Extension 29

We extend the analysis in two ways. First, we include time-variant lake specific water quality measures to check if improved water quality is not actually driving the rise in lake recreation in 2009, which we have attributed to unemployment during the recession. Second, we investigate the lake recreation during recession by exploiting cross county-cross period variation in county unemployment rate.

7.1 Water quality, employment status, and lake recreation behavior during recession Water quality varies across lakes and time periods. Water quality can be a major determinant of Iowans’ choice of lake for outdoor recreation [Egan et al. 2009]. Detail water quality data on 131 major lakes in Iowa are available from Iowa State University’s limnology lab.²⁷ The water quality data is well coordinated as well as temporally and spatially matched with

27 http://www.card.iastate.edu/lakes/ (last accessed on June 30^th, 2015).

the recreation data that we are utilizing for this analysis. In general, the measures reveal that average water quality has improved in 2009 compared to 2005 across the lakes in our sample.

The objective of this extension is to examine whether water quality improvement is playing a confounding role and biasing our estimates reported in the previous sections. Following Egan et al. (2009), we have considered six water quality indicators: secchi depth, total nitrogen, total

phosphorus, inorganic suspended solid, volatile suspended solid, and chlorophyll. Among all of these indicators, secchi depth, a measure of water clarity, is the most perceptible and direct water quality indicator to the recreationists.

We estimate the following specification including six water quality indicators 𝑇𝑟𝑖𝑝_𝑖𝑗𝑡 = 𝛼₀∑⁶_𝑘=1𝑊𝑄_𝑘𝑗𝑡 + 𝛼₁∗ 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡_𝑖𝑗+ 𝛼₂∗ 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡_𝑖𝑗 ∗ 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛 + 𝛼₃∗ 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛 + 𝛼₄𝑿 +∈_𝑖𝑗𝑡^,

where i =1,2,…,971 denotes households, j=1,2,3,…131 denotes lakes, and t=2005,2009 denotes time periods. Recession is an indicator variable that takes a value of 1 in 2009, and the vector 𝑿 contains a set of demographics. For each of the three treatment groups, we estimated the above specification twice: (a) including the demographics, and (b) including individual fixed effects. In the fixed effects model 𝛼₁ will not be identified. However, the statistical significance of 𝛼₂ will reveal if unemployment effect during recession is robust to the water quality improvement.

The results are reported in Table 13. In general, water quality indicators turn out to be small in magnitude and always statistically insignificant. The key coefficient, interaction between treatment status and recession indicator, is consistently positive and statistically significant for combined treatment group’s participation and count of total trips. After controlling for the water quality changes, the treatment group consisting of unemployed

households seems to participate more and take more trips during the recession year compared to

their counterfactual case. The retired group does not exhibit different participation behavior during the recession year. However, their frequency of trips dropped during the recession.

Overall, our findings in the previous section that unemployment during recession lead a

household to participate more in lake recreation is not altered when we address the water quality improvement in lakes in Iowa.²⁸

7.2 County unemployment and recreation

In this section, we adopt an alternative approach to investigating the impact of the recession on participation in lake recreation. Although Iowa lake project surveys were conducted in 2002-2005 and 2009, in the matching exercises we utilized surveys from the recession year and the nearest pre-recession year including individuals who provide complete information on employment status. In all of the survey rounds, many respondents did not respond to the questions on employment status and household income.²⁹ One way, we may still use their information is by using some proxy for their employment/economic status. Economics literature investigating the relationship between individual health behavior and recession have utilized group variable such as state level unemployment rate to represent business cycle [Ruhm (2000, 2005), Dehejia and Lleras-Muney (2004)].

We utilize a panel spanning the years 2002-2005 and 2009 to estimate a fixed effect model on how county level unemployment rate affect individual participation in lake recreation. In our setting, county unemployment rate is a good proxy for local economic condition. Since one cannot influence economic activities at the county level, household’s trip participation is less

28 The estimates obtained in this subsection are not directly comparable with the matching estimates since the later are more conservative. However, the smaller magnitude of water quality coefficients from a relatively less-restricted model imply that improved water quality is not playing a confounding role in our case.

29 In 2009, 853 individuals were silent about employment status although they provided relevant employment information in year 2005.

likely to affect county unemployment rate. The fixed effect model takes care of all individual specific time-invariant unobservable.³⁰ In the lake surveys, 3040 observations from year 2009 have a matching observation in at least one of the year 2002-2005. Out of them, 1498 individual responded across all the years 2002-2009 to form a balanced panel. We estimate the following specification

𝑇𝑟𝑖𝑝_𝑖𝑐𝑡 = (𝑅𝑒𝑐𝑌𝑟)𝛽₁+ (𝑈𝑛_𝑐𝑡)𝛽₂ + (𝑅𝑒𝑐𝑌𝑟 ∗ 𝑈𝑛_𝑐𝑡)𝛽₃+ 𝛾_𝑐𝑡+ 𝛾_𝑖+ 𝜖_𝑖𝑐𝑡,

where 𝑇𝑟𝑖𝑝_𝑖𝑐𝑡 is a binary variable indicating whether individual “i” in county “c” takes any lake trip in year “t” or not, 𝑈𝑛_𝑐𝑡 indicates unemployment in county “c” in year “t”, 𝑅𝑒𝑐𝑌𝑟 is an indicator variable assuming a value of 1 if year “t” is a recession year, 𝛾_𝑖 are individual specific fixed effects which take care of time-invariant demographics, such as race, gender, education, preference for recreation or work, risk attitudes etc., 𝜸_𝒄𝒕 are county-specific time trends.³¹ We are interested in the sign of the parameter 𝛽₃ on the interaction term between county level unemployment and indicator for recession. After controlling for the level effect of recession and county level unemployment along with individual fixed effect as well as various trends, if we find 𝛽₃ > 0 and statistically significant, we interpret it as a positive effect of unemployment during recession on participation in outdoor lake recreation. Since county level economic condition might exhibit correlation cross years, standard errors are clustered at county-year level [Wooldridge (2002)].

Table 14 shows the fixed effect estimates on the impact of county unemployment rate on recreation participation. Panel (a) in the Table reports results for the sample we use in matching exercises in previous section, while panel b and c report results for the unbalanced and a balanced panel respectively. We gradually increase controls. Column I does not incorporate any trend while

30 For instance if one’s preference for recreation is time-invariant; FE model would control for it.

31 We have tried to control time trends by including both of county specific linear and quadratic time trends. Instead of including year specific fixed effects we include a dummy variable for the recession year to disentangle the recession year effect from a normal year effect.

column II and III include linear and quadratic trend. Instead of general linear or quadratic trends, columns IV and V incorporate county specific linear and quadratic trends. The coefficients of county unemployment rate and recession turn out to be negative and statistically significant. The estimates in columns II-IV in Table 14 reveal that in a recession year, participation in outdoor lake recreation decreases in the range of 14 to 20 percentage points compared to a non-recession year.

Similarly, in a particular year, individuals from a high unemployment county participate less in outdoor lake recreation compared to an individual from a low unemployment county. A one percentage point increase in local county level unemployment rate decreases participation in lake recreation by 1.72 to 4.73 percentage points based on the specifications we have used. This pattern is common across all specifications except that with county level quadratic trend (specification V).

Note that in specification V, none of the variables display statistical significance-it seems like all variation in lake participation is absorbed by the county specific quadratic trend.

The coefficient of the interaction term between county level unemployment and recession year, after controlling for level effect of unemployment and recession, turns out to be positive and statistically significant in most of the cases(except for specification V consistently in all three samples). We interpret the statistically significant, and positive coefficient of the interaction term as a positive association between recessionary unemployment and outdoor recreation participation.

An individual from a county with high unemployment rate during the recession year of 2009 is more likely to participate in lake recreation compared to one from a low unemployment county.

Although unemployment, on its own, reduces participation in lake recreation, the recessionary unemployment affects participation in outdoor recreation in an opposite manner. This pattern is consistent across the balanced sample as well, as can be observed in panel c. Our findings overall

suggest that participation in lake recreation responds to unemployment in a different manner depending on whether the time is recessionary.