Processing technologies for undervalued grains in rural India: on target to help the poor?


Finger millet (ragi) is increasingly recognized as a nutritious staple by Indian consumers and policy makers. Though previously regarded as a poor person’s crop, the benefits of enhanced ragi consumption may bypass the poor. Because home processing is arduous, small flour mills have been introduced to help. With geo-referenced survey data from a pilot area in the Kolli Hills region of Tamil Nadu, India, we examined determinants of mill use and use intensity employing a two stage multinomial selection model. Overall, we found that the mill technology was not pro-poor, in that poor people do not tend to use the mills more than wealthier people, or use them at higher rates. We identified the location of mills as being a key factor in preventing more use of mills by the poor. Therefore, to better serve the poor, external agencies would have to deliberately locate mills in poor communities. For this to be feasible, changes to make this technology work better with poor communities may be required, such as the use of less capital intensive technology such as hand- or pedal-power, rather than reliance on electrical power.

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  1. 1.

    Our study area has a history of reduced dietary variety over the last 20 years (Huang et al. 2017). Changing consumer preferences due to greater public awareness and concern about the links between diet and obesity and related health problems are leading to a resurgence of millet consumption among urban consumers. Ragi is being promoted as a “nutri-cereal”: gluten free and high in calcium, fiber, minerals and amino acids. Several food processors are marketing breakfast products with ragi as a key ingredient (Shobana et al. 2013).

  2. 2.

    Rural consumers of ragi have strong preferences over the attributes of ragi flour and distinguish between processing alternatives that seem identical in terms of costs but produce flours that differ in attributes such as texture (Mal et al. 2010).

  3. 3.

    During the Green Revolution, federal and state government investments in irrigation infrastructure and fertilizer subsidies unleashed the productivity potential of dwarf varieties of rice and wheat. Productivity gains were primarily concentrated among irrigated rice and wheat producers in Punjab and Haryana States in Northwestern India, largely bypassing the millions of farmers who relied, and still rely, on rainfed agriculture (Rosegrant and Hazell 2000).

  4. 4.

    For crops in 2016–2017, the minimum support prices to be paid to farmers to produce crops were Rs 14.70 / kg for rice, Rs 14.50 / kg for wheat, and Rs 17.25 / kg for finger millet (Business Standard 2017). Targeted poor households were entitled to purchase set amounts of rice for Rs 3 / kg, wheat for Rs 2 / kg, and millet for Rs 1 / kg.

  5. 5.

    Based on data collected as part of our household survey in the study area.

  6. 6.

    We conducted a preliminary survey and found that, based on the frequency of customers who came to the mills, the average distances traveled to the mills, and the local population density, a random sample would not provide sufficient observations of adopters for our analysis. We therefore opted for this mixed sampling design. Standard errors of all econometric estimates were adjusted for the mixed sampling design using sampling weights, following Greene (2007).

  7. 7.

    In addition to the two mills established by the MSSRF, we identified seven privately operated mills that our sample households visited (see Fig. 2). The recent establishment of the private mills suggests that some entrepreneurs have overcome the barriers to entry into the milling market, without a publicly-funded intervention. Because only 10.7% of the households in our total sample processed ragi at one of the private mills, we do not expect that the presence of private mills affected the conclusions that can be drawn from our analysis of the SHG mills.

  8. 8.

    Block districts are local government subdivisions in Tamil Nadu, which are composed of several villages.

  9. 9.

    In our sample, a small fraction (9%) of households both buy and produce ragi flour from the mills. We subsumed this group into the mill-adopter category in our analyses.

  10. 10.

    Our measure of Adult Equivalent Household Size = (Number of Children Age 0 to 6) X 0.2 + (Number of Children Age 7 to 12) X 0.3 + (Number of Children Age 13 to 17) X 0.5 + Number of Adults Age 18 and Up; see Glewwe and van der Gaag (1990)

  11. 11.

    The following variables were included in the IHDS index, but not in the wealth index used here: air conditioner, clock / watch, electric fan, chair / table, cot, credit card, footware, piped indoor water, separate kitchen, flush toilet, electricity, LPG, and Pucca roof. Variables included in our index that were not included in the IHDS index were radio, DVC player, and tape player.

  12. 12.

    Following our earlier discussion we defined the intensity of mill use as the natural logarithm of the quantity of ragi flour produced (kg) in one month per adult.

  13. 13.

    Selection implies for example that unobserved household characteristics such as information, tradition, motivation and preferences, can affect both a households’ choice of processing technology and the amount of ragi they process at the mill.

  14. 14.

    A standard approach to estimating the mill adoption model in equation 1 would be to treat the household’s decision as binary, by defining Ai = 1 if the household is a mill-adopter and 0 otherwise, and assuming a probit or logit specification. The regression model of intensity of mill use in equation 2 could then be estimated consistently using a Heckman (1979) selection model. However, this approach would ignore the full choice set of alternatives available to households for processing ragi. Moreover, the intensity equation (2) would not identify the sources of selection. Similarly, multinomial selection models (see Lee 1983; Dubin and McFadden 1984) are inappropriate in our context because even though they allow for selection from multiple sources, they restrict the sign of the different selection effects to be the same.

  15. 15.

    The geography variables were not related to the sampling design, as the coordinates for the centroid were calculated after the sampling was completed. They were constructed by taking the average of the easting and northing measures of the Universal Transverse Mercator coordinates from each household.

  16. 16.

    The marginal effects of covariates on the other choice categories (market-adopter and non-adopter) are available upon request.

  17. 17.

    Ideally we would have data on the literacy of all adult females, including those in charge of cooking. In the absence of these data, we assumed that the household head is the main decision-maker in household decisions and his/her literacy about nutrition is shared among household members. Given assortative matching in marriage markets (Becker 1973) it is possible that the measure captures common preferences and beliefs of members.


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We would like to thank Vic Adamowicz for his help in the sampling design of data collected for this study. We acknowledge important inputs into the implementation of the survey from the M.S. Swaminathan Research Foundation as well as financial support of the International Development Research Centre and Global Affairs Canada (formerly Department for Foreign Affairs, Trade and Development) through the Canadian International Food Security Research Fund (IDRC Project Number 106505-001). We thank the following individuals at MSSRF for their contributions to the overall study design and implementation of the surveys: Nita Selena, Oliver King, Siddick Abubacker, Kumar Natarajan, Bala Murugam, and P. Hariharasudhan. We also extend our appreciation to the anonymous reviewers and editors for their helpful comments.

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Correspondence to Sandeep Mohapatra.

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Miller-Tait, E.J., Mohapatra, S., Luckert, M.K.(. et al. Processing technologies for undervalued grains in rural India: on target to help the poor?. Food Sec. 11, 151–166 (2019).

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  • Mill use
  • India
  • Nutrition
  • Women
  • Adoption
  • Multiple-selection model