In this paper, we undertake a systematic disaggregate analysis of the effects of government spending on economic activity in the US. The level of disaggregation we consider is the highest available and is unprecedented in the empirical literature. More specifically, public consumption and public investment are decomposed into various subcategories, which are measured at the levels of the federal (defence and non‐defence), and state and local governments. For each subcategory, we estimate a structural vector autoregression that identifies public spending shocks through the conditional heteroskedasticity of the structural disturbances, thus relaxing the identifying restrictions commonly used in the literature. Our analysis reveals significant heterogeneity in the effects of public spending shocks on output both across subcategories and government levels. Shocks to spending on durables and structures are found to have the largest and most persistent effect on aggregate output, with a peak multiplier that exceeds 1. Our results also suggest that there is little association between the size of a given category of public expenditures and the magnitude of its effect on output.
Separating the Wheat from the Chaff: A Disaggregate Analysis of the Effects of Public Spending in the USJune 7, 2018
The Institute of Medicine estimated that waste consumed 30 percent of US health dollars in 2009. Donald M. Berwick and Andrew D. Hackbarth, working from a 2011 baseline, pegged the midpoint of reasonable waste estimates even higher, at 34 percent. A crude extrapolation of these figures, given the steady rise in overall health expenditures, implies that wasted spending now comfortably exceeds $1 trillion annually (see Exhibit 1), a sum that could fund the entire Medicaid program twice over.
Health care spending rarely follows an ordinary, rational model. Yet even in that context, prescription drug prices are rising at a puzzling rate. What is causing the phenomenon? Quite simply, incentives percolating throughout the prescription drug market push players toward higher prices. At the center, lies the highly secretive and concentrated PBM industry — middle players who negotiate between drug companies and health insurers, arranging for rebates and establishing coverage levels for patients.
Contracts between drug companies and the middle players are closely guarded secrets. The PBM customers, including Medicare, private insurers, and even their auditors, are not permitted access to the terms. And the middle players are not alone; everyone is feeding at the trough.
Markets, like gardens, grow best in the sun; they wither without information. Thus, competitive distortions and suboptimal outcomes are unsurprising.
Despite the extreme secrecy, details have begun to seep out — through case documents (including recent contract disputes among parties), government reports, shareholder disclosures, and industry insider reports. Piecing together these sources, this article presents a picture of incentive structures in which higher-priced drugs receive favorable treatment, and patients are channeled towards more expensive medicines. In exchange for financial incentives structured in different ways to appeal to hospitals, insurers, doctors, and even patient advocacy groups, drug companies ensure that lower-priced substitutes cannot gain a foothold. It is a win-win for everyone, except of course for taxpayers and society. This article also analyzes popular proposals that are unlikely to work and suggests approaches for aligning incentives.
Declining Teen Employment: Minimum Wages, Other Explanations, and Implications for Human Capital InvestmentMay 29, 2018
We explore the decline in teen employment in the United States since 2000, which was sharpest for those age 16–17. We consider three explanatory factors: a rising minimum wage that could reduce employment opportunities for teens and potentially increase the value of investing in schooling; rising returns to schooling; and increasing competition from immigrants that, like the minimum wage, could reduce employment opportunities and raise the returns to human capital investment. We find that higher minimum wages are the predominant factor explaining changes in the schooling and workforce behavior of those age 16–17 since 2000. We also consider implications for human capital. Higher minimum wages have led both to fewer teens in school and employed at the same time, and to more teens in school but not employed, which is potentially consistent with a greater focus on schooling. We find no evidence that higher minimum wages have led to greater human capital investment. If anything, the evidence points to adverse effects on longer-run earnings for those exposed to these higher minimum wages as teenagers.
We study the impact of deportation fear on the incomplete take-up of federal safety net programs in the United States. We exploit changes in deportation fear due to the roll-out and intensity of Secure Communities (SC), an immigration enforcement program administered by the Immigration and Customs Enforcement Agency (ICE) from 2008 to 2014. The SC program empowers the federal government to check the immigration status of anyone arrested by local law enforcement agencies and has led to the issuance of over two million detainers and the forcible removal of approximately 380,000 immigrants. We estimate the spillover effects of SC on Hispanic citizens, finding significant declines in ACA sign-ups and food stamp take-up, particularly among mixed-status households and areas where deportation fear is highest. In contrast, we find little response to SC among Hispanic households residing in sanctuary cities. Our results are most consistent with network effects that perpetuate fear rather than lack of benefit information or stigma.
Innovation is a primary source of economic growth, and is accordingly the target of substantial academic and government attention. Grants are a key tool in the government’s arsenal of tools to promote innovation, but legal academic studies of that arsenal have given them short shrift. While patents, prizes, and regulator-enforced exclusivity are each the subject of a substantial literature, grants are typically addressed briefly, if at all. According to the conventional story, grants may be the only feasible tool to drive basic research, as opposed to applied research, but they are a blunt tool for that task.
Three critiques of grants underlie this narrative: grants are allocated by government bureaucrats who lack much of the relevant information for optimal decision-making; grants are purely ex ante funding mechanisms and therefore lack accountability; and grants misallocate risk by saddling the government all the downside risk and giving the innovator all the upside. These critiques are largely wrong. Focusing on grants awarded by the National Institutes of Health, the largest public funder of biomedical research, this Article delves deeply into how grants actually work. It shows that grants are awarded not by uninformed bureaucrats, but by panels of knowledgeable peer scientists with the benefit of extensive disclosures from applicants. It finds that grants provide accountability through repeated interactions over time. And it argues that the upside of grant-investments to the government is much greater than the lack of direct profits would suggest.
Grants also have two marked comparative strengths as innovation levers: they can support innovation where social value exceeds appropriable market value, and they can directly support innovation enablers — the people, institutions, processes, and infrastructure that shape and generate innovation. Where markets undervalue some socially important innovations, like cures for diseases of the poor, grants can help. Grants can also enable innovation by supporting its inputs: young or exceptional scientists, new institutions, research networks, and large datasets. Taken as a whole, grants do not form a monolithic, blunt innovation lever; instead, they provide a varied and nuanced set of policy options, and we should recognize and develop their usefulness in promoting major social goals.
In the domain of technology startups, biotechnology has often been considered as specific. Their unique technology content, the type of founders and managers they have, the amount of venture capital they raise, the time it takes them to reach an exit as well as the technology clusters they belong to are seen as such unique features. Based on extensive research from new databases, the author claims that the biotechnology startups are not as different as it might have been claimed: the amount of venture capital raised, the time to exit, their geography are indeed similar and even their equity structure to founders and managers have similarities. The differences still exist, for example the experience of the founders, the revenue and profit level at exit.