MODELING REGIONAL ECONOMIC GROWTH WITH A FOCUS ON AMENITIES

While the literature is clear that areas endowed with scenic beauty, lakes, forests, and wildlife among other natural and built amenities are experiencing robust economic growth, the theoretical foundations for this work are weak. In this study, we employ a Bayesian Modeling Average (BMA) approach to address the problem of model specification. Using data for US counties, we estimate a neoclassical growth model while looking at growth over the 1990s. We look at three separate ways of measuring amenities and recreational opportunities that build on those amenities. While the results suggest that higher amenity areas experience faster growth, still some level of value-added development may be required to realize that growth.     

POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.     

A MODEL OF GROWTH AUGMENTED WITH INSTITUTIONS

This paper shows that the inclusion of institutional indicators in a traditional model of growth substantially improves its explanatory capacity. The results have implications for economic policy, because not all the dimensions of institutional quality influence growth to the same extent. A large sample of 165 countries and estimation methods with instrumental variables are used to solve endogeneity problems.     

CORRUPTION'S DIRECT EFFECTS ON PER‐CAPITA INCOME GROWTH: A META‐ANALYSIS

Corruption is a symptom of weak institutional quality and could have potentially adverse effects on economic growth. However, heterogeneity in reported findings makes it difficult to synthesize the evidence base with a view to test competing hypotheses and/or support evidence‐based policy and practice. To address this issue, we have extracted 327 estimates of corruption's direct effect on per‐capita GDP growth from 29 primary studies, following a peer‐reviewed and pre‐published systematic review protocol. Precision‐effect and funnel asymmetry tests indicate that corruption has a negative effect on per‐capita GDP growth after controlling for publication selection bias and within‐study dependence. However, multivariate meta‐regression analysis results indicate that the overall effect is not robust to inclusion of moderating variables through a general‐to‐specific procedure for model specification. We report that the marginal effect of corruption on per‐capita GDP growth is more adverse when the primary study estimates relate to long‐run growth, are based on low‐income‐country data only, and extracted from journal papers. The effect is less adverse in studies that use the International Country Risk Guide corruption perceptions index and in those reporting estimates from two‐stage least‐squares estimations.     

MONETARY POLICY IN A NON‐REPRESENTATIVE AGENT ECONOMY: A SURVEY

It is well known that central bank policies affect not only macroeconomic aggregates, but also their distribution across economic agents. Similarly, a number of papers demonstrated that heterogeneity of agents may matter for the transmission of monetary policy to macro variables. Despite this, the mainstream monetary economics literature has so far been dominated by dynamic stochastic general equilibrium models with representative agents. This paper aims to tilt this imbalance towards heterogeneous agents setups by surveying the main positive and normative findings of this line of the literature, and suggesting areas in which these models could be implemented. In particular, we review studies that analyse the heterogeneity of (i) households’ income, (ii) households’ preferences, (iii) consumers’ age, (iv) expectations and (v) firms’ productivity and financial position. We highlight the results on issues that, by construction, cannot be investigated in a representative agent framework and discuss important papers modifying the findings from the representative agent literature.     

REAL‐TIME FORECASTING OF INFLATION AND OUTPUT GROWTH WITH AUTOREGRESSIVE MODELS IN THE PRESENCE OF DATA REVISIONS

We examine how the accuracy of real‐time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest‐available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple‐vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.     

MAPPING THE EVOLUTION OF ENERGY‐GROWTH NEXUS: SYNERGIES AND TRADE‐OFFS

The study explores the intellectual structure, development and evolution of energy crisis and economic growth research through bibliometric analysis of research articles on energy‐growth nexus from 2000 to 2017 by using Citespace where Gephi is used to analyse the authors collaboration. The analysis incorporates 27,152 references cited by 344 authors, in 1165 articles and from 330 journals. The results of study quantitatively present the most cited articles, authors, countries, institutions and intellectual structure with data visualization in the knowledge domain of energy‐growth nexus. The study categorizes the major research areas in energy‐growth nexus research as carbon dioxide emission, electricity consumption, heterogeneous Panel, real income, renewable energy and financial development. The study discusses emerging trends which provide the future research fronts and intellectual development within the framework of energy‐growth nexus.     

ECONOMETRICS FOR GRUMBLERS: A NEW LOOK AT THE LITERATURE ON CROSS‐COUNTRY GROWTH EMPIRICS

Abstract Since the seminal contribution of N. Gregory Mankiw, David Romer and David N. Weil in 1992 the growth empirics literature has used increasingly sophisticated methods to select relevant growth determinants in estimating cross‐section growth regressions. The vast majority of empirical approaches, however, limit cross‐country heterogeneity in production technology to the specification of total factor productivity, the ‘measure of our ignorance’. In this survey, we present two general empirical frameworks for cross‐country growth and productivity analysis and demonstrate that they encompass the various approaches in the growth empirics literature of the past two decades. We then develop our central argument, that cross‐country heterogeneity in the impact of observables and unobservables on output as well as the time‐series properties of the data are important for reliable empirical analysis.     

THE (NON‐)SIGNIFICANCE OF REPORTING ERRORS IN ECONOMICS: EVIDENCE FROM THREE TOP JOURNALS

We investigate the prevalence and sources of reporting errors in 30,993 hypothesis tests from 370 articles in three top economics journals. We define reporting errors as inconsistencies between reported significance levels by means of eye‐catchers and calculated ‐values based on reported statistical values, such as coefficients and standard errors. While 35.8% of the articles contain at least one reporting error, only 1.3% of the investigated hypothesis tests are afflicted by reporting errors. For strong reporting errors for which either the eye‐catcher or the calculated ‐value signals statistical significance but the respective other one does not, the error rate is 0.5% for the investigated hypothesis tests corresponding to 21.6% of the articles having at least one strong reporting error. Our analysis suggests a bias in favor of errors for which eye‐catchers signal statistical significance but calculated ‐values do not. Survey responses from the respective authors, replications, and exploratory regression analyses indicate some solutions to mitigate the prevalence of reporting errors in future research.     

FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: A META‐ANALYSIS

We analyze 1334 estimates from 67 studies that examine the effect of financial development on economic growth. Taken together, the studies imply a positive and statistically significant effect, but the individual estimates vary widely. We find that both research design and heterogeneity in the underlying effect play a role in explaining the differences in results. Studies that do not address endogeneity tend to overstate the effect of finance on growth. While the effect seems to be weaker in less developed countries, the effect decreases worldwide after the 1980s. Our results also suggest that stock markets support faster economic growth than other financial intermediaries.     

THE IMPACT OF RURAL OUTMIGRATION ON REGION OF ORIGIN IN KOREA*

To assess the impact of rural outmigration on regions of origin, this paper borrows from international trade theory to develop a model of rural-to-urban migration. Borrowing from theories of nontraded goods and Dutch Disease, a model is developed for application to Korea. The model finds that rural outmigration can be detrimental to the rural sector when outmigration reduces farm profitability and triggers deterioration in the rural service sector. Farm profitability falls because of rising labor costs that cannot be passed on to consumers. The rural service sector falters when outmigration reduces market demand while raising input costs. County (kun) level Korean census data are used to test the model.     

NONLINEAR GROWTH EFFECTS OF TAXATION: A SEMI‐PARAMETRIC APPROACH USING AVERAGE MARGINAL TAX RATES

One of the major challenges of empirical tax research is the identification and calculation of appropriate tax data. While there is consensus that average marginal tax rates are most suitable for studying the effects of tax policy on economic growth, because of data limitations the calculation of marginal tax rates has been limited to the USA and the UK. This paper provides calculations of average marginal tax rates for the four Scandinavian countries using the methodologies of Seater (1982, 1985) and Barro and Sahasakul (1983, 1986). Then, by pooling the newly calculated tax rates for the Scandinavian countries with the data for the USA and the UK, we investigate the effects of tax policy shocks on the per capita GDP growth rate. Our results suggest that an increase in average marginal tax rates has a negative impact on economic growth. Employing additive mixed panel models with penalized splines as estimation approach, we show that changes in tax rates have nonlinear effects. Increasing average marginal tax rates turn out to be the most distorting at relatively moderate tax rates. Copyright © 2013 John Wiley & Sons, Ltd.     

A SURVEY OF THE LITERATURE ON HUMAN CAPITAL‐INTENSIVE FIRMS

The aim of this paper is to provide an exhaustive survey of the academic research dedicated to the study of human capital‐intensive firms (HCIFs). We use a bibliometric analysis based on the three widely used databases (Econlit, Science Direct and Wiley) over the period 1992–2015. We are adapting a methodology developed in previous works to our specific object of study, and we generate a bottom‐up clustering on keywords, titles and abstracts. Our examination of the data sheds a light on five main clusters of works that respectively focus on (1) critical human resource management, (2) intellectual capital assessment, (3) funding risk and innovation (4) high‐skilled employees supporting economic development and (5) internal and external valuable social networks. In a critical appraisal of our results, we stress that the bibliometric analysis remains silent on how the different clusters are linked with each other. Based on our own appreciation of the HCIFs literature, we propose a further step in the identification of a central question linking up these clusters: a transversal review of the five clusters reveals that HCIFs appear as a network of specialized agents whose complementary human resources are decisive for the value‐creation process.     

ON BOOTSTRAPPING A CLASS OF DIFFERENTIABLE STATISTICAL FUNCNONALS WITH APPLICATIONS TO L– AND M– ESTIMATES

EFRON'S (1979) "bootstrap" method is justified for a class of differentiable statistical functionals which includes many L – and M –statistics. For illustration, a Monte Carlo study for the trimmed means is also included.     

THE FINANCIAL DEVELOPMENT AND GROWTH NEXUS: A META‐ANALYSIS

We conduct a meta‐analysis of the literature of financial development and economic growth. We cover a large number of empirical studies and estimations that have been published in journal articles. We measure the degree of heterogeneity and identify the causes of the observed differentiation. Among the most significant factors behind this heterogeneity is the choice of financial‐variable proxies, the kind of data used as well as whether a study takes into account the issue of endogeneity. Our results suggest that the empirical literature on the finance–growth nexus is not free from publication bias. Also, a genuine positive effect exists between financial development and economic growth.     

Measuring Business Cycles Intra‐Synchronization in US: A Regime‐switching Interdependence Framework

This paper proposes a Markov‐switching framework to endogenously identify periods where economies are more likely to (i) synchronously enter recessionary and expansionary phases, and (ii) follow independent business cycles. The reliability of the framework is validated with simulated data in Monte Carlo experiments. The framework is applied to assess the time‐varying intra‐country synchronization in the US. The main results report substantial changes over time in the cyclical affiliation patterns of US states, and show that the more similar the economic structures of states, the higher the correlation between their business cycles. A synchronization‐based network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that the US has become more internally synchronized since the early 1990s.     

ESTIMATION OF CENSORED PANEL‐DATA MODELS WITH SLOPE HETEROGENEITY

This paper considers estimation of censored panel‐data models with individual‐specific slope heterogeneity. The slope heterogeneity may be random (random slopes model) or related to covariates (correlated random slopes model). Maximum likelihood and censored least‐absolute deviations estimators are proposed for both models. The estimators are simple to implement and, in the case of maximum likelihood, lead to straightforward estimation of partial effects. The rescaled bootstrap suggested by Andrews (Econometrica 2000; 68 : 399–405) is used to deal with the possibility of variance parameters being equal to zero. The methodology is applied to an empirical study of Dutch household portfolio choice, where the outcome variable (portfolio share in safe assets) has corner solutions at zero and one. As predicted by economic theory, there is strong evidence of correlated random slopes for the age profiles, indicating a heterogeneous age profile of portfolio adjustment that varies significantly with other household characteristics. Copyright © 2013 John Wiley & Sons, Ltd.     

IMPACT OF URBAN AND RURAL HOUSEHOLD CONSUMPTION ON CARBON EMISSIONS IN CHINA

In this paper, we utilize input–output analysis and decomposition techniques to examine the direct and indirect urban and rural per-capita carbon emissions generated by household consumption in China from 1987 to 2007. The results show that indirect emissions are considerably larger than direct emissions due to households in urban and rural areas. Indirect urban emissions increase significantly because of growing expenditures, but indirect rural emissions do not register the same increase. Direct urban emissions decrease significantly because of changes in the energy mix, but direct rural emissions show only a slight decrease. The increase in the disparity of indirect urban–rural emissions and the decrease in the disparity in direct urban–rural emissions are evident. These findings imply that both energy-saving behavior in the production sector and residential lifestyle transition – particularly in the urban areas – are significant in mitigating carbon emissions in China.