Abstract
In this paper, the authors investigate whether better information about the macroeconomic environment of an economy has a positive impact on its capital inflows, namely portfolio and foreign direct investment (FDI). The purpose of the study is to explicitly quantify information asymmetries by compliance with the IMF’s Special Data Dissemination Standard (SDDS). The authors find that compliance with the SDDS increased FDI inflows by an economically relevant magnitude of 56 % while there are no such aggregate effects for portfolio flows. The empirical strategy demonstrates that the effect runs from SDDS to FDI and not vice versa, introduces a test for endogeneity bias due to omitted variables, and tests for spatial correlation in the residuals.
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Notes
All data displayed in the graph use unweighted averages over the 5 years prior to and after subscription, respectively. Outliers were deleted to provide the graph on a meaningful scale. For non-subscribers, we use the years 1995–1999 and 2000–2004 as the prior and post-subscription period, respectively, as the average year of subscription is 1998.8.
See also Sect. 4.3.1 where we show that capital flows are a poor predictor of joining SDDS.
A variable list with summary statistics of the data can be found in Table 6 of Appendix 1.
Because variable transaction costs would not explain the high turnover and because it seems generally improbable that the cumulated return on a well-diversified portfolio does not exceed the fixed barriers to entry in most markets.
Economies being geographically close tend to have higher correlations, portfolio diversification would thus suggest investing in distant economies.
This ‘pecking order’ of investment can be interpreted as evidence against the models of Razin et al. (1998) and Goldstein and Razin (2006) that suggests that portfolio should be more elastic to informational frictions. Alternatively, Mody et al. (2003) developed a model where FDI has an advantage over other forms of foreign investment in case of information asymmetries.
Similarly, Tesar and Werner (1995) argue that the negative impact of distance on capital flows and the associated lack of international diversification may have less to do with ‘international’ investment choices or transaction costs, but may simply reflect the tendency of individuals to hold ill-diversified portfolios.
After finishing our study, we became aware of an unpublished manuscript by Halvorsen and Vadlamannati (2013) that largely resembles our work and accordingly comes to nearly identical conclusions.
We hence implicitly focus on the investors’ motives (i.e., the supply side) toward host country effects in our empirical strategy. This is not to say that home country effects do not matter but source country fundamentals have to be taken as externally given. This is econometrically controlled for by using global year dummies.
Regressing stocks or the current home bias (as done, inter alia, by Mondria and Wu 2013) on current or lagged information measures is problematic because of the persistence of these dependent variables, i.e., the measure at time t is positively correlated with the measure at time \(t-h\) and the latter is likely to influence information acquisition at time \(t-q, \ q<h\) because holders of a foreign stock will be more interested in their development. This gives rise to an endogeneity bias which is less problematic in the empirical exercise of Mondria and Wu (2013) because it can be interpreted as a feature of their theoretical model but it conflicts with our aim to consistently identify the impact of information on capital flows empirically.
Note that in equilibrium flows will equal the depreciation of existing stock. Flows will hence (also) depend on levels of economic activity in the host country. Furthermore, the adjustment from one equilibrium to another will occur via flows which is exactly our research focus in the context of a policy change in informational asymmetries. See Wacker (2013) for details.
IFS data is balance of payments data as countries’ report them to the IMF. Since IFS data for most countries do not start before 1993, we use data from the IMF’s World Economic Outlook (WEO) where IFS data is not available but WEO data is. WEO data are compiled by IMF staff based on the information gathered by the IMF country desk officers in the context of their missions to IMF member countries and through their ongoing analysis of the evolving situation in each country. Historical data are updated on a continual basis, as more information becomes available, and structural breaks in data are often adjusted to produce smooth series with the use of splicing and other techniques. In order to correct for potential errors from mixing WEO to IFS data, we use a dummy variable that equals 1 if WEO data is used and equals 0 if IFS data is used. Furthermore, we show in a robustness check in Sect. 4.3 that relying exclusively on IFS data provides similar results.
Precise definitions of FDI and portfolio investment can be found in IMF (2009).
A list of SDDS members, their subscription and compliance dates and SDDS data coverage is given in Appendix 1 of the working paper version. The first wave of subscribers (1996) covered 20 industrialized and 22 emerging and transition economies. From the first group, it took countries like Australia, Austria, Belgium, or Switzerland up to four to five years to comply. Generally, the period between subscription and compliance shortened for later subscribers (mostly emerging economies), which potentially anticipated data requirements. Dziobek and Tanase (2007) highlight heterogeneity of subscribing countries and argue that most, but not all, of them have well-developed national statistical systems. For further information about complying countries and covered data, see also http://dsbb.imf.org/pages/sdds/home.aspx and Kester (2006).
Accordingly, 1999 is the first year where 1-values are observed for at least some of the countries in the sample. We perform a robustness check by looking at the impact of (lagged) SDDS subscription and investigating the dynamics of the process, see Sect. 4.3.
However, SDDS has the advantage of providing comparable metadata across countries.
Cf. footnote 29 on p. 1469. In follow-up work they find that investment promotion intermediaries that handle investors’ inquiries at a higher-quality level attract greater volumes of FDI. See Harding and Javorcik (2012).
We test for an omitted (time-dependent) variable problem in the robustness checks. Exclusion of the latter channel is trivial: On one hand, SDDS is a multilateral initiative and most countries joined at a single point in time (1996), so exogeneity can be assumed. The concern that international investors grew very strong over time and pushed both FDI flows and the implication of SDDS in 1996 is controlled for by the time-fixed effect. Furthermore, we look at the date when SDDS specifications are met by subscribers, which usually takes place three to four years after countries’ subscription to SDDS so that our main explanatory variable is predetermined.
Using a smoothing spline is an innovation over the method of Conley (1999), who uses a moving average estimator instead.
Also note that we are using FE estimation, so the fact that larger countries will generally have a lower trade share than smaller economies will not pose a problem.
Since the index also takes into account restrictions on the current account, one may argue that it is too broad for our purpose. However, according to Jeanne (2011), import restrictions can have exactly the same effect as controls on capital inflows and reserve accumulation.
For example, Lucas Jr. (1990), Kesternich and Schnitzer (2010), Javorcik and Wei (2009), Wei (2000a, b), Busse and Hefeker (2007), Papaioannou (2009), Daude and Fratzscher (2008), Sánchez-Martín et al. (2014). Interestingly, most of the previous studies did not control for the interest rate which is important to identify the impact of political risk (cf. footnote 30).
As for many other variables, we lag the PPP exchange rate by one year to avoid the problem of reversed causality since a capital inflow will automatically lead to an increase in the exchange rate if the latter is allowed to float freely, although it is ultimately the net inflow of all forms of capital that is relevant.
Note that results remain similar if we sum the deviation over the first three monts of year t and the last nine months of year \(t-1\).
Furthermore, previous studies on the impact of political instability that failed to control for the interest rate are likely to suffer from an omitted variable bias: instable countries are more likely to have higher interest rates. The relationship between stability and FDI hence also captures the cost of financing, not only an “instability tax”.
The MMR is the rate at which banks lend to each other for short term. Over alternative interest rates, it has the advantage that it is widely available and due to its economic relevance it can be seen as a proxy for interest rates more widely.
Despite cost-minimization playing an important role (cf. Badinger and Egger 2010), MNCs do not necessarily shy away from paying high wages (Lipsey 2002) and Haufler and Mittermaier (2011) even argue that governments in countries with high unionization rates (and thus probably higher wages) will have more incentives to attract FDI, e.g., by tax incentives. Scholes and Wolfson (1990) provide a framework where FDI flows grow as a result of a tax increase. The results of Davies et al. (2009) highlight that MNCs’ response to taxation is very complex.
Random effects is a generalized least squares technique under the restriction \(\alpha _i \mathop {=}\limits ^{!} \alpha \ \forall \ i\) in Eq. (2). The Hausman test does not suggest that random effects would provide consistent estimates for the FDI model but we cannot reject the null hypothesis that the difference between RE and FE is purely random in the portfolio model. Even in the first case, we think it is interesting to see what happens if cross-section variation is taken into account (since random effects is a matrix-weighted average of fixed and between effects estimation, cf. Maddala 1971).
Unless noted otherwise we refer to “significance” as statistical significance at the 95 % level. Furthermore, we use “highly significant” and “weakly significant” for the 99 % and the 90 % level of statistical significance, respectively.
Remember that a higher risk rating indicates more stability.
The appropriate test statistic is an F test for joint insignificance of both lags of exchange rate volatility and we can reject this null hypothesis at the 1 % level of statistical significance for the mentioned FDI models. This result is robust to excluding observations from the euro area.
Remember that wanting statistical significance could also be due to the fact that portfolio flows are more volatile and thus does not necessarily indicate that the effect is economically irrelevant.
This is the straightforward calculation of the marginal effect in log-linear models \({\text {ln}}(y) = X\beta \):
$$\begin{aligned} \frac{{\mathbb{E}}(y|x=1) - {\mathbb{E}}(y|x=0)}{{\mathbb{E}}(y|x=0)} = \frac{\exp {(\beta )} - \exp {(0)}}{\exp {(0)}} = \exp {(\beta )} -1. \end{aligned}$$An unbiased estimator for the marginal impact is discussed in Giles (1982).
We also estimated other, more parsimonious, model specifications of the baseline model. Consistent with the latter, the SDDS coefficient then ranges from 0.31 (mostly driven by sample effects as more parsimonious models allow the inclusion of more developing countries) to 0.51 and is also highly significant in a statistical sense. These results are available upon request.
Since SDDS compliance for most countries took place within a relatively narrow time frame, one could also argue that capital was not abundant enough to raise the capital stock in all countries to the new, higher, equilibrium level simultaneously. Under this restriction it seems reasonable that risk averse investors focus on the supposedly save havens in high-income countries.
This approach suffers from two problems: First, we cannot include time dummies because the model does not converge, so we use a time trend instead. Second, we cannot use FE because of the incremental parameter problem. Instead of country-dummies we hence use dummies that indicate whether a country belongs to the high, upper-middle, lower-middle, or low-income category. Note that this finding is a very strong statement against endogeneity since it still incorporates a significant degree of cross-country variation.
If SDDS has a positive impact on FDI flows, there will obviously arise a differing trend once the effect takes place.
Estimation inference is based on cluster-robust standard errors.
Only when SDDS starts kicking in in 1999, SDDS countries experienced a significantly higher trend growth in FDI inflows, as one would expect from the previous findings because the SDDS-countries’ trend picks up the SDDS dummy variable’s effect. Note that the power of this test will not necessarily be high but if we exclude all control variables from Eq. (4), we obtain p values of 0.0135 (1996), 0.0076 (1997), 0.0057 (1998), and 0.0052 (1999), indicating that the test has at least some power in finding an omitted variable bias.
The spline function is always below 0 and decreasing in distance, though this is not visible on the depicted scale.
We also did not find significant spatial correlation patterns in the residuals of the other models, even when estimated via RE.
As we did not find a significant impact for portfolio flows, we neglect them in this analysis.
Despite the fact that production index publication is a dummy variable, we use FE regressions (instead of logit or probit estimation) for the ease of interpretation and comparison.
‘Methodology’ captures not primarily the methodological aspects of data generation but is a broader measure of methodological compliance and reporting, especially for macro data. There seems to be no correlation of SDDS subscription with data periodicity and timeliness, and even a negative correlation with the SCI assessment of source data in developing countries. This should not be surprising as those two components mostly refer to micro, not macro data.
Given the data variation in the sample, we were not able to identify the effect using FE regressions (which led to weak identification in the first stage regression) but had to take into account cross-country variation and used random effects estimation accordingly. Note that FE and RE effects for our baseline model above were similar. Conventional standard errors are reported, as STATA does not support robust standard errors in this setting. However, as an alternative we estimated a (less efficient) pooled IV regression with robust standard errors. Results were similar, with p values below 0.15 in all three cases and the modest decline in statistical significance mostly emerging from the less efficient pooled IV estimation, as opposed to robust estimation. When clustering standard errors on the country level in this setting, their size increases considerably. Results are available upon request.
This assumption can neither be supported nor dismissed by the findings of Gelos and Wei (2005, p. 3000f).
Ausubel (1990) suggests that if outsiders can assume that—due to increased macroeconomic information—insiders cannot take as much advantage of them, they may increase their investment.
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Acknowledgments
We would like to thank an anonymous referee, Emma Angulo, John Cady, Natasha Che, Ron Davies, Philipp Grosskurth, Stephan Klasen, Silvia Matei, Anna Orthofer, Mohammed El Qorchi, reviewers from the IMF’s Research Department and seminar participants at the IMF’s Statistics Department, the University of Göttingen, the ECB, and the Vienna Institute for International Economics (wiiw) for helpful comments, inputs and ideas. The views expressed in this paper and any possible errors are ours.
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Appendices
Appendix 1: Sample, variables and descriptive statistics
List of countries in the sample (FDI model 2 in Table 1): Hong Kong, Korea (Rep.), Venezuela, Algeria, Argentina, Armenia, Australia, Austria, Bahrain, Belgium, Bolivia, Brazil, Bulgaria, Cote d’Ivoire, Canada, Chile, Colombia, Croatia, Cyprus, Czech Republic, El Salvador, Estonia, Finland, France, Germany, Ghana, Greece, Iceland, Indonesia, Ireland, Italy, Jamaica, Japan, Jordan, Kuwait, Latvia, Lithuania, Malaysia, Mali, Mexico, Morocco, Mozambique, Netherlands, New Zealand, Niger, Norway, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Romania, Russian Federation, Senegal, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Switzerland, Thailand, Togo, Turkey, Ukraine, United Kingdom, United States, Uruguay.
Appendix 2: First stage IV results
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Hashimoto, Y., Wacker, K.M. The role of information for international capital flows: new evidence from the SDDS. Rev World Econ 152, 529–557 (2016). https://doi.org/10.1007/s10290-016-0250-4
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DOI: https://doi.org/10.1007/s10290-016-0250-4