Where do you want to go and how do you get there?
Using a combination of tools, approaches and standards, we work with our clients to help them define their pathway to sustainability. This includes articulating objectives and expected outcomes, identifying where and what to measure, determining how much time is needed, what the most effective approach is and what can be done with the results.
Key components include:
COSA’s origins within the United Nations Conference on Trade and Development (UNCTAD) and the International Institute for Sustainable Development enable a broad perspective from which to functionally align with international accords and agreements. These range from the Bellagio Sustainability Assessment and Measurement Principles and the OECD Economic Guidelines to the Rio Declaration and the International Labor Organization’s Core 8 Labor Standards.
COSA’s alignment with dozens of important multilateral and multi-stakeholder instruments help ensure the global acceptance of its work. But its indicators and methods are particularly relevant because they were designed and evaluated using global participatory processes that involved hundreds of experts and practitioners covering every dimension and perspective of sustainability. Over many years, these inputs have come from developing country producer groups, commodity traders, leading food companies, NGOs, standards bodies or certifications, intergovernmental agencies, and research institutions.
As contributors to the UN Global Compact, COSA believes in the value of integrating the Food and Agriculture Business Principles into its work. The guidelines make good sense and are part of the alignment that COSA integrates into its work.
Defining COSA Indicators: COSA indicators are designed to quantify and clarify information in a manner that promotes the understanding of key environmental, social, and economic issues.
They can serve as proxies for complex phenomena that are difficult to perceive or measure in farming systems. They are calibrated to ensure comparability over time and across regions or countries.
Scientific rigor is a key feature of COSA indicators but they must also be practical so that they can be obtained at a reasonable cost using methods that are respectful to farmers and communities.
There is a direct relationship or pathway between a COSA indicator and the key longer term objectives sought by the sustainability community. A number of advances in recent years and the validation noted above, make COSA indicators particularly relevant and widely accepted as meaningful to the broader community of stakeholders.
Well-defined indicators are a good first step. When appropriately paired with clear metrics (specifically how to measure) and with sound methods for obtaining and analyzing data, COSA Indicators provide accurate insights into vital aspects of sustainability.
Indicators can only serve as signposts or objectives. The actual measurement of the indicators is where the real value is. For example, food security is a very useful indicator but its value and validity is determined by how appropriately, how specifically, and how consistently it is measured. When managed thoughtfully, indicators and their definitions include the precise survey questions and sound analytics that together provide a valid and reliable picture of sustainability.
Since the mid-2000s, COSA has been working with numerous global partnerships to find a common scientific language for measuring sustainability. We continue to facilitate multi-year discussions among leaders in the field of development, the scientific community, and private companies to maintain stat-of-the-art indicators and metrics. Our experience indicates that SMART indicators must at least be: Specific, Measurable, Achievable, Realistic, and Trackable (and Time-bound).
Sufficiently specific in definition
Specificity is vital to ensure that the same thing is measured in the same way each time. The definition of an indicator must therefore be thoroughly considered so that, over time, different people in different countries are considering or comparing the same thing. At the same time, an indicator cannot be so specific that it has only very limited applications. For example, “income” is a common indicator but unless the components of income are clearly and consistently defined, the indicator may or may not include: “household unpaid labor”, “off-farm income”, “full costs of production such as capital or credit costs” and the results would be so different as to be not comparable.
Measurable with reasonable cost and effort
The cost of collecting the data is often the most persistent and challenging constraint. In developing countries especially, field level data is notoriously expensive to collect. New technologies do help but some indicators are still costly to measure properly and so, from a feasibility perspective, reasonable tested proxies are used instead. For example, a useful understanding of biodiversity on a farm would require multiple levels of technical expertise and repeated visits to understand the annual cycles of flora and fauna. Instead, using a proxy developed and tested with many of the leading environmental organizations in the world gives COSA a low-cost and quick approximation that can be assessed easily by a non-expert in minutes.
Achievable and actionable
Indicators that measure something that is only rarely achieved are useless for most practical applications. Continuous improvement is more important in many cases than a static goal, so designing indicators that respond to such real-world application is preferable to having academic or theoretical goals.
A critical aspect of developing SMART indicators is asking this pragmatic question: “What can be done with the information once it is known?” The nature of some information means that it remains “interesting but not actionable.” If an indicator is not actionable, that means that it is unlikely to trigger a change in policy or investment and, as such, has limited value.
Realistic and comparable across different conditions
Indicators can be theoretical or naïve and do not serve to advance our understanding of best practices if they are not defined in realistic ways that are widely accepted as valid. For example, accurately measuring child labor is not only very difficult and costly but it can also depend on a number of ethical or moral judgments to be made by a researcher. Fundamentally, it does not work to ask a farmer or community such questions and the necessary long-term observations and interviews are impractical in many cases. COSA developed objective proxies that take into account two of the three inter-dependent core issues of child labor: denial of opportunity and dangerous work conditions (laboring or being detained against their will is the third).
Farmers often have choices to adopt a different crop or alter the use of the land (e.g. tree crops to grazing). Limited indicators prevent them and policymakers from comparing such options realistically. This can happen where researchers have only crop-specific or region-specific experience. For example, when the cost-benefit indicators for animal grazing are comparable to those for crops, we can better understand the dynamic of land use and deforestation that alter farm economies. This is even more true in the absence of balanced multi-dimensional assessment that takes into account key environmental and social indicators.
For example, having realistic and comparable indicators to determine the quality of life of a household means avoiding most site-specific, culture-specific, or asset-specific measures such as the type of flooring or roof materials in favor of more universal indicators that apply equally in most situations. COSA Indicators are adequately adapted to the crop type (e.g. coffee, cocoa, food crops) and yet are sufficiently standardized to allow comparison across countries and projects for most of the important measures such as net income, costs of production, resource-use efficiency, etc.
Trackable and oriented to capture change over time
Poor indicators are insufficiently sensitive to expected changes over reasonable periods of time or resemble compliance audits: asking for a “yes or no” response. For most situations (with minor exceptions related to clear dangers) this does not capture changes that occur in most farming systems. For example, if a food security indicator only captures months of food insecurity (as many do) then a family that moves from 3 days of insecurity to 25 days of insecurity would appear static (both are one month). Similarly, in the realm of biocides (i.e. herbicide, pesticide, fungicide), measuring only the amounts or only the costs (and not the type and class) will tend to overlook the important transition from a highly toxic broad spectrum product to a more specific and less systemically toxic product that has far fewer negative impacts on human and environmental health.
Sampling of Themes that Inform major COSA Indicators
Indices and insights.
There are a number of global themes or categories that we can readily calculate and present as indices and cross tabulations. Understanding results by income or gender might show notable differences in how wealthier or women farmers fare. Taking the basket of environmental indicators as a whole can provide a more landscape-level view of the direction of change in a community or region.
COSA also engages with well-tested indices and approaches such as IFPRI’s gender-related indicators. A prominent example is our testing and application of the Progress out of Poverty Index (PPI) – now used in more than 50 countries – in collaboration with the Grameen Foundation.
Caveats. Secondary data from supply chains and from audits can certainly be useful since it already exists and has little extra cost, it should be considered for certain applications. However, it also tends to have considerable bias and some indicators gathered in this was tend to be inaccurate. It is therefore worth testing accuracy before relying on secondary or third-had data.
To avoid misleading results, we must not focus solely on a single dimension or one type of indicator. For example, a good crop yield (economic indicator) may have come from deplorable environmental practices or the use of child labor. In nearly all cases, useful analyses provide a multi-dimensional perspective that encompasses a balanced look at the key social, economic, and environmental facets.
There is a tendency to oversimplify sustainability, and its intrinsic complexity makes this understandable. Although it is tempting to just measure simple factors such as farm yields or biodiversity as the proxy for sustainability, such simple assessments run a high risk of missing key factors that compromise projects, investments, and reputation. The reality is that sustainability, by definition, necessitates balancing social, environmental, and economic facets.
Any measurement that does not take into account a holistic view is simply not assessing sustainability. Managing the unexpected outcomes is important. For example, if higher yields are achieved by clear-cutting forested areas, which then results in soil erosion, silted waterways, and the loss of timber and firewood for the surrounding communities, it may not be a sustainable outcome. This can present quite a challenge for projects or investments whose focus is limited to only one or two desired outcomes.
COSA goes beyond SMART indicators and engages a variety of methods to understand the different dimensions of sustainability ranging from simple cross-tabulations of data points to stochastic frontier analysis and to relational analysis with, for example, the integration of the Progress out of Poverty Index (PPI) or the Multidimensional Poverty Index (MPI).