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FDI, growth and trade partisan conflict in the US: TVP-BVAR approach

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Abstract

This paper utilizes time-varying parameters Bayesian vector auto-regression model with stochastic volatility approach to investigate the time-varying dynamic relation between FDI, growth and trade partisan conflict in the US. The empirical results confirm that an increase in trade partisan conflict will deter FDI inflows to the US and discourage economic activities. Besides, we also examine the responses of equity investment, intra-company loans and reinvestment earnings given trade partisan conflict shock. In a robustness check, we consider different measurements on FDI and other control variables. The negative role of a trade partisan conflict shock is not altered, indicating the robustness of the findings. Moreover, trade partisan conflicts like GATT, Omnibus Bill Veto, NAFTA, Bush versus Kerry, the financial crisis and TPP are key factors which affect the dynamics. The US government should remedy the negative impacts of trade policy conflict on FDI inflows and economic growth.

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Notes

  1. The UNCTAD (2019) is available at https://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=1980, accessed on 14/03/2019.

  2. The OECD (2018) is available at http://www.oecd.org/daf/inv/investment-policy/FDI-in-Figures-April-2018.pdf, accessed on 14/03/2019.

  3. Based on the referees’ comments, we check the robustness against foreign direct investment, growth rate and interest rate, respectively.

  4. As suggested by the reviewer, we utilize the net FDI inflows in the following analysis as shown in equation (20).

  5. To solve the issue of negative values of FDI, Busse and Hefeker (2007) propose a method which is considered in the following robustness check. Azzimonti (2019) also uses such method to inspect the robustness of estimations against different proxies for foreign direct investment. The results suggest there are no significant changes.

  6. Following Primiceri (2005) and Franta et al. (2014), the effective federal funds rate and 3-month treasury bill entered into the model are in level. To examine the stationarity of other variables, we utilize Augmented Dickey–Fuller and Phillips–Perron unit root tests. The stationary results are upon request. All variables used in this paper are shown in “Appendix.”

  7. As Canova and Ciccarelli (2009) suggested, eliciting the priors over the whole sample is reasonable if a training sample is not available. Franta et al. (2014) also use such method to generate the priors because the data of Czech Republic cannot be reached pre-1996. The TPCI is freshly constructed, which is used in few studies. Thus, the priors in this study are actually the estimations on a fixed-coefficient VAR model on the full sample from January 1985 to December 2018.

  8. Primiceri (2005) presents three reasons behind for the choice of the time variation of the coefficients. In short, the model misbehaves by choosing a higher \(k_Q\), especially in forecasting (Stock and Watson 1996). Further, Kirchner et al. (2010) use Monte Carlo simulation to check the fits with different situations.

  9. The sign of the responses of \({\text {EI}}_t\), \({\text {IL}}_t\) and \({\text {RE}}_t\) is imposed like the main model shown in Table 3. Besides, the estimations in this part are based on the sample period from 1982Q1 to 2016Q4.

Abbreviations

GATT:

General Agreement on Tariffs and Trade

NAFTA:

North American Free Trade Agreement

TPP:

Trans-Pacific Partnership

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Authors and Affiliations

  1. Department of Ecconomics, Business School, The University of Western Australia, Perth, Australia

    Yifei Cai

  2. Department of Economics and Management of Tourist Units, Agricultural University of Athens, 33100, Amfissa, Greece

    Angeliki Menegaki

  3. Hellenic Open University, Patras, Greece

    Angeliki Menegaki

  4. Open University of Cyprus, Nicosia, Cyprus

    Angeliki Menegaki

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  1. Yifei Cai
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Correspondence to Yifei Cai.

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Work on this paper is supported by Australian Government International Research Training Program (RTP) Scholarship and University Postgraduate Award (UPA).

Appendix A: Variables and sources for the US data

Appendix A: Variables and sources for the US data

See Fig. 9.

Fig. 9
figure 9

Plots of variables

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In this appendix, we present the details of variables and sources for US data.

  • \({\text {FDIF}}_t\): Foreign direct investment flow in the USA from the rest of the world. In millions of dollars, quarterly, seasonally adjusted (annual rate). Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

  • \({\text {CFDIP}}_t\): Foreign direct investment levels in the USA from the rest of the world. In millions of dollars, quarterly, not seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

  • \({\text {EI}}_t\): Equity investment flow in the USA from the rest of the world. Millions of dollars, quarterly, seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1982Q1–2016Q4.

  • \({\text {IL}}_t\): Intra-company debt of US affiliates’ liabilities; asset, flow, millions of dollars, quarterly, seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1982Q1–2016Q4.

  • \({\text {RE}}_t\): Rest of the world; foreign direct investment in the USA: reinvested earnings; asset (current cost), flow, millions of dollars, quarterly, seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1982Q1–2016Q4.

  • \({\text {GR}}_t\): US real GDP growth. Corresponds to real gross domestic product, percent change from quarter one year ago, quarterly, seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

  • \({\text {IR}}_t\): Effective federal funds rate, percent, quarterly, not seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

  • \({\text {IPG}}_t\): Industrial Production Index, percent change from year ago, quarterly, seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

  • \({\text {TB}}_t\): 3-month treasury bill, secondary market rate, percent, quarterly, not seasonally adjusted. Source: FRED Economic data, Federal Reserve Bank of St. Louis. Web site: https://fred.stlouisfed.org/. Time span: 1981Q1–2016Q4.

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Cai, Y., Menegaki, A. FDI, growth and trade partisan conflict in the US: TVP-BVAR approach. Empir Econ 60, 1335–1362 (2021). https://doi.org/10.1007/s00181-019-01795-1

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