Learning and Behavioural Spillovers of Nutritional Information

ABSTRACT

This paper provides evidence for informational spillovers within urban slums in Chandigarh, India. I identify three groups, a treatment group, a neighbouring spillover group, and a non-adjacent pure control group. Mothers of children (aged three to six years) enrolled in government day-care centres are given recipe books in the treatment group to reduce malnutrition in their children. Spillovers to neighbouring (untreated) mothers can be through social learning or imitation. Results from a difference-in-differences analysis show that nutritional knowledge measured through a quiz increases among neighbouring untreated mothers relative to a control group. Neighbouring mothers exhibit learning spillovers, changes in dietary behaviour and a reduction in food expenditure regardless of their level of literacy. Spillovers not only raise the cost effectiveness of health information programmes but are important to consider when designing an experiment as causal effects of treatments can be attenuated if the spillover group is used as a control group.

Acknowledgments

I am grateful to Oriana Bandiera, Jere Behrman, Tim Besley, Lena Edlund, Seema Jayachandran, Melanie Khamis, Gerard Padró i Miquel, Rohini Pande, Chih Ming Tan, and Marcos Vera-Hernández for helpful comments, and to Darius Onul for research assistance. Thanks also go to seminar participants at Wesleyan University, Indian School of Business and participants at the NEUDC, SEA, LAC-Dev, and Mid-West International Economic Development conferences. I am deeply grateful to the team of enumerators, child care workers, mothers and their children who took part in this project. Finally, this project would not have been possible without co-operation from the staff at the Social Welfare Department, Food and Nutrition Board, and Health Department, Chandigarh.

Disclosure statement

No potential conflict of interest was reported by the author.

Supplemental Material

An Online Appendix is available for this article which can be accessed via the online version of this journal available at http://dx.doi.org/10.1080/00220388.2016.1208176

Notes

1. The other main role of the child care worker is to allocate mid-day meals to children in the Anganwadi. Tandon (1989) finds that the ICDS nutrition intervention programmes led to significant reductions in malnutrition among children in comparison to non-ICDS groups receiving information through other programmes.

2. The literature on the impact of informational campaigns is plagued with self-selection issues (Manski, 1993). Individuals could self-select into network groups based on their characteristics. However, I find the ex-ante observable characteristics of mothers between the different groups to be very similar and there is no migration between the sampled groups. Self-selection of participants within the treated and untreated groups is less of an issue as compliance among mothers is close to 95 per cent and similar across all groups.

3. These checks are conducted every three months and all children present are weighed by a doctor to determine the malnutrition rates in each block.

4. For selecting the adequate sample size, I used the software, Optimal Design Version 1.77. The number of children per centre or n was taken to be 25. Online Appendix Figure A5 shows power against intra-class correlation for different size effects. For instance, the intra-class correlation needed to detect a small effect of 0.2 standard deviations at the 5 per cent significance level and a power of 0.8 is about 0.05. If the effect size is slightly larger at δ = 0.30, intra-class correlation can be as high as 0.15 and the power would still be sufficiently high.

5. This was calculated by the local Nutritionist, Food and Nutrition Board (Rs. 4 = 9 cents). Figure A1 in the Online Appendix illustrates a recipe from the book (banana pie).

6. Enumerators were trained and supervised by me on the ground throughout the experiment. The weighing machines used in both rounds were re-used for the same set of children for accuracy.

7. Table A1.1 also shows probit results of being attrited on treatment and spillover groups. We find insignificant differences so there appears to be no effect of assignment on attrition.

8. Assuming 1$ = Rs. 45.

9. The infrastructure in the Anganwadi centres (blackboard, chart, toilet, drinking water, electricity) are very similar across all groups and wages of workers are identical.

10. This regression also includes individual dummies for other treatments implemented during the project in another block (incentive_j and combined_j) as well as their interactions with post: see Singh (2015) for details of these treatments. This is a conservative method and results remain very similar if we restrict the regression to only the recipe, spillover and control groups (available upon request).

11. It is possible that the weight may go up in the long run, and the time horizon of three months is not sufficient to capture spillovers. The window was chosen to coincide with the usual window between health check-ups at Anganwadis in Chandigarh which is deemed to be sufficient to observe changes in malnutrition status if children are given a more calorific diet (that is mothers prepare one recipe from the book daily, each of which is greater than 300 kcal). This is based on estimates that 300 kcal additional calories per day is estimated to lead to a 200 g increase in the body weight of a four year old child over a period of three months (WHO, 1983). However, it is not surprising that there is no effect on weight even though there are knowledge spillovers because at baseline the correlation between mothers’ nutritional knowledge and child’s weight is small and insignificant as shown in Online Appendix Table A15. One issue with using a difference-in-differences analysis is that even if baseline weights in the groups are similar, they may be on different pre-trends. For example, if the children in the recipe treatment are growing at a lower rate than the children in the spillover or control group, it may bias downwards our estimates of the direct impact of the recipe treatment by simply looking at difference-in-differences. However, pre-baseline weights (on average three months prior to baseline) that were recorded in the Anganwadi registers were also noted in the baseline survey. This helps us check for differential pre-trends in weights. Table A3 in the Online Appendix provides the results of a placebo check to see if trends were different prior to baseline. The weights of children have increased in the past three months (as they have become older), but there are no differential trends for the spillover or the recipe group children relative to control. The placebo test makes our assumption about similar pre-trends more plausible and confirms that the common trends assumption for interpreting diff-in-diff coefficients as causal is valid. Even after one year following the implementation of the treatment, there were no significant effects on child weight in the treatment or spillover groups relative to control. Even though the weight on average increased by 1.68 kilograms, the difference-in-difference coefficients for long-term impacts of the recipe treatment and spillovers are insignificant at the 10 per cent level, small and negative.

12. Including or excluding the observations from other treatments implemented in the project to a separate block does not make a difference to the interpretation of any of our results. These have been included in the background for improving efficiency but the gains are very minimal and results are extremely robust to only focusing on recipe and spillover groups.

13. I test for (a) effect of different quantiles of maternal knowledge on child’s health, and (b) effect of recipe and spillover treatments on different quantiles of child weight, and find a lack of threshold level effects of knowledge on health or of knowledge (implemented through treatments) on different health thresholds.

14. The general equilibrium effects of how such a policy would impact the price of food items is beyond the scope of this paper.

Additional information

Funding

This paper is an output from research funding by the UK Department for International Development (DFID) as part of the iiG, a research programme to study how to improve institutions for pro-poor growth in Africa and South-Asia. The views expressed are not necessarily those of DFID. I thank the Department of Economics at LSE for funding travel and to the Asia Research Centre for the Bagri Fellowship. All errors are my own.