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Generativity and social value orientation between rural and urban societies in a developing country
Accepted Manuscript Title: Generativity and social value orientation between rural and urban societies in a developing country Author: Raja Timilsina Koji Kotani Yoshio Kamijo PII: DOI: Reference:
S0016-3287(17)30281-1 https://doi.org/doi:10.1016/j.futures.2018.09.003 JFTR 2354
To appear in: Received date: Revised date: Accepted date:
21-7-2017 9-7-2018 14-9-2018
Please cite this article as: Raja Timilsina, Koji Kotani, Yoshio Kamijo, Generativity and social value orientation between rural and urban societies in a developing country, (2018), https://doi.org/10.1016/j.futures.2018.09.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Yoshio Kamijo*,†
Abstract
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September 16, 2018
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Koji Kotani*,†,‡,§,¶
Raja Timilsina*
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Generativity and social value orientation between rural and urban societies in a developing country
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Generativity, concern and commitment for the next generation, is an important factor in the sustainable development of a society, as intergenerational sustainability is claimed to have been compromised over the last decades. Generativity emerges through both prosocial and proself behaviors characterized by social preferences and is now hypothesized to have decreased in some urban societies; this is referred to as the “generativity crisis.” However, little is known about how ongoing urbanization of competitive societies, i.e., capitalism, and social preferences are related to generativity. To this end, we conduct field experiments on social value orientation and administer a generative behavior checklist in two strata of Nepalese society: (1) the urban and (2) the rural. The analysis finds that prosociality and the rural-specific effect are the two major factors that increase people’s generativity, while a larger proportion of prosocial people are found in rural areas than in urban areas. Overall, these results suggest that generativity will decrease with further urbanization, changing the economic culture and orientation of the populace so that there is less concern for future generations.
Key Words: Social value orientation; prosocial; proself; generativity
*Research Institute for Future Design, Kochi University of Technology, Japan †School of Economics and Management, Kochi University of Technology ‡Urban
Institute, Kyusyu University of Business, Rikkyo University ¶Corresponding author, E-mail: [email protected]
§College
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We empirically characterize generativity in relation to social value orientation and urbanization. We elicit generativity, social value orientations and socioeconomic factors through household surveys in rural and urban areas of Nepal. Prosociality and rural-specific factors positively affect generativity. A larger proportion of people are prosocial in rural areas than in urban areas. Further urbanization changes cultures and people’s value orientation to be less generative for the future.
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Contents 2
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Introduction
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Methodology
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Results
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Discussion
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Conclusion
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Bibliography
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Nomenclature
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List of Figures
Nomenclature
NPR Nepalese rupee
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GBC Generative behavior checklist
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ANOVA One-way analysis of variance
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List of Tables
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SVO Social value orientation USD US dollar
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Introduction Generativity, the concern for and commitment to the current and next generations, is an im-
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portant element in the sustainable development of a society; higher generativity within the current
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generation induces people to educate and help the next generation and even the next (Erikson,
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1963, Volckmann, 2014). Generativity is expressed in the daily practices of human interaction, for
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example, charity, mentoring, nursing, volunteering, teaching, religion and political activity, for the
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benefit of the next generation (McAdams and de St. Aubin, 1992). In reality, societies transition
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over time in “overlapping generations” in the sense that the members of one generation survive
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and remain as members of the next generations (Gaspar and Lauren, 2013). Unfortunately, it has
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been claimed that the current generation behaves more selfishly than previous generations, and this
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behavior compromises generativity and sustainability, e.g., environmental (resource) sustainabil-
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ity, incurring costs for the current and next generation, i.e., the “generativity crisis” (Sasaki, 2004,
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Fisher et al., 2004, Milinski et al., 2006, Pratt et al., 2013, Molnar and Vass, 2013, Jia et al., 2015,
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Lefebvre and Lefebvre, 2016). Thus, generativity is an urgent issue and essential if societies are to
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change for the benefit of the next generation.
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One of the most important issues today is the extent to which the interests of future generations
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must be addressed; however, a sustainable society must also satisfy the needs of the current gener-
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ation without jeopardizing the prospects of future generations, i.e., intergenerational sustainability
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(Howard, 2000, Masini, 2013, Tonn, 2009, 2018). The future studies literature has considered
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and addressed these issues by providing institutional proposals and theoretical frameworks (Inay-
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atullah, 1997, Pino, 2007, Balazs and Gaspar, 2010, Chen, 2016, Seo, 2017, Tonn, 2018). How-
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ever, these studies do not empirically examine how intergenerational sustainability changes among
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people in society. “Generativity” can be considered one of the best proxies for intergenerational
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sustainability, and a possible predictor for generativity is a social value orientation suggested by
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Murphy et al. (2011) that measures people’s concern for others (Van Lange et al., 2007b, 2011,
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Sutterlin et al., 2013, Hauser et al., 2014, Timilsina et al., 2017). This study empirically addresses
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the relationship between individual social preferences and generativity in different societies.
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Generativity has been studied by many researchers, and the generative behavior checklist
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(GBC) is established as one of the most reliable and internally consistent measures (McAdams
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and de St. Aubin, 1992, McAdams et al., 1993, McAdams and de St. Aubin, 1995). Most studies
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have sought to characterize the GBC as representing parts of innate human psychology, focusing on
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parenting, degree of well-being, life satisfaction and societal concern (Peterson and Stewart, 1993,
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Morfei et al., 2004, Huta and Zuroff, 2007, Newton et al., 2014). In particular, Hart et al. (2001)
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have empirically characterized generativity, finding that it has a positive association with social
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involvement related to parenting in both white and black Americans. Similarly, Hofer et al. (2008)
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have confirmed that the psychological mechanisms of the generativity model are consistently ap-
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plicable even in a cross-country comparison. In conclusion, these studies have demonstrated that
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the GBC can explain behaviors and preferences of social involvement in relation to people’s innate
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psychology, concerns and actual social behaviors.
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Economists and behavioral scientists have considered that the socioeconomic environment in-
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fluences people’s social preferences and actual behaviors (Henrich et al., 2005, 2010, Van Lange
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et al., 2007b, Leibbrandt et al., 2013). Schotter and Sopher (2003) and Hauser et al. (2014) have
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shown that the current generation can neither make sustainable decisions in an intergenerational
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setting nor take the balance of costs and benefits for future generations when faced with an ex-
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cessively competitive economic environment. Henrich et al. (2005, 2010) and Leibbrandt et al.
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(2013) have demonstrated that people’s social behaviors and preferences are affected by the degree
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of market integration in societies and workplace environments, respectively. Similarly, Ockenfels
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and Weimann (1999) and Brosig-Koch et al. (2011) have analyzed people’s cooperative and soli-
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darity behaviors in eastern and western Germany, demonstrating that subjects from eastern regions
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act more selfishly than those from western regions. They conclude that social history and socioe-
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conomic environment play an important role in shaping people’s social preferences and behaviors.
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In summary, the psychologists have addressed how generativity is associated with people’s innate
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psychology and actual behaviors, while the economists and behavioral scientists have determined
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how social preferences and behaviors are affected by economic environment.
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Rapid urbanization is taking place in Asia and Africa, and it is expected that 65 % - 75 %
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of the world population will be concentrated in urban cities in Asia and Africa, compromising a
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large part of environmental and resource sustainability (Bauman, 2013, Wigginton et al., 2016,
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Henderson et al., 2016, McDonnell and MacGregor-Fors, 2016). Generativity is hypothesized to
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be affected by such changes in societies, as generativity can be manifested through both prosocial
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and proself behaviors originating from people’s social preferences (Kotre, 1984, McAdams, 1985).
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Since societies are becoming more competitive and urbanized in the globalized market economy,
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it is expected that such cultural changes in societies might affect not only preferences but also
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generativity (Tajfel and Turner, 1979, Passini, 2013). However, no previous study has empirically
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addressed how generativity is evolving with economic development and changes in preferences.
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We consider ongoing urbanization of competitive societies as part of a culture and hypothesize
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that generativity changes with urbanization and social preferences. To this end, we conduct field
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experiments on social value orientation (SVO) and administer the generative behavior checklist
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(GBC) in two areas of Nepal: (1) urban and (2) rural, taking Nepal as a representative country that
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has experienced rapid urbanization.
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Methodology
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We implemented field experiments and questionnaire surveys in rural and urban areas and
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employed different approaches of random sampling because they possess distinct economic and
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sociodemographic characteristics. Kathmandu and Pokhara are chosen as urban areas, as they are
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the first and second largest urban societies in Nepal (figure 1). In the urban areas, we administered
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field experiments and surveys with 268 subjects. These cities are highly populated; most people
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are engaged in the business, service and government sectors. To maintain random sampling of
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subjects, an occupation-based randomization procedure was performed. First, we identify the
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proportion of each occupational category as part of the total population of the urban areas by
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referring to governmental and international non-governmental reports such as Central Bureau of
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Statistics (2011) and UNDP (2014). Next, we randomly select 15 to 20 organizations or companies
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from each area and we send approximately 600 invitations letters to participate in our survey and
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experiment. Based on their compliance, we select individuals from these organizations so that
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subjects do not know one another in the same session. Our field experiments and surveys were
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carried out in the city and community halls of the urban areas.
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[Figure 1 about here.]
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In the rural areas, we conducted field experiments and surveys with 260 subjects. Chitwan
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and Prabat are chosen as rural areas (figure 1). These districts consist of many small villages and
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are known as the least populated areas; most people engage in agriculture and forestry for their
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livelihood. In the rural areas, we conducted a household-level randomization procedure. First, we
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designated the number of samples for the selected villages based on the total number of households
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provided by each village development committee office. Each village consist of approximately
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150 to 400 number of households. After that, we selected the household number randomly and
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invited one household member by sending 400 invitation letters. Our monetary incentives and
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the conditions addressed in the invitation letters made it possible to collect a sufficient number of
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subjects. The field experiments and surveys were conducted in the schools of the rural areas.
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The SVO of the “slider method” was conducted to identify subjects’ social preferences as
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prosocial or proself in urban and rural areas (Murphy et al., 2011). Figure 2 shows six items of
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the slider measure that assign numbers to represent outcomes for oneself and the other for a pair
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of people, where the other is unknown to the subject. Subjects are asked to make a choice among
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nine options for each item. Each subject chooses her allocation by marking a line at the point that
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defines her most preferred distribution between oneself and the other. The mean allocation for
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oneself As and the mean allocation for the other Ao are computed from all six items (see figure 2).
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Then, 50 is subtracted from As , and Ao to shift the base of the resulting angle to the center of
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o )−50 the circle (50, 50). The index of a subject’s SVO is given by SVO = arctan (A . Depending (As )−50
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on the values generated from the test, social preferences are categorized as follows: 1. altruist:
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SVO > 57.15◦ , 2. prosocial: 22.45◦ < SVO < 57.15◦ , 3. individualist: −12.04◦ < SVO < 22.45◦
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and 4. competitive types: SVO < −12.04◦ . [Figure 2 about here.]
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The SVO framework assumes that people have different motivations and goals for evaluating
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resource allocations between oneself and others. Also, the SVOs or social preferences are estab-
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lished to be stable for a long time (see, e.g., Van Lange et al., 2007b, Brosig-Koch et al., 2011).
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Responses are yielded from six primary items that encompass the complete categories of social
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preferences. The major reasons for using the six primary slider measures of Murphy et al. (2011)
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are the simplicity and ease of implementation in the experimental settings of Nepal. Participants
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are able to understand it intuitively, even those with a limited level of education. As in the psy-
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chology research, we further simplify the four categories of social preferences into the prosocial
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and proself categories; “altruist” and “prosocial” types are categorized as prosocial subjects, while
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“individualistic” and “competitive” types are categorized as “proself” subjects (see Murphy et al.,
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2011).
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The GBC (generative behavior checklist) developed by McAdams and de St. Aubin (1992)
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checks the frequencies of the generative behaviors that each individual has engaged in previously.
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Specifically, the GBC asks how many times a person has performed 50 different behaviors, among
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which only 40 behaviors are suggestive of “generativity.”1 The examples are “taught somebody a
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skill,” “read a story to a child,” “served as a role model for a young person” and “made something
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for somebody and then gave it to them.” Subjects need to write “0” if they have not performed
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a specific generative behavior, “1” if they have performed the behavior once and “2” if they have
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performed the behavior more than once for the last one year. Scores on the 40 generative behaviors
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were summed for each subject to compute a total generativity score. In comparison with other
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psychological measures such as the Loyola Generativity Scale (LGS) and guided autobiography,
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we realize that GBC is easy for people to understand and respond to in the rural and urban areas of
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Nepal, which is a good proxy for the expression of people’s real behavior.
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The remaining 10 behaviors in the GBC questionnaire that are not counted as generativity are “fillers.”
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Considering the opportunity costs of time, we need to attract subjects to the experimental sites
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and motivate them to participate in the surveys and games. Therefore, we have incentivized the
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questionnaire surveys and the SVO game with monetary payments. In each session, we have
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collected 20 to 40 subjects in a site and provided experimental instructions to the subjects. Next,
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the experimenter (the first author) made oral presentations to confirm that the subjects understood.
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We also used six research assistants and helped the subjects. After eliciting the subjects’ SVOs, we
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conducted questionnaire surveys to collect individual sociodemographic information (See table 1
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for the definitions of the variables and the summary statistics).
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Subjects are paid on the basis of their earnings from the SVO game. After each subject has
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made his/her decisions, they write the resulting distribution of money on the spaces provided on
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the right-hand side of the SVO instruction sheets in figure 2. The total amount of points a subject
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is allocated by himself and by the other to oneself in the pair are calculated by summing the points
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of all six items. Depending on the points generated from the game, the points are converted into
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real money with an experimental exchange rate. In our experiment, we use 10 points equivalent to
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1 NPR. At the end, we randomly matched one subject with another to make pairs for calculating
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the total payoff of each subject and made payments. One session took 40 to 60 minutes, and
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the average payment was 200 NPR (approximately 2.10 USD) with a show-up fee of 100 NPR
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(1.05 USD).
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This study finally analyzes the association of generativity with people’s social preferences and
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the locations of two different areas. A dummy variable for controlling the urban and rural areas in
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the analysis is intended to represent different degrees of urbanization (equivalently, the degree of
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capitalism) in societies. To characterize which social preference and society lead to higher levels of
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generativity, nonparametric statistical and regression analyses are employed. The Mann-Whitney
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test is used to identify the distributional difference of generativity across the two areas and their
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social preferences. The regression model estimates the marginal impact on generativity when a
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key predictor, such as SVOs and an area dummy, increases, holding other factors fixed. The set of
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independent variables includes SVOs, area dummy, household income, age, education, gender and
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employment.2 Table 1 summarizes the definition of the variables in the analysis.
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Results Table 1 summarize the statistics of subjects’ sociodemographic information and generativity,
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respectively. Table 1 shows that 38 % of the subjects are male in rural areas, while 58 % of the sub-
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jects are male in urban areas. With respect to education, more than 50 % of subjects in urban areas
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have an undergraduate degree from a university (16 years of schooling as the median in table 1).
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On the other hand, subjects in rural areas possess 10 years of schooling as the median. Regard-
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ing employment, 90 % and 63 % of subjects in rural and urban areas are employed and engaged
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in agriculture or in a service sector, respectively, where a majority of rural (urban) subjects are
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farmers (nonfarmers such as office workers). This implies that the urban areas in our field do not
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depend on agriculture anymore, but rural areas are still agriculture-based or natural resource-based
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societies. Reflecting this difference of dependency on agriculture, household income is higher in
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urban areas than in rural areas (table 1). Overall, the summary statistics of sociodemographic in-
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formation in table 1 are in line with our initial expectations that urban societies are more advanced
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and urbanized in many aspects.
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[Table 1 about here.]
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Table 1 shows subjects’ SVOs to be prosocial or proself between the rural and the urban. The
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major difference can be seen in the “SVO” variable, indicating that 76 % of subjects in the rural
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areas are prosocial, while only 39 % of subjects are prosocial in urban areas. Specifically, 197
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out of 260 rural subjects are prosocial in rural areas, while 105 out of 268 urban subjects are
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prosocial in urban areas (table 1). The chi-square test reveals that subjects’ prosociality and areas
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are significantly dependent (χ2 = 72.16, p < 0.01). This implies that prosociality among people 2
Individual social preferences are established to remain the same for a long time (Van Lange et al., 2007b, BrosigKoch et al., 2011), while the GBC is a behavior checklist for the actions that subjects have taken over the last year. Therefore, taking SVOs as an independent variable in the regression of generativity does not cause any erogeneity problem or reverse causality.
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is different between rural and urban areas, and prosocial (proself) people are dominant in the rural
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(urban) areas. This SVO result appears to suggest that people tend to be more proself as societies
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are more urbanized and developed. Table 1 presents the summary statistics of subjects’ generativity. The mean generativity is
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different between rural and urban areas. The mean generativity in rural areas is 42.05 (SD =
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12.63), while that in urban areas is 37.91 (SD = 13.34). This implies that the mean of generativity
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in rural areas is higher than those in urban areas. We further analyze subjects’ generativity by
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SVOs in each area, for instance, the mean generativity of prosocial subjects in rural areas is 43.53
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(SD = 12.32), which is higher than that in urban areas, i.e., 40.23 (SD = 13.35). Similarly, the
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mean generativity for proself subjects in rural areas is 37.41 (SD = 12.57) which is higher than
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that for proself subjects in urban areas, i.e., 36.41 (SD = 13.17). Put simply, prosocials in rural
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areas, prosocials in urban areas, proselves in urban and proselves in rural areas are the descending
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orders of groups with respect to the central tendencies of generativity.3 Overall, this results suggest
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that prosociality and urban vs. rural areas are the keys to characterizing generativity.
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The one-way analysis of variance (ANOVA) is applied to examine whether there are any sta-
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tistically significant differences of the average generativities between every pair of groups with
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respect to SVOs and/or study areas. The null hypothesis is that the average generativity in one
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group is equal to that in the other group. We have applied the ANOVA to the following pairs of
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groups: (1) The rural vs. the urban, (2) the prosocial vs. the proself, (3) the prosocial in the rural vs.
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the proself in the rural, (4) the prosocial in the urban vs. the proself in the urban, (5) the prosocial
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in the rural vs. the prosocial in the urban and (6) the proself in the rural vs. the proself in the urban.
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We have identified that cases (1), (2) and (3) reject the null hypothesis at 1 % significance level
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(p < 0.01) with statistics of F1,526 = 13.40, F1,526 = 25.40 and F1,258 = 11.68, respectively. Cases
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(4) and (5) reject the null hypothesis at 5 % level (p < 0.05) with statistics of F1,266 = 5.33 and
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F1,300 = 4.63, respectively. The only pair that does not reject the null hypothesis even at the 10 %
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Regarding the medians of generativity, the almost same orders of groups follow in a descending order as the means do, except that the median generativity for proself subjects in rural areas is lower than that for proself subjects in urban areas. Overall, both means and medians of generativity as the central tendencies consistently demonstrate that both SVOs and areas are correlated with generativity.
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level is case (6) that comes with F1,224 = 0.27. Overall, these results of the ANOVA tests con-
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firm that average generativities between the rural and urban and/or between SVOs are statistically
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different, being consistent with table 1. For the robustness check, we also run Mann-Whitney tests for a pair of the same groups as
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the ANOVA.4 The null hypothesis is that the generativity distributions are the same between the
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two groups. We have obtained that (1) the rural vs. the urban (Z = 3.404, p < 0.01), (2) the
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prosocial vs. the proself (Z = 4.890, p < 0.01), (3) the prosocial in the rural vs. the proself in
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the rural (Z = 3.322, p < 0.01); (4) the prosocial in the urban vs. the proself in the urban (Z =
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2.300, p = 0.02); (5) the prosocial in the rural vs. the proself in the urban (Z = 4.978, p < 0.01);
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and (6) the prosocial in the rural vs. the prosocial in the urban (Z = 1.896, p = 0.06). These
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results of the Mann-Whitney tests statistically confirm that generativity may be characterized by
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prosociality in the areas between the rural and urban areas. Given the statistical significance of the
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generativity across areas and SVOs with ANOVA and Mann-Whitney tests, we further characterize
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generativity by running the regression model, taking the generativity as a dependent variable and
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the area dummy between the rural and the urban, SVOs and other sociodemographic information
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as independent variables.
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[Table 2 about here.]
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Table 2 reports the estimated coefficients and their respective standard errors with statistical sig-
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nificance in the regression of generativity. Model 1 in table 2 contains SVOs and the area dummy
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of the rural as independent variables. The result reveals that both variables exhibit statistical sig-
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nificances of p < 0.01 and p < 0.01, respectively, and are positively associated with generativity.
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To further characterize generativity, we add sociodemographic variables such as gender, education,
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age, employment, number of household members and income in model 2 of table 2. We find that
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the SVOs and the area dummy remains statistically significant with the same sign and magnitude, 4
The Mann-Whitney test is a non-parametric statistical hypothesis test based on ranks such as medians, and does not need to assume the underlying distributions of the samples in a parametric form (Conover, 1999). Therefore, it is generally known that the results are robust irrespective of the actual distributions of the samples.
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and education has a statistically significant positive correlation with generativity (p < 0.01). How-
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ever, the magnitude of education is rather small compared with that of the SVOs and area dummy.
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There are no significant associations of gender, employment, income and age in model 2. In model 3, with age squared added, both age and its squared variables are significant with
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positive and negative signs (p < 0.05), respectively. This result implies that generativity reaches
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its peak in midlife and starts to decline with old age. This is consistent with McAdams et al.
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(1993), Newton et al. (2014) and Schoklitsch and Baumann (2012) demonstrating that this single-
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peaked nature in generativity is due to health or physical weakness. Considering the consistent
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results across models 1, 2 and 3, it appears that SVOs and the area dummy are strong predictors
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of generativity. More specifically, in model 3, the generativity increases by 4.77 with a change in
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SVOs from being in the proself to being in the prosocial, and the generativity increases by 3.13 if
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the individual resides in rural as compared with residing in urban areas. Education is statistically
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significant in model 3; however, the magnitude remains small.5
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In summary, our results show that there are more prosocial people in the rural areas; prosocial
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and rural people possess higher generativity. This suggests that as societies become more urban-
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ized and competitive in a capitalistic way, people tend to be less concerned about others and even
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future generations. Among several predictors, the strong ones for generativity is identified to be
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SVOs and area dummy. In reality, generativity is expressed through daily practices and human
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interactions such as charity, mentoring, nursing, volunteering, teaching, religious practice and po-
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litical activities that guide the current and next generation (McAdams and de St. Aubin, 1992), and
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there are two channels to increase generativity. One channel is a direct effect of urbanization on
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generativity, which is the result of the difference between the rural and urban areas. The second
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channel is an indirect effect of urbanization on generativity; that is, the urbanization of societies
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induces people to be more proself, and generativity decreases through such a change in social pref-
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erences or SVOs (Van Lange et al., 2007a,b, 2011). Since the magnitudes of the impacts from both
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For further robustness check, we have tried various specifications, but we confirm that the main results do not change. For instance, we have also added an interaction term of SVO and area dummy on top of the specifications in model 3. However, the interaction term is not significant and the main results remain the same as those in model 3.
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SVOs and area dummies are large, the generativity crisis in urban areas may well be explained by
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these two factors.
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Discussion
There are differences between urban and rural areas in many respects, including the environ-
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ment, customs, technology, physical space and architecture that influence social interaction. Pop-
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ulation density is high in urban areas, and on a daily basis, people happen to meet or interact with
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numerous strangers and colleagues. However, in many cases, their social ties and connections are
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weak in the sense that there is less intimacy so that few act in concert (Beaudoin and Thorson,
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2004, Huddart-Kennedy et al., 2009, Macias and Nelson, 2011, Cope et al., 2016, Shahrier et al.,
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2016, Timilsina et al., 2017). To make matters worse, they may even be suspicious of strangers and
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must compete with colleagues. These phenomena are not uncommon in the capital city of Nepal,
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Kathmandu.
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Rural areas are nature-dependent and life is lived on the basis of natural vegetation and local
268
fauna. Therefore, people’s lives are agriculture based or nature-dependent as well. In such rural
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societies, both cultural learning and vocational training are passed on by family, relatives and
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neighbors because the transfer of such skills and knowledge must be made through close interaction
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in the local human network to survive. Young people seek to mimic and learn standard behaviors
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and beliefs from local communities, in particular from older people. Such transmission of norms
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can also be considered the perpetuation of conformist biases that are accumulated from previous
274
generations and neighbors (See, e.g., Henrich and McElreath, 2003, Sasaki, 2004).
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Urban settlements have advanced civic amenities and opportunities for education as well as fa-
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cilities for transport and business, and this standard of living does not induce people to interact with
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others or neighbors. In terms of employment, there is a division of labor and job specialization,
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and the number of opportunities leads to higher occupational mobility. Given these realities, we
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conjecture that the difference in how people interact with others leads to a change in generativity
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with urbanization and that the human network of intergenerational linkage and interaction in rural
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areas corresponds to a rural-specific effect on generativity in our statistical analysis. Urban areas are different in terms of their functionality and are classified according to land
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use and population density, but this can vary from developed countries to developing countries
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(McDonnell and MacGregor-Fors, 2016). Asia and Africa will have the world’s largest and fastest
285
growing cities in the future, and most of this growth will occur in emerging and developing coun-
286
tries (Wigginton et al., 2016). Such growth might help to lift millions of people out of poverty, but
287
it may be true that it comes with huge social costs to future generations (Henderson et al., 2016).
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There are several social, behavioral and economic challenges that need to be overcome to improve
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urban settlements and future sustainability. An individual lifestyle and decisions on how to live in
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urban areas directly affect societies, such as whether to unplug cell phones or use public transport
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to travel to work or install solar panels on a roof. Each of these individual decisions that affects
292
other people are dependent on people’s concern for others, i.e., individual social preferences (Van
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Lange et al., 2007a, 2011). More importantly, our findings suggest that not only social behaviors
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but also generativity in an intergenerational setting are affected by individual social preferences
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and ongoing urbanization.
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Conclusion
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This paper has explored how generativity is changing along with social preferences and the
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degree of capitalism in societies. To this end, we implemented a social value orientation (SVO)
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experiment and surveys of generative checklist questionnaires in two fields of Nepal: (i) urban and
300
(ii) rural areas. The results show that mainly individual generativity might have been positively
301
affected by two channels, i.e., prosociality and the rural-specific effect. Since we have found a
302
higher proportion of prosocial people in rural areas than in urban areas, we propose that as soci-
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eties are getting more urbanized and developed in a capitalistic way, generativity shall be further
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compromised through changes in social preferences and economic environments of urbanization.
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Our research shows evidence that economic environments may affect people’s preferences for
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and even behaviors toward intergenerational sustainability. We conjecture that the difference in
307
daily life between rural and urban areas influences how people interact with others including fam-
308
ily, relatives, neighbors and strangers. Rural areas might have induced people to interact with
309
others, and we consider that such interactions could be the main factor for higher generativity. On
310
the other hand, in urban areas, social network and the degree of interactions may be weak, al-
311
though more people happen to meet or come across one another. We believe that such differences
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in human interaction or networks between urban and rural areas is a key to explaining generativ-
313
ity. Greater attention seems to be required, particularly in urban areas, to induce people to have a
314
more generative expression in their daily life through institutional changes or some social device.
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First, we need to develop an extended public conversation about the responsibilities of the current
316
generation to future generations in urban areas. To this end, educational systems or public poli-
317
cies should be able to play vital roles in creating intergenerational linkages through its teaching
318
pedagogy and curriculum or in recommending a new type of urban life with the extended family
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through subsidies.
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Finally, we note some limitations to our study and future research. The study of generativity can
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be highly abstract, and this research does not fully address the details of rural-specific effects. In
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reality, rural-specific effects may be decomposed not only into ways of human interaction in daily
323
life but also other factors. Further data collection and analysis shall be required to confirm our
324
results and compare generativity among people of various ages in various societies. In the future,
325
we should collect more detailed data on human interactions, intergenerational transfer, attitudes of
326
rural and urban youth toward previous generations or elderly and other possible factors that may
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represent the wider differences between rural and urban areas. If such rich data are successfully
328
collected, new methodologies such as social network methods can be utilized to find the causes of
329
differences in generativity (Kim et al., 2015). We did not conduct such analysis or data collection
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because the main purpose of this research is to establish the relation between generativity and
331
urbanization of societies. These caveats notwithstanding, it is our belief that this research should
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be considered an important step in understanding societies that are becoming more urbanized and
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where intergenerational sustainability is claimed to be a pressing issue.
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Figure 1: The Map of Nepal
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Figure 2: Social value orientation measure by the slider method
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Instructions
In this task you have been randomly paired with another person, whom we will refer to as the other. This other person is someone you do not know and will remain mutually anonymous. All of your choices are completely confidential. You will be making a series of decisions about allocating resources between you and this other person. For each of the following questions, please indicate the distribution you prefer most by marking the respective position along the midline. You can only make one mark for each question.
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Your decisions will yield money for both yourself and the other person. In the example below, a person has chosen to distribute money so that he/she receives 50 dollars, while the anonymous other person receives 40 dollars.
There are no right or wrong answers, this is all about personal preferences. After you have made your decision, write the resulting distribution of money on the spaces on the right. As you can see, your choices will influence both the amount of money you receive as well as the amount of money the other receives.
You receive
Other receives
30
35
80
40
70
45
60
50
50
40
55
30
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Example: 60
20
85
You receive
85
85
85
1 Other receives
85
76
68
You receive
85
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Other receives
15
19
24
85
You
50
Other
40
85 You
24
15
93
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100
28
33
37
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46
50
Other
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Other
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54
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81
85
100
98
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Other
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Other receives
85
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You receive
3
50
85
0
41
d
2
59
85
70
10
an
a
65
You receive
50
54
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63
68
72
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81
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Other receives
100
89
79
68
58
47
36
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15
You receive
100
94
88
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75
69
63
56
50
Other receives
50
56
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69
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100
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100
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You
4
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3 4 5
6
7
8
2.00 0.00 10.00 1.00 5.00 1.00 42.00 43.00 34.00
0.00 0.00 1.00 0.00 1.00 0.00 5.00 8.00 5.00
5.00 1.00 16.00 1.00 6.00 1.00 72.00 72.00 65.00
1.62 0.58 13.07 0.63 4.809 0.39 37.91 40.23 36.41
Urban areas (268 subjects) SD Median Min Max 1.25 0.49 3.57 0.48 2.02 0.49 13.34 13.35 13.17
1.00 1.00 16.00 1.00 6.00 0.00 38.00 41.00 37.00
0.00 0.00 1.00 0.00 1.00 0.00 2.00 6.00 2.00
5.00 1.00 16.00 1.00 6.00 1.00 72.00 72.00 67.00
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The “SD” stands for standard deviation. The variable of age is defined as an ordered categorical variable of 0, 1, 2, 3, 4 and 5 where 0 is under 20 and 5 is above 60 and the rest is with an interval of 10 years. The variable of gender is a dummy variable that takes 1 when the subject is male and otherwise 0. The variable of education is defined as years of schooling. The variable of agriculture/employment is defined as 1 if engaged in agriculture or engaged in a service sector for employment. The variable of income is defined as an ordered categorical variable of 1, 2, 3, 4, 5, 6 with an interval of 250 USD where 6 represents as earning more than 1800 USD per year. The “SVO” represents a dummy variable taking 1 when a subject is prosocial and otherwise 0, based on SVO games. Generativity scores of subjects across areas and SVOs. Note that 197 (63) subjects are prosocial (proself) in rural areas, while 105 (163) subjects are prosocial (proself) in urban areas.
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1.09 0.49 3.40 0.27 2.10 0.43 12.63 12.32 12.57
Mean
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2.27 0.38 9.58 0.90 4.20 0.76 42.05 43.53 37.41
Rural areas (260 subjects) SD1 Median Min Max
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Age2 Gender3 Education4 Employment5 Income6 SVO7 Generativity8 Prosocial subjects Proself subjects
Mean
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Variables
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Table 1: Summary statistics of subjects’ sociodemographic information and SVOs
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Model 1
Model 2
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Table 2: Regression analysis of generativity Model 3
36.026*** 33.378*** 31.730*** (0.937) (3.061) (3.207) 1 SVOs (Prosocial = 1 & Proself = 0) 4.810*** 4.889*** 4.773*** (1.226) (1.227) (1.232) 2 Area dummy (Rural = 1 & Urban = 0) 2.383** 3.294** 3.129** (1.208) (1.415) (1.410) Gender −1.845 −1.445 (1.164) (1.182) Education 0.419** 0.391** (0.171) (0.171) Employment 0.414 −0.380 (1.641) (1.662) No. of household members −0.387 −0.282 (0.598) (0.592) Income −0.343 −0.270 (0.272) (0.274) Age −0.039 2.718* (0.556) (1.586) Age squared −0.630* (0.328)
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Constant
528 0.053
Observations R2 1
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528 0.071
528 0.077
The SVO represents a dummy variable of individual social value orientations that takes 1 when the individual is prosocial. Otherwise, it is zero. The area dummy takes 1 when the subject resides in the rural area and otherwise 0. The variables other than the SVO and area dummy follow the descriptions in table 1. Numbers in parentheses are robust standard errors ***= p < 0.01, **= p < 0.05 and *= p < 0.10.
26 Page 25 of 25
S0016-3287(17)30281-1 https://doi.org/doi:10.1016/j.futures.2018.09.003 JFTR 2354
To appear in: Received date: Revised date: Accepted date:
21-7-2017 9-7-2018 14-9-2018
Please cite this article as: Raja Timilsina, Koji Kotani, Yoshio Kamijo, Generativity and social value orientation between rural and urban societies in a developing country, (2018), https://doi.org/10.1016/j.futures.2018.09.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Yoshio Kamijo*,†
Abstract
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September 16, 2018
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Koji Kotani*,†,‡,§,¶
Raja Timilsina*
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Generativity and social value orientation between rural and urban societies in a developing country
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Generativity, concern and commitment for the next generation, is an important factor in the sustainable development of a society, as intergenerational sustainability is claimed to have been compromised over the last decades. Generativity emerges through both prosocial and proself behaviors characterized by social preferences and is now hypothesized to have decreased in some urban societies; this is referred to as the “generativity crisis.” However, little is known about how ongoing urbanization of competitive societies, i.e., capitalism, and social preferences are related to generativity. To this end, we conduct field experiments on social value orientation and administer a generative behavior checklist in two strata of Nepalese society: (1) the urban and (2) the rural. The analysis finds that prosociality and the rural-specific effect are the two major factors that increase people’s generativity, while a larger proportion of prosocial people are found in rural areas than in urban areas. Overall, these results suggest that generativity will decrease with further urbanization, changing the economic culture and orientation of the populace so that there is less concern for future generations.
Key Words: Social value orientation; prosocial; proself; generativity
*Research Institute for Future Design, Kochi University of Technology, Japan †School of Economics and Management, Kochi University of Technology ‡Urban
Institute, Kyusyu University of Business, Rikkyo University ¶Corresponding author, E-mail: [email protected]
§College
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We empirically characterize generativity in relation to social value orientation and urbanization. We elicit generativity, social value orientations and socioeconomic factors through household surveys in rural and urban areas of Nepal. Prosociality and rural-specific factors positively affect generativity. A larger proportion of people are prosocial in rural areas than in urban areas. Further urbanization changes cultures and people’s value orientation to be less generative for the future.
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Contents 2
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Introduction
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Methodology
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Results
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Discussion
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Conclusion
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Bibliography
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Nomenclature
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List of Figures
Nomenclature
NPR Nepalese rupee
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GBC Generative behavior checklist
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ANOVA One-way analysis of variance
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List of Tables
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SVO Social value orientation USD US dollar
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1
1
Introduction Generativity, the concern for and commitment to the current and next generations, is an im-
3
portant element in the sustainable development of a society; higher generativity within the current
4
generation induces people to educate and help the next generation and even the next (Erikson,
5
1963, Volckmann, 2014). Generativity is expressed in the daily practices of human interaction, for
6
example, charity, mentoring, nursing, volunteering, teaching, religion and political activity, for the
7
benefit of the next generation (McAdams and de St. Aubin, 1992). In reality, societies transition
8
over time in “overlapping generations” in the sense that the members of one generation survive
9
and remain as members of the next generations (Gaspar and Lauren, 2013). Unfortunately, it has
10
been claimed that the current generation behaves more selfishly than previous generations, and this
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behavior compromises generativity and sustainability, e.g., environmental (resource) sustainabil-
12
ity, incurring costs for the current and next generation, i.e., the “generativity crisis” (Sasaki, 2004,
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Fisher et al., 2004, Milinski et al., 2006, Pratt et al., 2013, Molnar and Vass, 2013, Jia et al., 2015,
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Lefebvre and Lefebvre, 2016). Thus, generativity is an urgent issue and essential if societies are to
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change for the benefit of the next generation.
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One of the most important issues today is the extent to which the interests of future generations
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must be addressed; however, a sustainable society must also satisfy the needs of the current gener-
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ation without jeopardizing the prospects of future generations, i.e., intergenerational sustainability
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(Howard, 2000, Masini, 2013, Tonn, 2009, 2018). The future studies literature has considered
20
and addressed these issues by providing institutional proposals and theoretical frameworks (Inay-
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atullah, 1997, Pino, 2007, Balazs and Gaspar, 2010, Chen, 2016, Seo, 2017, Tonn, 2018). How-
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ever, these studies do not empirically examine how intergenerational sustainability changes among
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people in society. “Generativity” can be considered one of the best proxies for intergenerational
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sustainability, and a possible predictor for generativity is a social value orientation suggested by
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Murphy et al. (2011) that measures people’s concern for others (Van Lange et al., 2007b, 2011,
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Sutterlin et al., 2013, Hauser et al., 2014, Timilsina et al., 2017). This study empirically addresses
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the relationship between individual social preferences and generativity in different societies.
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Generativity has been studied by many researchers, and the generative behavior checklist
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(GBC) is established as one of the most reliable and internally consistent measures (McAdams
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and de St. Aubin, 1992, McAdams et al., 1993, McAdams and de St. Aubin, 1995). Most studies
31
have sought to characterize the GBC as representing parts of innate human psychology, focusing on
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parenting, degree of well-being, life satisfaction and societal concern (Peterson and Stewart, 1993,
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Morfei et al., 2004, Huta and Zuroff, 2007, Newton et al., 2014). In particular, Hart et al. (2001)
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have empirically characterized generativity, finding that it has a positive association with social
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involvement related to parenting in both white and black Americans. Similarly, Hofer et al. (2008)
36
have confirmed that the psychological mechanisms of the generativity model are consistently ap-
37
plicable even in a cross-country comparison. In conclusion, these studies have demonstrated that
38
the GBC can explain behaviors and preferences of social involvement in relation to people’s innate
39
psychology, concerns and actual social behaviors.
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Economists and behavioral scientists have considered that the socioeconomic environment in-
41
fluences people’s social preferences and actual behaviors (Henrich et al., 2005, 2010, Van Lange
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et al., 2007b, Leibbrandt et al., 2013). Schotter and Sopher (2003) and Hauser et al. (2014) have
43
shown that the current generation can neither make sustainable decisions in an intergenerational
44
setting nor take the balance of costs and benefits for future generations when faced with an ex-
45
cessively competitive economic environment. Henrich et al. (2005, 2010) and Leibbrandt et al.
46
(2013) have demonstrated that people’s social behaviors and preferences are affected by the degree
47
of market integration in societies and workplace environments, respectively. Similarly, Ockenfels
48
and Weimann (1999) and Brosig-Koch et al. (2011) have analyzed people’s cooperative and soli-
49
darity behaviors in eastern and western Germany, demonstrating that subjects from eastern regions
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act more selfishly than those from western regions. They conclude that social history and socioe-
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conomic environment play an important role in shaping people’s social preferences and behaviors.
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In summary, the psychologists have addressed how generativity is associated with people’s innate
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psychology and actual behaviors, while the economists and behavioral scientists have determined
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how social preferences and behaviors are affected by economic environment.
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Rapid urbanization is taking place in Asia and Africa, and it is expected that 65 % - 75 %
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of the world population will be concentrated in urban cities in Asia and Africa, compromising a
57
large part of environmental and resource sustainability (Bauman, 2013, Wigginton et al., 2016,
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Henderson et al., 2016, McDonnell and MacGregor-Fors, 2016). Generativity is hypothesized to
59
be affected by such changes in societies, as generativity can be manifested through both prosocial
60
and proself behaviors originating from people’s social preferences (Kotre, 1984, McAdams, 1985).
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Since societies are becoming more competitive and urbanized in the globalized market economy,
62
it is expected that such cultural changes in societies might affect not only preferences but also
63
generativity (Tajfel and Turner, 1979, Passini, 2013). However, no previous study has empirically
64
addressed how generativity is evolving with economic development and changes in preferences.
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We consider ongoing urbanization of competitive societies as part of a culture and hypothesize
66
that generativity changes with urbanization and social preferences. To this end, we conduct field
67
experiments on social value orientation (SVO) and administer the generative behavior checklist
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(GBC) in two areas of Nepal: (1) urban and (2) rural, taking Nepal as a representative country that
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has experienced rapid urbanization.
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We implemented field experiments and questionnaire surveys in rural and urban areas and
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employed different approaches of random sampling because they possess distinct economic and
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sociodemographic characteristics. Kathmandu and Pokhara are chosen as urban areas, as they are
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the first and second largest urban societies in Nepal (figure 1). In the urban areas, we administered
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field experiments and surveys with 268 subjects. These cities are highly populated; most people
76
are engaged in the business, service and government sectors. To maintain random sampling of
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subjects, an occupation-based randomization procedure was performed. First, we identify the
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proportion of each occupational category as part of the total population of the urban areas by
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referring to governmental and international non-governmental reports such as Central Bureau of
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Statistics (2011) and UNDP (2014). Next, we randomly select 15 to 20 organizations or companies
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from each area and we send approximately 600 invitations letters to participate in our survey and
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experiment. Based on their compliance, we select individuals from these organizations so that
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subjects do not know one another in the same session. Our field experiments and surveys were
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carried out in the city and community halls of the urban areas.
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In the rural areas, we conducted field experiments and surveys with 260 subjects. Chitwan
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and Prabat are chosen as rural areas (figure 1). These districts consist of many small villages and
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are known as the least populated areas; most people engage in agriculture and forestry for their
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livelihood. In the rural areas, we conducted a household-level randomization procedure. First, we
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designated the number of samples for the selected villages based on the total number of households
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provided by each village development committee office. Each village consist of approximately
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150 to 400 number of households. After that, we selected the household number randomly and
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invited one household member by sending 400 invitation letters. Our monetary incentives and
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the conditions addressed in the invitation letters made it possible to collect a sufficient number of
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subjects. The field experiments and surveys were conducted in the schools of the rural areas.
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The SVO of the “slider method” was conducted to identify subjects’ social preferences as
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prosocial or proself in urban and rural areas (Murphy et al., 2011). Figure 2 shows six items of
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the slider measure that assign numbers to represent outcomes for oneself and the other for a pair
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of people, where the other is unknown to the subject. Subjects are asked to make a choice among
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nine options for each item. Each subject chooses her allocation by marking a line at the point that
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defines her most preferred distribution between oneself and the other. The mean allocation for
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oneself As and the mean allocation for the other Ao are computed from all six items (see figure 2).
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Then, 50 is subtracted from As , and Ao to shift the base of the resulting angle to the center of
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o )−50 the circle (50, 50). The index of a subject’s SVO is given by SVO = arctan (A . Depending (As )−50
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on the values generated from the test, social preferences are categorized as follows: 1. altruist:
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SVO > 57.15◦ , 2. prosocial: 22.45◦ < SVO < 57.15◦ , 3. individualist: −12.04◦ < SVO < 22.45◦
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and 4. competitive types: SVO < −12.04◦ . [Figure 2 about here.]
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The SVO framework assumes that people have different motivations and goals for evaluating
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resource allocations between oneself and others. Also, the SVOs or social preferences are estab-
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lished to be stable for a long time (see, e.g., Van Lange et al., 2007b, Brosig-Koch et al., 2011).
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Responses are yielded from six primary items that encompass the complete categories of social
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preferences. The major reasons for using the six primary slider measures of Murphy et al. (2011)
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are the simplicity and ease of implementation in the experimental settings of Nepal. Participants
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are able to understand it intuitively, even those with a limited level of education. As in the psy-
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chology research, we further simplify the four categories of social preferences into the prosocial
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and proself categories; “altruist” and “prosocial” types are categorized as prosocial subjects, while
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“individualistic” and “competitive” types are categorized as “proself” subjects (see Murphy et al.,
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2011).
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The GBC (generative behavior checklist) developed by McAdams and de St. Aubin (1992)
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checks the frequencies of the generative behaviors that each individual has engaged in previously.
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Specifically, the GBC asks how many times a person has performed 50 different behaviors, among
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which only 40 behaviors are suggestive of “generativity.”1 The examples are “taught somebody a
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skill,” “read a story to a child,” “served as a role model for a young person” and “made something
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for somebody and then gave it to them.” Subjects need to write “0” if they have not performed
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a specific generative behavior, “1” if they have performed the behavior once and “2” if they have
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performed the behavior more than once for the last one year. Scores on the 40 generative behaviors
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were summed for each subject to compute a total generativity score. In comparison with other
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psychological measures such as the Loyola Generativity Scale (LGS) and guided autobiography,
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we realize that GBC is easy for people to understand and respond to in the rural and urban areas of
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Nepal, which is a good proxy for the expression of people’s real behavior.
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The remaining 10 behaviors in the GBC questionnaire that are not counted as generativity are “fillers.”
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Considering the opportunity costs of time, we need to attract subjects to the experimental sites
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and motivate them to participate in the surveys and games. Therefore, we have incentivized the
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questionnaire surveys and the SVO game with monetary payments. In each session, we have
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collected 20 to 40 subjects in a site and provided experimental instructions to the subjects. Next,
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the experimenter (the first author) made oral presentations to confirm that the subjects understood.
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We also used six research assistants and helped the subjects. After eliciting the subjects’ SVOs, we
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conducted questionnaire surveys to collect individual sociodemographic information (See table 1
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for the definitions of the variables and the summary statistics).
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Subjects are paid on the basis of their earnings from the SVO game. After each subject has
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made his/her decisions, they write the resulting distribution of money on the spaces provided on
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the right-hand side of the SVO instruction sheets in figure 2. The total amount of points a subject
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is allocated by himself and by the other to oneself in the pair are calculated by summing the points
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of all six items. Depending on the points generated from the game, the points are converted into
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real money with an experimental exchange rate. In our experiment, we use 10 points equivalent to
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1 NPR. At the end, we randomly matched one subject with another to make pairs for calculating
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the total payoff of each subject and made payments. One session took 40 to 60 minutes, and
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the average payment was 200 NPR (approximately 2.10 USD) with a show-up fee of 100 NPR
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(1.05 USD).
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This study finally analyzes the association of generativity with people’s social preferences and
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the locations of two different areas. A dummy variable for controlling the urban and rural areas in
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the analysis is intended to represent different degrees of urbanization (equivalently, the degree of
153
capitalism) in societies. To characterize which social preference and society lead to higher levels of
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generativity, nonparametric statistical and regression analyses are employed. The Mann-Whitney
155
test is used to identify the distributional difference of generativity across the two areas and their
156
social preferences. The regression model estimates the marginal impact on generativity when a
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key predictor, such as SVOs and an area dummy, increases, holding other factors fixed. The set of
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independent variables includes SVOs, area dummy, household income, age, education, gender and
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employment.2 Table 1 summarizes the definition of the variables in the analysis.
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Results Table 1 summarize the statistics of subjects’ sociodemographic information and generativity,
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respectively. Table 1 shows that 38 % of the subjects are male in rural areas, while 58 % of the sub-
163
jects are male in urban areas. With respect to education, more than 50 % of subjects in urban areas
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have an undergraduate degree from a university (16 years of schooling as the median in table 1).
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On the other hand, subjects in rural areas possess 10 years of schooling as the median. Regard-
166
ing employment, 90 % and 63 % of subjects in rural and urban areas are employed and engaged
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in agriculture or in a service sector, respectively, where a majority of rural (urban) subjects are
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farmers (nonfarmers such as office workers). This implies that the urban areas in our field do not
169
depend on agriculture anymore, but rural areas are still agriculture-based or natural resource-based
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societies. Reflecting this difference of dependency on agriculture, household income is higher in
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urban areas than in rural areas (table 1). Overall, the summary statistics of sociodemographic in-
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formation in table 1 are in line with our initial expectations that urban societies are more advanced
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and urbanized in many aspects.
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[Table 1 about here.]
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Table 1 shows subjects’ SVOs to be prosocial or proself between the rural and the urban. The
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major difference can be seen in the “SVO” variable, indicating that 76 % of subjects in the rural
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areas are prosocial, while only 39 % of subjects are prosocial in urban areas. Specifically, 197
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out of 260 rural subjects are prosocial in rural areas, while 105 out of 268 urban subjects are
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prosocial in urban areas (table 1). The chi-square test reveals that subjects’ prosociality and areas
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are significantly dependent (χ2 = 72.16, p < 0.01). This implies that prosociality among people 2
Individual social preferences are established to remain the same for a long time (Van Lange et al., 2007b, BrosigKoch et al., 2011), while the GBC is a behavior checklist for the actions that subjects have taken over the last year. Therefore, taking SVOs as an independent variable in the regression of generativity does not cause any erogeneity problem or reverse causality.
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is different between rural and urban areas, and prosocial (proself) people are dominant in the rural
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(urban) areas. This SVO result appears to suggest that people tend to be more proself as societies
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are more urbanized and developed. Table 1 presents the summary statistics of subjects’ generativity. The mean generativity is
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different between rural and urban areas. The mean generativity in rural areas is 42.05 (SD =
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12.63), while that in urban areas is 37.91 (SD = 13.34). This implies that the mean of generativity
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in rural areas is higher than those in urban areas. We further analyze subjects’ generativity by
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SVOs in each area, for instance, the mean generativity of prosocial subjects in rural areas is 43.53
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(SD = 12.32), which is higher than that in urban areas, i.e., 40.23 (SD = 13.35). Similarly, the
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mean generativity for proself subjects in rural areas is 37.41 (SD = 12.57) which is higher than
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that for proself subjects in urban areas, i.e., 36.41 (SD = 13.17). Put simply, prosocials in rural
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areas, prosocials in urban areas, proselves in urban and proselves in rural areas are the descending
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orders of groups with respect to the central tendencies of generativity.3 Overall, this results suggest
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that prosociality and urban vs. rural areas are the keys to characterizing generativity.
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The one-way analysis of variance (ANOVA) is applied to examine whether there are any sta-
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tistically significant differences of the average generativities between every pair of groups with
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respect to SVOs and/or study areas. The null hypothesis is that the average generativity in one
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group is equal to that in the other group. We have applied the ANOVA to the following pairs of
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groups: (1) The rural vs. the urban, (2) the prosocial vs. the proself, (3) the prosocial in the rural vs.
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the proself in the rural, (4) the prosocial in the urban vs. the proself in the urban, (5) the prosocial
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in the rural vs. the prosocial in the urban and (6) the proself in the rural vs. the proself in the urban.
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We have identified that cases (1), (2) and (3) reject the null hypothesis at 1 % significance level
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(p < 0.01) with statistics of F1,526 = 13.40, F1,526 = 25.40 and F1,258 = 11.68, respectively. Cases
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(4) and (5) reject the null hypothesis at 5 % level (p < 0.05) with statistics of F1,266 = 5.33 and
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F1,300 = 4.63, respectively. The only pair that does not reject the null hypothesis even at the 10 %
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3
Regarding the medians of generativity, the almost same orders of groups follow in a descending order as the means do, except that the median generativity for proself subjects in rural areas is lower than that for proself subjects in urban areas. Overall, both means and medians of generativity as the central tendencies consistently demonstrate that both SVOs and areas are correlated with generativity.
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level is case (6) that comes with F1,224 = 0.27. Overall, these results of the ANOVA tests con-
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firm that average generativities between the rural and urban and/or between SVOs are statistically
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different, being consistent with table 1. For the robustness check, we also run Mann-Whitney tests for a pair of the same groups as
210
the ANOVA.4 The null hypothesis is that the generativity distributions are the same between the
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two groups. We have obtained that (1) the rural vs. the urban (Z = 3.404, p < 0.01), (2) the
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prosocial vs. the proself (Z = 4.890, p < 0.01), (3) the prosocial in the rural vs. the proself in
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the rural (Z = 3.322, p < 0.01); (4) the prosocial in the urban vs. the proself in the urban (Z =
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2.300, p = 0.02); (5) the prosocial in the rural vs. the proself in the urban (Z = 4.978, p < 0.01);
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and (6) the prosocial in the rural vs. the prosocial in the urban (Z = 1.896, p = 0.06). These
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results of the Mann-Whitney tests statistically confirm that generativity may be characterized by
217
prosociality in the areas between the rural and urban areas. Given the statistical significance of the
218
generativity across areas and SVOs with ANOVA and Mann-Whitney tests, we further characterize
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generativity by running the regression model, taking the generativity as a dependent variable and
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the area dummy between the rural and the urban, SVOs and other sociodemographic information
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as independent variables.
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[Table 2 about here.]
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Table 2 reports the estimated coefficients and their respective standard errors with statistical sig-
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nificance in the regression of generativity. Model 1 in table 2 contains SVOs and the area dummy
225
of the rural as independent variables. The result reveals that both variables exhibit statistical sig-
226
nificances of p < 0.01 and p < 0.01, respectively, and are positively associated with generativity.
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To further characterize generativity, we add sociodemographic variables such as gender, education,
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age, employment, number of household members and income in model 2 of table 2. We find that
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the SVOs and the area dummy remains statistically significant with the same sign and magnitude, 4
The Mann-Whitney test is a non-parametric statistical hypothesis test based on ranks such as medians, and does not need to assume the underlying distributions of the samples in a parametric form (Conover, 1999). Therefore, it is generally known that the results are robust irrespective of the actual distributions of the samples.
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and education has a statistically significant positive correlation with generativity (p < 0.01). How-
231
ever, the magnitude of education is rather small compared with that of the SVOs and area dummy.
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There are no significant associations of gender, employment, income and age in model 2. In model 3, with age squared added, both age and its squared variables are significant with
234
positive and negative signs (p < 0.05), respectively. This result implies that generativity reaches
235
its peak in midlife and starts to decline with old age. This is consistent with McAdams et al.
236
(1993), Newton et al. (2014) and Schoklitsch and Baumann (2012) demonstrating that this single-
237
peaked nature in generativity is due to health or physical weakness. Considering the consistent
238
results across models 1, 2 and 3, it appears that SVOs and the area dummy are strong predictors
239
of generativity. More specifically, in model 3, the generativity increases by 4.77 with a change in
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SVOs from being in the proself to being in the prosocial, and the generativity increases by 3.13 if
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the individual resides in rural as compared with residing in urban areas. Education is statistically
242
significant in model 3; however, the magnitude remains small.5
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In summary, our results show that there are more prosocial people in the rural areas; prosocial
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and rural people possess higher generativity. This suggests that as societies become more urban-
245
ized and competitive in a capitalistic way, people tend to be less concerned about others and even
246
future generations. Among several predictors, the strong ones for generativity is identified to be
247
SVOs and area dummy. In reality, generativity is expressed through daily practices and human
248
interactions such as charity, mentoring, nursing, volunteering, teaching, religious practice and po-
249
litical activities that guide the current and next generation (McAdams and de St. Aubin, 1992), and
250
there are two channels to increase generativity. One channel is a direct effect of urbanization on
251
generativity, which is the result of the difference between the rural and urban areas. The second
252
channel is an indirect effect of urbanization on generativity; that is, the urbanization of societies
253
induces people to be more proself, and generativity decreases through such a change in social pref-
254
erences or SVOs (Van Lange et al., 2007a,b, 2011). Since the magnitudes of the impacts from both
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5
For further robustness check, we have tried various specifications, but we confirm that the main results do not change. For instance, we have also added an interaction term of SVO and area dummy on top of the specifications in model 3. However, the interaction term is not significant and the main results remain the same as those in model 3.
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SVOs and area dummies are large, the generativity crisis in urban areas may well be explained by
256
these two factors.
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Discussion
There are differences between urban and rural areas in many respects, including the environ-
259
ment, customs, technology, physical space and architecture that influence social interaction. Pop-
260
ulation density is high in urban areas, and on a daily basis, people happen to meet or interact with
261
numerous strangers and colleagues. However, in many cases, their social ties and connections are
262
weak in the sense that there is less intimacy so that few act in concert (Beaudoin and Thorson,
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2004, Huddart-Kennedy et al., 2009, Macias and Nelson, 2011, Cope et al., 2016, Shahrier et al.,
264
2016, Timilsina et al., 2017). To make matters worse, they may even be suspicious of strangers and
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must compete with colleagues. These phenomena are not uncommon in the capital city of Nepal,
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Kathmandu.
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Rural areas are nature-dependent and life is lived on the basis of natural vegetation and local
268
fauna. Therefore, people’s lives are agriculture based or nature-dependent as well. In such rural
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societies, both cultural learning and vocational training are passed on by family, relatives and
270
neighbors because the transfer of such skills and knowledge must be made through close interaction
271
in the local human network to survive. Young people seek to mimic and learn standard behaviors
272
and beliefs from local communities, in particular from older people. Such transmission of norms
273
can also be considered the perpetuation of conformist biases that are accumulated from previous
274
generations and neighbors (See, e.g., Henrich and McElreath, 2003, Sasaki, 2004).
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Urban settlements have advanced civic amenities and opportunities for education as well as fa-
276
cilities for transport and business, and this standard of living does not induce people to interact with
277
others or neighbors. In terms of employment, there is a division of labor and job specialization,
278
and the number of opportunities leads to higher occupational mobility. Given these realities, we
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conjecture that the difference in how people interact with others leads to a change in generativity
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with urbanization and that the human network of intergenerational linkage and interaction in rural
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areas corresponds to a rural-specific effect on generativity in our statistical analysis. Urban areas are different in terms of their functionality and are classified according to land
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use and population density, but this can vary from developed countries to developing countries
284
(McDonnell and MacGregor-Fors, 2016). Asia and Africa will have the world’s largest and fastest
285
growing cities in the future, and most of this growth will occur in emerging and developing coun-
286
tries (Wigginton et al., 2016). Such growth might help to lift millions of people out of poverty, but
287
it may be true that it comes with huge social costs to future generations (Henderson et al., 2016).
288
There are several social, behavioral and economic challenges that need to be overcome to improve
289
urban settlements and future sustainability. An individual lifestyle and decisions on how to live in
290
urban areas directly affect societies, such as whether to unplug cell phones or use public transport
291
to travel to work or install solar panels on a roof. Each of these individual decisions that affects
292
other people are dependent on people’s concern for others, i.e., individual social preferences (Van
293
Lange et al., 2007a, 2011). More importantly, our findings suggest that not only social behaviors
294
but also generativity in an intergenerational setting are affected by individual social preferences
295
and ongoing urbanization.
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Conclusion
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This paper has explored how generativity is changing along with social preferences and the
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degree of capitalism in societies. To this end, we implemented a social value orientation (SVO)
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experiment and surveys of generative checklist questionnaires in two fields of Nepal: (i) urban and
300
(ii) rural areas. The results show that mainly individual generativity might have been positively
301
affected by two channels, i.e., prosociality and the rural-specific effect. Since we have found a
302
higher proportion of prosocial people in rural areas than in urban areas, we propose that as soci-
303
eties are getting more urbanized and developed in a capitalistic way, generativity shall be further
304
compromised through changes in social preferences and economic environments of urbanization.
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Our research shows evidence that economic environments may affect people’s preferences for
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and even behaviors toward intergenerational sustainability. We conjecture that the difference in
307
daily life between rural and urban areas influences how people interact with others including fam-
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ily, relatives, neighbors and strangers. Rural areas might have induced people to interact with
309
others, and we consider that such interactions could be the main factor for higher generativity. On
310
the other hand, in urban areas, social network and the degree of interactions may be weak, al-
311
though more people happen to meet or come across one another. We believe that such differences
312
in human interaction or networks between urban and rural areas is a key to explaining generativ-
313
ity. Greater attention seems to be required, particularly in urban areas, to induce people to have a
314
more generative expression in their daily life through institutional changes or some social device.
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First, we need to develop an extended public conversation about the responsibilities of the current
316
generation to future generations in urban areas. To this end, educational systems or public poli-
317
cies should be able to play vital roles in creating intergenerational linkages through its teaching
318
pedagogy and curriculum or in recommending a new type of urban life with the extended family
319
through subsidies.
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Finally, we note some limitations to our study and future research. The study of generativity can
321
be highly abstract, and this research does not fully address the details of rural-specific effects. In
322
reality, rural-specific effects may be decomposed not only into ways of human interaction in daily
323
life but also other factors. Further data collection and analysis shall be required to confirm our
324
results and compare generativity among people of various ages in various societies. In the future,
325
we should collect more detailed data on human interactions, intergenerational transfer, attitudes of
326
rural and urban youth toward previous generations or elderly and other possible factors that may
327
represent the wider differences between rural and urban areas. If such rich data are successfully
328
collected, new methodologies such as social network methods can be utilized to find the causes of
329
differences in generativity (Kim et al., 2015). We did not conduct such analysis or data collection
330
because the main purpose of this research is to establish the relation between generativity and
331
urbanization of societies. These caveats notwithstanding, it is our belief that this research should
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be considered an important step in understanding societies that are becoming more urbanized and
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where intergenerational sustainability is claimed to be a pressing issue.
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Figure 1: The Map of Nepal
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Figure 2: Social value orientation measure by the slider method
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Instructions
In this task you have been randomly paired with another person, whom we will refer to as the other. This other person is someone you do not know and will remain mutually anonymous. All of your choices are completely confidential. You will be making a series of decisions about allocating resources between you and this other person. For each of the following questions, please indicate the distribution you prefer most by marking the respective position along the midline. You can only make one mark for each question.
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Your decisions will yield money for both yourself and the other person. In the example below, a person has chosen to distribute money so that he/she receives 50 dollars, while the anonymous other person receives 40 dollars.
There are no right or wrong answers, this is all about personal preferences. After you have made your decision, write the resulting distribution of money on the spaces on the right. As you can see, your choices will influence both the amount of money you receive as well as the amount of money the other receives.
You receive
Other receives
30
35
80
40
70
45
60
50
50
40
55
30
us
Example: 60
20
85
You receive
85
85
85
1 Other receives
85
76
68
You receive
85
87
89
91
Other receives
15
19
24
85
You
50
Other
40
85 You
24
15
93
94
96
98
100
28
33
37
41
46
50
Other
You
Other
50
54
59
63
68
72
76
81
85
100
98
96
94
93
91
89
87
85
Other
You
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Other receives
85
33
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You receive
3
50
85
0
41
d
2
59
85
70
10
an
a
65
You receive
50
54
59
63
68
72
76
81
85
Other receives
100
89
79
68
58
47
36
26
15
You receive
100
94
88
81
75
69
63
56
50
Other receives
50
56
63
69
75
81
88
94
100
You receive
100
98
96
94
93
91
89
87
85
Other receives
50
54
59
63
68
72
76
81
85
You
4
Other
You
5
Other
You
6 Other
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3 4 5
6
7
8
2.00 0.00 10.00 1.00 5.00 1.00 42.00 43.00 34.00
0.00 0.00 1.00 0.00 1.00 0.00 5.00 8.00 5.00
5.00 1.00 16.00 1.00 6.00 1.00 72.00 72.00 65.00
1.62 0.58 13.07 0.63 4.809 0.39 37.91 40.23 36.41
Urban areas (268 subjects) SD Median Min Max 1.25 0.49 3.57 0.48 2.02 0.49 13.34 13.35 13.17
1.00 1.00 16.00 1.00 6.00 0.00 38.00 41.00 37.00
0.00 0.00 1.00 0.00 1.00 0.00 2.00 6.00 2.00
5.00 1.00 16.00 1.00 6.00 1.00 72.00 72.00 67.00
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The “SD” stands for standard deviation. The variable of age is defined as an ordered categorical variable of 0, 1, 2, 3, 4 and 5 where 0 is under 20 and 5 is above 60 and the rest is with an interval of 10 years. The variable of gender is a dummy variable that takes 1 when the subject is male and otherwise 0. The variable of education is defined as years of schooling. The variable of agriculture/employment is defined as 1 if engaged in agriculture or engaged in a service sector for employment. The variable of income is defined as an ordered categorical variable of 1, 2, 3, 4, 5, 6 with an interval of 250 USD where 6 represents as earning more than 1800 USD per year. The “SVO” represents a dummy variable taking 1 when a subject is prosocial and otherwise 0, based on SVO games. Generativity scores of subjects across areas and SVOs. Note that 197 (63) subjects are prosocial (proself) in rural areas, while 105 (163) subjects are prosocial (proself) in urban areas.
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2
1.09 0.49 3.40 0.27 2.10 0.43 12.63 12.32 12.57
Mean
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1
2.27 0.38 9.58 0.90 4.20 0.76 42.05 43.53 37.41
Rural areas (260 subjects) SD1 Median Min Max
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Age2 Gender3 Education4 Employment5 Income6 SVO7 Generativity8 Prosocial subjects Proself subjects
Mean
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Variables
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Table 1: Summary statistics of subjects’ sociodemographic information and SVOs
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Model 1
Model 2
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Table 2: Regression analysis of generativity Model 3
36.026*** 33.378*** 31.730*** (0.937) (3.061) (3.207) 1 SVOs (Prosocial = 1 & Proself = 0) 4.810*** 4.889*** 4.773*** (1.226) (1.227) (1.232) 2 Area dummy (Rural = 1 & Urban = 0) 2.383** 3.294** 3.129** (1.208) (1.415) (1.410) Gender −1.845 −1.445 (1.164) (1.182) Education 0.419** 0.391** (0.171) (0.171) Employment 0.414 −0.380 (1.641) (1.662) No. of household members −0.387 −0.282 (0.598) (0.592) Income −0.343 −0.270 (0.272) (0.274) Age −0.039 2.718* (0.556) (1.586) Age squared −0.630* (0.328)
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Constant
528 0.053
Observations R2 1
2
528 0.071
528 0.077
The SVO represents a dummy variable of individual social value orientations that takes 1 when the individual is prosocial. Otherwise, it is zero. The area dummy takes 1 when the subject resides in the rural area and otherwise 0. The variables other than the SVO and area dummy follow the descriptions in table 1. Numbers in parentheses are robust standard errors ***= p < 0.01, **= p < 0.05 and *= p < 0.10.
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