LACK OF TRUST IN GOVERNMENT LINKED TO HIGHER USAGE OF NONPROFIT SERVICES

According to a new analysis of the Human Needs Index (HNI) by researchers at Indiana University Lilly Family School of Philanthropy, state-level opinions and attitudes about government may influence the utilization of services by individuals in need. The study found that states with lower levels of trust in government had higher levels of need as measured by the HNI. In other words, when the citizenry of a state is less trusting of government, they are more likely to turn to a nonprofit organization (The Salvation Army) to receive needed assistance. The reverse is also true: When state-level trust in government is higher, usage of services provided by The Salvation Army is lower.

The analysis identified Colorado as one of the states having higher need (according to its HNI value) and lower trust in government. Kentucky, North Dakota, and South Dakota also each showed a similar pattern of high HNI values and low trust scores. On the other end of the spectrum, trust in government was relatively high in Delaware, Virginia, and Oregon, while HNI values in those states were lower.

In addition to looking at data on trust in government, the research team explored other state-level attitudes that might influence the provision and utilization of services, including opinions about federal government spending levels on programs such as welfare and Social Security. The results were similar to the findings on the relationship between trust and the HNI. In states where residents prefer that the government spend less on specific programs or policies such as welfare and Social Security, there is a higher level of need as measured by the HNI. In such states, residents are more likely to seek The Salvation Army’s services to meet basic human needs.

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HNI Overview

The HNI was developed in a unique collaboration between the Indiana University Lilly Family School of Philanthropy and The Salvation Army. It provides an analysis of The Salvation Army’s rich, enduring, and consistent collection of service data to expand the pathways through which individuals and communities in poverty may be identified. When combined with an assessment of governmental data, the HNI provides a new lens through which to understand human need across the U.S.

Unlike other traditional measures of poverty, the HNI comprises indicators that represent need substantiated on consumption rather than income. Thus, the HNI’s greatest strength is that it illustrates a more intimate portrayal of need than any other index.

Another important contribution of the HNI is that it provides a reliable instrument for measuring human need in near real time, and for tracking trends over time.

The advantage of this approach is that it provides critical observations informing the work of nonprofit organizations and public policymakers in reducing poverty.

CAN POVERTY BE MEASURED?

Official national poverty data have long been calculated using a single dimension: income. At best, income statistics provide only a hazy picture of the actual conditions facing the hungry, the homeless, the unemployed, and the underemployed.

The HNI is a multidimensional way to measure poverty and its effects. The key lies in the street-level knowledge of The Salvation Army, one of the country’s largest nonprofits delivering a safety net of services to those in need.

Published quarterly and developed from data amassed since 2004, the HNI provides the clearest illumination yet of trends in poverty and vulnerability. It is a critical new tool for policy makers, social services providers, and researchers. Sign up for email updates.

Insights and Analysis

Those Doing the Most Good^sm Have the Most Data Points

In addition to community outreach and support, The Salvation Army collects high-quality data on poverty-related social services provided in thousands of communities. It answers the call for increasingly accessible and rigorous data from nonprofit organizations to better illuminate trends in poverty over time.

The Human Needs Index – a Seven-Fold Picture

To provide a pathway through which the actual conditions of communities and individuals in poverty can be improved, the HNI analyzes seven indicator variables identifying the following kinds of assistance:

• Meals
• Groceries
• Housing (mortgage and rent payment)
• Clothing
• Furniture
• Medical bills and prescriptions
• Energy bills

The result is a comprehensive measure and score of need that can track changes in need-based demand month by month. These two advantages – specificity and immediacy – set the HNI apart from other measures.

Tracking the Trend Lines

The HNI measures need in the United States since 2004, suggesting ways public policy and social services can be used in the future to more precisely target human needs in an ever-changing society and economy.

National Perspective

The national HNI scores represent the degree of poverty-related need for the U.S. as a whole.

The scale of the HNI begins at 0, indicating a minimum level of need. Its lowest point was in 2004 (.78). The HNI reached its highest level in 2012 (1.33) and began to trend downward in 2013 (1.25) and 2014 (1.05) before an increase of need in 2015 (1.25). The HNI remained relatively even through 2016 (1.24) and dropped regularly between 2017 (1.15), 2018 (1.03), and 2019 (1.01).

Regional Perspective

Regional HNI scores over time suggest unique patterns based on geographical location, which are likely the result of the Great Recession and the length of recovery time needed in different states. In addition, regional HNI scores appear to be sensitive to disasters – like hurricanes, tornadoes, and snowstorms – that are geographically endemic but that often also traverse regional boundaries. States that have not returned to pre-Recession levels are: Nevada, South Dakota, Pennsylvania, Iowa, Wisconsin, Montana, Ohio, Indiana, Wyoming, West Virginia, Michigan, Kansas, Washington, Nebraska, Arkansas, Virginia, North Carolina, Minnesota, Utah, South Carolina, Kentucky, Oklahoma, New Hampshire, Tennessee, Maryland, Louisiana, and Hawaii.

What the HNI reveals about the continued impact of the Great Recession

National HNI scores reveal a pattern that mirrors the economic volatility in the U.S. during the Great Recession. The Great Recession exerted a powerful impact on unemployment rates, personal income, and wealth, as well as the housing market in the U.S. The national HNI scores reflect this reduced economic activity as demonstrated by the lowest HNI scores occurring during the pre-Great Recession period (2004-2007), moderate scores appearing in the midst of the Great Recession (2008-2009), and the highest scores represented during the immediate post-Great Recession era (2010-2013). It is not surprising that the first substantial increase in HNI scores – a representation of increased need – is witnessed in 2010, the year immediately following the end of the Great Recession. This lag in demonstrated need is likely attributed to the depth of the recession and economic insecurity that individuals and households experienced – a result of this profound economic downturn and weak recovery.

At the national-level, all services provided have decreased since 2018, yet in certain states, like Nevada which has a significantly higher level of need than any other state, the need for services is still great. A microcosm of the nation’s homeless crisis, housing assistance services in Nevada have increased 149.67% since 2018. The impact of the strengthened economy and lowered unemployment rate can be felt nationally, but in individual communities the need is still significant.

The HNI reveals that 2012 reflected a high point of need, with an annual score of 1.33. That was followed by two consecutive years of decreases in 2013 and 2014 (1.25 and 1.05). However, 2015 saw a reversal of this trend, increasing to a score of 1.25. In 2017, the HNI took a downturn to 1.15, then slid to 1.03 in 2018, and ended 2019 at 1.01.

How disasters impact the HNI

An important factor to consider when comparing national HNI scores over time is the effect of disasters. While these events are geographically specific, their impact is reflected in the national HNI score, as the elevation of specific poverty-related indicators are associated with services provided during and after disasters, therefore affecting the fluctuation of national HNI scores.

For example, when the national HNI score increased in 2008, the meal assistance indicator also reached its peak to the highest score among all other indicators. The increase in meal assistance is likely due to the Super Tuesday tornado outbreak of February 5-6, 2008, which included 87 tornados across nine states. Alabama, Arkansas, Kentucky, and Tennessee were among the hardest-hit states.

In 2012, the national HNI reached its decade-high score. A plausible explanation for this acute increase is the last few days of October 2012, when the eastern United States experienced Hurricane Sandy, which left thousands of people homeless and millions without electricity. Consequently, reflecting these trends, the three greatest demands for assistance during this time were for furniture, housing, and clothing.

How seasonality impacts the HNI

When monthly HNI scores are viewed, trends suggest that seasonality may be an important factor in anticipating human need. For example, in the 2013-14 winter season, HNI scores began to climb in November; December is markedly the highest score. Scores began to decrease in January and February, which represents the lowest score that season. Scores briefly increased again in March. As demonstrated in this example, the overall trend across years (and regions) illustrates poverty-related need increases late in the calendar year. This increase in need at the end of the year is further demonstrated by the rise in the use of the Supplemental Nutrition Assistance Program (SNAP) benefit, while unmet need is also prominent as evidenced by The Salvation Army’s referral data.

Methodology

The Human Needs Index (HNI) was developed through a rigorous methodology with the goal of informing nonprofit leaders and policymakers about the degree of poverty-related need and measuring the effectiveness of response to need, over time, in specific communities and across the country. To achieve this goal, a standardized index was constructed.

Constructing the HNI was an iterative process that employed empirical evidence, statistical methodologies, and expert consultation. We began this process by developing a conceptual framework for analyzing poverty by identifying appropriate line-item service variables that represented essential basic needs. The selection of variables was based on careful consideration of the academic literature and theoretical constructs associated with measuring poverty and human needs. Overall, we tested more than 450 combinations of 230 organizational service variables to create the HNI – at the national, regional, and state level. We initially selected 21 material assistance and personalized service variables representative of basic human need – that is, the delivery of food, clothing, shelter, or health/well-being services.

Initial tests of these variables were conducted at the national level and by year, although subsequently, data were disaggregated to test variables at the service center site, county, state and territorial levels, and by month. In testing the variables’ ability to measure human need, we relied on external governmental measures of poverty, including the poverty rate, unemployment rate, and the Supplemental Nutrition Assistance Program (SNAP) benefit usage. The statistically significant associations demonstrated with these government measures provided guidance in selecting which variables to include in preliminary modeling.

Next, the 30-member team of statisticians, program officers, economists, and National Advisory Board members from The Salvation Army and Indiana University Lilly Family School of Philanthropy engaged in detailed discussions about which variables dedicated to food, clothing, shelter, or health/well-being services were collected in all states and across all four regions during each year. As a result, three variables (Meals Provided, Clothing Provided, and Lodgings Provided) were initially selected for preliminary testing against the external government measures, individually and together as a test model. The results revealed positive and significant correlations. Thus, additional variables representing these basic needs were added for further model testing.

In the final iterations of creating the HNI, using the governmental measures as benchmark validation and the 21 material assistance and personalized service variables representative of basic human need, we employed three approaches to weighting variables to determine appropriateness for inclusion or exclusion in the final national and state-level models. The most parsimonious model included three variables representing food, shelter, and health/well-being services. The weighting for this model was based on the geometric mean of the included variables. The second technique was the most analytical and pragmatic, in that it used both theoretical and empirical justifications for the weighting and selection of variables. The initial testing of this approach included five variables representing food, shelter, clothing, and health/well-being services. The third and final approach was also an analytic model that included all 21 variables. This process concluded with the presentation of the six strongest models derived from these distinctive approaches.

Ultimately, the second approach using Principal Components Analysis (PCA) was used to build the HNI because it allowed for both intuitive variable selection as well as statistical confirmation of individual variables’ utility in the overall model measuring human need. Subsequently, to ensure the strongest, most appropriate model was selected, further discussions about, and statistical testing of, the second approach were conducted. This concluding phase testing necessitated the retention of four variables, the removal of one variable, and the addition of three variables. The final model, therefore, includes seven (7) line-item variables and demonstrates strong correlations with benchmark data. Another important strength of the final model used to construct the HNI is that it is built from variables representing essential aspects of human need that are measured by The Salvation Army consistently across time and region.

Downloads

Press Release September 2018

Press Release May 2018

Press Release September 2017

Handout: Human Needs Index Update

Press Release May 2017

Human Needs Index Fact Sheet 2017

Infographic: About Human Needs Index 2017

Infographic: About Perceptions Of Poverty Survey

Infographic: About The Salvation Army

Infographic: About The Human Needs Index 2016

Press Release April 2016

Press Release October 2015

Executive Summary October 2015

Report October 2015

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