extfieldsofstudy: Medicine Economics

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Land grabbing: a preliminary quantification of economic impacts on rural livelihoods

Global demands on agricultural land are increasing due to population growth, dietary changes and the use of biofuels. Their effect on food security is to reduce humans’ ability to cope with the uncertainties of global climate change. In light of the 2008 food crisis, to secure reliable future access to sufficient agricultural land, many nations and corporations have begun purchasing large tracts of land in the global South, a phenomenon deemed “land grabbing” by popular media. Because land investors frequently export crops without providing adequate employment, this represents an effective income loss for local communities. We study 28 countries targeted by large-scale land acquisitions [comprising 87 % of reported cases and 27 million hectares (ha)] and estimate the effects of such investments on local communities’ incomes. We find that this phenomenon can potentially affect the incomes of ~12 million people globally with implications for food security, poverty levels and urbanization. While it is important to note that our study incorporates a number of assumptions and limitations, it provides a much needed initial quantification of the economic impacts of large-scale land acquisitions on rural livelihoods. Electronic supplementary material The online version of this article (doi:10.1007/s11111-014-0215-2) contains supplementary material, which is available to authorized users.

Introduction/Background
Population growth, dietary changes and increasing use of crop-based biofuel are placing ever greater demand on food production and its requisite resources (Godfray et al. 2010). In addition, climate change is projected to adversely affect reliable and sufficient food supply in the future (Cline 2007). These changes in the demand and supply of agricultural products threaten food and water security as well as sustainable livelihoods. Due to these demographic and environmental pressures and the 2008 food crisis, many nations and corporations with the requisite capital are making large-scale investments in agricultural lands both domestically and abroad to either accumulate a reliable reservoir of land and water resources in the event of increased climatic uncertainty or to speculate on the price of cultivatable lands (Deininger and Byerlee 2011). While the potential benefits (e.g., insurance against food price shocks, increased global food supply) of these deals may be apparent, such transactions often take place at the expense of and without informed consent from the prior land users (Anseeuw et al. 2012;Deininger and Byerlee 2011;Cotula et al. 2009). This fact has been the source of wide discussion in the land rush literature (e.g., Vermeulen and Cotula 2010;Zoomers 2010;Borras et al. 2011;De Schutter 2011), but is all too often overlooked by the involved governments and investors. These large-scale land acquisition projects often emphasize the rapid increase in yield that they can produce and the additional employment they can provide. However, the benefits of this additional agricultural production are often not felt locally (Anseeuw et al. 2012;D'Odorico and Rulli 2013), so that the loss of access to land can ultimately spell significant dietary, social, cultural and economic consequences for rural communities in the targeted areas (Borras et al. 2011;De Schutter 2011). Given the lack of transparency in many of these transactions, it is understandable that a quantitative literature on the human impacts of this phenomenon is sparse. Despite this apparent difficulty, several studies have been able to broadly assess the amount of land appropriated (e.g., von Braun and Meinzen-Dick 2009;Deininger and Byerlee 2011). However, knowing the area controlled by investors can only inform the discussion so much, and a more pointed quantification of the specific impacts of the global land rush is now necessary. That is why steps are now being taken in the land rush literature to turn the focus from studies purely assessing the area affected by such land deals toward quantification of the potential environmental and human impacts (Edelman 2013;. One such study sought to quantify the potential for these land deals to impact malnourishment in the affected areas, estimating 200-300 million people at risk of greater food insecurity as a direct result (Rulli and D'Odorico 2014). Though this reduced ability to feed people locally is important to consider, it is only one way in which rural communities may experience the impacts of this global land rush. We focus here on a single question, namely: how many people in rural communities of targeted areas may potentially experience income loss as a direct impact of these agricultural land deals? We argue that, since the communities in these areas rely on agriculture for income, the loss of access to land and water resources as a result of land deals represents an inability to produce household income. While we only quantify this potential impact in lands intended for food crops, we should also note that large-scale land acquisitions can occur for several other reasons. For instance, recent increases in demand for agricultural land for biofuels is an effect of new energy policies (European Parliament 2009; UN DESA 2011) aimed at curbing the increase in atmospheric CO2 concentrations. Further, some large-scale investments in forested land can be driven by prospects of profitable investments in the carbon credit market for climate change mitigation (Meyfroidt et al. 2013;Fairhead et al. 2012;The Oakland Institute 2011c).
Targeted countries typically have lower levels of development and economies heavily reliant on the agricultural sector, in terms of both employment and value of domestic product (Fig. 1), making the livelihoods of their citizenry especially sensitive to climatic change, land degradation and this recent global land rush (IFPRI 2012). Specifically here, we consider how targeted land resources that would otherwise be used for local crop production translate into a reduced ability to sustain the livelihoods of the current population in the affected areas. This is especially important considering that rural households in agriculture-based economies are limited in their opportunities for non-farm employment unrelated to agricultural production (FAO 2010a, b;UN DESA 2011). The income lost from targeted agricultural land represents a reduced ability of the area to support a certain number of people. Thus, while there may be various contributing factors to the problem, the sole impact we explore here is the income loss by rural communities as a result of large-scale land acquisitions and how this impact varies across the most targeted countries. By calculating the total lost income due to confirmed large-scale land Fig. 1 Average percent contribution by agricultural sector to gross domestic product for the 28 significantly grabbed countries (solid/blue) and all other countries (dashed/green) from 1980 through 2010. Error bars represent the standard error of the mean deals, we examine the portion of a country's population with the potential to be directly economically impacted by these land deals and briefly suggest (while citing the limited available evidence) that this may result in increased urbanization and human migrations in order for rural communities to diversify their incomes (Tacoli 2011; The Government Office for Science 2011; UN DESA 2011). This study provides an initial, but much needed, quantification of the number of rural people whose livelihoods may be potentially impacted by large-scale land acquisitions. By providing empirical support, the intent of our work here is to act as a stepping stone for further studies with more definitive conclusions and to direct the attention of land deal research toward better quantifying the impacts of land deals on rural populations. Just as with tropical deforestation or urbanization, the issue of largescale land acquisitions is a rapidly evolving phenomenon (Cotula et al. 2014). This is compounded by the fact that information on land deals and their rural economic impacts suffers from a lack of transparency (Anseeuw et al. 2012;). Yet despite the difficulty that these issues can present in staying current with the dynamics of the phenomenon, our study provides a novel alternative approach with the potential to fill an important knowledge gap in our growing understanding of large-scale land acquisitions and their many possible impacts.

Methods/Data
We study the 28 countries (Table 1) most targeted by large-scale land acquisitions [comprising 87 % of reported cases and 27 million hectares (ha)]. We define largescale land acquisitions as transactions that target agricultural areas and that entail the transfer of rights to use, control or ownership through sale, lease or concession to commercial farming. Based on current yield scenarios (Mueller et al. 2012), country-specific crop yields for the year 2000 (which reflect national average yields before the land deal) were multiplied by land areas under contract from the new (June 2013) Land Matrix database (Land Matrix 2013) to calculate the agricultural production for each edible crop. Recently, criticism has been raised toward quantitative studies on large-scale land acquisitions relying on previous data sets of large-scale land acquisitions (Scoones et al. 2013;Oya 2013;Edelman 2013). Part of the criticism was based on lack of on-ground verification of the acquisition and on the fact that a substantial number of announced deals fail in the course of the negotiation stage. The new Land Matrix data set (Anseeuw et al. 2013;Land Matrix 2013) improves upon these criticisms and specifies whether each deal is just intended or concluded and also reports the area under contract. It also indicates whether the land has already been put under production by the investors (Land Matrix 2013). Here, we consider only concluded deals, for which the contract area was specified (Table S1), regardless of whether the land is under production because we assume that at this stage previous land users have already been excluded from accessing the acquired land. These criteria ensure that land rights have legally changed hands and that the ability of rural communities (who typically rely on traditional land tenure systems; Vermeulen and Cotula 2010;Anseeuw et al. 2012;White et al. 2012;Wily 2012) to access that land has been affected. The fact that these deals deny rural communities further access to agricultural land is all that is necessary for their incomes to be impacted. We readily acknowledge (as do the authors of the Land Matrix database) that conclusions from this database must be arrived at with caution and make every effort to ensure that our estimates are conservative. Also, since data are not available for the crops previously grown on targeted lands, our estimates of production represent the potential amount of crops able to be grown on these lands at current yields had the land continued to be available to local communities. We assume that the intended crop types were grown on the land prior to the land deal. This is reasonable since most prior land use is by smallholder agriculture (Robertson and Pinstrup-Andersen 2010;Anseeuw et al. 2012). This assumption in turn can lead to inconsistencies in certain instances (for sugar cane in particular) between FAO estimates of production and our own. As stated before, this is likely because targeted land may not yet be actively cultivated, but it is no longer accessible by rural communities. Thus, even if a community intended in the coming years to expand cultivation onto land that is now incorporated, this community would no longer have that option. Gross agricultural production values (USD $ for crops used as food, feed or seed) and gross agricultural production (tons of crop production used as food, feed or seed) were obtained from the FAOSTAT database (FAO 2013). Unit prices of crops were calculated as the total gross agricultural production value of each crop by country divided by the gross agricultural production of that crop for that country (FAO 2013). A value of $484 per metric ton was used for missing oil palm data, as this was the unit value given by the FAO for all African countries considered where data were available. To account for production costs, we first took the sum of the gross capital stock for the year 2007 (the most recent year available) for land development, plantation crops, machinery and equipment (FAO 2013). We then divided this by the total gross production value of crops by country to obtain the national average fraction of gross agricultural value lost to production costs. This further ensures that our estimate is conservative since a portion of the gross capital stock considered also takes into account land development, machinery and equipment used for livestock production and thus is an overestimate. We do not consider the cost of fertilizers, as sub-Saharan Africa, Latin America and South-East Asia have low levels of synthetic fertilizer consumption (Morris et al. 2007;Potter et al. 2010). We also do not consider transportation since the gross agriculture value represents the value of the production at farm gate. Oil palm production was converted to palm oil production by country-specific ratios of palm oil production to oil palm fruit production obtained from FAOSTAT (FAO 2013). The appropriate unit price was multiplied by the quantity of lost agricultural production, and the sum of these crop values gave the total lost agricultural income by a country as a result of recent land deals. This total was then divided by the average income per capita (The World Bank 2013) to give the number of people who could potentially lose their income as a result of large-scale land acquisitions (see supplementary materials for more details). Since data on average rural income were not available for the countries of interest, average income per capita was given as the gross national income (GNI) per capita in terms of purchasing power parity. These data were from the World Bank's World Development Indicators database (The World Bank 2013), as were population data for each country and percent value added by the agricultural sector. The use of GNI (as opposed to rural per capita income) may, in turn, underestimate the total number of people affected, thus ensuring that our estimate errs on the conservative side. Jatropha was conservatively excluded from these calculations because: (1) it is not yet clear if the crop is profitable and (2) it is typically grown on marginal land (Brown 2012). Data for the percent value added by agriculture to GDP were from the World Bank's World Development Indicators database (The World Bank 2013). As Edelman (2013) states, studies of insufficient rigor (both in the quality of data/ analyses used and the unjustified conclusions drawn) can be counterproductive and act to hurt the case of rural communities, as such studies will not hold up to scrutiny by opposing viewpoints. Our work here makes every effort not to do so by: (1) using an up-to-date database (that has addressed much of the criticism of its preceding versions; Anseeuw et al. 2013) and rigorous criteria to select land deals to include in our analysis, (2) using a simple, conservative yet powerful analysis to estimate the impact on rural income and (3) seeking only to approximate the potential number of rural people affected by large-scale land acquisitions [making no claims at establishing a conclusive ''killer fact'' as several authors caution against (Oya 2013;Scoones et al. 2013)]. Lastly, we should note that while the database used in this study is a significant improvement on its previous versions, it is still subjected to certain biases (e.g., countries' data policies, focus on international investments), which should be taken into consideration when drawing any conclusions.

Results
We estimate that in the 28 countries most affected by land deals from the year 2000 to present, more than 12.1 million people are potentially affected by the direct economic consequences of land acquisitions ( Table 1). The percent of a population potentially affected by lost income due to this phenomenon falls below 1 % for all but 7 countries (Gabon, Liberia, Malaysia, Mozambique, Papua New Guinea, Sierra Leone and South Sudan/Sudan). However, the impact on lost livelihood varies widely by country. In Papua New Guinea for example, an income that could support nearly one quarter (23 %) of the population is potentially lost. Conversely, in countries such as Russia (\0.01 %), Brazil (0.02 %), Peru (0.05 %) and Uganda (0.05 %), the relative impact on employment prospects is minimal. Of the countries in this study, 16 have a potential lost income equating to greater than 100,000 people, and 4 have [1.5 million people potentially affected. In absolute numbers, Mozambique tops the list with more than 2.7 million people, followed by Indonesia (1.8 million), South Sudan/Sudan (1.7 million), Papua New Guinea (1.5 million) and Ethiopia (0.78 million). Since there are no data in the peer-reviewed literature supporting these findings (Robertson and Pinstrup-Andersen 2010), comparisons are limited. However, several reports from NGOs indicate that our estimates are reasonable. For instance, our estimate for Ethiopia agrees well with a report (The Oakland Institute 2011b) placing the number of affected people at 1 million. Our approximation for Uganda corresponds well to an estimate for select affected districts of 20,000 people (Oxfam International 2011). Also, a major land deal in Tanzania will reportedly displace more than 160,000 people (The Oakland Institute 2011a). According to our findings, the regions with the potential to be most heavily impacted in terms of lost agricultural income are sub-Saharan Africa and Southeast Asia. While Africa accounts for 43 % of the appropriated area in this study, Africans comprise roughly two-thirds (8.2 million people) of all those potentially affected (Fig. 2). We estimate total lost income globally at *$34 billion, a number comparable to the *$35 billion loaned by the World Bank for development and aid in 2012 (The World Bank 2012). The local agricultural livelihoods of smaller countries in West Africa appear to be particularly vulnerable to the potential effects of land acquisition (Fig. 2). Again, we stress that the results presented here are conservative estimates. The analysis here thus provides a new and simple way to quantify a phenomenon with a reputation for lack of transparency and to gain a first approximation of how severely impacts on rural income may be across countries.

Discussion
From the outset of this discussion, it is important to note that while this study contributes important empirical evidence of the economic impacts of the global land rush on rural communities, the findings should by no means be viewed as conclusive. They should instead be considered as an upper limit (of potential impacts on rural people) against which future case studies can be measured. This is particularly noteworthy given the significant assumptions incorporated into our methodology (especially related to prior use and crop type) and, in turn, how they may influence our findings. Despite this, we find that where data are available our results agree quite well with case studies where rural communities were either displaced or their livelihoods were affected.
While the loss of income and employment opportunities by rural communities is an important impact to consider, we also acknowledge that with each land deal comes a unique set of benefits to, impacts on and responses by the affected local communities (Borras and Franco 2013). The fact of varying benefits, impacts and responses is true both between and within countries, as was highlighted in McCarthy's work (2010) in Indonesia. Here, he noted that the options presented to smallholders and the ways in which they choose to interact with commercial agriculture ultimately dictate whether change is positive or negative. In addition, as  Borras and Franco (2012) have previously described, the perspective from which a land deal is viewed plays an important role in how benefits of land deals are defined and whether they have been realized. For instance, a land deal that improves crop production or rural employment opportunities may result in environmental degradation. While potential benefits and impacts vary with each case and for each stakeholder, Li's work (2011) examining existing data on the land rush phenomenon and taken from a labor perspective demonstrates that poverty reduction is an unlikely result of large-scale land acquisitions. However, the question of benefits is far more certain at the national level for the target country where land deals are more likely to result in some economic and political benefits (Li 2011). This was notably the case in a study by the organization Welthungerhilfe of a recent land deal in Sierra Leone (IFPRI 2012;Melbach 2012). In this instance, local farmers were denied access to land without prior consultation and experienced a drastic loss of reliable income, making them less able to afford food for their households and school fees for their children. Except for a small one-time payment to farmers of USD 220 and minimal annual area-based payments of USD 6.25 per hectare for oil palm land only (compared to an average annual GNI per capita of USD 880), farmers are unable to obtain income from the land. Conversely, the various levels of government administration receive the other 50 % of the investors' yearly lease payment.
While this is a compelling example of what we seek to examine here, what is more broadly essential to consider is to what extent the potential benefits from land contracts (and the activities that follow) actually find their way to the populace just as the original agricultural income would. One way by which these changes in land tenure can potentially benefit and sustain the livelihoods of local communities is by providing employment opportunities with adequate income. While investing corporations regularly make estimates on new job creation, the actual number of jobs created is typically well below expectations, due to transitions to plantation style agriculture preferring mechanization and wage laborers (Deininger and Byerlee 2011;Cotula et al. 2009). In most cases, the opportunities for employment are low-quality and limited or nonexistence (Deininger and Byerlee 2011;Cotula et al. 2009;Li 2011). Moreover, land acquisitions largely affect rural (and generally poorer) communities in countries where wealth tends to be distributed less equally. Overall this means that vulnerable communities within vulnerable countries (i.e., those most impacted by changes in food prices) are also those more susceptible to livelihood loss due to the land rush. Where agricultural production is primarily contributed by subsistence farming, the loss of cropland can also be interpreted as a reduced ability to meet the dietary requirements of a targeted country's population (The Oakland Institute 2011b; Rulli and D'Odorico 2014).
The extent to which these land deals potentially affects employment prospects within a country varies widely and is unique to each case. The number of people potentially affected ranges from thousands to millions (1), highlighting the fact that countries are differentially affected by and sensitive to consequences of large-scale land acquisitions. As per capita income can vary greatly between targeted countries (e.g., USD 330 per year in the Democratic Republic of the Congo vs. USD 14,680 per year in Malaysia; World Bank 2013), a person's average income is an important consideration in assessing the consequences of such land deals. While we make every effort to keep our estimates conservative, our approximations of the potential number of people affected by income loss due to the global land rush provide important insight into which countries may expect to experience this impact most heavily (even if the country's land area under contract is comparatively small; e.g., Mozambique). How much of a country's income comes from agriculture (Fig. 1) and how many of its people are employed in that sector (Table 1) contribute to how vulnerable a country may be to the effects of large-scale land acquisitions. The strength of a targeted country's legal system, the extent of enforcement and the ease for investing countries in navigating its land tenure system also help determine which places are preferentially targeted (Anseeuw et al. 2012;Deininger and Byerlee 2011;. Ultimately, this can lead to the sudden marginalization of rural communities and leave them with limited options for alternative forms of household income. To worsen this vulnerability, less-developed countries (and rural areas in particular) are predicted to experience a disproportionately large amount of the adverse consequences of climate change (Rosenzweig and Parry 1994;Parry et al. 2004). While the analysis here focuses on people, the consequences of environmental change are likely to be compounded with a transition to a more commercialized means of agricultural production. These adverse effects typically associated with transition to commercial-scale agriculture include pollution from increased fertilizer usage and soil loss from mechanized planting and harvesting (Brown 2012). However, since the land rush has only taken place in the past several years, many of these potential adverse effects may require more time to be fully discernible. This is true not only for environmental impacts, but as Cotula et al. (2014) point out, also for impacts on rural livelihoods, since land deals across the world are at various stages of establishment and implementation. The fact that many land deals have taken longer than expected to implement can also mean significant opportunity costs, where it becomes less likely that positive outcomes will counter negative impacts (Cotula et al. 2014).
From the perspective of local communities, the economic consequences of land deals can often be thought of as analogous to those of crop failures. In both cases, the financial (e.g., transportation fare) and infrastructural (e.g., roads, bridges) means to seek employment through non-farm activities are often left intact, but the enduring livelihoods of households are threatened. Given the proximity of many land deals to urban areas (Anseeuw et al. 2012), the prospect of migration becomes all the more reasonable. In Bangladesh, a place visibly experiencing the early effects of climate change through increased flooding, it was found that crop failures (and not flooding) better explained people's propensity to migrate permanently (Gray and Mueller 2010). Thus, as with land acquisitions, loss of local profit from crop production for the foreseeable future can make migration a reasonable option for securing a household's income (Tacoli 2011; The Government Office for Science 2011). Similarly in China, migration due to the conversion from subsistence farming to commercial agriculture has been reported (Siciliano 2013). Also, in Ethiopia, large-scale land acquisitions have reportedly caused transboundary displacements of local farmers and pastoralists into Sudan (The Oakland Institute 2011b).

Conclusion
Overall, how affected communities are able to financially cope with the impacts of these land deals depends upon their access to assets, infrastructure and opportunities (UN DESA 2011).The effects of large-scale land investments can be multitudinous, with advocates on either side touting their positives (e.g., technology sharing, increased crop yields) and negatives (e.g., lost livelihoods, unjust land appropriations, environmental degradation) Toft 2013). This study offers a first insight into the impact of the recent land rush on rural livelihoods. Our conservative estimate of over 12 million people losing their incomes is more than one-third of the number of internally displaced people due to conflict (29 million people) (IDMC 2013a) and one quarter of the number of migrations induced by natural hazards in 2012 (32 millions) (IDMC 2013b). This relatively large number of people may contribute to issues of food insecurity and poverty in rural areas while challenging the sustainability of urban growth as affected people seek to diversify household income (UN DESA 2011).
Losing access to land can carry with it a variety of economic, social, nutritional and cultural consequences (De Schutter 2011), a full discussion of which is beyond the scope of this study. Income loss represents just one way through which these deals might adversely affect rural communities. By quantifying the number of rural people potentially affected by these land deals, we can also begin to understand the extent of the social and cultural impacts, an equally important aspect of the on-going conversation surrounding the global land rush (Edelman 2013). Our study provides estimates from an economic perspective against which field studies can be compared. Given the lack of transparency of this phenomenon, these findings provide a much needed initial empirical evaluation of the direct impacts of largescale land acquisitions on rural communities and their livelihoods. While our study and others like it ) are a good first step, on-ground verification is an essential next step toward firmly quantifying the human impacts of this process (Scoones et al. 2013;Oya 2013;Edelman 2013). Where our estimates best agree with such verifications can provide valuable information as to the primary impact (i.e. income loss) on rural households (and its magnitude) in these areas and help direct possible ways to address the problem.


The economic power of the Golden Rice opposition

Abstract Vitamin A enriched rice (Golden Rice) is a cost-efficient solution that can substantially reduce health costs. Despite Golden Rice being available since early 2000, this rice has not been introduced in any country. Governments must perceive additional costs that overcompensate the benefits of the technology to explain the delay in approval. We develop a real option model including irreversibility and uncertainty about perceived costs and arrival of new information to explain a delay in approval. The model has been applied to the case of India. Results show the annual perceived costs have to be at least US$199 million per year approximately for the last decade to explain the delay in approval of the technology. This is an indicator of the economic power of the opposition towards Golden Rice resulting in about 1.4 million life years lost over the past decade in India.

Introduction
'So, if introduced on a large scale, golden rice can exacerbate malnutrition and ultimately undermine food security.' This statement by Greenpeace (2012: 3) is in strong contradiction to the reported impacts of vitamin A deficiency and the nutritional impacts of vitamin A enriched diets. More than 125 million children under five years of age suffer from vitamin A deficiency (VAD). Dietary VAD causes 250,000-500,000 children to go blind each year.
More than half of the children who lose their sight because of VAD die within a year of becoming blind. Additionally, VAD compromises the immune systems of approximately 40 per cent of children under the age of five in the developing world, greatly increasing the risk The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence http:// creativecommons.org/licenses/by/3.0/ of severe illnesses from common childhood infections. Further health consequences include anemia, increased susceptibility to infection, and poorer growth (West and Darnton-Hill, 2008). Additionally, early childhood malnutrition has long-lasting effects that are difficult to reverse by better nutrition in later life (World Health Organization, 2001). Addressing VAD and zinc deficiency in nutrition has been ranked by the Copenhagen Consensus (2008) as the number one problem in developing countries for sustainable economic development and was reemphasized in 2012 (Copenhagen Consensus, 2012).
There are two major channels through which nutrition may affect economic development. Malnutrition limits (labor) productivity and effects human capital accumulation. Firstly, there is convincing evidence from past experience that nutritional status affects labor outcomes, particularly productivity. While the exact mechanisms underlying these relationships are not entirely clear, experimental and observational studies have documented sizeable effects of nutrition on productivity indicators (Thomas and Frankenberg, 2002). Fogel's (1994) study of the economic and health history of Europe illustrates the aforementioned relationship. At the end of the 18th century in England and France, agricultural production, and therefore food provision, was so low that approximately 20 per cent of the population was incapable of working more than a few hours of light work per day due to lacking food resources. Many people were chronically malnourished and died young, which resulted in premature losses of their human capital. Only the agricultural productivity increases in the 19th century permitted an escape from this developmental trap, and enabled individual productivity gains as well as increases in productivity on a macroeconomic level (Deaton, 2003). Similarly, Doblhammer and Vaupel (2001) investigate the relationship between month of birth and remaining life expectancy at age 50 in Austria, Denmark and Australia. Their results indicate that health appears to depend on factors like nutrition in utero or early childhood. These effects are attributed to the effects of intrauterine nutrition more than half a century ago, particularly to the seasonal availability of high-quality food like fresh fruit and vegetables (Deaton, 2003).
Secondly, malnutrition may also affect economic development indirectly via hampering cognitive abilities and human capital development. Richards et al. (2002), for example, use birth weight and cognitive indicators of the British 1946 birth cohort and find that birth weight was positively associated with cognition up to age 26, and with the likelihood of obtaining advanced educational qualifications. Similarly, Currie and Hyson (1999) find that malnutrition, measured by low birth weights, has significant long-term effects on health status, educational attainments and labor market outcomes. Moreover, Case and Paxson (2006) use stature of US and UK citizens as an indicator for their quality of diet during childhood and find that nutrition is positively associated with cognitive ability, which is rewarded in the labor market. They show that taller children have higher average cognitive test scores, and that these test scores explain a large portion of the height premium in earnings. In an historical study, Baten et al. (2010) show that nutrition during early childhood mattered for labor market outcomes: individuals who grew up in times and places characterized by high food prices tended to acquire fewer cognitive skills and were more likely to pursue occupations with limited intellectual requirements.
Vitamin A enriched rice has the potential to address the problem of micronutrient deficiencies in early childhood. Rice produces β-carotene in the leaves but not in the grain, where the biosynthetic pathway is turned off during plant development. In Golden Rice, two genes have been inserted into the rice genome by genetic engineering, which would not have been possible by traditional breeding, to restart the carotenoid biosynthetic pathway leading to the production and accumulation of β-carotene in the grains. The intensity of the golden color is an indicator of the concentration of β-carotene in the endosperm. Since a prototype Golden Rice was developed in 1999, new lines with higher β-carotene content have been generated. The breeding goal is to provide the recommended daily allowance of vitamin A in 100-200 g of β-carotene containing rice. This corresponds to the amount of rice eaten daily by children living in ricebased societies, such as India, Vietnam and Bangladesh. In other countries, Golden Rice could still be a valuable complement to children's diets, thus contributing to the reduction of clinical and sub-clinical VAD-related diseases. The different forms of Golden Rice contain between 1.6 and 35 μg β-carotene per gram of rice. A recent study with children has shown that the bioavailability of provitamin A from Golden Rice is as effective as pure β-carotene in oil, and far better than spinach in providing vitamin A to children. A daily intake of 60 g of rice (half a cup) would provide about 60 per cent of the Chinese Recommended Nutrient Intake of vitamin A for 6-8-year-old children and be sufficient to prevent vitamin A malnutrition (Tang et al., 2009(Tang et al., , 2012. Despite the expected nutritional benefits of Golden Rice, the technology faces strong opposition. Opponents to the technology argue that a sufficient daily vitamin A supply would require a two-year-old child to eat about 3 kg of rice per day; alternative and cheaper strategies would exist to address VAD and in the end the project is only an industry ploy to open doors for other genetically modified (GM) crops (Greenpeace, 2005). The argument about the daily intake has been dismissed as this assumes Golden Rice would be the only source of vitamin A (Enserink, 2008). Suggested alternatives such as alternative diets, industrial fortification or supplements have been available for many years but have not solved the problem (Stein et al., 2008). Hence, the possibility exists that Golden Rice may reduce VAD and save millions of lives, while other strategies have not been so successful.
Proof of concept of enhancing the vitamin A content of rice has existed since the late 1990s and the expectation was that by 2002 the first commercial rice varieties would be available. Developing a commercial product has not been delayed by solving a number of intellectual property right issues. The major stumbling block was -and still is -the regulation of genetically engineered (GE) crops. In 2000, the Golden Rice project started a public-private partnership with Syngenta to use their expertise in developing dossiers for the approval requirements of GE crops to ease problems caused by national regulations. Ten years have passed and still Golden Rice has neither been approved for cultivation in India and Bangladesh, two of the target countries of the Golden Rice Humanitarian Project, nor in other countries where its varieties are under development (Potrykus, 2010a, b).
Many argue that the additional regulations for approval required for GE crops compared with non-GE crops, and Golden Rice in particular, are not supported by science, and hence are overregulated. Justification for current regulation is based on the notion that the technology leads to 'unpredictable and uncontrolled modification of the genome'; this argument ignores the fact that all traditional breeding has been and is doing exactly the same (Baudo et al., 2006;Shewry et al., 2007;Batista et al., 2008;Herman et al., 2009). Although there have been studies showing the safety consequences of GM crops for rats, a careful assessment of those studies did not confirm the claims being made. Further, extensive reviews found no support for adverse human health effects from GM crops (Bennett et al., 2013). Developing Golden Rice requires the use of selectable marker genes. Regulatory authorities prefer that antibiotic selectable marker genes be deleted (e.g., Committee on Agriculture, 2012). This is technically possible, but requires substantial time and effort despite the fact that there is a wealth of scientific literature documenting that the antibiotic marker genes in use have no effect on consumer and environmental safety (Ramessar et al., 2007). Furthermore, requests for additional regulation are often based on the precautionary principle, while a strong interpretation of the precautionary principle has been shown to be a logically inconsistent line of reasoning for extra regulation (van den Belt, 2003), 1 while there is wide agreement among economists that optimal regulations related to food and environmental safety should be based on benefit-costs analyses (Arrow et al., 1996). Even the definition of what constitutes a GE crop is controversial from a scientific point of view (Herring, 2007).
Ex ante assessments have been done for Golden Rice in India (Stein et al., 2008) and the Philippines (Zimmermann and Qaim, 2004). These studies show that in India the costs per disability-adjusted life year (DALY) saved between US$3.1 and US$19.4: much lower than for alternative intervention strategies. The net social benefits for the Philippines have been calculated to be in the range of US$16 to US$88 million per year. These numbers are now expected to be on the lower side of this range as only direct health impacts have been considered and improvements in vitamin A enhanced rice breeding since 2004 were not included.
Despite the wealth of information available about the health benefits of Golden Rice, national governments, which are bound by the rules and regulations on the use of GE crops, are reluctant to approve its introduction (Potrykus, 2010a, b).
The objectives of this paper are to identify the costs and benefits of regulating Golden Rice, paying particular attention to possible overregulation, and to calibrate the model for Golden Rice in India. The simple model we present considers uncertainties related to the perceived costs by national governments of the technology and approval time, i.e., over time, additional information becomes available. This allows us to identify the costs of delaying the approval and in particular the perceived costs by national regulators equivalent to the costs imposed on society by the opposition to Golden Rice. Alternatively, ex ante assessments show substantial benefits of Golden Rice, but governments are still reluctant to approve the technology. Hence, there must be additional perceived costs for governments not approving the technology. This is the first study calculating those costs for the case of India. These perceived costs have been treated as being irreversible for being cautious. Hence, the opportunity to introduce Golden Rice has been valued following the quasi-option-value approach introduced by Arrow and Fisher (1974). Arrow and Fisher, among others (e.g., Dixit and Pindyck, 1994;Wesseler, 2009), show that decisions under uncertainty and irreversible benefits and costs either understate or overstate net benefits if irreversibility effects are ignored.
Results show that it pays to bring forward new arguments against the introduction of Golden Rice when the technology is close to being approved. The Indian case shows that the perceived costs by national governments are substantially larger than the costs of introducing Golden Rice, and has caused and continues to cause the death of thousands of children.

The real option model of perceived costs
The problem is described as follows. A benevolent developer of a vitamin A biofortification strategy in the form of Golden Rice, henceforth called the Golden Rice strategy (GRS) such as, for example, the 'Humanitarian Golden Rice' project, provides the technology for free. The benefits of the program are known to the developer as well as the national government and depend on the government's implementation strategy, the costs of which are known. The acceptance of the technology depends on national governments. The introduction of the GRS (the implementation strategy) costs the national government one-off administrative set-up costs and annual costs of running the strategy. This set-up serves as the reference model. If the benefits outweigh the costs of the GRS, a welfare maximizing regulator would implement the GRS. It is well known in the literature that regulators, other government agencies or governments in general do not follow this simple benefit-cost metric. Their objective function may not only be based on the direct benefits and costs of the GRS. A number of political economy models have been developed to analyze policies related to the introduction of GE crops in more detail considering two or more lobby groups (e.g., Graff et al., 2009;Swinnen and Vandemoortele, 2010) and the role of media (Vigani and Olper, 2014). Apel (2010) has argued that an anti-genetically modified organism (GMO) strategy has been a successful fundraising strategy for environmental lobby groups such as Greenpeace or Friends of the Earth. The role of anti-GMO lobby groups and their strong influence on decision making, particularly in developing countries, has been well described by political scientists (e.g., Paarlberg, 2008;Herring, 2010). Lobby groups with capital stocks at risk in the face of agricultural innovations use the political systems to protect their interests (Graff et al., 2013). Decision makers may consider the arguments raised by opponents for reasons of re-election, administrative power, budgetary power, sidepayments and more. In the end, whatever the specific reasons might be and whatever the detailed political economy process may look like, the final result is that decision makers take additional costs into account that result in the delay of the approval. 2 These costs we call the perceived costs -a term we use as those costs cannot be observed directly, and are a result of the political economy process of the regulatory decision.
If the inclusion of the perceived costs results in the delay of an approval, benefits of the GRS are foregone. We call the foregone benefits caused by a delay in approval the economic power of the opposition to a GRS.
We now develop the model to assess the economic power in more detail. The approval decision by the national government is exogenous to the developer as are the set-up and annual costs. At time t = 0 the government's view is that perceived costs, G c , of introducing the GRS exist and they are high, G c 0, while other benefits and costs discussed in more detail below are assumed to be known. This assumption is a simplification but can be justified by the studies investigating the costs and benefits of introducing the GRS. Hence, all remaining uncertainty is summarized under perceived costs. Over time, further information about the perceived costs arrive and at time κ either the strategy will be successful and perceived costs not be confirmed, G c = 0 with probability q, or confirmed to be high, G c 0 with probability (1 -q). Hence, the introduction mainly depends on the perceived costs of implementing the GRS. Based on that, the national government may decide the strategy will be introduced immediately (I) or postponed (P). Uncertainty about perceived costs will be resolved at some future point in time κ > 0. At κ > 0 the national government will know whether or not their perceived costs have been confirmed. At time t = 0 the arrival of κ > 0 is uncertain to the government and hence a random variable. k ∈ (0, ∞) is the date at which uncertainty about perceived costs is resolved; it follows from the exponential distribution f (κ) = he −hκ (Taylor and Karlin, 1984) This specification is not that restrictive as it will allow identifying threshold levels for perceived costs as shown below. The specification of the model considers the inherent uncertainty decision makers face as well as the fact that new information arrives over time, but that the specific point in time when information will be available is uncertain.
The health benefits of introducing the GRS are in the form of improved vitamin A supply. These benefits increase over time via the distribution of the seeds. As commonly done, we assume a logistic functional form for the diffusion of the Golden Rice seed where the slope parameter β ρ is known as the natural rate of diffusion, as it measures the rate at which adoption ρ increases with time t. The parameter α ρ is a constant of integration and the ceiling ρ max is the long-run upper limit on adoption. Benefits do follow a similar logistic pattern to the adoption curve ρ(t) with parameters α b , β b and ρ max . The health benefits are divided into annual irreversible, i b , and reversible, r b , health benefits with subscript b for benefits. Irreversible benefits refer to the benefits for children that in case of VAD could not be reversed in later life, such as premature death, blindness, stagnant growth, and cognitive capabilities, while reversible benefits refer to VAD symptoms that can be cured or at least reduced at every stage of life via an increase in vitamin A supply, such as diarrhea or infections like measles (West and Darnton-Hill, 2008). We have for the annual irreversible benefits i bt We get for the total irreversible benefits I b = ∞ 0 i(t)e −μt dt and for the total reversible benefits R b = ∞ 0 r (t)e −μt dt, where μ is the discount rate. From this we can deduct the average annual irreversible health benefits, i b = I b μ and the average annual reversible health benefits, The present value of the GRS benefits can be written as The costs of introducing the GRS from the government perspective include irreversible one-time administrative set-up costs for starting the GRS campaign, I c , average annual reversible costs for running the campaign, r c , and additional irreversible perceived costs of the GRS, G c , with subscript c for indicating costs: The state of nature and related benefits and costs the government faces can be summarized as follows: The net-present-value (NPV) of immediate introduction at t = 0 with subscript I for immediate: Postponed introduction but perceived costs are not correct, valued at t = 0 with subscript P for postponed and subscript e for error: Postponed introduction but perceived costs are correct with subscript ne for no error: where subscript ne indicates that the perceived costs in this case are at least as high that B < C ne holds. The value of a postponed GRS valued at t = 0 is Considering this setting, the developer faces two possibilities: either the GRS will be introduced immediately or postponed until time κ has arrived. On the one hand, the immediate introduction bears the risk that the GRS results in high perceived costs G c . On the other hand, postponing the GRS might cause foregone benefits of saved lives and health, NPV(GRS I ) − NPV(GRS P ). If the decision is postponed, the regulator gains additional information about the perceived costs and will know the true costs of implementing the GRS. Hence, the net benefits depend on weighing the benefits and costs of immediate against postponed introduction. Under this setting, whether or not the GRS will be introduced at time κ provides the following option value of the GRS strategy: The set-up allows us to identify the threshold when a national government might immediately introduce the GRS, which is NPV(GRS I ) − NPV(GRS P ) > 0, and yields: Solving for B: And solving for G c : where According to the result of equation (9), immediate introduction of the GRS will be economical if the reversible and irreversible benefits are larger than the irreversible and reversible costs plus the irreversible perceived costs of introducing GRS weighted by the leverage factor μ+h μ+(1−q)h > 1. The first part of the right-hand side of equation (9) captures the standard costs as part of a benefit-cost analysis; the second part adds the perceived costs. One unit of perceived irreversible costs of introducing the GRS weighs more than one unit of other costs. This can be explained by the fact that G c is uncertain, and due to the uncertainty about the perceived costs those costs weigh more than the other costs. This is an effect well known within the real option literature on regulation (e.g., Hennessy and Moschini, 2006;Ansink and Wesseler, 2009).
Equivalently, equation (10) shows that fewer perceived costs will be needed to outweigh the benefits minus the other reversible and irreversible costs for explaining a delay in approval. Using this equation, we can identify a minimum value for the economic power of the opposition towards the GRS.
We can compare these results with three alternative specifications for identifying the relative importance of uncertainty about perceived costs and uncertainty about the arrival date of information or date of decision: no uncertainty, uncertainty about approval date only, and uncertainty about perceived costs only. The first specification excludes any uncertainty, the approval will only be delayed, and at approval time a, G C will be zero. As in the previous specification, known benefits and costs remain constant over time. In this case the threshold value G * cn with subscript n for no uncertainty yields: The second specification adds uncertainty over the delay of approval. In this case the threshold value G * ch with subscript h for uncertainty about approval date yields: The third specification includes uncertainty about perceived costs only, the approval decision will only be delayed to a future point in time a, but from today's perspective the perceived costs will not be confirmed with probability q. This yields, for the threshold value of G * cq with subscript q for uncertainty about the presence of perceived costs, Comparing equations (10) to (13) we can observe that including uncertainty about the approval date results in the lowest threshold value for perceived costs as long as e μa > μ+h h followed by excluding any uncertainty, whereas considering uncertainty about perceived costs only results in a higher threshold than considering uncertainty about perceived costs in combination with uncertainty about the arrival time of additional information about whether or not the perceived costs will be confirmed. While the differences between equation (11) and (12), and (10) and (13) are substantial for q = 0.5, the differences between equation (10) and (13), and equation (11) and (12) are less pronounced and for some parameter values may even be reversed. In essence, ignoring any uncertainty substantially undervalues the perceived costs, while ignoring uncertainty about decision dates slightly overvalues the perceived costs. This will become more obvious in section 3 where the model will be calibrated for the case of India.

Regulations and delay strategies
The result of equation (9) also provides economic insight into the success of delay strategies of opponents to the GRS. One might be surprised by the fact that technology of this kind will not immediately be introduced but delayed by several means. Many developing countries either have or are discussing additional regulations for the approval of GE seed varieties. Most countries including those targeted for VAD have stringent biosafety regulations for the approval of GM crops (Paarlberg, 2008). The compliance with those regulations costs additional time and delays introduction. Take as an example India, whose government implemented a working group to assess biotechnology, which recommended that the Indian Government ban all GM food crops from cultivation, thereby stirring a debate about the approval process of GM crops. By looking at equation (9), an increase in h(∂( μ+h μ+(1−q)h )/∂h > 0), equivalent to availability of information about the perceived costs will soon become available, increases the weight of the perceived costs. In this sense, it pays for opponents to again raise concerns about perceived costs via different forms of protest or new but often unfounded claims about negative implications, when a decision to introduce the GRS is soon to be made. This is observed not only for Golden Rice, but in general for the approval of GE crops in many developing countries (e.g., Paarlberg, 2008 for Africa;Herring, 2010Herring, , 2012 for India) as well as the European Union (Herring, 2007;Wesseler et al., 2012).

Calibration of the model: the foregone benefits
The previous results will be used to quantify the foregone benefits of a delayed introduction of the GRS as well as the perceived costs by the Government of India. The model will be calibrated for India thanks to the study by Stein et al. (2008), in which detailed information about potential benefits and costs of the GRS for India are available. We consider the time period from 2002 when the technology was available and could have been introduced. For the purpose of our analysis, irreversible and reversible benefits and costs have been calculated based on Stein (2006;personal communication, 2013) and Stein et al. (2008), while the numbers employed for the 'pessimistic scenarios' have been on the lower side.
The benefits of the GRS are the reduced health costs of VAD. Those benefits, similar to other health benefits, are commonly assessed by calculating the DALYs (Murray and Lopez, 1996). The current burden of VAD has been calculated with about 2.3 million DALYs per year (over a 10-year period: about 23 million DALYs ignoring changes in the Indian population) (Stein et al., 2008, table 4). These include the burden of night blindness, corneal scars, blindness caused by corneal scars, measles and mortality of children five years old and younger, and night blindness for pregnant and lactating women. Corneal scars and blindness caused by corneal scars of children five years old and younger are VAD-related burdens that cannot be reversed and are considered to be irreversible. Reducing those burdens through the GRS is an irreversible benefit. Further, reducing child mortality through the GRS has been considered an irreversible benefit too; according to the numbers provided by Stein (2006, table A3), this amounts to about 71,600 child deaths annually.
The other health burdens can be reduced through the GRS. The benefits can be considered to be reversible as they normally do not result in health problems in later life. Using the information provided by Stein (2006), the share of irreversible and reversible health benefits can be calculated. About 74.4 and 25.6 per cent of the health benefits can be considered to be irreversible and reversible, respectively. Please note that the absolute numbers are on the lower side as a number of health effects for which a causal link has not yet been fully established have not been considered, such as stunted growth. Further, as an economic value of a DALY, a rather low value of US$500 has been used in the study. A low value can be justified in order not to overestimate the benefits of the GRS, but places a low value on the average statistical value of life and higher values would further increase the economic benefits of a GRS. 4 On the costs side, a number of cost items have been discussed within the literature. These include research and development costs at the international and national level but also costs for launching a media campaign to introduce the GRS and annual costs for maintenance breeding. While the costs for the media campaign will definitely arise and will be additional costs for the government, the annual costs for maintenance breeding are less obvious if they are additional. The media campaign, called social marketing, will be needed at the beginning when the GRS is introduced. The costs are treated as irreversible as they are sunk and will not matter at a later stage whether or not to continue the GRS, while the maintenance breeding costs still matter. Maintenance breeding by national agriculture research centers is a regular activity. If the Golden Rice trait were to be been introduced in Indian rice lines, they would be part of the ongoing maintenance breeding and additional costs would not arise. On the other hand, one can argue that the efforts for maintenance breeding might increase as the number of rice lines has increased. Therefore cost for maintenance breeding has been considered and treated as annual reversible costs. According to Stein (personal communication, 2013), these costs have been assessed to be about US$15,553,610 and about US$125,000 for the social marketing and the additional annual maintenance breeding costs, respectively. Table 1 The value for NPV( , using a value for h of 0.1, equivalent to an expected decision being made after 10 years, value for q of 0.5, and a discount rate of three per cent in discrete time (2.955 per cent in continuous time) as also used by Stein (2006) and commonly done for such kinds of health benefit assessments, provides the following result in US$: Using equation (10), the critical value G * C in US$: ,697,152,214. (16) This threshold level of about US$1.7 billion is substantial, or if annualized over a 10-year period is about US$199 million per year, and is an indicator of the economic power of GMO opposition in India. This is a minimum value as this only indicates what perceived costs at least have to be in order to explain a delay in approval following the specification of the model. A comparison with the two alternative specifications discussed in section 2 provides the following threshold value in case of no uncertainty by using equation (11):  (17) In case of uncertainty with respect to the approval date only provides the following threshold value in US$ by using equation (12): 630,549,798. (18) In case of uncertainty with respect to the perceived costs only provides the following threshold value in US$ by using equation (13): The differences in the weighing factors between the four specifications are substantial when comparing the results of equation (16) to (19). The weighing factor in equation (17) is 0.26 and 0.23 in equation (18), while in equation (16) the factor is 0.61 and 0.63 in equation (19). The difference in the weighing factors between equation (17) and (18), and equation (16) and (19) is a factor ranging between 2.40 and 2.75; the difference between these sets of equations is less pronounced with a factor of 1.12 and 1.02, respectively. A summary and comparison of the results with respect to the different model specification is provided in table 2. The results also illustrate the importance of information timing. The importance of perceived costs increases the sooner the date of new information arrives (figure 1). We can observe a sharp increase in the leverage effect for values of h below about 0.5 (two years). Figure 1 and equation (10) show that the leverage effect increases with an increase in q.
The reported results are the minimum amount based on the pessimistic or worst case scenario reported by Stein et al. (2008), who also report benefits and costs for an optimistic scenario. In that case the threshold level The differences in net present value from a 10-year delay are about US$707 million. This difference does not fully capture the minimum amount of perceived costs that the Government of India places on the introduction of a GRS. Considering uncertainty and irreversibility substantially increases the minimum amount of these perceived costs. Our calculation shows that the additional perceived costs by the Government of India are at least US$1.7 billion (about US$199 million annually). This is a substantial amount and reflects the economic power of the opposition against the introduction of Golden Rice and explains why it is more difficult to convince regulators when a strong vocal opposition exists that mainly stirs uncertainty about the GRS.
The comparison with alternative specifications shows that uncertainty about the irreversible costs is economically substantially more important than uncertainty about a specific date. Understanding and quantifying the causes of uncertainty about irreversible costs seems to be economically more important for assessing decision making than uncertainty with respect to the timing of decision making or the arrival of new information in general. Uncertainty with respect to the arrival of new information κ has been modeled by using an exponential distribution, which is memoryless and hence constant with respect to time. Other functional forms such as a Weibull or log-normal distribution are not necessarily memoryless (Billingsley, 2012). Nevertheless, we expect that the quality of the result will not change, but leave it for further research to assess the implications in more detail.
The size of the perceived costs is substantially larger, 85 times, than the cost of implementing the GRS. Having a better understanding of the political economy behind the perceived costs and how to reduce them seems to be economically much more important than additional investigations into the costs of social marketing and maintenance breeding.
The results further show that it pays for those opposing the GRS to raise concerns about the technology the sooner a decision by regulators is expected. The leverage factor of the perceived costs increases the closer the point of decision making is. This explains why the opposition to the GRS has substantial power and indicates that it will be difficult for those supporting the technology to change the view on perceived costs. In this context it is not so important to provide factual evidence, but to raise uncertainty. The opposition to GRS in India was able to link the GRS with the overall debate over GMOs in general (Enserink, 2008). Narratives about farmer suicides and dead sheep linked to GM cotton cultivation, environmental damages of Bt eggplant, and health damages linked to antibiotic marker genes are used to stir uncertainty among decision makers. Never mind that the narratives have not been correct (Herring, 2010), keeping them alive is sufficient. It has also been argued that the GRS is only an industry public relations strategy as the multinational company Syngenta is involved in further developing the technology to convince a skeptical society about the benefits of GM crops. If Golden Rice receives approval, other GM crops can be introduced more easily. In this case uniform regulation -i.e., to ban the cultivation of all GM food cropsmight be a cost-effective approach to avoid the introduction of potentially more 'dangerous' crops. While this argument has received support within the environmental economics literature (e.g., Kolstad, 1987), as identifying the marginal benefits and damage costs of a technology can be very costly, an important difference needs to be considered. Real damages to the environment from technologies assessed within the environmental economics literature have been demonstrated, such as damages from sulfate dioxide emission from steel factories or nitrogen emissions from intensive agriculture, while in the case of GM crops, net environmental benefits have been reported (e.g., Wesseler et al., 2011;Bennett et al., 2013).
One may then ask how new technologies can be introduced, if it is so easy to block them. There will always be a small, vocal group opposing a new technology. On the one hand, this is correct. Introducing new technologies will become more difficult. The advances being made over the past decade in information and communication systems reduce the costs of organizing the opposition as well as the costs of communicating technology-related uncertainties. On the other hand, uncertainties over a new technology can be balanced by uncertainties over not having access to a new technology stressed by a group of stakeholders as powerful as the opposition. This seems to explain why transgenic crops have been introduced within the US but not in Europe (Graff et al., 2013).
A countervailing power supporting the GRS and stirring uncertainties about not introducing the GRS has not been explicitly considered within the model presented, as this is currently not present within the GRS debate. The GRS has been developed by scientists. Scientists have the tendency to argue based on facts and not fiction, making it more difficult to be a countervailing power against the opposition to Golden Rice. Also, uncertainties and irreversibilities about the benefits and the other costs of the GRS can be included in the model. As a worst case scenario has been employed with respect to those benefits and costs, modifications of the model in the aforementioned direction will increase the calculated perceived costs. Further, recent nutritional studies show the factors translating β-carotene of Golden Rice into vitamin A are larger than expected (Tang et al., 2012).
One question that remains to be answered within this debate is: what are the incentives of the opposition to the GRS in India? This has not yet been well investigated empirically. Apel (2010) argues that this is driven by the financial support the opposition receives. A small industry has developed around the opposition to transgenic crops that survives mainly on donations and has to keep the debate about the risks of the technology alive. This strategy seems to be a successful strategy albeit, as the case of Golden Rice shows, at the cost of the lives of several thousand children.


Rejoinder of"Estimating the historical and future probabilities of large terrorist events"by Aaron Clauset and Ryan Woodard

Rejoinder of"Estimating the historical and future probabilities of large terrorist events"by Aaron Clauset and Ryan Woodard [arXiv:1209.0089].

University of Colorado, Boulder and ETH Zürich
We are pleased that our article has stimulated such thoughtful discussion, and we appreciate the discussants' interest in exploring the methodological and practical points of our study. The discussants make a number of excellent points worthy of future investigation and highlight the difficulty of making accurate statistical estimates of the likelihood of rare events in general, and of large terrorist events in particular. Our study is certainly not the final word on these topics and we look forward to future developments in these areas. In our rejoinder, we focus on selected points that will clarify the context of our study and open questions, including the choice of statistical models that are consistent with reasonable mechanistic models for the data under study, and the value of simple models in controlling uncertainty in complex social systems.
Not all tail models are equal. Two key motivations in our use of the powerlaw or simple Pareto tail model were (i) its previous use in modeling terrorist event severities, and (ii) its status as the only tail model with published mechanisms for the frequencies of large terrorist events. Although the debate is ongoing as to which mechanism, if any, is the correct explanation for the observed heavy-tailed pattern in event severities [see Clauset and Gleditsch (2012) for discussion], these mechanisms provide an important theoretical grounding for any statistical modeling of terrorism's upper tail. Without such mechanisms, there is little theoretical justification for favoring one particular tail model over another to estimate extreme event probabilities. Thus, we believe some amount of priority should be given to estimates derived from distributions like the power law, which have articulated and plausible underlying mechanisms for terrorist event severities. However, the surest way to reduce our ultimate uncertainty as to the likelihood of future large events is to identify and test alternative mechanisms for the heavytailed pattern in terrorist event severities, and we look forward to new work in that direction. Disagreement among tail models. The statistical framework we presented is entirely general and can thus be used in conjunction with (i) any welldefined, automatic method for identifying the upper tail region, and (ii) any well-defined probabilistic model of an upper tail. (Although we modeled severities as i.i.d. random variables, this is not a requirement, and a clear understanding of the statistical correlations among terrorist events at the global scale could, in principle, be incorporated into a more detailed model.) Given these choices, data-driven estimates are then produced.
Even without regard to their theoretical motivation, not all tail models are reasonable choices in this framework. Any model can be fitted to the data, but if it is a poor fit, we are under no obligation to trust its results. How then should we decide which models are good fits? The models used in our analysis (power law, log-normal and stretched exponential) were all previously demonstrated, under a combination of standard hypothesis tests and likelihood ratio tests, to meet this criteria [Clauset, Shalizi and Newman (2009)]. Of course, more flexible models, like the generalized Pareto distribution (GPD), the tapered Pareto or a piecewise Pareto, may also provide reasonable fits, as several discussants suggested. The difficulty, however, is how to interpret or reconcile models that produce conflicting estimates for the likelihood of a large event, and how to choose among models with different levels of flexibility.
Modern statistics does not offer clear answers to these questions because of the role played by x min , the smallest value for which the tail model holds. Unlike traditional model parameters, changing x min changes the sample size, which confounds changes in statistical power with the usual bias-variance trade-off. The result is an additional risk in overfitting, particularly with flexible models like those suggested by the discussants, as larger values of x min are considered. Simple models, like the power-law distribution, would seem to offer some protection against this risk. (We note that this problem of identifying reasonable tail models is ubiquitous in complex social systems with heavy-tailed variables, including financial markets [Farmer and Lillo (2004), Financial Crisis Inquiry Commission (2011)].) The method we used to choose x min has reasonable properties and performs well in practice [Clauset, Shalizi and Newman (2009)]. New research on how best to choose x min for out-of-sample forecasts and how to rigorously compare models estimated from differently sized samples would help resolve some of these difficulties.
Conclusions. The statistical modeling and forecasting of terrorist event severity is a relatively new endeavor that combines interesting methodological problems and tricky forms of uncertainty in an application area with REJOINDER 3 arguably genuine practical benefit. There are many interesting and important questions worthy of study, and the discussants have identified a number of them. Better estimates and a deeper understanding of the pattern of large terrorist events and the mechanisms that produce them can inform our expectations (as with large natural disasters) of how many such events will occur over a long time horizon and how to appropriately anticipate or respond to them.
In closing, we note that the relatively smooth distribution of the sizes of terrorist events worldwide presents a puzzle. Given the highly contingent nature of individual events and individual conflicts, 9/11 being an outstanding example, how can the global distribution be so regular? This striking pattern suggests both that accurate estimates of the probability of large events may be derived from modeling the relative frequency of much less severe events and that some aspects of global terrorism may not be as contingent or unpredictable as is often assumed. Understanding the origin of this global-level pattern, and the mechanisms by which local-level dynamics give rise to it, is itself an important research direction with real implications for understanding the fundamentals of violent political conflict. We look forward to new insights in these directions.


s2fieldsofstudy: Economics Business

text:
How to obtain high returns with lower volatility in emerging markets?

Abstract Emerging markets equity indexes are usually seen as high return with a high degree of volatility associated with them. However, this should not be the case, if you choose high-quality firms that have increasing returns and lower volatility. The intent of this paper is to introduce the risk weighted alpha (RWA) indexation method that helps identify stocks that have stable increasing returns with lower volatility. In order to review this method in the context of emerging markets scenario, this paper takes the example of the Sensex index listed on the Bombay Stock Exchange (BSE) that comprises India’s top 30 stocks by market capitalisation. Results show that some stocks like Hindustan Lever do show increasing returns and lower volatility. The RWA Sensex index outperforms the BSE Sensex index, while still maintaining a beta that is the same as that in the BSE Sensex index.

Introduction
Emerging equity markets are seen to provide higher yields when global stock markets are increasing. However, there is an exodus of funds from emerging markets to developed country markets when systematic risk seems to increase globally. In this paper, we take an example of the Sensex index that is the main index for the Bombay Stock Market (BSE) in India. This is a market capitalisation index that comprises the top 30 stocks by market capitalisation listed on the Bombay Stock Exchange. Most of the major stock indexes globally, like the Dow Jones Industrial Average, S&P500 and FTSE100, are market capitalisation or price-weighted indexes. Market capitalisation and price-weighted indexes have been developed based on modern portfolio theory. These indexes are, therefore, considered to be the most efficient portfolios, and as a result are the main indexes on these exchanges. However, Arnott and Hsu (2008), Arnott, Hsu, and Moore (2005) and Arnott, Kalesnik, Moghtader, and Scholl (2010) developed the fundamental index method stating that five fundamental factors (revenue, sales, cash flow, employment and book value) could help select stocks more efficiently to comprise an index compared to the market capitalisation and price-weighted index methods. Since then there have been numerous empirical studies that have differing views; however, it is inconclusive if the fundamental indexation method is superior to the market capitalisation and price-weighted indexes (see Basu & Forbes, 2013;Blitz & Swinkels, 2008;Blitz, van der Grient, & van Vliet, 2010;Chen, Chen, & Bassett, 2007;Estrada, 2006;Hemminki & Puttonen, 2008;Hsu & Campollo, 2006;Hsu, Kalesnik, & Xie, 2011;Kaplan, 2008;Mar, Bird, Casavecchia, & Yeung, 2009;Seigel, 2006). Instead, the fundamental index method is seen to have a value and small cap bias that may help it perform better at times than the market capitalisation weighted index.
Equal-weighted and risk-weighted indexes are the other index methods that are prominent. Equal-weighted index method simply applies an equal weight of each stock in the index. There are a myriad of risk-weighted indexes, minimum variance, equally weighted portfolio, maximum Sharpe ratio, most diversified portfolio, equally weighted risk distribution portfolio and semi-variance portfolio (Demey, Maillard, & Roncalli, 2010). While the fundamental index method aims to increase the portfolio's alpha, the risk-weighted indexes look to reducing the portfolio's risk through diversification. The disadvantage of the equal-weighted index method is that it needs to be rebalanced frequently, has a high tracking error and it works as a take-profit strategy, where the stocks that have increased in price are sold and the stocks that have fallen in price are purchased to bring all the stocks to equal weight (Benartzi & Thaler, 2001;DeMiguel, Garlappi, & Uppal, 2009;Windcliff & Boyle, 2004).
Similarly, the risk-weighted index method has a disadvantage in that in some cases it could be concentrated in too few stocks or may require a covariance between stocks to be calculated; due to the higher dimensionality of this covariance matrix it is hard to use this method (Amenc, Goltz, Martellini, & Retkowsky, 2010;Chia, Melas, & Zhou, 2011;Choueifaty & Coignard, 2008;Chow, Hsu, Kalesnik, & Little, 2011;Clarke, de Silva, & Thorley, 2006;Haugen & Baker, 1991). As emerging equity markets have high volatility, it would seem plausible that risk-weighted indexes would be a suitable index option. However, due to the diversification strategy used by these indexes, while unsystematic risk is reduced significant systematic risk remains. The intent of the risk weighted alpha (RWA) index method being introduced in this paper is to develop a method that selects stocks that have increasing returns and lower volatility. In effect, such an index will look to provide a higher return and have lower systematic risk than the market capitalisation weighted indexes.

RWA Index Construction for the BSE Index
RWA index method intends to provide higher weight to stocks that have higher returns and lower variance. In order to achieve this it uses Jensen's alpha to identify stocks that provide higher actual returns compared to their expected returns when considering systematic risk as derived by the capital asset pricing model. However, it is important that we find risk-weighted Jensen's alpha, as some of these stocks may take on higher risk in order to provide a higher return. As a result, the riskweighted Jensen's alpha will identify stocks that provide the highest alpha per unit risk. The following formula explains the RWA index method:

Risk Adjusted Jensen
where, R i = actual return on the stock R f = risk free rate R m = return on the market index β i = beta of the stock σ i = standard deviation of the stock In effect, once the RWA is obtained, this model assigns index weight to each stock based on this value. As it re-weighs the BSE index, it is possible that some stocks have a negative RWA. As a result, these stocks will have a negative index weight, which would mean that the investor should short these stocks as they do not provide sufficient return compared to the risk associated with them. It is also noticed that stocks with a positive RWA over a significant period are those stocks that have consistently increasing stock prices over time with lower stock price volatility. This paper uses the BSE index as an example in the next section in order to explain the RWA index method. Financial data used in this paper were obtained from the Thomson Reuters Tick History database using daily price data for the BSE index from 2 January 2002-31 December 2012.

Performance Characteristics-RWA and BSE Index
BSE index is a market capitalisation weighted index comprised of the top 30 stocks listed on the Bombay Stock Exchange (BSE). This paper analyses if the RWA method will provide superior return and lower variance by re-weighting the stocks in the BSE index. Performance of the RWA against the market cap-weighted BSE index is provided in Table 1. The RWA index seems to have provided a higher return (303.51%) from January 2002 to December 2012 compared to the BSE30 index, which is a market cap-weighted index, while the beta for both these indexes is close to .9. The reason why the RWA index has provided a higher return is due to the fact that it weighs stocks with increasing returns and lower volatility. Existing index methods do not specifically weight stocks based on their alpha while the fundamental indexation method uses five fundamental factors to increase the portfolio's alpha. The RWA index method assumes that only highperforming stocks will consistently be able to provide a positive alpha per unit risk over an extended period of time. If a stock, however, does provide a higher alpha, but it also has a higher level of volatility in its returns, then it is unlikely that this stock will be provided an above-average weight due to the higher volatility.
Let us look at the profiles of the best-and worst-performing stocks in each of these methods (see Figures 1-10). Using the RWA index method, data show that the Hindustan Unilever stock performed well as it had a higher RWA. If you notice Figure 1, the stock price has steadily increased from 2002 to 2013. On the contrary, the worst-performing stock in the RWA index was Wipro, and the figure below shows that the stock price has high volatility over the sample period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and is less than half of what it was in January 2002.
You can see that the returns for Hindustan Unilever are steadily increasing compared to those of Wipro. Also, the volatility of the returns is lower for the Hindustan Unilever stocks compared to the Wipro stock.
Why don't we analyse stocks within the same industry, for example let's compare three banks that are part of the BSE30 index to explain why the RWA identifies stocks that perform well over a long term. Most importantly, however, we need to make sure that we take significant amount of sample data before calculating the RWA. Usually, 10 years of daily data or 30 years of monthly data will be suitable. Any shorter duration will not work as it will possibly not consider a complete business cycle and it will result in providing incorrect index weights for stocks.
The three banks that are part of the BSE30 index which are being analysed are: the State Bank of India (index weight of 4.6951%), ICICI Bank (index weight of 3.1285%) and HDFC Bank (index weight of 2.5781%). You will notice that stocks with lower weights have either lower returns or higher volatility. Compare the three figures below.
We have also compared the three Indian IT companies (see figures above): Tata Consultancy Services (TCS), Infosys and Wipro. You will notice that the TCS stock has an increasing and stable stock price compared to that of Infosys. On the other hand, the Wipro stock price is more volatile and has a smaller increase compared to the other two stocks (TCS and Infosys). In order to prove the point that the RWA helps identify stocks with increasing returns and lower volatility, we also consider the stocks of two auto manufacturers: Bajaj Auto (index weight of 1.8401%) and Hero MotoCorp Limited (index weight of .2937%). These figures prove that the Bajaj Auto stock has higher returns as it has increased from 500 Indian rupees to 2000 Indian rupees from 2008 to 2012 compared to the Hero MotoCorp stock where the stock price increased by the same amount but it took longer (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) for this increase to occur.
The risk-return characteristics for the RWA index are provided below. Table 2 shows that the stocks that perform better and have lower volatility have a higher weight in this index.
So, how does an investor obtain a higher return with lower volatility in emerging markets? We require the investor to carefully weigh the index with stocks that provide stable and higher returns over the longer term with lower volatility. This will prevent the investor from suffering significant downward revisions in stock prices.
Further, an index should have stocks that represent an efficient portfolio, which means that other portfolios should not have a higher return or lower risk than them. As a result, it seems that the price or market cap-weighted index may not perform as well as the RWA index within this sample period (2 January 2002-31 December 2012), as the RWA index has lower risk and higher return than the BSE index. Effectively, the RWA index should only have long positions, as these stocks would have the highest RWAs. However, as this paper was comparing the BSE index weights with the RWA index method, based on the RWAs, it seemed that some stocks did not provide sufficient return in relation