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Category: Policy

Published May 10, 2012

Accounting for Fish and Seafood in Discussions of Food Prices

Discussions of world food prices in the media and among policy makers usually focus on a few select commodities (e.g., maize, wheat, rice, etc.).

Though this obviously omits many other food staples, the underlying assumption is that various kinds of food are substitutes (imperfect ones, but substitutes nonetheless) for one another.

In cases where a more refined notion of food prices is used for discussion, the food price measure used is often the Food and Agriculture Organization (FAO) of the United Nations’ food price index, which encompasses five categories of food: cereals, dairy, meat, oils and fats, and sugar.

The FAO’s food price index, however, does not include fish and seafood. But since fish and seafood are a key source of protein for almost half of the world’s population, this is an important omission that can lead to making the wrong policy recommendations.

Some of my coauthors thus developed a fish price index similar to the other food (i.e., cereals, dairy, meat, oils and fats, and sugar) price indices already used by the FAO. We recently wrote a paper discussing this new fish price index, which the FAO will incorporate in its food price index sometime this year.

Here is the abstract of our resulting PLoS ONE article on the FAO’s fish price index, titled “Fish Is Food: The FAO’s Fish Price Index,” which was published this week:

World food prices hit an all-time high in February 2011 and are still almost two and a half times those of 2000. Although three billion people worldwide use seafood as a key source of animal protein, the Food and Agriculture Organization (FAO) of the United Nations — which compiles prices for other major food categories — has not tracked seafood prices. We fill this gap by developing an index of global seafood prices that can help to understand food crises and may assist in averting them. The fish price index (FPI) relies on trade statistics because seafood is heavily traded internationally, exposing non-traded seafood to price competition from imports and exports. Easily updated trade data can thus proxy for domestic seafood prices that are difficult to observe in many regions and costly to update with global coverage. Calculations of the extent of price competition in different countries support the plausibility of reliance on trade data. Overall, the FPI shows less volatility and fewer price spikes than other food price indices including oils, cereals, and dairy. The FPI generally reflects seafood scarcity, but it can also be separated into indices by production technology, fish species, or region. Splitting FPI into capture fisheries and aquaculture suggests increased scarcity of capture fishery resources in recent years, but also growth in aquaculture that is keeping pace with demand. Regionally, seafood price volatility varies, and some prices are negatively correlated. These patterns hint that regional supply shocks are consequential for seafood prices in spite of the high degree of seafood tradability.

Tveterås S, Asche F, Bellemare MF, Smith MD, Guttormsen AG, Lem A, Lien K, & Vannuccini S (2012). Fish Is Food – The FAO’s Fish Price Index. PloS one, 7 (5) PMID: 22590598

Published May 7, 2012

Implementation Bias in Randomized Controlled Trials

From a new paper (link opens a .pdf file) by Oxford’s Tessa Bold and her coauthors:

The recent wave of randomized trials in development economics has provoked criticisms regarding external validity and the neglect of political economy. We investigate these concerns in a randomized trial designed to assess the prospects for scaling-up a contract teacher intervention in Kenya, previously shown to raise test scores for primary students in Western Kenya and various locations in India. The intervention was implemented in parallel in all eight Kenyan provinces by a nongovernmental organization (NGO) and the Kenyan government. Institutional differences had large e ffects on contract teacher performance. We find a signifi cant, positive effect of 0.19 standard deviations on math and English scores in schools randomly assigned to NGO implementation, and zero effect in schools receiving contract teachers from the Ministry of Education. We discuss political economy factors underlying this disparity, and suggest the need for future work on scaling up proven interventions to work within public sector institutions.

Bold et al.’s finding points to an important problem with the findings of many randomized controlled trials (RCTs): No matter how careful one is in ensuring that subjects are randomly assigned to the treatment and control groups, almost all RCTs rely on only one implementing partner.

Continue readingImplementation Bias in Randomized Controlled Trials
Published April 24, 2012

Identifying Causal Relationships vs. Ruling Out All Other Possible Causes

Portrait of Artistotle (Source: Wikimedia Commons.)

I was in Washington last month to discuss my work on food prices, in which I look at whether food prices cause social unrest, at an event whose goal was to discuss the link between climate change and conflict.

As many readers of this blog know, disentangling causal relationships from mere correlations is the goal of modern science, social or otherwise, and though it is easy to test whether two variables x and y are correlated, it is much more difficult to determine whether x causes y.

So while it is easy to test whether increases in the level of food prices are correlated with episodes of social unrest, it is much more difficult to determine whether food prices cause social unrest.

In my work, I try to do so by conditioning food prices on natural disasters. To make a long story short, if you believe that natural disasters only affect social unrest through food prices, this ensures that if there is a relationship between food prices and social unrest, that relationship is cleaned out of whatever variation which is not purely due to the relationship flowing from food prices to social unrest. In other words, this ensures that the estimated relationship between the two variables is causal. This technique is known as instrumental variables estimation.

Identifying Causal Relationships vs. Ruling Out All Other Causes

As with almost any other discussion of a social-scientific issue nowadays, the issue of causality came up during one of the discussions we had at that event in Washington. It was at that point that someone implied that it did not make sense to talk of causality by bringing up the following analogy:

Continue readingIdentifying Causal Relationships vs. Ruling Out All Other Possible Causes