Complete set of loan-level data on the recipients of Paycheck Protection Program loans

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in New Jersey who received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient.

Note: As per SBA, The Paycheck Protection Program (PPP) ended on May 31, 2021 so no updates has been made on this dataset.

Please see attached document on landing page for more details.

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in Connecticut that received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient. This dataset includes loans under $150,000 and loans of $150,000 and above made to Connecticut businesses through August 8, 2020. Please see attached document for more details.

This dataset is published by the Atlanta Regional Commission Research & Analytics group to show Paycheck Protection Program Loans for Georgia summarized by ZIP Code. Source: US Department of Treasuryhttps://home.treasury.gov/policy-issues/cares-act/assistance-for-small-businesses/sba-paycheck-protection-program-loan-level-data

Paycheck Protection Program loans are SBA loans that helps businesses keep their workforce employed during the Coronavirus (COVID-19) crisis, specific to Colorado. Includes < $150,000 loans and > $150,000 loans. Current law dictates that the Paycheck Protection Program (PPP) close at the end of August 8, 2020.

Conventional politics approaches, emphasizing party ideology, electoral dynamics, committee member-ship, campaign donations and industry clout, exercise a powerful hold over assessments of public policies and their distributional effects. Emerging from pluralist and business power perspectives, such accounts see “who gets what and why” as the result of how politics and power shape policies, their implementation, and distributional outcomes. This pervades our understanding of the Paycheck Protection Program (PPP), the US government’s effort to avert mass unemployment during the pandemic by lending $786 billion to small businesses to keep employees on payroll. Yet contrary to prior studies of the PPP, we find that such factors were strikingly uncorrelated with distributional outcomes, revealing limits to such approaches to this case. Instead, we find that an institutional politics or politics-in-time (IP-PIT) approach better explains the program and its trajectories. IP-PIT revises the causal sequence by empha-sizing how institutions and policies generate politics, distributional outcomes and feedback loops. We engage both approaches via a mixed-methods analysis of the PPP and two new datasets. We anchor our study in a qualitative process-tracing of temporal variation in policy architectures, politics, policy revi-sions, and shifting access to loans within and across the program’s three periods before presenting quan-titative analyses of loan flows across congressional districts and periods using data on the entire corpus of PPP loans. We use one of the largest economic interventions since the New Deal as a case to advance research and debate over the dynamics and outcomes of US policy making during crises and the Ameri-can political economy in general. Ours is the first study of the PPP to conduct a mixed-methods analysis of loans across congressional districts or to use conventional and institutional approaches to address its politics, policy and outcomes. We document varieties of critical junctures, contribute arguments about what might shape policy or institutional innovation in those moments, and use the PPP to identify conditions under which systems are “their own grave diggers,” fueling negative-transformative rather than positive-reproductive feedback.