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Review of Industrial Organization

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03-06-2020

Technology Adoption, Vertical Coordination in Value Chains, and FDI in Developing Countries: Panel Evidence from the Dairy Sector in India (Punjab)

Authors: Saule Burkitbayeva, Emma Janssen, Johan Swinnen

Published in: Review of Industrial Organization | Issue 2/2020

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Abstract

(Global) value chains have contributed to agricultural technology adoption in transition countries and food export commodities in developing countries through various forms of vertical coordination, often associated with FDI. Adoption of modern technologies in agriculture is crucial for improving the productivity and welfare of poor farmers. Much less is known about how (global) value chains do (not) affect technology transfer and/or adoption in staple food chains in developing countries, and the role that FDI plays. Our paper analyses the role of value chains and FDI in farm-level technology adoption with the use of panel data from representative farm surveys in 2008 and 2015 (a period of rapid growth) in the dairy sector in India (Punjab). We find important increases in the adoption of some technologies among traditional and poor dairy farms. However, the role of vertical coordination in value chains in stimulating technology adoption among these traditional dairy farmers seems to be minor, both for domestic and for FDI companies. At the same time a sub-sector of dynamic modern dairy farms has emerged. These farms are much larger, use only modern technology, and are fully integrated in vertically coordinated value chains that support these modern farms’ management and investments.

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Explanations in the literature for the failure of technology adoption include imperfections in credit, information, and labor markets: access to credit and information (Banerjee and Duflo 2014; Bardhan and Udry 1991; Feder et al. 1985; Feder and Umali 1993); the importance of learning through social interactions or extension services (Cameron 1999; Munshi 2004; Bandiera and Rasul 2006; Conley and Udry 2010; Genius et al. 2013); and labor constraints (Moser and Barrett 2006). Another reason for the farmer to not adopt the technology is when there is uncertainty about whether this investment will be rewarded. One possibility is buyer holdup at the time of delivery (Klein et al. 1978). Examples are late payments, renegotiation of prices at product delivery, and the absence of transparent and reliable quality evaluation procedures (which could lead to inappropriately rejecting produce) (Barrett et al. 2012; Cungu et al. 2008; Saenger et al. 2014).

Such interlinked contracts are well known in the traditional development literature for basic input provisions (Bardhan 1989; Bell and Srinivasan 1989).

India has also used these arguments in trade negotiations—both within the World Trade Organization (Jha 2003) and in bilateral negotiations (Mondal et al. 2012)—to keep the dairy sector largely off the negotiation table (Goswami 2007).

This conceptual framework draws on the models developed in Vandemoortele et al. (2012), Swinnen et al. (2015) and Swinnen and Kuijpers (2019).

Here, we keep the definition of “technology” general. Later we consider different types of technology.

We ignore the possibility that there is a trade-off between quantity and quality. Such trade off may well exist for given technologies and for some technological innovations. However, many of the technologies that are relevant in our analysis increase both quantity and quality, or at least increase one without reducing the other.

See Swinnen and Vandeplas (2010) for more details.

Here, we assume that this sharing rule β is determined through ex-ante bargaining. Swinnen and Kuijpers (2019) show how the ex post division of surplus depends on contract enforcement and holdup opportunities.

Although side-selling by farmers potentially affects the occurrence of technology transfer in a slightly different way than technology diversion, it is conceptually similar (see Swinnen et al. 2015). We have not included this to reduce the complexity of the analysis. The extended analysis for side-selling can be obtained from the authors.

We relax this assumption in the section where we look at different types of technologies.

Note that in this model τ and V₀ are “sunk” costs, which is why they do not directly show up in the buyer and farmer‘s payoffs. These costs will be reflected in the buyer and farmer‘s participation constraints.

Note that there may also be buyer hold-up of the contract. For example, the buyer may want to renegotiate the contract and refuse to pay the farmer the agreed share of the value at product delivery. Instead, he may offer to pay only as much as the farmer’s best alternative at that moment (e.g. the value of the produce when sold on the local market). We do not model this here and refer to Swinnen and Kuijpers (2019) for a formal analysis.

See, e.g., World Bank (2005) in transition countries and Maertens et al. (2011) in developing countries.

More formally, using parameter σ to represent the share of the gross surplus that is obtained in the contract period, the net surplus of technology adoption is \({\text{S}} =\upsigma\left( {{\text{V}}_{\uptau} - {\text{V}}_{0} } \right) -\uptau + \frac{{\upmu(1 - \sigma )}}{{1 +\updelta}}\left( {{\text{V}}_{\uptau} - {\text{V}}_{0} } \right):\) where μ represents the probability that the remaining gross surplus from the technology transfer is realized in the future (with 0 ≤ μ ≤ 1); and δ represents the discount rate. It follows that the surplus requires a larger value to be positive and grows slower with increasing value.

Our concept of “technology specificity” is obviously related to “asset specificity” in the transaction cost literature (e.g., Williamson 1985).

It was revealed that bottled water, milk and soft drinks contained dramatically high pesticide residue levels (Umali-Deininger and Sur 2007).

In 2006, Indian policy makers, pressured by civil society protests, drafted the Food Safety and Standards Act (FSSA). The Act determines which actor in the supply chain bears responsibility when food safety regulations are not complied with (Article 27 of FSSA 2006). For milk traders this implies that they are liable for any adulterants (e.g., water, oil, skimmed milk powder, soda) that is added to the milk, even if the responsibility for adulteration lies with their suppliers—unless they can specifically identify the culprit (Squicciarini and Vandeplas 2013). These regulations extend to informal traders including street hawkers and itinerant vendors. Opponents of the Act claim that poor street vendors will be unable to comply with the food standards that have been imposed. Despite the existence of the FSSA, a wide number of procedures, guidelines and mechanisms are yet to be framed by the Food Safety and Standards Authority of India (FSSAI).

Cross-breeding practices in India date back to mid 19th century (Sinha 1951). Cross-breeding on a large scale, however, began only in 1963 with the introduction of the Intensive Cattle Development Project (ICDP) as part of the Special Development programme during the Third Five-Year Plan period (Rao et al. 1995). Since then, the share of cross-bred cows in cattle milk production has been increasing steadily and reached 53% in 2011 (Kumar et al. 2013).

The north-west (Amritsar and Gurdaspur), the north-east (Hoshiarpur, Jalandhar, Kapurthala and Nawanshahar), the south-west (Bathinda, Faridkot, Ferozepur, Moga and Muktsar), the south-east (Mansa, Patiala, and Sangrur), and the central region (Fatehgarh Sahib, Ludhiana, and Ropar) (see Fig. 5).

Table 2 includes all dairy farmers in the sample. Table 10 in the Appendix shows the same table including only those dairy farms that had DA in both 2008 and 2015.

The index of hygienic practices is the average of the compliance levels for various sanitary practice indicators before and during milking which are demonstrated in Table 4.

Figures 7, 8, 10, and 12 include all dairy farmers in the sample. Figures 13, 14, 15 and 16 in the Appendix show the same graphs but include only those dairy farms that had DA in both 2008 and 2015.

In our survey, following Janssen and Swinnen (2019), we distinguished among four different types of concentrate feed: (a) grains and grain bran; (b) oilseeds and oilcakes; (c) mixed concentrated feed; and d) other concentrates, which includes salts and mineral mixtures. While grains, oilseeds/oilcakes, and salts/mineral mixtures are all part of a balanced diet, the mixed concentrates usually contain all these elements and are therefore the best indicator of whether a dairy farm has adopted enhanced feeding practices. In Punjab, different types of mixed concentrated feed can be found: First, branded pre-mixed feed with different compositions that are combined in such way that fulfill the requirements of different types of animals (cross-bred/desi) as well as match the status of the DA (pregnant, in milk, dry) can be purchased (if available). Second, there is unbranded pre-mixed feed with varying composition. The survey data does not allow us to distinguish between these two types of concentrated feed, so they are both classified as mixed feed. Finally, some dairy farmers also mix their own concentrates, although this is done by only a small minority in our sample.

Note that the provision of monetary funds in formal value chains is not provided by the processing firm themselves but rather by the managers of the collection centers who act like money lenders.

To verify that the Fixed Effects model is appropriate, we conduct the following steps: First we perform a Hausman test to decide between a FE and a Random Effects (RE) model. Next, we check if the assumption of strict exogeneity of explanatory variables holds by comparing outcomes of FE and first difference estimators (FD). The two estimators yield similar results, which means that explanatory variables do not correlate with past, current and future error terms. In short, the results showed that the FE model is preferred for studying both the adoption of improved hygienic practices and of cross-bred cows. The different models are presented in Tables 11 and 12 in the Appendix.

There exists no consensus on the econometric technique that should be used to estimate a model with a binary dependent variable in a panel setting. We follow Angrist and Pischke (2009) in using a Linear Probability Model. The LPM offers econometric advantages over a logit model when running estimations on panel data as using a logit model in this setting will give rise to the incidental parameter problem and lead to inconsistent parameter estimation, especially when T is small. However, as a robustness check, we estimate: (i) a probit model with FE and (ii) a correlated random effects probit model that was proposed by Mundlak (1978). This correlated random effects model relaxes the strong assumption of RE models that time-invariant unobserved heterogeneity is not correlated with the independent variables. Concretely, time averages of the time-varying explanatory variables are added for each dairy farm as a set of controls. As such, we are estimating the effect of changes in the time-varying explanatory variables while keeping the time average fixed (Wooldridge 2004). The results for all three models are similar and can be found in the Appendix Table 13.

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Title: Technology Adoption, Vertical Coordination in Value Chains, and FDI in Developing Countries: Panel Evidence from the Dairy Sector in India (Punjab)
Authors: Saule Burkitbayeva
Emma Janssen
Johan Swinnen
Publication date: 03-06-2020
Publisher: Springer US
Published in: Review of Industrial Organization / Issue 2/2020
Print ISSN: 0889-938X
Electronic ISSN: 1573-7160
DOI: https://doi.org/10.1007/s11151-020-09763-1

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