Ultimately, i gathered otherwise generated some other analysis source at the municipal ? 12 months top

//Ultimately, i gathered otherwise generated some other analysis source at the municipal ? 12 months top

Ultimately, i gathered otherwise generated some other analysis source at the municipal ? 12 months top

Ultimately, i gathered otherwise <a href="https://datingmentor.org/minnesota-saint-paul-dating/">Saint Paul dating ideas</a> generated some other analysis source at the municipal ? 12 months top

Speaking of (1) the population of women regarding reproductive ages (provided by new Federal Statistical Institute)? (2) new name, gender, and group of any municipal mayor with his/the woman choose share (regarding Electoral Services)? and you may (3) management information on any other contraceptive disbursements from the societal fitness services, and you can placebo fitness effects produced on the exact same administrative health facts (male morbidity, and you will morbidities about puerperium period).

That is used following de Chaise), where we can to see both immediate alter and transform along side following two years considering the variation inside the therapy adoption

I combined these research offer for the a data set of Chile’s 346 municipalities more than fifteen years, or 5,190 findings/files. A handful of observations features missing steps in some episodes. Specifically, the way of measuring EC tablet supply has actually 103 forgotten observations for many years in which municipalities don’t give information regarding the tablet disbursement status. Similarly, the newest way of measuring refused pills isn’t available for 2009. I document summation statistics regarding pursuing the section.


Here we follow the notation of Freyaldenhoven et al. (2018), where ?–step 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 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).

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. 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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