N.Chandrasekhara Rao Institute of Economic Growth Delhi, India

1. Continuing controversies on Bt cotton in IndiaRevisiting evidence on production, efficiency and employment N.Chandrasekhara Rao Institute of Economic Growth Delhi, India Presentation at the ICABR, 2013 Innovation and policy for the bioeconomy

2. Continuing controversies Committee on agriculture of members of Parliament Fresh round of mobilisation by NGOs on Bt cotton after 1st decade and creative efforts Unable to reject turnaround in cotton production Other interventions are attributed: Hybrids, increased irrigation, new pesticide molecules, better germplasm, lower pest populations and Bt effect While CSGs on one hand, some research scholars- Glover (2010 a&b; Stone, 2011, 2012)

3. Controversies contd… • Several research studies found significant yield increase in India unlike in USA or China • Qaim and Sadashivappa, 2008: Subramanian et al, 2009, 2010; Rao and Dev, 2010;Gandhi and Namboodiri, 2006; Narayanamoorty and Kalamkar, 2006: Bennet et al, 2005; Crost et al, 2007; Morse et al 2012; Lalitha et al, 2010; Kathage and Qaim, 2012 • Meta analysis by Gruere and Sengupta (2011) confirmed the above. Herring and Rao (2012) synthesised

4. Controversies continuing… Sahai&Rahman, 2002; Shiva and Jafri, 2003; Sakkari and Qayum, 2003,2005,2006,2008 Controversies continue- Kuruganti, 2009; Ho,2010; Shiva; Glover, 2010a,b&c; Prince Charles, 2010; CHRGJ,2011); Stone, 2011, 2012 Qaim et al, 2006; Smale et al, 2009,2012 2011 Census data on suicides- 16.3/11.1. Failure narrative centered on AP & MH

5. Issues addressed • What happens after adoption to the farmers? • What happens to net income if indirect costs are taken into account? (Smale et al 2012) • How much is technology effect, if farmer effect is isolated? (Stone, 2012) • Does biotechnology increase efficiency? (Kalirajan, 1991)

6. Data from longitudinal studies • Sample of 623 farmers (437+186) • All agro-climatic zones, groups of farmers • 2 surveys- 2005 (Period I) & 2007 (Period II) • By II survey, all 186 farmers shifted to new technology • Double difference method- ‘with and without’ &‘before and after adoption’ • Both direct and indirect costs. • Impact on employment

7. Costs and returns- Period I

8. Costs and returns in Period II

9. Costs and returns after adoption

10. Yield density function Period I

11. Yield density function in Period II

12. Yield density function after adoption

13. Fixed effects model Endogeneity problem in yield estimates if the explanatory variables are correlated with error term Socioeconomic variables of farm level variables Unobserved heterogeneity causing self selection bias Yit = α+ xit β +δit +γt+(ci +uit) Yit = Physical output in qtls/ac of farmer ‘i’ in period ‘t’ ci -impact of socioeconomic & farm level characteristics γt -season specific production shocks; δit- Bt dummy Uit - idiosyncratic disturbance; xit-variables influencing output

14. Panel data fixed effects model for yield

15. Stochastic frontier analysis: Why? Yield is a partial measure of productivity Efficiency of using the technology matters for raising production. Ruttan (1977): Efficiency differentials among farmers disappear in the long-run Kalirajan (1991) showed time invariance in rice Xu and Jeffrey (1995) showed TE may even decline with new technology due to socioeconomic factors Ruttan (1977)&(Hayami 1981) technology per se size neutral Glover (2010) questions this.

16. The model of SFA Yit = xitβ + (Vit - Uit) ………. 1 , i=1,..N; t=1,….T, Where Yit is the logarithm of the production of the i-th firm in the t-th period; • Xit is a kX1 vector of transformations of the input quantities of the i-th firm in the t-th period; • Β - vector of unknown parameters • Vit - random variables assumed to be iid. N(0,σ2v) & independent of • Uit -non-negative random variables assumed to account for technical inefficiency in production & to be independently distributed as truncations at zero of the N(mit, σ2u) distribution; where mit = zitδ, • Where zit is a pX1 vector of variables influencing eff • δ- 1Xp vector of parameters • Other variables are σ2 = σ2v +σ2u and γ= σ2u/ σ2v +σ2u

17. Efficiency analysis Ln(Yit) = β0 + β1 ln(Seedit) + β2 ln(Labour­it) + β3 ln(Other expenditureit) + β4 ln(landit) + + Vit - Uit …….. (1)  Where the technical inefficiency effects are assumed to be defined by Uit = δ0 + δ1(Bt dummyit) + δ2(Education) + δ3(Small farmer dummyit) + δ4(Dry land dummyit) + Wit ……………… (2)

18. Variables • Y is the physical quantity of output in quintals; • Seed is the expenditure on seeds in Rupees per farm; • Labour is the value of hired, family and attached labour in Rupees per farm; • Other expenditure- spending on fertilizers and pesticides, BL, ML, water in Rupees; • Land is the area in acres under cotton cultivation.

19. Determinants of inefficiency Bt dummy is defined as 1 if Bt technology is employed and O otherwise;  Education- number of years of education; SF dummy- 1 if small farmers with < 5 acres of land& 0 otherwise; DD dummy- 1 if in semi-arid and scanty rainfall zones and 0 otherwise

20. Final maximum likelihood estimates

21. Technical efficiency

22. Benefits across Groups of Farmers in 2004

23. Benefits across Groups of Farmers in 2006

24. Human Labour Utilisation- 2004 & 2006

25. Sum-up on Bt Performance • There was a significant positive yield effect even after controlling for indirect costs & farmer effect • Bt cotton introduction significantly decreased inefficiencies in production • Scale neutral and beneficial to all size and social categories of farmers • Secured employment as crop failures ruled out • Utilization of tools of biotechnology in cotton has sown the seeds of second green revolution in India

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