Explanatory variables are classified into three categories, following the model specification above and as described in Table 3.4. We use household traits (𝚽!!), farm attributes (𝚽!"#$) and market characteristics (𝚽!"#$%&) to explain variation in production diversity.
Table 3.4
Variable Names and Descriptions
VARIABLE NAME DESCRIPTION
Dependent Variables
Production Diversity
Crop count Total number of crops produced in the previous growing season
Livestock count Total number of different livestock kept by the household
Total count Sum of crop count and livestock count
Dietary Diversity
Food consumption score Sum of weighted frequencies of 8 food groups eaten in the past 7 days
Household dietary diversity score Number of 12 food groups eaten in the past 24 hours
Explanatory Variables
Farm Attributes
Farm size (ln) Log of cultivable land area in hectares managed by the household
Number of parcels Number of cultivable parcels managed by the household
Household Traits
Wealth Asset-based wealth index
Female household head Gender of household head (1 = female, 0 = male)
Age Age of household head in years
Age squared Squared age of household head
Education Years of education for household head
Household size Total number of household members
% Productive members Percentage of household members age 14 - 65 years
Market Access
Village fixed effects Village dummy variables − base option is Cullpa Alta
Note. Modern potato and traditional potato are counted as two separate crop types. Cow and bull are counted as two separate livestock types. Horses and burros are excluded from the livestock count.
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Under a strictly separable household model, socioeconomic characteristics and preferences of the household are hypothesized to affect consumption choices (dietary diversity) but not farm management (production diversity). Thus, significant coefficient estimates for variables representing household characteristics in the regression model explaining production diversity would support our prediction of nonseparability. We include age of the household head, plus a quadratic age term, to test whether older farmers maintain higher levels of production diversity. If older households in our sample prefer traditional crops such as oca, olluco and mashua, which are less readily available in the marketplace, then we expect to see a positive relationship between age and production diversity (Van Dusen and Taylor, 2005). Neither our model nor the literature provides any clear indication of the relationship between age and dietary diversity.
Household size (total number of members) and composition (proportion of productive adult members) are included as indicators of household labor available to undertake a wide range of tasks, including agricultural production and food preparation. Positively signed coefficient estimates are expected on variables representing household size and composition if production activities supporting higher levels of farm diversity − or food preparation activities supporting higher levels of dietary diversity − are intensive in family labor and lack perfect substitutes (e.g. hired labor) (Benjamin, 1992).
Gender is included as a variable that may affect access to productive resources. Although our model controls for cultivable land area, we predict lower production diversity for female- headed households if women face restricted access to other productive assets, such as shared pasture or water resources, relative to men. Both gender and education are included as traits that could affect household dietary preferences. Previous studies provide evidence that female control over income and productive assets can increase household food spending, dietary diversity and child nutritional status (Hoddinott and Hirvonen, 1995; Arimond et al., 2011; Meinzen-Dick et al., 2012; Jones et al., 2014b; Snapp and Fisher, 2015). Thus, female-headed households may
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have higher dietary diversity scores compared to male-headed households, ceteris paribus, due to a relative female preference for nutrition. Households with more education are likely to have better access to nutritional information and a higher ability to implement nutritional recommendations. We therefore predict a positive effect of education, indicated by years of schooling completed by the household head, on dietary diversity.
We use an asset-based index to control for household wealth. Our expectation that wealth positively impacts dietary diversity is consistent with previous theoretical and empirical work indicating that dietary diversity is a normal good (Ruel, 2003). Using Principal Components Analysis (PCA) we derive a continuous, normalized measure of socioeconomic status from factor scores for 41 indicators of housing quality and asset ownership (Vyas and Kumaranayake, 2006). To create this variable, we used all 492 observations from farming and non-farming households in the study to increase sample size and heterogeneity. PCA results are presented in Appendix 6.
We hypothesize that farm attributes affect both farm management (production diversity) and household consumption (dietary diversity). In this category we include indicators of farm size (logged cultivable land area) and farm fragmentation (number of cultivable parcels). The ecological literature indicates that land area and environmental heterogeneity tend to be positively correlated with biodiversity (Bellon, 1996; Benin et al., 2004). Thus, we predict positive coefficient estimates for farm size and fragmentation in the regression equation explaining production diversity. Significant coefficient estimates for variables representing farm size and fragmentation in the regression on dietary diversity will support our expectation of a direct relationship between agricultural production and dietary diversity.
A limitation of our data is a lack of household-level indicators of market access. Household distance from the nearest urban market is commonly used to proxy market access in the development literature (Brush et al., 1992; Benin et al., 2004; Sibhatu et al., 2015; Snapp and Fisher, 2015). Although transportation costs are considered reasonable approximations for farm-
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to-market transaction costs (Omamo, 1998), our dataset does not include individual household distances to Huancayo markets. However, households in our sample are relatively tightly clustered in their respective villages, and virtually all are situated alongside a road. It follows that the duration and cost of a trip to Huancayo is driven primarily by village characteristics (village distance from Huancayo and the frequency, reliability and price of public transportation). Thus, in all of our specifications, we include village dummy variables to control for village-level variation in market access. We use Cullpa Alta (the closest village to Huancayo) as our base for comparison.
Because the literature suggests that production diversity tends to increase with distance from markets (Omamo, 1998), we might expect to see positive coefficient estimates for all 𝜷𝟑 parameters (village effects), relative to Cullpa Alta, and we might predict that the size and significance of 𝜷𝟑 estimates would increase with village distance from Huancayo. However,
elevation, temperature and agroecological zone are also highly correlated with distance from Huancayo. The city of Huancayo is situated at the lowest point, which coincides with the warmest zone, in the Shullcas River Watershed. Biophysical factors likely have negative effects on crop diversity with increasing distance from Huancayo, as fewer crops are physiologically suited for higher elevations and colder temperatures. For this reason, although we specify village fixed effects, we are unable to isolate impacts of village-level market characteristics 𝚽𝑴𝒂𝒓𝒌𝒆𝒕𝒋 on household production diversity or dietary diversity. We also lack household and plot level data on elevation and climate, which would otherwise help to control for these effects.
To explore possible mechanisms relating agricultural production to dietary diversity, we introduce additional explanatory variables in subsequent models. Production diversity, as defined above, is expected to be positively associated with dietary diversity if a direct production- consumption linkage exists. Farm profit, as defined in Equation 10, is expected to be positively associated with dietary diversity if an income effect is present. We also include a dummy variable that equals one for commercial farming households, which sell some or all of their crop
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production in the market, and zero for subsistence farming households, which sell none of their harvest. Interacting the commercial farming indicator with production diversity and farm profit, respectively, allows us to evaluate whether mechanisms linking production and consumption differ for commercial farming households versus subsistence farming households.
𝜌! = !!!! 𝑝!"𝑄!"− (𝐼!" + 𝑤𝐿!") − 𝑟𝐻! (10)
We define farm profits 𝜌 for household i as the total value of crop production for all crops 𝑗 = 1, . . . , 𝐽 produced by the household, less the full economic cost of production. The value of crop production is defined as total quantity produced (𝑄!") multiplied by price (𝑝!"). For products sold in the marketplace, 𝑝!" equals the sale price for product j reported by household i. However, using a sale price for crops consumed entirely within the household would understate the value of those crops because production of goods for household consumption offsets the purchase of similar goods in the marketplace. Thus, if none of product j was sold by household i, then we set 𝑝!" equal to the consumer price for product j. We collected consumer price data in a market survey of Huancayo's central marketplace, Mercado Mayorista, during December 2015, within the same timeframe that the household surveys were completed. The economic cost of crop production includes the value of purchased inputs and the opportunity cost of labor and land. We include the crop-level input costs (𝐼!") reported by each household, and crop-level labor quantity (𝐿!") valued at a constant wage rate (𝑤) equal to the average daily wage of 30 PEN (approximately $9.25 USD) for the Huancayo region. We include the economic cost of land at the farm level by multiplying the total cultivable land area (𝐻!) by a constant price representing the typical rental rate for the study area (𝑟) equal to 1,000 PEN per hectare per season (approximately $310 USD).1
1 Our farm profit indicator is calculated with a constant per-hectare opportunity cost for land. However, it may be
unrealistic to assume that all households would be able to rent their land if they so desired, or that all cultivable land in the study area would be equally valued by renters. Thus, using a fixed opportunity cost for land may result in the
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