yojana jan 2014

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GIST OF THE HINDU VOL13 15

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G ist of

YOJANA

POVERTY MEASURE

Measuring Poverty in India has a long andvenerable tradit ion. In the pre-independence period,Dadabhai Naoroji sought to measure poverty with

a view to describe the consequences of colonial rulein India. His book

Povert y and Un-Bri ti sh Rule in I ndia drewattention t o the enormous drain on wealt h causedby colonial policy and was the foundation to manyintellectual arguments for independence.Subsequent ly, dur ing t he freedom str uggle t heCongress Party, the Planning Commission and manyeminent scholars have worked on t his issue.Srini vasan (2007) has a detailed review of thisbackground.

In fact , it would not be an understatementthat this discourse has been one of I ndias major

contributions to the field of development studies. Itis not a merely a scholarly exercise. The World Bankhas stated that fighting povert y is at t he core of i tswork. The United Nations when it outlined themil lennium development goals stated that the firstgoal is to eradicate ext reme poverty and hunger.Poverty is at the heart of almost all discourses ondevelopment policy.

In this context, when we seek to measurepoverty, there are at least three distinct types ofobjectives: (i) to build awareness on poverty and tokeep it in t he agenda of discourse; (i i) to designpolicies, programs and institutions to alleviate

poverty; (ii i) to monitor and evaluate these policies,programs and instit utions that are associated withit. Each of these objectives imposes very differentrequirements on data and the methodology ofmeasurement. In particular it could easily be argued

that the latter two are not single objectives but arein turn composit es of multiple objectives.

In so far as the fi rst is concerned, the objectiveis easily understood and is in fact the basis of

Dadabhai Naoroj it s book published in 1901, thepurpose of which was to influence British publicopinion about the consequences of colonial rule onIndia. It was principally to bring povert y in t hepolit ical discourse and inf luence policy with that inmind. An objective repeated by the National PlanningCommittee of the Congress and the authors of theBombay Plan before independence and by thePlanning Commission in more recent t imes. What iscommon in all these approaches is to state anormative criteria of what constitutes sociallyacceptable minimum necessary for the bare wantsof a human being, to keep him in ordinary goodhealth and decency (Naoroji 1901). Having done so,the aim is to estimate the proport ion of people whoon average in some defined peri od of t ime over someregiondo not meet this criteria. This est imation isthen achieved usually through a survey whichcanvasses (usually) households wit h a view to assessthe proportion of those who do not meet thedesired criteria. These results, based on the design ofthe survey are described by geography, communitiesas may be feasible. In India, this has been done sincethe 1970s using t he household consumerexpenditure survey, based on criteria established bythe Planning Commission task force in 1979. Theestimates are generated for rural and urban areas

separately in each state of the union. This profi le hasthem been the basis for our discussions on poverty.

Turning now to the second objective, theprincipal objective is to design programs and policiesso as to better target their objective. Thus the
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authori tative World Bank handbook (2009) statesClearly, one cannot help poor people withoutknowing who they are. The principal objective is toseek t o design t he program so as to al locateresources in a manner most likely to reach theintended beneficiaries. This target ing can be verybroad or coarse or very fine. In the former case thepoverty profiling done under the first objective isused to allocate resources to regions or programsconsistent wit h the orderings in povert y profi le.Alternatively, the finer t argeting can be sought tolocate beneficiaries directly as in the targeted PublicDistr ibut ion System or the Indira Awas Yojana, wherecaps on numbers of beneficiaries are reached basedon the estimates of the profi le. It is clear that while

both forms of targeting use the profile developed forthe first objective, the finer t he targeting, the moreintensive is the use of the data generating thepovert y profile. This then leads to the question as tohow appropriate is this? To answer this question weneed to understand the statistical properties of theprofile generated for the first objective.

Statistical Attributes of the Poverty Profile

As noted earlier, the poverty estimate is acalculation of a sample proportion who do notachieve a def ined crit er ia of needs. As such thestatistical properties of this estimate can bedescribed through formal techniques of statisticalanalysis.

The measurement process is that a sample ofhouseholds is selected from the population throughstratif ication at various stages. The household is thencanvassed about its consumption over a period oftime. Based on collat ing the data from the sampleover t he per iod of t he survey, we estimate theproport ion who did not meet the prescribed norm.This may differ from thetrueproport ion for a varietyof reasons:

First: The sample is a subset of t hepopulation, the estimate of the sample will dif ferfrom that of actual population, this difference will

depend on the design of the sample. These aretypically defined assampling errors.

Second:The households actual consumptionmay dif fer from the reported consumption becauseof t he design of the schedule of inquiry, the abili ty of

the respondent to recall and the ability of theinquirer to communicate his query and understandthe response of the respondent. These are furtherinf luenced by a variety of subjective factors like thetiming of the inquir y, the length of the process andthe actual modalities of t he dialogue and many othersuch considerations. This class of issues are describedin the li terature asnon-sampling error.

Both t hese issues are wel l known tostatisticians and are dealt in survey designs througha variety of means. Their overall impact on themeasurement is captured in the analysis, by theconcept of standard errors of estimates. Themagnitude of this error typically depends on thedesign of the sample and the overall size of the

sample. The latt er i n turn is inf luenced by thedesired degree of granularity in the estimates. Thus,for instance, estimates for India as whole over theent ire year will have lower standard errors, whereasin a given survey estimates for states, distr icts and forsub rounds will have higher standard errors.

The relevance of t his discussion f or t hedif ferent uses of poverty estimates is because, whenthe estimate is used for decision making like decidingthe quantum of target population in a region, theinherent randomness in the estimate induces errorsof inclusion and exclusion. These are over and aboveinclusion and exclusion errors generated through t he

operation of the target selection mechanism. Inother words, even if there were no errors in t heoperation of the selection mechanism, there wouldsti ll be individuals and households who areincorrectly included and excluded.

This too is well recognised. The correcttarget ing mechanisms would t hen adjust t heestimate derived in the first exercise by a factor tocorrect f or such errors. The form of adjustmentwould depend on the relative costs assigned to thedif ferent errors. To il lustrate, in the, proposed foodsecurity mechanism incorrect inclusion implies thata person who does not deserve state support will getsubsidised grain, where an incorrect exclusion impliesthat a poor person stays hungry. The balance of socialconsideration has been to argue to minimise suchwrongful exclusion and err on the side keeping thispossibility to a very low level even if this implies
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some degree of wrongful inclusion. Thus, thetargeted populat ion should exceed the averagenumber of poor calculated by our surveys by a factordependent on the standard error.

In order t o examine the implication of this, weturn t o some implications of povert y estimates fromthe NSS 68thround data from 2011-12 after a briefreview of t he history of poverty measures in India

Poverty Estimation in India

Poverty estimation in India, since 1979 hasbeen done by the Planning Commission using datafrom NSS surveys on household consumptionexpenditure. The methodology for estimation wasoutlined in a report of a Task Force (1979) of the

Planning Commission. The task force defined apoverty line for urban and rural areas. They firstdescribed an average calorie norm, worked out inconsideration of the age, sex and activitycomposit ion of the population. The monetary valuefor this norm was then derived from the expenditurepattern of the 1972-73 NSS consumer expendituresurvey. This was then termed as the base yearpoverty line. This base year line was then periodicallyrevised by adjusting for inf lation. The percentage ofpoor was then calculated in subsequent years usingthe distribution of consumption expenditure asrevealed in various NSS surveys with an adjustmentto the level of consumption to bring it in line withnational accounts estimates of householdconsumption.

This measure attracted a lot of attention,discussion and criticism as well. The PlanningCommission decided to have a comprehensive reviewby an Expert Group (1993). The group in their reportnoted The methodology foll owed in off icialestimates of povert y ... has been regarded by someas inappropriate and even inadequate in giving arepresentative picture of incidence of poverty inIndia. In fact, the use of State level estimates ofpovert y in allocat ing plan resources for povertyalleviation programmes has brought this debate into

sharper focus. The States have become very sensit iveabout their respective est imates of poverty. Thegroup t hen eventual ly recommended someadjustments to the procedure of the taskforce byremoving the link to national accounts, and allowing

for inter-state var iation in inf lation and also tochange the basis for inf lation correction using CPIrather t han wholesale prices. But the essential normremained as it was in the earl ier taskforce. Theconsequence was to bring about an adjustment forthe level of the estimate without alt eri ng itsstatistical attributes.

This method, in turn, received its share ofanalysis and crit icisms. These crit icisms centred onthe method used for price adjustment, the ruralurban differentials in poverty and inter-alia thecontinued relevance of the 1973 basket for povertycomparisons. A cont inuing concern was t hepurported under-counting of the poor specificallyfrom the view point of targeted programs. These

concerns were sought to be addressed by anotherexpert committee constituted under thechairmanship of Prof. Tendulkar. The report of thecommit tee addresses these concerns through asomewhat complex process: they suggested formallydropping the linkage to a calorie norm, which in anycase the expert group had implicitly done since theyhad recommended the expenditure associated withthe norm for 1973 without changing the underlyingbasket of goods. The commit tee suggested using theurban povert y rate for 2004-05 arr ived through theearli er method, as the reference rate; and t heassociated basket of goods as the new normative

basket, applicable to both urban and ruralhouseholds; and then updating this expenditureusing price data, implicitly captured in the NSS surveyin t he form of unit values in t he median householdclass. This maintained a continued comparabilit y withthe past, albeit through the urban povert y line, andprovided for a sli ghtly higher level of povert yestimates. This approach clearly did not address thecentral concern relating to under est imat ion of t heproport ion of poor for purposes of t argeti ng; aconcern that manifested it self in a huge, somewhatuninformed, public debate; leading to theconst it ution of yet another expert group, whosereport is stil l awaited.

We may note that a common element in allthe crit icisms is that the estimates are too low. Theresponse has been through calibration to adjust thelevel. This response seems to have missed a key facet

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of how poverty measures are used in t arget ingsystems. Consumpt ion as measured in the NSSsurveyor other povert y surveys is complexconceptually and is not feasible to measure througha census. The general approach of a targeting systemis to develop some observable attributes that can besimply measured in the population and arecorrelated with the consumption poverty profile.These att ri butes are t ypically t hose that can becaptured in a census and are often stable over t ime.These are then used to select ent it led and excludedpopulations usually subject to limits based on thepoverty profile assessed through consumption. Thecontroversy arises, because these limits set t hroughaverage measures induce large errors of exclusion.

In our earlier discussion we had noted that useof average thresholds in decision making can induceerrors of exclusion and inclusion. These errors areexpected to be li nked to t he vari abil it y of t hemeasure.

To explore this it is useful t o turn to someoperational characteristi cs of the NSS consumerexpenditure survey. The NSS Consumer expendituresurvey is conducted over a full year from July toJune (termed as a round, hence the 68thRoundcovered July 1, 2011 to 30lhJune, 2012). The surveyperiod is furt her divided int o four sub-rounds, eachwith a duration of three months, the 1st sub-round

period ranging from July t o September, the 2nd sub-round period from October to December and so on.An equal number of sample vil lages/blocks (FSUs) isallott ed for survey in each of these four sub-rounds.Thus each sub round is anindependent samplecapableof generating separate estimates for the country asa whole and for each state. Thus, while typically,poverty is estimated from the sample for the fullyear; it is in principal possible to also examine thedata across dif ferent sub-rounds.

The identification of the NSS year with theagricultural year and the sub rounds with broadagricult ural seasons, it is expected that someseasonal characteristics will be there in the data. Thusthe detailed result s for the Employment Survey ofthe NSS, which is carried out parallel to theconsumer survey, from 2009-10 show that averagewage earnings per day received by casual wage

labourers is the lowest in sub-round 1 for bothmales and females in rural and urban areas. What isalso interesting is the fact that this is also the case foraverage weekly remuneration from public works.Ref lecting, probably the lower amount of such worksin this time period. Sub Round l also has the highestincidence of unemployment. If we look at intensity ofemployment, in terms of t he number of days workedin the week, then again in sub round 1 thepercentage of people finding work all seven days isthe lowest, and the percentage not working on anyday is the highest.

Given the close association of employment andearnings wit h poverty, one should expect t o see asimilar patt ern in poverty. This has been attempted

for some states in t he Table. This has been doneapplying the state level poverty lines for the full yearto each sub round and by linear int erpolation in therelevant decile class. The results yield someinteresting characteristics:

As expected the 151sub-round and occasionallythe 2ndshow the highest level of poverty. Further the4thsub round has usually the lowest percentage ofpoverty. In these 18 states if we examine thepoorest, these would be Chhatt isgarh, Jharkhand,Bihar and Odisha; however t heir inter-se rankingsdepends on the sub round. Thus Odisha is secondonly to Chhat ti sgarh i n t he fi rst and thir d sub

rounds; Jharkhand appears to be the poorest in the4thsub round.The povert y percentage also varies quit e

sharply as well between sub rounds. In the case ofChhatt isgarh, the povert y estimates can differ by asmuch as 19 percentage points in rural areas and 16point s for t he state as a whole. In fact, MP, Odishaand Maharashtra also see large variations in their sub-round wise poverty percentage. This is largely onaccount of the fact that t he fourth sub-round has amuch lower poverty rate than any other sub-round.Further on expected lines, more developed states likePunjab and Kerala see relatively low sub-roundvariation but quite intriguingly so does a lessdeveloped state like Rajasthan! In general, the largerand poorer states have higher seasonal variabil itythan the better off states.

The seasonal variabil it y is only part of the

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problem. A related question is on its correlationstructure. If seasonal cross correlation across povertyprofi les is low then the overall measure cannot be asimple average but a suitably weighted sum! Wecannot easily measure correlation across seasonalmeasures because in the current design, householdsare only measured once and we do not have a senseof their seasonal variation in consumption. Indeveloped states, wit h less seasonal variation inemployment and also in states with strong activeant i poverty programs, the correlation is likely to behigh. In other cases correlation may be low. Ourmeasure of inter seasonal range is a part ial indicatorof this possibili ty. Thus, suggesting that if our concernis with targeting we should look for an all India

measure that aggregates seasonal peaks acrossstates. The degree of i nterstate and even higher

int erstate seasonal variabilit y suggests that this typeof variation would increase as we go down furtherint o sub State level measures. In fact i t is quite likelythat backward distri cts would have much higherseasonal variability in poverty. A targeting measureseeking to minimize exclusion would need to bebased on both a very granular estimate and takeaccount of the vari abil it y at that level; t hus anaverage based on district level seasonal maximawould be considerably higher than our currentestimates.

CONCLUSION

In this essay we have explored the basicapproaches to measure poverty and its areas of use.

Our discussion has pointed out that potential useshould describe the design of measurement.

Percentage below the poverty line (Tendulkar method) 2011-12 (selected states)2011-12 RURUAL URBAN

Full Year SRI SR2 SR3 SR4 Full Year SR1 SR2 SR3 SR4

Andhra Pradesh 10.96 14.8 10.7 9.5 9.3 5.86 9.4 8.2 7.2 6.3

Assam 33.89 37 327 34 30 20.49 17.6 24.3 20.5 18.3

Bihar 34.06 31.8 39.8 34.9 30.6 31.23 30.2 39.5 23.7 32

Chhatt isgarh 44.61 50.8 50.8 41.6 31.2 24.75 25.6 23.5 26 21.9

Gujarat 21.54 26 26 18.6 15.9 10.14 14.4 11.1 9.2 9.2

Haryana 11.64 12 11.3 15.6 8.2 10.28 14.8 9.2 7.5 8.3

Jammu & Kashmir 11.54 17.4 9.6 10 9 7.2 9.1 8.6 6.6 8.5

Jharkhand 40.84 42.5 43.4 34.9 36.3 24.83 25.3 31.5 20.1 15.5

Karnataka 24.53 29.3 25.2 21.7 20 15.25 16.6 17.1 13.2 15.7

Kerala 9.14 11.6 10 9.2 9.4 4.97 9.4 8.1 7.3 8.5

Madhya Pradesh 35.74 43.6 45 34.6 31.4 21 27.8 22 20.3 15

Maharashtra 24.22 33.2 23.8 25.7 17.5 9.12 13 9.2 8.2 9

Odisha 35.69 44.5 31.6 37.7 30.4 17.29 16.8 16.2 17 20.8

Punjab 7.66 8.6 8.6 8.3 8.7 9.24 8.6 9.5 11.3 9.4

Rajasthan 16.05 17.8 16.5 18 14.5 10.69 12.4 13.4 8.6 8.9

Tamil Nadu 15.83 18.3 18.7 18 10 6.54 10.9 8.3 8.2 6.4

Ut tar Pradesh 30.4 33.5 32.4 28.4 26.7 26.06 31.1 27.7 29 18

West Bengal 22.52 26.5 21.4 24 16.8 14.66 16.1 17.6 15.3 12.4

All India 25.7 28.1 26.9 25.6 22 13.7 16.2 14.5 13.5 10

The povert y percentages are based on l inearinterpolation in each decile. This leads to some overestimation in the lowest decile class. Specifically usingpoverty measures for targeting purposes implies thatwe should be sensitive to likely errors of inclusion

and exclusion. This suggests that in addit ion to beingconcerned about the location attributes of povertymeasures we should explore their variabil ity andsources and structures in the variabili ty. Existingmeasurement designs have principally been focussed

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on estimates of mean rather than those of varianceand covariance.

This has been part ly compounded by amisunderstanding on the nature of variance; itshould not be identi fied as a fault of t he design buta characterist ic of the process it self . There is clearlya need to develop f rom existing studies a deeperanalysis of variance and covariance. Further we have,in our discussion, examined variability only onaccount of seasonal variations in economic activity.But poverty is also affected by non-seasonal sourcesof variation like health expenses, economic anddemographic cycles, natural disasters etc. Ideally weshould seek to develop a study to understand thedynamics of poverty direct ly. To appreciate the

distinction; at present the survey focuses extensivelyon the nuances of consumer expenditure. Poverty isprincipally a distributional attribute of theconsumption distribution.

GROWTH &

EMPLOYMENT IN INDIA SOME TRENDS

Table 1 provides GDP growth, employmentgrowth, productivity growth, elasticity ofemployment wi th respect to GDP since the early1970s. The elasti cit y of employment declinedcontinuously from 0.52 in the 1970s to 0.02 in thesecond half of 2000s.

The story of I ndia shows that the relativelyhigh growth has not been jobless but it semployment content has been low and has declinedsharply over the decades since the early 19808.Overall productivity is increasing part icularly in theformal sector but new employment is being createdin t he low productive informal sector.

The numbers on GDP growth, employmentgrowth and elasticity by sectors for India are given inTable 2. Employment growth and elasticity havedeclined for the primary sector. Decline in t he shareof agriculture in employment is needed. However, itis declining in manufactur ing sector also. Theelasticity of employment in manufacturing declinedfrom 0.78 in the 1970s to 0.25 in 20005. Simi larly,the elut icity of tert iary sector has declined from 0.77to 0.30 during the same period. In the last twodecades, empl oyment was generated more in

construction sector trade, hotels, and storage.Twoother i mportant t rends are observed in Indianeconomy.

One observes a jobless growth phenomenonin organized manufacturing. The growth rate ofemployment in this sector recorded consistentlynegative growth since late 19808 wit h growth ratesof-0.8 in 1988-94, -2.5 in 1994-2000,-5.9 in 1999-2005 and -3.4 in 2005-2008. Secondly, the addit ionalemployment generated mainly relates to informalworkers.

Around 63 million workers were added duringthe period 1999-2000 to 2009-10. Out of t hat, 44.7million were added to unorganized sector and 18.8mil lions were informal organized workers. In other

words, all t he addit ional employment generated wasof informal nature.

There are large numbers of working poor inIndia. Around 92 per cent of t he workers are in theunorganized sector with low productivity, lowearnings, poor conditions of work and lack of socialprotection. The Indian experience thus suggests theneed for i ncrease in quant it y and qual it y ofemployment.

Global Experience: According to the Report ofthe Global Employment Trends 2013 (ILO, 2013),global unemployment is estimated to have increasedfrom 170 mill ion in 2007 to 197 mill ion in 2011.Around 39 million people have dropped out of t helabour market as they do not see job prospects.These are also called discouraged workers. ILO(2012a) indicates that although economies achievedhigh growth f rom the earl y 2000s, employmentelasticity to growth has been low. The employment-to-population ratio stagnated around 60 per centwhen the world economy was growing steadily. Thereport says that while the trends may mask region ntis not responding to growt h could be due tostructural changes that the global economy isundergoing.

Some of the structural changes are: (a) laboursaving technological advances; (b) workers are moving

to low productivity informal sector; (c) economiesare facing adjustments to ensure environmentalsustainabili ty to fight against climate change; (d)some demand is coming from extractive sectorswhich have low employment intensity.

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Table 1. GDP Growth, Employment, Productivity and Elasticity in India

Periods GDP Employment Productivity Elasticity of EmploymentGrowth (%) Growth (%) Growth (%) with respect to GDP

1972-73 to 1983 4.66 2.44 2.22 0.52

1983 to 1993-94 4.98 2.02 2.96 0.41

1993-94 t o 2004-05 6.27 1.84 4.43 0.29

1999-00 t o 2009-10 7.52 1.50 6.02 0.20

2004-05 to 2009-10 9.08 0.22 8.86 0.02

Source: Derived from Papola (2012)

Table 2. GDP Growth, Employment, Elasticity in India by Sectors

Sector GDP Growth (%) Employment growth (%) Elasticity of Employment GDP

72-73 83 to 93-94to 99-00/ 72-73 83 to 93-94 99-00/ 72-73to 83 to 93-94to 99-00/

to 83 93/94 04-05 09-10 to 83 93/94 04-05 09-10 83 93/94 04-05 09-10

Primary Sector 3.66 2.76 2.51 2.33 1.70 1.35 0.67 -0.13 0.46 0.49 0.26 -0.05

Manufacturing 5.47 4.94 6.70 7.97 4.28 2.00 3.17 1.95 0.78 0.41 0.47 0.25

Construct ion 3.08 4.88 7.63 9.20 4.43 5.67 7.19 9.72 1.44 1.16 0.94 1.06

Secondary sector 5.09 5.35 6.68 7.78 4.43 2.82 3.97 4.64 0.87 0.53 0.59 0.60

Trade, hoteling etc. 5.74 5.58 8.64 8.47 4.62 3.77 5.24 2.54 0.81 0.67 0.61 0.30

Transport &communica 6.48 6.03 10.57 14.50 5.88 3.39 5.16 3.68 0.91 0.56 0.49 0.25

Financing, insurance etc. 5.95 9.07 7.29 9.47 7.43 3.58 7.23 7.68 1.25 0.39 0.99 0.81

Community, social etc. 4.49 5.86 0.53 6.58 3.18 3.91 0.40 1.85 0.71 0.67 0.06 0.28

Tertiary Sector 5.46 6.58 8.00 9.35 4.21 3.77 3.41 2.83 0.77 0.57 0.43 0.30

AU non-agri. 5.31 6.12 7.54 8.84 4.30 3.36 3.64 3.61 0.81 0.55 0.48 0.41

Total 4.66 4.98 6.27 7.52 2.44 2.02 1.84 1.50 0.52 0.41 0.29 0.20

The conclusion of I LO (20l2a) i s that (a)growth is not a necessary condit ion for employmentgeneration although it is thought to be a necessary

condition (b) the structural changes in the worldeconomy do not seem to be conducive foremployment generation. The challenge at global levelis creating productive and decent jobs for theworking poor, the 200 million out of work and forthe 40 million people entering the labour force everyyear plus those discouraged workers.

RUPEE DEPRICIATION SAME FACTS

Rupee Valuation against USD has touchedhistoric low rate of Rs. 68.80 recently. More suchhistoric levels wit h corrections are expected in thenear future. Technically`is presently under volati le

trend, caused by sudden change in demand andsupply forces in forex markets. Valuation of `againstUS $ has depreciated more than t he 14 per cent inthe span of few weeks and daily change of `1 to `3has become common.

Brief volati le hist ory of Rupee movementalong with main inf luencing factors is as follows:

1947-No foreign borrowings on Indias

balance sheet USDINR=`1 1951-Int roduction of the Five-Year Plan, The

government started external borrowings tofinance welfare and development activit ies.USDINR =`4.8

1975-85 - INR was devalued due to oil pricehike in early 70s, lower domestic production,license raj and worsening BOP situation.USDINR =`12.36

1991 - Serious BOP crisis. The country wasin the grip of high inf lation, low growth andthe foreign reserves were not even worth tomeet three weeks of import s. USDINR =`17.90

1993-INR was let free to f low with t hemarket sentiments, The exchange rates werefreed to be determined by the market, withprovisions of intervention by the central

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Impact and Challenges for Exporters

Rupee depreciation against US$ is good newsfor our exporters specially in IT sector and those notusing imported inputs. Exporters using high contentsof imported raw material component like diamond,

jewellery, engineering goods etc. will be badly affectedby steep appreciat ion of US$. Rs. depreciationprovides right platform to our exporters, to competewith other suppliers in i nternational markets byreducing prices without affecting profitability.Export ers must avail this opportunity to penetratedeep int o exist ing markets by understandingbusiness model of their foreign buyers. Exportersmust make their export supply chain effective andefficient to meet future challenges. Such approach

wil l enhance competit ive capacity and cost efficiencywhich wil l help them to develop long term strategyIn international business.

Rupee depreciation has posed new challengesfor exporters. This is the best t ime for export ers todevelop brand image in their existing and newmarkets. Export sector must focus on improvingtechnology, reducing costs, improve qualit y, developcompetit ive manufacturing capacit y and improveefficiency of cheap labour. We may not get, in thenear f uture, such an encouragement f or exportpromotion. Policy making agencies should initiatecomprehensive packages for development of export

sector. This is probably t he best t ime for ourexporters to compete with China and replacing themfrom cert ain foreign markets.

Effect on Imports and Loans Portfolio

Depreciation of `is escalating the overall costof import business. Depreciating t rend of Rupee isagainst the interest of importers, borrowers offoreign currency on cheap interest rates, studentsplanning for higher studies abroad, tourists boundfor foreign destinations and for medical treatmentabroad, et c. As per RBI survey, major it y ofcompanies have not hedged t heir repayment offoreign currency loans raised in US$. Such casual

approach towards hedging of currency exposure hasmade cheap foreign currency loans more expensivethan Rupee loans due to fast appreciation of loancurrency USD. Similarly, foreign acquisit ion plans byIndian companies have become more expensive and

at the same time value of their old purchases havebecome high value assets.

Importers have to eit her pass on high cost tolocal consumers, pushing up inf lati on in I ndianeconomy or develop local sourcing like Indian Autosector. This sector is dominated by experiencedJapanese, Korean, European and Americanmult inat ional Auto companies. To meet t hechallenges of Rs. depreciation, auto industry hasdeveloped a long term strategy to arrange auto partsfrom Indian ancil lary units. Rupee depreciation willencourage locals in sourcing and will change businessmodels by encouraging exports to strong currencyareas and imports from weak currency countries.

The Way Ahead

History of US$/ rate reveals that once Rupeevalue goes down, it never returns back. Now,corporates must make future business strategies bytaking USD = 60 ` plus exchange rate intoconsideration. This is the right occasion to enhanceour exports and restrict non essential imports tocorrect Current Account Deficit and pressure onRupee. Big expor ters may establish warehousesabroad or build manufacturing capacities in theirmain int ernat ional markets. Long term exportpromotion strategies have to be developed.Gurumantra to do int ernat ional business would be toreduce the cost of each and every businesstransaction.

A focused policy approach is required toincrease inward remit tances and Non-Residentdeposits. Sincere efforts are required to encourageforeign inward remit tances like issuing Dollar basedlong period bonds and encouraging investments inIndia. Preserve the foreign exchange reserve toprotect the value of Rupee in unexpected marketsituations.

We have to reduce our dependence on USD inint ernational business. USD/ market t urnover issmall as compared to international forex market andstill controlled by RBI. Change in demand and supply

of USD causes volatil it y, destabil izing the externalsector of the economy. Surplus dollar reserves shouldbe used for high value crude oil and mil it ary imports,etc. so as to reduce the demand of USD in localonshore forex markets. Government must

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encourage bi or multilateral agreements, toencourage local currency payment in order to lessonthe dependence on third currency USD. Such policyinit iatives will lessen downward pressure on Rupee.Emphasis should also be to use local currencies indeveloping currency swap arrangements. Payment in

Rupee, negotiating for long credit periods for highvalue import s and encouraging local investments inproduction areas are some of the issues which willreduce dependence and demand of USD in local forexmarkets. Policy actions should be firm and growthoriented to give posit ive sent iments to Rs.

DO YOU KNOW?

WHATISUNSTRUCTUREDSUPPLEMENTARYSERVICEDATA(USSD)?

Unstructured Supplementary Service Data (USSD) is a protocol used by GSM Cellular telephonesfor communication with service providers computers. USSD is an int ernational system forcommunication technology, which is used for sending text between a mobile phone and anapplication program in the network. A USSD gateway routes messages from signalling networkto service application and back. In telecommunications, gateway is a central point at which several

dif ferent protocols or communications signals are controlled and routed. It is a technology uniqueto the GSM. I t is a session based communication which has a variety of applications. In interactiveapplications it takes less ti me than SMS, as it is a session based feature and not a store andforward service. With 182 alpha numeric characters USSD messages create real-t ime connectionduring a session. The connection allows a two-way exchange of a sequence of data; thus makingit a more responsive service. The process of interaction through USSD begins with t he usercomposing the message on the phone keyboard. It goes to the phone company network, whereit is received by a computer dedicated to USSD.The response from computer is sent back to the phone. Most GSM phones have USSD capability.A USSD message starts wit h an asterisk (*).The message ends with # sign. As far as the uses ofUSSD are concerned, the most common use in our day-to-day life is to enquire about how muchbalance we have in our mobile phone account at a particular t ime.The user sends a Process Supplementary Service Request (PSSR) to the home zone. Under the

guidance of the gateway, it is sent to t he correct appli cat ion. The appli cat ion sends anacknowledgement via USSD gateway. PSSR responds back. The balance appears on the screen.Balance notification at the end of charged call on our mobile phone screen is also done throughthe use of unstructured supplementary service.The use of above mentioned process is also done for voice chat . USSD is also a medium f orproduct-advert ising. These days, aggressive telemarketing has been a cause of i rrit at ion tocustomers who do not like being unnecessari ly disturbed in t he midst of their hectic schedules.USSD enabled advert ising is less invasive than telemarketing.The USSD services provide a virtual Home Environment (VHE) during roaming. This is becauseUSSD services are available in roamingnetworks and the USSD messages are directed towards the subscriber home network. In this waythe change of geographical location of subscriber and going beyond ones network area does notcome in the way of smooth communication. The same set of services are thus enjoyed by the

subscriber while on roaming.WHATISBRENTCRUDE?

Brent crude is a light crude oil. It got its name due to the fact that it was first produced fromthe Brent oilfield. It contains about 0.37% of sulphur. It is suitable for production of petrol and

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BROAD GENDER GAP IN EMPLOYMENT

The 2009-10 employment survey by NSSOrevealed a start ling fall in numbers of women in theworkforce since 2004-05. Notwi thstanding theimmediately following round of 20 11-12 showing aslight increase in t he number of women workers, the

fact remains that t he female workforce was reducedby more than 19 million between 2004-05 and2011-12, and female work participation ratesdropped to the lowest ever i n t he hist ory ofindependent India in 2011-12.

There is a widespread assumption that thisrecent slump in work part icipation rates is due toincreased participation in education, anunderstanding that is reflected in the 12th Plan aswell as the Economic Survey 2012-13. However,detailed analysis of the 2004-05 and 2009-10 data(when the fall in numbers of women workers was inexcess of 20 mill ion) has shown that most of the fallin womens employment cannot be accounted for by

education (Kannan, Raveendran 2012). Alt houghlow employment growth is indeed a general featureof the liberalization era, the starkness of t he absolutefall in numbers of workers across the last half decadeor so is specif ic to women. The evidence from NSSOsemployment surveys thus indicates that we arecurrently in the midst of a highly genderedemployment crisis. Taking a slightly longer view ofthe period of reforms as a whole, workforce figuresfrom 1993-94 to 2011-13 show that contrary to thegeneral assumption that globalization leads tofeminization of labour, female employment has beenlagging and t he gender gap in employment has

widened. Womens share of overall employment hasactually fallen from close to 33 per cent in 1993-94to around 27 per cent in 2011-12.

Some of us in womens studies have beenarguing that the aggregate workforce figures that are

put out by the NSSO do not give us a true picture ofwomens employment , since they include unpaid (andtherefore financially dependent) workers involved ineconomic activit ies. In the case of women - the shareof such unpaid workers is part icularly high andreached an all t ime high of 44 per cent of t he femaleworkforce in 2004-05, the only year across four

quinquennial rounds of employment surveys (1993-94 to 2009-10), when female work participationrates had shown a marked increase. In contrast, t heshare of unpaid helpers in the male workforce hasgenerally hovered around 15%. It has been arguedthat for the purposes of understanding trends inemployment opportunities for women, there is aneed to specifically count paid or income earningworkers among women rather than just presentingfigures that lump paid and unpaid workers together.Further, trends in unpaid work also require separateand specific attention. It is perhaps time that policymakers and analysts recognize that the separation of

paid and unpaid work at the macro-level isadditionally important for focusing attention on howdevelopments in womens work and employmentare inf luencing some of t he quali tati ve changestaking place in gender relations at several levels. Thesituat ion of the unpaid women workers assumesparticular significance in times of increasedmarketisat ion when money incomes have becomemore and more necessary f or even subsistenceproduction/consumption. It would be logical toassume that in such a situation, additionaldistinctions would inevitably emerge between thosewho bri ng in money incomes and those unpaid

workers who dont (as opposed to both workingtogether in a common production process forsubsistence). These, in turn would lead to shift ingalready unequal power equations further in favour ofmen - within families as well as in the broader

middle distillates. It is sourced from the North Sea. This type of oil is used as a benchmark to priceEuropean, African, Middle Eastern oil. It was discovered in early 1960s. it is sourced by UK,Norway, Denmark, the Netherlands and Germany. It is a light as well as sweet oil . It is a blendof UKs two North Sea oils. The production of this oil now stands at 500,000 barrels a day. Thereis a price dif ference between this oil and it s counterparts. The depletion of North Sea oil fieldshas also affected the prices. Dif ferences in the supply and demand sit uat ion have also led todif ference in prices. However, the gap in prices is now less.

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society. Our arguments in this paper however, aremore concerned with opportunit ies or rather lack ofopportunit ies for women in paid employment. Giventhe nature of the NSS data, some estimation of paidor income earning workers can be arrived at byexcluding all unpaid helpers f rom among the

category of self employed in the workforce figures(Mazumdar and Neetha, 2011). Provides estimationsof the numbers of paid or income earning workersfrom 1993-94 to 2009-10, based on such a procedureof excluding unpaid workers.

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