The Impact of Social Capital on Economic Attitudes and Outcomes
Iftekhar Hasan, Qing He, Haitian Lu
Journal of International Money and Finance,
November
2020
Abstract
This article traces the extant literature on the impact of social capital on economic attitudes and outcomes. Special attention is paid to clarify conceptual ambiguities, measurement techniques, channels of influence, and identification strategies. Insights derived from the literature are then used to analyze the marketplace lending industry in China, where the size of the peer-to-peer (P2P) lending market is larger than that of the rest of the world combined. Ironically, approximately two-thirds of these online P2P lending platforms have failed. Empirical evidence from the monthly operating data of 735 lending platforms and transaction level data from one prominent platform (Renrendai) shows that platforms in provinces with high social capital have low risk of failure, and borrowers in provinces with high social capital can borrow at low interest rate and are less likely to default. We also provide observations to guide future economic research on social capital.
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Special Issue on Productivity: Introduction
Filippo di Mauro
Singapore Economic Review,
No. 5,
2020
Abstract
At the time we write this introduction, the world is entering a second phase of the COVID-pandemic, where all countries in the world attempt to gradually reopen after the tremendous shock on lives and economic activity. The focus of the policies right now is very much on short-term interventions aimed at alleviating the financial strains on households and firms, thus fostering a quicker recovery. In the medium and long-term perspective, however, it would be essential to parallel such policies with appropriate interventions aimed at strengthening the aggregate productivity of the economy, with the objective of increasing resilience and foster more solid growth foundations.
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Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks
Christiane Baumeister, James D. Hamilton
American Economic Review,
No. 5,
2019
Abstract
Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.
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On DSGE Models
Lawrence J. Christiano, Martin S. Eichenbaum, Mathias Trabandt
Journal of Economic Perspectives,
No. 3,
2018
Abstract
The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks.
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On the Low-frequency Relationship Between Public Deficits and Inflation
Martin Kliem, Alexander Kriwoluzky, Samad Sarferaz
Journal of Applied Econometrics,
No. 3,
2016
Abstract
We estimate the low-frequency relationship between fiscal deficits and inflation and pay special attention to its potential time variation by estimating a time-varying vector autoregression model for US data from 1900 to 2011. We find the strongest relationship neither in times of crisis nor in times of high public deficits, but from the mid 1960s up to 1980. Employing a structural decomposition of the low-frequency relationship and further narrative evidence, we interpret our results such that the low-frequency relationship between fiscal deficits and inflation is strongly related to the conduct of monetary policy and its interaction with fiscal policy after World War II.
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Nested Models and Model Uncertainty
Alexander Kriwoluzky, Christian A. Stoltenberg
Scandinavian Journal of Economics,
No. 2,
2016
Abstract
Uncertainty about the appropriate choice among nested models is a concern for optimal policy when policy prescriptions from those models differ. The standard procedure is to specify a prior over the parameter space, ignoring the special status of submodels (e.g., those resulting from zero restrictions). Following Sims (2008, Journal of Economic Dynamics and Control 32, 2460–2475), we treat nested submodels as probability models, and we formalize a procedure that ensures that submodels are not discarded too easily and do matter for optimal policy. For the United States, we find that optimal policy based on our procedure leads to substantial welfare gains compared to the standard procedure.
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Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
Christiane Baumeister, James D. Hamilton
Econometrica,
No. 5,
2015
Abstract
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
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The Development of Cities and Municipalities in Central and Eastern Europe: Introduction for a Special Issue of 'Urban Research and Practice'
Martin T. W. Rosenfeld, Albrecht Kauffmann
Urban Research & Practice, Vol. 7 (3),
No. 3,
2014
Abstract
Since the 1990s, local governments in Central and Eastern European (CEE) countries have been confronted by completely new structures and developments. This came after more than 40 years (or even longer in the case of the former Soviet Union) under a socialist regime and behind an iron curtain which isolated them from the non-socialist world. A lack of resources had led to an underinvestment in the refurbishment of older buildings, while relatively cheap ‘prefabricated’ housing had been built, not only in the outskirts of cities, but also within city centres. A lack of resources had also resulted in the fact that the socialist regimes were generally unable to replace old buildings with ‘modern’ ones; hence, there is a very rich heritage of historical monuments in many of these cities today. The centrally planned economies and the development of urban structures (including the shifts of population between cities and regions) were determined by ideology, political rationality and the integration of all CEE countries into the production schemes of the Council for Mutual Economic Assistance and its division of labour by location. The sudden introduction of a market economy, private property, democratic rules, local autonomy for cities and municipalities and access to the global economy and society may be seen as a kind of ‘natural experiment’. How would these new conditions shape the national systems of cities and municipalities? Which cities would shrink and which would grow? How would the relationship between core cities and their surrounding municipalities develop? And what would happen within these cities and with their built environment?
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Determinants of Foreign Technological Activity in German Regions – A Count Model Analysis of Transnational Patents
Eva Dettmann, Iciar Dominguez Lacasa, Jutta Günther, Björn Jindra
Foresight-Russia,
No. 1,
2014
Abstract
Most research on R&D internationalisation focuses on comparative analysis of location factors at the national level of analysis. Very little work, however, has taken place in this field for the sub-national regional location behavior of multi-national enterprises (MNE). The paper contributes to the existing research by providing evidence on the determinants of foreign technological activities at the sub-national level for Germany, which hosts the largest share of foreign R&D within the EU27 and features the highest cross-regional dispersion of patented research. Using a pooled count data model, we estimate the effect of various sources for externalities on the extent of foreign technological activity across regions. Particular attention is paid to the role of local knowledge spillovers, technological specialization and diversification. We differentiate foreign and domestic sources of specialisation and account for region and sector-specific influences. This is the first time that the ‘cross-border-ownership’ principle to measure R&D internationalisation is combined with regionalised patent information.
To verify our findings we develop hypotheses. In particular, we expect and find that foreign technological activity is attracted by technologically specialised sectors of regions. In contrast to current empirical work, this effect applies both to foreign as well as domestic sources of specialization, although effects on foreign specialization seem more significant. We expect and find the same for science-industry spillovers. We postulate a negative impact of domestic specialization on foreign technological activities and a strong positive effect from diversificationspillovers, by comparison with specialisation spillovers, but these hypotheses are rejected. We find that the direction of the specialisation effect depends on dominance in the position of domestic firms as well as on the balance of knowledge flows between them and foreign actors.
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