HAS PRODUCTION MANAGEMENT IMPROVED SINCE 1984

Kim and Nelson (1999) and others have recently presented evidence of a dramatic decline in the volatility of real gross domestic product (GDP). When the data are plotted, the break date is easily identified as 1984. Kim and Nelson conclude that the enhanced stability is broadly based. But statistical analysis by McConnell and Perez-Quiros (2000; hereafter MPQ) points to reductions in inventory volatility in the durables sector as the source of the newfound stability. Although inventory investment is a very small component of GDP, changes in inventory investment are one of the most volatile components of changes in GDP. Ramey and West (1999) calculate that declines in inventory investment typically account for 49% of reductions in U.S. GDP during recessions. Inventories in the durables sector are substantially more volatile than in the nondurables sector, and thus inventory investment in this sector is more likely to contribute to macroeconomic instability. The 1984 break date is intriguing because it coincides with the rising popularity of new production management techniques, such as Just-in-Time (JIT). (1) Hall (1983) describes JIT as an emphasis on "producing exactly what is needed and conveying it to where it is needed precisely when required." He cites several components to the successful implementation of JIT, but one of the major components is flexible production implying rapid response and the ability to profitably produce only what is immediately required. In this ideal environment, no unfilled orders or finished goods are accumulated, and whatever raw materials and work-in-process must be accumulated are quickly processed. More generally, the more flexible and well-coordinated the production process, the more quickly output can respond to a demand shock. (2) The questions addressed here are as follows: 1. Is there evidence of structural change in output? 2. Did the structural change (if any) occur around 1984? 3. Did output track demand more closely following the structural change? The answers to all three questions must be yes to support the contention that improved production management is responsible for the stabilization of GDP. The analysis is carried out at the industry level to identify those industries that satisfy the criteria. The quality of production management is measured as the difference between the log of output and the log of new orders, both measured monthly. New orders represent demand and are equivalent to sales among firms that hold no unfilled orders. If the firm achieves the ideal efficiency, then output is identical to demand. More generally, efficiency is reflected in the time required for the firm to return inventories and unfilled orders to their target values following a shock to demand or output. An increase in efficiency implies a more rapid output response to demand shocks so that any differences between output and demand are resolved more rapidly. Structural change is identified with tests for structural change with unknown break dates developed by Andrews (1993) and Andrews and Ploberger (1994). Following Herrera and Pesavento's (2004) industry-level analysis, the tests allow for multiple breaks. Their tests for stability are conducted on univariate autoregressive models of sales and inventories by stages of fabrication. Here, the growth rate of output or the difference between the log of output and the log of new orders is the dependent variable allowing for a direct observation of structural change in the variable of interest. In addition, the models are richer in that variables such as new orders and raw materials are included on the right-hand side to determine the sensitivity of the break date to potential sources of any change in output behavior. Several studies in addition to Herrera and Pesavento's (2004) have examined industry-level data for evidence of structural change that might indicate improvements in production management. …