Research article Special Issues

Banking market competition in Europe—financial stability or fragility enhancing?

  • Received: 30 March 2019 Accepted: 20 May 2019 Published: 27 May 2019
  • JEL Codes: G21

  • Considering the importance of the competition-stability trade-off, contradictory theoretical predictions, and empirical evidence, its re-investigation from the angle of non-linearity is needed. Therefore, this paper focuses on the association between bank stability and competition in Europe by employing a Boone indicator and alternative competition measures. Bank stability is measured with the z-score and loan loss reserves ratio. System-GMM estimations are carried out on a panel of banks from 27 European Union member countries over the period of 2004–2014. The results confirm that when a linear association between bank stability and competition is assumed, competition-stability argument prevails. However, when potential non-linearity of this association is assumed, the results appear more diverse and complex across different competition proxies. We observe signs of U-shape association between bank stability and competition for the Boone indicator and weaker signs of an inverse U-shape association with Lerner index. This indicates that before taking policy measures, it is important to consider the potentially non-linear association between bank stability and competition and to define which aspect of competition regulators want to address. The results concerning mature and emerging Europe exhibit also some differences, indicating that suitable regulatory approaches applied even within the EU could be rather different.

    Citation: Kalle Ahi, Laivi Laidroo. Banking market competition in Europe—financial stability or fragility enhancing?[J]. Quantitative Finance and Economics, 2019, 3(2): 257-285. doi: 10.3934/QFE.2019.2.257

    Related Papers:

    [1] Dilvin Taşkın, Görkem Sarıyer . Use of derivatives, financial stability and performance in Turkish banking sector. Quantitative Finance and Economics, 2020, 4(2): 252-273. doi: 10.3934/QFE.2020012
    [2] Patrick Mumbi Chileshe . Banking structure and the bank lending channel of monetary policy transmission: Evidence from panel data methods. Quantitative Finance and Economics, 2018, 2(2): 497-524. doi: 10.3934/QFE.2018.2.497
    [3] Nereida Polovina, Ken Peasnell . The effects of different modes of foreign bank entry in the Turkish banking sector during the 2007–2009 Global financial crisis. Quantitative Finance and Economics, 2023, 7(1): 19-49. doi: 10.3934/QFE.2023002
    [4] Dimitra Loukia Kolia, Simeon Papadopoulos . The levels of bank capital, risk and efficiency in the Eurozone and the U.S. in the aftermath of the financial crisis. Quantitative Finance and Economics, 2020, 4(1): 66-90. doi: 10.3934/QFE.2020004
    [5] Jyh-Horng Lin, Shi Chen, Jeng-Yan Tsai . How does soft information about small business lending affect bank efficiency under capital regulation?. Quantitative Finance and Economics, 2019, 3(1): 53-74. doi: 10.3934/QFE.2019.1.53
    [6] Tram Thi Xuan Huong, Tran Thi Thanh Nga, Tran Thi Kim Oanh . Liquidity risk and bank performance in Southeast Asian countries: a dynamic panel approach. Quantitative Finance and Economics, 2021, 5(1): 111-133. doi: 10.3934/QFE.2021006
    [7] Raymond J. Hawkins, Hengyu Kuang . Lending Sociodynamics and Drivers of the Financial Business Cycle. Quantitative Finance and Economics, 2017, 1(3): 219-252. doi: 10.3934/QFE.2017.3.219
    [8] Albert Henry Ntarmah, Yusheng Kong, Michael Kobina Gyan . Banking system stability and economic sustainability: A panel data analysis of the effect of banking system stability on sustainability of some selected developing countries. Quantitative Finance and Economics, 2019, 3(4): 709-738. doi: 10.3934/QFE.2019.4.709
    [9] Hung Manh Pham, Nhi Yen Thi Nguyen . From responsibility to reward: Does corporate social responsibility perception enhance customer loyalty in Vietnamese banking sector?. Quantitative Finance and Economics, 2025, 9(2): 274-299. doi: 10.3934/QFE.2025009
    [10] Md Qamruzzaman, Wei Jianguo . Investigation of the asymmetric relationship between financial innovation, banking sector development, and economic growth. Quantitative Finance and Economics, 2018, 2(4): 952-980. doi: 10.3934/QFE.2018.4.952
  • Considering the importance of the competition-stability trade-off, contradictory theoretical predictions, and empirical evidence, its re-investigation from the angle of non-linearity is needed. Therefore, this paper focuses on the association between bank stability and competition in Europe by employing a Boone indicator and alternative competition measures. Bank stability is measured with the z-score and loan loss reserves ratio. System-GMM estimations are carried out on a panel of banks from 27 European Union member countries over the period of 2004–2014. The results confirm that when a linear association between bank stability and competition is assumed, competition-stability argument prevails. However, when potential non-linearity of this association is assumed, the results appear more diverse and complex across different competition proxies. We observe signs of U-shape association between bank stability and competition for the Boone indicator and weaker signs of an inverse U-shape association with Lerner index. This indicates that before taking policy measures, it is important to consider the potentially non-linear association between bank stability and competition and to define which aspect of competition regulators want to address. The results concerning mature and emerging Europe exhibit also some differences, indicating that suitable regulatory approaches applied even within the EU could be rather different.


    Malnutrition is a quiet emergency and it is one of the most widespread causes of morbidity and mortality among children and adolescents throughout the world [1],[2]. Malnutrition is a major public health problem throughout the developing world particularly in Southern Asia and Sub Saharan Africa [3][5]. In many of the developing countries, stunting, underweight, and micronutrient deficiencies among adolescents frequently result from inadequate nutrition and infections during early childhood combined with a diet insufficient to meet the intense nutritional demands of rapid growth during adolescence [6]. Adolescents are the persons of having age group 10–19 years. Adolescents experience a critical transition from childhood to adulthood, which is characterized by a rapid physical growth; psychological development and social changes [7]. Adolescents are nutritionally vulnerable due to their high requirements for growth, eating patterns and their susceptibility to environmental influences. Inadequate nutrition in adolescence can potentially retard growth and sexual maturation. Inadequate nutrition also puts adolescent at high risk of chronic disease although the detrimental effects appear after a long time [8]. Approximately 20% of the population of the WHO South-East-Asia (SEAR) consists of adolescents. The foundation of adequate growth and development is laid before birth, during childhood, and is followed during adolescence [9]. Adolescents in Nepal cover 23.45 percent of the total population that is nearly a quarter of population whereas they cover 22.34 percent of total population in Dang district [10]. Indicators of over nutrition such as overweight and obesity in children and adolescents now occur simultaneously with underweight, stunting and wasting [11][14]. The inconsistency of these two boundaries, repeatedly referred to as the “double burden of malnutrition” [14][16]. Obesity is a worldwide problem that is rising at an amazing rate. The children and adolescent obesity are the burning issues [17]. The World Health Organization reflects that poor nutrition is the single most significant threat to the world's health [18]. Poorer nutritional status becomes observable during adolescence, with a hindrance in maturation which may have consequence effect for consequent ability of the biologically immature female to bear a normal pregnancy [19]. The degree of the malnutrition is very high in Nepal [20]. According to Global School Based Student Health Survey (GSHS) 2015, 10.9% (male 13.8%, female 8.1%) adolescent students were underweight, 6.7% (male 7.6%, female 5.8%) were overweight (heavy for their height) and 0.6% (male 0.8% and female 0.4%) of the school going adolescent were obese [21]. A cross-sectional study conducted among school going adolescent girls, 9–16 years studying in various schools in rural area of Kavre district, Nepal found that overall prevalence of underweight, stunting and thinness was 31.98%, 21.08% and 14.94% respectively [22]. In most developing countries, nutrition initiatives have been focusing on children and women, thus neglecting adolescents. Addressing the nutrition needs of adolescents could be an important step towards breaking the vicious cycle of intergenerational malnutrition, chronic diseases and poverty [9]. Most of the studies are concerned with the nutritional status of under-five children and mother. Very limited research has been conducted to investigate the reason of having malnutrition among Adolescents in Nepal. Hence this study aimed to find out the malnutrition status and associated factors among school going adolescents of Dang district Nepal.

    Institutional based descriptive cross-sectional study was conducted in Dang district Nepal among secondary level school going adolescents studying in grade 9 and 10 between ages 14–17 years between July-December 2017. School adolescents both from government and private schools studying in grade 9 and 10 and between age group of 14–17 years of selected schools were included in the study while adolescents with severe mental problem and who were not available during the day of data collection were excluded in the study.

    The sample size of the study was 510 which was determined by using formula N = Z2pq/L2 [23] with 95% level of confidence interval, critical value Z=1.96, 3.5% margin of error, 10% non-response rate and 8.1% adolescents age 15–19 years from Kaski district were overweight and obese [24]. Hence, N = Z2pq/L2 = (1.96)2 × (0.081) × (0.919)/(0.035)2 = 232. Since multistage stratified probability random sampling was used as a sampling technique, initial sample size was multiply by design effect 2.0, hence n = 232 × 2 = 464. Further by adding 10% non-response rates i.e. 46, the final sample size of the study is 510. A multistage probability random sampling among total 142 secondary schools consisting of both government and private schools of Dang district was used. From each 5 existing electoral constituency, one government and one private school were selected by disproportionate stratified random sampling technique through non replacement lottery method. Further, 510 students was selected randomly from selected 10 secondary schools which consists 51 students from each government and private school by using non replacement lottery method (Figure 1).

    Figure 1.  Multistage stratified probability random sampling technique.

    Pretested semi structure self-administered questionnaire was performed for collecting data. Questionnaire was translated into Nepali and then retranslated into English language to find misinterpretation and then correction was made. The self administered questionnaire was pretested among 10% of total sample size (51 school adolescents) residing in Birgunj, Parsa. Both English and Nepali version Questionnaire was made and used according to familiarity of students. Immediately after self-administered questionnaire anthropometric measurement was done. Height was measured in centimeter from head to heel by removing shoes with a non-stretchable measuring tape on the wall of school. In order to maintain accuracy and consistency of measuring tape, the same measuring tape was used to measure height throughout data collection period. The measuring tape was calibrated on a regular basis in the field against standard height measurement instrument. Weight was measured in kilogram with same standard calibrated digital weighing machine with the school adolescents standing with shoes removed and on school dress. Two data collectors including 1st and 2nd authors with qualification of Master in Nursing and Master in Public Health as well as Master in Sociology were involved in data collection.

    After data collection, data were thoroughly screened, reviewed, compiled and checked for its completeness, consistency and accuracy by the researcher and data analysis was done as per the objectives of the study. Editing, classifying, coding and entry of data were done using Microsoft Excel and analysis was done using Statistical Package for Social Science (IBM SPSS) version 20. Descriptive analysis such as frequencies, percentage, means and standard deviations were calculated. Bivariate and multivariate analysis was done to find out association between dependent and independent variables. Odds ratio and corresponding 95% confidence interval was used to find out the significance of association. Variables which were found statistically significant at 95% CI (p < 0.05) during bivariate analysis were further analyzed using logistic regression model in multivariate analysis (stepwise backward likelihood ratio method). The data were summarized, adjusted odds ratios (AORs) were estimated and their corresponding value at 95% confidence intervals (95% CI) was computed.

    Dang district is located in the inner Terai and mid hills of Rapti zone in the mid-western development region of Nepal. Salyan and Rolpa are adjacent in the North, India in the south, Kapilvastu, Argakhachi and Pyuthan in the east, Surkhet and Bankae in the west [25]. During study time, there were 4 municipalities, 5 electoral consistencies and 31 VDCs out of which 5 VDCs lies in hilly region. According to Central Beuro of Statistics 2011 total population of Dang district was 552,583 among which 291,524 (52.76%) were female and 261,051 (47.24%) were male. Annual population growth rate of Dang district was 1.78 [10]. There are altogether 142 secondary schools including 86 private and 56 Government schools, similarly there are total 24,622 students in secondary level in Dang district which includes 12, 905 female and 11, 717 students [26].

    Anthropometric measurement of adolescent was done by using the standard calibrated digital weighing machine and non-stretchable measuring tape for weight and height respectively. After calculation of body mass index (BMI) through CDC BMI Percentile calculator software four indicators underweight, normal weight, overweight and obesity was recorded. CDC BMI Percentile calculator provides BMI and the corresponding BMI-for-age percentile based on the CDC growth charts. This calculator provides sex-specific CDC BMI-for-age growth charts where 5th percentile, between 5th percentiles to less than 85th percentile, between 85th percentiles up to less than 95th percentile, above 95th percentile of the sex specific CDC BMI-for-age growth charts gives underweight, normal weight, overweight and obesity respectively. For accuracy and consistency of anthropometric measurements, the same measuring tape and weighing machine was used to measure height and weight respectively throughout data collection period. The weighing machine and the measuring tape were calibrated on a regular basis in the field against standard weighing machine and height measurement instrument.

    This study obtained ethical approval from Institutional Ethical Review Board of National Medical College which is institutional review board of Nepal Health Research Council. Concerned stakeholders were officially contacted with letters and permission was obtained at all levels. Verbal informed consent was taken from parents and written informed consent was obtained from school teachers since the students were below 18 years old. Every study adolescents were informed about confidentiality and privacy.

    The mean age and mean family size was 15.28 ± 0.77 and 5.25 ± 1.56 respectively. Among total 510 school going adolescents two third (66.5%) of them were of less than equal to 15 years. Slightly more than half (51.4%) of total adolescents were male. In addition 42.7% of total adolescents were Brahmin/Chhetri followed by janajati, dalit, madeshi and muslim. Nearly two-third (64.3%) was from nuclear family. About half of the adolescents were from Government and Private schools respectively. Almost 12% of respondents' mother was found still unable to read or write and 6.4% of husbands and 13.6% of their wives had their informal classes. Nearly two-third (64.5%) of respondents' mother was engaged in household activities whereas about 45% of their fathers were engaged in different kind of services. Most 90.6% of the adolescents were from the families who have their own agricultural land (Table 1).

    Table 1.  Distribution of background related characteristics of study population.
    General Characteristics Frequency (n = 510) Percentage
    Age
    ≤ 15 years 339 66.5
    > 15 years 171 33.6
    Mean age ± SD; 15.28 ± 0.77
    Sex
     Male 262 51.4
     Female 248 48.6
    Ethnicity
     Dalit 73 14.3
     Janajati 150 29.4
     Madeshi 3 0.6
     Muslim 3 0.6
     Brahman/chhetri 218 42.7
     Others* 63 12.4
    Family Type
     Nuclear 328 64.3
     Joint 182 35.7
    Type of School
     Government 257 50.4
     Private 253 49.6
    Education of Mother
     Illiterate 63 12.4
     Informal education or primary (1 to 5) 172 33.7
     Lower secondary level (6 to 8 class) 144 28.2
     Secondary level (9 to 10 class) 64 12.5
     SLC Passed 50 9.8
     Intermediate +Bachelor and above 17 3.3
    Occupation of Mother
     Homemaker 329 64.5
     Small scale business 29 5.7
     Service 36 7.1
     Daily wage labor 11 2.2
     Agriculture 105 20.6
    Occupation of Father
     Small scale business 62 12.2
     Service 229 44.9
     Daily wage labor 40 7.8
     Agriculture 121 23.7
     Foreign job 58 11.4
    Own Agricultural Land 12.4
     Yes 462 90.6

     | Show Table
    DownLoad: CSV

    Based on 24 hours dietary recalls methods, most of the participants (99%) consumed cereals, slightly more than three-fourth (76.9%) green vegetables and fruits, nearly one-third (65.7%) pulses and few 11.6% consumed meat/fish /eggs within 24 hours of data collection time. Regarding the school Tiffin of adolescents less than one-fifth (19.2%) carry homemade foods such as roti/paratha and vegetables along with them where as more than half (53.5%) consumed junk food such as noodles/lays during school break (Table 2).

    Table 2.  Distribution of Food Diversity related characteristics of study population within 24 hours time period.
    Variable Yes (%) No (%)
    Twenty four hours intake of cereals 508 (99%) 2 (0.4%)
    Twenty four hours intake of Pulses 335 (65.7%) 175 (34.3%)
    Twenty four hours intake of Vegetables/fruits 392 (76.9%) 118 (23.1%)
    Twenty four hours intake of Meat/fish/eggs 59 (11.6%) 451 (88.4%)
    Twenty four hours intake of Milk/ milk product 225 (44.1%) 285 (55.9%)
    Tiffin food items
     Roti/paratha and vegetables 98 (19.2%) 412 (80.8%)
     Packed food(noodles/lays) 273 (53.5%) 237 (46.5)
     Fast Food from school canteen 139 (27.3%) 371 (72.7%)

     | Show Table
    DownLoad: CSV

    The result about frequency of intake of food items showed that more than half 294 (57.6%) took junk food daily, nearly half 49.4% took three times meal in 24 hours. Nearly one-fourth (24.7%) of the respondents were vegetarians, among 384 non vegetarian adolescents, more than one-fourth (26.3%) adolescents took meat and fish occasionally, 35.6% took eggs occasionally. Among total, less than half (46.3%) took milk and milk product and 49.8% green leafy vegetables daily whereas less than one-fifth (17.8%) have habits of taking fruits daily in their diet (Table 3).

    Regarding the nutritional status of the adolescents, among 510 adolescents almost three fourth (74.3%) had normal weight. Similarly 21.8% were underweight followed by overweight and obesity that is 3.1% and 0.8% respectively (Table 4).

    Table 5 represents the multiple regression analysis of underweight with associated factors. Regarding religion the odds of underweight was found 0.19 times less likely (OR = 0.19, CI: 0.05–0.65) on Hindus rather than Christian. Similarly, adolescent living in Nuclear family was 0.28 times less likely (OR = 0.28, CI: 0.13–0.61) to have underweight than in joint family. Further, adolescents studying in Government schools were 0.46 times less likely (OR = 0.46, CI: 0.22–0.98) to be underweight than adolescents studying in private school. About the earning status adolescents whose family members were unable to earn to live their livelihood were more likely to have underweight by factor 4.52 (OR = 4.52, CI: 1.44–14.16) than the adolescents' whose family members earned money for their livelihood. In addition adolescents having daily intake of green leafy vegetables were significantly associated by factor 0.49 (OR = 0.49, CI: 0.26–0.93) than adolescent who did not take vegetables. Likewise, the adolescent who were not engaged in competitive sports were found 0.49 times less likely to be underweight (OR = 0.49, CI: 0.25–0.96) (Table 5).

    Table 3.  Distribution of frequency of intake of food items among study population.
    Characteristics Frequency (N = 510) Percentage
    Frequency of having junk food
     Daily 294 57.6
     Twice a week 102 20.0
     Weekly 35 6.9
     Occasionally 79 15.5
    Frequency of meal in 24 hours
     Two times 24 4.7
     Three times 252 49.4
     Four times 160 31.4
     More than four times 74 14.5
    Vegetarian/Non vegetarian
     Vegetarian 126 24.7
     Non vegetarian 384 75.3
    Frequency of having meat/fish (n=384)
     Daily 53 10.4
     Twice a week 85 16.7
     Weekly 110 21.6
     Occasionally 134 26.3
    Frequency of having eggs (n=384)
     Daily 33 6.5
     Twice a week 77 15.1
     Weekly 93 18.2
     Occasionally 180 35.3
    Frequency of having milk and milk product
     Daily 236 46.3
     Twice a week 114 22.4
     Weekly 45 8.8
     Occasionally 115 22.5
    Frequency of having green leafy vegetables
     Daily 254 49.8
     Twice a week 256 50.2
    Frequency of having fruits
     Daily 91 17.8
     Twice a week 128 25.1
     Weekly 34 6.7
     Occasionally 257 50.4

     | Show Table
    DownLoad: CSV
    Table 4.  Prevalence of Nutritional Status of Adolescents.
    Characteristics Frequency (N = 510) Percentage
    Underweight 111 21.8
    Normal Weight 379 74.3
    Overweight 16 3.1
    Obesity 4 0.8
    Total 510 100

     | Show Table
    DownLoad: CSV
    Table 5.  Multiple regression analysis of underweight with associated factors.
    Characteristics Unadjusted OR (95% CI) P-value Adjusted OR (95% CI) P-value
    Religion
     Hindu 1 1 0.008*
     Christian 3.85 (1.56–9.50) 0.002 0.19 (0.05–0.65)
    Family Size
     ≤ 5 members 1 1
     > 5 members 3.21 (2.08–4.96) < 0.001 0.78 (0.35–1.71) 0.538
    Family Type
     Nuclear 1 1 0.001*
     Joint 0.25 (0.16–0.39) < 0.001 0.28 (0.13–0.61)
    Type of School
     Government 1 1 0.044*
     Private 0.49 (0.32–0.76) 0.001 0.46 (0.22–0.98)
    Education of Mother
     Illiterate 1 1
     Informal or Primary 4.28 (1.67–10.99) 0.007 1.62 (0.47–5.57) 0.439
     Lower Secondary 2.19 (0.92–5.20) 1.16 (0.41–3.26) 0.769
     Secondary 2.06 (0.85–5.01) 1.55 (0.54–4.48) 0.410
     SLC and Above 3.89 (1.51–10.01) 0.045 1.90 (0.65–5.55) 0.237
    Duration of food sufficiency
     ≤ 6 months 1 1
     > 6 months 2.24 (1.34–3.77) 0.002 1.54 (0.79–2.99) 0.197
    Earning status
     Not earning 1 1 0.010*
     Earning(cash) 9.43 (3.77–23.57) < 0.001 4.52 (1.44–14.16)
    History of 24 hours milk/milk product
     No 1 1
     Yes 1.61 (1.04–2.49) 0.031 0.99 (0.52–1.87) 0.975
    Daily intake milk and milk product
     Yes 1 1
     No 0.45 (0.29–0.70) < 0.001 0.55 (0.29–1.05) 0.071
    Daily intake of green leafy vegetables
     Yes 1 1 0.031*
     No 0.591 (0.38–0.90) 0.015 0.49 (0.26–0.93)
    Sleeping hours
     ≤ 6 hours 1 1
     > 6 hours 3.45 (1.63–7.33) 0.001 0.49 (0.16–1.50) 0.212
    School sports
     No 1 0.002 1 0.040*
     Yes 0.47 (0.24–0.77) 0.49 (0.25–0.96)
    Sufferance from diseases 1
     No 1 0.016 0.914
     Yes 0.59 (0.38–0.90) 1.03 (0.57–1.84)

    *Significant (p < 0.05)—AOR in bold denotes significant

     | Show Table
    DownLoad: CSV

    Table 6 presents the multiple regression analysis of overweight/obesity with associated factors. Overweight/obesity was found 0.20 times more likely in male adolescents than female adolescents (OR = 0.20, CI: 0.04–0.97). Likewise, overweight/obesity was found 9.75 times less likely in Hindus rather than Christian (OR = 9.75, CI: 2.24–42.39) (Table 6).

    Table 6.  Multiple regression analysis of overweight/obesity with associated factors.
    Characteristics Unadjusted OR (95% CI) P-value Adjusted OR (95% CI) P-value
    Gender
     Male 1 1
     Female 3.96 (1.30–12.03) 0.007 0.20 (0.04–0.97) 0.046*
    Religion
     Hindu 1 1
     Christian 0.09 (0.03–0.29) <0.001 9.75 (2.24–42.39) 0.002*
    History of 24 hours milk/milk product
     No 1 1
     Yes 0.32 (0.12–0.85) 0.017 5.82 (0.47–71.05) 0.167
    Daily intake of eggs
     Yes 1 1
     No 4.25 (1.27–14.19) 0.035 0.37 (0.09–1.48) 0.162
    Daily intake of milk and milk product
     Yes 1 1
     No 3.65 (1.30–10.20) 0.009 0.14 (0.01–1.22) 0.076

    *Significant (p<0.05)—AOR in bold denotes significant

     | Show Table
    DownLoad: CSV

    The present study revealed that 21.8% of school going adolescents between the age of 14–17 years in Dang district Nepal at the time of survey were underweight. The findings of the present study is similar to the study conducted by Roba KT, Abdo M and Wakayo T in Adama City, Central Ethiopia which found that 21.3% of adolescent girls were underweighted [27], this finding is also supported by Nepal Demographic Health Survey, 2011 where 25.8% of late adolescent girls cut off BMI value 18 [28]. However different studies such as study conducted in 2014 in Kaski district of Nepal, further analysis of Demographic and Health survey 2008 in Ghana 2015 documented the lower prevalence of underweight 15.3% and 13.8% respectively among adolescents [24],[29]. In spite of this, most of the studies conducted in rural areas of different countries such as study conducted in 2015 in Kavre district of Nepal among rural school going adolescent girls of 9–16 years, cross-sectional study conducted by Bisai, Bose, Ghosh and De, 2011 among rural school children aged 11–18 years of West Bengal, India and data from the Global School-based Student Health Survey (GSHS) in seven African countries between 2006 and 2010, by Taru, Hesham, David and Jason in 2014 from randomly selected schools going adolescents of 11–17 years showed the prevalence of underweight among adolescents were high 31.98%, 28.3% and 31.9% respectively than the present study [22],[30],[31].The variation of underweight in different study might be due to the difference in study setting of different schools, different age group of adolescents included in the study and methodologies applied in the study.

    Regarding the prevalence of overweight and obesity the present study revealed that 3.9% of school going adolescents between the age of 14–17 years were found to be overweight and obesity. This finding is supported by the study conducted in Adama City, Central Ethiopia which found 4.3% of school adolescent girls were overweight and obesity [27]. However finding of this study is higher in compare to Nepal Demographic Health Survey, 2011 which reveals 2.9% overweight and obesity among late adolescent girls [32]. In spite the separate comparison of overweight and obesity, the present study revealed overweight and obesity 3.1% and 0.8% respectively among school going adolescents of 14–17 age groups. The finding of this study is slightly less in compare to study done by Acharya, Chauhan, Thapa, Kaphle and Malla in 2014 which revealed 5.8% overweight and 2.3% obesity among school adolescents [24]. Similar pattern was also seen in other studies such as study conducted in India by Vinoth, Shanthi , Lakshmi, Umakanthan and Fatima, 2016 in six schools in a semi urban area of Southern part of India revealed prevalence of more overweight and obesity as 10.9% and 6% respectively [33], next study conducted by Gurung and Gurung in 2014 among school going adolscents in Belgaum city in India documented the prevalence of overweight and obesity as 12% and 3.3% respectively [34] which are the higher than the present study. However, cross-sectional data from the Global School-based Student Health Survey (GSHS) conducted by Taru, Hesham, David, and Jason, 2014 in seven African countries indicate prevalence of obesity of Benin was 0.6% which is similar to the finding of the present study [31]. This disparity in over nutrition in different studies might be due to the different study setting, different age group of adolescents included in the study and methodologies applied in the study.

    The current study showed statistical significant p = 0.001 between type of family and underweight. Consistently the study done by Pal, Pari, Sinha, Prakash and Dhara, 2016 in West Bengal state, India found significant association of underweight with family type, however showed different result of that adolescents of nuclear families (family size < 4) were more likely to be under nutrition [2] in contrast to this study thatadolescent living in Nuclear family was 0.28 times less likely to have underweight than in joint family. The finding of the present study found the significant association p = 0.010 of underweight with earning status of adolescents. Similar result was found in community based cross sectional study done by Pal, Pari, Sinha, Prakash and Dhara, 2016 in West Bengal India, which revealed poverty is found to be an important factor of under nutrition among the adolescents [2]. Adolescents' mother education in this study was found to be statistically significant (p = 0.007) with underweight in bivariate analysis. Similar pattern follows in most studies one of the study conducted by Vinoth, Shanthi, Lakshmi, Umakanthan and Fatima, 2016 in Southern part of India showed mother's education have significant associations with under nutrition [33]. Similarly a study conducted by Senbanjo, Oshikoya, Odusanya and Njokanma, 2011 in Abeokuta, Nigeria revealed that low maternal education was the major contributory factor to stunting [35]. Another cross sectional study done by Mukherjee, Chaturvedi and Bhalwar, 2008 found mothers' educational level was significantly associated with the nutritional status of the child [36]. Study done in West Bengal conducted by Pal, Pari, Sinha, Prakash and Dhara, 2016 showed adolescents of women with higher education were less likely to be undernourished than adolescents of poor and uneducated women [2].

    The current study found gender of adolescent was significantly associated with overweight/obesity. Similar result was found in the study conducted by Goyal, Shah, Saboo, Phatak, Shah and Gohel, 2010 in school adolescent of 12–18 found age-adjusted prevalence of overweight was found to be 14.3% among boys and 9.2% among girls where as the prevalence of obesity was 2.9% in boys and 1.5% in girls [37]. However in contrast to the present study, Taru, Hesham, David and Jason, 2014 studied Cross-sectional study data from the Global School-based Student Health Survey (GSHS) in seven African countries among 11–17 years old school adolescents revealed that females had a higher overweight in five of the countries [31]. This contraction might be due to the male dominant Nepalese society.

    The present study revealed that Christian was 0.19 times less likely to be underweight in comparison to Hindus. Similar result was found in the study conducted by Adeladza A. in 2009 in Kwale district of Kenya and Sabharwal NS in 2011 in rural India where Christian have better nutritional status [38],[39]. In concurrent with the present study nutritional status and religion was statistically significant in the study conducted by Arepalli S, Rao GV in Kallur Primary Health Center, Kurnool District of India in 2016 [40]. Similarly in this study overweight/obesity was found 9.75 times more likely in Christian rather than Hindus, this difference might be due to the difference in intake of diet, majority of Christian in the study area own cattle and engaged in agricultural work, however the finding of this study was supported by the study done by Peltzer et al., 2014in which high prevalence of organized religious activity was associated with overweight/obesity [41]. In simultaneous with the present study religious affiliation was associated with overweight/obesity in study conducted by Bharmal NH, McCarthy WJ, Gadgil MD, Kandula NR, Kanaya AM in 2018 [42].

    The prevalence of underweight, overweight and obesity among school going adolescents between the age of 14–17 years in Dang district Nepal at the time of survey were 21.8%, 3.1% and 0.8% respectively. Prevalence of both under nutrition and over nutrition in school going adolescents demonstrated the existence of double burden of malnutrition. Malnutrition was significantly higher among adolescents living in joint family, family having no earning status. Male adolescents were found more likely to be overweight and obesity than female adolescents. Hence to tie up the good nutrition it is recommended that integrated nutritional intervention and health related services should also be focused on adolescents.



    [1] Agoraki MEK, Delis MD, Pasiouras F (2011) Regulations, competition and bank risk-taking in transition countries. J Financ Stabil 7: 38-48. doi: 10.1016/j.jfs.2009.08.002
    [2] Allen F, Gale D (2000) Financial contagion. J Polit Econ 108: 1-33. doi: 10.1086/262109
    [3] Allen F, Gale D (2004) Competition and financial stability. J Money Credit Bank 36: 453-480. doi: 10.1353/mcb.2004.0038
    [4] Andrieş AM, Căpraru B (2012) How does EU banking competition impact financial stability? Proceedings of the 13th International Conference on Finance and Banking, Ostrava, Czech Republic Oct 12-13, 2011.
    [5] Anginer D, Demirguc-Kunt A, Zhu M (2014) How does competition affect bank systemic risk? J Financ Intermed 23: 1-26. doi: 10.1016/j.jfi.2013.11.001
    [6] Arellano M, Bover O (1995) Another look at the instrumental variables estimation of error components models. J Econometrics 68: 29-51. doi: 10.1016/0304-4076(94)01642-D
    [7] Bain J (1956) Barriers to New Competition, Cambridge: Harvard Press.
    [8] Beck T, Coyle D, Dewatripont M, et al. (2010) Bailing out the banks: Reconciling stability and competition. Centre for Economic Policy Research.
    [9] Beck T, De Jonghe O, Schepens G (2013) Bank competition and stability: Cross-country heterogeneity. J Financ Intermed 22: 218-244. doi: 10.1016/j.jfi.2012.07.001
    [10] Berger AN, Klapper LF, Turk-Ariss R (2009) Bank competition and financial stability. J Financ Serv Res 35: 99-118. doi: 10.1007/s10693-008-0050-7
    [11] Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econometrics 87: 11-143.
    [12] Bolt W, Humphrey D (2015) Assessing bank competition for consumer loans. J Bank Financ 61: 127-141. doi: 10.1016/j.jbankfin.2015.09.004
    [13] Bolt W, Tieman A (2004) Bank competition, risk and regulation. Scand J Econ 44: 1-34.
    [14] Boone J (2008) A new way to measure competition. Econ J 118: 1245-1261. doi: 10.1111/j.1468-0297.2008.02168.x
    [15] Boyd JH, Graham SL (1988) The profitability and risk effects of allowing bank holding companies to merge with other financial firms: A simulation study. Fed Reserve Bank Minneapolis Quart Rev 12: 3-20.
    [16] Boyd JH, De Nicoló G (2005) The theory of bank risk taking and competition revisited. J Finance 60: 1329-1343. doi: 10.1111/j.1540-6261.2005.00763.x
    [17] Boyd JH, De Nicolò G, Jalal AM (2006) Bank risk-taking and competition revisited: New theory and new evidence. IMF Working Papers No. 297.
    [18] Broecker T (1990) Credit-worthiness tests and interbank competition. Econometrica 58: 429-452. doi: 10.2307/2938210
    [19] Brůha J, Kočenda E (2018) Financial stability in Europe: Banking and sovereign risk. J Financ Stabil 36: 305-321. doi: 10.1016/j.jfs.2018.03.001
    [20] Carbó S, Humphrey D, Maudos J, et al. (2009) Cross-country comparisons of competition and pricing power in European banking. J Int Money Financ 28: 115-134. doi: 10.1016/j.jimonfin.2008.06.005
    [21] Carletti E (2008) Competition and regulation in banking, In: Boot A, Thakor A (Eds.), Handbook in Financial Intermediation, Elsevier, North Holland, 449-482.
    [22] Claessens S (2009) Competition in the financial sector: Overview of competition policies. IMF Working Paper No. 45.
    [23] Coelli TJ, Rao DSP, O'Donnell CJ, et al. (2005) An Introduction to Efficiency and Productivity Analysis, Springer Science & Business Media.
    [24] Cubillas E, González F (2014) Financial liberalization and bank risk-taking: International evidence. J Financ Stabil 11: 32-48. doi: 10.1016/j.jfs.2013.11.001
    [25] De Jonghe O, Diepstraten M, Schepens G (2016) Competition in EU banking, In: Beck T, Casu B (Eds.), The Palgrave Handbook of European Banking, Palgrave Macmillan UK, 187-211.
    [26] De Nicolò G, Loukoianova E (2007) Bank ownership, market structure and risk. IMF Working Paper No. 215.
    [27] Fiordelisi F, Mare DS, Molyneux P (2015) State-aid, stability and competition in European banking. MPRA Paper No. 67473.
    [28] Forssbæck J, Shehzad CT (2015) The conditional effects of market power on bank risk-Cross-country evidence. Rev Financ 19: 1997-2038. doi: 10.1093/rof/rfu044
    [29] Fu X, Lin Y, Molyneux P (2014) Bank competition and financial stability in Asia Pacific. J Bank Financ 38: 64-77. doi: 10.1016/j.jbankfin.2013.09.012
    [30] Goetz MR (2018) Competition and bank stability. J Financ Intermed 35: 57-69. doi: 10.1016/j.jfi.2017.06.001
    [31] Greene WH (1993) The econometric approach to efficiency analysis, In Fried HO, Lovell CAK, Schmidt SS (Eds.), The Measurement of Productive Efficiency, New York: Oxford University Press.
    [32] Hellmann TF, Murdock KC, Stiglitz JE (2000) Liberalization, moral hazard, and prudential regulation: Are capital requirements enough? Am Econ Rev 90: 147-165. doi: 10.1257/aer.90.1.147
    [33] Hesse H, Čihák M (2007) Cooperative banks and financial stability. IMF Working Papers No. 2.
    [34] Hope CJ, Gwatidzo T, Ntuli M (2013) Investigating the effect of bank competition on financial stability in ten African countries. Int Bus Econ Res J 12: 755-768.
    [35] Houston JF, Lin C, Lin P, et al. (2010) Creditor rights, information sharing, and bank risk taking. J Financ Econ 96: 485-512. doi: 10.1016/j.jfineco.2010.02.008
    [36] Inderst R (2013) Prudence as a competitive advantage: On the effects of competition on banks' risk-taking incentives. Eur Econ Rev 60: 127-143. doi: 10.1016/j.euroecorev.2012.10.001
    [37] Jeon JQ, Lim KK (2013) Bank competition and financial stability: A comparison of commercial banks and mutual savings banks in Korea. Pac-Basin Financ J 25: 253-272. doi: 10.1016/j.pacfin.2013.10.003
    [38] Jiménez G, Lopez JA, Saurina J (2013) How does competition affect bank risk-taking? J Financ Stab 9: 185-195. doi: 10.1016/j.jfs.2013.02.004
    [39] Kasman S, Kasman A (2015) Bank competition, concentration and financial stability in the Turkish banking industry. Econ Syst 39: 502-517. doi: 10.1016/j.ecosys.2014.12.003
    [40] Keeley MC (1990) Deposit insurance, risk, and market power in banking. Am Econ Rev 80: 1183-1200.
    [41] Kick T, Prieto E (2015) Bank risk and competition: Evidence from regional banking markets. Rev Financ 19: 1185-1222. doi: 10.1093/rof/rfu019
    [42] Koetter M, Kolari JW, Spierdijk L (2012) Enjoying the quiet life under deregulation? Evidence from adjusted Lerner indices for U.S. banks. Rev Econ Stat 94: 462-480.
    [43] Kumbhakar SC, Lovell CAK (2000) Stochastic Frontier Analysis, Cambridge University Press.
    [44] Kumbhakar SC, Wang H-J, Horncastle A (2014) A Practitioner's Guide to Stochastic Frontier Analysis, Cambridge University Press, Cambridge, England.
    [45] Laeven L, Levine R (2007) Is there a diversification discount in financial conglomerates? J Financ Econ 85: 331-367. doi: 10.1016/j.jfineco.2005.06.001
    [46] Laidroo L (2016) Bank ownership and lending-Does bank ownership matter? Emerg Mark Financ Tr 52: 285-301 doi: 10.1080/1540496X.2015.1095032
    [47] Laidroo L, Männasoo K (2017) Do credit commitments compromise credit quality? Res Int Bus Financ 41: 303-317. doi: 10.1016/j.ribaf.2017.04.010
    [48] Lapteacru I (2017) Market power and risk of Central and Eastern European banks: Does more powerful mean safer? Econ Model 63: 46-59 doi: 10.1016/j.econmod.2017.01.022
    [49] Leroy A, Lucotte Y (2017) Is there a competition-stability trade-off in European banking? J Int Financ Mark I: 199-215
    [50] Lepetit L, Strobel F (2013) Bank insolvency risk and time-varying z-score measures. J Int Financ Mark I: 73-87.
    [51] Liu H, Molyneux P, Nguyen LH (2012) Competition and risk in South East Asian commercial banking. Appl Econ 44: 3627-3644. doi: 10.1080/00036846.2011.579066
    [52] Liu H, Molyneux P, Wilson JOS (2013) Competition and stability in European banking: A regional analysis. Manch Sch 81: 176-201. doi: 10.1111/j.1467-9957.2011.02285.x
    [53] Maudos J, de Guevara JF (2007) The cost of market power in banking: Social welfare loss vs. cost inefficiency. J Bank Financ 31: 2103-2125. doi: 10.1016/j.jbankfin.2006.10.028
    [54] Noman AHM, Gee CS, Isa CR (2017) Does competition improve financial stability of the banking sector in ASEAN countries? An empirical analysis. PLoS ONE 12: 1-27.
    [55] Martínez-Miera D, Repullo R (2010) Does competition reduce the risk of bank failure? Rev Financ Stud 23: 3638-3664. doi: 10.1093/rfs/hhq057
    [56] Repullo R (2004) Capital requirements, market power, and risk-taking in banking. J Financ Intermed 13: 156-182. doi: 10.1016/j.jfi.2003.08.005
    [57] Roodman DM (2009) A note on the theme of too many instruments. Oxford B Econ Stat 71: 135-158. doi: 10.1111/j.1468-0084.2008.00542.x
    [58] Ruckes M (2004) Bank competition and credit standards. Rev Financ Stud 17: 1073-1102. doi: 10.1093/rfs/hhh011
    [59] Rumler F, Waschiczek W (2016) Have changes in the financial structure affected bank profitability? Evidence for Austria. Eur J Financ 22: 803-824. doi: 10.1080/1351847X.2014.984815
    [60] Samantas I (2013) Bank competition and financial (in) stability in Europe: A sensitivity analysis. MPRA Paper No. 51621.
    [61] Samantas I (2017) On the optimality of bank competition policy. Int Rev Financ Anal 54: 39-53. doi: 10.1016/j.irfa.2017.09.005
    [62] Schaeck K, Cihák M (2014). Competition, efficiency, and stability in banking. Financ Manage 43: 215-241. doi: 10.1111/fima.12010
    [63] Shaffer S (1998) The winner's curse in banking. J Financ Intermed 7: 359-392. doi: 10.1006/jfin.1998.0251
    [64] Soedarmono W, Machrouh F, Tarazi A (2011) Bank market power, economic growth and financial stability: Evidence from Asian banks. J Asian Econ 22: 460-470. doi: 10.1016/j.asieco.2011.08.003
    [65] Soedarmono W, Machrouh F, Tarazi A (2013) Bank competition, crisis and risk taking: Evidence from emerging markets in Asia. J Int Financ Mark I 23: 196-221. doi: 10.1016/j.intfin.2012.09.009
    [66] Stiglitz JE, Weiss A (1981) Credit rationing in markets with imperfect information. Am Econ Rev 71: 393-410.
    [67] Tabak BM, Fazio DM, Cajueiro DO (2012) The relationship between banking market competition and risk-taking: Do size and capitalization matter? J Bank Financ 36: 3366-3381. doi: 10.1016/j.jbankfin.2012.07.022
    [68] Turk Ariss R (2010) On the implications of market power in banking: Evidence from developing countries. J Bank Financ 34: 765-775. doi: 10.1016/j.jbankfin.2009.09.004
    [69] Uhde A, Heimeshoff U (2009) Consolidation in banking and financial stability in Europe: Empirical evidence. J Bank Financ 33: 1299-1311. doi: 10.1016/j.jbankfin.2009.01.006
    [70] Van Leuvensteijn M, Bikker JA, van Rixtel AARJM, et al. (2011) A new approach to measuring competition in the loan markets of the euro area. Appl Econ 43: 3155-3167. doi: 10.1080/00036840903493234
    [71] Wagner W (2010) Loan market competition and bank risk-taking. J Financ Serv Res 37: 71-81. doi: 10.1007/s10693-009-0073-8
    [72] Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econometrics 126: 25-51. doi: 10.1016/j.jeconom.2004.02.005
    [73] Yeyati E, Micco A (2007) Concentration and foreign penetration in Latin American banking sectors: Impact on competition and risk. J Bank Financ 31: 1633-1647. doi: 10.1016/j.jbankfin.2006.11.003
  • This article has been cited by:

    1. Siming Liu, Peng Hou, Yingkun Gao, Yong Tan, Innovation and green total factor productivity in China: a linear and nonlinear investigation, 2020, 0944-1344, 10.1007/s11356-020-11436-1
    2. Laivi Laidroo, Mari Avarmaa, The role of location in FinTech formation, 2020, 32, 0898-5626, 555, 10.1080/08985626.2019.1675777
    3. Pavlo Illiashenko, Laivi Laidroo, National culture and bank risk-taking: Contradictory case of individualism, 2020, 51, 02755319, 101069, 10.1016/j.ribaf.2019.101069
    4. Tinghui Li, Junhao Zhong, Hai Zhang, Pierre Failler, Chinese financial cycle spillovers to developed countries, 2019, 1, 2643-1092, 364, 10.3934/GF.2019.4.364
    5. Yue Liu, Siming Liu, Xueying Xu, Pierre Failler, Does Energy Price Induce China’s Green Energy Innovation?, 2020, 13, 1996-1073, 4034, 10.3390/en13154034
    6. Siming Liu, Qing Wei, Pierre Failler, Hong Lan, Fine Particulate Air Pollution, Public Service, and Under-Five Mortality: A Cross-Country Empirical Study, 2020, 8, 2227-9032, 271, 10.3390/healthcare8030271
    7. Adriana Novotná, Teoretické východiská skúmania konkurenčných paradigiem medzi bankovou stabilitou a konkurenciou, 2022, 23, 27298213, 50, 10.24040/eas.2022.23.1.50-68
    8. Abayomi Oredegbe, Competition and Banking Industry Stability: How Do BRICS and G7 Compare?, 2022, 21, 0972-6527, 7, 10.1177/09726527211045759
    9. Toni Richter, Bankenwettbewerb und die Stabilität von Finanzsektoren, 2021, 70, 2366-0317, 1, 10.1515/zfwp-2021-2044
    10. Pavla Klepková Vodová, Iveta Palečková, Daniel Stavárek, 2023, 9781009092166, 10.1017/9781009092166
    11. Sa Xu, Cunyi Yang, Zhehao Huang, Pierre Failler, Interaction between Digital Economy and Environmental Pollution: New Evidence from a Spatial Perspective, 2022, 19, 1660-4601, 5074, 10.3390/ijerph19095074
    12. Fathi Mohamed Bouzidi, Aida Arbi Nefzi, The Impact of Foreign Bank Entry on the Efficiency and Sustainability of Domestic Banks in Developing Countries: A Meta-Frontier Approach, 2024, 16, 2071-1050, 10932, 10.3390/su162410932
    13. Didar Erdinç, 2025, Chapter 13, 978-3-031-84318-1, 219, 10.1007/978-3-031-84319-8_13
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(7339) PDF downloads(2037) Cited by(13)

Figures and Tables

Figures(1)  /  Tables(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog