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Chapter 7: Current Account Imbalances: Can Structural Policies Make a Difference?

Author(s):
Ashoka Mody
Published Date:
April 2013
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Author(s)
Anna IvanovaThe author is grateful for invaluable support and guidance from Ashoka Mody. She also thanks Fabian Bornhorst for allowing her to use some of his analysis. The chapter has benefited from the insightful comments by participants at several workshops at the Germany in an Interconnected World Economy conference organized by the Federal Ministry of Finance of Germany, with particular thanks to the discussant, Carsten-Patrick Meier; “Preventing and Correcting Macro Economic Imbalances in the Euro Area” co-organized by the Central Bank of the Netherlands and the IMF; and the IMF internal group on macroeconomic imbalances in Europe. Special thanks also go to Akito Matsumoto for helpful discussions. Susan Becker provided valuable research assistance.

Anna Ivanova

The discussion of global and regional imbalances has put the spotlight on a possible link between current accounts and structural policies. Drawing on standard empirical current account models, the paper finds that the commonly recommended structural factors cannot explain the widening of imbalances prior to the 2008–09 crisis. That said, structural factors do help explain some part of long-standing cross-country differences in the current account levels. In particular, countries with stricter credit market regulation, higher taxes on businesses, lower minimum wage (in particular, in slow-growing economies), and generous unemployment benefits tend to have higher current account balances than others.

Introduction

Although the relationship between global current account imbalances and the financial crisis of 2008–09 is far from obvious, concern remains that such imbalances are a continuing source of global instability and a threat to a sustainable recovery (Blanchard and Milesi-Ferretti 2009). The seriousness with which the imbalances are viewed is reflected in the far-reaching actions that have been proposed to limit them, including a suggestion for imposing quantitative targets on the current account balances.1 Underlying these proposals is the premise that some sizeable fraction of both the surpluses and the deficits represents “distortions.” In other words, where the current account balance is the outcome of an “optimal” allocation of resources (“good imbalance”), it is not a problem; but the imbalances that result from policy distortions or externalities are “bad.” Since distortions are undesirable even from the country perspective, their mitigation by policy action is twice blessed, since this also scales back the threat from global instability.

In this paper, I empirically examine the contribution of structural factors—the presumed locus of the distortions—to current account imbalances. While the analysis covers an extended period, 1975–2009, I use the results to interpret developments during the last phase of the sample period. It was in those years that the unprecedented global expansion and exuberance were accompanied by widening imbalances. I conclude that a significant fraction of the imbalances in the run-up to the crisis reflected the global cycle. Yet, since much policy attention has been focused on possible structural causes and remedies, the bulk of the paper is devoted to assessing the link between structural policies and the current account. I apply these findings in particular to Germany, where the current account surplus surged to 7.5 percent of GDP in 2007.

In practice, the specific distortions at the root of imbalances remain a matter of some speculation, with competing explanations for the observed behavior of the current account. For example, high current account surpluses due to low investment may reflect a variety of factors, including lack of competition in the financial sector, high corporate taxation, or expectations of low potential growth. More seriously, the same package of structural policies is at times prescribed to both surplus and deficit countries. That package often includes deregulation of product, services, and credit markets, reduction in employment protections, removal of rigidities in the labor market, and taxation. While these policies may be good for many reasons, their impact on the current account is not clear a priori. Structural policies, which may influence productivity growth and/or access to credit, could impact both savings and investment decisions. The variety of channels and the complex interactions between them make the issue an empirical one, a perspective that I adopt.

For a panel of 106 advanced, emerging, and developing countries, I estimate an equation to determine the correlates of the current account balance, using five-year non-overlapping averages. As is standard practice, to represent the intertemporal consumption and investment decisions underlying the current account, I include such control variables as income growth and level, population age structure, fiscal balance, initial net foreign asset position, and the degree of financial integration. In line with other recent studies, I find that these standard determinants of the current account did not evolve significantly during the final years of the global exuberance and so cannot be used to explain the emergence of global imbalances. I then add a number of variables representing structural factors. Even more so than the standard variables, structural factors changed little over time or changed in the same direction in both surplus and deficit countries. Therefore, these factors can explain very little of the emergence of imbalances prior to the crisis.

I infer from these findings that the emergence of imbalances was likely linked to cyclical factors. Germany, in particular, was able to benefit from the global increase in demand for technology-intensive goods, in the production of which Germany has a comparative advantage. However, the “windfall” profits of German firms due to their export success did not immediately translate into an increase in domestic investment, since German firms apparently viewed the boom as temporary, and the German growth potential remained low.

As a further consideration, I ask if structural policies, while not directly influential, may have helped shape the response of the current account to the standard variables. The evidence presented in this paper suggests that even in their role as absorbers or amplifiers of changes in fundamentals, structural factors account for only a small fraction of the imbalances.

To be clear, even if they are not candidates for explaining the rise in imbalances, some structural factors do have a meaningful correlation with the current account balance and so can explain longstanding differences in the current account balances across countries. Even these findings need to be qualified, however, as they are often not robust across country samples and time periods, with some commonly recommended policies increasing and some reducing the current account balance. With these caveats, the empirical results suggest that lower business taxation, credit market regulation,2 and unemployment benefits can reduce the current account surplus. Consistent with earlier studies, I find that a lower minimum wage and less strict employment protection, often recommended for making the labor market “more flexible,” are associated with larger current account surpluses. In the application to Germany, this would imply that the minimum wage would have to be raised and employment protection strengthened to reduce the current account surplus, although this may not be desirable since a higher minimum wage and stricter employment protection might also raise unemployment. These findings’ relevance to Germany is therefore unclear. However, in some of Europe’s peripheral economies, reducing minimum wage and lowering employment protection could contribute to reducing their current account deficits.

The empirical evidence therefore points to select structural measures that would need to be tailored to particular countries, rather than a package of broad structural policies for addressing imbalances. For Germany, these results suggest that lower taxes on businesses, further reduction in the gross unemployment replacement rate, and a smaller public share in the banking system3 could reduce the surplus. Altogether, however, the impact on the German current account surplus will likely be modest.

Literature Review

The relationship between structural policies and the current account remains an open one. The literature agrees that fundamentals such as income per capita, demographics, fiscal policy, and other traditional factors are important determinants of the current account. But beyond that, while several recent studies point to imbalances in the run-up to the 2008–09 crisis as “excessive” compared to the fundamentals, the role of structural factors in the emergence of these imbalances remains an open question. The overall impact of the commonly recommended package of structural policies—such as liberalization of product, services and credit markets, reduction in employment protection, removal of other labor market rigidities, and reduction in business taxation—remains unclear.

Chinn and Prasad (2003), Abiad, Leigh and Mody (2009), Jaumotte and Sodsriwiboon (2010), and Lane and Milessi-Ferretti (2011) find that current account balances are largely driven by such fundamentals as relative per-capita income, fiscal stance, demographics, oil prices, the initial net foreign assets position, and the degree of financial integration conditional on income level. The studies find a positive and significant relationship between relative income per capita and the current account, possibly capturing the fact that capital flows from rich countries to poor countries, where there are higher growth “catching up” opportunities. The current account balances are also found to be relatively large where the fiscal balances are relatively large, suggesting that private sector savings provide only a partial Ricardian offset to changes in public savings (the coefficient is often found to be less than one).

Higher old and young dependency ratios are associated with lower current account balances, since relatively higher dependency ratios are associated with the lower aggregate savings. However, the expected change in the old dependency ratio has a positive association with the current account, since countries that age rapidly are saving more. For oil producers, the current account is positively related to the oil balance, which captures fluctuations in the oil price. The literature also finds that the current account is positively associated with the initial net foreign assets position. While it is somewhat counterintuitive, this finding likely reflects the fact that the net foreign assets position is generating net investment income, which is part of the current account. Financial integration is also found to facilitate access to capital for poor countries; hence, poorer countries tend to have lower current account balances at a given state of financial integration. Some studies also find that among developing countries, the degree of trade openness is negatively associated with the current account balance. Chinn, Eichengreen, and Ito (2011) also find weak evidence that countries with more developed financial markets have weaker current accounts, but their results are not robust.

While a substantial body of literature exists on the link between current accounts and macroeconomic fundamentals, the literature on the link between structural policies and the current account is scarce and inconclusive. Following is a summary of the recent studies, which should allow one to view this chapter in proper perspective.

Kennedy and Sløk (2005) conclude that current account imbalances are structural in nature because they deviate from the current accounts projected under unchanged fiscal policies, unchanged real exchange rates, and monetary policy aimed at closing the output gap in the medium term. They also find that cyclically adjusted current accounts are correlated with the potential growth, although this correlation is largely driven by cross-country differences. On the other hand, they do not find a robust link between specific structural policies and the current account in their reduced-form pooled time series and cross-country regressions, which they conducted on a sample of 14 OECD countries. However, there is some evidence that more open product and financial markets are associated with weaker current accounts. The other variables under investigation included indicators of labor market regulation, foreign direct investment (FDI) restrictiveness, financial market development (stock market capitalization), and labor market performance (trend participation rate and non-accelerating inflation rate of unemployment, or NAIRU). Nevertheless, they encourage policymakers to undertake structural policies because a faster growing economy will improve welfare, though it may or may not reduce imbalances.

Kerdrain, Koske and Wanner (2010) estimate reduced-form regressions in a large panel of 117 advanced, emerging, and developing countries to assess the impact of structural policies on savings, investment, and the current account. They conclude that structural policies may influence savings, investment, and the current account, not only through their impact on macroeconomic conditions such as productivity growth or public revenues and expenditures but also directly. In particular, social spending, notably spending on health care, is associated with lower savings rates, possibly due to lower precautionary savings, and with a lower current account. Stricter employment protection is associated with lower savings rates, if unemployment benefits are low, as well as higher investment rates, perhaps due to a greater substitution of capital for labor, leading to lower current account balances. Product market liberalization is found to temporarily boost investment, though direct impact on the current account could not be detected. Financial market deregulation may lower the savings rate, although only in less developed countries, and again the direct impact on the current account could not be detected.

While the Kerdrain, Koske and Wanner (2010) study is rather comprehensive, their regression includes country-specific fixed effects, which may absorb some of the cross-country variation in the current account, possibly related to the structural variables, which do not change significantly over time. Also, some of their other variables, such as user cost of capital and productivity growth, might reflect structural conditions. As a result, their study does not allow one to fully answer the question of the individual impact of various structural policies on the current account.

Kerdrain, Koske, and Wanner (2010) find little evidence that structural policies affect the speed of adjustment of the current account to the equilibrium. In contrast, Ju and Wei (2007) provide evidence that rigid labor markets reduce the speed of adjustment of the current account to the long-run equilibrium. The latter authors use a two-step approach: first, they estimate a speed of convergence of the current account ratio to the steady state for each country separately, using a vector-error correction model, and second, they relate the speed of convergence to the degree of labor market rigidity in a cross-section of countries. However, large economies, such as the United States, Japan, and Germany, are excluded from this analysis, because the authors suggest that the current accounts in large economies could behave systematically differently due to the importance of not only their domestic labor market flexibility but also foreign labor market flexibility.

Jaumotte and Sodsriwiboon (2010) estimate pooled current account regressions with traditional determinants as controls in a smaller sample of 49 advanced and emerging economies to test for the importance of the European Monetary Union and the potential impact of policies. They find that financial liberalization and higher minimum wage lower the current account, while no direct link could be detected between the level of employment protection or the level of unemployment benefits and the current account. In an econometric study covering 100 advanced, emerging, and developing countries for the period 2001–09 (annual data), Bayoumi, Vamvakidis, and Vitek (2010) find that countries with more (less) credit market regulation have higher (lower) current account balances while controlling for traditional fundamentals.4

Berger and Nitsch (2010) investigate the link between employment protection and product market regulation and the bilateral trade balances as a fraction of total bilateral trade in a sample of 18 European countries over a long time horizon (1948 through 2008). They find that countries with less flexible labor and product markets exhibit systematically lower bilateral trade surpluses than others.

A recent body of literature also identifies imbalances in the period preceding the crisis as “excessive” compared to fundamentals. These studies including Barnes, Lawson, and Radziwill (2010), who estimate current account regression with traditional factors in a sample of 25 OECD countries; Lane and Milessi-Ferretti (2011), in a sample of 65 advanced and emerging economies; and Chinn, Eichengreen, and Ito (2011), in a sample of 109 industrial and developing countries. Barnes, Lawson, and Radziwill (2010) and Chinn, Eichengreen, and Ito (2011) find some evidence that such excesses could partly be explained by housing investment, real housing appreciation, and stock market performance. However, large residuals remain, in particular, for the United States and China. Lane and Milessi-Ferretti (2011) conclude that the countries with the largest excesses before the 2008–09 crisis have experienced the largest corrections thereafter, and also find that the adjustment in deficit countries has been achieved primarily through demand compression rather than expenditure switching. They further conclude that the high output costs that have been associated with the rapid current-account corrections provide support for research that assesses whether current account deficits during good times might partly reflect distortions that fail to internalize the risk of a subsequent sudden stop. It is not clear, however, what exactly these distortions are.

Finally, theoretical literature (see, for example, Vogel 2011) suggests that while structural policies that mainly target supply-side weaknesses may help regain competitiveness in economies with competitiveness problems in the short run, in the longer run this effect is offset by the income effect as imports rise. Consequently, the lasting long-term rebalancing of external accounts also requires the correction of demand imbalances.

The current paper contributes to the existing empirical literature in the following three dimensions. First, it attempts to shed some light of the role of structural policies in the emergence of imbalances in the run-up to the 2008–09 financial crisis. Second, it assesses the direct impact of a commonly recommended package of structural policies on the current account in a large sample of advanced, emerging, and developing countries while controlling for traditional macroeconomic fundamentals. Third, it assesses the potential size of the current account reduction due to these policies in Germany, which has come under a spotlight due to its large current account surplus.

While the results in this paper support some of the earlier findings, they point to the lack of robustness of many results in determining the level of the current account. Moreover, the paper emphasizes the muted role of structural factors in causing the growth of imbalances just prior to the recent crisis. For Germany, the paper offers some policy directions for change but cautions that the quantitative effects may be small.

Baseline Model

This section introduces the results of the baseline econometric model. The baseline model is estimated using a random effects model in a sample of 106 advanced, emerging, and developing countries. It includes traditional fundamentals, which were found to be important current account determinants in the earlier literature. As a robustness check, an Ordinary Least Squares OLS model with cluster robust standard errors (not including fixed effects) is also estimated and yields similar results. The current account is averaged over five-year non-overlapping periods spanning the period of 1975–2009, since the goal is to identify the determinants of the medium term or so to speak “structural current account.” Many of the explanatory variables enter as deviations from the PPP-weighted sample average in a given period, which captures the fact that current accounts are determined by the countries’ positions relative to their trading partners. Data sources are described in the Appendix.

The baseline model (Table 7A.1)5 largely confirms the findings in the literature. Higher relative income per capita, fiscal balance, and initial net foreign assets position as well as higher oil prices for oil producers are associated with the higher current account balances.6 Countries with relatively high current dependency ratios have lower current account balances, as the elderly tend to draw on savings more. However, countries with the higher expected increases in the dependency ratio, capturing the speed of aging, are found to have higher current account surpluses.

The regression also includes the degree of financial integration, measured by the sum of foreign assets and liabilities in percent of GDP and the interaction of the financial integration with the GDP per capita growth in the previous period (column 2). The link between financial integration and the current account works more robustly through growth than through the income level. In particular, it reduces the current account balance in the countries with higher previous-period growth. However, high-growth countries also tend to be poorer countries, so this finding is consistent with that in Abiad, Leigh and Mody (2009).

The model presented in this chapter does not include any crisis dummies, unlike some of the earlier studies. The reason is that the goal is to explain the developments in the current account with the known set of factors, including structural policies, while the dummies could capture some of the effects without identifying the policies and factors behind the crisis.

The relationship between the fundamentals and the current account balance in the sample of OECD countries is somewhat different (Tables 7A.1, 7A.2, 7A.4, and 7A.5, columns 1 and 2). The relationship between the current account and income per capita, fiscal balance, the ratio of net foreign assets to GDP, old dependency ratio, an increase in the old dependency ratio, and the interaction of financial integration and past growth remains broadly unchanged in the OECD sample, although in some cases the coefficients become insignificant. In contrast, the coefficient on the young dependency ratio becomes positive and significant. While this result appears counterintuitive, it is consistent with the findings of Kerdrain, Koske, and Wanner (2010) as well as Barnes, Lawson and Radziwill (2010). It could perhaps be explained by the fact that richer OECD countries can afford to save more for future generations, for example for education purposes. The degree of trade openness also appears to matter more in a sample of OECD countries; in particular, the higher the trade openness the higher is the current account surplus, perhaps reflecting the fact that richer countries that are also more open tend to export capital to the poorer countries.

The baseline model generates a fairly good fit, especially for advanced countries, explaining about 35 percent of the variation in the current account balances in the sample. The model explains cross-country variation better than time-series variation, with the between R-square of 0.5 (Figures 7.1a and 7.1b).

Figure 7.1aActual and Fitted Current Account to GDP, Advanced Economies

Figure 7.1bActual and Fitted Current Account to GDP, Other Countries

Sources: Annual Macroeconomic Database of the European Commission (AMECO); World Economic Outlook database; and IMF staff estimates.

Nonetheless, the residuals from the current account regression largely mirror the imbalances that emerged in mid-2000. The “fundamentals” therefore did not evolve to generate the imbalances. This is the case even when accounting for the potential impact of the financial integration and trade openness. The fact that imbalances widened across the globe suggests that some global forces were at work, although country-specific factors probably determined the direction of change in the current accounts (Figures 7.2a and 7.2b).

Figure 7.2aCurrent Account Balance, Germany and Four Large Economies, 1975–2005

(Five-year averages, percent of GDP)

Figure 7.2bResiduals from the Baseline Model, Germany and Four Large Economies, 1975–2005

(Five-year averages, percent of GDP)

Sources: Annual Macroeconomic Database of the European Commission (AMECO); IMF, World Economic Outlook database; and IMF staff estimates.

Structural Policies and the Current Account

This section introduces structural policies such as regulation, taxation, and the level of minimum wage and unemployment benefits, and discuss the results of the estimation when these factors are added to the baseline regression as five-year averages.

With respect to the emergence of imbalances, two distinct possibilities emerge regarding what role structural policies could have played. First, structural policies on their own could have directly impacted the current accounts. For example, a high level of business taxation may have reduced investment incentives, leading to lower investment and higher current account balances. Second, structural policies could have shaped the response of the current account to changes in macroeconomic fundamentals and shocks, including global shocks. But even if structural policies did not play a major role in the emergence of imbalances in any way, they might help explain the differences in the levels of the current accounts across the globe. In that case, they could also be used as a tool for reducing imbalances. These three possibilities are investigated below.

First, I note that many structural indictors either did not change substantially over time, in particular during the period of the emergence of global imbalances, or if they did, often changed in the same direction for the surplus and deficit countries (Figures 7.3a-d).7 China is the notable exception, where its credit market was substantially deregulated in 2005 and there was a substantial increase in the ratio of minimum to mean wages in 2002. However, given the earlier findings in the literature on the impact of the financial liberalization and the minimum wage, one could expect both of these changes to reduce China’s current account surplus. So it is unlikely that structural factors on their own can explain the emergence of imbalances.

Figure 7.3aEmployment Protection, Germany and Three Selected Countries, 1989–2007

Figure 7.3bGross Unemployment Replacement Rate, Germany and Four Selected Countries, 1980–2004

Figure 7.3cCredit Market Regulation, Germany and Four Selected Countries, 1985–2006

Figure 7.3dRegulation in Electricity/Transport/Communication, Germany and Three Selected Countries, 1980–2007

Sources: Aleksynska and Schindler (2011); Fraser Institute; and Organisation for Economic Cooperation and Development.

Second, I augment baseline regressions with the structural indicators that vary over time8 (see Table 7A.1 for the full sample and Table 7A.4 for an OECD sample). Generally, the results do not indicate a robust relationship between the current account and structural policies, although in some specifications in the full sample the coefficient on the unemployment gross replacement ratio is positive and significant, while that on the ratio of the minimum-to-mean wage and employment protection indicator is negative and significant. No significant association is found for OECD countries, although the sample there is rather small.

The positive association between the current account and the gross unemployment replacement rate could reflect the fact that generous unemployment systems might contribute to higher unemployment rates by reducing incentives to seek new jobs (Bassani and Duval 2006, Vandenberg 2010). In such an environment, the unemployment rate and the probability of becoming unemployed are higher, which could lead to higher precautionary savings by households. However, there might be a counteracting impact as high unemployment benefits provide higher income in the event of job loss. However, to have a negative impact on the current account-to-GDP-ratio, this higher income would have to lead to an increase in the marginal propensity to consume. The results suggest that the latter effect has not been important historically.9

The negative association between the ratio of the minimum wage to the mean wage and the current account is consistent with earlier findings and may reflect the fact that higher minimum wage may lead to higher labor costs and, therefore, hurt competitiveness. This, in principle, could work through both savings and investment channels. Higher labor costs may reduce corporate profitability and savings. However, higher labor costs may also encourage companies to substitute capital for labor, when the latter is expensive.

Finally, higher employment protection is associated with a lower current account, which is consistent with the findings in the literature that higher employment protection reduces savings and increases investment. Higher employment protection raises implicit and explicit labor costs, so the impact can be similar to that of the minimum wage.

Not surprisingly, the residuals from the regression where three of the structural variables are included (unemployment gross replacement rate, ratio of the minimum-to-mean wage, and employment protection index) continue to mirror the imbalances (Figure 7A.2). Hence, structural factors on their own did not evolve to generate the imbalances either.

While structural policies may not have contributed directly to the emergence of imbalances, they may have helped shape the response of the current account to macroeconomic shocks and changes in the fundamentals. In other words, structural factors might have played a role as macroeconomic shock absorbers or amplifiers. This hypothesis is tested by analyzing the interaction of structural factors with the more dynamic fundamentals.

Figure 7.4Residuals from the Model with Structural Variables, Germany and Three Selected Countries, 1990–2005

(Five-year averages, percent of GDP)

Source: IMF staff estimates.

Long-Standing Structural Differences and the Current Account

The long-standing differences in the levels of structural variables on the figures above are striking. They reflect not only policy differences but also differences in institutional arrangements and social norms. As such, these factors could have been important in explaining the long-standing cross-country differences in the current accounts.

To test this hypothesis, given a rather small sample size, all structural variables over all available years were averaged to construct a structural indicator for each country that captures that country’s long-term structural characteristic. The relationship between the current account and these structural variables, which do not vary over time, are investigated, while controlling for fundamentals as before (see Tables 7A.1 and 7A.2 for the global sample; Table 7A.4 for the OECD sample). In most cases, structural variables enter as deviations from a PPP GDP-weighted sample mean to capture the relative standing of a country compared to its trading partners. This formulation essentially allows us to test whether structural factors help explain country-specific fixed effects. The results are summarized in Table 7.1.

TABLE 7.1Impact of Selected Structural Reforms on Current Account, All Countries and OECD Countries
Advanced, Emerging, and Developing EconomiesOECD Sample
Impact on the current accountImpact on the current account
DirectionStatistically significantPossibly strengthened byDirectionStatistically significant
Structural reforms that could REDUCE the current account balance
Deregulation of the credit marketYesNo
Reducing taxes (profit, labor and other business taxes) and simplifying procedures for tax paymentsYesNo
Reducing Unemployment gross replacement rateYesthe higher initial value of the net foreign assets and lower previous period growthLargely Yes
Product market deregulationNANANo
Deregulation in retail tradeNANANo
Structural reforms that could INCREASE the current account balance
Deregulation of professional servicesNANANo
Reducing the ratio of minimum wage to mean wageYesthe higher previous period growthYes
Reducing employment protectionLargely NoLargely Yes
Source: IMF staff estimates.
Source: IMF staff estimates.
TABLE 7.2Germany: Potential Impact of the Selected Structural Reforms on the Current Account
Change in the current account in percent of GDP
Credit Market Deregulation to OECD average−0.5
Reduction in taxes and simplification of tax procedures to US rank−0.3
Reduction in gross unemployment replacement ratio to OECD average−0.4
Total−1.2
Source: IMF staff estimates.
Source: IMF staff estimates.

The most robust result is for the ratio of the minimum-to-mean wage, which has a negative sign and is significant in almost all specifications. The positive impact of the unemployment gross replacement rate is also fairly robust. However, the impact of other structural indicators is not robust across two different samples. Moreover, some commonly recommended policies would increase the current account while others would reduce it. In particular, lower business taxation, less credit market regulation, and lower unemployment benefits can reduce the current account surplus. However, lower minimum wage and employment protection, often recommended for making the labor market “more flexible,” are associated with larger current account surpluses.

The two new indicators that become significant in the overall sample when structural variables enter as averages over time are corporate income tax rate/ indicator of doing business paying taxes10 and credit market regulation (the higher value of this index means less regulation). Countries with a long-standing tradition of relatively high business taxes are found to have, on average, higher current account balances. This could reflect the fact that higher corporate taxation reduces investment incentives and so may raise the current account balance.11

The credit market regulation index, which is constructed by the Fraser Institute, includes several components, namely: the degree of public ownership of the banking system, control of interest rates, percentage of credit extended to the private sector, and competition from foreign banks. For example, in the case of Germany this index indicates strict regulation largely on account of the high public ownership of the banking system. The results suggest that stricter credit market “regulation” raises the current account. Stricter credit market regulation can work through both savings and investment channels. In particular, the lack of access to credit may constrain investment. However, lack of access to credit may also encourage household and corporate savings. Given that the index captures a broader set of components than just credit extended to the private sector, the results could indicate that it is the broader effectiveness and efficiency of the banking sector that affects the current account.

To be clear, though, these relationships are not evident in the OECD sample. The indicators of the degree of regulation in product and services markets, which are available only for OECD countries, generally are not significantly associated with the current account. The results for the OECD sample, however, should be interpreted with caution due to a relatively small number of observations.

Following Chinn and Ito (2007) and Abiad, Leigh, and Mody (2009), as a robustness check two additional financial measures were included, namely the degree of financial development measured by the ratio of private credit to GDP and the measure of capital account openness constructed in Chinn and Ito (2008). Unlike Chinn and Ito (2007), however, I included a measure of financial development at the start of the period rather than the five-year period average to mitigate the potential endogeneity problem, since financial development is measured by the ratio of private credit to GDP. Both financial development and capital account openness were not significant when included on their own. However, similarly to Abiad, Leigh and Mody (2009), I find that fast growing countries (typically, these are poorer countries), which a have higher degree of capital account openness, also have lower current account balances, which could be interpreted as greater capital account openness helping the inflow of capital to poorer countries.

The inclusion of these variables does not alter the conclusions for other structural variables with the exception of the credit market regulation variable, the coefficient on which becomes insignificant. While in principle, credit market regulation and the degree of capital account openness are conceptually different, they appear to capture similar aspects of the availability of credit. For simplicity of exposition, capital account openness is not included in the tables, but the result on credit market regulation should be treated with caution in this light.

Overall, empirical evidence points to select structural measures, rather than a broad and diffuse package of structural policies, for addressing imbalances. Moreover, there may be a trade-off between reducing the current account imbalance and achieving other policy objectives, so the choice of the policy instruments should not be based purely on their impact on the current account.

Interaction of Structural Factors and Fundamentals

This section investigates whether long-standing structural differences may have shaped the response of the current account to changes in fundamentals.

To this end, I augment regressions in Table 7A.112 with the interaction terms of the structural variables averaged over time with the fundamentals. Due to a substantial reduction in the degrees of freedom with the inclusion of the interaction terms, I experimented with the groups of variables both separately and together and chose the variables that turned out to be significant based on a set of these regressions. The results are summarized in Table 7A.5.13

The evidence of the indirect impact of structural policies on the current account is inconclusive, since most of the findings, with the exception of the interaction of the minimum-to-mean ratio and the previous period growth, are not robust across specifications. The most robust finding is that the negative impact of the minimum-to-mean wage ratio on the current account is stronger in countries that experience rapid income growth. This finding could be consistent with the interpretation that a higher minimum wage increases labor cost and reduces companies’ savings or forces them to substitute capital for labor. Higher labor costs in the fast growing countries may provide stronger incentives for companies to substitute capital for labor, leading to higher investment and a lower current account balance. This finding also suggests that the relationship between the minimum wage and the current account may be stronger for less developed countries, which tend to have higher rates of growth.

The impact of the gross unemployment replacement rate depends on the initial net foreign assets position and the previous period’s per capita income growth. In particular, the positive impact of the unemployment benefits on the current account may be reduced in countries that experience rapid income growth. This finding would be consistent with the explanation that high unemployment benefits increase the rate of unemployment and the probability of becoming unemployed, which in turn lead to higher precautionary savings, since such a probability would be reduced in an environment of rapid income growth. The finding that the positive impact of unemployment benefits on the current account is strengthened in countries with a high initial net-foreign-assets position is difficult to interpret; it could be related to the fact that the net-foreign-asset position might capture the persistence of the current account beyond the factor-income contribution.

Nonetheless, the residuals from the regression with interaction terms (Table 7A.5, column 2; and Figure 7.5) track the imbalances, though they are closer to zero than in the baseline model for all countries except Japan. So even as absorbers or amplifiers of changes in the fundamentals, the commonly evoked structural policies cannot account for the emergence of imbalances. There might be other important structural differences in the economies of the surplus and deficit countries, not necessarily representing policy distortions, which translated global shocks into the differing responses of the current accounts.

Figure 7.5Residuals from the Model with Structural Variables Interacted with Fundamentals, Germany and Four Selected Countries, 1975–2005

(Five-year averages, percent of GDP)

Source: IMF staff estimates.

In addition, the emergence of imbalances coincided with the global cyclical upswing and a rapid expansion of world trade; cyclical factors have therefore likely played a role. The correlation of the “excess imbalances” with the housing investment/housing real price as well as with the performance of the stock market found in the literature provide further support to this proposition. A further investigation into the role of structural policies and broader structural factors in the impact of cyclical shocks on the current account may therefore be warranted.

Implications for Germany

This section analyzes what the empirical findings imply for a country like Germany, where the current account surplus reached a historical high of 7.5 percent in 2007. The improvement in the current account in Germany was driven by an improvement in Germany’s trade balance on goods and coincided with the expansion of global trade. Germany’s trade surplus has been consistently positive over the past half-century. Its export competitiveness derives from a comparative advantage in a large number of specialized product varieties. Germany was able to hold its market share when other European countries lost it.14 (Figures 7.6 and 7.7)

Figure 7.6Specialized Products Varieties and World Market Share, Selected Countries.

Germany’s competitive advantage arises from specialized product varieties.

Sources: See footnote 14; UN Comtrade Database; and staff estimates.

Note: Computed with Standard International Trade Classification (SITC) 4 level trade data for 2007.

Figure 7.7Contribution of World Trade Growth and Changes in Market Share to Export Increases, 15 Selected Countries

Germany’s export growth is mainly due to growth in world trade, not increasing market share.

Sources: UN Comtrade Database; and IMF staff estimates.

Note: Increase in exports 2001–08, as percent of exports in 2001, decomposed into the effect of world trade growth and that of increased market share. Computed with Standard International Trade Classification (SITC) 4 level trade data.

While Germany has increased both exports to and imports from Europe as part of increased trade integration, its imports are increasingly tilted toward products produced most cost-effectively by China.15 (Figures 7.8a and 7.8b) Thus, while German exports have remained largely unaffected by the competition from Asia and Eastern Europe, much of the rest of Europe was affected. European imbalances therefore largely reflect the loss of competitiveness of other countries.

Figure 7.8aOrigin of German Imports, 2000

The share of imports from China has grown rapidly from a low base.

Source: UN Comtrade Database.

Figure 7.8bGrowth of Germany’s Imports, by Region 2000–2009

Source: UN Comtrade Database.

While German exports have been performing strongly for a good reason, it is somewhat puzzling why imports did not catch up. A look at domestic demand suggests that while all sectors contributed to increased current account surplus, the largest contributor was German corporate sector (Figure 7.9), which did not match a substantial increase in profits with increased investment despite the latter being consistently low. Germany’s corporate investment remained low compared to European peers, even accounting for foreign direct investment (FDI) outflows. The reluctance to invest domestically reflects long-standing low returns to investment in Germany, but pinning down particular policy distortions that could hold back investment is difficult. One possible explanation, consistent with the findings for the German labor market in the years preceding the 2008–09 crisis (Burda and Hunt, 2011), is manufacturing employers’ lack of confidence that the boom would last. The estimated potential growth in Germany remained low (close to 1 percent) during those years, and the companies chose to save a substantial portion of the “windfall profits” while increasing investment only slowly.16

Figure 7.9Combined Domestic Corporate Investment (Gross) and Outward FDI, Germany and European Union (EU), 1999–2009

German investment has been low compared to EU peers even after accounting for outward foreign direct investment.

(Percent of GDP)

Sources: Eurostat; and Organisation for Economic Cooperation and Development.

Nonetheless, the results of the estimation would suggest that in application to Germany, lower taxes on businesses, further reduction in the gross unemployment replacement rate, and a smaller public share in the banking system could help reduce the surplus, albeit only moderately.

Despite a comprehensive reform of the corporate income tax in 2008, the combined federal and local corporate tax rates in Germany remain above the OECD average. German unemployment benefits also remain rather generous. Public sector banks occupy an important place in the German system, more so than in other advanced economies. These banks have implicit government backing and low profitability. The package of measures, which includes scaling down the public provisioning of banking services, reducing unemployment benefits in the direction of the OECD average, and reducing and simplifying business taxes to move Germany to the U.S. rank in the World Bank’s “Doing Business” indicator could reduce the surplus by about 1.25 percent of GDP. Reduction in taxes and unemployment benefits, however, should be undertaken in a way that does not jeopardize long-term fiscal sustainability goals.

Conclusion

This chapter reported on my econometric investigation into the possible links between the current account balance and the commonly recommended package of structural policies, including financial regulation, tax policy, and labor market flexibility. I find little evidence that this set of policies contributed substantially to the emergence of global imbalances. The large imbalances likely reflected mainly a booming world economy. Moreover, while the structural factors might have helped shape the response of the current account to macroeconomic shocks and fundamentals, even in their role as shock absorbers/amplifiers those factors only partially account for the emergence of imbalances.

Nonetheless, structural policies do help explain long-standing cross-country differences in the current account levels. While the results are not always robust, there is evidence that stricter credit market regulation—encompassing the degree of public ownership of the banking system, interest rate controls, percentage of credit extended to the private sector, and competition from foreign banks—is associated with a higher current account balance. Countries with higher taxes on businesses, generous unemployment benefits, lower minimum wage, and less strict employment protection also tend to have higher current account balances than others. To the extent that less developed countries tend to experience higher rates of growth, lowering the minimum wage is likely to be more effective in reducing the current account deficits of these countries than of advanced countries. Some of the commonly proposed structural policies would reduce—while others would increase—the current account balance. These findings point to select structural measures tailored to the specific country circumstances, rather than a package of broad and diffuse structural policies, for addressing imbalances. It is also important to keep in mind that current account balance is not the only objective of policy makers, and the design of a policy package should take other objectives into account. For example, some of the policies that could lower the current account may increase inequality, which could be undesirable from the social point of view.

In relation to Germany, which experienced a large increase in the current account surplus in mid-2000, these findings imply that the most promising avenues for Germany to pursue in reducing its current account surplus through structural policies is to lower the tax burden, liberalize the banking system to allow greater private sector participation, and reduce unemployment benefits. However, altogether, the impact of these structural policies on the surplus will likely be modest, so a broader strategy for raising potential growth and raising domestic consumption and investment in the medium term will be essential.

Appendix

Data Description

The analysis included a sample of 106 advanced, emerging, and developing countries with populations exceeding one million. The OECD sample included 27 countries. The new EU member states are included starting from the year 1994 to avoid structural breaks. Most of the traditional variables determining the current account were computed following Abiad, Leigh, and Mody (2009).

The current account as a ratio to GDP was taken from the Annual Macroeconomic Database (AMECO) of the European Commission’s Directorate General for Economic and Financial Affairs (http://ec.europa.eu/economy_finance/indicators_en.htm) where available, and from the IMF’s World Economic Outlook (WEO) database in other cases. Income per capita is real PPP GDP per capita in 2005 constant prices with 1996 reference year from Penn World Tables 7.3 up to year 2007 (http://pwt.econ.upenn.edu). The rest of the years were extrapolated using per capita real GDP growth from the WEO database. Fiscal balance as a share of GDP was computed as general government net lending/ borrowing from the WEO database where available, otherwise general government overall fiscal balance was used from the same database. Net foreign assets as a ratio to GDP were computed as foreign assets minus foreign liabilities divided by GDP. All the variables are from the External Wealth of Nations (1970–2007) database, which can be downloaded from http://www.philiplane.org/EWN.html. Financial integration was computed as the sum of foreign assets and foreign liabilities divided by GDP from the same data source.

Old (young) dependency ratios were computed using the data from the World Development Indicators (WDI) database. The old (young) dependency ratio was defined as the ratio of the population aged above 64 (below 15) relative to the population aged 15–64. The increase in the old dependency ratio was computed over the five-year period (see below) to capture the underlying demographic trend. Trade openness is calculated as the sum of exports and imports divided by GDP; it is obtained from the Penn World Tables 7.3 database (‘openc’/100). Oil price is taken from IMF’s WEO database.

Several macroeconomic variables (current account to GDP ratio, GDP per capita growth, fiscal balance, oil price) were averaged over the 5-year non-overlapping periods, namely, 1975–79, 1980–84, 1985–89, 1990–94, 1995–99, 2000–04, and 2005–09. Other variables were included as of the year preceding the beginning of the five-year period, e.g. 2004 for the period 2005–09. Many of the variables were also included as the deviations from the PPP-weighted sample average (growth, fiscal balance, young and old dependency ratios) while real GDP per capita was computed as the ratio to the U.S. real GDP per capita in a given year.

Credit market regulation is obtained from the Fraser Institute (http://www.freetheworld.com/) and comprises an index consisting of four components, measuring the degree of public ownership of the banking system, control of interest rates, percentage of credit extended to the private sector, and competition from foreign banks. The index ranges between zero and 10 with the higher values implying less regulation.

The gross unemployment replacement rate is obtained from Aleksynska and Schindler (2011) and is the average of the gross unemployment replacement rates over two years of unemployment. The ratio of minimum wage to mean wage is taken from the same database. Employment protection indicator for OECD countries was obtained from the OECD database (http://www.oecd.org). For other countries, employment protection index was constructed as an out-of sample forecast from the regression of the OECD employment protection index on the measures of the stipulated advance notice period (in months) and severance pay after nine months (in months), which were obtained from Aleksynska and Schindler (2011).

For OECD countries, central government corporate income tax rates were obtained from the OECD database. The corporate income tax rate comprises the basic central government statutory (flat or top marginal) corporate income tax rate, measured gross of a deduction if any for sub-central tax. The corporate income tax rate for other countries was obtained from the IMF Fiscal Affairs Corporate Income Tax rate database. The indicator of doing business paying taxes is a country’s rank among 183 countries based on the indicator that combines measures of the level of taxes and mandatory contributions that a medium-size company must pay in a given year with the measures of the administrative burden of paying taxes and contributions. The data is available at http://www.doingbusiness.org/rankings. Labor tax wedge for OECD countries is a total tax wedge of the average earner from the OECD database. It is computed as a combined central and sub-central government income tax plus employee and employer social security contribution taxes and expressed as a percentage of labor costs, defined as gross wage earnings plus employer social security contributions. The tax wedge is also adjusted for cash transfers. The indicators of product market regulation, regulation in energy transport and communication as well as regulation in professional services and retail trade are available only for OECD from the OECD database.

The indicators of product market regulation are a comprehensive and internationally comparable set of indicators that measure the degree to which policies promote or inhibit competition in areas of the product market. This indicator is available only for a subset of years, namely 1998, 2003, and 2008. When structural variables were included as time-varying, the values for available years were assigned to the corresponding five-year periods. The OECD indicator of regulation in energy, transport and communications (ETCR) summarizes regulatory provisions in seven sectors: telecoms, electricity, gas, post, rail, air passenger transport, and road freight. While this indicator is not as broad as that of product market regulation, it is available as longer time-series, namely, annual data for the period 1975–2007 with gaps for some countries. The data in available years were attributed to the five-year periods.

The indicator of regulation in professional services covers entry and conduct regulation in the legal, accounting, engineering, and architectural professions. The indicator of regulation in retail trade covers barriers to entry, operational restrictions, and price controls in retail distribution. Both of these indicators are available for the years 1996, 2003, and 2008 and in econometric analysis the data for available years were assigned to the corresponding five-year periods.

TABLE 7A.1Current Account and Structural Policies: Random Effects Model with Robust Standard Errors, Structural Variables are Averages over Five Year Periods, Total Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)(6)(7)(8)(9)
1975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-2009
Log of GDP per capitaa,b0.0247***0.0236***0.0265***0.0211***0.0176**0.01070.00470.01070.0083
[4.49][4.36][4.33][3.01][2.55][1.18][0.47][1.18][1.26]
Previous period growthc,d−0.00020.00100.00120.00030.00110.00160.00200.00160.0022
[−0.25][1.03][1.18][0.34][1.09][1.09][1.25][1.09][1.41]
Fiscal balance to GDPc,d0.3853***0.3782***0.3790***0.1882**0.2325***0.13280.02190.13280.1450
[4.56][4.42][3.73][2.42][2.84][1.13][0.21][1.13][1.45]
Net foreign assets to GDPb0.0146**0.0197***0.0194***0.0253***0.0248**0.0229**0.0282**0.0229**0.0196
[2.26][3.28][2.62][2.61][2.05][2.21][2.15][2.21][1.45]
Old dependency ratiob,d−0.3226***−0.3400***−0.3744***−0.3810***−0.12730.01910.03730.0191−0.2631**
[−3.73][−3.93][−4.15][−3.64][−1.29][0.20][0.29][0.20][−2.09]
Young dependency ratiob,d−0.0138−0.0177−0.0253−0.01160.03540.0699**0.0899**0.0699**0.0110
[−0.54][−0.67][−0.88][−0.43][1.25][2.33][2.08][2.33][0.31]
Trade opennessb−0.0080−0.0117−0.0079−0.00330.00220.00460.01460.00460.0152
[−0.82][−1.12][−0.64][−0.23][0.25][0.41][1.17][0.41][1.19]
Increase in the old dependency ratio over 5 years0.7330***0.6511**0.6117**0.5964**0.25930.6761***0.8173**0.6761***1.0855***
[2.91][2.54][2.19][2.02][0.96][3.37][2.28][3.37][3.35]
Contemporaneous oil price*Oil producerc0.0005**0.0004**0.0005**0.0006***0.00030.00010.0003*0.00010.0004**
[2.29][2.07][2.09][2.73][1.42][0.89][1.88][0.89][2.10]
Financial integrationb0.00340.0035*0.00310.0032*0.0004−0.00100.00040.0009
[1.61][1.73][1.44][1.71][0.28][−0.69][0.28][0.38]
Financial integration*Previous period growthe−0.0010**−0.0013***−0.0011***−0.0011***−0.0003−0.0005−0.0003−0.0010**
[−2.53][−3.59][−3.44][−3.46][−0.77][−1.49][−0.77][−2.04]
Credit market regulationc,d−0.0025−0.0010−0.0018−0.0020−0.0017−0.0020
[−1.45][−0.63][−1.04][−1.16][−0.88][−1.16]
Gross replacement ratec,d,f0.0572**0.04210.03140.0816*0.03140.0709
[2.12][1.49][0.89][1.79][0.89][1.60]
Corporate income tax ratec,d0.00020.0002−0.00040.0002
[0.78][0.40][−0.86][0.40]
Ratio of minimum wage to mean wagec,d−0.0169−0.0271*−0.0169−0.0194
[−1.31][−1.92][−1.31][−1.23]
Employment protection indexc,d,g−0.0125**−0.0053
[−2.06][−0.82]
Observations548548501371242153124153172
Number of countries106106101776548484859
Source: IMF staff estimates, see Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates, see Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.2Current Account and Structural Policies: Random Effects Model with Robust Standard Errors, Structural Variables are Averages over the Whole Period, Total Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)(6)(7)(8)(9)
1975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-19941995-2009
Log of GDP per capitaa,b0.0264***0.0205***0.0206***0.0157***0.0158***0.0187***0.0191***0.01300.0263***
[4.83][3.58][3.42][3.01][3.00][3.49][3.65][1.50][2.99]
Previous period growthc,d0.00120.00140.00150.00160.00160.00170.00180.0009−0.0009
[1.17][1.31][1.35][1.31][1.31][1.45][1.51][0.26][−0.35]
Fiscal balance to GDPc,d0.3941***0.3123***0.3369***0.3202**0.3201**0.2954**0.2925**0.1979**0.2683
[4.58][2.90][3.02][2.39][2.38][2.31][2.32][2.23][1.59]
Net foreign assets to GDPb0.0211***0.0194**0.0193**0.0228**0.0226**0.0277***0.0283***0.02090.0611***
[3.34][2.47][2.29][2.49][2.40][3.11][3.18][1.53][3.33]
Old dependency ratiob,d−0.3481***−0.3966***−0.4024***−0.4652***−0.4657***−0.4644***−0.4671***−0.0167−0.4961***
[−4.07][−4.46][−4.39][−4.93][−4.92][−4.94][−5.00][−0.10][−3.39]
Young dependency ratiob,d−0.0211−0.0193−0.0170−0.0236−0.0232−0.0191−0.02030.0647*0.1191
[−0.80][−0.79][−0.66][−0.88][−0.86][−0.76][−0.81][1.69][1.49]
Trade opennessb−0.00560.00320.01000.01140.01170.00800.0064−0.01690.0155
[−0.48][0.25][0.81][1.00][1.01][0.63][0.52][−1.17][1.06]
Increase in the old dependency ratio over 5 years0.6131**0.6208**0.6741**1.0845***1.0905***0.9556***0.9197***0.6809*1.6603***
[2.37][2.13][2.22][3.13][3.14][2.92][2.82][1.90][2.95]
Contemporaneous oil price*Oil producerc0.0004**0.0005**0.0005**0.0005**0.0005**0.0005**0.0005**0.00000.0004*
[2.06][2.48][2.38][2.16][2.15][2.11][2.08][0.08][1.83]
Financial integrationb0.00340.00320.00300.00160.00150.00230.00270.0098−0.0002
[1.60][1.64][1.54][0.72][0.67][0.98][1.14][1.23][−0.09]
Financial integration*Previous period growthe−0.0011***−0.0013***−0.0014***−0.0013**−0.0013**−0.0013***−0.0014***0.0012−0.0006
[−2.81][−4.10][−4.00][−2.37][−2.36][−2.63][−2.69][0.33][−1.35]
Credit market regulationd,f−0.0047**−0.0081***−0.0080***−0.0059**−0.0059***−0.0070***−0.0067***−0.0001−0.0088
[−2.16][−3.11][−2.70][−2.29][−2.58][−3.07][−2.81][−0.04][−1.44]
Gross replacement rated,f,g0.0971***0.0998***0.0929**0.0945**0.1130***0.1007**0.05110.1128*
[2.73][2.65][2.16][2.50][2.80][2.12][1.15][1.79]
Corporate income tax rated,f0.00050.0014**0.0014**0.0003−0.0007
[0.90][2.15][2.53][0.54][−0.67]
Ratio of minimum wage to mean waged,f−0.0400**−0.0399**−0.0325*−0.0328*−0.0122−0.0669**
[−2.42][−2.48][−1.89][−1.88][−0.69][−1.99]
Employment protection indexd,f,h−0.0004−0.0048
[−0.07][−0.71]
Doing business paying taxes rankf0.0001*0.0001*
[1.82][1.76]
Observations532426400323323349349114118
Number of countries1017873606065654359
Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.3Current Account and Structural Policies: OLS with Cluster Robust Standard Errors, Structural Variables are Averages over the Whole Period, Total Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)(6)(7)(8)(9)
1975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-19941995-2009
Log of GDP per capitaa,b0.0215***0.0149***0.0143**0.0113**0.0111**0.0146***0.0153***0.01130.0202**
[4.51][2.90][2.58][2.16][2.09][2.87][3.07][1.35][2.31]
Previous period growthc,d0.0017*0.00160.00150.00180.00180.0020*0.0021*0.0009−0.0003
[1.72][1.48][1.43][1.45][1.44][1.72][1.79][0.26][−0.12]
Fiscal balance to GDPc,d0.3643***0.3084***0.3418***0.3219**0.3222**0.2965**0.2910**0.1589*0.2666
[4.31][3.34][3.44][2.62][2.61][2.55][2.55][1.80][1.59]
Net foreign assets to GDPb0.0339***0.0347***0.0354***0.0383***0.0386***0.0431***0.0429***0.0338***0.0661***
[5.72][4.47][4.26][4.16][4.03][4.78][4.74][2.70][3.47]
Old dependency ratiob,d−0.3061***−0.3373***−0.3240***−0.4374***−0.4378***−0.4487***−0.4504***−0.0533−0.6214***
[−3.35][−4.19][−3.91][−5.12][−5.13][−5.22][−5.24][−0.31][−4.48]
Young dependency ratiob,d−0.0204−0.0238−0.0216−0.0318−0.0313−0.0281−0.02940.05020.0442
[−0.76][−1.03][−0.88][−1.19][−1.17][−1.16][−1.21][1.33][0.57]
Trade opennessb−0.0061−0.00020.00420.00570.00600.00510.0034−0.01840.0207
[−0.54][−0.01][0.35][0.48][0.49][0.43][0.29][−1.44][1.67]
Increase in the old dependency ratio over 5 years0.33010.42430.49380.8303**0.8340**0.7158**0.6772*0.46771.5275***
[1.16][1.27][1.50][2.41][2.41][2.15][1.98][1.25][2.71]
Contemporaneous oil price*Oil producerc0.0005**0.0006***0.0006***0.0005**0.0005**0.0005**0.0005**0.00020.0005**
[2.10][3.29][3.30][2.57][2.59][2.41][2.33][0.43][2.01]
Financial integrationb0.0061***0.0047***0.0048***0.0044*0.0043*0.0050*0.0054**0.01220.0027
[2.95][2.69][2.71][1.74][1.68][1.91][2.03][1.50][0.78]
Financial integration*Previous period growthe−0.0016***−0.0015***−0.0016***−0.0017***−0.0017***−0.0018***−0.0018***0.0009−0.0014**
[−4.21][−5.29][−5.40][−3.05][−3.05][−3.57][−3.58][0.26][−2.15]
Credit market regulationd,f−0.0039**−0.0069***−0.0066**−0.0050**−0.0052**−0.0064***−0.0061***−0.0006−0.0082
[−2.04][−3.04][−2.57][−2.21][−2.51][−3.05][−2.76][−0.32][−1.39]
Gross replacement rated,f,g0.0789**0.0760**0.0768**0.0798**0.0955***0.0837*0.04920.1009*
[2.46][2.30][2.01][2.46][2.81][2.00][1.18][1.70]
Corporate income tax rated,f0.00060.0010*0.0010*0.0002−0.0005
[1.23][1.75][2.00][0.36][−0.52]
Ratio of minimum wage to mean waged,f−0.0348**−0.0344**−0.0287*−0.0293*−0.0089−0.0684**
[−2.30][−2.36][−1.80][−1.81][−0.53][−2.13]
Employment protection indexd,f,h−0.0010−0.0043
[−0.19][−0.70]
Doing business paying taxes rankf0.0001*0.0001*
[1.74][1.68]
Observations532426400323323349349114118
R-squared0.3710.3990.4010.3680.3680.3630.3610.3110.549
Source: IMF staff estimates. See Data Description for data sources.Note: OLS: ordinary least squares standard linear regression procedure.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates. See Data Description for data sources.Note: OLS: ordinary least squares standard linear regression procedure.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.4Current Account and Structural Policies: Random Effects Model with Robust Standard Errors, Structural Variables are Averages over Five Year Periods, OECD Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)(6)(7)(8)
1975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-2009
Log of GDP per capitaa,b0.0372**0.0401**0.0402**0.0384*0.0444**0.01270.01400.0207
[2.05][2.28][2.12][1.94][2.29][0.89][0.80][0.92]
Previous period growthc,d−0.0021−0.0005−0.0005−0.0007−0.00220.00060.00120.0015
[−1.09][−0.21][−0.27][−0.31][−0.97][0.37][0.56][0.70]
Fiscal balance to GDPc,d0.2813**0.2814**0.2796**0.2928**0.3608***0.3052*0.12790.1275
[2.21][2.16][2.16][2.24][2.70][1.66][0.69][0.67]
Net foreign assets to GDPb0.02910.03070.03040.03480.03100.0311**0.0530***0.0692***
[1.41][1.35][1.33][1.50][1.31][2.32][3.96][5.90]
Old dependency ratiob,d0.0055−0.0562−0.0588−0.0376−0.04300.08620.0525−0.1104
[0.05][−0.47][−0.46][−0.27][−0.28][0.66][0.40][−0.55]
Young dependency ratiob,d0.1513**0.1422**0.1447*0.1843***0.1483**0.0994*0.14310.1367
[2.14][1.98][1.88][2.76][2.33][1.94][1.61][1.21]
Trade opennessb0.0261*0.0295**0.0295**0.0346***0.0420***0.0380***0.0527***0.0534***
[1.95][2.18][2.00][2.71][3.44][2.96][3.61][3.36]
Increase in the old dependency ratio over 5 years0.5264**0.4982*0.4958*0.5353*0.5232*0.9795***1.5232***1.6074***
[1.97][1.76][1.76][1.89][1.85][4.05][4.00][4.19]
Contemporaneous oil price*Oil producerc0.00020.00020.00020.00010.00010.00010.00020.0001
[0.94][0.81][0.80][0.34][0.45][0.64][1.61][0.53]
Financial integrationb0.00020.0001−0.0004−0.0004−0.0017−0.0029**−0.0018
[0.08][0.06][−0.22][−0.23][−1.49][−2.28][−1.10]
Financial integration*Previous period growthe−0.0008**−0.0008**−0.0007**−0.0006−0.0004−0.0007**−0.0010***
[−2.07][−1.98][−1.97][−1.59][−1.35][−2.04][−3.81]
Credit market regulationc,d0.00020.0004−0.0027−0.00200.00160.0014
[0.09][0.15][−0.73][−0.69][0.27][0.20]
Gross replacement ratec,d,f0.0090−0.0043−0.01280.02820.0384
[0.32][−0.16][−0.46][0.46][0.83]
Corporate income tax ratec,d0.00030.00060.00050.0001
[0.61][0.90][0.52][0.07]
Ratio of minimum wage to mean wagec,d0.0192−0.0020−0.0271
[0.77][−0.09][−1.25]
Employment protection indexc,d,g−0.0041−0.0121
[−0.39][−1.14]
Regulation in energy transport and communicationc,40.0048
[0.72]
Observations160160160148142976866
Number of countries2727272626191919
Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.5Current Account and Structural Policies: Random Effects Model with Robust Standard Errors, Structural Variables are Averages over the Whole Period, OECD Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)
1975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-20091975-2009
Log of GDP per capitaa,b0.0407*0.03020.03140.01680.00890.01850.02270.01530.01850.01330.0113
[1.67][1.30][1.33][0.62][0.30][0.71][1.16][0.76][0.71][0.67][0.65]
Previous period growthc,d−0.0005−0.0010−0.00120.00020.00040.00020.00050.00070.00020.00070.0007
[−0.23][−0.42][−0.46][0.08][0.16][0.10][0.19][0.31][0.10][0.33][0.30]
Fiscal balance to GDPc,d0.2807**0.2643**0.2612*0.08930.07460.07950.10920.09710.07950.07170.0831
[2.13][1.98][1.94][0.59][0.48][0.51][0.72][0.65][0.51][0.55][0.59]
Net foreign assets to GDPb0.03020.03300.03100.0612***0.0641***0.0666***0.0665***0.0712***0.0666***0.0728***0.0720***
[1.34][1.44][1.30][4.42][4.82][5.29][4.49][4.57][5.29][5.74][5.44]
Old dependency ratiob,d−0.0606−0.0893−0.0911−0.2026*−0.2107*−0.2047*−0.3575***−0.4064***−0.2047*−0.3607***−0.3429***
[−0.47][−0.72][−0.70][−1.77][−1.73][−1.69][−2.64][−3.07][−1.69][−2.83][−3.62]
Young dependency ratiob,d0.1438**0.1384**0.1435**0.05570.0612*0.0719**0.0503*0.03890.0719**0.0465*0.0491*
[1.96][2.08][2.08][1.41][1.96][2.14][1.87][1.45][2.14][1.80][1.69]
Trade opennessb0.0299**0.0321**0.0321**0.0373***0.0377***0.0378***0.0385***0.0323***0.0378***0.0360***0.0388***
[2.10][2.38][2.29][2.88][3.11][2.89][3.55][3.36][2.89][3.61][3.41]
Expected increase in the old dependency ratio0.4963*0.4769*0.47740.7237**0.7191*0.7086*1.0304**0.9610**0.7086*0.9749**0.9351**
[1.75][1.65][1.64][2.00][1.89][1.94][2.22][2.14][1.94][2.09][2.07]
Contemporaneous oil price*Oil producerc0.00020.00020.00010.00010.00010.00000.00020.00020.00000.00020.0001
[0.81][0.78][0.78][0.66][0.53][0.25][0.94][1.08][0.25][0.99][0.65]
Financial integrationb0.0001−0.0001−0.0002−0.0014−0.0017−0.0018−0.0015−0.0015−0.0018−0.0015−0.0014
[0.06][−0.03][−0.11][−0.99][−1.41][−1.42][−1.03][−1.07][−1.42][−1.21][−1.06]
Financial integration*Previous period growthe−0.0008*−0.0007*−0.0006−0.0009***−0.0009***−0.0010***−0.0012***−0.0013***−0.0010***−0.0012***−0.0011***
[−1.88][−1.84][−1.55][−3.05][−3.28][−3.57][−2.94][−2.97][−3.57][−3.21][−2.72]
Credit market regulationd,f−0.00010.00060.0013−0.0003−0.0039−0.0032−0.0011−0.0012−0.0032−0.0044−0.0031
[−0.02][0.10][0.20][−0.06][−0.67][−0.44][−0.16][−0.22][−0.44][−1.03][−0.72]
Gross replacement rated,f,70.03670.03600.02260.0804*0.0821**0.0785*0.0808**0.0821**0.0823**0.0777*
[0.81][0.79][0.58][1.95][2.00][1.87][2.01][2.00][2.27][1.82]
Corporate income tax rated,f−0.00040.00110.00120.00060.00080.00110.0011
[−0.63][0.99][1.18][0.81][1.24][1.41][1.38]
Ratio of minimum wage to mean waged,f−0.0386***−0.0468***−0.0483***−0.0649***−0.0658***−0.0483***−0.0507***−0.0511***
[−3.00][−3.42][−2.96][−3.99][−4.70][−2.96][−3.83][−4.15]
Employment protection indexd,f,h−0.0121*−0.0123−0.0126**−0.0150**−0.0123−0.0134**−0.0134**
[−1.72][−1.48][−2.06][−2.43][−1.48][−2.02][−2.04]
Doing business paying taxes rankf0.00000.0000
[0.26][0.26]
Labor tax wedged,f0.00070.00060.00070.0006
[1.15][0.97][1.40][1.41]
Regulation in professional servicesd,f−0.0058−0.0041−0.0029
[−1.08][−0.67][−0.55]
Regulation in retail traded,f0.00200.00290.0021
[0.41][0.64][0.50]
Product market regulationd,f0.0139
[0.83]
Regulation in energy transport and communicationd,f0.0097
[1.43]
Observations160157157115115115115115115115115
Number of countries2726261919191919191919
Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

At the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

5-year period average.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.6Current Account and Structural Policies (interaction with fundamentals): Random Effects Model with Robust Standard Errors, Structural Variables are Averages over the Whole Period, Total Sample
Dependent variable=current account to GDP (5-year average)(1)(2)(3)(4)(5)
1975-20091975-20101975-20091975-19941995-2009
Log of GDP per capitaa,b0.0130**0.0169***0.0134***0.01110.0200**
[2.53][3.17][2.74][1.45][2.07]
Previous period growthc,d0.00110.00110.00160.0012−0.0037
[0.60][0.96][1.24][0.49][−1.60]
Fiscal balance to GDPc,d0.3104**0.3107**0.2846**0.1732*0.2735*
[2.43][2.17][2.55][1.81][1.69]
Net foreign assets to GDPb0.0237**0.0293**0.0424***0.0268**0.0769***
[2.41][2.26][5.44][2.19][5.03]
Old dependency ratiob,d−0.4416***−0.4878***−0.4353***−0.0229−0.4896***
[−4.86][−5.33][−5.21][−0.14][−3.48]
Young dependency ratiob,d−0.0238−0.0133−0.02650.04630.0566
[−0.89][−0.50][−1.04][1.22][0.80]
Trade opennessb0.00830.00780.0050−0.01710.0102
[0.73][0.75][0.45][−1.00][0.51]
Increase in the old dependency ratio over 5 years1.0614***1.0954***1.0508***0.8079**1.5315**
[3.16][2.98][3.29][2.37][2.48]
Contemporaneous oil price*Oil producerc0.0005**0.0004*0.0005**−0.00020.0004*
[2.26][1.84][2.39][−0.63][1.69]
Financial integrationb0.00200.00480.00180.0099−0.0020
[0.90][1.12][0.96][1.16][−1.14]
Financial integration*Previous period growthe−0.0013**−0.0007−0.0014***0.0001−0.0001
[−2.16][−1.37][−3.35][0.03][−0.21]
Credit market regulationd,f−0.0053**−0.0110***−0.0059***−0.0008−0.0105***
[−2.34][−2.58][−3.11][−0.34][−2.79]
Gross replacement rated,f,g0.0843**0.1608***0.1275***0.03850.1962***
[2.38][3.12][4.41][0.81][3.91]
Corporate income tax rated,f0.0015***0.0011*0.0011**0.0003−0.0004
[2.81][1.72][2.10][0.39][−0.53]
Ratio of minimum wage to mean waged,f−0.0495***−0.0462***−0.0460***−0.0388**−0.0565**
[−3.37][−2.66][−3.43][−2.07][−1.98]
Employment protection indexd,f,h−0.0004−0.0089−0.00270.0018−0.0002
[−0.07][−1.32][−0.62][0.30][−0.03]
Crerdit Market Regulation*Previous period growthc,d,f−0.0002
[−0.30]
Gross replacement rate*Previous period growthc,d,f,g−0.0145−0.0166**−0.0115−0.0257**
[−1.43][−2.17][−1.21][−2.05]
Corporate income tax rate*Previous period growthc,d,f0.00030.0004**0.00010.0003
[1.26][1.96][0.57][0.72]
Ratio of minimum wage to mean wage*Previous period growthc,d,f−0.0119**−0.0110**−0.0136***−0.0199**
[−2.26][−2.24][−2.75][−2.01]
Employment protection index*Previous period growthc,d,f,h−0.0003
[−0.19]
Ratio of minimum wage to mean wage*Financial integrationb,d,f0.0130
[1.30]
Gross replacement rate*Trade opennessb,d,f,g
Gross replacement rate*Net foreign assets to GDPb,d,f,g0.2037***0.06320.2415**
[4.66][0.86][2.44]
Observations323323323116118
Number of countries6060604459
Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

Fundamentals are included as of the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

fundamentals are included as 5-year period averages.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

Source: IMF staff estimates. See Data Description for data sources.

Deviation from US level in a given year.

Fundamentals are included as of the beginning of the period, for example for a 5-year period covering 2005-2009, 2004 value was used.

fundamentals are included as 5-year period averages.

Deviation from a PPP GDP-weighted sample average.

Financial Integration is one year before the beginning of a given 5-year period; growth is the average over the previous 5-year period.

Structural variable are country averages over all available years in a given period.

Gross replacement rate is the average over 2 years of unemployment.

For OECD countries OECD employment protection index was used. For a broader sample an index was constructed as an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after 9 months. The latter two indicators are available for a large sample of advanced, emerging and developing countries (Aleksynska & Schindler, 2010).

TABLE 7A.7Structural Indicators: Germany in Comparison
Latest available yearUnited StatesaFranceSpainJapanGermanyOECD Average
Credit Market Regulationa20087.79.29.38.98.29.0
Combined Corporate Income Tax Rateb2009393430403026
Average Labor Tax Wedge (single earner w/o children at 100 percent of average wage)2009294938295136
Doing business rank on paying taxes200954558611580NA
Unemployment Gross Replacement Ratec2008556661546056
Product Market regulation20080.81.51.01.11.31.4
Regulation in Professional Services20081.12.12.11.52.92.0
Regulation in Retail Trade20082.63.12.72.42.42.4
Regulation in Energy Transport and Communication20071.82.21.62.21.12.1
Employment Protection Regulation20080.22.52.51.93.02.2
Ratio of Minimum Wage to Mean Waged20050.30.50.20.40.00.3
Sources: Fraser Institute; Organisation for Economic Co-operation and Development (OECD); Business Indicators; and Aleksynska and Schindler (2011). See Data Description for more detailed information.

Higher value means less regulated. For the United States the index declined from 8.9 to 7.7 between 2007 and 2008.

The regression included federal corporate income tax rate, which was available for a wide set of countries. The table presents combined corporate income tax rate, including sub-central tax rates, which provides a better assessment of the actual tax burden on corporations.

The regression included a two-year average unemployment gross replacement ratio, which was available for a wide set of countries but the series ended in 2005. The table presents unemployment net replacement ratio for a single person, no children, at 100 percent of average wage, which is available for OECD countries up to 2008.

While Germany has no minimum wage in most sectors except for construction workers, electrical workers and some others, the de facto floor may be higher as wages are set by collective bargaining agreements and enforceable by law.

Sources: Fraser Institute; Organisation for Economic Co-operation and Development (OECD); Business Indicators; and Aleksynska and Schindler (2011). See Data Description for more detailed information.

Higher value means less regulated. For the United States the index declined from 8.9 to 7.7 between 2007 and 2008.

The regression included federal corporate income tax rate, which was available for a wide set of countries. The table presents combined corporate income tax rate, including sub-central tax rates, which provides a better assessment of the actual tax burden on corporations.

The regression included a two-year average unemployment gross replacement ratio, which was available for a wide set of countries but the series ended in 2005. The table presents unemployment net replacement ratio for a single person, no children, at 100 percent of average wage, which is available for OECD countries up to 2008.

While Germany has no minimum wage in most sectors except for construction workers, electrical workers and some others, the de facto floor may be higher as wages are set by collective bargaining agreements and enforceable by law.

Figure 7A.1Germany and EU: Savings and Investment by Sector, 1999–2009

Source: Eurostat.

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See, for example, the proposal by U.S. Treasury Secretary Timothy Geithner to the meeting of G-20 ministers in South Korea in 2010 (http://graphics8.nytimes.com/packages/pdf/10222010geithnerletter.pdf).

The measure of credit market regulation employed in this paper includes four components measuring the degree of public ownership of the banking system, control of interest rates, percentage of credit extended to private sector, and competition from foreign banks.

Germany scores well on all of the subcomponents of the index of credit market regulation except the degree of public ownership of the banking system due to the large presence of publicly owned banks (Landesbanken and Sparkassen).

Bayoumi, Vamvakidis, and Vitek (2010) employ an index of credit market regulation constructed by the Fraser Institute, which is also utilized in this paper (see Appendix for details).

Tables 7A.1 through 7A.6 may be found at the end of this chapter.

Although some of the variables, e.g. financial integration, openness, and oil price, may be nonstationary, the residuals from the baseline regression estimated on annual data are found to be stationary using an augmented Dickey-Fuller test, though they exhibit serial correlation. Therefore, the results of the random effects model were estimated using standard errors robust for heteroscedasticity and serial correlation.

In Germany, the 2004 Hartz IV reform reduced unemployment benefits and social transfers and increased the flexibility of temporary employment. The subcomponent of the employment protection indicator, which measures protection of temporary employment, did decline, but the overall index increased.

Some of the variables to replace the missing values were interpolated, as some of these variables are not available on an annual basis. I also extrapolated the values of some structural variables to 2009, since for this year many of the structural variables were not available. The index of employment protection is available only for OECD countries, but there are subcomponents of this indicator, such as advance notice period and severance pay after nine months, available for a broader set of countries in (Aleksynska and Schindler, 2011). I constructed an employment protection index for a broader set using an out-of-sample forecast from the regression of the employment protection index on advance notice period and severance pay after nine months.

At the time of the financial crisis, however, the impact of the reduction in unemployment benefits may have been different from that observed historically, since the level of unemployment may be largely a reflection of lower demand for labor rather than lower labor supply. Hence, the finding on unemployment benefits should be interpreted in the medium-term context.

This variable captures the amount and administrative burden of paying taxes and contributions for a medium-size company; it is a rank of a country among all countries.

There is evidence from firm-level data that lower corporate tax rates or higher depreciation allowances are associated with higher investment (e.g., Vartia, 2008; Schwellnus and Arnold, 2008).

Tables 7A.1 through 7A.6 are located in the appendix to this chapter.

The analysis included various interaction terms, but the table reports only a subset of the results. In particular, no robust link between the interaction of credit market regulation/demographics and the current account could be established, although some theoretical research (Coeurdacier, Guibaud, and Jin, 2012) suggests that such interaction may be important.

The charts on competitiveness and imports were provided by Fabian Bornhorst as part of the joint column on VOXEU, which can be found at http://www.voxeu.org/index.php?q=node/6873.

In view of East Asia’s deep and extensive industrial division of labor, China’s exports to Germany include export value added from other countries.

In addition, overall low private investment in the 2000s reflected a prolonged period of normalization in housing construction following the reunification boom and restructuring in the commercial real estate construction.

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