The welfare impacts of rising quinoa prices: evidence from Peru

IntroductionConceptual Framework

DataEmpirical FrameworkEstimation Results

Summary and Concluding Remarks

The Welfare Impacts of Rising Quinoa Prices:Evidence from Peru

Marc F. Bellemare, Johanna Fajardo-Gonzalez, and Seth R.Gitter

International Center for Tropical Agriculture � Cali, Colombia

May 4, 2015

Bellemare, Fajardo-Gonzalez, and Gitter The Welfare Impacts of Rising Quinoa Prices




IntroductionQuinoa (Chenopodium quinoa) is an Andean grain that is relativelyhigh in protein as well as in essential amino-acids. It wasdomesticated over 3,000 years ago in the Andes and has beengrown in Bolivia, Ecuador, and Peru since then.

As a consequence of quinoa being seen as some kind of�superfood� in the US, the UK, and other rich countries, there hasbeen a sharp increase in the demand for quinoa since 2007.Peruvian exports of quinoa to the United States, for example,totaled $80 million in 2013�up from $5 million in 2008(AgroVision, 2014).

Similarly, the price of quinoa has tripled since 2007, and it showsno sign of falling back down.





Introduction





Introduction

Given the foregoing, one might reasonably want to know what thismeans for those who have traditionally relied on quinoa for theirsubsistence, viz. Andean peasants.

And indeed, a few years ago, journalists made all kinds ofcontradictory statements about the welfare impacts of risingquinoa prices.





Introduction

Johanna Blythman, The Guardian (Manchester), January 16, 2013:

[T]here is an unpalatable truth to face for those of uswith a bag of quinoa in the larder. The appetite ofcountries such as ours for this grain has pushed up pricesto such an extent that poorer people in Peru and Bolivia,for whom it was once a nourishing staple food, can nolonger a¤ord to eat it. Imported junk food is cheaper. InLima, quinoa now costs more than chicken. Outside thecities, and fueled by overseas demand, the pressure is onto turn land that once produced a portfolio of diversecrops into quinoa monoculture.





Introduction

Doug Saunders, The Globe and Mail (Toronto), January 19, 2013:

The people of the Altiplano are indeed among thepoorest in the Americas. But their economy is almostentirely agrarian. They are sellers � farmers or farmworkers seeking the highest price and wage. The quinoaprice rise is the greatest thing that has happened tothem. And it is a deliberate strategy: Quinoa had all butdied out as a staple in Bolivia, replaced by beans andpotatoes, until farmers began planting it in the 1980swith exports to North America in mind.





Introduction





IntroductionIn my post, I highlighted three research questions one needs toanswer before knowing what the welfare impacts of rising quinoaprices are:

1. Are most households in the Altiplano net buyers or net sellersof quinoa, or are they autarkic?

2. Do net seller households produce under contract, as part of aquinoa value chain, or do they sell to processors on the spotmarket?

3. Is it possible to store quinoa for a relatively long period?

In this paper, we ask a question related to the �rst question above,and we ask:

What e¤ects, if any, did rising quinoa prices have onthe welfare of quinoa-cultivating households?





IntroductionTo answer this question, we use ten rounds of the PeruvianEncuesta Nacional de Hogares (ENAHO), a nationallyrepresentative, large-scale household survey conducted annually.

Because the ENAHO is not longitudinal (i.e., it is a repeatedcross-section in that it surveys a new sample of households everyyear), we construct a pseudo-panel (Antman and McKenzie, 2007)by using geographical units (districts, provinces, and departments)instead of households as our units of observation, and by lookingat geographical unit-level averages instead of household-speci�cmeasures of welfare and quinoa cultivation.

We combine this with a di¤erence-in-di¤erences (DiD) approach toassess the causal impact of quinoa cultivation on household welfare.





IntroductionOur results suggest that rising quinoa prices have had positivee¤ects on the welfare of quinoa-producing households through twochannels:

I First, the average level of consumption (our proxy for income,and thus for welfare) of quinoa-producing householdsincreased at a higher rate than that of households that did notproduce quinoa in 2012 and 2013, after the sharp increase inquinoa prices.

I Second, the variance of consumption of quinoa-producinghouseholds decreased at a higher rate than that of householdsthat did not produce quinoa in 2012 and 2013.

Thus, it looks as though quinoa production has both �rst- andsecond-order e¤ects on household welfare.





Outline

1. Introduction

2. Conceptual Framework

3. Data

4. Empirical FrameworkI Estimation StrategyI Identi�cation Strategy

5. Results

6. Summary and Concluding Remarks





Conceptual FrameworkIn order to investigate the welfare e¤ects of a change in the priceof good i on a household�s welfare, Deaton (1989) de�ned theconcept of net bene�t ratio as follows:

NBRi = �piqim, (1)

where pi denotes the price of good i , qi denotes the household�snet purchases of good i (net sales if qi < 0), and m denotes thehousehold�s income.

Obviously, the sign of NBRi depends directly on the sign of qi .With qi > 0, NBRi < 0 and the household loses out from a priceincrease. With qi < 0, NBRi > 0 and the household bene�ts froma price increase.





Conceptual Framework

The idea of the NBR has found much support in the empiricalliterature (Budd 1993; Barrett and Dorosh 1996; Lasco et al.2008).

One also needs to realize that the presence of transactions costswill also drive whether some households will be net buyers (q > 0)or net sellers (q < 0). See for example de Janvry et al., 1991;Goetz, 1992; Key et al., 2001; Bellemare and Barrett, 2006; etc.

So as the price of quinoa goes up, theory suggests thosehouseholds who are net sellers will bene�t, and those who are netbuyers will lose out.





Data SourceWelfare MeasureNet Buyers and Net Sellers

Data

We use the Peruvian ENAHO Condiciones de Vida y Pobreza forthe period 2004-2013.

We have ten years worth of nationally representative data on227,400 households across 1,401 districts in 194 provinces in 25departments.

The data cover 8,216 quinoa-producing households household-yearobservations, or about 3.5% of the sample.






Welfare Measure

For our welfare measure, we use total household consumption,which the Peruvian INEI imputed based on a two-week recall ofconsumption at the household level.

If you are not an economist, treating household consumption as�welfare�might seem strange. Ideally, one would want to use ameasure of income to capture welfare� for economists, welfare isincreasing in income� but as Deaton (1997) points out, collectingdata on income is both tedious and di¢ cult. Consumption is agood proxy measure for income, and so it is our proxy measure forwelfare here.






Welfare Measure

In order to avoid obvious endogeneity issues, we subtract theconsumption of quinoa from our consumption measure (we includeit as part of robustness checks later).

Our consumption measure also includes the consumption of goodsproduced by the household� for quinoa growers, this representsabout 40% of their total consumption.

All data is expressed in 2004 PEN using in�ation data obtainedfrom the Central Reserve Bank of Peru.






Welfare Measure

Quinoa producers have total consumption that is about one thirdthat of households that do not grow quinoa. In other words, theystart out much poorer than households that do not produce quinoa.

The NBR for quinoa is much larger (in absolute value) for quinoasellers than it is for quinoa buyers. In other words, the budget shareof quinoa for sellers exceeds the budget share of quinoa for buyers.






Welfare Measure

But we are also interested in second-order e¤ects. In other words,we are interested in knowing whether the variability ofconsumption is di¤erent for those households that produce quinoa.

In an expected utility theory sense, assuming people are risk averse(which is not unlikely for households in a country such as Peru), itwould be interesting to know if the production of quinoa isassociated with a more stable household consumption.

So we use the variance of total household consumption as anadditional measure of welfare.






Welfare Measure

0.2

.4.6

Den

sity

4 6 8 10 12 14Ln(Real Annual Expenditure)

Growers of quinoa Nongrowers of quinoaData source: ENAHO

Figure: Distribution of Welfare by Quinoa Production Status.Bellemare, Fajardo-Gonzalez, and Gitter The Welfare Impacts of Rising Quinoa Prices





Net Buyers and Net Sellers

Most quinoa-producing households produce quinoa for their ownconsumption. In 2013, only 17% of them sold any quinoa, up fromroughly 8% for the period 2004-2010.

Less than 0.5% of quinoa producers reported having purchasedquinoa over the last two weeks.

Roughly 30% of all Peruvian households consumed quinoa over thelast two weeks. This �gure was the same in 2004 and 2013, withsome variation in between.





Estimation StrategyIdenti�cation Strategy

Estimation StrategyAs I mentioned above, the ENAHO is a repeated cross-section, i.e.,it does not follow households over time, and so it is not possible touse standard panel techniques (e.g., household �xed e¤ects) toidentify the potential causal relationship �owing from quinoaproduction to welfare.

What we can do, however, is to treat geographical units (i.e.,districts, provinces, and departments) as our unit of observation.Because households are randomly selected in each community ineach round, this is akin to matching households across roundsalong both their observable and unobservable characteristics.

It is this random sampling (as well as the use of controls for thetime period) on which our identi�cation strategy hinges� more onthat in a minute.






Estimation Strategy

So to be clear: We treat a geographical unit as our unit ofobservation. For each unit, we take the average of householdconsumption, and the proportion (i.e., average) of households whogrow quinoa and regress the former on the latter.

We do this for three di¤erent geographical units (i.e., district,province, and department, with varying sample sizes) so as toensure that our results are robust.






Estimation Strategy

The equation we estimate is thus

ln cgt = α0 + β0Dgt +2013

∑t=2005

αtTt

+2013

∑t=2005

βtDgt xTt +G

∑g=2

γgdg + εgt , (2)

where cgt is average household consumption in region g in year t,Dgt is the proportion of households who produce quinoa ingeographical unit g in year t, T are �xed e¤ects for each year t,dg are �xed e¤ects for each geographical unit g , and εgt is an errorterm with mean zero






Identi�cation Strategy

Might this not only capture a correlation between householdconsumption and quinoa cultivation instead of the causal e¤ect ofquinoa cultivation on household consumption?

We argue that our combined use of (i) pseudo-panel techniques,and (ii) a di¤erence-in-di¤erences design yield the causal e¤ect ofquinoa production on household welfare.






Identi�cation Strategy

This is because

1. The fact that households are randomly selected each year ineach community means they are matched along bothobservables and unobservables, and the inclusion of time �xede¤ect corrects for changes over time in their characteristics.

2. Given that, the di¤erence-in-di¤erences estimator (withclustered standard errors) should yield an estimate of thee¤ect of quinoa production on household welfare. that isplausibly causal.





Estimation Results: Household ConsumptionVariables District Province Department

Year 2005*Quinoa -0.155* -0.093 -0.091(0.084) (0.127) (0.141)

Year 2006*Quinoa -0.004 0.092 0.191(0.085) (0.106) (0.223)

Year 2007*Quinoa 0.005 0.098 0.207(0.093) (0.163) (0.308)

Year 2008*Quinoa 0.06 0.033 0.107(0.098) (0.195) (0.413)

Year 2009*Quinoa 0.079 0.129 0.228(0.084) (0.161) (0.378)

Year 2010*Quinoa 0.061 0.082 0.369(0.09) (0.157) (0.233)

Year 2011*Quinoa 0.022 0.029 0.278(0.09) (0.144) (0.292)

Year 2012*Quinoa 0.161* 0.257* 0.474(0.085) (0.146) (0.336)

Year 2013*Quinoa 0.377*** 0.562*** 1.050**(0.092) (0.164) (0.502)

N 9613 1919 250R2 0.217 0.426 0.778Number of districts 1401Number of provinces 194Number of departments 25Signi�cant at: *** p<0.01, ** p<0.05, * p<0.1Robust standard errors in parenthesesclustered at regional level





Estimation Results: Household Consumption

How should you interpret those �ndings? Here, the marginale¤ects are complicated by the semi-logarithmic speci�cation of theequation of interest. In order to recover a marginal e¤ect, theestimates need to be transformed as per the formula given byKennedy (1980).

So for Year 2013*Quinoa, for example, the marginal e¤ect is 46percentage points. That is, for a community whose proportion ofquinoa growers increases by 10%, households would see an increasein their consumption that is 4.6% faster on average.





Estimation Results: Household Consumption

We conduct a number of robustness checks:

I We use only quinoa-producing regions.I We control for baseline (i.e., 2004) quinoa productionI We include quinoa purchases as part of overall householdconsumption

I We did the analysis at the household level (with all theendogeneity problems that this entails)

By and large, our �ndings are preserved: Signi�cant positivewelfare impacts of quinoa production, but only in most recent years(i.e., 2012 and 2013).





Estimation Results: Variance of Household ConsumptionVariables District Province Department

Year 2005*Quinoa 0.021 0.081 0.314(0.075) (0.122) (0.19)

Year 2006*Quinoa 0.015 0.014 0.138(0.065) (0.13) (0.217)

Year 2007*Quinoa -0.159** -0.143 -0.198(0.072) (0.115) (0.197)

Year 2008*Quinoa -0.125 -0.151 -0.083(0.08) (0.124) (0.25)

Year 2009*Quinoa -0.064 -0.061 -0.12(0.084) (0.125) (0.162)

Year 2010*Quinoa -0.072 0.083 -0.099(0.082) (0.142) (0.156)

Year 2011*Quinoa -0.106 -0.033 0.032(0.082) (0.115) (0.254)

Year 2012*Quinoa -0.183** -0.264** -0.363**(0.079) (0.121) (0.155)

Year 2013*Quinoa -0.079 -0.198 -0.356**(0.082) (0.121) (0.149)

Intercept 0.368*** 0.460*** 0.563***-0.008 -0.013 -0.017

N 9613 1919 250R2 0.008 0.032 0.239Signi�cant at: *** p<0.01, ** p<0.05, * p<0.1Robust standard errors in parenthesesclustered at regional level





Estimation Results: Variance of Household Consumption

Our results indicate that over time, within district variance ofconsumption has been increasing.

It was only in 2012, however, that the production of quinoaconsistently led to a decrease in the variance of consumption.

The same might be true for 2013, although the result here is muchless robust.






Given our empirical results, it looks as though the dramaticincrease in quinoa prices translated into improved welfare outcomesfor quinoa-producing households in Peru, but only in 2012 and2013, after the quinoa price spike.

Before the price spike, quinoa-producing households hadconsumption growth rates similar to the rest of the country.






Our next steps are to conduct additional robustness checks tomake sure that our �ndings robust to various speci�cations of ourequation of interest.

Two of us are currently doing �eldwork in Peru, surveying a sampleof 150 households every four months, in an e¤ort to study seasonalpatterns of production and consumption, and truly get at thewelfare e¤ects of rising quinoa prices.


Science

The welfare impacts of rising quinoa prices: evidence from Peru