UK Productivity: Measurement & Puzzle

This subsection establishes a fundamental connection between productivity and living standards. It emphasizes that a country's ability to improve its standard of living relies heavily on its capacity to increase its output per worker. This is supported by a citation of Paul Krugman, a Nobel Prize winner, who famously stated that while productivity isn't everything, in the long run it's almost everything. The section uses real GDP per hour worked as a measure of labor productivity and real GDP per head as an indicator of living standards. Data for Britain from 1856 to 2016 shows a significant increase in both, illustrating the historical link between the two. However, the section also points to the recent stagnation in productivity growth since the start of the 2008 Great Recession, a phenomenon referred to as the 'productivity puzzle,' which the study intends to address.

This part focuses on the 'productivity puzzle'—the notable stagnation in productivity and living standards that emerged after the 2008 Great Recession. The paper introduces productivity, defined as output per unit of input, with a primary focus on labor productivity (output per unit of labor). While acknowledging that it doesn't aim to definitively explain the puzzle, the study lays the groundwork for investigating it. Initial observations from charts (later clarified) might suggest slow productivity growth before World War II followed by a sharp increase until 2007. However, the use of a log scale in Chart 2 reveals a more nuanced picture: relatively rapid initial growth, a decline from 1874 to 1939, faster growth post-World War II, a drop after 1973, and near-zero growth after 2007, which is explicitly identified as the productivity puzzle. The growing gap between GDP per hour and GDP per head throughout this period is also noted, suggesting further investigation is needed.

This subsection highlights the importance of proper data visualization techniques for analyzing long-term economic trends. The use of log scales in charts is emphasized as crucial for accurately representing steady growth over extended periods. The section contrasts Chart 1, which uses a linear vertical scale and misrepresents the data, with Chart 2, employing a log scale to accurately reflect growth rates (represented by slopes) and percentage differences (represented by vertical gaps). The discussion underscores the critical role of data presentation in avoiding misinterpretations of long-term trends in productivity and living standards. Different methods of calculating growth rates (geometric and exponential) are introduced, with a preference for the exponential method amongst economists due to the logarithmic nature of many economic relationships. The geometric method, commonly used by statistical agencies, is acknowledged, with a note that discrepancies between these methods become significant at higher growth rates, such as those seen in China and India.

This subsection explores whether the benefits of productivity growth since 1856 have been equitably distributed. It addresses the possibility that the gains primarily accrued to the wealthy, potentially invalidating Krugman's conclusion about the importance of productivity for raising living standards. However, the section counters this by citing evidence from historical sources, including novels by Charles Dickens and systematic studies of the lives of the poor in Victorian times, indicating that the benefits of growth were not exclusively enjoyed by the rich. Furthermore, it mentions that the UK's Gini coefficient (a measure of income inequality) suggests inequality is lower now than two centuries ago, further challenging the notion that productivity gains solely benefit the affluent. The possibility of redistributing income from the wealthy to the poor as an alternative to productivity growth is considered, though deemed less effective. As an example, the section estimates that confiscating the banking industry's income and redistributing it would only increase average incomes for non-bankers by less than 10 percent, given the banking industry's relatively small share (around 7%) of GDP before the 2008 Great Recession.

This subsection tackles the intricacies of defining and measuring labor productivity. It begins by establishing that labor productivity is defined as output per unit of labor. However, the seemingly straightforward concept of 'hours worked' is examined, revealing complexities and potential sources of inconsistencies. The text distinguishes between 'hours worked' (time spent actively working) and 'hours paid for' (including paid absences), highlighting that the former is preferred for productivity analysis. The importance of consistent measurement across countries is emphasized to avoid distortion in international comparisons. The study underscores the fact that even 'hours worked' is not perfectly unambiguous; short breaks might be included, leading to variations in measurement approaches across nations. This inconsistency in methodology serves as a crucial point in understanding the challenges of accurate cross-national productivity comparisons.

This section dives into the calculation and representation of productivity growth rates. It emphasizes the use of a logarithmic scale for long-term productivity analysis, highlighting that the slope of the line on a log scale represents the growth rate and the vertical gap represents the percentage difference between series. This corrects for inaccuracies caused by using a linear scale. Two different methods for calculating growth rates are discussed: the exponential method and the geometric method. The exponential method, favored by economists due to its compatibility with logarithmic representations of economic relationships, is contrasted with the geometric method, typically used by national statistical agencies. While similar for low growth rates, the methods differ significantly at higher rates, such as those observed in recent Chinese and Indian economic growth. The choice of method is presented as a key factor influencing the interpretation of productivity trends, underscoring the technical considerations that shape productivity measurement.

The section outlines the Office for National Statistics' (ONS) recommendation to use Gross Value Added (GVA) per hour worked instead of GVA per head as the preferred measure for productivity. The rationale emphasizes that people do not always live and work in the same place, and commuting patterns can distort measures based on residents. The City of London serves as an extreme example: its small resident population contrasts sharply with the vast number of commuters, resulting in a misleadingly high output per resident. The ONS’s approach avoids this distortion by focusing on where the work is actually performed. Moreover, the section presents a contrasting example of regional productivity disparities within Britain. It notes the divergence between productivity and living standards, particularly comparing London's high productivity and high living standards with the South West region's low productivity yet relatively average standard of living. This difference is attributed to workforce concentration in low-productivity sectors (agriculture, tourism) in the South West combined with a significant retired population, while London's high productivity is linked to high wages and skilled labor. This discussion highlights the importance of choosing appropriate productivity measures to capture regional and economic realities accurately.

This subsection examines the long-term relationship between productivity growth and income distribution in Britain. It challenges the potential argument that productivity growth since 1856 primarily benefitted the wealthy, questioning whether this undermines the importance of productivity growth in raising living standards. The analysis uses historical evidence, including literary sources like the novels of Charles Dickens, and more systematic data on the lives of the poor during the Victorian era, to refute the idea that only the wealthy benefited. This evidence suggests that productivity growth, even in earlier periods, improved the lives of a broad segment of the population. Further support is drawn from studies showing a lower Gini coefficient (a measure of income inequality) in the UK in 1999 compared to earlier centuries (1290, 1688, 1759, and 1801), indicating a more equitable distribution of income in recent times, albeit with some caveats that later sections may address.

The section explores alternative strategies for improving living standards besides productivity growth. It identifies two main options: income redistribution and productivity growth. Income redistribution, such as transferring wealth from the rich to the poor, is discussed as a possibility. However, the analysis indicates that its potential impact is significantly limited compared to the effect of productivity growth. The section uses an illustrative example of confiscating the banking industry's total income (wages, bonuses, and profits) and redistributing it to the broader population. This scenario is expected to raise average incomes for non-bankers by less than 10%, highlighting the relatively small contribution of the banking sector to the overall economy (around 7% of GDP pre-2008 Great Recession). This comparison emphasizes the dominant role of productivity growth in driving increases in living standards and underlining the importance of sustaining productivity growth, given the limitations of alternative methods.

This subsection explores the arguments for and against continued productivity growth. The text acknowledges that some may believe current living standards are already sufficient to meet basic needs, rendering further productivity gains unnecessary. However, it counters this view by suggesting that productivity growth enables increased leisure time without sacrificing consumption levels. Furthermore, it highlights that the perspective of those in developed nations may differ from those in developing countries, where even modest improvements in living standards resulting from productivity growth would make a significant impact. This point brings to the forefront the global implications of sustained productivity growth and contrasts the perspective of an individual whose needs may already be met with the global need for improved living standards in less-developed nations.

This subsection examines regional variations in labor productivity within Britain. It analyzes differences in labor productivity levels across the main regions of Britain, highlighting disparities in both productivity and living standards. The example of the South West region illustrates a situation where low productivity is counterbalanced by factors like a high proportion of prosperous retirees, leading to a relatively average standard of living despite low regional productivity. In contrast, London's labor productivity gap with the rest of the country is similar to its gap in living standards, suggesting a correlation between higher wages, skills, and higher productivity in London compared to other regions. This regional comparison reveals the complex interplay between productivity, wages, skills, and population demographics, which affects living standards across different parts of the country.

This section focuses on international comparisons of labor productivity levels, emphasizing the use of Purchasing Power Parity (PPP) to convert different currencies to a common monetary unit for meaningful comparison. The limitations of using exchange rates alone are highlighted due to factors like imperfect free trade and volatile exchange rate fluctuations influenced by capital movements, which are not directly related to productivity or living standards. The section introduces the International Comparison Program (ICP) as a method for converting currencies into a common basis using PPP. The ICP's procedure for gathering prices is briefly explained, emphasizing its focus on comparing prices of identical products across different countries while acknowledging the practical challenges in achieving perfect comparability due to variations in products offered across different markets. The 2011 ICP round is mentioned, involving 199 countries (covering 97% of the world's population and 99% of global GDP), which helps to establish the scale and scope of international productivity comparisons using this method.

This subsection presents data from the 2011 round of the International Comparison Program (ICP), showing the 30 largest economies in the world ranked by GDP at Purchasing Power Parity (PPP). The United States and China are highlighted as the top two economies (with China likely surpassing the US since 2011). The UK is noted to be the 9th largest economy, slightly behind France. A key finding is that poorer countries tend to show a higher GDP at PPP relative to their GDP measured using exchange rates, with China's GDP being 87% of the US level at PPP but only 47% using the exchange rate. This disparity is attributed to lower prices of non-traded, labor-intensive services in poorer countries. The subsection discusses the complexities of enforcing transitivity in international comparisons, where bilateral PPP indices might not be perfectly transitive. The ICP addresses this limitation by averaging direct and indirect comparisons, thereby ensuring consistency across countries, although this comes at the cost of reducing the 'characteristicity' of the comparisons. Additionally, it's pointed out that industry-level productivity comparisons are not directly feasible within the ICP framework due to its expenditure-side focus which doesn't capture intermediate goods and services, creating a limitation on detailed international analysis of sector-specific productivity.

This subsection introduces Multi-Factor Productivity (MFP) as a more comprehensive measure of efficiency than labor productivity. It explains that to measure overall efficiency, all inputs—not just labor—must be considered. This necessitates creating an index of total input, where each input is weighted by its cost. A simplified model with two inputs (labor and capital) is presented to illustrate the calculation of a cost-weighted index of total inputs. The section highlights the conceptual difference between MFP and simple labor productivity measurements, pointing towards the necessity of incorporating capital, and potentially other inputs, to achieve a holistic view of productive efficiency. The relationship between MFP growth and innovation is briefly discussed with a caveat, acknowledging that much innovation is embodied in capital goods and improved quality, factors that should ideally be reflected in the price indices of capital assets (using examples such as computer price indices). The ongoing and imperfect process of improving quality adjustments in price indices is noted as a source of potential inaccuracy in MFP calculation.

This part delves into the complexities of measuring capital input for MFP calculations. It explains that estimating capital input presents significant challenges compared to measuring labor input. While the Office for National Statistics (ONS) has detailed labor market surveys providing data on wages, employment, and industry breakdowns, equivalent data for capital are scarce. Firms' published accounts, while providing some information on assets, often lack the necessary detail on asset types, using historic cost (which conflates different price levels), and accounting for depreciation influenced by tax considerations. The section explains the process of deriving missing information by applying economic theory, focusing on estimating capital services. The ONS approach of estimating rental prices using observed gross investment, price indices, and depreciation rates derived from second-hand asset prices is introduced. Two different assumptions about asset decay (geometric decay and the 'lightbulb' pattern) are described, acknowledging the ongoing debate on which assumption is more realistic. The lack of empirical evidence to definitively choose one model is also highlighted.

This subsection focuses on the concept of 'user cost of capital' and its role in MFP calculations. It explains the user cost as a firm's cost of using an asset for a year, incorporating the required rate of return (interest rate or opportunity cost) and the asset's depreciation. The user cost is presented as the relevant factor in investment decisions under perfect competition, driving the marginal product of capital into equality with the real user cost. However, it acknowledges the deviation from the perfect competition assumption, introducing the complexities of imperfect competition and potential markups (the difference between price and marginal cost) in the market. The challenges of estimating markups and the data requirements for more sophisticated models are discussed; high-quality, long-term industry-level data are highlighted as necessary for reliable estimates, and the lack of such data in Britain is noted. This subsection concludes by mentioning assumptions made in calculating MFP, including constant utilization rates, and the implications of these assumptions for interpreting MFP growth over the business cycle, noting potential biases introduced by variations in utilization during recessions and expansions.

This final part explores an alternative approach to measuring MFP growth using prices rather than quantities. The consistent results under consistent accounting systems are highlighted. While acknowledging this, it also points out that the price approach provides further insights and can be advantageous when quantity data are scarce, particularly for pre-modern periods. The section then discusses potential biases in price indexes, specifically focusing on investment goods like computers, where inadequate allowance for quality change might lead to upward price bias and the underestimation of capital's contribution to MFP, overstating MFP in practice. It notes that this bias might be offset by underestimating GDP growth (due to investment), but that the upward bias in MFP is likely to dominate in Britain because of the high volume of imported high-tech goods. Finally, it mentions limitations in the current ONS data, specifically, the omission of inventories and land, and the limited number of intangible assets included, all of which impact the accuracy of MFP calculation.

This section summarizes the current state of productivity statistics published by the Office for National Statistics (ONS) in the UK. The ONS publishes a wide array of data on productivity, covering labor productivity at the whole-economy, industry, and regional levels. International comparisons of whole-economy labor productivity are also available, although limited to a select group of countries. Beyond labor productivity, the ONS provides annual and quarterly estimates of multi-factor productivity (MFP) for the whole economy and individual industries. These MFP estimates are based on labor input measures that account for quality and compositional differences, and capital input measures that aggregate multiple tangible and intangible asset types. The underlying theoretical framework used by the ONS is broadly neoclassical, aligning with the OECD's manuals on productivity and capital measurement. This summary underscores the significant data collection and publication efforts of the ONS, providing a detailed picture of UK productivity.

This subsection identifies limitations in current productivity measurement and suggests avenues for future research. The analysis points out several areas where improvements are needed. Firstly, the list of asset types included in capital input measures should be broadened to incorporate land, inventories, and potentially additional intangible assets. Secondly, a critical limitation is the lack of official international comparisons of productivity (both labor and MFP) at the industry level. This limitation stems from the International Comparison Program (ICP) methodology, which focuses on the expenditure side of national accounts rather than the output side, making direct industry-level comparisons across countries infeasible. This points to a need for improvements in data collection and international cooperation to facilitate more detailed cross-country comparisons. The need for more sophisticated methodologies, like taking into account imperfect competition instead of the perfect competition assumption currently used by national statistical agencies, is highlighted. This requires substantially more data, and is noted as an area that requires further investigation.