Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing

Begashe, Betty Amos; Matotola, Salum; Mgonja, John Thomas Article Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Begashe, Betty Amos; Matotola, Salum; Mgonja, John Thomas (2024) : Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-16, https://doi.org/10.1080/23311975.2024.2340127 This Version is available at: https://hdl.handle.net/10419/326236 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing Betty Amos Begashe, Salum Matotola & John Thomas Mgonja To cite this article: Betty Amos Begashe, Salum Matotola & John Thomas Mgonja (2024) Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing, Cogent Business & Management, 11:1, 2340127, DOI: 10.1080/23311975.2024.2340127 To link to this article: https://doi.org/10.1080/23311975.2024.2340127 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 22 Apr 2024. Submit your article to this journal Article views: 1027 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Marketing | research article Cogent Business & ManageMent 2024, VoL. , no. 1, 2340127 Demographic determinants of wildlife attraction selection among international return tourists in Tanzania: implications for strategic marketing Betty amos Begashea , salum Matotolaa and John thomas Mgonjab aDepartment of Business administration and Management, the university of Dodoma, Dodoma, tanzania; bDepartment of tourism and Recreation, sokoine university of agriculture, Morogoro, tanzania ABSTRACT this study examines the demographic determinants for the choice of wildlife attractions among international repeat tourists in tanzania to provide valuable insights for destination planning and marketing. the study employed binary logistic regression analysis and cross-tabulation to assess the influence of demographic characteristics on the selection of wildlife attractions from the total 1550 international repeat tourists. the demographic attributes of tourists, including family size, employment status, income level, and region of origin, exert a substantial impact on the selection of the wildlife attraction, as indicated by a significant p-value <0.05. While individuals with medium-level annual income, retirees, students, and those individuals who are from asia and north america are drawn to wildlife experiences. Unlike, tourists residing from small size family (less than members) and those who are coming from south america, show reduced interest in wildlife attractions. Moreover, the results from the cross-tabulation revealed that individuals with a moderate annual income, especially those who are from large family sizes, exhibit a more pronounced preference for wildlife. additionally, the findings indicated that repeat tourists showing a higher inclination towards wildlife are individuals who are retired and have a family size exceeding three members. these findings shed light on possibilities for marketers to enhance their strategies, ensuring that information regarding the diverse range of wildlife offerings and new wildlife packages reaches individuals displaying a strong inclination for repeat visits. 1. Introduction tourism, a multifaceted industry encompassing travel, leisure, and exploration, holds immense global importance (UnWtO, 2021). tourism fuels growth by generating revenue, fostering job creation, and bolstering foreign exchange earnings (UnWtO, 2021). Beyond economics, tourism promotes cross-cultural understanding, preserves heritage, and nurtures community development (Olalere, 2019). also, it can drive infrastructure enhancements and environmental conservation efforts, while serving as a platform for education, research, and diplomatic engagement (ahmed & Jahan, 2013). setting aside the industry’s standpoint, intense rivalry prevails among tourist destinations, thereby exposing attractions at significant risks (almeida-santana & Moreno-gil, 2018; Okamura & Fukushige, 2010; Oppermann, 2000). to avoid exposure to risks, the recommendation put forward is to focus on retaining its loyal customers (almeida-santana & Moreno-gil, 2018). a fundamental assumption underlying this perspective is that repeat visitors tend to be more financially advantageous (e.g. due to reduced marketing costs) and that their positive word of mouth (WOM) is vital for drawing in new tourists to the destination (Čaušević et al., 2020; lim et al., 2016). Moreover, retaining tourists is considered more cost-effective than attracting new ones (Oppermann, 2000). © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Betty amos Begashe b[email protected] Department of Business administration and Management, the university of Dodoma, Dodoma, tanzania https://doi.org/10.1080/23311975.2024.2340127 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 4 February 2024 revised 28 March 2024 accepted 30 March 2024 KEYWORDS Demographic characteristics; international tourists; repeat tourists; tourists choices; wildlife attractions REVIEWING EDITOR len tiu Wright, De Montfort University Faculty of Business and law, United kingdom of great Britain and northern ireland SUBJECTS hospitality; Marketing communications; the hospitality industry; hospitality Marketing 2 B. a. Begashe etal. Despite the importance of repeat tourists, tanzania faces the challenge of attracting a satisfactory number of repeat tourists compared to its competitors, such as kenya and south africa. For instance, between 2016 and 2019, tanzania received an average of 20% repeat tourists, compared to south africa and kenya, which received an average of 80 and 42% repeat tourists, respectively (Musembi et al., 2020; sia, 2020; tri, 2020). knowingly, the country contains varieties and diversity of natural attractions over its competitors; kenya and south africa (Travel & Tourism Competitive Report, 2019). the expectation was, due to the availability of unique and diversity of natural attractions including wildlife, mountains, beach, and forest reserves which primarily attract tourists to the destination, tanzania should gain a competitive advantage over its competitors by attracting enough number of tourists (Wamboye et al., 2020). tourism in tanzania is highly focused on natural resources and the country is recognized as a wilderness and safari capital of the east african. in line with that, the country is positioned as one of the top 25 global biodiversity hotspots in terms of the number of endemics species (Travel & Tourism Competitive Report, 2019). reports show that tanzania tourism depends much on wildlife attractions as 36.4% of tourists who visit tanzania, prefer to visit wildlife attractions (Tanzania Tourism Sectoral Survey Report, 2019). this is due to the availability of varieties of wildlife areas ranging from national parks, game reserves, game-controlled areas, and conservation areas which offer diverse of experiences to tourists. these areas are the homes of wildlife animals which attracts thousands of first timer and repeat tourists to visit the country for leisure, recreation, and education purposes. holding those significances, it validate the need to have a clear understanding of this attraction segment especially its driving force to enhance the likelihood of repeated visits in the country (ayazlar, 2017; Moorhouse et al., 2017). through empirical investigation, it has been established that factors like tourists’ contentment, the quality of services, socio-demographic traits, the perception of destination reputation, tourists’ prior encounters, and the attributes of the destination are identified as precursors to repeat visits within the destination (Matolo etal., 2021; Mlozi, 2014; Mlozi & Pesämaa, 2013; Perovic etal., 2018; tan & Wu, 2016; tosun et al., 2015; Wadood et al., 2020). to the best of the authors’ knowledge, there is a scarcity of studies conducted on repeat visitation to wildlife attractions in tanzania. additionally; the factors that influence tourists to choose wildlife attractions during their repeat visits are not known. scholars recommended that marketers and tour providers should have a clear understanding of the nature of the market that they save (Mckercher etal., 2012). this involves having the knowledge of who exactly are these repeat tourists who prefer wildlife attractions during repeat visitation to the country. according to literature, tourists’ choices within the destination can be controlled by their demographic characteristics (n. kara, 2016; Oh etal., 2004; Padrón-Ávila & hernández-Martín, 2019; tomić et al., 2019; Valek et al., 2014). in line with that almeida-santana and Moreno-gil (2018) suggested that, for a destination to gain a competitive advantage, tour providers and marketers must thoroughly grasp the characteristics of the market they serve. tourists choices can be affected by demographic characteristics, such as age, gender, occupation, income (kattiyapornpong & Miller, 2009; n. kara, 2016; Valek et al., 2014). Furthermore, in the body of knowledge, little is known about the influence of socio-demographic characteristics on the choice of wildlife attractions among international repeat tourists in tanzania. available studies put more focus on the choice of activities (n. kara, 2016); and the choice of wildlife for domestic tourists (Mariki et al., 2012). therefore, the main interest of the current study is to investigate the influence of demographic characteristics, including age, gender, family size, marital status, occupation, income level, and region of origin on the selection of wildlife attractions among international repeat tourists in tanzania. the study aims to provide valuable insights into destination planning and marketing strategies in the tourism industry. 2. Literature review 2.1. Wildlife tourism in Tanzania Wildlife tourism involves travelers journeying from their homes to observe and engage in various activities related to wild animals (scott & higginbottom, 2004). these activities encompass bird watching, cOgent BUsiness & ManageMent 3 game viewing, and photography, primarily designed to provide tourists with experiences related to wildlife, birds, vegetation, and ecosystems (curtin & kragh, 2014; scott & higginbottom, 2004). Moreover, wildlife provides tourists with an opportunity to gain knowledge about biodiversity and the interconnected relationships between animals and plants (curtin & kragh, 2014; scott & higginbottom, 2004). the intersection of tourism and wildlife attractions forms a symbiotic relationship that holds profound implications for both the natural world and the travel industry (curtin & kragh, 2014). Wildlife attractions act as significant draws for tourists seeking unique and immersive experiences globally (Fennell & Yazdan Panah, 2020). People are naturally drawn to observe and interact with wildlife in their natural habitats or well-designed enclosures (Fennell & Yazdan Panah, 2020). as a result, these attractions become focal points for tourism campaigns, attracting tourists from around the world (scott & higginbottom, 2004). tanzania’s wildlife tourism stands as a prominent and internationally acclaimed sector, drawing global travelers to engage with the nation’s remarkable natural magnificence and diverse animal life (Wade etal., 2001). Positioned in east africa, tanzania boasts a spectrum of iconic wildlife species and renowned natural wonders, positioning it as an ideal destination for nature enthusiasts, photographers, and those captivated by the outdoors (Wade etal., 2001; Wamboye etal., 2020). among its principal wildlife attractions includes, serengeti national Park, which is globally recognized as one of the most diverse and well-known conservation areas for wildlife (Figure 1). the park gains its renown from the annual great Migration, a remarkable spectacle where expansive herds of wildebeests traverse vast distances in pursuit of fresh grazing, captivating tourists seeking to witness this awe-inspiring event (eagles etal., 2006). Furthermore, the ngorongoro conservation area emerges as another magnet, encompassing the ngorongoro crater, a volcanic caldera that provides a distinctive and varied habitat for an extensive array of wildlife species (Figure 1). this area is often dubbed the ‘eighth Wonder of the World’, celebrated for its incredible biodiversity and the harmonious coexistence of human communities alongside wildlife (Masao et al., 2015). Mount kilimanjaro national Park, while primarily acclaimed for its majestic snow-crowned peak, extends its offerings to encompass wildlife experiences at lower elevations (Figure 1). those who journey Figure 1. Map showing tanzania national Parks, game reserves, and conservation areas. Source: tanzania national Parks Website. 4 B. a. Begashe etal. through its different ecological zones encounter distinct flora and fauna, adding to the allure of the expedition (adili & robert, 2016). similarly, the tarangire national Park distinguishes itself with substantial elephant populations, stately baobab trees, and an array of bird species. Providing an exceptional and less crowded safari venture in comparison to more famed parks, it ensures a distinctive encounter with nature (sachedina, 2006). lake Manyara national Park, although relatively compact, garners attention for its abundant biodiversity (Figure 1). the park plays host to a diverse collection of wildlife, including elephants, giraffes, zebras, buffalo, lions, leopards, and an array of bird species. notably, the park is acclaimed for its distinctive tree-climbing lions, a phenomenon that adds a unique dimension to the region’s allure (Okello & Yerian, 2009). therefore, due to the fact that 36.4% of tourists who visit tanzania prefer to visit wildlife (tanzania tourism sectoral survey report, 2019), it is fruitful to have a clear understanding of traits and their drivers towards visiting wildlife attractions especially for international repeat tourists. 2.2. International repeat tourists’ tourists who revisit a destination on multiple occasions are commonly referred to as repeat tourists (Juaneda, 2016). the term ‘visited destination’ can encompass various locations, including countries, regions, districts, villages, sites, or attractions (Pike, 2007). the concept of repeat visitation is rooted in the idea of tourism destination loyalty, which represents a behavioral inclination for a tourist to return to a specific destination (Oppermann, 1999). as defined by Oppermann (1999, pp. 78–84), tourism destination loyalty is characterized as ‘a biased (i.e. nonrandom) behavioral response, expressed over time, by an individual with respect to one or more alternative holiday destinations’. it stems from both the psychological tendencies and the attitudes of individuals toward revisiting a destination for future holidays. consequently, within consumer research, the term ‘customer loyalty’ is frequently gauged through indicators, such as the ‘intent to continue purchasing the same product’, ‘intent to make repeat purchases of the same product’ (behavioral measures), or ‘willingness to recommend the product to others’ (an attitudinal indicator reflecting product advocacy) (Oppermann, 1999). Wildlife tourism and repeat visits are intertwined aspects of the broader travel industry, each influencing and complementing the other in unique ways (Okello & Yerian, 2009). Wildlife tourism, which involves experiencing and engaging with natural environments and diverse animal species, often plays a significant role in encouraging repeat visits to specific destinations (curtin & kragh, 2014; Fennell & Yazdan Panah, 2020). hence, because of the interrelatedness of these two elements, it is beneficial to gain a more profound comprehension of the attributes and driving factors influencing international repeat tourists when it comes to wildlife experiences. 2.3. Socio-demographic characteristics and the choice of wildlife attraction From a tourism perspective, socio-demographic factors are defined as a descriptive segmentation technique (Mazilu & sabrina, 2010, p. 159). socio-demographic variables are normally used as a market segmentation approach, whereby individuals are segmented based on gender, age, region of origin, occupation, education level, income, household size, and family size Mkwizu (2018). according to kara and Mkwizu (2020) and Mkwizu (2018), age, gender, family life cycle, education, income, and nationality are examples of socio-demographic factors commonly used by tourism experts. these variables are believed to be accurate in describing the tourism market and predicting travel behavior patterns (hughes, 2001). traditionally, the studies of destination choices aim to provide the link between a tourist’s characteristics and what the destination has to offer (kalenjuk, 2022; kattiyapornpong & Miller, 2009; M. n. kara, 2016; Mak & Jim, 2019). according to almeida-santana and Moreno-gil (2018), it is very important for destination managers and tour providers to understand the nature of the market that they serve. the understanding of this part is very important because it will enable them to design and provide products and services according to the market demand. this will ensure tourist satisfaction and encourage repeat cOgent BUsiness & ManageMent 5 business. Furthermore, based on the aim of this study, the understanding of how tourists’ socio-demographic characteristics influence the choice of wildlife attractions among international repeat tourists is very useful for the country given this competitive edge (kara & Mkwizu, 2020). several studies proved the effectiveness of demographic characteristics in predicting tourists decision making in the destination (almeida-santana & Moreno-gil, 2018; Mohsin & ryan, 2004; n. kara, 2016; Odunga, 2005; Padrón-Ávila & hernández-Martín, 2019; tomić et al., 2019). Focusing on the choice of attractions; studies found that, demographic characteristics have a direct relationship with the choice of specific attractions within the destination. the attractions include those which are related to, sports (Valek et al., 2014); shopping (Oh et al., 2004); food (kalenjuk, 2022); nature based (Meric & hunt, 1998); island (Padrón-Ávila & hernández-Martín, 2019); and culture (kim et al., 2007). Demographic characteristics, such as education and income tend to influence tourists’ choice for sports, shopping, nature, island, and cultural attractions. For instance, Padrón-Ávila and hernández-Martín (2019) found that older tourists had greater probabilities than younger ones of visiting cultural attractions. On another hand, Meric and hunt (1998) revealed that ecotourists tend to be middle age with higher education and income levels than the tourists who travel for other purposes. in addition apart from age and education kalenjuk (2022) also found that the tourist’s preferences for local food differ depending on their region of origin. Unlike factors age and gender has a major influence on individual shopping behavior (Oh et al., 2004). generally, in the body of literature, there are plenty of studies that explain the relationship between tourist’s socio-demographic characteristics and destination offerings. Focusing on the influence of demographic characteristics on the choice of wildlife attractions among international repeat tourists is the main focus of this study. there is a limited study that has been conducted on this area, especially in tanzania context. Previous studies failed to capture how tourists’ socio-demographic characteristics, such as age, gender, family size, marital status, occupation, income level, and region of origin can influence the choice of wildlife attractions among international repeat tourists in tanzania as a tourist destination. Understanding this relationship will enable tourism marketers and tour providers to have a clear understanding of the market that they serve based on their socio-demographic characteristics. 3. Methods 3.1. Research design, study area, and sample size the study employs a cross-sectional research design to collect data from international tourists who make repeat visits to tanzania. in a broader sense, a cross-sectional design involves gathering data from a subgroup of a population at a single point in time (Mann, 2003). this approach offers significant advantages in terms of time and cost efficiency, making it particularly suitable for research involving multiple variables and a large number of participants. 3.2. Sampling design and data collection this paper aims to present the findings of survey data collected from a sample of 1550 international repeat tourists during the period spanning from november 2022 to august 2023. this period covers two tourism seasons in tanzania; high and low season. the collection of data for both seasons enables the researcher to get the diversity of tourist’s preferences in terms of attractions from different seasons. additionally, M. n. kara (2016) recommended that tourists who travel in different seasons might be different in terms of their choice behavior within the destination. a convenience sampling procedure was employed to get the appropriate respondents for the study. it is known that convenience sampling has inherent limitations, primarily related to potential sampling biases and constraints in data collection. to reduce these limitations Ferber (1977) and M. n. kara (2016) noted that convenience sampling is one of the forms of non-probability sampling which can be used when there is a control in the research design to reduce the impact of non-random sampling by making sure that the generated findings are from the true representative of the population. to enhance that, the data were collected from the major international airports in tanzania. these airports are known as the major exit point for international tourists (Tanzania Tourism Sectoral Survey Report, 2019). therefore, the 6 B. a. Begashe etal. chances of having a significant effect on the results for not adopting a probability sampling technique are very high. additionally, convenience sampling is one of the appropriate sampling techniques to be used when collecting data from actual tourist settings. Other studies that used convenient sampling include the study conducted by M. n. kara (2016) which assesses the influence of socio-demographic characteristics on tourists’ choices of activities in the tanzania northern circuit. also Mgonja etal. (2017) employed a convenience sampling method to assess international visitors’ perceptions about local foods in tanzania. the questionnaire survey was disseminated using a drop-and-pick approach at three key international airports: Julius nyerere international airport (Jnia), kilimanjaro international airport (kia), and abeid aman karume international airport (aakia). these airports were chosen because they serve as the primary exit points for international tourists visiting both tanzania Mainland and Zanzibar. international tourists who were found at the departure lounge were approached conveniently and kindly asked to take part in the survey exercise. the main intention of the study was introduced to tourists and the decision to participate in the study was left entirely to tourists. therefore, tourists who agreed to participate in the study were given a survey questionnaire to fill. to ensure a variety of responses and avoid redundancy, in cases where tourists traveled as a group, a single respondent was chosen from each group to represent the collective views. according to this study travel group were the group of tourists who plan and travel together without anyone left behind thought the whole trip. every group that entered the departure terminal was traced by a researcher. Once the group made it through security check and seat at the departure lounge, one person was requested to fill out the survey on behalf of other members. international repeat tourists who were found at the departure lounge at the time the researcher was collecting data were considered as a sample. Because the study covered two seasons, the researcher managed to collect data from 1594 respondents. Forty-four respondents didn’t fill the questionnaire effectively therefore those questionnaires were discarded as incomplete. therefore, the remaining 1550 completed questionnaires were used for analysis. 3.3. Variable measurements the questionnaire was divided into two major parts. the general information about respondents including their socio-demographic details, such as age, sex, family size, region of origin, marital status, educational attainment, and income level, was collected in the first part. these demographic variables were selected firstly; due to limited studies conducted on their implications on the choice of wildlife pattern in the context of tanzania as explained previously. secondly; the chosen demographic characteristics are considered to be more effective in segmenting tourists in relation to their behavior within the destination (kara & Mkwizu, 2020; M. n. kara, 2016; Ma etal., 2018). therefore, by assessing the influence of the selected demographic characteristics on the choice of wildlife attractions marketers and promoters will be able to segment wildlife repeat tourists based on their family size, employment status, income level, and region of origin. the dependent variable was measured as a binary variable ‘0 or 1’. 0 stands for otherwise and 1 stands for the act of tourists to choose wildlife attractions during their repeat visit to tanzania. it also represents only a behavior of returning for subsequent visits, aligning with previous research in this domain (alegre & cladera, 2006; kastenholz et al., 2013). Before carrying out the exercise of data analysis, several diagnoses were tested to validate the assumptions underlying the binary logistic regression model as follows: 3.4. Diagnostic checks Diagnostics play a crucial role in assessing the assumptions of a binary logistic regression before fitting the model. these assumptions, including linearity of logit (log odds), no perfect multicollinearity, and no influential outliers, are essential for providing reliable estimates of the binary logistic regression model. in this study, diagnostic checks were performed to ensure that any violations of the assumptions were identified and addressed appropriately before fitting the binary logistic regression model. cOgent BUsiness & ManageMent 7 3.4.1. Linearity of logit (log odds) this assumption requires that the relationship between the continuous independent variables and the log odds of the dependent variable is linear. the Box-tidwell transformation is a useful diagnostic tool in logistic regression for assessing the linearity assumption and determining if additional model adjustments are needed, hence was used to check for linearity of logit in this study. to assess the linearity of logit we examine the significance of the transformed continuous independent variables in the model. if the transformed variable is significant, it suggests that the relationship between the original independent variables and the log odds of the dependent variable is not linear. in this case, the transformed variable (age of respondent) was not significant (p = 0.491), indicating linearity of logit as shown in table 1 below. 3.4.2. Multicollinearity Variance inflation Factor (ViF) and tolerance values were used to assess the presence of multicollinearity. high ViF values for independent variables suggest multicollinearity, indicating that certain variables are highly correlated. ViF values <10 (ideally close to 1) and tolerance values >0.1 are generally considered acceptable. in this case, the ViF and tolerance values are reasonable, indicating no severe multicollinearity issues as shown in table 2 below. 3.4.3. Outlier and influential data points cook’s distance, Mahalanobis distance, standard and studentized residuals were used to assess the presence of outlier and influential data points. Based on cook’s and Mahalanobis distance, the points with high cook’s and Mahalanobis distance were considered influential. in our case, a relatively small distance ranging from 0.000 to 0.039 and from 0.147 to 10.897 for cook’s and Mahalanobis distance, respectively was observed, suggesting no influential outliers. Moreover, standardized residuals, which indicate the number of standard deviations an observation’s residual deviates from the mean, exhibit a range from −2.774 to 2.711 suggesting the absence of Table 1. Multiple binary logistic regression model with the Box-tidwell transformation. Variable Coefficient ( β )std error 95% Ci p-Value Constant 2.09111 1.207351 [−0.27525, 4.45747] 0.083 age −0.09528 0.1371 [−0.36399, 0.17343] 0.487 age*ln (age) 0.019747 0.028667 [−0.03644, 0.07593] 0.491 Marital status single Ref Married 0.078145 0.150077 [−0.216, 0.37229] 0.603 others 0.025655 0.296552 [−0.55558, 0.606887] 0.931 Household size Below 3 (small size) −0.80574 0.140101 [−1.08034, −0.53115] <0.001 above 3 (large size) Ref Income inc1 0.252868 0.232612 [−0.20304, 0.70878] 0.277 inc2 0.544339 0.229809 [0.09392, 0.99476] 0.018 inc3 0.895896 0.251931 [0.40212, 1.38967] <0.001 inc4 0.195079 0.317762 [−0.42772, 0.81788] 0.539 inc5 Ref Occupation employed Ref Retired 0.461642 0.215448 [0.039372, 0.88391] 0.032 student 0.528782 0.339216 [−0.13607, 1.19363] 0.119 Gender Female Ref Male −0.1367 0.13703 [−0.40528, 0.13187] 0.318 Region of entry europe Ref north-america 0.650694 0.270749 [0.12004, 1.181352] 0.016 south-america −0.59741 0.289407 [−1.16464, −0.03018] 0.039 asia 0.868573 0.384085 [0.11578, 1.62137] 0.024 sub-saharan africa 0.322426 0.241085 [−0.15009, 0.79494] 0.181 Education level Primary education −0.39052 0.567958 [−1.5037, 0.72265] 0.492 secondary education Ref College education −0.28868 0.282114 [−0.84161, 0.26426] 0.306 university education −0.11187 0.256045 [−0.61371, 0.389972] 0.662 14 B. a. 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