We provide empirical evidence that the impact of quantitative easing (QE) programs on investment is weaker for countries with high-credit market regulations. We then extend a simple DSGE model with segmented financial markets to include credit regulation and examine its impact on the transmission of conventional and unconventional monetary policies. In our model, the government requires banks to hold a fraction of their assets in government debt. We show that the presence of such regulation can invert monetary transmission under QE policy: An expansionary QE program raises term premiums on corporate bonds and causes a contraction instead of an expansion in the economy. Such a perversion is absent under conventional policy. Further, in contrast to Carlstrom et al. (2017), we show that a simple Taylor rule welfare dominates a term premium peg under financial shocks, while the peg does better in the case of non-financial shocks.

What is the relationship between short-run fluctuations in economic activity and the long-run evolution of the economy? There is empirical evidence that more perturbed economies tend to grow less. Yet matching this evidence has proven challenging for growth models without market failures. This paper examines the relationship between short-term fluctuations and long-term growth within a complete-market economy featuring Epstein-Zin preferences and unbounded growth driven by human and physical capital accumulation. With these preferences, risk aversion and intertemporal elasticity of substitution are allowed to be independent of each other. When the model is plausibly calibrated, the relationship between the mean and variance of growth turns out to be negative. In most cases, the effect of fluctuations on welfare is found to be negative and sizable, even when the long-run effect on growth is positive.

Motivated by distributional concerns raised by recent breakthroughs in AI and robotics, we ask how workers would prefer to manage an episode of automation in a task-based model, which distinguishes between automation and traditional technical progress. We show that under majority voting with the option to implement a “partial” UBI (as transfers to workers) it is optimal to tax capital at a higher rate than labor in the long run to fund the partial UBI. We show that, unlike traditional technical progress, automation always lowers the labor share in the long run, justifying distributional concerns. A quantitative analysis of an episode of automation for the US economy shows that it is optimal from the workers’ perspective to lower capital taxes and transfers over the transition. Nevertheless, this policy increases worker welfare by only 0.7% in consumption-equivalent terms, compared with a 21.6% welfare gain to entrepreneurs, because the welfare gains to workers from lower capital taxes are second-order, while the gains to entrepreneurs are first-order.

In the COVID-19 pandemic, people’s dwellings suddenly became a predominant site of economic activity. We argue that, predictably, policy-makers and employers took the home for granted as a background support of economic life. Acting as if home is a cost-less resource that is free for appropriation in an emergency, ignoring how home functions as a site of gendered relations of care and labour, and assuming home is a largely harmonious site, all shaped the invisibility of the imposition. Taking employee flexibility for granted and presenting work-from-home as a privilege offered by generous employers assumed rapid adaptation. As Australia emerges from lockdown, ‘building back better’ to meet future shocks entails better supporting adaptive capabilities of workers in the care economy, and of homes that have likewise played an unacknowledged role as buffer and shelter for the economy. Investing in infrastructure capable of providing a more equitable basis for future resilience is urgent to reap the benefits that work-from-home offers. This article points to the need for rethinking public investment and infrastructure priorities for economic recovery and reconstruction in the light of a gender perspective on COVID-19 ‘lockdown’ experience.

Only half of investment by firms is in physical capital, such as buildings and machinery. The other half is in intangible assets, such as branding, software and training. This has been true for the past two decades or more in the UK, but only if you step beyond the measures in the National Accounts, which include only some of the recognised intangible assets. This paper surveys ongoing work at the Office for National Statistics to develop measures of investment in intangible assets, using new insights and innovative approaches. In particular, this paper reviews developments in three areas: in-house branding investments, employer-funded training investments, and in-house investments in organisational capital. We reconsider some of the key assumptions made in the literature and propose alternative approaches to measurement. The paper concludes by considering implications of this work, and identifies some of the remaining gaps in the evidence base for measuring intangible assets.

The level of private sector labour productivity has been particularly weak since the start of the crisis. In this paper we explore whether impairment to capital reallocation has been contributing to this weakness. The recent increase in the dispersion of output, prices and rates of return across firms and sectors is stark, and suggests that resources have had incentives to move. Efficient allocation requires that capital moves to firms and sectors where rates of return are relatively high. And the change in capital levels across sectors has been particularly low, suggesting there has been an unusually slow process of capital reallocation since 2008 compared to previous UK recessions and other banking crises. This result is also apparent within sectors. We use a simple and general model to show that increased price dispersion can be a consequence of frictions to efficient capital allocation. And the size of this dispersion can usefully inform us about the size of the associated output and productivity loss. We then find that – using firm level data – the relationship between rates of return and subsequent capital movements has changed since the financial crisis. Overall, our results suggest that impaired capital reallocation across the UK economy is likely to have been one factor contributing to the recent weakness in productivity growth.

This note responds to the recent critique by Goodridge, Haskel and Wallis (GHW) of our 2012 study of Britain's productivity puzzle. We show that a correct reworking of the latest official data on changes in labour composition overturns GHW's main criticism. We also question the strength of the empirical evidence offered by GHW in support of their alternative explanations of the productivity puzzle.

This note responds to a critique of our recent paper, “Can Intangible Investment Explain the UK Productivity Puzzle?”. In that critique Martin and Rowthorn (MR) present a re-working of data on labour composition which they feel refutes an element of our argument. In this response we argue that the data are still in support of our original position. Essentially, the puzzle is that measured labour productivity has been falling, but labour quality is rising, so, at least on this measure, changes in labour quality cannot explain falling productivity.

This paper investigates whether intangibles might explain the UK productivity puzzle. We note that since the recession: (a) firms have upskilled faster than before; (b) intangible investment in R&D and software has risen whereas tangible investment has fallen; and (c) intangible and telecoms equipment investment slowed in advance of the recession. We have therefore tested to see if: (a) what looks like labour hoarding is actually firms keeping workers who are employed in creating intangible assets; and (b) the current slowdown in TFP growth is due to the spillover effects of the past slowdown in R&D and telecoms equipment investment. Our main findings are: (a) measured market sector real value added growth since the start of 2008 is understated by 1.6 per cent due to the omission of intangibles; and (b) 0.75 per cent per annum of the TFP growth slowdown can be accounted for by the slowdown in intangible and telecoms investment in the early 2000s. Taken together intangible investment can therefore account for around 5 percentage points of the 16 per cent productivity puzzle.

We investigate and interpret some of the properties of a multi-sectoral growth model with endogenous embodied technical change in the light of the ongoing debate on the viability of an IT based growth regime. In particular, we illustrate the two main views of the 1995-2000 IT boom in the USA. If it only comes from productivity gains in the production of hardware and/or softwares, and even though these gains are permanent, the story could be just one of temporary massive capital deepening and no long term growth effect. In contrast, if this boom relies on productivity gains in R&D, there is room for a permanent IT growth regime associated with a permanent accumulation of both hardware and software.

The evidence on U.S. investment in high-tech equipment and labor productivity in the 1990s is briefly reviewed and some implications discussed. First, capturing the role of information technologies has raised a number of important measurement issues, which have led to a change in the construction of aggregate real series in the U.S. national accounts, such as real GDP. Second, the recent period provided an important confirmation for traditional neoclassical theories of business investment and productivity. Third, there is a discussion of what type of theoretical and empirical models of economic growth are likely to prove helpful in the future.

Acknowledging that changes in factor utilization rates explain variations in factor productive services flows, this paper investigates a dynamic general equilibrium model incorporating quasi-fixed inputs, weak complementarity between capital and labour, and displaying variations in the following three margins: (i) individual hours, (ii) aggregate employment and (iii) the workweek of capital. Both instantaneous amplification of shocks and mid-term persistence are substantially magnified, witnessing much richer propagation mechanisms. This high persistence does not entirely rely on assumptions regarding adjustment of employment, but, on the contrary, on faster capital accumulation enabled by large increases in output due to periods of high factor utilization.

Trigger values are derived for investment projects that are affected by uncertainty and construction periods. The importance of both aspects on the decision to invest is shown by simulation results and empirically corroborated by estimation results obtained with national (non-)residential structures and equipment data. The main conclusion is that time-to-build projects faced with uncertainty, like non-residential structures, are disproportionally more affected than projects that do not have either of these aspects.

Ce modèle étudie les effets joints de l’irréversibilité et des coûts de faillite sur la décision d’investissement. Les coûts de faillite supportés par le prêteur accentuent son conflit d’intérêt avec l’emprunteur: il est principalement concerné par la partie basse de la distribution du risque, alors que l’emprunteur est concerné par la partie restante. On établit l’existence d’un optimum du second ordre, où l’investissement dépend non seulement de la profitabilité et de l’espérance de la demande mais aussi de la richesse ou des fonds propres de l’entreprise. Le taux d’utilisation des capacités de production optimal diminue en présence de coûts de faillite. Le coût du capital est alors plus élevé puisqu’il inclue les coûts de faillite.

Dans le but d’analyser le cycle de productivité de l’emploi, cet article présente un modèle de cycles réels avec coûts d’ajustement croisés sur l’investissement et le travail. Le modèle est simulé et confronté aux faits stylisés du marché du travail. Nous évaluons plus particulièrement la capacité du modèle à reproduire un cycle de productivité de l’emploi. Nous montrons alors que l’amplitude du cycle de productivité ne dépend pas uniquement du poids des coûts d’ajustement de l’emploi, mais peut aussi résulter du degré de complémentarité, en termes de coûts d’ajustement, de l’embauche et de l’investissement. Au-delà de son explication du cycle de productivité, le modèle permet aussi de rendre compte de la dynamique de l’investissement et de l’emploi.