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Role of interactive radio programming in advancing women’s and youth’s empowerment and dietary diversity: Mixed method evidence from Malawi

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Abstract

Much progress has been achieved over the years in reducing gender gaps in resources and opportunities, but major gaps persist in the area of agricultural information and extension services. The study contributes to filling this gap by assessing the effect of low-cost communication tools, i.e., interactive radio programming, on women’s empowerment and dietary diversity, employing nationally representative household panel data, intrahousehold data, and qualitative interviews in Malawi. Three major findings can be highlighted. First, radio programming is the preferred source of agricultural and nutrition advice among many subpopulations: younger women and men used radio more than other sources for their agricultural information needs, while younger and older men used radio more than other sources for nutrition education. Second, results show positive impacts of access to advice from radio on dietary diversity among the rural population. Men’s access to agricultural and nutrition advice from radio contributes to improving household dietary diversity. Third, results show a strong association between access to interactive radio programming and greater women’s and men’s empowerment scores. Access to agricultural advice from radio by older women is associated with greater effect on their empowerment score than other groups. Access to nutrition advice from radio by younger men and women is associated with greater effect on their empowerment than older men and women.

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Notes

  1. Youth (also referred to as young) is defined as 15–34 years of age (based on Malawi National Statistics Office’s definition) and used in Benson et al. (2019).

  2. An alternative to the MC/CRE approach is a fixed-effects model. However, fixed effects models are generally inconsistent in nonlinear models as the number of observations (N) grows with fixed time (T) as in our case (since we have only two years of data) (see Wooldridge, 2019; Papke & Wooldridge, 2008). As experts have noted, fixed effects models in short panels are generally not estimable due to the incidental parameters problem (Papke & Wooldridge, 2008; Wooldridge, 2019). Experts recommend an MC/CRE model for shorter time series (Wooldridge, 2019; Papke & Wooldridge, 2008). In general, the MC/CRE device unifies the fixed effects and the random effects estimation approaches. By including the vector of time-averaged variables, we still control for time-constant unobserved heterogeneity, as with fixed effects, while avoiding the problem of incidental parameters in nonlinear models. At the same time, the MC/CRE device allows measurement of the effects of time-constant independent variables, just as in a standard random effects environment (Papke & Wooldridge, 2008; Wooldridge, 2002; Wooldridge, 2019).

References

  • Abubakar, N. H., & Dasuki, S. I. (2018). Empowerment in their hands: Use of WhatsApp by women in Nigeria. Gender, Technology and Development, 22(2), 164–183.

    Article  Google Scholar 

  • Aker, J. (2011). Dial ‘A’ for Agriculture: A Review of Information and Communication Technologies for Agricultural Extension in Developing Countries. Agricultural Economics, 42, 631–647.

    Article  Google Scholar 

  • Aker, J., Ghosh, I., & Burrell, J. (2016). The Promise (and Pitfalls) of ICT for Agriculture Initiatives. Agricultural Economics, 47(S1), 35–48.

    Article  Google Scholar 

  • Aker, J. C., & Ksoll, C. (2016). Can Mobile Phones Improve Agricultural Outcomes? Evidence from a Randomized Experiment in Niger. Food Policy, 60, 44–51.

    Article  Google Scholar 

  • Becker, S., & Caliendo, M. (2007). Sensitivity Analysis for Average Treatment Effects. Stata Journal, 7(1), 71–83.

    Article  Google Scholar 

  • Benson, T., Erman, A., & Baulch, B. (2019). Change and rigidity in youth employment patterns in Malawi. In V. Mueller & J. Thurlow (Eds.), Youth and jobs in rural Africa: Beyond stylized facts (Chapter 6, pp. 137–171). New York, NY: International Food Policy Research Institute (IFPRI) and Oxford University Press. https://doi.org/10.1093/oso/9780198848059.003.0006

    Chapter  Google Scholar 

  • Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31–72.

    Article  Google Scholar 

  • Camacho, A., & Conover, E. (2019). The impact of receiving SMS price and weather information on small scale farmers in Colombia. World Development, 123, 104596.

  • Chamberlain, G. (1984). Panel Data. In Z. Grilliches & M. D. Intriligator (Eds.), Handbook of Econometrics (Vol. 2, pp. 1247–1318). North-Holland.

    Google Scholar 

  • Chapman, R., Blench, R., Kranjac-Berisavljevic, G., & Zakariah, A. B. T. (2003). Rural Radio in Agricultural Extension: The Example of Vernacular Radio Programmes on Soil and Water Conservation in N. Ghana. Network Paper 127. London: Agricultural Research and Extension Network, Overseas Development Institute.

  • Chisama, B. F. (2016). Farmers’ Use of Mobile Phone Technology for Agricultural Information Services in Lilongwe District, Malawi. Purdue e-Pubs, Open Access Theses, 934. https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1915&context=open_access_theses

  • DiPrete, T., & Gangl, M. (2004). Assessing Bias in the Estimation of Casual Effects: Rosenbaum Bounds on Matching Estimators and Instrumental Variables Estimation with Imperfect Instruments. Sociological Methodology, 34(1), 271–310.

    Article  Google Scholar 

  • Eissler, S., Heckert, J., Myers, E., Seymour, G., Sinharoy, S., & Yount, K. M. (2021). Exploring gendered experiences of time-use agency in Benin, Malawi, and Nigeria as a new concept to measure women’s empowerment. IFPRI Discussion Paper 2003. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.134275

  • Fardon, R., & Furniss, G. (2000). African Broadcast Cultures: Radio in Transition. Harare, Zimbabwe: Baobab Publishing; Oxford, UK: James Currey.

  • Farnworth, C., Stirling, C., Chinyophiro, A., Namakhoma, A., & Morahan, R. (2018). Exploring the potential of household methodologies to strengthen gender equality and improve smallholder livelihoods: Research in Malawi in maize-based systems. Journal of Arid Environments, 149(2018), 53–61.

    Article  Google Scholar 

  • FTF (Feed the Future) FEEDBACK. (2013). Feed the Future Malawi: Zone of Influence Baseline Report. Rockville, MD, US: Westat.

  • FTF (Feed the Future) FEEDBACK. (2015). Feed the Future Malawi 2015: Zone of Influence Interim Assessment Report. Rockville, MD, US: Westat.

  • Guney-Frahm, I. (2018). A new era for women? Some reflections on blind spots of ICT-based development projects for women’s entrepreneurship and empowerment. Gender, Technology and Development, 22(2), 130–144.

    Article  Google Scholar 

  • Hambly Odame, H., & Kassam, A. (2002). Listening to Stakeholders: Agricultural Research and Rural Radio Linkages. Briefing Paper 48. The Netherlands: ISNAR.

  • Hirano, K., & Imbens, G. W. (2001). Estimation of Causal Effects Using Propensity Score Weighting: An Application to Data on Right Heart Catheterization. Health Services and Outcomes Research Methodology, 2(3–4), 259–278.

    Article  Google Scholar 

  • Hoddinott, J., & Yohannes Y. (2002). Dietary diversity as a household food security indicator. Food and Nutrition Technical Assistance Project, FHI 360.

  • IFPRI (International Food Policy Research Institute) (2016). Agricultural extension services and technology adoption survey, 2016, IFPRI, Harvard Dataverse, V1. https://doi.org/10.7910/DVN/1VSK7V

  • IFPRI (International Food Policy Research Institute) (2018). Agricultural extension services and technology adoption in Malawi, 2018. IFPRI, Harvard Dataverse, V1. https://doi.org/10.7910/DVN/JCXY6E

  • IFPRI, & World Bank (2010). Gender and governance in rural services: Insights from India, Ghana, and Ethiopia. World Bank.

    Google Scholar 

  • Jain, R., Rani, U., & Kumar, A. (2012). ICTs and farm women: Access, use and impact. Indian Journal of Agricultural Economics, 67(3), 385–393.

    Google Scholar 

  • Kabeer, N. (1999). Resources, Agency, Achievements: Reflections on the Measurement of Women’s Empowerment. Development and Change, 30, 435–464.

    Article  Google Scholar 

  • Klugman, J., Hanmer, L., Twigg, S., Hasan, T., McCleary-Sills, J., & Santamaria, J. (2014). Voice and Agency: Empowering Women and Girls for Shared Prosperity. The World Bank.

    Book  Google Scholar 

  • Kumar, N., Nguyen, P. H., Harris, J., Harvey, D., Rawat, R., & Ruel, M. (2018). What it takes: Evidence from a nutrition- and gender-sensitive agriculture intervention in rural Zambia. Journal of Development Effectiveness, 10(3), 341–372.

    Article  Google Scholar 

  • Lambrecht, I., Vanlauwe, B., & Maertens, M. (2016). Agricultural Extension in Eastern Democratic Republic of Congo: Does Gender Matter? European Review of Agricultural Economics, 43(5), 841–874.

    Article  Google Scholar 

  • Malapit, H., Pinkstaff, C. Sproule, K. Kovarik, C. Quisumbing, A. & Meinzen-Dick, R. S. (2017). The Abbreviated Women’s Empowerment in Agriculture Index (A-WEAI). IFPRI Discussion Paper 1647. IFPRI, Washington, DC.

  • Manfre, C., & Nordehn, C. (2013). Exploring the promise of information and communication technologies for women farmers in Kenya. Cultural practice, LLC, MEAS Case Study #4. Urbana, USA: Modernizing Extension and Advisory Services (MEAS).

  • Mbo’o-Tchouawou, M., & Colverson, K. (2014). Increasing access to agricultural extension and advisory services: How effective are new approaches in reaching women farmers in rural areas? Nairobi, Kenya: International Livestock Research Institute (ILRI).

  • Mundlak, Y. (1978). On the Pooling of Time Series and Cross Section Data. Econometrica, 46, 69–85.

    Article  Google Scholar 

  • Norrish, P. (1998). Radio and Video for Development. In D. Richardson & L. Paisley (Eds.), The First Mile of Connectivity: Advancing Telecommunications for Rural Development through a Participatory Communication Approach. Rome: Food and Agriculture Organization of the United Nations.

    Google Scholar 

  • Papke, L., & Wooldridge, J. (2008). Panel Data Methods for Fractional Response Variables with an Application to Test Pass Rates. Journal of Econometrics, 145, 121–133.

    Article  Google Scholar 

  • Pirracchio, R., Resche-Rigon, M., & Chevret, S. (2012). ‘Evaluation of the Propensity Score Methods for Estimating Marginal Odds Ratios in Case of Small Sample Size. BMC Medical Research Methodology, 12, 70 Open Access.

  • Ragasa, C. (2014). Improving Gender-Responsiveness on Agricultural Extension. In A. Quisumbing, R. Meinzen-Dick, T. Raney, A. Croppenstedt, J. Behrman, & A. Peterman (Eds.), Gender in Agriculture and Food Security: Closing the Knowledge Gap (pp. 411–430). Springer.

    Chapter  Google Scholar 

  • Ragasa, C., Aberman, N. L., & Alvarez-Mingote, C. (2019). Does Providing Agricultural and Nutrition Information to Both Men and Women Improve Household Food Security? Evidence from Malawi. Global Food Security, 20, 45–59.

    Article  Google Scholar 

  • Ragasa, C., & Niu, C. (2017). The State of Agricultural Extension and Advisory Services Provision in Malawi: Insights from Household and Community Surveys. IFPRI Technical Report (February). Washington, DC: International Food Policy Research Institute.

  • Ragasa, C., Mahrt, K., Aung, Z. W., & Lambrecht, I. (2020). Gender, Crop Diversification and Nutrition in Irrigation Catchment Areas in the Central Dry Zones in Myanmar: Implications for Agricultural Development Support, draft report. IFPRI.

    Google Scholar 

  • Rola-Rubzen, F., Paris, T. R., Hawkins, J., & Sapkota, B. (2020). Improving Gender Participation in Agricultural Technology Adoption in Asia: From Rhetoric to Practical Action. Applied Economic Perspectives and Policy, 42(1), 113–125.

    Article  Google Scholar 

  • Rosenbaum, R., & Rubin, D. (1983). The Central Role of the Propensity Score in Observational Studies for Casual Effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Sekabira, H., & Qaim, M. (2017). Can mobile phones improve gender equality and nutrition? Panel data evidence from farm households in Uganda. Food Policy, 73, 95–103.

  • Seymour, G., Malapit, H., & Quisumbing, A. (2020). Measuring time use in Developing Country Agriculture: Evidence from Bangladesh and Uganda. Feminist Economics, 26(3), 169–199.

    Article  Google Scholar 

  • Swindale, A., & Bilinsky P. (2006). Household dietary diversity score (HDDS) for measurement of household food access: Indicator guide (v.2). FHI 360/FANTA.

  • Van Campenhout, B., Spielman, D., & Lecoutere, E. (2018). The role of gender in ICT-mediated agricultural information campaigns. Selected paper presented at the International Conference of Agricultural Economists, Vancouver, BC, July 28–August 2.

  • Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.

    Google Scholar 

  • Wooldridge, J. (2019). Correlated Random Effects Models with Unbalanced Panels. Journal of Econometrics, 211(1), 137–150.

    Article  Google Scholar 

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Authors and Affiliations

  1. Development Strategy and Governance Division, International Food Policy Research Institute (IFPRI), 1201 Eye Street NW, Washington, DC, 20005, USA

    Catherine Ragasa

  2. Agricultural Technical Vocational Education and Training (ATVET) for Women Programme, Lilongwe, Malawi

    Diston Mzungu

  3. Ministry of Agriculture, Irrigation and Water Development (MoAIWD), Lilongwe, Malawi

    Kenan Kalagho

  4. IFPRI Malawi Office, Lilongwe, Malawi

    Cynthia Kazembe

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  1. Catherine Ragasa
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  2. Diston Mzungu
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  3. Kenan Kalagho
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Correspondence to Catherine Ragasa.

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Ragasa, C., Mzungu, D., Kalagho, K. et al. Role of interactive radio programming in advancing women’s and youth’s empowerment and dietary diversity: Mixed method evidence from Malawi. Food Sec. 14, 1259–1277 (2022). https://doi.org/10.1007/s12571-022-01284-x

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