Ultimately, we gathered or produced another data present at municipal ? season peak

Ultimately, we gathered or produced another data present at municipal ? season peak

These are (1) the people of women of reproductive years (provided by new National Statistical Institute)? (2) new name, gender, and you will group of every civil gran along with his/this lady vote share (about Electoral Service)? and (3) administrative info on the all other birth control disbursements from societal health provider, and placebo health outcomes produced about same management fitness records (men morbidity, and you can morbidities throughout the puerperium period).

We shared such data supply into a document number of Chile’s 346 municipalities over fifteen years, otherwise 5,190 observations/data. A small number of observations features destroyed measures in a few periods. Likewise, this new way of measuring rejected pills is not designed for 2009. I file summary analytics about following the section.

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Here we follow the notation of Freyaldenhoven et al. (2018), where ?–1 = 0, so that our reference period is one year prior to adoption in each municipality. We are interested in the nine yearly leads and eight yearly lags of the policy change, where leads capture any prevailing trends prior to the reform in earlier versus later adopting municipalities, and lags show the change in health outcomes following EC pill availability. Given variation in reform timing, initial leads and lags capture differences in treatment status (treated vs. untreated), while later periods capture pure variation in timing. Year and municipal fixed Kent escort service effects ??? and ??? absorb time and municipal invariant factors, and standard errors are clustered by Chile’s 346 municipalities. As well as capturing any dynamic impacts of the reform (e.g., growing knowledge diffusion), specification (1) provides evidence in favor of parallel (pre)trends if we can reject that each ?j = 0 ? j 11 It is important to note that in all cases, EC Pill refers to free provision by the public health system. In Chile, following the passage of the EC pill laws, the pill was also sold at private pharmacies. Unlike public data, official data on EC pill usage in the private system are not available (Fernandez et al. 2016). Thus, all estimates refer to the impact of the public reform. Although we cannot formally assess the impact of private market provision without data on disbursements, if private provision fills gaps not met by the public health system “spilling over” to areas not yet treated by the public system, our estimates will understate the actual full effect of EC pill availability (Clarke 2019).

In particular, our way of measuring EC pill availability has 103 destroyed observations to possess many years where municipalities did not render information regarding its pill disbursement standing

where EC Pillct is a binary variable indicating that the EC pill is available in municipality c and time t. Specifically, such models take care of recent critiques that single-coefficient models may be biased if effects are heterogeneous over time (Goodman-Bacon 2018). However, recent advances by de Chaise) propose an estimator to avoid issues relating to heterogeneous impacts over time and time-varying adoption of policies. We thus follow their proposed DIDM estimator in line with Eq. (2) (full details of this method are included in the online Appendix C). 12 This estimator consists of comparing outcomes between all units that change their EC pill status with those that have not yet changed, around the time that the policy change occurs. This is implemented following de Chaise), where we can observe both immediate changes and changes over the following two years given the variation in treatment adoption. In addition, we estimate mirrored leads as placebo tests, which implement the same comparisons between changing and unchanging units, but in periods entirely before treatment is adopted. Besides allowing for a single summary estimate, this method offers the benefit that all identification is drawn off the time period in which the staggered adoption of the EC pill occurred. We consistently conduct inference using a block-bootstrap procedure allowing for within-municipality correlations over time. We also explore one specification where EC Pillct is replaced with Pill Rejectedct, indicating whether each municipality refused to disburse requested EC pills in a given year.

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