UNDEERRSSTTAANND DI IN NGG R TRREENNDSS NIN S …S(prw2mtmorfsbbyfygmth4w2s... · compensation and...

1 UUNNDDEERRSSTTAANNDDIINNGG TTRREENNDDSS IINN SSAALLAARRYY EESSCCAALLAATTIIOONN RRAATTEESS IINN

IINNDDIIAANN PPRRIIVVAATTEE SSEECCTTOORR

A paper prepared by the Research Department- Institute of Actuaries of India-Oct’13 |

FFOORREEWWOORRDD

The era of LPG (Liberalisation, Privatisation, Globalisation) began in early nineties have integrated the Indian economy to global markets and so the labour market. The private sector in India have demonstrated its potential of growth and offered tremendous opportunities of employment. Compensations become global in nature for similar or equivalent skill sets, however, the growth levels in compensation differed market to market. HR organisations have made attempts to understand the linkage and influencing factors of salary escalation levels as a part of their routine exercise and projected short term trends for different sectors/ industry. Not much work done to codify and to understand salary escalation trends over long periods. Reasons and challenges were many.

Influence of market forces to compensation levels and escalation of salary are another important area of interest for market watchers. The subject of understanding such inter relationships also assumes a lot of importance and significance for long term projections of salary and salary linked long term and terminal benefits such as pensions and gratuity. At this juncture, a minimum level of understanding of behavioural patterns of escalation in salary and any possible future trends emerging is desirable. The paper “Understanding salary trends in Indian Private sector” prepared by the Research department of Institute of Actuaries of India attempts to meet this objective.

MM KKaarruunnaanniiddhhii

PPrreessiiddeenntt,, IInnssttiittuuttee ooff AAccttuuaarriieess ooff IInnddiiaa




MMEESSSSAAGGEE

One would agree to the words of Sir Terry Pratchett, “Only in our dreams are we free. The rest of the time we need wages.” Moreover, one needs to take into consideration the upsurge in the cost of need fulfillment and salary levels with time. In the context of developing India, majority of employed population is associated with private sector companies. Keeping these aspects in mind, the Research department of the Institute of Actuaries of India has undertaken a project named “Salary trends in Indian Private Sector”. The objective of the project is to understand the underlying trends in salary escalation rates in private sector in India and shed some light upon possible salary level movement in future. Besides, an attempt has been made to derive the influence of key economic indicators on salary escalation rates through analysis of historical data and creation of salary index benchmark.

I am sure that, the interesting insights brought out in the paper would, definitely, prove valuable for Benefits Actuaries among others and would lay the foundation for further research in this space.

TTaanniiaa CChhaakkrraabbaarrttii

CChhaaiirr PPeerrssoonn,, AAddvviissoorryy GGrroouupp oonn RReesseeaarrcchh aanndd PPuubblliiccaattiioonnss

http://www.brainyquote.com/quotes/quotes/t/terrypratc125780.html%5Coviewquote

http://www.brainyquote.com/quotes/quotes/t/terrypratc125780.html%5Coviewquote




IINNDDEEXX

Item No. Description Page No.

1 Background 4

2 Introduction 5

3 Key Findings 6

4 Methodology 7

5 Application of findings and Results-Limitations 8

6 Historical Background 9

7 Salary trends-Recent Past 11

8 Analysis of Historical data 13

9 Future likelihoods 16

10 Salary escalations and macro economic variables 18

Appendix I 22

Appendix II 26

Appendix III 28

Appendix IV 30

Bibliography 36




11.. BBAACCKKGGRROOUUNNDD

Salary and salary increases are outcomes of number of rational, irrational, subjective and arbitrary factors. Leaving any irrational and arbitrary factors, it may be broadly put into some baskets such as;

(a) Regional practises in the economy

(b) Industry/company performances

(c) Compensation policy of the institution

(d) Supply and demand of labour, and

(e) Individual’s job performance.

It may be difficult to create a single model for the market by capturing all dynamics which decide salary increases in time to time. The possibility of a model within a Company/ Sector can only be codified on a broad basis for/over a period. Leaving the company policy and individual performance factors, other factors together decide performance of the economy to a large extent.

Understanding salary increases in turn require sensible projections of driving forces of market and economy which has always been among major challenging and confronting issues for any political and economic regime. Investigating a historical trend in salary movements is straightforward and may lead to trends and correlations, however, any attempt to project a trend needs to be justified with all above relevant factors moving in tandem. Projecting salary to future based on its links to the market factors may lead to projecting economy and its deciding parameters and such modelling could be very complex and beyond the scope of understanding salary projections. This justifies the situation that not many serious attempts had been made by agencies, institutions and individuals on the subject in the past. Notwithstanding these difficulties, the salary projections are integral part of the actuarial practice on the Employee benefits side, hence some efforts have been made and results are presented in this Paper.




22.. IINNTTRROODDUUCCTTIIOONN

People most concerned about future movements of salary include Actuaries which relate to their role for making financial sense of the future. Reporting and funding of Employee benefits has been a major service area for consulting actuaries and salary data and projections of employee specific salary are inputs to the model. There are number of industrial and service sectors in the economy and numerous companies in each category. Salary data of any institution in general are confidential in nature and confined to the HR departments, which prevents any agency to undertake a detailed view on the structural and incremental changes. This restricts any attempt to understand salary trends in companies, industries and sectors in a wider canvas. Hence, any attempt to discover salary trends to be within the broad frame of economy as a whole, considering private or public sector. The public sector salary growth has a pattern by way of existence of specific designs in career growth and associated salary scales and defined structure of compensation and reviews, which is not the case for private sector. This paper attempts to discuss on salary escalation trends in the private sector during 21st century and its linkages with some of the key market indicators and possibilities of setting some bench marks for the future for understanding possible salary movements.

There are quite a good number of economic indicators which make broad sense of the market which include GDP growth rate, CPI or inflation, WPI and Stock market indices. Some or all of these indicators might have influenced salary escalation in different times or all times. The paper also examines and attempts to understand this possibility with regard to some of the market indicators.




33.. KKEEYY FFIINNDDIINNGGSS

Historical indicators of salary escalation show 12.24%, 12.60% and 12.21% as mean, median and compounded annual rate respectively for the last 12 years period.

The linear, quadratic and exponential models closely fit to the historical salary index data.

For a period of 10-15 years, projections under linear, quadratic and exponential models are close to each other.

For periods exceeding 15 years, the exponential model shows a sharp increase in comparison to linear and quadratic models.

It is observed that the salary growth rates estimated from the salary indices as predicted by the exponential model are independent of time, moving at a compound rate of 12.42% for all future time periods.

Salary growth rates of the Indian private sector have closely conformed to swings in the macroeconomic conditions of the country. Hence, salary decisions are not influenced significantly to firm level dynamics like performance, attrition rate etc.

Growth rates of GDP and CPI Inflation are most significant macroeconomic factors that affect salary growth rates.




44.. MMEETTHHOODDOOLLOOGGYY

The following methodology has been used to investigate the values and creating trends:

Analysis of historical data – calculation of mean, median, outliers.

Creation of a salary index bench mark, with the base value as 100 in the year 2000-01.

Fitting trend lines to the salary indices, using linear, quadratic and exponential functions.

Exploring the relative suitability of each fitted trend function.

Identification of macroeconomic variables those are most influential in affecting salary budget decisions using principles of economic theory and regression analysis.




55.. AAPPPPLLIICCAATTIIOONN OOFF FFIINNDDIINNGGSS AANNDD RREESSUULLTTSS--LLIIMMIITTAATTIIOONNSS

Albeit all our judgements and validations on figures collated from market sources as above, indicators emerged out of the investigation are very crude in nature and are subject to further testing and validation before its application to a real time situation. Indicators are reflecting market as a whole; an application of results to be a sole discretion of the individual based on the understanding as to how salary trends of any industry/sector/ category moved in tandem with the market in the past and significance of any other influencing factors applicable. Future salary escalations in the market will have a big role in tuning the projected indexes and to be updated on a real time basis. The Research department does not recommend any result or values in the report for any commercial purpose; however, any findings in the report may be used as background information of the private sector salary market in India.




66.. HHIISSTTOORRIICCAALL BBAACCKKGGRROOUUNNDD

Researching about salary trends and its interrelationships with economic indicators has been an interesting and desired subject at any time; however, it appears that such attempts are not been taken place regularly. There are references of occasional investigations done by Institutions specialised in Human Resources and their findings regarding correlation of Salary escalation with GDP and/or CPI, however, content, methodology and basis of any such investigations are either not available or not authenticated. Reports based on salary and salary increase surveys provide cross sectional analysis of salary trends across various sectors of the Indian economy as well as across different managerial hierarchy. Most salary increase surveys explore the factors affecting attrition, hiring and salary budget optimization techniques that change labour demand and supply conditions.




In an economy which embraces a number of reforms and ambitious to grow exponentially is subject to a number of social, structural and economical and political factors influencing over periods ,both short and long time. The privatisation, globalisation and liberalisation policies have affected Indian pay structures and packages. In the pre-liberalization era, salaries were capped but the executives were compensated by various other perks. With the advent of the MNC’s, maintaining such diverse benefits packages became complex and expensive; most of these reimbursements became taxable. Compensation was homogenized in

accordance with international norms. Salary became performance linked. The new salary revision method has favoured middle and junior level executives much more than the senior executives.

While India strives towards parity between salary rates in the domestic and international markets in the globalised world, increasing labour costs threaten to erode India’s advantage in producing goods and services at low cost. Here arises the natural comparison of India’s trade fundamentals vis-à-vis China. China’s salary increase had been much lower than that of India in the past decade. In fact, even after the global economic slump of 2008; Indian salary growth rates were the highest in the Asia Pacific region.

Setback to the export sector does not always signal doom for the economy if high salaries cause a spurt in domestic consumption and investment demand. However, how much salary growth is translated to aggregate domestic demand depend upon the consumption propensity of the salaried class, the savings mobilizing financial infrastructure, the overall investment climate and the import basket of the economy. Another crucial factor influencing purchasing power of the country’s salaried class is the rate of inflation. Monthly average wage adjusted for inflation has been identified by ILO as the “decent work indicator”. Perhaps the most important driver of

salary growth rates is the economic growth of a country, as indicated by its GDP growth rates. High economic growth does not necessarily trickle down to high salaries, but nevertheless it affects the firm’s net profitability which is the necessary condition to ensure growth in its employees’ salaries. It is the importance of these linkages of salary growth rates with the overall macroeconomic health of the country which necessitated this project; contrary to the conventional research on the interrelationship between salary rates and firm level issues like promotion, recruitment, attrition etc.




77.. SSAALLAARRYY TTRREENNDDSS--RREECCEENNTT PPAASSTT

The report needs to be understood with the data by its sources, definitions and limitations. Compilation of data and it validation process has been quite challenging, however, every single indicator picked up from a source of information and/or published reports are evaluated on a case to case basis and have consolidated for arriving a final picture. It may be useful to readers to keep a brief summary of information related to sources, definitions and limitations of data used before proceeding for further reading.

Data sources, definitions and limitations:

Some of the HR organizations published reports on the basis of survey conducted by them and also projected salary increase in the succeeding year.

Agencies include, Aon, Towers Watson, Mercer, Hay, ECA.

There were few reports showing sector/industry/ category wise trends published recently, however, no historical data.

Yearly salary growth rates for the private sector as a whole collected from all above sources from year 2001 onwards.

The salary growth rate for year 2013 has been taken as the forecasted figure by Aon Hewitt

Yearly salary growth rates for the private sector as a whole were compared between the reports of various HR organizations, media report and other publications.

Salary refers to total compensation as understood and taken from data sources and reports

No agency, governmental or non-governmental published salary data on a regular basis.

A complete time series data on actual salary growth rates were not available from just one organization

Aon appeared to be a front runner in doing yearly reporting of salary increases and creating trends for the next year

Limited information regarding salary increase available from newspaper, media reports

Not a single research work done and available in both hard/ soft form both in the public domain and in publications, irrespective of country of reference.

Confidentiality of salary related data appears to be paramount for companies and collecting even a sample data is nearly impossible.

No methodology referred/ explained in any of the published reports.




There are reasons to believe that Government agencies are not engaged in reporting of salary trends in the Indian private sector on a periodical basis. Some specialised HR organisations conduct surveys in the market annually and key outcomes, particularly with regard to salary projections for the current year and performance indicators of different sectors of economy are published in the media. Historical data shows that the forecasted salaries for the next year by such agencies tend to be close to the actual growth rate albeit projections differ marginally from one agency to another, thanks to difference in methodologies adopted for such investigations. A researcher doing a salary trend related investigation in India is bound to be satisfied with the limited sources of verification of available and published figures and its accuracy and regularity. Information on salary increases for the Private sector is collected from year 2001 to 2012 and forms the basic historical data for any investigation and is summarised in the graph below:

Graph 1

12.8%

9.7%

11.5%

13.7% 14.1% 14.4% 15.1%

13.3%

6.6%

11.1%

12.6% 12.0%

2001-'02 2002-'03 2003-'04 2004-'05 2005-'06 2006-'07 2007-'08 2008-'09 2009-'10 2010-'11 2011-'12 2012-'13

Year

Private sector salary growth rate




88.. AANNAALLYYSSIISS OOFF HHIISSTTOORRIICCAALL DDAATTAA

History never repeats by all its characteristics, it only leaves foot prints to future for a reasonable period of time. The question of “how long to the future” depends on the event and impact /severity and frequency of incidents. Economic indicators also often follow this theory and principles of history and applicable to salary trends as well. It would be worth to understand what historical figures have indicated to carry something to future trends from the above figures of salary increments. Takeaways from the past may only help us to set bench marks for a reasonable short period of time and there could be a number of unpredictable factors which finalise the shape of the curve along with factors carried from the past.

Order Year Salary Quartile

1 2009-‘10 6.6% 2 2002-‘03 9.7%

3 2010-‘11 11.1% Q1

4 2003-‘04 11.5% 5 2012-‘13 12.0%

6 2011-‘12 12.6% Q2= Median

7 2001-‘02 12.8%

8 2008-‘09 13.3%

9 2004-‘05 13.7% Q3

10 2005-‘06 14.1%

11 2006-‘07 14.4% 12 2007-‘08 15.1%

Table 1

Looking the above table, the historical values during the past 12 years moved with a median value of 12.6% and have ìnter-quartile range of 2.6%. The outliers in the values are to be those observations less than Q1- 1.5* 2.6% or greater than Q3+ 1.5* 2.6% which circles on one value recorded on the year 2008-’09 only and is obvious with valid reasons such as economic down turn during the period. The median make a sense of the historical trend and could be a bench mark on projecting near future values. When year wise salary rates are converted into salary index starting from a base value of 100 in the year 2000-’01, it leads to take a broad view that the salary increased 4 times over a period of 12 years which is equivalently to a compounded annual rate of 12.21%. The average value is 12.24% and summarised below:

Measure Value (historical)

Mean 12.24%

Median 12.60%

Compounded annual rate 12.21% Table 2




Actual salary increases after converting to salary index format with starting value 100 from year 2000-’01 appear as a best fit for linear, quadratic and exponential models. The table 3 and graph 2 below reveals this feature:

Year Salary

increase Index

2001-‘02 12.8% 112.80

2002-‘03 9.7% 123.74

2003-‘04 11.5% 137.91

2004-‘05 13.7% 156.80

2005-‘06 14.1% 178.91

2006-‘07 14.4% 204.68

2007-‘08 15.1% 235.58

2008-‘09 13.3% 266.92

2009-‘10 6.6% 284.53

2010-‘11 11.1% 316.11

2011-‘12 12.6% 355.94

2012-‘13 12.0% 398.66

Table 3

The salary trend lines reflecting exponential and two degree polynomial models appear to be a close fit to the historical index as:

Graph 2

Barring two years in 2007-’08 and 2008-’09, trend lines almost coincides with the actual salary index values and the salary index values estimated for exponential and quadratic models are worth noting.

100.00 112.80 123.74

137.91 156.80

178.91 204.68

235.58

266.92 284.53

316.11

355.94

398.66

2000-'01 2001-'02 2002-'03 2003-'04 2004-'05 2005-'06 2006-'07 2007-'08 2008-'09 2009-'10 2010-'11 2011-'12 2012-'13

Salary Index- Historical

Salary Index Expon. (Salary Index) Poly. (Salary Index)




Year Actual Exponential Quadratic

2001-‘02 112.80 112.07 110.71

2002-‘03 123.74 125.98 124.60

2003-‘04 137.91 141.63 140.89

2004-‘05 156.80 159.22 159.58

2005-‘06 178.91 179.00 180.67

2006-‘07 204.68 201.23 204.16

2007-‘08 235.58 226.22 230.06

2008-‘09 266.92 254.32 258.35

2009-‘10 284.53 285.90 289.04

2010-‘11 316.11 321.41 322.14

2011-‘12 355.94 361.33 357.63

2012-‘13 398.66 406.21 395.53

Table 4

For the historical data, the exponential and quadratic models fit closely and these models may be tested for salary index projections.




99.. FFUUTTUURREE LLIIKKEELLIIHHOOOODDSS

It may be interesting to see as to how the future projections of the above models making sense to future projections, starting from time 1 as year 2000-’01. Graphs projected with linear, quadratic and exponential models for next 50 years with year 1 as 2013-’14 as:

Graph 3

The values of projected salary index along with model summary, ANOVA and table of coefficients are available in Appendix-I for reference.

Models have been fitted by using SPSS using historical values and have parameters as per the summary table here under:

Type Model

Linear Salary Index= 48.13+ (24.69*Time)

Quadratic Salary Index= 90.14+(7.89*Time)+(1.2*Time^2)

Exponential Salary Index= 88.67*Exp(0.12*Time)

Table 5

The exponential values outperform very aggressively compared to the linear and quadratic model values as and when the time progresses and maintains a uniform year to year rate of 12.42%. The compounded annual salary growth rate and median values of historical data being 12.21% and 12.6% respectively, the exponential model makes more sense to the future projected salary index values. The parameters above are likely to be modified year to year for the exponential model as and when actual salary data replaces the projected figures. The clustering of average compounded rate on a year to year basis is also in the

0

5000

10000

15000

20000

25000

30000

35000

40000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

S

a

l

a

r

y

I

n

d

e

x

Time

Salary Index Projection

Linear Quadratic Exponential




range of 12.21% to 12.6% and likely to move in the same pattern on a short and long term basis.

The economic slowdown and recovery is a matter of a number of global and domestic factors and future levels of salary following a recession/ shock on the economy always carries such indicators of recent past. The salary expectations for the near future years seem to be carrying ghosts of the current and recent pasts, as is evident from graph 1 and also decided by economic indicators which always set the ground for compensation levels. This part is analysed in the Appendix-II

For applying the salary index trends to specific industries/ sectors/ regions, one needs to keep in mind as to how the historical salary levels of those specific industries/ sectors/ regions moved with the private sector as a whole in the past. The subjective judgement to calibrate the projected salary index for different situations is to be based on the level of understanding of this relationship.

Observations and suggestions from readers:

Any observations/ feedbacks and suggestions may please be communicated to [email protected]




1100.. SSAALLAARRYY EESSCCAALLAATTIIOONNSS AANNDD MMAACCRROO EECCOONNOOMMIICC VVAARRIIAABBLLEESS

Macroeconomic variables, in isolation and in groups have large say in salary levels. The question is, whether the relationship is to be read as a single or multi variable model for interpreting salary levels.

As mentioned earlier, salary rates are the outcomes of various firm, industry and economy level phenomena. While data unavailability constrains analysis of industrial policies and performance, labour market movements and employee performance for the private sector, an attempt is made to understand the salary determination issue at a broad nationwide level.

Following the global economic meltdown in Oct 2008, the Indian economy slowed down, with sharp falls in profit levels of those sectors, particularly which are highly dependent on the international market. The corresponding fall in the salary budget for 2009 once again bears the fact that salary rates indeed depend upon macroeconomic conditions; internal firm level dynamics are not the sole determinants of salary in the Indian economy.

Salary growth rates are linked to various macroeconomic variables, often in complex ways. Our objective is to disentangle the effects of each of these economic variables on salary growth rate to have a clear understanding of how Indian private sector salary actually grows. Firstly, those economic indicators have to be identified which can reasonably affect the macroeconomic environment of the country and in turn have a logical relationship with salary budget decisions. Here lies the trap of confusing correlation with causation; this calls for intuitive understanding of the linkages between economic processes. Some screening is required as many such economic variables are difficult to model or their historical data are unavailable.

The crucial determinants of the macroeconomic conditions are GDP growth rate, rate of inflation, private sector performance as indicated by stock market movements, index of industrial productivity, exchange rates and the prevalent monetary policies which affect interest rates, liquidity and investment potential of a country.

These factors are closely interrelated with one another; they may or may not be related with salary growth rates of the private sector. For example, variations in the bank rate, repo rate, cash reserve ratio and open market operations by the central bank can be policy measures taken to control inflation. How frequently they are adjusted as per inflationary trends depend upon the significance of excess liquidity in affecting inflation and the state of the banking sector. Hike in interest rates following hike in bank rate and repo rate make investment expensive, thereby reducing domestic investment and in turn GDP. Economic data shows weak negative correlation between Gross Fixed Capital Formation (GFCF) growth rate (a proxy for investment in fixed capital of the economy), repo rate and bank rate. Post economic crisis of 2008, GFCF fell and rose again; just like GDP and salary growth rates. So for the sake of simplifying the analysis, it may be assumed that salary growth rates would be affected by the money market via GDP and inflationary changes, ignoring the above liquidity determinants.

Rise in the index of industrial productivity (IIP) will cause GDP to rise, depending upon the contribution of the manufacturing sector to GDP. Year 2008 onwards, the yearly average of the index of industrial productivity grew or remained stagnant; while GDP and salary growth rates fluctuated. No apparent relation between IIP and salary rates has been




observed. As such, the index of industrial productivity may be dropped from the list of important determinants of salary growth rate.

The sector that mostly influences GDP growth rates is the service sector. The share of services in GDP at factor cost (current prices) was 55.2% in 2009-’10; if construction is included the share is 63.4%. The advance estimates for 2012-‘13 pegs the share of the service sector to GDP at 56.5%. Export of services has been growing at a faster rate than the export of manufactured goods. In 2009-10, NSSO estimated that out of 1000 Indians, 679 people are employed by the agricultural sector, 80 in the industrial sector and 241 in the service sector (including construction). The most prominent industry in the service sector has been the IT and ITeS industry, which has not only captured a sizeable portion of the overseas market but has also provided 2.8 million people with direct employment and 8.9 million with indirect employment in 2011. The worldwide recession has decelerated the export growth of this industry and in turn its total revenue. Correspondingly, median salary of the IT sector remained stagnant at 10% from 2011 to 2012 while for the IteS sector the drop was from 15.5% to 12%. Once again, the vulnerability of Indian salaries in the globalised world is apparent.

While it is practically unfeasible to study economic conditions of all major trading partners of India for understanding domestic salary trends; we choose exchange rate as one important reflector of global economic movements. However, exchange rates do not provide conclusive evidence of the state of the world economy; while US was reeling under the subprime crisis and investment bank failures in 2008, the exchange rate of Indian rupee vis-a-vis the US Dollar shot up steeply. Again we can find indirect linkages of exchange rates with salary rates. For example, high exchange rates increase our import burden (India is a net importer of goods and services with a current account deficit, petro products being the most vital import item); which often cause increase in imported raw material inputs. Government’s subsidy burden on petro products is occasionally passed onto the citizens, causing almost immediate cost push inflation all over the country. Rising cost of living can be an important factor in deciding an employee’s compensation. While a higher exchange rate of the Indian rupee against the US Dollar suggests that exported Indian products are getting cheaper in the international market compared to the exports of the other countries; how well the export sector performs will also depend upon the demand levels in the international market. For example, although the exchange rate grew by 14.24% in 2008, the Indian export sector witnessed sharp downturn during this time following the recession in the OECD countries. Thus, the conclusion that exchange rate affects the profitability of the export-import sector, and in turn its salary decisions, is ambiguous. For the other sectors of the economy, it is logical to assume that salary decisions would never be based upon the exchange rate changes of the national currency. Rather, alterations in the economic conditions due to exchange rate swings may be reflected in salary budget changes by some proxy parameter, most likely the GDP growth rate. There is no visible similarity in the path of exchange rate growth rate movement and GDP growth rate movement; particularly 2001 onwards which marked the beginning of the period of salary data availability. So in spite of exchange rate being a significant indicator of the macroeconomic conditions in a country, assuming that it is not an important determinant of salary rates of the overall private sector of the Indian economy; we drop it from further investigation.

Rise in the stock market indices does not necessarily indicate economic development since stock market prices are subjected to speculation, contagion effects of nearby countries and continuous changes in the international financial markets. Yet stock market indices of various corporations are an indicator of their financial robustness. While there seems to be




no apparent relation between GDP growth rates and the high volatility of Indian stock markets, we incorporate stock market indices as a proxy for the profitability of the private sector, which in turn should be a significant determinant of the compensation policy of the firms.

While each of the above factors is weakly correlated with salary growth rates, all of them do not directly cause its changes. The most important determinants of salary have now been narrowed down to GDP growth rate, inflation and stock market index. As we start screening available data on these macroeconomic indicators, it was observed that there exist numerous variations of the same measure. With the primary objective of this paper in view, i.e. understanding salary escalation rates, we choose the best variant of the macroeconomic indicators.

Given below are the year-to-year percentage growth rates of salary and some macroeconomic variables. The method and source of data selection for calculation and verification of macroeconomic variables below are explained in Appendix-III

Year Salary GDP CPI WPI CNX500 Index

2001-‘02 12.8% 5.52 3.67 3.60 -26.70

2002-‘03 9.7% 3.99 4.47 3.41 4.81

2003-‘04 11.5% 8.06 3.71 5.46 44.42

2004-‘05 13.7% 6.97 3.89 6.48 50.37

2005-‘06 14.1% 9.48 3.97 4.47 45.19

2006-‘07 14.4% 9.57 6.27 6.59 29.51

2007-‘08 15.1% 9.32 6.37 4.74 147.29

2008-‘09 13.3% 6.72 8.35 8.05 -59.39

2009-‘10 6.6% 8.39 10.88 3.80 8.41

2010-‘11 11.1% 8.39 11.99 9.56 28.69

2011-‘12 12.6% 6.48 8.86 8.94 3.44 Table 6

For the purpose of understanding, the direction of movement of salary growth rates vis-à-vis movements of macroeconomic indicators, mainly GDP Growth, CPI,WPI and CNX500 Index over years are summarised below :-

Year Salary GDP CPI WPI CNX500 Index

2001-2002 Fall Fall Rise Fall Rise

2002-2003 Rise Sharp Rise Fall Rise Rise

2003-2004 Rise Fall Slight Rise Rise Rise

2004-2005 Rise Rise Slight Rise Fall Fall

2005-2006 Rise Rise Rise Rise Fall

2006-2007 Rise Slight Fall Slight Rise Fall Sharp Rise

2007-2008 Fall Fall Rise Rise Sharp Fall

2008-2009 Sharp Fall Rise Rise Fall Rise

2009-2010 Rise Unchanged Rise Rise Rise

2010-2011 Rise Fall Fall Fall Fall Table 7




WPI takes into account the prices of all primary articles, fuel and manufactured goods. It is the broadest measure of inflation, which shed light on cost of raw materials and in turn on profit levels of firms. Yet, for this project CPI has been chosen as an indicator of inflationary conditions in the economy since it is a better reflector of cost of living changes than WPI. The salaried class is more affected by the food and non-food articles covered under CPI than the prices of the vast array of goods prices which the WPI covers. Hence, the study of understanding correlation between salary escalation and macro economic variables is narrowed down to the remaining variables viz., GDP growth, CPI and CNX500 Index and are explored in Appendix IV




APPENDIX-I

Salary Index

Time Linear Quadratic Exponential

1 72.82 99.22 99.69

2 97.51 110.71 112.07

3 122.20 124.60 125.98

4 146.89 140.89 141.63

5 171.58 159.58 159.22

6 196.28 180.67 179.00

7 220.97 204.16 201.23

8 245.66 230.06 226.22

9 270.35 258.35 254.32

10 295.04 289.04 285.90

11 319.74 322.14 321.41

12 344.43 357.63 361.33

13 369.12 395.53 406.21

14 393.81 435.82 456.66

15 418.50 478.52 513.37

16 443.20 523.62 577.13

17 467.89 571.12 648.81

18 492.58 621.02 729.39

19 517.27 673.32 819.98

20 541.96 728.02 921.82

21 566.66 785.12 1,036.31

22 591.35 844.62 1,165.02

23 616.04 906.52 1,309.72

24 640.73 970.82 1,472.38

25 665.42 1,037.53 1,655.25

26 690.11 1,106.63 1,860.83

27 714.81 1,178.14 2,091.94

28 739.50 1,252.04 2,351.76

29 764.19 1,328.35 2,643.84

30 788.88 1,407.06 2,972.20

31 813.57 1,488.16 3,341.35

32 838.27 1,571.67 3,756.34

33 862.96 1,657.58 4,222.87

34 887.65 1,745.89 4,747.34

35 912.34 1,836.60 5,336.96

36 937.03 1,929.71 5,999.80

37 961.73 2,025.23 6,744.96

38 986.42 2,123.14 7,582.68




39 1,011.11 2,223.45 8,524.43

40 1,035.80 2,326.17 9,583.16

41 1,060.49 2,431.28 10,773.37

42 1,085.18 2,538.80 12,111.41

43 1,109.88 2,648.71 13,615.63

44 1,134.57 2,761.03 15,306.67

45 1,159.26 2,875.75 17,207.73

46 1,183.95 2,992.86 19,344.91

47 1,208.64 3,112.38 21,747.52

48 1,233.34 3,234.30 24,448.52

49 1,258.03 3,358.62 27,485.00

50 1,282.72 3,485.34 30,898.59

Table 8

SUMMARY OF MODELS:

Linear:

Model Summary

R R Square Adjusted R Square Std. Error of the Estimate

.986 .973 .971 16.742

ANOVA

Sum of Squares df Mean Square F Sig.

Regression 110963.443 1

110963.443

395.866 .000

Residual 3083.359 11 280.305

Total 114046.801 12

Coefficients

Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

Case Sequence 24.692 1.241 .986 19.896 .000

(Constant) 48.125 9.850

4.886 .000




Quadratic:

Model Summary


.999 .998 .998 4.459

ANOVA


Regression 113847.956 2 56923.978 2862.719 .000

Residual 198.846 10 19.885

Total 114046.801 12

Coefficients

Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

Case Sequence 7.887 1.434 .315 5.501 .000

Case Sequence ** 2

1.200 .100 .690 12.044 .000

(Constant) 90.137 4.365

20.652 .000

Exponential:

Model Summary


.999 .997 .997 .024

ANOVA


Regression 2.494 1 2.494 4243.443 .000

Residual .006 11 .001

Total 2.501 12




Coefficients

Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

Case Sequence .117 .002 .999 65.142 .000

(Constant) 88.672 1.265

70.104 .000




APPENDIX-II

ANALYSIS OF MACROECONOMIC FACTORS AND THEIR RELATION WITH SALARY

To study the effect of GDP, CPI and CNX500 Index fluctuations on salary growth rates, the trends of salary vis-a-vis these factors needs to be considered, one at a time.

i) Salary and GDP

Graph 5

Salary rate movement broadly resembles movements in GDP growth rates. At times, the effect of GDP growth rate swings on salary is not realised immediately, but in the next year.

ii) Salary and CPI

Graph 6

12.80

9.70 11.45

13.70 14.10 14.40 15.10 13.30

6.60

11.10 12.60

5.52 3.99

8.06 6.97

9.48 9.57 9.32

6.72 8.39 8.39

6.48

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

Year

Salary & GDP rates (Percentage)

Salary GDP

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

Salary and CPI (Percentage)

Salary CPI




CPI Inflation enhances the cost of living; this should be reflected in higher compensation to employees. However Indian data on salary growth rate and CPI inflation rate shows opposite trends, the two curves cross twice.

iii) Salary and CNX500 Index

Graph 7

The CNX500 Index exhibits wide volatility in comparison with the salary growth rate curve. However, the possibility of its effect on salary growth rate cannot be ignored all together; since it can explain salary increases at times when all other economic variables had become unfavourable to positive salary growth movements.

-100.00

-50.00

0.00

50.00

100.00

150.00

200.00

0 2 4 6 8 10 12

Salary & CNX 500 Index (percentage)

Salary CNX500 Index




APPENDIX-III

DATA SELECTION

Gross Domestic Product: GDP at Factor Cost (in Rs. Billion) at 2004-05 prices

Source: RBI Database.

The sum of Net Value Added in various economic activities is known as GDP at factor cost.

GDP at factor cost plus indirect taxes minus subsidies = GDP at Market Price.

Hence, GDP at factor cost indicates the true value of the net output produced by the private sector; within the geographical boundaries of the country. GDP at market prices includes the government’s income in addition to the net value of the private sector’s output. Since our objective is to study how the salary rates of the private sector respond to macroeconomic changes; GDP at factor cost is a better measure than GDP at market prices.

Value of goods and services = Price* Quantity. Changes in either price or quantity can cause the value of output to change. To examine the change in the quantity of output produced, prices must be held fixed. This is the justification behind using GDP at constant prices instead of GDP at market prices. In this data, all outputs of goods and services over the years are valued at 2004-05 prices.

The GDP at factor cost, valued at constant prices gives the real change in the output of goods and services produced in the economy; the measure is completely free of inflationary impacts on the output value.

The rate of growth of GDP shows the growth in GDP values in percentage; from one time period to the immediately next time period.

GDP values and rates of growth cross checked with MOSPI data.

Data checks: GDP at Market Price/ GDP at Factor Cost = GDP Deflator = Inflation rate from 2004-05 prices. - GDP Deflator for 2004-05 = 1. - GDP Deflator = WPI /100.(approx.)

Consumer Price Index (CPI)

The best measure of inflation which captures the prices by the salaried class is Consumer Price Index (CPI) for the Urban Non Manual Employees.

CPI for Urban Non Manual Employees (UNME) is available till Dec 2010. 2011 onwards, CPI calculation is based on rural-urban classification instead of occupational category like Agricultural workers (AW), Industrial workers (IW) and Urban Non Manual Employees.

No linking factor could be found between these two measurement criteria. The base year calculations are also different for the two series.

The only continuous CPI series available is that of the Industrial Workers. Data collection centres for the UNME and IW series are almost the same, weightage given to the food and non-food commodities are slightly different.

Hence as a measure of inflation in the urban areas, the IW series of CPI has been chosen.




The data available from International Monetary Fund, World Economic Outlook Database, October 2012 has been cross checked with the data from CSO Database. 2011 onwards CPI figures are not actual, but that estimated by IMF staff.

Inflation rate of CPI checked with media reports.

Wholesale Price Index (WPI)

The WPI is the broadest index reflecting changes in overall price levels of the economy. Price of industrial outputs, which determine the profitability of companies are captured by the WPI.

The nodal agency for collecting WPI related data is the Office to the Economic Advisor to the Govt of India. (Ministry of Commerce and Industries). The data used in this project has been collected from the database of the aforementioned agency

The raw data was available in two series; one with 1993-94 as the base year and another with 2004-05 as the base year. The 1993-94 base year indices were converted to 2004-05 prices by the linking factor prescribed by the data source agency. A consolidated WPI series calculated with 2004-05 as the base year price.

Inflation rate calculated in percentage from the consolidated WPI series.

S&P CNX 500 Index

S&P CNX 500 Equity Index reflects the market as closely as possible. It is the broadest stock market indicator.

The S&P CNX 500 represents 95.53% of total free float market capitalization of the universe of the stocks traded on NSE and 94.76% of the total turnover on the NSE as on September 28, 2012.

The daily index is calculated by taking average of “open”, “close”, “high” and “low” values of the index observed over a trading day.

The daily averages are averaged from the first trading day of April to the last trading day of March to obtain the financial year average.




APPENDIX - IV

REGRESSION

. The trend lines indicating growth rates of salary vis-a-vis growth rates of macroeconomic indicators indicate some relationship between salary and economic factors. To add clarity to the analysis, this relationship requires quantification. However, direct regression of salary on these macroeconomic variables does not yield reasonable results. Regression with such variables is difficult as there are possibilities of co linearity and spurious correlations; which can vitiate the understanding of salary growth rates. The regression results are reasonable (co linearity statistics, autocorrelation, homoscedasticity of error variance, goodness of fit statistics within limits) when CAGR Salary is regressed on CAGRs (Compounded Annual Growth Rates) of the macroeconomic variables.

Method 1: Regression of CAGR Salary on CAGR of macroeconomic variables

The ordinary least square regression results are summarized below:-

CAGR GDP and CAGR CPI are the chief explanatory variables for CAGR Salary. However a rise in the adjusted R squared value on inclusion of CAGR CNX500 for the Three Year CAGR Salary shows that it can be used as an explanatory variable; but it is still not very significant.

The above regression models suggest that CAGR Salary is positively related with CAGR GDP and negatively related with CAGR CPI. These are the most significant factors that explain movements in CAGR Salary.

Given below are the CAGR Salary rates predicted by the above regression models using corresponding duration’s CAGR GDP, CPI and CNX500 values:-

GDP CAGR CPI Inflation CAGR CNX500 CAGR PREDICTED CAGR SALARY

Year 3 yr 4 yr 5 yr 3 yr 4 yr 5 yr 3 yr 3 yr 4 yr 5 yr

2004 6.33 6.12 5.76 4.02 3.94 3.93 31.54 10.72 10.91 11.44

2005 8.16 7.10 6.79 3.86 4.01 3.94 46.64 12.83 12.19 12.40

2006 8.67 8.51 7.59 4.70 4.46 4.46 41.40 13.22 13.90 12.98

2007 9.46 8.83 8.67 5.53 5.12 4.84 66.91 13.33 14.09 13.86

2008 8.53 8.77 8.41 6.99 6.23 5.76 9.15 12.72 13.62 13.27

2009 8.14 8.50 8.69 8.52 7.95 7.14 2.87 11.68 12.66 13.05

2010 7.83 8.20 8.48 10.40 9.38 8.75 -17.26 10.89 11.77 12.27

2011 7.75 7.49 7.86 10.57 10.01 9.27 13.01 10.07 10.60 11.50

CAGR Salary

Constant CAGR GDP

CAGR CPI Inflation

CAGR CNX500

R Square

Five Year Coefficient 7.439 0.939 - 0.357

0.761 Significance .226 .207 .073

Four Year Coefficient 4.145 1.329 - 0.35

0.911 Significance .188 .012 .014

Three Year

Coefficient 4.795 1.509 - 0.598 -0.021 0.911

Significance .130 .014 .027 .334




The predicted CAGR Salary values for three, four and five years compounded duration can be compared with the actual CAGR Salary values in the table below:-

Predicted CAGR Salary Actual CAGR Salary

Year 3 yr 4 yr 5 yr 3 yr 4 yr 5 yr

2004-'05 10.72 10.91 11.44 11.60

2005-'06 12.83 12.19 12.40 13.08 12.22

2006-'07 13.22 13.90 12.98 14.07 13.41 12.66

2007-'08 13.33 14.09 13.86 14.53 14.32 13.74

2008-'09 12.72 13.62 13.27 14.26 14.22 14.12

2009-'10 11.68 12.66 13.05 11.61 12.30 12.66

2010-'11 10.89 11.77 12.27 10.30 11.48 12.06

2011-'12 10.07 10.60 11.50 10.07 10.87 11.70

The Chi Square test employed to test for closeness of predicted CAGR Salary values to actual CAGR Salary values yields the following results:-

Three year CAGR

Salary Four year CAGR

Salary Five year CAGR

Salary

Chi Square statistic 0.46 0.22 0.29

Degrees of freedom

4 4 3

Level of significance

0.977 0.994 0.963

The Chi Square statistic is calculated as Σ {(Actual CAGR Salary) - (Predicted CAGR Salary)} / (Predicted CAGR Salary) Degrees of freedom = No. of observations – No. of parameters

The high levels of significance show that all the models have closely conformed to the actual values. The Four year CAGR Salary model has fitted the historical data best.

Derived values of year-to-year percentage salary growth rates from the Salary CAGRs predicted by regression models are not close to actual yearly salary growth rates. This limits the scope of regression to some extent. A possible way of utilizing these regression models is to apply CAGR GDP and CAGR CPI forecasted by various agencies for the immediate future time period and obtaining the corresponding CAGR Salary rates. The forecasted CAGR Salary rates can give an indication of yearly percentage salary growth rate, even though it cannot precisely predict it.

Method 2: Regression of CAGR Salary on CAGR GDP, Time Cube

The previous regression analysis shows GDP CAGR as a significant variable influencing CAGR Salary. GDP growth rates by various agencies are available for the immediate future. Cubic functions of time closely fit the historical CAGR GDP data. Movements in CAGR Salary can be attributed to movements in CAGR GDP once the influence of ‘time’ is




separated from CAGR GDP fluctuations. Regression of CAGR Salary on CAGR GDP and Time Cube yields the following results:-

Here, year 2001 corresponds to time 1. The positive relation between CAGR GDP and CAGR Salary holds for three, four and five years compounded duration.

Using the regression model given above, the following CAGR Salary values can be predicted from the corresponding CAGR GDP and time cube figures:

GDP CAGR

Predicted CAGR Salary

Year 3 yr 4 yr 5 yr Time Cube 3 yr 4 yr 5 yr

2004-'05 6.33 6.12 5.76 64.00 11.43 11.07 11.72

2005-'06 8.16 7.10 6.79 125.00 13.63 12.19 12.48

2006-'07 8.67 8.51 7.59 216.00 14.04 13.80 13.00

2007-'08 9.46 8.83 8.67 343.00 14.71 13.96 13.70

2008-'09 8.53 8.77 8.41 512.00 13.06 13.58 13.21

2009-'10 8.14 8.50 8.69 729.00 11.97 12.84 13.10

2010-'11 7.83 8.20 8.48 1,000.00 10.84 11.98 12.48

2011-'12 7.75 7.49 7.86 1,331.00 9.84 10.48 11.44

The predicted values are compared with the actual CAGR Salary values to test for goodness of fit:

Predicted CAGR Salary Actual CAGR Salary

Year 3 yr 4 yr 5 yr 3 yr 4 yr 5 yr

2004-'05 11.43 11.07 11.72 11.60

2005-'06 13.63 12.19 12.48 13.08 12.22

2006-'07 14.04 13.80 13.00 14.07 13.41 12.66

2007-'08 14.71 13.96 13.70 14.53 14.32 13.74

2008-'09 13.06 13.58 13.21 14.26 14.22 14.12

2009-'10 11.97 12.84 13.10 11.61 12.30 12.66

2010-'11 10.84 11.98 12.48 10.30 11.48 12.06

2011-'12 9.84 10.48 11.44 10.07 10.87 11.70

CAGR Salary Constant CAGR GDP Time Cube R Square

Five Year Coefficient 7.035 .831 -.002

.685 Significance .298 .304 .115

Four Year Coefficient 3.483 1.258 -.002

.869 Significance .328 .028 .031

Three Year Coefficient 3.450 1.289 -.003

.897 Significance

.201

.006

.005




Three year CAGR

Salary Four year CAGR

Salary Five year CAGR

Salary

Chi Square statistic 0.180152 0.108447 0.10736

Degrees of freedom

5 4 3

Level of significance

0.999 0.9985 0.991

The CAGR Salary values predicted by the above models are very close to the actual CAGR Salary values. With the availability of GDP forecasts from credible sources, CAGR Salary can be predicted by these models.

Method 3: Regression of CAGR Salary on CAGR GDP, Sine Time

If the issue of forecasting is considered with CAGR GDP as the only explanatory economic variable, the continuously rising CAGR GDP values predicted by a cubic trend curve fitted to the historical data result in ever-increasing CAGR Salary forecasts. The problem can be tackled by incorporating another explanatory variable that exhibits cylicity. Usage of sine time as a second explanatory variable of CAGR Salary, arrests the problem of continuously rising CAGR Salary forecasts to some extent.

As regression of CAGR Salary on CAGR GDP and Sin Time does not produce statistically sound results for three and four years compounded amount duration; the results of regressing Five Year CAGR Salary on Five Year CAGR GDP and Sin Time can only be employed for forecasting purposes. The results of this regression are mentioned below:

Five Year CAGR Salary values predicted from the above regression model can be compared with the actual Five year CAGR Salary values from the table below:-

Constant 5 yr CAGR GDP Sine Time R Square

Five Year CAGR Salary

Coefficient 16.958

-.506

1.336 .926

Significance

.015

.305

.012

Year Predicted

Value Actual Value

2006-'07 12.75 12.66

2007-'08 13.45 13.74

2008-'09 14.03 14.12

2009-'10 13.11 12.66

2010-'11 11.95 12.06

2011-'12 11.65 11.70




The high level of significance indicated by the Chi Square statistic shows close fit of the predicted values with the actual values.

A Comparison of the Regression Methods

Year Actual Five Year

CAGR Salary

Predicted Five Year CAGR Salary

Method 1 Method 2 Method 3

2006-'07 12.66 12.98 13.00 12.75

2007-'08 13.74 13.86 13.70 13.45

2008-'09 14.12 13.27 13.21 14.03

2009-'10 12.66 13.05 13.10 13.11

2010-'11 12.06 12.27 12.48 11.95

2011-'12 11.70 11.50 11.44 11.65

Sig Level of Chi Square

Statistic 0.963 0.991 0.999

The three methods are applicable for estimation of Five year CAGR Salary values; method 3 is unsuitable for estimation of three and four year CAGR Salary.

Usage of the regression models cited above for forecasting purpose would essentially depend upon the availability of credible CAGR CPI forecasts (for method 1) and CAGR GDP forecasts (for methods 1, 2 and 3).

Since the small size of the actual Five year CAGR Salary data prevents drawing of strong conclusions regarding the ‘best’ method; the three methods together can give an idea about the range of CAGR Salary forecast. Once again, range does not imply that the actual CAGR Salary would lie strictly between the upper and lower limits as predicted by the regression models. For example, the values of Five year CAGR Salary predicted by all the three methods for the year 2011 is lower than the actual Five year CAGR Salary value of 11.7%. Yet, it would not be erroneous to infer from the regression methods that the five year CAGR Salary for 2011 would lie close to the range 11.44% - 11.65%.

The forecasting potential of the above regression models essentially depends upon availability of credible forecasts of macroeconomic variables. Forecasting these macroeconomic variables is not only difficult without sophisticated modelling tools, but also beyond the scope of this study. Although cubic trend lines fitted well to available historical CAGR GDP data; as time increases the forecasted CAGR GDP values cease to be reliable. Curve estimation of CAGR CPI and the highly volatile CAGR CNX500 involves immense complexities. So, the regression models can give a broad idea regarding the likely values of CAGR Salary for near future only, till the period for which forecasted macroeconomic indicators are credible and are likely to closely conform to actual values. Unforeseen economic shocks can reduce the credibility of the forecasts of these macroeconomic indicators, rendering salary forecasts inaccurate.

Chi Square statistic 0.02

Degrees of freedom 3.00

Level of significance 0.99899




The positive relation between CAGR Salary and CAGR GDP can be inferred from the first two regression methods. Although a negative relation is observed between Five Year CAGR Salary and Five Year CAGR GDP in the last regression method involving sine time; the small size of data which shows reverse movements of the two series in just two years may not necessarily invalidate the inference of a positive relation between CAGR GDP and CAGR Salary in the Indian economy. Another important observation from the regression exercise is the opposite trends of CPI Inflation and Salary. Salary growth rate does not increase as CPI Inflation rises, rather it falls. So, rising cost of living may be secondary to other impacts of inflation on profitability of a firm in influencing salary budget decisions.

Analysis of macroeconomic factors provides a holistic view of how Indian private sector salaries respond to economic changes. Salary decisions are not limited to labour market movements and firm level issues like profitability, employee performance etc. Developments in the macro economy also influence Indian private sector salaries. There are possibilities to explore these economic relationships and arrive at different ways of projecting salary growth rates.




Bibliography:

Determination of Executive Compensation in an Emerging Economy: Evidence from India:Arijit Ghosh; Indira Gandhi Institute of Development Research.

Liberalization of Remuneration Guidelines in India and its Effect on Managerial Pay: Evidence from Large Corporations: Sen and Sarkar; Vikalpa 1999

Employment Outlook and Salary Guide 2012: Kelly Services. Gulf Compensation Trends 2007: Gulf Talent.com Mid Year View on Performance and Rewards Trends in India, July 2007: Hewitt Consulting. India Salary Handbook 2008-09: Kelly Services India Salary Guide 2006: Kelly Services Global Employment Trends 2011: International Labour Office. The interrelationship between Inflation Rates, Salary Rates, Interest Rates and Pension

Costs: Allison, Winklevoss; Transactions of Society of Actuaries, 1975. Monetary Transmission Mechanism in India- A Quarterly Model: Kapur,Behera; RBI Working

Paper Series 2012. Global Wage Report 2010/11: Wage Policies in times of crisis: International Labour Office. Manual on Consumer Price Index 2010: Central Statistics Office, Govt of India. Salary Policy Projections-Developing an Analytical Framework: Lindsay Scott and Associates. Handbook of Statistics on Indian Securities Market 2011: Securities and Exchange Board of

India. Total Rewards Quarterly, Volume 1 Issue 2: Aon Hewitt How Should Inflation be Measured in India: Patnaik, Shah,Veronese;NIPFP DEA Research

Programme. Financial Stability Report, Issue 5: RBI July 2012. Compensation Trends Survey 2012: Deloitte Human Capital Advisory Services. Total Rewards Quarterly, Volume 2 Issue 3: Aon Hewitt Total Rewards Quarterly, Volume 1 Issue 4: Aon Hewitt Annual HR Salary Survey : Hrhelpdesk.in Salaries on the Rise: Globalization Brings New Pressures to India, April 2011:

IndiaKnowledge@Wharton. Salary and Employment Forecast 2012-13: Michael Page International Results of the 19th Round (Q4:2011-12) of Survey of Professional Forecasters of

Macroeconomic Indicators: RBI. Current Statistics Feb 2 2013: Economic and Political Weekly Research Foundation. S&P CNX 500: India Index Services Private Limited. World at Work 2012-13 Salary Budget Survey Yet More on a Stochastic Economic Model: Part 1: Updating and Refitting, 1995 to 2009:

Wilkie, Sahin, Cairns, Kleinow. Annals of Actuarial Science, Vol 5, Institute and Faculty of Actuaries 2010.

A Review of Wilkie’s Stochastic Investment Model: Huber 1995. A Stochastic Asset Model & Calibration for Long Term Financial Planning Purposes: Hibbert,

Mowbray, Turnbull. 2001. A Stochastic Investment Model for Actuarial Use: Wilkie 1984. Stochastic Models for Inflation, Investments and Exchange Rates: Wilkie 1993 Stochastic Asset Liability Modelling: An Indian Perspective: Basu, Maini, Narayanan Regression Modeling with Actuarial and Financial Applications: Edward W. Frees Introductory Econometrics: Jeffrey M. Wooldridge Econometric Analysis of Cross Section and Panel Data: Jeffrey M. Wooldridge Explaining Inflation in India: The Role of Food Prices – Mishra, Roy

UNDEERRSSTTAANND DI IN NGG R TRREENNDSS NIN S …S(prw2mtmorfsbbyfygmth4w2s... · compensation and...

Documents

Transcript of UNDEERRSSTTAANND DI IN NGG R TRREENNDSS NIN S …S(prw2mtmorfsbbyfygmth4w2s... · compensation and...