Hence, our data strongly suggest an SUTVA violation. As a result, without further structure on the nature of the interference between beneficiary and non-beneficiary households, we cannot identify the usual ‘intent-totreat’ or ‘average treatment’ effects. What we can identify is what biostatisticians, public health experts, or communicable disease specialists sometimes call the ‘direct effect’ of the program (see <u>Özler 2016): a</u> comparison of beneficiaries and non-beneficiaries living in close proximity (or in the same network). Baird et al. (2018), in the context of cluster-RCTs, call this estimand the ‘value of treatment,’ or VT, because it is “... <mark>the individual value of receiving treatment in the treated cluster.” For example, the value of getting vaccinated could be zero for an individual if everyone else in her community is already vaccinated. The intuition here is that, unless spillover effects are zero, then the ITT will not be equal to the VT (VT = ITT - spillover effects).</mark> Therefore, throughout this paper, we present ‘value of treatment’ estimates, which is the ‘value of being an ESSN beneficiary’ for an applicant household (that lives among and interacts with other beneficiary and nonbeneficiary households). This implies that if the ‘value of treatment’ is zero for an outcome of interest, it does not necessarily mean that the ESSN was ineffective in improving that outcome. This depends on the size of the spillover effects on non-beneficiary households.<sup>22</sup>

# _4. Program eligibility_

A final issue concerning the interpretation of the impacts of ESSN is program eligibility. While the ‘treatment sample’ for the study was drawn from the group of successful applicants initially deemed eligible for the program and the ‘control sample’ from those initially deemed ineligible, the beneficiary status of refugees in either group were not fixed over time. As discussed above, all applicant households are checked against the eligibility criteria on a monthly basis. If ISAS identifies that the household no longer meets the eligibility criteria (for instance, because of a dependent turning the age of 18, or the expiration of a disability report), the case is removed from the ESSN program. Conversely, households that were deemed ineligible at the time of their first application to the program, can become eligible at a later date through monthly digital eligibility checks (for instance if they register a new dependent such as a newborn baby on official MERNIS records, or obtain a disability report from the designated public hospitals, which get updated on their ISAS records).<sup>23</sup> <mark>As a result, the difference between the share of those receiving cash transfers in the treatment vs. the control groups of our study declined over time. The top panel in T</mark> able <mark>5 shows the time trend in the share of the</mark>

> 22 The spillover effects are likely to be positive if we assume away general equilibrium effects on increased prices. Such an assumption is plausible as ESSN beneficiaries constitute a small share (about 2 percent) of Turkey’s population. Sävje, Aronow, and Hudgens (2019) introduce _expected average treatment effect_ , an estimand that generalizes the _average treatment effect_ to settings with interference, which is similar to VT.

> 23 In October 2018, the program initiated a complementary support, called ‘discretionary allowance,’ for households that are vulnerable but do not fit the demographic eligibility criteria. The quota for this support was capped at 5% of applications made until September 2018. Not many households fit the socio-economic assessment done to be eligible for this support based on the Decision Support Mechanism Algorithm (DSMA) of the Integrated Social Assistance System. Therefore, by August 2019, only 19% of the total quota had been used.

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