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Just-in-Time and the Great Moderation: Longitudinal Evidence from Firms Xiaodan Gao∗† National University of Singapore

Abstract The volatility of real GDP growth declined substantially from the mid-1980s to 2007. This paper uses a sample of just-in-time (JIT) adopters to directly test one of the main competing explanations for this phenomenon: the implementation of JIT inventory management. Exploiting the panel nature of the data and using difference-in-differences (DID) estimation, I find no support for the hypothesis that JIT plays a role in reducing output-growth volatility. Instead, my analysis suggests that JIT tends to destabilize real output by increasing the variabilities of inventory and sales growth.

JEL Classification: E32, G31, C21, C23 Keywords: Great Moderation; Just-in-Time; Inventory Holdings.

∗ Department of Strategy and Policy, NUS Business School, 15 Kent Ridge Drive, Singapore. Email address: [email protected]. I would like to thank Ivan Png for insightful comments and suggestions. † First draft: March 2017.

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1

Introduction

A long-standing puzzle in macroeconomics is the significant decline in the volatility of U.S. real output growth from the mid-1980s to 2007, known as the Great Moderation. Three main competing explanations have been proposed in the literature for this phenomenon: (i) improvements in monetary policy (Clarida, Gali, and Gertler, 2000; Canova, 2009); (ii) a reduction in the volatility of aggregate economic shocks (Ahmed, Levin, and Wilson, 2004; Leduc and Sill, 2007; Justiniano and Primiceri, 2008); and (iii) structural changes—and in particular, the adoption of just-in-time (JIT) inventory management (McConnell and Perez-Quiros, 2000; Kahn, McConnell, and Perez-Quiros, 2002; Irvine and Schuh, 2005; Davis and Kahn, 2008). This paper focuses on the third explanation and examines the hypothesis that JIT reduces output-growth volatility. So far, no direct empirical test of this hypothesis has been performed, and no clear consensus regarding the role of JIT has been reached. A large number of previous studies on this topic argue that implementing JIT reduces the volatility of firms’ real output growth due to better inventory control and smaller forecast errors.1 However, Wen (2011) develops a general equilibrium model of inventory and finds that JIT tends to destabilize sales—which, in turn, increases the volatility of real output. Meanwhile, other studies conclude that JIT has negligible effects on output volatility (Clarida, Gali, and Gertler, 2000; Khan and Thomas, 2007). In this paper, I aim to resolve the debate and contribute to the literature by directly assessing the effect of JIT. More specifically, I use a matched sample of JIT adopters and non-adopters to identify the causal effect of JIT on firms’ output-growth volatility from the differences between pre-adoption and post-adoption, within-firm differences of JIT adopters and non-adopters. The list of JIT adopters is compiled from LexisNexis Academic SEC filings, and a matched control group is constructed based on 4-digit SIC industries and firms’ total assets 1

See, for instance, the recent studies by Hererra, Jung, and Rossana (2014) and Strasser (2014).

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(Gao, 2017). To validate the sample, I show that (i) adopters and non-adopters share quantitatively similar characteristics before JIT adoption, (ii) the assumptions of common trends in inventory behavior and real output growth before JIT adoption are satisfied, and (iii) JIT effectively reduces firms’ inventory holdings. I then use the validated sample to answer the question of whether JIT reduces the volatility of output growth. Using difference-in-differences (DID) estimation, I find that JIT tends to increase firms’ output-growth volatility, consistent with the theoretical prediction derived by Wen (2011). In particular, after the implementation of JIT, firms’ outputgrowth volatility rises by 10%, which is statistically significant. In addition, I find that the volatilities of aggregate TFP shocks and firm-level productivity shocks have different impacts on firms’ output volatility. The former plays no role, which goes against the “good luck” hypothesis. The latter is a key determinant: Its one-standard-deviation increase causes the volatility of output growth to increase by 43%, which is economically and statistically important. To understand the channels by which JIT increases output-growth volatility, I investigate the effects of JIT on the volatilities of inventory growth and sales growth and the correlation between the two. I find that JIT tends to increase the former two and has no effects on the correlation. Specifically, JIT drives up inventory-growth volatility and sales-growth volatility by 17.8% and 12%, respectively. My finding of increased fluctuations in sales after adopting JIT supports the central insight of Khan and Thomas (2007) and Wen (2011), who argue that inventory tends to stabilize sales. The remainder of the paper is organized as follows. Section 2 provides some background regarding JIT management and its relationship with the Great Moderation. Sections 3 and 4 provide an overview of the data and identification methods. Section 5 discusses the estimation results, and Section 6 concludes.

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2

Background

This section briefly describes the JIT inventory strategy and provides an overview of the arguments that shape the empirical analysis linking JIT to the Great Moderation.

2.1

JIT Inventory Strategy

JIT is a business operational strategy that aims to manufacture products based on immediate needs and to reduce inventory generated from waiting and overproduction. The JIT strategy encompasses both JIT purchasing (speedy delivery of materials from suppliers) and JIT manufacturing (production of goods to meet current demand). The former reduces rawmaterial inventories, while the latter reduces work-in-progress and finished-goods inventories. The JIT strategy was developed for Toyota’s manufacturing plants, and was widespread in Japan by the mid-1970s. When Japanese manufacturers claimed a major market share in the U.S. in the early 1980s, JIT started attracting attention from U.S. companies, and has gradually been adopted since then.

2.2

JIT and the Great Moderation

As a result of the contemporaneous occurrence of the start of the Great Moderation and the spread of JIT in the U.S., many researchers hypothesize that the former can be attributed to the latter (McConnell and Perez-Quiros, 2000; Kahn, McConnell, and Perez-Quiros, 2002; Davis and Kahn, 2008; Strasser, 2014). In particular, since output (Yt ) is the sum of final sales (St ) and inventory investment (∆It ), the variance of output can be decomposed into the sum of variances of sales and inventory investment and the covariance between them,

V ar(Yt ) = V ar(St ) + V ar(∆It ) + 2Cov(St , ∆It ).

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Empirical data suggest a positive correlation between final sales and inventory investment, Cov(St , ∆It ) > 0, which has two implications. First, the volatility of output is greater than the volatility of sales. Second, inventory investment propagates shocks and exacerbates output fluctuations. As such, when changes in final sales are taken as given, a reduction in inventory holdings resulting from JIT tends to stabilize output and lower output volatility. I next turn to regression analysis to formally test the hypothesis that JIT smooths output fluctuations.

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Data

I use data from several sources to perform the empirical analysis. LexisNexis Academic SEC filings are used to identify JIT adopters, and Compustat Fundamentals Quarterly files to collect financial information for those firms and construct their counterparts. The NBER-CES Manufacturing Industry Database provides information about wage rate, which, along with firms’ financial information from Compustat, is used to compute firms’ value added. National Income and Product Accounts (NIPA) and Current Employment Statistics (CES) are used to construct aggregate TFP shocks over time. The JIT-adopter sample is from Gao (2017). Based on the initial list of JIT adopters provided by Kinney and Wempe (2002), Gao extends the sample by searching for keywords “JIT,” “Just in Time,” and “lean” in LexisNexis Academic SEC filings and deleting nonmanufacturing firms or firms with low data quality. The final sample contains 149 JIT adopters, with the description provided in Figure 1. [Figure 1 about here.] The upper panel plots the distribution of JIT adoption year. Firms in the sample started adopting JIT in 1982. The adoption rate steadily increased, and reached its peak in the late 1980s and early 1990s. The lower panel plots the distribution of industries in which JIT adopters operate. As shown, most of them operate in four industries: industrial equipment 5

(SIC 35), electronic equipment (SIC 36), motor vehicles (SIC 37), and instrumentation (SIC 38), while the remaining adopters are relatively evenly distributed among other industries.

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Methods

I construct a control group similar to the adopters by matching the 4-digit SIC industries in which firms operate and their total assets in the year preceding JIT adoption, and identify the effect of JIT on the volatility of real output growth by employing a DID approach. The identification comes from the differences between pre-adoption and post-adoption, within-firm differences of JIT adopters and non-adopters, with the following two assumptions. First, all of the unobserved heterogeneity that leads to the correlation between JIT adoption and the error term is captured by firm fixed effects. Second, variations in the dependent variable due to changes in the macroeconomic environment (e.g., monetary policy and aggregate shocks) are captured by industry-specific year fixed effects, which are common to firms in both the treatment and control groups within the same industry. These, along with controlling for a list of key firm attributes and matching on observed pre-adoption characteristics, help alleviate concerns about the endogeneity of JIT adoption. Moreover, I validate my sample in three ways. I compare the characteristics of adopters and non-adopters in the year preceding JIT adoption, show the trends in inventory behavior and real output growth before JIT adoption, and examine whether JIT reduces firms’ inventory holdings.

4.1 4.1.1

Sample Validation Characteristics of Adopters and Non-Adopters

I extract firms’ financial data from Compustat Fundamental Quarterly and show the similarities between JIT adopters and the matched control group in the year before adoption in Table 1. 6

[Table 1 about here.] I consider the following key characteristics of firms: inventory behavior, firm size, marketto-book ratio, cash flow, volatility of cash flow, working capital net of inventory, capital investment, debt financing, R&D investment, and operating profitability. To limit the effects of outliers in the sample, I winsorize variables as follows. Leverage and inventory ratios are winsorized between zero and one. EBITDA margin, R&D, and capital investment ratios are winsorized at the top and bottom 1%. Cash flow and net working capital ratios are winsorized at the bottom 1%, and market-to-book ratio is winsorized at the top 1%. On average, JIT adopters and control firms are quantitatively similar along most dimensions. They share similar average inventory-to-assets ratio, firm size, cash-flow ratio, cash-flow volatility, net working capital ratio, capital and R&D investment rates, and EBITDA margin. In other dimensions, adopters and the control group differ slightly: The former have better investment opportunities, proxied by higher market-to-book ratios, and carry less debt.

4.1.2

Trends before JIT adoption

To use DID estimation, I next verify the common trend assumption. I plot the dynamics of inventory turnover ratio (i.e., sales-to-inventory ratio) for JIT adopters (green line with circles) and matched firms (red line with diamonds) over 80 quarters in Figure 2. Quarter 0 is the first quarter of the year that adopters start implementing JIT. Quarters before adoption take on negative values, and quarters after adoption take on positive values. [Figure 2 about here.] The upper panel of Figure 2 presents the median inventory turnover ratio for both groups. As shown, adopters and non-adopters manage their inventory holdings in the same way before adoption—the two lines track each other closely. After adoption, non-adopters continue their inventory management practices, as suggested by the same trend, while 7

adopters manage their inventory more efficiently, as implied by the steeper upward-sloping trend. The relative average inventory turnover ratio is plotted in the bottom panel of Figure 2, where I normalize the average ratio in the adoption quarter to be 0. The pre-adoption trends of JIT firms and control group are parallel, although with different volatilities. The post-adoption trends suggest that JIT adopters tend to increase their inventory turnover ratios, which is consistent with JIT strategy, while the control group keeps the turnover ratio almost unchanged. To answer the key question—whether JIT plays a role in reducing firms’ output-growth volatility—I next show the dynamics of median and relative average real output growth in Figure 3. [Figure 3 about here.] The upper panel plots the median output growth over time. Before the adoption, the two output growth rates co-move with quantitatively similar volatilities. In the post-adoption period, the volatilities of both groups’ output growth drop. The relative average output growth rates, shown in the bottom panel of Figure 3, appear to be less volatile and overlap with each other within the last few years preceding adoption. After adopting JIT, the two output growth rates diverge. In particular, adopters’ growth rate stops declining. It is smooth in the first few years and becomes a bit more volatile afterwards. The control group’s output growth rate continues to fall and continues to fluctuate. Overall, this subsection demonstrates that the assumption of common trends before JIT is satisfied, which lends support for performing DID analyses in the subsections below. 4.1.3

JIT and Inventory Holdings

The role of JIT in reducing inventory holdings has been broadly documented in the inventory literature. In this subsection, I further validate my sample by examining whether adopters manage their inventories in a way that is consistent with JIT strategy. 8

I consider the following regression specification:

inventoryi,t = β0 + β1 JITi,t + β20 Xi,t−1 + γi + σI,T + i,t .

(1)

The dependent variable, inventory, is the ratio of inventory to total assets. Dummy variable JITi,t takes the value one if firm i at time t implements JIT and zero otherwise. The control variables, Xi,t−1 , include firm size, market-to-book ratio, cash-flow volatility, operating cash flow, working capital net of inventory, capital investment, and leverage ratio at the end of previous period t − 1. γi and σI,T are firm fixed effects and industry-specific year fixed effects, respectively. Summary statistics for these variables are provided in Table 2, and estimation results are reported in Table 3. [Table 2 about here.] [Table 3 about here.] Consistent with the role of JIT in reducing inventory holdings, the coefficient of the JIT dummy reported in Columns (1)-(2) of Table 3 is negative and statistically significant at the 1% level. That is, the adoption of JIT leads firms to free up resources from inventory. Results are robust to different measures of inventory ratio. In Columns (3)-(4), I use inventory-to-sales ratio as the dependent variable and re-estimate regression model (1). Results are qualitatively the same. In particular, after implementing JIT, firms choose to lower their inventory-to-sales ratio by roughly 6 percentage points. The non-zero inventory holdings after implementing JIT could be caused by the imperfection of the supply chain in reality, such that firms choose to maintain some level of inventory. Other important determinants of firms’ inventory policies include working capital net of inventory and leverage. Firms with higher net working capital, on average, have lower inventory ratios, because other components of working capital are substitutes for inventory for operational purposes. In addition, firms with higher leverage ratios are more cautious in their investment decisions and tend to reduce inventory holdings. 9

4.2

Effects of JIT on Output-Growth Volatility

After validating the sample, we are ready to estimate the effect of JIT on the volatility of firms’ output growth. The specification I consider is analogous to regression equation (1),

volatilityi,t = α0 + α1 JITi,t + α20 Xi,t−1 + γi + σI,T + i,t ,

(2)

where volatility is measured by a centered five-period moving standard deviation of real output growth. I measure the firm’s output as value added, which is defined as the sum of operating income before depreciation plus wage expenses, and scale the measure by price index. Independent variables Xi,t−1 are the same as those in regression equation (1). The coefficient estimate of the dummy variable JITi,t is of particular interest.

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Empirical Results

In this section, I first report estimation results for regression model (2) and examine how JIT affects output-growth volatility. I then explore the sources of the resulting effect.

5.1

Volatility of Output Growth

Estimates of equation (2) are presented in Table 4. The first column reports results when control variables X are not included. The coefficient of JIT is negative, yet not statistically different from zero. The second column shows the results of the full model. Controlling for other important firm-level characteristics changes the sign of the coefficient of JIT, which becomes positive. That is, JIT tends to raise the volatility of output growth, although the effect remains insignificant. [Table 4 about here.]

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Both real output and cash flow are defined as a function of operating income before depreciation (item oibdp). The volatility of cash flow, therefore, may absorb most variation in the dependent variable by variable construction, which possibly explains its remarkably large coefficient. To address this issue and alleviate the concern, I consider an alternative measure of firm-level risk: idiosyncratic productivity risk. I construct the variable as follows. I assume a Cobb-Douglas production function and regress firms’ logged value added on their respective logged capital stock, logged employment, a constant term, firm fixed effects, and time fixed effects. I then collect predicted residuals from the regression model and estimate an AR(1) process. The standard deviation of the resulting residuals over the previous five periods gives the idiosyncratic productivity risk. Estimation results are reported in Column (3). The coefficient estimate of JITi,t remains positive, but becomes statistically significant. Its value suggests that JIT implementation tends to increase output-growth volatility by approximately 10%, which is economically significant. Moreover, the estimation results imply that idiosyncratic risk is another important determinant of output-growth volatility. A one-standard-deviation increase in firm-specific risk raises output-growth volatility by 0.069, or 43%. In addition to JIT, a drop in the volatility of TFP shocks is hypothesized as another potentially important explanation for the Great Moderation (i.e., the “good luck” hypothesis). I consider this explanation by isolating aggregate productivity risk from time fixed effects and report the results in the last column of Table 4.2 Including aggregate productivity risk in the specification barely changes the coefficient estimates of other covariates. Moreover, the coefficient of aggregate risk is positive yet not statistically significant, which suggests that the “good luck” explanation may not be important. 2

I follow the traditional approach to construct TFP and measure aggregate productivity risk as the standard deviation of TFP shocks over the previous five periods.

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5.2

Channel Exploration

Contrary to the popular view that JIT reduces output-growth volatility, I find the opposite and support the theoretical prediction of Wen (2011). In this subsection, I aim to identify the sources of the increase in output-growth volatility by examining the effects of JIT on the volatility of sales growth, the volatility of inventory growth, and the inventory-sales correlation. Regression specifications are the same as for regression model (2), but with different dependent variables.

5.2.1

Volatilty of Sales Growth

Khan and Thomas (2007) and Wen (2011) argue that a decrease in inventory holdings does not reduce the volatility of output, because procyclical inventory investment shifts resources from final production and stabilizes sales. In other words, lower inventory holdings tend to amplify fluctuations in sales. Results in Table 5 support their views: JIT implementation leads to a more volatile sales-growth rate. In particular, JIT adopters experience a rise in sales-growth volatility by 0.0156, as reported in Column (4). This effect is both statistically and economically significant. In addition, idiosyncratic productivity risk also plays an important role. A one-standarddeviation increase in idiosyncratic risk increases sales-growth volatility by 5.5%. [Table 5 about here.]

5.2.2

Volatility of Inventory Growth

Estimation results reported in Table 6 suggest that JIT also leads to an increase in inventory volatility, which is robust to different model specifications and different measures of firmlevel risk. According to Column (4), in which both idiosyncratic and aggregate productivity risks are controlled for, implementing JIT, on average, increases inventory-growth volatility by 0.0160, or 17.8%. 12

[Table 6 about here.] This effect may arise from the fact that in the absence of a stockout-avoidance motive, firms have lower incentives to keep a large amount of inventory on hand and become more responsive to contemporaneous demand shocks, which cause inventory to fluctuate more. The more pronounced reaction of inventory to demand shocks after JIT can be partially evidenced by the small effect of idiosyncratic productivity risk: A one-standard-deviation increase in firm-level productivity risk causes inventory-growth volatility to rise by 0.0017, which is roughly 10% of the effect of JIT and not statistically significant.

5.2.3

Inventory-Sales Correlation

Lastly, I examine how the correlation between inventory growth and sales growth changes with JIT adoption. Results are reported in Table 7. As shown, none of the coefficient estimates of JIT is statistically different from zero, which implies that JIT has limited effects on the inventory-sales correlation and does not significantly change the cyclical behavior of inventory. [Table 7 about here.]

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Discussion and Conclusion

JIT implementation is one of the main potential explanations for the Great Moderation. However, there is a lack of direct empirical evidence on this hypothesis. In this paper, I address the issue by investigating a sample of JIT adopters and examine how the volatility of their output growth changes after adoption. The main finding of my analysis is that JIT tends to destabilize real output. To understand the sources of the induced increase in output-growth volatility, I decompose it into three components: the volatility of inventory growth, the volatility of sales growth, and

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the correlation between them. Estimation results suggest that JIT affects output volatility mainly by increasing the former two. This paper establishes some empirical facts about inventory behavior after JIT adoption, which can be used to guide development of new inventory models for business-cycle analysis.

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References Ahmed, S., A. Levin, and B. A. Wilson (2004). Recent U.S. Macroeconomic Stability: Good Policies, Good Practices, or Good Luck? Review of Economics and Statistics 86 (3), 824–832. Canova, F. (2009). What Explains the Great Moderation in the U.S.? A Structural Analysis. Journal of the European Economic Association 7 (4), 697–721. Clarida, R., J. Gali, and M. Gertler (2000). Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory. Quarterly Journal of Economics 115 (1), 147–180. Davis, S. J. and J. A. Kahn (2008). Interpreting the Great Moderation: Changes in the Volatility of Economic Activity at the Macro and Micro Levels. Journal of Economic Perspectives 22 (4), 155–180. Gao, X. (2017). Corporate Cash Hoarding: the Role of Just-in-Time Adoption. Management Science, forthcoming. Hererra, A. M., Y.-G. Jung, and R. Rossana (2014). Just-in-Time Inventories , Business Cycles , and the Great Moderation. Working Paper, Univeristy of Kentucky. Irvine, O. and S. Schuh (2005). Inventory Investment and Output Volatility. International Journal of Production Economics 93-94 (SPEC.ISS.), 75–86. Justiniano, A. and G. E. Primiceri (2008). the Time Varying Volatility of Macroeconomic Fluctuations. American Economic Review 98 (3), 604–641. Kahn, J. A., M. M. McConnell, and G. Perez-Quiros (2002). On the Causes of the Increased Stability of the US Economy. Federal Reserve Bank of New York Economic Policy Review 8 (1), 183–202.

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Khan, A. and J. K. Thomas (2007). Inventories and the Business Cycle: An Equilibrium Analysis of ( S, s ) Policies. American Economic Review 97 (4), 1165–1188. Kinney, M. and W. Wempe (2002). Further Evidence on the Extent and Origins of JIT’s Profitability Effects. The Accounting Review 77 (1), 203–225. Leduc, S. and K. Sill (2007). Monetary policy, oil shocks, and TFP: Accounting for the decline in US volatility. Review of Economic Dynamics 10 (4), 595–614. McConnell, M. M. and G. Perez-Quiros (2000). Output Fluctuations in the United States: What Has Changed Since the Early 1980s? American Economic Review 90 (5), 1464–1476. Strasser, G. (2014). Just-in-Time Production and the Great Moderation: Inventories in German Manufacturing. Working Paper, European Central Bank . Wen, Y. (2011). Input and Output Inventory Dynamics. American Economic Journal: Macroeconomics 3 (October), 181–212.

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A A.1

Appendix Variable Definitions

I define the variables used in the regression analysis as follows: Inventory is the ratio of total inventories over total assets; Inventory turnover ratio is the ratio of total sales to total inventories; Firm size is the natural logarithm of total assets; Cash-flow volatility is computed as the standard deviation of operating cash-flow ratio in the previous five periods, with operating cash-flow ratio defined as the ratio of earnings after interest, dividends, and taxes but before depreciation over total assets; Market-to-book ratio is the sum of market value and debt over total assets; Net working capital is equal to working capital net of inventory over total assets; Capital investment is the ratio of capital expenditures over total assets, with capital expenditures defined as the first difference in gross property, plant, and equipment; Leverage is the sum of long-term debt and debt in current liabilities normalized by total assets; R&D investment is the ratio of research and development expenses to total assets; EBITDA margin is the ratio of operating income before depreciation to revenue; Output-growth volatility is the centered five-period moving standard deviation of the growth rate of value added, with value added computed as the earnings before depreciation plus wage expenses (scaled by price index); Inventory-growth volatility is computed as the centered five-period moving standard deviation of the growth rate of total inventories; Idiosyncratic risk is computed as the standard deviation of idiosyncratic shocks over the previous five periods; 17

Aggregate risk is computed as the standard deviation of TFP shocks over the previous five periods.

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.15 .1 fraction .05 0 1980

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2−Digit Industry Classifications

Figure 1: Descriptive Statistics for 149 JIT Adopters. This figure presents descriptive statistics for the JIT adopter sample. The upper panel plots the distribution of JIT adoption year, and the lower panel the 2-digit SIC industry distribution.

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Median Inventory Turnover (Quarterly) 1.4 1.6 1.8 2 1.2

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Figure 2: Inventory Turnover Ratios for JIT and Control Firms. This figure plots the dynamics of median and relative average inventory turnover ratios for JIT adopters and matched control firms. The sample is constructed from Compustat Fundamentals Quarterly over the period 1972Q1-2005Q1.

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.1 Median Real Output Growth (Quarterly) 0 .05 −.05

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Figure 3: Real Output Growth for JIT and Control Firms. This figure plots the dynamics of median and relative average real output growth rates for JIT adopters and matched control firms. The sample is constructed from Compustat Fundamentals Quarterly over the period 1972Q1-2005Q1.

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Table 1: Summary Statistics: Pre-adoption Year Table 1 presents descriptive statistics for the adopter sample and matched control group in the pre-adoption year. Columns A and B report the mean and median value of each characteristic for adopters and non-adopters, respectively. Column C reports the p-value for the paired t-test. The sample is constructed from Compustat Fundamentals Quarterly. A detailed definition of variables is provided in Appendix A.1. A: JIT Sample B: Control Sample C: Difference (p-value) Variables Mean Median Mean Median Mean Median Inventory ratio 0.24 0.22 0.24 0.23 0.90 0.58 Firm size (log) 5.65 5.50 5.55 5.48 0.31 0.83 Market-to-Book ratio 1.20 1.02 1.09 0.93 0.006 0.007 Cash flow ratio 0.04 0.04 0.04 0.03 0.25 0.22 Cash-flow volatility 0.01 0.007 0.01 0.009 0.11 0.002 Net working capital ratio 0.08 0.07 0.07 0.05 0.38 0.005 Capital investment ratio 0.01 0.011 0.01 0.009 0.27 0.03 Leverage ratio 0.23 0.20 0.26 0.25 0.002 0.003 R&D ratio 0.02 0.02 0.02 0.02 0.16 0.69 EBITDA margin 0.11 0.11 0.12 0.11 0.32 0.79

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Table 2: Summary Statistics: Regression Analysis Table 2 presents descriptive statistics for the variables used in regression analyses. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. Variables Mean Median Std. Dev. 25% 75% Obs. Inventory 0.22 0.20 0.11 0.14 0.29 28417 Size 6.08 5.91 2.05 4.62 7.49 28512 Cash flow volatility 0.01 0.01 0.01 0.004 0.012 20041 Market-to-Book 1.25 1.02 0.77 0.75 1.49 25724 Cash flow 0.04 0.04 0.03 0.02 0.05 23488 Net working capital 0.09 0.07 0.17 -0.01 0.17 27887 Capital investment 0.01 0.01 0.03 0.002 0.019 20877 Leverage 0.23 0.21 0.17 0.10 0.32 26759 Output-growth volatility 0.16 0.10 0.16 0.06 0.19 11464 Inventory-growth volatility 0.09 0.07 0.06 0.04 0.11 25949 Sales-growth volatility 0.13 0.10 0.10 0.06 0.17 31592 Idiosyncratic risk 0.13 0.09 0.17 0.05 0.15 7794 Aggregate risk 0.004 0.004 0.002 0.003 0.005 32576 Inventory-sales correlation -0.107 -0.164 0.524 -0.541 0.300 25839

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Table 3: Effects of JIT on Inventory Table 3 reports estimation results for the inventory regression on the JIT-adoption dummy and firms’ characteristics, which include firm size, market-to-book ratio, cashflow volatility, cash flow, net working capital, capital investment, and leverage. Firm fixed effects and industry-specific year fixed effects are included in the regressions, and the heteroskedasticity-consistent standard errors reported in parentheses account for possible correlation within a firm cluster. Significance levels are indicated by ∗ , ∗∗ , and ∗∗∗ for 10%, 5%, and 1%, respectively. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. (1) (2) (3) (4) inventory inventory inventory inventory assets assets sales sales ∗∗∗ ∗∗∗ ∗∗∗ JIT -0.0343 -0.0206 -0.0990 -0.0591∗∗ (0.0076) (0.0076) (0.0280) (0.0301) ∗∗∗ Size -0.0166 0.0172 (0.0046) (0.0157) Market-to-book -0.0011 -0.0122 (0.0029) (0.0134) Cash flow volatility -0.0687 -0.6897 (0.2072) (0.8558) Cash flow -0.0527 -2.4706∗∗∗ (0.0558) (0.4303) ∗∗∗ Net working capital -0.1729 -0.1725∗∗∗ (0.0153) (0.0598) Capital investment 0.0433 0.0862 (0.0274) (0.1128) Leverage -0.0718∗∗∗ -0.1015∗ (0.0169) (0.0530) Industry (2-digit) × Year FE Yes Yes Yes Yes Observations 28,417 14,065 28,374 14,053 R-squared 0.382 0.4460 0.1356 0.2091

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Table 4: Effects of JIT on Output-Growth Volatility Table 4 reports estimation results for the output-volatility regression on the JITadoption dummy and firms’ characteristics, which include firm size, market-tobook ratio, cash-flow volatility, cash flow, net working capital, capital investment, and leverage. Firm fixed effects and industry-specific year fixed effects are included in the regressions, and the heteroskedasticity-consistent standard errors reported in parentheses account for possible correlation within a firm cluster. Significance levels are indicated by ∗ , ∗∗ , and ∗∗∗ for 10%, 5%, and 1%, respectively. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. (1) (2) (3) (4) JIT -0.0133 0.0071 0.0162∗ 0.0162∗ (0.0124) (0.0127) (0.0095) (0.0095) Size 0.0065 0.0058 0.0058 (0.0081) (0.0065) (0.0065) Market-to-book 0.0153∗∗ 0.0135∗∗∗ 0.0135∗∗∗ (0.0065) (0.0050) (0.0050) ∗∗∗ Cash flow volatility 5.9761 (0.6486) Idiosyncratic Productivity Risk 0.4057∗∗∗ 0.4057∗∗∗ (0.0443) (0.0443) Aggregate Productivity Risk 0.5649 (0.9587) Cash flow -1.5421∗∗∗ -0.7490∗∗∗ -0.7490∗∗∗ (0.2609) (0.1457) (0.1457) Net working capital -0.0172 -0.0297 -0.0297 (0.0356) (0.0273) (0.0274) Capital investment -0.0483 -0.0451 -0.0451 (0.0538) (0.0506) (0.0506) Leverage 0.0589∗∗ 0.0206 0.0206 (0.0279) (0.0202) (0.0202) Industry (2-digit) × Year FE Yes Yes Yes Yes Observations 11,464 7,451 6,558 6,558 R-squared 0.1169 0.3326 0.4450 0.4450

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Table 5: Effects of JIT on the Volatility of Sales Growth Table 5 reports estimation results for the sales-volatility regression on the JITadoption dummy and firms’ characteristics, which include firm size, market-tobook ratio, cash-flow volatility, cash flow, net working capital, capital investment, and leverage. Firm fixed effects and industry-specific year fixed effects are included in the regressions, and the heteroskedasticity-consistent standard errors reported in parentheses account for possible correlation within a firm cluster. Significance levels are indicated by ∗ , ∗∗ , and ∗∗∗ for 10%, 5%, and 1%, respectively. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. (1) (2) (3) (4) JIT 0.0063 0.0159∗∗ 0.0156∗ 0.0156∗ (0.0052) (0.0067) (0.0091) (0.0091) Size 0.0035 0.0120∗∗ 0.0120∗∗ (0.0035) (0.0053) (0.0053) Market-to-book 0.0005 0.0018 0.0018 (0.0033) (0.0039) (0.0039) Cash flow volatility 2.3136∗∗∗ (0.2772) Idiosyncratic Productivity Risk 0.0721∗∗∗ 0.0721∗∗∗ (0.0195) (0.0195) Aggregate Productivity Risk 0.0594 (0.7148) Cash flow -0.2301∗∗∗ -0.1363 -0.1363 (0.0744) (0.0943) (0.0943) Net working capital 0.0159 -0.0122 -0.0122 (0.0128) (0.0167) (0.0167) Capital investment 0.0577∗ 0.0434 0.0434 (0.0311) (0.0374) (0.0374) Leverage 0.0184 -0.0019 -0.0019 (0.0157) (0.0207) (0.0207) Industry (2-digit) × Year FE Yes Yes Yes Yes Observations 31,592 13,910 7,014 7,014 R-squared 0.1067 0.1975 0.2116 0.2116

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Table 6: Effects of JIT on the Volatility of Inventory Growth Table 6 reports estimation results for the inventory-volatility regression on the JIT-adoption dummy and firms’ characteristics, which include firm size, market-tobook ratio, cash-flow volatility, cash flow, net working capital, capital investment, and leverage. Firm fixed effects and industry-specific year fixed effects are included in the regressions, and the heteroskedasticity-consistent standard errors reported in parentheses account for possible correlation within a firm cluster. Significance levels are indicated by ∗ , ∗∗ , and ∗∗∗ for 10%, 5%, and 1%, respectively. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. (1) (2) (3) (4) JIT 0.0062 0.0165∗∗∗ 0.0161∗∗∗ 0.0160∗∗∗ (0.0040) (0.0047) (0.0061) (0.0061) Size -0.0011 0.0053 0.0053 (0.0027) (0.0037) (0.0037) Market-to-book 0.0027 0.0037 0.0037 (0.0018) (0.0027) (0.0027) Cash flow volatility 1.0657∗∗∗ (0.1631) Idiosyncratic Productivity Risk 0.0128 0.0130 (0.0091) (0.0091) Aggregate Productivity Risk -1.2068∗∗ (0.5762) Cash flow -0.0441 -0.0303 -0.0299 (0.0641) (0.0718) (0.0718) Net working capital 0.0190∗ -0.0100 -0.0100 (0.0100) (0.0135) (0.0135) Capital investment 0.0815∗∗∗ 0.0228 0.0230 (0.0209) (0.0254) (0.0254) Leverage 0.0142 0.0068 0.0067 (0.0113) (0.0158) (0.0158) Industry (2-digit) × Year FE Yes Yes Yes Yes Observations 25,949 13,853 7,003 7,033 R-squared 0.0822 0.1526 0.2003 0.2007

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Table 7: Effects of JIT on the Inventory-Sales Correlation Table 7 reports estimation results for the inventory-sales correlation regression on the JIT-adoption dummy and firms’ characteristics, which include firm size, market-to-book ratio, cash-flow volatility, cash flow, net working capital, capital investment, and leverage. Firm fixed effects and industry-specific year fixed effects are included in the regressions, and the heteroskedasticity-consistent standard errors reported in parentheses account for possible correlation within a firm cluster. Significance levels are indicated by ∗ , ∗∗ , and ∗∗∗ for 10%, 5%, and 1%, respectively. The sample is constructed from Compustat Fundamentals Quarterly for the period 1976Q1-2011Q4. (1) (2) (3) (4) JIT -0.0137 -0.0283 0.0105 0.0103 (0.0310) (0.0441) (0.0607) (0.0607) Size 0.0161 -0.0105 -0.0103 (0.0267) (0.0400) (0.0400) Market-to-book 0.0743∗∗∗ 0.0670∗∗∗ 0.0671∗∗∗ (0.0188) (0.0231) (0.0231) Cash flow volatility -1.8609 (1.1990) Idiosyncratic Productivity Risk 0.0627 0.0637 (0.0755) (0.0754) Aggregate Productivity Risk -5.9122 (6.2100) Cash flow -0.7017∗∗ 0.0110 0.0132 (0.3166) (0.5023) (0.5026) Net working capital -0.0547 -0.0578 -0.0577 (0.1036) (0.1397) (0.1397) Capital investment -0.3002 -0.1444 -0.1436 (0.1910) (0.2412) (0.2414) Leverage 0.0477 0.1030 0.1028 (0.1032) (0.1395) (0.1396) Industry (2-digit) × Year FE Yes Yes Yes Yes Observations 25,839 13,835 6,996 6,996 R-squared 0.0583 0.1066 0.1835 0.1835

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