The role of gender in consumer markets for electric vehicles

This dataset contains data from a survey of new-car buying households in
13 US states conducted December 2014 to January 2015. The original study
is described in these technical reports: Kurani, K S., N. Caperello, J.
TyreeHageman New Car Buyers' Valuation of Zero-Emission
Vehicles: California, Institute of Transportation Studies, University of
California, Davis, Research Report UCD-ITS-RR-16-05 (2016).
https://escholarship.org/uc/item/28v320rq Kurani, K.S., N. Caperello, J.
TyreeHageman NCST Research Report: Are We Hardwiring Gender Differences
into the Market for Plug-in Electric Vehicles? Institute of Transportation
Studies, University of California, Davis, Research Report UCD-ITS-RR-18-05
(2018). https://itspubs.ucdavis.edu/publication_detail.php?id=2888 This
dataset is associated specifically with a subsequent technical
report:  Kurani, K.S. and K. Buch Across Early Policy
and Market Contexts Women and Men Show Similar Interest in Electric
Vehicles, National Center for Sustainable Transportation, University of
California, Davis, Research Report. 2019.
 https://escholarship.org/uc/item/9zz8n5x5 Data are from
households who had a acquired at least one household vehicle as new
(rather than used) since January 2008. The questionnaire was administered
on-line to households in the following US states: California, Connecticut,
Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New
York, Oregon, Rhode, Island, Vermont, and Washington. Most of these states
are so called “ZEV states,” i.e., they had adopted California’s Zero
Emission Vehicle (ZEV) Mandate. Those states that were not ZEV states were
included to facilitate regional analysis or because they were otherwise
important to the initial launch of retail ZEV sales in 2011. The primary
regional analysis was for the Northeast States for Coordinated Air Use
Management (NESCAUM). The NESCAUM member states are Connecticut, Maine,
Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode
Island, and Vermont. The total sample size is 5,654 for all states;
individual state samples sizes are available in the above referenced,
Kurani et al (2016). Analyses were conducted at the state and regional,
i.e., NESCAUM, levels. Thus, there are individual data sets for each state
for which there is a state-level analysis (California, Delaware, Maryland,
Massachusetts, New Jersey, New York, Oregon, and Washington) and NESCAUM.
Data for California are included in this release despite the fact its
analysis was previously conducted under a separate study. California
serves as the reference case because it has the most supportive policy and
market context for ZEVs and its analysis is specifically referenced in the
report associated with these data sets. Since the goal was to produce the
best possible analysis for each state or region, there are differences in
their data sets. While variable names and codes follow consistent rules
across all the data sets, which variables are in the data does vary across
states and the NESCAUM region. The data released here are those required
to replicate the analyses in the associated report. For each state and
region, data are available in two formats indicated by their file
extensions: .jmp and .csv. Files with the .jmp extension are proprietary
to the JMP© statistics program from SAS Institute. These files contain the
data and as well as information about variable coding, variable values,
value ordering, and other information in column notes. In effect, the .jmp
files contain the data and the code book. The .csv files are generally
accessible for import into a wide variety of analytical software but
contain no explanatory notes. Finally, an annotated version of the on-line
questionnaire is available as Appendix F of the original report from
California (Kurani et al 2016) cited above. The on-line instrument is
customized to each respondent as they complete it. More than simple skip
patterns, as respondents answer questions content of subsequent questions
is populated with information participants provide. Some of this requires
calls to data external to the survey instrument; some of these data are
proprietary and some are no longer available. Therefore, no “live” version
of the on-line questionnaire from 2014 is maintained. The annotated
version and the description of the survey provided in the linked report
are provided to assist data users. While household ownership and purchase
of all light-duty passenger cars and trucks approach gender parity, to
date zero emission vehicles (ZEVs) are being purchased by far more men
than women. Prior analysis of data from California finds no reason based
in the prospective interest in ZEVs of female and male respondents why
this difference should persist. The present report extends the California
analysis to 12 other US states with varying ZEV policy and market
contexts.  Among many other contextual, socio-economic,
demographic, and attitudinal measures, the survey solicited participants’
prospective interest in acquiring an ZEV, that is, their interest in their
next new car. Participants then indicated why they were motivated to
select a ZEV or what motivated them to not select one. Factor analysis was
used to reduce the dimensionality of participants’ prior awareness,
experience, knowledge, and assessments of ZEVs. Via nominal logistic
regression modeling, differences in prospective interest in ZEVs between
female and male respondents are examined. Given their prospective
interest, the motivations of female and male respondents are
compared.  Overall, no difference between female and male
participants in prospective interest in a ZEV rises to the level of the
observed differences in real markets. Further, the multivariate modeling
indicates no statistically significant effect of a sex indicator on
prospective interest in ZEVS almost anywhere in these states. Where there
is a difference, female participants are estimated to be more likely to
choose a ZEV than their male counterparts.  While participants
from both sexes tend to give high scores to the same ZEV (de)motivations,
differences in their rank orders repeat generalizations from other
research. On average, female respondents score environmental motivations
higher than do male respondents. On average, interest in “new technology”
is more motivating to male than female participants. Conversely, on
average female respondents who do not select a ZEV score “unfamiliar
technology” more highly than their male counterparts.  Within the
variation in policy and market contexts represented by the states in this
study, no finding here explains why similar prospective interest among
female and male participants in ZEVs from the beginning of 2015 has yet to
be turned toward equal participation in ZEV markets. Explanations may lie
in factors not modeled here.