Temporal and Spatial Variations in Agricultural Growth and Its ...

REVIEW OF RURAL AFFAIRS

Temporal and Spatial Variations in Agricultural Growth and Its Determinants Ramesh Chand, Shinoj Parappurathu

The agriculture sector has gone through different phases of growth, embracing a wide variety of institutional interventions, and technology and policy regimes. From the late 1960s onwards, the green revolution helped the sector maintain steady growth for more than two decades. But the challenges that swept through the economy in the 1990s after the initiation of economic reforms arrested this growth. Conscious efforts have brought about a recovery of growth since the middle of the first decade of the 2000s. It is important to assess whether the recent turnaround is sustainable in the long run. This paper analyses the trends in agricultural productivity at the national and state levels and attempts to identify the major factors responsible for the varied performance of agriculture in different periods and in different states.

This paper draws heavily on a study carried out by the authors for the Food and Agricultural Organisation, Country Office, New Delhi. Ramesh Chand ([email protected]) and Shinoj Parappurathu (pshinoj@ ncap.res.in) are at the National Centre for Agricultural Economics and Policy Research, New Delhi. Economic & Political Weekly Supplement

EPW

june 30, 2012

Introduction

T

he agriculture sector contributed more than half the output of the Indian economy when the country embarked on its First Five-Year Plan in 1950-51. Over a period of six decades, the share of agriculture has gradually declined to less than 15%. Even in the rural economy, the share of the agriculture sector has declined to 38% of the total income generated in rural areas (2004-05). Despite its shrinking share in national income and losing its dominance in rural income, the performance of the agriculture sector remains a matter of central concern to policymakers and the public at large. The main reasons for this are that, one, till date, more than half the total workforce in the country remains employed in this sector and agriculture is a source of livelihood for a majority of the population; two, the performance of agriculture is much more important than other sectors for inclusive growth and for reducing poverty (Ravallion and Datt 1996; Datt and Ravallion 1998; Virmani 2008); three, the performance of agriculture determines the food and nutrition security of the population of the country, which cannot depend on external sources of supply; four, the growth of agriculture has a significant bearing on food and overall inflation and macroeconomic stability; and five, much of trade and commerce and industrial activity are linked to agriculture. In the course of development, the agriculture sector has had to go through different phases of growth, embracing a wide variety of institutional interventions, and technology and policy regimes. The implications of such institutional, technological and policy variations on growth, instability and farmers’ income have been captured in several studies such as Rao (1996), Radhakrishna (2002), Chand and Raju (2009) and Vaidyanathan (2010). The literature shows that in the initial years after Independence, it was institutional reforms and an expansion in the area under cultivation that helped the sector realise an annual growth in output close to 3%. However, this momentum could not be sustained for long and agricultural growth decelerated during the 1960s. This trend was reversed with the adoption of new technology which came to be known as the green revolution and which helped the sector maintain steady growth for more than two decades. But the challenges that swept through the economy after the initiation of economic reforms in the early 1990s put pressure on the momentum of growth. The 1990s saw the agriculture sector losing its past vigour, as pointed out by Bhalla and Singh (2001), Rao (2003), Balakrishnan (2000) and others. The slowdown in growth was

vol xlviI nos 26 & 27

55

REVIEW OF RURAL AFFAIRS Figure 1: Trend Growth in GDP-Agriculture (at 2004-05 Prices) based on 10-Year Periods (Decade Ending with 1960-61 to 2010-11, %) 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50

1960-61 1962-63 1964-65 1966-67 1968-69 1970-71 1972-73 1974-75 1976-77 1978-79 1980-81 1982-83 1984-85 1986-87 1988-89 1990-91 1992-93 1994-95 1996-97 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11

0.00

Source: National Accounts Statistics, CSO, Government of India.

was first smoothened by taking two-year moving averages to moderate the effects of abrupt weather variations and other shocks. Further, trend growth rates were estimated for 10-year periods by fitting a semi-log trend to the smoothened data. The series begins with the 10-year period from the biennium ending (BE) 1950-51 to BE 1960-61 and extends to the latest decade starting with BE 2000-01 and ending with BE 2010-11. The growth rates derived from this exercise are plotted in Figure 1. The above analysis suggests that the decade before the green revolution was characterised by a steep decline in growth in GDP-agriculture, with growth rates plummeting from close to 3% in the decade ending (DE) 1960-61 to less than 1% in the DE 1968-69. The green revolution, owing to the adoption of superior technology and institutional reforms, began showing an effect on growth from 1969-70 onwards. The subsequent period witnessed a turnaround in growth with growth rates moving in the range of 2% to 3% for a sustained period of nearly three decades, though with occasional slumps. A deceleration of growth came in the latter half of the 1990s, followed by a quick recovery in the middle of the first decade of the 2000s. In a nutshell, the growth series clearly establishes a steady increase in the growth rate for three decades after the advent of the green revolution, followed by a gradual tapering off and decline after the mid-1990s, which lasted for a decade. This was succeeded by an unambiguous turnaround in the years coinciding with the Eleventh Five-Year Plan (2007-12).

attributed to a variety of factors such as “technology fatigue”, the reduction of public spending on irrigation and water management and scientific research, the gradual breakdown of the agricultural extension system in the country, and so on (Thamarajakshi 1999; Vyas 2001; Bhalla and Singh 2009). Moreover, with an increase in population pressure and a shrinking resource base, the agriculture sector became increasingly prone to a number of formidable problems such as a degradation of productive resources, a slowdown in total factor productivity (TFP), and a sluggish growth in farm income. This was exacerbated by factors like climate change. The indiscriminate use of inputs aided by a highly subsidised input regime had begun taking its toll by decreasing input use efficiency, Structural Breaks in GDP-Agriculture degrading soil quality and lowering the groundwater table. Even though the above analysis based on decadal growth However, some initiatives by the government in the middle of trends helps to reveal the broad pattern of growth, it falls short of the first decade of the 21st century like the Rashtriya Krishi clearly identifying any structural breaks or secular acceleration/ Vikas Yojana, the National Food Security Mission and a special deceleration that could have been present in the series due to emphasis on certified seed production have aided in arresting abrupt changes in technology or policies. There are a number the slide and bringing about a recovery of growth. Since then, a of alternative methodologies available in the time series litemarked improvement in terms of trade (TOT) in favour of agri- rature to determine structural breaks in longitudinal data. culture has turned out to be a boon for farm producers amid an The conventional approach is to apply a “chow test” for a staescalation of input prices and decreasing profitability of farm- tistically significant difference in the parameters of linear time ing in general. Nevertheless, it is important to understand series regression fitted across two periods which have been whether the turnaround since the middle of the first decade of segmented based on an exogenously identified break date. this century is just a passing phase or whether it is Table 1: Results of the Multiple Break Points Estimation sustainable in the long run. Against this backdrop, Particulars Estimated Number of Breaks with Minimum Break Length (h) = 6 m=1 m=2 m=3 m=4 m=5 m=6 m=7 this paper analyses trends in agricultural productiBreak points 1988-89 1980-81 1974-75 1969-70 1969-70 1968-69 (+) 1968-69 vity at the national and state levels in India and [1967-68/1970-71] attempts to identify the major factors responsible 1993-94 1987-88 1980-81 1980-81 1975-76 (-) 1974-75 for the varied performance of agriculture in different [1974-75/1976-77] periods and in different states. Efforts have also been 1997-98 1988-89 1988-89 1982-83 (+) 1980-81 [1981-82/1983-84] made to understand the broad nature of growth in dif1998-99 1995-96 1988-89 (+) 1986-87 ferent sub-sectors of agriculture and its implications [1987-88/1989-90] for the sector as a whole. 2004-05

Historical Growth Trends in Agriculture

We attempt an analysis based on decadal trend growth rates to capture the effects of major changes in technologies and policies on the agriculture sector and understand the broad trends in growth. The gross domestic product (GDP)-agriculture series 56

RSS BIC

1.91 -7.0

0.86 -39.4

0.49 -60.5

0.30 -77.3

0.18 -95.7

1995-96 (-) 1992-93 [1994-95/1996-97] 2004-05 (+) 1998-99 [2003-04/2005-06] 2004-05 0.12 0.12 -109.2 -101.8

Figures in parentheses indicate 95% confidence intervals using the procedure given by Bai (1997); the signs (+/-) in brackets indicate acceleration and deceleration respectively.

june 30, 2012


EPW

Economic & Political Weekly Supplement


However, in this procedure, the researcher has to choose an arbitrary break date based on prior information about the timing of significant changes. Such a method suffers from the problem of relying too much on the subjective judgment of the researcher. Moreover, this methodology often leads to identifying false break dates on the basis of some feature of the data (Wallack 2003; Balakrishnan and Parameshwaran 2007). Table 2: Trend Growth Rates in GDP (2004-05 Prices) of Various Sub-sectors in India during Various Phases of Growth (%/Year) Sector

PGR EGR WTD DIV (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1968-69) 1975-76) 1988-89) 1995-96)

PR REC (1995-96/ (2004-05/ 2004-05) 2010-11)

Agriculture and allied

1.03

1.98

2.42

3.24

2.35

3.31

Agriculture*

0.70

1.93

2.71

3.21

2.30

3.37

Forestry and logging

3.70

2.01

-1.77

0.74

2.05

2.25

Fishery

3.91

4.19

3.45

7.37

3.28

4.42

Non-agriculture

4.90

3.67

5.23

5.91

7.05

9.68

All sectors

3.19

2.99

4.25

5.14

5.95

8.57

*Agriculture includes crops and livestock. Source: National Accounts Statistics, CSO, Government of India.

Recently, several advanced econometric methods have been developed to estimate critical turning points endogenously. The methodology suggested by Zivot and Andrews (1992) is one such procedure based on a unit root hypothesis, incorporating appropriate structural breaks in an endogenous manner. Ghosh (2010) employed this methodology on India’s GDPagriculture data for the period 1960-61 to 2006-07 and identified 1988-89 and 1967-68 as two critical break dates. Wallack (2003) applied the methodology suggested by Vogelsang (1997) to identify break dates based on sup-F or sup-Wald statistics. Clemente et al (1998) and Lumisdane and Papell (1997) also devised alternative methodologies to identify structural breaks based on shifts in levels and slopes in the time series. However, all these methods are ineffective when multiple breaks are present in the series. It was a seminal paper published by Bai and Perron (1998) that addressed the theoretical issues in identifying multiple structural breaks in longitudinal data. A subsequent paper by the same authors (Bai and Perron 2003) dealt with practical considerations and empirical application of the methodology for estimating multiple structural breaks in data series using an efficient dynamic programming algorithm. Balakrishnan and Parameshwaran (2007) followed this approach to identify break dates in the GDP series of various sectors of the economy. Hypothesis on Growth

In this paper, we proceed on the hypothesis that India’s GDPagriculture series is characterised by multiple break points at various intervals. Accordingly, we applied the Bai and Perron (2003) methodology to the GDP-agriculture series (at constant prices) for the period 1960-61 to 2010-11 to simultaneously estimate multiple break points endogenously. The “strucchange” package developed by Zeileis was used for this purpose and the computations were done with R software. The use of this procedure is outlined in Zeileis et al (2005). A detailed account of the Bai and Perron methodology and its estimation details in the present context are provided in Annexure I (p 64). Economic & Political Weekly Supplement

EPW

june 30, 2012

The results of the estimation are presented in Table 1 (p 56). For the selected minimum break length (h) of 6, the dynamic estimation procedure returned seven possible results ranging from one break point to seven break points. The optimum number of breaks was determined on the basis of the Bayesian Information Criteria (BIC), a suitable indicator suggested by Bai and Perron (2003) and later found superior to other information criteria by Wang (2006). Accordingly, an optimum number of six breaks were selected, which also corresponds to minimum residual sum of squares (RSS). Trend growth rates for the seven phases1 corresponding to the six break points identified were worked out and were found to be 0.70%, 1.93%, 2.26%, 2.34%, 3.21%, 2.31% and 3.13% respectively for the periods 1960-61 to 1968-69, 1968-69 to 1975-76, 1975-76 to 1982-83, 1982-83 to 1988-89, 1988-89 to 1995-96, 1995-96 to 2004-05 and 2004-05 to 2010-11. As GDPagriculture grew more or less in the same fashion during the third and fourth phases with comparable trend growth rates, it was logically sound to treat them as a single phase. Consequently, for the overall GDP-agriculture series, six distinct phases of growth were chosen for further analysis: (i) Phase I: Pre-green revolution period (PGR) – 1960-61 to 1968-69. (ii) Phase II: Early green revolution period (EGR) – 1968-69 to 1975-76. (iii) Phase III: Period of wider technology dissemination (WTD) – 1975-76 to 1988-89. (iv) Phase IV: Period of diversification (DIV) – 1988-89 to 1995-96. (v) Phase V: Post-reform period (PR) – 1995-96 to 2004-05. (vi) Phase VI: Period of recovery (REC) – 2004-05 to 2010-11.2 Table 3: Trend Growth Rates in VOP (2004-05 Prices) of Major Crop Groups during Various Phases of Growth (%/Year) Crop Group

Cereals Pulses Oilseeds Drugs and narcotics Fruits and vegetables Fibres Spices and condiments All crops

PGR EGR WTD DIV (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1968-69) 1975-76) 1988-89) 1995-96)

1.20 -2.14 0.76 2.71 5.14 0.54 1.40 1.11

1.75 -1.02 2.31 2.51 5.33 1.79 3.10 1.90

2.63 0.75 2.95 2.23 3.13 1.65 4.11 2.56

2.52 0.22 4.42 2.11 4.07 5.31 3.60 2.64

PR REC (1995-96/ (2004-05/ 2004-05) 2010-11)

0.51 0.23 -1.07 2.89 3.85 -1.17 4.95 1.88

2.60 1.31 1.43 2.68 5.02 7.97 3.52 3.01

Source: Same as Table 2.

To further understand the growth performance of the agriculture sector and to compare and contrast it with that of the overall economy and non-agriculture sectors, trend growth rates of these disaggregated series corresponding to the phases identified above were worked out. They are presented in Table 2. It was observed that the overall economy has progressed steadily with a sustained increase in GDP growth in each subsequent phase except during the period 1968-69 to 1975-76. The annual average trend growth improved from 3.19% in the pre-green revolution period to 8.57% in the last phase of recovery. However, as mentioned earlier, growth of both the agriculture and allied sector and the agriculture sector in isolation decelerated during the post-reform period, followed by a recovery in the last phase. Negative


57


growth (-1.77%) was observed in the forestry and logging series during the period of wider technology dissemination and a feeble growth of 0.74% per annum during the period of diversification. Table 4: Trend Growth Rates in Production of Major Crops during Various Phases of Growth (%/Year) Crop Group

Rice Wheat Maize Gram Arhar Groundnut Rapeseed and mustard Soyabean Onion Potato Sugar cane Cotton

PGR EGR WTD DIV (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1968-69) 1975-76) 1988-89) 1995-96)

0.47 4.39 4.42 -3.19 -1.05 0.33 1.69 NA NA 6.88 1.14 1.41

1.35 4.75 0.18 -2.30 -0.27 1.25 4.85 NA NA 5.25 2.79 3.19

2.75 4.79 1.25 -1.12 2.57 0.78 5.79 22.84 2.46 5.43 2.26 1.17

2.69 3.63 2.80 3.26 -1.81 -0.34 5.34 20.78 4.57 3.19 3.70 5.16

PR REC (1995-96/ (2004-05/ 2004-05) 2010-11)

0.58 0.85 4.30 -1.67 -0.20 -2.98 -1.02 2.66 3.70 2.28 -0.97 -1.02

0.60 3.31 3.21 5.86 2.12 -3.28 -1.50 6.09 13.98 9.66 3.26 9.92

Source: Agricultural Statistics at a Glance 2011, Ministry of Agriculture, Government of India.

The fishery sector witnessed the highest growth (7.37%) during the period of diversification, quite in line with the surmise that growth became more broad-based with a greater focus on non-crop sectors during this period. It is worth noting that unlike agriculture, the non-agriculture sector registered higher growth during the post-reform period. This indirectly suggests that for a brief spell after reforms there was more emphasis on the non-agriculture sector in terms of resource allocation, probably at the expense of the agriculture sector, a point that will be discussed in subsequent sections. The performance of the non-agriculture sector has been particularly impressive during 2004-05 to 2010-11 as obvious from the growth rate of 9.68%.

importance and priority given to agriculture was diluted in the Second (1956-61) and Third (1961-66) Five-Year Plans. Growth of the sub-sector decelerated during the early 1960s, leading to food shortages and the import of huge quantities of foodgrains. However, in the mid-1960s, a new agricultural strategy was adopted, which emphasised the spread of dwarf and highyielding varieties (HYVs) of wheat and rice. Policy support, the adoption of improved production technologies and public investment in infrastructure, research and extension also contributed to growth in the crop sector. The new strategy paid dividends and resulted in the acclaimed green revolution. A further acceleration (2.56%) was experienced in the period of wider dissemination of green revolution technologies. As seen in Table 3, crops such as cereals, pulses, oilseeds, spices and condiments fared better in terms of growth during this phase than the previous two phases. However, drugs and narcotics, and fruits and vegetables experienced slower growth during this phase. The period of diversification maintained the tempo of growth in the crop sector, but a perceptible slowdown in performance was noticed in the post-reform period. The period after reforms was a phase of lacklustre performance for most crops. Overall growth in the crop sub-sector dipped to 1.88% from 2.64% in the previous period. The deceleration was aided by negative growth in oilseeds (-1.07%) and Table 6: State-wise Comparison of Trend Growth (1999-2000 to 2008-09) in NSDP-Agriculture at 2004-05 Prices Low (up to 2.0) State

Medium (< 4 > 2) TGR

Jharkhand -0.9 Karnataka 0.4 Assam 0.8 Kerala 1.0 Uttar Pradesh 1.6 Tamil Nadu 1.8 West Bengal 2.0

High (> 4)

State

TGR

State

Uttarakhand Himachal Pradesh Punjab Bihar Jammu and Kashmir Haryana Orissa

2.2 2.4 2.4 2.5 3.4 3.5 3.6

Madhya Pradesh 4.1 Rajasthan 4.3 Maharashtra 4.7 Andhra Pradesh 5.2 Chhattisgarh 6.1 Gujarat 11.5

TGR

Agriculture Performance at Disaggregate Level


Growth in the crop sub-sector gradually gained momentum in the first four phases. The pre-green revolution period registered a modest growth of 1.11% per annum, which improved to

fibres (-1.17%) and the shoddy performance of cereals (0.51%) and pulses (0.23%). It was only drugs and narcotics, fruits and vegetables, and spices and condiments that did well in this phase. A turnaround was observed in 2004-05 with renewed growth in cereals (2.60%), pulses (1.31%), oilseeds (1.43%), fruits and vegetables (5.02%) and fibres (7.97%). Even though the period of recovery has helped most of these crops regain their past vigour in growth, the overall rate of growth of crops (3.01%) in this phase has been modest. The performance of individual crops during various phases of growth was assessed by estimating their annual trend growth rates in production for the respective phases (Table 4). Even though a wide disparity in growth was observed across the crops during successive phases, a general deceleration was visible after the initial years of reforms. The period of diversification was characterised by a deceleration in traditional crops such as rice, wheat, coarse cereals, oilseeds and pulses but relatively better growth in crops such as onion, potato, sugarcane and cotton. In general, pulses such as gram and arhar performed poorly in all phases. Among oilseeds, groundnut, rapeseed and mustard did not do well

Table 5: Trend Growth Rates in VOP (2004/05 Prices) of Livestock Sub-sectors during Various Phases of Growth (%/Year) Commodity

Milk group Meat group Egg Wool and hair Others Total livestock

PGR EGR WTD DIV (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1968-69) 1975-76) 1988-89) 1995-96)

1.02 0.11 4.73 0.39 -0.83 0.40

3.53 0.15 2.15 0.77 1.21 2.69

5.58 4.37 8.34 -3.32 1.88 4.89

4.56 5.41 5.27 1.65 0.18 4.12

PR REC (1995-96/ (2004-05/ 2004-05) 2010-11)

3.72 3.25 3.81 2.59 0.78 3.43

3.68 5.05 6.25 -1.58 2.65 4.29


1.90% after the green revolution (Table 3, p 57). This was made possible by a substantial allocation of resources to agriculture and the introduction of superior technologies and institutional reforms. The First Five-Year Plan (1951-56) allocated a substantial part of its outlay to this sector. This plan also witnessed progress in tenancy land reforms, institutional changes, and the operationalisation of some major irrigation projects. The 58

june 30, 2012


EPW



in most of the phases, particularly in the last two. Growth in soyabean was remarkable during the period of wider technology dissemination and period of diversification but it slowed down in the later phases. Commercial crops such as sugar cane and cotton exhibited a steady improvement in growth in the initial phases, but a slump was observed in the post-reform period, followed by a turnaround in the recovery phase. Livestock Sub-sector

As with the crop sector, growth in the livestock sub-sector was modest in the first phase before the green revolution. However, the momentum picked up during the green revolution owing to substantial investment for reviving the sector and due to the inception of programmes like the Intensive Cattle

growth was experienced both at the aggregate level and at the level of individual products during the post-reform phase, livestock products did not suffer a major setback in growth as happened in the case of crops. Similar to the crop segment, there was a recovery in growth in the livestock sector in the six years after 2004-05 except in the case of wool and hair. Nevertheless, it is to be noted that the growth in the livestock sector in the last two decades has been driven by an increase in the stock of livestock population than a growth in productivity (GoI 2011), which is not a healthy trend. Agricultural Performance at State Level

The performance of agriculture at the state level in the last decade was examined on the basis of the growth rate in net state domestic product (NSDP) Table 7: State-wise Comparison of Trend Growth (2000-01 to 2009-10) in Production of Major Crops Commodity Low (< 2.0%) Medium (>2% and 4%) agriculture from 1999-2000 to Rice BH, HP, ASM, TN, UP, UTL, WB, MH, MP J&K, PJ, KAR, AP HR, CHT, OR, GJ, JH, RJ 2008-09 at 2004-05 prices. The Wheat ASM, HP, WB, PJ, UP, HR, UTL, BH RJ KAR, JH, J&K, MP, MH, GJ growth rates were estimated from Maize MP, UP, GJ, HP, J&K, BH PJ, RJ JH, KAR, AP, MH, WB, TN two-year moving averages of the Total cereals BH, UP, WB, PJ, TN, HR, MH, MP, KAR, RJ, AP CHT, OR, JH, GJ data series. The states were then Gram WB, UP, BH, ASM, UTL, HAR – RJ, MP, KAR, CHT, OR, MH, AP, GJ classified into three categories of Arhar TN, UP, BH, AP MH, MP OR, KAR, GJ, JH growth rate – more than 4% Total pulses PJ, TN, WB, BH, UP, MP HAR, MH, RJ KAR, CHT, AP, OR, GJ, JH (high); more than 2% but less Total foodgrains BH, ASM, UP, WB, UTL, TN, PJ HR, MP, MH, RJ, KAR, AP CHT, OR, JH, GJ than 4% (medium); and less than Groundnut KAR, TN, MH, UP, AP, MP – GJ, RJ, OR 2% (low). Rapeseed and mustard PJ, ASM, UP, BH HAR GJ, RJ, MP As can be seen from Table 6 Soyabean – – MP, KAR, RJ, MH, AP (p 58), there is great variation in Total oilseeds TN, PJ, ASM, UP, KAR, AP, HR BH, WB OR, GJ, MP, RJ, MH the growth performance of agriSugar cane PJ, HP, WB, KAR, UTL, OR, AP, UP, – MP, MH culture across states. The NSDPASM, BH, GJ, TN agriculture in Gujarat increased Cotton – – MH, TN, KAR, HP, RJ, PJ, AP, MP, GJ by more than 10% per year in the AP: Andhra Pradesh; ASM: Assam; BH: Bihar; CHT: Chhattisgarh; GJ: Gujarat; HR: Haryana; HP: Himachal Pradesh; J&K: Jammu and Kashmir; JH: Jharkhand; KAR: Karnataka; KL: Kerala; MP: Madhya Pradesh; MH: Maharashtra; OR: Orissa; PJ: Punjab; RJ: Rajasthan; TN: last decade, which was a remarkTamil Nadu; UP: Uttar Pradesh; UTL: Uttarakhand; WB: West Bengal. able achievement for an individSource: Agricultural Statistics at a Glance (various years), Ministry of Agriculture, Government of India. ual state. A close examination of Table 8: State-wise Comparison of Trend Growth (2000-01 to 2009-10) in Production of Major Livestock Products the data shows that Gujarat made Commodity Low (2% and 4%) appreciable progress in raising Milk KL, KAR, ASM, HP, J&K CHT, HR, PJ, TN, WB, UTL, MH, RJ UP, MP, GJ, AP, OR, BH agricultural production, particuEgg JH, KL, ASM, MP, RJ, MH WB, PJ, J&K, KAR, UP, CHT AP, BH, UTL, OR, TN, GJ, HR larly after 2002-03. Several facWool TN, JH, HR, PJ, MP, RJ, BH, UTL, J&K AP tors such as an innovative agriUP, HP, MH, GJ, WB, CHT, KAR culture development programme Meat WB, KL KAR, UTL, BH RJ, GJ, AP, MH, MP, OR, UP, TN, HR, PJ Same as Table7. adopted by the state, liberalised Source: Basic Animal Husbandry Statistics (various years), Ministry of Agriculture, Government of India. markets, private capital, a reinDevelopment Project (ICDP) in the mid-1960s. Consequently, vention of agricultural extension, an improvement in rural inthe growth rate of the overall livestock sector averaged 2.69% frastructure, mass-based water harvesting and farm power during the phase of the green revolution and 4.89% in the reforms have been cited as drivers of the agrarian growth phase of wider dissemination of technology (Table 5, p 58). story of Gujarat (Shah et al 2009). Chhattisgarh came second The latter phase also coincided with the Operation Flood pro- with a 6.1% growth rate. The other states which recorded more gramme, which gave impetus to institutional reforms of the than 4% annual growth in agricultural output were Madhya sector through the establishment of milk marketing coopera- Pradesh, Rajasthan, Maharashtra and Andhra Pradesh. In eastern India, Orissa had more than 3% growth but other tives and other initiatives. The value of production (VOP) of the milk group increased at 3.53% per annum while that of states continued to have low growth. Agriculture in Jharkhand the meat group and the egg group increased by 0.15% and had negative growth in the last decade. Karnataka and Assam 2.15% respectively during the early green revolution. Produc- had less than 1% growth, accentuated by low levels of production of these major livestock products continued to grow im- tivity. State-wise growth trends show that despite the usual pressively through the phases of wider technology dissemina- explanations for low growth such as changing climate, soil tion and diversification. Though a minor deceleration in degradation, stress on water resources, inadequate technology Economic & Political Weekly Supplement

EPW

june 30, 2012


59


Input

PGR EGR WTD DIV (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1968-69) 1975-76) 1988-89) 1995-96)

Quality/certified seeds NA Fertilisers (NPK/GCA) 24.22 Irrigation (GIA) 2.62 All inputs* 2.16

NA 7.35 2.77 2.62

NA 9.29 2.34 4.48

3.54 2.84 2.67 2.27

PR 1995-96/ 2004-05)

REC (2004-05/ 2009-10)

5.56 2.59 0.75 2.07

22.93 6.95 2.18 3.51

NA denotes data not available; *in value terms at 2004-05 prices. Source: Same as Table 2.

of low, medium and high growth and those in each category were further ordered in ascending magnitude of growth rate. As can be seen in Table 7 (p 59), there was low growth in rice production (below 2%) in Bihar, Himachal Pradesh, Assam, Tamil Nadu, Uttar Pradesh, Uttarakhand, West Bengal, Maharashtra and Madhya Pradesh. At the other end, rice production grew at more than 4% in Haryana, Chhattisgarh, Orissa, Gujarat, Jharkhand and Rajasthan. It was found that traditional wheat-growing states such as Punjab, Haryana, Uttar Pradesh and Bihar fell in the category of low growth while small-scale producers such as Karnataka, Jharkhand, Maharashtra and Gujarat experienced a remarkable growth in production in the last decade. Even though this could be due to a high base value effect and capacity saturation in the traditional growing states, a strong indication of the emergence of new areas of wheat production is obvious in the data. In pulse production, promising performances were displayed by Andhra Pradesh, Orissa, Jharkhand and Gujarat. As in the past, large oilseed producing states such as Rajasthan, Madhya Pradesh, Maharashtra and Gujarat continued to lead the way in the last decade with exceptional growth in production. A majority of the sugar cane-producing states fell in the category of low growth (negative growth in seven states) with the only exceptions being Madhya Pradesh and Maharashtra. Though it is not wise to jump to conclusions about the dismal plight of this crop, it is an indication of stagnating yield levels of sugar cane in recent years. In contrast with all the other crops examined, cotton was the only one where all major producing states showed high growth, a testimony to the remarkable success of Bt technology in the country. Similar to crops, the state-wise growth of major livestock products such as milk, egg, wool and meat in the decade 2000-01 to 2009-10 was assessed and the states were classified into categories of low, medium and high growth. Milk production grew at impressive rates in Uttar Pradesh, Madhya Pradesh, Gujarat, Andhra Pradesh, Orissa and Bihar (Table 8, p 59). Haryana, Punjab and Rajasthan, which are the major milkproducing states in the country, fell in the medium-growth 60

Pattern of Growth of Major Determinants

In this section, an attempt is made to analyse the key factors that resulted in the variable growth performance of the agriculture sector in the six decades after Independence. For this, the behaviour of major drivers of agricultural growth such as capital formation, primary inputs (quality seeds, fertilisers and irrigation), terms of trade of agriculture vis-à-vis non-agriculture, technology, and other factors such as cropping intensity, institutional credit and electricity for agriculture were probed during the respective periods of growth. Though trends in each of the identified factors may not necessarily match with exact trends in output growth during the various phases, broad correlations can be derived. Investment in Agriculture

Investment is a key driver of growth in all sectors of the economy, including agriculture. Total fixed capital formation in agriculture moved upwards from the mid-1960s onwards when both government and private spending increased with the commencement of the green revolution. In the initial years of this phase, it was private capital formation that received a real impetus. However, public investment picked up in the mid1970s. The share of public capital formation in GDP – agriculture remained in the range of 3% to 4% throughout the 1970s, but started dipping from the mid-1980s, falling below 2% in 1996-97 and reaching a trough at 1.87% in 2000-01 (Figure 2). Figure 2: Gross Fixed Capital Formation in Agriculture as a Share of GDP-Agriculture at Current Prices (%) 20.00 18.00 16.00

Total

14.00 12.00 10.00 8.00

Private

6.00 4.00 2.00 0.00

Public 1960-61 1962-63 1964-65 1966-67 1968-69 1970-71 1972-73 1974-75 1976-77 1978-79 1980-81 1982-83 1984-85 1986-87 1988-89 1990-91 1992-93 1994-95 1996-97 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2009-10

Table 9: Trend Growth Rates in Use of Agricultural Inputs during Various Phases of Growth (%/Year)

category. However, it is a matter of concern that highly productive states like Kerala and Karnataka lagged behind with sluggish growth in production during the last decade. Egg production surged during the period in Uttarakhand, Orissa, Tamil Nadu, Gujarat and Haryana (all with greater than 10% growth), thanks to a quick transition from backyard poultries to commercial hatcheries. A majority of the woolproducing states languished with slow growth except a few. The meat sector also demonstrated an impressive resurgence during the period under study.

Share of investment (%)

and policy constraints, some states have done exceedingly well. It also shows that action at the state level matters a lot in determining the performance of agriculture as a whole in the country. There is a need to learn from better-performing states so that relevant experiences can be replicated in low-growth states, particularly those with high potential. The performance of individual crops in different states was assessed on the basis of growth in production over the period 2000-01 to 2009-10. The states were classified into categories

Source: Same as Figure 1.

As with public investment, private investment also experienced a dip in the early 1990s. Both declined not only in terms of share in GDP but also in absolute terms during this period. This led to a perceptible slowdown in agricultural growth,

june 30, 2012


EPW



To understand the contribution of primary inputs to the growth pattern of the agriculture sector during various phases, the trend growth rates for primary inputs such as certified seeds, fertilisers and irrigation were estimated. They are presented in Table 9 (p 60). Quite in line with expectations, the value of total inputs grew during the early green revolution and the period of wider dissemination. The total value of primary inputs grew per annum 2.62% and 4.48% during these periods respectively. In accordance with the pattern of growth in VOP/GDP agriculture, total input use decelerated in the post-reform period (2.07%), and showed a reversal in trend during the phase of recovery. This pattern of deceleration in yield/crop output with a reduced use in inputs in the post-liberalisation phase has been acknowledged by Bhalla and Singh (2009) and Chand (2010). It also supports the assumption that a part of the deficit in public investment was translated into reduced availability/use of inputs and thereby contributed to a deceleration in output growth. A plough back in investment during the middle of the first decade of this century reversed the consumption pattern of primary inputs, as evident from a substantial growth in certified seed distribution (22.93%), renewed consumption of fertilisers (6.95%) and an increase in area under irrigation (2.18% growth in gross irrigated area). Table 10: Total Seed Production by Public and Private Sectors Year

Total Seed Production (lakh qntl)

Share of Private Sector (%)

Quantity of Seed Produced by Private Sector (lakh qntl)

132.27

47.48

62.80

69.47

2004-05

140.51

45.02

63.26

77.25

2005-06

148.18

46.80

69.35

78.83

2006-07

194.31

41.00

79.67

114.64

2007-08

194.23

42.59

82.72

111.51

2008-09

250.40

39.78

99.61

150.79

2009-10

280.00

38.93

109.00

171.00


EPW

june 30, 2012

The terms of trade for agriculture measured as the ratio of the implicit price index of GDP-agriculture to that of GDP-nonagriculture witnessed a gradual increase during the 1980s and Figure 3: Terms of Trade Based on Ratio of Implicit Price Index of GDP Agriculture to GDP Non-agriculture 140.0

Quantity of Seed Produced by Public Sector (lakh qntl)

2003-04


Terms of Trade

130.0 120.0 110.0 100.0 90.0 80.0 70.0 60.0

1960-61 1962-63 1964-65 1966-67 1968-69 1970-71 1972-73 1974-75 1976-77 1978-79 1980-81 1982-83 1984-85 1986-87 1988-89 1990-91 1992-93 1994-95 1996-97 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11

Primary Inputs

It is important to understand the overwhelming role played by quality seeds in bringing about a recovery in the growth of major crops. Strong support was provided to the seed sector in the latter half of the last decade and this has led to success on two fronts. One, production of certified seeds doubled in the four years after 2004-05 (Table 10). Two, the public sector has emerged as a competitor to the private sector in the seed market (Malik and Chand 2011). As a result, the share of the private sector in seed production declined substantially in this period. Yet the ratio of certified seeds to total seeds is much lower than the prescribed norm and there is tremendous scope to raise productivity and production of crops by raising the share of quality seeds in total seeds used by farmers. As with seeds, there has been a discernible change in the role of the public sector in the development of hybrids after 2001-02. After 2001-02, the private sector developed 150 hybrids of cotton compared to 15 by the public sector before that year. Similarly, in maize, the number of hybrids developed by the private and public sectors was 67 and 3. But in the following seven years, the share of the public sector increased from 8% to 19% in cotton, 4% to 40% in maize and 25% to 58% in rice (Malik and Chand 2011). Similar changes are observed in the case of other crops as well. Cotton and maize have been the favourite crops for development of hybrids both by public and the private sectors. The private sector also showed strong interest in pearl millet, sunflower and sorghum. Considering all crops together, the private sector accounts for three-fourths of the total hybrids developed so far in the country. In recent years, particularly after 2001-02, the gap between the private and public sector in development of hybrids has narrowed, though the private sector continues to dominate in cross-pollinated crops such as cotton, maize, pearl millet and sorghum.

Terms of Trade (Agri/Non-agri

particularly in the later years of the 1990s and the early 2000s. That a deficit in public expenditure on agricultural infrastructure and extension services substantially contributed to the slowdown in agricultural growth has been pointed out by studies such as Mahendradev (2000), Vyas (2001), Rao (2003) and Chand and Kumar (2004). A deceleration in growth of total factor productivity in the north-western region, especially in rice and wheat-growing areas, put pressure on the production of major staples like rice and wheat and forced the government to take measures to reverse such trends. Since then conscious efforts have been made to raise investment in agriculture. It is clear from Figure 2 that the share of public investment returned to the level of the 1980s and private investment grew at an exponential rate after 2000-01. At a level of 18.12%, 2008-09 marked the highest share in total agricultural sector investment in the history of planned development. The recovery in the production of major crops and livestock products in recent years can, to a large extent, be attributed to this rise in investment.

Source: Same as Figure 1.

the early years of the 1990s. However, a sharp deterioration was experienced during the late 1990s and it extended up to 2004-05 (Figure 3). This can be perceived as having been one of the contributing factors behind the deceleration of agricultural growth during this period. After 2004-05, a notable turnaround in the terms of trade was observed, which could


61


be attributed to faster growth in agricultural prices (around 30% higher) than those of non-farm commodities. The indications from factors underlying the increase in agri-food prices are that food prices are likely to grow faster than non-food prices in the comTable 11: Growth in TFP and Its Contribution to ing years. This Agricultural Growth Year Growth Rate (%) Share of TFP has a positive efGDP Agriculture TFP in Agricultural fect on farm inGrowth (%) come and pro1950-51 to 1960-61 3.03 1.65 54.5 vides incentives 1960-61 to 1970-71 2.31 0.88 38.1 for raising pro1970-71 to 1980-81 1.50 -0.35 -23.3 1980-81 to 1990-91 3.43 1.89 55.1 ductivity and proSource: Sivasubramonian (2004). duction. Some important reasons for the continuing improvement in the terms of trade are strong consumer demand for agricultural products, rising marginal cost of production, rising wages in rural Table 12: Annual Growth in TFP for Major Crops and Its Share in Output Growth (%) Crop

Rice Wheat Maize Jowar Bajra Barley Gram Moong Arhar Urad Soyabean Groundnut Rapeseed and mustard Sugar cane Cotton Jute

TFP Growth/Annum (%) 1986-1995

1996-2005

0.74 2.51 0.67 0.74 0.39 0.44 0.09 -0.59 0.21 -0.22 0.83 0.55 0.74 -1.32 0.92 1.59

0.40 1.61 1.64 -0.42 1.50 0.61 0.34 1.70 -0.54 -0.73 0.63 1.30 0.08 -0.65 0.80 0.25

Share of TFP Growth in Output Growth (%) 1986-95 1996-2005

23.5 68.3 11.6 47.7 9.4 30.5 5.7 (-) 33.8 (-) 4.6 25.4 8.0 (-) 21.5 83.6

43.5 60.4 31.0 (-) 55.9 (-) 71.4 17.8 (-) (-) 6.7 30.8 7.7 (-) 46.0 70.5

Other Factors

Source: Chand et al (2011).

areas across the country, high producers’ margin, the shrinking natural resource base, and a deficit in breakthrough of technology. Strong indications are emerging in India and at the global level that, in future, price increases will drive supply growth, rather than supply growth resulting in a fall in prices. Technology

Technology has played a key role in altering the trajectory of growth of Indian agriculture. The phenomenal success of the green revolution strategy in increasing the productivity of major crops was evidence of this process. An appreciable growth in area under HYV (19.11%) during the early green revolution period demonstrated the power of technology to unleash the productivity potential of Indian farms. TFP is a commonly used indicator to gauge the effect of growth in inputs and other factors such as technology, infrastructure, institutions and farmers’ knowledge on productivity growth. A study by Sivasubramonian (2004) looked at the growth in TFP during different periods and estimated its share in total 62

agricultural growth in the country. The results of this study suggest that TFP grew at 1.65% during 1950-51/1960-61 and at 0.88% during 1960-61/1970-71. A negative growth (-0.35) in TFP was reported during 1970-71 to 1980-81 and a recovery at 1.89% during 1980-81 to 1990-91 (Table 11). It was estimated that the growth in TFP contributed 54.5%, 38.1%, -23.3% and 55.1% to agricultural growth during the four periods respectively. However, the study stopped short of analysing the next decades and trends in TFP growth in recent years are not available from the study. A recent study by Chand et al (2011) updated the information on TFP growth till 2005. The crop-wise growth in TFP was estimated for two periods 1986-95 and 1996-2005 (Table 12). The results suggest that TFP growth increased in crops such as rice, maize, bajra, gram, moong, soyabean, groundnut and cotton in 1996-2005 compared to 1986-95, but decreased in wheat, rapeseed and mustard and jute while the rate was negative in jowar, barley, arhar, urad and sugar cane. Notwithstanding this, it is unclear to what extent the TFP growth in overall agriculture fared well in the two periods under consideration.

The pattern of growth in other key determinants such as cropping intensity, institutional credit and electricity supply for agriculture were also examined. Cropping intensity increased at a greater pace (0.36%) during the green revolution period than the period immediately before it (Table 13). An even faster rate (0.44%) in intensification of land use for crop production was seen in the periods of wider technology dissemination and period of diversification, but a marked deceleration (0.15%) occurred during the period after reforms. As in the case of other determinants, a gain in the momentum of intensification was observed in the recovery phase. A phenomenal growth in the distribution of institutional credit – 12.93% per annum – was the highlight during the period 1995-96 to 2004-05. This was a result of rapid expansion of the banking infrastructure across the country, which Table 13: Trend Growth Rates in Key Determinants during Various Phases of Growth (%/Year) Input

PGR EGR WTD DIV PR REC (1960-61/ (1968-69/ (1975-76/ (1988-89/ 1995-96/ (2004-05/ 1968-69) 1975-76) 1988-89) 1995-96) (2004-05) 2009-10)

Cropping intensity Institutional credit Electricity for agriculture

0.05 NA NA

0.36 3.25 13.26

0.44 5.96 11.66

0.46 1.30 12.28

0.15 12.93 -0.04

0.63 12.78 5.02

NA denotes data not available. Source: Same as Table 4.

has been a boon to the farming community. The delivery of electricity shot up by 13.26%, 11.66% and 12.28% during the periods of early green revolution, wider dissemination and period of diversification respectively, but registered a negative growth (-0.04%) in the subsequent period, followed by a recovery (5.02%) in the last phase. In general, the growth behaviour of almost all major determinants of agricultural production in the different phases of growth more or less coincided with the observed pattern of growth in GDP-agriculture.

june 30, 2012


EPW



Conclusions A historical and spatial growth analysis of Indian agriculture suggests that growth of the sector has been highly uneven across time and regions. The green revolution technologies introduced in the late 1960s played an active role in lifting the growth trajectory from below 1% to 3% in a short time. During the period of wider technology dissemination, the spread of these technologies across regions aided in maintaining the tempo of growth realised in the early green revolution period. Subsequent years saw growth becoming broadbased with faster diversification of production towards horticultural and cash crops. However, the post-reform period witnessed a deceleration of growth in most of the major crops and this could be attributed to a significant diversion of resources away from agriculture. Both public and private investment suffered a setback during this period and the effect was a sluggish performance of the sector as a whole. The use of primary inputs in the sector also slowed down, which resulted in the yield levels of many crops stagnating or even Notes 1

2

The break points were made common for successive phases while estimating trend growth rates to capture the shifts in level (intercept). The last year of the period of recovery has been taken either as 2009-10 or 2010-11 depending on the data availability on variables while working out growth rates.

References Bai, Jushan (1997): “Estimating Multiple Breaks One at a Time”, Econometric Theory, Vol 13, pp 315-52. Bai, Jushan and Pierre Perron (1998): “Estimating and Testing Linear Models with Multiple Structural Changes”, Econometrica, Vol 66, pp 47-78.

declining. The retardation of growth continued up to 2004-05, after which there was a sharp recovery. This could be attributed to a conscious hike in public and private investment and a substantial improvement in terms of trade in favour of the agriculture sector. More than a matter of chance or a brief spell of improvement, the recovery can be considered the result of a significant change in strategy. This saw a rapid expansion of agricultural credit, reinvigorated growth in the distribution of quality seeds and substantial public and private investment in the agriculture sector. The spurt in farm production was partly driven by the favourable prices of farm products. Such price-led growth is a morale booster for the farming community and has positive implications for farm income and profitability, but also poses the larger question of sustainability. Future growth of the sector relies a lot on the manner in which the resources in the sector are put to productive use and the degree to which farmers are incentivised to continue with farming as a profession.

– (2003): “Computation and Analysis of Multiple Structural Change Models”, Journal of Applied Econometrics, Vol 18, pp 1-22. Balakrishnan, Pulapre (2000): “Agriculture and Economic Reforms: Growth and Welfare”, Economic & Political Weekly, Vol 35 (12), pp 999-1004. Balakrishnan, Pulapre and M Parameshwaran (2007): “Understanding Economic Growth in India: A Prerequisite”, Economic & Political Weekly, Vol 42 (7), pp 2915-22. Bhalla, G S and Gurmail Singh (2001): Indian Agriculture: Four Decades of Development (New Delhi: Sage). – (2009): “Economic Liberalisation and Indian Agriculture: A State-wise Analysis”, Economic & Political Weekly, Vol 44 (52), pp 34-44. Chand, Ramesh (2010): “Achieving 4% Growth in Agriculture during the Eleventh Five-Year

Plan: Feasibility and Constraints” in Pulin B Nayak, Bishwanath Goldar and Pradeep Agrawal (ed.), India’s Economy and Growth, Essays in Honour of VKRV Rao (New Delhi: Sage). Chand, Ramesh and Pramod Kumar (2004): “Determinants of Capital Formation and Agriculture Growth: Some New Explorations”, Economic & Political Weekly, Vol 39 (52), pp 5611-16. Chand, Ramesh, Praduman Kumar and Sant Kumar (2011): “Total Factor Productivity and Contribution of Research Investment to Agricultural Growth in India”, Policy Paper No 25, National Centre for Agricultural Economics and Policy Research, New Delhi. Chand, Ramesh and S S Raju (2009): “Instability in Indian Agriculture during Different Phases of Technology and Policy”, Indian Journal of

REVIEW OF AGRICULTURE June 26, 2011 Farm Size and Productivity: Understanding the Strengths of Smallholders and Improving Their Livelihoods —Ramesh Chand, P A Lakshmi Prasanna, Aruna Singh Spread and Economics of Micro-irrigation in India: Evidence from Nine States —K Palanisami, Kadiri Mohan, K R Kakumanu, S Raman Water Harvesting Traditions and the Social Milieu in India: A Second Look —Shri Krishan Irrigation in Telangana: The Rise and Fall of Tanks —Gautam Pingle Farmers’ Suicides in Punjab: A Census Survey of the Two Most Affected Districts —R S Sidhu, Sukhpal Singh, A S Bhullar Reorienting Land Use Strategies for Socio-economic Development in Uttar Pradesh —Arun Chaturvedi, N G Patil, S N Goswami National Water Policy: An Alternative Draft for Consideration —Ramaswamy R Iyer Revitalising Higher Agricultural Education in India —J Challa, P K Joshi, Prabhakar Tamboli

For copies write to: Circulation Manager, Economic and Political Weekly, 320-321, A to Z Industrial Estate, Ganpatrao Kadam Marg, Lower Parel, Mumbai 400 013. email: [email protected] Economic & Political Weekly Supplement

EPW

june 30, 2012


63

REVIEW OF RURAL AFFAIRS Agricultural Economics, Vol 64 (2), pp 283-88. Clemente, Jesus, Antonio Montanes and Marcelo Reyes (1998): “Testing For a Unit Root in Variables with a Double Change in Mean”, Economic Letters, Vol 59 (2), pp 175-82. Datt, Gaurav and Martin Ravallion (1998): “Farm Productivity and Rural Poverty in India”, Journal of Development Studies, 34 (1). Ghosh, Madhusudan (2010): “Structural Breaks in Indian Agriculture”, Indian Journal of Agricultural Economics, Vol 65 (1), pp 59-79. GoI (1951): “Census 1951”, Office of the Registrar General and Census Commissioner, Ministry of Home Affairs, Government of India. – (2011): “Report of the Working Group on Animal Husbandry and Dairying for Twelfth Five Year Plan”, Planning Commission, Government of India. Joshi, P K, P S Birthal and Nicholas Minot (2006): “Sources of Agricultural Growth in India: Role of Diversification towards High Value Crops”, MTID Discussion Paper No 98, International Food Policy Research Institute, Washington DC. Malik, Harbir and Ramesh Chand (2011): “The Seeds Bill, 2011: Some Reflections”, Economic & Political Weekly, Vol 46 (51), pp 22-25. Lumsdaine, R L and D H Papell (1997): “Multiple Trend Breaks and the Unit Root Hypothesis”,

Review of Economics and Statistics, 79 (2), pp 212-21. Mahendradev, S (2000): “Economic Reforms, Poverty, Income Distribution and Employment”, Economic & Political Weekly, Vol 35 (10), pp 823-35. Radhakrishna, R (2002): “Agricultural Growth, Employment and Poverty: A Policy Perspective”, Economic & Political Weekly, Vol 37 (3), pp 243-49. Rao, V M (1996): “Agricultural Development with a Human Face”, Economic & Political Weekly, Vol 31 (26), pp A-50–A-62. Rao, C H Hanumantha (2003): “Reform Agenda for Agriculture”, Economic & Political Weekly, Vol 21 (29), pp 1943-48. Ravallion, Martin and Gaurav Datt (1996): “How Important to India’s Poor Is the Sectoral Composition of Economic Growth?”, World Bank Economic Review, 10 (1), pp 1-25. Shah, Tushaar, Ashok Gulati, P Hemant, Ganga Shreedhar and R C Jain (2009): “Secret of Gujarat’s Agrarian Miracle after 2000”, Economic & Political Weekly, Vol 44 (52), pp 45-55. Sivasubramonian (2004): Sources of Economic Growth in India (New Delhi: Oxford University Press). Thamarajakshi, R (1999): “Agriculture and Economic Reforms”, Economic & Political Weekly, Vol 34 (14), pp 2393-95.

estimates of β and δj are obtained by minimising the sum of squared residuals

Annexure I Bai and Perron (2003) Methodology for Estimating Multiple Structural Breaks in Longitudinal Data

m+1

We consider the following multiple linear regression model with m breaks (m+1 regimes) with h as the minimum length assigned to a segment: y t = x´t β + z´t δj + utt = Tj–1 + 1, ... ... ... ...,Tj

(1)

For j = 1, ... ..., m + 1. In this model, y t is the observed dependent variable at time t; x t (p × 1) and z t (q × 1) are vectors of covariates and β and δj (j = 1, ... , m+1) are the corresponding vectors of coefficients; ut is the disturbance at time t. The indices (T1..., Tm) or the break points, are explicitly treated as unknown (we use the convention that To = O and Tm+1 = T). The purpose is to estimate the unknown regression coefficients together with the break points when T observations on (y t, x t, z t) are available. This is a partial structural change model since the parameter vector β is not subject to shifts and is estimated using the entire sample. When p = O, we obtain a pure structural change model where all the coefficients are subject to change. The variance of ut needs not be constant. Indeed, breaks in variance are permitted provided they occur at the same dates as the breaks in the parameters of the regression. The multiple linear regression system (1) may be expressed in matrix form as, – Y = Xβ + Z δ + U Where Y = (y1, ..., yT)´, X = (x1, ..., xT)´, U = (u1, ..., ur)´, δ = (δ´, 1 δ´, 2 ..., – δ´m+1)´, and Z is the matrix which diagonally partitions Z at (T1, ..., Tm), – that is, Z = diag (Z1, ..., Zm+1) with Zi = (zrt-1 + 1, ... zri)´. We denote the

– – (Y – Xβ – Z δ)´(Y – Xβ – Z δ) = 

´ )´ and (T1o, ..., Tmo) are used to denote, respectively, the true values δom+1 –o of the parameters δ and the true break points. The matrix Z is the one which diagonally partitions Z at (T1o, ..., Tmo). Hence, the data-generating (2)

The method of estimation considered is that based on the least-squares principle. For each m-partition (T1 , ..., Tm ), the associated least-squares

64

Ti

2 [yt – x´β t – z´δ t i]

i=1 t=Ti–1+1

Let βˆ({Tj }) and δˆ({Tj }) denote the estimates based on the given m-partition (T1 , ..., Tm ) denoted {Tj }. Substituting these in the objective function and denoting the resulting sum of squared residuals as ST(T1 , ..., Tm ), the estimated break points (Tˆ , ..., Tˆ ) are such that (Tˆ , ..., Tˆ ) = argmin 1

... ...Tm

m

1

m

T1...

ST (T1 ,... ..., Tm ), where the minimisation is taken over all partitions

(T1 ,... ..., Tm ) such that Ti – Ti–1 ≥ q2. Thus the break point estimators are global minimisers of the objective function. The regression parameter estimates are the estimates associates with the m-partition {Tˆ }, i e, βˆ = j

βˆ ({Tˆj }), δˆ = δˆ({Tj}). Since the break points are discrete parameters and can only take a finite number of values, they can be estimated by a grid search. This method becomes rapidly computationally excessive when m>2. Instead a dynamic programming algorithm that allows computation of estimates of the break points as global minimisers of the sum of squared residuals was devised to efficiently estimate the optimal break points for the series, starting from one to the maximum allowed by T and h. In the present case, we had a sample size of 50 observations and with the selected value of h = 6 (12% trimming of total observations), the maximum allowed breaks were seven. Among the various combinations of break points, the best one was selected based on BIC criterion. We applied this break point estimation procedure on GDP agriculture at constant prices for the period 1960-61 to 2010-11. The “strucchange” package was used for this purpose and the computations were done with R software.

true value of a parameter with a 0 superscript. In particular, δo = (δo1´, ...,

process is assumed to be – Y = Xβo + Zoδo + U

Vaidyanathan, A (2010): Agricultural Growth in India, Role of Technology, Incentives and Institutions (New Delhi: Oxford University Press). Virmani, Arvind (2008): “Growth and Poverty: Policy Implications for Lagging States”, Economic & Political Weekly, Vol 43 (2), pp 54-62. Vogelsang, Timothy (1997): “Wald-Type Tests for Detecting Shifts in Trend Function of Dynamic Time Series”, Econometric Theory, Vol 13, pp 818-49. Vyas, V S (2001): “Agriculture: Second Round of Economic Reforms”, Economic & Political Weekly, Vol 36 (14), pp 829-36. Wallack, Jessica Seddon (2003): “Structural Breaks in Indian Macroeconomic Data”, Economic & Political Weekly, Vol 46 (10), pp 4312-15. Wang, Z (2006): “The Joint Determination of the Number and Type of Structural Changes”, Economic Letters, Vol 93, pp 222-27. Zeileis, A, F Leisch, K Hornik, and C Kleiber (2005): “Strucchange: An R Package for Testing for Structural Change in Linear Regression Models,” at http://www.R-project.org/ Zivot, E and D W K Andrews (1992): “Further Evidence on the Great Crash, the Oil Price Shock, and the Unit Root Hypothesis”, Journal of Business and Economic Statistics, Vol 10, pp 251-70.

EPW Index An author-title index for EPW has been prepared for the years from 1968 to 2010. The PDFs of the Index have been uploaded, year-wise, on the EPW web site. Visitors can download the Index for all the years from the site. (The Index for a few years is yet to be prepared and will be uploaded when ready.) EPW would like to acknowledge the help of the staff of the library of the Indira Gandhi Institute for Development Research, Mumbai, in preparing the index under a project supported by the RD Tata Trust.

june 30, 2012


EPW
