Deciphering the causes for the post-1990 slow output recoveries

Economics Letters 176 (2019) 28–34

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Economics Letters journal homepage: www.elsevier.com/locate/ecolet

Deciphering the causes for the post-1990 slow output recoveries Wen Zhang School of Economics, Renmin University of China, 59 Zhongguancun St, Haidian, Beijing, 100872, China

highlights • • • •

This paper estimates a time-varying parameter VAR with stochastic volatilities. The model includes routine to total employment ratio and two financial variables. Routine biased technological changes weaken all three post-1990 recoveries. Structural changes that affect investment lowers the two post-2000 recoveries.

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Article history: Received 20 October 2018 Received in revised form 30 November 2018 Accepted 2 December 2018 Available online 15 December 2018 JEL classification: C31 E32 E37

a b s t r a c t This paper explores the causes for the slow output recoveries in the U.S. since 1990, via estimating a time-varying parameter vector autoregressive model with stochastic volatilities. The model incorporates routine employment as a share of total employment and two financial variables, to simultaneously evaluate the roles of labor-market-related and credit-related factors in determining output dynamics. Based on the estimated model, the counterfactual experiments highlight the crucial roles of routine biased technological changes and structural changes that shift investment dynamics in explaining post-1990 slow output recoveries. © 2018 Published by Elsevier B.V.

Keywords: Time-varying parameter VAR Slow recoveries Routine biased technological shock Structural changes Financial shocks

1. Introduction Since 1990, the U.S. economy has experienced a significant slowdown in the output recovery: The average cumulative GDP growth rates in two years following recession troughs is 5.9 percentage points before 1990, while falls to 2.5 percentage points after 1990, according to Table 1.A.1 In particular, contrary to the historical experience of the U.S. economy, that deep recessions are followed by strong recoveries, the output growth after the Great Recession – the most severe postwar recession – is disappointingly slow. A host of literature has explored the causes of this ‘‘new norm’’. Many studies attribute it to financial factors, e.g. the household deleverage or the housing price collapse (Hall, 2011; Prieto et al., 2016), to insufficient policy reactions, e.g. muted fiscal stimulations when the monetary policy is constrained by the zero E-mail address: [email protected]. 1 This paper uses the cumulative changes in real GDP growth rates in four quarters or eight quarters after the recession troughs as the measures of recovery speed. According to both measures, the reduction in recovery speeds is highly significant for the U.S. economy since 1990. https://doi.org/10.1016/j.econlet.2018.12.003 0165-1765/© 2018 Published by Elsevier B.V.

lower bound (Albonico et al., 2017), and to their interactions (Galí et al., 2012; Korinek and Simsek, 2016). A small literature, however, argues that the output recovery after the Great Recession is mainly held back by the supply-side factors that lead to the decline in the total factor productivity and the labor market participation rate (Gordon, 2015; Fernald et al., 2017). Overall, the existing works focus mostly on the post-Great-Recession instead of post1990 sluggish recoveries, and even fewer works have examined the link between the changes in the labor market and the changed output dynamics. The U.S. labor market has become polarized also since 1990: The employment at the middle of the skill distribution persistently decline relative to the employment at the two ends of the skill distribution (e.g. Autor et al., 2006). The literature finds the increased offshoring activities and the automation technology adoption are the key drivers of this phenomenon, with the latter being more important (e.g. Goos et al., 2014). Moreover, the recent analysis shows the losses of routine jobs happened overwhelmingly during the recessions after 1990 (e.g. Jaimovich and Siu, 2012; Gaggl and Kaufmann, 2014), as the recessions precipitate the routine biased technological changes (Hershbein and Kahn, 2018). The timing of

W. Zhang / Economics Letters 176 (2019) 28–34

the labor market polarization and slow output recoveries suggests that these two phenomena may be related. In fact, the costly employment reallocation across different occupational groups can depress aggregate demand following post-1990 recessions, possibly leading to the slow output recoveries. This paper contributes to the literature by comparing the roles of the financial factors and the labor-market-related factors in determining the output dynamics, based on a time-varying parameter vector autoregression model with stochastic volatilities. To achieve this goal, the dynamic system incorporates the routine employment as a share of total employment and two financial variables. The model estimation adopts the Bayesian method developed by Cogley and Sargent (2005) and Primiceri (2005). As the VAR coefficients and the volatilities are time-varying, the model is able to distinguish between the changes in the sizes of the exogenous shocks and the changes in the transmission mechanisms. In the context of this paper, the candidate cyclical explanations are the credit-related shocks and the routine biased technological shock (RBTS henceforth). The candidate structural explanations are the changes in the financial market that result in excessive leverage buildup in household and financial sectors, and the technology advances that bring about the labor market polarization. The empirical analysis indicates that both the sizes of the exogenous shocks as well as the transmission mechanisms have evolved over the past five decades. Based on the estimated model, the counterfactual analysis reveals that the RBTS and the structural changes that affect the investment dynamics together can account for the bulk of the reduction in post-1990 output recovery speeds. The former significantly weakens all three post-1990 recoveries, and the latter significantly weakens the two post-2000 recoveries. The rest of this paper is organized as follows. Section 2 presents the econometric model and the shocks’ identification strategy. Section 3 shows the estimation results, Section 4 illustrates the counterfactual results, and Section 5 concludes. 2. Methodology The empirical model is a second order vector autoregression with time-varying parameters and stochastic volatilities yt = αt + β1,t yt −1 + β2,t yt −2 + µt

(1)

where yt is a 5 × 1 vector that contains the real GDP Gt , the yearover-year inflation based on GDP deflator Πt , the routine to total employment ratio Rt , the investment to output ratio It and the S&P 500 index St .2 The categorization of the routine and the nonroutine employment is based on the skills required to perform the tasks of each occupation, following Jaimovich and Siu (2012). Routine jobs are often found in the middle of skill distribution, and can be implemented by following a series of well-defined procedures. On the contrary, the non-routine jobs require more creativity, flexibility and interpersonal skills, which are at the two ends of the skill distribution.3 The sample covers the period from 1960:q1 to 2017:q4. By stacking the time-varying coefficients αt , β1,t and β2,t in Eq. (1) into a 55 × 1 vector Bt , and decomposing the covariance ma1 ′ −1 ′ trix of the residual term µt into Ωt = v ar(µt ) = A− t Σt Σt (At ) , where At is a lower triangular matrix that contains contemporaneous correlations among endogenous variables with ones along the 2 The real GDP, the routine to total employment ratio and the S&P 500 index enter the dynamic system in the form of quarterly growth rates. In fact, the qualitative results are not affect by replacing the GDP deflator with the consumer price index or the personal consumption expenditure to compute the inflation. 3 The not-for-publication Appendix documents the detailed sources of all the data and the exact categorization of the employment variables.

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diagonal, and Σt is a diagonal matrix with the standard deviations along the diagonal. The reduced form Eq. (1) can be rewritten as 1 yt = Xt Bt + A− t Σt ϵ t

(2)

where Xt = I(5)⊗[1, Gt −1 , Πt −1 , Rt −1 , It −1 , St −1 , Gt −2 , Πt −2 , Rt −2 , It −2 , St −2 ] and I(5) denotes a 5 × 5 identity matrix. ϵt is a 5 × 1 vector that contains uncorrelated disturbances. Let αt be a 10 × 1 vector that contains the non-zero and non-one elements in At , and let σt be a 5 × 1 vector that contains the diagonal elements in Σt . The dynamics of the time varying parameter are given by Bt = Bt −1 + νt

νt ∼ N(0, Q )

(3)

αt = αt −1 + ζt

ζt ∼ N(0, S)

(4)

ηt ∼ N(0, W )

(5)

log(σt ) = log(σt −1 ) + ηt

where the uncorrelated innovation terms νt , ζt and ηt follow normal distributions. Q is a 55 × 55 covariance matrix, S is a 10 × 10 block diagonal matrix, and W is a 5 × 5 diagonal matrix. I use the Markov Chain Monte Carlo algorithm outlined in Primiceri (2005) and Cogley and Sargent (2005) to estimate the time varying parameters (Bt , αt and σt ), and the hyperparameters (Q , S and W ). The algorithm is implemented 100 000 times with the first 50 000 draws discarded as burn-ins. The calibrations of the prior distributions follow Primiceri (2005), and the details are described in the not-for-publication online appendix. This paper uses sign identification scheme developed by Uhlig (2005) and Faust (1998) to identify five structural shocks (a routine augmenting technological shock, a RBTS, a demand shock, an investment shock, and a financial shock). Positive technological shocks raise output while reduce inflation. They are disentangled by imposing restrictions on the relative responses of the routine versus non-routine employment. Based on the definition in Violante (2008) that a positive RBTS reduces the relative demand of routine employment to non-routine employment, the routine employment as a share of total employment falls in response to a positive RBTS.4 The rest of the supply shock is captured by the routine augmenting technological shock, whose positive realizations raise the share of routine employment in total employment. As for the rest three shocks, their positive realizations raise both output and inflation. Following Furlanetto et al. (forthcoming), a positive demand shock drives down the investment to output ratio, as the demand shock is assumed to raise the component of aggregate demand other than investment. A positive investment or financial shock augments the investment to output ratio, and they are separately identified by imposing that the asset price (measured by S&P 500 index) falls in response to a positive investment shock, while rises in response to a positive financial shock, since the investment shock is interpreted as the capital supply shock, while the financial shock is interpreted as the capital demand shock. These sign restrictions are imposed only on impact of the impulse responses. The sign identification strategy is implemented using the algorithm outlined in Rubio-Ramírez et al. (2010).5 The algorithm provides a structural impact matrix for each posterior draw of the parameters P˜ t ,i i = 1, . . . , 50000. It can be decomposed as P˜ t ,i = Ct−,i1 Dt ,i , where Ct ,i is a 5 × 5 matrix of the contemporaneous correlation coefficients with ones along its diagonal, and Dt ,i is a 5 × 5 diagonal matrix with standard deviations of structural shocks along its diagonal. The structural model (Eq. (2)) can be written as Ct ,i yt = γt + Γ1,t yt −1 + Γ2,t yt −2 + Dt ,i ξt

(6)

4 A routine biased technology shock can be interpreted as the combination of the cyclical factors that have heterogeneous effects on workers of different skill groups, such as the advances in the automation or the telecommunication technology, e.g. Mandelman (2016), Cortes et al. (2017) and Goos et al. (2014). 5 The not-for-publication online appendix describes the detailed procedures.

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W. Zhang / Economics Letters 176 (2019) 28–34 Table 1 (A) Changed recovery speed: Data. (B) Changed recovery speed: Simulated data from counterfactual analysis. A Variables

GDP growth rate

Four-quarter growth

Eight-quarter growth

Pre-1990

Post-1990

Changes

Pre-1990

Post-1990

Changes

5.8757

2.5203

−3.3553**

10.969

5.5702

−5.3992***

B Counterfactual scenarios

Four-quarter

Eight-quarter

Post-1990

Changes

Post-1990

Changes

3.5781 2.6398 3.4839 3.3505 2.7575 2.7532 2.931

−2.2976* −3.2359** −2.3917** −2.5252** −3.1182** −3.1225** −2.9446**

7.9039 6.3454 7.2206 6.7306 5.7012 6.0057 6.3478

−3.0655** −4.624** −3.7488** −4.2388** −5.2682*** −4.9637*** −4.6216***

2.3074 2.5324 2.6083 3.1749 2.5988

−3.5682** −3.3432** −3.2674** −2.7007** −3.2769**

5.1838 5.6791 5.6633 6.8193 5.6329

−5.7856*** −5.2903*** −5.3061*** −4.1501*** −5.3365***

4.1428 4.3087

−1.7328 −1.567

8.4308 9.144

−2.5386* −1.8254

Role of different shocks Supply shocks Technology (RA) Technology (RB) Demand shocks Demand Investment Financial Role of structural changes Output Inflation Routine employment share Investment share S&P 500 Role of cyclical and structural changes Technology (RB) and investment share Supply shocks and financial variables

Notes: Each row reports the mean values of the cumulative GDP growth rates after pre-1990 or post-1990 business cycle recession troughs, and their differences. The results in Table (A) are calculated based on the data, and the results in Table (B) are calculated based on the medians of the simulated data obtained from the counterfactual experiments. To avoid redundancy, the pre-1990 output recovery speeds are omitted from Table (B), as they are unchanged. *Indicate significance level at 10% of a standard t-test on the equality of means across two subsamples. **Indicate significance level at 5% of a standard t-test on the equality of means across two subsamples. ***Indicate significance level at 1% of a standard t-test on the equality of means across two subsamples.

where ξt = [ξtra , ξtrb , ξtd , ξtinv , ξt ] is the vector that contains the routine augmenting technological shock, the RBTS, the demand shock, the investment shock and the financial shock. fin

3. Estimation results The first column in Fig. 1 shows the time-varying standard deviations of the five structural shocks (the diagonal elements of matrix Dt ). There is a substantial time variation in the volatilities of the identified shocks except for the investment shock. The two supply shocks and the demand shock exhibited declined volatilities since late-1970s, and remained to be low and stable from mid1980 through 2000.6 Their volatilities resurged during the Great Recession in line with the findings in Stock and Watson (2012). The volatility of the financial shock exhibits the greatest variation, and it peaks during the Great Recession similar to the findings in Prieto et al. (2016). This time variation in the sizes of exogenous shocks can potentially account for the changed dynamics of the U.S. output. In order to further understand the contributions of the identified shocks to the output dynamics, the second and third columns in Fig. 1 present the medians of the realized structural shocks in pre-1990 and post-1990 subsamples respectively.7 The behaviors of the identified shocks are consistent with their perceived roles in driving the output dynamics from 1970 to 2017. In particular, 6 There has been ample evidence that volatilities of the shocks to the U.S. economy have declined during 1980s and 1990s, e.g. Primiceri (2005) and Justiniano and Primiceri (2008). This phenomenon is referred to as ‘‘Good Luck’’ explanation for the Great Moderation. 7 As the sign identification provides a set of candidate structural impact matrices corresponding to each set of posterior draws of parameters, the medians are taken across the 50 000 sets of the identified structural shocks.

as the severity of the Great Recession is mainly caused by run on wholesale fundings and the housing price collapse induced household deleverage (Bernanke, 2018), the financial shock takes the deepest plunge among all the identified shocks during the Great Recession. In addition, comparing the historical path of the RBTS in pre-1990 and post-1990 subsamples confirms that the two recent recessions aggravated the routine job losses as shown in Gaggl and Kaufmann (2014) and Hershbein and Kahn (2018). Fig. 2 presents the contributions of the structural shocks to the deviations of GDP growth rate from its deterministic trend.8 The two supply shocks and the financial shock explain the most variations of the GDP growth rate, although their roles vary over time.9 Two identified shocks exhibit more obvious changes than others do. Firstly, the RBTSs boost the recoveries of GDP after the pre-1990 recessions, while have negligible or even negative effects on GDP growth after the post-1990 recessions. Secondly, the effects of the financial shocks on the GDP growth rate rise after 2000, playing a dominating role during mid-2000s, Great Recession and its immediate aftermath, consistent with the findings in Prieto et al. (2016). Fig. 3 presents the impulse responses of the cumulative GDP growth rates to a unit increase in the identified structural shocks from 1971 to 2016. The impulse responses for each period are computed based on the posterior draws of the parameters for that period, thus they summarize how the GDP growth rate responds to the structural shocks at a particular point in time. By construction, 8 The detailed procedures for computing the Historical Decomposition are provided in the not-for-publication online appendix. 9 The combination of the two supply shocks and the financial shocks together can account for around 70% variations of the GDP growth rate, according to the variance decomposition at various horizons, which is not presented in the paper due to the space constraint.

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Fig. 1. Structural shock volatilities and the realized structural shocks. The red solid lines are median values and the red dashed lines in the first column are the 68% confidence intervals. ‘RA’ stands for routine augmenting and ‘RB’ stands for routine biased.

Fig. 2. Contributions of structural shocks to the deviations of GDP growth from its deterministic trend. ‘RA’ stands for routine augmenting and ‘RB’ stands for routine biased.

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Fig. 3. Median values of the impulse responses of the cumulative GDP growth rates to a unit increase in identified shocks. ‘RA’ stands for routine augmenting and ‘RB’ stands for routine biased.

the variations in the impulse responses solely reflect the changes in the transmission mechanisms. The results reveal two interesting features of the data. Firstly, the volatilities of GDP growth rate conditional on all the identified structural shocks have declined since the early-1980s, but to varied degrees, indicating that the Great Moderation can at least be partly explained by the structural changes in the economy. Secondly, the conditional volatilities of the GDP growth rate have risen again since the early 2000. This second phase of evolution in the transmission mechanisms is not as well documented in the literature as the Great Moderation, and it may be related to several structural changes, e.g. the changes in the labor market that lead to the declined labor share or to the labor market polarization, and the development in the capital market that leads to the falling safe interest rate. Overall, the estimation results suggest that the sizes of the shocks and their transmission mechanisms have evolved over the past five decades, and both of them potentially contribute to the slowdown in the post-1990 output recoveries. 4. Counterfactual analysis This section conducts three groups of counterfactual analysis to illustrate the roles of identified shocks and structural changes in explaining the slow output recoveries.10 For each counterfactual experiment, Table 1.B reports the average post-1990 output recovery speeds, and calculates the difference between the post-1990 counterfactual and the pre-1990 actual recovery speeds. For three representative counterfactual experiments, Fig. 4 shows the medians and the 68% confidence intervals of the difference between the 10 The TVP-VAR literature often conducts counterfactual experiments to explore the implications of an identified structural shock (e.g. Belongia and Ireland, 2016) or a structural change (e.g. Primiceri, 2005) for the rest of the economy.

counterfactual and the actual cumulative GDP growth rates after each post-1990 recession. The differences are normalized to equal zero at the trough of each respective recession. The first group of counterfactual experiments explores the role of each identified shock, by replacing its realizations during five years after each post-1990 recession trough, with its fiveyear realizations following the last pre-1990 recession trough (i.e. 1982:Q4).11 The purpose of this exercise is to explore, what would happen to the trajectories of the GDP growth rate, if the behavior of the chosen structural shock during post-1990 recoveries had remained the same as that during a representative pre-1990 recovery, i.e. 1983:Q1–1987:Q4.The quantitative results in Table 1.B indicate all the structural shocks contribute to the slow output recoveries, as the post-1990 recovery speeds are improved. Among the five structural shocks, the contribution of RBTS is the greatest, and the two supply shocks play a greater role in holding back the output recoveries compared with the three demand shocks. Moreover, as shown in Fig. 4, the output recoveries would be significantly faster after each post-1990 recession, if the behavior of the RBTS had not changed during post-1990 recovery phases. In the second group of counterfactual experiments, the post1990 coefficients in an endogenous variable’s equation are drawn, not from their own posterior distributions, but instead from the posterior distributions from 1989:Q4. This group of experiments is designed to infer the behavior of GDP growth rate, when the structural changes that shift the dynamics of the chosen endogenous variable had not happened since 1990.12 According to Table 1.B, 11 The results are similar to the current ones when using the earlier recovery phases’ realizations of the structural shocks to replace their actual realizations. 12 Preventing the structural changes that affect the investment to output ratio from happening since 1985 moderately improves the post-90 output recovery speeds from the current results.

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Fig. 4. Differences between the counterfactual and the actual cumulative GDP growth rates for each post-1990 recovery phase. The first column illustrates the counterfactual, in which the behavior of the routine biased technological shocks during the post-1990 recoveries are set to be the same as those during 1983:Q1–1987:Q4. The second column illustrates the counterfactual, in which the post-1990 structural changes that affect investment dynamics are removed. The third column illustrates the combine effects of the first two counterfactual experiments. The red sold lines are the median values and the pink regions are 68% confidence intervals. ‘RB’ stands for routine biased.

the structural changes that altered the dynamics of investment to output ratio are more important than other structural changes. Fig. 4 indicates that holding the post-1990 coefficients in investment to output ratio equation unchanged significantly boosts the output recoveries from the recent two recessions, while has an ambiguous effect on the speed of output recoveries from 1991 recession. The third group of counterfactual experiments examines the combined effects of the cyclical factors and the secular factors. Fig. 4 and Table 1.B confirm the crucial roles of RBTS and the structural changes that govern the investment dynamics in explaining the post-1990 slow output recoveries. According to Table 1.B, the reduction in the post-1990 output recovery speed is no longer significant, when replacing the historical realizations of the RBTS in five years after each post-1990 recession trough with its fiveyear realizations after 1982:Q4, in tandem with holding the economic conditions that govern the investment to output dynamics unchanged after 1990. Moreover, the improvement of the output growth rate shown in Fig. 4 is highly significant for all the post1990 recoveries, especially for the one after the Great Recession. 5. Conclusion This paper investigates the causes of the slow post-1990 output recoveries with an estimated time-varying parameter VAR model. By incorporating the ratio of routine to total employment and two financial variables, this flexible framework is capable of evaluating

several candidate explanations. The empirical results highlight the role of the identified RBTS in slowing the post-1990 output recoveries, and the role of the structural changes that govern the dynamics of investment in slowing the post-2000 output recoveries. Thus, both the cyclical changes in the labor market and the structural transformations in the financial market contribute to the slow post-1990 recoveries, shedding light on the directions for future works to elaborate the underlying mechanisms. Acknowledgments I would like to thank the referee for the very helpful comments. This work was supported by Major Program of National Social Science Foundation of China [Grant No: 14ZDB123]; and the fund for building world-class universities (disciplines) of Renmin University of China. References Albonico, Alice, Paccagnini, Alessia, Tirelli, Patrizio, 2017. Great recession, slow recovery and muted fiscal policies in the U.S.. J. Econom. Dynam. Control 81, 140–161, International Conference Large-scale Crises: 1929 vs. 2008. Autor, David H., Katz, Lawrence F., Kearney, Melissa S., 2006. The polarization of the U.S. labor market. Amer. Econ. Rev. 96 (2), 189–194. Belongia, Michael T., Ireland, Peter N., 2016. The evolution of U.S. monetary policy: 2000–2007. J. Econ. Dyn. Control 73, 78–93. Bernanke, Ben S., 2018. The real effects of the financial crisis. Brookings Papers on Economic Activity Conference Draft, Fall. Cogley, Timothy, Sargent, Thomas J., 2005. Drift and volatilities: Monetary policies and outcomes in the post WWII U.S. Rev. Econ. Dyn. 8 (2), 262–302.

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