Li, Zhao. 2018. "How Internal Constraints Shape Interest Group Activities: Evidence from Access-Seeking PACs." American Political Science Review 112(4):792--808.
Abstract. Interest groups contribute much less to campaigns than legally allowed. Consequently, prevailing theories infer these contributions must yield minimal returns. I argue constraints on PAC fundraising may also explain why interest groups give little. I illuminate one such constraint: access-seeking PACs rely on voluntary donations from affiliated individuals (e.g., employees), and these PACs alienate donors with partisan preferences when giving to the opposite party. First, difference-in-differences analysis of real giving shows donors withhold donations to access-seeking PACs when PACs contribute to out-partisan politicians. Next, an original survey of corporate PAC donors demonstrates they know how their PACs allocate contributions across parties, and replicates the observational study in an experiment. Donors' partisanship thus limits access-seeking PACs' fundraising and influence. This provides a new perspective on why there is little interest group money in elections, and has broad implications for how partisan preferences and other internal constraints shape interest group strategy.
Li, Zhao. 2019. "Looking Inside the Black Box of Firms: A Proposal for A Research Agenda." The Political Economist 15(2): 17-19.
Li, Zhao. "Analyzing the Evidence: Who's Funding Google's PAC?" Forthcoming in American Politics: Power and Purpose, 16th edition, Theodore J. Lowi, Benjamin Ginsburg, Ken Shepsle, and Stephen Ansolabehere. New York: W.W. Norton.
Li, Zhao. Review of Game Changer: How Dark Money and Super PACs Are Transforming U.S. Campaigns, Henrik M. Schatzinger and Steven E. Martin, forthcoming at Political Science Quarterly.
Abstract. Voters reward extremist politicians following economic shocks, with potentially adverse policy implications. However, whether campaign donors in the U.S. respond similarly to economic crises remains an open question. Linking nationwide campaign finance and real estate transactions, I examine how the foreclosure crisis (2007-2010) affected Republican donors’ support for Tea Party candidates, a right-wing faction of the Republican party that opposed government relief for underwater homeowners. Individual-level longitudinal analyses show that Republican donors in distressed neighborhoods reduced contributions to Tea Partiers, but not moderate Republican candidates. In contrast, proximity to foreclosures mobilized grassroots and electoral support for the Tea Party movement. One potential explanation is that Republican donors disproportionately resided in racially homogeneous neighborhoods, and reduced support for Tea Partiers when in-group neighbors suffered from housing distress. Campaign donors, whose lived experiences during economic crises defy those of the broader electorate, may exert distinct influences on post-crisis elections and policymaking.
Abstract. The Supreme Court upholds mandatory disclosure of itemized contributions as an important means to help voters place candidates on the political spectrum “more precisely than is often possible solely on the basis of party labels and campaign speeches” (Buckley v. Valeo). To assess the informational value of disclosure for democratic accountability, we apply supervised machine learning to predict candidates' issue-specific positions based on itemized contributions they received prior to entering Congress. Our models accurately distinguish candidates on different issues both within and across parties, generally outperforming predictions based on DW-NOMINATE scores. Moreover, leveraging only non-incumbent fundraising records, our algorithms can impute issue scores for candidates lacking office-holding experiences. We also identify donors that provide the most marginal information on candidates' policy stances. Itemized contributions can facilitate voters' discernment of candidates on issues of interest, and are uniquely valuable for differentiating co-partisan candidates in the absence of legislative records.
Abstract. Do firms' presence in local communities lead them to concentrate their political activities in these communities? If so, what are the implications for corporate political strategy and business influence in society? To these ends, we leverage the fracking boom as a natural experiment on the geography of drilling activities across the United States. Using panel analysis and an instrumental variable design, we show that state legislative districts where drilling expanded during the fracking boom received an influx of oil and gas campaign contributions. Moreover, these contributions overwhelmingly benefitted Republican candidates, particularly those in historically Democratic districts, and may have facilitated Republican victories in fracking-intensive districts. Firms' geographic ties to districts not only lead them to channel more money in elections, but also adopt an electoral strategy in campaign giving. Business influence may therefore transform democratic representation across a broad range of constituencies in communities where firms have a local presence.
Lemons in the Political Marketplace: A Big-Data Approach to Detect ‘Scam PACs’ (Email for an early draft)
Abstract. ‘Scam PACs’ are political action committees (PACs) in the United States that raise campaign contributions to enrich their creators (e.g., political consultants) instead of advancing the campaigns or causes they purport to champion. In the 2018 election cycle alone scam PACs collectively raised $57 million, which could have fully funded 74 average House campaigns. The proliferation of and the lack of regulatory oversight over scam PACs not only undermine PACs’ accountability to donors, but also generate a lemons problem in the political marketplace. To reduce the information asymmetry that donors face in discerning scam PACs, I first quantitatively assess how scam PACs that have been identified by media reports differ from comparable non-scam PACs on fundraising and expenditure patterns, donor characteristics, and PAC donor and personnel networks. Building on these descriptive analyses, I construct a supervised machine learning algorithm that systematically detects scam PACs in U.S. federal elections.