This paper examines the link between structural change and growth in India. It constructs indices of structural change, and performs a time series analysis of the data. It finds that 1988 marks a break in the time series of growth and structural change. There is one-way causality from structural change to growth in the period 1988-2007, whereas there is no evidence for this linkage before 1988.Indian economy; structural change; growth; causality

Cortuk, Orcan

Singh, Nirvikar

English

Research Papers in Economics

Structural Change and Growth in India 
 
Orcan Cortuk
+ 
Nirvikar Singh
*  
 
 
February 10, 2010 
 
 
Abstract 
 
This paper examines the link between structural change and growth in India. It constructs 
indices of structural change, and performs a time series analysis of the data. It finds that 
1988 marks a break in the time series of growth and structural change. There is one-way 
causality from structural change to growth in the period 1988-2007, whereas there is no 
evidence for this linkage before 1988. 
 
JEL classification: O1, O5 
Keywords: Indian economy; structural change; growth; causality 
 
                                                 
+ Department of Economics, University of California, Santa Cruz, ocortuk@ucsc.edu 
* Professor of Economics, University of California, Santa Cruz, Santa Cruz, CA 95064, boxjenk@ucsc.edu   1 
 
INTRODUCTION 
 
The  Indian  economy  has  been  one  of  the  world’s  star  growth  performers  in  recent 
decades. Debates about its growth process have focused on the timing of changes in the 
trend rate of growth, with implications for judgments on the efficacy of market-oriented 
reforms  (e.g.,  Panagariya,  2008),  and  on  the  nature  of  that  growth  in  terms  of  skill-
intensity  (e.g.,  Kochhar  et  al,  2006)  and  services-intensity  (e.g.,  Singh,  2006),  with 
implications for it future pattern and sustainability. This paper examines the link between 
structural  change  and  growth  in  India.  It  constructs  indices  of  structural  change,  and 
performs a time series analysis of the data. It finds that 1988 marks a break in the time 
series of growth and structural change. There is one-way causality from structural change 
to growth in the period 1988-2007, whereas there is no evidence for this linkage before 
1988. By establishing the nature of the link between structural change and growth, this 
analysis provides new insight into the growth process in India at the aggregate level. 
 
 
DATA AND METHODOLOGY 
 
We start by describing the two variables we use in the analysis, namely growth rates and 
structural  change  indices.  Growth  rates  are  calculated  from  National  Accounts  gross 
domestic product (GDP) data.
1 The data cover the period from 1951 to 2007 at 1999-
2000  prices
2.  For  structural  change,  two  different  indices  are  calculated,  following 
Dietrich  (2009).  The  first  is  the  simplest  measure  of  structural  change,  the  Norm  of 
Absolute Values (NAV)
3: 
 
(1)  ∑
=
- =
n
i
is it x x NAV
1
| | 5 . 0  
 
                                                 
1 The data were obtained from the Reserve Bank of India’s website, www.rbi.org.in. 
2 More detail regarding the data is given in the Appendix B. 
3 Also called the Michaely-Index (Michaely, 1962) or Stoikov-Index (Stoikov, 1966)   2 
For its computation first the differences of the sector shares xi between two points in time s and t 
are calculated.
4 Then the absolute amounts of these differences are summed up and divided by 
two (since each change is counted twice).  
 
The second index is the modified Lilien index (MLI). The Lilien (1982) index originally 
measured the standard deviation of the sectoral growth rates of employment from period 
s to period t. Stamer (1999) modified this index in order to fulfill the characteristics of a 
metric. The MLI is constructed as follows: 
 
(2)  
2
ln .  


 


=
is
it
is it x
x
x x MLI  where xis > 0 and xit > 0.  
 
The use of two indices allows us to check the robustness of our analysis with respect to 
the structural change measure. We constructed two annual series of structural change for 
the Indian economy, one for each index. 
 
We  begin  with  a  VAR  analysis  of  growth  and  structural  change.  The  lag  length  is 
determined by the Schwarz  Information Criterion (SIC)  which indicates a lag of one 
period.
5 The VAR analysis does not indicate any significant coefficients for the period 
from 1951 to 2007. A possible explanation for this insignificance is structural breaks 
(Wallack, 2003; Balakrishnan and Parameswaran, 2007), and so we look for a structural 
break in the growth rate series, innovating by allowing for structural change to affect 
growth. We continue our investigation after splitting our period into two sub-periods at 
the structural break. 
 
The standard test for structural change (Chow, 1960) requires the assumption that the 
break date is known a priori (Hansen, 2001). One potential solution to this problem is to 
treat the break date as unknown, carry out the procedure for all the possible years and 
then select the largest statistic over all possible break dates.  However, there may also be 
                                                 
4 Sectoral shares are calculated for two disaggregations of the GDP data. In the first disaggregation, there 
are three main sectors, namely agriculture, industry and services. In the second disaggregation, there are 
nine subsectors (Appendix B). We report the latter results here – the more aggregate results are 
qualitatively the same. 
5 The Akaike Information Criterion (AIC) also indicates to one lag. By using SIC, we penalize the models 
having higher number of parameters more strongly.   3 
multiple breaks in the data. Therefore, we follow Bai and Perron’s (1998) test procedure 
for multiple structural breaks. The first step tests for a single structural break taking the 
entire sample.  If the test rejects the null hypothesis that there is no structural break, the 
corresponding year is taken as the candidate break date and the sample is split into two 
sub-periods around that year. The test is reapplied to each sub-sample. If we find a break 
date in any of the samples, the entire sample is split around this new candidate break-date 
and two new subsamples are tested for structural breaks.  This sequence continues until 
each subsample test fails to find evidence for a break (Hansen 2001). 
 
We  estimate  the  equation  1 1 t t t T Tt t G H G DU u a b g d - - = + + + +   where  G  denotes  the 
growth rate, H represents the structural change index and u is the random disturbance 
term.  The  break  is  captured  by  DUTt  ,  a  dummy  variable,  which  is  1  if  t  >T  and  0 
otherwise. All values of T are tried, with breaks being identified by rolling F-tests. In the 
case of significant structural breaks, we replicate the VAR analysis in the subsamples in 
order  to  get  some  significant  relation  between  growth  and  structural  change  indices. 
Assuming significant results from VAR analysis for the subsamples, we test for Granger 
causality between growth and structural change. 
 
 
EMPIRICAL FINDINGS 
 
We first present our findings from the results of the VAR analysis for the whole sample 
(Table 1), for both structural change indices.  
 
Table 1: VAR Analysis, 1951 to 2007 
 
      GROWTH        MLI INDEX 
Variable   Coefficient     t stat    Coefficient     t stat 
Growth(-1)  0.04      0.02    -0.0001    -0.33 
MLI(-1)  59.26      1.00    -0.10      -0.69 
Constant  3.85      2.62     0.018       4.92 
 
      GROWTH        NAV INDEX 
Variable   Coefficient     t stat    Coefficient     t stat 
Growth(-1)  0.014      0.09    -0.00009    -0.27 
NAV(-1)  82.85      1.27    -0.12      -0.82 
Constant  3.43      2.25     0.018       5.36   4 
 
As mentioned previously, no significant relation between growth and structural change is 
found for the period from 1951 to 2007. Hence, we next check for structural breaks in the 
whole sample. Exploiting the procedure described above, our results are presented here 
with the MLI index as the structural break measure. For robustness, we also did the same 
analysis with the NAV index.
6  
 
Structural Breaks for MLI Index 
 
We initially find the year 1988 as a structural break over the whole sample of 1951-2007 
(Figure 1). Furthermore, after splitting the whole sample of 1951-2007 into two, as 1951-
1988 and 1988-2007, we continued to search for an additional break but found none. 
 
 
Figure 1: Structural Break Test 
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Growth and Structural Change 
 
The only structural break we find is the year 1988. Hence, we perform the VAR analysis 
once more to check whether a significant relationship between growth and the structural 
break indices exists in the period 1951 to 1988 (Table 2) and from 1988 to 2007 (Table 
3). As seen from the tables, the only significant parameter is the coefficient of MLI(-1) in 
                                                 
6 Results found by using NAV index can be found in the Appendix A.   5 
the  later  period,  at  the  1%  level,  indicating  that  structural  change  positively  affected 
growth in this period. Table 2 shows that there is no significant relationship between 
growth and structural change for the period 1951-1988. 
 
Table 2: VAR Analysis, 1951 to 1988  
        GROWTH        MLI INDEX 
Variable     Coefficient     t stat    Coefficient     t stat 
Growth(-1)    -0.38      -2.00    0.00005    0.09 
MLI(-1)    0.64      0.009    0.38      -0.09 
Constant    5.46      3.13    0.015       3.71 
 
Table 3: VAR Analysis, 1988 to 2007 
        GROWTH        MLI INDEX 
Variable     Coefficient     t stat    Coefficient     t stat 
Growth(-1)    0.28      1.38    0.0005     0.85 
MLI(-1)    167.86     2.39    0.38      -1.73 
Constant    2.38      1.32    0.015      2.82 
 
 
 
Granger Causality Test: 
We also performed a Granger causality test with the growth and MLI series. Table 4 
below shows the results for this test. 
 
Table 4: Granger Causality Test 
 
Variable             F stat    Prob. value 
“Growth” does not Granger Cause “MLI”    1.38    0.25 
“MLI” does not Granger Cause “Growth”    7.99    0.009 
 
Accordingly, the results indicate Granger Causality from “MLI of structural change” to 
growth which is consistent with the VAR analysis shown previously.  
 
Robustness: NAV Index as a Structural Change Measure 
We replicate our analysis and tests with the NAV index in order to check the sensitivity 
of our analysis to structural measure definitions. We find the same results: 1988 is the 
only  structural  break,  and  there  is  only  one  significant  causality  relationship  from 
structural change (NAV) to growth (Appendix A).  
   6 
Robustness: 1980 as the Structural Break  
As seen in Figure 1, 1980 has the second highest F-statistic, which makes it a potential 
candidate for the year of structural break. We replicated all our analysis for the period of 
1980-2007 and obtained the same significant impact from structural change to growth.
7 
 
Hence, our result is robust both to different structural change indices, and to the structural 
break year employed in our analysis. 
 
 
CONCLUSION 
 
Our results show that India’s economy has only one structural break, which is at 1988, 
for the period from 1951 to 2007. This structural break allows us to identify a significant 
positive  impact  from  structural  change  to  growth,  but  only  in  the  latter  period. 
Furthermore, there we find Granger Causality from structural change to growth for this 
period, 1988 to 2007. However, there is no such relationship for the period 1951-1988. 
Therefore, one of the sources for increasing growth rates observed in the last two decades 
is the structural change of the Indian economy.  
                                                 
7 These results are available from the authors.   7 
APPENDIX A: Analysis Performed with NAV Index (1988-2007) 
 
Figure A1:  
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Table A1: VAR Analysis, 1988 to 2007 
 
        GROWTH        NAV INDEX 
Variable     Coefficient     t stat    Coefficient     t stat 
Growth(-1)    0.26      1.31    0.0006     1.15 
NAV(-1)    205.16     2.56    -0.41      -1.96 
Constant    1.90      1.02    0.016      3.39 
 
 
 
Table A2: Granger Causality Test 
 
Variable             F stat    Prob. Value 
“Growth” does not Granger Cause “NAV”    1.66    0.21 
“NAV” does not Granger Cause “Growth”    8.86    0.006 
 
 
APPENDIX B: Data 
 
GDP data have three main sectors and nine subsectors shown below: 
·  Agriculture and allied activities, 
o  Agriculture 
o  Allied activities 
·  Industry 
o  Mining and Quarrying 
o  Manufacturing 
o  Electric, gas and water supply   8 
·  Services  
o  Construction 
o  Trade, Hotel, Transport and Communication 
o  Finance, Insurance, Real Estate and Business Services 
o  Community, Social and Personal Services 
 
The time series cover 1950-1951 to 2007-2008.  We have indicated this period as 1950-
2007 for simplicity. The year 1950 is lost in growth calculations, so the study covers the 
period 1951 to 2007. 
 
REFERENCES 
Bai, J. and Perron, P. (1998), ‘Testing for and Estimation of Multiple Structural 
Changes’, Econometrica, 66, pp 817-58. 
 
Balakrishnan, P. and Parameswaran, M. (2007), “Understanding Economic Growth in 
India: A Prerequisite”, Economic and Political Weekly, July 14. 
 
Chow,  G.C.,  (1960),  “Tests  of  Equality  between  Sets  of  Coefficients  in  Two 
Regressions”, Econometrica, 28, 591-603. 
 
Dietrich,  A.  (2009),  “Does  Growth  Cause  Structural  Change,  or  Is  it  the  Other  Way 
Round?  A  Dynamic  Panel  Data  Analysis  for  Seven  OECD  Countries”  Jena 
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Hansen, B. (2001), ‘The New Econometrics of Structural Change: Dating Breaks in US 
Productivity’, Journal of Economic Perspectives, Vol 15, pp 117-28. 
 
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   9 
Lilien, D.M. (1982), “Sectoral Shifts ad Cyclical Unemployment”, Journal of Political 
Economy, vol. 90, pp. 777-793. 
 
Michaely, M. (1962), Concentration in International Trade, Amsterdam: North Holland. 
 
Panagariya, A. (2008), India: The Emerging Giant, New York: Oxford. 
 
Singh, N. (2006), Services-Led Industrialization in India: Assessment and Lessons, in 
Industrial Development for the 21st Century: Sustainable Development 
Perspectives, ed. David O’Connor, New York: UN-DESA, pp. 235-291. 
 
Stamer, M. (1998), “Interrelation between Subsidies, Structural Change and Economic 
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44, no. 3, pp. 231-253.  
 
Stoikov, V. (1966), “Some Determinants of the Level of Frictional Unemployment: A 
Comparative Study”, International Labour Review, vol. 93, pp. 530-549. 
 
Wallack, J. (2003), ‘Structural Breaks in Indian Macroeconomic Data’, Economic and 
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Structural Change and Growth in India

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