# New Orleans business recovery in the aftermath of Hurricane Katrina. 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. data ## 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 ## 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). ```r data(Katrina) ``` ## 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. ## Examples ```r ## 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) ``` ## 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.