Employment Forecasts Research Articles

Federal Reserve forecasts of nonfarm payroll employment across different political regimes

PurposeThis study is motivated by the view that Democrats are concerned with reducing unemployment in the short‐run, while Republicans are concerned with keeping inflation low to promote economic stability and growth. The purpose of this paper is to ask whether the Federal Reserve forecasts of nonfarm payroll employment are accurate and free of systematic bias during the 1977‐2000 Democratic and Republican administrations.Design/methodology/approachThe authors employ comparable forecasts from a univariate autoregressive integrated moving‐average (ARIMA) model to assess forecast accuracy. An ARIMA model efficiently utilizes past information and thus yields desirable forecasts commonly used as benchmarks.FindingsFederal Reserve forecasts during the Democratic Administrations, while failing to outperform the ARIMA forecasts, display systematic under‐prediction. Such evidence is consistent with a discretionary approach to monetary policy when the bias is in the direction of full employment. During the Republican Administrations, the Federal Reserve forecasts, while superior to the ARIMA forecasts, are free of systematic bias. This, we argue, is consistent with a monetary policy that approximated the Taylor rule.Research limitations/implicationsThe distinct behavior over the two administrations is unique to the nonfarm payroll employment forecasts and cannot be substantiated for the Federal Reserve forecasts of other macroeconomic variables.Originality/valueThis study provides new insights into the monetary policies pursued during the 1977‐2000 Democratic and Republican administrations. The findings are useful and informative for the design and implementation of monetary policy.

The ACVP/STP Coalition for Veterinary Pathology Fellows Adapts to Changing Employment Demographics*

In late 2004, the American College of Veterinary Pathologists (ACVP) and Society of Toxicologic Pathology (STP) partnered to form the ACVP/STP Coalition for Veterinary Pathology Fellows. The purpose of the Coalition was to provide a unified mechanism to solicit and allocate funds for new training positions in response to the well-documented shortage of veterinary anatomic and clinical pathologists at that time. Sponsors and training institutions enthusiastically responded to this educational initiative; to date, the Coalition has established 29 new training positions at 16 North American universities, supported by >$6.8 million in financial commitments from sponsors, as detailed in the following table. Notwithstanding the Coalition’s efforts, the shortage of veterinary pathologists remained until relatively recently. In a 2007 North American veterinary pathology supply and demand survey, training programs reported 278 graduates in the past 5 years and 258 currently enrolled trainees, and employers reported 142 job openings and an additional 370 anticipated openings in the next 5 years, 2008–2012 (http://www. toxpath.org/acvp_workforce.asp). Despite this relatively optimistic employment forecast, continuing mergers, acquisitions and reorganizations within industry, and state and federal budgetary restrictions imposed on academia have clearly affected traditional job opportunities for veterinary pathologists in recent years. These changes in employment demographics, as well as the scope of investigations and introduction of new diagnostic tools, have required the Coalition to continue to enhance training opportunities in veterinary pathology to supply traditional and emerging markets. The purpose of this communication is to highlight 3 recent efforts that reflect these adaptations. First, owing to its experience in implementing new training positions, the ACVP asked the Coalition to administer fellowships created through the generosity of 2 recently deceased prominent veterinary pathologists, Drs Charles Capen and Linda Munson. Accordingly, the Coalition is in the process of implementing the ‘‘Charles and Sharron Capen Fellowship in Veterinary Pathology’’ and the ‘‘Linda Munson Fellowship for Research in Wildlife Pathology Research.’’ These 2 fellowships are unique for several reasons; they are the first to be supported with funds other than from industry, they require specific areas of PhD dissertation research, and they require cost sharing by the training institution. It is hoped that other

Accuracy of Zonal Socioeconomic Forecasts for Travel Demand Modeling: Retrospective Case Study

Modeling for urban travel demand begins with 20-year forecasts of population, households, vehicle ownership, and employment for a region's individual transportation analysis zones. Yet even though these models rely on socioeconomic forecasts, the long-term accuracy of such models has not received attention, especially for smaller regions with limited planning staff. This paper reports on a case study of the socioeconomic predictions made in 1980 for a horizon year of 2000, by comparing predicted and actual results in Lynchburg, Virginia. The region percentage error reflects the difference between forecast and observed values for the entire region. Although regional forecasts for the number of vehicles and employment showed errors of less than 10%, those forecasts for population and households showed errors of 48% and 14%, respectively. The failure of planned development in two of the region's 68 zones accounted for much of this error, such that removal of these two zones lowered population and household region percentage errors to 10% and 1%, respectively. The zone percentage error is the average of all individual zone percentage errors. Even after removal of the two aforementioned zones, population, households, vehicles, and employment had strikingly large zone percent errors of 39%, 48%, 45%, and 136%, respectively. These results make a compelling case for executing the regional travel demand model twice: once with the given socioeconomic forecasts and once with forecasts modified on the basis of expected errors. For regions that have not conducted an assessment such as that presented here, the expected errors from this paper may be used.

A Real Time Evaluation of Employment Forecasts in Switzerland

We evaluate nowcasts and one- to four-quarter-ahead forecasts of Swiss full time equivalent jobs from 1998q1-2011q4, comparing forecasts of the KOF Swiss Economic Institute and of the State Secretariat for Economic Affairs with the outcome of the reference series. Both forecasts are biased downward, and they do not capture the trend of the reference series well. In addition, we show that the KOF Employment Indicator as well as the Employment Indicator of the Federal Statistical Office are useful to predict the evolution full time equivalent jobs with a lead of about one quarter.

Regional Aggregation in Forecasting: An Application to the Federal Reserve’s Eighth District

Hernández-Murillo and Owyang (Economics Letters, March 2006, 90(3), pp. 335-39) showed that accounting for spatial correlations in regional data can improve forecasts of national employment. This article considers whether the predictive advantage of disaggregate models remains when forecasting subnational data. The authors conduct horse races among several forecasting models in which the objective is to forecast regional- or state-level employment.

Neural networks for regional employment forecasts: are the parameters relevant?

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models—in terms of explanatory variables and NN structures—we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models.

A Simultaneous Equation Model of the Office Cycle for the Parisian Region (French)

This paper presents a simultaneous equation model for the office market. We determine supply and demand relationships to link main fundamentals of commercial real estate: net absorption, vacancy rate, real rents and construction. This model is applied to the office market in the Paris region over the 1994-2008 period. Office market cycles are then established according to different employment forecasts from 2009 to 2020. The results show that the employment cycle determines the cycle of office market fundamentals. Interest rates have a limited impact on the decisions of property actors. From 2009 to 2010, empirical results show that real rents may be historically low because of the economic shock in 2008-2009.

Visualizing Long-Range Health of Rural Recreational and Agricultural Corridors

This paper describes a system developed by the Idaho Transportation Department (ITD) and DKS Associates to produce visual displays of how growth in the key rural corridors in eastern Idaho will affect the health of state facilities and how investments can reduce the long-term impacts of that growth. The system includes a geographic information system (GIS) as the base platform where a variety of data on roadway characteristics, land use, population and employment forecasts, and traffic counts are stored and organized. The system also includes a module for producing travel forecasts from population and employment growth forecasts and from historical trends in growth in traffic volumes to reflect growth in the corridor and growth in recreational travel to the region. For a future scenario, the GIS program generates a graphic display of the level of service for different times of the day, days of the week, and seasons of the year. It also can generate a graphic display of the health of the corridor where “health” is defined by a set of factors and criteria that describe how the corridor would be perceived by travelers who use it and how the roadway facility affects the economic health of the corridor. The visualization tool has been successfully used by ITD to help the counties in eastern Idaho develop plans that anticipate and acknowledge the growth and other changes in the critical state route corridors.

Management and Policy Implications of Coastal Tourism Forecasts

One of the fastest growing segments of the world's tourism industry is coastal tourism. As interest in coastal tourism intensifies, the increasing demand on coastal resources will require local governments and private businesses to obtain information on future tourism demand. However, management agencies often fail to incorporate forecasting results into the strategic planning process. The purpose of this article is to present comprehensive forecasting information of coastal tourism demand using various tourism-related time series variables, and provide public and private agencies with a framework for integrating coastal demand forecasts into the strategic planning process. Two different quantitative forecasting techniques were used to project future coastal tourism demand in South Carolina: Auto Regressive Integrated Moving Average (ARIMA) methods for short-term and midterm forecasts and Naive 1 method for long-term forecasts. Short-term forecast results suggest accommodation taxes in coastal counties will steadily increase over the next several years, while midterm forecasts of coastal tourism-related employment indicates a significant increase in labor force requirements. Long-term forecasts imply visitation to and demand of beaches and coastal resources will continue to intensify. In the coming years, tourism-related businesses in coastal destinations will be challenged to provide satisfactory experiences due to a small labor pool of quality workers. Failure to incorporate forecasts in the strategic planning process may result in the degradation of tourism resources, resulting in a reduction of tourist visitation. Integrating accurate forecasts into the strategic planning process are essential to improving the likelihood of sustainable development at tourism destinations.

A Geographically Weighted Regression Method to Spatially Disaggregate Regional Employment Forecasts for South East Queensland

In this paper we present a new methodology by which regional employment forecasts can be spatially disaggregated to smaller administrative units. We develop a statistical model for disaggregating spatial data based upon related employment determinants (for example, the proximity of an area to a shopping centre), demonstrating there is a degree of spatial dependence and spatial heterogeneity in relationships. Applying an advanced statistical procedure, Geographically Weighted Regression (GWR), to account for these spatial effects this method utilises the locally fitted relationships to estimate employment numbers at the smaller geography whilst being constrained by the regional forecast. Results demonstrate that our GWR method generates superior estimates over a global regression model for spatially disaggregating regional employment forecasts.

Back to the Future with Keynes

This article analyzes Keynes's Economic Possibilities for our Grandchildren- an essay presenting Keynes's views about economic growth into the 21st century - from the perspective of modern growth theory. I find that the implicit theoretical framework used by Keynes to form his expectations about the 21st-century world economy is remarkably close to modern growth models, featuring a stable steady-state growth path driven by technological progress. On the other hand, Keynes's forecast of employment in the 21st century is far off the mark, reflecting a mistaken view that the income elasticity of leisure is much higher than that of consumption.

Forecast of Employment and Job Openings in Pennsylvania Plastics Manufacturing, 2004-1027

Between 2004 and 2017, Pensylvania is forecast to lose employment and experience declining numbers of job opening in occupations in its plastics manufacturing industry as a result of manufacturing productivity increases and in spite enjoying advantage relative to the entire US in delivered prices and in costs of production.

Forecasting Regional Labor Market Developments under Spatial Autocorrelation

Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level and, therefore, require regional forecasts. The data available to compute regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models.

Forecasting in the Labour Market

An assessment of future labour market outcomes is of considerable interest to policy makers and for those making operational decisions with respect to training. These could take the form of forecasts of employment by occupations and skill levels or needs at the national, sectoral or regional level. Often forecasts are make of the demand for labour, both additional and replacement demand, the latter often surpassing the former due to the present demographic composition of the labour force. In order to assess likely future shortages (or surplus) of labour, knowledge of the current supply (not always known with certainty as in the case of seasonal labour) and an understanding of future supply is required, all of which pose considerable challenges. In this paper, an overview of the forecasting needs of the Department of Labour, approaches and methodology likely to be applicable and the required level of accuracy and relevance of forecasts are discussed in relation to national, sectoral, regional and seasonal labour market outcomes. Some preliminary and provisional results are presented as an illustration of likely outcomes. In summary, the total employment level is expected to show and annual average growth of 1.8% from 2005 to 2010, following a higher annual average growth of 2.8% from 2001 to 2005. However, for the Primary and Manufacturing Sectors, the employment is likely to transform to a positive growth at an average rate of 1.0% and 0.3% respectively from 2005 to 2010 after having experienced a decline from 2001 to 2005. However, for the Private and Public sectors services, employment is expected o increase at an average rate of 1.7% and 2.8% respectively from 2005 to 2010 following a higher growth rate of 3.3% and 4.8% respectively from 2001 to 2005. The consequences of a number of variations in GDP growth and employment ratios by industry are explored in terms of occupational forecasts.

The information content of regional employment data for forecasting aggregate conditions

We consider whether disaggregated data enhance the efficiency of aggregate employment forecasts. We find that incorporating spatial interaction into a disaggregated forecasting model lowers the out-of-sample mean squared error from a univariate aggregate model by 70% at a two-year horizon.

The Information Content of Regional Employment Data for Forecasting Aggregate Conditions

We consider whether disaggregated data enhances the efficiency of aggregate employment forecasts. We find that incorporating spatial interaction into a disaggregated forecasting model lowers the out-of-sample mean-squared-error from a univariate aggregate model by 70 percent at a two-year horizon.

Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns

This article analyzes artificial neural networks (ANNs) as a method to compute employment forecasts at a regional level. The empirical application is based on employment data collected for 327West German regionsover a periodof fourteenyears. First, the authors compare ANNs to models commonly used in panel data analysis. Second, they verify, in the case of panel data, whether the common practice of combining forecasts of the computed models is able to produce more reliable forecasts. The technique currently employed by the German authorities to compute such regional employment forecasts is comparable to a simple naïve no-change model. For this reason, ANNs are also compared to this undemanding technique.

Accounting for migration in regional occupational employment projections

Occupational employment projections are one of the primary products produced by state labor market information agencies to assist with state and regional job training and worker assistance programs. In theory, the information from occupational employment forecasts should improve both interregional and intertemporal labor market efficiency through better matching between training efforts and job openings. Until recently, the projections methodology was predominantly a demand-requirements approach that failed to incorporate important labor supply effects and interstate/interregional dependencies. Recent research has focused on improving the labor supply specification. This paper reports on one such effort to evaluate the importance of interstate occupational migration and to develop methods to incorporate migration into the existing projections methodology. Initial results indicate that the total number of estimated job openings by occupation have to be revised significantly upwards when migration is taken into account.

Manpower Coefficients and the Forecasting of Manpower Requirements in Nova Scotia

Manpower Coefficients and the Forecasting of Manpower Requirements in Nova Scotia

Regional Occupational Employment Projections: Modeling Supply Constraints in the Direct‐Requirements Approach*

Detailed industry‐occupation employment forecasts are an important class of regional labor market information produced by the U.S. Bureau of Labor Statistics. In theory, the forecasts should improve the national, interregional, and intertemporal matching efficiency of labor markets. But the efficiency argument is dependent on the quality of the forecasts. The methodology used to produce the projections is still fundamentally a demand‐requirements approach that implicitly assumes that labor supply is infinitely elastic for every occupation. This paper examines the validity of that assumption and evaluates a demographically based labor supply module as an adjunct to the current methodology.