Appendix 1: Cross-Country Evidence

Section III of the working paper provides cross country evidence on the persistent effects of recessions. This appendix provides methodological details.

Methodology

We use standard local projection models (LPMs) to estimate the medium-term dynamics around recession episodes. LPMs involve estimating impulse responses to shocks by running separate regressions for each time horizon (h) of the form:

Yi,t+hYi,t1=αih+βhRECi,t+γhXi,t+εi,t(A1.1)

where RECi,t is a dummy variable for the start of a recession taken from Martin et al (2015), Xi,t is a vector of control variables including lagged values of the recession dummy and lagged differences of the dependent variable, and αih are country fixed effects. The dependent variable represents changes over different horizons of our variable of interest, Yi,t. For Yi,t, we use different variables, including log GDP and its various components: log of TFP, log of the capital stock, log of employment, the unemployment rate, and the labor force participation rate. Separate regressions are estimated for each variable. The coefficient βh directly estimates the impulse response for horizon h in response to a shock to the recession variable. Standard errors are clustered at the country level.

In our baseline specification we do not include year fixed effects. This is because the current crisis is a synchronized shock impacting all countries, therefore, to get context on how GDP and its components may respond to this crisis, excluding year fixed effects from the crosscountry analysis is appropriate. We report robustness of our main result to including year fixed effects in Figure 4 of the main paper.

Sample

We focus attention on advanced economies as these are most comparable to Australia and New Zealand. Our exact country and time coverage is dictated by the availability of recession start dates.

In our baseline specification, we use recession start dates for 23 advanced economies as identified by Martin et al. (2015). The exact time coverage differs from country to country but broadly covers the period 1970 to 2012.

As a robustness check, we also use recession dates as identified by Dovern and Zuber (2020). This allows us to include a slightly broader country coverage (27 countries), though the time coverage is reduced to the period 1990 to 2017.

Data

Our specifications use data at the annual frequency. Data on our main dependent variables (GDP and its components) is taken from Penn World Table 9.1 (PWT) and the OECD. In particular, we take data for GDP, TFP, employment and capital stock from PWT. We supplement this with data on unemployment rate and labor force participation rate from the OECD.

In addition, we use data on crisis episodes by Laeven and Valencia (2018). We use this data to identify large recessions that were not accompanied by a banking or currency crisis i.e. recessions which did not have a banking or currency crisis a year before or after the start of the recession.

Appendix 2: Estimation of potential output before the pandemic

Section IV of the working paper provides estimates of potential output for Australia and New Zealand before the pandemic. This appendix provides methodological detail.

Methodology

The estimates of potential output presented in Figure 5 of Section IV are computed using a semi-structural multivariate filter model that incorporates a Phillips curve and Okun’s law.1 The structure of the model can be summarized as follows. The output gap yt is defined as the deviation of observable log real output Yt from log potential output Yt*.

yt=YtYt*(A2.1)

The dynamics of output can be defined by following three equations.

Yt*=Yt1*+Gt+εtY*(A2.2)
Gt=θGss+(1θ)Gt1+εtG(A2.3)
yt=Φyt1+εty(A2.4)

The level of potential output evolves according to potential growth Gt and shock term εtY*, which can be interpreted as supply-side shocks. Potential growth is subjected to shock εtG, and converges to steady-state growth rate. Output gap follows AR (1) process and is also subject to shock εty, which is interpreted as demand shocks.

The model also incorporates a (hybrid) Phillips curve, which links output gap to observable inflation.

πt=λπt+1+(1λ)πt1+βyt+εtπ(A2.5)

where πt denotes inflation.

In addition, the model employs the observed unemployment rate to help identify unobservable variables. Okun’s law links the output gap to the unemployment rate gap ut, the deviation of unemployment rate Ut from the nonaccelerating inflation rate of unemployment rate (NAIRU) Ut*.

ut=γut1+δyt+εtu(A2.6)
ut=Ut*Ut(A2.7)

Equation A2.6 is an Okun’s law relationship. In Equation A2.7, NAIRU Ut* is time-varying and follows:

Ut*=κUss+(1κ)Ut1*+μt+εtU*(A2.8)
μt=ημt1+εtμ(A2.9)

where USS denotes steady state unemployment rate, and μt denotes trend in NAIRU, which is subject to shock εtμ.

The estimation uses three observable variables: real GDP, inflation, and the unemployment rate. Annual data (1990–2019 for Australia, and 1995–2019 for New Zealand) are used for estimation, and parameters are estimated with Bayesian estimation techniques.2

Estimated potential growth can be decomposed into underlying drivers, namely, capital, NAIRU, trend labor force participation rate, working age population, and total factor productivity, based on Cobb Douglas production function in Equation A5.1 in Appendix 5.3 Figure 5 reports estimated potential growth rates before the pandemic for Australia and New Zealand and their decompositions.

Appendix 3: Projecting Medium-Term Growth

Section IV of the working paper analyzes medium-term potential output for Australia and New Zealand. This appendix provides methodological details.

Growth Accounting Framework and Medium-term Projection

The paper employs growth accounting framework to analyze potential output in the medium term. The framework is based on the standard Cobb-Douglas production function augmented with detailed labor input items.

 ln (Yt*)= ln (zt*)+α ln (Kt)+(1α) ln (WAPt×LPt*×(1Ut*))(A3.1)

where Yt* denotes potential output, zt* denotes cyclicality adjusted TFP, α is the constant capital share of the economy (set as 0.4 both for Australia and New Zealand), kt denotes capital level, WAPt denotes working age population, LPt* denotes cyclicality adjusted labor force participation rate and Ut* denotes Non-Accelerating Inflation Rate of Unemployment (NAIRU).

The medium-term projection is conducted in terms of the deviation from pre-COVID potential output projections, using potential output projections from the January 2020 World Economic Outlook data vintage as benchmark. First, the trajectory of each right-hand-side variable in the production function in Equation A3.1 is analyzed in terms of its deviation from pre-COVID projections. These deviations are then aggregated into a predicted revision of potential output from pre-COVID trends based on the Cobb-Douglas production function in Equation A3.1.1

Figure 15 and 16 of Section IV show each component of potential output and potential output in deviation from pre-COVID projection. Figure 17 shows potential output under different scenarios in level.

Capital Accumulation

In the simulations above, capital accumulation is Kt is endogenously determined in line with growth theory. In the long-run, capital accumulation is assumed to follow a balanced growth path, where capital and output grow at same rate. Balanced growth path of capital is given as

Δ ln (KtBGP)=Δ ln (zt*)1α+Δ ln (Lt*)=Δ ln (zt*)1α+Δ ln (WAPt×LPt*×(1Ut*))(A3.2)

where KtBGP denotes the capital level at balanced growth path, and Lt* denotes cyclicality adjusted labor input, and α denotes the constant capital share of the economy.

In the simulation, it is assumed that capital accumulation converges gradually to this balanced growth path.2 In addition, debt overhang effects and uncertainty effects discussed in Appendix 5 also affect the trajectory of capital accumulation.

 ln (Kt)=(1γ) ln (KtBGP)+γ ln (Kt1)+DOt+Ut(A3.3)

where γ denotes persistence parameter (set at 0.66 both for Australia and New Zealand), DOt denotes debt overhang effects and Ut denotes uncertainty effects, both discussed in Appendix 5.

Effects of labor reallocation on productivity

As discussed in Section IV, the pandemic has induced large sectoral reallocation. The simulations in this paper incorporate such effects of labor reallocation across sectors on productivity following Duarte and Restuccia (2010) and Goodridge et al. (2018). Following Goodridge et al. (2018), output growth can be decomposed into within-sector labor productivity growth, labor reallocation effects and aggregate labor inputs,

Δ ln (Yt*)=Δ ln (LProdt*,within)+RtL+Δ ln (WAPt×LPt*×(1Ut*))(A3.4)

where LProdt*,within denotes labor productivity growth within sectors, and RtL denotes labor reallocation effects on labor productivity. RtL captures effects of change in labor composition across sectors, which can be given as follows,

RtL=i=1nLprodi,t1Lprodt1×(Li,tLtLi,t1Lt1)(A3.5)

where Lprodt-1 denotes aggregate labor productivity, Lprodi,t-1 denotes sector i’s labor productivity, Li,t denotes employment in sector i and Lt denotes aggregate employment. Reallocation effects are obtained with the change in employment share in sectors, multiplied by sector-level labor productivity in the previous period. It takes positive value if there is labor shift from a low-productivity sector to a high-productivity sector.

Figure 11 in Section III shows labor reallocation effects in Australia during the recession in 1990s and COVID-19 episode.3 In aggregate growth accounting in Equation A3.1, reallocation effects are included in total factor productivity.4

Appendix 4: Sectoral Reallocation and ITs effect on Unemployment Rate

Section IV of the working paper analyzes sectoral reallocation and the effects of shocks to sector allocation on the unemployment rate. This appendix provides methodological details.

Sectoral Reallocation Index

The degree of sectoral reallocation is analyzed using the method developed by Lilien (1982).1 Sectoral reallocation index is defined as the weighted standard deviation of sectoral differences, computed separately for the stock markets and labor markets:

σt=[i=1nsi(Δlnxi,tΔlnXt)2]12(A4.1)
  • For the stock market specification, Si denotes the market capitalization share of sector i,xi,t denotes sectoral stock return at time t, and Xt denotes the total stock market return at time t. The index computes weighted standard deviation of sectoral stock returns, therefore quantifies sectoral dispersion at time t. Sectoral stock prices are obtained from Financial Times Stock Exchange (FTSE) database. Data for 25 and 12 industries are used for Australia and New Zealand, respectively. The sectoral reallocation index is calculated at monthly frequency. Figure 8 in Section III and the left panel of Figure A4.1 display the estimated sectoral reallocation index for Australia and New Zealand.

  • For the labor market specification, Si denotes share of employment in sector i, and xi,t denotes sectoral employment at time t, and Xt denotes the total employment at time t. Data for 19 and 16 industries are used for Australia and New Zealand, respectively. The index is calculated at quarterly frequency.

Figure A4.1:
Figure A4.1:

The Speed of Economic Reallocation Across Sectors Is Unprecedented

(Index of sectoral reallocation)

Citation: IMF Working Papers 2020, 272; 10.5089/9781513563282.001.A999

Source: FTSE, ABS, Stats NZ and IMF staff calculations.Notes: The left panel shows Lilien (1982) sectoral reallocation index calculated from FTSE industry level stock price data (25 industries for Australia, 12 industries for New Zealand). The right panel shows Lilien (1982) sectoral reallocation index calculated from sector level employment data (19 industries for Australia, 16 industries for New Zealand).

Figure A4.1 shows sectoral reallocation indices estimated separately from stock markets and labor markets. For Australia, large sectoral reallocation is observed both in the stock market and the labor markets. For New Zealand, labor market-implied sectoral reallocation remains low in the second quarter, despite a sharp increase in the stock-implied sectoral reallocation index, as reallocation in labor market is masked by a large-scale wage subsidy scheme provided by the government.

Effects of Reallocative Shocks

A simple structural vector autoregression model is employed to analyze the effects of reallocation shocks on the unemployment rate. The model is given as:

yt=β0+k=1mβkytk+ut(A4.2)

Where vector yt includes the change in the unemployment rate and the sectoral reallocation index based on stock market returns obtained from Equation A4.1, βk is the coefficient matrix on kth lag of y, and vectors β0 and ut represent the constant terms and reduced-form error terms. The equation is estimated at monthly frequency for Australia using data over September 1995-May 2020, and at a quarterly frequency for New Zealand using data over 2000Q4–2020Q1, where unemployment rate is only available at quarterly frequencies. Number of lags are set at 12 for Australia, and 6 for New Zealand.

Following the literature (e.g. Campbell and Kuttner 1996, Tase 2019), sectoral reallocation shocks are identified, using Cholesky decomposition, such that they do not affect the unemployment rate contemporaneously. Figure 9 of Section IV reports cumulative impulse responses of unemployment rate changes to identified reallocation shocks scaled to the magnitude in COVID-19 episode.2

Appendix 5: Effects of Debt overhang and uncertainty on investment

Section IV of the working paper analyzes effects of debt and uncertainty on firm-level investment behavior. This appendix provides methodological details.

Determinants of firm-level investment behavior

The following panel regression model, a Tobin’s Q model augmented with firm-level financial variables and uncertainty, is employed to analyze effects of debt and uncertainty on firms’ investment:

Ii,tKi,t1=α+τi+δt+βXi,t+εi,t(A5.1)

where Ii,t denotes firm i’s capital expenditure at time t, Ki,t-1 denotes firm i’s capital stock, and Xi,t includes a set of firm-level variables. X[tincludes the debt level (debt-to-asset ratio), firm-level uncertainty (measured as firm-lev el stock volatility), the cost of debt (interest rate expenditure-to-debt), liquidity (current-asset-to-current-liability ratio), and Tobin’s Q (measured as the sum of market value of equity and book value debt divided by book value of asset).1 The regression includes firm-level and time fixed effects, and firm-clustered robust standard errors are estimated. All explanatory variables are included with a one-year lag to preempt endogeneity issues. Firm-level data are of annual frequency, obtained from the IMF Corporate Vulnerability Unit Database, which is based on the Worldscope database. Firms in the financial sector are excluded from the sample and firm-clustered robust standard errors are estimated.

Estimated parameters on leverage and uncertainty are reported in Figure 12 of Section IV. Table A5.1 reports the comprehensive regression results.

Table A5.1

Determinants of firm-level investment

article image
Note: In the table, *** and * indicate statistical significance at 1 and 10 percent level, respectively. Time fixed effects and firm fixed effects are controled.

Effects of debt overhang

To analyze the effects of rising debt due to COVID-19, first, the increase in debt is estimated using business survey data. Business survey data compiled by the Australian Bureau of Statistics provide information on revenue losses due to COVID-19 at the sectoral level (Figure A5.1).2 Based on firm-level data used for the above panel regression and sectoral information on revenue losses, the increase in debt level is projected using the following equation:

ΔDi,t=δ*(1T)*14*{Si,t75%*(.75)*Ri,t1+Si,t50%*(.625)*Ri,t1+Si,t25%*(.375)*Ri,t1+Si,tL25%*(.125)*Ri,t1}(A5.2)

where ΔDi,t denotes change in debt, T denotes effective corporate tax (which is set at 0.3 in the simulation), Ri,t-1 denotes revenue in previous period, and Si,tj denotes share of firms in the sector that report revenue loss at range j, δ denotes elasticity of debt to revenue loss (set as 0.8, based on cross-country analysis by De Vito and Gomez, 2020).3

Figure A5.1.
Figure A5.1.

Impact of COVID-19: Revenue Loss of Australian Firms

(percent of firms, revenue loss compared to previous year)

Citation: IMF Working Papers 2020, 272; 10.5089/9781513563282.001.A999

Source: ABS

Based on the projected firm-level increase in debt and sensitivity parameters obtained in Equation A5.1, firm-level debt overhang effects can be estimated as:

Δ(I/K)i,t=βdebtΔDi,tAi,t1(A5.3)

where Δ(I/K)i,t denotes the impact of debt on firm i’s investment (expressed as the change in its investment-to-capital ratio), βdebt denotes the regression coefficient on the debt level (debt-to-asset ratio) from Equation A5.1, and Ai,t-1 denotes firm i’s total asset at previous period (ΔDi,tAi,t1 is estimated change in debt-to-asset ratio). Figure 13 of section III displays the distribution of firm-level debt overhang effects for Australian firms. The aggregate level of debt impact on capital accumulation is obtained using a weighted average of firm-level impacts.4

Effects of uncertainty

Similarly, firm-level impacts of increased uncertainty can be obtained based on parameters obtained in the panel regression in Equation A5.1. The impact of uncertainty is given as:

Δ(I/K)i,t=βσΔσt(A5.4)

where Δ(I/K)i,t denotes the uncertainty effect on firm i’s investment (expressed as the change in the investment-to-capital ratio), βσ denotes the regression coefficient on uncertainty (firm-level stock volatility), and Δσt denotes the change in uncertainty. Due to data limitations, we calculate the aggregate level change in uncertainty based on S&P/ASX200 VIX index and apply that to firm-level uncertainty.5 Therefore, aggregate level effects are also obtained by Equation A5.4.

References

  • Abiad, A., R. Balakrishnan, P. Koeva Brooks, D. Leigh, and I. Tytell, 2009, “What’s the Damage? Medium-term Output Dynamics after the Banking Crises,” IMF Working Paper WP/09/245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, G., R. Duval, D. Furceri, S. Celik, K. Koloskova, and M. Poplawski-Ribeiro, 2017, “Gone with the Headwinds: Global Productivity,” IMF Staff Discussion Note SDN/17/04.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alichi, A., O. Bizimana, S. Domit, E. Fernandez Corugedo, D. Laxton, K. Tanyeri, H. Wang, and F. Zhang, 2015, “Multivariate Filter Estimation of Potential Output for the Euro Area and the United States,” IMF Working Paper WP/15/253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alichi, A., H. Avetisyan, D. Laxton, S. Mikhatrishvili, A. Nurbekyan, L. Torosyan, and H. Wang, 2019, “Multivariate Filter Estimation of Potential Output for the United States: An Extension with Labor Market Hysteresis,” IMF Working Paper, WP/19/35.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angelini, E., M. Damjanovic, M. Darracq Paries and S. Zimic, 2020, “ECB-BASIR: a primer on the macroeconomic implications of the COVID-19 pandemic,” European Central Bank, Working Paper No. 2431, June 2020.

    • Search Google Scholar
    • Export Citation
  • Arsov, I. and B. Watson, 2019, “Potential Growth in Advanced Economies,” Research Bulletin, Reserve Bank of Australia, December 2019.

    • Search Google Scholar
    • Export Citation
  • Ball, L., 2014, “Long-Term Damage from the Great Recession in OECD Countries,” European Journal of Economics and Economic Policies: Intervention, Vol. 11, No. 2, pp. 149160.

    • Search Google Scholar
    • Export Citation
  • Barrero, J. N. Bloom, and S. Davis, 2020, “COVID-19 Is Also a Reallocation Shock,” NBER Working Paper 27137.

  • Bekaert, G., E. Engstrom, and A. Ermolov, 2020, “Aggregate Demand and Aggregate Supply Effects of COVID-19: A Real-Time Analysis,” Finance and Economics Discussion Series 2020–049. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2020.049.

    • Search Google Scholar
    • Export Citation
  • Benati, L., 2012, “Estimating the Financial Crisis’ Impact on Potential Output,” Economics Letters, No. 114, pp. 11319.

  • Bernanke, Ben S, 1983, “Irreversibility, Uncertainty and Cyclical Investment,” The Quarterly Journal of Economics, Feb., 1983, Vol. 98 No. 1, 85106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blagrave, P., R. Garcia-Saltos, D. Laxton, and F. Zhang, 2015, “A Simple Multivariate Filter for Estimating Potential Output,” IMF working Paper, WP/15/79.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bloom, N., 2009, “The Impact of Uncertainty Shocks,” Econometrica, 77 (3), 623685.

  • Bloom, N., S. Bond, and J. Van Reenen, 2007, “Uncertainty and Investment Dynamics,” The Review of Economic Studies, 74 (2), 391415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bluedorn, J. and D. Leigh, 2018, “In the Cycle the Trend? Evidence from the Views of International Forecasters,” IMF working Paper, WP/18/163.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bluedorn, J. and D. Leigh, 2019, “Hysteresis in Labor Markets? Evidence from Professional Long-Term Forecasts,” IMF working Paper, WP/19/114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bond, S., and J. V. Reenen, 2007, “Microeconometric Models of Investment and Employment.” Handbook of Econometrics, 6: 44174498.

  • Bosworth, B. and S. Collins, 2008, “Accounting for Growth: Comparing China and India,” Journal of Economic Perspectives, Vol. 22, No. q, pp.4566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouis, R. and R. Duval, 2011, “Raising Potential Growth after the Crisis: A Quantitative Assessment of the Potential Gains from Various Structural Reforms in OECD Area and Beyond,” OECD Economics Department Working Paper, No. 835.

    • Search Google Scholar
    • Export Citation
  • Bouis, R., R. Duval, and J. Eugster, 2016, “Product Market Deregulation and Growth: New Country-Industry-Level Evidence,” IMF Working Paper, WP/16/114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bourlès, R., G. Cette, J. Lopez, J. Mairesse, and G. Nicoletti, 2013, “Do Product Market Regulations in Upstream Sectors Curb Productivity Growth? Panel Data Evidence for OECD Countries,” Review of Economics and Statistics, Vol. 95, No. 5, pp. 17501768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brainard, S. and D. Cutler, 1993. “Sectoral Shifts and Cyclical Unemployment Reconsidered.” Quarterly Journal of Economics, No. 108.1, pp. 219243.

  • Brinca, P., J. Duarte, and M. Faria-e-Castro, 2020, “Measuring Sectoral Supply and Demand Shocks During COVID-19,” COVID Economics, No. 20, pp. 5459.

    • Search Google Scholar
    • Export Citation
  • Campbell, J. and K. Kuttner, 1996, “Macroeconomic Effects of Employment Reallocation,” Carnegie-Rochester Conference Series on Public Policies, No. 44, pp. 87116.

    • Search Google Scholar
    • Export Citation
  • Canon, M., M. Chen, and E. Marifian, 2013, “Labor Mismatch in the Great Recession: A Review of Indexes Using Recent U.S. Data,” Federal Reserve Bank of St. Louis Review, Vol. 95, No. 3, pp. 23771.

    • Search Google Scholar
    • Export Citation
  • Celik, K., M. Kose, and F. Ohnsorge, 2020, “Subdued Potential Growth: Sources and Remedies,” World Bank Policy Research Working Paper, No. 9177.

    • Search Google Scholar
    • Export Citation
  • Cerra, V. and S. Saxena, 2008, “Growth Dynamics: The Myth of Economic Recovery,” American Economic Review, Vol. 98, No. 1, pp. 43957.

  • Chodorow-Reich, G. and J. Wieland, 2020, “Secular Labor Reallocation and Business Cycles,” Journal of Political Economy, Vol. 128, No. 6, pp. 224587.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • David, J. M., 2020, “Will the Covid-19 Pandemic Lead to Job Reallocation and Persistent Unemployment?Chicago Fed Letter, No. 444.

  • David, S. and J. Haltiwanger, 1999, “On the Driving Forces Behind Cyclical Movements in Employment and Job Reallocation,” American Economic Review, Vol. 89, No. 5, pp. 123458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deb, Pragyan, Davide Furceri, Jonathan D. Ostry and Nour Tawk, 2020, “The Economic Effects of COVID-19 Containment Measures,” CEPR Discussion Paper 15087, Center for Economic and Policy Research.

    • Search Google Scholar
    • Export Citation
  • de Brouwer, Gordon 1998, “Estimating Output Gaps,” Research Discussion Paper 9809, Reserve Bank of Australia.

  • De Vito, A, and J. P. Gomez 2020, “Estimating the COVID-19 Cash Crunch: Global Evidence and Policy.” Journal of Accounting and Public Policy (2020): 106741

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doven, J. and C. Zuber, 2020, “Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects,” The Scandinavian Journal of Economics,No. 1, pp. 136.

    • Search Google Scholar
    • Export Citation
  • Duarter, M. and D. Restuccia, 2010, “The Role of Structural Transformation in Aggregate Productivity,” The Quarterly Journal of Economics, February, pp. 12973.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duval R. and C. Maisonneuve, 2010, “Long-Run Growth Scenarios for the World Economy,” Journal of Policy Modeling, Vol. 32, pp. 6480.

  • Eberly, J. and Wang, N., 2009, “Capital Reallocation and Growth,” American Economic Review, 99(2), pp.56066.

  • Fernald, J. G., 2015, “Productivity and Potential Output Before, During, and After the Great Recession,” NBER Macroeconomics Annual, 29(1), pp.151.

    • Search Google Scholar
    • Export Citation
  • Foster, L., C. Grim, and J. Haltiwanger., 2016, “Reallocation in the Great Recession: Cleansing or Not?Journal of Labor Economics 34, No. S1: S293-S331.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furceri, D., and A. Mourougane, 2012, “The Effect of Financial Crises on Potential Output: New Empirical Evidence from OECD Countries,” Journal of Macroeconomics, Vol. 34, Iss. 3, pp. 82232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furlanetto, F., and Ø. Robstad, 2019, “Immigration and the Macroeconomy: Some New Empirical Evidence,” Review of Economic Dynamics, Vol. 34, pp. 119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gianella, C., I. Koske, E. Rusticelli, and O. Chatal, 2008, “What Drives the NAIRU? Evidence from a Panel of OECD Countries.” OECD Economic Department Working Papers 649

    • Search Google Scholar
    • Export Citation
  • Goodridge, P., J. Haskel, and G. Wallis, (2018). “Accounting for the UK Productivity Puzzle: a decomposition and predictions.” Economica, Vol. 85(339), pp. 581605.

    • Search Google Scholar
    • Export Citation
  • Gordon, R., 2015, “Secular Stagnation: A Supply-Side View,” American Economic Review, Vol. 105, No. 5, pp. 5459.

  • Grant, A.L. and Chan, J.C.C., 2017, “A Bayesian Model Comparison for Trend-Cycle Decompositions of Output.” Journal of Money, Credit and Banking.

  • Hambur, J. and K. Jenner, 2019, “Can Structural Change Account for the Low Level of Non-Mining Investment?”, Reserve Bank of Australia, RBA Bulletin, June 2019.

    • Search Google Scholar
    • Export Citation
  • Hennessy, Christopher A., 2004, “Tobin’s Q, Debt Overhang and Investment,” The Journal of Finance, August 2004, Vol. 59 No. 4, 17171742.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2009, “U.S. Potential Growth in the Aftermath of the Crisis,” in Selected Issues Papers for the 2009 Article IV consultations, pp. 315.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2012, “Fiscal Policy and Employment in Advanced and Emerging Economies.” IMF Policy Paper

  • International Monetary Fund, 2013, “How Fast Can Brazil Grow?Selected Issues Papers for the 2013 Article IV consultations, pp. 414.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2015, “Where Are We Headed? Perspectives on Potential Output,” IMF, World Economic Outlook, April, Chapter 4.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2017, “Labor Market Adjustment to Shocks in Australia,” in Selected Issues for the 2017 Australia Article IV consultation, pp. 434.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2018, “Labor Force Participation in Advanced Economies: Drivers and Prospects.” IMF, World Economic Outlook, April, Chapter 2.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2020a, “The Great Lockdown: Dissecting the Economic Effects.” IMF World Economic Outlook, October, Chapter 2.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, 2020b, Regional Economic Outlook: Asia and the Pacific, IMF, October.

  • Jackman, R. and S. Roper, 1987, “Structural Unemployment.” Oxford Bulletin of Economics and Statistics, vol.49, Iss. 1, pp. 936.

  • Jorgenson, D., M. Ho, J. Samuels, and K. Stiroh, 2007, “Industry Origins of the American Productivity Resurgence.” Economic Systems Research, Vol. 19, Iss. 3, pp. 22952.

    • Search Google Scholar
    • Export Citation
  • Kido Y., D. Muir, M. Nozaki, Y. Wong, Y. Zhou, and S. Hlatshwayo, 2020, “Why Has Investment Slowed Down in Australia,” in Australia: Selected Issues, IMF Country Report No. 20/69.

    • Search Google Scholar
    • Export Citation
  • King, T. and J. Morley, 2007, “In Search of The Natural Rate of Unemployment.” Journal of Monetary Economics, Vol. 54, No. 2, pp. 55064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kozlowski, J., Veldkamp, L., & Venkateswaran, V., 2020, “Scarring Body and Mind: The Long-Term Belief-Scarring Effects of COVID-19.” Covid Economics, Vol.8.

    • Search Google Scholar
    • Export Citation
  • Laeven M. and M. Valencia, 2018, “Systemic Banking Crises Revisited,” International Monetary Fund.

  • Lienert, A. and D. Gillmore, 2015, “The Reserve Bank’s Method of Estimating Potential Output,” Reserve Bank of New Zealand Analytical Note AN 2015/01.

    • Search Google Scholar
    • Export Citation
  • Martin, R., T. Munyan, and B. Wilson, 2015, “Potential Output and Recessions: Are We Fooling Ourselves?FRB International Finance Discussion Paper, No. 1145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehan, L., 2014, “Structural Change and New Zealand’s Productivity Performance.” New Zealand Productivity Commission Staff Working Paper.

    • Search Google Scholar
    • Export Citation
  • Mitra, P., A. Hosry, G. Minasyan, M. Fischer, and G. Abajyan, 2016, “Avoiding a New Mediocre; Raising Long-Term Growth in the Middle East and Central Asia,” International Monetary Fund, Middle East and Central Asia Department, Washington D.C.

    • Search Google Scholar
    • Export Citation
  • Miyamoto, W., T. Nguyen, D. Sergeyev, 2018, “Government Spending Multipliers Under the Zero Lower Bound: Evidence from Japan.” American Economic Journal: Macroeconomics, Vol. 10, No. 3, pp. 24777.

    • Search Google Scholar
    • Export Citation
  • Organisation for Economic Co-operation and Development (OECD). 2010. “The OECD Innovation Strategy: Getting a Head Start on Tomorrow,” Directorate for Science, Technology and Innovation, Paris.

    • Search Google Scholar
    • Export Citation
  • Ostry, J., A. Prati, and A. Spilimbergo, 2009, “Structural Reforms and Economic Performance in Advanced and Developing Economies,” International Monetary Fund, Occasional Paper No. 268, Washington DC.

    • Search Google Scholar
    • Export Citation
  • Ostry, J., A. Berg, and S. Kothari, 2018, “Growth-Equity Trade-offs in Structural Reforms,” IMF Working Paper, WP/18/5, International Monetary Fund.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Owyang, M., V. Ramey, and S. Zubairy, 2013, “Are Government Spending Multipliers Greater During Periods of Slack? Evidence from Twentieth-Century Historical Data,” American Economic Review, Vol. 103, No. 3, 12934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Podpiera, J., 2018, “Business Cycle in Ireland: Accounting for Open Labor Market and Multinationalsin Ireland: Selected Issues, International Monetary Fund, IMF Country Report No. 18/195.

    • Search Google Scholar
    • Export Citation
  • Teulings, C. and N. Zubanov, 2014, “Is Economic Recovery a Myth? Robust estimation of impulse responses,” Journal of Applied Econometrics, Vol. 29, No. 3, pp. 497514.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van der Merwe, M., L. Cockerell, M. Chambers, and J. Jääskelä, 2018, “Private Non-Mining Investment in Australia,” Reserve Bank of Australia, RBA Bulletin, June 2018.

    • Search Google Scholar
    • Export Citation
1

The authors are grateful for helpful discussions with, and input from, Davide Furceri, Angelia Grant, Deniz Igan, Nigel Ray, Brian Walker, and Fan Zhang. The paper also incorporates helpful comments received from staff of the Australian Treasury, Reserve Bank of Australia, New Zealand Treasury, and IMF staff, including in several webinars. Ioana Hussiada and Nadine Dubost provided excellent support in the production of this paper.

2

Throughout the paper, scarring effects refer to the negative impact of the crisis on medium-term potential output, acting through various channels discussed below.

3

It should be noted that uncertainty to these projections is high as our analysis relies largely on data to 2020Q2 or July 2020.

4

Similarly, there are few studies of potential output in Australia and New Zealand. Two useful references are de Brouwer (1998) and Lienert and Gillmore (2015). Arsov and Watson (2019) review potential growth in advanced economies.

5

See Ball (2014), Benati (2012), Celik, Kose, and Ohnsorge (2020), Fernald (2015), Hallaeert et al. (2013), IMF (2009, 2013a, 2013b, and 2015), and Martin, Munyan, and Wilson (2015) among others.

7

Including for the United States (IMF, 2009, and Fernald, 2015, Grant and Chan, 2017), France (Hallaert et al., 2013), Portugal (IMF, 2013a), and Brazil (2013b).

8

Duarter and Restuccia (2010), Campbell and Kuttner (1996), Valetta and Cleary (2008), Tase (2019), Aarson, Rissman, and Sullivan (2004), Ngai and Pissarides (2007), David and Haltiwanger (1999), Hobijn and Sahn (2013), Goshen and Potter (2003), and ElFayoumi, Ndoye, Nadeem, and Auclair (2018).

9

Related to this, Kozlowski et al (2020) find that scarring of belief, a persistent change in the perceived probability of extreme, negative shocks in the future, would reduce investment and output in the long run.

11

The impact ranges from 1 percent to nearly 20 percent. Estimated impacts for Australia and New Zealand are lower than the OECD average as both countries have relatively good institutions.

12

In Australia and New Zealand, the decline in GDP in 2020Q2 relative to peak was 7.2 and 13.4 percent, respectively, well above the threshold for a large recession.

13

Banking and financial crises are based on Laeven and Valencia (2018). Recessions which had a banking or currency crisis in the same year, or the year before or after the start of the recession, are excluded in the robustness check.

14

As the current crisis is a global recession, the decline in world growth is likely to have a negative effect on individual countries, including Australia and New Zealand. As such, we do not control for aggregate shocks by including year fixed effects in our baseline specification as these shocks are highly relevant in the current crisis. Results are also robust to other changes in specification, such as including leads of the recession dummy as suggested by Teulings and Zubanov (2013).

15

See Kido et al. (2020), Hambur and Jenner (2019) and Van der Merwe et al. (2018) for determinants of non-mining business investment in Australia.

16

The impact would have been significantly larger without the large-scale policy support both countries have implemented. The observed decline in agricultural sector employment in New Zealand largely reflects a previous, temporary increase in late 2019. For government economic responses to the COVID-19 pandemic, see the IMF Policy Trucker (https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19)

17

We assume that the Coronavirus Supplement in Australia and the COVID-19 Income Relief Payment in New Zealand will not affect NAIRU as they are temporary measures which were implemented at a time of extreme slack in the labor market and are therefore unlikely to have a significant impact on job search incentives.

18

Gianella et al. (2008) estimate the elasticity of structural unemployment to the unemployment replacement ratio in OECD economies. They find that a 1 percentage point increase in the unemployment replacement ratio leads to 0.03 percentage point increase in the NAIRU. After the pandemic, the net unemployment replacement ratio has been increased by 6 percentage points in New Zealand.

19

Using cross country data of 23 advanced economies, IMF (2018) finds that 10 percentage point change in the relative service employment share (the ratio of services to manufacturing employment) leads to 0.1 percent increase in the labor force participation rate.

20

The impact is calculated from the change in the relative service employment share in the second quarter.

21

It should be noted that labor productivity, an alternative measure of productivity, tended to rise in Australia during previous downturns.

22

For example, Adler et al. (2017) reports a within sector productivity decline of 2 percent after the global financial crisis.

23

The other possible channel is reallocation of labor within sectors. Foster et al. (2016) find that reallocation to more productive producers accelerated during the downturn prior to the GFC, but the intensity of reallocation fell rather than rose in the GFC. They also find that the reallocation during the GFC was less productivity-enhancing compared to prior recessions.

24

Another possible channel that would affect TFP is reallocation of capital, which we do not analyze explicitly in this paper. The speed of capital reallocation tends to be slower than labor reallocation due to adjustment costs (for example, Eberly and Wang, 2009). In Australia, capital reallocation played a nonnegligible role during the mining investment boom, but otherwise had relatively small impacts on the economy.

25

Firm level data are obtained from IMF Corporate Vulnerability Unit database, which is based on Worldscope database.

26

This estimation does not incorporate the fiscal support that has been provided to affected firms by the Australian and New Zealand governments. In Australia, the size of government support provided to firms was large enough to offset the initial impact of the pandemic on corporate balance sheets.

27

The magnitude of the impact of the pandemic inherently depends on its persistence. Our simulations assume that the authorities will continue to contain health risks successfully and that no major local outbreak will occur in Australia and New Zealand after 2020.

28

Labor productivity of Australia and New Zealand are both estimated to be 2½ percent lower than pre-pandemic trend.

29

Labor productivity in Australia and New Zealand will remain ¼–4¾ below pre-COVID trends under alternative scenarios.

30

Similar policy implications were recommended in the aftermath of the GFC for OECD countries (Bouis and Duval, 2011) and for Middle Eastern and Central Asian countries (Mitra et al., 2016).

31

Demand support would not only help close the output gap but would also support capital deepening and adoption of technologies embodied in new physical capital.

32

Australia’s recent FY2021 Commonwealth budget contains significant additional fiscal measures and constitutes welcome support for the post-COVID recovery.

33

Our policy discussion assumes no major local outbreak of the virus after 2020 and an onset of economic recovery from 2020Q3. Policies should be reprioritized flexibly if health risks emerge again.

34

Support for workers and firms will be all the more important as some structural reforms have been found to affect the distribution of income, while still having a positive effect on growth (see for example, Ostry et al., 2018).

35

Implementing a policy to support viable firms will be challenging as it is often difficult to distinguish between solvent and insolvent firms, especially in an environment of heightened uncertainty.

1

Discussion in this appendix is based on IMF (2015) and Blagrave et al. (2015). See Blagrave et al. (2015) for further details about the model.

2

Priors used in the estimations are reported in Blagrave et al. (2015).

3

Trend labor force participation rate is obtained by the Hodrick-Prescott filter with smoothing parameter λ=100.

1

Shocks on each variable are assumed to decay over the forecast horizon. In each year, sectoral reallocations are assumed to decay by 10 percent, debt overhang effects are assumed to decay by 20 percent, and uncertainty effects are assumed to decay by 50 percent.

2

Capital is assumed to adjust gradually due to adjustment costs. It is also implicitly assumed that capital level before the pandemic was on balance growth path.

3

Reallocation effects in the 90s’ calculated based on labor reallocation from 1990Q3 to 1993Q2, and reallocation effects after COVID-19 is calculated based on labor reallocation from March 14, 2020 to July 25, 2020 (weekly payroll data).

4

Jorgenson et al. (2007) show labor reallocation effects and capital reallocation effects (not considered in this paper) are included in change in aggregate total factor productivity. For New Zealand, labor reallocation effects in Australia is used, as the labor adjustment after the pandemic is masked by a large-scale wage subsidy program.

1

Lilien (1982) analyzes sectoral reallocation in labor market. Brainard and Cutler (1993) apply its methodologies to stock market.

2

Results are broadly unchanged if the change in terms of trade is included in the vector autoregression.

1

Although theoretically Tobin’s Q is a sufficient statistic for investment under certain assumptions, other variables have commonly been found to have important additional explanatory power (Bond and Reenen, 2007).

2

Business Impacts of COVID-19, June 2020 (5676.0.55.003).

3

The survey measures revenue losses at five ranges, namely 0–25 percent, 25–50 percent, 50–75 percent, and greater than 75 percent. In Equation A5.2, Si,tj is determined based on the sector the firm i belongs.

4

For New Zealand, we assume aggregate debt effects similar to Australia due to data availability issues. In doing so, difference in parameters reported in Table A5.1 is adjusted.

5

Due to limited data availability, change in S&P/ASX200 VIX is also applied to New Zealand data.

Addressing the Pandemic's Medium-Term Fallout in Australia and New Zealand
Author: Mr. Geoffrey J Bannister, Mr. Harald Finger, Yosuke Kido, Siddharth Kothari, and Ms. Elena Loukoianova