Abstract

This paper investigates the optimal path of subsidies when a new business, such as electric vehicles (EV), with low externality and market competitiveness, competes against conventional businesses, such as internal combustion engines (ICE), with high externality and market competitiveness. I build a dynamic model that addresses market competition, the channel of sales leading to technology improvements, and the externality of carbon emissions. This paper compares dynamic purchase and investment subsidies with currently fixed subsidies in the US and no policy. To run the model, I utilize collocation methods and use coefficients from the estimation of aggregated real market data: 531 vehicle models sold in the US from 2008 to 2019, as well as SEC filing reports. I find that the optimal path should begin at high values and then approach zero as the capital of EV firms increases. Both purchase and investment subsidies increase the total social net benefit compared to the constant subsidy, and also more, compared to no policy. It suggests that constant subsidies based on the Pigouvian concept will not fully correct the externalities from carbon emissions in the automobile market, which has a strong potential for improvement via investments.

JEL codes: Q58, Q55, H23, R48, L98 

Abstract

This paper quantifies peer effects of electric vehicles (EV) by area and subgroups in Michigan, USA. The peer effect has been widely used to explain consumer behavior, and it plays an important role in product adoption under imperfect information. Consumers may initially hesitate to purchase an EV; however, if they observe more EVs in their neighborhood, on the road, or in parking lots, their reluctance to buy one may be alleviated. I build a system of equations to identify peer effects from lagged cumulative EV adoption by area and subgroups of type and brand values, with other effects from charging stations and socio-economic controls. This paper provides evidence that peer effects from the cumulative adoption of a specific model play a substantial role in the new adoption of the same model. While peer effects also increase overall adoption within the same ZCTA, competing types or brand values negatively affect the adoption of new EVs. I discuss the implications of these findings and how they affect the real impact of subsidization with peer effects. This paper also finds that the impact of subsidization would be significantly larger when peer effects are considered.

JEL codes:  D12, Q40, Q50, R11 

Abstract

The environmental justice literature has examined the association between economic disadvantage and industrial emissions, but rarely examined is the association between rurality, open space, and emissions. In a fossil fuel economy, open space may be valued to disperse industrial disamenities and reduce public opposition to emission-intensive production. However, open space may also be valued economically to produce less energy dense renewables that are space intensive. This research uses the 2014 and 2018 versions of the Manufacturing Energy Consumption Survey to investigate local characteristics that are strongly associated with poor carbon performance. Quantile regression is used to identify whether industrial activities that produce the most carbon pollution may be more amenable to transitioning to renewable energy.

Abstract

The longer society waits to reduce carbon emissions on an eventual path to net zero, the more critical environmental innovation becomes to averting a catastrophic rise in global temperatures above 2°C. Innovation surveys such as the EU Community Innovation Survey (CIS) and the US Annual Business Survey (ABS) now include questions on environmental innovation related to reducing a firm’s carbon footprint. However, as with all self-reported innovation questions that comport with the Oslo Manual (OECD/Eurostat 2018), the requirements for reporting are only that the innovation is available to users and that it comprises a substantive change from what was done before. Neither the success nor the type of environmental innovations are investigated in the innovation surveys making the findings unpersuasive due to potential social desirability bias (Tourangeau, et al. 2000) and of limited usefulness for informing environmental innovation policy related to the energy transition.

Abstract

State governments use various policies to encourage energy production from non- traditional sources to promote clean energy transitions, including renewable portfolio standards. Despite their widespread adoption, their farm-level impacts remain understudied. Adoption by farmers is especially important as they often have the land needed to accommodate new forms of electricity production. This paper examines how state RPS targets influence energy adoption on farms and economic outcomes, using panel data from 50 US states for 2012, 2017, and 2022, and a system of equations with inverse probability weighting to address endogeneity and selection biases. In states with higher standards, the share of farms adopting solar and small hydro renewable energy systems is significantly higher, especially among smaller farms. Farm-level adoption of solar and geothermal but not wind energy also raises net farm income, mostly by increasing gross income while only marginally increasing total costs, thus contributing to farm survival and regional resilience.

JEL codes: Q15, Q42, Q48, R11 

Abstract

Energy consumption has been strongly linked to economic growth for several centuries. Modern economic activities require considerable energy consumption: i.e., burning fossil fuels for transportation, generating electricity, or heating buildings. Therefore, modern economies are vulnerable to external energy price shocks, and this has been demonstrated several times in history. In response to such external shocks, local economies may (1) experience short-term economic decline or (2) reduce their long-term dependence on energy, indicating a lower energy intensity. Energy intensity measures how much energy is used to produce one unit of added economic value, such as Gross Domestic Product. Therefore, both responses reduce production costs associated with energy consumption. We investigate how US local industries adapted to external energy shocks with panel data from about 3,100 US counties over the years 2002 to 2022, and do the same for 50 US states from 1970 to 2022. We construct Bartik instruments considering local industry structures and relevant energy consumption as well as external energy price shocks. Systems of equations are then used to analyze the causal relationships among energy price shocks, local energy intensity, and local GDP. We find that local GDP declines in the short-term following an energy price shock, and that local energy intensity has increased in the long-term. Further, and as expected, energy exporting states and counties are more resilient to external energy price shocks. These findings reveal that local economies adapt to external shocks by decoupling energy consumption and economic growth.

JEL codes: Q43, R11, Q40, O44 

Abstract

The Office of Science and Technology Policy requested questions on both R&D and use of renewable energy and battery storage technologies in the 2022 Annual Business Survey. The definition of R&D for process innovation focuses on creating, improving, or adapting existing production or delivery methods, including significant changes in techniques or equipment that would include evaluating or implementing onsite renewable energy. Use of onsite renewable energy, with or without battery storage, may provide evidence of an energy transition from fossil to renewable sources if adopted at significant scale. The critical regional science question investigated in this paper is where these emerging phenomena are located. Space-intensive renewable energy production is likely to be cost prohibitive in more densely populated areas where the opportunity cost of open space is higher. The one limitation of the current analysis is the inability to connect local characteristics to renewable energy initiatives of multi-plant firms. However, the phenomenon in large single-unit firms may be informative while we wait for data on establishments in multi-unit firms available in the 2022 Manufacturing Energy Consumption Survey. 

JEL codes: Q42, R14, C11 

Abstract

Despite federal mitigation efforts since the 1950s, rural poverty rates have remained 3–5 percentage points higher than those in metropolitan areas in the United States. This study uses panel data to examine whether USDA Rural Development (RD) programs reduced poverty in nonmetro counties from 2004 to 2022. We apply a Bartik instrument based on national industry shocks and historical RD investment composition to identify program effects. Greater per capita RD investments cause reductions in county poverty rates over time. This effect spans RD investment types, with housing programs showing the strongest impact. Results underscore the importance of targeted rural development for effective federal strategies. 

JEL codes: I30, R11, R58

Abstract

In recent decades, investments in controlled environment agriculture (CEA) have increased markedly, resulting in the number of CEA operations doubling, even as the share of CEA in total agriculture remains low. The productivity of CEA is exceptionally high compared to conventional outdoor agriculture, and it provides stable production conditions regardless of weather conditions. Increased adoption of the technology is expected over time as more farmers move up the learning curve because CEA is still at the beginning of the technology adoption phase in the US. In this paper, we investigate how CEA affects farm survival and preservation of farmland using panel data for 50 US states from 1998 to 2019. Data comes from the USDA Census of Agriculture, Censuses of Horticultural Specialties, Horticultural Specialties Reports, Vegetable Annual Summary Reports, and the US EIA State Energy Data System. We use a system of equations to investigate (1) how climate change has affected farmers’ decisions on CEA adoption, and (2) how CEA adoption contributes to farmers’ income and energy consumption. We find that more severe climate conditions lead farmers to install CEA, and CEA adoption has increased farmers’ income substantially more than associated cost increases. CEA adoption also decreases energy consumption. These results demonstrate that CEA simultaneously contributes to farmers’ income and energy security, suggesting important implications for agricultural sustainability and climate adaptation strategies.

JEL codes: Q15, Q16, Q19, Q49 

Abstract

Trade shocks at the national level are external to and largely unavoidable for local economies, and in the past shocks associated with Chinese imports have profoundly affected local businesses. These impacts are observable in local economies in poverty and unemployment rates, economic growth, and population (out-)migration. A county having more industries influenced by Chinese imports has higher poverty. Intense competition with Chinese imports may force firms to exit the market, or reduce employment, and consequently, workers may move to other regions. Here we analyze the short-term and long-term impacts of trade-induced employment and income shocks on about 3,100 US counties between 2002 and 2022. Bartik-type instruments are constructed to indicate trade shock on local economies by applying a Chinese import shock at the three-digit NAICS code to the share of employment and payrolls at the county-level three-digit NAICS code. We find that a trade shock significantly increases the poverty rate, and the effect is worse in denser areas. More people aged over 65 absorb the impact of trade shock on the poverty rate, while more people under 5 increase the poverty rate with the shock. Wage employment-dependent local economies are vulnerable to the trade shock, while more self-employment can alleviate the negative from the trade shock. Overall, import shocks worsen local poverty, and local areas with more children and wage employees are also more vulnerable to such external forces.

JEL codes: F14, R11 

Abstract

Microdata from surveys that have been used successfully to study local characteristics associated with innovative behavior are largely silent on local innovation rates for all but the largest substate areas. This is because estimating a proportion from a binomial distribution requires a sampling fraction that is cost prohibitive outside of large conurbations. However, there is growing policy interest in the causes and consequences of innovation at the local level across OECD member countries as new initiatives are introduced to ensure that all regions in a country fully participate in the innovation economy. This study summarizes development of small area innovation rate estimation in which detailed innovation data in the Annual Business Survey (ABS) “borrow strength” from the much larger Economic Census (EC) to more accurately estimate innovation incidence at the local level. It does this by combining observed innovation in ABS with modeled innovation in EC using auxiliary variables that are common to both datasets. Bayesian spatial dependence in concert with random effects that allows partial pooling greatly increases the efficiency of small area innovation rate estimates even for areas with a relatively small number of direct observations. Possible use cases of the small area innovation rate estimation motivated by the Regional Innovation Engines program at the U.S. National Science Foundation that funded this research are demonstrated.