Cross-sectional dependence is inevitable among industries, in which each sector serves as a supplier to the other sectors. However, the chains of such interconnections cause indirect relationship among industries. Spatial analysis is one of the approaches to address cross-sectional dependence by using a priori a specified spatial weights matrix.
We exploit the linkage patterns from the input-output tables and use them to assign spatial weights to describe the economic interdependencies.
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By using the spatial weights matrix, we can estimate the industry-level production functions and productivity of the U. Economic Development, Innovation, Governance and Institutions 4. Sustainable Development, Innovation and Societal Transitions 5. Innovation Systems Indicators and Policy 6. Migration and Development 7. Simone Vannuccini, A growing number of studies identify a generalized slowdown in labor productivity growth. Venue: Conference room 0.
Next workshop:. Finally, the IDBR provides a variable to identify firms that are foreign-owned. This differs from the measure used in ONS c in that it refers to ultimate foreign ownership — not immediate foreign ownership — and does not capture outward foreign direct investment FDI of British businesses. In this analysis we present measures of labour productivity in current and constant prices.
These deflators were derived by allocating national accounts product-level deflators to industries, weighting them using information on industry-level output shares from the supply and use framework. As such, these deflators are constant across businesses in the same industry and survey year.
Unless otherwise stated, the results in this article are weighted to reflect total employment: that is, we assign the observed level of labour productivity for each business to each of its employees, and calculate points of interest across the ranks of employees. This has the effect of weighting larger businesses — those that employ more people — more heavily than smaller businesses. Finally, recent changes to these data sources — in particular, the re-optimisation of the ABS sample in 5 — have had some impact on our analysis.
The change to the structure of the ABS sample — which affects the and estimates — involved two changes:. The simultaneous reduction in the sample of micro-businesses and the addition of a new group of micro-businesses to the ABS universe has complicated this analysis.
In particular, it is difficult to distil changes in the overall performance of micro-businesses into those due to changes in methods and those due to changes in underlying performance. In our results, we find a notable fall in both mean and median productivity of micro-businesses in , which may be linked to the previously described changes to the ABS. We find that micro-businesses registered solely for PAYE have lower levels of productivity on average than other micro-businesses, indicating that their inclusion is likely to act as a drag on the productivity of this group.
We also observe that the reduction in the number of micro-businesses sampled has increased the standard errors of our productivity estimates for and , likely reflecting an increase in the volatility of this series. In an attempt to mitigate these effects, we conducted several robustness checks.
Aggregate and Industry-Level Productivity Analyses
Firstly, we excluded PAYE-only businesses from the results for and , so as to ensure consistency in the ABS universe, and reweighted the remaining observations accordingly. We also excluded businesses that record zero turnover and purchases in a period. However, even when these businesses are removed, the fall in micro-business productivity is still observed. We also assessed the likelihood that the reduction in sample size — and a consequent increase in volatility — might be driving the fall in micro-business productivity.
To test this, we drew sub-samples of micro-business ABS data from previous years and used bootstrapping techniques to compare the distribution of resulting productivity estimates for previous years with the more recent data. This work indicated that a fall in micro-business labour productivity remained, even after accounting for the change in the sample size through time 6.
The industries covered by the ABS are non-financial services, distribution, production, construction and parts of agriculture. Industry-level deflators experimental. Annual Business Survey: UK non-financial business economy, revised results. Using bootstrapping methods, we find that there is a 1. Consistent with earlier work, we find that levels of labour productivity vary widely across businesses Figure 1.
Despite little change at the median, the shape and position of this distribution has changed somewhat over the past decade. With the onset of the economic downturn, productivity fell across the majority of the distribution — low-productivity and high-productivity businesses alike were affected.
This is shown by the downward and rightward shift between and reducing the number of employees at higher levels of labour productivity. Since the downturn, the extent of the recovery varies across the distribution.
In , businesses at the top tail of the distribution — the highest-productivity businesses — and the lower tail — the least-productive businesses — were more productive than their pre-downturn counterparts; over these portions of the horizontal axis, the more recent cumulative distribution lines are above their earlier equivalents.
However, the middle of the productivity distribution saw relatively little recovery, with the level of productivity between the 40th and 60th percentiles remaining marginally lower in than in Businesses can have negative levels of value added per worker in specific periods when they report larger values of purchases than their total turnover. Businesses that record zero turnover and purchases in a period are excluded.
The data are in constant prices and weighted by ABS sample weights and employment. Download this chart Image. To examine these changes in the distribution of labour productivity in more detail, we follow Schneider and present the average annual change in the level of productivity at each centile of the productivity distribution Figure 2.
This approach — which shows the between-year differences in the curves in Figure 1 — allows easy observation of how levels of labour productivity among the most- and least-productive employees in Great Britain has changed over time. The most striking feature of Figure 2 is the negative effect of the economic downturn on the productivity distribution.
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All but the lower tail of the distribution experienced a fall in productivity between and , pointing to a widespread shock, which affected businesses across the economy. However, in percentage terms, the size of the fall in productivity was fairly similar across businesses — if slightly smaller for the highest-performing firms 2. Secondly, Figure 2 suggests that changes in productivity levels before and after the economic downturn were quite concentrated. By contrast, productivity was fairly stagnant across much of the rest of the distribution.
In the most recent period to , the middle third of the distribution 33rd to 67th percentiles experienced a fall in the level of productivity. This chart excludes the period to The majority of the recovery of labour productivity following the economic downturn is concentrated in this period, rendering it less comparable with the periods presented.
Businesses that record zero turnover and purchases in a period. As these changes reflect the contributions of different businesses to aggregate UK productivity, Figure 2 also suggests that some parts of the distribution have made a larger contribution to overall productivity growth than others. In particular, Figure 2 suggests that the most productive businesses made quite a large contribution to aggregate productivity growth immediately prior to the downturn between and , and that this contribution has fallen somewhat in the post-downturn period.
This result is consistent with Schneider — who also found that the contribution of the most productive businesses had fallen somewhat in the post-downturn period. Extending this analysis further back in time indicates that at least some of this contribution may have been a temporary phenomenon. The contributions distribution in Figure 2 for to is quite similar to that for the post-downturn period excepting the very highest-productivity businesses , which may indicate that to was a period of unusually strong growth at the top of the labour productivity distribution.
This presentation makes clear that the highest-productivity businesses supported productivity growth over the post-downturn period. However, whether this support, from the most productive businesses, was greater or less than the pre-downturn average depends on the comparison period. The data exclude the top and bottom one per cent of businesses in terms of productivity and businesses that record zero turnover and purchases in a period.
The striking variation in business-level productivity shown in the previous sections raises questions about our understanding of the economic processes that support this variation. Economic theory predicts that over time, more-productive businesses should grow in size and market share — drawing in a growing fraction of factor inputs and accounting for a larger share of total output — while less-productive businesses will see their factor input and total output shares shrink.
The finding of wide and persistent productivity variations across businesses suggests either that the forces that re-allocate resources across businesses are not as strong as they might be, or that there are some factors that do not move readily across businesses or industries. To better understand the variation in productivity at the business level, the following sections examine labour productivity for different groups of firms. Table 1 summarises mean and median gross value added GVA per worker for businesses of different sizes, for different ages and with different foreign direct investment FDI relationships in A wider range of productivity metrics including firm- and worker-weighted estimates are included in the datasets attached to this release.
The following sections take these three characteristics in turn, and provide some brief commentary on our findings. Consistent with our earlier findings and those in the wider literature, we find that firms that are larger — in terms of employment — are more productive on average Table 1. However, this trend only extends to businesses with fewer than 1, workers: Table 1 suggests that the largest businesses are much less productive than those with to workers and are more comparable with micro one to nine workers and small-sized 10 to 49 workers firms in labour productivity terms.
This result appears to be driven by the industrial composition of the largest firms. These are low productivity industries 3. To isolate the association between size and productivity more clearly, Figure 4 presents the results of conditional analysis, which compares the relative size of businesses horizontal axis with the relative productivity of those same businesses vertical axis , after controlling for their industry and the survey year. Firms to the left bottom of this plot have lower employment lower productivity than the average for their industry, while businesses to the right top of this plot are larger have higher productivity.
This depiction shows that, when industry and survey year are controlled for, there is a positive relationship between size employment and productivity, but that this does not appear to hold for micro one to nine workers businesses. This relationship is concave, suggesting that this relationship is weaker the larger the business.