Welcome! I’m Mark Hand. I study teams and organizations in politics and policy. Prior to starting a Ph.D. program at the LBJ School of Public Affairs at UT-Austin, I worked in start-up investing, impacting investing and community development. Here are some of the research projects I’m working on now:
Double Whammy: Rural Leaders as Policy Narrators During the Pandemic and Oil Bust of 2020 (with Megan Morris and Varun Rai; in preparation)
Energy-producing areas in the rural United States faced a two-fold crisis in early 2020: The plunging price of oil, on which their economies depend, and the onset of a global pandemic. During such crises, local leaders act in entrepreneurial ways, helping their constituents make sense of change and enact policy. This paper builds on the Narrative Policy Framework (NPF) by conceptualizing local leaders as policy narrators, and in doing so proposes a novel theoretical element for inclusion in the NPF. To explore who policy narrators are, what they do, and what factors enable or constrain them, we build seven case studies of rural counties located in a major oil and gas formation in Texas, constructed from semi-structured interviews and accompanying quantitative data on those counties.
We test four sets of hypotheses about policy narrators: (1) where policy narrators source their ideas; (2) how they choose what kind of story they tell, in terms of breach with the status quo; (3) how narrators select and position the characters in their narrative; and (4) whether narrators’ worldviews are congruent with those of their audiences. We then put forward a working definition of policy narrators as “agents in the policy process who interpret, compose, and advocate for a narrative of the world and current events that supports the adoption of their favored policies, and who strategically collect and assemble ideas and culture elements into a coherent narrative designed to build their credibility, persuade a policy audience, and shape the policy agenda.” We argue for the inclusion of the policy narrator as a foundational component within the Narrative Policy Framework. Lastly, we offer insight into how leaders in rural America formulate policy in response to crisis.
The Impermanent Campaign: Team Formation in the Project Networks of Political Campaigns (IN PREPARATION)
How do political campaign teams form? What drives the hiring decisions of campaigns and the decisions of personnel to join a campaign? Using eight inductive case studies of campaigns for the U.S. House of Representatives in the U.S. South in 2020, I examine the motivations behind key moments in the formation of political campaign teams, including candidates’ first and subsequent hiring decisions, personnel’s decisions to join a campaign, and even a candidate’s decision to run. I find those decisions are shaped primarily by the structure of the extended party networks in which campaigns are embedded. These network structures differ by party in their structure and their effects on campaign team formation. At the center of the Democratic network are a central party organization and an abortion rights campaign committee. At the center of the Republican network are large, privately owned consulting shops.
I also find that on both sides of the aisle, hiring occurs primarily by familiarity and word-of-mouth networks, in response to the absence of clear signals about either personnel or candidate performance that might inform decision-making. Based on these results and on prior literature on political campaigns, I argue that campaigns are best understood as project networks, a type of temporary organization studied by scholars of organizational theory. I advance temporary organizations literature by studying team formation in a temporary organizing context and add to our understanding of U.S. political campaigns by explaining the formation of the teams that battle to determine the makeup of the U.S. Congress.
Predicting Firm Growth in Rural Texas: A multi-method machine learning approach to a complex policy problem (IN PREPARATION with Vivek Shastry and Varun Rai)
What factors predict higher or lower rates of entrepreneurship in rural America? Policymakers looking for an answer to this question face two serious obstacles. First, existing research on firm creation is by and large focused on the growth of high-tech firms based in urban areas. Second, academic work on rates of firm creation is housed across multiple disciplines, often using econometric methods whose aim is to identify the effect of a specific variable (such as ease of doing business, or access to finance) on firm creation rather than compare the likely importance of all the variables advocates claim will lead a policymakers’ desired outcome.
In this paper, we combine multiple machine learning methods (linear regression, subset selection, lasso and random forests) to a novel dataset in order to answer the question of what social and economic factors are predictive of firm growth in rural Texas counties from 2008-2018. Our results suggest that some of the factors commonly discussed in association with promoting entrepreneurship (access to broadband and patents) may not be as predictive as other population-level variables (age distribution, ethnic diversity, social capital, and migration patterns). We also find that the strength of specific industries (oil, wind, and healthcare) is predictive of firm growth, as is the number of local banks. Most of the factors predictive of firm growth in rural counties are distinct from those predictive in urban counties, providing support for arguments that rural entrepreneurship is a distinct phenomenon worthy of greater, and distinct, focus. We use these results as a demonstration how this approach can serve policymakers looking for high-level guidance about what factors to focus on given a desired policy outcome.
The Devil You Know: Team Experience, Team Familiarity, and Performance in U.S. House Campaigns from 2012-2016 (IN PREPARATION)
Every two years, candidates for the U.S. House of Representatives make a series of rapid decisions about whom to hire to operate their campaigns. Yet most of the guidance they receive is made up of untested, often contradictory rules of thumb. Which of that advice should candidates follow? This paper examines two factors that previous research on entrepreneurial team formation shows contribute to team effectiveness: (1) The average experience level of personnel, and (2) their history of working together, or team familiarity. For U.S. House campaigns from 2012-2016, I build measures of both, and use multilinear regression models to test their correlation with campaign outcomes, as measured by vote share in the general election and fundraising totals.
For most candidates, hiring more experienced personnel is not associated with either raising more money or earning more votes, with the exception of Republican incumbents, for whom having more experienced consultants may be associated with greater vote share. The strongest and most consistent finding is the positive association between team familiarity and vote share: Teams with more experience working together earn more votes. More familiar Republican teams raise more money, too, though Democratic teams do not. Together, I interpret these results to mean that in political campaigns, the inability of most campaigns to use experience as a proxy for quality of personnel leads candidates and their hiring managers to build a team from the people they already know, which helps Republican candidates raise more money and all candidates earn more votes.
Hacks and Wonks: The Effects of Hiring Campaign Staff into Congressional Offices from 2005-2016 (IN PREPARATION)
At the end of a successful electoral campaign, a winning candidate is rewarded with a new challenge: What happens to the organization they built to win the campaign, and how should they staff their legislative office? Some policymakers solve both problems at a stroke by hiring their political campaign staff onto their legislative teams. This paper examines the effects of such staff crossover on U.S. House Members’ legislative effectiveness and vote share in subsequent elections.
Contrary to lay expectations, I find that from 2005-2016, members of Congress who hired more campaign staff into their congressional offices were more effective legislators and performed no better or worse in subsequent campaigns. This paper extends recent scholarship on legislative staff by examining the campaign staff that cross over into legislative offices, and their effect on policymakers’ effectiveness and electoral success, two dimensions of performance studied by political scientists and policy scholars.