EMERGENCY RESPONSE SERVICES: The Determinants of the 911 System Adoption 2

As described in Section П, we expect that the level of 911 technology will respond to political demand as well as demographic factors related to the efficiency of the service in a particular locality. While much of our productivity analysis will focus on a subset of cardiac patients in Pennsylvania, a within-state analysis can provide only limited insight as to the factors which determine the allocation of 911 services (and their productivity benefits) to different subsets of the population. Thus, in Table 4, we consider the determinants of adoption of the level of 911 service in a national cross-section of counties. As expected, POPULATION is significantly correlated with adoption; politically, counties with a relatively high proportion of Perot voters tend to adopt lower levels of 911, consistent with the emphasis of the Peroi movement on limited government expenditure. As well, counties in states with regulations about training had higher levels of 911 adoption. This legislation either requires or recommends standardized training programs in association with 911 programs, and may farther proxy for the institutional support for 911 provided by the state boards which oversee 911 centers. We intapret this result to indicate that states which provide legislative support and guidance for 911 systems have a higher propensity to adopt 911 services. Thus, we conclude that 911 adoption responds to efficiency motivations as well as political and regulatory factors which may be unrelated to efficiency.

DEPENDENT VARIA lBLE = 911 LEVEL
BASE BASE INCLUDE INCLUDE
REGRESSION REGRESSION COUNTY HOSPINF
(OLS) (ORDERED HOSPITAL (ORDERED
LOGIT) INFRASTRUCTURE LOGIT)
COUNTY HOSPITAL INFRASTRUCTURE
CERT. TRAUMA -0.06458 -0.44596
CNTR. (0.06221) (0.29835)
COUNTY DEMOGRAPHIC CHARACTERISTICS
L POPULATION 0.11172 0.37180 0.11555 0.37754
(0.02972) (0.13783) (0.02995) (0.13877)
DENSITY 0.000004 0.00078 0.000008 0.00088
(0.000037) (0.00069) (0.000037) (0.00070)
INCOME PER CAP 0.01207 0.06675 0.01266 0.06894
(0.00956) (0.05483) (0.00958) (0.05516)
CRIMERATE 0.27501 4.34417 0.33910 4.77066
(1.14780) (5.69929) (1.14940) (5.74605)
POLICE EXP -0.00108 -0.01212 -0.00104 -0.01176
(0.00081) (0.00747) (0.00081) (0.00781)
HEALTH EXP 0.00088 0.04322 0.00113 0.04601
(0.00243) (0.03065) (0.00244) (0.03077)
COUNTY POLITICAL CHARACTERISTICS
% REPUBLICAN -0.00332 -0.01023 -0.00344 -0.01050
(0.00284) (0.01282) (0.00285) (0.01286)
%PEROT -0.00854 -0.04217 -0.00873 -0.04301
(0.00402) (0.01774) (0.00386) (0.01781)
STATE LEGISLATION
911 _TRAIN_L A W 0.16148 0.47014 0.15724 0.46095
(0.05926) (0.23082) оrfOn

ln © о

(0.23085)
911 _TRAIN_PLAN 0.26570 1.04053 0.26365 1.04598
(0.06372) (0.27448) (0.06375) (0.27465)
CONSTANT 0.41650(0.31374) 0.38349(0.31533)
ORD. LOGIT Insignificant Insignificant
PARAMETERS
OBSERVATIONS 722 722 722 722
LOG-LIKELIHOOD -444.51963 -443.44308
R-SQUARED 0.1192 0.1206

The latter two specifications in Table 4 include a variable whieh measures the highest level of in-hospital emergency care offered in the county (in addition to the controls described above). Even though the unconditional correlation between 911 and the level of in-hospital emergency care is positive (.19) and significantly different from zero, most of that positive relationship is accounted for by common factors which affect the adoption of both (e.g., population). Thus, despite the potential for strategic complementarities between hospital technology adoption and 911 services when higher levels of 911 better allocate patients to high-technology hospitals, we do not see strong evidence of this interaction in our national sample. Electronic Payday Loans Online