Table 3. Regression results for dog rabies cases, reported exposures, PEP completions, and human rabies cases, respectively.

Regression 1		Regression 2		Regression 3		Regression 4
Dep. Var: $\text{ln}\left(R_{i,t}^D\right)$a		Dep. Var: ln(Ei,t)		Dep. Var: ln(Pi,t)		Dep. Var: ${\mathit{R}}_{\mathit{i},\mathit{t}}^{\mathit{H}}$
Var.	Coef.	Var.	Coef.	Var.	Coef.	Var.	Coef.
$\text{ln}\left(R_{t-1}^D\right)$	0.56***	ln(Et−1)	0.07	ln(Et)	0.31	$\widehat{\mathrm{l}\mathrm{n}\left(P_{it}\right)}$	-0.71***
ln(Vt)	-0.23	ln(Et−2)	-0.86***	$\text{ln}\left(R_t^D\right)$	-0.01	$\text{ln}\left(R_t^D\right)$	0.63***
ln(Vt−1)	-0.48	$\text{ln}\left(R_t^D\right)$	0.10***	$\text{ln}\left(R_{t-1}^D\right)$	0.13**	ln(Et)	-0.27
ln(Vt−2)	-0.31**	$\text{ln}\left(R_{t-1}^D\right)$	0.09***	$\text{ln}\left(R_{t-1}^H\right)$	0.03**	$\text{ln}\left(H_t^u\right)$	-0.36
ln(Dt)	-0.83	$\text{ln}\left(R_{t-1}^H\right)$	0.02*	$\text{ln}\left(H_t^u\right)$	-2.87**	$\text{ln}\left(H_t^r\right)$	-0.21
ln(Dt−1)	-1.73**	ln(t)	1.28***	$\text{ln}\left(H_t^r\right)$	1.02	BMP	1.36***
Const.	30.1***	ln(Dt)	-0.02	ln(It)	2.05***	Const.	12.3***
		$\text{ln}\left(H_t^u\right)$	-5.86***	ln(Xt)	1.72***	αc	0.05**
		$\text{ln}\left(H_t^r\right)$	-1.10	ln(Ot)	-0.81***
		Const.	85.4***	Const.	-0.28
N = 60b, χ2 = 6597		N = 39, χ2 = 99.1		N = 67, R2 = 0.59		N = 67, R2 = 0.27

***p<0.01,

**p<0.05,

*p<0.1

^aR^D = Dog Rabies Cases, V = Dog Vaccinations, D = Dog Population, E = Reported Exposures, R^H = Human Rabies Cases, H^u = Urban Population, H^r = Rural Population, P = PEP Completions, I = [per capita] Income, X = Health Expenditures, O = Out of Pocket [health expenditures], and BMP = indicator variable for Brazil, Mexico, Peru. See Table 1 for descriptions.

^bReported sample sizes are different from summary statistics in Table 2 because of missing values for some variables, and because the use of lagged variables decreases the number of observations available for the dependent variable in initial time periods.

^cParameter α is the dispersion parameter for the Negative Binomial regression.