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Volume 11 Issue 6 (2020)
IMPACTS OF FOREIGN DIRECT
INVESTMENT ON INDIAN RETAIL
BAZAAR: AN EMPIRICAL STUDY
ALGORITHMIZATION FOR PROCESSES OF
REGIONAL DIFFERENTIATION AND
CONCENTRATION OF INVESTMENTS IN
FACTORS AFFECTING ACCEPTANCE AND
THE USE OF TECHNOLOGY IN YEMENI
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
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
DEVELOPMENT OF TECHNOLOGY FOR THE
PRODUCTION OF MULTICOMPONENT FEED
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
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
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
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
EVALUATION OF ERGONOMIC DESIGN OF DESK AND CHAIR FOR
PRIMARY SCHOOLS IN ERBIL CITY
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
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.
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,
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
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
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
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:
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
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 ):
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
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.
4.1 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
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)
itFIIMicroSarma −: 0.022**
- - - -
itFIIMicroDFM −: - -0.007
- - -
itLnBanks - - 0.004**
itLnDA - - - 0.024**
itLnBA - - - - 0.029**
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
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
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
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.
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:
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.
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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
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
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.
2 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid
Heart failure (HF) is a serious chronic progressive disease with significant worldwide prevalence
and mortality . 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 .
The leading pathological factors of HF are hypertension, cardiomyopathy, congenital heart
disease, lung disorder, diabetes and obesity . Anxiety and depression are common among the
patients of HF. It may worsen their symptoms requiring hospitalization for intensive emergency
. 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
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.
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 .
Another study  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. During hospitalization HF patients generally observe anxiety and
depression which can intensify clinical physical ailments, along with sluggishness and social
isolation . Unfortunately, even after coronary artery bypass grafting (CABG), depression remains
untreated then certainly the morbidity rate and mortality will be high .
Heart failure patients with depression remain anxious to readmit in the hospital also have a high
risk of mortality . 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 . The
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.
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 (%)
<31 Years 33(17.2)
31-45 Years 122(63.5)
45-60 Years 33(17.2)
> 60 Years 4(2.1)
Male 112 (58.0)
Female 80 (42.0)
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).
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.
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*
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
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
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.
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. A meta-analysis  reported the incidence and frequency of anxiety disorders in heart
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
8 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid
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