The Role of Agricultural Economics Research in Policymaking

Agricultural economics research uses a multitude of methods and approaches to assess existing and new policy measures. This is the basis for agricultural policy that demonstrably makes a difference, i.e. is evidence-based.  

Evaluating policy measures before their introduction

Ex-ante evaluations allow us to assess the costs and benefits of policy measures before they are implemented. Simulation and optimisation models that can depict the behaviour of farms, sectors and markets play a major role here. In this way, Agroscope uses the ‘SWISSland’ model depicting individual farms and summarising their behaviour at sectoral level as a standard tool for ex-ante agricultural policy evaluations in Switzerland. Increasingly, behavioural experiments are also used for ex-ante policy evaluation. In these experiments, some of the participating farmers are allowed to choose a measure to be evaluated, whilst the control group is not. Comparing both groups after the experiment allows us to evaluate the effectiveness and efficiency of agricultural policy measures before their introduction at national level, and to reveal the behavioural mechanisms underlying them.

Evaluating policy measures after their introduction

Ex-post evaluations aim to analyse the impact and goal attainment of policy measures after their introduction. In this way, they provide the scientific basis for adapting, expanding or abandoning established policy measures, and drawing lessons for future measures from this. Econometric measures in particular are key here, and often use data from farms, e.g. census, accountancy or survey data. The aim is to determine the impact of an agricultural policy measure, in other words, to ascertain e.g. whether biodiversity payments actually have an effect on biodiversity.

Further ex-post evaluation methods seldom used to date include systematic literature surveys, meta-analyses and replication studies. Based on such studies, the scientific evidence for a problem can be improved and existing knowledge gaps bridged.

A mix of data and methods is key

In general, a stronger mix of methods and data should be aimed at for policy analyses.  Often, diverse agricultural policy objectives, measures and issues cannot be comprehensively analysed by means of a single method. Many different possible combinations can be employed here – for example, ex-ante and ex-post studies or even quantitative and qualitative methods can be combined. Combining different methods allows researchers to answer questions as to ‘how’ and ‘why’ in greater detail. Data on economic, ecological and social aspects at farm level are key for comprehensively evaluating the impact of agricultural policy measures. The aim should therefore be to expand existing monitoring systems and data surveys as well as improve the use of existing datasets such as e.g. remote sensing. Transparent and accessible knowledge, i.e. open science, can contribute substantially to an improved state of knowledge and help make agricutural policy even more effective.