Policy Implications of Projecting the Multiplier Effects of Social Safety Net Programs Using IMPLAN

Abstract

This study employs input–output (I/O) modeling at the state and county level to compare national estimates of the federal Supplemental Nutrition Assistance Program (SNAP). The results show that more localized economies cannot achieve the results predicted by national models likely because of leakage of economic activity outside the boundaries of a single state or county. This study sheds light on methodological tools that forecasters and policy makers can use in making decisions based on economic impact of social safety net programs.

The economic, fiscal, and workforce impacts of coal‐fired power plant closures in Appalachian Ohio

“…These models can be useful tools in predicting how an economy will respond to an external or exogenous shock, such as a power plant closure (Deller, Sumathi, & Marcouiller, ). IMPLAN has been used in a wide variety of contexts, such as measuring the impact of recreational fishing (Steinback, ), tourism events (Brown, Var, & Lee, ; Chhabra, Sills, & Cubbage, ; Johnson, Obermiller, & Radtke, ; Strauss & Lord, ), industry closures (Burrows, Cheney, & Rahn, ), Supplemental Nutrition Assistance Program (SNAP) spending (Paynter, Jolley, & Nousaine, ), and pension benefits (Furdek & Lucas, ), among many others. IMPLAN utilization has been extended to measuring the effects or contribution of sectors and subsectors of the economy, such as health (Doeksen, Johnson, Biard‐Holmes, & Schott, ), agriculture (Tanjuakio, Hastings, & Tytus, ), and forestry products (Aruna, Cubbage, Abt, & Redmond, ; Brandeis & Hodges, ; Michaud & Jolley, ).…”

Basic income: a 50-state economic impact analysis

“…Brun et al (2014) estimated the total and state-by-state impact of increased federal spending on infrastructure using IMPLAN. Paynter et al (2014) estimated both the county and the state impacts of Supplemental Nutritional Assistance Program (SNAP) spending in North Carolina; this study used a modeling approach that treated SNAP spending using two scenarios: as an increase in retail sales and as an increase in household income because of substitute effects arising because SNAP freed up money a household might spend on food. The authors apportioned the increase in SNAP spending to the appropriate household income category depending on the distribution of households receiving the benefits.…”

“…$6,000 increase per adult in each income category) in IMPLAN. This paper uses a similar approach developed by Paynter et al (2014) where they modeled SNAP spending as a change in household income per income category in IMPLAN. IMPLAN does not provide income categories for individuals, but one can model the economic impact of a change in household spending in household income categories of less than $10,000, $10,000–$14,999 and $15,000–$24,999, respectively.…”

The Impact of Social Safety Net Programs on Poverty, Food Security and Standard of Living in Bangladesh

“…The social safety net program (SSNP) is an effective policy instrument, which provides economic and social security to the improvised people (Barket-e-khuda, 2011; Pradhan et al, 2012). It aims to relieve distress and prevent destitution to the disadvantaged people (Grosh et al, 2008; Munro, 2005; Paitoonpong et al, 2008). The SSNP is mainly designated to provide money, food, education or treatment facilities to the distressed people (Pradhan et al, 2012; Suryahadi et al, 1999; Vivian, 1994).…”

“…A wide range of social safety net programs (SSNPs) are being implemented in Bangladesh and these are mainly rural-based programs. Past studies document that SSNPs intend to lessen the severity of social destitutions such as poverty, food insecurity, illiteracy, unemployment, disability, sickness, vulnerability through giving direct and indirect long-term benefits to the recipients (Barrett et al, 2002; Bhushan, 2004; Camacho et al, 2014; Devereux, 2002; Erhabor, 2015; Fiszbein et al, 2009; Grosh et al, 2008; Hossain & Ali, 2017; Khandker et al, 2011; Masud-All-Kamal & Saha, 2014; Melkamu & Mesfin, 2015; Ninno et al, 2013; Paynter et al, 2014; Perry et al, 2007; Slater, 2011; Tovar & Urrutia, 2017; Wetengere & Sabuni, 2015; Wheaton et al, 2011). For example, Mamun (2019) examines the effectiveness of old age allowance in Bangladesh, finding that old age allowance has a positive impact for reducing poverty among elderly people.…”