This paper presents a new framework for the memoranda of understanding that central public sector enterprises sign with the government for benchmarking their performance. The framework goes beyond standard financial ratios. It uses these ratios and other information to develop state financial parameters, dynamic indicators and sector and enterprise-specific variables for public sector enterprises in each sector. The paper, based on a study done for the government of India, suggests a new analytical tool for measuring public sector performance.

Memorandum of Understanding and Business Performance Appraisal of the Public Sector

R Venkatesan

This paper presents a new framework for the memoranda of understanding that central public sector enterprises sign with the government for benchmarking their performance. The framework goes beyond standard financial ratios. It uses these ratios and other information to develop state financial parameters, dynamic indicators and sector and enterprise-specific variables for public sector enterprises in each sector. The paper, based on a study done for the government of India, suggests a new analytical tool for measuring public sector performance.

The useful comments received on the draft version of this article from the author’s colleague, Diana Rai, are gratefully acknowledged.

R Venkatesan (rvenkatesaniitmiimb@gmail.com) is with the National Council of Applied Economic Research.

he memorandum of understanding (MoU) is the most talked about and visible document in the public forum on central public sector enterprises (CPSEs) in India. The MoU system was introduced in the late 1980s as an instrument to increase accountability and autonomy. The MoU, during the late 1990s, had transcended to become an instrument for performance appraisal looking at how the CPSEs performed in utilisation of strategic assets.

In 2004, a research study was commissioned by the central g overnment and was carried out at the National Council of Applied Economic Research (NCAER) by the author. The government’s concern was that by then 40 per cent of the enterprises had been graded as “excellent” and the question was therefore whether the MoU system was still relevant or was just one that kept everyone happy. Also, the MoU system had to incorporate changes brought out by the complexities of globalisation and there was a need to reorient the instrument to the new c omplex environment.

It was evident that the redesigned MoU instrument had to look at business performance appraisal, at how the management exploited new opportunities that arose in business as well as how it responded to serious threats to the organisation. In other words, gone were the days when the MoU instrument could remain content with examining the efﬁciency in utilisation of strategic assets. It had to go a step further in appraising business performance. With this basic purpose set as the project goal, a team from NCAER set upon the journey of redesigning the MoU system with the active participation of all stakeholders. The stakeholders included those involved in managing the CPSEs and in monitoring the performance in the ministries.

Choice of Indicators

We ﬁrst decided that the MoU system, which relies on ﬁnancial ratios derived from proﬁt and loss accounts and balance sheets of CPSEs should design its ratios to reﬂect the capital, labour and total factor productivities rather than liquidity, debt, coverage and proﬁtability. This aimed at remedying the fact that many CPSEs had improved ﬁnancial performance without any accompanying improvement in total factor productivity (TFP).

We named the ﬁnancial ratios derived from proﬁt and loss accounts and balance sheets of CPSEs and designed to reﬂect the capital, labour and total factor productivities as “static i ndicators”. This was done to contrast them from variables/ ratios that depict the health of the company. These variables/ ratios are dynamic in nature and show up with a lag of about

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Economic & Political Weekly

REVIEW OF INDUSTRY AND MANAGEMENT

three years. This was the second major decision that had the stake holders’ consensus.

For the purpose of the design of the instrument, it was recognised that enterprises belonging to the social, ﬁnancial, trading and consulting services factor in a certain amount of heterogeneity with regard to static and dynamic parameters. Thus these enterprises had a different design of MoU from the main instrument that was common to CPSEs belonging to syndicate groups such as petroleum, energy, mining and metals, electronics or communications, heavy industry, process industry and fertilisers. These enterprises were grouped under the category christened as CPSEs not belonging to social, ﬁnancial, trading and consulting sectors.

Productivity growth from factors other than those associated with capital deepening such as growth in the absolute amount of capital stock and labour productivity is measured by TFP. Normally, productivity indicators commonly used by industrialeconomists include labour productivity. Traditionally, the gross product per worker is used as an indicator of the efﬁciency with which labour is used. TFP or multi-factor productivity measures output per unit of all inputs, including labour and capital. Capital productivity can be conceived as the return associated with c apital deepening while TFP can be associated with the effects on productivity by factors such as new technologies, economies of scale, scope, managerial skills, improved organisational structure, etc. Armed with this basic understanding, the analytic team probed into ways of estimating TFP from the conventional published annual ﬁnancial statements and tried to understand whether the “static factors” can be expanded to include productivity measures such that a meaningful comparison across periods and between enterprises is possible.

Financial Performance Indicators

For CPSEs not belonging to social, ﬁnancial, trading and consulting sectors, proﬁtability ratios on gross margin, gross block, net proﬁt, net worth, gross proﬁt and capital employed, reﬂect capital productivity ratios. The ratio “gross margin/gross block” is an indicator of the overall yield on cumulative ﬁxed assets in the enterprise and is an especially important variable for public s ector enterprises (PSEs) with high investments in ﬁxed assets. The ratio “gross proﬁt/capital employed” captures the yield on capital employed rather than the investments in ﬁxed assets and is particularly important for PSEs who deploy signiﬁcant funds in working capital requirements. The ratio net proﬁt/net worth reﬂects the prudence by the management of shareholders’ fund in the organisation, since access to low interest/optimal set of debt instruments can leverage this particular ratio.

However, keeping in view the differences between the nature of activities across sectors we identiﬁed a separate set of parameters reﬂecting proﬁtability for the ﬁnancial, trading, consulting and social sectors. For the ﬁnancial sector, these parameters include disbursement, resource mobilisation, loan sanctions, projects commissioned in value terms and ﬁnancial returns (the difference between cost of borrowings and disbursements). For the trading and consulting sectors, the gross margin/gross sales and operating turnover/employees have been considered appropriate ﬁnancial performance variables. In the case of the social sector, a

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PSE has to show that its operations make surplus on the funds deployed and/or meets at least the cost of capital. The performance review should be considered based on activity and efﬁciency considering the mission and objective of the organisation.

Size-Related Financial Indicators

Size is an important parameter as many PSEs have listed maximisation of sales revenue as one of their key objectives. A select few of them are either companies belonging to the Fortune 500 category or those aspiring to be listed in the Fortune 500. Thus we consciously selected size as one of the important static factors. Besides, the optimal ratios can be achieved easily in smaller enterprises than those involved in coordination of activities in a large enterprise. Thus it was felt that use of ratios alone might not capture complexities associated with managing mega companies.

Annexure 1: Static, Dynamic, Enterprise Specific and Sector Specific Indicators

The definitions and rationale for selection, of various static variables is given in the chart below

Financial Returns

Besides the size and proﬁtability, productivity factors were also a key consideration. For instance, a PSE with access to cheap inputs and/or assured market conditions may have an incentive to expand without due attention to improvement of productivity. It could use more inputs in achieving additional production if this action improves size and proﬁtability ratios. On the other hand, improvement of productivity through optimal use of input per unit of output improves the long-term competitiveness when market situations change or when price of inputs vary abruptly. Productivity can be of three types – labour productivity, capital productivity and TFP. While capital productivity is indirectly

REVIEW OF INDUSTRY AND MANAGEMENT REVIEW OF INDUSTRY AND MANAGEMENT

c aptured by proﬁt and size related variables, TFP reﬂects techno	you assume that the “going concern” is broken up and the
logy change and labour productivity is a key variable tracked	resources are ploughed back into other PSEs. In other words a
by economists. A fundamental ratio reﬂecting labour pro	company’s performance is measured by the ratio of added value
ductivity is proﬁt before depreciation, interest and taxes (PBDIT)/	per unit sale, which is an indicator of the multi-factor productivity
number of employees. Here PBDIT was chosen as the numerator	or the value added by the business unit per unit sale.
(in the place of value added) as it was felt that wages and salaries	In practical terms, the surplus over and above all these returns
may not fully reﬂect marginal productivity. This is borne out by the	on capital, labour and the supply of inputs is the added value cre
fact that the voluntary retirement schemes (VRS) have been	ated. A company’s performance is measured by the ratio of added
improving the ﬁnancial returns as well as productivity related	value per unit sales, which is an indicator of the efﬁciency in
f actors in many PSEs.	activity of the organisation. An increase in gross margin would
	cause an increase in every other performance indicator in the
Annexure 2: Proposed Overall Weights Assigned to the Static and Dynamic Parameters for PSEs Other Than Financial, Trading and Consulting and Social Sector	company. This is reﬂected as an improvement in the ﬁnancial
Indicators Final Overall Weight	parameters. However, discussions with PSEs suggested that the
(in %)	deﬁnition and the weight assigned to the parameter added value
1 Static financial parameters (50 %) (a) Financial performance indicators – profit related 22	should consider the nature of activity of the PSE. This was taken
J (SPTTNBSHJOHSPTTCMPDL	care of by altering the discount factor for the PSEs in social sector
JJ /FUQSPGJUOFUXPSUI	and changing its deﬁnition for the PSEs in ﬁnancial services. In
JJJ (SPTTQSPGJUDBQJUBMFNQMPZFE	case of the ﬁnancial sector this would be the returns on funds
(b) Financial indicators – size related 12	lent, which should at least cover the cost of borrowings and other
J (SPTTNBSHJO	expenses. The social sector gets capital at a lower cost and hence
JJ (SPTTTBMFT	the discount rate applicable will be lower vis-à-vis other PSEs.
(c) Financial return indicators – productivity related 16	Variables or ratios that depict the health of the company, which
J 1#%*5UPUBMFNQMPZNFOU	are dynamic in nature, and the effect of which shows up with a
JJ EEFEWBMVFTBMFT Sub-total 1 (a+b+c) 50	lag of about three years, were christened “dynamic indicators”.
%ZOBNJDGBDUPST E 2VBMJUZ *40DFSUJGJDBUJPOJOUFSOBMJTBUJPOPGRVBMJUZXJUIJO4#6QSPEVDUT	Dynamic Indicators
F $VTUPNFSTBUJTGBDUJPO DVTUPNFSPSJFOUBUJPO	Among the dynamic indicators, the basis for choosing “quality” is
G )3% NQMPZFFUSBJOJOHBOENPUJWBUJPO	that it reﬂects the effort on the part of the companies to attain the
H 3%GPSTVTUBJOFEBOEDPOUJOVPVTJOOPWBUJPO	Indian Standard Organisation (ISO) certiﬁcation that is an inter
I 1SPKFDUJNQMFNFOUBUJPO NPEFSOJTBUJPOBOEFYQBOTJPO	nationally recognised criterion of quality control. Internalisation
J $BQJUBMFYQFOEJUVSFHSFFOGJFMEJOWFTUNFOUTKPJOUWFOUVSFT K YUFOUPGHMPCBMJTBUJPO JOUFSOBUJPOBMJTBUJPOKPJOUWFOUVSFT	of quality enhancement at the SBU level or product level needs
exports, strategic alliances, market presence in emerging	also to be factored in. Customer Satisfaction includes ratios of the
FDPOPNJFTJOUFSOBUJPOBMJTBUJPOBMPOHWBMVFDIBJO	number of complaints to the total number of transactions, the
4VCUPUBM EFGHIJK	number of major customers retained by initiatives of previous
5PUBM Sector-specific variables	years, etc. Human resource development (HRD) measures include
(Task force to decide in consultation with PSEs) 10	the training of employees (number of man-days), the implemen
Enterprise-specific variables	tation of HRD training programmes, the upgradation of skills, etc.
(Task force to decide in consultation with PSEs) 10	Research development (RD) is undertaken by PSEs to
5PUBM MM.P6SFMBUFEJUFNT	increase the stock of knowledge and the use of this knowledge to
6OUJNFMZESBGUJOHBOETVCNJTTJPOPGUIF.P6 1FOBMUZPGNBSLT	derive new applications in an effort to develop sustainable, insti
	tutionalised and appropriate technological innovations. Various
TFP of a CPSE was assessed by a new ﬁnancial ratio “added	aspects of RD have been considered as valuable such as RD
value” per unit turnover. TFP answers the question as to what	projects completed on the basis of approved schedules, value of
would be the net proﬁt or loss to the national economy or group of	items and services commercially offered, RD investments as a
companies if one were to wind up this enterprise or strategic business	percentage of gross sales, etc. Project implementation includes
unit (SBU) and invest the proceeds in other CPSEs or other SBUs.	projects implemented (modernisation and expansion) per cost
Added value is a measure of TFP based on inputs from the ﬁnan	and time schedules without time-overruns and cost-overruns.
cial statements. The concept of added value is that wages and sal-	Also there was a need to factor in enterprise speciﬁc parameters
aries reﬂect to a certain extent the marginal productivity of labour.	that do not get reﬂected in proﬁts such as investments for environ-
Expenses on supplies, etc, reﬂect the true value of these inputs,	ment or ecological considerations for example. The performances
assuming no price distortion. The capital employed earns a return,	of PSEs are also affected by the international industry environment
which is equal to the average cost of capital for the industry group	and the general domestic economic performance. Hence, sector
or an average of PSE returns in the economy. The surplus over and	speciﬁc parameters for the latter and enterprise speciﬁc para
above all these returns that is capital, labour and the supply of	meters for the former have been introduced to allow ﬂexibility.
inputs is the “added value” created. Another interpretation may be	Thus, besides the static and dynamic indicators, s ector and enter
that “added value” measures the contribution to the economy, if	prise variables were also included in the new MoU instrument.
84	september 27, 2008 Economic Political Weekly

Annexure 3: Distribution of Weights for Financial Sector PSEs

For the ﬁnancial sector, the relevant parameters reﬂecting proﬁtability are disbursements, resource mobilisation, loan sanctions, projects commissioned in value terms and ﬁnancial returns (difference in the cost of borrowing and disbursements). Apart from the proﬁtability parameters, the concept of added value has also been modiﬁed for the ﬁnancial sector. The parameter added value was deﬁned as r evenue – operating expenditure – expected returns on capital employed, while the capital recovery factor remains 10 per cent. The table below shows the parameters and distribution of weights for the PSEs in ﬁnancial sector.

Proposed Overall Weights Assigned to the Static and Dynamic Parameters for PSEs – Financial Sector

Indicators Final Overall Weight (in %)

1 Static financial parameters (50 %)

(a) Financial performance indicators – profit related 22

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(v) Financial returns (difference in cost of borrowing and disbursements) (Indicators to be selected by the task force in consultation with the relevant PSEs)

(b) Financial indicators – size related 12

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(c) Financial returns – productivity related 16

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strategic alliances, market presence in emerging economies,

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Sector-specific variables (Task force to decide in consultation with PSEs) 10

Enterprise-specific variables (Task force to decide in consultation with PSEs) 10

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Sector and Enterprise Specific Variables

The enterprise could opt for investments which might not earn a rate of return but are considered necessary for certain broader goals. Such investments include environmental safeguards, safety instruments, etc. There could also be other variables, which are not reﬂected in the ﬁnancial indicators for the year or for s ubsequent years. These should be factored in as enterprise s peciﬁc. The e nterprise speciﬁc variables are equally important to reﬂect the unique characteristics of the enterprises with respect to MoUs.

The sector speciﬁc variables arising from factors outside the c ontrol of CPSEs also need to be factored in for they affect the

Economic Political Weekly

september 27, 2008

p erformance of these enterprises. Foreign factors include for example disruptions in supplies of key raw materials, sudden spurts in prices of ﬁnished products, performance affected by the international in dustry environment or general domestic e conomic performance.

The parameters that need to be factored in were arrived at using the factor analytic technique. Parameters so chosen were categorised into static and dynamic. Static or quantitative parameters reﬂected costs and beneﬁts associated with the operation of the enterprise in a given period of time, while the dynamic or qualitative parameters normally resulted in beneﬁts in the future when costs are incurred in the reference year. The effect of the dynamic parameter showed up with a lag of about two to three years. The dynamic factors could be considered as indicators of the health of the ﬁrm.

Data

The data for the study were obtained from the Public Enterprises Survey published by the department of public enterprises, m inistry of heavy industries and public enterprises. For the

Annexure 4: Distribution of Weights in PSEs in Trading and Consulting

In the trading and consulting sectors there is very little input of capital or equipment. Hence in order to judge the performance of the organisation gross margin/turnover and operating turnover/total employees are the appropriate ﬁnancial performance variables. The table below shows the parameters and distribution of weights for the PSEs in the trading and consulting sectors.

Proposed Overall Weights Assigned to the Static and Dynamic Parameters for PSEs – Trading and Consulting Sector

Indicators Final Overall Weight (in %)

1 Static financial parameters (50 %)

(a) Financial performance indicators – profit related

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(b) Financial indicators – size related

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(c) Financial returns – productivity related

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Sub-total 1 (a+b+c)

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strategic alliances, market presence in emerging economies,

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Sector-specific variables (Task force to decide in consultation with PSEs)

Enterprise-specific variables (Task force to decide in consultation with PSEs)

5PUBM

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REVIEW OF INDUSTRY AND MANAGEMENT

dynamic indicators we used the data contained in volumes on MoU published by the same department.

The Model

The objective of the present study required an appropriate model that would indicate the optimum combination of performance indicators and the corresponding weights. Therefore, the statistical tool should combine the variables in such a manner that it reﬂects the underlying correlation between them and reduces the dimensionality of the original data set.

The Factor Analytic model fulﬁlled all the above criteria. The main application of this technique is to reduce the number of variables needed for analysis and to detect the structure in the relationships between the variables. In performing the latter function, it also calculates the optimum weights to the original variables, which are combined to form the factors. The variance

Annexure 5: Distribution of Weights in PSEs in Social Sector

The social sector PSEs have to show that all operations make surplus on funds deployed and/or meet their cost of capital. The performance review should be considered based on activity and efﬁciency considering the mission and objective of the organisation. The discount rate for the calculation of the added value for these PSEs should be 4 per cent since they are not solely proﬁt-making organisations and thus market rates of returns should not be applicable. The table below shows the distribution of weights between static and dynamic parameters for these PSEs.

Proposed Overall Weights Assigned to the Static and Dynamic Parameters for PSEs –Social Sector

Indicators Final Overall Weight (in %)

1 Static factors (50 %)

(a) Financial performance indicators – profit related 22 Task force should set parameters in consultation with the PSEs which reflect returns on funds deployed in programmes for comparisons with the cost of raising these funds.

(b) Financial indicator – size related 12

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(c) Financial returns – productivity related 16

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strategic alliances, market presence in emerging economies,

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of the retained data was maximised so that it was the optimum representation of the original data set. The weighting scheme was based on this statistical property. To put it simply, one can summarise the correlation between two variables in a scatter diagram. A regression line can then be ﬁtted that represents the “best” summary of the linear relationship between the variables. Now if a variable can be deﬁned that would approximate the regression line in such a plot, then the variable would capture most of the “essence” of the two items.

Principal Components Analysis (PCA) is a powerful factor analytic tool that helped us identify patterns in data with a dimensionality that is more than three, where graphs cannot be used. The main advantage of this technique is that it compresses the data without much loss of information. We ﬁrst used PCA to compress the static variables to one static indicator, which gave us the optimum linear combination of the variables.

The schematic diagram in Annex 1 (p 83) shows the factors selected under each category of parameters for the quantitative and qualitative analyses and the weights associated with the static and dynamic indicators. Annexures 2 (p 84), 3 and 4 (p 85) and 5 list the weights provided for each factor/variable in different sectors.

Conclusions

Today, the task of a PSE leader is more demanding than ever before. Big enterprises often operate in many countries or p roduct markets and through joint ventures. Outsourcing and alliances add further complexity. The pace of innovation is quicker, new technologies have to be applied faster and the product life cycles have become shorter. A MoU can no longer be a document about performance appraisal or one that aids in increasing accountability and autonomy. The MoU in today’s globalised context has to be one that facilitates the performance of an enterprise. The performance appraisal has to be one that provides the ability to an enterprise to excel, not one of c ategorisation. Globalisation has seen increased ﬁnancial liberalisation. This spells the need for an MoU instrument that expands it’s list of static factors needed to conduct productivity measures. This can be done by estimating the TFP from the c onventional, published, annual ﬁnancial statement in addition to proﬁt and size-related indicators. Besides static factors, a MoU needs to:

* Include variables that depict the health of the company. These variables are dynamic in nature and their effect shows up with a lag.

* Incorporate enterprise speciﬁc parameters, which are important from the viewpoint of society though they do not get reﬂected in increased proﬁts.

* Incorporate sector speciﬁc parameters that reﬂect how the PSE is affected by the macro-environment.

* Recognise that for enterprises belonging to the social, ﬁnancial, trading and consulting services, a certain amount of h eterogeneity with regard to static and dynamic parameters has to be brought in. Increasingly, PSEs perceive public enterprise governance as an

add-on tool to overcome control by the government, to overcome handicaps that hamper the efﬁciency and competitiveness.

september 27, 2008

Economic Political Weekly

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Memorandum of Understanding and Business Performance Appraisal of the Public Sector

Memorandum of Understanding and Business Performance Appraisal of the Public Sector

Choice of Indicators

Financial Performance Indicators

Size-Related Financial Indicators

Financial Returns

Sector and Enterprise Specific Variables

Data

REVIEW OF INDUSTRY AND MANAGEMENT

The Model

Conclusions

EPW

Term & Policy

Circulation

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