Energy scarcity, food supply chain transformation, and poverty reduction in the emerging economies: the case of Brazil, China, and India

Chen, Kevin (2018). Energy scarcity, food supply chain transformation, and poverty reduction in the emerging economies: the case of Brazil, China, and India. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-852252

Rational. Three existing knowledge gaps motivate this study. First, there has been little research on linking energy, transformation, and poverty reduction in the developing countries. Second, there has been little research analyzing energy costs in the various segments of the food supply chains, differentiating over products, tracing both patterns in energy intensity by segment as well as the impacts of these costs on net incomes of actors. Third, there has been little empirical research empirically linking energy policy and public energy system investments with energy costs and performance in food supply chains in developing countries.

Objectives. 1) to develop an integrated conceptual framework for modeling the relations among three interrelated factors, transformed versus traditional food supply chains; energy costs from electricity and fuel; and net incomes of supply chain participants and food prices; 2) to apply the framework to analyze horticulture and dairy supply chains in China, India, and Brazil, to assess how energy costs are generated and affect behavior in the segments of the supply chain and what the implications of these are for food costs to consumers and incomes to producers; and 3) to formulate policy pathways for moving towards more optimal energy use practices that contribute to supply chain development and reduction of poverty.

Data description (abstract)

In order to generate a more reliable value chain actors’ data, we use stratified random sampling in each of the value chain segments to the extent possible. The data come from 3,253 interviews of economic actors from 6 sets of surveys, using comparable questionnaires covering all the segments of the dairy and potato value chains in Brazil, China, and India.
The initial set of questionnaires for all segments including farmers, wholesale/logistics, processors, and retailers were developed in English and then adapted and translated into Chinese, Portugal and Hindi. Detailed information on input use and technologies, output, logistical interface, energy costs, procurement systems, and institutional arrangements of each actors were collected.

Data creators:

Creator Name	Affiliation	ORCID (as URL)
Chen Kevin	International Food Policy Research Institute	https://orcid.org/0000-0001-7927-4132

Sponsors:

Economic and Social Research Council

Grant reference:

ES/J017841/1

Topic classification:

Economics
Labour and employment

Keywords:

Energy Costs, Food Value Chains, Dairy, Potato, Brazil, China, India

Project title:

Energy Scarcity, Food Supply Chain Transformation, and Poverty Reduction in the Emerging Economies: the Case of Brazil, China, and India

Grant holders:

Kevin Z. Chen, Tom Reardon, Bharat Ramaswami , Alexandre Nicolella, Fong Song, Elizabeth Farina, Sylvia Saes , Yazhen Gong

Project dates:

From	To
1 October 2012	31 December 2015

Date published:

27 Oct 2017 15:17

Last modified:

30 Jul 2018 13:04

Coverage and Methodology

Collection period:

Date from:	Date to:
1 October 2012	31 December 2015

Geographical area:

Brazil, China, and India

Country:

United Kingdom, Brazil, China, India

Spatial unit:

Postal > Postcode (Area)
Postal > Postcode (District)
Postal > Postcode (Sector)
Postal > Postcode (Unit)

Data collection method:

Inventory per country the energy policies and public and private-sector investments related to energy costs and access for all segments of the value chains studied.
Inventory and “map” the different value chains for the two products, in two study provinces/states.
Collect detailed data in “stacked surveys”, with a representative sample survey in each segment of the value chains, as well as supplemental case studies, as discussed above.
Analyze the data from all the segments of the value chain surveys.
Use the findings from step four, arrayed as parameters in simulations at different levels to model the impacts of energy policies and investments on energy costs, intensity, efficiency, and energy cost burdens of the poor.

Observation unit:

Household, Housing unit, Organization, Other

Kind of data:

Numeric, Numeric, Other, Text

Type of data:

Other surveys, Qualitative and mixed methods data

Resource language:

English, Chinese, Indian, Brazilian

Access and Administration

Data sourcing, processing and preparation:

Detailed data on value chain actors’ energy use/costs and other input/output behavior and costs are collected using common questionnaires during the period from 2013 to 2015 in Brazil, China, and India. A full representative sample survey for each segment of each supply chain (farmers, wholesale/logistics, processors, retailers) for dairy and potato is implemented. The database provides information on input use (technologies) and output, logistical interface, energy costs, procurement systems and institutional and organizational arrangements, such as collaborative arrangements among supermarkets and processors. The data are collected with a recall of 5 years which is a combination of feasible recall and adequate recall in a situation of rapid change in the value chains. It is notable that to ensure data quality we use stratified random sampling in each segment. For farm households, in each country we choose a more-developed and less-developed province/state for comparison. We then do stratified random sampling over districts, then villages, and within villages, over farm households supplying to different types of value chains; an example for dairy in India would be to 16 select villages that have private dairy and cooperative dairy collection centers and other villages have only traditional milk brokers; within each village we stratify the censored farm households producing the study products in order to get a sample of farmers selling to modern and traditional buyers, and then choose random samples within those strata. For wholesalers/brokers we first stratify by rural study zone and urban study areas. Per study zone, per product, we randomly sample cold storages (for potato) or collection centers (for dairy) and first stage-processors or packers. Per urban area (of which there are two per country), per product, we randomly sample traditional retailers, and for two urban areas for both products, supermarkets. There are three data collection steps. First, inventory per country the energy policies and public and private-sector investments related to energy costs and access for all segments of the value chains studied. Second, inventory and “map” the different value chains for the two products, in the study provinces/states. Third, collect detailed data in “stacked surveys”, with a representative sample survey in each segment of the value chains. Analytical Model: To help better understand the model used in this research, we'd like to first introduce the analytical process. The data from the value chain surveys has been analyzed in several ways. First, characterize and compare the current the transformation status of potato and dairy value chains in Brazil, China and India. Second, analyze econometrically the participation of small-scale/poor actors in selected segments of the value chain, in terms of patterns and determinants of participation. Third, per segment in the different categories of value chains, analyze energy intensity and costs by type of actor, in terms of averages and distributions. This analysis features our development of a value chain extension of the macro and meso input-output (energy input process output) matrix approaches used in Ramirez et al. (2006), Bhattacharyya and Ussanarassamee (2005), and Bullard and Herendeen (1975). Forth, analyze the distribution of costs and value added over segments of the different value chains. This serves as comparative context for the energy cost findings, so one can identify the energy share in costs over different value chains. Fifth, decompose the cost determinants of consumer food prices in the value chains under study in the major urban areas, so that energy versus other costs can be assessed in terms of consumer food security impacts. Analyze how the energy costs/access affect the transformation of value chains transformation. Sixth, cross the information about the participation of the poor as producers in the different segments of the value chains, with energy intensity and cost findings, to show the correlation of energy costs and poverty in the different value chains. Using the findings from the above analysis, arrayed as parameters in simulations at different levels, we model the impacts of energy policies and investments on energy costs, intensity, efficiency, and energy cost burdens of the poor, as well as final outcome indicators in particular food prices in the value chains: (a) a firm (farm)-level model assuming all the economic parameters are given; (b) a partial equilibrium model that only looks at the value chain; (c) a general equilibrium model that can include the feedback of economic forces of different sectors. Oral consent was obtained from all participants.

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