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11(6) 2020 ITJEMAST Research Articles

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Single brand retailing; Organized retailing; Single-brand stores; Indian retail sector; Managers' perception of FDI; FDI impacts Algorithmization; Agricultural investment; Differentiation; Credit resources; State subsidies; Statistical groups; Lorentz curve Indian personification; Retail Industry, Organized and unorganized stores sector; Income-based Purchase; Intention purchase products Horticultural crops; Intensive gardening; Horticulture farming enterprise; Fruit crops; NPK Fertilization; Orchards and berry plants; Horticulture effectiveness. Smart analog model (SAM); Small Spatial Spaces (SSS); Environmental influence; Studio apartments, Technological influences; Smart technology; Formal influence; Holistic change; Flexibleness of small spaces. Unit Root; Stationarity; Cointegration Granger Causality test; Stock price and exchange rate; Relationship of Stock price and gold price; Pakistan Stock Exchange (PSX). Rice crop production; Environmental stress; Rice yield; EC tolerant; Irrigation scheduling; Rice response to drought; Soil moisture content (MC); Electrical conductivity at saturation extract (ECe); Manageable allowable depletion (MAD).

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11(6) 2020 ITJEMAST Research Articles

  1. 1. Volume 11 Issue 6 (2020) ISSN 2228-9860 eISSN 1906-9642 http://TuEngr.com IMPACTS OF FOREIGN DIRECT INVESTMENT ON INDIAN RETAIL BAZAAR: AN EMPIRICAL STUDY ALGORITHMIZATION FOR PROCESSES OF REGIONAL DIFFERENTIATION AND CONCENTRATION OF INVESTMENTS IN RUSSIAN AGRICULTURE FACTORS AFFECTING ACCEPTANCE AND THE USE OF TECHNOLOGY IN YEMENI TELECOM COMPANIES STUDENTS' FINANCIAL LITERACY AND POLICIES FOR ITS DEVELOPMENT 3D MODELING IN AUTOCAD? AS A BASIC COMPONENT OF THE INITIAL TRAINING OF MECHANICAL ENGINEERS USE OF FULL-FAT SOY FLOUR IN COMPOUND FEEDS FOR MEAT CHICKENS OF THE INITIAL LINES AND BROILER CHICKENS A ROBUST VIDEO STABILIZATION ALGORITHMS FOR GLOBAL MOTION ESTIMATION USING BLOCK MATCHING EVALUATION OF ERGONOMIC DESIGN OF DESK AND CHAIR FOR PRIMARY SCHOOLS IN ERBIL CITY MICRO AND MACRO FINANCIAL INCLUSION AND THEIR IMPACTS ON ECONOMIC GROWTH: EVIDENCE FROM ASIAN ECONOMIES WITH ALTERNATIVE APPROACHES DEVELOPMENT OF TECHNOLOGY FOR THE PRODUCTION OF MULTICOMPONENT FEED SUPPLEMENT HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS WITH HEART FAILURE IN SOUTH PUNJAB PAKISTAN: A SECTIONAL SURVEY STUDY ROLES OF PHYSICAL ENVIRONMENT IN CHARACTERIZING THE IDENTITY OF MALAY ROYAL TOWN IN ALOR SETAR, KEDAH GREEN HUMAN RESOURCE MANAGEMENT PRACTICES AND ORGANIZATIONAL CITIZENSHIP BEHAVIOUR FOR ENVIRONMENT: THE INTERACTIVE EFFECTS OF GREEN PASSION CHARACTERISTICS OF POST-GLOBALIZATION APPLICATION OF NUMERICAL MODELING TO PREDICT AND EVALUATE THE EFFECTS OF MANAGEMENT SCENARIOS IN GROUNDWATER RESOURCES EFFECTS OF IRRIGATION SCHEDULING AT DIFFERENT MANAGED ALLOWABLE DEPLETION IN SALINE SOIL ON THREE RICE VARIETIES DYNAMIC NEXUS AMONG GOLD PRICE, EXCHANGE RATES, AND EQUITY RETURN OF PAKISTAN SMALL SPACES NEED SMART SOLUTIONS: IMPACTS OF SMART INTERIOR DESIGN SOLUTIONS ON ACHIEVING FLEXIBLE SPACES STATE AND EFFECTIVENESS OF THE RUSSIAN ENTERPRISE OF HORTICULTURE PRODUCTION IMPACTS OF SUBSTANTIAL STRATEGIC ISSUES AND TRENDS OF FOREIGN DIRECT INVESTMENT ON INDIAN RETAIL CONSUMER SERVICE INDUSTRY
  2. 2. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com International Editorial Board Editor-in-Chief Ahmad Sanusi Hassan, PhD Professor Universiti Sains Malaysia, MALAYSIA Executive Editor Boonsap Witchayangkoon, PhD Associate Professor Thammasat University, THAILAND Editorial Board: Assoc. Prof. Dr. Mohamed Gadi (University of Nottingham, UNITED KINGDOM) Professor Dr.Hitoshi YAMADA (Yokohama National University, JAPAN) Professor Dr. Chuen-Sheng Cheng (Yuan Ze University, TAIWAN ) Professor Dr.Mikio SATOMURA (Shizuoka University, JAPAN) Professor Dr.Chuen-Sheng Cheng (Yuan Ze University, TAIWAN) Emeritus Professor Dr.Mike Jenks (Oxford Brookes University, UNITED KINGDOM ) Professor Dr.I Nyoman Pujawan (Sepuluh Nopember Institute of Technology, INDONESIA) Professor Dr.Toshio YOSHII (EHIME University, JAPAN) Professor Dr.Neven Duić (University of Zagreb, CROATIA) Professor Dr.Dewan Muhammad Nuruzzaman (University Malaysia Pahang MALAYSIA) Professor Dr.Masato SAITOH (Saitama University, JAPAN) Scientific and Technical Committee & Editorial Review Board on Engineering, Technologies and Applied Sciences: Associate Prof. Dr. Paulo Cesar Lima Segantine (University of São Paulo, BRASIL) Associate Prof. Dr. Kurt B. Wurm (New Mexico State University, USA ) Associate Prof. Dr. Truong V.B.Giang (Vietnam National University, Hanoi, VIETNAM) Associate Prof. Dr. Fatemeh Khozaei (Islamic Azad University Kerman Branch, IRAN) Assistant Prof.Dr. Zoe D. Ziaka (International Hellenic University, GREECE) Associate Prof.Dr. Junji SHIKATA (Yokohama National University, JAPAN) Assistant Prof.Dr. Akeel Noori Abdul Hameed (University of Sharjah, UAE) Assistant Prof.Dr. Rohit Srivastava (Indian Institute of Technology Bombay, INDIA) Assistant Prof. Dr.Muhammad Yar Khan (COMSATS University, Pakistan) Assistant Prof. Dr. David Kuria (Kimathi University College of Technology, KENYA ) Dr. Mazran bin Ismail (Universiti Sains Malaysia, MALAYSIA ) Dr. Salahaddin Yasin Baper (Salahaddin University - Hawler, IRAQ ) Dr. Foong Swee Yeok (Universiti Sains Malaysia, MALAYSIA) Dr.Azusa FUKUSHIMA (Kobe Gakuin University, JAPAN) Dr.Yasser Arab (Ittihad Private University, SYRIA) Dr.Arslan Khalid (Shandong University, CHINA) ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
  3. 3. i ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. :: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies Volume 11 Issue 6 (2020) ISSN 2228-9860 http://TuEngr.com eISSN 1906-9642 FEATURE PEER-REVIEWED ARTICLES MICRO AND MACRO FINANCIAL INCLUSION AND THEIR IMPACTS ON ECONOMIC GROWTH: EVIDENCE FROM ASIAN ECONOMIES WITH ALTERNATIVE APPROACHES 11A06A DEVELOPMENT OF TECHNOLOGY FOR THE PRODUCTION OF MULTICOMPONENT FEED SUPPLEMENT 11A06B HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS WITH HEART FAILURE IN SOUTH PUNJAB PAKISTAN: A SECTIONAL SURVEY STUDY 11A06C ROLES OF PHYSICAL ENVIRONMENT IN CHARACTERIZING THE IDENTITY OF MALAY ROYAL TOWN IN ALOR SETAR, KEDAH 11A06D GREEN HUMAN RESOURCE MANAGEMENT PRACTICES AND ORGANIZATIONAL CITIZENSHIP BEHAVIOUR FOR ENVIRONMENT: THE INTERACTIVE EFFECTS OF GREEN PASSION 11A06E CHARACTERISTICS OF POST-GLOBALIZATION 11A06F APPLICATION OF NUMERICAL MODELING TO PREDICT AND EVALUATE THE EFFECTS OF MANAGEMENT SCENARIOS IN GROUNDWATER RESOURCES 11A06G EFFECTS OF IRRIGATION SCHEDULING AT DIFFERENT MANAGED ALLOWABLE DEPLETION IN SALINE SOIL ON THREE RICE VARIETIES 11A06H DYNAMIC NEXUS AMONG GOLD PRICE, EXCHANGE RATES, AND EQUITY RETURN OF PAKISTAN 11A06I SMALL SPACES NEED SMART SOLUTIONS: IMPACTS OF SMART INTERIOR DESIGN SOLUTIONS ON ACHIEVING FLEXIBLE SPACES 11A06J STATE AND EFFECTIVENESS OF THE RUSSIAN ENTERPRISE OF HORTICULTURE PRODUCTION 11A06K IMPACTS OF SUBSTANTIAL STRATEGIC ISSUES AND TRENDS OF FOREIGN DIRECT INVESTMENT ON INDIAN RETAIL CONSUMER SERVICE INDUSTRY: AN EMPIRICAL INVESTIGATION 11A06L
  4. 4. ii IMPACTS OF FOREIGN DIRECT INVESTMENT ON INDIAN RETAIL BAZAAR: AN EMPIRICAL STUDY 11A06M ALGORITHMIZATION FOR PROCESSES OF REGIONAL DIFFERENTIATION AND CONCENTRATION OF INVESTMENTS IN RUSSIAN AGRICULTURE 11A06N FACTORS AFFECTING ACCEPTANCE AND THE USE OF TECHNOLOGY IN YEMENI TELECOM COMPANIES 11A06O STUDENTS' FINANCIAL LITERACY AND POLICIES FOR ITS DEVELOPMENT 11A06P 3D MODELING IN AUTOCAD? AS A BASIC COMPONENT OF THE INITIAL TRAINING OF MECHANICAL ENGINEERS 11A06Q USE OF FULL-FAT SOY FLOUR IN COMPOUND FEEDS FOR MEAT CHICKENS OF THE INITIAL LINES AND BROILER CHICKENS 11A06R A ROBUST VIDEO STABILIZATION ALGORITHMS FOR GLOBAL MOTION ESTIMATION USING BLOCK MATCHING 11A06S EVALUATION OF ERGONOMIC DESIGN OF DESK AND CHAIR FOR PRIMARY SCHOOLS IN ERBIL CITY 11A06T Contacts: Professor Dr.Ahmad Sanusi Hassan (Editor-in-Chief), School of Housing, Building and Planning, UNIVERSITI SAINS MALAYSIA, 11800 Minden, Penang, MALAYSIA. Tel: +60-4-653-2835 Fax: +60-4-657 6523, Sanusi@usm.my, Editor@TuEngr.com Associate Professor Dr.Boonsap Witchayangkoon (Executive Editor), Thammasat School of Engineering, THAMMASAT UNIVERSITY, Klong-Luang, Pathumtani, 12120, THAILAND. Tel: +66-2-5643005 Ext 3101. Fax: +66-2-5643022 DrBoonsap@gmail.com, Editor@TuEngr.com Managing Office TUENGR Group, 88/244 Moo 3, Moo Baan Saransiri, Klong#2, KlongLuang, Pathumtani, 12120, THAILAND. Tel/WhatsApp: +66-995535450. P l P id i MALAYSIA/THAILAND Side image is the colorful models of Corona COVID19 virus.
  5. 5. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 1 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com PAPER ID: 11A06A MICRO AND MACRO FINANCIAL INCLUSION AND THEIR IMPACTS ON ECONOMIC GROWTH: EVIDENCE FROM ASIAN ECONOMIES WITH ALTERNATIVE APPROACHES Jamshed Ali 1* , Muhammad Arshad Khan 2 1 Department of Management Science, COMSATS University Islamabad, PAKISTAN. 2 Department of Economics, COMSATS University Islamabad, PAKISTAN. A R T I C L E I N F O A B S T RA C T Article history: Received 19 June 2019 Received in revised form 10 December 2019 Accepted 27 December 2019 Available online 06 January 2020 Keywords: Micro-FII; Macro-FII; Financial inclusion indices; Economic growth; Dynamic Factor Model (DFM); Financial development indicators; GMM. This study examines the impacts of financial inclusion on economic growth in a panel of 20 Asian economies using annual data over the period 1995-2017. Two separate indices, namely micro- financial inclusion and macro-financial inclusion were constructed following Sarma (2008) and the Dynamic Factor Model. The impact of each index, as well as individual indicators of financial inclusion, is analyzed to get deeper insights into the impact of financial inclusion on economic growth in Asian economies. The impact of the micro- financial inclusion index based on Saram’s (2008) methodology is found to be significant, while the impact of macro-financial inclusion index remains insignificant. In contrast, the impact of DFM-based micro and macro-financial inclusion indices exerts an insignificant impact on economic growth. The difference in results could be due to the fact that Sharma’s index measures the level of financial inclusion, while DFM-based indices reflect the growth of financial inclusion indicators. With regard to the individual indicators of financial inclusion, the results show that a number of banks, borrower accounts, and deposit accounts have a significant positive impact on economic growth, while financial system deposits and insurance premium exerts an insignificant impact on economic growth. Disciplinary: Financial Management and Economic Sciences. ©2020 INT TRANS J ENG MANAG SCI TECH. 1 INTRODUCTION Financial inclusion is a multi-dimensional term used to describe a financial system that provides easy access to financial services at affordable costs to households and businesses, irrespective of their size and market value (Mohan, 2006). It is a process that ensures access to financial services to every member of society in a transparent manner and at affordable prices through the proper institutional setup. World Bank (2008, p. 27) defines financial inclusion as “the ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
  6. 6. 2 Jamshed Ali, Muhammad Arshad Khan access to financial services without non-price and price barriers to all members of society”. Deposits, credit, payments, and insurance are generally considered as the main indicators of financial inclusion. Allen et al. (2012) argued that bank account increases savings, empowers women, boost household consumption and raise productive investment, which in turn accelerate economic growth. Financial inclusion enables institutions to operate under comprehensive regulations, organizational framework and industry-specific performance standards. It brings sustainability in the performance of institutions and ensures financial stability through continuity of funds. It helps in allocating financial risks to those agents who have the capacity to bear it without hurting their financial position (Demirguc-Kunt et al, 2008). Financial inclusion enhances market competition through increasing choices among different financial products and affordability for potential customers. It also augments inclusive growth, economic development, and financial deepening thereby reduce poverty and income inequality. In contrast, financial exclusion leads to the emergence of unorganized and exploitative financial markets and financial illiteracy (Sharma, 2016). Schumpeter (1911) is the pioneer detecting a link between finance and economic growth that an effective and organized financial system is important for facilitating economic growth through the channelization of funds. Economic growth can be simulated by having a well-functional financial system with reasonable financial depth. The deepening of the financial sector increases the supply of financial services, which in turn stimulates economic growth. In the modern socio- economic development policy agenda, broader accessibility to various financial products and services is considered an important enabling factor (Buera et al, 2011). Financial inclusion is vital for economic development and decreasing poverty (Beck & Torre, 2006). The available literature on the impact of Asian financial inclusion and economic growth is scant. Many studies concentrated on specific geographical areas like the Organization of Islamic Countries (OIC) and the Middle East and North Africa (MENA), while Asian has been largely neglected. It would be very interesting to examine the impact of financial inclusion on Asian economic growth. Asia is diverse in terms of culture, political system, economic size and level of economic development. Asia is the biggest continent on the basis of population as it hosts 60% of the world’s population and it covers 30% of the land area. The average Asian GDP growth rate was more than 5% per annum. The Organization for Economic Cooperation and Development (OECD, 2017) projected that the Asia-pacific region is expected to grow at a rate of more than 5% in the coming years. The growth of China and India is exceptional in the region. The other economies are also growing at an average rate of 5%. The nominal GDP of Asia was $31.58 trillion; while per capita income was $7351 (IMF, 2019). Despite the large economic size and exceptional economic growth, a large number of population has still a very low level of financial access in Asia as compared to other parts of the world. According to the Global Findex (2017) more than half of the financially excluded peoples living in China, India, Bangladesh, Pakistan, Indonesia, Nigeria, and Mexico. This indicates that Asia is also hosting the largest segment of the world’s financially excluded peoples. Thus, improving financial access can lead to Asia as an important stimulus for achieving sustainable economic growth. Several empirical studies (inter alia by Demirguç-Kunt et al, 2013; Efobi et al, 2014; Allen et al, 2016; Muhammad et al, 2017) focused on individual characteristics of financial inclusion and
  7. 7. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 3 overlooked the role of a macro-level factor in determining economic growth. Given the importance of financial inclusion in economic growth, the main objective of this study is to examine the impact of micro-based and macro-based indicators of financial inclusion on economic growth for a panel of 20 Asian countries for 1995-2017, as no previous studies found. This study uses the Dynamic Factor Model (DFM), which is a relatively new approach for the construction of micro-based and macro-based financial inclusion indices. The DFM method is useful to deal with the issues of multicollinearity and dimensionality. To check the robustness we also construct micro-based and macro-based financial inclusion indices using Sarma (2008) methodology. The empirical analysis based on micro and macro-based indices of financial inclusion allows us to understand their relative importance in the determination of economic growth in the Asian region. This study also investigates the impact of each component of financial inclusion on economic growth in 20 Asian countries. 2 LITERATURE REVIEW Theoretically, Schumpeter (1911) argued that a well-functional financial sector facilitates the real sector’s growth which enhances the economic growth of an economy. Shaw (1973) and Mckinnon (1973) asserted that a well-developed financial sector decreases monitoring and transaction costs and minimizes asymmetric information and thus enhances financial intermediation in an economy. Financial inclusion received much attention after the late 1990s and the early 2000s. In 2010, G20 countries accepted financial services accessibility as one of the major pillars of the world development agenda (Zins & Weill, 2016). Many researches on financial inclusion shown mixed results on the relationship between finance and economic growth, divided into three groups. The first group concluded positive relationships between financial inclusion and economic growth, using a variety of financial inclusion indicators (FII). For example, Wong (2015) found positive impacts of financial access on economic growth in developing countries. Inoue and Hamori (2016) detected positive impacts of commercial bank branches on economic growth in 37 Sub-Saharan African economies. Subrahmanyam & Acharya (2017) demonstrated that financial inclusion as a component of the supply-leading strategy of the financial development model can clearly create faster economic growth. Kim et al. (2018) examined the impact of financial access on economic growth in 55 economies of the OIC region. Their results showed that a high level of financial access causes faster economic growth. Likewise, Sethi & Acharyaa (2018) concluded that financial inclusion exerted a positive impact on economic growth in a panel of 31 countries. At the country level, the positive effects of financial inclusion on economic growth were reported by (Aduda & Kalunda, 2012; Onalapo, 2015; Babajide et al, 2015; Sharma, 2016) using different approaches. They concluded that access to finance spurs economic growth in different countries. For instance, Dev (2006) demonstrated that in India financial inclusion can work as a strategic tool for improving rural enterprises and livelihood of underprivileged farmers. Mehrotra et al. (2009) reported that access to financial services enables saving, transferring and investing money, which faster economic growth. Similarly, Swamy (2010) emphasized the importance of access to finance for long term inclusive growth. Lenka & Sharman (2017) revealed a positive association between economic growth and access to finance in India. The second group demonstrated that financial inclusion has a negative impact on economic growth. For example,
  8. 8. 4 Jamshed Ali, Muhammad Arshad Khan Naceur et al. (2007) explored that in MENA region bank development adversely affected economic growth, meaning that banking sector development has a negative effect on economic growth. They also observed that private sector credit exerted an insignificant impact on growth in MENA countries. Pearce (2011) demonstrated that financial inclusion enhances the economic growth of a smaller social class, while the disadvantaged groups like underprivileged, elders, women and uneducated populations cannot get benefits from the financial system. Thus, financial inclusion does not affect the economic growth of disadvantaged groups. The third group explored the causal links of financial inclusion and economic growth. For instance, Apergis et al. (2007) found bidirectional causality between financial inclusion and economic growth in a panel of 65 OECD and non-OECD countries. Sharma (2016) explored bi-directional causality between financial inclusion and economic growth. Similarly; Kim et al. (2018) also revealed the presence of a bi-directional causal relationship between financial inclusion and economic growth in OIC countries. The conflicting evidence with respect to the link between financial inclusion and economic growth creates a need for an investigation to gain new insights. It is worthwhile for developing regions like Asia to examine how micro and macro-level financial inclusion impacts economic growth. Since financial intermediaries work as catalysts for economic growth in developing economies like Asia, therefore, economic growth is assumed to be dependent on financial sector development. 3 MODEL, METHODOLOGY, AND DATA The present study investigates different dimensions of financial inclusion in 20 Asian countries in a more comprehensive way. To get deep insights into financial inclusion and economic growth nexus, we have used different statistical techniques such as Sarma (2008), DFM and dynamic panel Generalized Moment Method (GMM) estimator. 3.1 FINANCIAL INCLUSION AND ECONOMIC GROWTH To examine the impact of financial inclusion on economic growth, we have used a two-step procedure. In the first step, we have examined the impact of financial inclusion on economic growth in a panel of 20 Asian countries. We have constructed two separate indices for micro and macro- financial inclusion. Then, we have examined the impact of individual indicators of financial inclusion on economic growth. Following Kim et al. (2018), we specify the empirical model itiitit itititititit FIIUnemp TradeSEPOPINFLnGDPPCLnGDPPC enββ ββββθβ ++++ +++++= − 65 432110 (1), where cross-sectional units are represented by i, while t represents the time period, that is, 1995- 2017. The term iv represents country-specific effects, while the error term itε is assumed to be identically and independently distributed (iid ) over the whole sample period. In Equation (1), one lag of itLnGDPPC is included to control the endogeneity problem. The dependent variable LnGDPPC is the logarithmic value of GDP per capita which is used as a proxy of economic growth, FII is the financial inclusion index. INF is the inflation rate, POP is the population growth rate, SE indicates school enrolment and Unemp represents the unemployment rate. To examine the impact of individual indicators of financial inclusion on economic growth, we can rewrite Equation (1) as:
  9. 9. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 5 }{ itiitititititit i i ititititittiit LnPCLnFSDLnIPLnBALnDALnBanks UnempTradeSEPOPINFLnGDPPCLnGDPPC enγ βββββθβ +++ ++++++= ∑= − ,,,,,, 6 1 5432110 (2), where DA, BA , IP , FSDand PC are the deposit accounts, borrower accounts, insurance premium relative to GDP, total deposits in the financial system and private sector credit relative to GDP, respectively. The description of variables and data sources is given in Table 1. Table 1: Definition of variables and data source. Variable Symbol Definition of Variable Data Source Real GDP per capita itGDPPC Real GDP per capita is obtained as gross domestic product adjusted for inflation dividing by the population of each country. World Bank Population growth itPOP The population growth rate for the year t is the growth rate of the midyear population from 1−t to t . World Bank Unemployment rate itUnemp The unemployment rate measures the number of people actively looking for jobs as a percentage of the total labor force. International Labour Organization Inflation rate itINF Inflation is calculated as changes in Consumer Price Index (CPI) with base year 2010=100. World Bank/ International Monetary Fund (IMF) School enrolment itSE Net primary enrollment is obtained as the ratio of school age who are enrolled in school and population of official school age. UNESCO Institute of Statistics Trade Openness itTrade Sum of exports and imports relative to GDP. World Bank/IMF Deposit accounts itDA A number of deposit accounts with commercial banks per 100000 of the population. IMF/World Bank’s World Development Indicators and respective country central bank and statistics department Borrower accounts itBA A number of borrower accounts with commercial banks per 100000 of the population. No of banks itBanks Number of commercial bank per 100000 of population Private sector credit relative to GDP itPC Total credit distributed to the private sector as a percentage of GDP. World Bank/IMF Financial system deposits relative to GDP itFSD Total demand, time and saving deposits with banks and other financial institutions as a percentage of GDP. World Bank/ IFS Insurance premium relative to GDP itIP Total life and non-life insurance premium as a percentage of GDP. World Bank 3.2 DATA This study main object is to explore the impact of financial inclusion on economic growth in 20 Asian economies. The available 1995-2017 data on various variables shown in Table 1 are extracted from the World Development Indicators (WDI), IMF’s World Governance Indicators, United Nations Education, Scientific, and Cultural Organization (UNESCO), United Nation Development Programme (UNDP), International Financial Statistics (IFS), Oxford University Database, Central Banks, and Statistical Ministries of respective countries. 3.3 METHODOLOGY One objective of this study is to construct a financial inclusion index to overcome the issues of multi-dimensionality and multicollinearity among the different indicators of financial inclusion. To this end, we have used Sarma’s (2008) methodology and DFM advanced by Stock and Watson (2002) for the construction of micro-based and macro-based financial inclusion indices.
  10. 10. 6 Jamshed Ali, Muhammad Arshad Khan 3.3.1 SHARMA’S METHODOLOGY Following Sarma (2008), we have constructed a micro-based financial inclusion index by considering the following indicators (justification given in Pina (2018)): o A number of branches of commercial banks per 100,000 adults. o A number of commercial banks deposit accounts per 100,000 adults. o A number of borrowers from commercial banks per 100,000 adults. For the construction of macro-financial inclusion index, we consider the following indicators: o Private credit as a percentage of GDP. o Insurance premium as a percentage of GDP. o Deposits in the financial system as a percentage to GDP. The average of each indicator for the selected economies is computed first and then we calculated the index of each indicator following Sarma (2008) as jj jj i mM mA D − − = (3), where jA is the actual value of the indicator j , jm is the minimum value of indicator i and jM is the maximum value of the indicator j . Financial inclusion index for the country I is measured by the normalized inverse of Euclidean distance of point id computed in Equation (3) from the ideal point which is considered as 1. We used the following formula to compute financial inclusion index ( FII ): n ddd FII n i 22 2 2 1 )1(................)1()1( 1 −++−+− −= (4), where numerator of the second term in Equation (4) is the Euclidean distance from an ideal point which is assumed to be 1, it is normalized by the square root of the number of observations and subtracting id from ideal value 1, giving the inverse normalized distance. All indicators are normalized for making it standardized and its value lies between 0 and 1, where 1 shows the highest level of financial inclusion and 0 reflects the lowest level of financial inclusion. It can be argued that Saram’s (2008) methodology is based on static factor models that involved the issue of multicollinearity. It also reflects the level of financial inclusion regardless of growth in FII. To overcome this issue, we have used the DFM technique to construct the financial inclusion index that is a relatively new methodology. 3.3.2 DYNAMIC FACTOR MODEL The DFM has the ability to overcome problems of high dimensionality and multicollinearity. The DFM method can decompose each variable into common and idiosyncratic components which make the variables different. Following Stock & Watson (2002) we have constructed micro-based and macro-based financial inclusion indices using the DFM methodology. 3.4 TRENDS OF FINANCIAL INCLUSION INDICES The trends of financial inclusion index based on Sarma’s (2008) methodology are depicted in Figure 1. In order to get deeper insights into the accessibility and usage of financial services, we have constructed two different indices, the micro-FII that reflects the micro-side of financial inclusion. Micro-FII is constructed using the information on number of bank branches per 100,000
  11. 11. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 7 adults, number of deposit accounts per 100,000 adults and number of saving accounts per 100.000 adults, while macro-FII deals with macro-side of financial inclusion, which is constructed using the financial development indicators such as insurance premium relative to GDP, private sector credit relative to GDP and total deposits in financial system of economy. The score of FII lies between 0 and 1, 0 indicates a minimal level of financial inclusion; while 1 reflects the highest level of financial inclusion. Figure 1: Trends of Macro and Micro financial inclusion indices (1995-2017) (Sources: Authors calculations) Figure 1, Japan has the highest score in micro-FII and macro-FII, followed by Indonesia, South Korea, and Singapore. The relatively high discrepancy between micro and macro-financial inclusion indices in some countries could be due to the difference between the level of financial accessibility and financial sector development. For example, Thailand has quite a good financial development level as compared to the level of financial accessibility. We have ranked countries based on the level of financial inclusion. Table 2 depicts macro-FII and micro-FII ranking. Table 2: Ranking of Asian Countries by Micro-FII and Macro-FII (1995-2017). (Sources: Authors calculation). Rank Country Micro-FII Rank Country Macro-FII 1 Japan 0.601 1 Japan 0.735 2 South Korea 0.356 2 South Korea 0.591 3 Singapore 0.325 3 Thailand 0.421 4 Brunei-DS 0.317 4 China 0.277 5 Magnolia 0.256 5 Singapore 0.276 6 Malaysia 0.206 6 Malaysia 0.265 7 Uzbekistan 0.182 7 Kyrgyz rep 0.234 8 India 0.146 8 India 0.205 9 Thailand 0.145 9 Brunei-DS 0.174 10 Indonesia 0.116 10 Magnolia 0.171 11 Sri Lanka 0.104 11 Vietnam 0.135 12 Khazikistan 0.088 12 Indonesia 0.133 13 Azerbaijan 0.085 13 Sri Lanka 0.114 14 China 0.074 14 Philippine 0.109 15 Bangladesh 0.070 15 Bangladesh 0.109 16 Pakistan 0.070 16 Khazikistan 0.084 17 Philippine 0.063 17 Pakistan 0.046 18 Tajikistan 0.042 18 Azerbaijan 0.043 19 Kyrgyz Rep 0.039 19 Tajikistan 0.037 20 Vietnam 0.038 20 Uzbekistan 0.035 Table 2, on the basis of micro-FII, Japan has the highest level of financial inclusion, followed
  12. 12. 8 Jamshed Ali, Muhammad Arshad Khan by South Korea, Singapore, and Brunei Darussalam, while Vietnam and the Kyrgyz Republic have the lowest level. Pakistan is in the 16th position; it means that access to financial services in Pakistan is better than Philippine, Tajikistan, Kyrgyz Republic and Vietnam. Macro-FII reflects the level of financial sector development. Again, Japan has the highest score, followed by South Korea, Thailand, China, Singapore, and Malaysia, while Uzbekistan has the lowest score. Pakistan is in the 17th position based on the level of financial sector development. 3.5 DYNAMIC FACTOR MODEL-BASED FINANCIAL INCLUSION INDICES The micro-FII and macro-FII based on the DFM approach are depicted in Figure 2. Figure 2: Trends of Macro-FII and Micro-FII based on DFM Approach (1995-2017) (Sources: authors’ calculations). The Micro-FII depicted in Figure 2 shows higher variation across countries. This means that countries with a high level of financial inclusion have a higher score based on the DFM approach. However, macro-FII indicates relatively less variation as compared to the fluctuation associated with micro-FII. This clearly indicates that micro-FII is more volatile than macro-FII and in terms of access to finance because Asian economies are more heterogeneous. 4 RESULT 4.1 DESCRIPTIVE STATISTICS In Table 3, the mean values of all variables are positive. More importantly, positive mean values of Micro-FII and Macro-FII indicates an important role of access to financial services and financial development in economic growth in selected Asian countries. The mean value of micro- level financial inclusion, such as the number of bank branches, the number of deposit accounts and a number of borrower accounts is positive. Among the micro-level FII, the mean value of a number of deposit accounts is highest (112865.6), followed by a number of borrower accounts (23755.96), showing the key role of these variables in economic growth. Similarly, the large difference between maximum and minimum values for all variables indicating that the selected sample is heterogeneous with large and small countries. On the other hand, the mean value of macro-level FII is also positive with a mean value of private sector credit is 53.40, followed by financial system deposits relative to GDP (49.46). The standard deviation of all variables is positive. However, the standard deviation of deposit accounts (DA) and borrower accounts (BA) has the highest variation among all the variables, showing large volatility in these variables. The standard deviation of
  13. 13. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 9 Sarma’s micro and macro-financial inclusion indices is lowest because of the standardization of indices values between 0 and 1. Table 3: Descriptive statistics. Variable Mean SD Min Max itLnGDPPC 8.93 1.15 6.65 11.45 itFIIMacroDFM −: 0.37 0.99 -2.51 6.66 itFIIMicroDFM −: 1.58 2.55 -5.02 8.10 itFIIMacroSarma −: 0.21 0.18 0.01 0.79 itFIIMicroSarma −: 0.17 0.15 0.01 0.67 itSE 91.08 8.91 42.23 99.92 itTrade 95.73 74.67 16.68 437.3 itUnemp 5.15 3.10 0.39 16.5 itPOP 1.30 0.79 -1.75 5.32 itINF 9.49 32.38 -8.52 512.3 itBanks 13.55 13.38 0.081 71.61 itDA 112865.6 153097.5 462.79 726909 itBA 23755.96 26136.8 207.42 123300 itPC 53.40 48.40 0.94 192.46 itFSD 49.46 48.58 1.42 221.13 itIP 2.33 2.83 0.02 13.18 To examine the possibility of multicollinearity among the variables under consideration, we performed Pearson’s correlation analysis and the results are reported in Table 4. Table 4: Correlation analysis. Ln (GDPPC) SE Trade Unemp POP INF Banks DA BA PC FSD IP Ln (GDPPC) 1.00 SE 0.44 1.00 Trade 0.38 0.32 1.00 Unemp -0.23 0.02 0.01 1.00 POP -0.12 -0.29 0.27 -0.12 1.00 INF -0.24 -0.13 0.02 0.27 -0.01 1.00 Banks 0.41 0.22 0.01 -0.09 -0.02 -0.10 1.00 DA 0.62 0.35 0.01 -0.17 -0.35 -0.13 0.39 1.00 BA 0.81 0.37 0.39 -0.21 -0.13 -0.16 0.33 0.77 1.00 PC 0.38 0.45 0.01 -0.33 -0.38 -0.16 0.34 0.55 0.37 1.00 FSD 0.44 0.34 -0.09 -0.19 -0.39 -0.15 0.19 0.84 0.61 0.64 1.00 IP 0.51 0.36 0.16 -0.28 -0.33 -0.13 0.09 0.79 0.68 0.50 0.74 1.00 The correlation coefficients reported in Table 4 show that unemployment, population growth, and inflation rate are negatively correlated with real GDP per capita. The reason for the negative correlation of these variables with real GDP per capita could be the adverse effect of macro and micro variables on economic growth. Population growth without a proper plan and economic resources leads to serious socioeconomic problems; the main issue is that it can cause unemployment. An increase in unemployment may decreases productivity and reflects the underutilization of human resource. Similarly, inflation adversely affects a household’s consumption and savings, which in turn have a negative impact on real GDP per capita growth. Among the financial inclusion indicators, BA and DA have a positive correlation with real GDP per capita with correlation coefficients are 0.62 and 0.81 respectively. It reflects that an increase in borrower accounts and deposit accounts causes to increase in real GDP per capita in Asian
  14. 14. 10 Jamshed Ali, Muhammad Arshad Khan countries. The positive correlation could be due to the fact that it facilitates a pro-growth environment that enables inclusive growth. 4.2 IMPACT OF MICRO FINANCIAL INCLUSION ON ECONOMIC GROWTH Using a dynamic GMM estimator, we have estimated five models by considering micro-FII and individual components of financial inclusion. The main aim of using individual indicators of financial inclusion is to get deeper insights regarding the impact of each indicator on economic growth. Table 5 presents the estimated result when micro-level FII is considered. Table 5: Estimates of micro-FII and economic growth. (Dependent variable: itLnGDPPC ). Variable Model (A) Model (B) Model (C) Model (D) Model (E) 1−itLnGDPPC 0.787*** (7.05) 0.90*** (12.13) 0.085*** (11.75) 0.81*** (7.02) 0.76*** (9.73) itSE 0.0024 (0.53) 0.0043 (0.63) 0.0008 (1.55) 0.0032 (0.36) 0.002** (2.26) itTrade 0.0015** (2.05) 0.0013* (1.89) 0.0003** (2.05) 0.0022*** (3.07) 0.0012* (1.82) itINF 0.00007 (0.09) -0.0008 (-1.64) -0.010*** (-3.75) 0.00023 (0.26) -0.052** (-2.05) itPOP -0.16 (-1.10) -0.094* (-1.99) -0.024** (-2.22) -0.11 (-1.48) -0.028 (-0.76) itUnemp -0.041** (-2.11) -0.03** (-2.18) -0.0023 (-0.74) -0.042*** (-3.53) -0.038*** (-2.91) itFIIMicroSarma −: 0.022** (2.19) - - - - itFIIMicroDFM −: - -0.007 (-1.15) - - - itLnBanks - - 0.004** (2.15) - - itLnDA - - - 0.024** (2.21) - itLnBA - - - - 0.029** (2.14) Observations 460 460 460 460 460 AR(2) 0.27 0.13 0.21 0.19 0.52 Hansen test 0.63 0.68 0.64 0.55 0.71 F-state 230.23*** 435.16*** 683.4*** 110.86** 184.01*** Note: Values in parentheses are the t-stat. *, **, *** respectively indicate significance at the 10%, 5% and 1% level. The results associated with Model (A) indicate the impact of the financial inclusion index based on Sarma’s (2008) method. The finding shows that financial inclusion based on micro indicators exerts a significant positive impact on economic growth. This implies that an increase in the accessibility of financial services plays an important role in enhancing economic growth in the Asian region. Furthermore, one period lagged GDPPC, trade openness and unemployment has a statistically significant impact on economic growth. Concerning the control variables, the result reveals that trade openness exerts a significant positive impact on economic growth as it improves the usage of financial services and also increases the circulation of capital. The impact of the unemployment rate on economic growth is negative and significant. It could be due to the fact that when unemployment increases, economic activities slowed down, which in turn deteriorates economic growth. Other control variables such as school enrolment, inflation rate, and population growth exert an insignificant impact on economic growth. Model (B) shows the impact of DFM- based micro-FII on economic growth. The result indicates that DFM-based micro-FII exerts an insignificant negative impact on economic growth in Asian countries. Other variables such as lagged GDPPC, trade openness, population growth and unemployment rate have a significant
  15. 15. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 11 impact on economic growth, while school enrolment and inflation rate are insignificant. With regard to the individual indicators of financial inclusion, in the model (C) a number of bank branches are positively related to economic growth. This implies that an increase in the number of bank branches providing access to financial services, which in turn exerts a positive impact on economic growth. The positive association shows that the increase in banking penetration is a key factor in deriving economic growth. Furthermore, the impact of lagged GDPPC, trade openness, inflation rate and unemployment rate are statistically significant. Model (D) indicates that the impact of deposit accounts on economic growth is positive and statistically significant. This reveals that an increase in the number of deposit accounts enhances economic growth in Asian countries. This finding is important in the sense that an increase in the number of deposit accounts is a very basic step towards achieving financial inclusion. Additionally, lagged GDPPC, trade openness, inflation rate and population growth have a significant impact on economic growth. Finally, in Model (E) the impact of borrower accounts on economic growth is positive and significant, implying that an increase in loan accounts leads to enhances economic growth in Asian countries. Other variables entered in the Model (E) with a statistically significant impact on economic growth. Overall, the results reported in Models (C) to (E) reveal a positive and significant relationship of a number of banks, deposits accounts and borrower’s accounts with economic growth which supports the financial inclusion-growth nexus in selected Asian countries. 4.3 IMPACT OF MACRO FINANCIAL INCLUSION ON ECONOMIC GROWTH Table 6 estimates the impact of macro-financial inclusion index as well as individual macro- indicators of financial inclusion on economic growth. It gives useful insights into the impact of financial deepening on economic growth in Asian countries. Table 6: Estimations of macro-FII and economic growth in Asian countries (Dependent variable: itLnGDPPC ). Variable Model (F) Model (G) Model (H) Model (I) Model (J) 1−itLnGDPPC 0.877*** (10.08) 0.867*** (8.24) 0.961*** (13.05) 0.864*** (11.74) 0.910*** (13.34) itSE 0.006 (0.45) 0.011 (1.08) -0.022** (-2.20) 0.0027 (0.20) 0.0032 (0.51) itTrade 0.001* (1.94) 0.001 (1.33) 0.002*** (3.07) 0.001** (2.28) 0.001*** (3.24) itINF -0.0004 (-0.20) 0.001 (1.13) 0.001 (-1.35) 0.0007 (0.32) -0.0001 (-0.04) itPOP -0.160 (-1.41) -0.21 (-1.34) -0.054* (-1.86) -0.13 (-1.02) -0.190 (-1.37) itUnemp -0.043* (-1.88) -0.470** (-2.15) -0.037*** (-2.77) -0.052** (-2.22) -0.046 (-1.39) itFIIMacroSarma −: -0.007 (-0.56) - - - - itFIIMacroDFM −: - -0.010 (-0.58) - - - itLnPC - - 0.003* (1.73) - - itLnFSD - - - 0.003** (2.06) - itLnIP - - - - -0.029 (-1.18) Observations 460 460 460 460 460 AR(2) 0.21 0.20 0.89 0.14 0.23 Hansen test 0.80 0.39 60.4 0.45 0.77 F-stat 309.05*** 340.35*** 88.41*** 141.01*** 429.2*** Note: 1) Values in parentheses are the t-stat. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.
  16. 16. 12 Jamshed Ali, Muhammad Arshad Khan It is evident from Table 6 (Model (F)) that the impact of macro-financial inclusion index (macro-FII) due to Sarma’s (2008) on economic growth is insignificant. This means that the financial deepening-side of financial inclusion does not influence economic growth significantly in selected countries of the Asian region. In addition, lagged GDPPC, trade openness and the unemployment rate has a statistically significant impact on economic growth. Similarly, in Model (G) the impact of macro-financial inclusion index based on DFM (DFM-macro-FII) on economic growth has also insignificant. However, lagged GDPC, trade openness, and the unemployment rate has a significant impact on economic growth in selected Asian countries. This implies that the growth of the financial sector does not produce any significant impact on economic growth in Asian countries. With respect to individual macro components of financial inclusion, Model (H) reflects that private sector credit relative to GDP has a significant positive impact on economic growth. This implies that an increase in private sector credit exerts a positive impact on economic growth, although the impact of this variable on economic growth is too small. The impact of total deposit with the financial sector ( itLnFSD ) on economic growth in Model (I) is positive and statistically significant. This also implies that the growth of total deposits of the financial system has a positive impact on economic growth. However, the coefficient of this variable is also too small. On the other hand, in Model (J) the effect of insurance premium ( itLnIP ) on economic growth is found to be negative but statistically insignificant. This means the insurance sector does not play any role in economic growth in Asian countries. The impact of lagged GDPPC and other control variables, such as trade openness, population growth, and the unemployment rate is statistically significant in Model(s) (H) through (J), suggesting the key role of these macroeconomic variables in the growth process in Asian countries. Overall, this study result is consistent with the existing literature. For example, Kendall et al. (2010) revealed that economic development is associated with bank branches, deposit accounts, loan accounts and ATM’s availability. Andrianaivo & Kpodar (2011) found a positive and statistically significant effect of financial inclusion on economic growth. Similarly, Demirguc & Klapper (2013) observed that those economies where the commercial bank branches and the volume of deposits are high, the income level of households in those areas also increases at a faster pace. Wong (2015) revealed that growth in developing countries was positively affected by access to finance. Likewise, Inoue & Hamori (2016) detected the positive impact of financial inclusion on the economic growth of selected countries. More recently Kim et al. (2018) found that a high level of financial access causes faster economic growth in OIC economies. They also detected the significant positive impact of ATMs, bank branches, borrower accounts, deposit accounts and private sector credit was significant on economic growth, while the role of insurance premium on economic growth was insignificant. Likewise, Sethi & Acharyaa (2018) concluded that economic growth in OIC economies was positively affected by financial inclusion. They reported bi- directional causality between financial inclusion and economic growth. Thus, the above-cited literature confirms that financial access is among the main drivers which cause economic growth in various part of the world.
  17. 17. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 13 5 CONCLUSION To measure financial inclusion, we have constructed two separate indices, namely micro and macro-financial inclusion, using Sarma (2008) and Stock & Watson’s (2002) Dynamic Factor Model. The result of micro-financial inclusion based on Sarma’s (2008) method reveals a positive association of financial inclusion and economic growth, while the impact of DFM-based financial inclusion index on economic growth is insignificant. The difference between the results could be due to the fact that Sarma’s index measures the level of financial inclusion, while DFM considers the growth of FII. This suggests that the level of financial inclusion contributes to economic growth rather than the growth of financial inclusion. Furthermore, we have also examined the impact individual indicator of micro-level financial inclusion on economic growth and the results show that the number of banks, number of borrower accounts and number of deposit accounts have a statistically significant positive impact on economic growth in selected Asian countries. The impact of macro-financial inclusion indices based on Sarma and DFM methods on economic growth was found to be insignificant. Additionally, the impact of individual indicators of financial deepening- side of financial inclusion is also examined. We find that the impact of private credit relative to GDP and financial system deposits relative to GDP has a significant impact on economic growth, while the impact of insurance premium on economic growth remains insignificant. The results show that micro-level financial inclusion index and indicators of financial inclusion such as the number of bank branches, number of borrower accounts and number of deposit accounts have a significant positive impact on economic growth in Asian countries. Therefore, to stimulate economic growth in Asian economies, policymakers may strengthen financial services accessibility. It can be argued that improved accessibility of financial services works as a stimulus of capital accumulation and financial resources mobilization, which in turn gradually promotes economic growth and social welfare. Therefore, Asian countries may increase the penetration of financial services and eliminate barriers to financial access in order to stimulate economic growth in the region. Furthermore, the expansion of the financial sector can also contribute to economic growth in these economies. 6 DATA AND MATERIAL AVAILABILITY Information regarding this study is available by contacting the corresponding author. 7 ACKNOWLEDGEMENT The authors acknowledge the financial support provided by the Higher Education Commission of Pakistan for the first author’s PhD study. The authors acknowledge the contribution of Mr. Abdur Rehman Rana, a PhD scholar at Department of Management Sciences COMSATS University Islamabad in construction of the financial inclusion indices via DFM for this study. 8 REFERENCES Aduda, J., & Kalunda, E. (2012). Financial inclusion and financial sector stability with reference to Kenya: A review of literature. Journal of Applied Finance and Banking, 2(6), 95-120. Allen, F. (2012). Trends in financial innovation and their welfare impact: an overview. European Financial Management, 18(4), 493-514.
  18. 18. 14 Jamshed Ali, Muhammad Arshad Khan Apergis, N., Filippidis, I., & Economidou, C. (2007). Financial deepening and economic growth linkages: a panel data analysis. Review of World Economics, 143(1), 179-198. Babajide, A. A., Adegboye, F. B., & Omankhanlen, A. E. (2015). Financial inclusion and economic growth in Nigeria. International Journal of Economics and Financial Issues, 5(3), 629-637. Beck , T., & Torre, A. (2006). The basic analysis of access to financial services. World Bank . Buera, F. J., Kaboski, J. P., & Shin, Y. (2011). Finance and development: A tale of two sectors. American Economic Review, 101(5), 1964-2002. Demirguc-Kunt, A., Honohan, P., & Beck, T. (2008). Finance for all? Policies and Pitfalls in Expanding Access. World Bank. Demirguc-Kunt, A., Klapper, L., & Randall, D. (2013). Islamic finance and financial inclusion: measuring use of and demand for formal financial services among Muslim adults. The World Bank: Washington DC. Dev, S. M. (2006). Financial inclusion: Issues and challenges. Economic and Political Weekly, 4310-4313. Efobi, U., Beecroft, I., & Osabuohien, E. (2014). Access to and use of bank services in Nigeria: Micro- econometric evidence. Review of Development Finance, 4(2), 104-114. En, F., Demirguc-Kunt, A., Klapper, L., & Peria, M. S. M. (2012). The foundations of financial inclusion: Understanding ownership and use of formal accounts. The World Bank: Washington DC. Global Findex (2017). Global Findex database. http://globalfindex.worldbank.org. Hariharan, G., & Marktanner, M. (2012). The growth potential from financial inclusion. ICA Institute and Kennesaw State University. Inoue, T., & Hamori, S. (2016). Financial access and economic growth: Evidence from Sub-Saharan Africa. Emerging Markets Finance and Trade, 52(3), 743-753. Kim, D. W., Yu, J. S., & Hassan, M. K. (2018). Financial inclusion and economic growth in OIC countries. Research in International Business and Finance, 43, 1-14. Kpodar, K., & Andrianaivo, M. (2011). ICT, financial inclusion, and growth evidence from African countries. IMF Working Paper No. 11-73, International Monetary Fund, Washington DC. Lenka, S. K., & Sharma, R. (2017). Does financial inclusion spur economic growth in India?. The Journal of Developing Areas, 51(3), 215-228. Mckinnon, R. I. (1973). Money, capital and banking. Brooklyn Institution, Washington DC. Mehrotra, N., Puhazhendhi, V., Nair, G., & Sahoo, B. B. (2009). Financial inclusion-an overview. Occasional Paper No.48, Department of Economic Analysis and Research, National Bank for Agriculture and Rural Development (NABARD). Mohan, R. (2006). Economic growth, financial deepening and financial inclusion. In M. Shadrma , Dynamics of Indian Banking; View and Vistas (p. 442). Naceur, B. S., Ghazouani, S., & Omran, M. (2007). The determinants of stock market development in the Middle-Eastern and North African region. Managerial Finance, 33(7), 477-489. Pearce, D. (2011). Financial inclusion in the Middle East and North Africa: Analysis and roadmap recommendations. The World Bank: Washington DC. Pina, G. (2018). Macro and micro financial liberalizations, savings and growth. Journal of Financial Economic Policy, 10(2), 290-309. Sarma, M. (2008). Index of financial inclusion. Working Paper No. 215, Indian Council for Research on International Economic Relations (ICRIER). Schumpeter, J. (1911). The theory of economic development. Leipzig, Duncker & Humblot, translated as Schumpeter, J. (1934) The Theory of Economic Development. An Inquiry into Profits, Capital, Credit, Interest and Business. Sethi, D., & Acharya, D. (2018). Financial inclusion and economic growth linkage: Some cross country evidence. Journal of Financial Economic Policy, 10(3), 369-385.
  19. 19. *Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101 15 Sharma, D. (2016). Nexus between financial inclusion and economic growth: Evidence from the emerging Indian economy. Journal of Financial Economic Policy, 8(1), 13-36. Shaw, E. S. (1973). Financial deepening in economic development. New York Oxford University Press. Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20(2), 147-162. Subrahmanyam, G., & Acharya, D. (2017). Financial inclusion fosters growth: Simple multiplier and AK growth model analysis. Universal Journal of Accounting and Finance, 5(3), 55-59. Swamy, V. (2010). Financial development and inclusive growth: Impact of government intervention in prioritized credit. Zagreb International Review of Economics and Business, 13(2), 55-72. Wong, Y. H. (2015). Quantifying macroeconomic impacts of financial inclusion. Available at: http://mastercardcenter.org/insights/imf-mit-study-shows-financial-inclusion-drives-economic- growth World Bank (2008). Banking the poor: Measuring banking access in 54 economies. World Bank: Washington, DC. Zins, A., & Weill, L. (2016). The determinants of financial inclusion in Africa. Review of Development Finance, 6, 46-57. Jamshed Ali is a PhD scholar at the Department of Management Sciences, COMSATS University Islamabad, Pakistan. He got his Master’s degree in Finance, from SZABIST Islamabad, Pakitan. His research interests include Financial Development, Financial Inclusion, Economic Growth, Poverty, Indices Construction, Sovereign Rating and Dynamics of Financial Markets Cointegration. Dr. Muhammad Arshad Khand is an Associate Professor and Chairman, Department of Economics, COMSATS University Islamabad, Pakistan. Dr. Khan holds a PhD in Economics from the Pakistan Institute of Development Economics (PIDE), Islamabad. He is interested in Financial Engineering & Financial Dynamics, International Economics, Econometrics, International Money and Finance, Environmental Economics, Agricultural and Resource Economics and Data Science.
  20. 20. *Corresponding author (Sergei Dotsenko). Tel: +7-989-99-87887. Email: lyudmila0511@mail.ru ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06B http://TUENGR.COM/V10A/10A06B.pdf DOI: 10.14456/ITJEMAST.2020.102 1 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com PAPER ID: 11A06B DEVELOPMENT OF TECHNOLOGY FOR THE PRODUCTION OF MULTICOMPONENT FEED SUPPLEMENT Sergei Dotsenko a* , Lyudmila Kryuchkova b , Andrei Burmaga c a Department of Building Production and Engineering Structures, Far Eastern State Agrarian University, RUSSIA. b Department of Higher Mathematics, Far Eastern State Agrarian University, RUSSIA. c Department of Transport and Energy Means and Mechanization of Agribusiness, Far Eastern State Agrarian University, RUSSIA. A R T I C L E I N F O A B S T RA C T Article history: Received 06 April 2019 Received in revised form 12 December 2019 Accepted 24 December 2019 Available online 09 January 2020 Keywords: Feed technology; Insoluble pulp residue; Calcium supplement feed; Protein supplement; β-carotene supplement; Energy intensity; Feed production efficiency. This article presents the results of the development of a new highly effective feed additive based on the use of secondary fractions in the production of a substitute for whole milk. The resulting additive is characterized by high calcium content (10.8 times), as well as a content of β-carotene for 2 mg/100g of the product, which provides it with high biological and nutritional value, as well as antioxidant activity. The developed technology for energy intensity is more than 3 times lower than the base one recommended for small and medium capacity farms. The implementation of the developed technology, as well as the set of equipment adopted for it, makes it possible to increase the efficiency of the functioning of the mechanized system of feeding animals on small and medium-sized farms by reducing the cost of energy, labor, and money. Disciplinary: Animal Sciences (Animal Nutrition and Feed Technology). ©2020 INT TRANS J ENG MANAG SCI TECH. 1. INTRODUCTION An analysis of the data characterizing the condition of the feed base at small and medium-sized enterprises for the production of livestock and poultry products shows that currently there is a shortage of protein, vitamins, macro and micronutrients in feed rations. So, in particular, the use of low-fat soybean meal does not allow to have vitamin E in the diets, and the absence of a root component in the diets, in the form of pumpkin or carrot - β–carotene (Tutelian, 2004, Dotsenko et al., 2020). When developing substitutes for milk-containing feeds, still few include vegetable protein and vitamin substances, and in particular, soy, root, vegetable, and others. Low energy-intensive technologies and technical means for extracting proteins and vitamins from soy grain and root crops ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
  21. 21. 2 Sergei Dotsenko, Lyudmila Kryuchkova, Andrei Burmaga have not been developed. Today, neither small nor medium producers have special small-sized modular-block technology for these purposes (Tutelian, 2005). Protein-vitamin supplements, protein-vitamin calcium supplements, and others are homogeneous mixtures of high-protein fodder products and micro additives, crushed to the required size, used for the preparation of animal feed based on grain fodder. Obtaining soy protein feed products of high nutritional and biological value is a promising direction in the development of complete feeds. Get fodder products from soy flour and carrot, or beetroot, or pumpkin pastes or their compositions, or their combinations based on the principle of averaging dry matter content under mild conditions and a shorter cooking time with a higher protein and fat content, the presence of biologically active substances in them - carotene and vitamins C and R (Kochetkova, 1999). At the same time, non-fat-free flour is prepared on the basis of soybean seeds, which contains vitamin E and more than 40% protein, balanced for essential amino acids. In this regard, obtaining protein-vitamin products based on these raw materials, for example, in granular physical form, is an urgent task. Deficiency of proteins and calcium can be eliminated in diets by using meat or fishmeal meals, but the cost of their production is very high (Petibskaya, 2012). A theoretical approach to achieving the goal is that upon receipt of soy protein feed products, including mixing, molding and heat treatment of a mixture of soy protein and carbohydrate-vitamin components, soy protein-free flour containing dry matter 88-92 is used as a soy protein component %, and carbohydrate and vitamin - carrot, beetroot or pumpkin paste or their composition, or combinations thereof with a solids content of 8-12% (Dotsenko et al., 2018). At the same time, the production technology of so-called “soy milk” based on soy-pumpkin or soy-carrot compositions, a waste fraction in which soy-pumpkin or soy-carrot pulp residue is known (Dotsenko, et al, 2016). Using this waste product in combination with chalk in powder form, you can get granular feed protein-vitamin-calcium supplement (Ostroumov, 1998). This study develops a technological scheme for producing protein-vitamin-calcium supplements and justifies the technology using soya-pumpkin and soya-carrot pulp residue in the technology for producing granular protein-vitamin-calcium supplements with less labor and money. 2. MATERIALS AND METHODS Studies have established that pulp obtained as a waste in the production of soya-pumpkin or soya-carrot product in the form of a substitute for whole milk contains 60-70% water, δ = 10% protein, β-carotene on average up to 10 mg/kg, however, it has a limited shelf life [1, 2]. According to the proposed technology (Figure 1), on the basis of pre-soaked soybean seeds and crushed root crops (carrots and pumpkins), with the help of a grinder-extractor -1 (Figure 2), so-called "soy milk" and the waste fraction - insoluble soybean - are obtained pulp root crop, in which in a weight ratio of 1: 1, is mixed with chalk, transformed into flour form using a mixer -3. At the same time, chalk is fed from the feeder - 2. The moisture content due to its diffuse transition into the chalk component of the composition. A composition with average humidity is formed into granules with a diameter of 2-3 mm using a
  22. 22. *Corresponding author (Sergei Dotsenko). Tel: +7-989-99-87887. Email: lyudmila0511@mail.ru ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06B http://TUENGR.COM/V10A/10A06B.pdf DOI: 10.14456/ITJEMAST.2020.102 3 screw - 4 and a matrix - 5. The granules are accumulated in a mesh tray - 6 and dried in a cabinet - 7 with active ventilation. 3. RESULT AND DISCUSSION The energy intensity of traditional and innovative technologies is determined by the formula. 100 l r N E Q P × = × (1) where N – energy consumption for producing granulate, kW; Ql – line productivity kg/h; Pr – pellet strength, %. The installed power of electric motors in the set of equipment K7 - FKE is Nt = 254.5 kW, with a pellet strength of Pr = 93% and a productivity of Ql = 100 kg / h, which gives an indicator value 100 254.5 2.736 100 93 % t kWh E kg × = = × . Figure 1: The technological scheme of the process of obtaining feed products based on soy-pumpkin and soy-carrot compositions. For an innovative technology variant, Ei is 100 83.4 0.877 100 95 % i kWh E kg × = = × . Comparative characteristics of the energy intensity of production and nutritional value of feed additives are given in Table 1. An analysis of the data given in Table 1 shows that the energy intensity by innovative technology is 3.119 times lower compared to the basic version. Comparative characteristics of the energy intensity of production and nutritional value of additives.
  23. 23. 4 Sergei Dotsenko, Lyudmila Kryuchkova, Andrei Burmaga Figure 2: Structural-technological (hardware) scheme of the line for producing karate-calcium additives for farm animals and poultry. Table 1: The results of evaluating the effectiveness of the developed technology Product Electricity costs, % kWh kg Content Strength, % Calcium, g/100 g β-carotene mg/kg The product according to a known method (K7 - FKE) 2,736 1,8 - 93,0 Protein-carotene-calcium supplement 0,877 19,5 2,0 95,0 Moreover, in an innovative product, the calcium content is 10.8 times higher. At the same time, the presence of β-carotene in the additive in an amount of 2 mg / 100 g provides it with high biological and nutritional value, as well as antioxidant activity. The developed non-waste technology, with the specified values of the modes and parameters, allows obtaining granules with a diameter of 2-3 mm and a length of 2-3 cm (see Figure 3). The strength of the granules was 93-95% and moisture content 92%. Figure 3: Multicomponent feed supplement.
  24. 24. *Corresponding author (Sergei Dotsenko). Tel: +7-989-99-87887. Email: lyudmila0511@mail.ru ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN 2228-9860 eISSN 1906-9642 CODEN: ITJEA8 Paper ID:11A06B http://TUENGR.COM/V10A/10A06B.pdf DOI: 10.14456/ITJEMAST.2020.102 5 4. CONCLUSION Based on the adopted innovative approaches, the possibility and feasibility of obtaining an effective additive using soya-pumpkin and soya-carrot pulp residue, which is a waste fraction in the production of a substitute for whole milk using soya-pumpkin and soya-carrot compositions, are substantiated. The implementation of the developed technology, as well as the set of equipment adopted for it, makes it possible to increase the efficiency of the functioning of the mechanized system of feeding animals on small and medium-sized farms by reducing the cost of energy, labor, and money. 5. AVAILABILITY OF DATA AND MATERIAL Data can be made available by contacting the corresponding authors 6. REFERENCES Dotsenko, S.M., Goncharuk, O.V., Makarenko, V.V., et al. (2016). Monograph. Scientific and practical aspects of creating food products of a given composition and properties using soy-plant compositions. Blagoveshchensk. 355. Dotsenko, S.M. (2018). A unit for continuous preparation of a substitute for whole milk and animal feed. etc. RF patent No. 2663610 Published in B.I. No.22. Dotsenko, S., Kryuchkova, L., Samuylo, V. (2020). Development of Technology for Production of Protein-Vitamin Granulate. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. 11(5), 11A05M: 1-6. Tutelian, V.A. (2004). Nutrition and Health. Food Industry. 5, 5-6. Tutelian, V. A., Poznyakovsky, V.M. (2002). The policy of healthy eating. Federal and regional levels. Novosibirsk Siberian University Publishing House, 144. Kochetkova, A. A. (1999). Functional products in the concept of a healthy diet. Food industry. 4, 4. Tutelian, V.A. (2005). Health is in your hands. Food industry. 4, 6-8. Petibskaya, V.S. (2012). Soya: chemical composition and use. Maykop, 432. Stepanova, L.I. (1999). Handbook of a dairy production technologist. Technology and recipes. SPb. GIOGD, 190. Ostroumov, L.K. (1998). Combined dairy protein products using plant materials. Storage and processing of agricultural raw materials. 38, 28 - 30. Ginzburg A.S. (1976). The technology of drying food. Food Industry, 248. Professor Dr.Sergei Dotsenko is Professor, Far Eastern State Agrarian University. He holds a Doctor of Technical Sciences degree. His research includes Animal Nutrition, Production Line, Feed Technology, Binder, Vitamins, Protein Substances, Biological Value. Lyudmila Kryuchkova, is an Associate Professor, Department of Higher Mathematics, Far Eastern State Agrarian University. She is a Candidate of Technical Sciences. Her research involves Applications of Math. Professor Dr.Andrei Burmaga is Professor, Department of Transport and Energy Facilities and Mechanization of Agriculture, Far Eastern State Agrarian University. He holds a Doctor of Technical Sciences. His researches are Feed Technology, Production Line, Feed Technology, Binder, Vitamins, Protein Substances, Biological Value.
  25. 25. *Corresponding author (S.Bibi, shaguftamalik409@yahoo.com; A.Khalid arslankhalid1989@yahoo.com) ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN2228-9860 eISSN1906-9642 CODEN: ITJEA8 Paper ID:11A06C http://TUENGR.COM/V10A/11A06C.pdf DOI: 10.14456/ITJEMAST.2020.103 1 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com PAPER ID: 11A06C HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS WITH HEART FAILURE IN SOUTH PUNJAB PAKISTAN: A SECTIONAL SURVEY STUDY Abdul Sattar Ghaffari 1 , Ruqia Safdar Bajwa 2 , Mureed Hussain 3 , Muhammad Tahir 4 , Shagufta Bibi 5* , Arslan Khalid 6* 1 Zhongtai Securities, Institute for Financial Studies, School of Mathematics, Shandong University, Jinan, CHINA. 2 Department of Applied Psychology, Bahauddin Zakariya University, Multan, PAKISTAN. 3 Department of Psychology, International Islamic University, Islamabad, PAKISTAN. 4 School of Medicine, Shandong University, Jinan, CHINA. 5 School of Psychology, Shaanxi Normal University, Xi’an, CHINA 6 Department of Health Psychology, School of Nursing, Shandong University, Jinan, CHINA. A R T I C L E I N F O A B S T RA C T Article history: Received 15 July 2019 Received in revised form 16 December 2019 Accepted 26 December 2019 Available online 09 January 2020 Keywords: Heart failure patients; Hospitalized Patients; Borderline of anxiety and depression; Hospital Anxiety and Depression Scale (HADS); Heart failure (HF). Depression and anxiety are highly prevalent in Heart failure patients. The main objective of the study is to check the prevalence of anxiety and depression and associated factors of Heart Failure Patients of South Punjab Pakistan. A sample of 192 Hospital admitted patients whose age range was between 27-71 years were selected from various cardiac hospitals of south Punjab Pakistan for January-July 2017 through a purposive sampling technique. The Hospital Anxiety and Depression Scale (HADS) was used to assess the level of depression and anxiety among patients. The social and psychological parameters including family care, gender, and relevant support were identified. Findings revealed a highly significant correlation between the level of anxiety and depression among Hospitalized heart failure patients. According to results anxiety and depression is higher among unmarried and patients with low family care and support. In addition, data has indicated that economic factors associated with poverty mediate the frequency of anxiety and depression. It was determined that approximately 50% and 54% of peoples were involved in anxiety and depression respectively, however, 31% of people were regarded in the borderline of anxiety and depression and most patients were accompanied by heart disease. Furthermore, marital status, the difference in income level also promotes anxiety and depression in patients associated with heart disease. Disciplinary: Health Sciences (Health Management), Psychological Sciences. ©2020 INT TRANS J ENG MANAG SCI TECH. ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
  26. 26. 2 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid 1. INTRODUCTION Heart failure (HF) is a serious chronic progressive disease with significant worldwide prevalence and mortality [1]. The clinical picture of HF patients manifested by a group of symptoms including pleurisy, difficult breathing, wheezing cough, generalized edema, decrease appetite, tiredness, fatigue, malaise, rapid heartbeat, sleep apnea, confused thinking, loss of memory, poor social and psychological functioning due to resultant depression, anxiety and stress [2]. The leading pathological factors of HF are hypertension, cardiomyopathy, congenital heart disease, lung disorder, diabetes and obesity [3]. Anxiety and depression are common among the patients of HF. It may worsen their symptoms requiring hospitalization for intensive emergency care[4] . Such malign psychological disorders eventually distort patients' physical health. Research also highlights that cardiologists only focus on the clinical pictures of HF by ignoring the resultants psychological stressors i.e. anxiety and depression, which further deteriorate the HF patient's health. Depression plays a vital role to cause high blood pressure and many other cardiac ailments including CVD and CAD [5, 6]. Several studies revealed that anxiety and depression and CAD are correlated with one another[7] . Recently research revealed that depressive patient with MI has a high mortality rate whereas during hospitalization the depressive state of patient leads to CVD [8,9]. The hospitalized HF patients who seldom visit by their family or those who are unmarried often seek a high level of anxiety and depression which may cause impairments of their routine activities [10, 11]. Several types of research show that there is an association between anxiety, depression and hospitalized HF patients[9]. Although it is highly critical to surviving with advanced heart failure because of the sudden worsening situation the patient has to be readmitted in the hospital. In the modern developed countries, health care teams adopted various strategies with the aim to improve quality of life, enhancing patient endurance along with reduced hospital stay. Such action may play a vital role to curtail the rate of anxiety and depression among HF patients. Patients with heart failure admitted to the hospital have a high incident rate (>50%), have a 10 to 15% mortality rate whereas it is up to 30 to 40 % in case of re-hospitalization within 6 months duration of discharge [12,13]. Studies revealed that approximately less than 1/3 hospitalized HF patients were assessed by a cardiac health professional in the early 90 days after discharged from hospital [13, 14]. To reduce the re-hospitalization rate it is necessary to adopt new strategies including family care at home, long-term hospital care programs involving palliative care treatment [15]. Another study [16] shows that depression expressed by physical complaints or hospitalization the non-serious attitude by a cardiologist, nursing staff, and attendants to ignoring the magnitude of depression ultimately damage the HF patient's health and quality of life. Early understanding, delectation, and curative treatment of anxiety and depression certainly improve the HF patient’s health and quality of life[9]. During hospitalization HF patients generally observe anxiety and depression which can intensify clinical physical ailments, along with sluggishness and social isolation [10]. Unfortunately, even after coronary artery bypass grafting (CABG), depression remains untreated then certainly the morbidity rate and mortality will be high [17]. Heart failure patients with depression remain anxious to readmit in the hospital also have a high risk of mortality [14]. Numerous studies show that HF patients living style and standards associated with anxiety and depression such as poor diet, alcohol consumption, smoking/tobacco habits, lack of exercise and social support will interfere with treatment and allied healthcare support [18]. The
  27. 27. *Corresponding author (S.Bibi, shaguftamalik409@yahoo.com; A.Khalid arslankhalid1989@yahoo.com) ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN2228-9860 eISSN1906-9642 CODEN: ITJEA8 Paper ID:11A06C http://TUENGR.COM/V10A/11A06C.pdf DOI: 10.14456/ITJEMAST.2020.103 3 cardiac patient plus generalized anxiety disorder has a high risk of severe morbidity as compared to those who not suffered from anxiety or generalized anxiety disorder (GAD) [19]. Social and family environment also affects cardiac events. The studies found that there is a positive correlation between the socioeconomic statuses, family psychosocial environment and cardiovascular diseases [20]. The literature clearly indicates a high level of prevalence of depression and anxiety among heart failure patients in different regions of the world. As such study has not conducted in Pakistan, so it was planned to check the prevalence of hospital anxiety and depression and related social and psychological factors in the south part of Pakistan. 2. METHODS For this study, the samples were collected from 192 Hospitalized Heart failure (HF) patients were chosen from Nishtar Hospital, Multan, Pakistan and Chaudhry Pervaiz Elahi Institute of Cardiology, Multan, Pakistan (78 and 114 respectively) from January 2017-to- July 2017 by using the method of purposive sampling. Most of HF patients were re-hospitalized. The demographic variables were age, education, residential area, gender, marital status, and family care and support. To collect the detailed information and observing basic parameters including anxiety and depression among hospitalized heart failure patients. Purposely, heart failure patients with the phenomena of anxiety and depression issues were targeted. The questionnaire was filled completely by the consent of all participants. All the instructions and relevant assistance were provided from the hospital team to fill this Performa honestly. The same methodology was applied to observe the differences among people with low and high income and support from the family Ethics and Consent: Ethical approval was taken from Shandong University China to conduct the study and verbal consent was taken from patients after briefing them about the purpose of the study. The Hospital Anxiety and Depression Scale (1983).The Hospital Anxiety and Depression Scale (HADS) developed by Zigmond and Snaith[21] to gauge anxiety and depression in a general therapeutic populace of patients. The scale consisted of 14 items with responses being scored on a scale of 0-3 where a higher score reflects frequencies of symptoms. In spite of the fact that the tension and despondency questions are blended inside the survey, it is indispensable that these are scored independently. Cut-off scores are accessible for evaluation, for instance, a score of at least 8 for uneasiness has a specificity of 0.78 and affectability of 0.9, and for gloom a specificity of 0.79 and an affectability of 0.83. Before starting, patients were made a request to give permission that they have complete knowledge about this study and willing to take an interest in this study. Instructions were provided to all candidates to complete the questionnaire with honesty to minimize any chances of error. The demographic sheet and Hospital Anxiety and Depression Scale were provided to all candidates to examine the anxiety and depression index in these patients. Purposive sampling method was applied to collect the sample from these enrolled patients associated with heart failure disease. Subjects were also given the assurance that their information will not be disclosed. Participated patients were approached at hospitals to administer the study instruments and help to complete the questionnaire. The questionnaire took 2 to 5 minutes to complete. To determine the scoring scale of HADS, the data were analyzed by using SPSS 21. Pearson correlation coefficients tool was used to finding the linkage of all the variable parameters with each other. Comparisons between marital status for depression and
  28. 28. 4 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid anxiety were analyzed via an independent samples t-test with corrections made for assumed variance equality. We adopted the same methodology to find the difference among participants with less family support and care with participants with more family support and care. One way ANOVA and post hoc tests were used to analyze the differences between depression and anxiety among different income levels. A P-value of less than 0.05 was considered significant. 3. RESULTS Table 1 shows descriptive statistics of socio-demographic variables for this study. From overall 192 patients, most patients 122(63.5%) are between the age of 31 to 45 years, 112 (58%) male and 119 (62%) are single. Furthermore, 104 (54%) of the respondents who give their response as low family care. Similarly, 51 (27%) of the patients have less than 15000 PKR, 50 (26%) belong to 30000-45000 PKR of the patients’ monthly income level. Table 1: Descriptive Statistics of sociodemographic variables of the participants (n=192) Individual factors Category Frequency (%) Age <31 Years 33(17.2) 31-45 Years 122(63.5) 45-60 Years 33(17.2) > 60 Years 4(2.1) Gender Male 112 (58.0) Female 80 (42.0) Marital Status Single 119 (62.0) Married 73 (38.0) Family Care and Support Low 104 (54.0) High 88 (46.0) Monthly Income (Rupees) <15000 51 (27.0) 15000-3000 36 (19.0) 30000-45000 50 (26.0) 45000-60000 31 (16.0) >60000 24 (12.0) Table 2: Categories of Anxiety and Depression on the Basis of Scores Categories Anxiety Frequency (%) Depression Frequency (%) Normal 36 (19.0) 28 (15.0) Borderline Abnormal 60 (31.0) 59 (31.0) Abnormal 96 (50.0) 105 (54.0) Table 2 and diagram 1 shows the prevalence of hospital anxiety and depression level in the sampled patients. There are 60 (31%) and 96 (50%) of the patients who have a borderline abnormal and abnormal level of anxiety respectively. Similarly, 59 (31%) and 105 (54%) of the heart failure patients show borderline abnormal and abnormal depression level respectively. Table 3: Pearson Correlation between anxiety and depression (n=192). Depression Anxiety 0.736*** (***p<0.001) Table 3 explores the correlation between anxiety and depression in patients associated with heart failure. The value of the correlation coefficient shows that anxiety and depression are significantly positively correlated with each other.
  29. 29. *Corresponding author (S.Bibi, shaguftamalik409@yahoo.com; A.Khalid arslankhalid1989@yahoo.com) ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN2228-9860 eISSN1906-9642 CODEN: ITJEA8 Paper ID:11A06C http://TUENGR.COM/V10A/11A06C.pdf DOI: 10.14456/ITJEMAST.2020.103 5 Table 4: Mean, Standard Deviation and t- value for the score of single (n=119) and married (n=73) on the scale of anxiety and depression. Variable Marital Status N M SD T P Anxiety Single 119 10.85 2.91 2.856 <.003Married 73 9.64 2.72 Depression Single 119 11.26 2.73 3.357 <.001Married 73 9.90 2.70 Table 4 depicts the frequency of depression and anxiety for the single and married hospitalized heart failure patients. The results show that the level of anxiety and depression is higher in single as compared to married patients. Table 5: Mean, SD and t-value for the score of low family care and support (n=104) and high family care and support (n=88) on the scale of anxiety and depression. Variable Family Care and Support N M SD T P Anxiety Low 104 10.85 2.94 2.404 <.009High 88 9.85 2.75 Depression Low 104 11.20 2.81 2.502 <.007High 88 10.24 2.68 Table 6: One Way ANOVA: Anxiety and depression level among hospitalized heart failure patients with variation in income. Variable Sum of Squares Df Mean Square F Sig. Anxiety Between Groups 106.952 4 26.738 3.359 <.011 Within Groups 1488.752 187 7.961 Total 1595.703 191 Depression Between Groups 147.364 4 36.841 5.145 <.001 Within Groups 1339.131 187 7.161 Total 1486.495 191 Table 7: Post Hoc Tests Multiple Comparisons. Dependent Variable (I) Income Status (J) Income Status Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Anxiety Up to 15000 15000-30000 .312 0.614 0.612 -0.900 1.524 30000-45000 .518 0.562 0.358 -0.590 1.625 45000-60000 1.956* 0.643 0.003 0.689 3.224 Above 60000 1.743* 0.698 0.013 0.365 3.121 15000-30000 30000-45000 .2056 0.617 0.739 -1.011 1.422 45000-60000 1.644* 0.691 0.018 0.280 3.008 Above 60000 1.431 0.744 0.056 -0.036 2.897 30000-45000 45000-60000 1.439* 0.645 0.027 0.166 2.711 Above 60000 1.225 0.701 0.082 -0.157 2.607 45000-60000 Above 60000 -.214 0.767 0.781 -1.727 1.300 Depression Up to 15000 15000-30000 .444 0.583 0.446 -0.705 1.594 30000-45000 .767 0.533 0.152 -0.284 1.817 45000-60000 2.151* 0.609 0.001 0.948 3.353 Above 60000 2.333* 0.662 0.001 1.027 3.640 15000-30000 30000-45000 .322 0.585 0.582 -0.832 1.476 45000-60000 1.707* 0.656 0.010 0.413 3.000 Above 60000 1.889* 0.705 0.008 0.498 3.280 30000-45000 45000-60000 1.384* 0.612 0.025 0.177 2.591 Above 60000 1.567* 0.665 0.019 0.256 2.878 45000-60000 Above 60000 -.183 0.728 0.802 -1.618 1.253 Table 5 explores that the index of anxiety and depression was higher in patients with less care
  30. 30. 6 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid and support from the family compared to the patients having more family care and support. The significant difference in anxiety and depression among different income levels patients was observed in Tables 6 and 7 that the p-value for anxiety and depression are 0.011* and 0.001** respectively, which show that there is a highly significant difference in depression and anxiety among different income status. Furthermore, from the Post Hoc test, we can also conclude that the maximum difference in the level of anxiety is between income group “up to 15000” and “45000-60000”. Similarly, depression is much higher in the patients belongs to the “up to 15000” income group also the group belongs to “above 60000 income group”. Table 8: Regression Analysis: Anxiety and Depression on Age, Gender, Marital Status, Family Care and Support and Income Level. Dependent Predictor Β SE (β) T P 95% Confidence Interval for β Lower Bound Upper Bound Anxiety Age 0.025 0.023 1.065 <.288 -0.021 0.071 Gender 1.211 0.390 3.102 <.002 0.441 1.981 Marital Status -1.207 0.400 -3.018 <.003 -1.996 -0.418 Family Care and Support -0.693 0.392 -1.769 .079 -1.466 0.080 Income level -0.527 0.144 -3.652 <.001 -0.812 -0.242 Depression Age 0.023 0.022 1.034 .302 -0.021 0.067 Gender 0.158 0.375 0.421 .674 -0.581 0.897 Marital Status -1.364 0.384 -3.555 <.001 -2.122 -0.607 Family Care and Support -0.765 0.376 -2.035 <.043 -1.507 -0.024 Income level -0.616 0.139 -4.446 <.001 -0.889 -0.343 Results Table 8 shows that gender, marital status, and income level have a significant effect on anxiety while no significant effect seen on the basis of age and support and care from family. Similarly marital status, support, and care from family and income level have significant on depression which there is no significant effect of age and gender on depression. 4. DISCUSSION The heart failure patients generally experience frequent hospitalization within 3 to 6 months due to their sudden health situation. About one-half of heart failure patients have to experience re-hospitalization. In this way, the mortality rate among heart failure patients is higher generally. Hospitalization incurs depression and anxiety among heart failure patients which further deteriorates the status of the heart. The study revealed that epidemiology of anxiety and depression is mainly due to the poor prognosis of heart failure and longer hospitalization. Results revealed a high level of abnormal depression and anxiety in the majority of the candidates associated with a heart problem. Upon extensive review, it has been found that several patients associated with heart problems were involved in anxiety and depression during the course of their disease [22, 23]. Almost 15-20 percent of hospitalized patients with heart failure is diagnosed with major depressive disorder [24, 25]. A systematic review revealed that Anxiety is a big predictor of hospitalization in heart failure patients[26]. A meta-analysis [27] reported the incidence and frequency of anxiety disorders in heart failure patients. A highly significant positive correlation was found between anxiety and depression among hospitalized heart failure patients. According to existing literature anxiety and depression are highly
  31. 31. *Corresponding author (S.Bibi, shaguftamalik409@yahoo.com; A.Khalid arslankhalid1989@yahoo.com) ©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN2228-9860 eISSN1906-9642 CODEN: ITJEA8 Paper ID:11A06C http://TUENGR.COM/V10A/11A06C.pdf DOI: 10.14456/ITJEMAST.2020.103 7 prevalent among heart failure patients [28, 29, 30, 31]. It was hypothesized in this study that single patients who don’t have life partners will have more anxiety and depression as compared to married counterparts. The results supported the hypotheses and found a significant difference in both married and unmarried groups. The frequency of depression and anxiety was higher among young unmarried patients. Previous literature also supports this finding that elderly unmarried patients have depressive symptoms and it had a strong impact on their recovery as well[32, 33, 34] . Another remarkable finding of this study discusses that lack of family care and support also triggers the anxiety and depression among hospitalized heart failure patients. Studies have enormously focused on the role of social support, it has been found as a very protective factor against unpleasant effects [35,36]. Re-hospitalization has a strong association with anxiety and depression [37]. Further, the study revealed a highly significant difference in anxiety and depression among patients with different income status [38]. Depression epidemiology among HF hospitalized patients is higher and associated with fear of readmission. Re-hospitalization ultimately exerts a havoc blow on the economic status of HF patients [39]. This triggers the level of anxiety and depression among hospitalized HF patients. 5. CONCLUSION In conclusion, anxiety and depression among hospitalized heart failure patients are certainly significant. The level of the psychological clinical picture entirely depends upon the state of HF and admission or readmission in the hospital. Acknowledgment of this causative factor and their outcomes is essential for future studies to curtail the barrier to curative effective HF treatment. The cardiologists should try their best to adopt certain advance approaches to reduce the hospitalization rate among HF patients. The psychologist should also visit HF patients to reduce the level of depression. 6. AVAILABILITY OF DATA AND MATERIAL Data can be made available by contacting the corresponding author. 7. REFERENCES [1] Ruiz-Hurtado G, Sarafidis P, Fernández-Alfonso MS, Waeber B, Ruilope LM. Global cardiovascular protection in chronic kidney disease. Nature Reviews Cardiology. 2016, 13(10):603. [2] Yelwanti CG, Desai VA. KEYWORDS Heart Failure, Coronary Artery Disease, Dilated Cardiomyopathy, Hypertension, Rheumatic Heart Disease, Cor Pulmonale, Anaemia. A Study on Clinical and Aetiological Profile of Heart Failure at Kbn Teaching and General Hospital. 2016, 17(95168) [3] Lee WS, Kim J. Diabetic cardiomyopathy: where we are and where we are going. The Korean Journal of internal medicine. 2017, 32(3): 404. [4] Cowie MR, Anker SD, Cleland JG, Felker GM, Filippatos G, Jaarsma T, Jourdain P, Knight E, Massie B, Ponikowski P, López‐Sendón J. Improving care for patients with acute heart failure: before, during and after hospitalization. ESC Heart Failure. 2014, 1(2):110-45.
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