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Q&A on the Economics of Resilience

This post is about the following event(s)

On January 25, Agrilinks and Microlinks co-hosted international development economists Courtenay Cabot Venton and Mark Lawrence and resilience M&E and strategic analytics advisor Tiffany Griffin for a webinar titled, "Economics of Resilience: An Ounce of Prevention, a Pound of Cure," which shared the results of a recent study commissioned by USAID’s Center for Resilience. The study demonstrates that investing in resilience and a more proactive response to avert humanitarian crises in the Horn of Africa could reduce the cost to international donors by 30 percent, while also protecting billions of dollars of income and assets for households and communities impacted by droughts. If you missed the event, reference this page for transcripts, recordings, reports and other resources.

The presenters fielded a flurry of questions from 230 attendees tuning in from all over the globe; in this Q&A, they address a few they did not have time to answer during the event.

Audience: Great presentation and discussion — thank you! Did you gain insights on agricultural choices and diversity shifts, i.e., beyond productivity/yield trends, which you mentioned — possible link to/factor for resilience?

Tanya Boudreau: This would be an interesting issue to investigate further in the context of the livelihood change analysis being undertaken in a number of countries where we have two full sets of Household Economy Analysis (HEA) baselines from ten years apart (including Ethiopia, Malawi, Swaziland, and parts of Kenya and the Sahel). In many areas, we've seen households moving away from growing lower-value food crops towards cultivating higher-value cash crops. For example, in Amhara Region, eucalyptus tree cultivation has increased substantially in some zones (driven by the strong demand for building poles in the burgeoning construction industry) as households turn away from cultivating rainfed cereal crops on diminishing plots. In other zones, pepper production has taken over, with households responding to increasing prices associated with large regional towns and urban demand. This is also evident in the riverine agro-pastoral livelihood zone in Somali Region (AGA).

And it’s not just crops. In 10 of the 27 rural livelihood zones in Amhara Region, livestock income is now the most important source of cash for better-off households even in these traditional cropping areas. Livestock prices in East Africa have increased steadily over the past decade, outstripping the rate of inflation. Maize and teff prices, on the other hand, have remained in line with inflation. There have also been shifts away from certain livestock, like cattle, in places where grazing is severely constrained. In a number of these areas, tree cultivation has become increasingly important. It would be interesting to test the degree to which these particular diversity shifts are correlated (or not) to changes in the resilience scores in these areas. 

Audience: It would be interesting to discuss how early interventions apply in the context of conflicts and what good years would mean. I think one of the peculiarities of conflict context is that refugees and internally displaced persons lose access to the means of production, and host countries do not facilitate access to the labor market.

Courtenay Cabot Venton: Very much agree, and this is a topic that has come up regularly during the course of this study. We are scoping out a potential study related to conflict to other interested donors to see if we can start to tease out some of these issues and look, at least qualitatively, at what it means to build resilience in a context where those efforts are consistently undermined.

Audience: There was a large dip in the livelihoods threshold line in a couple of the years studied. Can you please explain why?

Mark Lawrence: The simple answer is that this is linked to changes over time in the prices of the items that make up the livelihoods protection basket.

The longer explanation is as follows. In HEA, we generally measure income in food terms rather than cash terms. The unit of measurement (the y-axis on the graphs) is percentage of minimum kilocalorie requirements (2100 kcals per person per day). The conversion of cash income into kcals is based on the cost of a basic staple food basket providing 100 percent kcals per person for the year. So, if you sell a goat for $30, then this might purchase, for example, 10 percent of the household’s food needs in the baseline year and so on.

The reason we do this is that it means that the survival threshold (basically the cost of covering minimum food needs) does not change from year to year; it is always close to 100 percent. But the cost of the livelihoods protection basket does change from one year to the next, in line with changes in the cost of the items that are included in that basket (e.g., livestock drugs, seeds and fertilizer).

Audience: Can you point to a great example of shock-responsive adaptive management in practice?

Cabot Venton: I am starting to see more examples of adaptive management in this context, but it is still nascent. I think the most obvious starting point is where flexible funding is starting to allow people to pivot quickly to arising needs. So for example in the 2015 drought in Ethiopia, UNICEF pivoted its development funding from DFID for the OneWash program to humanitarian-focused water interventions and were able to do this very rapidly. I know that Concern also has a program in Somalia, where they have been able to be very responsive to changing community needs. Again, I'm focusing on outcomes rather than activities.

Audience: How do interventions that support economic growth at the national and system levels contribute to building resilience of poor and vulnerable people and households in vulnerable zones in-country?

Cabot Venton: This is a big question and not something that came directly out of the study. However, I do think that when you look at this question from the bottom up, achieving changes in household status is very hard to imagine if there isn’t access to the relevant infrastructure. You can train people, provide them with loans/grants etc., but if the wider systems-level work to support economic growth (e.g., trade policy, access to road/markets) isn’t in place, you won’t get very far.

Audience: What changes were made in the livestock component of the total income for the agro-pastoralist livelihood groups?

Lawrence: In terms of the way that livestock and livestock income are dealt with in the model, there is no general difference between pastoral and agropastoral livelihood zones. The difference comes from the specifics of each zone, i.e., the type and number of animals owned, the livestock products that are consumed and sold, the markets that different groups access and the area within which livestock graze in most years (defined by the borders of the livelihood zone). 

The HEA baseline data includes a range of information on livestock (at the household level), including types of animals owned (camels, cattle, goats, sheep), numbers owned, food consumption (milk and meat), numbers sold and total cash income from livestock (animal sales, milk/butter/ghee sales, sale of hides, renting out of pack animals etc.); plus information on herd dynamics during the reference (or baseline) year, i.e., total herd size at the beginning and end of the year, number of adult females and numbers of births, deaths, sales, purchases and slaughters during the year. The HEA baseline data we collect is specific to each livelihood zone and wealth group. The types and numbers of livestock owned and the contribution that these make to household food and cash income for different wealth groups will vary from zone to zone. 

For the 15-year analysis (the outcome analysis), we combine that baseline data with outputs from the herd model for the same livelihood zone (i.e., using rainfall from that livelihood zone as the input into the model) along with price data (for staple crops and livestock). The outputs from the herd model estimate how herd numbers (specifically for cattle, camels, goats and sheep) will change, and how milk yields will change given different levels of rainfall (which is used as a proxy for grazing). This is then tied back to a change in the availability of milk (for household food) and the numbers of animals available to sell, combined with changing prices from year to year. Thus, total income (food and cash income combined) changes from year to year depending on one, the HEA baseline data; two, the outputs from the herd model; and three, prices (of both staple food and livestock).

Audience: During the webinar, I asked about the caloric base for the estimate of dietary needs, and the answer was simple 2,100 kcal/day, which is well recognized as the basic metabolism needs for someone like us with a mostly sedentary lifestyle. However, the economic opportunities for the project beneficiaries are all based on substantial physical exertion either as herders or agriculturists, which may require a diet of up to or exceeding 4,000 kcal/day. Anything less will hinder agronomic output, food security, and resilience. I think when the caloric demands for the beneficiaries’ limited economic activity is factored in it will substantially downwardly skew the resilience analysis. I wonder how many studies have been based on basic metabolism without taking into consideration the work energy, and if this represents a somewhat superficial understanding of the plight of your intended beneficiaries and the limited conceptualization by the development effort on the operational limits of innovations.

Boudreau: The primary reason that HEA uses 2,100 kcal per person per day as the minimum energy requirement is that this is the figure used by all implementing agencies (WFP and UNHCR, especially) and it has been the generally accepted intervention threshold for humanitarian assistance since 1997. It was revised upwards from 1,900 kcal at the prompting of USAID in 1995. The revision was based on a number of empirical studies, in which average energy requirements for a range of sub-Saharan African and Asian countries were computed based on the demographic composition of the population. These studies came up with figures ranging from 2,060 to 2,160. (Smith, Alderman, and Aduayom 2006; Smith and Subandoro, 2005). (See full references below.) 

The key thing to keep in mind is that the 2,100 kcal figure is an average for a typical household, not for a particular individual. So, let’s take the following as a typical family breakdown: two adults (26-35), two children under five years of age, one teenage child (12-13 years), one elder (> 60). The daily calorie needs would be as follows:

 

Minimum kcal/day

Child 1 – 2 years old (active)

1,000

Child 2 – 4 years old (active)

1,400

12-year old (active)

2,400

Adult 1 Female (active)

2,200

Adult 2 Male (active)

3,000

Elder, 65 years old (moderately active)

2,400

Total

12,400

Average for household

2,066.67

Source: https://health.gov/dietaryguidelines/2015/guidelines/appendix-2/.

Even if you increase the male adult to 4,000 kcal per day, the average is still only 2,233 kcal per day for the whole household. Given the range of family structures, with some having more children and fewer adults and others having more active adults and fewer children, the average of 2,100 calories was settled upon some time ago as an acceptable compromise. There is no empirical basis on which 4,000 kcal would be the average minimum requirement for a typical household, even if a very active adult male engaged in heavy labor might need this much. It is also important to keep in mind that this is an intervention threshold, not a development threshold. If households fall below this point, an intervention should be triggered to help save livelihoods/lives. It is, by definition, the minimum, not the ideal. 

References

Smith, L. C., and A. Subandoro, 2005. Improving the empirical basis for assessing food insecurity in developing countries: Asia. Report submitted to the Department for International Development of the United Kingdom and to the Australian Agency for International Development by International Food Policy Research Institute. International Food Policy Research Institute, Washington, D.C.

Smith, L.C., H. Alderman, and D. Aduayom. 2006. Food Insecurity in Sub-Saharan Africa: New estimates from household expenditure surveys. Research Report 146. Washington, D.C.: International Food Policy Research Institute.

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