Is Private Investment in Advanced Economies Too Low?* Title page Is Private Investment in Advanced Economies Too Low?* Seok Gil Park Research Department * Preliminary version, please do not cite or circulate.

Summary Question / Methodology Question: Is investment in AEs in line with output dynamics? Has investment behavior changed after the crisis? Models: Output accelerator. Checking with uncertainty, Tobin’s Q, etc. Countries: France, Germany, Italy, Spain, Japan, United States Findings Investment explained by output dynamics, once allowing for a change in coefficients after the crisis. Firms’ target capital stock (to output ratio) increased during 1990-2000’s, but returned to previous level after GFC

Weak Investment in AEs: Disappointing Recovery Contributions to Forecast Errors: 2011-14 (percentage-point difference between actual and predicted growth 1/) “Financial markets have been optimistic, … However, this has not translated into a pickup in investment, which—particularly in advanced economies—has remained subdued.” (WEO 2014 October) Growth forecast errors mostly from unexpectedly low investment Sources: IMF, World Economic Outlook; and staff calculations. 1/ Average of one-year-ahead forecast errors for the years from 2011 to 2014. 2/ Crisis economies defined as in Laeven and Valencia (2013).

Methodology and Data

Accelerator Model An old idea: Optimal (target) capital stock as a linear function of output level Then investment should respond to changes in output level (Accelerator). Txt slide

Model Variations 1) Accelerator + Uncertainty Real I/K ratio, as a function of output accelerator and uncertainty (Unc) 2) Augmented Tobin’s Q model Controls for firms’ financial status (alternative Tobin’s Q, operating surplus), user cost of capital (Cost), and capacity utilization ratio (Util) 3) All-inclusive model Txt slide

Data Definition / Robustness Check Investment: Real, gross fixed capital formation for equipments Capital stock: Real net stock of equipments, extrapolated with implied depreciation rate Uncertainty: Standard deviation of output accelerator Alternative Q: Equity to Asset ratio of nonfinancial corporations User cost of capital: (Real) Bank lending rate, adjusted for relative price of investment goods (with GDP deflators) <Robustness Check> Using output level as denominator instead of capital stock Txt slide

Regression Results

Investment Became Less Sensitive to Accelerators Structural Break: In overall assessments, impact of output accelerator on investment decreased after GFC. Robust: for most of countries (U.S. an exception) and most of specifications (See tables in Appendix)   Coefficients for accelerator1/ p-value for Adj. R squared All sample Pre-crisis2/ Post-crisis3/ Chow test w/o SB w/ SB Pooled sample 1.202*** 1.300*** 0.414*** 0.000 0.935 France 2.874*** 2.334*** 0.535*** 0.715 0.853 Germany 0.939*** 0.953** 0.513*** 0.561 0.265 0.240 Italy 1.569*** 1.509*** 0.030 0.566 0.650 Japan 1.467*** 1.977*** 0.866*** 0.438 0.554 Spain 1.600*** 1.850*** 0.856*** 0.885 0.913 United States 0.601 1.363 1.957*** 0.001 -0.032 0.055 Source: Haver Analytics; IMF staff calculation. Model was estimated with Newey-West type standard error. 1/ Sum of coefficients for lagged accelerators. 2/ 1990Q1 – 2008Q4, 3/ 2009Q1 – 2014 Q2 3/ *** and ** denote statistical significance at the 1% and 5% level, respectively.

Recent Trends Explained with Structural Break Real Private Investment: Equipment (In percent of GDP; Fitted values are from the all-inclusive model without structural break) (In percent of GDP; Fitted values are from the all-inclusive model with structural break) Source: Haver Analytics; IMF staff calculation. Note/ Standard errors of predictions were calculated from simple OLS.

Puzzle, If Any, Maybe from Ignoring Structural Break Residuals from Regression (In percent of GDP, Average 2011-13) Source: Haver Analytics; IMF staff calculation

Then Why Structural Break? A Peek from Time-Varying β Rolling Window Regression Coefficient for Accelerator (40-Quarters window, equipment investment, accelerator model without uncertainty) Source: Haver Analytics; IMF staff calculation

Time-Varying β: Using Y as denominator Rolling Window Regression Coefficient for Accelerator (40-Quarters window, equipment investment, accelerator model without uncertainty, output level as denominator) Source: Haver Analytics; IMF staff calculation

Time-Varying β: Shorter Rolling Window Rolling Window Regression Coefficient for Accelerator (20-Quarters window, equipment investment, regressed only on average accelerator) Source: Haver Analytics; IMF staff calculation

Interpretations Ratio of target capital stock to output fluctuated, partly reflecting longer-frequency business cycle. For example, IT boom in 1990’s seems to have raised the ratio (accelerator coefficient) notably in US, but the coefficient returned to previous levels. Returning to mediocre normal? Longer-history data suggest that the coefficient had not been significantly high before 1990’s. Secular stagnation? Some downward trend in the coefficient since 2000’s Txt slide

Appendix: Regression Table

Regression Coefficients, Equipment Investment France   Pre Post Output accelerator1/ 2.334 0.535 2.651 0.826 2.842 0.616 2.248 0.355 2.222 0.565 1.834 0.800 1.732 0.701 (0.000) (0.001) (0.003) 0.853 Uncertainty -0.931 -0.287 1.791 0.366 -0.074 0.098 (0.488) (0.179) (0.060) (0.929) (0.045) 0.875 Tobins Q ratio 0.100 1.179 -1.806 -1.246 2.745 -0.577 0.071 -1.154 (0.951) (0.392) (0.084) (0.005) (0.958) (0.013) 0.672 0.876 Surplus 0.316 0.425 0.066 0.280 -0.119 0.294 -0.142 0.104 (0.099) (0.641) (0.388) (0.122) 0.670 0.852 User cost of capital 0.108 -0.134 0.090 0.026 0.311 0.001 0.158 -0.056 (0.095) (0.070) (0.390) (0.921) (0.023) 0.688 0.910 Utilization ratio 17.249 4.578 4.605 -1.970 13.940 2.547 9.864 -1.055 (0.440) (0.175) (0.407) 0.777 0.829 0.889 0.908 Germany Output accelerator 0.953 0.513 1.429 0.511 0.845 0.459 1.292 0.373 1.241 0.510 -0.611 0.292 0.509 0.220 (0.018) (0.008) (0.022) (0.036) (0.083) (0.534) (0.009) 0.240 (0.561) -1.247 -0.204 -2.047 -0.002 -0.476 -0.048 (0.056) (0.053) (0.963) (0.648) (0.030) 0.223 0.542 4.191 2.136 4.310 0.478 1.071 0.428 1.673 (0.002) (0.185) (0.451) (0.180) (0.172) (0.155) 0.417 0.506 0.168 0.178 0.210 0.070 0.114 0.043 0.138 0.103 (0.043) (0.012) (0.047) 0.372 (0.025) 0.603 0.133 0.044 -0.006 0.067 0.061 0.142 0.075 (0.203) (0.619) (0.686) (0.685) (0.399) (0.093) 0.094 (0.466) 0.175 (0.660) 13.316 3.673 20.683 1.518 7.913 2.692 4.708 (0.120) (0.459) (0.787) 0.560 0.537 0.622 0.660 Source: Haver Analytics; IMF staff calculation. 1/ Sum of coefficients for lagged accelerators 2/ Figures within parenthesis in second rows are p-values for coefficients. Figures in italics in third rows (left columns) are OLS adjusted R-squared. Figures in italics within parenthesis in third rows (right columns) are p-values from Chow test.

Regression Coefficients, Equipment Investment Italy   Pre Post Output accelerator1/ 1.509 0.030 1.512 0.871 1.179 0.377 0.907 0.675 1.601 0.630 0.799 0.821 1.155 0.989 (0.007) (0.926) (0.009) (0.000) (0.384) (0.008) (0.002) (0.004) (0.048) 0.650 Uncertainty 0.070 0.594 0.158 0.999 1.146 1.199 (0.907) (0.001) (0.798) 0.536 0.681 Tobins Q ratio 0.398 -0.106 0.677 0.779 0.523 -0.827 0.151 0.363 (0.112) (0.739) (0.431) (0.234) (0.073) (0.029) 0.781 0.832 Surplus 0.020 -0.005 0.057 -0.543 -0.112 -0.010 0.037 0.254 (0.793) (0.976) (0.511) (0.010) (0.177) (0.951) (0.494) 0.769 0.801 User cost of capital -0.057 -0.117 -0.052 -0.320 -0.019 -0.167 -0.067 -0.140 (0.062) (0.550) (0.013) (0.005) 0.628 0.780 Utilization ratio 8.876 -0.329 6.268 -7.361 5.282 0.524 2.312 1.567 (0.819) (0.053) (0.036) (0.682) (0.022) (0.044) 0.687 0.704 0.811 0.939 Spain Output accelerator 1.850 0.856 1.851 0.496 1.767 0.900 1.855 0.861 2.260 0.886 1.714 0.696 1.690 0.646 0.913 -0.174 -0.726 0.007 -0.314 -0.589 -0.401 (0.567) (0.927) 0.529 0.912 1.761 1.602 0.493 1.110 2.648 2.231 2.036 (0.201) (0.216) (0.054) 0.595 0.916 0.014 -0.066 -0.068 -0.065 0.026 (0.773) (0.207) (0.651) (0.854) (0.122) (0.049) 0.516 0.911 0.035 0.029 0.059 -0.033 0.013 0.109 0.034 0.032 (0.591) (0.732) (0.023) (0.498) (0.731) (0.003) (0.046) 0.741 0.963 19.203 6.077 2.678 1.454 12.557 5.293 4.836 -2.327 (0.231) (0.071) 0.752 0.928 0.988 Source: Haver Analytics; IMF staff calculation. 1/ Sum of coefficients for lagged accelerators 2/ Figures within parenthesis in second rows are p-values for coefficients. Figures in italics in third rows (left columns) are OLS adjusted R-squared. Figures in italics within parenthesis in third rows (right columns) are p-values from Chow test.

Regression Coefficients, Equipment Investment Japan   Pre Post Output accelerator1/ 1.977 0.866 2.490 0.425 1.948 0.814 1.049 0.543 2.100 0.794 0.484 0.579 0.551 0.302 (0.000) (0.291) (0.001) (0.323) (0.006) 0.552 Uncertainty 1.059 -0.883 -0.820 -0.702 -2.115 -0.855 (0.204) (0.222) (0.021) 0.142 (0.040) 0.605 Tobins Q ratio 1.258 0.512 1.142 0.388 -0.067 -1.013 0.494 -0.206 (0.167) (0.038) (0.819) (0.030) (0.081) (0.225) 0.355 (0.072) 0.632 Surplus 0.463 0.152 0.361 0.124 0.254 0.188 -0.077 -0.185 (0.022) (0.161) (0.220) (0.702) (0.013) 0.606 User cost of capital 0.008 -0.042 -0.034 -0.045 -0.062 0.007 -0.043 (0.891) (0.052) (0.347) (0.014) -0.021 (0.649) 0.581 Utilization ratio 3.613 0.789 3.073 0.822 1.168 0.372 2.229 1.399 (0.153) (0.395) (0.681) (0.029) 0.712 0.748 0.732 0.783 United States Output accelerator 1.363 1.957 0.679 0.968 -0.276 1.590 1.183 1.024 2.565 2.300 3.110 -0.250 1.302 -0.059 (0.169) (0.523) (0.827) (0.140) (0.023) (0.008) (0.074) (0.768) 0.055 -3.595 -2.237 -3.083 -1.262 -0.215 0.231 (0.059) (0.181) (0.002) (0.606) (0.349) 0.262 (0.062) 0.165 1.776 2.953 2.030 0.894 1.944 0.380 1.256 -0.069 (0.009) (0.025) (0.343) (0.746) 0.281 0.187 0.350 0.382 0.346 0.201 0.133 0.341 0.024 (0.037) (0.186) 0.577 0.591 -0.055 -0.666 -0.221 0.258 0.160 -0.093 0.196 0.037 (0.555) (0.024) (0.018) (0.203) (0.686) 0.062 0.199 -6.134 15.280 -21.809 18.741 -21.703 7.928 -27.944 18.368 (0.108) (0.004) (0.073) 0.131 0.333 0.882 0.913 Source: Haver Analytics; IMF staff calculation. 1/ Sum of coefficients for lagged accelerators 2/ Figures within parenthesis in second rows are p-values for coefficients. Figures in italics in third rows (left columns) are OLS adjusted R-squared. Figures in italics within parenthesis in third rows (right columns) are p-values from Chow test.