1) The document analyzes wage-employment dynamics in India's organized manufacturing sector from 1981 to 2017 using time series econometric techniques. 2) It finds a cointegrating relationship between employment, wages, output, and prices in the long run. In particular, employment and wages are positively correlated contrary to expectations. 3) Impulse response functions show that positive wage and output shocks increase employment in the long run, while a positive price shock decreases employment as firms adopt capital-intensive methods. Variance decomposition also shows employment has a greater impact on wages, prices, and output than vice versa.

1. Ruhani
Wage-Employment Dynamics
in India’s Organized Manufacturing Sector

2. 1.Introduction and
Research
Objective
3.Trends in
wages and
employment
5.Conclusion
and Policy
Implications
4.Method
• VECM
• Impulse
Response
• Variance
Decomposition
2.Literature
Review
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35 40 45 50
ResponseofLN_PRICEtoLN_EMP
ROADMAP
(At all-India and state-level)

3.  Labour is the ultimate source of
wealth of a nation
 Despite his objections to
legislative and other restrictions
on the free play of market forces,
Smith's sympathy with the
position of the labourer is
evident in his explicit remark that
a just and equitable wage
regulation imposes no real
hardship upon the masters
 We try to examine the impact of
wage incentives on employment
in the organised manufacturing
sector in a time-series framework

4. Can wages create more
employment?
Find ways of creating more jobs
1
2
3
Can growth create more
employment?
Can prices create more
employment?
RESEARCH OBJECTIVE

5. Study by Elasticity of employment with respect to wage
World Bank(1989) -0.8
Bhalotra (1998) −0.28 to −0.44(very low)
Goldar (2000) -0.51 during 1980–1981 to 1990–1991,
-0.67 over 1990– 1991 to 1997–1998
Mitra (2013) −0.54 (statistically insignificant)
Mitra(2018) -0.25( as per 2SLS estimate, not highly significant)
0.10 (insignificant , as per OLS estimate)
LITERATURE REVIEW

6. DATA SOURCES AND VARIABLES
• ASI Time Series data(from 1981 to
2017)
• Deflate nominal wages and salaries
by the consumer price index for indu
strial workers (CPI-IW)
• Deflate gross value added by the
wholesale price index for
manufactured products (WPI-MP).
• Data for CPI-IW and WPI-MP were
obtained from the RBI Handbook of
Statistics.
• Throughout our analysis, we consider
2011-12 as the base year.
4 Variables of interest:
1. EMPLOYMENT(total persons engaged)
2. WAGES(real emoluments)
3. RGVA(real GVA at 2011-12 prices)
4. PRICE(wholesale price index)
All variables are taken in their logarithmic
forms.

7. AGGREGATE ANALYSIS OF TRENDS
Focus on three distinct
regimes :
First period: employment
and wages grew slowly.
Second period: decline and
stagnation in both, and
Third: employment and
wages growing much faster.
We see improvement in performance of
wages as well as employment, starting
2004–05
Reason: Shift in the labour force from
unorganised to organised sector(Thomas
et al,2018)
Positive association between employment
and wages,
is opposite to the view that wages have to be
adjusted downward to increase employment.

8. METHODOLOGY
UNIT ROOT TEST
Variable ADF Test Statistic PP Test Statistic Conclusion
Ln_emp -0.29 0.23 Unit root exists
Ln_rgva -0.27 -0.34 Unit root exists
Ln_price -2.49 -2.37 Unit root exists
Ln_wages -0.27 -1.82 Unit root exists
Rog in emp -4.77 -4.97 No unit root
Rog in rgva -4.64 -4.66 No unit root
Rog in price -3.64 -3.64 No unit root
Rog in wages -14.58 -6.02 No unit root
Table 1: Unit Root Test on Log Levels and First difference forms
Note: The critical value at 1% level for both the tests is around -3.62.
ADF (Augmented Dickey Fuller)
and Philip Perron Test on the
4 series-EMP,RGVA,PRICE,
WAGES(all in logarithmic form).
All the four series are found to
be non-stationary in their level
form but stationary in their first
difference form(growth rate
form).

9. Series: LN_EMP LN_PRICE LN_RGVA LN_WAGES
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.702822 72.36079 47.85613 0.0001
At most 1 * 0.387558 31.10437 29.79707 0.0352
At most 2 0.252686 14.43414 15.49471 0.0718
At most 3 * 0.124766 4.530989 3.841466 0.0333
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level4
* denotes rejection of the hypothesis at the 0.05 level
Performing Johansen Cointegration Test, we find a cointegrating relationship among the
variables.
Johansen Cointegration Test
Since the variables have a cointegrating relationship, we estimate a Vector error correction
model.

10. VECTOR ERROR CORRECTION MODEL
So the estimated long-term relationship between employment and other variables is:
ln 𝑒𝑚𝑝𝑡 = −0.35 − 1.59 ln 𝑝𝑟𝑖𝑐𝑒𝑡 + 1.38 ln 𝑟𝑔𝑣𝑎𝑡 + 0.23 ln 𝑤𝑎𝑔𝑒𝑠𝑡 + 𝜀𝑡
This is the cointegrating equation.
• ‘Real wages’ and ‘total persons engaged’
are positively and significantly correlated
in the long run, which is quite contrary
to their expected negative association.
• Long-term wage elasticity of
employment is 0.23.
• Employment and RGVA have a positive
long-run relationship as expected.
• Employment and price have a negative
long-run relationship which is
unexpected since price rise is expected
to be beneficial for employment creation

11. ERROR CORRECTION TERM
The estimated adjustment coefficient
is significant at 10% level in the emplo
yment equation (the p-value of error
correction term is 0.06).
Thus, in the long run, it is the
employment variable that adjusts to
maintain the long-run relationship.
The adjustment coefficient is -0.1782
which shows the speed of adjustment
towards equilibrium.
Thus,17.82 percent per year
adjustment will occur in employment
to restore the equilibrium.

12. IMPULSE RESPONSE FUNCTIONS

13. -.010
-.008
-.006
-.004
-.002
.000
1 2 3 4 5 6 7 8 9 10
Response of LN_EMP to LN_WAGES
-.016
-.012
-.008
-.004
.000
.004
1 2 3 4 5 6 7 8 9 10
Response of LN_PRICE to LN_WAGES
-.05
-.04
-.03
-.02
-.01
.00
1 2 3 4 5 6 7 8 9 10
Response of LN_RGVA to LN_WAGES
.12
.16
.20
.24
.28
1 2 3 4 5 6 7 8 9 10
Response of LN_WAGES to LN_WAGES
Response to Cholesky One S.D. Innovations
Dynamic Responses to Wage shock
• Wage shock is like an
input cost shock.
• When wages rise,
employment as well
as real GVA decrease
in the short run as
expected.
• When wages rise,
prices fall a little but
then rise which is exp
ected as firms pass on
their costs to
consumers.
Over time, firms do cost-cutting in other areas. Hence, firms reduce their other costs to
make up for the increase in cost of labour. Hence, cost shocks are no longer passed on to
product prices in the long run and thus, prices stabilize at a lower level in the long run.
Moreover, more competition with foreign firms in the long run will drive down the prices
and ensure that cost-push inflation does not persist forever.

14. .00
.01
.02
.03
.04
1 2 3 4 5 6 7 8 9 10
Response of LN_EMP to LN_RGVA
.000
.002
.004
.006
.008
.010
1 2 3 4 5 6 7 8 9 10
Response of LN_PRICE to LN_RGVA
.02
.03
.04
.05
.06
1 2 3 4 5 6 7 8 9 10
Response of LN_RGVAto LN_RGVA
-.07
-.06
-.05
-.04
-.03
1 2 3 4 5 6 7 8 9 10
Response of LN_WAGES to LN_RGVA
Response to Cholesky One S.D. Innovations
Dynamic Responses to RGVA Shock
Higher output is like a positive
supply shock.
An output shock increases em
ployment as expected.
Initially, prices rise in response
to an output shock which is
contrary to the expectation th
at higher output production
causes reduction in prices.
But in the long run, prices fall in response to an output shock as expected.
In case of wages, we notice a slight increase at first which is expected as higher growth
should lead to higher incomes. Thereafter, we notice an oscillation in wages.

15. -.016
-.012
-.008
-.004
.000
1 2 3 4 5 6 7 8 9 10
Response ofLN_EMP to LN_PRICE
.02
.03
.04
.05
.06
.07
.08
.09
1 2 3 4 5 6 7 8 9 10
Response ofLN_PRICE to LN_PRICE
-.02
-.01
.00
.01
.02
.03
1 2 3 4 5 6 7 8 9 10
Response of LN_RGVAto LN_PRICE
.05
.10
.15
.20
.25
1 2 3 4 5 6 7 8 9 10
Response of LN_WAGES to LN_PRICE
Response to Cholesky One S.D. Innovations
Dynamic Responses to Price Shock
Higher prices incentivize
producers to produce
more. Hence RGVA
increases in response to a
price shock as expected.
However, we note a
decline in employment
which is probably linked
to firms choosing to
adopt capital-intensive
methods of production.
Wages rise in response to a price shock which is expected as workers demand for a
wage hike when product prices increase.

16. .04
.06
.08
.10
.12
.14
1 2 3 4 5 6 7 8 9 10
Response of LN_EMP to LN_EMP
.004
.008
.012
.016
.020
.024
1 2 3 4 5 6 7 8 9 10
Response of LN_PRICE to LN_EMP
.04
.06
.08
.10
.12
.14
1 2 3 4 5 6 7 8 9 10
Response of LN_RGVAto LN_EMP
.00
.02
.04
.06
.08
.10
1 2 3 4 5 6 7 8 9 10
Response of LN_WAGES to LN_EMP
Response to Cholesky One S.D. Innovations
Dynamic Responses to EMP Shock
Employment shock is like a
labour supply shock.
RGVA rises in response to a
shock in employment which i
s expected.
We also note an increase in
prices because employment
expansion raises the effective
demand for goods and
services which results in
increase in prices.
Wages decline due to a positive employment shock. This is expected because if there is a
sudden labour absorption in the organised manufacturing sector, firms will have to reduce
wages of workers in order to employ all of them.

17. VARIANCE DECOMPOSITION

18. VARIANCE
DECOMPOSITION
OF
PERIOD CONTRIBUTION OF
EMP PRICE RGVA WAGES
EMP(largely driven
by its own
innovations)
1
100.0000 0.000000 0.000000 0.000000
2
93.86321 0.356618 5.699607 0.080563
4
94.01875 0.248442 5.200552 0.532254
6
92.64045 0.400490 6.456234 0.502823
8
92.03040 0.586889 6.932895 0.449811
10
91.81299 0.801995 7.009256 0.375763
PRICE(largely driven
by its own
innovations)
1
4.393462 95.60654 0.000000 0.000000
2
3.266260 95.06126 1.469478 0.202999
4
7.295605 91.33569 1.287232 0.081472
6
7.933814 90.25108 1.395632 0.419476
8
6.868384 90.92788 1.282102 0.921636
10
5.433641 92.11918 1.101221 1.345954
RGVA(largely driven
by employment
innovations)
1
70.96569 3.040117 25.99419 0.000000
2
84.45098 1.136678 13.92989 0.482456
4
78.63624 0.426098 14.29603 6.641630
6
79.04157 1.022439 13.06255 6.873448
8
78.28392 1.629850 13.09525 6.990984
10
77.52786 2.198264 13.06249 7.211395
WAGES(largely
driven by price
innovations)
1
3.843808 4.738551 4.101510 87.31613
2
2.959634 14.00169 3.825876 79.21280
4
5.936354 22.26841 3.976450 67.81879
6
5.710367 30.11188 4.158900 60.01885
8
4.598554 37.48173 4.021821 53.89790
10
3.642083 44.20938 4.115384 48.03316

19. EMP-WAGE Causality
EMP-RGVA Causality WAGE-RGVA Causality
EMP-PRICE Causality WAGE-PRICE Causality
RGVA-PRICE Causality
Contribution of EMP to WAGE
variation is 3.64% whereas
contribution of WAGE to EMP var
iation is only 0.37%. Thus, EMP
has a greater impact on wage
rather than vice-versa
Direction of Causality using Variance Decomposition
Contribution of EMP to PRICE variation is
5.43% whereas contribution of PRICE to
EMP variation is only 0.8%. Thus, EMP has
a greater impact on price rather than vice-
versa. Employment expansion raises the ef
fective demand for goods and services
which results in increase in prices.
Contribution of EMP to RGVA variation is
77.5% whereas contribution of RGVA to
EMP variation is only 7.009%. Thus, employ
ment causes growth rather than vice-versa.
Hence, India must focus more on employm
ent rather than growth. (Bhagwati debate)
Contribution of WAGE to PRICE variation
is 1.34 % whereas contribution of PRICE to
WAGE variation is 44.2%. Thus, prices
cause wages and wages do not cause pric
es. This is an evidence against the Keynesi
an model that states that firms set prices
as a mark-up over wages
Contribution of WAGE to RGVA variation is
7.21 % whereas contribution of RGVA to
WAGE variation is 4.11%. Thus, wage has a
greater impact on output rather than
vice-versa. This impact is negative since
higher input costs discourage output
production.
Contribution of RGVA
to PRICE variation is
1.1% whereas contrib
ution of PRICE to
RGVA is 2.2%. So price
s cause RGVA. Higher
prices encourages pr
oducers to produce
more.

20. STATE-WISE ANALYSIS

21. State-wise analysis of impact of different incentives on
employment generation
Using ASI’s state-level time Series data for
the organized manufacturing sector
available on the website of EPW Research
Foundation, variance decomposition of
employment was computed using the
same variables that we used for the
all-India analysis.
Our final data set spans 18 years (2000-01
to 2017–18) and covers all Indian states
except 4 states-Arunachal Pradesh,
Mizoram, Sikkim and Telangana for which
data was not available.
The contribution of different variables to the variance of employment for
different states is shown in next slide.

22. STATE EMP PRICE RGVA WAGES
ANDHRA PRADESH 69.86517 29.68717 0.409258 0.038398
ASSAM 45.78257 45.00338 6.112874 3.101177
BIHAR 98.53142 0.475851 0.460132 0.532596
CHATTISGARH 33.4657 10.31085 34.12608 22.09736
GOA 64.22771 14.2365 13.50913 8.026657
GUJARAT 59.12662 31.37382 7.452146 2.047416
HARYANA 95.21304 2.413618 1.788676 0.584666
HIMACHAL PRADESH 92.78331 2.459234 0.141132 4.616321
J&K 90.81885 3.042147 1.245851 4.893155
JHARKHAND 86.98115 6.537341 3.533138 2.948375
KARNATAKA 95.02573 0.601932 3.975734 0.3966
KERALA 80.13832 3.474506 16.26509 0.122092
MADHYA PRADESH 41.05244 8.352686 48.72881 1.866066
MAHARASHTRA 83.07441 13.18713 3.627523 0.110941
MANIPUR 94.6321 1.797718 0.587687 2.982494
MEGHALAYA 94.18897 5.177528 0.567206 0.066293
NAGALAND 76.20995 0.614112 20.09062 3.085319
ODISHA 8.735129 28.52069 58.16744 4.576742
PUNJAB 79.76852 8.953928 11.24068 0.036876
RAJASTHAN 84.58782 5.997337 1.378888 8.035956
TAMIL NADU 68.8898 25.81032 0.840466 4.459417
TRIPURA 89.84943 9.147232 0.533239 0.470095
UTTARAKHAND 72.60347 12.03207 1.506738 13.85772
UTTAR PRADESH 69.52946 24.26058 5.229726 0.980233
WEST BENGAL 91.44226 5.007045 3.183123 0.367572
Variance Decomposition of employment for different states

23. States where price incentives create jobs
The state where price variation explains the
maximum variation in employment is Assam.
45% variation in employment of Assam is
explained by variation in prices.
We also note from our VECM that prices have a
positive long-run relationship with employment in
Assam.
Thus,higher prices generate higher employment
in Assam.This could probably happen because
Assam government provides a huge interest
subsidy on working capital loans to its
industries,especially the tea industry.
Easy credit availability and low interest rates raise
prices as well as promote investment resulting in
employment generation.
Similar pattern is observed in states like
Gujarat,Andhra Pradesh and Uttar Pradesh as
credit is being made easily available due to faster
digitization in these states.

24. States where wage incentives create jobs
States are colored according to the contribution of
wages to the variance of employment.
The state where wage variation explains the
maximum variation in employment is Chattisgarh.
22.1% variation in employment of Chattisgarh is
explained by variation in wages.
A possible reason for this is that Chattisgarh is still a
very labour-intensive state and hence,wage shocks
affect employment here.Other states are relatively
more mechanised and hence wage shocks do not
have a significant impact on employment in other
states.
We also note from our VECM that wages have a
negative long-run relationship with employment in
Chattisgarh.
Thus,lowering wages in Chattisgarh can generate
more jobs.But this does not suggest that wage cuts
will have extraordinary impact on employment
creation because wages account for only around
one-fifth variation in employment.

25. States where output incentives create jobs
States are colored according to the
contribution of output to the variance of
employment.
The state where output variation explains the
maximum variation in employment is Odisha.
58.16 % variation in employment of Odisha is
explained by variation in output. In Madhya
Pradesh and Chattisgarh,the contribution of
output to variance of employment is 48% and
34% respectively.
All these 3 states are mineral-rich states and
mining occurs at a large scale in these states.
If financial incentives are given to produce
more output,these states have room for
excessive mining and extraction of metals and
thus,produce more and employ more people.
Thus, resource-rich states, if incentivised
through Production-linked incentive schemes,
create more employment.

26. At the aggregate level, we found that
wages and employment have a positive
long-run relationship. Hence, the usual
argument that lower wages lead to
higher employment is not valid in the
long-run.
In the short-run, wages have a negative
impact on employment but the impact is
insignificant as wages explain a negligible
amount of variation in employment.
WAGE INCENTIVES
Output incentives are effective in employment
generation both in the short-run as well as long-run.
Output-linked incentive schemes have a huge
potential to create jobs. Thus, production-linked
incentive scheme introduced by the government is a
step in the right direction at the aggregate level and
the scheme can generate massive employment in
resource-rich states.
OUTPUT INCENTIVES
Prices have a negative long-run
relationship with employment at
the aggregate level. However, in
states like Assam, Andhra Pradesh
and Gujarat, prices have a positive
relationship with employment and
thus, price incentives are effective
in job creation in these states.
PRICE INCENTIVES
1
3
2
CONCLUSION AND POLICY IMPLICATIONS