Author contributions: M. Despite widespread adoption of genetically modified crops in many countries, heated controversies about their advantages and disadvantages continue.
Especially for developing countries, there are concerns that genetically modified crops fail to benefit smallholder farmers and contribute to social and economic hardship. Many economic studies contradict this view, but most of them look at short-term impacts only, so that uncertainty about longer-term effects prevails. We address this shortcoming by analyzing economic impacts and impact dynamics of Bt cotton in India. These benefits are stable; there are even indications that they have increased over time.
We conclude that Bt cotton has created large and sustainable benefits, which contribute to positive economic and social development in India. Despite widespread adoption of genetically modified GM crops in many countries 1 , controversies about their advantages and disadvantages continue.
In the public debate, negative attitudes often seem to dominate. Civil society groups tend to emphasize potential risks of GM crops and question reports about positive agronomic and economic effects 2 — 5. Especially with a view to developing countries, there are widespread concerns that GM crops fail to benefit smallholder farmers and contribute to social and economic hardship 4 , 5.
Much of this debate focuses on Bt cotton 5 — 9 , as this is currently the most widely used GM crop technology among smallholders. Using comprehensive data from India, we show that these concerns about negative social and economic impacts are not backed by representative empirical evidence.
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Bt cotton contains genes from Bacillus thuringiensis that make the plant resistant to the cotton bollworm complex. Several studies have shown that Bt cotton adoption is associated with significant benefits to farmers in various countries 10 — In addition to productivity gains 15 — 19 , Bt adoption entails reduced incidence of acute pesticide poisoning among smallholders However, the available literature on Bt cotton impacts has four important shortcomings, which may also explain why controversies continue.
First, with very few exceptions 21 , most of the evidence is based on data from field trials or from the first few growing seasons after the commercial release of Bt varieties in a country.
This evidence is unsatisfying because it does not allow analysis of longer-term developments.
For example, resistance build-up in pest populations or growing importance of secondary pests may potentially lower Bt benefits over time 22 — Second, most impact studies do not properly control for nonrandom selection bias 17 , which may occur when more successful farmers adopt the new technology earlier or more widely As these successful farmers may have higher crop yields and profits anyway, this can result in inflated benefit estimates.
Third, most available studies focus on agronomic impacts of Bt, such as yield and pesticide use effects, but economic effects, such as profit changes, are not analyzed at all or only based on simplistic comparisons. Fourth, and related to the previous point, many existing studies concentrate on impacts at the plot level, without considering possible broader welfare effects for farm households.
We address these shortcomings by using comprehensive panel data collected in India in four waves between and Estimation of panel data models allows us to account for selection bias and also analyze impact dynamics. In particular, we estimate fixed-effects specifications of yield, profit, and consumption expenditure models to derive net impacts of Bt adoption on cotton yield per acre, profit per acre, and household living standard.
To our knowledge, this economic impact assessment of any GM crop technology that builds on more than 2 y of panel data is unique. In India, cotton is primarily grown by smallholder farmers with farm sizes of less than 15 acres and cotton holdings of 3—4 acres on average. The first Bt cotton hybrids were commercially released in India in We carried out a survey of Indian cotton farmers in four waves between and This survey covered a total of farm households in four principal cotton-producing states see Materials and Methods.
The sample is representative of Bt and conventional cotton farmers in central and southern India. Given that we purposively oversampled Bt adopters in the first wave, sample adoption rates differ from actual adoption rates. In the , , and survey waves, we also asked farmers for their adoption of Bt hybrids in , , and , respectively.
However, further details about the cultivation experience were only asked for the respective survey years. A similar trend is also observed for individual adoption intensities, defined as the Bt acreage relative to the total cotton acreage on a farm.
Alongside a range of household characteristics, data on all cotton plots of surveyed households were recorded, leading to a total of 1, plot observations. Table 1 compares selected variables between Bt and conventional cotton plots and farms for a more detailed overview, see Table S1. We differentiate between early — and late — adoption periods.
Most previous studies on Bt cotton impacts in India concentrated on the early period; evidence for the later period is thin.
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Bt seed costs per acre were more than three times higher than conventional seed costs during the early period. During —, the cost difference was lower because of government interventions in seed pricing and increasing competition in the market for Bt technology 19 , Pesticide costs were significantly higher on conventional plots than on Bt plots during —, and there was no difference during — Widespread adoption of Bt has led to area-wide suppression of bollworm populations, so that conventional cotton farmers also substantially reduced their pesticide applications Similar positive spillover effects were observed for Bt cotton in China and Bt maize in the United States 28 , Descriptive statistics for 1, plots and associated households averages for — and — Mean values are shown with SDs in parentheses.
Household expenditures were deflated using the consumer price index. Rs, Indian Rupees. Additional variables are shown in Table S1. In terms of yield per acre, Bt strongly outperformed conventional cotton in both time periods Table 1. This finding is not because of higher yield potentials of Bt hybrids, but because of more effective pest control and thus lower crop losses.
Higher yields are also the main reason for much higher profits on Bt cotton plots. These observed differences provide interesting insights into Bt effects, but they cannot be interpreted as net impacts of the technology, because confounding factors and possible nonrandom selection bias have to be controlled for.
This process requires regression analysis. Results of panel fixed-effects specifications of a cotton yield function are shown in Table 2 full model results with all control variables are shown in Table S2.
The positive and significant coefficient of Bt in column 1 indicates that Bt has a positive net impact on cotton yield per acre. The Bt dummy variable captures Bt adoption in any year, whereas the additional Bt — dummy takes a value of one only when Bt was used in the or survey waves. In the first column, the Bt — coefficient is insignificant, indicating that the Bt yield effect was stable over time and did not increase or decrease in the later compared with the earlier period.
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Coefficient estimates are shown with SEs in parentheses. Estimates are based on panel regressions with household fixed effects to control for nonrandom selection bias.
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The reference year is Not all explanatory variables included in the models e. The Hausman test results show that fixed-effects are preferred over random-effects specifications. The dummies for the three survey waves in column 1 of Table 2 are all positive and significant, indicating that overall yield levels were higher in , , and , compared with the reference year Omitting these year dummies in column 2 leads to a large positive and significant Bt — coefficient.
These results suggest that the Bt yield gain was in a magnitude of kg per acre sum of Bt and Bt — coefficients in the later period and thus more than doubled compared with — As Bt adoption strongly increased over time, there is a close correlation between Bt — and the year dummies. Hence, some of the Bt effects are captured by the year dummies in column 1.
Not including year dummies, as in column 2, may overestimate the Bt yield gains, because Bt — may then also capture time effects that are unrelated to the technology. However, systematic changes in temperature or rainfall did not occur during the period of analysis 30 , 31 , and there were also no other breakthrough technologies in Indian cotton production 20 , Nor did we find evidence of attrition bias.
Because we have an unbalanced panel, there is the possibility of attrition bias, which could emerge when farmers who obtained lower than average yields with Bt cotton in — dropped out of the sample in the later — period.
This drop could potentially hide a decrease in Bt impact over time.
Analyses with different subsamples that we carried out do not support this hypothesis. We re-estimated the model in column 1 of Table 2 excluding the dropout farmers. With this smaller sample, the Bt coefficient is Hence, we conclude that there is no attrition bias.
Therefore, Bt was probably the main factor contributing to the observed time effects. Bt technology can influence cotton profit mainly through three channels, namely changes in yield, changes in pesticide cost, and changes in seed cost To assess net profit changes per acre, we estimated fixed-effects specifications of a profit function Table 2 ; full model results with all control variables are shown in Table S3.
In this specification, the Bt impact per acre does not change significantly over time. However, total cotton profits per farm rose, because farmers increased their Bt adoption intensity. Similar to the yield analysis above, the year dummies in column 3 of Table 2 are all significant. This result may partly be explained by lower Bt seed prices during — However, as seeds only account for a relatively small share of total production costs, the more important reason for larger profits per acre are higher yield gains and thus higher sales revenues.
Cotton is often the major crop for cotton-producing households in India, so that profit gains through Bt technology are also likely to increase household living standard. A common way of measuring living standard in the development literature is to look at household consumption expenditures, because expenditure is usually a more reliable indicator than income We use a fixed-effects specification of a consumption expenditure model.
The results suggest that Bt had no significant effect on consumption expenditures in the early adoption period, but it increased household living standard significantly in the later period Table 3 ; full model results with all control variables are shown in Table S4. This finding is plausible. Although Bt-adopting households also increased cotton profit during —, they did not immediately change their consumption behavior but waited until they realized that the profit gains are sustainable.
Control variables include cotton area, so that the coefficients of Bt area and Bt area — can be interpreted as the net effect of Bt technology full model results with all control variables are shown in Table S4.
The Hausman test result shows that fixed-effects are preferred over a random-effects specification. Based on this finding, we can also calculate the total living standard effect per household by multiplying with the mean Bt area of adopting farms. The results show that Bt cotton adoption has caused sizeable socioeconomic benefits for smallholder farm households in India.
These effects are similar in magnitude to the ones shown in earlier studies for India based on cross-section data 15 — 19 , The panel data used here confirm that impacts per acre of Bt cotton have been stable over time.