Do Regional Price Levels Converge?: Paneleconometric Evidence Based on German Districts

We investigate price index convergence on the base of regional data for 439 German districts. Prices refer to the overall consumer price index as well as to the index without housing prices. To increase the efficiency of the testing framework, a panel unit root analysis is performed, where cross section dependencies are taken into account. The tests indicate a lack of regional price convergence. While the idiosyncratic component of price differentials is mostly stationary, their common component is driven by a unit root. The results are very similar for the overall price index and the index without housing prices, and for the Western and Eastern part of the German economy. Obviously the elimination of housing prices is not sufficient to obtain a price index where tradable products dominate. One rationale of our findings is the persistent west-east divide in consumer prices. A second argument is related to the persistence of the price gradient between urban and rural regions.Regional price differentials, price convergence, panel unit roots

A Test Strategy for Spurious Spatial Regression, Spatial Nonstationarity, and Spatial Cointegration

A test strategy consisting of a twofold application of a Lagrange Multiplier test is suggested as a device to reveal spatial nonstationarity and spurious spatial regeression. It is further illustrated how the test strategy can be used as a diagnostic for presence of a spatial cointegrating relationship between two variables. Using Monte Carlo simulations it is shown that the small sample behaviour of the test strategy is as desired in these cases.

The Role of R+D-intensive Clusters for Regional Competitiveness

Modern cluster theory provides reasons for positive external effects that ac-crue from the interaction of spatially proximate firms operating in common and related fields of economic activity. In this paper, we examine the impact of R+D-intensive clus-ters as a key factor of regional competitiveness on productivity and innovation growth. In analogy to the industry-oriented concepts of related and unrelated variety (Frenken, Van Oort, Verburg 2007), we differentiate between effects of cluster specialisation and diversity. The identification of R+D-intensive clusters is based on a hybrid approach of qualitative input-output analysis and spatial scanning (Kosfeld and Titze 2017). Our empirical study is conducted for a panel of German NUTS-3 regions in 2001-2011. To comprehensive account for specialisation and diversity effects of clustering we adopt a spatial econometric approach, which allows us to identify these effects beyond the geographical boundaries of a single region. After controlling for regional characteristics and unobserved heterogeneity, a robust ‘cluster strength’ effect (i.e. specialization) on productivity growth is found within the context of conditional convergence across Ger-man regions. With regard to the underlying mechanisms, we find that the presence of a limited number of R+D-intensive clusters in specific technological fields is most strongly linked to higher levels of regional productivity growth. While we also observe a positive effect of cluster strength on innovation growth once we account for spatial spillovers, no significant effects of ‘cluster diversity’ can be identified. This indicates that some but not all cluster-based regional development strategies are promising pol-icy tools to foster regional growth processes

Market Access, Regional Price Level and Wage Disparities: The German Case

In this paper we use the NEG framework of the Helpman model to investigate the spatial distribution of wages across German labour market regions under different assumptions. As the assumptions of equal regional price level and equal real wages are strongly rejected for the German economy, standard approaches may fail to reveal the role of market access in explaining regional wage disparities. In part substantial changes occur when market potential is measured with the aid of regional price levels. With the so-called price index approach, the importance of market access in explaining regional wage differentials is clearly revealed. When controlling for heterogeneity of labour force and spatial dependence, the relationship still remains highly significant. From the price index approach, limited demand linkages of reasonable reach are inferred

On the Stability of the German Beveridge Curve. A Spatial Econometric Perspective

In this paper, we use the Beveridge relationship to address the effectiveness of the matching process, that brings workers searching for jobs together with employers searching for workers. For a fixed matching technology, the curve yields a negative relation between the unemployment rate and the rate of vacancies. Movements along a curve reflect adjustments over the business cycle. In a recession vacancies are closed, and workers enter the unemployed. Shifts of the curve are more important here, as they point to structural change. For example, an outward shift of the curve indicates an in-creased mismatch, perhaps due to a deterioration in human capital of the unemployed or changes in the unemployment benefit system, which affects the willingness of the un-employed to fill out vacancies. Empirical estimates rely on panel data. A sample of 180 regional labour markets is em-ployed, and the sample period runs from 1993 to 2004. The regional labour markets are seperated on the base of flows of the job commuters and correspond to travel-to-work areas. Due to common or idiosyncratic shocks, however, the cross sections are not inde-pendent. Instead, they are tied together to some extent, and the spillovers account for spatial effects. As these patterns can have an impact on the correlation between unem-ployment and vacancy rates, the results of OLS regressions are eventually biased. Thus the Beveridge curve is efficiently estimated by a spatial procedure, where regional de-pendencies are taken into account. No previous paper has investigated a similar broad regional dataset so far. The eigenfunction decomposition approach suggested by Griffith (1996, 2000) is used to identify spatial and non-spatial components in regression analysis. As the spatial pat-tern may vary over time, inference is conducted on the base of a spatial seemingly unre-lated regressions (spatial SUR) model. Due to this setup, efficient estimates for the Beveridge relationship are obtained. Time dummies are used to control for shifts in the curve. The empirical results provide some indication that the degree of job mismatch has increased over the recent periods.

Spatial Point Pattern Analysis and Industry Concentration

Traditional measures of spatial industry concentration are restricted to given areal units. They do not make allowance for the fact that concentration may be differently pronounced at various geographical levels. Methods of spatial point pattern analysis allow to measure industry concentration at a continuum of spatial scales. While common distancebased methods are well applicable for sub-national study areas, they become inefficient in measuring concentration at various levels within industrial countries. This particularly applies in testing for conditional concentration where overall manufacturing is used as a reference population. Using Ripley’s K function approach to second-order analysis, we propose a subsample similarity test as a feasible testing approach for establishing conditional clustering or dispersion at different spatial scales. For measuring the extent of clustering and dispersion, we introduce a concentration index of the style of Besag’s (1977) L function. By contrast to Besag’s L function, the new index can be employed to measure deviations of observed from general spatial point patterns. The K function approach is illustratively applied to measuring and testing industry concentration in Germany

Thresholds for Employment and Unemployment - a Spatial Analysis of German Regional Labour Markets 1992-2000

Changes in production and employment are closely related over the course of the business cycle. However, as exemplified by the laws of Verdoorn (1949, 1993) and Okun (1962, 1970), thresholds seem to be present in the relationship. Due to capacity reserves of the firms, output growth must exceed certain levels for the creation of new jobs or a fall in the unemployment rate. While Verdoorn's law focuses on the growth rate of output sufficient for an increase in employment, in Okun's law, the fall in the unemployment rate becomes the focus of attention. In order to assess the future development of employment and unemployment, these thresholds have to be taken into account. They serve as important guidelines for policymakers. In contrast to previous studies, we present joint estimates for both the employment and unemployment threshold. Due to demographic patterns and institutional settings on the labour market, the two thresholds can differ, implying that minimum output growth needed for a rise in employment may not be sufficient for a simultaneous drop in the unemployment rate. Second, regional information is considered to a large extent. In particular, the analysis is carried out using a sample of 180 German regional labour markets, see Eckey (2001). Since the cross-sections are separated by the flows of job commuters, they correspond to travel-to-work areas. Labour mobility is high within a market, but low among the entities. As the sectoral decomposition of economic activities varies across the regions, the thresholds are founded on a heterogeneous experience, leading to more reliable estimates.The contribution to the literature is twofold. First, to the best of our knowledge, no previous paper has investigated a similar broad regional dataset for the German economy as a whole before. By using a panel dataset, information on the regional distributions around the regression lines as well as theirs positional changes is provided for each year. Second, the methods applied are of new type. They involve a mixture of pooled and spatial econometric techniques. Dependencies across the regions may result from common or idiosyncratic (region specific) shocks. In particular, the eigenfunction decomposition approach suggested by Griffith (1996, 2000) is used to identify spatial and non-spatial components in regression analysis. As the spatial pattern may vary over time, inference is conducted on the base of a spatial SUR model. Due to this setting, efficient estimates of the thresholds are obtained. With the aid of a geographic information system (GIS) variation of the spatial components can be made transparent. With Verdoorn’s and Okun’s law the figures show some significant patterns become obvious over time. In respect to Verdoorn’s law, for instance, a stripe of high values in the north-western part from Schleswig-Holstein via Lower Saxony and North Rhine Westfalia to Rhineland Palatinate is striking in all years but 1994 and 1995. In most periods the spatial component is likewise concentrated in Saxony. Clusters of low values can be found in northern Bavaria and, in some periods, in Thüringen and Mecklenburg-Vorpommern. Other parts of Germany appear to be more fragmented consisting of relative small clusters of low, medium and high values of the spatial component. With Okun’s law some changing spatial patterns arise. In all, spatially filtering provides valuable insights into the spatial dimensions of the laws of Verdoorn and Okun. Threshold employment and unemployment, regional labour markets, spatial filtering techniques, spatial SUR analysis

Local and Spatial Cointegration in the Wage Curve– A Spatial Panel Analysis for German Regions

The wage curve introduced by Blanchflower and Oswald (1990, 1994) postulates a negative correlation between wages and unemployment. Empirical results focus on particular theoretical channels establishing the relationship. Panel models mostly draw on unionized bargaining or the efficiency wage hypothesis. Spatial econometric approaches can be rationalized by monopsonistic competition. However, the approaches either ignore the issue of nonstationarity or treat the data as if it were nonspatial. In this paper, we adopt a global cointegration approach recently proposed by Bienstock and Felsenstein (2010) to account for nonstationarity of regional data. By specifying a spatial error correction model (SpECM), equilibrium adjustments are considered in both space and time. Applying the methodology for West German labour markets, we find strong evidence for the existence of a long-run wage curve with spatial effects

Benchmark Value Added Chains and Regional Clusters in German R+D Intensive Industries

Although the phase of euphoria seems to be over, policymakers and regional agencies have maintained their interest in cluster policy. Modern cluster theory provides reasons for positive external effects that may accrue from interaction in a group of proximate enterprises operating in common and related fields. While there is some progress in locating clusters, in most cases only limited knowledge on the geographical extent of regional clusters is established. The present paper presents a hybrid approach to cluster identification. While dominant buyer-supplier relations are derived by qualitative input-output analysis (QIOA) from national I-O tables, potential regional clusters are identified by spatial scanning. This procedure is employed to identify clusters of German R+D intensive industries. In a sensitivity analysis, good robustness properties of the hybrid approach are revealed with respect to variations in the quantitative cluster composition

Towards an East German wage curve – NUTS boundaries, labour market regions and unemployment spillovers

The relevance of spatial effects in the wage curve can be rationalized by the model of monopsonistic competition in regional labour markets. However, distortions in extracting the regional unemployment effects arise in standard regional (i.e. NUTS) classifications as they fail to adequately capture spatial processes. In addition, the nonstationarity of wages and unemployment is often ignored. Both issues are particularly important in high unemployment regimes like East Germany where a wage curve is difficult to establish. In this paper, labour market regions defined by economic criteria are used to examine the existence of an East German wage curve. Due to the nonstationarity of spatial data, a global panel cointegration approach is adopted. By specifying a spatial error correction model (SpECM), equilibrium adjustments are investigated in time and space. The analysis gives evidence on a locally but not a spatially cointegrated wage curve for East Germany