# 统计代写|经济统计代写Economic Statistics代考|ECON502

## 统计代写|经济统计代写Economic Statistics代考|Identifying Franchise- Affi liated Yelp- Queried Establishments

One of the disadvantages of the Yelp-queried data is ambiguity regarding the franchise with which an establishment is affiliated. Unfortunately, when a franchise name is used to query Yelp’s API, not all harvested establishments are actually affiliated with the queried franchise. For instance, a query for “franchise A” might yield several establishments affiliated with that franchise but might also yield other nearby establishments affiliated with “franchise B” (or nearby establishments not affiliated with any franchise). Thus, it is crucial to identify which establishments harvested from a query for a franchise are actually affiliated with that franchise.

We address this issue by taking advantage of the fact that Yelp URLs typically embed the name of the franchise with which each establishment is affiliated. Moreover, each URL is augmented with information that distinguishes the establishment from other establishments affiliated with the same franchise. This allows us to identify, with a fairly high level of confidence, all establishments in the Yelp database that are affiliated with a given franchise. To illustrate, consider the Yelp URLs listed below.

• https://www.yelp.com/biz/franchise-a-boston-downtown-seaport -boston-2
• https://www.yelp.com/biz/franchise-a-boston-back-bay-fenway-boston
• https://www.yelp.com/biz/franchise-b-atlanta-ne-atlanta-2
• https://www.yelp.com/biz/franchise-b-austin-austin
• https://www.yelp.com/biz/nonfranchise-establishment-1-boulder -longmont
• https://www.yelp.com/biz/nonfranchise-establishment-2-brooklyn -queens-queens

The bold fragments of each URL indicate the name of the establishment. The italicized fragments give information on the location of the establishment, which differentiates URLs affiliated with different establishments but the same franchise. For instance, the bold fragment of the first two URLs suggests that the establishments are attiliated with franchise $\mathrm{A}$, and the italicized fragment suggests the establishments are located in different neighborhoods in Boston. The bold fragment of the second two URLs suggests that the establishments are affiliated with franchise B, and the italicized fragment suggests that one establishment is located Atlanta and the other in Austin.

## 统计代写|经济统计代写Economic Statistics代考|The Case for NAICS Codes

The NAICS is the system by which multiple federal and international statistical agencies assign business establishments to industrial sectors or classes. Economic statistics, such as the Business Dynamics Statistics (Haltiwanger, Jarmin, and Miranda 2008), and survey sampling frames rely on timely and accurate industrial classification data. Currently, NAICS codes are produced by multiple statistical agencies: The Census produces classifications through multiple surveys, most notably the Economic Census (EC). The Bureau of Labor Statistics (BLS) generates and uses NAICS codes in its surveys, and the Social Security Administration (SSA) produces codes for newly established businesses via information on the SS4 Application for Employee Identification Number (EIN) form. NAICS classification provides an ideal testbed for use of public data-more accurate, timely, and consistent NAICS codes would save Census considerable effort, and improve statistical quality and timeliness. For example, the EC uses “classification cards,” which are forms sent to a business prior to the EC in an attempt to identify its correct NAICS code, which enables the correct EC electronic survey path for that business. Filling out such an additional “classification card” form adds substantial burden to respondents, increases survey costs, and may also suffer from lower response rates. Our proposed methodology has the potential to allow Census to avoid such costly classification procedures and deliver better data products at a faster rate. Another compelling reason to develop NAICS codes from public data sources is that laws that govern data sharing between agencies prevent reconciliation between agency codes. A standardized set of assigned classifications would allow agencies to coordinate their lists and ensure all establishments receive the same code. Figure $8.1$ shows the percentage of agreement, at the 2-digit level, between NAICS codes produced by the 2012 EC, BLS, and SSA for the same set of single-unit establishments active in 2012. It shows that the Census and BLS, when coding the same cases, agree on the NAICS sector in approximately 86 percent of cases, whereas the BLS and SSA concur in around 70 percent of cases.

## 统计代写|经济统计代写经济统计代考|识别特许经营-附属Yelp-查询的机构

. .

yelp查询的数据的缺点之一是关于企业所属的特许经营的模糊性。不幸的是，当使用特许经营名称查询Yelp的API时，并不是所有收获的机构都与查询的特许经营有关。例如，对“特许经营a”的查询可能会产生与该特许经营有关联的几家机构，但也可能产生与“特许经营B”有关联的其他附近机构(或与任何特许经营没有关联的附近机构)。因此，识别从一个特许经营查询中获得的哪些机构实际上与该特许经营有关联是至关重要的

• https://www.yelp.com/biz/franchise-a-boston-downtown-seaport -boston-2
• https://www.yelp.com/biz/franchise-a-boston-back-bay-fenway-boston
• https://www.yelp.com/biz/franchise-b-atlanta-ne-atlanta-2
• https://www.yelp.com/biz/franchise-b-austin-austin
• https://www.yelp.com/biz/nonfranchise-establishment-1-boulder -longmont
• https://www.yelp.com/biz/nonfranchise-establishment-2-brooklyn -queens

## 统计代写|经济统计代写经济统计代考| NAICS代码的案例

NAICS是多个联邦和国际统计机构将商业机构划分到工业部门或等级的系统。经济统计数据，如业务动态统计数据(Haltiwanger, Jarmin，和Miranda 2008)和调查抽样框架依赖于及时和准确的行业分类数据。目前，NAICS代码由多个统计机构制定:人口普查通过多次调查进行分类，最著名的是经济普查(EC)。劳工统计局(BLS)在其调查中生成并使用NAICS代码，社会安全管理局(SSA)通过SS4雇员身份号码申请(EIN)表格上的信息为新成立的企业生成代码。NAICS分类为使用公共数据提供了一个理想的测试平台——更准确、及时和一致的NAICS代码将为人口普查节省大量工作，并提高统计质量和及时性。例如，欧委会使用“分类卡”，这是在欧委会之前发送给企业的表格，试图识别其正确的NAICS代码，从而为该企业提供正确的欧委会电子调查路径。填写这种额外的“分类卡”表格会给受访者增加很大的负担，增加调查成本，还可能导致回复率降低。我们提出的方法有可能使人口普查局避免这种昂贵的分类程序，并以更快的速度提供更好的数据产品。从公共数据源开发NAICS代码的另一个令人信服的原因是，管理机构间数据共享的法律阻止了机构代码之间的协调。一套指定的标准分类将使各机构能够协调它们的清单，并确保所有机构收到相同的代码。图$8.1$显示了2012年EC、BLS和SSA为同一组活跃的单一单位机构制定的NAICS代码在2位数水平上的一致性百分比。它表明，当人口普查局和劳工统计局对相同的案例进行编码时，在大约86％的案例中，NAICS部门是一致的，而劳工统计局和社会保障协会在大约70％的案例中是一致的

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