
Of which:
With meeting 35
28.0 20.0; 36.0
125 100.0
Without meeting
182 14.2
12.3; 16.2
1,278 100.0
Total 701
16.7 15.5; 17.8
4,209 100.0
Notes: 1 All fi gures in these columns are numbers of individuals. 2 All fi gures in these columns are percentages.
The exceptional fi nancial aid offered to the individuals in Treatment Groups 1 and 2 appears to have more successfully targeted eligible benefi ciaries—that is to say, the
poorest individuals in the experimental sample, as the rate of refusals due to income levels above the eligibility threshold is much lower in Treatment Groups 1 and 2 than
in the control group Table 7.
B. Impact of the proposed meeting
