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Convergence of per capita income: the case of U.S. metropolitan statistical areas.

1. Introduction

Convergence is defined as poorer regions or countries growing faster than rich regions or countries, so that the gap in relative incomes between the two will become smaller over time. Much empirical work has been done on this concept over the past 25 years. This is due to the fact that data on growth over many countries--has been highly dissimilar over the last 3 decades. The empirical studies have been done to ascertain whether or not the convergence phenomenon is indeed valid, or merely a theoretical prediction of the seminal Solow (1956) model. The study of convergence is also important in order to analyze the determinants of growth, and why certain regions/ countries tend to grow faster than others.

There are numerous empirical works testing the convergence hypothesis, with varying results. The main methodology used in the literature involves fitting cross-country regressions, where convergence is confirmed if there is a negative relationship between the average growth rate and initial income of a specific country, i.e. the higher the initial income, the slower the average growth rate. Studies by the pioneering Baumol (1986), as well as those by Barro and Sala-i-Martin (1991, 1992), and Mankiw et al. (1992) all find that convergence exists among OECD/industrial countries, as well as the regions within them.

For illustration, Barro and Sala-i-Martin (1991) make use of per capita income data for the U.S. states for the time period 1840-1990. They find significant evidence for the existence of convergence across the U.S. states for the time period. They then compare their original results for the U.S. to a cross country sample, using real GDP per capita from 1985-1990 for 85 countries, including industrialized as well as non-industrialized countries. Again they find significant evidence for the convergence of income.

The above mentioned methodology has been criticized for its simplicity, and other techniques applied to the convergence hypothesis. In contrast to the cross-sectional studies above, Quah (1993), making use panel data, and Bernard and Durlauf (1995), using a univariate form of cointegration tests that defines convergence as a stochastic process, cannot confirm convergence across a large sub-section of countries.

Time series methodologies appear to hold weight, as recently the study by Strazicich et al. (2002) makes use of a time series approach, utilizing a minimum Lagrange multiplier unit root test which endogenously determines two structural breaks in level and trend. This approach is not subject to restrictions on the null hypothesis, as many other tests are. Strazicich et al. empirically find that incomes are stochastically converging across 15 OECD countries for the time period 1870-1994. This means that the shocks to each country analyzed are temporary, and do not affect incomes in the long run. They find two structural breaks for all countries, most often around the period of the two World Wars. These results contrast with other time series tests that did not allow for structural breaks, which gives weight to the inclusion of structural breaks in our analysis.

Further evidence of the validity of the inclusion of structural breaks are provided by Carlino and Mills (1996) as well as by Loewy and Papell (1996), who utilize one-break exogenous and endogenous unit root tests, respectively, and find support for convergence among the U.S. regions in the period 1920-1990. Fleissig and Strauss (2001) examine OECD countries with a variety of panel unit root tests and find, in general, support for convergence only in the post-WWII period.

Other methods that find support for convergence include spatial econometrics, which incorporates regional factors into the analysis, as well as Instrument Variable approaches making use of 2SLS. Rey and Montouri (1999) use a spatial econometric approach, in which they incorporate effects specific to their region of study, such as geography, to test convergence of the U.S. region relative incomes. They revealed strong evidence of spatial autocorrelation in the levels of state per capita incomes over the sample period of 1929-1994. They further found that state income growth rates had a high degree of autocorrelation. This implies that, while states may be converging in relative incomes, they do this together, i.e. not independently, but rather tend to display similar movements to that of their regional neighbors.

As far as the IV approach goes, Higgins, Levy and Young (2003) use 2SLS--making use of instrumental variables--to study growth determination and the speed of income convergence across the U.S. They find that the U.S. states are converging, as confirmed by both methodologies. However, they further emphasize that convergence rates are not constant across the U.S., for example, the counties in the Southern states converge at a rate that is more than two and half times faster than the counties located in the New England states. Further, the authors confirm that large presences of finance, insurance, real estate industry, and entertainment industry are positively correlated with growth.

Finally, in line with this papers methodology, Fousekis (2007) makes use of relative stochastic convergence and specifically, stationarity tests on panel data to analyze convergence of per capita incomes for the period 1929-2005 in US state data. This covers the period just before the global crisis of 2008, and gives a good indication of convergence pre-crisis in the U.S. region. The author discovers that not all states were stationary for every sub-period after the 1960s (which is when the data is considered to become free of any deterministic or stochastic trends). This implies that there is some degree of divergence in state per capita income, although more than 80% of the states have converged, or reached their steady-state equilibrium values.

Thus, considering the importance of testing convergence, and in light of the recent turmoil that engulfed the world economy in the form of the global crisis, this paper will investigate the convergence hypothesis for the U.S., using metro regions in the period subsequent to the crisis. Note that, existence of convergence implies that national-level policies will have uniform effects on the regions of the economy, in our case the 384 US metros. Also, convergence implies, if it exists, that shocks originating from one region is likely to spread to other regions over time, i.e., in the long-run. Hence, determining the existence or non-existence of convergence is of paramount information from a policy-makers perspective, as this allows the policy authorities to understand if specific regions require additional attention following the changes in policy.

In order to achieve this, we make use of multivariate tests for stability, and the existence of unit roots, as used in the literature by Abuaf and Jorion (1990), and more recently by Harvey and Bates (2003). The stationarity tests are used to conclude whether the regions have converged, while the unit root tests come in handy to conclude whether the regions are converging. We account for the presence of structural breaks in the series, due to the highly volatile nature of the economy over the period of study, considering our data the recent global crisis, as well as numerous other possible structural breaks. Further accounting for potential breaks in our data, we conclude our empirical analysis with the panel stationarity test that accounts for structural changes, as proposed by Carrion-i-Silvestre, Del Barrio-Castro and LopezBazo [CBL] (2005).

We make use of U.S. data for studying convergence in a post-crisis world, due to the fact that we have a vast resource of data for over 40 years, on what is similar to over 384 separate economies, spread over 50 US states. There is substantial heterogeneity among the U.S. metros, in both terms of wealth, income, regulatory institutions, geographical location, and income per capita. The economies are also very open, which allows for high mobility of capital, labor and technology--all cornerstones of neo-classical models on which the idea of convergence is based. Exchange rate fluctuations are eliminated, as all the regions use the same currency. Price variations in consumer goods across countries also tend to be smaller than in a cross country analysis. We thus use the U.S as a benchmark, for what may be observed across the world. Additionally, due to our methodology, as Bernard and Durlauf (1996) mention "time series tests of convergence are not appropriate for those countries positioned far from the steady state as occurs with developing countries, in this case, the data would not be characterized by well-defined population moments, since the data are far from their limiting distribution."

To the best of our knowledge, this is the first paper that analyzes the convergence of per capita income across the 384 metros of the US economy, using not only standard unit root tests, but also tests of stability, and unit root tests with breaks, which previous studies on this topic, like Drennan et al., (2004), Drennan (2005), ignored, and failed to find evidence of convergence. We also work with data that covers more metros than these studies and also updated data that includes the "Great Recession"--a major structural break for the US economy. Note that many studies at the metro-level have analyzed convergence in prices (Huang et al. (2013) and references cited there in) and house prices (Canarella et al. (2012)), but not extensively income as we do. Most of the literature on convergence in income is only restricted to US states (see Heckelman (2013) and references cited therein), but for policy making, it is important that a more disaggregated analysis is conducted, and hence, the choice of metros in our paper. The rest of the paper is structured as follows: Section 2 discusses the methodology we employ, after which we detail the data we use in section 3. The empirical results are outlined in section 4. Finally we conclude in section 5.

2. Methodology

We study a group of metros, attempting to identify if the metro's income per capita trend is stationary--indicating convergence. For this we make use of multivariate tests for stability. We take cognizance of the fact that there are probable structural breaks in our data, and due to this we make use of the Carrion-i-Silvestre et al.'s (2005) test, which appreciates structural changes. All unit root tests we perform, excluding the KPSS, have a null hypothesis of a unit root, meaning that a rejection of null indicates convergence. The KPSS test has the opposite null of no unit root. The Hadri Lagrange multiplier (LM) test that we make use of has as the null hypothesis that all the panels are (trend) stationary.

2.1 Multivariate tests for stability and unit roots

Multivariate tests are appropriate, if the aim is studying across a group of observations. Let [x.sub.t] be n vector of contrasts between each of the metros, and a benchmark, e.g. [x'.sub.t] = ([y.sup.1.sub.t], [y.sup.2.sub.t], ..., [y.sup.N.sub.t]). The simplest multivariate convergence model is the zero mean VAR(l) process:

[x.sub.t] = [alpha][x.sub.t-1] + [[omega].sub.t]. (3)

where [alpha] is a N x N matrix and [[omega].sub.t] is N dimensional vector of martingale differences innovations with constant variance [summation over [omega]] []. The model is said to be homogeneous if [alpha] = [phi][I.sub.N]. Following Abuaf and Jorion (1990), Harvey and Bates (2003) propose the use of the multivariate unit root test from the homogeneous model. Specifically, they used the Wald-type statistic on [rho] = [phi] - 1, that is


and referred to as the multivariate homogeneous Dickey-Fuller (MHDF) statistic, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is initially estimated by the sample covariance matrix of first-differenced data and then re-estimated by iterating the estimation of [phi] to convergence. Under the null hypothesis, [H.sub.0]: [rho] = 0,


where [W.sub.i](r) are independent standard Brownian motion processes, i=1, ..., N; if N is large, [[psi].sub.0](N) is approximately Gaussian. The null hypothesis is rejected when [[psi].sub.0](N) less than a given critical value [delta].

An interesting feature of the MHDF test is that it is invariant to any nonsingular transformation of [x.sub.t]. Consequently, it is invariant to which country is chosen as benchmark. This feature is lost in case of heterogeneous model in which [alpha] is diagonal.

One can designate a parametric correction of the variance of the errors, through the addition of lagged differenced terms of [x.sub.t]. to cope with serial correlation of the errors. The critical values of the test can be obtained from Harvey and Bates (2003); see also O'Connell (1998).

A generalization of the KPSS test can be applied to xt to test whether the N metros have converged in the context of stability analysis. Then, the involved statistic is given by


where C = [T.summation over (t=1)]([t.summation over (j=1)][x.sub.j])([t.summation over (j=1)][x.sub.j])' and [[??].sup.-1] is a non-parametric estimation of the long run variance of [x.sub.t]. Under the null hypothesis of zero mean stationarity, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], with d denoting weak convergence in distribution. Critical values are provided in Nyblom (1989) and Hobijn and Franses (2000). A non-rejection of the null hypothesis would suggest overall evidence of stability, in the sense that the n countries should have converged absolutely.

There may be confusion as to the role that unit root and stationarity tests play in detecting convergence. As described by Busetti et al. (2007), the two types of tests are in fact meant for different purposes and cannot be arbitrarily interchanged. Unit root tests are used for estimating whether series are in the process of converging, dependent on initial conditions. Stationarity tests however, are used for exploring whether series have converged. This implies that the difference between the series is stable. This is again confirmed by Harvey and Carvalho (2002).

It is therefore important to distinguish between convergence and stability. Convergence is analyzed by testing the null hypothesis of unit root, whereas stability is tested by way of the null of stationarity. Thus the unit root tests come in handy to conclude whether the metros are converging, while the stationarity tests are used to conclude whether the metros have converged.

2.2 Panel stationarity test with structural changes: The Carrion-i-Silvestre, Del Barrio-Castro and Lopez-Bazo [CBL] (2005) test

The reasons for taking into account structural breaks in the income per capita series are due to the potential shifts in the data due to shocks--such as the stock market crashes, and more recently the global crisis. It goes without saying the economic system is subject to capricious up and down-swings. Therefore, the income per capita series will be subjected to a number of structural changes. That is why we have taken account of Carrion-i-Silvestre et al.'s (2005) test in the analysis of convergence. In what follows, we briefly describe the CBL (2005) test, which, by design, has the ability to test the null hypothesis of panel stationarity while allowing multiple structural breaks. It will be described as follows:


where [x.sub.i,t] is the logarithm of the series of income per capita, i=1 ..., N represents the number of cross section units and [[epsilon].sub.i,t] is the error term. The dummy variables [Du.sub.i,k,t] and [DT.sup.*.sub.i,k,t] are defined as [DU.sub.i,k,t] = 1 for t > [T.sup.i.sub.b,k] and 0 otherwise, and [DT.sup.*.sub.i,k,t] = t - [T.sup.i.sub.b,k] for t > [T.sup.i.sub.b,j] and 0 wise; and [T.sup.i.sub.b,k] denotes the kth date of the break for the ith individual, k = {1, ... [m.sub.i]}, [m.sub.i] [greater than or equal to] 1.

The model in equation (6) constitutes a generalization of that of Hadri (2000) and it includes individual effects, individual structural break effects (i.e., shift in the mean caused by the structural breaks known as temporal effects where [[beta].sub.i] [not equal to] 0), and temporal structural break effects (i.e., shift in the individual time trend where [[gamma].sub.i] [not equal to] 0). In addition, the specification given by equation (6) considers several structural breaks, which are located on different unidentified dates and where the number of structural breaks is allowed to vary between the members of the panel. The test statistic is constructed by running individual KPSS regressions for each member of the panel, and then taking the average of the N individual statistics. The general expression of the test statistic is


where [S.sub.i,t] = [t.summation over (j=1)][[??].sub.i,j] represents the partial sum process that is obtained using the estimated OLS residuals of equation (6), and [[??].sup.2.sub.i] is the consistent estimate of the long-run variance of residual [[epsilon].sub.i,t]; this allows the disturbances to be heteroscedastic across the cross-sectional dimension.

In equation (7), [lambda] is defined as the vector


The test statistic for the null hypothesis of a stationary panel with multiple shifts is


As in the case of the univariate KPSS test statistic, the null hypothesis of stationarity in the panel is rejected for large values of Z([lambda]). [bar.[zeta]] and [bar.[zeta]] are the cross-sectional average of the individual mean and variance of [[delta].sub.[down arrow]]i([lambda]) = [[??].sup.-2.sub.i][T.sup.-2] [T.summation over (t=1)][S.sup.2.sub.i,t].

3. Data

We use annual US data, measuring real metro level Income per capita across the U.S. Data incorporating 384 metros from all 50 States is available from 1969-2011, obtained from the Bureau of Economic Analysis regional economic accounts. Note that our metric for the tests of stationarity is the ratio of income per capita of a specific metro relative to the cross-sectional average. So if this ratio shows stationarity, we can conclude of convergence, as this means that the income of the specific metro is moving towards the overall average of the entire country.

We use U.S. data to study convergence, as it represents a unique and vast resource that covers 40 years, and it mimics many separate global "economies;" the states represent over 50 separate economies as the metro areas are dispersed over the states. This allows more in depth analysis and isolation of particular phenomena. Moreover, there is substantial heterogeneity among the U.S. metros, in terms of wealth, income, regulatory institutions, geographical location, and income per capita.

These separate economies are very open due to the fact that they lie in the same country. This allows for unrestricted movement of capital, labor and technology--high mobility of factors is a cornerstone of neo-classical models on which the idea of convergence is based. Fluctuations in exchange rates are all but eliminated, as the regions use the same currency. The effects of inflation and differing consumer prices are also somewhat mitigated, as price variations in consumer goods across countries (i.e. within a country) also tend to be smaller than in a cross country (i.e. between countries) analysis. We make use of the U.S. regions as they act as a benchmark for what may be observed in an analysis that makes use of global data, and to which other studies of this nature may be compared.

All the U.S. metros used are available in the appendix if needed for reference.

4. Empirical Results

Before we proceed with the presentation of our results, we make a note of the specification of our KPSS unit root test. The literature establishes that the KPSS test without a constant has power against a stationary process with a non-zero mean, as well as against a non-stationary process (Busetti and Harvey, 2002). Thus we perform the KPSS test without a constant term, given its increased power when performed in this manner. In this way, we test if the series have converged individually or in groups, and if the involved convergence is absolute.

The Levin-Lin-Chu (2002) and Im-Pesaran-Shin (2003) tests have as the null hypothesis that all the panels contain a unit root. The Hadri (2000) Lagrange multiplier (LM) test has as the null hypothesis that all the panels are (trend) stationary. The KPSS has a null of stationarity, as do the CBL and Hadri LM tests.

With regard to multivariate tests, the cross-section mean was subtracted from each individual series. This can be beneficial in two ways: First, the cross-section mean series can be regarded as a benchmark, and then, the study of all individual series is guaranteed. Second, this subtraction can mitigate the effects of not taking into account the cross-sectional dependence. This assumption reflects reality, as the analysis of macroeconomic time series for different metros may be affected by similar events that could introduce dependency between individuals in the panel data set. We therefore follow Levin et al. (Levin, Lin and Chu [LLC]) (2002), who suggested removing the cross-section mean, which is equivalent to include temporal effects in the panel data. We applied the LLC test to the series to compare its results with those from the other multivariate homogeneous test, including, the MHDF test. Given that the MHDF test does not take account of heterogeneity, we decided to add the Im et al. (Im, Pesaran and Shin [IPS]) (2003) test that has been formulated by allowing for this heterogeneity.

From Table 1 above, we can see that all the tests reject the null of a unit root, both under the homogeneity and heterogeneity assumptions, which indicates that the series are in the process of converging, as they do not have a permanent memory and are heading towards their respective steady states. This does not necessarily imply convergence; however, given the time frame it confirms that in the long run the income of the respective metros should all reach their steady states--we can say that the metros are converging. This is as predicted by contemporary macroeconomic models.

Next, we consider Multivariate tests for stability which, as described, are used to test if the series have converged.

As we see from Table 2, the MKPSS, Hadri and CBL tests all reject the null hypothesis of stationarity. For comparison to the MKPSS test, we used the Bai-Ng (2004) PANIC test for stationarity hypothesis. A specific feature of PANIC is that it tests the data's unobserved co mponents instead of the observed series. From a cursory glance, this procedure is based on the factor structure of the large dimensional panels to reveal the nature of nonstationarity in the data. Using the PANIC test, we failed to reject the null of stationarity.

Due to the fact that the MKPSS test does not take heterogeneity into account, we include the panel stationarity test of Hadri (2000), which allows for heterogeneity. With regards to convergence, this may be advantageous, as the homogeneity assumption restricts every metro to converge at the same rate. The Hadri test that includes structural breaks, as well as Hadri test with individual and temporal effects, both reject the null of stationarity.

Referring in particular to the CBL test, we see that the null hypothesis of stationarity with structural breaks is rejected. A little digression is absolutely essential here. From an econometric point of view, the conflicting results of stationarity and unit root tests may be an indicator of nonlinearity and structural changes. We are therefore justified in using the CBL test.

Based on the results from Table 2, we reject the null of stationarity for the panel as a whole. This would indicate that rather than no convergence, there is actually a divergence of income per capita in the U.S. metros. This result is reasonable within the current investigation, as given the literature, the income per capita in the U.S. metros were typically close together, and have now tended to widen. This divergence terminology is supported by Busetti et al. (2007).

Overall, based on test results from Tables 1 and 2, our finds are that the unit root tests reject the null hypothesis of unit root, and the stationarity tests reject the null hypothesis of stationarity. We can thus conclude from the unit root tests that the series are in the process of converging. However, given the rejection of stationarity hypothesis in the stationarity tests, we can conclude that there is no evidence for overall stability of the income per capita among the U.S. metros, and this implies that the series have diverged.

Potential reasons for this divergence of income per capita in the U.S. metros are outlined by Ganong and Shoag (2012). They argue that migration of labor can account for all of the observed change in convergence. In their study, it is shown that the relationship between migration and housing prices has changed in the recent past. Even though housing prices have always been higher in richer areas, housing prices now capitalize a far greater proportion of the income differences across states. Due to the fact that housing prices are now a bigger divider between income groups, labor markets no longer clear through migration, but rather by skill-sorting. Thus, the divergence in income per capita may be attributed to a divergence in the skill-specific returns to productive places, a redirection of low-skilled migration, diminished human capital convergence, and continued convergence among places with unconstrained housing supply.

Consequently, the reason that convergence of income per capita has ceased, can be answered by a divergence in workers with tertiary education across places--where high-skilled workers are becoming relatively more concentrated. As argued by the authors, there is an increase in consumers' desire to live in places that are highly educated, as well as an increase in firms' desire for highly skilled labor. The fact that they are willing to pay increasingly large amounts to live in their place of choosing, drives up the price of housing in these areas.

From the firm side, the rise of the so-called information economy and a bias towards highly skilled technological change cause an upsurge in the demand for skilled labor, due to their higher productivity levels. This means that firms in highly-skilled areas may pay higher wages, however, due to the fact that migration of labor has been prevented by the excessive housing prices, only a select few highly-skilled individuals can take advantage of the higher wages in these areas. The fact that housing prices constrain the movement of labor means that workers from poorer regions are not able to move to regions where their remuneration may be higher, and this is due to the upsurge of housing prices in the areas that offer higher wages. Further, because low-workers are no longer able to move to well-educated regions, there is a slowdown of human capital convergence--which may be due to diminished learning by doing and skills-sharing, all caused by increased housing prices.

5. Conclusion

The confirmation of convergence is of utmost importance to policy makers and economists, since it is necessary to find out what impact certain policies might have on growth, as well as to test to see if model predictions hold in the real world--which would indicate the validity of certain macroeconomic models. Having recently undergone a major global crisis which may have affected the distribution of income, we test the convergence hypothesis across the U.S metros for the time period 1969-2011.

Firstly, we make use of multivariate tests for existence of unit roots. Given that the series are likely to contain structural breaks, and that these breaks must be accounted for, we proceed with the analysis using the panel stationarity test accounting for structural changes as proposed by Carrion-i-Silvestre et al. (2005).

Overall, our results indicate that while the U.S metros are indeed converging, they have not converged but have indeed diverged. So, just like earlier studies, allowing for more sophisticated techniques with breaks and updated data, cannot reverse the evidence of divergences of US metros. Potential reasons for this are vast, and to be sure a full study needs to be undertaken to determine probably causes. However, given the increase in housing prices in rich areas, migration of labor may be constrained, which limits the convergence of human capital and productive returns to specific skills. This may limit the "catch up" factor that drives convergence of income per capita.

Given the finding in this paper, future studies may test for convergence over a large sample of countries, both developed and potentially developing. A limitation of this study, which may be rectified in future work, is that a more in-depth study focusing on the reasons for the divergence found in the U.S. metros could be carried out.

Our findings have important implications for literature related to macroeconomic modeling, but also policy regarding land-ownership. Literature may need to take into account the role of the price of land, and how it affects labor, in modeling economic growth, while policy makers should take note of the fact that increases in house prices may limit the migration of labor, and ultimately affect productivity. Policy that encourages the continued migration of labor by way of ensuring realistic housing prices for the working class may ensure future convergence.


University of Manouba


University of Pretoria


University of Pretoria

Received 26 August 2015 * Received in revised form 2 October 2015

Accepted 3 October 2015 * Available online 25 January 2016


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Solow, R. (1956), "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics 70(1): 65-94.

Strazicich, M. C., J. Lee, and E. Day (2002), "Are Incomes Converging among OECD Countries? Time Series Evidence with Two Structural Breaks," Journal of Macroeconomics 26: 131-145.

a) List of all Metro's

1     hp10180   Abilene, TX (Metropolitan Statistical Area)

2     hp10420   Akron, OH (Metropolitan Statistical Area)

3     hp10500   Albany, GA (Metropolitan Statistical Area)

4     hp10580   Albany-Schenectady-Troy, NY (Metropolitan
                Statistical Area)

5     hp10740   Albuquerque, NM (Metropolitan Statistical Area)

6     hp10780   Alexandria, LA (Metropolitan Statistical Area)

7     hp10900   Allentown-Bethlehem-Easton, PA-NJ (Metropolitan
                Statistical Area)

8     hp11020   Altoona, PA (Metropolitan Statistical Area)

9     hp11100   Amarillo, TX (Metropolitan Statistical Area)

10    hp11180   Ames, IA (Metropolitan Statistical Area)

11    hp11260   Anchorage, AK (Metropolitan Statistical Area)

12    hp11300   Anderson, IN (Metropolitan Statistical Area)

13    hp11340   Anderson, SC (Metropolitan Statistical Area)

14    hp11460   Ann Arbor, MI (Metropolitan Statistical Area)

15    hp11500   Anniston-Oxford, AL (Metropolitan Statistical

16    hp11540   Appleton, WI (Metropolitan Statistical Area)

17    hp11700   Asheville, NC (Metropolitan Statistical Area)

18    hp12020   Athens-Clarke County, GA (Metropolitan Statistical

19    hp12060   Atlanta-Sandy Springs-Marietta, GA (Metropolitan
                Statistical Area)

20    hp12100   Atlantic City-Hammonton, NJ (Metropolitan
                Statistical Area)

21    hp12220   Auburn-Opelika, AL (Metropolitan Statistical Area)

22    hp12260   Augusta-Richmond County, GA-SC (Metropolitan
                Statistical Area)

23    hp12420   Austin-Round Rock-San Marcos, TX (Metropolitan
                Statistical Area)

24    hp12540   Bakersfield-Delano, CA (Metropolitan Statistical

25    hp12580   Baltimore-Towson, MD (Metropolitan Statistical

26    hp12620   Bangor, ME (Metropolitan Statistical Area)

27    hp12700   Barnstable Town, MA (Metropolitan Statistical

28    hp12940   Baton Rouge, LA (Metropolitan Statistical Area)

29    hp12980   Battle Creek, MI (Metropolitan Statistical Area)

30    hp13020   Bay City, MI (Metropolitan Statistical Area)

31    hp13140   Beaumont-Port Arthur, TX (Metropolitan Statistical

32    hp13380   Bellingham, WA (Metropolitan Statistical Area)

33    hp13460   Bend, OR (Metropolitan Statistical Area)

34    hp13644   Bethesda-Rockville-Frederick, MD (MSAD)

35    hp13740   Billings, MT (Metropolitan Statistical Area)

36    hp13780   Binghamton, NY (Metropolitan Statistical Area)

37    hp13820   Birmingham-Hoover, AL (Metropolitan Statistical

38    hp13900   Bismarck, ND (Metropolitan Statistical Area)

39    hp13980   Blacksburg-Christiansburg-Radford, VA
                (Metropolitan Statistical Area)

40    hp14020   Bloomington, IN (Metropolitan Statistical Area)

41    hp14060   Bloomington-Normal, IL (Metropolitan Statistical

42    hp14260   Boise City-Nampa, ID (Metropolitan Statistical

43    hp14484   Boston-Cambridge-Quincy, MA-NH (Metropolitan
                Statistical Area)

44    hp14500   Boulder, CO (Metropolitan Statistical Area)

45    hp14540   Bowling Green, KY (Metropolitan Statistical Area)

46    hp14740   Bremerton-Silverdale, WA (Metropolitan Statistical

47    hp14860   Bridgeport-Stamford-Norwalk, CT (Metropolitan
                Statistical Area)

48    hp15180   Brownsville-Harlingen, TX (Metropolitan
                Statistical Area)

49    hp15260   Brunswick, GA (Metropolitan Statistical Area)

50    hp15380   Buffalo-Niagara Falls, NY (Metropolitan
                Statistical Area)

51    hp15500   Burlington, NC (Metropolitan Statistical Area)

52    hp15540   Burlington-South Burlington, VT (Metropolitan
                Statistical Area)

53    hp15764   Cambridge-Newton-Framingham, MA (MSAD)

54    hp15804   Camden, NJ (MSAD)

55    hp15940   Canton-Massillon, OH (Metropolitan Statistical

56    hp15980   Cape Coral-Fort Myers, FL (Metropolitan
                Statistical Area)

57    hp16020   Cape Girardeau-Jackson, MO-IL (Metropolitan
                Statistical Area)

58    hp16180   Carson City, NV (Metropolitan Statistical Area)

59    hp16220   Casper, WY (Metropolitan Statistical Area)

60    hp16300   Cedar Rapids, IA (Metropolitan Statistical Area)

61    hp16580   Champaign-Urbana, IL (Metropolitan Statistical

62    hp16620   Charleston, WV (Metropolitan Statistical Area)

63    hp16700   Charleston-North Charleston-Summerville, SC
                (Metropolitan Statistical Area)

64    hp16740   Charlotte-Gastonia-Rock Hill, NC-SC (Metropolitan
                Statistical Area)

65    hp16820   Charlottesville, VA (Metropolitan Statistical

66    hp16860   Chattanooga, TN-GA (Metropolitan Statistical Area)

67    hp16940   Cheyenne, WY (Metropolitan Statistical Area)

68    hp16974   Chicago-Joliet-Naperville, IL-IN-WI (Metropolitan
                Statistical Area)

69    hp17020   Chico, CA (Metropolitan Statistical Area)

70    hp17140   Cincinnati-Middletown, OH-KY-IN (Metropolitan
                Statistical Area)

71    hp17300   Clarksville, TN-KY (Metropolitan Statistical Area)

72    hp17420   Cleveland, TN (Metropolitan Statistical Area)

73    hp17460   Cleveland-Elyria-Mentor, OH (Metropolitan
                Statistical Area)

74    hp17660   Coeur d'Alene, ID (Metropolitan Statistical Area)

75    hp17780   College Station-Bryan, TX (Metropolitan
                Statistical Area)

76    hp17820   Colorado Springs, CO (Metropolitan Statistical

77    hp17860   Columbia, MO (Metropolitan Statistical Area)

78    hp17900   Columbia, SC (Metropolitan Statistical Area)

79    hp17980   Columbus, GA-AL (Metropolitan Statistical Area)

80    hp18020   Columbus, IN (Metropolitan Statistical Area)

81    hp18140   Columbus, OH (Metropolitan Statistical Area)

82    hp18580   Corpus Christi, TX (Metropolitan Statistical Area)

83    hp18700   Corvallis, OR (Metropolitan Statistical Area)

84    hp18880   Crestview-Fort Walton Beach-Destin, FL
                (Metropolitan Statistical Area)

85    hp19060   Cumberland, MD-WV (Metropolitan Statistical Area)

86    hp19124   Dallas-Fort Worth-Arlington, TX (Metropolitan
                Statistical Area)

87    hp19140   Dalton, GA (Metropolitan Statistical Area)

88    hp19180   Danville, IL (Metropolitan Statistical Area)

89    hp19260   Danville, VA (Metropolitan Statistical Area)

90    hp19340   Davenport-Moline-Rock Island, IA-IL (Metropolitan
                Statistical Area)

91    hp19380   Dayton, OH (Metropolitan Statistical Area)

92    hp19460   Decatur, AL (Metropolitan Statistical Area)

93    hp19500   Decatur, IL (Metropolitan Statistical Area)

94    hp19660   Deltona-Daytona Beach-Ormond Beach, FL
                (Metropolitan Statistical Area)

95    hp19740   Denver-Aurora-Broomfield, CO (Metropolitan
                Statistical Area)

96    hp19780   Des Moines-West Des Moines, IA (Metropolitan
                Statistical Area)

97    hp19804   Detroit-Warren-Livonia, MI (Metropolitan
                Statistical Area)

98    hp20020   Dothan, AL (Metropolitan Statistical Area)

99    hp20100   Dover, DE (Metropolitan Statistical Area)

100   hp20220   Dubuque, IA (Metropolitan Statistical Area)

101   hp20260   Duluth, MN-WI (Metropolitan Statistical Area)

102   hp20500   Durham-Chapel Hill, NC (Metropolitan Statistical

103   hp20740   Eau Claire, WI (Metropolitan Statistical Area)

104   hp20764   Edison-New Brunswick, NJ (MSAD)

105   hp20940   El Centro, CA (Metropolitan Statistical Area)

106   hp21060   Elizabethtown, KY (Metropolitan Statistical Area)

107   hp21140   Elkhart-Goshen, IN (Metropolitan Statistical Area)

108   hp21300   Elmira, NY (Metropolitan Statistical Area)

109   hp21340   El Paso, TX (Metropolitan Statistical Area)

110   hp21500   Erie, PA (Metropolitan Statistical Area)

111   hp21660   Eugene-Springfield, OR (Metropolitan Statistical

112   hp21780   Evansville, IN-KY (Metropolitan Statistical Area)

113   hp21820   Fairbanks, AK (Metropolitan Statistical Area)

114   hp22020   Fargo, ND-MN (Metropolitan Statistical Area)

115   hp22140   Farmington, NM (Metropolitan Statistical Area)

116   hp22180   Fayetteville, NC (Metropolitan Statistical Area)

117   hp22220   Fayetteville-Springdale-Rogers, AR-MO
                (Metropolitan Statistical Area)

118   hp22380   Flagstaff, AZ (Metropolitan Statistical Area)

119   hp22420   Flint, MI (Metropolitan Statistical Area)

120   hp22500   Florence, SC (Metropolitan Statistical Area)

121   hp22520   Florence-Muscle Shoals, AL (Metropolitan
                Statistical Area)

122   hp22540   Fond du Lac, WI (Metropolitan Statistical Area)

123   hp22660   Fort Collins-Loveland, CO (Metropolitan
                Statistical Area)

124   hp22744   Fort Smith, AR-OK (Metropolitan Statistical Area)

125   hp22900   Fort Smith, AR-OK

126   hp23060   Fort Wayne, IN (Metropolitan Statistical Area)

127   hp23104   Fort Worth-Arlington, TX (MSAD)

128   hp23420   Fresno, CA (Metropolitan Statistical Area)

129   hp23460   Gadsden, AL (Metropolitan Statistical Area)

130   hp23540   Gainesville, FL (Metropolitan Statistical Area)

131   hp23580   Gainesville, GA (Metropolitan Statistical Area)

132   hp23844   Gary, IN (MSAD)

133   hp24020   Glens Falls, NY (Metropolitan Statistical Area)

134   hp24140   Goldsboro, NC (Metropolitan Statistical Area)

135   hp24220   Grand Forks, ND-MN (Metropolitan Statistical Area)

136   hp24300   Grand Junction, CO (Metropolitan Statistical Area)

137   hp24340   Grand Rapids-Wyoming, MI (Metropolitan Statistical

138   hp24500   Great Falls, MT (Metropolitan Statistical Area)

139   hp24540   Greeley, CO (Metropolitan Statistical Area)

140   hp24580   Green Bay, WI (Metropolitan Statistical Area)

141   hp24660   Greensboro-High Point, NC (Metropolitan
                Statistical Area)

142   hp24780   Greenville, NC (Metropolitan Statistical Area)

143   hp24860   Greenville-Mauldin-Easley, SC (Metropolitan
                Statistical Area)

144   hp25060   Gulfport-Biloxi, MS (Metropolitan Statistical Area)

145   hp25180   Hagerstown-Martinsburg, MD-WV (Metropolitan
                Statistical Area)

146   hp25260   Hanford-Corcoran, CA (Metropolitan Statistical

147   hp25420   Harrisburg-Carlisle, PA (Metropolitan Statistical

148   hp25500   Harrisonburg, VA (Metropolitan Statistical Area)

149   hp25540   Hartford-West Hartford-East Hartford, CT
                (Metropolitan Statistical Area)

150   hp25620   Hattiesburg, MS (Metropolitan Statistical Area)

151   hp25860   Hickory-Lenoir-Morganton, NC (Metropolitan
                Statistical Area)

152   hp25980   Hinesville-Fort Stewart, GA (Metropolitan
                Statistical Area)

153   hp26100   Holland-Grand Haven, MI (Metropolitan Statistical

154   hp26180   Honolulu, HI (Metropolitan Statistical Area)

155   hp26300   Hot Springs, AR (Metropolitan Statistical Area)

156   hp26380   Houma-Bayou Cane-Thibodaux, LA (Metropolitan
                Statistical Area)

157   hp26420   Houston-Sugar Land-Baytown, TX (Metropolitan
                Statistical Area)

158   hp26580   Huntington-Ashland, WV-KY-OH (Metropolitan
                Statistical Area)

159   hp26620   Huntsville, AL (Metropolitan Statistical Area)

160   hp26820   Idaho Falls, ID (Metropolitan Statistical Area)

161   hp26900   Indianapolis-Carmel, IN (Metropolitan Statistical

162   hp26980   Iowa City, IA (Metropolitan Statistical Area)

163   hp27060   Ithaca, NY (Metropolitan Statistical Area)

164   hp27100   Jackson, MI (Metropolitan Statistical Area)

165   hp27140   Jackson, MS (Metropolitan Statistical Area)

166   hp27180   Jackson, TN (Metropolitan Statistical Area)

167   hp27260   Jacksonville, FL (Metropolitan Statistical Area)

168   hp27340   Jacksonville, NC (Metropolitan Statistical Area)

169   hp27500   Janesville, WI (Metropolitan Statistical Area)

170   hp27620   Jefferson City, MO (Metropolitan Statistical Area)

171   hp27740   Johnson City, TN (Metropolitan Statistical Area)

172   hp27780   Johnstown, PA (Metropolitan Statistical Area)

173   hp27860   Jonesboro, AR (Metropolitan Statistical Area)

174   hp27900   Joplin, MO (Metropolitan Statistical Area)

175   hp28020   Kalamazoo-Portage, MI (Metropolitan Statistical

176   hp28100   Kankakee-Bradley, IL (Metropolitan Statistical

177   hp28140   Kansas City, MO-KS (Metropolitan Statistical Area)

178   hp28420   Kennewick-Pasco-Richland, WA (Metropolitan
                Statistical Area)

179   hp28660   Killeen-Temple-Fort Hood, TX (Metropolitan
                Statistical Area)

180   hp28700   Kingsport-Bristol-Bristol, TN-VA (Metropolitan
                Statistical Area)

181   hp28740   Kingston, NY (Metropolitan Statistical Area)

182   hp28940   Knoxville, TN (Metropolitan Statistical Area)

183   hp29020   Kokomo, IN (Metropolitan Statistical Area)

184   hp29100   La Crosse, WI-MN (Metropolitan Statistical Area)

185   hp29140   Lafayette, IN (Metropolitan Statistical Area)

186   hp29180   Lafayette, LA (Metropolitan Statistical Area)

187   hp29340   Lake Charles, LA (Metropolitan Statistical Area)

188   hp29404   Lake County-Kenosha County, IL-WI (MSAD)

189   hp29420   Lake Havasu City-Kingman, AZ (Metropolitan
                Statistical Area)

190   hp29460   Lakeland-Winter Haven, FL (Metropolitan
                Statistical Area)

191   hp29540   Lancaster, PA (Metropolitan Statistical Area)

192   hp29620   Lansing-East Lansing, MI (Metropolitan Statistical

193   hp29700   Laredo, TX (Metropolitan Statistical Area)

194   hp29740   Las Cruces, NM (Metropolitan Statistical Area)

195   hp29820   Las Vegas-Paradise, NV (Metropolitan Statistical

196   hp29940   Lawrence, KS (Metropolitan Statistical Area)

197   hp30020   Lawton, OK (Metropolitan Statistical Area)

198   hp30140   Lebanon, PA (Metropolitan Statistical Area)

199   hp30300   Lewiston, ID-WA (Metropolitan Statistical Area)

200   hp30340   Lewiston-Auburn, ME (Metropolitan Statistical

201   hp30460   Lexington-Fayette, KY (Metropolitan Statistical

202   hp30620   Lima, OH (Metropolitan Statistical Area)

203   hp30700   Lincoln, NE (Metropolitan Statistical Area)

204   hp30780   Little Rock-North Little Rock-Conway, AR
                (Metropolitan Statistical Area)

205   hp30860   Logan, UT-ID (Metropolitan Statistical Area)

206   hp30980   Longview, TX (Metropolitan Statistical Area)

207   hp31020   Longview, WA (Metropolitan Statistical Area)

208   hp31084   Los Angeles-Long Beach-Santa Ana, CA (Metropolitan
                Statistical Area)

209   hp31140   Louisville-Jefferson County, KY-IN (Metropolitan
                Statistical Area)

210   hp31180   Lubbock, TX (Metropolitan Statistical Area)

211   hp31340   Lynchburg, VA (Metropolitan Statistical Area)

212   hp31420   Macon, GA (Metropolitan Statistical Area)

213   hp31460   Madera-Chowchilla, CA (Metropolitan Statistical

214   hp31540   Madison, WI (Metropolitan Statistical Area)

215   hp31700   Manchester-Nashua, NH (Metropolitan Statistical

216   hp31740   Manhattan, KS (Metropolitan Statistical Area)

217   hp31860   Mankato-North Mankato, MN (Metropolitan
                Statistical Area)

218   hp31900   Mansfield, OH (Metropolitan Statistical Area)

219   hp32580   McAllen-Edinburg-Mission, TX (Metropolitan
                Statistical Area)

220   hp32780   Medford, OR (Metropolitan Statistical Area)

221   hp32820   Memphis, TN-MS-AR (Metropolitan Statistical Area)

222   hp32900   Merced, CA (Metropolitan Statistical Area)

223   hp33124   Miami-Fort Lauderdale-Pompano Beach, FL
                (Metropolitan Statistical Area)

224   hp33140   Michigan City-La Porte, IN (Metropolitan
                Statistical Area)

225   hp33260   Midland, TX (Metropolitan Statistical Area)

226   hp33340   Milwaukee-Waukesha-West Allis, WI (Metropolitan
                Statistical Area)

227   hp33460   Minneapolis-St. Paul-Bloomington, MN-WI
                (Metropolitan Statistical Area)

228   hp33540   Missoula, MT (Metropolitan Statistical Area)

229   hp33660   Mobile, AL (Metropolitan Statistical Area)

230   hp33700   Modesto, CA (Metropolitan Statistical Area)

231   hp33740   Monroe, LA (Metropolitan Statistical Area)

232   hp33780   Monroe, MI (Metropolitan Statistical Area)

233   hp33860   Montgomery, AL (Metropolitan Statistical Area)

234   hp34060   Morgantown, WV (Metropolitan Statistical Area)

235   hp34100   Morristown, TN (Metropolitan Statistical Area)

236   hp34580   Mount Vernon-Anacortes, WA (Metropolitan
                Statistical Area)

237   hp34620   Muncie, IN (Metropolitan Statistical Area)

238   hp34740   Muskegon-Norton Shores, MI (Metropolitan
                Statistical Area)

239   hp34820   Myrtle Beach-North Myrtle Beach-Conway, SC
                (Metropolitan Statistical Area)

240   hp34900   Napa, CA (Metropolitan Statistical Area)

241   hp34940   Naples-Marco Island, FL (Metropolitan Statistical

242   hp34980   Nashville-Davidson-Murfreesboro-Franklin, TN
                (Metropolitan Statistical Area)

243   hp35004   Nassau-Suffolk, NY (MSAD)

244   hp35084   Newark-Union, NJ-PA (MSAD)

245   hp35300   New Haven-Milford, CT (Metropolitan Statistical

246   hp35380   New Orleans-Metairie-Kenner, LA (Metropolitan
                Statistical Area)

247   hp35644   New York-Northern New Jersey-Long Island, NY-NJ-PA
                (Metropolitan Statistical Area)

248   hp35660   Niles-Benton Harbor, MI (Metropolitan Statistical

249   hp35840   North Port-Bradenton-Sarasota, FL (Metropolitan
                Statistical Area)

250   hp35980   Norwich-New London, CT (Metropolitan Statistical

251   hp36084   Oakland-Fremont-Hayward, CA (MSAD)

252   hp36100   Ocala, FL (Metropolitan Statistical Area)

253   hp36140   Ocean City, NJ (Metropolitan Statistical Area)

254   hp36220   Odessa, TX (Metropolitan Statistical Area)

255   hp36260   Ogden-Clearfield, UT (Metropolitan Statistical

256   hp36420   Oklahoma City, OK (Metropolitan Statistical Area)

257   hp36500   Olympia, WA (Metropolitan Statistical Area)

258   hp36540   Omaha-Council Bluffs, NE-IA (Metropolitan
                Statistical Area)

259   hp36740   Orlando-Kissimmee-Sanford, FL (Metropolitan
                Statistical Area)

260   hp36780   Oshkosh-Neenah, WI (Metropolitan Statistical Area)

261   hp36980   Owensboro, KY (Metropolitan Statistical Area)

262   hp37100   Oxnard-Thousand Oaks-Ventura, CA (Metropolitan
                Statistical Area)

263   hp37340   Palm Bay-Melbourne-Titusville, FL (Metropolitan
                Statistical Area)

264   hp37380   Palm Coast, FL (Metropolitan Statistical Area)

265   hp37460   Panama City-Lynn Haven-Panama City Beach, FL
                (Metropolitan Statistical Area)

266   hp37620   Parkersburg-Marietta-Vienna, WV-OH (Metropolitan
                Statistical Area)

267   hp37700   Pascagoula, MS (Metropolitan Statistical Area)

268   hp37764   Peabody, MA (MSAD)

269   hp37860   Pensacola-Ferry Pass-Brent, FL (Metropolitan
                Statistical Area)

270   hp37900   Peoria, IL (Metropolitan Statistical Area)

271   hp37964   Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
                (Metropolitan Statistical Area)

272   hp38060   Phoenix-Mesa-Glendale, AZ (Metropolitan
                Statistical Area)

273   hp38220   Pine Bluff, AR (Metropolitan Statistical Area)

274   hp38300   Pittsburgh, PA (Metropolitan Statistical Area)

275   hp38340   Pittsfield, MA (Metropolitan Statistical Area)

276   hp38540   Pocatello, ID (Metropolitan Statistical Area)

277   hp38860   Portland-South Portland-Biddeford, ME
                (Metropolitan Statistical Area)

278   hp38900   Portland-Vancouver-Hillsboro, OR-WA (Metropolitan
                Statistical Area)

279   hp38940   Port St. Lucie, FL (Metropolitan Statistical Area)

280   hp39100   Poughkeepsie-Newburgh-Middletown, NY (Metropolitan
                Statistical Area)

281   hp39140   Prescott, AZ (Metropolitan Statistical Area)

282   hp39300   Providence-New Bedford-Fall River, RI-MA
                (Metropolitan Statistical Area)

283   hp39340   Provo-Orem, UT (Metropolitan Statistical Area)

284   hp39380   Pueblo, CO (Metropolitan Statistical Area)

285   hp39460   Punta Gorda, FL (Metropolitan Statistical Area)

286   hp39540   Racine, WI (Metropolitan Statistical Area)

287   hp39580   Raleigh-Cary, NC (Metropolitan Statistical Area)

288   hp39660   Rapid City, SD (Metropolitan Statistical Area)

289   hp39740   Reading, PA (Metropolitan Statistical Area)

290   hp39820   Redding, CA (Metropolitan Statistical Area)

291   hp39900   Reno-Sparks, NV (Metropolitan Statistical Area)

292   hp40060   Richmond, VA (Metropolitan Statistical Area)

293   hp40140   Riverside-San Bernardino-Ontario, CA (Metropolitan
                Statistical Area)

294   hp40220   Roanoke, VA (Metropolitan Statistical Area)

295   hp40340   Rochester, MN (Metropolitan Statistical Area)

296   hp40380   Rochester, NY (Metropolitan Statistical Area)

297   hp40420   Rockford, IL (Metropolitan Statistical Area)

298   hp40484   Rockingham County-Strafford County, NH (MSAD)

299   hp40580   Rocky Mount, NC (Metropolitan Statistical Area)

300   hp40660   Rome, GA (Metropolitan Statistical Area)

301   hp40900   Sacramento-Arden-Arcade-Roseville, CA
                (Metropolitan Statistical Area)

302   hp40980   Saginaw-Saginaw Township North, MI (Metropolitan
                Statistical Area)

303   hp41060   St. Cloud, MN (Metropolitan Statistical Area)

304   hp41100   St. George, UT (Metropolitan Statistical Area)

305   hp41140   St. Joseph, MO-KS (Metropolitan Statistical Area)

306   hp41180   St. Louis, MO-IL (Metropolitan Statistical Area)

307   hp41420   Salem, OR (Metropolitan Statistical Area)

308   hp41500   Salinas, CA (Metropolitan Statistical Area)

309   hp41540   Salisbury, MD (Metropolitan Statistical Area)

310   hp41620   Salt Lake City, UT (Metropolitan Statistical Area)

311   hp41660   San Angelo, TX (Metropolitan Statistical Area)

312   hp41700   San Antonio-New Braunfels, TX (Metropolitan
                Statistical Area)

313   hp41740   San Diego-Carlsbad-San Marcos, CA (Metropolitan
                Statistical Area)

314   hp41780   Sandusky, OH (Metropolitan Statistical Area)

315   hp41884   San Francisco-Oakland-Fremont, CA (Metropolitan
                Statistical Area)

316   hp41940   San Jose-Sunnyvale-Santa Clara, CA (Metropolitan
                Statistical Area)

317   hp42020   San Luis Obispo-Paso Robles, CA (Metropolitan
                Statistical Area)

318   hp42044   Santa Ana-Anaheim-Irvine, CA (MSAD)

319   hp42060   Santa Barbara-Santa Maria-Goleta, CA (Metropolitan
                Statistical Area)

320   hp42100   Santa Cruz-Watsonville, CA (Metropolitan
                Statistical Area)

321   hp42140   Santa Fe, NM (Metropolitan Statistical Area)

322   hp42220   Santa Rosa-Petaluma, CA (Metropolitan Statistical

323   hp42340   Savannah, GA (Metropolitan Statistical Area)

324   hp42540   Scranton-Wilkes-Barre, PA (Metropolitan
                Statistical Area)

325   hp42644   Seattle-Tacoma-Bellevue, WA (Metropolitan
                Statistical Area)

326   hp42680   Sebastian-Vero Beach, FL (Metropolitan Statistical

327   hp43100   Sheboygan, WI (Metropolitan Statistical Area)

328   hp43300   Sherman-Denison, TX (Metropolitan Statistical

329   hp43340   Shreveport-Bossier City, LA (Metropolitan
                Statistical Area)

330   hp43580   Sioux City, IA-NE-SD (Metropolitan Statistical

331   hp43620   Sioux Falls, SD (Metropolitan Statistical Area)

332   hp43780   South Bend-Mishawaka, IN-MI (Metropolitan
                Statistical Area)

333   hp43900   Spartanburg, SC (Metropolitan Statistical Area)

334   hp44060   Spokane, WA (Metropolitan Statistical Area)

335   hp44100   Springfield, IL (Metropolitan Statistical Area)

336   hp44140   Springfield, MA (Metropolitan Statistical Area)

337   hp44180   Springfield, MO (Metropolitan Statistical Area)

338   hp44220   Springfield, OH (Metropolitan Statistical Area)

339   hp44300   State College, PA (Metropolitan Statistical Area)

340   hp44600   Steubenville-Weirton, OH-WV (Metropolitan
                Statistical Area)

341   hp44700   Stockton, CA (Metropolitan Statistical Area)

342   hp44940   Sumter, SC (Metropolitan Statistical Area)

343   hp45060   Syracuse, NY (Metropolitan Statistical Area)

344   hp45104   Tacoma, WA (MSAD)

345   hp45220   Tallahassee, FL (Metropolitan Statistical Area)

346   hp45300   Tampa-St. Petersburg-Clearwater,
                FL (Metropolitan Statistical Area)

347   hp45460   Terre Haute, IN (Metropolitan Statistical Area)

348   hp45500   Texarkana, TX-Texarkana, AR (Metropolitan
                Statistical Area)

349   hp45780   Toledo, OH (Metropolitan Statistical Area)

350   hp45820   Topeka, KS (Metropolitan Statistical Area)

351   hp45940   Trenton-Ewing, NJ (Metropolitan Statistical Area)

352   hp46060   Tucson, AZ (Metropolitan Statistical Area)

353   hp46140   Tulsa, OK (Metropolitan Statistical Area)

354   hp46220   Tuscaloosa, AL (Metropolitan Statistical Area)

355   hp46340   Tyler, TX (Metropolitan Statistical Area)

356   hp46540   Utica-Rome, NY (Metropolitan Statistical Area)

357   hp46660   Valdosta, GA (Metropolitan Statistical Area)

358   hp46700   Vallejo-Fairfield, CA (Metropolitan Statistical

359   hp47020   Victoria, TX (Metropolitan Statistical Area)

360   hp47220   Vineland-Millville-Bridgeton,
                NJ (Metropolitan Statistical Area)

361   hp47260   Virginia Beach-Norfolk-Newport News,
                VA-NC (Metropolitan Statistical Area)

362   hp47300   Visalia-Porterville, CA (Metropolitan Statistical

363   hp47380   Waco, TX (Metropolitan Statistical Area)

364   hp47580   Warner Robins, GA (Metropolitan Statistical Area)

365   hp47644   Warren-Troy-Farmington Hills, MI (MSAD)

366   hp47894   Washington-Arlington-Alexandria, DC-VA-MD-WV

367   hp47940   Waterloo-Cedar Falls, IA (Metropolitan Statistical

368   hp48140   Wausau, WI (Metropolitan Statistical Area)

369   hp48300   Wenatchee-East Wenatchee, WA (Metropolitan
                Statistical Area)

370   hp48424   West Palm Beach-Boca Raton-Boynton Beach, FL

371   hp48540   Wheeling, WV-OH (Metropolitan Statistical Area)

372   hp48620   Wichita, KS (Metropolitan Statistical Area)

373   hp48660   Wichita Falls, TX (Metropolitan Statistical Area)

374   hp48700   Williamsport, PA (Metropolitan Statistical Area)

375   hp48864   Wilmington, DE-MD-NJ (MSAD)

376   hp48900   Wilmington, NC (Metropolitan Statistical Area)

377   hp49020   Winchester, VA-WV (Metropolitan Statistical Area)

378   hp49180   Winston-Salem, NC (Metropolitan Statistical Area)

379   hp49340   Worcester, MA (Metropolitan Statistical Area)

380   hp49420   Yakima, WA (Metropolitan Statistical Area)

381   hp49620   York-Hanover, PA (Metropolitan Statistical Area)

382   hp49660   Youngstown-Warren-Boardman,
                OH-PA (Metropolitan Statistical Area)

383   hp49700   Yuba City, CA (Metropolitan Statistical Area)

384   hp49740   Yuma, AZ (Metropolitan Statistical Area)

Table 1 Summary of the results of the tests for unit roots

Test                         Statistic   p-value   Decision

Multivariate homogeneous     -29.0945    0.0000    Reject [H.sub.o] of
  Dickey-Fuller (MHDF)                               a unit root
Levin Lin Chu (LLC)          -3.6540     0.0000    Reject [H.sub.o] of
                                                     a unit root
In, Pesaran and Shin (IPS)   -3.5929     0.0000    Reject [H.sub.o] of
                                                     a unit root

Table 2 Summary of the results of the panel and
multivariate test for stationarity

Test                 Statistic   p-value   Decision

MKPSS                46668       0.000     Reject [H.sub.o] of
Bai-Ng Panic test    1611.134    0.144     Cannot Reject [H.sub.o]
  for stationarity                           of stationarity
Hadri LM Test        15.794725   0.0000    Reject the [H.sub.o] of
CBL Test             77.181      0.000     Reject the [H.sub.o] of
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Author:Montasser, Ghassen El-; Gupta, Rangan; Smithers, Devon
Publication:Economics, Management, and Financial Markets
Article Type:Report
Geographic Code:1USA
Date:Dec 1, 2016
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