Is Rising Returns to Scale a Figment of Poor Data?

While using detailed firm-level data from the private business sector, this study identifies two empirical puzzles: (i) returns-to-scale (RTS) parameter estimates rise at higher levels of data aggregation, and (ii) estimates from the firm level suggest decreasing returns to scale. The analysis shows that, although consistent with rising estimates, the Basu-Fernald (1997) aggregation-bias effect does not drive this result. Rather, rising and too low returns-to-scale estimates probably reflect a mixture of random errors in factor inputs. It turns out, in fact, that a 7.5-10 percent error in labor (hours worked) can explain both puzzles.Business cycles; Data aggregation; External economies; Factor hoarding; Firm-level data; Monte Carlo simulation; Random errors; Returns to scale

New fixed links across the Öresund – what is the point?

During the last years, several investigations on new fixed links across the Öresund have been conducted and presented to the public. These new fixed links intend to supplement the already existing bridge between Malmö and Copenhagen, the Öresund Bridge. We review these proposals and outline their intended function in the regional cross-border transport system as well as the European transport system (TEN-T) in order to identify the objective(s) of the proposals. New fixed links are generally proposed as a “one-solution-only” alternative, focusing more on the proposed alternative, than identifying the transport problem. The overall aim of the paper is to achieve a deeper understanding of the basic transport-planning question: What is the problem? Based on a review of reports, we conclude that four key-objectives exist for a new fixed link across the Öresund. Each proposal meets one or more of these objectives

Direkta undanträngningseffekter av arbetsmarknadspolitiska åtgärder

Dahlberg och Forslunds (1999, 2000) studie av undanträngningseffekter fokuserar på resultat från det andra steget i den GMM-metod som utvecklats av Arellano och Bond (1991). Tidigare Monte Carlo simuleringar samt simuleringen för det aktuella datamaterialet visar dock att de skattade standardfelen från det andra stegets skattningsmetod underskattar de faktiska standardfelen. Det innebär att punktskattningar av undanträngningseffekter förefaller vara mer precist skattade än vad de faktiskt är. Vår simulering visar vidare att det första stegets skattade standardfel för den skattade undanträngningseffekten är i paritet med de faktiska standardfelen. Vi finner att det första stegets skattade 95-procentiga konfidensintervall för undanträngningseffekten för Beredskapsarbete är mellan minus 22 och plus 127 procent. Punktskattningen är inte statistiskt skild från noll på 10 procents signifikansnivå. Detta resultat skiljer sig kvalitativt från slutsatsen i DF. För Övriga subventionerade anställningar är det skattade 95-procentiga konfidensintervallet för undanträngningseffekten mellan 32 och 92 procent.arbetsmarknadspolitiska åtgärder; GMM; paneldata; små stickprov; direkta undanträngningseffekter

Spatial and Semantic Validation of Secondary Food Source Data

Governmental and commercial lists of food retailers are often used to measure food environments and foodscapes for health and nutritional research. Information about the validity of such secondary food source data is relevant to understanding the potential and limitations of its application. This study assesses the validity of two government lists of food retailer locations and types by comparing them to direct field observations, including an assessment of whether pre-classification of the directories can reduce the need for field observation. Lists of food retailers were obtained from the Central Business Register (CVR) and the Smiley directory. For each directory, the positive prediction value (PPV) and sensitivity were calculated as measures of completeness and thematic accuracy, respectively. Standard deviation was calculated as a measure of geographic accuracy. The effect of the pre-classification was measured through the calculation of PPV, sensitivity and negative prediction value (NPV). The application of either CVR or Smiley as a measure of the food environment would result in a misrepresentation. The pre-classification based on the food retailer names was found to be a valid method for identifying approximately 80% of the food retailers and limiting the need for field observation