Once the resilience indexes are calculated the proportional resilience measure can be used, at the level of the individual or at the area (PUMA) level, as a variable to be explained. Initially, at the PUMA-level, PUMA-level factors are considered such as industry structure and aggregate educational attainment measures as explanatory factors for PUMA level resilience. This allows for a determination of whether the industrial structure of the economy can convey resilience to an area while also controlling for educational levels in the area as well as age structure. This is captured in equation (5.6)
(5.6)
where is the proportional resilience of PUMA r in time period t and is a matrix of PUMA-level variables controlling for the proportion of employees in a range of sectors (listed in Table 5.3), the proportion of individuals over the age of 20 by educational category, and the proportion of individuals in each age category in the PUMA. Also in equation (5.6), proportional resilience by PUMA depends on log market potential ( ), log employment density ( ), state fixed effects ( ) and unmodelled residual effects ( ). The terms are scalar parameters to be estimated and is a vector of coefficients to be estimated. rt s rt rt rt rt P Z P E R f µ µ s b a+ + + + + = 1 ln ln rt P R Zrt lnPrt lnErt µS µrt , and a s f b
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At the individual level I use individual-specific proportional resilience as the dependent variable as in equation (5.7)
(5.7)
in which is the proportional resilience of person i in time period t where t is 2011 and the matrix X contains individual-specific variables. This is similar to equation (5.3) except that rather than explaining wages here I am attempting to explain resilience.
5.5 Data
In this Chapter I am interested in exploring the impact of the 2008 economic crisis on individual level wages in the US. To contextualise this analysis Figure 5.1 shows the change in the log of wages per employee from 2007 to 2011 at the PUMA level of aggregation. It is possible to see that while some PUMAs (with dark shading) have experienced positive growth in average wage levels, others (with lighter shading) have experienced decreases in average wages. In this Chapter I ask what would wages have looked like had the crisis not occurred and compare the observed wages in 2011 with counterfactual predictions obtained under a no-recession counterfactual. Essentially I am concerned with examining whether, by 2011, wages had been depressed by the crisis to a level below their counterfactual level or whether they had proven resilient (i.e. actual wages had rebounded to their counterfactual level or had not been impacted by the crisis). If they have been resilient, I ask what factors contributed to that resilience.
Figure 5.1: Change in Log Wages per Employee 2007-2011
it s rt rt it it P X P E R f µ µ s b a + + + + + = 1 ln ln it P R
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The data used are derived from The Integrated Public Use Microdata Series (IPUMS-USA); specifically the data are from the American Community Surveys (hereafter ACS) of 2005-2011 which is an on-going statistical survey by the U.S. Census Bureau, delivered to approximately 250,000 addresses monthly (or 3 million per year). It has the advantage of providing snapshots at frequent and regular intervals of socio-economic phenomena that were only previously available in the decennial census, and is the largest survey other than the decennial census that the Census Bureau administers. The ACS is a repeated cross sectional survey, and therefore it is not a panel dataset but a pseudo-panel as it surveys different individuals32 in each wave. However, the questions are consistent across years allowing the data to be pooled in a manner similar to Dalmazzo and de Blasio (2007b; 2007a) and Canton (2009) who construct pseudo- panels for various repeated cross-sectional surveys. Table 5.1 summarises the variables and gives descriptive statistics of the ACS. This shows that the average wage across years varies between $45,000 and $48,000 while the average age of survey respondents is just over 45 years. The sample is predominantly male with a roughly 60/40 split, and the majority of individuals surveyed are married. The ethnic composition is mixed but the majority of people are white. Most have at least a Grade 12 education, while approximately 35% have at least 4 years college education.
The ACS microfiles also contain the Public Use Microdata Areas (PUMA) data. PUMAs (as in Figure 5.1) are non-overlapping regions which partition each state into areas each containing about 100,000 residents, and were first made available in ACS micro files in 200533. The presence of geographical identifiers in the dataset allows for the incorporation of measures of market potential and employment density into the model specification. In total, given about 2,100 PUMAs in the US, there are about 2,100 measures of market potential and employment density alongside approximately 650,000 individual observations annually.
The information required for the generation of the market potential variables is obtained from ‘The American Factfinder’ and is derived from ACS estimates of employment at the PUMA level. Specifically, I acquire data on sectoral employment in each PUMA34 as well as income
32Note that the sample size each year is approximately 650,000 people. These are people in
the ACS who are in employment in the year in question.
33ACS files from 2000-2004 did not include the PUMA variables.
34Where the industries available are (i) Agriculture, forestry, fishing and hunting, and mining;
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and this enables the calculation of market potential in a way that is broadly consistent with NEG theory. The starting point is equation (5.8),
(5.8)
in which denotes market potential in area i, and I sum across a set of R areas to obtain this. The variable is the level of income in area r, is the price index for the M sector35 in area r, and is the transport cost between areas i and r. Also, following from established literature the elasticity of substitution is set to be = 6.25, an assumption based on the summary of empirical estimates presented in Head and Mayer (2003). This value is also used in Fingleton (2011). Note that this is the same as in equations (5.1, 5.3 and 5.4) but in these equations it is an estimate based on empirical data. Note that strictly this equation relates to M sector wages, but I simplify by setting the price index equal to 1 across all areas, so that market potential then relates simply to income levels and transport costs, and this more informal specification can then be related to wages overall.
and warehousing, and utilities; (vii) Information; (viii) Finance and insurance, and real estate and rental and leasing; (iix) Professional, scientific, and management, and administrative and waste management services; (ix) Educational services, and health care and social assistance; (x) Arts, entertainment, and recreation, and accommodation and food services; (xi) Other services, except public administration; and (xii) Public administration. I use these to define the M and C sectors.
35In NEG theory, the economy is divided in the M sector under a monopolistic competition
market structure, and the competitive sector (C). 1 1 1 ( ) R M i r r ir r P Y G s-T -s = =
å
i P r YG
rM irT
s s119
Table 5.1: Descriptive Statistics of American Community Survey
Variable 2005 2006 2007 2011 Wage ($) 45,017 (50,502) 45,992 (52,152) 48,308 (55,657) 48,580 (55,414) Age (years) 45.13 (12.54) (12.78) 45.05 (12.85) 45.32 (13.39) 45.96 Gender (%) Male 0.61 0.60 0.59 0.57 Female 0.39 0.40 0.41 0.43 Marital Status (%)
Married, spouse present 58.29 56.95 57.02 53.54
Married, spouse absent 1.75 1.84 1.87 2.00
Separated 2.68 2.69 2.62 2.75 Divorced 15.90 15.88 15.78 15.97 Widowed 3.43 3.35 3.31 3.45 Never married/single 17.95 19.30 19.40 22.30 Race (%) White 82.17 81.24 81.13 80.02 African American 8.41 8.74 8.83 9.78
American Indian or Alaska Native 0.69 0.68 0.68 0.83
Chinese 0.95 1.01 0.99 1.20
Japanese 0.30 0.30 0.30 0.28
Other Asian or Pacific Islander 2.52 2.68 2.80 3.18
Other Race 3.77 4.08 3.93 2.91
Two major races 1.10 1.18 1.25 1.64
Three or more major races 0.08 0.09 0.09 0.15
Education (%)
No Schooling 0.33 0.37 0.33 0.71
Nursery School to Grade 4 0.36 0.37 0.33 0.37
Grade 5, 6, 7 or 8 1.99 2.05 2.01 1.77 Grade 9 1.15 1.16 1.13 0.96 Grade 10 1.42 1.40 1.34 1.13 Grade 11 1.61 1.57 1.49 1.39 Grade 12 33.78 33.76 33.12 31.72 1 year of college 15.36 15.55 15.39 16.66 2 years of college 8.73 8.78 8.87 9.19 4 years of college 21.52 21.46 21.97 21.57 5+ years of college 13.74 13.54 14.00 14.55
Source: ACS 2005, 2006, 2007 and 2011.
Note 1: Standard deviations are given in brackets for select variables.
When defining trade costs in equation (5.8) I use the distance between PUMAs, thus
where is the straight line distance between the main towns of area i and area r respectively and the parameter defines the rate at which trade costs increase with distance. Ideally, this would be estimated using trade data as in Redding and Venables (2004), however,
ir D ir
e
T
=
tln ir D t120
at the PUMA level this is not possible as no statistics for trade are available. Therefore, following the published literature I assume a value for equal to 0.1 (Fingleton, 2006). This assumption produces plausible levels of market potential which accord with the a priori notions, as described in Figure 5.2. Varying the assumed value of within a reasonable range does not distort the resulting geographical pattern too greatly, so I are reasonably confident that the market potential variable is a robust and reasonable measure.
The market potential map presented in Figure 5.2 shows the highest concentration (darker shading) is on the East coast of the US with two pockets of high market potential on the West coast, centred around major urban concentrations.36 Low market potential prevails across the central and Western states, with obvious exceptions for large urban concentrations.
36These are centred around Los Angeles and Seattle.
t t
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Figure 5.2: Log of Market Potential 2007
In contrast to the NEG motivated market potential variable, the link to UE theory is simply via employment density, defined as employment per square kilometre. Figure 5.3 presents the 2007 employment density map again using the geographical framework of the PUMAs. Quite naturally employment density is also highest around the core urban areas of the US, as depicted on the map by the regions with darkest shading.
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