Empirical Economics

The aim of this work is to analyse the influence of spatial effects in the evolution of regional employment, thus improving the explanation of the existing differences. With this aim, two non-parametric techniques are proposed: spatial shift-share analysis and spatial filtering. Spatial shift-share models based on previously defined spatial weights matrix allow the identification and estimation of the spatial effects. Furthermore, spatial filtering techniques can be used in order to remove the effects of spatial correlation, thus allowing the decomposition of the employment variation into two components, respectively related to the spatial and structural effects. The application of both techniques to the spatial analysis of regional employment in Spain leads to some interesting findings and shows the main advantages and limitations of each of the considered procedures, together with the quantification of their sensitivity with regard to the considered weights matrix.

Recent literature gives emphasis on the services provided by telecommunication capital which might be subject to internal and external-networking effects, as well as costly irreversible investment pointing out to a more important and complicated effect from these services on productivity and growth. The objective of this paper is to model these effects using a production function framework, along with a newly constructed dataset. A TFP growth approach is employed taking into consideration all the above characteristics, and a smooth coefficient semiparametric model is used. From the analysis, we observe that the services of telecommunication capital depend on the level of telecommunication capital stock. There is a significant positive, but heterogeneous, relationship between telecommunication capital and country productivity. Even though the effect is positive, it diminishes with its level but increases with networking, captured by number of users. Also, telecommunication capital growth and its own effect appear to be raising income in all countries used in our sample, even those who experienced output reduction. These characteristics should be taken into consideration when new telecommunication investment policy is considered within a country.

Using a recently proposed Euclidian measure to quantify inter-regional inequality in per capita GSP (gross state product), we study the convergence among Mexican states during the 1940–2010 period. In ten cases, we find that GSP inequality diverges while the rest of the states’ GSP inequality can be described as the following catching-up or lagging behind processes. For the later processes, we also find evidence of structural breaks. The shifts in the evolution of regional inequality in Mexico for various cases coincide with two distinct periods: the “Mexican Miracle” phase and the “oil/debt crisis” phase.

Many studies have identified that eligible claimants do not participate in means-tested income support programs. We examine the determinants of the decision to take-up social assistance in Canada using the 1997 Canadian Out of Employment Panel dataset. Using a conditional maximum likelihood approach to take account of the potential endogeneity of the level of benefits available to potential claimants, we find that benefit levels and recent receipt of Social Assistance (SA) are important determinants of the take-up decision. The results are important for the fiscal implications of changing benefit levels as the take-up rate is systematically related to the benefits potential recipients are entitled to receive. Further, it suggests that stigma and transaction costs associated with program use are important in explaining the take-up decision.

This paper aims to investigate whether the predictive performance and behaviour of professional forecasters are different during the COVID-19 pandemic as compared with the global financial crisis of 2008 and normal times. To this end, we use a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting records for GDP growth and CPI inflation for the period 2000Q1–2021Q4. We first examine the point forecasts to document the extent of forecast failure during the two crises and explore various explanations for it, such as leader-following and herding behaviour. Then, using percentile-based summary measures of probability distribution forecasts, we study how the degree of consensus and extent of subjective uncertainty among forecasters were affected by crisis conditions. A trend break is observed in the subjective uncertainty associated with growth projections after the onset of the COVID-19 crisis. In contrast, both subjective uncertainty and the degree of consensus in inflation projections were essentially unchanged in crises, suggesting that the short-term inflation expectations of forecasters were strongly anchored.

We challenge the view that the relationship between money and prices is too loose in countries with low inflation rates and argue that cross-border portfolio shifts are the root cause of the volatility in real money balances. The novelty of this paper is that we model jointly in the euro area and the USA (1) the equilibrium in the money market that takes into account the cross-border portfolio shifts and (2) the equilibrium in the domestic asset markets, by finding a relation between domestic long-horizon expected stock and bond returns. We estimate a stable money demand in the long-run and find that the short-run correlation between annual inflation and model-based excess money growth is not statistically different from unity in both the euro area and the USA. We also find that the resulting long-run equity risk premium comoves counter-cyclically with quarterly real GDP growth in both economies.

If repeated observations on the same individuals are not available it is not possible to capture unobserved individual characteristics in a linear model by using the standard fixed effects estimator. If large numbers of observations are available in each period one can use cohorts of individuals with common characteristics to achieve the same goal, as shown by Deaton (1985). It is tempting to analyze the observations on cohort averages as if they are observations on individuals which are observed in consecutive time periods. In this paper we analyze under which conditions this is a valid approach. Moreover, we consider the impact of the construction of the cohorts on the bias in the standard fixed effects estimator. Our results show that the effects of ignoring the fact that only a synthetic panel is available will be small if the cohort sizes are sufficiently large (100, 200 individuals) and if the true means within each cohort exhibit sufficient time variation.

We examine the impact of a comprehensive set of measures of human capital on recently created, direct measures of technology adoption using country-level panel data for the period 1964–2003, covering a wide range of technologies in various sectors of the economy. We consider many dimensions of human capital, using both qualitative and quantitative measures, as well as indirect measures that capture the role of “learning by doing” intrinsic to the process of technological diffusion. Our analysis, which examines the human capital and technological diffusion link more comprehensively relative to previous studies, suggests that the link is a conditional one, resting on various aspects of human capital and the nature of the technology in question. Overall, the results suggest that the type of human capital that is formed via the learning-by-doing mechanism may be the most important determinant of technological diffusion, followed by, to a substantially less degree, qualitative determinants such as cognitive skills (measured using test scores) and quantitative or other measures (such as years of schooling and life expectancy). Our conclusions are robust to the inclusion of institutional variables and other factors that determine technological diffusion.