Eventually, we collected or made another analysis source in the municipal ? year peak

Eventually, we collected or made another analysis source in the municipal ? year peak

These are (1) the people of females away from reproductive many years (available with brand new National Statistical Institute)? (2) the latest title, gender, and you can team of each civil mayor with his/the woman choose display (from the Electoral Service)? and you may (3) management details to your any other contraceptive disbursements from the personal health solution, and placebo fitness outcomes made from the same administrative wellness records (male morbidity, and morbidities on puerperium several months).

I joint this type of analysis sources with the a document number of Chile’s 346 municipalities more fifteen years, otherwise 5,190 observations/files. A small number of findings has actually forgotten procedures in certain symptoms. Also, the brand new measure of declined pills isn’t readily available for 2009. I file realization analytics from the following the section.

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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).

In particular, our measure of EC tablet availability have 103 forgotten observations for age in which municipalities did not provide information regarding their pill disbursement position

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 hookupreviews.net/bbw-hookup/ 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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