Please refer to important disclosures/disclaimers in Appendix A

Dhananjay Sinha dhananjay.sinha@centrum.co.in +91 22 4215 9619 Komal Taparia komal.taparia@centrum.co.in +9 1 22 4215 9195

The fragility of the rural growth India’s rural growth is pivoted on farm sector policy interventions, including aggressive procurement & minimum support prices (MSP), input subsidies and massive credit infusion. But is this strategy sustainable? We foresee inconsistencies leading to a decline in the farm sector’s terms of trade, volatility in farm incomes, credit concerns and aggravation of the fiscal burden. Ironically, the food subsidy policy may have hurt consumption.

Food shortages did not cause inflation: We believe the steep rise in food inflation was not caused by inadequate supplies, or even a cyclical burst of inflation. Rather, it was a manifestation of policies favouring price escalation.

High MSP not sustainable: The elevation of the farm sector’s terms of trade (TOT) to a 30-year high would create structural limitations. Normalization would require either a 12-14% fall in food grain prices, or 15-16% rise in manufactured products prices. Ensuring remunerative prices for the farm sector would be a challenge, going forward.

Is the rising food subsidy helping? The food subsidy bill has been expanding, but cereal consumption has been falling. This inconsistency has been aggravated due to aggressive MSP increases. While benefiting a relatively small number of producers, high food prices has hurt aggregate consumption & encouraged inconsistent cropping patterns.

Rising leverage and risk of renewed defaults: Institutional lending to the farm sector has risen to 91% of Agri GDP and could rise further to 170%. Ironically, however, farm sector growth has declined. Policy interventions may have created moral hazard problem for both lender and borrowers. We foresee NPA ratios rising to 10-15%, with an upside bias.

Themes to play: Based on our assessment and possible policy responses, we believe the agri-driven story would sober a little. Consumption-led demand recovery would be oriented towards non-durables, but the volatility in food grain procurement-related flows would slow growth for durables and autos. Concerns on credit risk would have implications for PSU banks. The big worry is on the fiscal health.

India’s food grain buffer (‘ 000 tonnes)

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Exhibit 1: Agri sector ToT at a 30 year high Exhibit 2: Rising agri debt leveraging, but weak growth

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3 May 2010

Macro theme

INDIA

Economy

2 Economy

Summary: The fragility of the rural growth

India’s rural growth story is structured around policy interventions in the farm sector. While there is an attempt to improve long-term productivity, through higher public spent on rural infrastructure, the emphasis continues to be centred on the cereal sector rather than rural industrialization.

Interventions in the farm sector are mainly in the form of high support prices for food grains; demand support by creating abnormally high buffer stocks; cost-side support through various subsidies (mainly power and fertilizer), and massive infusion of institutional credit.

The question is how sustainable is this strategy? We highlight some of the inconsistencies the correction of which would lead to decline in the farm sector’s terms of trade (ToT) and result in volatile rural incomes. Elevated indebtedness increases the risk of renewed defaults, eventually aggravating the fiscal burden. We are of the view that food subsidy policy has harmed consumption. The expected correction in food grain prices would gradually reverse this adversity.

Food shortage did not cause inflation: We believe the steep rise in food inflation has been not caused by supply shortage, or even a cyclical burst of inflation. Rather it a manifestation of policies favouring price escalation. The cumulative rise in MSP since FY06 has been the steepest since 1970s. The buffer stock of food grains has remained in excess since April 2008.

Steep rise in MSP not sustainable: The farm sector’s ToT has risen to 30-year high, thereby creating structural limitations. Normalization would either require a 12-14% decline in food grain prices or 15-16% rise in prices of manufacturing products. Hence, the fundamental reason for the fall in market prices is unlikely to be measures taken to curb inflation. It will rather be driven by misalignment in prices, supply burden arising from excess buffer and supply response in the next season and term of trade adjustments. The moderation in cereal prices since Jan 2010 (-1.6% for cereal WPI till Mar 2010 and 3.7% for wheat) is likely to accelerate going forward.

Volatile prices, elevated cost would be a burden: The long-term impact of higher food prices on productivity is weak as overtime, the MSP benefits contribute to escalation of cost. The combination of downward inelastic cost side and volatile market prices can create volatility in farm income and hence, engender renewed credit default concerns.

Food subsidy policy plagued by inconsistency: The inverse trend of structural decline in cereal consumption and expansion in the food subsidy bill is a glaring inconsistency, largely resulting from aggressive increase in MSP. While the policy benefits relatively less number of producers, the price effect of the rising subsidy bill in absence of productivity growth, is ironically hurting cereal consumption. It is also creating mismatch between changing consumption pattern which is de-emphasising cereal consumption and cropping pattern which encourages it.

Rising indebtedness– a recipe for more defaults: Policy thrust have resulted in an eight-fold rise in institutional lending to the agri sector over FY00, or 91% of agri sector GDP and could rise to 170% by FY18 (as per our simulation). Ironically, however, farm sector growth has slowed. Apart from the high level indebtedness, the risk of renewed default is elevated due to the moral hazard problem engendered by frequent debt restructuring & waiver programs and changes in NPA norms: While lenders are induced to treat Agri lending as quasi-sovereign, such initiatives also encourage poor repayment culture.

Is the income effect of higher procurement prices broad-based? The glaring divergence between booming consumer durable & vehicle sales and declining growth for non-durables, suggests expenditure inequality. A broad-based spill-over impact of higher MSPs on rural income is not evident. On the contrary, by elevating food prices it may be hurting three-fourth of the rural population and most of the urban population. High negative price elasticity for food grains (-0.6) during FY05-09 suggest that demand conditions may have worsened in FY10. Private final consumption expenditure (PCE) grew modestly at 3.5% YoY during Q1-Q3 FY10.

Possible policy options:

– De-linking from MSP-based procurement

– Moratorium on raising MSP and temporary suspension of official procurement of food grains

– Promoting exports (but that would depend on global food prices)

– Increase issue prices for public distribution

3 Economy

Key takeaways

Elevation in NPAs for public sector banks

Expected rise in agri-credit NPA ratio to 10-15% will be a concern for the public sector banks. While overall share of agri-credit to overall credit is still lower than the targeted 18%, sustenance of NPA ratios at these levels can impair their asset quality. Policy interventions in the past viz. loan waivers and change in NPA norms, does provide comfort, banks would still have face the burden as policy responses typically lag. Given the capital constrains arising from 51% government holding, a weakening in loan quality may not be beneficial.

Biggest concern on fiscal health

We believe strains on fiscal condition will arise from multiple factors:

Expected enlargement of central government’s food subsidy bill by 35-24% over FY10 estimate (depending on whether PDS is made universal or not) due to addition to number of BPL families (implication from the Tendulkar Committee Recommendation, 2009). FY11 Union Budget has earmarked lower food subsidy at Rs556bn (Rs560bn in FY10). The consolidated fiscal impact may be lower if state governments respond by lowering their food subsidy budgets.

– The new poverty line estimate will also require higher public spending on health services and medicine, and free education those that qualify as BPL.

– Food management itself can result in higher burden for several reasons a) reluctance to increase PDS issue price, b) rising carrying cost for high food buffer and c) possible export subsidy.

Potential burden from rising farm loan defaults: If NPA level rises to 10-15%, the eventual fiscal burden for the next waiver scheme large-around Rs650-800bn per year, with upside bias. Actual burden can be larger if the fiscal response is delayed (Rs716bn debt waiver/relief package 2007 was 30% of FY06 direct institutional credit balance).

Taking into consideration the near-term concerns of expected under-recovery of oil PSU companies at Rs900bn, the additional food subsidy burden, and medium term possibility of renewed debt waiver, we believe constrains on fiscal management can be significant.

While expected tax reforms viz. GST and DTC can provide comfort, strong cyclicality in Tax/GDP ratio during 2000s suggest that rise in structural expenditure can still upset the fiscal balance.

Decline in food prices will help, but a steep decline is required for bigger gains

The expected correction in food grain prices should benefit overall consumption spending. But given the steep rise in food prices since FY07, we believe a serious recovery would require sharp correction (>15%). Decline in food prices should also benefit FMCG companies from the cost side.

A 41% expansion in number of households under the BPL category will help reduce cost of cereal consumption for the additional BPL households in the rural area and can potentially boost overall consumption, particularly FMCG.

The relaxation in income tax slabs (Union Budget FY11) is positive on monthly disposable incomes. However, the relaxation would impact only 2% of households.

Volatility in farm income could impact demand for durables

In the past cycle, increase in MSPs and market prices benefited the rural rich. For these sections, positive income elasticity is higher than the absolute negative price elasticity. Hence, the fall in food grain prices would be negative for their income flows. This could impact demand for durables and vehicles.

To the extent that upsurge in rural credit was feeding into demand for durables and autos, expected slowdown in agri credit growth in response to credit concerns can impact demand for these sectors.

Tractors: Aided by increase in food grain prices and boost to credit flows resulted in a 24% YoY growth in tractor sales in FY10, ahead of the typical 7 year demand cycle. The growth upsurge in FY10 is significantly higher than the 6% long term average. Volatile in farm income can slow volume growth.

4 Economy

Are supply shortages the real cause of high food inflation?

Contrary to the common view1, we believe the steep rise in food inflation is not caused by supply shortage (except pulses) or drought conditions in FY10, or even a cyclical burst of inflation2. Rather, it is largely a manifestation of policies favouring price escalation. The cumulative rise in MSPs since FY06 has been the steepest since 1970s. Inflationary impulses have also been aided by aggressive government consumption spending. But contrastingly, food grain buffer has remained in excess since April 2008 and is currently at 8 year high. We believe food inflation beyond the threshold inflation of 4-5% is inconsistent from demand-supply perspective.

1) Long-term data refute arguments that droughts necessarily result in a sustained rise in food prices. The steepest fall in food grain production in FY03 was not accompanied by escalation of food prices. Out of the three big episodes of sustained rise in food grain prices (including FY07-10), the earlier two (FY90-92 and FY98-00) occurred when food grain production was rising. But all three episodes were characterised by steep rise in MSPs (Exhibit 12). However, the short-term impact of drought on price expectation cannot be entirely ruled out.

2) Recent steep rise in CPI and food inflation has been caused by sustained increase in MSPs. The MSP for wheat increased 69% during FY06-10, paddy 72% and pulses 60-80% (Exhibits 4 and 5).

1 Budget speech FY11: Momentum in food prices due to flare-up of global commodity prices preceding the financial crisis in 2008. It was expected that food inflation will cool off during the agriculture season beginning June 2009. However, the erratic monsoons and drought like conditions reinforced the supply side bottlenecks. 2 Prime Minister Feb 6, 2010: This is, however, not the first time we are facing high rates of inflation in food articles. We had a similar upsurge in 1998. Food prices are subject to cyclical bursts of inflation…. Procurement prices of have been increased significantly in recent years deliberately to provide an adequate incentive for farmers to produce more food.

Unlike the consensus view, we believe the steep rise in food inflation has not been caused by drought like conditions in FY11 Key factors behind the consistent rise in food prices are consistent rise in MSPs across most food grains and boost in government consumption spending MSP for paddy has increased to 72% over FY06 but wholesale price index for rice rose lesser at 42%

Exhibit 3: Production shortfalls have seldom caused price rise

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Updated till March 2010; Red boxes denote rise in production of paddy and grey boxes denote periods of significant fall

Source: CMIE, Centrum Research

Exhibit 4: Steepest rise in MSPs for cereals during FY07-FY10 Exhibit 5: Highest ever rise in MSPs for pulses

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5 Economy

3) Per capita availability (including change in food grain buffer with FCI) of food grains is 5% higher than FY05. Buffer stock of food grains has been higher than the minimum norm since April 2008 and is currently at 3.5x the norm (Exhibit 8). This implies procurement interventions have helped constrain supplies and fuelled market prices.

Exhibit 8: India’s food grain buffer stock is 3.5x the minimum norm

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4) Prolonged period of net negative imports of food grain implies that India is essentially a food surplus country. India was exporting even in FY03, when production fell 18%. In the past food grain export has been encouraged to support higher food domestic prices.

5) Structurally demand pressures also seem to be lacking given the 18.4% decline in per capita cereal consumption over the past two decades. Recent PCE data (FY05-09) also shows that real consumption expenditure on food has decreased to 28% from 34% (discussed later, Exhibit 16 & 53)

Exhibit 9: India has been an exporter of food grains since FY96

Net imports (Mn T)

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Exhibit 6: Unlike FY10 earlier production shortfalls and drought in FY03 did not cause runaway CPI inflation

Exhibit 7: Improved food grain availability (including change in buffer stock)

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India’s buffer of total food grains has been consistently higher than the norm since Apr 2008

Prolonged period of net negative imports of food grain indicate that India is essentially a food surplus country. We were exporting even in FY03 when production fell 18%. In the past exports have been encouraged support higher domestic prices

6 Economy

6) The correlation of CPI inflation with real government consumption spending is high – CPI rural (82%, FY99-FY10) and CPI urban labour (69%). This high sensitivity indicates pressures arising from factors (Exhibit 11) like aggressive food grain procurement and spill over impact of huge rise in government consumption spending.

Given that ToT for the farm sector vs manufacturing has risen to a 30-year high (next section), we believe increases in MSPs would be minimal or remain stagnant, going forward. While this may be touted as success of measures taken to curb inflation, the fundamental reasons for the fall in food grain prices would be supply responses and a glut in FCI’s stocks. Assuming normal monsoon, supply response can be strong. Initial signs are already visible; cereal prices have softened since Jan 2010 (-1.6% for cereal WPI till Mar 2010 and -3.7% for wheat). In future, the bigger challenge would be preventing market prices from falling sharply, curbing food subsidy bill and finding alternative means of to offload stocks viz. exports or larger allocation under public distribution system (PDS).

Misaligned prices and supply responses could manifest in volatile farm incomes

Phases of spikes in MSPs typically result in misalignments between harvest, wholesale, retail and import parity prices. Several studies highlighted this phenomenon during 1999-20033. Hence, it’s not surprising that we are witnessing similar conditions now (Prime Minister’s statement, Feb 2010). Misalignments arise from inflationary expectation built up at different stages of distribution chain. Misalignments have also been caused by the fact that while procurement is concentrated in few states, off-take in many other states under PDS has been poor.

These misalignments can cause sharp drop in market prices later due to lagged supply responses and prevailing large buffer stock. The ToT for the agriculture sector (vs manufacturing WPI) have spurted sharply to 30-year high (Exhibit 13). Normalization to the trend would either require manufacturing index rising by 15-16% or a 12-14% decline in primary food/food grain prices. The actual scenario may be somewhere mid-way and will also be a function of how services sector inflation responds.

Exhibit 12: Phases of steep rise in MSPs are followed by high volatility in market prices

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3 Ashok Gulati 2003: Policy reforms & farm sector adjustments in India

Exhibit 10: Strong relation between inflation and real government consumption spending

Exhibit 11: Inflation correlation and impact from government consumption expenditure

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Source: Bloomberg, CSO, Centrum Research Source: Bloomberg, CMIE, Centrum Research

Wholesale prices for food grain have not increased in line with MSPs indicating possibility of significant misalignments between MSPs, farm gate, wholesale, retail and import parity prices.

Market driven prices could be volatile going ahead due to near term lagged supply responses

Recent official statements of linking procurement prices to free market pricing strengthens our argument for correction in primary food price going forward

7 Economy

Exhibit 13: Rise in MSPs have resulted in surge of ToT for farm sector, but likely to reverse

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WPI Terms of trade (ToT) w.r.t manufacturing sector

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Terms of trade indexed at April 1982 levels; based on monthly WPI data Source: CSO, Centrum Research

Exhibit 14: Primary food inflation could fall into negative territory

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MSPs to remain unchanged for a while: The combination of weak aggregate consumption, adverse ToT for the non-farm sector and the fiscal constraints, would imply that MSPs for food grains would remain unchanged for a while. Phases of intervention-driven rise in cereal inflation are often followed by prolonged stagnancy as the normalization of ToT happens over several years (Exhibit 13).

Farm income to witness volatility: Downward inelastic cost side and decline in market prices could create volatility in farm income as margin is typically narrow (10-15% for paddy). The long-term impact of higher food grain prices on productivity is weak as overtime the benefits of higher MSP contribute to escalation of costs (rentals, labour, management and other costs, Exhibit 15), while market prices can be volatile. The new nutrient-based fertilizer policy would likely induce upside pressure on production costs in the medium-term. Also there is some evidence that labour shortages induced by the rural employment guarantee scheme (NREGA) is increasing wage rate in the farm sector and in absence of productivity growth is increasing the cost of cultivation. Sustained lack of productivity growth is posing viability issues for the cereal sector.

Exhibit 15: Labour and rental account for 60% of cost of cultivation (paddy: 6 major states)

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Nearly 88% of rental cost is imputed rental value of owned land, the balance 22% is the actual rent paid Source: Fertilizers Statistics, 2006-07, Fertilizers Association of India, Centrum Research

While steep in crease in MSP since FY07 helped elevate the terms of trade for the farm sector to a 30 year high, the next round of normalization will emanate from correction food prices and or rise in manufacturing sector inflation

Steep rise in food inflation is typically followed by declines to negative territory

Long term impact of increase in prices on productivity is weak as over time the MSP benefits contribute to escalation of cost. Downward inelastic cost side and volatile market prices can potentially create volatility in farm income and credit concerns

A large portion of non-labor working capital cost is subsidized, fertilizer, seeds and irrigation (including diesel)

Normalization of ToT takes a considerable period and characterized by prolonged relative stagnancy in farm sector prices

8 Economy

Food subsidy bill rising, but cereal consumption declining

The inverse relationship of falling cereal consumption and expanding food subsidy is incongruous. While the cereal consumption/capita has declined 18.4% since 1991, the central government’s food subsidy bill (Rs582.3bn FY10) has risen to 23% of total expenditure on cereals (30% including subsidy from states).

Exhibit 16: Falling cereal consumption and rising food subsidy

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Note: Per capita cereal consumption represents NSS rounds 12 month ending Jul of every year Source: NSSO, CSO, Union Budgets and Centrum Research

Exhibit 17: Trend decline in per capita food grain consumption kg/month FY00 FY08 Rural Urban Rural Urban

Rice 6.75 5.21 6.36 4.74 Wheat 4.49 4.76 4.19 4.51 Coarse Cereals 1.48 0.45 1.12 0.42 Cereals 12.72 10.42 11.67 9.68 Pulses 0.84 1.00 0.67 0.78

Source: 55th (1999-2000) and 64th (2007-2008) rounds of NSSO, Centrum Research

Three principal causes for the surge in food subsidy bill are:

1) Unchanged issue prices of food grains under the public distribution system (PDS) since 2002

2) Aggressive government interventions through massive increase procurement prices and aggressive procurement of food stock by FCI. Government has been consistently giving additional incentive bonus over MSPs for paddy since FY07 (Exhibit 18)

3) Increase in taxes and levies on procurement by surplus producing states

Points 2 and 3 are more important factors contributing to rise in subsidy as they have caused significant increase economic cost4.

Exhibit 18: Widening gap between procurement price and issue price

Rs/quintalSeason Wheat Paddy Wheat Rice APL BPL AAY APL BPL AAY

2002-03 620 a 530 a 884 1,165 610 415 200 795 565 300 2003-04 630 550 919 1,236 610 415 200 795 565 300 2004-05 640 560 1,019 1,304 610 415 200 795 565 300 2005-06 650 b 570 1,032 1,351 610 415 200 795 565 300 2006-07 750 d 580 c 1,178 1,391 610 415 200 795 565 300 2007-08 1,000 645 e 1,353 1,564 610 415 200 795 565 300 2008-09 1,080 850 f 1,393 1,790 610 415 200 795 565 300 2009-10 1,100 1000 g 1,403 1,894 610 415 200 795 565 300

Procurement price PDS issue priceWheat Rice MSP Economic cost

a One-time special drought relief of Rs20/ql & Rs 10/ql for paddy & wheat respectively; b Incentive bonus of Rs50/ql for wheat in 2006-07 c Bonus of Rs40/ql allowed for paddy in 2006-07 till Mar 07. Extended up to Sept 07 for Andhra Pradesh, Tamil Nadu, Orissa, West Bengal and Chhattisgarh & to Mar 07 for Bihar and Kerala. d Bonus of Rs 100/ql given for wheat procured in 2007-08. e Bonus of Rs 100/ql for paddy/rice in the entire 2007-08. Rs50/ql allowed for paddy for 2008-09. g Bonus of Rs50/ql for paddy for 2009-10 in Oct 2010. APL: above poverty line, BPL: below poverty line and AAY: Antodaya Anna Yojna

Source: Economic Survey FY10, Centrum Research

4 Economic cost of food grains includes MSP (and bonus), procurement incidentals and the cost of distribution. The FCI is reimbursed the difference between the economic cost and the issue price creates food subsidy. High incidence of taxes and levies of over 10% on the procurement of food-grains in Punjab, Haryana and Andhra Pradesh increases the economic costs.

While real per capita consumption of cereals has declined by 20% over the past two decade food subsidy in FY10 has risen to about 25% of total consumption expenditure on cereals

Structural disparity in food policy reflects in inverse trends in expanding food subsidy and falling cereal consumption and rising food prices

Per capita consumption of food grain has been declining both in rural and urban areas

Rising economic cost of procurement and unchanged issue prices

9 Economy

While the move benefiting relatively fewer number of people (nearly one-fifth of rice and wheat grain producers in surplus states of Punjab, Haryana, Uttar Pradesh, Madhya Pradesh, Andhra Pradesh and Chhattisgarh benefited from MSP), a large part of the population is adversely impacted due to rising inflation. Other related issues are:

Mismatch between subsidy policy and consumption pattern. Aggressive increase in MSPs incentives misaligned cropping pattern (including associated ecological damage) and prevents allocation for much-needed crop diversification.

The surplus buffer stock with the FCI and rising economic cost leading to fiscal burden constraining FCI’s ability to procure food grains in the subsequent seasons.

What are the possible government responses?

De-linking food grain prices from MSP: Shifting to market price-based procurements as recommended by the Planning Commission is an indication that MSP would lose relevance. Reliance on free market prices, in the current context, could imply reduction in realisation prices for producers.

Moratorium on raising MSP: The government could put a moratorium on MSP and temporarily suspend official procurement, rollback bonuses.

Promoting exports: This would depend on global food prices. The softening of global wheat prices would make exports difficult without substantial subsidy. A 34% jump in the combined harvests of Australia and Russia over two years is creating the biggest wheat glut since 2002 (Bloomberg).

Increase issue price: The government could hike the issue for both below poverty line (BPL) and above poverty line (APL) categories to cover a part of the holding costs.

New poverty line: The Tendulkar Committee (2009) recommendation adds 26.5mn (or 40% more) households (largely rural) to the below poverty line (BPL) category, which might increase the food subsidy bill of the central government by 35-24% of FY10 estimate to Rs700-750bn (depending on whether PDS is made universal or not).

The pursuit of benefiting food grain sector through aggressive increase in MSPs and build up of buffers in the backdrop of declining per capita food grain consumption is fundamentally inconsistent. The disparity is discernable given that India is a food grain surplus country, exporting food grains

10 Economy

Agriculture credit: Is there a debt sustainability issue?

Agriculture debt-to-GDP ratio could grow to over 170%

The policy thrust5 towards increased rural lending (largely agriculture credit) and directives to banks to grow farm credit 30% annually has resulted in an eight-fold rise in institutional Agri lending (including direct & indirect lending; excluding food credit) over the past decade. Contrastingly, however, the contribution of agriculture sector to overall GDP has fallen: 36% FY81, 29% FY91, 22% FY01 and 18% currently.

Ironically, the steep rise in lending has not been matched by surge in the agriculture real GDP growth, suggesting declining productivity of capital deployed. The sector grew 2.3% during 2000s (including 4.6% during FY06-08) lower than 3.2% in 1990’s, when credit availability was poor. Multiplying credit balance may be feeding into escalating inflation for inputs and output or household consumption without enhancing productivity. The proportion of debt going into productive purpose has been declining (62.9% 2002 from 71.6% in 1981, NSSO).

At 91% of Agri GDP, the current credit-to-GDP ratio (both nominal values, FY10E) is higher than that of the entire economy at 55%. Assuming a moderate 30% share of farm lending from informal sources (42.4% in FY09, NSSO) this ratio could closer to 140%.

Our simulations indicate that in the base case (Exhibit 22), the institutional credit/GDP for the sector can rise to 170% by 2018 (assuming agri GDP growth at 3% and inflation at 5%). The risk of enlargement is high in the near-term as we expect food grain prices to correct or remain stagnant. For an unchanged debt-to-GDP ratio at 91%, nominal agri GDP growth will have to sustain at 21%. Such a scenario is unlikely given the structural decline in per capita food consumption.

Exhibit 21: Steep rise in agri debt leveraging and falling real growth

0

10

20

30

40

50

60

70

80

90

100

FY71

FY74

FY77

FY80

FY83

FY86

FY89

FY92

FY95

FY98

FY01

FY04

FY07

FY10

E

(15)

(10)

(5)

-

5

10

15

20

Total Credit/Agri GDP (% lhs) Agri GDP Real (%YoY, rhs) 5 Yr MA

Estimated

Note: Total credit includes direct and indirect outstanding balance from all sources, including Banks, Cooperative banks, RRBs & REC Source: RBI, Centrum Research

5 RBI initiated policy measures in pursuance to the Union Budget FY05 to achieve enhanced credit flow to agriculture. Banks were advised to grow agricultural credit flow at 30% per year. Direct credit: Lending to farmers for agricultural purposes, largely short-term loans for raising crops. Also includes medium to long-term loans for agricultural implements. Indirect credit: finance provided by banks to through other agencies

Exhibit 19: Build up of institutional credit to Agri sector Exhibit 20: Increasing contribution of banks in Agri credit

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

FY90

FY91

FY92

FY93

FY94

FY95

FY96

FY97

FY98

FY99

FY00

FY01

FY02

FY03

FY04

FY05

FY06

FY07

FY08

FY09

SCBs Co-operatives RRBs REC

(Rsbn)

Outstanding credit (Rs bn) FY00 FY08 FY09E*DirectCo-operatives 420 957 1056SCBs 334 2254 2818RRBs 60 332 421Total direct 814 3543 4294IndirectCo-operatives 674 1480 1624SCBs 130 934 1121REC 122 386 507Total indirect 925 2800 3252Total direct+ indirect 1739 6344 7547Agri GDP: Nominal

New^ 8154 8984Old* 4465 7826 8618

SCB-schedule commercial banks, RRB-Regional rural banks, REC-Rural electrification corporation; Source: RBI, Centrum Research

Partly estimated, ^ New GDP series base FY04; * base year FY00

Source: RBI, Centrum Research

At 91% Agri GDP leverage in FY10 is higher than the credit leverage of the entire economy at 55%. The ratio could close to 140% if informal lending is included Institutional credit to Agri sector has rising eight fold in the last decade but ironically its growth has declined over the previous decade The key risk is that debt/GDP ratio for the sector will continue to enlarge Leveraging in the Agri sector has been fasted than the overall economy. But falling real growth exposes the sector to debt sustainability issues which can lead to aggravating defaults unless food inflation is allowed to keep escalating. This could be a tight spot for the economy

11 Economy

Exhibit 22: Simulated projections for debt/GDP ratio for agriculture sector

24

91

228

171

91

0

50

100

150

200

250

FY97

FY98

FY99

FY00

FY01

FY02

FY03

FY04

FY05

FY06

FY07

FY08

FY09

FY10

FY11

FY12

FY13

FY14

FY15

FY16

FY17

FY18

Scenario 1 Scenario 2 Scenario 3

Projections(% Agri GDP)

Note: Projections based on Debt/Agri GDP=91% in FY10, Rate of interest= 8%, Incremental debt less interest/Agri GDP=10%p.aand assumptions on real & nominal GDP growth for the sector. Scenario1: Real GDP=3% & zero Agri sector inflation Scenario 2: Real GDP=3% & Agri sector inflation=5% Scenario 3: Agri GDP growth = 21% (16% real+5% inflation) if Agri sector Debt/GDP has to remain unchanged at FY10 level Source: RBI, Centrum Research Estimates

We see multiple implications arising from the current situation— increased debt defaults, impairment of banks asset quality, leading to larger debt waiver schemes, extenuating fiscal problem, constraining capital formation in agri sector.

Volatility in agriculture production and market prices of food grains can be major reasons for impairment of repaying capacity of the farmers. Though MSP mechanism aims to provide some protection, it has limitations of spatial coverage. Inability to control cost of cultivation, lack of productivity growth and volatility in output increases the probability of financial distress.

Going by past experience of directed Agri lending in the 1970s and 1980s and the subsequent debt and relief scheme in 1990, a relapse of farm sector debt defaults could slow lending to the agri sector. If credit-to-GDP has to fall by 10% from the current level over the next four years (as it did between FY88-92), aggregate institutional credit growth would have to fall 5-6% from the average of 23% recorded during FY05-10 (31% for banks).

Agri credit has been rising over the past two decades (including 1990s) as a share of both the value of inputs and output. Long-term credit/private investment has also been rising. But the steep rise in credit is not feeding into investments. Farm sector capital formation/credit ratio declined to 25% in FY09. The rising credit intensity could, ironically, lead to greater credit risk6.

6 Rakesh Mohan 2004: Agricultural Credit in India: Status, Issues and Future Agenda

Debt/GDP ratio for agriculture sector could rise to over 170%

Exhibit 23: Institutional lending is largely direct loans, and rising in proportion since FY00

Exhibit 24: Capital formation in agri sector has not matched the pace of growth of credit

0%

20%

40%

60%

80%

100%

FY90

FY91

FY92

FY93

FY94

FY95

FY96

FY97

FY98

FY99

FY00

FY01

FY02

FY03

FY04

FY05

FY06

FY07

FY08

FY09

Total: Direct lending Total: Indirect lending

25

35

45

55

65

75

85

FY73

FY75

FY77

FY79

FY81

FY83

FY85

FY87

FY89

FY91

FY93

FY95

FY97

FY99

FY01

FY03

FY05

FY07

FY09

Agri gross capital formation/Direct agri credit (%)

Source: RBI, Centrum Research Source: RBI, Centrum Research

12 Economy

Pattern bank farm lending: Continued focus on southern states

The rapid growth in agriculture credit (30% CAGR over FY04-08 in 13 select states, Exhibit 25) has been associated with the steep rise in average size of credit (17% CAGR over FY04-08). However, the strong growth in the number of accounts also indicates increasing spread. The share of lending to farmers up to 2.5 acres increased to 26% in FY07 (all-India) from 24% in FY00. Both SBI and other schedule commercial banks (SCBs) have aggressively increased direct lending. Currently, direct credit accounts for over 80% of total agri credit.

Distribution of direct credit, however, indicate that notwithstanding strong growth in less- serviced states like Bihar, Assam, Rajasthan, Orissa and West Bengal, the highly indebted southern states (Tamil Nadu, Karnataka and Andhra Pradesh) continue to dominate (Exhibit 26). If we club Maharashtra with the southern states, allocation to these states would be above 62% of the total. The only exception is Gujarat which has shown steep rise in its share of direct credit.

Taking into account the more active cooperative and informal lending, particularly in Andhra Pradesh and Tamil Nadu, their share of agricultural credit would be even higher. Most of these states, except for Andhra Pradesh, have shown less-than-average agriculture GDP growth (Exhibit 27). The low share in bank credit in the western region could be explained by dominant role of informal lending (50% of lending business in Punjab) and cooperatives.

It is notable that the rural stress in recent years has been concentrated in the southern region. Irrespective of the level of formal lending or the purpose of borrowing, high level of indebtedness in states like Maharashtra, Andhra Pradesh, Karnataka, Punjab and Kerala was the most important factor for distress7.

7 IGIDR: Expert Group on Agriculture Debt 2007

Exhibit 25: More than half of bank credit expansion contributed by growth in average credit size (Rsbn) Distribution of direct credit: Schedule commercial banks Share in direct credit

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Direct:No.of acc ('000) Direct: Credit outstanding (Rs bn.)

Nationalised banks RRBs SBI+ Other SCBs

16% CAGR:FY08-FY04

32% CAGR:FY08-FY04

FY08

0%10%20%30%40%50%60%70%80%90%

100%

Direct:No.of acc ('000) Direct: Credit outstanding (Rs bn.)

Nationalised banks RRBs SBI+ Other SCBs

FY08

Represents select states: Haryana, Punjab, Bihar, Orissa, West Bengal, Chhattisgarh, MP, UP, Gujarat, Maharashtra, Andhra, Karnataka and Tamil Nadu; SBI+ stands for SBI and associates

Source: RBI, Centrum Research

Distribution of direct credit indicates that notwithstanding the strong growth in less serviced states the highly indebted southern states have continued to dominate. If we club Maharashtra along with the southern states, allocation to these states would be above 62% of the total

Exhibit 26: Credit flows dominated by cereal surplus states Exhibit 27: Gujarat and Andhra Pradesh are the only statesshowing rapid Agri sector growth

02468

1012141618

Har

Pun

Bih

Ori

WB

Chh

at MP

UP

Guj

Mah

a

And

hra

Kar

TN

FY08 FY04

% share in 13 states

(2)

-

2

4

6

8

10

12

Guj

And

hra

Har

Indi

a

WB

Pun

MP

UP

Mah

a

TN Kar

Agri real GDP growth % yoy (avg FY00-FY09)

Source: RBI, Centrum Research Source: RBI, Centrum Research

13 Economy

Why default risk can rise?

Agriculture credit NPA ratio for all PSU banks declined from 17.2% in FY03 to 2% in FY09 and has possibly fallen even lower subsequently (Exhibit 32). But we believe the trend would likely reverse. Based on our assessment of structural and regulatory factors, we believe NPA ratios are likely to rise to 10-15% in the coming years. In view of the poor realisation rate in the 1990s (averaging of 60% for banks), the bias on NPA ratio is on the higher side. We believe NPA levels may have been on the rise in H2FY10. The announcement in the Union Budget extending interest repayments for restructured loans by another 6 months to June 2010 corroborates our hypothesis.

Exhibit 32: Regulatory changes and policy support have helped improve recovery performance

02468

101214161820

FY03

FY04

FY05

FY06

FY07

FY08

FY09

FY03

-FY0

6

Ave

rage

Public Sector Banks Nationalised Banks

Agri NPA FY09% total Agri credit: Trailing

Source: RBI and Centrum Research estimate

Exhibit 28: SBI group: Above average growth, specially in the east, Gujarat and Tamil Nadu

Exhibit 29: Nationalised banks: Broad-based expansion

0

10

20

30

40

50

60

Har

Pun

Bih

Ori

WB

Chh

at MP

UP

Guj

Mah

a

And

hra

Kar

TN

All

13

No. of acc. Credit outstanding

SBI+: Direct credit (%CAGR FY04-FY08)

05

10152025303540

Har

Pun

Bih

Ori

WB

Chh

at MP

UP

Guj

Mah

a

And

hra

Kar

TN

All

13

No. of acc. Credit outstanding

Nat Banks: Direct credit (%CAGR FY04-FY08)

Source: RBI, Centrum Research

Exhibit 30: RRBs: Uniform growth but less aggressive Exhibit 31: All SCBs: Stronger growth in Bihar, Orissa & WB, But Gujarat leads

05

1015202530354045

Har

Pun

Bih

Ori

WB

Chh

at MP

UP

Guj

Mah

a

And

hra

Kar

TN

All

13

No. of acc. Credit outstanding

RRBs: Direct credit (%CAGR FY04-FY08)

0

10

20

30

40

50

60

Har

Pun

Bih

Ori

WB

Chh

at MP

UP

Guj

Mah

a

And

hra

Kar

TN

All

13

All SCBsNo of A/c All SCBsOutstanding

All SCBs: Direct credit (%CAGR FY04-FY08)

Source: RBI, Centrum Research

We believe that NPA ratios are likely to rise to 10-15% in the coming years, somewhat lower than the peak of FY03. Given poor realisation rate in the 1990s (averaging at 60%) the bias on NPA ratio is on the higher side. The NPA levels may have been rising in H2FY10

14 Economy

Factors contributing to the fall in NPA ratio during FY03-10 are: (1) Changes in NPA norms; (2) restructuring of loans initiated after March 2004; (3) debt waiver scheme which proposed to write off or restructure a massive Rs716bn of bad loans; and (4) supportive food policies, including steep elevation in MSPs and aggressive government procurements.

Reasons why we think the trend could reverse:

– Loan restructuring on account of droughts and natural calamities in 2003-2004 resulted in the clubbing of overdue interest and principal outstanding. The amount was payable within 5 years with a moratorium of two years. The fact that termination of the restructuring scheme was followed by loan waiver scheme (implemented during FY09-11) suggests persistence of recovery problems notwithstanding the fall in NPA ratios.

– These programs were supplemented by new NPA norms, which allows banks longer NPA recognition period for direct credit to two cropping season for short-duration crops and one cropping season for long-duration crops. Removal of earlier restriction of “not exceeding two half years” has effectively extended the NPA recognition period beyond one year. Hence, the real impact of NPA will be seen with a lag.

– While the above two factors enabled scope for fresh loans for the farmers, the enormous policy thrust to grow direct credit seems to have compressed the NPA ratios.

– With a major portion of debt waiver scheme over by FY10 and debt restructuring program coming to an end, we believe the overdue ratio will see an upward trend.

– In our assessment, the repayment culture may have been weakened due to successive restructuring and loan waiver programs. Volatile cereal prices, concentration of lending in food-grain surplus states, fast pace of credit expansion in the past and liberal collateral requirements will increase in default rates.

– Structural decline in land holding have increased the proportion of small and marginal farmers (0-2 hectares) who have highly vulnerable to risk factors. Mid level farmers (2-5 hectares) do not enjoy strong banking relationship. Credit comfort is high for large farmer (>5 hectares) as they enjoy better banking relationship and are better equipped to withstand the risks. Given this structural backdrop and the fact that banks, being competed out of equipment financing, are increasing crop lending portfolio increase prospects of default risk.

Exhibit 33: Public sector banks: NPA ratio likely rise after a secular decline from FY03 peaks

0

10

20

30

40

50

60

PSU

Ban

ks

Nat

Ban

ks

Allh

a Bn

k

And

hra

Bnk

BoB

BoI

BoM

ah

Can

Bnk

Cen

t Bnk

Cor

p B

nk

Den

a Bn

k

Ind

Bnk

IOB

OBC

PNB

P&S

Bnk

Synd

Bnk

UC

O B

nk

Uni

on B

nk

Uni

ted

Bnk

Vija

ya B

nk

IDBI

Bnk

SBI g

rp SBI

SBBJ

SBH

yd

SBIn

d

SBM

ys

SBPa

t

SBSa

u

SBTr

av

NPA FY09% total cre Average: FY03-FY09 FY09 FY03-FY06

NPA ratio %: Trailing

Source: RBI and Centrum Research Estimates

In our assessment, the repaymentculture may have been weakened due to successive restructuring and loanwaiver programs. Volatile cereal prices,concentration of lending in food-grain surplus states, fast pace of creditexpansion in the past and liberalcollateral requirements will increase indefault rates

15 Economy

Private consumption: Price shocks hurting endogenous demand

In contrast to the consensus view that steep rise in food prices shocks would benefit the rural economy through positive income effect, and is a sign of rising overall private consumption, both historical data and near-term trends indicate otherwise. While the positive income effect on agri income is evident, it may not be broad based enough to compensate for the adverse price effect.

With CPI inflation rising to 16% (dominated by food prices, 14.9% for Mar 2010), aggregate consumption demand is impacted adversely. Long-term data (Exhibit 34) suggest that low inflation (PCE based) of 4-5% is most beneficial for consumption (+ve correlation during FY00-FY04). At higher inflation the impact on real consumption (and income) is adverse (-ve correlation).

CPI inflation has been rising since FY05. This period has also exhibited high inverse correlation (-78%) and elasticity between inflation and PCE growth. As a result, consumption spending growth fell dramatically since early 2009 from over 10% to sub 2%.

The period FY05-09 (Exhibit 46 and 47) exhibited high negative price elasticity for broad categories of food, clothing and furnishing. This suggests that with higher inflation demand conditions during FY10 may have worsened. Private consumption grew modestly at 3.5% in FY10 (till Dec 09). However, the positive elasticity for hotel & restaurants, education and miscellaneous goods (luxury and health care items), which together contribute 19% of PCE, imply that some segments are positively impacted even with rising inflation.

Long-term estimates show the uncompensated elasticity of demand for food (representing changes in the quantity demanded as a result of changes in prices, capturing both price and income effects) is negative for both rural and urban India.

Exhibit 36: Own-price elasticity of demand for major food groups in India Groups Rural Urban All-India

Uncompensated own-price elasticity

Cereals (0.50) (0.44) (0.48)

Pulses (0.77) (0.77) (0.77)

V&F (0.97) (0.98) (0.98)

Milk (0.73) (0.84) (0.78)

Edible oil (0.78) (0.81) (0.80)

Sugar (0.73) (0.72) (0.73)

MFE (2.38) (2.13) (2.26)

Compensated own-price elasticity

Cereals (0.46) (0.43) (0.45)

Pulses (0.74) (0.75) (0.75)

V&F (0.92) (0.93) (0.92)

Milk (0.62 ) (0.74) (0.68)

Edible oil (0.76) (0.79) (0.78)

Sugar (0.72 ) (0.70) (0.71)

MFE (-2.33) (2.09) (2.22)

Note: V&F=Vegetables and fruits; MFE= Meat, fish and eggs. The uncompensated elasticity of demand represents changes in the quantity demanded as a result of changes in prices, capturing both price effect and income effect. Compensated elasticity of demand refers to the portion of change in quantity demand, which captures only the price effect

Source: Structural Shift in Demand for Food: Projections for 2020, ICRIER, 2006

Negative elasticity for broad categories of food, clothing and furnishing during FY05-FY09, suggest that high inflation may have worsened demand conditions during FY10

Exhibit 34: High inflation adversely impact real private consumption expenditure (PCE)

Exhibit 35: CPI inflation and real private consumption expenditure (PCE, quarterly)

0

2

4

6

8

10

12

FY90

FY91

FY92

FY93

FY94

FY95

FY96

FY97

FY98

FY99

FY00

FY01

FY02

FY03

FY04

FY05

FY06

FY07

FY08

FY09

0

2

4

6

8

10

12

14

PCE real (% LHS) PCE inflation (% RHS)

High inflationCorrel = -70%

Lowering inflationCorrel = +62%

Low inflationCorrel = +74%

Rising inflationCorrel = -78%

New series (Base FY04)

Old series (Base FY00)

0

2

4

6

8

10

12

14

16

Jun-

00

Dec

-00

Jun-

01

Dec

-01

Jun-

02

Dec

-02

Jun-

03

Dec

-03

Jun-

04

Dec

-04

Jun-

05

Dec

-05

Jun-

06

Dec

-06

Jun-

07

Dec

-07

Jun-

08

Dec

-08

Jun-

09

Dec

-09

CPI (3m avg) Pvt consumption

(% YoY)

Source: RBI, Centrum Research Source: Bloomberg, CSO, Centrum Research

Estimates show the combinedresponse to rise food prices on demand, including both price and income effects is negative for both rural and urban India

16 Economy

Studies show that given nominal incomes, a 10% increase in cereal price reduces the money metric value of the rural bottom decile by 3.7% and that of the rural top decile by 1.1%; and of the urban bottom decile by 2.2% and urban top decile by 0.5%. There is clear evidence that an increase in cereal price would hurt the poor the most and aggravate income inequality.

If price effect on income is incorporated, the relative position of bottom deciles will be further worsened since the existing PDS only partially compensate the income loss experienced by the poor whereas incomes of non-poor are likely to be indexed for inflation and, moreover, some food producers and food traders may as well benefit from food inflation8.

Decline in cereal consumption and nutritional intake across rural & urban areas; trend more pronounced in rural areas

Exhibit 37: Growth in food and cereal expenditure at 1993-94 prices and per capita calorie intake

**Includes NSS 55th round; Source: Ravi (2007)

Diverging consumption trend indicate inequality The spectacular growth in consumer durable and vehicle sales during FY10 (which together contributes about 5% of PCE) and the contrasting decline in non-durables suggest expenditure inequality. NSSO data indicate that only the top 10 fractile consumers have high propensity for consumer durables in both rural and urban areas (Exhibits 40 and 41).

Exhibit 38: Spurt in consumer durable growth Exhibit 39: Slowing growth in non-durables

Consumer durable: 5.4% of IIP

(20)(10)

01020304050

Feb

-95

Feb

-96

Feb

-97

Feb

-98

Feb

-99

Feb

-00

Feb

-01

Feb

-02

Feb

-03

Feb

-04

Feb

-05

Feb

-06

Feb

-07

Feb

-08

Feb

-09

Feb

-10

(YoY %)

Consumer non-durable: 23.2% of IIP

(15)(10)

(5)05

1015202530

Feb

-95

Feb

-96

Feb

-97

Feb

-98

Feb

-99

Feb

-00

Feb

-01

Feb

-02

Feb

-03

Feb

-04

Feb

-05

Feb

-06

Feb

-07

Feb

-08

Feb

-09

Feb

-10

(YoY %)

Source: CSO, Centrum Research Source: CSO, Centrum Research

Exhibit 40: Top 10% percentile spends on durables Exhibit 41: High allocation to food at lower percentile

0

1

2

3

4

5

6

7

8

9

0 – 10 10 – 20 20 – 30 30 – 40 40 – 50 50 – 60 60 – 70 70 – 80 80 – 90 90 – 100

Rural Urabn

Durables/Total expenditure

Fractile of monthly private consumption expenditure

Low High

20

25

30

35

40

45

50

55

60

65

0 – 10 10 – 20 20 – 30 30 – 40 40 – 50 50 – 60 60 – 70 70 – 80 80 – 90 90 – 100

Rural Urabn (rhs)

Food/Total expenditure (%)

Fractile of monthly private consumption expenditure

Low High

Includes furniture and fixtures, good of recreation ,transport vehicles, personal goods & residential building related items Source: NSS 64th Round 07/2007-07/2008, Centrum Research

Source: NSS 64th Round 07/2007-07/2008, Centrum Research

8 Radhakrishana (2008): Economic Well Being and Deprivations in India

(% per ann) Bottom Middle Top All (% per ann) Bottom Middle Top All

30% 40% 30% classes 30% 40% 30% classes Rural Urban Per capita cereal expenditure 1970-1980 0.1 (0.5) (1.2) (0.6) 1970-1980 0.1 (0.3) (0.1) (0.1) 1990-2005 (1.3) (1.9) (1.6) (1.6) 1990-2005 (0.9) (0.9) (0.6) (0.8) 1970-2005 (0.6) (1.2) (1.6) (1.2) 1970-2005 (0.4) (0.6) (0.4) (0.5) Per capita food expenditure 1970-1980 1.2 0.9 0.7 0.8 1970-1980 1.2 1.1 0.4 1.0 1990-2005 - (0.2) - (0.1) 1990-2005 0.4 0.3 0.3 0.3 1970-2005* 0.6 0.3 0.1 0.3 1970-2005 0.8 0.8 0.5 0.7 Per capita calorie intake 1970-1990 0.2 0.2 0.4 0.2 1970-1990 0.3 0.1 0.3 0.2 1990-1998 (1.0) (1.6) (1.8) (1.5) 1990-1998 (0.6) (0.7) (0.1) (0.4) 1990-2000** (0.4) (0.9) (1.4) (1.4) 1990-2000* 0.1 0.1 0.2 0.1

17 Economy

Evidence across the world indicates that food shocks induce more vulnerability among the urban poor vs rural poor. However, rural poor, landless, and net buyers are in no better position9. NSSO data corroborates this as the bottom 20 factile consumer spends heavily on food and urban poor spends as high as rural poor (Exhibit 41). A majority of about ¾ of the rural population is net buyers of food grains and they are the small & marginal farmers, agricultural labourers and other casual labourers.

Structural change rural consumption

The increasing share of spending on education, rentals and medical cares possibly reflects improvement in living standard. Share of food essentials like cereal, milk & milk products and edible oil has declined. While these may suggest presence of Engle’s Law10, the reduction in calories intake hints at vulnerability (Exhibit 37). The worrisome part is the decline in per capita calorie intake (1600-1700 kcal/day) of the bottom 30% which falls short of FAO’s minimum norm of 1800 calorie for India.

Overall spending pattern indicates:

Declining share of food consumption vs non-food. Declining share of cereals, pulses, milk & milk products, edible oil and pulses.

– This is a structural trend and attributable to changes in consumer tastes and preference from food to non-food items, and within the food group from cereals to non-cereal food items and from coarse to fine cereals.

– Research11 shows that the decline in cereal consumption has been greater in the rural areas, where improvement in rural infrastructure made other food and non-food items available. It has also been contributed by reduction in manual work in agriculture due to farm mechanization and expansion of informal services sector.

Significant rise in share of miscellaneous expenditure including education, medical care, rents and taxes. Surge in allocation for beverages.

Exhibit 42: Declining share of food consumption (%) Exhibit 43: Steady decline in cereal budget

0

20

40

60

80

100

FY88 FY94 FY00 FY05 FY06 FY07 FY08

Food items Non-food items

Cereals/Total expenditure %

0

5

10

15

20

25

30

FY88 FY94 FY00 FY05 FY06 FY07 FY08

Source: NSSO various rounds, Centrum Research Source: NSSO various rounds, Centrum Research

Exhibit 44: Non-cereal food consumption expenditure Exhibit 45: Non-food expenditure: Major items

0

2

4

6

8

10

FY88 FY94 FY00 FY05 FY06 FY07 FY08

Milk & milk pdts Vegetables Edible oil

Pulses Beverages Fruits

% of total expenditure

0

5

10

15

20

25

30

FY88 FY94 FY00 FY05 FY06 FY07 FY08

Misc. goods & ser* Fuel & light Clothing Durable

% of total expenditure

Source: NSSO various rounds, Centrum Research *Includes education, medical care, rents and taxes Source: NSSO various rounds, Centrum Research

9 Ruel, Garrett, Hawkes and Cohen (Jan 2010): The Food, Fuel, and Financial Crises Affect the Urban and Rural Poor Disproportionately: A Review of the Evidence 10 Ernst Engel (1857): The proportion of expenditure spent on food decreases with income, assuming unchanged prices 11 Rao Hanumanta. (2000), “Declining Demand for Foodgrains in Rural India: Causes and Implications”

While structural changes in rural consumption patter suggests presence of Engle’s Law , the reduction in calories intake hints at vulnerability

18 Economy

Exhibit 46: High inverse correlation across consumption items (FY06-FY09)

Real PCE by item (domestic market) FY06 FY07 FY08 FY09 FY06 FY07 FY08 FY09 Correl (%)Price

elasticity

1 Food, beverages & tobacco 7.5 3.8 7.2 2.7 3.9 7.1 5.4 7.5 -95 -1.4 1.1 food 6.6 1.5 6.6 0.3 3.9 7.5 5.2 7.3 -93 -1.8

1.1.1 cereals & bread 6.9 2.5 3.5 1.7 4.3 10.0 6.1 6.3 -64 -0.6 1.1.2 pulses 4.6 4.1 8.4 -0.7 15.9 21.6 -0.3 5.1 -15 -0.1 1.1.3 sugar & gur 2.1 4.5 13.1 -36.0 9.2 0.4 -11.8 11.8 -74 -1.5 1.1.4 oils & oilseeds -4.2 -10.8 5.9 10.2 -7.1 5.1 19.9 10.2 59 0.5 1.1.5 fruits & vegetables 9.8 2.0 12.6 2.2 6.0 3.2 3.0 10.0 -44 -0.7 1.1.6 potato & other tubers -0.7 -6.5 25.1 4.8 17.1 14.7 10.0 -11.6 -16 -0.2 1.1.7 milk & milk products 6.8 2.0 1.5 1.4 0.4 6.3 8.4 7.6 -99 -0.7 1.1.8 meat, egg & fish 4.3 5.8 5.6 6.5 6.0 8.1 6.2 3.2 -39 -0.2 1.1.9 coffee, tea & cocoa -1.2 -1.9 1.7 -1.5 -0.2 2.7 -11.9 2.4 -99 -0.2 1.1.10 spices & other food 18.5 -3.3 1.2 -6.7 -2.7 18.1 5.1 10.0 -84 -1.1

1.2 beverages, pan & intoxicants 12.8 19.2 23.3 16.2 3.7 5.9 4.3 7.1 1 0.0 1.3 tobacco & its products 8.0 1.9 -8.2 2.3 2.6 3.9 11.2 12.4 -64 -0.9 1.4 hotel & restaurants 17.5 25.8 12.9 17.5 4.9 5.9 4.5 7.9 27 1.0

2 Clothing & footwear 24.0 23.2 8.1 -0.6 -4.8 1.5 -0.4 5.9 -74 -2.0 2.1 clothing 25.5 23.9 4.1 0.9 -6.8 2.6 -1.6 7.1 -56 -1.2 2.2 footwear 15.8 18.6 33.8 -8.1 7.1 -4.4 4.2 1.1 17 0.6

3 Gross rent, fuel & power 3.4 4.0 4.8 3.4 6.8 8.1 9.0 12.8 -18 0.0 3.1 gross rent & water charges 3.1 3.0 3.4 3.5 7.0 9.3 13.1 18.2 92 0.0 3.2 fuel & power 4.2 6.0 7.5 3.2 6.5 5.8 1.0 0.9 -11 -0.1

4Furniture, furnishings, appliances & services 14.1 15.9 14.6 3.7 3.1 5.4 4.2 5.4 -43 -2.2 4.1 furniture, furnishings & household equipment etc. 15.6 17.5 15.9 3.2 3.0 5.1 3.8 4.8 -28 -1.9 4.2 services 6.9 7.0 7.1 7.2 4.1 7.0 6.6 9.2 95 0.1

5 Medical care & health services 5.8 4.5 2.5 8.1 4.1 5.4 7.5 1.9 -100 -1.0 6 Transport & communication 5.0 7.6 8.8 12.3 5.3 6.1 0.5 3.8 -37 -0.5

6.1 personal transport equipment -11.2 15.5 5.7 2.0 3.3 1.7 2.5 5.1 -50 -3.8 6.2 operation of personal transport equipment 4.9 4.8 5.1 6.6 10.3 8.9 -1.7 7.8 2 0.0 6.3 purchase of transport services 5.8 6.2 5.3 6.0 3.5 6.3 4.1 7.2 70 0.1 6.4 communication 19.4 18.1 39.1 55.0 -0.9 -0.2 -5.8 -9.0 -100 -4.2

7 Recreation, education & cultural services 8.9 7.0 13.2 5.4 3.1 5.5 4.5 7.5 -62 -1.2 7.1 education 5.4 2.7 10.5 7.3 4.2 7.0 6.4 9.2 13 0.2 7.2 others 14.6 13.5 17.0 2.9 1.5 3.6 2.0 4.6 -85 -3.7

8 Miscellaneous goods & services 15.9 21.2 25.6 19.3 -2.6 4.8 3.3 8.2 41 0.4 9 Private final consumption expenditure 8.6 8.3 9.6 6.8 3.2 6.0 4.1 7.0 -77 -0.5

Real PCE (% YoY) PCE inflation (% YoY)

Source: CSO, Centrum Research

Exhibit 47: High allocation to food but real allocation falling; Real Misc. spending rising faster than nominal

PCE by item (domestic market) FY05 FY06 FY07 FY08 FY09 FY05 FY06 FY07 FY08 FY09 1 Food, beverages & tobacco 39.6 39.2 37.6 36.8 35.3 39.6 39.5 38.3 37.9 36.6

1.1 food 33.5 32.9 30.8 30.0 28.1 33.5 33.1 31.5 31.0 29.2 1.1.1 cereals & bread 8.4 8.2 7.8 7.4 7.0 8.4 8.3 8.2 7.9 7.5 1.1.2 pulses 0.8 0.8 0.7 0.7 0.7 0.8 0.9 1.0 0.9 0.8 1.1.3 sugar & gur 1.7 1.6 1.5 1.6 0.9 1.7 1.7 1.5 1.3 0.8 1.1.4 oils & oilseeds 2.0 1.7 1.4 1.4 1.4 2.0 1.6 1.3 1.4 1.5 1.1.5 fruits & vegetables 7.9 8.0 7.5 7.7 7.4 7.9 8.2 7.5 7.7 7.5 1.1.6 potato & other tubers 0.9 0.8 0.7 0.8 0.8 0.9 0.9 0.9 1.1 0.9 1.1.7 milk & milk products 6.9 6.8 6.4 5.9 5.6 6.9 6.6 6.2 6.0 5.7 1.1.8 meat, egg & fish 3.2 3.1 3.0 2.9 2.9 3.2 3.2 3.2 3.1 3.0 1.1.9 coffee, tea & cocoa 0.3 0.3 0.3 0.3 0.2 0.3 0.3 0.3 0.2 0.2 1.1.10 spices & other food 1.4 1.5 1.4 1.3 1.1 1.4 1.5 1.5 1.4 1.2

1.2 beverages, pan & intoxicants 1.9 1.9 2.1 2.4 2.6 1.9 1.9 2.1 2.4 2.6 1.3 tobacco & its products 2.2 2.1 2.0 1.7 1.6 2.2 2.1 2.0 1.8 1.8 1.4 hotel & restaurants 2.1 2.3 2.6 2.7 3.0 2.1 2.3 2.7 2.8 3.1

2 Clothing & footwear 6.6 7.6 8.6 8.5 7.9 6.6 7.0 7.6 7.2 6.6 2.1 clothing 5.6 6.5 7.4 7.1 6.7 5.6 5.9 6.5 5.8 5.5 2.2 footwear 1.0 1.1 1.2 1.4 1.2 1.0 1.1 1.1 1.3 1.1

3 Gross rent, fuel & power 13.0 12.4 11.9 11.4 11.0 13.0 12.8 12.6 12.6 12.9 3.1 gross rent & water charges 8.7 8.3 7.9 7.4 7.2 8.7 8.6 8.4 8.6 9.2 3.2 fuel & power 4.3 4.1 4.1 4.0 3.8 4.3 4.3 4.2 4.0 3.6

4Furniture, furnishings, appliances & services 3.4 3.5 3.8 4.0 3.9 3.4 3.5 3.8 3.9 3.8 4.1 furniture, furnishings & household equipment etc. 2.8 3.0 3.2 3.4 3.3 2.8 3.0 3.2 3.4 3.2 4.2 services 0.6 0.6 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6

5 Medical care & health services 5.0 4.8 4.7 4.4 4.4 5.0 4.9 4.7 4.5 4.4 6 Transport & communication 19.6 19.0 18.9 18.7 19.7 19.6 19.4 19.3 18.5 18.8

6.1 personal transport equipment 1.9 1.5 1.6 1.6 1.5 1.9 1.5 1.6 1.5 1.4 6.2 operation of personal transport equipment 6.5 6.3 6.1 5.9 5.8 6.5 6.7 6.7 6.1 6.1 6.3 purchase of transport services 9.6 9.4 9.2 8.8 8.8 9.6 9.4 9.3 8.9 8.9 6.4 communication 1.6 1.8 1.9 2.5 3.6 1.6 1.7 1.8 2.0 2.5

7 Recreation, education & cultural services 3.4 3.4 3.4 3.5 3.4 3.4 3.4 3.3 3.5 3.4 7.1 education 2.1 2.1 1.9 2.0 2.0 2.1 2.1 2.0 2.0 2.1 7.2 others 1.3 1.3 1.4 1.5 1.5 1.3 1.3 1.4 1.4 1.3

8 Miscellaneous goods & services 9.4 10.0 11.2 12.8 14.4 9.4 9.5 10.5 11.9 13.5 9 Private final consumption expenditure 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Private final consumption expenditure (Rs bn) 19269 20916 22657 24834 26518 19269 21583 24772 28254 32268

Real PCE (% Share) Nominal PCE (% Share)

Source: CSO, Centrum Research

19 Economy

Manufacturing sector — Next round of inflationary pressure

The key factors driving up manufacturing sector inflation are:

The feedback impact of high CPI inflation over the past year averaging at 12% and adverse terms of trade vs the agriculture sector. As mentioned earlier, the normalization process provides a structural upside bias for manufacturing sector price index.

Pass through of increased production costs: Several sectors are experiencing raw material contracts renewed at higher levels in response to rise in commodity prices.

Positive output gap: Spurt in IIP growth of 16% against the long term average of 6.7% (30 year average).

The increase in excise duty (200bp increase announced in the budget), increase in railway freight charges and hike in fuel prices (following the budget announcement). Possibility of further fuel price hikes as oil sector under recovery is estimated at Rs900bn for FY11.

Recent developments

Steel prices expected to increase 15-20%: Steel prices are expected to increase 15-20% as we move further into FY11 on the back of rising raw material cost. With the gap between spot iron ore and contracted prices widening, contracts for FY11 would be 50% higher. Coking coal spot prices coal prices are also expected to rise further, taking the cumulative rise to 35% from their Jan 2010 levels. Aluminum prices are also slated to go up on similar grounds.

Cascading impact on other sectors: The increase in steel and aluminium prices is also having a similar impact on other sectors. For instance, we expect auto makers to hike prices in FY11 as margins get squeezed. For auto ancillaries and two wheelers, margins have peaked.

Cross elasticity impact: Sectors exposed to high competition (for example, premium brands in the FMCG sector) whose demand is also exposed to cross prices elasticity impact of elevated food prices are facing intense pricing pressure.

Fuel prices: Indications that under-recovery for oil companies could be higher at Rs900bn (FY11) is increasing the possibility of further hikes in fuel prices.

Taking all these factors into account, we believe manufacturing sector inflation would rise to over 11% from the current level of 7.5%, notwithstanding the correction in food prices. However, a lot will depend also on global commodity price outlook. Sustained hardening of crude and metal prices can aggravate the scenario. But we are of the view that commodity prices can undergo a correction in H2FY11 in response to expected slowdown in economic growth, excess capacities worldwide and significant inventory build-up globally, especially in China. Overall WPI inflation is likely to peak at 12-13% around by mid FY11.

Exhibit 48: Iron ore contract price likely to go up 50% Exhibit 49: Coking coal spot prices rise by 35% YTD

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2004 2005 2006 2007 2008 2009 2010

Avg spot price Long term contract price

(INR/Tn)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Jan 10 Feb 10 Mar 10 Apr 10E

Coking coal (Australia FoB, INR/Tn)

up 35%

Source: SSB, Centrum Research Source: SSB, Centrum Research

Key factor driving up the manufacturing sector inflation is the intensifying margin pressure and the need to pass on increase production costs, at least partially. On top of these other factors impacting inflation will be increase in excise duty, increase in railway freight charges and hike in fuel prices

20 Economy

Exhibit 50: Steep prices can potentially rise 15-20% in response to higher raw material cost

Exhibit 51: Margin pressure and escalating cost to see price actions

-

10,000

20,000

30,000

40,000

50,000

60,000

Dec

-03

Jun-

04

Dec

-04

Jun-

05

Dec

-05

Jun-

06

Dec

-06

Jun-

07

Dec

-07

Jun-

08

Dec

-08

Jun-

09

Dec

-09

Jun-

10

Flat HRC price Scrap price

(INR/Tn)

Expected to increase 25% over Mar 10

2468

101214161820

Jun-

05

Oct

-05

Feb

-06

Jun-

06

Oct

-06

Feb

-07

Jun-

07

Oct

-07

Feb

-08

Jun-

08

Oct

-08

Feb

-09

Jun-

09

Oct

-09

Comm Veh Passenger cars Tyres & Tubes 2&3Wheelers Automobile Ancil

Auto sector margins are on a downhill(

PBDIT Margin

Source: SSB, Centrum Research Source: CMIE, Centrum Research

Exhibit 52: Rising commodity prices Exhibit 53: …Will induce further margins stress

80

130

180

230

280

330

Jan-

08

Mar

-08

May

-08

Jul-0

8

Sep

-08

Nov

-08

Jan-

09

Mar

-09

May

-09

Jul-0

9

Sep

-09

Nov

-09

Jan-

10

Mar

-10

Brent Aluminium Copper

Index as on Feb 09

46

48

50

52

54

56

Mar

-05

Jul-0

5

Nov

-05

Mar

-06

Jul-0

6

Nov

-06

Mar

-07

Jul-0

7

Nov

-07

Mar

-08

Jul-0

8

Nov

-08

Mar

-09

Jul-0

9

Nov

-09

(%)

20

22

24

26

28

30(%)

RM/Net sales (%) PBDIT/Total income (% RHS)

Source: Bloomberg , Centrum Research Source: Bloomberg, Centrum Research

Exhibit 54: Spill-over impact of fuel price hike will be visible after a lag; further hikes in petro product prices expected

Exhibit 55: Manufacturing sector inflation will rise to over 11%

(15)(10)

(5)-

5101520

Mar

-03

Sep

-03

Mar

-04

Sep

-04

Mar

-05

Sep

-05

Mar

-06

Sep

-06

Mar

-07

Sep

-07

Mar

-08

Sep

-08

Mar

-09

Sep

-09

Mar

-10

(% YoY) Fuel inflation: Monthly average

0

2

4

6

8

10

12

14

Mar

-03

Sep

-03

Mar

-04

Sep

-04

Mar

-05

Sep

-05

Mar

-06

Sep

-06

Mar

-07

Sep

-07

Mar

-08

Sep

-08

Mar

-09

Sep

-09

Mar

-10

(% YoY) Manufactured products inflation

Expected to peak at

Source: Bloomberg, CMIE, , Centrum Research Source: Bloomberg, Centrum Research

21 Economy

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22 Economy

T. S. Baskaran Managing Director & CEO ts.baskaran@centrum.co.in 91-22-4215 9620/87 Research Girish Pai Head–Equity Research Strategy & IT Services girish.pai@centrum.co.in 91-22-4215 9699 Dhananjay Sinha Economist Economy & Strategy dhananjay.sinha@centrum.co.in 91-22-4215 9619

Abhishek Anand Analyst Telecom, Education a.anand@centrum.co.in 91-22-4215 9853

Adhidev Chattopadhyay Analyst Real Estate adhidev@centrum.co.in 91-22-4215 9632

Ankit Kedia Analyst Media, FMCG ankit.kedia@centrum.co.in 91-22-4215 9634

Ajay Shethiya Analyst Automobiles/Auto Ancillaries ajay.shethiya@centrum.co.in 91-22-4215 9855

Madanagopal R Analyst Power, Capital Goods r.madanagopal@centrum.co.in 91-22-4215 9684

Manish Kayal Analyst Infrastructure manish.kayal@centrum.co.in 91-22-4215 9313

Pranshu Mittal Analyst Sugar & Retail p.mittal@centrum.co.in 91-22-4215 9854

Rajan Kumar Analyst Cement rajan.kumar@centrum.co.in 91-22-4215 9640

Rohit Ahuja Analyst Oil & Gas rohit.ahuja@centrum.co.in 91-22-4215 9636

Siddhartha Khemka Analyst Logistics, Shipping siddhartha.khemka@centrum.co.in 91-22-4215 9857

Sriram Rathi Analyst Pharmaceuticals s.rathi@centrum.co.in 91-22-4215 9643

Abhishek Kumar Associate IT Services abhishekkumar@centrum.co.in 91 224215 9644

Janhavi Prabhu Associate Sugar, Retail janhavi.prabhu@centrum.co.in 91-22-4215 9864

Jatin Damania Associate Metals & Mining, Pipes jatin.damania@centrum.co.in 91-22-4215 9647

Komal Taparia Associate Economy & Strategy komal.taparia@centrum.co.in 91-22-4215 9195

Rahul Gaggar Associate Hotels, Healthcare rahul.gaggar@centrum.co.in 91-22-4215 9683

Rishabh Saraogi Associate Oil & Gas rishabh.saraogi@centrum.co.in 91-22-4215 9927

Sarika Dumbre Associate Telecom sarika.dumbre@centrum.co.in 91-22-4215 9194

Shweta Mane Associate Banking & Financial Services shweta.mane@centrum.co.in 91-22-4215 9928

Vijay Nara Associate Automobiles/Auto Ancillaries vijay.nara@centrum.co.in 91-22-4215 9641

Vishal Desai Associate IT Services vishal.desai@centrum.co.in 91-22-4215 9930 Sales V. Krishnan +91-22-4215 9658 v.krishnan@centrum.co.in +91 98216 23870

Ashvin Patil +91-22-4215 9866 ashvin.patil@centrum.co.in +91 98338 92012

Rajagopal Ramanathan +91-22-4215 9675 rajagopal.ramanathan@centrum.co.in +91 98193 99031

Siddharth Batra +91-22-4215 9863 s.batra@centrum.co.in +91 99202 63525

Centrum Securities (Europe) Ltd., UK

Dan Harwood CEO +44-7830-134859 dan.harwood@centrum.co.in

Michael Orme Global Strategist +44 (0) 775 145 2198 michael.orme@centrum.co.in

Centrum Securities LLC, USA

Melrick D’Souza +1-646-701-4465 melrick.dsouza@centrumsecurities.com

Key to Centrum Investment Rankings

Buy: Expected outperform Nifty by>15%, Accumulate: Expected to outperform Nifty by +5 to 15%, Hold: Expected to outperform Nifty by -5% to +5%, Reduce: Expected to underperform Nifty by 5 to 15%, Sell: Expected to underperform Nifty

by>15%

Centrum Broking Private Limited

Member (NSE, BSE, MCX-SX), Depository Participant (CDSL) and SEBI registered Portfolio Manager

Regn Nos

CAPITAL MARKET SEBI REGN. NO.: BSE: INB 011251130, NSE: INB231251134 DERIVATIVES SEBI REGN. NO.: NSE: INF 231251134 (TRADING & SELF CLEARING MEMBER)

CDSL DP ID: 12200. SEBI REGISTRATION NO.: IN-DP-CDSL-20-99 PMS REGISTRATION NO.: INP000000456

MCX – SX (Currency Derivative segment) REGN. NO.: INE 261251134

Website: www.centrum.co.in Investor Grievance Email ID: investor.grievances@centrum.co.in

REGD. OFFICE Address Bombay Mutual Bldg.,2nd Floor, Dr. D. N. Road, Fort,

Mumbai - 400 001

Correspondence Address Centrum House, 6th Floor, CST Road, Near Vidya Nagari Marg,

Kalina, Santacruz (E), Mumbai 400 098. Tel: (022) 4215 9000