Guys, I am attaching the project that needs to be done. Thanks
MATH 4350/MSDS 5350/MBA 5393 Final Project How risky is it to open a restaurant? To answer this question, read the article “Nine out of 10 fail? Check, please” (posted on Blackboard). Ideally, we would be able to recreated what was done in the article but cannot do so due to the lack of access to data. Instead, we will use restaurant inspection data as proxy data for how long restaurants stay in business. This data is posted on Blackboard, and can also be found at the following website: https://data.cityofchicago.org/Health-Human-Services/Food-Inspections/4ijn-s7e5 With this data you will need to create a time and censoring variable, and each restaurant should only be included once. For the time variable, the difference between when a license is issued and when it is out of business or date of its last inspection can serve as a time variable. You will need to decide how you want to count the time (months or years). For a censoring variable, you will need to look at the last inspection date (if it is not listed as out of business) and decide if that is an indication if the restaurant is still open or not (e.g., if an inspection has not been conducted in four years does that mean the restaurant is closed or hasn’t been inspected) On Blackboard there is a script file to get you started with the data set. The script file gives an example of way to create a time variable, but you will need to create the censoring variable. Make sure to look at the facility type – we only want to use restaurants for this project. Once you have the data set up, conduct a basic survival analysis for restaurant survival in Chicago. The final project is due on Sunday, December 12. You will need to submit a write-up of your analysis and the code used. Nine out of 10 restaurants fail? Check, please Remember that fabulous restaurant you “discovered” when it opened three months ago? It’s dark now; closed for ever. Another great new place is gone! Was it bad luck – or was failure par for the course? It’s most likely the latter, right? It happens all the time, or so you’ve heard. Don’t 9 out of 10 restaurants fail in their first year? They do not. Entrepreneurs, lenders, and the media consider restaurants to be particularly risky start-ups. But there has been a steady 2% per year growth in the number of restaurants in the US over the past decade. On average, US households spend 5–6% of their income eating out, which equates to over $50 per week. And a number of studies in the last 15 years find restaurants to have far lower failure rates than received wisdom would suggest.1 We are fortunate to have detailed data to measure just how risky restaurants are: 20 years of microdata from the US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) in the western US. These longitudinal data are tantamount to a census of businesses, allowing us to estimate failure rates with low bias and no sampling error. In contrast, typical studies of business survival are sample-based and tend to be local or to have relatively small sample sizes, making even regional extrapolation uncertain. Longitudinal studies of business mortality are typically cohort-based, which controls for some sources of confounding, but limits sample sizes and exacerbates other sources of confounding, including macroeconomic events. For this analysis, we compare single- establishment independently owned full-service restaurants to all other single-establishment service- providing businesses in the western US. We exclude multi-establishment and “chain” restaurants because Figure 1. Quarterly birth and death rates of (a) service-providing businesses and (b) restaurants 0% 1% 2% 3% 4% 5% 1992 1995 1998 2001 2004 2007 2010 B irt h an d D ea th R at es Quarterly Birth and Death Rates of All Service-Providing Businesses NBER Recession All services: Birth rate All services: Death Rate 0% 1% 2% 3% 4% 5% 1992 1995 1998 2001 2004 2007 2010 B irt h an d D ea th R at es Quarterly Birth and Death Rates of Restaurants NBER Recession Restaurants: Birth Rate Restaurants: Death Rate (a) (b) Nine out of 10 restaurants fail? Check, please Tian Luo and Philip B. Stark use Bureau of Labor Statistics data to put paid to a persistent myth about the riskiness of the restaurant business Ph ot o: s nv v/ iS to ck /T hi nk st oc k 25april2015 business their operational structure, management and capitalisation are so different. For details of data and methodology, see the box on page 29. Let us start by looking at birth and death rates over time for all service businesses, and for restaurants specifically. Figure 1 (page 25) shows that but for seasonality, there is no discernible pattern to either measure between 1992 and 2011, so the QCEW Figure 2 shows that business failure rates generally decrease as the number of employees at birth increases. Compared to businesses that started with 5 or fewer employees (small), start-ups with 6–20 employees (medium) had an annual failure rate 1.6% lower, while those with 21 or more staff (large) had a failure rate 2.8% lower. Seventy-nine per cent of small start-ups failed by age 15, compared to 73% of medium data include roughly equal numbers of businesses born in each year. (The spikes in birth rates in 1997 and 1998 are apparently caused by administrative changes that affected unemployment insurance reporting requirements.) However, it is important to note that different birth cohorts affect different parts of the cumulative failure function: businesses born late in the window do not contribute to the estimate of the failure function in old age. Figure 2. Failure rates of service-providing businesses born after the first quarter 1992, grouped by number of employees at birth: (a) cumulative failure rate; (b) conditional annual failure rate, which is the rate of failure in a given year, given that the establishment was alive at the beginning of that year 0% 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Fa ilu re R at e Age (years) Conditional annual failure rate, by startup size 5 or fewer 6 to 20 21 or more 0% 20% 40% 60% 80% 100% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Fa ilu re R at e Age (years) Cumulative failure rate, by startup size 5 or fewer 6 to 20 21 or more (a) (b) Ca nd yB ox Im ag es /i St oc k/ Th in ks to ck 26 april2015 start-ups and 67% of large start-ups. Larger start-ups may need more initial capital, but tend to survive longer. One may view the failure curves in Figure 2 as simply a summary of the observations, or as an estimate of an underlying theoretical population failure curve from which the businesses in the census are assumed to be a sample. We prefer the former view since the data are in fact a census. In the latter view, one assumes that the survival times of different establishments (of the same type) are random, independent and identically distributed. This implies, in particular, that the survival time of an establishment cannot depend on when the establishment was born. To check whether this assumption is consistent with the data, we examine whether date of birth is related to longevity. Figure 3 shows only negligible differences among failure rates of establishments born in different phases of economic cycles. Figure 3. Cumulative failure rates of service-providing businesses by birth year 0% 20% 40% 60% 80% 0 1 2 3 4 5 6 7 8 9 10 Fa ilu re R at e Age (years) Cumulative Failure Rate 1992 Q2 to 2000 Q4 Expansion (871,423 establishments) 2001 Q1 to 2001 Q4 Recession (108,159 establishments) 2002 Q1 to 2007 Q3 Expansion (635,578 establishments) 2007 Q4 to 2009 Q2: Recession (158,688 establishments) All years 1992 Q1 to 2011 Q4 (1,928,333 establishments) Entrepreneurs, lenders, and the media consider restaurants to be risky start-ups. But there has been a steady growth in the number of restaurants in the US over the past decade sn vv /i St oc k/ Th in ks to ck 27april2015 How do restaurants fare? From the QCEW data, we can see that about 17% of restaurants in the western US failed in the first year – lower than the average first- year failure rate of 19% for all other service- providing businesses. Not only is the first-year failure rate far lower than the commonly cited 90% figure, the 15-year cumulative failure rate is less than 80%. Restaurants have median lifetimes of roughly 4.5 years, compared to 4.25 years for other service-providing businesses. Figure 4 shows the failure rate and conditional quarterly failure rate of restaurants and service-providing businesses. Restaurants have slightly lower failure rates than other service start-ups, but that difference is highly statistically significant. The quarterly conditional failure rates (rate of failure in a given quarter, given that the establishment was alive at the beginning of that quarter) are fairly low but rising in the first year. They peak at the start of the second year, but decrease in a convex fashion thereafter. Previous studies have also found that conditional failure rates generally decrease with age. The ‘liability of adolescence’ suggests that the first-year failure rate for a firm is lower because businesses generally can survive for a year on initial resources.4 Table 1 compares failure rates of restaurants and other businesses for various start-up sizes and birth epochs. In each size group, the difference in failure rates between restaurants and other service establishments is about the same. In every birth period, restaurants as a group have slightly longer median lifetimes than other service businesses, but risk does depend on size at birth. The median lifespan of restaurants that started with 20 or fewer employees is about 3 months shorter than other businesses of the same start-up size, but restaurants with 21 or more employees had median lifespan about 9 months longer than other businesses with the same start-up size. Of over 500 different types of single- establishment service start-ups (by 6-digit Figure 5. (a)