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

Description Usage Format Details Note Source Examples

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

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Format

Katrina.raw 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.raw 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 www.blackwellpublishing.com/rss/Volumes/Av174p4.htm and brought to RData format by Wilhelm and Godinho de Matos (2013).

Examples

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## 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 <- SpatialProbitFit(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 <- SpatialProbitFit(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 <- SpatialProbitFit(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 <- SpatialProbitFit(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 <- SpatialProbitFit(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 <- SpatialProbitFit(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 <- SpatialProbitFit(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)

ProbitSpatial documentation built on May 2, 2019, 12:20 p.m.