Mi Vida Enterprises v. Mark A. Steen-Adams v. Nancy Ciddio Steen-Adams and Charles A. Steen, III : Brief of Appellant

BRIEF OF APPELLANTS APPEAL FROM A FINAL ORDER OF THE SEVENTH DISTRICT COURT HONORABLE LYLE ANDERSON, PRESIDING

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed

Prescribing Behavior of General Practitioners: Competition Matters!

Background: General Practitioners have limited means to compete. As quality is hard to observe by patients, GPs have incentives to signal quality by using instruments patients perceive as quality. Objectives: We investigate whether GPs exhibit different prescribing behavior (volume and value of prescriptions) when confronted with more competition. As there is no monetary benefit in doing so, this type of (perceived) quality competition originates from GPs satisfying patients’ expectations. Method: We look at market level data on per capita and per contact number of items prescribed by GPs and the value of prescriptions for the Belgian market of General Practitioners. We test to which extent different types of variables explain the observed variation. We consider patient characteristics, GP characteristics, number and type of GP contacts and the level of competition. The level of competition is measured by GP density, after controlling for the number of GPs and a HHI. Results: We find that a higher number of GPs per capita results in a higher number of units prescribed by GPs, both per capita and per contact. We argue that this is consistent with quality competition in the GP market. Our findings reject alternative explanations of GP scarcity, availability effect in GP care consumption and GP dispersing prescription in time due to competition

Prescribing Behavior of General Practitioners: Competition Matters!

Background: General Practitioners have limited means to compete. As quality is hard to observe by patients, GPs have incentives to signal quality by using instruments patients perceive as quality. Objectives: We investigate whether GPs exhibit different prescribing behavior (volume and value of prescriptions) when confronted with more competition. As there is no monetary benefit in doing so, this type of (perceived) quality competition originates from GPs satisfying patients’ expectations. Method: We look at market level data on per capita and per contact number of items prescribed by GPs and the value of prescriptions for the Belgian market of General Practitioners. We test to which extent different types of variables explain the observed variation. We consider patient characteristics, GP characteristics, number and type of GP contacts and the level of competition. The level of competition is measured by GP density, after controlling for the number of GPs and a HHI. Results: We find that a higher number of GPs per capita results in a higher number of units prescribed by GPs, both per capita and per contact. We argue that this is consistent with quality competition in the GP market. Our findings reject alternative explanations of GP scarcity, availability effect in GP care consumption and GP dispersing prescription in time due to competition

Entry and Competition in Differentiated Products Markets

We propose a methodology for estimating the competition effects from entry when firms sell differentiated products. We first derive precise conditions under which Bres- nahan and Reiss'entry threshold ratios (ETRs) can be used to test for the presence and to measure the magnitude of competition effects. We then show how to augment the traditional entry model with a revenue equation. This revenue equation serves to adjust the ETRs by the extent of market expansion from entry, and leads to unbiased estimates of the competition effects from entry. We apply our approach to seven different local service sectors. We find that entry typically leads to significant market expansion, implying that traditional ETRs may substantially underestimate the com- petition effects from entry. In most sectors, the second entrant reduces markups by at least 30%, whereas the third or subsequent entrants have smaller or insignificant effects. In one sector, we find that even the second entrant does not reduce markups, consistent with a recent decision by the competition authority.competition;entry;local services sectors;entry threshold ratios

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed