Why Do People Commute to Other Counties to Work?

Publication Date: 
Wednesday, August 17, 2016

Retirees want to live where the weather and scenery are great and houses are cheap and rent is low. If they move from where they worked before retiring, they're likely to wind up in a pleasant and inexpensive place. Workers also like to live in pleasant and inexpensive places but they also want high wages. A dilemma they face is that other workers have the same preferences for nice, cheap cities or counties, which increases the supply of labor and pushes wages down. Wages may be highest where the cost of living is also high. Some workers solve this dilemma by commuting, from where wages and the cost of living are low to places where wages and the cost of living are high. They're willing to spend more time and money commuting in order to have the best of both worlds—low costs and high wages. That suggest that most commuters will live in counties with lower wages than where they work. It turns out that the majority of Florida's workers who commute from one county to another, that's 18% of all Florida workers, do just that [1].

To try to understand why almost a fifth of Florida workers do not live in the same county they work in, we have to look at the differences in the Florida Price Level Index (FPLI) between counties. The FPLI was established by the Legislature as the basis for the District Cost Differential (DCD) in the Florida Education Finance Program. Since 2000, the University of Florida Bureau of Economic and Business Research (BEBR) has been responsible for calculating this. Prior to 2003, the FPLI was a weighted average of the relative prices of goods and services. Since 2003, the FPLI has been calculated using statistical techniques to estimate an index of relative wages for comparable workers across Florida’s 67 counties [2]. So if County A has a lower FPLI than County B, then it can be assumed that wages in County A are lower than in County B, for comparable work.

To see how wage differences contribute to the decision process of workers, we must first look at the percentage of workers in a county that work in a specific, different county. For example, the percentage of workers that live in Baker County but work in Duval County is 37% [1]. We then compare the FPLI of each county. Baker County has an FPLI of 97.053, while Duval County has an FPLI of 101.453 [2]. We also have to account for distance because a difference in FPLI will not explain why a worker does not commute from Calhoun County to Collier County.  So we look at the distance between Baker and Duval and see that it is 39 miles [3]. So this singular example shows that workers from a county with lower wages will commute to a county with higher wages as long as the distance between them is not too large. To see if this holds true on a broader scale, we have to increase the sample size.

To take a deeper look, we examined the number of workers who live in County A and work in County B, as a percentage of total workers who live in County A, for all Florida counties, using data from the National Bureau of Economic Research. We then find the ratio between the FPLIs of County A and County B. We must also find the distance, in miles, between County A and County B. Once every combination of counties is examined, it is apparent that the FPLI ratio is twice as powerful as the distance between counties, and thus confirms that people will choose live in a place with lower cost of living and commute to the higher wage counties to work, as long as the distance is not too great.

The graph below shows the top 10 county-to-county flows, as a percentage of how many workers that live in one commute to the other. The average ratio between the FPLI of each county is 1.03 and the average distance is only 26.5 miles [2][3]. Duval attracts many workers from other counties because of its high FPLI of 101.45, but Leon only has an FPLI of 96.02 [2]. Leon’s attractiveness stems from the fact that there are many counties nearby that have especially low wages.  

In the matrices below, the first column represents County A and the top row represents County B. The matrices show the ratio of the FPLI of B/A, and the distance between County A and B, respectively. The cells highlighted green represent the top 10 commuter flows.

Table 1.  Ratio of the FPLI between counties with commuter flows

County/ County

Alachua

Baker

Clay

Duval

Gadsden

Gilchrist

Jefferson

Leon

Nassau

Orange

Osceola

St. Johns

Seminole

Wakulla

Alachua

1.00

1.00

1.02

1.05

0.96

0.97

0.96

0.99

1.02

1.03

1.01

1.04

1.00

0.97

Baker

1.00

1.00

1.02

1.05

0.96

0.97

0.96

0.99

1.02

1.03

1.01

1.04

1.00

0.97

Clay

0.98

0.98

1.00

1.02

0.94

0.95

0.94

0.97

1.00

1.01

0.99

1.02

0.98

0.95

Duval

0.95

0.96

0.98

1.00

0.92

0.92

0.92

0.95

0.97

0.99

0.97

0.99

0.95

0.93

Gadsden

1.04

1.04

1.06

1.09

1.00

1.00

1.00

1.03

1.06

1.07

1.05

1.08

1.04

1.00

Gilchrist

1.03

1.03

1.06

1.08

1.00

1.00

0.99

1.02

1.05

1.07

1.05

1.07

1.03

1.00

Jefferson

1.04

1.04

1.06

1.09

1.00

1.01

1.00

1.03

1.06

1.08

1.05

1.08

1.04

1.01

Leon

1.01

1.01

1.03

1.06

0.97

0.98

0.97

1.00

1.03

1.04

1.02

1.05

1.01

0.98

Nassau

0.98

0.98

1.00

1.03

0.95

0.95

0.94

0.97

1.00

1.02

0.99

1.02

0.98

0.95

Orange

0.97

0.97

0.99

1.01

0.93

0.94

0.93

0.96

0.98

1.00

0.98

1.01

0.97

0.94

Osceola

0.99

0.99

1.01

1.03

0.95

0.96

0.95

0.98

1.01

1.02

1.00

1.03

0.99

0.96

St. Johns

0.96

0.96

0.98

1.01

0.93

0.93

0.92

0.95

0.98

0.99

0.97

1.00

0.96

0.93

Seminole

1.00

1.00

1.02

1.05

0.97

0.97

0.96

0.99

1.02

1.04

1.01

1.04

1.00

0.97

Wakulla

1.03

1.03

1.06

1.08

1.00

1.00

0.99

1.02

1.05

1.07

1.05

1.07

1.03

1.00


Table 2.  Distance in miles between geographic centers of counties with commuter flows

County/ County

Alachua

Baker

Clay

Duval

Gadsden

Gilchrist

Jefferson

Leon

Nassau

Orange

Osceola

St. Johns

Seminole

Wakulla

Alachua

0.0

44.9

36.8

62.3

148.5

26.5

105.3

127.0

73.4

101.7

133.8

60.2

100.5

125.0

Baker

44.9

0.0

35.3

39.0

138.7

50.9

94.9

118.1

37.4

138.2

171.5

62.5

133.1

124.4

Clay

36.8

35.3

0.0

27.1

169.4

59.1

125.1

148.1

43.1

106.7

140.1

29.2

99.7

150.9

Duval

62.3

39.0

27.1

0.0

177.4

80.6

133.8

156.9

20.0

127.3

160.2

34.5

117.8

163.3

Gadsden

148.5

138.7

169.4

177.4

0.0

123.6

44.3

21.6

169.4

243.8

272.2

198.5

246.4

33.5

Gilchrist

26.5

50.9

59.1

80.6

123.6

0.0

81.4

102.1

86.7

122.0

152.5

85.4

123.2

98.9

Jefferson

105.3

94.9

125.1

133.8

44.3

81.4

0.0

23.2

127.2

203.1

232.9

154.2

204.6

34.9

Leon

127.0

118.1

148.1

156.9

21.6

102.1

23.2

0.0

149.9

222.8

251.6

177.3

225.1

22.8

Nassau

73.4

37.4

43.1

20.0

169.4

86.7

127.2

149.9

0.0

146.9

180.0

54.4

137.7

158.9

Orange

101.7

138.2

106.7

127.3

243.8

122.0

203.1

222.8

146.9

0.0

33.4

95.2

16.8

215.4

Osceola

133.8

171.5

140.1

160.2

272.2

152.5

232.9

251.6

180.0

33.4

0.0

127.4

43.6

242.6

St. Johns

60.2

62.5

29.2

34.5

198.5

85.4

154.2

177.3

54.4

95.2

127.4

0.0

84.3

179.8

Seminole

100.5

133.1

99.7

117.8

246.4

123.2

204.6

225.1

137.7

16.8

43.6

84.3

0.0

219.4

Wakulla

125.0

124.4

150.9

163.3

33.5

98.9

34.9

22.8

158.9

215.4

242.6

179.8

219.4

0.0

[1][2][3]

In a nutshell, the differences in wages has a commanding effect on whether or not a person commutes to a different county than they live in to work. This effect is lessened by the distance between counties, but the wage ratio is twice as powerful.

 

References

[1]"Residence County to Workplace County Flows for the United States and Puerto Rico Sorted by Residence Geography: 2006-2010." United States Census Bureau. United States Census Bureau, 18 Jan. 2013. Web. 4 Apr. 2016.

[2]Dewey, Jim. 2015 Florida Price Level Index. Rep. Florida Polytechnic University. Florida Polytechnic University, 30 Dec. 2015. Web. 4 Apr. 2016.

[3]Roth, Jean. "County Distance Database." The National Bureau of Economic Research. The National Bureau of Economic Research, 11 June 2014. Web. 4 Apr. 2016.

ACKNOWLEDGEMENT:  Thanks to Dr. David Denslow for his assistance

Art adapted from photo by Daniel Olnes licensed under a Creative Commons Attribution 2.0 Generic License

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