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Labor Market Effects of Technology Shocks

Profile image of Claudio MichelacciClaudio Michelacci

2007, SSRN Electronic Journal

Abstract

We analyze the labor market effects of neutral and investment-specific technology shocks along the intensive margin (hours worked) and the extensive margin (unemployment). We characterize the dynamic response of unemployment in terms of the job separation and the job finding rate. We find that the job separation rate accounts for a major portion of the impact response of unemployment. Over the adjustment path, instead, unemployment is mainly explained by fluctuations in the job finding rate. Neutral shocks prompt an increase in unemployment while investment specific shocks are expansionary on employment and hours worked. Neutral shocks explain a substantial portion of the volatility of unemployment and output; investment specific shocks mainly explain hours worked volatility. We show that these findings are consistent with the view that neutral technological progress prompt Schumpeterian creative destruction, while investment-specific technological progress operates essentially as in a neoclassical growth model.

Figures (37)
Figure 1: First graph: the dashed line is the aggregate number of hours worked per capita; the con- tinuous line is civilian unemployment both series in logs. Second graph: (logged) hours per employee. Third graph: rate of growth of labor productivity in the non-farm business sector. Fourth graph: growth rate of the relative price of investment goods (multiplied by 100). Fifth and sixth graph: job finding rate and the job separation rate (both in logs), respectively. The solid line corresponds to the approximated rate, the dashed to the exact rate. The areas in grey correspond to the NBER recessions. Figure 1: First graph: the dashed line is the aggregate number of hours worked per capita; the con- job finding rate and the job separation rate (both in logs), respectively. ‘The solid line corresponds
Figure 1: First graph: the dashed line is the aggregate number of hours worked per capita; the con- tinuous line is civilian unemployment both series in logs. Second graph: (logged) hours per employee. Third graph: rate of growth of labor productivity in the non-farm business sector. Fourth graph: growth rate of the relative price of investment goods (multiplied by 100). Fifth and sixth graph: job finding rate and the job separation rate (both in logs), respectively. The solid line corresponds to the approximated rate, the dashed to the exact rate. The areas in grey correspond to the NBER recessions. Figure 1: First graph: the dashed line is the aggregate number of hours worked per capita; the con- job finding rate and the job separation rate (both in logs), respectively. ‘The solid line corresponds
Figure 2: First graph: average quarterly growth rate of the relative price of investment (dotted line) and unemployment rate (solid line). Second graph: average quarterly growth rate of labour productivity (dotted line) and unemployment rate (solid line). Third graph: Hodrick Prescott trend of labor productivity growth (dotted line) and hours per capita (solid line). Fifth graph: Hodrick Prescott trend of labor productivity growth (dotted line) and unemployment rate (solid line). Sixth and seventh graphs: Hodrick Prescott trend of finding and separation rates (dotted lines) and unemployment rate (solid line). The smoothing coefficient A = 1600. in figure 2. We follow the growth literature and choose 1973:2 and 1997:1 as a break
Figure 2: First graph: average quarterly growth rate of the relative price of investment (dotted line) and unemployment rate (solid line). Second graph: average quarterly growth rate of labour productivity (dotted line) and unemployment rate (solid line). Third graph: Hodrick Prescott trend of labor productivity growth (dotted line) and hours per capita (solid line). Fifth graph: Hodrick Prescott trend of labor productivity growth (dotted line) and unemployment rate (solid line). Sixth and seventh graphs: Hodrick Prescott trend of finding and separation rates (dotted lines) and unemployment rate (solid line). The smoothing coefficient A = 1600. in figure 2. We follow the growth literature and choose 1973:2 and 1997:1 as a break
Figure 3: Each line corresponds to a VAR estimated over a different sample period. Each VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates.
Figure 3: Each line corresponds to a VAR estimated over a different sample period. Each VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates.
full sample output increases very little while it increases substantial in the relevant sub-period, both in the short and in the long run. The two specifications agree in characterizing the response of hours and the job finding rate although both variables increase more in the 1973-1997 sub-sample. The unemployment rate rises on impact in the full sample while it falls in the relevant sub-sample. The separation rate falls in the relevant subperiod while it increases on impact when considering the full sample. the relevant subperiod while it increases on impact when considering the full sample. Again, the bias in the estimated responses for the full sample, is in line with the low frequency correlations previously discussed. In fact, in the full sample, a permanent change in the rate o f growth of the relative price of investment is at least partly identified as a series of investment specific technology shocks. Thus, over the period 1973:11-1997:I when t he price of investment falls at a faster rate on average, the full sample specification tends to identify a series of positive investment specific technology
full sample output increases very little while it increases substantial in the relevant sub-period, both in the short and in the long run. The two specifications agree in characterizing the response of hours and the job finding rate although both variables increase more in the 1973-1997 sub-sample. The unemployment rate rises on impact in the full sample while it falls in the relevant sub-sample. The separation rate falls in the relevant subperiod while it increases on impact when considering the full sample. the relevant subperiod while it increases on impact when considering the full sample. Again, the bias in the estimated responses for the full sample, is in line with the low frequency correlations previously discussed. In fact, in the full sample, a permanent change in the rate o f growth of the relative price of investment is at least partly identified as a series of investment specific technology shocks. Thus, over the period 1973:11-1997:I when t he price of investment falls at a faster rate on average, the full sample specification tends to identify a series of positive investment specific technology
Figure 5: Full sample with intercept deterministically broken at 1973:II and 1997:I. Six variables VAR. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to the median estimate. The reported bands correspond to the 90 percent confidence interval (obtained from
Figure 5: Full sample with intercept deterministically broken at 1973:II and 1997:I. Six variables VAR. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to the median estimate. The reported bands correspond to the 90 percent confidence interval (obtained from
Figure 6: Full sample with intercept deterministically broken at 1973:II and 1997:I. Six variables VAR. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to the median estimate. investment specific technology shocks makes the price of investment fall permanently. Figure 6 plots responses to an investment specific shock. The estimated responses
Figure 6: Full sample with intercept deterministically broken at 1973:II and 1997:I. Six variables VAR. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to the median estimate. investment specific technology shocks makes the price of investment fall permanently. Figure 6 plots responses to an investment specific shock. The estimated responses
Figure 7: Exact rates (dotted lines) and approximated rates (solid lines). Both VAR. includes dummies corresponding to the breaks in technology growh. Each VAR has 8 lags and six variables. Reported are point estimates of the responses. sponses to a neutral technology shock. We plot the response obtained with the exact
Figure 7: Exact rates (dotted lines) and approximated rates (solid lines). Both VAR. includes dummies corresponding to the breaks in technology growh. Each VAR has 8 lags and six variables. Reported are point estimates of the responses. sponses to a neutral technology shock. We plot the response obtained with the exact
Figure 8 reports responses to an investment specific technology shock when exact
Figure 8 reports responses to an investment specific technology shock when exact
Figure 9: Left column corresponds to neutral technology shocks; right column to investment specific technology shocks. The first row plots the correlation of the corresponding technology shock with the consumption-output ratio, the second with the investment-output ratio, the thrid with the inflation rate. The shocks are estimated from the six variables VAR with approximated rates in the dummy specification. The horizontal lines correspond to an asymptotic 95 percent confidence interval centered around zero. technology shocks. The first row plots the correlation of the corresponding technology shock with the tunnel around zero are reported in Figure 9. The technology shocks are obtained from pute correlations up to 6 leads and lags between each of our technology shocks and
Figure 9: Left column corresponds to neutral technology shocks; right column to investment specific technology shocks. The first row plots the correlation of the corresponding technology shock with the consumption-output ratio, the second with the investment-output ratio, the thrid with the inflation rate. The shocks are estimated from the six variables VAR with approximated rates in the dummy specification. The horizontal lines correspond to an asymptotic 95 percent confidence interval centered around zero. technology shocks. The first row plots the correlation of the corresponding technology shock with the tunnel around zero are reported in Figure 9. The technology shocks are obtained from pute correlations up to 6 leads and lags between each of our technology shocks and
Figure 10: Nine variables VAR. 1955:1-2000:IV sample with intercept deterministically broken at 1973:II and 1997:I. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate. the VAR in the dummy specification wit btained with the exact rates). There is atio and the investment output ratio hel h the approximated rates (similar results a: some evidence that the consumption outpt p to predict neutral technology shocks, whi one of the three potentially omitted variables significantly correlate with investme! pecific shocks. Hence, we investigate w hat happens when we enlarge the system 1 nclude these three new variables. Figures 10 and 11 present the responses of the eig! ariables of interest when considering a VAR which includes the original six variabl lus the consumption to output and the investment to output ratios and the inflatic ate in the dummy specification, when approximated rates are used. It is apparent th: ione of conclusions are affected and this is still the case when exact rates are used.
Figure 10: Nine variables VAR. 1955:1-2000:IV sample with intercept deterministically broken at 1973:II and 1997:I. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate. the VAR in the dummy specification wit btained with the exact rates). There is atio and the investment output ratio hel h the approximated rates (similar results a: some evidence that the consumption outpt p to predict neutral technology shocks, whi one of the three potentially omitted variables significantly correlate with investme! pecific shocks. Hence, we investigate w hat happens when we enlarge the system 1 nclude these three new variables. Figures 10 and 11 present the responses of the eig! ariables of interest when considering a VAR which includes the original six variabl lus the consumption to output and the investment to output ratios and the inflatic ate in the dummy specification, when approximated rates are used. It is apparent th: ione of conclusions are affected and this is still the case when exact rates are used.
Figure 11: Nine variables VAR. 1955:1-2000:IV sample with intercept deterministically broken at 1973:II and 1997:I. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate. rates. They argue that recessions are mainly explained by a fall in the job finding rate.
Figure 11: Nine variables VAR. 1955:1-2000:IV sample with intercept deterministically broken at 1973:II and 1997:I. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate. rates. They argue that recessions are mainly explained by a fall in the job finding rate.
Figure 12: Nine variables VAR with approximated rates. Full sample with deterministic time dummies. Reported are median estimates from 500 bootstrap replications. 3 deals with the exact rates. In both cases a nine variable VAR is used. In eacl "More specifically, fictional and actual unemployment might differ if either transitional dynamics (for given transition rates) is important or if other labour market flows explain unemployment. From expression (3) it follows that the rate of convergence of unemployment to its steady state depends upon S and F’. Given that the values of the separation and finding rate are high enough in the data, Shimer (2005) forcefully argues that the first possibility is unlikely to be cause of concern. This suggests that differences between fictional and actual unemployment are mainly due to other labor market flows. Figure 12 reports the results for the specification with approximated rates, Figure
Figure 12: Nine variables VAR with approximated rates. Full sample with deterministic time dummies. Reported are median estimates from 500 bootstrap replications. 3 deals with the exact rates. In both cases a nine variable VAR is used. In eacl "More specifically, fictional and actual unemployment might differ if either transitional dynamics (for given transition rates) is important or if other labour market flows explain unemployment. From expression (3) it follows that the rate of convergence of unemployment to its steady state depends upon S and F’. Given that the values of the separation and finding rate are high enough in the data, Shimer (2005) forcefully argues that the first possibility is unlikely to be cause of concern. This suggests that differences between fictional and actual unemployment are mainly due to other labor market flows. Figure 12 reports the results for the specification with approximated rates, Figure
Contribution of Separation rate Figure 13: Nine variables VAR with Exact rates. Full sample with deterministic time dummies. Impulse responses correspond to median estimates from 500 bootstrap replications. contribution of the separation rate to fluctuations in fictional unemployment. figure, the response of the true unemployment rate appears with a solid line and the
Contribution of Separation rate Figure 13: Nine variables VAR with Exact rates. Full sample with deterministic time dummies. Impulse responses correspond to median estimates from 500 bootstrap replications. contribution of the separation rate to fluctuations in fictional unemployment. figure, the response of the true unemployment rate appears with a solid line and the
Figure 14: Response of the distribution of technological gaps T, f;(7) to a neutral technology shock: bold line corresponds to steady state, dotted line to the distribution in the same year of the z-shock.
Figure 14: Response of the distribution of technological gaps T, f;(7) to a neutral technology shock: bold line corresponds to steady state, dotted line to the distribution in the same year of the z-shock.
Table 1: Parameters values used in the baseline specification.
Table 1: Parameters values used in the baseline specification.
when using the specification with exact rates, see Figure 7. In the quarter immediately Figure 15: Impulse responses to a 2-shock leading to a long run increase in labor productivity of one per cent. The separation and finding rates correspond to the implied average monthly rate. All impulse responses are multiplied by 100. ing to a long run increase in labor productivity of one per cent. The separation and finding rates correspond to the implied average monthly rate, denoted S?” and F/”, respectively.'? Neutral technology shocks bring about a simultaneous increase in the destruction of technologically obsolete jobs and in the creation of new highly produc- tive units. But since labour market frictions make reallocation sluggish, this process prompts a contractionary period during which employment (and output) temporarily fall. As z increases, jobs with a given technology becomes obsolete relatively to the technological frontier so the distribution of old jobs f,(7) shifts to the right on impact— see the dotted line in Figure 14. This leads to an initial cleansing of technologically outdated jobs which makes the separation rate and consequently the unemployment rate increase. Quantitatively, a one per cent increase in long run productivity leads to an increase of about five per cent in the monthly separation rate and of four percent in the unemployment rate, which are closely in line with the empirical results obtained week nw aeotaee Lan armnnz pnetounwn <ihh ~acxwewnt wveataa ann Di nwwn 7% Tr thn nwwartaw tenmaniatal<- in the unemployment rate, which are closely in line with the empirical results obtained
when using the specification with exact rates, see Figure 7. In the quarter immediately Figure 15: Impulse responses to a 2-shock leading to a long run increase in labor productivity of one per cent. The separation and finding rates correspond to the implied average monthly rate. All impulse responses are multiplied by 100. ing to a long run increase in labor productivity of one per cent. The separation and finding rates correspond to the implied average monthly rate, denoted S?” and F/”, respectively.'? Neutral technology shocks bring about a simultaneous increase in the destruction of technologically obsolete jobs and in the creation of new highly produc- tive units. But since labour market frictions make reallocation sluggish, this process prompts a contractionary period during which employment (and output) temporarily fall. As z increases, jobs with a given technology becomes obsolete relatively to the technological frontier so the distribution of old jobs f,(7) shifts to the right on impact— see the dotted line in Figure 14. This leads to an initial cleansing of technologically outdated jobs which makes the separation rate and consequently the unemployment rate increase. Quantitatively, a one per cent increase in long run productivity leads to an increase of about five per cent in the monthly separation rate and of four percent in the unemployment rate, which are closely in line with the empirical results obtained week nw aeotaee Lan armnnz pnetounwn <ihh ~acxwewnt wveataa ann Di nwwn 7% Tr thn nwwartaw tenmaniatal<- in the unemployment rate, which are closely in line with the empirical results obtained
Figure 16: Impulse responses to a one-per-cent g-shock at time zero. The separation and finding rates correspond to the implied average monthly rate. All impulse responses are multiplied by 100. (.4 The intensive margin
Figure 16: Impulse responses to a one-per-cent g-shock at time zero. The separation and finding rates correspond to the implied average monthly rate. All impulse responses are multiplied by 100. (.4 The intensive margin
Table 2: Forecast Error Variance Decomposition: percentage of variance explained by neutral or investment-specific technology shocks at different time horizons for the selected variables. All VARs have nine variables with intercept deterministically broken at 1973:II and 1997:I. Panel A deals with a VAR with approximated rates, Panel B restrict the analysis to the 1973:II-2000:IV sub-sample, Panel deals C with the exact rates.
Table 2: Forecast Error Variance Decomposition: percentage of variance explained by neutral or investment-specific technology shocks at different time horizons for the selected variables. All VARs have nine variables with intercept deterministically broken at 1973:II and 1997:I. Panel A deals with a VAR with approximated rates, Panel B restrict the analysis to the 1973:II-2000:IV sub-sample, Panel deals C with the exact rates.
Figure 17: Effects of technology shocks in data and model. Solid line refers to raw data, the dotted line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The areas in grey correspond to the NBER recessions. from the nine variables VAR in the dummy specification with the exact rates. All
Figure 17: Effects of technology shocks in data and model. Solid line refers to raw data, the dotted line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The areas in grey correspond to the NBER recessions. from the nine variables VAR in the dummy specification with the exact rates. All
Figure 18: Contribution of neutral technology shocks. Solid line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates; dotted line is teh component due to neutral technology shocks alone. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The areas in grey correspond to the NBER recessions. Finally we study further the episode of the recession of the early 90 and the subse quent recovery. This episode have been extensively investigated, yet its causes are stil
Figure 18: Contribution of neutral technology shocks. Solid line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates; dotted line is teh component due to neutral technology shocks alone. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The areas in grey correspond to the NBER recessions. Finally we study further the episode of the recession of the early 90 and the subse quent recovery. This episode have been extensively investigated, yet its causes are stil
Figure 19: The jobless recovery of the 90s. Solid line refers to raw data (either unemployment or output), the dotted line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The area in grey corresponds to the NBER recession. ilter with smoothing parameter equal to 1600. It is apparent that technology shocks
Figure 19: The jobless recovery of the 90s. Solid line refers to raw data (either unemployment or output), the dotted line refers to the component due to technology shock (either neutral or investment specific) as recovered from the nine variables VAR with the exact rates. All series are detrended with a Hodrick Prescott filter with smoothing parameter equal to 1600.The area in grey corresponds to the NBER recession. ilter with smoothing parameter equal to 1600. It is apparent that technology shocks
Figure 20: Left column corresponds to neutral technology shocks; right column to investment specific technology shocks. The first row plots the correlation of the corresponding technology shock with relative oil price shocks(i.e. relative to consumption). The second row with Federal fund rate shocks at different time horizons. The shocks are estimated from the nine variables VAR, approximated rates, full sample with deterministic dummies. The horizzontal lines correspond to an asymptotic 95 percent confidence interval centered around zero. for other potential sources of supply disturbances.To check this concern, we have corre
Figure 20: Left column corresponds to neutral technology shocks; right column to investment specific technology shocks. The first row plots the correlation of the corresponding technology shock with relative oil price shocks(i.e. relative to consumption). The second row with Federal fund rate shocks at different time horizons. The shocks are estimated from the nine variables VAR, approximated rates, full sample with deterministic dummies. The horizzontal lines correspond to an asymptotic 95 percent confidence interval centered around zero. for other potential sources of supply disturbances.To check this concern, we have corre
where f; and V; are functions of technological gap while the remaining quantities are scalar, that satisfies the following conditions:
where f; and V; are functions of technological gap while the remaining quantities are scalar, that satisfies the following conditions:
Figure 21: Response to a neutral technology shock in two different sub-periods: 1973:II-1997:1 and 1973:II-2000:IV. The VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates. The continuous line correponds to the 1973:II-2000:IV period, and the dash-dotted line to the 1973:II- 1997:II period. Impulse responses correspond to point estimates. In this appendix we report some figures that are discussed in the main text. They are reported here just for completeness.
Figure 21: Response to a neutral technology shock in two different sub-periods: 1973:II-1997:1 and 1973:II-2000:IV. The VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates. The continuous line correponds to the 1973:II-2000:IV period, and the dash-dotted line to the 1973:II- 1997:II period. Impulse responses correspond to point estimates. In this appendix we report some figures that are discussed in the main text. They are reported here just for completeness.
Figure 22: Response to an investment specific in two different sub-periods: 1973:I-1997:I, and 1973:11-2000:IV. The VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates. The continuous line correponds to the 1973:II-2000:IV period, and the dash-dotted line to the 1973:II- 1997:II period. Impulse responses correspond to point estimates.
Figure 22: Response to an investment specific in two different sub-periods: 1973:I-1997:I, and 1973:11-2000:IV. The VAR has 8 lags and six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) unemployment rate, and the (logged) aggregate number of hours worked per capita, the log of separation and finding rates. The continuous line correponds to the 1973:II-2000:IV period, and the dash-dotted line to the 1973:II- 1997:II period. Impulse responses correspond to point estimates.
Figure 23: The continous line corresponds to dummy specification, the dotted line to the case where the intercept is a 3rd order in time. The dashed lines are the responses after detrending the original series with an Hodrick Prescott filter with smoothing parameter A=10000. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates. rigure 23: The continous line corresponds to dummy specification, the dotted line to the case where
Figure 23: The continous line corresponds to dummy specification, the dotted line to the case where the intercept is a 3rd order in time. The dashed lines are the responses after detrending the original series with an Hodrick Prescott filter with smoothing parameter A=10000. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates. rigure 23: The continous line corresponds to dummy specification, the dotted line to the case where
Figure 24: The continous line corresponds to dummy specification, the dotted line to the case where the intercept is a 3rd order in time. The dashed lines are the responses after detrending the original series with an Hodrick Prescott filter with smoothing parameter A=10000. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates.
Figure 24: The continous line corresponds to dummy specification, the dotted line to the case where the intercept is a 3rd order in time. The dashed lines are the responses after detrending the original series with an Hodrick Prescott filter with smoothing parameter A=10000. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates.
Figure 29: Results from VAR in the dummy specification when the variables in VAR are deflated with adifferent price index: continuous line corresponds to baseline specification, dotted line corresponds to the VAR where output and price of investment are deflated by using the CPI index, the dashed line corresponds to the case where output is deflated with the output deflator and the price of investment with the CPI index. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates.
Figure 29: Results from VAR in the dummy specification when the variables in VAR are deflated with adifferent price index: continuous line corresponds to baseline specification, dotted line corresponds to the VAR where output and price of investment are deflated by using the CPI index, the dashed line corresponds to the case where output is deflated with the output deflator and the price of investment with the CPI index. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates.
Figure 30: Results from VAR in the dummy specification when the variables in VAR are deflated with adifferent price index: continuous line corresponds to baseline specification, dotted line corresponds to the VAR where output and price of investment are deflated by using the CPI index, the dashed line corresponds to the case where output is deflated with the output deflator and the price of investment with the CPI index. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates. Figure 30: Results from VAR in the dummy specification when the variables in VAR are deflated with
Figure 30: Results from VAR in the dummy specification when the variables in VAR are deflated with adifferent price index: continuous line corresponds to baseline specification, dotted line corresponds to the VAR where output and price of investment are deflated by using the CPI index, the dashed line corresponds to the case where output is deflated with the output deflator and the price of investment with the CPI index. VAR with approximated rates, with 8 lags, and six variables. Plotted impulse responses correspond to point estimates. Figure 30: Results from VAR in the dummy specification when the variables in VAR are deflated with
Figure 31: Response of the Ins and Outs of unemployment to a neutral technology shock. The sample period is 1955:1-1973:1. The VAR has eight lags and contains six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) job finding rate, the (logged) job separation rate, the (logged), unemployment rate (logged), and the (logged) aggregate number of hours worked per capita. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate from bootstrap replications.
Figure 31: Response of the Ins and Outs of unemployment to a neutral technology shock. The sample period is 1955:1-1973:1. The VAR has eight lags and contains six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) job finding rate, the (logged) job separation rate, the (logged), unemployment rate (logged), and the (logged) aggregate number of hours worked per capita. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate from bootstrap replications.
Figure 34: Response of the Ins and Outs of unemployment to an investment specific technology shock. The sample period is 1973:I1-1997:II. The VAR has eight lags and contains six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) job finding rate, the (logged) job separation rate, the (logged), unemployment rate (logged), and the (logged) aggregate number of hours worked per capita. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate from bootstrap replications. Figure 34: Response of the Ins and Outs of unemployment to an investment specific technology
Figure 34: Response of the Ins and Outs of unemployment to an investment specific technology shock. The sample period is 1973:I1-1997:II. The VAR has eight lags and contains six variables: the rate of growth of the relative price of investment, the rate of growth of labour productivity, the (logged) job finding rate, the (logged) job separation rate, the (logged), unemployment rate (logged), and the (logged) aggregate number of hours worked per capita. Dotted lines represent the 5% and 95% quantiles of the distribution of the responses simulated by bootstrapping 500 times the residuals of the VAR. The continuous line corresponds to median estimate from bootstrap replications. Figure 34: Response of the Ins and Outs of unemployment to an investment specific technology

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Technology Shocks, Employment, and Labor Market Frictions
Francesco Zanetti

Recent empirical evidence suggests that a positive technology shock leads to a decline in labor inputs. However, the standard real business model fails to account for this empirical regularity. Can the presence of labor market frictions address this problem, without otherwise altering the functioning of the model? We develop and estimate a real business cycle model using Bayesian techniques that allows, but does not require, labor market frictions to generate a negative response of employment to a technology shock. The results of the estimation support the hypothesis that labor market frictions are the factor responsible for the negative response of employment.

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