The Distributional Effects of Building Codes
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1 The Distributional Effects of Building Codes Chris Bruegge Stanford University Tatyana Deryugina University of Illinois and NBER Erica Myers University of Illinois and E2e June 2, 2017
2 Research Questions 1. How much energy do building codes save? 2. Which parts of the income distribution are most affected by the codes?
3 ...comparisons between residences constructed just before and after an increase in the stringency of Florida s energy code in We find that the code change is associated with a decrease in the consumption of electricity by 4% and natural gas by 6%. Jacobsen and Kotchen, ReStat 2013
4 compare current electricity use by California homes of different vintages constructed under different standards...[and find ] no evidence that homes constructed since California instituted its building energy codes use less electricity today than homes built before the codes came into effect Levinson, AER 2016 (Citation from NBER WP Version)
5 Challenges Temporal Variation in Code Stringency Can t separate home vintage from code stringency: Jacobsen and Kotchen (2013), Levinson (2016) Spatial Variation in Code Stringency Cross-state differences: Aroonruengsawat et al. (2012), Levinson (2016)
6 Our Innovation
7 Current Findings 1. How much energy do building codes save? Electric savings of 3%; Gas savings of 8% 2. Which parts of the income distribution are most affected by the codes? Largest distortions at top of income distribution
8 CA Building Code Timeline (Temporal Code Variation) Building energy code est d (1977) Climate zones est d (1982)
9 CA Building Code Timeline (Temporal Code Variation) Sample Start (1947) Building energy code est d (1977) Climate zones est d (1982) Sample End (2008)
10 Building Climate Zones (Spatial Code Variation)
11 Building Climate Zones (Spatial Code Variation)
12 Building Climate Zones (Spatial Code Variation)
13 Data Summary Stats Energy Consumption Data Monthly billing from PG&E, SCE, SDG&E, and SoCal Gas Home Characteristics Parcel attributes from Corelogic Year built Square footage Sales price history Demographic Characteristics Household-level from RefUSA Income
14 Diff-in-Diffs Specification log(y ijkt ) = α YBi + β j + γ kt + δ (YB i > 1981) Strict ij + ɛ ijkt Dependent Vars Energy use (BTU), home size, home price Subscripts home i, border j, zipcode k, billmonth t (e.g. January 2015)
15 Identifying Variation
16 Overall Energy Savings (1) (2) log(electric BTU) log(gas BTU) YB > 1981 x Strict *** *** (0.005) (0.005) Observations 22,350,372 22,350,372 Adjusted R-squared Significance levels: * 10 percent, ** 5 percent, *** 1 percent. Controls include year built fixed effects, zip-by-month-of-sample fixed effects, and border-byelevation-quintile fixed effects. Standard errors (in parentheses) two-way clustered by dwelling and month-of-sample. Parallel Trends
17 Energy Savings by Income Decile Electric Savings Electric Savings per Sqft Gas Savings Gas Savings per Sqft
18 Attribute Distortions
19 Why Size Might Be Affected Proposed plans compared to reference home with same square-footage Reference home assumed to be square Conductive heat loss proportional to surface area
20 Conclusion Today Novel identification, Herculean data collection Savings between 3-8% Distortions in high end of income distribution In Progress Incorporating weather data
21 Thanks!
22 Electricity Savings back
23 Gas Savings back
24 Electricity Savings per Square Foot back
25 Gas Savings per Square Foot back
26 Parallel Trends in Square Feet back
27 Parallel Trends in Bedrooms back
28 Table: Summary statistics Mean Standard deviation Observations Natural gas + electricity usage, thousands of BTUs ,350,688 Electricity usage, thousands of BTUs ,350,688 Natural gas usage, thousands of BTUs ,350,688 Year built 1, ,011 Living square feet 1, ,011 Number of bedrooms ,011 Number of bathrooms ,011 Home sale price 420, , ,862 Household income 129,448 75, ,011 Unit of observation is dwelling-by-month for energy use, dwelling-by-sale for the home sale price, and dwelling for the remaining variables. back
29 Table: Summary statistics by income decile Income (dollars) Home sale price Living square feet Price per square foot Electric use (1000 s BTUs) Gas use (1000 s BTUs) Decile 1 34, ,248 1, Decile 2 57, ,612 1, Decile 3 74, ,908 1, Decile 4 90, ,624 1, Decile 5 105, ,788 1, Decile 6 121, ,555 1, Decile 7 140, ,136 1, Decile 8 163, ,318 2, Decile 9 198, ,528 2, Decile , ,402 3, Unit of observation is dwelling-by-month for energy use, dwelling-by-sale for the home sale price, and dwelling for the remaining variables.
30 Table: Effect of building energy codes on home characteristics (1) (2) (3) (4) log(sq. ft.) log(bedrooms)log(sq. ft.) log(bedrooms) Post 1981 x Strict ** ** ** ** (0.028) (0.015) (0.028) (0.014) Radius around border 5km 5km 2.5km 2.5km Observations 319, , , ,175 Adjusted R-squared Significance levels: * 10 percent, ** 5 percent, *** 1 percent. Controls include year built fixed effects, zip code fixed effects, and border-by-elevation-quintile fixed effects. Standard errors (in parentheses) clustered by zip code.
31 Table: Effect of building energy codes on total energy use (1) (2) (3) (4) log(btu) log(btu/sq. log(btu) log(btu/sq. ft.) ft.) Post 1981 x Strict *** 0.012*** *** 0.015** (0.005) (0.004) (0.007) (0.006) Radius around border 5km 5km 2.5km 2.5km Observations 22,350,372 22,350,372 9,735,724 9,735,724 Adjusted R-squared Significance levels: * 10 percent, ** 5 percent, *** 1 percent. Controls include year built fixed effects, zip-by-month-of-sample fixed effects, and border-byelevation-quintile fixed effects. Standard errors (in parentheses) two-way clustered by dwelling and month-of-sample.
32 Table: Effect of building energy codes on electricity and natural gas use (1) (2) (3) (4) log(electric log(gas BTU/sq. BTU) ft.) log(electric BTU) log(gas BTU/sq. ft.) Post 1981 x Strict *** 0.042*** *** (0.005) (0.005) (0.005) (0.005) Observations 22,350,372 22,350,372 22,350,372 22,350,372 Adjusted R-squared Significance levels: * 10 percent, ** 5 percent, *** 1 percent. Controls include year built fixed effects, zip-by-month-of-sample fixed effects, and border-byelevation-quintile fixed effects. Standard errors (in parentheses) two-way clustered by dwelling and month-of-sample.
33 Table: Effect of building energy codes on home prices (1) (2) (3) (4) log(price) log(price per sq. ft.) log(price) log(price per sq. ft.) Post 1981 x Strict * *** * (0.045) (0.038) (0.048) (0.050) Radius around border 5km 5km 2.5km 2.5km Observations 420, , , ,606 Adjusted R-squared Significance levels: * 10 percent, ** 5 percent, *** 1 percent. Controls include year built fixed effects, zip code fixed effects, border-by-elevation-quintile fixed effects, month-of-sale fixed effects, and an indicator for the first sale. Standard errors (in parentheses) clustered by zip code.
The Distributional Effects of Building Energy Codes *
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