SpringerLink
Log in

Crop yield convergence across districts in India’s poorest state

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

Bihar, India’s poorest state, witnessed impressive yield growth in its three principal crops during the last decade or so. This paper examines whether a reduction in regional yield disparities accompanied these state-level productivity gains. The convergence test allows the district-specific element of productivity to vary with time, thus permitting divergence in interim phases. District rice yields converged towards a common growth path. The results for wheat and maize are not as conclusive. However, there is strong evidence of divergence for each crop in recent years. This divergence, nonetheless, happens in the presence of club convergence. The club classification transcends agro-climatic boundaries, indicating a role for policy to aid growth in the lagging districts. The shifts in credit allocation over the years do not appear to be driving the yield divergence, highlighting the limitations of a macro credit-driven policy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. The absence of absolute convergence indicates a rise in the cross-district disparity in yields.

  2. Section 2 sketches the procedure of identifying convergence clubs. See Phillips and Sul (2007) for a detailed discussion.

  3. Yet, the evidence on the credit-growth relationship is varied. For instance, Binswanger and Khandkher (1995) and Subbarao (2012) estimate a positive effect, while Burgess and Pande (2005) and Narayanan (2016) find a negligible to weak impact.

  4. The parameter a in the logt regression equals \(\log H_1 - 2\log \left( {\frac{{\left( {N^{ - 1}\left( {1 - N^{ - 1}} \right)\mathop {\sum}\nolimits_i {\sigma _i^2} } \right)^{\frac{1}{2}}}}{b}} \right)\). For the derivation of the regression Eq. (8), see Phillips and Sul (2007).

  5. Level convergence also requires regularity conditions on the common growth component μt.

  6. Refer to Phillips and Sul (2007) and Phillips and Sul (2009) for details on the clustering procedure.

  7. It is possible to aim for more clubs by increasing the threshold for t-test statistic. I restrict my choice of the critical value to zero to minimize the number of convergence clubs.

  8. The club identification follows a slightly different algorithm relative to Phillips and Sul (2007, 2009). Specifically, I follow the revision suggested in Schnurbus et al. (2017) which makes the manual interventions in the former procedure redundant. The analysis makes use of STATA’s psecta package (Du 2018).

  9. 100 period (year) replications with districts used to identify resampling clusters. The results are robust when employing higher or lower replications.

  10. One-tailed test statistic corresponding to 36 degrees of freedom.

  11. Table 8 in the appendix lists the districts according to their club classification for each crop.

  12. Agriculture commands around 60 percent of the priority sector lending, illustrating the sector’s central role in development policy. In contrast, small and medium enterprises account for approximately 25 percent.

  13. The Wald supremum statistic is used to test the null hypothesis of no structural break in district yields (Perron 2006). I use asymmetric trimming of 17%, which is slightly higher than the 15% proposed by Andrews (1993) because the latter value is unable to resolve the identification problem.

References

  • Abate GT, Rashid S, Borzaga C, Getnet K (2016) Rural finance and agricultural technology adoption in Ethiopia: does the institutional design of lending organizations matter? World Dev 84:235–253

    Article  Google Scholar 

  • Andrews, DWK (1993) Tests for parameter instability and structural change with unknown change point. Econometrica: J Econom Soc 821–856

  • Avila AFD, Evenson RE (2010) Total factor productivity growth in agriculture: the role of technological capital. Handbook of agricultural economics. p 3769–3822

  • Ball VE, Bureau J-C, Butault J-P, Nehring R (2001) Levels of farm sector productivity: an international comparison. J Prod Anal 15.1:5–29

    Article  Google Scholar 

  • Ball VE, Hallahan C, Nehring R (2004) Convergence of productivity: an analysis of the catch-up hypothesis within a panel of states. Am J Agric Econ 86.5:1315–1321

    Article  Google Scholar 

  • Bandyopadhyay, D (2009) Lost opportunity in Bihar. Econ Political Wkly. p 12–14

  • Banerjee, A (2004) Who is getting the public goods in India? Some evidence and some speculation. India’s Emerging Economy p 183–213

  • Banerjee A, Iyer L (2005) History, institutions, and economic performance: the legacy of colonial land tenure systems in India. Am Econ Rev 95.4:1190–1213

    Article  Google Scholar 

  • Bansil PC (2011) Bihar agriculture: a perspective. Concept Publishing Company, Delhi, India

    Google Scholar 

  • Barro, RJ, X Sala-i-Martin, OJ Blanchard, and RE Hall (1991) Convergence across states and regions. Brookings Pap Econ Act 107–182

  • Barro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100.2:223–251

    Article  Google Scholar 

  • Baumol, WJ (1986) Productivity growth, convergence, and welfare: what the long-run data show. Am Econ Rev 1072–1085

  • Bhalla GS, Singh G (2010) Growth of Indian agriculture: a district level study. Planning Commission, Government of India

    Google Scholar 

  • Binswanger HP, Shahidur RK (1995) The impact of formal finance on the rural economy of India. J Dev Stud 32(2):234–262

    Article  Google Scholar 

  • Bhaduri A (1973) A study in agricultural backwardness under semi-feudalism. Econ J 83.329:120–137

    Article  Google Scholar 

  • Burgess R, Pande R (2005) Do rural banks matter? Evidence from the Indian social banking experiment. Am Econ Rev 95(3):780–795. (2005)

    Article  Google Scholar 

  • Census of India (2011) Table B7: Main workers classified by industrial category, educational level and sex—2011. Ministry of Home Affairs, Government of India, New Delhi, India

    Google Scholar 

  • Chirwa EW (2005) Adoption of fertiliser and hybrid seeds by smallholder maize farmers in Southern Malawi. Dev South Afr 22(1):1–12

    Article  Google Scholar 

  • Conning J, Udry C (2007) Rural financial markets in develo** countries. Handbook of agricultural economics. pp 2857–2908

  • Das A, Senapati M, John J (2009) Impact of agricultural credit on agriculture production: an empirical analysis in India. Reserve Bank India Occas Pap 30(2):75–107

    Google Scholar 

  • Davies RB (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74.1:33–43

    Google Scholar 

  • De Long JB (1988) Productivity growth, convergence, and welfare: comment. Am Econ Rev 78.5:1138–1154

    Google Scholar 

  • Dercon S (1996) Risk, crop choice, and savings: evidence from Tanzania. Econ Dev Cult Change 44(3):485–513

    Article  Google Scholar 

  • Du K (2018) Econometric convergence test and club clustering using Stata. Stata J 17.4:882–900

    Google Scholar 

  • Giné X, Yang D (2009) Insurance, credit, and technology adoption: field experimental evidencefrom Malawi. J Dev Econ 89(1):1–11

    Article  Google Scholar 

  • Government of Bihar (2010) Bihar economic survey. Finance Department, Government of Bihar, Patna, 2019

    Google Scholar 

  • Heltberg R (1998) Rural market imperfections and the farm size—productivity relationship: evidence from Pakistan. World Dev 26(10):1807–1826

    Article  Google Scholar 

  • Kishore, A (2004) Understanding agrarian impasse in Bihar. Econ Political Wkly p 3484–3491

  • Martin W, Mitra D (2001) Productivity growth and convergence in agriculture versus manufacturing. Econ Dev Cult Change 49.2:403–422

    Article  Google Scholar 

  • McErlean S, Wu Z (2003) Regional agricultural labour productivity convergence in China. Food Policy 28.3:237–252

    Article  Google Scholar 

  • Mukherjee AN, Kuroda Y (2003) Productivity growth in Indian agriculture: Is there evidence of convergence across states? Agric Econ 29.1:43–53

    Article  Google Scholar 

  • Mukhopadhyay D, Sarkar N (2019) Convergence of foodgrains productivity In Indian agriculture. Int J Food Agric Econ 7.1128-2019-3386:229–241

    Google Scholar 

  • Narayanan S (2016) The productivity of agricultural credit in India. Agric Econ 47(4):399–409

    Article  Google Scholar 

  • Pandey S, Pal S (2000) The nature and causes of changes in variability of rice production in eastern India: a district-level analysis. Risk analysis and management in rainfed rice systems. International Rice Research Institute, Los Banos

    Google Scholar 

  • Perron P (2006) Dealing with structural breaks. Palgrave handbook of econometrics. p 278–352

  • Phillips PCB, Sul D (2007) Transition modeling and econometric convergence tests. Econometrica 75.6:1771–1855

    Article  Google Scholar 

  • Phillips PCB, Sul D (2009) Economic transition and growth. J Appl Econom 24.7:1153–1185

    Article  Google Scholar 

  • Prasad, PH (1974) Reactionary role of usurer’s capital in rural India. Econ Political Wkly p 1305–1308

  • Reserve Bank of India (2019) Table 10: Per capita net state domestic product—state-wise (at constant prices). Handbook of statistics on Indian economy, 2018–19, Mumbai, India

  • Rezitis AN (2010) Agricultural productivity and convergence: Europe and the United States. Appl Econ 42.8:1029–1044

    Article  Google Scholar 

  • Schnurbus J, Haupt H, Meier V (2017) Economic transition and growth: a replication. J Appl Econom 32.5:1039–1042

    Article  Google Scholar 

  • Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70.1:65–94

    Article  Google Scholar 

  • Stiglitz JE, Weiss A (1981) Credit rationing in markets with imperfect information. Am Econ Rev 71.3:393–410

    Google Scholar 

  • Subbarao, D (2012) Agricultural credit-accomplishments and challenges. Speech delivered at NABARD

  • Wu Y (1995) Productivity growth, technological progress, and technical efficiency change in China: a three-sector analysis. J Compar Econ 21.2:207–229

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to two anonymous referees and the associate editor for their comments and suggestions, which led to significant improvements. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Author information

Authors and Affiliations

  1. The World Bank, 1818 H St NW, Washington, DC, 20433, USA

    Rishabh Sinha

Authors
  1. Rishabh Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rishabh Sinha.

Ethics declarations

Conflict of interest

The author declares no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Tables 7–9

Table 7 (a): Descriptive statistics—Southern districts. (b): Descriptive statistics—Northwestern districts. (c): Descriptive statistics—Northeastern districts
Full size table
Table 8 Convergence clubs
Full size table
Table 9 Agricultural credit across sub-periods, periods split using statistical break in district yields
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sinha, R. Crop yield convergence across districts in India’s poorest state. J Prod Anal 57, 41–59 (2022). https://doi.org/10.1007/s11123-021-00618-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11123-021-00618-9

Keywords

Search

Navigation