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Branch level efficiency and its decomposition of Meghalaya co-operative Apex Bank Ltd.


Co-operative banks, though much smaller in size as compared to the commercial banks, play a greater role in rural financial intermediation. The rural financial system in India operates through two sets of institutions in the formal and informal sectors. Though the formal sector has adopted multi agency approach, commercial banks and cooperative banks are the two prime channels to intervene in the rural financial intermediation process. The cooperative banks however have gained renewed importance in view of the recent thrust on financial inclusion. This role of co-operative bank is crucial mainly in the North-Eastern region of the country. The region is characterized by lower volume of advances, deposits and investments and consequently a lower ratio of credit to deposit as compared to other regions. Against this backdrop of poor pace of financial intermediation in the region, co-operative banks possess greater potentialities in view of its wider presence and accessibility in the rural and semi-urban vicinity. Since the network of co-operative banks is widespread across different parts of the country, these institutions are considered as a potential instrument to bring people from far-flung areas under the formal banking network.

However, the poor financial health of cooperative banks in general, and grass root level cooperatives in particular remains as an impediment, which needs to be addressed in order to fully utilize the benefits of wide spread network of these institutions. It is indeed necessary for cooperative banks to devote adequate attention for maximizing their returns on every unit of resources through effective services. Thus, ensuring healthy performance of such banks is crucial for the overall economic development of the region. Thus, the present study is intended to evaluate the level of efficiency at the branch level of one of the State Cooperative Banks operational in one of the states of the region, i.e.; The Meghalaya Co-operative Apex Bank Limited having its presence in the state of Meghalaya at its branch level.

There exists blaring volume of researches devoted to measurement of efficiency of banks. These studies make use of parametric as well as non-parametric (DEA) techniques to measure efficiency. But, owing to lack of accessibility to branch level data there is scanty of studies on efficiency of a bank at its branch level. Branch data are generally confidential and are not meant for public consumption. But measuring efficiency of a bank's branch is crucial in view of the fact that bank level measures of efficiency could be misleading when some of its branches are inefficient.

Review of Literature

In the post reform period Das, A (1997) analysed overall efficiency of PSBs in the period 1990-96 and found a decline in overall efficiency. This occurred because there was a decline in technical efficiency, both pure and scale, which was not offset by an improvement in allocative efficiency. Ram Mohan and Ray (2004) made an attempt to compare between PSBs and their private sector counterparts using Tornqvist and Malmquist total factor productivity growth. They could find no significant differences in productivity growth between the public and private sectors in the post reform period 1992-2000. The authors speculate that the huge scale of operation of PSBs may possibly offset the inefficiencies ascribed to government ownership. Hooda, V. (2011), made a comparative study of scheduled commercial banks and state cooperative banks based on three financial ratios i.e. CD ratio, Investment Deposit ratio and Cash Deposit ratio. Their result indicates a high cash deposit and investment deposit ratio of state cooperative banks as compared to scheduled commercial banks. But scheduled commercial banks exhibited consistency in CD ratio as compared to state cooperative banks. A. Feroze, PS. (2012) studied the technical efficiency of District Co-operative Banks in Kerala using Data Envelopment Analysis for the year 2005 to 2009. His study reported that the banks operate at an efficiency level of 74 per cent on an average and display inefficiency to the extent of 26 per cent.

Studies encompassing the issue of branch level efficiency have also enriched the banking literature, though limited in numbers.

Golany, B. and Storbeck, J.E. (1999) performed a multiperiod data envelopment analysis (DEA) study of the efficiencies of selected branches of a large US bank over six consecutive quarters (second quarter of 1992 to the third quarter of 1993). Their study revealed that about half of the branches (92 out of 182) were efficient in that quarter, while the rest were mostly distributed around the 80 to 89 percent efficiency range with just five branches exhibiting low efficiencies (69 percent and below). Further, a positive correlation between size and efficiency was identified.

In a study by Das, Abhiman, Ray, S.C. and Nag, Ashoke (2005) labour use efficiency of a large public sector bank operational in different regions of India was studied. The study was based on 222 branches of a leading Public Sector Bank and labour cost efficiencies were estimated using DEA. Their study revealed that the corporate policies, procedures and incentives cannot fully neutralize the influence of local culture over the bank branches. Further, Most of the potential reduction in labor cost appears to be coming from possible downsizing the clerical and subordinate staff. Their study also recommended gainful mergers of inefficient branches with that of efficient branches.

In a similar study Ray, S.C. (2011) studied the cost efficiency of a demand constraint branch network. The objective of the study was to determine the optimal number of branches within a postal district that could provide the observed amounts of banking services to the customers in that area at the minimum operating cost. Branch level data on 138 branches of a large public sector bank within the city of Calcutta were collected. The DEA results showed that while in many cases, consolidating multiple branches would be more cost efficient, there are numerous instances, where increasing the number of branches seemed optimal.

Co-Operative Credit Scenario in North-East India

The emergence of cooperative credit in the North-Eastern region is only a new development in comparison to other parts and regions of the country. The cooperative movement took its start with the setting up of 'Gaonlia Banks' in 1912 in Assam. This gave an impetus in its later stage to the establishment of 'Assam Cooperative Apex Bank' in 1948. With the reorganization of the erstwhile Assam State Cooperative bank over a period of time, the cooperative movement spread to other states in the region. The region's youngest state Cooperative Bank was established in Sikkim which became operational with effect from 1998.

In almost all the states of NER, there exists two-tier structure of Cooperative Credit, with the State Cooperative Bank at the apex level and the Primary Agriculture Credit Societies, Service Cooperative Societies, LAMPS (Large Sized Adivasi Multi Purpose Societies), Farmer Service Societies, etc at the grass root level. However there exists separate Long Term Cooperative Credit structure in the form of Agricultural and Rural Development Bank (ARDB) in the states of Assam, Tripura and Manipur whereas in other states the Short Term Cooperative Credit structure itself caters to the long term credit portfolios.

The Meghalaya Cooperative Apex Bank Ltd (MSCABL)

The Meghalaya State Cooperative Apex Bank Limited (MCABL) was set up in 1971.Over its 41 years of existence, the bank has grown into a premier institution in the State. It is characterised by a two-tier credit structure with the bank at the Apex level operating through 47 branches spread over the seven districts of the state. The MSCABL commenced Banking operations with just two branches--one at Shillong and the other at Tura. Currently, the Bank has a wide network of 47 branches, spread throughout the State providing effective banking products and other related services to the general public. Apart from mobilising deposit resources of more than 911.68 crores and accounting for advances of more than 263.23 crores as on March 31, 2010 for various developmental activities in the state, the bank has also engaged itself in various unique activities like, channelizing concessional credit of NSTFDC (National Scheduled Tribes Finance and Development Corporation), NHFDC (National Handicapped Finance and Development Corporation) and NSCFDC (National Scheduled Tribes Finance and Development Corporation) to the tribals, handicapped and scheduled castes respectively of the state since 2007 with the support from the state govt. Besides, MSCABL is the first bank to have two branches to be exclusively run and managed by women and also have mobile banking service to cover 7 rural centres in the East Khasi Hills District.

Measures of Efficiency

Establishing an effective technique for measuring bank's performance has always been stressed upon by researchers and practitioners. It is found that estimates of efficiency are sensitive to the choice of the technique. The whole lot of literature available that attempt to measure the performance of banks can be divided into two categories based on the methodologies adopted viz., traditional measures and frontier approaches. The techniques used in the traditional approach are Ratio Analysis, Regression Analysis, Index Number Approach, Taxonomic Method, Multivariate Analysis, Translog Function etc. Traditionally accounting based cost ratios were used by bank analysts to measure cost efficiency. Although cost ratios are easy to construct and use, they are often difficult to interpret and may prove to be misleading. An alternative to accounting-based efficiency ratios is the Cost Frontier Analysis (CFA). In CFA, the analysts attempt to estimate the maximum amount that a bank could reduce its costs while still producing the same amount and combination of financial services. Frontier inefficiency, also known as X-inefficiency, imply the scope of potential cost savings.

Data Envelopment Analysis (DEA) is a linear programming-based technique for measuring the performance efficiency of organizational units like bank known as Decision Making Unit (DMU) relative to other DMUs. The performance of DMUs is assessed in DEA using the concept of efficiency or productivity which is the ratio of weighted outputs (virtual output) to weighted inputs (virtual inputs). The best performing DMU is assigned an efficiency score of unity (or 100 percent) and thus the performance of a DMU vary between 0 and 1.The DEA approach forms the efficiency frontier out of piecewise linear stretches thereby forming a convex production possibility set. The firms on the frontier are considered 100 per cent efficient. When the inefficient units are projected onto the envelopment surface, the efficient units closest to the projection and whose linear combination comprises this virtual unit form the peer group for that particular DMU.

The DEA mathematical model used is as follows: Let there be N number of DMUs whose efficiencies have to be compared. Let us take one of the DMUs, say the mth DMU and maximize its efficiency according to the formula given below:


Where [], [] [greater than or equal to] 0; i = 1, 2, K, I; j = 1, 2, K, J

And [E.sub.m] is the efficiency of the mth DMU; [] is the [] output of the mth DMU; [] is the weight of that output; [] is the [] input of the mth DMU; Uim is the weight of that input; [Y.sub.jn] and [] are the [] output and [] input respectively of the DMU, n= 1, 2, ..., N.

The objective function defined by [E.sub.m] aims to maximizethe ratio of weighted outputs to weighted inputs of the bank. This is subject to the condition that any other bank in the sample cannot exceed unit efficiency by using the same weight.

The problem setting in (1) is a fractional program. It is generally difficult to solve fractional programs. If they are converted to simpler formulations, such as the linear programming (LP) formats, then they can be solved easily. The simplest way to convert these fractional programs to linear programs is to normalize the numerator or the denominator of the fractional programming objective function. This is done by restricting the denominator of the objective function [E.sub.m] to unity, and adding this as a constraint to the problem. This is shown as follows:


The maximizing linear programming setting in (2) assumes constant returns to scale technologies. When the formulation constraints the weighted sum of the inputs to unity and maximizes the outputs this becomes an input based efficiency measurement as shown above. Similarly a general input minimization CCR DEA model can also be constructed.

Using DEA model, the efficiencies at the branch level are estimated for the sample from the period March 31, 2008 to March 31, 2012. The sample branches are 29 proportionally selected from the five districts of Meghalaya. The study measures the Cost Efficiency also termed as Overall Cost Efficiency (OCE) of the branches. The present study deploys both BCC and CCR model to estimate the efficiency scores. Under BCC model the study decomposes the Technical Efficiency outcomes into Pure Technical Efficiency (PTE) and Scale Efficiency (SE). The present study considers three inputs viz, Deposits, Physical Capital and Number of Employees and three outputs viz; Advances, Interest Income and Non-Interest Income. Cost DEA requires specification of Input prices. Thus for the present study Interest Expenses is taken as measure of price of Deposits whereas payments and provisions for employees is taken as a price of Employees, Depreciation and Rental charges are taken as a measure for price of physical capital.

Empirical Results

Table 1 presents the results of average OTE [the term overall technical efficiency (OTE) and technical efficiency (TE) have been used interchangeably in this study] scores with standard deviation of the sample branches of the bank during the period from 2009 to 2013. The results have been obtained through running CCR and BCC model separately for each year. The basic difference between CCR and BCC model is the assumption of returns to scale. While the former restricts DMUs to operate with Constant Returns to Scale (CRS), the latter assumes Variable Returns to Scale (VRS). The empirical findings reported that average TE under CCR is 0.62 with Standard Deviation of 0.050. Thus, the overall technical inefficiency [OTIE (%) = (1- OTE) X 100] of branches came out to be almost 38 percent. This indicates that the branches can curtail their input expenditures on Deposits, fixed assets and labour by 38 percent by adopting best practices.

Under BCC model, TE can further be decomposed into Pure Technical Efficiency (PTE), the efficiency arising out of a manager's ability to utilize resources most efficiently and get the maximum possible returns, and Scale Efficiency (SE), that is the ability to increase/decrease the scale of operation to the optimum and operate at the Constant Returns to Scale. Thus, Pure Technical Inefficiency (PTIE) also represents wastages that are devoid of Scale Inefficiency (SIE).

It can be seen from Table 1; the mean PTE score is estimated to be 79 percent with standard deviation measure of 0.040. This indicates that 38 per cent of TIE is explained by 21 per cent of PTIE that is due to the incapability of the management to utilize the resources. The rest part of the TIE may be attributed to the fact that the banks are operating at below the optimal level.

SE of banks can be measured as the ratio of OTE to PTE. The value of SE equal to 1 indicates that a DMU is operating at most productive scale size and value of less than one indicates that a DMU is not operating at optimal scale. The mean SE score is estimated at 78 per cent with standard deviation measure of .062 which implies that average scale inefficiency (SIE) as much as 22 per cent is due to the choice of sub-optimal level of operation.

Branch Wise Efficiency Analysis

From Table II, it can be observed that two branches B2 and B21 is found to be efficient under CCR input oriented DEA model, whereas under BCC input oriented DEA model eight branches, that is, B1, B2, B5, B10, B18, B19 and B21 were found to be the best practice branches under the measure of pure technical efficiency. The comparison of CCR and BCC model gives us the estimates of scale efficiency. Table reflects that three branches, that is, B2, B6 and B21 are the scale efficient branches whereas others are scale inefficient. Analysis on the scale of operation of the sample branches reveals that in the year 2009, nine branches, that is, B2, B6, B7, B9, B10, B20, B21, B22 and B29 operated at constant return to scale. In the Year 2010 only six branches were identified as the best practice branches. These branches are B2, B6, B9, B10, B21 and B25. Again in the year 2011, nine branches exhibited constant return to scale. These branches are B2, B5, B7, B9, B10, B16, B18, B21 and B22. In the year 2012 only five branches were identified as to be operating at the best scale. These are B2, B6, B18, B20, B21 and B22. Again in 2012 nine branches exhibited CRS. These branches are B2, B6, B7, B10, B13, B16, B18, B21 and B22. B2 exhibited Constant Returns to Scale in all the years. B6 exhibited CRS in four out of five years.

Distribution of Branches under Technical Efficiency

The sample branches are classified into different efficiency range based on TE scores. Based on the mean TE scores, it can be seen from Table III that only two branches B2 and B21 were found to be in the most efficient group. It can be seen that majority of the branches (35 per cent) concentrate in the efficiency range of 0-.49. This is followed by concentration of 21 per cent of the branches in the efficiency range of 0.50 to 0.59

Productivity Growth and its Decomposition

The present makes use of Malmquist Total Factor Productivity (TFP) Index to measure the productivity growth/decline during the study period. Further, it tries to decompose the TFP change (tfpch) into Technical Efficiency change (effch) and Technological Change (techch). The technical efficiency change (effch) is further decomposed into pure technical efficiency change (pech) and scale efficiency change (sech). The MTFPI measures changes in total output relative to inputs. It is one of the most frequently used methods to evaluate productivity change. The MTFPI measures the TFP change between two data points by calculating the ratio of the distances of each data point relative to a common technology. The Malmquist input oriented TFP change index between the base period t and the following period t + 1 is defined as:


Thus Malmquist Productivity Index is a product of two: (i) Change in Technical Efficiency (TE) or how close a bank is to the efficient frontier (Catching -Up Index) and (ii) Technological Change i.e., the change in best practice index or how much the benchmark production function shifts at each bank's observed input mix (Innovations and Shocks). This is also termed as the 'frontier shift' effect. Thus equation (1) can be decomposed into:

Efficiency Change = [d.sup.t.sub.1]([Y.sub.t],[X.sub.t])/[d.sup.5.sub.0]([Y.sub.5], [X.sub.5])] ... (2)


If Malmquist productivity index is greater than one it indicates progress in TFP and if it is less than one it indicates decline in TFP. Productivity changes reflect changes in technological progress as well as technical efficiency: M = (E) X (T) where E and T stand for technical efficiency change and technology change respectively.

For the purpose of the present study, year 2009 is taken as the base against which productivity growth or decline is studied.

Table-IV shows an average decline of 8 per cent in the total factor productivity over the study period. The tfpch is found mainly due to a decline in techch index which registered a decline of 12 per cent whereas a growth in effch of 5 per cent is observed. The decomposition of effch index into pech and sech components indicates a growth in sech of 6 per cent whereas a very marginal decline of 0.004 is identified in pech. In all the years, productivity decline is observed on an average. Further, except for the year 2011, technological regress is found to be the major cause of productivity decline.

The distribution of branches based on productivity change index. It is seen that only eight out of twenty nine branches exhibit productivity growth. Majority of the branches are found to be concentrated in the productivity range of 0.80 to 1 (Branch Code 29,27,25,24,23,22,20,17,16,1 5,14,13,10,7,4,3,2). This includes 17 branches constituting 59 per cent of the total sample. This indicates that majority of the sample branches exhibit productivity decline between 1-20 per cent. Six branches constituting 21 per cent of the sample exhibit productivity range of 1-1.20 (Branch Code 26, 18, 11,8, 5, 1). Only 2 branches representing only 7 per cent of the total sample are classified in the productivity range of 1.20 - 1.40 (Branch Code 12, 6). 4 branches comprising 14 per cent of the sample are classified in the productivity range of 0.60-0.80 (Branch Code 28, 21, 19, 9). Thus, only 8 branches (28 percent of the sample) exhibit productivity growth whereas other exhibit productivity decline.

Determinants of Inefficiency

This section attempts to ascertain the determinants of efficiency or inefficiency. The efficiency scores as obtained under the measure of TE are regressed over certain relevant explanatory variables. Since the DEA efficiency score lies in the interval 0 and 1, the dependent variable is 'a limited dependent variable'. Therefore, it is appropriate to use the Tobit model, which is a censored regression model, applicable in cases where the dependent variable is constrained in some way. The Tobit model may bedefined as:

[y.sup.*] ; 0 < = [y.sup.*] < = 1

y = 0 ; [y.sup.*] < 0;

1 ; 1< [y.sup.*]

[y.sup.*] = [beta]xi + [epsilon]t

Where y is the DEA VRS TEscore. [epsilon]t ~ i e N(0, [sigma]2) [y.sup.*] is a latent (unobservable) variable.

The dependent variable is the DEA Score obtained from Stage 1 of DEA. The explanatory variables which are considered in the Tobitmodel to estimate the factors which determine efficiency of the selected branches are size, profitability, Market Power and Business. These variables are described as follows:

1. Size (SZ): Volume of Total Assets is taken as the proxy of Size. It is hypothesized that Large Sized Branches are more efficient.

2. NOI/TA (NOITA): The ratio of Net Operating Income (NOI) to Total Assets (TA) is taken as a measure of profitability. It is hypothesized that a higher NOI/TA increases efficiency.

3. Market Power (MP): Market Power is expressed as a proportion of individual branch Deposit to Total Deposit. It is hypothesized that Increase in Market Power has a positive impact on Efficiency.

4. Liquidity Risk (LQ): This is expressed in terms of ratio of Total Loans and Advances to Total Assets. It is hypothesized that Increase in Liquidity risk negatively impacts efficiency.

[beta] is the vector of unknown parameters which determines the relationship betweenthe independent variables and the latent variable.xi is the vector of explanatory variables.

Thus, the Tobit Model can be specified as

[y.sup.*] = [alpha] + [beta]1SZ+ [beta]2PFT+ [beta]3MP+ [beta]4LQ+e

[y.sup.*] represents the dependent variable which is the DEA score of TE.

Results of Tobit Regression Model
Variables   Coefficient
SZ          2.34e-11
NOITA       -4.387271 **
MP          -0.1505802
LQ          -0.2218774 **
CONS        0.7204055 **

** Significant at 5 per cent level of significance

The efficiency scores changes by the coefficient for each unit increase in the corresponding predictor variable. Size is found to have a positive impact on TE. Thus, it can be viewed that large sized branches account for higher degree of technical efficiency. The Net Operating Income to Total Assets (NOITA) ratio is found to be negatively influencing the efficiency score. NOITA represents the ratio of Net Operating Income to Total Assets. Net Operating Income is the difference between Interest Earned and Interest Expended. In many cases, the branches exhibited negative spread implying that the interest earned are lower than the interest income. This negative difference has a negative impact over the efficiency scores. Market power has negative impact on efficiency. Increasing market power in terms of deposits simultaneously brings increased cost of deposits which negatively impacts the efficiency. Liquidity risk in banks is a trade-off between bank intermediary performance roles and having a crisis of cash Liquidity which is found to be negatively influencing the efficiency.


The study reveals that there exists wider scope of cost savings for the sample branches. 38 per cent of TIE is explained by 21 per cent of PTIE that is due to the incapability of the management to utilize the resources. The rest part of the TIE may be attributed to the fact that the banks are operating at below the optimal level. The branches can curtail their input expenditures on Deposits, fixed assets and labour by 38 percent by adopting best practices. On an average the branches exhibit productivity decline and Technological change index is the major source of productivity regress. Small branches are found to be relatively better in terms of efficiency than large sized branches. Malmquist Total Factor Productivity (TFP) Index shows an average decline of 8 per cent in the total factor productivity over the study period. The tfpch is found mainly due to a decline in techch index which registered a decline of 12 per cent whereas a growth in effch of 5 per cent is observed.

Joyeeta Deb

Assistant Professor, Department of Commerce, Assam University, Silchar, Assam.

Paper received on July 16, 2015


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Year-Wise Mean Technical Efficiency of Branches under Input Oriented
CCR DEA Model and BCC Model

Year    Technical Efficiency    Pure Technical    Scale Efficiency (SE)
        (TE) under CCR model   Efficiency (PTE)

2009            0.60                0.828                 0.708
2010            0.61                0.824                 0.758
2011            0.64                0.762                 0.83
2012            0.56                0.736                 0.746
2013            0.70                0.798                 0.859
Mean            0.62                0.790                 0.78
SD             0.050                0.040                 0.062

Source: Estimated using DEAP 2.1 based on data pertaining to Branch
Financial Statements (2009-2013)

Note: SD denotes standard deviation.

Branch- Wise OTE,PTE and SE Scores (Input Oriented)

Branch Code    OTE     PTE     SE

1             0.584   1.000   0.550
2             1.000   1.000   1.000
3             0.295   0.500   0.589
4             0.470   0.734   0.768
5             0.819   1.000   0.763
6             0.754   0.834   1.000
7             0.938   0.961   0.916
8             0.218   0.380   0.621
9             0.816   0.873   0.913
10            0.970   1.000   0.970
11            0.428   0.968   0.430
12            0.323   0.400   0.820
13            0.688   0.988   0.671
14            0.545   0.675   0.858
15            0.448   0.530   0.887
16            0.810   0.982   0.866
17            0.428   0.788   0.475
18            0.925   1.000   0.931
19            0.746   1.000   0.697
20            0.757   0.709   0.979
21            1.000   1.000   1.000
22            1.000   1.000   0.935
23            0.548   0.710   0.815
24            0.410   0.802   0.515
25            0.406   0.596   0.852
26            0.341   0.534   0.659
27            0.413   0.650   0.651
28            0.438   0.738   0.534
29            0.451   0.543   0.966
Mean          0.620   0.789   0.780

Source: Estimated using DEAP 2.1 based on data pertaining to Branch
Financial Statements (2009-2013).

Classification of Branches in Efficiency Range
(based on TE scores)

Efficiency Range       Branch Code   TE Score (Mean)
(Based on TE Scores)

0-.49 (N = 10)              3            0.2892
                            8             0.232
                           11            0.4174
                           12             0.327
                           15             0.478
                           17            0.3664
                           24            0.3988
                           26            0.3382
                           27            0.4156
                           28            0.3998

.50-.59 (N = 6)             1            0.5496
                            4            0.5796
                           14            0.5626
                           23            0.5816
                           25            0.5208
                           29            0.5226

.60-.69 (N = 3)            13            0.6644
                           19            0.6974
                           20             0.693

.70-.79 (N = 1)             5             0.763

.80-.89 (N=4)               6            0.8334
                            7            0.8832
                            9             0.816
                           16            0.8524

.90-.99 (N = 3)            10            0.9704
                           18            0.9306
                           22            0.9352

100 (N = 2)                 2               1
                           21               1

Malmquist Index Summary of Annual Means for All Branch

Year           effch   techch   pech    sech    tfpch

2nd            1.035   0.929    0.978   1.058   0.961
3rd            1.06    0.791    0.94    1.128   0.839
4th            0.862   1.062    0.965   0.893   0.915
5th            1.304   0.755    1.111   1.174   0.985
Average (GM)   1.054   0.876    0.996   1.058   0.923

Source: Estimated from DEAP 2.1
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Date:Jan 1, 2017
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