Posted: February 16th, 2015

location quotient analysis

LQ content analysis. You are asked to write a half-page (350-500 word) analysis of the table. Of course, there are more numbers than you have space to write about them. Much like the economic profile assignment, you have to be selective upon what you write. What story are the numbers telling us? What I don’t want is a play-by-play description of the numbers. ex “The agriculture LQ was 0.6, which is low, compared to 1.2 for utilities, which is high.” That may be factually correct, but you are writing to a reader who wants an interpretation of the data, noit a description. Remember what I said at the beginning of the lab: LQs are a tool to show whether the share of people working in a sector in your region is weaker (0<x<1.00) equal (x = 1.00) or stronger (x > 1.00) than the national average. When the LQ value is high (ex. 1.20) or above, this suggests your region is specialized in that sector (industry). The LQ values can be very high or very low in small regions (eg Saint John), but tend not to fl;uctuate so much in very large regions (ex Toronto), so don’t use my 1.20 rule as absolute. LQs will thus help confirm that Toronto is a major financial centre and that energy (utilities) is important to Saint John. My suggestion is therefore to highlight 2 or 3 sectors where LQ values are at their highest. Then, in your analysis, you can either
a) compare your LQ numbers with what you wrote in the economic profile (ex the sources you consulted were talking about diversifying away from agriculture, but your LQ numbers show employment has rather been concentrating in that sector);
b) tell me if there’s a trend between 2004-2014. Is the region further specializing or rather diversifying? Don’t forget to look at all sectors. The LQs for financial services may have been low (0.60) in 2004, but now at 1.10, which may not suggest a stroing specialization, however it is phenomenally hiugher than in 2004. Alternatively, a sector at 1.00 today but at 1.80 ten years ago suggests something dramatic happened in that sector.
c) hypothesize what could be behind the numbers or the changes over time? If there’s a drop in LQ values in resource sectors or manufacturing, what might this be telling us about the global competitiveness ofthese sectors (or technological change). A hypothesis is an educated guess that whose answers yoiu could research at another time.
Table 282-0125 Labour force survey estimates (LFS), employment by economic region based on 2011 census boundaries and North American Industry Classification System (NAICS), annual (persons x 1,000)(1)
Survey or program details:
Labour Force Survey – 3701
Geography (2) North American Industry Classification System (NAICS) (3) 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Canada Total employed, all industries (4)
Canada Agriculture [111-112 1100 1151-1152] (6)
Canada Forestry, fishing, mining, quarrying, oil and gas [21 113-114 1153 2100] (7,8)
Canada Utilities [22]
Canada Construction [23]
Canada Manufacturing [31-33]
Canada Trade [41 44-45]
Canada Transportation and warehousing [48-49]
Canada Finance, insurance, real estate and leasing [52-53]
Canada Professional, scientific and technical services [54]
Canada Business, building and other support services [55-56] (10)
Canada Educational services [61]
Canada Health care and social assistance [62]
Canada Information, culture and recreation [51 71]
Canada Accommodation and food services [72]
Canada Other services [81]
Canada Public administration [91]
Ontario Total employed, all industries (4) 1
Ontario Agriculture [111-112 1100 1151-1152] (6) 0.603503701
Ontario Forestry, fishing, mining, quarrying, oil and gas [21 113-114 1153 2100] (7,8) 0.299023749
Ontario Utilities [22] 1.091376611
Ontario Construction [23] 0.982457367
Ontario Manufacturing [31-33] 1.213823485
Ontario Trade [41 44-45] 0.970310674
Ontario Transportation and warehousing [48-49] 0.940324157
Ontario Finance, insurance, real estate and leasing [52-53] 1.136396613
Ontario Professional, scientific and technical services [54] 1.088412795
Ontario Business, building and other support services [55-56] (10) 1.116948572
Ontario Educational services [61] 0.954560756
Ontario Health care and social assistance [62] 0.92
Ontario Information, culture and recreation [51 71] 1.05
Ontario Accommodation and food services [72] 0.92
Ontario Other services [81] 0.92
Ontario Public administration [91] 0.94
Kingston-Pembroke, Ontario [3515] Total employed, all industries (4) 0.08 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Kingston-Pembroke, Ontario [3515] Agriculture [111-112 1100 1151-1152] (6) 0.10 1.02 1.35 1.53 0.87 0.55 0.65 0.95 1.31 1.74 1.25
Kingston-Pembroke, Ontario [3515] Forestry, fishing, mining, quarrying, oil and gas [21 113-114 1153 2100] (7,8) 0.20 0.43 0.51
Kingston-Pembroke, Ontario [3515] Utilities [22] 0.07 1.83 3.26 2.38 2.07 1.85 1.95 2.02 2.45 2.33 1.30
Kingston-Pembroke, Ontario [3515] Construction [23] 0.09 1.07 0.84 0.91 0.95 1.12 0.90 1.00 1.01 0.88 1.06
Kingston-Pembroke, Ontario [3515] Manufacturing [31-33] 0.85 0.84 1.02 0.97 0.94 0.90 1.11 0.89 1.01 0.89 1.02
Kingston-Pembroke, Ontario [3515] Trade [41 44-45] 0.99 0.96 0.90 1.00 0.99 1.09 1.15 0.99 0.99 0.96 1.01
Kingston-Pembroke, Ontario [3515] Transportation and warehousing [48-49] 1.08 0.96 0.81 0.90 0.73 0.90 0.72 0.89 0.83 0.84 0.92
Kingston-Pembroke, Ontario [3515] Finance, insurance, real estate and leasing [52-53] 0.61 0.56 0.68 0.78 0.71 0.54 0.63 0.74 0.76 0.67 0.72
Kingston-Pembroke, Ontario [3515] Professional, scientific and technical services [54] 0.63 0.67 0.52 0.53 0.61 0.58 0.68 0.63 0.58 0.68 0.55
Kingston-Pembroke, Ontario [3515] Business, building and other support services [55-56] (10) 1.04 1.25 1.37 1.30 1.29 1.36 1.07 1.35 1.18 1.41 1.34
Kingston-Pembroke, Ontario [3515] Educational services [61] 1.19 1.26 1.37 1.25 1.38 1.16 1.12 1.20 1.11 1.29 1.19
Kingston-Pembroke, Ontario [3515] Health care and social assistance [62] 1.32 1.29 1.23 1.11 1.20 1.12 1.10 1.34 1.30 1.22 1.12
Kingston-Pembroke, Ontario [3515] Information, culture and recreation [51 71] 0.57 0.71 0.73 0.67 0.81 0.83 0.61 0.69 0.58 0.77 0.79
Kingston-Pembroke, Ontario [3515] Accommodation and food services [72] 1.24 1.06 1.26 1.12 1.07 1.21 1.10 1.08 1.21 1.11 1.05
Kingston-Pembroke, Ontario [3515] Other services [81] 1.26 1.07 1.01 1.02 0.87 1.00 1.22 0.96 0.75 0.99 0.99
Kingston-Pembroke, Ontario [3515] Public administration [91] 1.25 1.52 1.05 1.31 1.45 1.33 1.29 1.25 1.32 1.21 1.40
Legend:
x Suppressed to meet the confidentiality requirements of the Statistics Act
Footnotes:
1 The Labour force survey collection of tables, starting with number 282-, is large with many possible cross-tabulations for the 10 provinces and other geographic regions. To ensure respondent’s confidentiality, detailed data are suppressed. Data for Canada, Quebec, Ontario, Alberta and British Columbia are suppressed if the estimate is below 1,500, for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan, if the estimate is below 500, and for Prince Edward Island, under 200. For suppression levels within census metropolitan areas (CMAs) and economic regions (ERs), use the respective provincial suppression levels above. While suppressing to protect respondent confidentiality has the added effect of blocking-out the lowest-quality LFS data, some remaining non-suppressed data in these very large LFS CANSIM tables may be of insufficient quality to allow for accurate interpretation. Please be warned that the more detailed your LFS CANSIM download, the smaller the sample size upon which your LFS estimates will be based, and the greater the risk of downloading poorer quality data.
2 An economic region is a geographical unit generally composed of several census divisions within a province. While 73 regions are defined for the purposes of sampling, some areas are too small to enable production of independent estimates from the survey. These small regions are grouped with a neighbouring region with similar economic characteristics. In the case of Prince Edward Island, the province constitutes one economic region. For geographic descriptions of economic regions, refer to the Guide to the Labour Force Survey (Catalogue no. 71-543-GIE).
3 Industry estimates are based on the 2007 North American Industry Classification System (NAICS). Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). In January 1999, the industry classification switched from Standard Industrial Classification (SIC 1980) to North American Industrial Classification System (NAICS), which is quite different. While the titles of some groupings have not changed, the detailed industries that they represent have. For this reason, detailed historical data based on NAICS is provided going back to January 1987. In addition, data from 1976 to 1986 is provided at a less detailed level than the more recent data. Historical data have been created by imputation.
4 This combines the North American Industry Classification System (NAICS) codes 11 to 91.
6 Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 – Farming – not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (example: mixed farming).
7 Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 – Mining – not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished.
8 Also referred to as Natural resources.
10 Formerly “Management of companies and administrative and other support services”.
Source:
Statistics Canada. Table 282-0125 – Labour force survey estimates (LFS), employment by economic region based on 2011 census boundaries and North American Industry Classification System (NAICS), annual (persons)
(accessed: February 05, 2015)

 

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