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Model
An ideal experimental set-up to study the effect of LCRs on bid prices would include (at least) two identical countries, completely independent of each other, with an auction scheme identical apart from the LCR feature. Any price difference that emerged between the two auction schemes could then be fully attributed to the differences in the LCR feature. Even better would be to include additional identical countries with varying levels of stringency in the level of the LCRs (in weight, value or number of components), to study whether there are discontinuities in bid prices that seem to be attributable to increasing levels of LCR stringency. For instance, one might expect a non-linear increase in bid prices if very high levels of LCRs (say, 95%) were introduced given that the manufacturing capabilities for wafers would be near zero in India35.
The Indian case comes close to an ideal policy experiment given that many tendered capacities were distributed between LCR and non-LCR auctions in similar geographic areas—albeit not always equally in terms of capacity. Importantly, firms were free to bid in both LCR and open auctions, and many firms submitted bids in both auction windows. However, to address the possible remaining issue of selection bias (that is, firms self-selecting into auctions with or without LCRs), we divide firms into two groups: firms that only bid in auctions without LCRs (59 firms, 134 bids) and firms that bid in both auction types, open and closed (26 firms, 143 bids). The data used in the study come from various sources from the Indian government and firm-level data from Mergent Intellect (described in more detail in the Data section).
We considered using a multinomial selection model that would divide firms into three groups: those that only bid in auctions with LCRs, those that only bid in auctions without LCRs and those that bid in both auction types. However, since there are only seven firms in the category of ‘only LCR auctions’, we were unable to run the model. Yet we believe that the main question is whether a firm bid in an LCR auction, because it indicates whether the firm has sufficient local knowledge to either liaise with a local manufacturer or to use its own existing manufacturing facilities. Firms that only bid in the open auction, in contrast, could merely import the required parts. Hence, we expect there to be systematic differences between these two groups.
Thus, we test whether firms that do not bid in the LCR category are different from those that do bid in the LCR category. It could be, for instance, that firms that bid in LCR auctions have more experience in local development than firms that only bid in the open auctions (where there are no restrictions in using imported material). Similarly, firms that only bid in the open auctions might be able to more effectively exploit economies of scale by producing solar PV cells and modules for several markets (for example, Canadian Solar, which has manufacturing capabilities in China).
In addition to using standard ordinary least squares regressions, we therefore make use of a Heckman regression model, which accounts for this possible selection bias. Heckman’s 1979 seminal paper proposes a two-step statistical approach37. In the first step, an economic model is defined in which plausible factors for the probability of falling into (in our case) either Group 1 or 2 are considered. This is modelled as a probit regression,

$${mathrm{Pr}}(G = 1|Z) = Phi (Zb)$$
(1)

where G indicates whether the firm belongs to Group 1 (G = 0 otherwise), Z is a vector of explanatory variables, b is a vector of unknown parameters and Φ is the cumulative distribution function of the standard normal distribution. The explanatory variables we consider are the number of employees of a given firm, whether it is a state-owned enterprise (SOE) and whether the firm is itself a manufacturer or is merely a project developer (an indication of the degree of vertical integration). We also consider whether the firm is primarily focused on energy or merely attempts to diversify from an unrelated field, indicating limited technical experience, and whether the company already bid in the NSM Phase I. The latter factor captures advantages that firms might have in the NSM Phase II due to prior experience with the auction system.
The second stage of the Heckman model then uses the probability that a firm will self-select into Group 1, based on its characteristics, by including that probability as an explanatory variable in the ordinary least squares regression.
For our standard ordinary least squares and Heckman regression model, we also created a number of explanatory variables that we assume influence bid price. We recognize that competition differed substantially between rounds and was on average twice as high in open auctions as in LCR auctions (as measured by our variable defined in equation (2)). Firms are likely to anticipate, or at least have beliefs about, the level of competition in an upcoming auction round, which leads them to adapt their bids accordingly (that is, to make higher bids when less competition is expected; this is well documented in the literature38). In order to exclude the possibility that bids under LCR regulation are higher solely due to this effect, we control for the degree of competition within each round. Therefore, we define the competition for each tender as follows:

$${mathrm{Competition}}_r = frac{{mathop {sum }nolimits_{n = 1}^N B_r}}{{AC_r}}$$
(2)

where B is the capacity in MW of each of the bids submitted for a particular auction round r, AC is the total capacity in MW auctioned in round r and N is the overall number of bids received for each auction round r. For instance, if 20 MW are auctioned off and firms submit 100 MW in bids, the competition would be 5.
We also include the cumulative installed capacity of each developer within the auction windows we cover to account for learning-by-doing of the developers and capacity building (for example, through greater local knowledge and connection to suppliers)39,40. Our time dummy controls for exogenous technological change, such as decrease in the cost of solar PV modules and other equipment over time, that is not directly related to the deployment in India (that is, exogenous technical progress)41. We do not include a state dummy as the variable is correlated too strongly with our time dummy (as certain states only conducted auctions in specific years, leading to high multicollinearity). We do, however, include the mean solar irradiation (annual average kWh m−2 d−1) per state to control for differences in the solar resources across different states (we also use the maximum solar irradiation for each state as a robustness check, which does not affect the results42).
In addition, we include a dummy for the utility that purchases the electricity generated by the awarded projects. It is well documented that the financial solvency of the utility buying the electricity (that is, the offtaker) is an important factor in assessing the risk associated with a project (that is, if an offtaker is less financially stable, the risk of a default increases, making capital more expensive, which in turn increases the cost of power43). Lastly, we consider whether a PV project being in a solar park has an effect on bid price. Solar parks are designated areas where environmental impact assessment, land procurement and interconnection are already taken care of. However, these increased costs may be reflected in the land price for the solar projects. By differentiating between solar parks and normal land, we are able to capture the price differences between the two approaches.
Thus, we use the following specification to study the effect of LCRs on bid price:

$$begin{array}{ll}{mathrm{bid}}_i & = alpha + beta _1{mathrm{LCR}}_r + beta _2{mathrm{Competition}}_r + beta _3{mathrm{Year}} + beta _4{mathrm{Cum}}_{{mathrm{MW}}} \ & + beta _5{mathrm{Offtaker}} + beta _6{mathrm{Solar}},{mathrm{park}} + beta _7{mathrm{Sol}} + varepsilon _iend{array}$$
(3)

where bidi is the individual bid of each firm, r is the auction round, LCRr is the dummy for whether local content was required or not in the auction, Year is the time dummy to control for temporal shocks, CumMW is the cumulative installed capacity prior to the given auction in the NSM Phase II, Offtaker is a dummy for the utility buying the power (1 = SECI, 0 = NTPC), Solar park indicates whether the project is within a solar park, Sol refers to the annualized average solar resources (kWh m−2 d−1) in each state and εi is the error term. We also include an interaction term between LCR and our time dummy, to test whether the effect of LCRs changed over time.
Part of the auctioned capacity was tendered under the viability gap funding (VGF) scheme, where the government fixed a base power purchase agreement (PPA) price and companies could request a top-up on the existing base price to make their project financially viable. Since price-only auctions have been implemented in India, the bidders who quoted the lowest amount of VGF were awarded the contracts until the auctioned capacity was reached (it should be noted that bidders were allowed to quote a lower PPA tariff than proposed and waive the VGF, but this rarely happened). Given that the VGF is dispensed as a capacity-based payment at the beginning of the lifetime of a power plant instead of as a constant subsidy for each unit of electricity generated, we had to levelize the amount to compare the outcomes with the generic auction results, where the payments are made across the entire lifetime of the power plant. Therefore, we applied the following method, which is based on the commonly used levelized cost of electricity (LCOE) calculation44, to calculate levelized VGF:

$${mathrm{VGF}}_{{mathrm{levelized}}} = frac{{{mathrm{VGF}}_{{mathrm{total}}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{E_t}}{{(1 + d)^t}}}} = frac{{C{mathrm{VGF}}}}{{mathop {sum }nolimits_{t = 1}^{25} frac{{C{mathrm{Flh}}}}{{(1 + d)^t}}}} = frac{{mathop {sum }nolimits_{t = 0}^5 frac{{{mathrm{VGF}}_t}}{{(1 + d)^t}}}}{{{mathrm{Flh}}mathop {sum }nolimits_{t = 1}^{25} frac{1}{{(1 + d)^t}}}}$$
(4)

In equation (4), Et is the electricity generated in year t, C is the project’s capacity in MW and Flh is its full-load hours. We assume constant, region-specific full-load hours, which can be found in ref. 45. For bids that did not indicate the project’s location in India, we assume a capacity factor of 20% and thus full-load hours of Flh = 1,752 h. Moreover, we assume a discount rate of d = 10% and a plant life of t = 25 years. With our approach, we are also able to capture the time value of money induced by the different VGF disbursement methods applied throughout Phase II (Supplementary Table 7; note that there was no VGF disbursement in Batch II). We then add the resulting levelized VGF support to the specific PPA price.
To estimate the possible range of values for the additional cost borne directly by the Indian government due to LCRs, we used the average estimates from our Heckman regression of the additional cost of power of LCR bids when compared to the open bids. We compute this overall cost to the Indian government via an NPV model in which we discount all future payments from the Indian government to solar power plant owners and compare the cost to the clean technology budget in India. We use discount rates of 10%, 12% and 14% and a capacity factor of 20% for the solar PV plants and a 25-year running time based on REN21 (2018) data. These numbers are roughly similar (apart from possibly lower discount rates in this study) for other developing and emerging economies. These discount rates are based on information used by the Indian government for evaluating public projects46. Given the well-known challenges of choosing social discount rates (SDRs)47, we perform a sensitivity analysis by varying the discount rate between 10%, 12% and 14%. Taken together, these values for the SDRs encompass typical values of SDRs used in other developing and emerging economies, something that helps make our results more comparable to other countries46. We use the average real bid price from all open category auctions as our base price to calculate the additional cost of LCRs over the lifetime of an average solar project subject to LCRs.
In order to analyse the possible benefits of the LCR policy, we select a small set of indicators commonly used in the innovation systems and catching-up literature to determine whether a country is ‘narrowing’ the gap between the innovation leader and itself. While there are no perfect sets of metrics, we employ three different metrics commonly used in the innovation and economics literature: (1) domestic and international patent filings in the technology of interest40, (2) domestic production versus international imports5 and (3) exports to other countries from the country of interest4.
This analysis should be understood as correlational rather than causal, in contrast to the first part of our analysis. In addition, given how relatively recent the policy is, this analysis captures only short-term manufacturing and innovation effects. This is a limitation because some of the impacts of the policy, such as ongoing consolidation of the local industry through mergers and acquisitions, may take more time to materialize. Hence, the main contribution of this paper is the empirical assessment of the additional costs of LCRs, while the analysis of the possible benefits provides indicatory evidence of the evolution of important manufacturing and innovation metrics.
Data
In our analysis, we focus on the NSM Phase II auction results from 2014 to 2017. We did not include the bids and results from NSM Phase I since the majority of the projects (61% of total capacity deployed48) in the auction relied on thin film technology (as opposed to silicon panels), which was exempt from LCRs. Furthermore, we focused on the results of the PPA-based scheme and did not consider the EPC programme, which has a different focus: the auctioneer procures and owns the project and does not remunerate the electricity generated over 25 years to the project developer. The different remuneration mechanism, limited availability of data and different auction design elements, as well as different offtakers, hinder the comparability of the data. For the same reason, we neglect auctions conducted by state governments and focus solely on central government tenders conducted by either SECI or NTPC.
Contrary to most other countries conducting auctions, the Indian government shows a high degree of transparency in terms of publishing bids in the NSM Phase II auctions—including information on firms and the bid prices of both successful and unsuccessful applicants. We collected the data about the bid prices and the respective bidders from various government sources, either directly through governmental bodies (for example, SECI) or indirectly through different industry and reputable news sites, such as Mercom India or EQ International Magazine. We include all auction rounds of Phase II that had LCR regulations in place for a total of 28 auction windows across 10 Indian states. As shown in Supplementary Fig. 5, we intentionally excluded from the analysis the state-wise utility-scale PV tenders (around 14 GW by September 2017). In addition, we exclude the central government EPC tenders (1.6 GW) as well as the ‘open category’ rounds in central government auctions in which no counterfactual LCR auction took place (around 4.6 GW), such as the 100 MW auction in Uttar Pradesh in Batch III.
We consider our dataset with 277 bids complete in terms of auction rounds, since LCRs were abolished on 14 December 2017 due to a ruling of the WTO, with NTPC’s 250 MW Indian-wide auction being the last one under LCR regulation (the auction was later cancelled due to the negotiated phase-out of LCR). For further analysis, we consider the available submitted bids, rescale those to 2014 US dollar values to reflect inflation, and use logged bid values in our regression to normalize them. In summary, we consider bids with a total capacity of 21.7 GW, of which 18.7 GW were submitted in the open category and 3 GW under the LCR scheme.
We also collect detailed firm data for all 85 firms within our sample. For each firm, we analyse whether it belongs to a bigger firm. Several firms are so-called special purpose vehicles, which are created merely to bid in a given auction. Given that these firms have access to the human, financial and technical capital of the bigger firm that they belong to, we use the firm characteristics of the parent company. In addition, we collect data on the employment numbers (which could be found for all firms, as opposed to sales numbers, which were unavailable for many privately owned firms), check whether the firm is an SOE and research whether the firms themselves have manufacturing capacities (that is, are vertically integrated). We analyse whether the firm had already bid in the first phase of the NSM, which might give firms a distinct advantage over newcomers due to experience with local regulations. We also check whether the main focus of the company is energy or whether it has just recently diversified its firm activities into energy. Lastly, we analyse whether the firm was founded in India or was registered abroad. We posit that all of these characteristics may influence whether a firm participates in a given auction (for example, we assume that firms that have local manufacturing capabilities are more likely to participate in LCR auctions).
We used solar irradiation maps from the National Renewable Energy Laboratory (NREL) and converted them via QGIS (version 2.8.14) into mean, maximum and minimum values for each state. The NREL dataset provides solar resource in India for surface cells of 0.1 degrees in both latitude and longitude, or nominally 10 km in size. The NREL calculations are based on data from the Meteosat-5 and Meteosat-7 geostationary meteorological satellites42.
Patent data were collected from the Indian Patent Database using web scraping methods (Python package Selenium), as the patent office does not offer an application programming interface (API). We employ a typology from a recent, comprehensive review of international patent classification (IPC) terms and their correspondence to PV system components published in Renewable and Sustainable Energy Reviews49. This typology covers 284 distinct IPC codes in seven groups: cells, panels, electronics, energy storage, monitoring/testing, devices and combined. Studies comparing global trends in patenting to track innovation normally rely on large patent databases such as the European patent database PATSTAT, which aggregates patent statistics across many domestic offices. However, for India the PATSTAT data are woefully incomplete, leading us to resort to web scraping techniques.
The data on domestic production and imports in Fig. 4c are based on the Directorate General of Trade Remedies investigation on the imposition of safeguards on solar PV cells and modules on behalf of five Indian solar producers. The export data in Fig. 4d were exported from the global United Nations trade database Comtrade using the commodity code ‘HS 854140’, which describes ‘photosensitive semi-conductor devices, including photovoltaic cells whether or not assembled in modules or made up into panels’50. More

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Experimental design
The trials were conducted in five regions (Boro, Ugunja, Ukwala, Wagai and Yala) of Siaya County in western Kenya. Siaya County is located at 00°08.468′ N, 34°25.378′ E, and at an altitude of 1,336 m above sea level. The experimental sites were in lower midland 1(LM1) and lower midlands 2 (LM2) agro-ecological zones, which experience bimodal rainfall with long rains (LR) starting in March to July and short Rains (SR) starting in Late August to December41 and receive average annual rainfall of 1,500 mm42. The soils are mainly Ferralsols and Acrisols in the higher areas and Vertisols in the low areas.
Trials were conducted in 48 randomly selected villages, using stratification at the sub-county level. Half of them (randomly selected) participated in the trials in the long and short rain of 2014 and the long rain of 2015. The other half started in the short rain of 2014 and continued throughout the long and short rain of 2015. In each village 10 farmers participated in the trial. Half of them were specifically selected for participation in a community meeting. In those meetings, the researchers explained the objectives of the trials and asked the community members to nominate 5 farmers (as well as 5 potential substitutes), including two women, thought to be good farmers and interested in participating in the trials. Such non-random selection of farmers is common practice in research trials (Supplementary Table S1 online). The other half was selected randomly from the list of all the farmers in the village. All selected farmers were visited to obtain consent for the trials and identify the potential trial parcel (chosen by the farmer, conditional on fitting with some criteria for suitability to the research trials). A small number of replacements was done (but always keeping 5 selected by the community and 5 random). In each village 4 random farmers (2 random and 2 selected) were assigned to participate in the maize trial, while 3 random farmers (at least 1 random and 1 selected) were assigned to participate in the soybean trial. The other 3 farmers participated in a maize-soybean intercrop trial. During implementation, the assignment of inputs for the intercrop trial was, however, contaminated. As a result none of the plots in the intercrop trial received a best-bet input package, making the agronomic findings from that trial hard to interpret, and therefore not necessarily of interest for the decomposition proposed in this paper. Nevertheless, for completeness, results for these intercrop trials are shown in Supplementary Table S14 online.
Researcher-designed and farmer-managed trials
The trials would qualify as researcher-designed and farmer-managed (under the supervision of the researchers). The research team had full control over the design of the trials, from the choice of inputs to spacing and other management practices. All inputs were provided by the research team, with the exception of the local maize seed tested in two out of the six plots. A researcher (local expert agronomist) was present and led planting, gapping and thinning, all fertilizer applications, and harvesting. In these activities, labor was typically provided by the farmer. Planting dates were mostly decided by the researchers to best target the onset of rains, also responding to the farmers’ feedback on beginning of rains and availability to schedule the visit for planting. The farmer was in charge of land preparation, weeding and other management, with the researchers providing guidelines on those practices. In each village, a contact person (typically one of the ten farmers) visited the trials weekly to verify that the farmers fulfilled their responsibilities. Farmers were also asked to inform the contact person in case of any pest or disease, in which case the researcher provided the required pesticide or fungicide.
Treatment structure and application
Supplementary Table S2 online presents the full factorial designs of the multi-locational trials for maize and soybean, including details on crop varieties and quantity of inputs. The plot sizes were 4.5 × 5 m and the treatments were completely randomized between the six plots on each parcel. Plot sizes are of a similar order of magnitude as those found in other recently published work. A 1 m inter-plot spacing was planted with sweet potatoes to act as a buffer between plots to prevent inter-plot contamination. The sweet potatoes were planted at 50 cm from each plot, and border rows of the maize and soya plots were excluded for yield estimations to limit any edge effect. Hence the area harvested was 12.9 m2 for maize and 13.5 m2 for soybean. The experiments were repeated for three seasons, and plot layout and treatments were maintained for three seasons.
For the soybean trials, a soybean rhizobia inoculant was tested alone, with Minjingu hyper phosphate (0-30-0 + 38CaO) or Sympal (0:23:15 + 10CaO + 4S + 1MgO + 0.1Zn) in a full factorial design. Phosphorus rate of 30 kg P ha−1 was used to determine the quantity of Sympal and Minjingu hyper phosphate to be applied. On each farm only one replicate was used; hence, 6 plots were installed on each farm. Inoculation was done at planting as a seed coating using the directions for use in the respective product labels. Each plot had 6 soybean lines of 5 m in length each spaced at 5 cm from plant to plant within row and 50 cm from row to row. Inoculation was done on all the rows. Soybean variety TGx1740-2F with medium maturity (95–100 days)43 was used as the test crop. The spatial variability of the soybean response is studied in44. The soybean trials demonstrated that the combination of rhizobia inoculant and P-source led to important yield gains44.
The choice of inputs resulted from prior research conducted as part of the Compro project. Soybean was chosen as test crop mainly because in the prior phase of the project it had shown good response to rhizobia inoculation45 and was agro-ecologically suitable to the region. Kenya is an importer of soybean and multiple efforts are geared towards raising local production. In Compro I, the two rhizobia inoculants were tested and shown to be effective in increasing nodulation, nitrogen fixation and yield when inoculated on the tested soybean variety. Minjingu and Sympal were chosen based on their formulation with respect to the chemical characteristics of the soils in the test sites and results of earlier research46. The soils generally lack phosphorus and are acidic. A mapping study47 specifically identified Western Kenya as a potential K deficient area, and soil acidity has long been identified as a constraining factor in Western Kenya48 hence the importance of CaO. Results from soil sampling of the trial plots confirmed that more than 56.87% of soils were acidic (pH  More

Spatial patterns of stability proxies and background variables
Figure 2 a, b show the spatial distribution of our two proxy variables, the average rank of Rs per position (rankRs) and of the range of the ranks per position (rangeRs) in kriged maps. The middle to southern areas were found to have the largest, whilst the north-eastern areas the smallest rankRs values, whereas a slightly different pattern was characteristic for rangeRs with some additional north-western large values. Similarly, larger average soil organic carbon content (meanSOC) and average soil water content (meanSWC) (Fig. 2 c, d) were detected at the western-middle-southern regions and smaller at the north-eastern part of the study site.
Figure 2

Kriged patterns of stability proxies, rankRs (a) and rangeRs (b), as well as of background factors, meanSOC (%, c) and meanSWC (%, d).

Full size image

Correlations between stability proxies and background variables along DEMs: entire dataset
We investigated the potential direct effects of the different terrain attributes (local mean elevation (mALT), standard deviation of elevation (SD), topographic position index (TPI), slope (Sl), Easterness and Northness (East, North)) on the spatial distributions of our proxy variables by using the terrain attributes originating from differently smoothed DEM rasters. DEM1 was the original, 0.2 m resolution model, while DEMs 2–6 were progressively smoothed by a factor of two resulting in different resolution DEM rasters (DEM2: 0.4 m, DEM3: 0.8 m, DEM4: 1.6 m, DEM5: 3.2 m, DEM6: 6.4 m, respectively), and finally DEM7 met the resolution of the field measuring campaigns (10 m). The terrain attributes were filtered out from the rasters for the 78 measuring positions of the sampling grid.
On the basis of the correlation analysis we found an important difference in terrain attribute features between DEM 5 and 6, especially in SD, Sl, North and East. All subsequent results are then based on DEMs 1–5, which were found to be more similar to each other and to the original DEM1. The maps of terrain attributes with the box blur kernel from DEM1-5 can be found in the Supplementary Information (SI) together with the descriptions and calculations. As we couldn’t find any of the blur kernels superior to the other when considering correlations, the results hereafter are only presented for the box blur kernel calculations for simplicity.
When we considered the entire dataset (named hereafter: “A” dataset), we could only find significant correlation between rangeRs and TPI at less smoothed DEMs but the correlation was very weak (black symbols and line in Fig. 3).
Figure 3

Direct correlation between TPI and stability proxy, rangeRs at less smoothed DEMs, DEM1-2 for datasets A (black symbols and line) and S (blue symbols and line, see the information later on). The correlations were significant at p = 0.0076 and p  = 0.0172 levels, although they were weak, r2 = 0.09, r2 = 0.42 for A and S (see the information later on), respectively.

Full size image

Any other correlation between the proxies and the terrain attributes could only be deduced indirectly from the positive correlations between rankRs and meanSOC, meanSWC (cf. Table 1b). These correlations were scale-independent, i.e., we detected them at every DEMs. In general, the larger the soil carbon content and soil moisture at a position (cf. Figure 2c,d, showing quite similar patterns to the proxy patterns in the figure upper row), the larger the Rs activity detected and the opposite was true for lower carbon content and soil moisture positions.
Table 1 (a) Statistically significant (p  More

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