15 September 2016
Economy
Diving into Trending Themes
How does monsoon impact Indian economy?
2016 turning out to be third consecutive year of below-normal monsoon
As against expectations of an above-normal monsoon season, 2016 south-west (SW) monsoon has turned from
a surplus of 3% of long-period average (LPA) a month ago, to a deficit of 5% now. With only two weeks
remaining in the current monsoon season, 2016 could be the third consecutive year of below-normal monsoon.
Below-normal monsoon in 2016 will lead to lowering of real GVA growth projections for FY17 by 35-40bp.
Although the correlation between monsoon and inflation is difficult to establish, worse-than-expected
monsoon may reduce market expectations of sharper rate cuts. Further, while rural wages are more correlated
with fiscal policies than rainfall, weak monsoon could act as dampener for rural sector-related stocks.
Moreover, excess rainfall in Rajasthan and Madhya Pradesh, and deficit rainfall in Gujarat and Uttar Pradesh
may impact production of coarse cereals, pulses, oilseeds and cotton.
Finally, although area sown under pulses has increased dramatically in 2016, it is important to note that two-
thirds of production occurs in the Rabi season, for which good levels of water reservoirs are needed. The third
consecutive year of below-normal monsoon does not give much confidence.
is Motilal Oswal’s new
product, in which we deep-dive into trending
macroeconomic themes. It complements our
existing “Ecoscope” product, which is reserved for
regular updates on macroeconomics.
“EcoKnowLedge”
SW monsoon from 3% surplus a month ago to 5% deficit
now:
2016 SW monsoon (June-September period) saw a
Exhibit 1: SW monsoon has faltered since mid-August
weak start in June (11% deficit), but picked up
Weekly
Cumulative
(% of LPA)
impressively in July to wipe out the entire deficiency. As
40
per the
Indian Meteorological Department (IMD),
20
cumulative rainfall for the first half (June-July) of the
0
monsoon season was normal with no departure from
long-period average (LPA). However, the performance
-20
has deteriorated significantly since mid-August – with a
-40
cumulative deficit of ~22% in past five weeks, SW rainfall
has turned from a surplus of 3% of LPA a month ago to a
deficit of 5% as of September 14, 2016
(Exhibit 1).
IMD and Skymet forecasts:
The IMD, in its
second long-
range forecast (LRF) released on August 1, 2016,
maintained its
first LRF
of SW rainfall to be 106% of LPA
in the country as a whole. The IMD has not released any
forecast update since then, but Skymet, the other
weather forecasting agency, lowered its forecasts from
109% of LPA
to
100% in mid-August 2016.
40
20
0
-20
-40
With a cumulative deficit of ~22% in
past five weeks, SW rainfall has
turned from a surplus of 3% of LPA a
month ago to a deficit of 5% as of
September 14, 2016
Source: Indian Meteorological Department (IMD), MoSL
Latest data is for the week ended September 14, 2016
Nikhil Gupta
(Nikhil.Gupta@MotilalOswal.com); +91 22 3982 5405
Investors are advised to refer through important disclosures made at the last page of the Research Report.
Motilal Oswal research is available on www.motilaloswal.com/Institutional-Equities, Bloomberg, Thomson Reuters, Factset and S&P Capital.

In this note, we discuss in detail the impact of monsoon
on domestic economic activity from various
perspectives.
1. Impact of monsoon on national gross value added
(GVA):
We find that SW monsoon is directly and very
positively correlated with agricultural output and
thus GVA.
2. Linkages of monsoon and food inflation:
Although
highly correlated with GVA, monsoon’s relationship
with food inflation is very weak. We cross-check our
findings by dividing the performance of the Indian
economy over the past two decades based on
monsoon development.
3. State-wise dependence on monsoon:
Of the eight
most agriculture-oriented Indian states, three are
experiencing deficient (<20% of LPA) monsoon, while
two are witnessing excess (>20% of LPA) rainfall,
thereby putting the production of coarse cereals,
pulses and oilseeds at risk.
4. How has been the 2016 sowing season?
With better
monsoon in 2016 than last year, area sown under
several crops in 2016 too has increased. Pulses, in
particular, have seen 33% more area sown versus
last year. We complete this section by looking at the
trends in yield – the other side of the coin - of these
crops.
5. Level of water reservoirs:
Water reservoir level this
year has been 97% of the average in the last 10
years, much better than 81% in the corresponding
period last year. This will help ensure good
agriculture in the Rabi season too.
Key implications for the markets:
1.
With monsoon at 5% deficit as of yesterday, it
implies agricultural output growth of ~3% in FY17.
This will wipe off 35-40bp from real GVA growth
expectations, which may be revised down from 7.7%
to ~7.3%. (We will wait till the end of monsoon
season to revise our growth forecasts).
2.
We argue in this note that it is difficult to establish
direct relationship between food inflation and
monsoon. However, the markets’ perceive better
monsoon to help inflation ease faster and thus open
the window to cut rates sharply. Weak monsoon will
dampen such expectations.
3.
As we had argued in
our detailed note in June 2016,
the rural sector is
to
expected
remain
under
prolonged stress.
With
weaker-
than-expected
monsoon,
our
argument
strengthens
further, and thus
we could see
pressure on rural
sector-related
equity stocks.
4.
Weaker-than-expected monsoon for the straight
third year may keep cotton prices higher (or may
push even higher) and thus hurt spinning companies.
5.
Notably, only one-third of pulses production occurs
in the ongoing Kharif season, while the remaining
happens in the Rabi season. Below-normal monsoon
may keep pulses prices high for longer.
6.
Weaker-than-expected monsoon will increase
pressure on the government to support the stressed
rural sector. This may imply compromise on the
government’s ability to increase capital spending.
15 September 2016
2

Impact of monsoon on national GVA
Accordingly, we assume real agriculture output growth
of ~6% in FY17, as against meager growth of 1.2% in
FY16 and a decline of 0.2% in FY15. Thus, the agriculture
sector will add almost 90bp to real GVA this year, as
against 20bp last year. Hence, the biggest delta in
economic growth would come from the agriculture
economy, excluding which we expect real GVA to grow
The two exhibits below show relationship between 8% in FY17, lower than 8.3% in FY16.
monsoon and agricultural output in the Indian economy.
Exhibit 2
shows a strong positive correlation between SW
…but lower rainfall could wipe off 35-40bp from GVA
monsoon and real agricultural output.
Exhibit 3
shows
growth
that irrespective of the extent of SW monsoon surplus, On the other hand, years with a rainfall deficit of up to
real agricultural output has always been better than LPA 5% of LPA (eight years since 1980) have witnessed
of 3.4% in the years of surplus monsoon.
average growth in agricultural output of 2.6% per annum
– less than half of our FY17 estimate. The most recent
A look at historical data suggests that since the 1980s, year with such rainfall deficit (1.7% of LPA) was 2008-09,
agriculture output grew at an average of 8.4% in the nine when real agriculture output grew 0.1% YoY.
years associated with surplus SW monsoon. Weak
agricultural output in last two years should also support
higher growth in real agricultural output due to
If monsoon forecasts are revised down
favourable base effect, provided the economy witnesses
meaningfully, lower agricultural
surplus monsoon this year.
output could shave off 35-40bp from
Surplus monsoon has historically boosted agriculture
and thus GVA growth…
The IMD, as per its latest update, expects 2016 SW
monsoon to be 106% of LPA. If true, it will be the best
monsoon season in more than two decades and certainly
help boost agriculture output, in our view.
GVA growth in FY17
Since the 1980s, agricultural output
grew at an average of 8.4% in the nine
years associated with surplus SW
monsoon
In other words, if monsoon forecasts are revised down
meaningfully (bringing down even marginally below
100% of LPA), lower agricultural output could shave off
35-40bp from GVA growth in FY17, taking it down from
our current expectation of 7.7% to ~7.3%, as against
7.2% growth in FY16.
Exhibit 3: Surplus SW monsoon has always resulted in very
high agriculture output growth
Exhibit 2: Real agriculture output is positively correlated
with SW monsoon
Source: IMD, Central Statistics Office (CSO), MOSL
The dots in
exhibit 3
represent years with surplus monsoon
15 September 2016
3

No clear relationship between monsoon
and food inflation
There are two interesting things to note: 1) the
coefficient was positive in both the models, as against
the conventional expectation of a negative coefficient,
and 2) the R-square was as low as 5%-10%, implying low
explanatory powers of monsoon, as far as food inflation
is concerned.
Another widely held belief is that good monsoon has
positive impact on food inflation (i.e. negative
relationship). The pair-wise correlation between the two
variables in the past 26 years (since 1990) is only 22%.
However, the correlation does not imply causation, and
thus further examination is required.
What then drives food inflation is a debate (we will
discuss this in another note). However, here, we convey
that rainfall is not as important a variable for food
The pair-wise correlation between the
inflation in India, as it is commonly perceived.
two variables in the past 26 years (since
1990) is only 22%; however, the
correlation does not imply causation
Exhibit 4
below analyses the relationship between
monsoon and retail food inflation based on annual data.
The weak relationship between the two – monsoon
(independent variable, x-axis) and retail food inflation
(the dependent variable, y-axis) – is apparent. We have
also given the linear and quadratic regression equation
between these two variables.
Two interesting things to note: 1) the
coefficient was positive in both the
models, as against the conventional
expectation of a negative coefficient,
and 2) the R-square was as low as 5%-
10%
Exhibit 4: No clear correlation between monsoon and food inflation
Source: CSO, IMD, MoSL
Red and blue dots represent 2014 (FY15) and 2015 (FY16) data respectively
15 September 2016
4

How does monsoon affect (real) rural
wages?
Exhibit 6
compares same macroeconomic indicators on
annual basis. Irrespective of monsoon development, real
rural wages were almost flat in the entire decade up to
The development of SW monsoon is also generally FY10. From April 2010, however, it picked up and
correlated with the rural sector. One of the high- averaged 8% YoY per month for the next three years
frequency key data to gauge the situation in the rural (FY11-FY13). Notably, India received rainfall of about
sector is monthly wage data on various agricultural and 102% of LPA in FY11 and FY12.
non-agricultural jobs.
Exhibits 5-6
below compare
monsoon development with real rural wage growth.
Overall, although monsoon is expected to impact rural
wages directly, historical data do not support this thesis.
Major growth in rural wages occurred
We believe rural wages are highly correlated with
in FY11 and FY12, when India witnessed
government revenue spending rather than monsoon.
back-to-back surplus (by ~2% of LPA in
each year) monsoon
Exhibit 5
compares rainfall development with real rural
wages. As is apparent from the exhibit, there is no clear
relationship between these two indicators. We use 12-
month moving average to smoothen long time-series
data. Interestingly, major growth in rural wages occurred
in FY11 and FY12, when India witnessed back-to-back
surplus (by ~2% of LPA in each year) monsoon.
Although monsoon is expected to
impact rural wages directly, historical
data do not support this thesis. We
believe rural wages are highly
correlated with government revenue
spending rather than monsoon
Exhibit 5: Strong relation between monsoon, food grain
production and thus agricultural output…
Exhibit 6: …but not-so-strong correlation between monsoon
and inflation
Source: Labour Bureau, IMD, MOSL
Rural wages are daily wages for agricultural and non-agricultural jobs for men, deflated by CPI for rural workers
15 September 2016
5

The tale of six episodes over the past
two decades
In order to understand the impact of monsoon on
various macroeconomic parameters, we have created
Exhibit 7
below. We classify the past two decades into six
periods based on monsoon developments. Notably,
there was average rainfall deficit of 4.3% for the entire
two-decade period, with 12 years of deficit and 8 years
of surplus monsoon. Notably, agriculture output growth
has averaged ~3% over this period.
Better monsoon implies higher GVA…:
As expected,
surplus monsoon periods (FY97-99, FY06-08 and FY11-
14) are associated with higher food grains production,
which implies better growth in agriculture output
(Exhibit 8 on next page).
Since agriculture output is
directly included into GVA, higher growth in the former
pushes the latter higher.
…but inflation impact not clear:
Nevertheless, as
discussed above, the relationship between monsoon and
inflation is not as clear
(Exhibit 9).
During the first half of
the first decade of the 21
st
century, while monsoon gods
were unhappy, retail food inflation eased drastically
from ~10% in late 1990s to ~2% in early 2000s, while it
decelerated from ~9% to under 3% on the wholesale
level. Similarly, when the economy saw better rainfall
over the subsequent three years (FY06-08), food inflation
actually picked up, which deteriorated even further
during FY09-10 when rainfall was in deficit. In the recent
past also, although monsoon deficit was high in FY15-16,
food inflation has decelerated substantially.
As expected, surplus monsoon periods
are associated with higher food grains
production, which implies better
growth in agriculture output
While monsoon gods were unhappy,
retail food inflation eased drastically
from ~10% in late 1990s to ~2% in early
2000s
Exhibit 7: The tale of six episodes over the past two decades
FY97-99
South-west monsoon (% deviation from LPA)
Food grains production (% YoY)
Real agricultural GDP (% YoY)
Real private consumption (% YoY)
Food consumption (% YoY)
Real GDP (% YoY)
Consumer price index (CPI)
CPI-food inflation
3
3
3
2
2
2
2
1
FY00-05
(8.6)
1.6
1.9
4.9
0.8
6.2
3.9
2.2
4.9
2.6
5.1
(0.4)
2
FY06-08
1.3
5.5
5.0
8.8
4.6
9.5
5.7
7.3
5.3
5.7
4.6
0.8
FY09-10
(11.7)
(7.2)
0.5
7.3
1.6
6.2
10.7
13.8
5.9
11.8
21.6
2.0
FY11-14
0.5
5.7
4.8
7.5
6.0
7.3
9.7
10.1
8.0
9.3
13.7
7.3
1
FY15-16
(12.7)
(1.8)
0.5
6.8
0.0
6
3.0
2.7
4.6
5.8
4.9
5.9
9.9
9.8
5.0
8.9
7.4
6.0
6.3
(0.2)
3.7
2.4
0.9
Wholesale price index (WPI)
WPI-food inflation
3
4
Minimum support prices (% YoY)
Real rural agricultural wages (% YoY)
5
7.0
n/a
For kharif season
2011-12 series merged with 2004-05 using splicing technique
3
Based on industrial workers (IW). Food index comprises primary + manufactured food series
4
Simple average of MSPs for paddy (common), jowar (hybrid), bajra, maize, ragi, arhar, moong, urad, cotton, groundnut, sunflower, soyabean (yellow),
sesamum and sugarcane
5
For men including five agriculture activities (ploughing/tilling, sowing, harvesting, picking and animal husbandry)
6
Data for FY15 only; FY16 not yet available
Source: CEIC, Labour Bureau, Government of India (GoI), MoSL
15 September 2016
6

Exhibit 8: Strong relation between monsoon, food grain
production and thus agricultural output…
Exhibit 9: …but not-so-strong correlation between monsoon
and inflation
Source: CEIC, IMD, MOSL
Monsoon affects supply as well as demand of food:
Another important thing to note is growth in demand
and supply of food items during various periods
(Exhibit
10).
Interestingly, as food production suffers during bad
monsoon years, demand also witnesses a setback.
Nevertheless, monsoon elasticity of supply of food grains
is much higher than food demand, as movements in the
latter are not as sharp as in the former. For instance,
while food grains production declined 7.2% p.a. between
FY09 and FY10 (vis-à-vis growth of 5.5% p.a. between
FY06 and FY08), food consumption growth decelerated
from 4.6% to 1.6% during the corresponding periods. In
the most recent period also, while production fell 1.8%
p.a., consumption was almost flat (data for FY16 not yet
available).
Exhibit 10: Food demand also moves in line with monsoon,
but with less intensity than supply
MSP hikes not always in line with monsoon
development:
Finally, the increases in minimum support
prices (MSPs) have also not been in line with monsoon
developments
(Exhibit 11).
Notwithstanding better
monsoon during FY11–FY14 period, MSP hike was ~14%
p.a. vis-à-vis ~22% p.a. increase during FY09-FY10.
Similarly, in the past two years, MSP growth was 2.4%
p.a., the lowest in any period discussed in this section.
Rural wages get impacted by MSP hikes but with a lag.
Exhibit 11: Government support via MSPs, and rural wages,
also not clearly correlated with monsoon
Source: CSO, IMD, Labour Bureau, CEIC, MoSL
15 September 2016
7

Which states are most reliant on
monsoon and what do they produce?
Eight Indian states are agriculture oriented…:
Another
important perspective to look at is the dependence of
various states on monsoon.
Exhibit 12
below shows the
share of agriculture in states’ gross domestic product
and compares it with 2016 monsoon progress. Eight
states in India – Arunachal Pradesh, Madhya Pradesh,
Punjab, Andhra Pradesh, Rajasthan, Uttar Pradesh and
Assam – are agriculture oriented, as agriculture accounts
for more than 20% of the state GDP. This may make
monsoon more important for them than the rest of
India.
facing rainfall deficit. Neither bodes well for agricultural
output and thus GDP growth in these states. As of
September 14, 2016, Rajasthan has had received rainfall
to the magnitude of 132% of LPA, MP (118% of LPA),
Assam (76% of LPA), Nagaland (71% of LPA) and Punjab
(76% of LPA). Importantly, since Assam and Nagaland are
very small states economically (account for only 0.1% or
less of the national GDP), the impact on national basis
would not be large. On the other hand, Gujarat, where
agriculture is ~17% of state GDP, is an important
agriculture state with 25% rainfall deficit.
Not all states are monsoon dependent:
Notably, not all
of the agriculture-oriented Indian states are dependent
on monsoon. Punjab enjoys almost universal irrigation,
while UP has more than three-fourth of its area (under
all crops) irrigated (last row in
Exhibit 13).
Other states,
however, have less than 50% of their area under
irrigation, which increases their reliance on monsoon.
Eight states in India – Arunachal
Pradesh, MP, Punjab, Andhra Pradesh,
Rajasthan, UP and Assam – are
agriculture oriented, with agriculture
accounting for more than 20% of
respective state GDP
…of which three are facing rainfall deficit and two
excess rainfall:
As
Exhibit 12
shows, Madhya Pradesh
and Rajasthan are reeling under excess rainfall (more
than 20% of LPA), while Assam, Nagaland and Punjab are
Not all states are monsoon dependent -
Punjab enjoys almost universal irrigation,
while UP has more than three-fourth of
its area irrigated. Other states have less
than 50% area under irrigation
Exhibit 12: Role of monsoon for various states
Source: CEIC, CSO, IMD, MOSL
Data as of September 14, 2016
15 September 2016
8

Several crops’ production at risk:
Excess rainfall may be
as bad as deficient rainfall for crop production.
Exhibit 13
below looks at the share of large agriculture-oriented
states in the production of six crops considered in this
analysis. There are four important things to note:
1. MP and Rajasthan – both witnessing excess
rainfall – are among the top producers of pulses
in India, together accounting for 38% of total
pulses production.
2. Rajasthan is the largest producer of coarse
cereals in the country, followed by Karnataka
(rainfall deficit of 14%) and Andhra Pradesh
(surplus of 4%).
3. Madhya Pradesh, Gujarat and Rajasthan are the
top three producers (in that order) of oilseeds in
the country, together accounting for more than
60% production of the crop.
4. Gujarat, by far, is the largest producer of cotton
in the country.
These four facts may have huge implications for demand
and supply of various crops considered in this analysis.
Madhya Pradesh (MP) and Rajasthan –
both witnessing excess rainfall – are
among top producers of pulses in India,
together accounting for 38% of total
pulses production
Madhya Pradesh, Gujarat and Rajasthan
are the top three producers (in that
order) of oilseeds in the country, together
accounting for more than 60% of crop
production
Exhibit 13: Share of several states in production of six key crops
Ton m
Total*
Rice
Coarse cereals
Pulses
Oilseeds
Cotton@
Sugarcane
Total irrigated area (%)^
104.3
37.9
16.5
25.3
30.1
352.2
19.9
Andhra Pradesh
11.3
12.5
7.9
5.3
19.8
4.2
45.9
Gujarat
1.5
4.5
3.4
19.1
28.5
3.7
46.9
@ Bale mn
% of total production#
Madhya Pradesh
2.9
6.5
26.4
23.5
5.4
1.0
38.8
Punjab
10.7
1.3
0.3
0.2
3.8
1.9
98.4
Rajasthan
0.3
16.7
11.6
18.2
n/a
0.1
39.5
Uttar Pradesh
13. 0
8.9
9.9
3.2
n/a
38.5
78.2
* As per advance estimate of 2015-16
Source: Agriculture Pocket Book 2015, MOSL
# As per the recent 3-yr average
^ Data for 2012-13
15 September 2016
9

Area sown is improving, but
deteriorating yield a concern
Area sown is better in 2016…:
There is also a strong
relationship between monsoon progress and area sown
under different crops for obvious reasons. Data in this
regard is available on weekly basis for some key crops.
Exhibit 14
shows that area sown under pulses was 14.4m
hectare as of September 9, 2016, up 29% from 11.1m
hectare a year ago. Excluding pulses also, area sown
under these crops was 2.3% higher than a year ago (v/s
Overall, although area sown under various crops has
growth of 0.6% YoY in 2015).
increased ~4% YoY in the current sowing season (better
than ~2% growth in the corresponding period last year),
Although higher area sown certainly is positive, this
the deterioration in yield for some of these crops is
reflects only half the picture. The other piece is the yield
concerning. The recent trends imply that more area
(or productivity) of various crops. In general, if the yield
needs to be sown to produce the similar level of output.
is falling (rising), the similar amount of output for a
This does not bode well for a consumption-driven
particular crop will be produced with higher (lower)
economy.
sowing area. Therefore, it is important to note how the
yield of these crops is moving.
…but yield has fallen in recent years:
Exhibit 15
shows
that the yield for most of these crops declined in 2014-15
(the year for which recent data is available). We also look
at the average yield in the 3-year period (FY13-FY15) to
gauge the trend in the yield for these crops. For crops
like rice, oilseeds, sugarcane and cotton, the yield seems
to have deteriorated. Nevertheless, improving yield in
pulses and coarse cereals is encouraging.
Area sown under pulses was 14.4m
hectare as of September 9, 2016, up 29%
YoY from 11.1m hectare a year ago.
Excluding pulses also, area sown under
these crops was 2.3% higher than a year
ago
For crops like rice, oilseeds, sugarcane
and cotton, the yield seems to have
deteriorated. Nevertheless, improving
yield in pulses and coarse cereals is
encouraging
Exhibit 14: Area sown under pulses has increased in 2016…
Exhibit 15: …but yield performance is disappointing
Data up to September 9, 2016
Source: Press Information Bureau (PIB), CEIC, MOSL
15 September 2016
10

Good monsoon must to replenish water
reservoirs
Water storage better than last year, but less than long-
period average:
Finally, one of the most important
aspects of normal-to-better monsoon is to replenish our
water reservoirs. As per the recent available data (as of
September 8, 2016), total water available in live storage
of 91 reservoirs in the country was 108.1 BCM – 69% of
total live storage capacity of these reservoirs. This is
117% of live storage of the corresponding period last
year and 97% of storage of average of last ten years
(Exhibit
16).
Rabi crops primarily dependent on SW monsoon:
Good
water reservoirs not only ensure better availability of
water to citizens, but also help the agriculture sector
during Rabi season (almost 80% of annual rainfall
happens during June-September period). Notably, almost
50% of food grains are produced during the Rabi season
(Exhibit
17).
In fact, unlike coarse cereals and oilseeds,
about two-third of pulses, which has been witnessing
very high inflation in recent months, are produced during
the Rabi season. If water reservoir levels are not good
enough, some crops may get adversely impacted (note
that less than 20% of total crop under pulses and coarse
cereals and <30% for oilseeds are irrigated).
Total water available in live storage of 91
reservoirs in the country was 108.1 BCM –
69% of total live storage capacity of these
reservoirs
Unlike coarse cereals and oilseeds,
about two-third of pulses, which have
been witnessing very high inflation in
recent months, are produced during Rabi
season
Exhibit 16: Good monsoon helps replenish water reservoirs
Exhibit 17: Rabi season is as important as Kharif season
% of
production
Kharif
Rabi
Food
grains
49.2
50.8
Rice
87.5
12.5
Wheat
0.0
100.0
Coarse
cereals
71.6
28.4
Pulses Oil seeds
33.6
66.4
65.6
34.4
As of early-Sept
Source: Central Water Commission, MOSL
Source: CEIC, MOSL
15 September 2016
11

Key market implications based on
4. Cotton prices may increase further or remain high:
Raw cotton prices have increased 30% YoY in July
2016, from below Rs100/kg a couple of months ago
Based on the above analysis and the fact that 2016 SW
to Rs117/kg. The following facts may keep cotton
monsoon has turned from a surplus a month ago to a
prices higher (or may push even higher) and thus
considerable deficit now, there are six key implications
hurt spinning companies: a) Gujarat, the largest
from the market’s perspective.
producer of cotton, was facing rainfall deficit of 25%
as of September 14, 2016; b) area sown under cotton
1. Worse-than-expected monsoon may lead to
is down 11% as of September 9, 2016, and c) yields
lowering of real GVA growth projections…:
As
of cotton have fallen in the recent past.
explained above, surplus monsoon (irrespective of
the magnitude of surplus) implies ~6% agricultural
5. Pulses prices may be reluctant to come down:
output growth, which we had factored into our GVA
Notably, only one-third of pulses production occurs
forecasts. However, with monsoon at 5% deficit as
in the Kharif season, while the rest happens in the
of yesterday, it implies agricultural output growth of
Rabi season. In case 2016 turns out to be the third
~3% in FY17. This will wipe off 35-40bp from real
consecutive year of below-normal monsoon, the
GVA growth expectations – may get revised down
level of water reservoirs may remain subdued (if
from 7.7% to ~7.3%.
there are no late pick-up in monsoon), hurting
monsoon development
2. …and dampen expectations of sharper monetary
easing:
We have argued that it is difficult to establish
direct relationship between inflation and monsoon.
However, the market continues to believe that
6. Will government be able to resist support to farm
better monsoon may help inflation ease faster, and
sector?
Finally, the ruling government has attracted
thus open the window for sharp rate cuts. Weak
a lot of flak for its lack of financial support to the
monsoon will dampen such expectations.
rural sector. Although expectations of above-normal
monsoon have weakened such demands, weaker-
3. Rural sector to remain under stress:
As we had
than-expected rains will increase pressure on the
argued in our detailed note in June 2016, we expect
government to support the stressed rural sector. This
the rural sector to remain under prolonged stress.
may imply compromise on the government’s ability
With
weaker-than-expected
monsoon,
the
to increase capital spending.
acceptability of this argument will increase, thereby
hurting rural sector-related equity stocks.
Exhibit 18: Raw cotton prices have hardened considerably
Exhibit 19: Pulses prices are reluctant to come down
sentiment on Rabi season output. Accordingly,
pulses prices may be reluctant to come down
(inflation rate, however, will come down).
Source: CEIC, MOSL
Pulses price is an average of Gram, Urd, Masur, Moong and Tur
15 September 2016
12

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