Assignment on Empirical part of the research

  Analysis of the result dataset of The microfinancing industry of Sri Lanka.

           The focus of the present study was to find out the key factors which effects micro financing and determining the efficiency of institutes in Sri Lanka The sample of the present study was taken from five banks Regional development bank, Cooperative rural bank, Samurdhi Authority, Department of Agrarian and Lankaputhra development bank. The data is collected from the secondary data (annual reports). The data employment analysis (DEA) was done for the technical analysis. The researcher identified three inputs (Desposit, Number of deposit and number of branches) and two outputs (number of Borrowers and Loan Portfolio). The DEA (Data Envelopment Analysis) model was used for the construction of and measurement of efficiency score.  According to Jayamaha and Mula (2011) in Srilanka, efficiency can be measured through partial and total factors. Partial efficiency factors measured one input change due to the change in output factors whereas change which occurs in more than one input factor due to the change in output factors called total efficiency factors. DEA usually used to evaluate the given efficiency of firm, industry, and performance of the firms due to the inputs factors and output factors.

Table 3.1

Technical efficiency score of  2013 to 2017

Table 3.1 continued

          Table 3.1 shows that  DMU and technical efficiency scores in 2013 regional development bank, Samurdhi Authority, department of agrarian development, lanka saving bank have high technical efficiency scores where as department of co-operative development and lankaputhra development bank have low technical efficacy scores. In 2014, regional development bank, Samurdhi Authority, department of agrarian development and lankaputhra development bank have high technical efficiency scores where as department of co-operative development have low technical efficacy scores. In 2015, regional development bank, Samurdhi Authority, department of agrarian development and lankaputhra development bank have high technical efficiency scores where as department of co-operative development have low technical efficacy scores. In 2016, regional development bank, department of agrarian development and lankaputhra development bank have high technical efficiency scores whereas Samurdhi Authority and department of co-operative development have low technical efficacy scores.In 2017, regional development bank, department of agrarian development, Samurdhi Authority and lankaputhra development bank have high technical efficiency scores where as department of co-operative development have low technical efficacy scores. Lanka saving bank observations were not reported in 2014, 2015, 2016 and 2017. Rank of the table shows that which company got high and lower efficiency (

see.Tab 3.2)

 Table 3.2

Rank of the technical efficiency score

Table 3.2 continued

The DMU, technical scores and ranks of banks revealed that in 2013 Department of Co-operative Development and Lankaputhra Development Bank were ranks high where as Regional Development Bank, Samurdhi Authority, Department of Agrarian Development and lanka saving bank were ranks low. In 2014, Department of Co-operative Development and Samurdhi Authority were ranks high where as Regional Development Bank, Lankaputhra Development Bank, Department of Agrarian Development were ranks low. In 2015, Department of Co-operative Development was ranks high where as Regional Development Bank, Samurdhi Authority, Department of Agrarian Development, Lankaputhra Development Bank were ranks low. In 2016 and 2017, Department of Co-operative Development and Samurdhi Authority were ranks high where as Regional Development Bank, Lankaputhra Development Bank, Department of Agrarian Development were ranks low. Lanka saving bank observations were not reported in 2014, 2015, 2016 and 2017.

 Table 3.3

Efficiency score of the models

  The table of efficient scores explained that Regional Development Bank efficiency scores from 2013 to 2017 were equal to 1 which represents good scores.  Samurdhi Authority efficiency scores related to TE, PTE, SE equal to 1 in 2013 and 2015. But these scores were declined below 1 in 2014, 2016 and 2017. Department of Co-operative Development efficiency scores were below from 2013 to 2017. Department of Agrarian Development TE, PTE, SE efficiency scores were equal to 1 from 2013 to 2017. The efficiency scores of Lankaputhra Development Bank were below 1 in 2013 but these scores were improved from 2014 to 2017. Lanka saving bank efficiency score in 2013 were also equal to 1.

Table 3.4

 Distribution of con respondents

  The scores of above revealed that(see.Tab 3.4) four DMUs (66 %) in 2013, 3 DMUs (60 %) in 2014 and again four in 2015 (80 %) were the evaluated as efficient as there efficiency scores were equal to 1 where as these scores decline as the DMUs declined to three in 2016 which was 60 % DMUs and again three in 2017  DMUs (60 %). These DMUs values are according to the companies operation in Sri lanka. The PTE efficient scores showed the value of four in 2013 (66 %). PTE scores rise to five in 2014 (100 %), again these score decline in 2015 to four scores (80 %). These scores were further decline in 2016 and 2017 to three (60 %) respectively.  These DMUs values efficient scores of companies’ operation in Sri lanka.  The scale efficient scores from 2013 to 2017 were consistent with DMUs TE score.

           The overall efficiency scores analysis showed that 66 % banks (four banks out of six) were classified as the efficient in the period on 2013. This efficiency scores were decrease to 60 % in 2014. The efficient number again increases in 2015 to 80 %. These values again decrease in 206 and 2017 to 60 % (to the three numbers of banks out of five). The efficient scores were equal to 1 in the period of 2013, 2014 and 2015. The technical efficiency scores showed the average range of scores in 2013 (M = 0. 7647), 2014 (M = 0. 8843), 2015 (M = 0. 8501), 2016 (M = 0. 8178) and 2017 (M= 0. 8843) respectively over the period of time in Sri lanka. So the results showed that there is no major significant differences were found in the technical efficiency scores in this inputs and outputs factor only minor mean difference were report.

1.2 Descriptive statistic analysis of The microfinancing industry of Sri Lanka.

Table 3.5

Descriptive statistic of input and output

 Table 3.5 continued

The results of descriptive statistic correlation values of input and output factors showed positive relationship with passing of year. Results reported that p value is less than .05. It explained about the significant relationship between the correlation three inputs (Deposit, Number of deposit and number of branches) and two outputs (number of Borrowers and Loan Portfolio. The direction of the relationship is explained in correlation co efficient table of inputs and outputs factors.

The descriptive statistics correlation results also showed that in 2013 number of branches (N  B) have the strong relationship with the number of deposit (N D), deposit (Mn) (D) and weak relationship with the number of borrowers (N W) and loan portfolio (Mn) (L P).  It means that in 2013 number of branches strongly influence the number of deposit and deposit (Mn) as compare to the number of borrowers and loan portfolio (Mn). The results analysis of 2014 showed that number of branches has strong relationship with number of deposit and number of borrowers and weak relationship with D (Mn) and L P (Mn). In 2014 number of branches strongly influence the number of borrowers and number of deposit as compare to the deposit (Mn) and loan portfolio (Mn). 2015 results showed that number of branches strong relationship with number of deposit and deposit (Mn) and weak relationship with number of borrowers and loan portfolio (Mn). In 2015, number of branches more influenced the number of deposit and deposit as compare to the number of borrowers and loan portfolio (Mn). 2016 results showed that number of branches have the strong relationship with the number of deposit, deposit (Mn) and weak relationship with the number of borrowers and loan portfolio (Mn).  It means that in 2016 number of branches strongly influence the number of deposit and deposit (Mn) as compare to the number of borrowers and loan portfolio (Mn). The results of 2017 showed that number of branches has strong relationship with number of deposit and number of borrowers and weak relationship with D (Mn) and L P (Mn). In 2017 number of branches strongly influence the number of borrowers and number of deposit as compare to the deposit (Mn) and loan portfolio (Mn).

           The findings of results also showed relation between numbers of deposit, deposit (Mn), number of borrowers and loan Portfolio (Mn). The results analysis showed that in 2013 number of deposit has the strong relationship with loan portfolio (Mn) and number of borrowers and weak relationship with deposit (Mn).  In 2013, number of deposit put more influence on loan portfolio (Mn) and number of borrowers as compare to the deposit (Mn).  Results of 2014 analysis showed that number of deposit has strong relationship with number of borrowers and deposit (Mn)  and weak relationship with loan portfolio (Mn). In 2014, number of deposit put more influence on number of borrowers and deposit (Mn) as compare to the loan portfolio (Mn). 2015 results analysis showed that number of deposit has strong relationship with the deposit (Mn) and loan portfolio (Mn) and weak relationship with number of borrowers. In 2015, number of deposit put more influence on deposit (Mn) and loan portfolio (Mn) as compare to the number of borrowers.  2016 results analysis showed that number of deposit has strong relationship with the deposit (Mn) and weak relationship with number of borrowers and loan portfolio (Mn). In 2016, number of deposit put more influence on deposit (Mn) as compare to the number of borrowers and loan portfolio (Mn).  Results of 2017 analysis showed that number of deposit has strong relationship with number of borrowers and deposit (Mn) and weak relationship with loan portfolio (Mn). In 2017, number of deposit put more influence on number of borrowers and deposit (Mn) as compare to the loan portfolio (Mn).

           Finding also revealed that deposit (Mn) was also put influence on number of borrowers and loan portfolio (Mn) in 2013, 2014, 2015, 2016 and 2017. Following results showed the iflunec of deposit on number of borrowers and loan portfolio (Mn) in different years.  2013 results revealed deposit (Mn) has weak relationship with the number of borrowers and loan portfolio (Mn). It explained that in 2013 number of borrowers and loan portfolio (Mn) were less influenced by the deposit (Mn). In 2014, this relationship was enhanced and deposit (Mn) has strong relationship with loan portfolio (Mn) and weak relationship number of borrowers. It means that deposit (Mn) put more influence on loan portfolio (Mn) as compare to the number of borrowers in 2014.  The results analysis of 2015 also showed the same results but with minor difference in values of relationship. In 2015, deposit (Mn) has strong relationship with loan portfolio (Mn) and weak relationship number of borrowers. It means that deposit (Mn) put more influence on loan portfolio (Mn) as compare to the number of borrowers.  In 2016, this relationship was weak as compare to previous years. 2016 results revealed deposit (Mn) has weak relationship with the number of borrowers and loan portfolio (Mn). It explained that again in 2016 number of borrowers and loan portfolio (Mn) were less influenced by the deposit (Mn) like 2013. These results again changed in 2017, this relationship was enhanced and deposit (Mn) has strong relationship with loan portfolio (Mn) and weak relationship number of borrowers. It means that deposit (Mn) put more influence on loan portfolio (Mn) as compare to the number of borrowers in 2017.

           The results finding of present research also revealed the influence of number of borrowers on loan portfolio (Mn). Number of borrowers showed strong relation with loan portfolio (Mn) in 2013 and 2015 where as weak relationship was measure in 2014, 2016 and 2017. It means that loan portfolio (Mn) was more influenced by number of borrowers in 2013 and 2015 as compare to 2014, 2016, 2017.

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