Regional productivity growth and plant-level dynamics

The mix of companies in the economy is always changing. The more-productive ones expand, and the less-productive ones are driven out of the market, freeing resources such as labor and capital for new ventures. This reallocation contributes more to aggregate productivity growth than the productivity gains achieved by individual businesses. The efficiency with which the process takes place is a key factor affecting rates of productivity growth in different regions and explaining why they differ.Industrial productivity ; Labor productivity

The importance of reallocations in cyclical productivity and returns to scale: evidence from plant-level data

Procyclical productivity plays an important role in many models of aggregate fluctuations. However, recent studies using aggregate data to directly measure technology shocks in the Solow residual find that technology shocks are not procyclical. This paper provides new evidence that, due to countercyclical composition changes between producers, the procyclicality of productivity observed in aggregate data may be understated. Using plant-level microdata, this paper finds that the reallocation of output shares across continuing plants, as well as the entry and exit of plants, creates a countercyclical component in aggregate productivity. This paper shows that such composition changes may cause a downward bias in industry-level estimates of returns to scale. The findings of this paper suggest that, without correcting for the countercyclical effects of reallocations, estimates based on aggregate data may not reflect the true cyclicality of technology shocks, which a representative agent faces over the business cycle.Productivity ; Business cycles

Employment growth, job creation, and job destruction in Ohio

Over the past several years, Ohio’s employment has grown much more slowly than the national average. If we look at patterns of job creation and destruction in the state, we can start to get a handle on why. In the late 1990s, not only was the rate of job creation sluggish relative to the nation, but the rate of job destruction climbed rapidly.Employment - Ohio ; Economic conditions - Ohio

Labor productivity growth across states

Labor productivity growth, a measure of output per unit of work, is closely tied to gains in wages and living standards, and it provides a direct measure of a country’s competitive position over time. The same holds true for states. Since the last business cycle peak in 2000, states boosted their average labor productivity growth to 2.3 percent. In Ohio, this growth came as a result of modest output growth accompanied by sharp employment losses. Although this has been a painful transition for the Fourth District, solid productivity gains have made the remaining firms and workers more competitive and may prepare the way for future growth.Labor productivity ; Economic conditions ; Federal Reserve District, 4th

Estimating GSP and labor productivity by state

In gauging the health of state economies, arguably the two most important series to track are employment and output. While employment by state is available about three weeks after the end of a month, data on output, as measured by Gross State Product (GSP), are only available annually and with a significant lag. This Policy Discussion Paper details how more current estimates of GSP can be generated using U.S. Gross Domestic Product and personal income along with individual states’ personal income. A straightforward share approach yields reasonable GSP estimates, but a more sophisticated econometric approach, at a cost of imposing more structure, yields even better ones. Both techniques are also applied to estimate nonfarm-business GSP in order to calculate a measure of labor productivity at the state level that follows as closely as possible the method used by the Bureau of Labor Statistics to calculate the national measure of labor productivity. We then briefly examine how labor productivity varies across states.Labor productivity ; Gross state product

Entry, exit and plant-level dynamics over the business cycle

This paper analyzes the implications of plant-level dynamics over the business cycle. We first document basic patterns of entry and exit of U.S. manufacturing plants, in terms of employment and productivity between 1972 and 1997. We show how entry and exit patterns vary during the business cycle, and that the cyclical pattern of entry is very different from the cyclical pattern of exit. Second, we build a general equilibrium model of plant entry, exit, and employment and compare its predictions to the data. In our model, plants enter and exit endogenously, and the size and productivity of entering and exiting plants are also determined endogenously. Finally, we explore the policy implications of the model. Imposing a firing tax that is constant over time can destabilize the economy by causing fluctuations in the entry rate. Entry subsidies are found to be effective in stabilizing the entry rate and output.Business cycles ; Manufacturing industries

Cross–Sectoral Variation in Firm–Level Idiosyncratic Risk

We estimate firm–level idiosyncratic risk in the U.S. manufacturing sector. Our proxy for risk is the volatility of the portion of growth in sales or TFP which is not explained by either industry– or economy–wide factors, or firm characteristics systematically associated with growth itself. We find that idiosyncratic risk accounts for about 90% of the overall uncertainty faced by firms. The extent of cross–sectoral variation in idiosyncratic risk is remarkable. Firms in the most volatile sector are subject to at least three times as much uncertainty as firms in the least volatile. Our evidence indicates that idiosyncratic risk is higher in industries where the extent of creative destruction is likely to be greater.Schumpeterian Competition, Creative Destruction, Product Turnover, R&D Intensity, Investment–Specific Technological Change

Cross-Sectoral Variation in The Volatility of Plant-Level Idiosyncratic Shocks

We estimate the volatility of plant–level idiosyncratic shocks in the U.S. manufacturing sector. Our measure of volatility is the variation in Revenue Total Factor Productivity which is not explained by either industry– or economy–wide factors, or by establishments’ characteristics. Consistent with previous studies, we find that idiosyncratic shocks are much larger than aggregate random disturbances, accounting for about 80% of the overall uncertainty faced by plants. The extent of cross–sectoral variation in the volatility of shocks is remarkable. Plants in the most volatile sector are subject to about six times as much idiosyncratic uncertainty as plants in the least volatile. We provide evidence suggesting that idiosyncratic risk is higher in industries where the extent of creative destruction is likely to be greater.

Cross-sectoral variation in the volatility of plant-level idiosyncratic shocks

We estimate the volatility of plant–level idiosyncratic shocks in the U.S. manufacturing sector. Our measure of volatility is the variation in Revenue Total Factor Productivity which is not explained by either industry– or economy–wide factors, or by establishments’ characteristics. Consistent with previous studies, we find that idiosyncratic shocks are much larger than aggregate random disturbances, accounting for about 80% of the overall uncertainty faced by plants. The extent of cross–sectoral variation in the volatility of shocks is remarkable. Plants in the most volatile sector are subject to about six times as much idiosyncratic uncertainty as plants in the least volatile. We provide evidence suggesting that idiosyncratic risk is higher in industries where the extent of creative destruction is likely to be greater

Cross–Sectoral Variation in Firm–Level Idiosyncratic Risk

We estimate firm–level idiosyncratic risk in the U.S. manufacturing sector. Our proxy for risk is the volatility of the portion of growth in sales or TFP which is not explained by either industry– or economy–wide factors, or firm characteristics systematically associated with growth itself. We find that idiosyncratic risk accounts for about 90% of the overall uncertainty faced by firms. The extent of cross–sectoral variation in idiosyncratic risk is remarkable. Firms in the most volatile sector are subject to at least three times as much uncertainty as firms in the least volatile. Our evidence indicates that idiosyncratic risk is higher in industries where the extent of creative destruction is likely to be greater