Abstract
This chapter re-investigates the empirical studies claimed for a solution to the Lucas paradox using cross-sectional regression analysis with new specifications of the conditional mean capital inflow function. By replicating a closely related empirical work (Alfaro et al. 2008), this empirical chapter shows that differences in institutional quality (e.g., property rights) between rich and poor countries are not sufficient to explain fully why such large capital flows are observed flowing from poor to rich countries. This finding implies that, when the empirical model is specified more appropriately, differences in institutional quality cannot fully explain why capital flows to rich countries. This chapter confirms this finding using an updated dataset on capital inflows covering the most recent decade. This updated analysis shows that institutional quality alone does not solve the Lucas paradox because capital is still observed flowing to rich countries.
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Notes
- 1.
Alfaro et al. (2008) is one of the most highly cited works in the empirical literature on the determinants of capital inflows that uses a cross-section approach and this chapter reviews their work.
- 2.
According to the IMF (BPM6), an investment is considered as direct if a foreigner holds a controlling stake of at least 10% of the local firm’s equity. Portfolio equity holdings measure ownership of voting stock below the 10% threshold.
- 3.
A transformation method is followed as is used by Burbidge et al. (1988) and is known as inverse hyperbolic sine (IHS) transformation: \( y = { \ln }\left( {x + \sqrt {x^{2} + 1} } \right) \). For simplicity, it is referred as log* and this transformation is used throughout the thesis unless otherwise stated. Busse and Hefeker (2007) also used this transformation. See details of this transformation in Appendix A.
- 4.
The author is grateful to the Alfaro et al. for sharing their dataset.
- 5.
Outlier observations have large residuals. To identify outliers, studentized residuals are used. Eight outliers in the log* model (2**) are: Singapore, South Africa, Trinidad and Tobago, Sweden, Ukraine, Senegal, Iran, and Zimbabwe.
- 6.
The studentized residual plot shows that there are five outliers in linear model and sorting the 10 largest and the 10 smallest residuals, the residuals of Sweden exceeds −2.5 and those of Finland, Denmark, Netherlands, and Great Britain exceed 2.5.
- 7.
See details of the diagnostic tests in Appendix A.
- 8.
Outliers are also identified by using studentized residuals and it is necessary to remove eight outliers that exceed +2 and −2; otherwise the estimates do not satisfy the diagnostic tests.
- 9.
King and Roberts (2014) argue that a large difference between conventional and robust standard errors is a ‘bright red flag’ of model misspecification.
- 10.
The Barro and Lee (2010) data set provides schooling data for 82 countries out of Alfaro et al.’s 98 country sample, although Alfaro et al. report that N = 92 in their Table 4. Alfaro et al.’s own data set also contains schooling data for only 82 countries.
- 11.
Alfaro et al.’s (2008) country sample was retained as it contains a sufficiently large number of countries excluding outlier countries and countries with population less than a million. Moreover, using the same country sample for the updated data helps us to understand better the empirical findings compared to a benchmark estimation result.
- 12.
For additional descriptions of sources of data and data construction, see Appendix B.
- 13.
In this chapter, sum of direct and portfolio equity inflows are used as capital inflows and as the dependent variable; because, including debt might complicate the results because private debt may be partially substitutable for public debt and in this case private debt may be influenced by factors beyond the scope of this analysis. See Sect. 4.3.4 for further discussion on this point.
- 14.
The IMF’s Balance of Payments manual suggests that, the net recording in the financial account means aggregations whereby all debit entries of a particular asset type are netted against all credit entries in the same asset type or in the same liability type. However, changes in financial assets should not be netted against changes in liabilities (IMF BPM6 p. 134, Chap. 8). That said, net capital inflows (inflows with a positive sign) arise when new bonds issued to foreign residents are netted against redemption of bonds issued but acquisition of bond assets is not netted against incurrence of bond liabilities.
- 15.
Barro and Lee schooling data are measured at five-year intervals.
- 16.
Details of the construction of the TFP measure are given in Appendix B.
- 17.
Since 1979, the PRS provided ‘political risk’ services and was developed by Coplin and O’Leary; in 1993. The ICRG rating is used for forecasting financial, economic, and political risk. For details of each sub-index of the ICRG measure of the institutions index, see Appendix B.
- 18.
See details of the World Bank Institute’s Governance Indicators in Appendix B.
- 19.
See details of the variables used to measure each controls and the data sources in Appendix B.
- 20.
An index measure is used based on six Governance Indicators, see Appendix B.
- 21.
Based on coefficient estimates (Table 4.3) and sample mean and sample standard deviation (Summary Statistics 1), a one standard deviation improvement in log* of per capita income, going from the income level of Ecuador to Estonia, has a similar impact on per capita inflows as a 0.36 standard deviation improvement in institutions, such as going form a level of institutional development of Ecuador to Italy.
- 22.
Simple log-transformation means y = log(x) whereas our preferred log-transformation follows IHS’s transformation (log*).
- 23.
Cross country capital stock data are constructed to measure TFP following the approach in Hall and Jones (1999), details of the construction of the TFP and capital stock are given in Appendix B. Hall and Jones’s (1999) TFP index also uses Barro and Lee’s human capital data, which seems more appropriate than the standard Solow residual estimates of TFP, as used in Alfaro et al. (2008), that include human capital in the residual. Assume that the functional form of the model is appropriate, the potential measurement error in estimating TFP and that TFP differences may be sensitive to parameter estimates. Also acknowledged, that this decomposition may not capture completely the effects of the income variable on capital inflows and that the income variable is affected by other factors as well.
- 24.
Valuation effects are discussed in more detail in Chap. 4 (Sect. 4.3.1).
- 25.
Moreover, LM’s capital data also suggests that the histogram plots (see Fig. C.1 in Appendix C) of the log*-transformed dependent variable is approximately normally distributed compared to untransformed dependent variable.
- 26.
This new infrastructure index is only available in recent periods with a maximum of two time series observations available in the WDI.
- 27.
Other indicators of financial development are also examined (results not reported) and similar results obtained.
- 28.
PPP adjusted capital inflows are defined as: capital inflows × (GDP per capita, PPP/GDP per capita, constant US dollars).
- 29.
Hall and Jones (1999) use the term ‘social infrastructure’ to incorporate institutions and openness to trade.
- 30.
For details of these requirements and the discussion, see Stock and Watson (2003).
- 31.
Acknowledging the Acemoglu et al.’s (2001, 2002) argument that European setter mortality as a preferred instrument over others; however, it is worth examining how robust our findings are across other available instruments. More importantly, relying on European settler mortality as the only instrument reduces the number of observations in our sample by almost 50%.
- 32.
When the number of endogenous explanatory variables, n = 1, the number of instruments, K = 1, and maximal size, r = 15%, then the critical value is 8.96. See critical values in Stock and Yogo’s (2005), Table 2, p. 40.
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Akhtaruzzaman, M. (2019). Estimating the Determinants of Capital Inflows Using Cross Sectional Data. In: International Capital Flows and the Lucas Paradox. Springer, Singapore. https://doi.org/10.1007/978-981-13-9069-2_4
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