R: New Orleans business recovery in the aftermath of Hurricane...

Katrina {ProbitSpatial}

R Documentation

New Orleans business recovery in the aftermath of Hurricane Katrina.

Description

This dataset has been used in the LeSage et al. (2011) paper entitled "New Orleans business recovery in the aftermath of Hurricane Katrina" to study the decisions of shop owners to reopen business after Hurricane Katrina. The dataset contains 673 observations on 3 streets in New Orleans and can be used to estimate the spatial probit models and to replicate the findings in the paper.

Usage

data(Katrina)

Format

Katrina is a data frame with 673 observations on the following 15 variables:

code: a numeric vector
long: longitude coordinate of store
lat: latitude coordinate of store
street1: a numeric vector
medinc: median income
perinc: a numeric vector
elevation: a numeric vector
flood: flood depth (measured in feet)
owntype: type of store ownership: "sole proprietorship" vs. "local chain" vs. "national chain"
sesstatus: socio-economic status of clientele (1-5): 1-2 = low #' status customers, 3 = middle, 4-5 = high status customers
sizeemp: "small size" vs. "medium size" vs. "large size" firms
openstatus1: a numeric vector
openstatus2: a numeric vector
days: days to reopen business
street: 1=Magazine Street, 2=Carrollton Avenue, 3=St. Claude Avenue

Katrina is a data frame with 673 observations on the following 13 variables.

long: longitude coordinate of store
lat: latitude coordinate of store
flood_depth: flood depth (measured in feet)
log_medinc: log median income
small_size: binary variable for "small size" firms
large_size: binary variable for "large size" firms
low_status_customers: binary variable for low socio-economic status of clientele
high_status_customers: binary variable for high socio-economic status of clientele
owntype_sole_proprietor: a binary variable indicating "sole proprietor" ownership type
owntype_national_chain: a binary variable indicating "national_chain" ownership type
y1: reopening status in the very short period 0-3 months; 1=reopened, 0=not reopened
y2: reopening status in the period 0-6 months; 1=reopened, 0=not reopened
y3: reopening status in the period 0-12 months; 1=reopened, 0=not reopened

Details

The Katrina dataset contains the data found on the website before some of the variables are recoded. For example, the socio-economic status of clientele is coded as 1-5 in the raw data, but only 3 levels will be used in estimation: 1-2 = low status customers, 3 = middle, 4-5 = high status customers. Hence, with "middle" as the reference category, Katrina contains 2 dummy variables for low status customers and high status customers.

The dataset Katrina is the result of these recoding operations and can be directly used for model estimation.

Note

When definining the reopening status variables y1 (0-3 months), y2 (0-6 months), and y3 (0-12 months) from the days variable, the Matlab code ignores the seven cases where days=90. To be consistent with the number of cases in the paper, we define y1,y2,y3 in the same way: y1=sum(days < 90), y2=sum(days < 180 & days != 90), y3=sum(days < 365 & days != 90). So this is not a bug, its a feature.

Source

The raw data was obtained from the Royal Statistical Society dataset website and brought to RData format by Wilhelm and Godinho de Matos (2013).

References

LeSage et al. (2011): P. LeSage, R. K. Pace, N. Lam, R. Campanella and X. Liu. New Orleans business recovery in the aftermath of Hurricane Katrina. Journal of the Royal Statistical Society A, 174, 1007–1027, 2011.
Wilhelm and Godinho de Matos (2013): S. Wilhelm and M. Godinho de Matos. Estimating Spatial Probit Models in R. The R Journal 5, 130–143, 2013.

Examples

## Not run: 
	data(Katrina)
	attach(Katrina)
	table(y1) # 300 of the 673 firms reopened during 0-3 months horizon, p.1016
	table(y2) # 425 of the 673 firms reopened during 0-6 months horizon, p.1016
	table(y3) # 478 of the 673 firms reopened during 0-12 months horizon, p.1016
	detach(Katrina)


	# replicate LeSage et al. (2011), Table 3, p.1017
	require(spdep)
 
	# (a) 0-3 months time horizon
	# LeSage et al. (2011) use k=11 nearest neighbors in this case
	nb <- knn2nb(knearneigh(cbind(Katrina$lat, Katrina$long), k=11))
	listw <- nb2listw(nb, style="W")
	W1 <- as(as_dgRMatrix_listw(listw), "CsparseMatrix")

	fit1_cond <- ProbitSpatialFit(y1 ~ flood_depth + log_medinc + small_size + 
		large_size +low_status_customers +  high_status_customers + 
		owntype_sole_proprietor + owntype_national_chain, 
		W=W1, data=Katrina, DGP='SAR', method="conditional", varcov="varcov")
	summary(fit1_cond)

	fit1_FL <- ProbitSpatialFit(y1 ~ flood_depth + log_medinc + small_size + 
		large_size +low_status_customers +  high_status_customers + 
		owntype_sole_proprietor + owntype_national_chain, 
		W=W1, data=Katrina, DGP='SAR', method="full-lik", varcov="varcov")
	summary(fit1_FL)

	fit1_cond_10nn <- ProbitSpatialFit(y1 ~ flood_depth+ log_medinc+ small_size+
		large_size +low_status_customers +  high_status_customers + 
		owntype_sole_proprietor + owntype_national_chain, 
		W=W1, data=Katrina, DGP='SAR', method="conditional", varcov="varcov",
		control=list(iW_CL=10))
	summary(fit1_cond_10nn)

# (b) 0-6 months time horizon
# LeSage et al. (2011) use k=15 nearest neighbors
nb <- knn2nb(knearneigh(cbind(Katrina$lat, Katrina$long), k=15))
listw <- nb2listw(nb, style="W")
W2 <- as(as_dgRMatrix_listw(listw), "CsparseMatrix")

fit2_cond <- ProbitSpatialFit(y2 ~ flood_depth + log_medinc + small_size + 
	large_size + low_status_customers + high_status_customers + 
	owntype_sole_proprietor + owntype_national_chain, 
	W=W2, data=Katrina, DGP="SAR", method="full-lik", varcov="varcov")
summary(fit2_cond)  

fit2_FL <- ProbitSpatialFit(y2 ~ flood_depth + log_medinc + small_size + 
	large_size + low_status_customers + high_status_customers + 
	owntype_sole_proprietor + owntype_national_chain, 
	W=W2, data=Katrina, DGP="SAR", method="full-lik", varcov="varcov")
summary(fit2_FL)  

# (c) 0-12 months time horizon
# LeSage et al. (2011) use k=15 nearest neighbors as in 0-6 months
W3 <- W2
fit3_cond <- ProbitSpatialFit(y3 ~ flood_depth + log_medinc + small_size + 	
	large_size + low_status_customers + high_status_customers + 
	owntype_sole_proprietor + owntype_national_chain, 
	W=W3, data=Katrina, DGP="SAR", method="conditional", varcov="varcov")
summary(fit3_cond)

fit3_FL <- ProbitSpatialFit(y3 ~ flood_depth + log_medinc + small_size + 
	large_size + low_status_customers + high_status_customers + 
	owntype_sole_proprietor + owntype_national_chain, 
	W=W3, data=Katrina, DGP="SAR", method="full-lik", varcov="varcov")
summary(fit3_FL)

# replicate LeSage et al. (2011), Table 4, p.1018
# SAR probit model effects estimates for the 0-3-month time horizon
effects(fit1_cond)  

# replicate LeSage et al. (2011), Table 5, p.1019
# SAR probit model effects estimates for the 0-6-month time horizon
effects(fit2_cond)

# replicate LeSage et al. (2011), Table 6, p.1020
# SAR probit model effects estimates for the 0-12-month time horizon
effects(fit3_cond)

## End(Not run)

[Package ProbitSpatial version 1.1 Index]