Advancing Public Entrepreneurship through State Government Capacity and Competitiveness: The Impact of Discretionary Fiscal Policy of American States on Worldwide Manufacturing Industries

Abstract

Many policymakers and administrators have directed efforts to increase foreign manufacturing investment (FMI) due to its potential to raise the employment rate, technological progress, and productivity in their regions. Despite foreign manufacturers’ significant influence on the economies of their host countries, institutional and policy uncertainty creates significant entry barriers for multinational manufacturers. Focusing solely on American state performance in economic development as measured by amounts of FMI, this study suggests that different institutional designs and regulations that affect state taxing and spending decision-making make a difference in FMI in American states. This research empirically assesses the relationship between fiscal federalism and FMI by focusing on the level of fiscal decentralization, federal grants, and fiscally constraining institutions. Testing two different FMI datasets that cover all 50 American states by source country between 1987–2006 and 2008–2016, this study finds that manufacturing firms increase their investment in the states that exercise higher discretion in managing fiscal policy, receive more federal grants, and implement more restrictive taxing and spending regulations. The observed positive impact of fiscal institutions and constraints is more prominent for foreign manufacturing firms in the tax-exemption group.

Keywords

state government performance foreign manufacturing investment fiscal decentralization fiscal institutions tax limit supermajority rule institutional stability

Administrators and policymakers are evaluated by performance in terms of policy outcomes. Research on both public management and policy implementation has accounted for government performance largely as a function of potential management quality (Boyne, 2003) or external environments to the agency and resources (Boyne et al., 2005). While these studies have provided critical information that can be translated into evidence-based advice for policy practitioners regarding how to improve government policy performance, there remains insufficient evidence of sizable factors influencing government performance. The reality is that policymakers are almost always constrained by statutory factors that limit management activities or policy implementation (Heckman, 2013). A good example is government performance in economies. Despite scholars’ efforts to predict both short- and long-term economic growth, forecasting regional growth has been challenging because state and local governments exercise different levels of regulations that yield different results for production costs, technological advances, and outputs (Tannenwald, 1997).

Functional theory of American federalism suggests that the functional efficiency of government management increases when each level of government manages policies—that is, the federal government manages redistributive policies and state and local governments manage economic development policies—that can best perform (Peterson, 1995; Shin, 2019). Considering that states have traditionally made major efforts to increase economic performance by competing with one another, the economic performance of a state government can be measured, in part, by its capacity to attract more industries and businesses to its jurisdiction to raise employment rates and improve productivity. Noting that multinational corporations have significantly increased regional economic development and growth, many state policymakers and administrators have directed efforts to increase foreign manufacturing investment (FMI) in their jurisdiction. To outperform their rivals, state governments have been particularly interested in implementing exceptional tax policies and fiscal incentives to attract more FMI.

Contrary to the expectations of state policymakers, however, existing FMI studies have identified that the effects of such direct benefits have not been equal, even among states that have the same or similar investment environments compared with competing states. Some states have been very successful, but in others, effects have been at best marginal or have even diminished. A survey of multinational enterprise managers showed that such direct tax and expenditure benefits are not major considerations in investment decisions (Tavares-Lehmann et al., 2012). This study suggests that different institutional designs and regulations that influence state taxing and spending decision-making significantly affect the FMI of states within the United States. Accordingly, examining the direct impact of fiscal incentives does not help our understanding of FMI because unique state fiscal institutions and regulations limit policy manipulation before policymakers can intervene.

This paper is particularly interested in examining the relationship between fiscal federalism and FMI inflows in the American states. Foreign direct investment (FDI), including manufacturing and services, is defined as international capital flows that entail a 10% ownership stake in a business unit in a foreign country. These investments exclusively include the manufacturing sector, which accounted for nearly 42.4% of total FDI stock in the United States at the end of 2021 (BEA, 2022). FDI is attained by establishing a new subsidiary/branch, acquiring a control share of an existing firm, or participating in a joint venture. These types of investments extend the firm's corporate network across national political boundaries, allowing it to maintain ownership over a package of resources transferred abroad including capital, equipment, engineering expertise, and managerial and marketing skills (Liu, 1997). FMI is a significant catalyst for raising the employment rate, technological progress, productivity, and ultimately economic growth.

Moran (2011) suggests that “manufacturing FDI brings the host directly to the cutting edge of the latest technology and best practices in production, quality control, and marketing worldwide in any given industry—a cutting edge that is continuously pushed forward and improved over time as the parent multinational uses its affiliates to reinforce its competitive position in international markets” (p. 39). Particularly, multinational manufacturers have contributed to economic development in their host regions by actively helping local suppliers increase their facilities and improve their reputation (Moran, 2011; Potter et al., 2003).

Despite the significant role of foreign manufacturers in their host countries’ economies, institutional and policy uncertainty creates significant entry barriers for multinational manufacturers. Mishra et al. (2018) have suggested that foreign manufacturers located in countries with higher government stability can benefit from the increased international manufacturing network that facilitates transparent information sharing and a sustainable supply chain. According to Moran (2011), foreign manufacturing industries’ contribution to the host has noticeably relied on the target country's policy environment. Overall, FDI studies have suggested that FMI's positive impact on host economies is noticeably high in regions where the host government has a stable institutional and policy environment. Thus, foreign manufacturing investors prefer to visit those regions because they can benefit from larger-scale economies as a consequence of the host's development.

As such, fiscal federalism provides a composite measure of investment for foreign manufacturers because each state has different levels of taxation and spending policies, fiscal conditions, policy constraints, and intergovernmental relationships. As a result, the role of fiscally decentralized institutions demonstrates how stable policy and regulatory environments determined by policymakers in institutional dynamics influence FMI inflows beyond FDI's economic implications.

The article proceeds as follows. First, this study reviews the literature that examines determinants of multinational corporations’ locations in the United States. The following section provides a theoretical linkage between fiscal federalism and FMI by focusing on the fiscal decentralization level, federal grants, and fiscally constraining institutions (including spending and taxing limitations). After describing the research design, I present the results from time-series cross-sectional analyses for the period of 1987–2006. In the conclusion, I discuss the key findings and offer avenues for future research.

Existing Literature on the Determinants of FDI in the American States

Studies on FDI that directly examine within-state factors in the United States have stagnated since the 2000s. Existing econometric studies of firm location decisions have suggested that macroeconomic conditions attract potential firms to invest in American states. Although most studies have not directly examined foreign manufacturing firms, a large body of literature on FDI has created a puzzle as to what factors determine the investment decisions of multinational corporations, including the manufacturing sector. Those studies have employed three theories—firm-specific ownership theory, internalization theory, and location-specific theory—to explain important determinants affecting investment decisions (Dunning, 1981; Gordon & Lees, 1986). In ownership and internalization advantages, firms are more attracted to governments that encourage the internalization of production and/or the sale of technology. In location-specific advantages, multinational firms choose one country that has specific advantages in economic and financial impact, political stability, and the cognitive closeness of cultures between the home and host countries. Other studies that have emphasized macroeconomic factors have clarified two types of firms: horizontal firms known as “market-seeking” firms and vertical firms known as “efficiency-seeking” firms (Jensen, 2003). While horizontal firms maintain similar production facilities in multiple countries, vertical firms geographically separate the firm's headquarters according to a country's factor endowment. Thus, horizontal firms are more interested in lower tariffs and transport costs (Caves, 1996; Markusen, 1984), while vertical firms consider classic motivating factors such as lower wages or natural resources (Helpman & Krugman, 1985).

Despite a large body of research on the economic implications of firm location decisions, studies of multinational corporations have suggested that non-market-driven factors such as political interventions and policy processes affect firms’ investment decisions by designing and implementing economic development policies (e.g., Büthe & Milner, 2008; Campos & Nugent, 2002; Fox, 1996; Halvorsen & Jakobsen, 2013; Rodrik, 1996; Scheve & Slaughter, 2006). Kobrin (1982) defined political instability as an unexpected change in the general environment of the government, while Rodrik (1996) understood it as the host government's policies or decisions that affect the business climate. For example, Rodrik (1996) and Garrett (1995) have found that lower levels of political instability are likely to attract more FDI inflows because politicians that are more committed to property rights are likely to be more responsible in observing labor standards. However, Campos and Nugent (2002) as well as Scheve and Slaughter (2006) have found that political instability has a positive effect on FDI inflows when political instability is measured by the level of unexpected change in government politics.

Kline (1984) claimed that the eclectic relationship between national and subnational governments is an important factor in attracting more foreign firms because both labor and physical infrastructure in eclectic relationships promote healthier business operations. Some studies have suggested that constraints imposed on political leaders in intergovernmental relationships have affected foreign firms’ investment decisions. Kudrle and Bobrow (1982) suggested that the role of public politics versus intergovernmental or inter-bureaucratic decision-making determines the amount of firm investment. Specifically, they demonstrated that the regularities of various veto players within both national and subnational governments attract more foreign investment because veto players act as constraints and increase policy predictability. Kang (1997) argued that elected policymakers play a critical role in characterizing FDI inflows in the United States. He suggested that higher FDI inflows are a product of institutionally mediated conflict between the President and policy entrepreneurs in Congress as well as between national and subnational political leaders.

Some studies have suggested that partisan composition influences firm location decisions. While some have suggested that control of both legislative chambers by the Democratic Party attracts more manufacturing firms by increasing direct policy incentives (lower corporate and capital taxation) (Fox, 1996; Fox & Lee, 1996), other scholars have suggested that regardless of party or ideological preference, firms do not favor one party over the other; rather, they prefer a useful blend of policies by both parties (Halvorsen & Jakobsen, 2013).

Other studies on FDI in the United States have directly investigated policy impacts on FDI inflows in states. For example, some studies have indicated that right-to-work (RTW) policies influence foreign manufacturers’ investment decisions in American states (Dinlersoz & Hernandez-Murillo, 2002; Eren & Ozbeklik, 2016; Kim, 2018). For example, Dinlersoz and Hernandez-Murillo (2002) and Eren and Ozbeklik (2016) have found that Idaho, which has an RTW law, has attracted more FMI due to the increased emphasis on the employees’ right not to join a union. Focusing on the impact of states’ corporate tax on FDI, Agostini (2007) found that corporate tax elasticity is negatively associated with FMI amounts, holding for both endogenous and exogenous factors in the tax-setting process. More recently, Shin (2018) has suggested that the negative effect of corporate tax rates on multinationals’ investment decisions differs according to the level of fiscal decentralization.

Fiscal Federalism and Multinational Corporations in the American States

Despite the relative breadth of economic, policy, and political explanations offered for multinational firms’ location decisions in the United States, previous econometric studies on FDI have not sufficiently explored the potential impact of governmental institutions, particularly on the level of state governments’ autonomy in which budgeting and financial decisions take place. This omission is surprising because a growing number of studies on American fiscal federalism have supported fiscal decentralization—that is, the devolution of the central government's fiscal responsibilities to lower levels of government, influencing economic development and growth (e.g., Oates, 1972; Shin, 2018, 2019; Teune, 1982; Weingast, 1995).

Examining state governments’ different abilities and regulations regarding taxing and spending their own revenue can be substantial—particularly for foreign manufacturers. Most studies have shown that manufacturing firms are more likely than non-manufacturing firms to be situated in regions that offer a lower tax burden because the former tend to be more capital intensive than the latter (Bartik, 1991; Buss, 2001; Fisher, 1997; Moran, 2011). Drawing on these findings, a connection between fiscal federalism and FMI is expected. Given the most common debates on American fiscal federalism, I directly examine whether the characteristics of fiscal institutions (i.e., fiscal decentralization, fiscal regulations, and grants-in-aid) that influence state taxing, spending, and budgetary decision-making affect foreign manufacturing firms’ investment in American states.

As the historical truth of American federalism reveals that states existed before the nation, and each state has pursued different paths of cultural and economic development, the necessity of federalism was a natural compromise to unite citizens in diverse states and territories into a single country. As such, the fiscal-federalism system established a two-tier tax-administration system (national and subnational) that separates taxes into different levels of government (Montinola et al., 1995). Many scholars have argued that a prominent feature of fiscal institutions in federalism is the use of intergovernmental grants (Nicholson-Crotty, 2004; Nicholson-Crotty et al., 2006; Oates, 1972). While state governments have used their independent powers to levy taxes, tax revenue as both a fiscal function and instrument has limited their power. Over one-third of state and local revenue is derived from federal government grants while the federal government relies almost exclusively on its tax revenues (O’Toole, 2007). Thus, the fiscal reality of federalism suggests that “the national government plays a critical role in subsidizing a variety of areas of public policy that are administered at the state and local levels” (Theodoulou & Kofinis, 2004, p. 48). If state governments cannot exclusively increase their tax revenue, as they cannot ensure that “decentralized units have sufficient funds to make available a minimal level of a particular good to all constituents” (Gillette, 2004, p. 101), they will depend on federal grants. This suggests a likely connection between states’ tax efforts and the amount of federal grants.

The theoretical premise between tax efforts and federal grants is “what states do with the portion of own source revenue that they supplant with federal grants” (Nicholson-Crotty, 2008, p. 111). Bradford and Oates (1971) argued that the relative effectiveness of grants is “a fiscal choice model where recipient jurisdiction operates within a budget constraint and has preferences defined over a mix of public and private goods” (as cited in Nicholson-Crotty, 2008). In this basic form of the fiscal choice model, the rest of the grant monies will be diverted to jurisdictional citizens with reduced taxes after some portion of federal grants are spent on specific policies determined by the donor (Gramlich & Galper, 1973; Case et al., 1993). That is, when the states compete to attract more firms to secure more grant monies, they might offer favorable tax rates to host foreign industry. Given the important implications for grants-in-aid as a slack resource for state governments, it is reasonable to suggest that federal grants help states by providing tax relief or corporate income tax credits, and thus states that receive more grants-in-aid will exert lower levels of tax incentives. Thus, I posit:

Hypothesis 1: State governments that receive more federal grants are more likely to attract foreign manufacturing firms.

Another aspect of fiscal federalism is fiscal decentralization, that is, the devolution of the central government's fiscal responsibilities to lower levels of government, influencing economic development and growth (e.g., Oates, 1972; Teune, 1982; Weingast, 1995). Focusing solely on American federalism, the devolution of fiscal responsibilities to state and local governments features the effects of the aforementioned factors to determine the level of FMI. It is important to note the theoretical importance of the results for fiscal federalism. Foundational studies of fiscal decentralization have emphasized that increased fiscal authority to subnational governments promotes the managerial efficiency of government, which in turn increases regional economic growth (Oates, 1972; Tiebout, 1956). Weingast (1995) asserted that “market-preserving federalism” (MPF), a concept derived from public choice models of competitive federalism, allows politicians to promote more economic development.

Although the effect of the level of fiscal decentralization on economic development is mixed, most studies have indicated that its impact is substantial, particularly in developed countries. For instance, in their empirical studies of state-level data, Xie et al. (1999) found no significant relationship between state spending shares and state economic growth, and they expected that further decentralization might negatively impact economic growth in the United States. Using new state-level data controlling for both periods of high economic growth and cultural differences, however, Akai and Sakata (2002) provided evidence that fiscal decentralization enhances economic growth at the state level in the United States. Investigating the relationship between local decentralization and local economic growth in U.S. metropolitan areas, Stansel (2005) also identified a positive and significant relationship between local decentralization and per capita income growth. At the cross-national level, Davoodi and Zou (1998) found a negative impact of fiscal decentralization on growth in developing countries but no such impact in developed countries. More recently, focusing on American states, Shin (2018) has examined the conditional impact of the level of fiscal decentralization on the relationship between corporate tax rates and FDI inflows. He has found that states with higher levels of fiscal decentralization are more likely to attract FDI inflows than states with lower levels of fiscal decentralization, although the former have higher corporate tax rates. Thus, I expect:

Hypothesis 2: State governments that are more fiscally decentralized are more likely to attract foreign manufacturing firms.

As discussed above, given state governments’ responsibility to tax and spend their own revenue in the context of fiscal federalism, it is important to consider how state regulations regarding revenue streams affect foreign marketing firms’ investment decisions. Particularly, tax incentives for foreign investors refer to preferential host government policies of value-added tax, corporate income tax, property tax, licensing fees, import duties, and sales tax (Li, 2006).¹ Because new manufacturing firms—whether they are capital-intensive manufacturers or labor-intensive manufacturers—are sensitive to potential tax costs, foreign manufacturing firms prefer to operate in states with more stringent regulations which would limit the potential of an increase in tax rates. As a result, state governments compete to attract manufacturing firms by limiting “the taxation of mobile tax bases and the use of the tax laws to redistribute wealth” (Brunori, 2007). Thus, state tax regulation is an important factor for foreign manufacturing firms because firms can benefit from both the increased investment incentives and reduced aggregate tax costs.

In addition to the impact of tax limitations on FMI, state fiscal institutions and constraints on spending could benefit foreign manufacturing firms. Some existing studies have suggested that more stringent spending policies are negatively associated with state economic growth by increasing deficits (borrowing) and limiting spending, especially during economic downturns (Deller et al., 2012; Hou, 2003). There is also the argument that binding a government's tax and spending levels can lower public wage premiums and employment rates, which in turn hinder economic growth in the short term (McGuire & Rueben, 2006; Poterba & Rueben, 1995).

However, debates on the relationship between state fiscal constraining institutions and economic growth do not necessarily mean that firms are not likely to relocate to states with more stringent spending regulations. Indeed, profit-seeking firms make investment decisions based on their long-term expectations of the economic stability in a potential location (Shin et al., 2016). Most foreign firms are mobile ex ante but relatively immobile ex post (Vernon, 1971). Manufacturing firms are especially inclined to build large plants and factories in a region. Once they install their machines and equipment, they do not want to pull their facilities out of a host region because relocating is costly (Jensen & McGillivray, 2005). Thus, profit-maximizing manufacturers are likely to invest in a region that is expected to provide long-term market potential. In this sense, some studies have viewed fiscally constraining institutions as cyclical and long-term effects on economic stability have appeared more persuasive (e.g., Deller et al., 2012; Staley, 2017).

The existence of fiscally constraining institutions, regardless of their variation among states, may create an investment-friendly environment due to their potential signaling effect of reduced fiscal volatility. For example, Patrick (2021) has suggested that an increase in firms due to fiscal incentives will create increased competition, which may induce existing firms to close. Thus, state constitutional limits may prevent governments from increasing wasteful incentives to attract firms and thus may reduce the potential risk of governments falling into long-term debt obligations and default. Appleby (2021) has suggested that tax supermajority requirements exert downward pressure on state tax efforts, and thus, “these design principles will best position the state to attract businesses and wealthy individuals, achieve fiscal stability, and minimize the many drawbacks that commonly accompany tax supermajority requirements” (p. 1015). As a result, the existence of fiscally constraining institutions not only induces states’ low-tax competitive advantages but also provides more transparent and stable tax regimes that are imperative to potential business and corporate taxpayers.

Fiscally constraining institutions provide potential opportunities for foreign manufacturing firms not only by spurring sustained economic growth but also by lowering fiscal volatility. Focusing on the two most prominent fiscal institutions—tax and expenditure limitations (TELs) and supermajority requirements (SMRs)—in American states, I therefore posit:

Hypothesis 3: States with more restrictive tax and expenditure limitations are more likely to attract foreign manufacturing firms.

Hypothesis 4: States with more restrictive supermajority requirements are more likely to attract foreign manufacturing firms.

Data, Variables, and Methods

This study's empirical tests utilized time-series cross-sectional regressions (TSCS) to examine how fiscal federalism affects FMI inflows for all 50 states between 1987 and 2006, which date range was largely determined by data availability. All independent and control variables are lagged by one year, except for the fiscal decentralization variable, to control for the possible contemporaneous effects of FMI inflows.² The TSCS econometric equation is:

\begin{aligned} FMI INFLOW S_{i t} & = β_{0} \\ + β_{i} (INDEPENDENT VARIABLE S_{i t - 1}) \\ + ε_{i}, ε_{i t} \sim N (0, σ_{ε}^{2} \end{aligned})

Dependent Variable

The dependent variable is the number of foreign manufacturing firms entering each American state over the years. However, to my knowledge, there are no consistent panel data that measure net FMI values by state. The U.S. Bureau of Economic Analysis (BEA) has collected the real annual book values of gross property, plant, and equipment (PPE) of all non-bank affiliates in each state, but consistent data is available only since 2006. The only consistent and available data that measures foreign investment in manufacturing sectors by state is the number of employees in each state. BEA also provides this manufacturing data differentiated by major investing countries (Australia, Canada, France, Germany, Japan, the Netherlands, Switzerland, and the United Kingdom). To conduct a more rigorous empirical test, I chose the latter data—manufacturing employment of affiliates in each state by seven source countries of ultimate beneficial owner (UBO), except for Australia³—because it is important to control for factors of each home country, both observed and unobserved, where the headquarters of manufacturing firms are located. In 2006, the number of employees who worked in manufacturing firms from these seven countries was 1,811,900, which accounts for 86.9% of total employees in all foreign-owned manufacturing firms.⁴

Because the data is a cross-sectional time series, it is important to determine whether the unit roots are available for the dependent variable. The test indicates that the state-level FMI series is non-stationary,⁵ and thus using the raw FMI variable is problematic. To ameliorate the existing unit root, I measured the difference in the number of employees of the manufacturing firms from one year to the next. In addition, it is not realistic to believe that firms’ investment decisions are made hastily but rather that firms incrementally make decisions over the span of more than a year (Halvorsen & Jakobsen, 2013). To be more realistic, therefore, I transformed the difference measure of FMI as a 3-year running average of the number of employees (average employment change). Descriptive statistics for FMI and other variables are presented in Table 1, and the measure of dependent and independent variables is shown in Table 2.

Table 1.

Descriptive Statistics.

Variables	Obs.	Mean	Std. Dev.	Min.	Max.
FMI	6403	55.62	923.99	−8733.33	12300
Fiscal decentralization	6980	0.52	0.09	0.20	0.74
TEL	6980	7.75	7.98	1	30
SMRs	6980	0.53	0.06	0.51	0.75
Federal grant	6980	969.08	379.32	363.73	3307.65
Education expenditures	6980	1128.39	337.93	432.38	2857.61
Corporate tax	6980	6.59	2.86	0	12.25
Government ideology	6980	50.47	21.41	4.05	88.99
Unified government	6980	0.58	0.49	0	1
Per capita GSP	6980	35813.46	7277.27	21679.8	73079.4
Agglomeration	6980	0.02	0.003	0.02	0.03
Land costs	6980	177.39	244.89	1	1178
Unit labor costs	6980	33691.39	39703.55	131.4	202601
Unionization rate	6980	13.41	6.04	2.3	30.3

Table 2.

Variable Descriptions and Sources.

Variable	Description	Source
FMI	The real annual book values of gross property, plant, and equipment (PPE) of all non-bank affiliates by state	The U.S. Bureau of Economic Analysis (BEA)
Fiscal decentralization	The level of fiscal decentralization as expenditure indicator (state and local sum of expenditure/total government expenditures)	Statistical Abstract, U.S. Census Bureau of U.S. Department of Commerce
TEL	Index of stringency of state tax and expenditure limitations	Amiel et al., 2009
SMRs	Index of stringency of the legislative supermajority requirement to raise taxes (no supermajority = 0.51)	National Conference of State Legislatures
Federal grant	Per capita aggregate federal grants awarded to the states	Statistical Abstract, U.S. Census Bureau of U.S. Department of Commerce
Education expenditures	Per capita Federal education expenditures	State Government Finances, US Census Bureau
Corporate tax	The highest marginal state corporate income tax rate	World tax database from the tax policy research at the University of Michigan Tax Foundation
Government ideology	Index of state government ideology	Berry et al., 2010 (http://rcfording.wordpress.com/state-ideology-data/)
Unified government	Unified party control over the governorship and both chambers of the legislature (1 = unified Dem or Rep control of both the legislature and governor's office)	The Council of State Governments Klarner Politics (http://www.indstate.edu/polisci/klarnerpolitics.htm)
Per capita GSP	Per capita Gross State Product	Bureau of Economic Analysis of U.S. Department of Commerce
Agglomeration	The number of establishments per capita	U.S. Census Bureau
Land costs	Population per square mile	U.S. Census Bureau
Unit labor costs	The average wage rate divided by the average product of labor	Union membership, coverage, and earnings from the CPS (http://www.unionstats.com/)
Unionization rate	The percentage of total members of a union divided by total employed	Union membership, coverage, and earnings from the CPS (http://www.unionstats.com/)

Note. All financial variables are adjusted in constant 2001 United States dollars.

Independent Variables

Major independent variables include four key measures of fiscal federalism: the fiscal decentralization level, federal grants, TELs, and SMRs. First, measuring the level of fiscal decentralization is not straightforward. Generally, scholars have measured state fiscal decentralization by indicating the degree of discretion given to the local governments. A popular method has been to calculate the ratio of local government spending or revenue to total state and local government spending or revenue (Akai et al., 2007; Oates, 2005; Xie et al., 1999). Despite the difficulty in measurement, I used the expenditure measure because it is the best-known indicator used frequently in the literature (e.g., Oates, 1985; Thiessen, 2003).⁶ A smaller ratio means a lower level of fiscal decentralization within a state, whereas a larger ratio means a higher level of fiscal decentralization within a state.⁷

Another key independent variable is the level of grant funding awarded to a state. The models include the aggregate federal grants which represent sums of total grant monies awarded directly to the states by the federal government in a given year. Federal grants are divided by the total population in each state. Both spending and grants-in-aid data were extracted from the U.S. Statistical Abstract.

Lastly, to understand more fully the variation in FMI inflows that states receive, specifically regarding state revenue regulations, this study's models include two major state fiscally constraining institutions—tax and expenditure limitations (TELs) and supermajority voting requirements (SMRs). TELs are the most widespread fiscal constraint of tax revolt. TELs are constitutional or statutory provisions restricting government spending and over-taxation in the states. Similar to TELs, SMRs limit spending and taxation by requiring, generally, a supermajority approval by the state legislature, a majority of citizens, or some combination of the two. It is important to note, however, that each state exercises a different level of stringency on all fiscal institutions and constraints. The TELs variable is an index that quantifies the TEL severity of both state and local governments from 1 to 30, which is taken from Amiel et al. (2009). The supermajority requirements variable is measured as the percentage of the legislature required to raise the tax rates or impose new taxes.⁸ These data are from the National Conference of State Legislatures.

Control Variables

To control for potential influences on FMI inflows, this study's models include a set of control variables capturing policy instruments, political characteristics, economic and market conditions, and labor conditions of each state. First, the models include the indicators of policy instruments, which represent states’ strategic efforts to increase innovative economic development programs and traditional efforts to provide direct policy incentives. While the government in the supply-side theory prioritizes supporting established firms and relocating existing industries by providing competitive incentives that lower capital, land, and labor costs, the government in the demand-side theory attempts to selectively assist unique products that promise competitive market power (Mahroum & Al-Saleh, 2013). To measure the quality of the labor market, I used data from the U.S. Census of Government Finances measuring total state spending on total educational resources, which was divided by state population and measured in 2001 United States dollars. I expected that the education spending variable would be positively correlated with FMI. To control for state direct incentive policy, which is defined by the older supply-side theory, I included measures of the highest marginal statutory tax rates of the state corporate income tax, which data were obtained from World Tax Database, the Office of Tax Policy Research at the University of Michigan, and the Tax Foundation. Since manufacturing firms are sensitive to changing corporate tax rates, I expected state corporate tax rates to correlate negatively with FMI.

The models also control for political-indicating variables: state government ideology and partisan composition. Studies on the American states have indicated that ideological changes and partisan differences in state governments are not stable but rather dynamic over time (e.g., Berry et al., 2007; Lupton et al., 2015). The indicator of state government ideology is taken from Berry et al. (1998), but the most recently updated measure is taken from Richard Fording's (2018) website.⁹ The ideology measure ranges from 0, representing the most conservative position, to 100, representing the most liberal position (Berry et al., 1998). Traditionally, while more liberal governments are driven to make demand-side policies that pursue a consumption-driven long-term growth strategy by creating new markets (Leigh, 2008), more conservative governments favor supply-side policies that provide short-term direct incentives such as tax incentives, lower-cost labor, and land (Halvorsen & Jakobsen, 2013).

However, it should be noted that the main goal of state policymakers is to increase and retain foreign investments in their state regardless of changes in government ideology (Halvorsen & Jakobsen, 2013; Shin, 2018). Although states’ changing ideological environments have oriented different development policies, there has been no clear evidence of which ideologically valued strategies have been more successful in attracting more industries and businesses (Halvorsen & Jakobsen, 2013; Shin, 2018). Therefore, it is reasonable to expect that government ideology does not significantly affect investment because liberal and conservative governments work to create beneficial institutional environments that develop more conducive investment policies.

Partisan composition generally matters in American politics. Although there is little evidence suggesting which type of partisan control—unified Republican or unified Democratic—has greater policymaking advantages, scholars seem to agree that states with a unified government, regardless of control by Republicans or Democrats, are more efficient and accountable than divided governments (e.g., Bjornskov & Potrafke, 2013; Krause et al., 2013; Miras & Rouse, 2022). Therefore, I expected that states with a unified government are more likely than those with a divided government to attract foreign manufacturers. The effect of unified government is captured by a dummy measure that scores 1 for states that have unified party control over the governorship and both houses of the legislature and 0 for those that do not. This variable is drawn from the Klarner State Partisan Balance Data.¹⁰

In addition, this study's models control for state economic and market conditions. First, the models control for the state's economic condition, measured as gross state product (GSP) per capita. Studies have suggested that states with higher GSP attract more foreign manufacturing industries by exercising greater purchasing power and local demand (Friedman et al., 1992; Fox, 1996). Significant literature also exists that has examined the impact of agglomeration on firm location. There is a broad consensus among investment studies that rational firms are interested in investing in regions where they are provided a large network of firms, market accessibility, transportation facilities, and well-developed infrastructure by being located near each other (e.g., Bobonis & Shatz, 2007; Halvorsen, 2012; Kandogan, 2012). To measure the agglomeration effect on FMI, I used the number of establishments per capita. I also included the level of land costs, which is the same estimation of population density measured by state population per square mile (Halvorsen, 2012). I expected all these economic and market variables except for the population density variable to correlate positively with FMI. All these variables are available from the U.S. Census Bureau.

Finally, the models include labor conditions: the unionization rate and unit labor cost. Previous studies in this area have suggested that unionization could be negatively correlated with FDI not only because unionization tends to increase wage levels, but also because it makes managerial control of production more difficult (e.g., Naylor & Santoni, 2003; Owen, 2013). While there are mixed findings regarding the impact of labor costs on foreign investment, there is a broad consensus that large firms (especially within the OECD) are attracted to high-skill, high-wage locations (Alsleben, 2005; Blonigen et al., 2007; Moosa & Cardak, 2006). Thus, I expected that the unit labor cost variable would be positively related to FMI. The unionization variable is measured as the percentage of union membership per employed population. The unit labor cost variable is computed as the average wage rate divided by the average product of labor (Axarloglou, 2004).¹¹ These variables were obtained from the U.S. Bureau of Labor Statistics.

Statistical Methods

Panel data contain various sources of bias, such as non-stationarity, correlated error terms across time and states, and differing variances in different panels (heteroscedasticity). The larger problem in testing the impact of fiscal institutions on FMI is endogeneity in the modeling. Intuitively, everything on the right-hand side is endogenous and correlated with many other variables. To take one example, federal grants are not random but rather dependent on such factors as state population and state per capita income. All other variables are in part determined by some combination of the state economy and the state's politics. Without identifying exogenous changes in these variables, it is difficult to determine whether the estimated coefficients of the variables reflect the causal effects of the variables or whether the included variables are correlated with unobserved variables.

The most appropriate method to manage endogeneity is to use two-staged least-squares instrumental variable estimation or a Heckman selection model if it can find valid instruments that affect only explanatory variables including the measures of fiscal institutions not related to FMI. Realistically, however, it is difficult to find reliable instruments to account for such potential endogeneity of all variables on the right-hand side of the equations. Furthermore, relatively few time periods and many UBO countries by state in this study would produce significant biases in the estimates. In addition, the dynamic panel data contain various sources of bias, such as non-stationarity, correlated error terms across time and states, and differing variances in different panels (heteroscedasticity). While the inclusion of the lagged dependent variable (LDV) not only ameliorates one potential source of bias (the presence of serial correlation), the inclusion of LDV could also inflate the coefficients of the LDV due to the correlation with the error term. However, this study's models not only include LDV due to the theoretical reason that the previous FMI affects the current FMI, but they also control for investing country and state fixed effects due to the need to account for the potential impact of unobserved state and industry effects, as ordinary least squares estimates would be biased (Nickell, 1981).

Given these problems, this study adopts Arellano and Bond's (1991) generalized method of moments (GMM) system because it is the most efficient estimation to correct for the endogenous explanatory variables, autocorrelation, heteroscedasticity, and the issue of small t (i.e., years) and large n (UBO countries by states). Another form of GMM, the difference GMM, could be an alternative estimator. However, the system GMM is more suitable than the difference GMM where the dependent and the explanatory variables are stationary (Blundell & Bond, 1998).¹² To deal with endogeneity bias, the system GMM estimation incorporates LDV and other potentially endogenous explanatory variables in the form of the second lag length as instrumental variables.¹³ To ameliorate the problem of heteroscedasticity and autocorrelation, I estimated the regression via the two-step approach.

Findings

The models considering the impact of different measures of fiscal decentralization on FMI are presented in Table 3. The results are presented in six separate models. Roodman (2009) has suggested that several diagnostic tests should be conducted to see if the results from the system GMM estimator are valid. First, the results for the serial correlation tests from all models indicate that the coefficients of AR(1) are statistically significant, but those of AR(2) are statistically insignificant, which collectively indicates no serial correlation of the first differenced residuals. Second, the system GMM estimation should be free from the “too many instruments” problem. That is, including overidentified instruments can overfit the endogenous variables and thereby inflate standard error estimates. As presented in Table 3, Hansen tests of overidentification restrictions fail to reject the null hypothesis that the instruments are not overidentified. Lastly, the exogeneity test should identify that the instruments are uncorrelated with the error term. Difference-in-Hansen tests of exogeneity of instrument subsets indicate that the null hypothesis of exogeneity of instruments is not rejected. In sum, all models in Table 3 pass the diagnostic tests that Roodman (2009) suggested.

Table 3.

The Impact of Fiscal Federalism on FMI, System GMM.

	(1)	(2)	(3)	(4)	(5)
Fiscal federalism
Fiscal decentralization	2570.401***				4101.459***
	(924.044)				(905.770)
TELs		7.576*			7.158*
		(5.881)			(4.837)
SMRs			59.182**		50.410*
			(31.086)		(31.935)
Federal grants				0.463***	0.572***
				(0.147)	(0.150)
Control Variables
Education expenditures	0.199*	0.241*	0.180*	0.107	0.182
	(0.137)	(0.154)	(0.130)	(0.136)	(0.149)
Corporate tax	−19.342	−29.783	−38.904	−16.843	−16.444
	(44.369)	(43.318)	(48.19)	(49.753)	(42.620)
Government ideology	−0.325	−1.020	−0.837	−0.937	0.051
	(1.656)	(1.690)	(1.789)	(1.727)	(1.574)
Unified government	135.931***	137.542***	137.093***	135.095***	128.595***
	(51.376)	(53.307)	(51.959)	(49.499)	(51.347)
Per capita GSP	0.0331***	0.0422***	0.042***	0.039***	0.023**
	(0.010)	(0.011)	(0.010)	(0.011)	(0.009)
Previous FMI	0.094***	0.097***	0.093***	0.094***	0.088***
	(0.015)	(0.015)	(0.015)	(0.015)	(0.012)
Agglomeration	37962.77*	36644.72	20821.38	17591.44	31306.26
	(29239.74)	(28810.73)	(28104.13)	(25797)	(27873.89)
Land costs	−5.158***	−5.126***	−5.002***	−3.700***	−3.801**
	(1.654)	(1.705)	(1.707)	(1.587)	(1.621)
Unit labor costs	0.023***	0.023***	0.024***	0.024***	0.023***
	(0.004)	(0.004)	(0.004)	(0.004)	(0.004)
Unionization rate	−14.286	−18.582	−23.252	−31.833**	−20.456
	(18.038)	(19.236)	(19.162)	(19.138)	(19.103)
Year	−28.527***	−43.307***	−39.285***	−56.189***	−41.659***
	(10.141)	(11.056)	(10.202)	(12.253)	(12.145)
Constant	−2726.322***	−1313.173**	−947.659*	−829.463*	−3412.889***
	(750.270)	(616.813)	(613.338)	(550.62)	(808.444)
AB test for AR(1) (p-value)	0.000	0.000	0.000	0.000	0.000
AB test for AR(2) (p-value)	0.495	0.463	0.455	0.432	0.485
H₀: Exogeneity of instruments	Not Rejected	Not Rejected	Not Rejected	Not Rejected	Not Rejected
Hansen test chi-square	316.01	313.40	311.65	314.84	319.28
State FE	yes	yes	Yes	yes	yes
UBO country FE	yes	yes	Yes	yes	yes
Observations	6115	6115	6115	6115	6115
Number of Groups	347	347	347	347	347

Note. Dynamic panel regressions are estimated via two-step system GMM; robust standard errors are in parentheses. Controls for fixed effects for state and ultimate beneficial owner country included but results suppressed. *p < 0.10. **p < 0.05. ***p < 0.01; one-tailed tests.

Each model uses a key independent variable of the fiscal federalism indicator. In Column 1 of Table 3, I examined whether the level of fiscal decentralization attracts more FMI. The results indicate that states with a higher level of fiscal decentralization are more likely to attract foreign manufacturing firms than states with a lower level of fiscal decentralization. Substantively, comparing states with the lowest level of fiscal decentralization (0.20) and the highest level of fiscal decentralization (0.74) indicated in the original data, holding all else constant, the average change in number of employees in manufacturing increases from 514 to 1902, a 270% increase.

Focusing on the impact of fiscal institutions on FMI (Columns 2 and 3), Column 2 indicates that states with more stringently binding TELs receive more FMI. As the TELs level increases by one, there is an increase of 7.6 in the average change in number of manufacturing employees over a 3-year period. However, the FMI level corresponding with TELs is not considerable when compared with the effect of SMRs on FMI. As shown in Column 3, the coefficients of SMRs are positive and significant, indicating that a 1-level increase in the stringency of SMRs produces an average change of 59 in the number of manufacturing employees over a 3-year period.

As presented in Column 4, the impact of federal grant dollars on FMI is positive and highly statistically significant. Substantively, the size of the coefficient indicates that a one-dollar increase in per capita federal grants results in a 0.5 increase in the average change in number of manufacturing employees. This result suggests that intergovernmental transfer from the federal government to state governments spurs FMI into the states by helping them potentially increase slack resources to be diverted to investment incentives than they otherwise would.¹⁴

Lastly, in Column 5, I examined the robustness of these findings by including all fiscal variables in the models. As shown in Column 5, the coefficients of all key independent variables are positive and statistically significant, suggesting that fiscal federalism is a critical determinant of FMI.

The control variables also performed largely as expected. The effect of state spending on education is important to encourage foreign investment, but its statistical significance is only supported by three models (Columns 1, 2, and 3). The unified government variable is positive and statistically significant, suggesting that unified governments attract more FMI than divided governments, all else being equal. As expected, the measures of state economic condition are also statistically robust. Our results suggest that states with a higher GSP per capita, previous levels of FMI, and agglomeration rate attract more FMI, but the agglomeration effect is supported only by the model in Column 1. The measure of industrial land costs is negative and statistically significant, indicating that higher land costs have a negative impact on manufacturing size. One of the labor condition variables, unit labor costs, displays a positive and statistically significant relationship with FMI. As expected, this result is consistent with the existing argument that high wages are more attractive for large investments. The coefficients of unionization rate are negative across the models, suggesting that high unionization signals a less friendly business climate. However, its effect is distinguishable to zero only in Model 4. The coefficients of year-count variables are negative and statistically significant across all models.

While the results from Table 3 provide sufficient evidence that fiscal federalism attracts more FMI, I ran an additional test to assess the robustness of its impact by considering different government policies of the source countries. Specifically, the home-country tax regime is important to understand firms' investment decision. Foreign manufacturing firms are responsible for paying taxes in both the host and home countries based on their foreign income. All home countries where foreign firms originate provide tax incentives to avoid double taxation on hosting foreign firms. Popular methods to avoid duplicate tax liabilities are foreign-tax exemptions and foreign tax credits. Following Hines (1996), Canada, France, Switzerland, the Netherlands, and Germany directly exempt foreign income from taxation, while Japan and the United Kingdom use a tax credit regime that issues a credit for taxes paid to the host government. Thus, foreign manufacturing firms from tax-credit countries would be less sensitive to corporate tax rates than those from tax-exempt countries because the latter is still responsible for paying taxes to their investing countries. The question naturally arises whether a state's fiscally constraining institutions are still positively and significantly associated with foreign manufacturers from tax-credit countries. To test this question, I split two different samples: a sample of tax-exempt countries and a sample of tax-credit countries. Since a country's tax regime is related to only corporate tax rates incurred from firms’ investing countries, I replaced the original SMRs variable with the modified SMRs variable, which is a dummy measure that scores 1 for states that have SMRs requiring votes for taxes including corporate income tax, and 0 otherwise.

As presented in Table 4, all variables of fiscal federalism are positively associated with FMI in both exemption and credit countries, but the coefficients of all key independent variables but federal grants are statistically significant only in the tax-exemption group. It is noteworthy that the coefficients of the TELs and SMRs variables are positive and significant only in the sample of tax-exempt countries. These results suggest that foreign manufacturing firms from tax-credit countries are not sensitive to the fiscally constraining institutions that limit even corporate tax rates. Overall, these results reaffirm that manufacturing firms are very responsive to fiscal federalism and continue to invest in states with high levels of fiscal institutions and constraints.

Table 4.

The Impact of Fiscal Federalism on FMI by Host Country Tax Regimes, System GMM.

	Tax-exempt countries	Tax-credit countries
Fiscal federalism
Fiscal decentralization	2701.154***	4543.944
	(1032.149)	(3653.086)
TELs	0.197*	7.683
	(5.751)	(27.560)
SMRs	60.248**	12.163
	(30.648)	(85.284)
Federal grants	0.080	0.567
	(0.091)	(0.602)
Control Variables
Education expenditures	0.133	0.061
	(0.146)	(0.453)
Corporate tax	−27.774	−73.548
	(40.640)	(94.557)
Government ideology	0.462	−2.407
	(1.168)	(4.655)
Unified government	55.957*	199.682
	(36.891)	(183.275)
Per capita GSP	0.021**	−0.015
	(0.011)	(0.027)
Previous FMI	0.117***	0.217***
	(0.034)	(0.033)
Agglomeration	12413.35	−33290.46
	(20564.18)	(85300.82)
Land costs	−5.443***	−22.400
	(1.991)	(18.588)
Unit labor costs	0.013***	0.059***
	(0.002)	(0.012)
Unionization rate	1.391	−45.904
	(14.048)	(61.849)
Year	−12.927	15.482
	(9.052)	(36.796)
Constant	−1789.665**	−1882.449
	(791.994)	(3249.086)
AB test for AR(1) (p-value)	0.000	0.000
AB test for AR(2) (p-value)	0.895	0.337
H₀: Exogeneity of instruments	Not Rejected	Not Rejected
Hansen test chi-square	207.91	64.35
State FE	Yes	Yes
UBO country FE	Yes	Yes
Observations	4285	1830
Number of Groups	247	100

The Debate on the Impact of Fiscal Institutions in a Changing Global Economy

One potential criticism of this study might arise from the concern that, due to the limited data, it is difficult to explain whether the positive impact of fiscal institutions on FMI still holds even in a rapidly changing world economy.

Importantly, rational firms are more likely to invest their production facilities in regions where long-term economic returns are expected because it is hard to relocate their plants and capital after firms have made the investment (Shin, 2020). In this matter, institutional and regulatory stability in target governments would not only lower the risk of investment but would also increase the present values (Panibratov, 2017). Indeed, institutional and regulatory stability would increase the host governments’ reputation and credibility, which would potentially induce foreign investment that could transcend various administrative capacities and policy incentives (Büthe & Milner, 2008; Panizza, 2014). Thus, those governments have attracted foreign firms even in periods of financial crisis and macroeconomic uncertainty by enhancing the security of contracts and property rights, which are necessary conditions for an investment decision (Chopra & Negi, 2010). When a global financial crisis occurs, one rational choice for foreign investors is to invest their capital in the United States because the United States has a steady stream of labor supply and provides the potential ability to recover their economy (Shin, 2020). As a result, given the United States’ highly decentralized institutional system, foreign manufacturing firms could benefit from competitive incentives from state governments as well as fiscally stringent institutions that limit potential tax increases.

To examine whether the impact of fiscal institutions on FMI could still hold in a changing world economy, the ideal method would be to compare the data before and after the 2008 recession. There have been some difficulties, however, that limit the ability to obtain consistent and up-to-date data. The only available data that can expand the time period is the BEA's data on majority-owned manufacturing employment of all affiliates, which data is available from 1987 to 2016. Unlike the original data used in this research, this data is not classified by seven source countries of UBO. Further, BEA used net FMI values only including non-bank affiliates before 2007, while it used FMI values including both bank and non-bank affiliates after 2007. However, in comparing two different time periods, this does not seem to be a source of bias, not only because this research uses the dependent variable as a 3-year running average of the number of employees, but also because the portion of bank affiliates versus all affiliates is small. I excluded 2007 data that cannot be included in the 1987–2016 data not only because the 2007 FMI data is only available at both bank and non-bank affiliates, but also because the financial crisis began in 2008. Thus, this alternative data should be adequate for comparing between the two different time periods of 1987–2006 and 2008–2016. I conducted additional empirical tests with the same statistical estimator (the system GMM) to determine any statistical difference in the effects of fiscal institutions on FMI across these two different time periods.

Looking at the results from two separate datasets, Appendices A and B show that decentralized institutions have remained relatively unchanged even after the 2008 financial crisis. The level of fiscal decentralization is positively and statistically significantly related to FMI both before and after 2007. Additionally, more stringent fiscal institutions (TELs and SMRs) attracted more FMI during these two time periods. More federal grants attracted more FMI across states before and after 2007. These results mirror the original results based on the data from seven UBO countries between 1987 and 2006 and are presented to show the potential effects of fiscal federalism on attracting more FMI regardless of the influence of a global financial crisis. Importantly, these results suggest that rational firms seem to prioritize the potential benefits of fiscal federalism that create economically responsible governments (Jensen & McGillivray, 2005) even in a rapidly changing global economy.

Discussion and Conclusions

This study examines how state fiscal institutions affect the level of FMI in states by considering perspectives of government performance, especially regarding economic development. While the concept of government or organizational performance is still unclear and ambiguous (Andersen et al., 2016), many studies in public administration and management with a New Public Management perspective have sought to measure outcome-based performance by government or organization (e.g., Gerrish, 2016; Hall et al., 2022). Focusing on the functional theory's suggestion that states’ intrinsic motivation is to economically compete with one another, this study identifies state government performance as a government capacity attracting FMI in these states. As a function of inputs, Hall and Kanaan (2021) have suggested that a government's economic development capacity will increase “as they are increasingly informed by evidence from scholarship that is focused not only on outputs but also on outcomes against which to measure mission achievement and that control for various forms of preexisting capacity to measure the impact of particular strategies” (p. 460).

The major point of this paper is that institutional stability and strict regulatory requirements of state governments can improve state governments’ economic performance by exercising broader discretionary fiscal policies. Focusing solely on state performance in economic development as measured by amounts of FMI, this paper illustrates the importance of state-level fiscal institutions in attracting more foreign manufacturing firms to extend manufacturing industries in the United States. This paper asserts that focusing on fiscal federalism could expand understanding of foreign manufacturing firms’ choice of location in the American states, and it provides considerable evidence that fiscal institutions and constraints affect FMI. More specifically, states with a higher level of fiscal decentralization attract more FMI than states with a lower level of fiscal decentralization. These results also suggest that states that receive more federal grant money are more likely to host foreign manufacturing firms. Furthermore, our empirical results indicate that states with more restrictive laws that limit the incentives for and ability of governments to increase revenues and spending attract more FMI. This observed positive impact of fiscal institutions and constraints is more prominent for foreign manufacturing firms in the tax-exemption group.

This research on fiscal federalism marks a significant departure from previous studies whose focus was whether direct tax policy and fiscal incentives are effective in increasing economic development or growth in the states. These findings have potentially significant implications in the current era of government expansion. Increased fiscal autonomy for state governments produces institutional advantages that could lead to better macroeconomic performance, which in turn attracts foreign manufacturers. Narrowing the role of fiscal federalism as a primary responsibility of taxing and spending of subnational governments, scholars of fiscal federalism have claimed that federal grants not only stimulate state and local spending but also influence state governments to lower tax efforts. Proponents of fiscal devolution have also claimed that taxation and spending powers delegated to subnational governments can promote economic development by improving the efficiency of government (Akai and Sakata, 2002; Shin, 2019). Scholars of public finance have also suggested that stringent fiscal institutions not only increase economic stability but also lower fiscal volatility, especially during periods of economic downturn. As a result, rational foreign manufacturing firms that target American states benefit substantially from fiscal federalism, which drives the “market promoting” competition between states.

From a practical standpoint, increasing the fiscal independence of local governments in a state could lead to an increase in investment by foreign manufacturing firms in the state. Second, creating more stringent taxing and spending regulations helps states attract more foreign manufacturing firms. Lastly, a state's administrative capacity to receive more federal funds increases the use of slack resources in enhancing economic well-being, which in turn can help states host more foreign manufacturing firms.

Footnotes

Appendix A.

Table A1.

The Impact of Fiscal Federalism on Foreign Manufacturing Investment by All Countries, 1987–2006, System GMM.

	(1)	(2)	(3)	(4)	(5)
Fiscal federalism
Fiscal decentralization	57007.36***				50493.76***
	(17580.07)				(21132.05)
TELs		58.456*			16.172*
		(89.492)			(125.804)
SMRs			854.051*		152.02*
			(12,810)		(148.86)
Federal grants				1.004*	1.100**
				(1.910)	(2.031)
Control variables
Education expenditures	2.194	2.478	1.289	1.416	4.199
	(2.535)	(2.972)	(2.670)	(3.058)	(3.063)
Corporate tax	−1357.102**	−1076.283**	−1404.988**	−1322.583**	−1100.371*
	(673.572)	(496.781)	(849.309)	(789.283)	(666.405)
Government ideology	−15.999	−31.934*	−34.548*	−31.767*	−20.035
	(19.144)	(22.528)	(25.903)	(23.640)	(18.174)
Unified government	202.664	200.533	318.663	303.567	296.356
	(687.7)	(729.023)	(885.666)	(739.939)	(888.459)
Per capita GSP	0.125	0.263**	0.237*	0.260**	0.119
	(0.116)	(0.158)	(0.153)	(0.151)	(0.156)
Previous FMI	0.201***	0.208***	0.205***	0.210***	−0.188***
	(0.038)	(0.042)	(0.043)	(0.040)	(0.045)
Agglomeration	352492.7	525606.7	749114.5	588417.7	155385.7
	(441118.0)	(540048.8)	(652727.2)	(509503.9)	(487101.0)
Land costs	−97.704	−66.085	−15.965	−1.960	−238.069*
	(133.119)	(225.334)	(198.581)	(173.328)	(155.933)
Unit labor costs	0.172***	0.171***	0.178***	0.172***	0.173***
	(0.031)	(0.035)	(0.038)	(0.035)	(0.033)
Unionization rate	−169.409	−346.705	−352.008*	−311.162	−167.618
	(271.883)	(273.726)	(265.479)	(264.153)	(248.723)
Year	167.079	−155.618	−206.217	−173.627	238.728
	(234.889)	(333.552)	(305.745)	(313.438)	(215.023)
Constant	14382.19	19010.86	16110.1	14353.25	−19705.73
	(45320.97)	(27935.2)	(21005.26)	(28304.22)	(17566.19)
AB test for AR(1) (p-value)	0.010	0.013	0.014	0.014	0.014
AB test for AR(2) (p-value)	0.715	0.693	0.733	0.719	0.782
H₀: Exogeneity of instruments	Not Rejected	Not Rejected	Not Rejected	Not Rejected	Not Rejected
Hansen test chi-square	37.66	40.41	39.59	41.13	33.48
State FE	yes	yes	Yes	yes	yes
Observations	919	919	919	919	919
Number of groups	49	49	49	49	49

Note: Dynamic panel regressions are estimated via two-step system GMM; robust standard errors are in parentheses. Controls for fixed effects for state included but results suppressed. *p < 0.10. **p < 0.05. ***p < 0.01; one-tailed tests.

Appendix B.

Table A2.

The Impact of Fiscal Federalism on Foreign Manufacturing Investment by All Countries, 2008–2016, System GMM.

	(1)	(2)	(3)	(4)	(5)
Fiscal federalism
Fiscal decentralization	27800.13**				22604.56**
	(41823.03)				(40331.78)
TELs		37.831*			31.837*
		(33.697)			(31.803)
SMRs			422.59**		198.486*
			(261.58)		(174.939)
Federal grants				2.146*	2.558**
				(1.574)	(1.645)
Control variables
Education expenditures	2.222	3.113	0.586	1.879	8.477
	(4.509)	(4.343)	(3.023)	(3.383)	(7.302)
Corporate tax	−127.535	−323.459	−504.211	−145.193	−29.579
	(622.424)	(559.565)	(546.231)	(393.827)	(366.923)
Government ideology	−93.214**	−102.358**	−124.753***	−135.652*	−58.995
	(44.092)	(46.091)	(43.152)	(87.325)	(60.752)
Unified government	964.794	735.873	715.155	1824.443*	1180.169
	(1326.922)	(1203.782)	(1193.764)	(1274.084)	(1074.804)
Per capita GSP	0.017	0.019	0.068	0.071	0.181
	(0.329)	(0.314)	(0.255)	(0.195)	(0.351)
Previous FMI	0.016	0.015	0.009	0.044	0.027
	(0.122)	(0.113)	(0.111)	(0.089)	(0.076)
Agglomeration	131623.8	194404	273740.5	531606.9	1.695e + 06
	(1.299e + 06)	(837481.8)	(420394.5)	(857831.4)	(1.694e + 06)
Land costs	−426.284*	−393.147*	−508.603**	−343.953**	−318.955*
	(301.1)	(308.110)	(279.003)	(180.170)	(210.466)
Unit labor costs	0.094	0.074	0.129	0.038	0.074
	(0.377)	(0.434)	(0.297)	(0.246)	(0.355)
Unionization rate	−178.507	−242.540	−273.713	−624.363	−1.363
	(302.927)	(335.353)	(281.414)	(610.103)	(410.590)
Year	−331.195	−238.540	−446.272*	−333.878	−13.292
	(332.709)	(357.348)	(274.888)	(326.818)	(377.189)
Constant	−52913.5	−40542.47	−55416.64*	−58489.57**	−64052.18*
	(53022.09)	(45129.88)	(41596.52)	(27055.8)	(36652.36)
AB test for AR(1) (p-value)	0.037	0.011	0.043	0.062	0.009
AB test for AR(2) (p-value)	0.898	0.945	0.919	0.881	0.610
H₀: Exogeneity of instruments	Not Rejected	Not Rejected	Not Rejected	Not Rejected	Not Rejected
Hansen test chi-square	31.12	28.65	23.86	25.54	30.90
State FE	yes	yes	Yes	yes	yes
Observations	392	392	392	392	392
Number of Groups	49	49	49	49	49

Note. Dynamic panel regressions are estimated via two-step system GMM; robust standard errors are in parentheses. Controls for fixed effects for state included but results suppressed. *p < 0.10. **p < 0.05., ***p < 0.01; one-tailed tests.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Kookmin University.

ORCID iD

Geiguen Shin

Notes

Author Biography

Geiguen Shin is an assistant professor in the Department of Public Administration at Kookmin University, Seoul, South Korea. His research focuses on public management and performance, with a specific application to public school and law enforcement, public sector innovation, fiscal policy, a linkage between redistribution and development, experimental methods, and time-series forecasting.

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