2.8 Appendix
Table A2.1: Product range of different retail formats in small towns
Typical products categories: Supermarket Small
self-service store Traditional kiosk
Non-food items of daily use Yes Yes Yes
Crisps & salted snacks Yes Yes Yes
Milk and yoghurt Yes, fresh & long life Yes, long life No
Meat and fish Yes, cooled sausages,
frozen chicken & fish No No
Cooking fat, incl. cholesterol free Yes Yes Yes
Fortified products (e.g. added vitamins) Yes Yes No
Tinned products Yes, but very limited No No
Instant noodles, breakfast cereals Yes Yes Yes
Soft drinks, juices with sugar added, drinking
chocolate Yes Yes Yes
Fruit juice without added sugar Yes No No
Alcoholic Beverages Yes, but limited No No
Built-in over the counter retail (e.g. bakery,
butchery, fast food stall) No (only few cases) No No
Fresh fruits & vegetables No (if yes, only very limited) Source: Own observation.
Table A2.2: Summary statistics of main dependent variables DEPENDENT VARIABLES
All Njabini
(no SM) Mwea
(SM since 2011) Ol Kalou (SM since 2002) Mean Mean Diff to
others Mean Diff to
others Mean Diff to others Food expenditure
shares:
Unprocessed 0.63 0.65 0.03*** 0.62 -0.02 0.62 -0.02
(0.11) (0.12) (0.01) (0.12) (0.01) (0.10) (0.01)
Primary processed 0.25 0.24 -0.00 0.25 0.01 0.24 -0.00
(0.11) (0.12) (0.01) (0.10) (0.01) (0.09) (0.01)
Highly processed 0.12 0.10 -0.03*** 0.13 0.01 0.13 0.02**
(0.10) (0.10) (0.01) (0.11) (0.01) (0.08) (0.01)
All processed 0.36 0.34 -0.04*** 0.38 0.02* 0.38 0.02*
(0.11) (0.12) (0.01) (0.12) (0.01) (0.10) (0.01) Calorie shares:
Unprocessed 0.48 0.50 0.03** 0.47 -0.02 0.47 -0.01
(0.12) (0.13) (0.01) (0.12) (0.01) (0.11) (0.01)
Primary processed 0.42 0.42 0.00 0.43 0.01 0.42 -0.01
(0.13) (0.14) (0.01) (0.12) (0.01) (0.12) (0.01)
Highly processed 0.10 0.08 -0.03*** 0.11 0.01 0.11 0.02*
(0.09) (0.09) (0.01) (0.10) (0.01) (0.08) (0.01)
All processed 0.52 0.50 -0.03** 0.53 0.02 0.52 0.01
(0.12) (0.13) (0.01) (0.12) (0.01) (0.11) (0.01) Calories p.c. per day
(adult equivalent)
2841.78 2565.22 -431.70*** 2878.87 52.91 3100.31 392.63***
(1127.36) (1015.78) (109.23) (1148.33) (116.43) (1161.08) (110.89)
Chapter 2.8: Appendix
For means, standard deviations for rest, standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
Source: Own calculation.
Table A2.3: Expenditure shares 1st stage results of main models (1) 1st stage Education of head in years 0.0051***
(0.001) 0.0050*** Age of cook squared 0.0000
(0.000) 0.0000
Chapter 2.8: Appendix
Table A2.4: Selected robustness checks, only main variable of interest shown Expenditure share highly processed foods
Chapter 2.8: Appendix
Table A2.5: Expenditure shares: Interaction effects (2) IV (4) IV (6) IV
Cluster robust standard errors in parentheses.
* p < 0.10, ** p < 0.05, *** p < 0.01. Source: Own calculation.
Table A2.6: Share of calories from different food categories – OLS and IV estimates
(1) OLS (2) IV (3) OLS (4) IV (5) OLS (6) IV (7) OLS (8) IV
Robust (1)-(4) and cluster robust (5)-(8) standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
Source: Own calculation.
Chapter 2.8: Appendix
Table A2.7: Selected robustness checks – only main variable of interest shown Calorie share all processed food
Cluster robust standard errors in parentheses.
Calorie availability p. c. per day
OLS Standard
Standard controls as in model (1), Table 2.6. Source: Own calculation.
Table A2.8: Calorie shares: Interaction
Cluster robust standard errors in parentheses.
* p < 0.10, ** p < 0.05, *** p < 0.01.
Source: Own calculation.
Chapter 2.8: Appendix
Table A2.9: Food budget shares and prices per calories, OLS and IV estimation
(1) OLS (2) IV (3) OLS (4) IV (5) OLS (6) IV
Table A2.10: Food diversity indicators, OLS and IV estimation
(1) OLS (2) IV (3) OLS (4) IV
3 Do Supermarkets Contribute to the Obesity Pandemic in Developing Countries?
19Abstract
In this chapter, we employ instrumental variable techniques to analyse the impact of supermarket purchases on the nutritional status of adults and of children and adolescents. We use household survey data collected in Kenya in 2012. We also estimate causal chain models to examine the pathways through which supermarkets may affect the nutritional status of individuals. Controlling for other factors, buying in a supermarket increases the body mass index of adults and raises the probability of adult overweight or obesity. For children and adolescents we do not find a significant impact on overweight. Instead, buying in a supermarket tends to decrease child undernutrition in terms of height-for-age z-scores. Impacts of supermarkets depend on many factors, including people’s initial nutritional status. Kenya and many other developing countries face a dual burden of malnutrition, where adult overweight coexists with childhood stunting. For both, adults and children, the nutrition impacts of supermarkets occur through higher calorie consumption and changes in their dietary composition.
19 This chapter is co-authored by Simon C. Kimenju, Stephan Klasen and Matin Qaim. The author’s contributions are as follows:
All authors contributed to the design of the research. SCK & RR performed research; SCK undertook data analysis; SCK and MQ wrote the manuscript; all authors reviewed the manuscript, RR edited the manuscript to include it in this dissertation.
Chapter 3.1: Introduction
3.1 Introduction
ased on the same motivation as the previous chapter, that is to understand the drivers and consequences of the nutrition transition in developing countries, in this chapter we follow up on the effects of supermarkets on nutritional outcomes: In chapter two, we have established that supermarket purchases are indeed associated with changing consumption patterns on the level of households. This was in the context of small urban towns in Kenya. Here, we aim to shed light on the consequences on nutritional outcomes for individuals. More explicitly, we aim to understand if supermarket purchases and the changes in consumption patterns they induce contribute to increasing rates of overweight and obesity.
Rising rates of overweight and obesity have been associated with serious negative implications for people’s health (Hawkes et al., 2009; Popkin et al., 2012; Rtveladze et al., 2014). In 2008, 34% of all adults in the world were overweight or obese (Finucane et al., 2011). While average overweight rates are still higher in most industrialized countries, many developing countries are rapidly catching up. In chapter 2.2., we discussed the spread of supermarkets in developing countries and mechanisms in which retail formats, and supermarkets in particular, influence the types of products offered, marketing practices including sales prices, the shopping atmosphere, and ultimately consumer food choices. One important question to follow up on is whether supermarkets contribute directly to rising rates of overweight and obesity in developing countries. We address this question building on the same observational data used in chapter 2 and collected in Kenya.
Empirical studies on the impact of supermarkets on the nutritional status of consumers in developing countries are rare. Studies in the context of the US show that access to supermarkets is nowadays often associated with lower obesity rates (Drewnowski et al., 2012; Lear et al., 2013; Michimi and Wimberly, 2010; Morland et al., 2006a), but the situation in developing countries is different. One study for Guatemala found that food purchases in supermarkets increase the BMI of consumers (Asfaw, 2008).
However, the research for Guatemala builds on a household living standard survey that was not specifically designed for analysing the nutritional impact of supermarkets. Hence, a few variables of interest, such as item specific food quantities purchased in different retail outlets, were not properly captured. Moreover, the impact on BMI was analysed for all individuals in the sample above 10 years of age, an approach that masks possible differences between adults and children. BMI is a suitable indicator of nutritional status only for individuals who have reached their final body height. For children and adolescents, it is recommended to use indicators such as height-for-age or BMI-for-age Z-scores, which set individual measures in relation to a reference population of the same age (de Onis et al., 2007).
We address these shortcomings in the previous literature by using data from a survey of Kenyan consumers that was specifically designed for this purpose. As we detailed in chapter 2.3, Kenya has recently witnessed a rapid spread of supermarkets and large chain supermarkets now account for about 10% of national grocery sales (PlanetRetail, 2013). This retail share of supermarkets in Kenya is lower than in many middle-income countries, but it is already higher than in most other low-income countries in Sub-Saharan Africa and Asia. Hence, trends observed in Kenya may be helpful to predict future developments in other poor regions. Using household as well as individual specific data, we analyse the impact of supermarket purchases on the nutritional status of individuals. We also examine impact pathways. The analysis is carried out separately for adults and for the group of children and adolescents, because impacts