State Employment Data Research Articles

Labor market impact of the Massachusetts state licensure performance assessment for leaders

PurposeThis paper explores the school leadership career outcomes, timing and educator evaluation of those who complete the Massachusetts Performance Assessment for Leaders (PAL) in comparison with others who did not. It also compares outcomes for those with different PAL score completion requirements.Design/methodology/approachUsing PAL assessment results and state employment data for years 2015 through 2019, the authors examined trends and timing in PAL completers' career advancement into an initial school leader position (assistant principal or principal), by assessment cohort (based on assessment year and passing (cut) score requirements) and with who never had to complete the assessment for licensure (non-PAL completers). Using regression analysis, the authors evaluated potential race/ethnicity and gender differences in advancement. Using chi-square tests of association, the authors compared non-PAL and PAL completers on their demographic attributes and on retention and promotion from assistant principal and on their educator evaluation scores. The authors also examined differences in advancement based on the cut score requirements and preparation pathways.FindingsPAL completers made steady career advances over time and at faster rates than non-PAL completers. Further, PAL completers subject to higher cut score requirements advanced more quickly than those with lower or no score requirements. PAL completers' gender and race/ethnicity seemed to matter less in career advancement than was found in other studies. In 2019, almost half who advanced were employed in the same districts as they had been in 2014 and were more likely to be new leaders in urban districts. When compared with other career-related measures, PAL completers outperformed non-PAL completers who first became school leaders since 2014: they were more likely to be rated as exemplary on educator evaluation and more likely to be retained or promoted after two years in their first school leader position.Originality/valueUntil now little research has existed on the career effects of licensure assessments. Because it requires candidates to demonstrate proficiency in core areas of school leadership work, the PAL assessment appears to be a superior means of screening initial school leaders (based on rate of hiring) and of signaling future performance (based on subsequent educator evaluation ratings) than other assessment forms (such as the School Leader Licensure Assessment [SLLA] exam).

Estimation of short‐run predictive factor for US growth using state employment data

We estimate a predictive single factor model targeted to unobserved common growth in gross domestic product and gross domestic income (GDI) using a state-space framework with select state employment data. We use likelihood-based comparison to select the states to estimate the dynamic factor. The results show improved in-sample and out-of-sample performance than threshold principal component factors and financial spreads. Out-of-sample evaluations indicate larger gains for GDI growth with 14% to 20% lower mean squared forecast errors than other alternatives. Sectoral employment factors based on selected sectors using the state-space framework also show forecasting gains. An expanded model using both sectoral and state employment data shows that their common component is the primary predictive factor.

Predicting benchmarked US state employment data in real time

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data 5–16 months after the reference period. This article develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: (1) an explicit model of the data revision process and (2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model’s predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.

Months for benchmark dominance: A new accuracy measure for state employment data

Employment growth is one of the most commonly used measures of the economic health of U.S. states. While the CES jobs data is timely and available by industry, for many states the early estimates are subject to large revisions, significantly reducing the usefulness of the series. Much of the error, however, cancels out when taking growth over several months. In this paper we evaluate the benchmark revisions by state and produce a quality guidepost that determines the months of growth needed to be calculated such that the early estimates are more representative of the benchmark growth than due to error.

Seasonal adjustment of hybrid time series: An application to U.S. regional jobs data

Hybrid time series data often require special care in estimating seasonal factors. Series such as the state and metro area Current Employment Statistics produced by the U.S. Bureau of Labor Statistics (BLS) are composed of two different source series that often have two different seasonal patterns. In this paper we address the process to test for differing seasonal patterns within the hybrid series. We also discuss how to apply differing seasonal factors to the separate parts of the hybrid series. Currently, for state employment data, the BLS simply juxtaposes the two different sets of seasonal factors at the transition point between the benchmark part of the data and the survey part. We argue that the seasonal factors should be extrapolated at the transition point or that an adjustment should be made to the level of the unadjusted data to correct for a bias in the survey part of the data caused by differing seasonal factors at the transition month.

Acceptable losses? The relative impacts of natural hazards in the United States, 1980–2009

Local and regional hazard management initiatives undertake loss and impact studies to prioritize funds for future mitigation programs. However, hazard loss records typically include only direct damages to property and crops. More flexible measures of hazard losses and impacts are needed to allow comparisons across local places and different time frames, as one mechanism for prioritizing disaster aid and mitigation resources.This study introduces a relative loss ratio at the county level for the US. All losses from geophysical, meteorological, and hydrologic hazards are collected for the period 1980–2009. The loss data are normalized by a measure of economic capacity calculated using county and state employment data and state Gross Domestic Product estimates. A statistical regionalization technique is utilized to identify hazard regions of the US which consistently suffered heavy losses in comparison to their economic capacities. For the nation, the average relative loss ratio is 0.15% of the US GDP, suggesting that overall relative impact of disasters has not been that large. However, downscaling to regional or county levels shows a different result with the relative impact exceeding 10% of county GDP for many places, and more than 3% of regional GDP in the central US and the Gulf Coast. While the national capacity to absorb and recover from disasters is quite high, local capacities are often exceeded by singular large events or the cumulative impacts of smaller events, necessitating the influx of external aid to help these places recover from hazards.

The Effect of Contract Regulation: The Case of Franchising

States and the federal government have enacted laws intended to police franchisors' use of termination provisions in franchise contracts to opportunistically take over profitable establishments. This regulation may, however, reduce the total number of chain outlets because franchising is a valuable form of contracting and termination rights allow franchisors to police franchisee free-riding on the franchised trademark. On the other hand, no such effect is implied if the regulation reduces franchisors' extra gains from skimming profitable franchises. We exploit two new sources of data to provide new empirical evidence on the effects of franchise regulation. Panel data on fast food establishments extracted from uniform franchise offering circulars show that laws restricting franchisor termination rights lead to a reduction in franchising, and this reduction is not offset by the concomitant increase in franchisor-operated establishments. This article also examines how Coasian bargaining between the franchisor and franchisee can mitigate the effect of regulation. In particular, regulation may be apparently important but actually inconsequential because affected parties can easily waive the regulation or avoid it through contractual choice-of-law and choice-of-forum clauses. To examine this, we use state employment data to more broadly examine the effects of franchise regulation. We find that employment in franchise industries is significantly reduced when states enact restrictions on franchisor termination rights and the effect is larger when states limit the ability to contract around these restrictions.

Applications of State Employment Data to Transportation Planning

A major problem in transportation modeling is the acquisition of quality employment data. Employment data are used in transportation planning to model the journey to work and other types of trips, but they rarely are collected solely for transportation purposes because of the large expense involved. Census data, on the other hand, are inexpensive but are collected only every 10 years (although the American Community Survey provides some continuous data). This presentation outlines and assesses a process to develop systems and protocols for using an already-collected set of detailed employment data for an alternative purpose, transportation planning. Tapping this source may provide an ongoing, economical, and annual source of important transportation data that allows planners to better model and predict transportation needs, from the commuter and the business perspective. A better understanding of travel patterns, in turn, helps planners to evaluate investments in fixed physical infrastructure and public transportation. The data set also has valuable uses in other fields, such as workforce development planning, economic development, land use planning, and social service delivery. The success of this project required intergovernmental cooperation from several key partners.

The textile bill of 1985: The determinants of congressional voting patterns

The textile bill of 1985: The determinants of congressional voting patterns