The Effect of the Existing Housing Stock on Neighborhood Growth

4 Dynamics of Ethnic Neighborhoods

4.2 The Effect of the Existing Housing Stock on Neighborhood Growth

Generally, the growth of any particular neighborhood should depend on the characteristics of the existing residents and the characteristics of potential new residents. For example, if there is a new wave of high skilled Chinese coming to the New York metro area then we might expect larger population growth in the richer Chinese neighborhoods than in Manhattan’s Chinatown. Unfortunately, we do not have individual level data and therefore cannot assess why specific neighborhoods grew and others shrank. Instead, we ex-amine whether pre-existing characteristics of a city affect growth. The previous section documented that ethnic neighborhoods primarily grow over time by expanding geographically into adjacent tracts. In Table 2 we found significant housing differences between ethnic neighborhoods and other locations in the city, especially in terms of rental housing and housing age. Further, in Table 4 we found systematic changes in these housing variables as existing neighborhoods expand. Since it seems unlikely that the initial migrants into a neighborhood chose the location based on its capacity for future migrants, it’s possible that the charac-teristics of the pre-existing housing stock could affect neighborhood expansion. Therefore, in this section we look at how the age of the housing stock age and the supply of rental housing affect neighborhood growth.

Our data classifies the housing stock into the percent built less than five years earlier, 5-10 years earlier, 10-20 years, 20-30 years, and more than 30 years earlier. We define the mean housing age of a tract as the average of these ranges weighted by percent, using the median for the first four categories and 30 years for

25The extensive margin share and intensive margin share must sum to one, thus the intensive margin shares are negative on average for Canada, Cuba, and Italy (-0.03,-0.92,-3.37).

Table 7: Ethnic Neighborhoods and Growth

(1) (2) (3) (4) (5) (6) (7) (8)

Native Native Canada Canada China China India India

ethtract -269.64*** -101.18*** -29.23*** -26.91*** 27.30*** 11.43*** 15.57*** 5.41***

(24.46) (36.49) (0.44) (0.46) (1.77) (1.32) (1.44) (1.22)

adjacent to ethtract -110.65*** -66.81*** 1.34*** 1.36*** 11.77*** 10.46*** 11.74*** 10.12***

(12.04) (16.46) (0.12) (0.14) (0.30) (0.29) (0.31) (0.29)

neighborhood pop -42.78*** -5.86*** 2.25*** 4.84***

(9.02) (1.20) (0.16) (0.53)

adjacent neighborhood pop -12.33*** -0.10 0.52*** 1.65***

(3.56) (0.44) (0.06) (0.17)

Observations 244268 244268 244292 244292 244292 244292 192238 192238

Neigh. Pop Mean 3.94 0.40 7.29 2.18

Adj. Neigh. Pop Mean 3.54 0.21 1.94 0.94

(a) Selected Groups

(1) (2) (3) (4) (5) (6) (7) (8)

Italy Italy Jamaica Jamaica Mexico Mexico Vietnam Vietnam

ethtract -43.27*** -38.35*** 0.65 -3.75*** 112.18*** 124.92*** -1.20 -10.50***

(0.73) (0.69) (1.20) (1.06) (2.65) (3.01) (1.45) (1.10)

adjacent to ethtract -0.80*** -0.77*** 6.37*** 5.20*** 45.66*** 41.91*** 9.79*** 8.34***

(0.13) (0.13) (0.28) (0.24) (1.01) (0.98) (0.31) (0.29)

neighborhood pop -1.44*** 0.71*** -0.23*** 2.71***

(0.14) (0.13) (0.02) (0.25)

adjacent neighborhood pop -0.08 0.84*** 0.21*** 1.09***

(0.06) (0.10) (0.02) (0.13)

Observations 244292 244292 192238 192238 244292 244292 192238 192238

Neigh. Pop Mean 3.60 6.84 56.11 3.56

Adj. Neigh. Pop Mean 1.17 1.41 17.62 1.16

(b) Selected Groups

Note: Dependent variable in each regression is future difference in ethnic population, ethpop_t+1−ethpop_t. All regressions include CBSA-by-year fixed effects. The variable “ethtract” indicates the status of the tract in the current period while “adjacent to ethtract” indicates the tract is next to an ethnic tract (but not one itself). The “neighborhood pop” is the population of the neighborhood in the current period for an ethnic tract (zero for non-ethnic tracts). The adjacent neighborhood population measures the population for non-ethnic tracts that border a neighborhood in the current period (zero for tracts not bordering neighborhoods).

Both population variables are measured in thousands; the last two rows of each table show the means over all non-zero cases.

Standard errors are clustered at the tract level.

Table 8: Share of Neighborhood Growth in New Ethnic Tracts

(1) (2) (3) (4) (5) (6) (7) (8)

Canada China Cuba India Italy Jamaica Mexico Vietnam

Ext. Share 1.03 0.80 1.92 0.74 4.37 0.88 0.61 0.87

(0.04) (0.10) (0.44) (0.05) (2.65) (0.14) (0.03) (0.05)

N 297 559 159 568 152 184 801 433

Note: We partition a neighborhood’s tracts in decadet into those that were already ethnic tracts in the previous decade (intensive margin) and those that became an ethnic tract int (extensive margin). We sum the ethnic population change between periods among all neighborhood tracts and then calculate the share of the neighborhood change in extensive margin tracts (“Ext. Share”).

We use all years of data but limit the sample to neighborhoods with at least five tracts and an increase in total population. We then calculate the mean over all neighborhoods—each neighborhood is one observation—and put the standard error in parentheses.

For some groups, intensive margin tracts lose population on average, and thus the extensive share is larger than one (see text).

the last category. The rental percentage is simply the number of rental housing units divided by the total count of housing in the tract. Then, for a given groupg, we first select all of the non-ethnic tracts that border an ethnic neighborhood (including neighborhoods of a single ethnic tract), across all cities and all years²⁶. Next we calculate the normalized difference between a neighborhood housing characteristic—age or rental percentage—and an adjacent tract. The neighborhood housing characteristic is a weighted average of the values in its component tracts. Lethdi f f_jt be the normalized difference in age or rental percentage between tract jand adjacent neighborhooda(j,t)in citycand yeart:

hdi f f_jt =hvaljt−hval_a(_j,t)

sd_ct(hval) , wherehval_a(_j,t)=

∑

l∈a(j,t)

n_lt∗hval_lt

∑

l∈a(j,t)

n_lt (11)

In the above equation, hval_jt is the housing age or rental percentage in tract j in period t. The variable sdct(hval)is the standard deviation of age or rental percentage across all tracts in citycand yeart.

We then examine whether the housing differences of adjacent tracts affect groupgpopulation growth in the next decade. For ease of interpretation, we use growth as the dependent variable, rather than our binary indicator for neighborhood status; patterns would generally be the same if we were to use neighborhood status. We control for the tract-level ethnic population, total population, median housing price, and median housing rent, since each of these could be important independent of the housing differences. Lastly, we also include adjacent neighborhood fixed effects, µ_a(_j,t), and thus we are comparing growth in adjacent tracts next to the same neighborhood. Our specification is:

△_(t+1)n_j=β₁hdi f f_jt+β₂n_j,t+β₃tot pop_j,t+β₄hprice_jt+β₅hrent_jt+µ_a(_j,t)+ε_jt (12) We first examine the effect of housing stock age on the growth in adjacent tracts. Unlike the rental percentage, the durability of housing implies that the age of the housing stock mostly depends on supply decisions made years earlier (Glaeser and Gyourko 2005). Nonetheless, it’s still possible that differences in the current housing age of adjacent tracts reflect trends in ethnic population growth. For example, the housing supply decisions of developers or the maintenance decisions of current housing owners could reflect expectations of neighborhood growth, thus endogenously changing the housing age in adjacent tracts. To mitigate these issues we replace the current period’s housing age difference with that calculated using the housing stock from two decades earlier, meaning we replace the hval_t terms in equation 11 with lagged values,hval_t−2. The identifying assumption for estimatingβ₁is that the difference in the age of the neigh-borhood housing stock and adjacent tracts from twenty years earlier is uncorrelated with anything affecting

26If a tract is adjacent to two different neighborhoods then we assign it to the neighborhood with the larger groupgpopulation.

ethnic population growth in adjacent tracts twenty years later, conditional on the control variables from twenty years later. We believe this assumption is reasonable because the adjacent tracts were not next to a neighborhood twenty years earlier, and in fact, in many cases the neighborhood did not even exist (not a single tract was an ethnic tract twenty years earlier).

In Table 9 we show the results from estimating equation 12, using two decade lagged differences. We report the mean of theabsolutedifference in housing age in row three since positive and negative differences offset each other (in levels the values are 0.1, 0.21, 0.03, 0.26, 0.03, 0.02, -0.18, 0.04). For every group we find that as the age of the housing stock in adjacent tracts increases, relative to the age of the neighborhood housing stock, ethnic population growth decreases (the coefficient for Cuba is insignificant). Consistent with the results in Table 4, ethnic neighborhoods are expanding into younger housing stock. However, the magnitude of these effects is modest. Since the difference variable is measured in standard deviations of the city-year housing age, a one standard deviation increase in age from the neighborhood mean lowers the growth in tracts adjacent to Chinese neighborhoods by 4.5 people. The average growth in these tracts is 18.4 people (fourth row below table), and thus the effect is about 25% of mean growth. We find similarly sized effects for most of the other groups, ranging between 10 and 50 percent of mean growth. Nonetheless, these results suggest that the pre-existing housing stock can indeed have an effect on neighborhood growth.

One potential mechanism for this effect is that the age of the housing stock is related to the supply of rental housing. Two papers by Rosenthal provide persuasive evidence of housing stock “filtering,” a process where the income of residents decreases as a house ages (depreciates) and older housing transitions from owner-occupied to rental (Rosenthal 2008, Rosenthal 2014). We now estimate 12 using differences in the rental percentage. However, the current rental percentage is much more likely to be endogenous than the housing stock age since the same stock can be converted from owner-occupied to rental. Therefore we instrument for the difference in rental percentage between a neighborhood and adjacent tracts using the predicted rental percentage difference, based on the housing stock age from two decades earlier. We first estimate the rental percentage in each tract in period t as a function of the housing stock age in t−k and city-year fixed effects,µct:

rent pct_jt=δ₁hAge5_j,t−k+δ2hAge5.10j,t−k+δ₃hAge10.20j,t−k+δ4hAge20.30j,t−k+δ5hAge30_j,t−k+µ_ct+ε_{c jt} (13) Theδ coefficients are the same for all cities and years, and thus one interpretation of this equation is that we are predicting the percentage rental using lagged housing age variables and a national filtering process.

We show the results from estimating specification 13 in Appendix Table 15, using a one decade lag in the first column and a two decade lag in the second column. Rental percentage and housing age percentages are measured as fractions, 0 to 1. The first coefficient in column 1 shows that a ten percentage point increase in the percent of housing built within five years is associated with a 1.2 percentage point decrease in the rental percentage, one decade later. On the other hand, a ten percentage point increase in the share of housing older than thirty years is associated with a 3.1 percentage point increase in the rental percentage. The two decade lag specification has similar coefficients and we use this specification in creating our instrument. We replace hval_jt andhval_lt in equation 11 with the predicted values,rent pctˆ _jt, from equation 13 with two lags. Note that we are using the actual ethnic population weights when calculating the predicted neighborhood rental percentage.

In panel A of Table 10 we first show OLS results from estimating specification 12 with the period t rental percentage. For most groups, increasing the rental percentage of an adjacent tract, relative to the neighborhood, is associated with lower growth. An exception are Indian neighborhoods, for which the coefficient is positive.

In panel B of Table 10 we show the results when instrumenting housing values differences using the prediction from housing age variables twenty years earlier. We cluster standard errors by adjacent

neigh-Table 9: Neighborhood Expansion and Age of Housing Stock: OLS

(1) (2) (3) (4) (5) (6) (7) (8)

Canada China Cuba India Italy Jamaica Mexico Vietnam

2-lag housing age diff -1.06*** -4.51*** -6.23 -6.96*** -1.11*** -6.27*** -9.27*** -4.39***

(0.26) (0.65) (4.10) (0.74) (0.27) (2.22) (3.05) (0.69)

ethnic pop. -0.67*** -0.10** 0.02 -0.00 -0.42*** -0.18** 0.02 -0.13**

(0.02) (0.04) (0.08) (0.08) (0.02) (0.08) (0.05) (0.07)

total pop. 1.82*** 0.89** -0.83 0.83** 0.91*** 0.70 11.37*** 1.46***

(0.17) (0.35) (1.26) (0.41) (0.13) (0.62) (1.54) (0.38) median price 2.15*** 3.11*** -2.64 3.14*** 1.29*** -4.44*** -40.31*** -1.94**

(0.35) (1.07) (1.70) (1.08) (0.32) (0.84) (3.70) (0.92)

median rent 0.58*** 0.50 1.06 0.94*** 0.13 0.68** -0.04 0.73**

(0.13) (0.32) (0.88) (0.34) (0.11) (0.34) (1.13) (0.33)

Observations 17266 18135 9053 20740 12613 8709 19202 17475

Clusters 2522 2202 1234 2570 1702 1132 2243 2216

Abs. H. Diff. Mean 0.64 0.69 0.55 0.71 0.60 0.56 0.67 0.64

D. Var. Mean 4.1 18.4 10.8 21.8 -0.5 12.1 90.8 15.0

Nb. Ct. Mean 3.4 10.9 8.9 9.2 6.4 11.0 22.0 7.7

Note: Dependent variable in each regression is future difference in ethnic population,ethpop_t+1−ethpop_t. The variable “2-lag housing age diff” is the difference between the average age of the housing stock in a tract and in the adjacent ethnic neighborhood, measured in standard deviations of the housing age in the city, see equation 11 in text. We calculate this variable using data from two decades earlier, often before the neighborhood existed. The variable ethnic pop. is measured in individual people, the total pop. is measured in thousands of people, median housing price in hundred thousands of dollars, and median rent in hundred dollars. The sample is limited to non-ethnic tracts adjacent to ethnic neighborhoods. All specifications include adjacent neighborhood fixed effects and standard errors are clustered at adjacent neighborhood level. The Nb. Ct. Mean statistic is the average number of tracts in an adjacent neighborhood.

borhood, and therefore report the Kleibergen Paap F statistic (KP F) in the third row below the table as a measure of instrument strength. We show the first stage results for each specification in Appendix Table 16.

In Table 10 we again report the mean of the absolute difference in rental percentage in the third row below the estimates; the values in levels are 0.1, -0.17, -0.1, -0.27, 0.09, -0.17, -0.54, -0.16. For all groups, the coefficients are negative and much larger in magnitude than in panel A. Further, the coefficients are signifi-cantly different from zero, with the exception of Cuban neighborhoods; this specification also has a lower F statistic. A one standard deviation increase in the rental percentage difference lowers the ethnic population growth in tracts adjacent to Chinese neighborhoods by 53 people, which is nearly three times the average growth in these tracts. Other groups also have large coefficients, ranging from 1.5 to 3 times the mean pop-ulation increase. Tracts next to Italian neighborhoods are losing Italian poppop-ulation on average, but lose this population much faster as the rental percentage difference increases.

One potential concern with our identification strategy is that despite our use of two decade lagged hous-ing, some neighborhoods may have already started to form and affect the rental percentage in the area, even if the neighborhood boundaries were quite different in the earlier period. To try and address this we now limit our sample to tracts adjacent to neighborhoods formed less than twenty years earlier. These are cases where we are predicting the future rental percentage with the housing age from a period where not a single tract of the future neighborhood was an ethnic tract. We show the results from estimating our two stage least squares specification with this sample in panel A of Table 11. As can be seen in the observation count and mean neighborhood count rows below the table, this restriction reduces the sample by about a third for most groups and significantly decreases the neighborhood size by excluding older neighborhoods. Perhaps as a result, the instrument is now much weaker for Chinese, Cuban, Italian, and Jamaican neighborhoods.

Nonetheless, the coefficients are all still negative and roughly in line with the results in panel B of Table 10, although the standard errors are much larger.

A remaining concern is that there may be some unobserved effect that is persistent over time and cor-related with both differences in the housing stock age and differences in ethnic population growth. For example, two adjacent tracts may be in different school districts, which could affect both demographic and housing stock characteristics of the tracts (Bayer, Ferreira, and McMillan 2007). While school district boundaries alone are unlikely to explain our results—we test a large number of neighborhoods that change over time and have different component tracts for different groups—time persistent differences in adjacent tracts could result from many different phenomena. To try and address this we limit the sample to only those neighborhood adjacent tracts that had ethnic tract status in at least one period. This strategy uses variation in housing differences only among tracts in ethnic neighborhoods (at some point), and thus removes the concern that some tracts never have large ethnic populations due to unobserved fixed characteristics. For example, consider two tracts j andl, that are both adjacent to neighborhoodbin periodt. Tract jbecomes an ethnic tract int+1 whilel becomes an ethnic tract int+2, thus we can test whether differences in the rental percentage in periodt help explain why j grew faster than l betweent andt+1. Not surprisingly, this restriction greatly reduces the sample by roughly 75% and significantly increases the average neighbor-hood size and average population growth rate. Further, the instrument is weak for all groups except Italians, Mexicans, and possibly Canadians. The coefficients again are all negative and generally much larger than in earlier specifications, although as a fraction of the dependent variable mean they are actually smaller.

We interpret the results in Tables 9, 10, and 11 as strong evidence that the pre-existing housing stock can affect the expansion of ethnic neighborhoods, but as only suggestive evidence that one channel for this effect is the rental percentage. Given our results, it seems unlikely that future ethnic neighborhoods could be influencing housing along neighborhood boundaries that don’t yet exist, but we cannot rule out that differences in the housing stock are associated with other factors that could affect ethnic population growth. Therefore, from the perspective of an urban planner or real estate analyst, the existing housing stock

Table 10: Neighborhood Expansion and Rental Housing Percentage

(1) (2) (3) (4) (5) (6) (7) (8)

Canada China Cuba India Italy Jamaica Mexico Vietnam

rental pct. diff -0.55** 0.67 -4.40 5.35*** -1.32*** -0.98 -3.66 -3.47***

(0.26) (0.65) (3.27) (0.88) (0.23) (0.69) (2.97) (0.72) ethnic pop. -0.67*** -0.09** 0.02 0.01 -0.43*** -0.17** 0.02 -0.12*

(0.02) (0.04) (0.08) (0.07) (0.02) (0.08) (0.05) (0.07) total pop. 1.86*** 1.04*** -0.83 1.13*** 0.97*** 0.78 11.80*** 1.58***

(0.17) (0.35) (1.29) (0.41) (0.13) (0.61) (1.55) (0.37) median price 2.07*** 3.37*** -3.08 4.91*** 1.06*** -4.22*** -40.22*** -2.04**

(0.36) (1.11) (2.00) (1.16) (0.32) (0.78) (3.68) (0.91)

median rent 0.56*** 0.86*** 0.57 2.17*** -0.02 0.81** -0.01 0.33

(0.13) (0.33) (0.57) (0.37) (0.12) (0.40) (1.09) (0.34)

Observations 17266 18135 9053 20740 12613 8709 19202 17475

Clusters 2522 2202 1234 2570 1702 1132 2243 2216

Abs. R. Diff Mean 0.75 0.82 0.74 0.83 0.71 0.78 0.89 0.78

D. Var. Mean 4.06 18.36 10.83 21.78 -0.52 12.12 90.77 14.99

Nb. Ct. Mean 3.4 10.9 8.9 9.2 6.4 11.0 22.0 7.7

(a) OLS

(1) (2) (3) (4) (5) (6) (7) (8)

Canada China Cuba India Italy Jamaica Mexico Vietnam

rental pct. diff -8.47** -53.09*** -88.40 -59.77*** -11.86*** -38.87** -117.73*** -52.57***

(3.43) (14.08) (55.64) (10.62) (2.69) (18.30) (21.10) (10.89)

ethnic pop. -0.69*** -0.02 0.01 0.29*** -0.48*** -0.02 0.05 -0.06

(0.02) (0.05) (0.09) (0.09) (0.03) (0.11) (0.05) (0.07)

total pop. 1.92*** 0.66 -2.71 -0.20 1.07*** 0.06 8.79*** 1.04**

(0.17) (0.44) (2.44) (0.54) (0.14) (0.89) (1.66) (0.45)

median price 0.69 -6.43** -16.44 -10.04*** -1.28* -10.49*** -49.14*** -11.97***

(0.66) (2.89) (10.16) (2.74) (0.68) (3.43) (4.67) (2.56)

median rent -0.51 -7.80*** -14.57 -7.65*** -1.55*** -6.00* -21.95*** -9.57***

(0.49) (2.37) (9.20) (1.67) (0.41) (3.14) (4.10) (2.21)

Observations 17266 18135 9053 20740 12613 8709 19202 17475

Clusters 2522 2202 1234 2570 1702 1132 2243 2216

Abs. R. Diff Mean 0.75 0.82 0.74 0.83 0.71 0.78 0.89 0.78

KP-F Stat 44.26 22.34 11.54 47.52 57.48 21.45 136.44 44.20

D. Var. Mean 4.06 18.36 10.83 21.78 -0.52 12.12 90.77 14.99

Nb. Ct. Mean 3.4 10.9 8.9 9.2 6.4 11.0 22.0 7.7

(b) 2SLS

Note: Dependent variable in each regression is future difference in ethnic population,ethpop_t+1−ethpop_t. The variable of interest is the difference in the percentage of the housing stock for rent in a tract versus the adjacent ethnic neighborhood, measured in standard deviations of the tract-level rental percentage in that city-year. In the top table we show OLS results. In the bottom table we instrument the rental percentage difference with the predicted difference, using the age of the housing stock from two decades earlier and a national filtering process, see equation 13. The tract-level control variables are ethnic population (measured in individual people), total population (thousands of people), median housing price (hundred thousands of dollars), and median housing rent (hundreds of dollars). The sample is limited to non-ethnic tracts adjacent to ethnic neighborhoods. All specifications include adjacent neighborhood fixed effects and standard errors are clustered at adjacent neighborhood level.

is likely to be a useful variable in predicting how neighborhoods expand. The availability of rental housing is a plausible mechanism, but to understand the exact mechanism will probably require detailed individual level data on neighborhood residents and housing choice.

5 Conclusion

In this paper we derived a new statistical definition of an ethnic neighborhood from a choice model and using the native population as a reference distribution. We then applied our definition to census tract data in the United States from 1970-2010, across many immigrant groups. We found that ethnic neighborhoods capture a large percentage of the ethnic population in each city, that these neighborhoods are systematically different from other locations where the ethnic population lives, and that they have a spatial structure similar to a city sub-center, with a population density gradient declining from a central point. Most of these neighborhoods disappear after a couple decades and the number of large neighborhoods for many European groups has declined precipitously. However, for many Asian and Latin American groups, ethnic neighborhoods are growing rapidly. Ethnic neighborhoods grow primarily through spatial expansion into adjacent locations, and this expansion can be partly predicted with the pre-existing housing stock.

The results from this paper raise an intriguing question: can government policy or planning have a causal effect on neighborhood formation and growth? Some of the patterns we have found in ethnic neighborhoods, including the high percentage of people commuting without a car, the percentage of renters, and the age of the

Im Dokument The Structure and Growth of Ethnic Neighborhoods (Seite 30-42)