This paper analyzes the effects of corporate tax liability on firm-level total factor productivity (TFP) as the key driver of economic performance. This is a new dimension in the UK productivity puzzle that has not attracted attention so far. We use 6559 manufacturing firms over 2004–2011 to investigate whether higher levels of corporate tax affect the productivity catch-up process by reducing after-tax earnings that could alternatively be used for productivity-enhancing investment, particularly focusing on R&D- and export-intensive firms. Our key results are summarized as follows: first, higher levels of corporate taxation impact adversely on TFP and this finding is robust to different tax measures and insensitive to endogeneity bias; second, as R&D- and export-intensive firms tend to have relatively higher TFP growth, higher levels of tax liability as a share of earnings before interest and taxes decelerate TFP growth of these firms.

We study the main shocks driving current account (CA) fluctuations for the G6 economies, using a standard two-good intertemporal model. We build a structural vector autoregression model including the world real interest rate, net output (NO), the real exchange rate, and the CA and identify four structural shocks. Our results suggest four main conclusions: (i) there is substantial support for the two-good intertemporal model with time-varying interest rate, since both external supply and preference shocks account for an important proportion of CA fluctuations; (ii) temporary domestic shocks account for a large proportion of CA fluctuations, albeit smaller than in previous studies; (iii) our results alleviate the puzzle in the literature that a shock that explains little about NO changes can explain a large proportion of CA changes; (iv) the nature of the shock matters to shape the relationship between the CA and the real exchange rate.

I analyze the impact of intellectual property and product market competition regulations on innovation and long-run growth in an endogenous growth model with two R&D performing sectors. I show that strengthening intellectual property rights and competition in a sector increases its R&D investments. However, these policies adversely affect R&D investments in the other sector because of increased factor competition between the sectors. As a result, the overall impact of such policies on economic growth is ambiguous. I perform a numerical exercise in an attempt to resolve this ambiguity. This exercise suggests that strengthening intellectual property rights increases economic growth, but higher competition has a very limited effect on growth.

For previously identified weakly separable blockings of goods and assets, we construct aggregates using four superlative index numbers, the Fisher, Sato-Vartia, Törnqvist, and Walsh, two non-superlative indexes, the Laspeyres and Paasche, and the atheoretical simple summation. We conduct several tests to examine how well each of these aggregates “fit” the data. These tests are how close the aggregates come to solving the revealed preference conditions for weak separability, how often each aggregate gets the direction of change correct, and how well the aggregates mimic the preference ranking from revealed preference tests. We find that, as the number of goods and assets being aggregated increases, the problems with simple summation manifest.

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

We examine the effect of sovereign credit default swaps (CDS) trading initiation on the occurrence of sovereign debt crises (SDC). Estimations on a large sample of 141 countries for 1980–2013 reveal that, by affecting the fiscal stance, CDS initiation increases by around 1.5 percentage points on average the probability of SDC in countries with CDS compared to the other countries. This result holds for different robustness tests and is found to be stronger for developing countries, for countries with initial lower creditworthiness, and when the degrees of central bank independence and public sector transparency are low. Consequently, compared to existing work emphasizing favorable effects, CDS trading initiation is found to have adverse effects, by increasing the occurrence of SDC. These opposite effects should fuel the literature on measuring the consequences of CDS trading initiation, and its design and implementation from a policy perspective.

Rapid growth in large emerging economies has been spread unequally across their regions leading to growing income disparities. This paper examines the distribution of regional output per capita and the evolution of its shape in Brazil, China, India, and Russia from the mid-1990s to the mid-2010s. A comparative analysis of convergence is conducted using a nonparametric methodology. The results reveal different distribution dynamics across the four economies. Chinese regions with below-median output per capita have the highest probability of transitioning toward higher-income levels, while their Brazilian counterparts are trapped at the bottom of the distribution. Although India has the lowest and Russia one of the highest regional income inequalities, they display similar divergence patterns exemplified by high persistence at both ends of the distribution. Our findings indicate that government spending and the rule of law are the major driving forces behind regional convergence, except in Russia where they have the opposite effect. Innovation and property rights also promote convergence in China, but cause regional divergence in India.

We present an analysis of interregional consumption risk sharing in Russia between 1999 and 2009 using novel estimation methods. In addition to standard fixed-effects panel estimations, we use system and difference GMM estimators to reflect time dynamic properties and possible endogeneity between output and consumption. Furthermore, we apply spatial models that control for spatial dependence across regions. The results show that regional consumption deviations from the national average are highly persistent in time and space. Nevertheless, the regional consumption risk sharing in Russia is relatively high with 70%–90% of idiosyncratic risk being smoothed. Finally, fiscal policy and the degree of financial development appear to contribute to the consumption smoothing.

The period of low interest rates since the global financial crisis provides a unique opportunity to examine monetary policy reaction functions near the zero lower bound (ZLB). Using smooth transition regressions for the Euro area and a panel of industrialized countries we show that central banks anticipate the ZLB by less aggressive policies in its vicinity while we do not find a significant difference between both regimes for the US.

In this paper, we make the case that an argument for price-level targeting over inflation targeting need not to be based on some overly restrictive assumptions. We adopt a theoretical framework that deviates from the assumption of rational expectation, and that takes into account the cognitive limitations and a “trial and error” learning mechanism of the agents. The (im)perfect credibility of various monetary policies (e.g., a Taylor-type rule, strict domestic inflation targeting, strict consumer price index (CPI) inflation targeting, exchange rate peg, and domestic price-level and CPI-level targeting) may lead agents to react according to their expectation rules, and then create various degrees of booms and busts in output and inflation. Therefore, relaxing the rational expectation hypothesis has potential consequences for policy planning. We find that price-level targeting prevails over inflation targeting even under different expectation formation and even when the announced inflation target is not fully credible. The counterfactual analysis and sensitivity test confirm that CPI-level targeting is the most effective for improving social welfare and stability in an open economy. The business cycles induced by animal spirits are enhanced by strict inflation targeting.

It has repeatedly been shown that properly constructed monetary aggregates based on index number theory (such as Divisia money) vastly outperform traditional measures of money (i.e. simple sum money) in empirical models. However, opponents of Divisia frequently claim that Divisia is “too complex” for little gain. And indeed, at first glance it looks as if simple sum and Divisia sum exhibit similar dynamics. In this paper, we want to build deeper understanding of how and when Divisia and simple sum differ empirically using monthly US data from 1990M1 to 2007M12. In particular, we look at how they respond differently to monetary policy shocks, which seems to be the most essential aspect of those differences from the perspective of the policy maker. We use a very rich, fairly agnostic setup that allows us to identify many potential nonlinearities, building on a smoothed local projections approach with automatic selection of the relevant interaction terms. We find, that—while the direction of change is often similar—the precise dynamics differ sharply. In particular in times of economic uncertainty, when the proper assessment of monetary policy is most relevant, those existing differences are drastically augmented.

We analyze the channels through which institutional quality can impact the corruption–growth nexus. To do this, we develop an endogenous growth model and test its implications empirically, through panel data models using GMM and PSTR settings. Our sample consists of 136 developed and developing countries analyzed over the period 1984–2015. We show, both theoretically and empirically, that (i) the corruption–growth relation can be subject to nonlinearities highly influenced by countries’ institutional development; and (ii) private investment and public spending are two main channels through which institutional quality affects, positively or negatively, the relation between corruption and economic growth.