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Towards Zero Hunger

By August 2, 2022No Comments
Towards Zero Hunger

The Global Hunger Index [1] ranks India 101 out of 116 countries. The index is calculated by normalizing and aggregating multi-dimensional data that includes data on children under-five mortality rates, child under-nutrition, and data on food supply shortages.

India has a very robust food security program through its Public Distribution System (PDS) [2] and a history of providing mid-day meals in its schools. Several regional governments and NGOs provide and distribute free and subsidized meals to vulnerable populations using community kitchens. Have all these efforts not had much impact towards reducing hunger? Or, is the low score a result of regional imbalances? Can we use the underlying data to guide us in formulating policies and help us in directing investments that can lead us to zero hunger?

While the global hunger index does help in ranking regions and countries, it is not actionable. A low or high ranking does in no way guide policy makers in taking corrective actions that can improve the outcome. Further, the degree of correlation of the three selected dimensions to the desired outcome is unknown and providing equal weightage to all the dimensions only increases the opacity of the model. In addition, most of the data that goes towards the index are lagging indicators. Even if the goal were to be met today, the index may reflect that only some years later.

Another problem with the metric is that more than two-thirds of the weighting towards the score is based on childhood hunger. Data on elderly hunger is indirectly attributed through data on food supply shortages. Many countries that rank high in the global hunger index recognize that under-nutrition and food insecurity among older adults, especially after COVID-19, is now at epidemic levels [3].

In the same report, Sri Lanka ranked 65 out of 116 countries. It had a moderate score and showed a positive trend with improvements over previous years. As recent events have shown [4], the index is neither predictive nor prescriptive.

From Vanity to Actionable metrics

A given metric can be categorized as vanity or actionable. A vanity metric looks good on the surface but offers limited business value, does not inform any future strategic action, or provide insights into return on investment. Actionable metrics tie actions to observed results and provide the needed insights to make informed decisions that get you closer to your goals.

An actionable metric guides you in taking decisions. A positive trend of the metric will indicate that policies, investments, and actions are working and a negative trend of the metric informs the corrective actions that can improve outcomes.

An actionable metric can track targets, identify gaps, and provide a standard way to benchmark and compare status across regions and nations. They allow for transparency in policy-making and guide in directing investments that can have the maximum impact on the most vulnerable populations. Once the investments are made, the same metrics can now quantify the impact and the return on investments.

SDG 2 (Zero Hunger)

The UN Sustainable Development Goals (SDG) [5] provides a shared aspirational blueprint for achieving economic prosperity for all nations and a sustainable future for the planet. There are 17 goals that recognize that ensuring economic prosperity and preserving the environment starts with removing inequities, creating opportunities, and making sure that basic services like food, health, and education are available to all.

The sustainable development goals list specific targets that should be met by the year 2030. Some of these are macro-level targets that are measured and reported by governments and typically require government level policies to achieve the targets. The targets for SDG 2 (Zero Hunger) include, doubling agriculture productivity and farmer incomes, ensuring sustainable food production systems, and maintaining diversity of seeds. Others are micro-level targets that specifically target the individual, one of which is –

By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round.

How do we benchmark status and measure progress towards these micro-level targets? What are the actionable metrics that we can use to baseline status, study trends, and help us take corrective actions?

To create an actionable metric that can track progress (or lack thereof), you have to start with the desired outcome and work your way backwards, keeping the end customer in mind. The end customer here is someone vulnerable to hunger and to that person, the only thing that matters is the availability and accessibility to food.

The individual and household enumeration done during a national census does identify the vulnerable populations who require social assistance. This data needs to be supplemented with data on the availability and accessibility of food.

Tracking Zero Hunger

For SDG 2, there are 8 targets and 14 indicators [6]. The indicators are supposed to shine a light on progress towards the goal and light the path forward. Many of the SDG 2 indicators track supply-side secondary data. These track targets for annual grain production, unit production volumes, and investments in agriculture. The other indicators track demand-side data, which are, typically, collected using household surveys.

Supply-side secondary data cannot be disaggregated at the local levels and cannot be used as an indicator of local resilience. Household surveys tend to be expensive undertakings. To control costs, sample sizes are kept low, which can amplify errors at the local levels [7]. The only effective way to light the path forward to zero hunger is to make sure that last-mile data is accurate.

The facility-based data model [8] flips the narrative from supply-side data to demand-side data. Instead of doing individual or household enumerations, specific facilities of interest are surveyed and data collected. This reduces the survey and data collection costs and the last-mile facility data can provide the primary data for measuring a community’s resiliency in combating hunger.

Using the facility-based data model, free or subsidized meals served per day was tracked. To meet the Zero Hunger target for a region, the free and subsidized meals that are available must match the total individuals that are in the ‘social assistance base’ in that region. The availability of subsidized meals when juxtaposed with the ‘social assistance base’ will show where the vulnerable populations are, highlight gaps, and where new investments should be made to plug the gaps.

The SDG 2 metrics should be able to answer questions like:

  • Is the service accessible? This is a measure of the proximity of the service to the vulnerable populations that require social assistance.
  • Does the service have adequate capacity for the served population? Do the facilities collectively provide sufficient free or subsidized meals to meet the needs of the local community?
  • What is the quality of service? A facility may have the capacity but if there is a food supply shortage, or if it has an erratic power supply, it might reflect on its preparedness to deliver service. A poor infrastructure leading to an unreliable supply chain will affect service readiness and timeliness. A good way to model the quality of service is to have users review and rate the facility.

Standardized actionable metrics for SDG 2 will provide a common denominator for benchmarking and be comparable across regions and countries. For Zero Hunger, the key indicators are the total number of vulnerable individuals that require social assistance and the number of free and subsidized meals available. The social assistance base is estimated to be anywhere between 300 – 400 million for India. To meet the Zero Hunger target, the aggregate meals capacity of the community kitchens across India must match that.

Vulnerability Data

During a national census, a range of geographic, demographic, social, and economic information is collected about individuals, families, and dwellings. This information is aggregated at local, regional, and national levels and this data is invaluable for policy formation. The census survey is carried out about once in ten years and the data for in-between years is extrapolated with adjustments made for accounting for disasters, epidemics, and climate change events.

The UN Statistics Division (https://unstats.un.org/home/) [9] provides national population census data as open data. This data includes, populations at city, region, and national levels, and the demographic and economic characteristics of the population.

The Hawkai Data platform provides this population and demographics data at local, regional, and national levels as a service. This data when linked with facility-based data can offer analytic value and insights into service availability, service capacity, and service quality for the vulnerable populations.

Vulnerability data by locality and region

The vulnerability data can be aggregated at district, regional, and country levels as shown in the visualization below. This data when layered with the facility data will provide the needed insights into gaps and distance to targets. Periodic monitoring of the facility data will provide insights into trends, the pacing towards the goal, and measure the impact of new investments.

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Facility-Based Data

In order to make timely and informed decisions, accurate, up-to-date data on the availability of services, capacity, and the readiness to deliver services is required. To establish a baseline for SDG 2, data about the presence and distribution of  PDS outlets and community kitchens, and the capacity and availability of these services was collected.

The following was the process that we went through to collect data about community kitchens, food banks, and organizations focused on eliminating hunger.

  •  Created a list of all the organizations focused on SDG 2.
  • Reached out to the organization heads and asked for appropriate contacts for the data. Analyzed the data, cleansed it, mapped the locations, and made sure that data is consistent, complete, and accurate.
  • Identify/recruit a contact at each facility who would be responsible for providing updates periodically
  • Train the contact on the web form that is used to provide updated data.

The table below shows the data coverage for some of the facilities that were mapped and the extrapolated capacities for the organizations.

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The figure below shows the share of each organization in providing the meals. The data below only includes the capacities of the facilities that were mapped.

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The figure below shows the availability of food units aggregated by state.

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Once the facilities are mapped, the self-serve web form allows an organization to provide updated data on meal capacities and new facilities on a periodic basis.

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Transforming the Data

The Hawkai Data platform transforms the data to create visualizations that provide actionable insights, and answer questions like where the vulnerable populations are, how different regions are tracking on goals, and how regions are trending over time.

The figure below shows the summary data for the Zero Hunger category.

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Specific categories can be browsed and service availability and gaps can be determined visually.

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Facility level drill-downs can be used for visually identifying food availability and accessibility. The state of Kerala has over a thousand Kudambashree Janakeeya Hotels that provide subsidized meals. It has 14000 PDS outlets that provide subsidized food grains to over 200,000 most vulnerable individuals. Kerala has a population of about 35 million. The vulnerable population in the state (0.7% poverty rate) is around 245,000. The Janakeeya Hotels have an aggregate meal capacity per day of over 250,000. The figure below shows the locations of the subsidized food outlets in Ernakulam district. Kerala has clearly met its SDG 2 target. The Kudambashree food grid has the required capacity and makes food accessible to everyone who needs it through its network of kitchens. The grid is also resilient enough to scale down or scale up services as needed.

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Local data use and data skills along with these tools and dashboards will help communities and cities to identify and respond to local trends and build local resiliency. These dashboards can be used to drive effective responses during pandemics, and increase resilience and social capital. The figure below shows the food grid in the city of Chennai, Tamil Nadu.

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Over time, these metrics will provide a quantifiable way for a region to measure and justify the impact of its spend on initiatives while also providing a way for regions to measure the longer-term effects of natural disasters, climate change, conflicts, and epidemics.

Activating the Data — Connecting People and Food

Activating the data is about converting the data into information and making it useful, relevant, and easy to access for all. During the first phase of the COVID-19 crisis in India, migrant workers journeyed back to their hometowns as all work was halted during the shutdowns. Without wages, they were unable to meet basic needs like food or healthcare in the cities where they worked. Even where these services were available, the migrant workers did not have the information as to be able to access the services. Information about basic services like local food distribution points needs to be made available to all who may require these services.

Disasters are inherently discriminatory. The socially disadvantaged, the economically disadvantaged, and even the informationally disadvantaged are disproportionately impacted. One way to restore informational equity is to make sure that information is in the hands of the people who need it.

Activating the data using mobile apps puts this information into the hands of people who need it. The ExtraPortions mobile app is simple to install, simple to navigate, and informs the user to the availability of free and subsidized foods near them and how to get there.

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Getting to Zero Hunger

India has a fairly effective food security program through its Public Distribution System outlets. The PDS outlets distribute food grains and other commodities at affordable prices and have been effective in managing food inflation and ensuring access to food. During the COVID-19 lockdowns and later, it was recognized that a large number of the population that included the homeless, the migrant workers, the elderly, and other marginalized groups without access to or not able to utilize a kitchen were excluded from its benefits.

Community kitchens are key to providing universal food security. They serve localized, healthy, and inexpensive food, which keeps street food prices in check, extending its benefits beyond the marginalized communities to the daily-wager and the urban poor. Kudambashree, Kerala’s women community network, worked with local self-governing bodies and started over 1000 community kitchens which served over 350,000 cooked meals every day during the COVID-19 lockdowns. The Akshaya Patra Foundation operates automated mega community kitchens across India that can each serve over 100,000 meals a day. Amma’s Unavagam, a state funded initiative of Tamil Nadu, operates over 600 canteens serving inexpensive food. The Gautam Gambhir Foundation uses individual and corporate donor funds to serve inexpensive, nutritional, and hygienic meals at its community kitchens in Delhi.

Community kitchens, whether centralized or distributed, whether state or privately funded, have been effective at fighting hunger. The challenge now is to scale these models, remove regional imbalances, and create a national food grid that is responsive, maintains service quality, and provides food security for all.

To track the progress towards zero hunger, the data on PDS outlets need to be supplemented with data on the service availability, service accessibility, and service quality of these community kitchens. The facility-based model and the tools and dashboards provided by Hawkai Data to collect and visualize geospatial information will provide the needed transparency and the necessary actionable insights for local and regional officials to chart their paths to zero hunger. The ExtraPortions mobile app ensures that the information about access and availability of meals is available to all who need it.

 References

 [1] Global Hunger Index Ranking, https://www.globalhungerindex.org/ranking.html

 [2] National Food Security Portal, Government of India, https://nfsa.gov.in/portal/PDS_page

 [3] The Facts behind Senior Hunger, https://aginginplace.org/the-facts-behind-senior-hunger/

 [4] Sri Lanka facing imminent threat of starvation, https://www.theguardian.com/world/2022/apr/06/sri-lanka-facing-imminent-threat-of-starvation-senior-politician-warns

 [5] UN Sustainable Development Goals, https://sdgs.un.org/goals

 [6] UN SDG Indicators Database, https://unstats.un.org/sdgs/dataportal

 [7] An assessment of India’s readiness for tracking SDG targets on Health and Nutrition; Nandita Saikia, Purushottam M. Kulkarni; https://www.orfonline.org/research/an-assessment-of-indias-readiness-for-tracking-sdg-targets-on-health-and-nutrition/

 [8] Measuring SDG Progress, https://www.linkedin.com/pulse/measuring-sdg-progress-hawkai-data/

 [9] UN Statistics Division, https://unstats.un.org/home/

* Header image uses photos by Loren Joseph | Unsplash, Jacopo Maia | Unsplash, Sreehari Devadas | Unsplash