Abstract
This paper examines the effect of administrative restrictions on cross-border capital transactions. Using highly disaggregated data from the German balance of payments statistics for the period from 1999 through 2017, we document several stylized facts about the effectiveness of such capital control policies introduced by other countries. Capital controls are associated with economically and statistically significant declines in capital flows; they affect bilateral financial relationships along both the extensive and the intensive margin.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The latest version of the dataset contains data up to the year 2019; see http://web.pdx.edu/~ito/Chinn-Ito_website.htm.
The percentage of 1996 controls persisting in 2012 varies by asset category, but is always sizably above 50%. For instance, for capital market securities, 127 countries had control measures in 1996, 116 (~91%) of which had such controls in 2012.
The assumption that there may be reverse causality from a country’s bilateral financial relationship with Germany to the country’s overall degree of financial openness does not seem to be particularly plausible to us. According to Davis, Valente and van Wincoop (2019), the correlation of German capital flows with world gross flows is substantial, but not exceptional and close to the median in their sample of advanced countries. Cerutti, Claessens and Rose (2019) note that most of the variation in capital flows is unexplained by global factors; see also Barrot and Serven (2018).
German outflows are defined as the purchases of foreign assets by domestic residents and the sales of German assets by foreign residents. German inflows are defined as the sales of foreign assets by German residents and the purchases of German assets by foreign residents. In our baseline analysis, therefore, we focus on the direction of the payment stream. However, in a robustness check, we also examine purchases of assets separately, thereby focusing on the instrument.
The data are available online at http://www.columbia.edu/~mu2166/fkrsu/.
Appendix Table 9 summarizes the key features of the coverage of selected capital controls indicators.
As of September 2022, the Google Scholar citations count of this article is in excess of 2700.
Chinn and Ito (2006) obtain their raw data from the AREAER. However, in contrast to Fernández et al. (2016), they use not only information on regulatory controls over current or capital account transactions, but apply a broader approach, which also takes information on the existence of multiple exchange rates and the requirements of surrendering export proceeds into account.
Our raw data set contains information on the cross-border financial activities of 45,077 German declarants in 104 different types of assets with counterparts in 98 countries. At this level of disaggregation, a balanced sample (including zeros) would yield, for a period of 19 years, (45077*104*98*19=) 8,729,070,896 observations.
Although the sample is balanced, the actual number of observations varies due to singletons.
In general, inflow and outflow controls are highly correlated on a country-by-country basis. However, Fernández et al. (2016) also document a somewhat higher prevalence of outflow controls than of inflow controls, such that inflow controls may have effectively more bite.
In unreported results, we replace stock market capitalization with other measures of a country’s financial conditions, without much effect.
The analysis of a balanced sample may be of particular relevance when capital controls are prohibitive and drive capital flows to zero.
The CRE estimator is a general formulation of a panel regression estimator which considers the within variation and the between variation simultaneously. Under certain conditions, it transforms into the more convenient fixed effects estimator.
Appendix Fig. 1 provides a scatter plot of the maximum value of a country’s capital account restrictions index during the sample period against the corresponding minimum. The figure illustrates considerable variation in capital account openness across countries as well as over time.
The results are unchanged when we analyze different subperiods.
We refrain from using the terms gates and walls because these terms implicitly link different dimensions of capital control restrictions (such as duration and coverage of asset categories), and consider them as largely synonymous, although they are not necessarily correlated with each other. More importantly, since our fixed effects estimator exploits variation over time, we ignore several types of policies (such as long-standing controls) by definition.
From our micro-data, we are able to identify and match transactions in five of Fernandez et al., (2016) ten asset categories. An asset category which may be of particular interest because of its growing relevance in international transactions, but is excluded from our analysis, is financial derivatives. For financial instruments in this asset category, it may not be feasible to measure transactions on a gross basis such that only net transactions are recorded (see paragraph 6.60 in International Monetary Fund 2009). In Appendix Table 10, we assess the sensitivity of our results to this (omitted) asset category. The table reports the benchmark estimates (analogous to Table 2) when financial derivatives are additionally included in the sample; the results are, if anything, stronger with this extension. Similar to other asset categories, we also examine transactions in financial derivatives only and tabulate the results as a memo item in Table 7. For this sample, the β estimates are negative but statistically indifferent from zero
Rather than reducing the volume of capital flows, capital controls may change the composition of cross-border financial transactions. In unreported robustness checks, however, our results remain essentially unchanged when we analyze the effectiveness of capital controls on the shares of different asset types (instead of their volumes).
Germany’s financial relationship with a country can be described along various dimensions, in addition to the volume of capital flows, each of which may be affected differently by capital control measures. Variations in cross-border financial flows along the extensive margin include, in our sample, variations in the number of statistical entries in the German balance of payments, variations in the number of German declarants, and variations in the number of traded asset classes and asset categories. Variations along the intensive margin are variations in the capital flow per entry and variations in the average capital flow per asset class per declarant.
References
Barrot, Luis-Diego and Luis Serven. 2018. Gross Capital Flows, Common Factors, and the Global Financial Cycle. World Bank Policy Research Working Paper 8354.
Besedeš, Tibor, Stefan Goldbach and Volker Nitsch. 2017. You’re Banned! The effect of sanctions on German cross-border financial flows. Economic Policy. 90: 263–318.
Cerutti, Eugenio, Stijn Claessens and Andrew K. Rose. 2019. How important is the global financial cycle? Evidence from capital flows. IMF Economic Review 67: 24–60.
Chinn, Menzie D. and Hiro Ito. 2006. What matters for financial development? Capital controls, institutions, and interactions. Journal of Development Economics 81: 163–192.
Davis, J. Scott, Giorgio Valente and Eric van Wincoop. 2019. Global capital flows cycle: Impact on gross and net flows. NBER Working Paper 25721.
Gregorio, De., Sebastian Edwards José and Rodrigo O. Valdés. 2000. Controls on capital inflows: Do they work? Journal of Development Economics 63: 59–83.
Eichengreen, Barry and Andrew K. Rose. 2014. Capital controls in the 21st century. Journal of International Money and Finance 48: 1–16.
Erten, Bilge, Anton Korinek and Jose Antonio Ocampo. 2021. Capital controls: Theory and evidence. Journal of Economic Literature. 59: 45-89.
Erten, Bilge and Jose Antonio Ocampo. 2016. Macroeconomic effects of capital account regulations. IMF Economic Review 65: 193–240.
Fernández, Andrés, Michael W. Klein, Alessandro Rebucci, Martin Schindler and Martín Uribe. 2016. Capital control measures: A new dataset. IMF Economic Review 64: 548–574.
International Monetary Fund. 2009. Balance of Payments and International Investment Position Manual (BPM6). Washington, DC: International Monetary Fund.
Jahan, Sarwat and Daili Wang. 2016. “Capital Account Openness in Low-income Developing Countries: Evidence from a New Database,” IMF Working Paper WP/16/252.
Klein, Michael W. 2012. Capital controls: Gates versus walls brookings papers on economic activity. 317-367.
Magud, Nicolás E., Carmen M. Reinhart and Kenneth S. Rogoff. 2018. Capital controls: Myth and reality. Annals of Economics and Finance 19: 1–47.
Ostry, Jonathan D., Atish R. Ghosh, Marcos Chamon and Mahvash S. Qureshi. 2012. Tools for managing financial-stability risks from capital inflows. Journal of International Economics 88: 407–421.
Acknowledgements
We thank Prachi Mishra, three anonymous referees, Ulrich Grosch, Hiro Ito, Stephan Jank, Axel Jochem and participants at the Bundesbank research seminar for helpful discussions and comments. This paper represents the authors’ personal opinions and do not necessarily reflect the views of the Deutsche Bundesbank or the Eurosystem.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
