BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Eastern Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20190401T140926Z DESCRIPTION:Dr. Mejia and Dr. Parker will present two recent papers as part of this year’s theme of Experiments and Causal Inference:\n \nWhen Tran sparency Fails: Bias and Financial Incentives in Ridesharing Platforms\n \ nPassenger discrimination in transportation systems is a well-documented p henomenon. With the advent and success of ridesharing platforms\, such as Lyft\, Uber and Via\, there has been hope that discrimination against unde r-represented minorities may be reduced. However\, early evidence has sugg ested the existance of bias in ridesharing platforms. Several platforms re sponded by reducing operational transparency through the removal of inform ation about the rider's gender and race from the ride request presented to drivers. However\, following this change\, bias may still manifest after a request is accepted\, at which point the rider's picture is displayed\, through driver cancelation. Our primary research question is to what exten t a rider's gender\, race\, and perception of support for lesbian\, gay\, bisexual\, and transgender (LGBT) rights impact cancelation rates on rides haring platforms. We investigate this through a large field experiment usi ng a major ridesharing platform in North America. By manipulating rider na mes and profile pictures\, we observe drivers' patterns of behavior in acc epting and canceling rides. Our results confirm that bias at the ride requ est stage has been eliminated. However\, at the cancelation stage\, racial and LGBT biases are persistent\, while biases related to gender appear to have been eliminated. We also explore whether dynamic pricing moderates ( through increased pay to drivers) or exacerbates (by signaling that there are many riders\, allowing drivers to be more selective) these biases. We find a moderating effect of peak pricing\, with consistently lower biased behavior.\n \nShow or Tell? Improving Agent Decision Making in a Tanzanian Mobile Money Field Experiment\n \nWhen workers make operational decisions \, the firm's global knowledge and the worker's domain-specific knowledge complement each other. Oftentimes workers have the final decision-making p ower. Two key decisions a firm makes when designing systems to support the se workers are: 1) what guidance to deliver\, and 2) what kind of training (if any) to provide. We examine these choices in the context of mobile mo ney platforms?systems that allow users in developing economies to deposit\ , transfer\, and withdraw money using their mobile phones. Mobile money ha s grown quickly\, but high stockout rates of currency persist due to sub-o ptimal inventory decisions made by contracted employees (called agents). I n partnership with a Tanzanian mobile money operator\, we perform a random ized controlled trial with 4\,771 agents over eight weeks to examine how d iffering types of guidance and training impact the agents' inventory manag ement. We find agents who are trained in person and receive an explicit\, personalized\, daily text message recommendation of how much electronic cu rrency to stock are less likely to stock out. These agents are more likely to alter their electronic currency balance on a day (rebalance). In contr ast\, agents trained in person but who receive summary statistics of trans action volumes or agents who are notified about the program and not offere d in-person training do not experience changes in stockouts or rebalances. We observe no evidence of learning or fatigue. Agent-level heterogeneity in the treatment effects shows that the agents who handle substantially mo re customer deposits than withdrawals benefit most from the intervention.\ n \nJorge Mejia is an Assistant Professor in the Kelley School of Business . Christopher Parker is an Assistant Professor in the Smeal College of Bus iness at the Pennsylvania State University.\n \nhttps://go.iu.edu/wim \n DTEND;TZID="Eastern Standard Time":20190405T160000 DTSTAMP:20190401T140926Z DTSTART;TZID="Eastern Standard Time":20190405T140000 LAST-MODIFIED:20190401T140926Z LOCATION:Social Science Research Commons Grand Hall (Woodburn Hall 200)\, 1 100 E. 7th St.\, Bloomington\, IN 47405 PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:Workshop in Methods: Dr. Jorge Mejia & Dr. Chris Par ker TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000A0AEFBEC72E8D401000000000000000 010000000CE1357F289B335438A88BDD09DE14738 X-ALT-DESC;FMTTYPE=text/html:

Dr. Mejia and Dr. Parker will present two recent papers as part of this year& #8217\;s theme of Experiments and Causal Inference:

 \;

When Transparency Fails: Bias and Financial Incentive s in Ridesharing Platforms

  \;

Passenger discrimination in transportation systems is a well-documented phenomenon. With the advent and success of ri desharing platforms\, such as Lyft\, Uber and Via\, there has been hope th at discrimination against under-represented minorities may be reduced. How ever\, early evidence has suggested the existance of bias in ridesharing platforms. Several platforms responded by reducin g operational transparency through the removal of information about the ri der's gender and race from the ride request presented to drivers. However\ , following this change\, bias may still manifest after a request is accep ted\, at which point the rider's picture is displayed\, through driver can celation. Our primary research question is to what extent a rider's gender \, race\, and perception of support for lesbian\, gay\, bisexual\, and tra nsgender (LGBT) rights impact cancelation rates on ridesharing platforms. We investigate this through a large field experiment using a major ridesha ring platform in North America. By manipulating rider names and profile pi ctures\, we observe drivers' patterns of behavior in accepting and canceli ng rides. Our results confirm that bias at the ride request stage has been eliminated. However\, at the cancelation stage\, racial and LGBT biases a re persistent\, while biases related to gender appear to have been elimina ted. We also explore whether dynamic pricing moderates (through increased pay to drivers) or exacerbates (by signaling that there are many riders\, allowing drivers to be more selective) these biases. We find a moderating effect of peak pricing\, with consistently lower biased behavior.

 \;

Show or Tell? Improving Agent Decision Maki ng in a Tanzanian Mobile Money Field Experiment

 \;

When workers make oper ational decisions\, the firm's global knowledge and the worker's domain-sp ecific knowledge complement each other. Oftentimes workers have the final decision-making power. Two key decisions a firm makes when designing syste ms to support these workers are: 1) what guidance to deliver\, and 2) what kind of training (if any) to provide. We examine these choices in the con text of mobile money platforms?systems that allo w users in developing economies to deposit\, transfer\, and withdraw money using their mobile phones. Mobile money has grown quickly\, but high stockout rates of currency persist due to sub-optima l inventory decisions made by contracted employees (called agents). In par tnership with a Tanzanian mobile money operator\, we perform a randomized controlled trial with 4\,771 agents over eight weeks to examine how differ ing types of guidance and training impact the agents' inventory management . We find agents who are trained in person and receive an explicit\, perso nalized\, daily text message recommendation of how much electronic currenc y to stock are less likely to stock out. These agents are more likely to a lter their electronic currency balance on a day (rebalance). In contrast\, agents trained in person but who receive summary statistics of transactio n volumes or agents who are notified about the program and not offered in- person training do not experience changes in stockouts< /span> or rebalances. We observe no evidence of learning or fatigue. Agent -level heterogeneity in the treatment effects shows that the agents who ha ndle substantially more customer deposits than withdrawals benefit most fr om the intervention.

 \;

Jorge Mejia is an Assistant Professor in the Kelley School of Business. Christopher Parke r is an Assistant Professor in the Smeal College of Business at the Pennsylvania State University.

 \;

https://go.iu.edu/w im

X-MICROSOFT-CDO-BUSYSTATUS:BUSY X-MICROSOFT-CDO-IMPORTANCE:1 X-MICROSOFT-DISALLOW-COUNTER:FALSE X-MS-OLK-AUTOFILLLOCATION:FALSE X-MS-OLK-CONFTYPE:0 BEGIN:VALARM TRIGGER:-PT15M ACTION:DISPLAY DESCRIPTION:Reminder END:VALARM END:VEVENT END:VCALENDAR