November, 2000, Vol. 123, No. 11
Précis
IT improves emergency outcomes
Social Security's earnings test
Productivity and LMDs
IT improves emergency outcomes
It still often seems difficult to find computers in the macro-productivity figures outside the computer industry itself or the relatively easy-to-measure durable goods manufacturing sector. Indeed, at least one study reviewed for this department indicated that there are specific service-sector applications in which productivity may not be improved by computers. (See the March 2000 Précis.)
On the plus side of the ledger, however, Susan Athey and Scott Stern find significant improvements in emergency services in a recent NBER study, The Impact of Information Technology on Emergency Health Care Outcomes. Their study combines information from an original survey of information technology (IT) and job design in emergency call (911) centers with detailed data on virtually all ambulance rides to emergency room admissions in Pennsylvania in 1994 and 1996.
Their results indicate that the adoption of the more IT-intensive "Enhanced 911" systems that link automatic caller identification to a GIS database is associated with significant improvements in the outcomes of cardiac distress calls to the emergency service. These improved outcomes include better intermediate health care at the scene, such as measurement of blood pressure, respiration, and pulse, increased survival rate, and better outcomes in terms of mortality and cost once the patient reaches the hospital.
In a general conclusion about productivity measurement in the service sector, Athey and Stern remark, "In contrast to studies that attempt to evaluate the gains from IT by aggregating across a wide variety of heterogeneous establishments and applications of IT, our approach has been to identify a specific application and to tailor both the measurement of IT and the productivity analysis to fit the application." Perhaps this indicates that as much as quality adjustment is the "house-to-house combat" of price measurement, these detailed studies will be the street-to-street fighting needed to understand service sector productivity.
Social Security's earnings test
In April of this year, the Senior Citizens Freedom to Work Act was signed into law. This new law eliminated the earnings test for recipients of Social Security benefits who are over the normal Social Security retirement age (now 65). The test reduced current payments to recipients whose labor income was above a certain level. While benefits were later increased to make up for such a reduction, the test was often viewed as a tax on earnings. A version of the test still affects recipients aged 62-64, and will affect more as the normal retirement age is raised to 67. Therefore, the test’s impact on the behavior of older workers is still of interest.
Jonathan Gruber of Massachusetts Institute of Technology and National Bureau of Economic Research [NBER] and Peter Orszag of Sebago Associates and University of California Berkeley investigate this impact in "Does the Social Security Earnings Test Affect Labor Supply and Benefits Receipt?" (NBER Working Paper 7923). They analyze data from the March Supplement to the Current Population Survey during the 1973–98 period. One noteworthy feature of their analysis is that it studies data on both women and men. Earlier studies of the earnings test have tended to focus solely on men.
Gruber and Orszag find that the earnings test does not have a robust influence on men’s labor supply choices. They do find more evidence that suggests that the earnings test affects women’s labor supply decisions. Gruber and Orszag’s analysis also indicates that removing the earnings test may increase the early receipt of Social Security benefits, which lowers benefit levels. This could raise concerns regarding the standard of living of some of these elderly as they reach very advanced ages.
What are LMDs and what do they have to do with productivity? A longitudinal micro-level data set (LMD) follows a large number of firms or establishments over a period of time. LMDs allow researchers to conduct certain types of productivity studies that are not possible with aggregate data.
In "Understanding Productivity: Lessons from Longitudinal Microdata" (Journal of Economic Literature, September 2000), Eric J. Bartelsman of Free University, Amsterdam and Mark Doms of the Federal Reserve Board of Governors review recent productivity studies that have used LMDs. They pay particular attention to the Longitudinal Research Database (LRD), a large data set of U.S. manufacturing establishments that has been developed by the U.S. Census Bureau. The authors have both worked with the LRD and are quite familiar with it. The LRD combines data from the Census of Manufactures and the Annual Survey of Manufactures. The establishment-level data from the two have been linked starting in 1972 and continuing (as of now) through 1997. Data from supplementary surveys have also been linked to the basic data.
There have been numerous valuable findings from studying productivity with data sets such as the LRD. Among the findings discussed by Bartelsman and Doms is that "the amount of productivity dispersion is extremely large—some firms are substantially more productive than others." They also note that "a large portion of aggregate productivity growth is attributable to resource reallocation. The manufacturing sector is characterized by large shifts in employment and output across establishments every year—the aggregate data belie the tremendous amount of turmoil underneath."
We are interested in your feedback on this column. Please let us know what you have found most interesting and what essential reading we may have missed. Write to: Executive Editor, Monthly Labor Review, Bureau of Labor Statistics, Washington, DC. 20212, or e-mail MLR@bls.gov |
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