Food and Water Safety

Monitoring and Evaluation

Area 4: To assess the uptake of food/water safety interventions on food security, health, and nutrition to develop tools for monitoring and evaluating the impact of interventions

One of the innovative aspects of this theme will be to implement research projects along the three areas previously discussed that have the potential to be scaled up. The goal is that once cost-effective control strategies are identified, the willingness to accept or adopt, as well as willingness to pay of the producers or other actors along the value chain needs to be understood in order to implement such measures effectively and efficiently. When these projects are introduced on a large scale, robust monitoring and impact evaluation (M&E) approaches will be implemented.

Research questions

  1. What are the factors that affect the uptake of cost-effective strategies in controlling/reducing food safety risks?
  2. How can the bottlenecks preventing emergence of effective demand for food safety and quality attributes in products and processes be ameliorated? To what extent the demand for food safety and quality is a function of ability and willingness to pay and to what extent it is a function of information?
  3. What are the information problems that result in adverse selection issues in food markets resulting in market failures?
  4. What institutional solutions such as screening or signaling mechanisms can overcome the information problems with regard to product safety and quality in food markets? how are the institutional solutions for ensuring uptake of food safety and quality differentiated within the class of developing countries?

Hypotheses

The following are the main hypotheses that this theme is likely to focus on.

  1. Factors that determine the uptake of cost-effective strategies in controlling/reducing food safety risks are related to household characteristics (such as total income, location, livelihood strategies available), individual characteristics (such as gender, education, risk perceptions) and institutional characteristics (such as whether the institutions are public or private).
  2. Significant demand for food safety and quality exists in developing countries particularly in fast growing countries like India and China.
  3. The demand for food safety and quality imposes constraints but offers a big window of opportunity to share value that it creates and thus help towards rural poverty alleviation.
  4. The demand for food safety and quality in developing countries is low not only because of low ability to pay but also because of several government and market failures.
  5. Food safety and quality will be ensured in developing countries if credible institutions are entrusted with it. The most credible agencies in the developing countries are not necessarily in the public sector.
  6. Improving food safety and quality in developing countries can have a significant impact on development indicators such as public health and can also have positive impact on livelihoods.
  7. With institutional support smallholders can provide the desired levels of product quality and safety.

Research approach and methods that may be used for impact evaluation depending on case being studied

There are three main components of methodology in this segment

  1. Assessment of uptake in hypothetical settings where interventions have not been implemented. – The methods that will be used in this segment fall under the class of choice experiments.
  2. Impact evaluation of actual interventions under randomized allocation setting.
  3. Impact evaluation of actual interventions under non-randomized setting.
  1. Experiments under hypothetical setting (the choice experiment method)- The Choice Experiment Method (CEM) is a Stated Preference Method (SPM) of economic valuation, adopted from marketing and transport economics literature (see for example, Louviere and Hensher, 1982; Louviere and Woodworth, 1983; Louviere 1988; Louviere 1992). Similarly to the other SPM, namely the Contingent Valuation Method (CVM), CEM can elicit the total economic value (i.e., both use and non-use values) of goods, which can in turn be used to inform their efficient and effective provision.

    In CEM once attributes of a product (in this case for example a technology) and their levels are identified, experimental design theory is used to generate different profiles of the good (technology) in terms of its attributes and levels these attributes take. These profiles are then assembled in choice sets, which are presented to the respondents, who are asked to state their preferences in multiple occasions. Hanley et al. (1998) define the CEM as a highly ‘structured method of data generation’. One of the attributes which is typically included in a choice experiment is a monetary cost/benefit attribute. The monetary attribute and the random utility framework on which the CEM method is based allow for the estimation of welfare estimates, i.e., willingness to pay (WTP) or willingness to accept (WTA) compensation, for changes in the levels of attributes (Hanemann, 1984). Specifically, the CEM can provide four types of information about the values of goods: (i) which attributes are significant determinants of the values that stakeholders place on the good; (ii) The implied ranking of these attributes amongst the relevant stakeholders; (ii) The value of changing more than one of the attributes at once; and (v) The total economic value of a good (Bateman et al., 2003).

  2. Impact evaluation under randomized experiment setting - In an ideal impact evaluation setting, the methodological approach will imply a comparison of the indicators of participants with those of non-participants, with the selection of program beneficiaries being done randomly, either at the individual level or at the cluster (e.g. household, community or institutional) level. This approach is usually considered the strongest in terms of allowing to infer causality, that is, to infer that the impact achieved is actually due to the program and not due to some other confounding factor. Because the allocation of individuals (or households, or communities) to program and non-program groups is not linked to any of their observable or unobservable characteristics (other than eligibility), both external selection bias and self-selection bias are neutralized. In addition, the use of a control group ensures that the influence of confounding factors is also limited. This design will include a pre/post (or baseline and a post-intervention data collection) approach; the advantage of using a pre/post approach in addition to a randomized control group is that it allows to control for differences at baseline and use a “difference-in-differences” type of analysis.
  3. Impact evaluation under non-randomized setting -In the situation that randomization cannot be achieved other tools such as propensity score matching will be employed to assess the impact of the interventions. The approach works by creating statistical comparison groups (to the people affected by intervention) that are similar in the sense that they have similar probabilities of being included in the program but are in the control group. Comparing the outcomes for the two groups (treatment and control) allows isolating the effect of the intervention on the affected.

Projects to date

  • Testing for the demand for food safety in developing countries through certification- The research aims to quantify the possible price premium that producers can appropriate for providing food with higher quality and safety standards in developing countries.
  • Assessing uptake of bio-fortified staples The research aims to investigate producers acceptance of bio-fortified seeds/crops.