How Consumers Responded to the 2014-2015 Oil Price Shock ... · the shock from lower gas prices. Jan13. Jul13. Jan14. Jul14. Jan15. Jul15. Treatment group. Control group. Assumption

How Consumers Responded to the 2014-2015 Oil Price Shock?Evidence from the Consumer Expenditure Survey

Patrick Alexander and Louis Poirier (2017)July, 2017

Presented by: Louis Poirier2017 CE Microdata Workshop20 July 2017

The views expressed in the presentation are those of the authors. No responsibility for them should be attributed to the Bank of Canada

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MotivationBetween June 2014 and December 2015, global oil prices fell by almost 50%. This decline was expected to help U.S. GDP growth to accelerate to 3.6 % in 2016 and

3.3 % in 2016 (IMF). However, U.S. economic growth was only 2.6% in 2015 and 1.6 % in 2016.

Where was the impact from lower oil prices on consumption? “...the decline of oil prices over the last two years hasfailed to deliver the usual economic benefits. ” (New York Times, 2016)

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This paper

Therefore to find the source of the “missing stimulus”, we have looked into the consumer’s reaction to lower gas prices.

Data: Consumer Expenditure Survey (CE)

Question: Did total consumption increase more for households that consume gasoline than it did for people that do not consume it (after June 2014)?

This PaperContribution to the literature: The first study to examine this episode using:

– Representative microdata– Isolate the impact of lower gas prices with:

• Information on vehicle ownership status • Intensity of gasoline consumption

Summary of findings:– Evidence of significant divergence in consumption response among

vehicle owners and high gasoline spenders.– Suggests the consumption stimulus was sizable.

Handling the CE data

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Cleaning the data

Consumer Expenditure Survey (CE) Interview Survey (FMLI), 2013-2015 Append each fmli file including the ones in Stata

• use "$inipath2013\fmli131x.dta"• append using "$inipath2013\fmli132.dta"• append using "$inipath2013\fmli133.dta"• append using "$inipath2013\fmli134.dta"• append using "$inipath2013\fmli141.dta"

Cleaning the data

1. Steps to transform the “quarterly” data in monthly space:2013Q4 2014Q1x

xyz_pq = 500$ xyz_cq =1000$

(1) (2) (3)Oct Nov Dec Jan Feb Mar

if qintrvmo =

Results:Dec_xyz_2013= 500$Jan_xyz_2014= 500$Feb_xyz_2014= 500$

Cleaning the data

2. “Weighting” the data to get a representative sampleResults:

Dec_xyz_2013_w = 500$* finlwt21

Jan_xyz_2014_w = 500$* finlwt21

Feb_xyz_2014_w = 500$* finlwt21

Cleaning the data3. Removing aberrant or irrelevant observations:

– CU spends nothing or has zero income– Delete CU who bought gasoline and report

having no vehicle– Remove 1 per cent lowest and 1 per cent

highest income percentile as in Coibion et al. (2012)

– Delete some data points that are extreme

Our microdata pretty much in line with CE aggregates

Micro CE Agg CE Micro CE Agg CE Micro CE Agg CETotal spending 48,993$ 51,100$ 51,031$ 53,495$ 52,777$ 55,978$ Total spending ex gas 46,332$ 48,489$ 48,497$ 50,966$ 50,644$ 53,885$ Non-discretionnary spending 34,424$ 33,345$ 36,215$ 35,004$ 37,762$ 36,747$ Food spending 5,060$ 3,977$ 5,165$ 3,971$ 5,238$ 4,015$ Shelter spending 9,863$ 10,080$ 10,283$ 10,491$ 10,411$ 10,742$ Transportation spending 6,303$ 6,392$ 6,454$ 6,605$ 7,277$ 7,414$ Baby care spending 311$ N.A. 327$ N.A. 384$ N.A.Health care spending 3,552$ 3,631$ 4,267$ 4,290$ 4,247$ 4,342$ Personal insurance spending 5,560$ 5,528$ 5,750$ 5,726$ 6,289$ 6,349$ Utilities spending 3,775$ 3,737$ 3,969$ 3,921$ 3,915$ 3,885$ Discretionnary spending 11,908$ 15,144$ 12,282$ 15,962$ 12,883$ 17,138$ Alcoholic beverage spending 378$ 445$ 406$ 463$ 462$ 515$ Apparel spending 909$ 1,604$ 933$ 1,786$ 988$ 1,846$ Entertainment spending 2,190$ 2,482$ 2,413$ 2,728$ 2,485$ 2,842$ Personal care spending 296$ 608$ 307$ 645$ 321$ 683$ Education spending 944$ 1,138$ 936$ 1,174$ 927$ 1,309$ Books spending 109$ 102$ 109$ 103$ 91$ 114$ Food away from home spending 2,290$ 2,625$ 2,441$ 2,787$ 2,555$ 3,008$ House expenses spending 1,598$ 3,331$ 1,609$ 3,387$ 1,786$ 3,782$ Miscelleaneous spending 3,194$ 2,809$ 3,127$ 2,889$ 3,268$ 3,039$ Gasoline spending 2,661$ 2,611$ 2,534$ 2,468$ 2,133$ 2,090$

2013 2014 2015

Theoretical approach

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TheoryEdelstein and Killian (2009) describe three distinct types of responses to negative gasoline price shocks:1. Discretionary income effect:

– Suppose: MPC > 0, 0 > ndg > -1

– Increased spending on gasoline and other products. Back-of-the-envelope calculation for discretionary income effect:

– Pg fell by ≈ 25% – nd

g ≈ -0.42– MPC = 1– Average CU spend 250$/month on gas

Discretionary savings: (0.25)*(0.58)*(1)*(250) <= 36.25$

Theory2. Income switching effect

– Suppose: -1 > ndng

– Increased spending on gasoline-related products.3. Precautionary savings effect

– Suppose: incomplete insurance markets– Increased MPC due to higher future real income.

Implication: the upper bound of consumption response is unclear. However, with additional effects, max > 36.25$

– Edelstein & Kilian (2009) mentions the response could be 4 times as large than the discretionary income effect, when you take into account additional effects.

Empirical Strategy

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Empirical strategy

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To do a diff in diff, you should only capture one time-invariant shock between 2 groups (i.e.: impact of lower gas prices).

• One group is affected by the shock and the other one is not.

• Therefore, the only parameter that should impact the difference in consumption between your 2 groups and the 2 time periods is the shock from lower gas prices.

Jan13 Jul13 Jan14 Jul14 Jan15 Jul15

Treatment group Control group

Assumption for consumption for a diff in diff regression

Last observation: December 2015

Before the shock After the shock

2. Control group

Impact of lower gas prices

1. Treated group

Oil price shock (Jun 14)

Spending for treated group

Spending for control group

Empirical strategy

Changes in Medicare in 2014 ? This is not consumption. Spike in January 2013 ?

Remove some sub-categories of spending to create our core spending (Yngit ):

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Education Spending

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Average Household Expenditures (USD)

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Low gas spenders (LHS) Normal gas spenders (RHS)

Retirement Savings

Last observation: December 2015Source: Bureau of Labor Statistics

Empirical strategy

1. Difference-in-difference specification:

where highgasi = 1 if gas spending in top 80th percentile.2. Alternative difference-in-difference specification:

where vehownri = 1 if CU reports owning a vehicle.Identifying assumption: car ownership “sticky”, contemporaneously unrelated to gasoline prices

Yngit = Spending ex gas, health insurance, savings and education

Empirical strategy

Car owners Non-Car owners

• Annual Income $ 65,184.72 $ 21,160.21

• Average number of persons in the CU

2.6 1.7

• Average age of the CU head

51.3 54.4

• Is the CU headed by a woman?

52% 62%

• Is the CU living in an urban area ?

94% 96%

• Is the CU having a mortgage?

39% 5%

• Is the head of the CU working ?

68% 39%

High gas spenders

Low gas spenders

• Annual Income $69,578.92 $ 29,437.28

• Average number of persons in the CU

2.7 1.7

• Average age of the CU head

50.0 57.5

• Is the CU headed by a woman?

51% 61%

• Is the CU living in an urban area ?

94% 93%

• Is the CU having a mortgage?

58% 14%

• Is the head of the CU working ?

71% 41%

Our control variables and their average between sub-groups:

Empirical strategy

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A visual inspection of the pattern between the treated and control groups before and after the shock shows an acceptable behaviour for normal versus low gas spenders.

• The slopes are still not that similar for car versus non-car owners.

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2013 2014 2015Normal gas spenders (RHS)

Slope for normal gas spenders (LHS)

Low gas spenders (LHS)

Slope for low gas spenders (RHS)

Core spending evolution pre- and post-shock between the 2 groups respects the diff in diff principles

Average Household Expenditures (USD)


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Car owners (LHS)Slope for car owners (LHS)Non-car owners (RHS)Slope for non-car owners (RHS)

Core spending evolution pre- and post-shock between the 2 groups does not respect the diff in diff principlesAverage Household Expenditures (USD)


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Treatment group Control group

Assumption for consumption for a diff in diff regressionBefore the shock After the shock

Results

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1. Results for high gas spenders with core spending

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Table 1.Regression on core spending ex gas for high gas spenders as treated

Robust t statistics in parentheses * p<0.05 ** p<0.01 *** p<0.001 \ controls * = number of persons, age, sex, in the CU, mortgage, urban, working

Basecase IV on income Month fixed effects Cluster by CU

Income0.0220*** 0.0272*** 0.0220*** 0.0220***(95.78) (117.62) (97.75) (51.09)

After July 2014 72.59*** 60.55** 213.3*** 72.59*(3.87) (3.22) (7.79) (2.36)

high gasoline 535.2*** 444.0*** 534.6*** 535.2***(27.67) (23.16) (22.66) (16.29)

Combined effect 104.5*** 93.19*** 105.6** 104.5*(4.18) (3.72) (2.97) (2.56)

Constant Y Y Y YControls* X X X X

Observations 190852 190852 190852 190852

Adjusted R-squared 0.219 0.212 0.219

2. Subcomponents of consumption

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Table 1.2. Regression with spending on essential products, by sub-group


Essential Food Shelter Transportation Auto Baby care HealthcarePersonal insurance Utilites

Income 0.0130*** 0.00111*** 0.00591*** 0.00373*** 0.00178*** 0.000438*** 0.000604*** 0.000336*** 0.000824***[121.80] [104.07] [150.32] [40.34] [19.85] [60.95] [40.74] [30.09] [108.89]

High gasoline 57.11* 7.471** 28.42** 24.37 14.11 -0.511 -6.637 -1.193 5.191**[2.34] [3.06] [3.16] [1.15] [0.69] [-0.31] [-1.95] [-0.47] [2.99]

After July 2014 336.8*** 48.93*** -36.03*** 220.5*** 127.3*** -11.59*** 34.13*** 6.518** 74.42***[16.03] [23.21] [-4.64] [12.06] [7.18] [-8.16] [11.65] [2.96] [49.77]

Combined effect 41.39 2.938 -22.45* 51.47* 44.09 5.418** 4.711 1.145 -1.844[1.51] [1.07] [-2.22] [2.16] [1.91] [2.93] [1.23] [0.40] [-0.95]

Constant Y Y Y Y Y Y Y Y Y

Controls* X X X X X X X X X

Observations 190852 190852 190852 190852 190852 190852 190852 190852 190852Adjusted R-squared 0.152 0.284 0.180 0.021 0.007 0.056 0.026 0.009 0.304

3. Subcomponents of consumption

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Table 1.3. Regression with spending on non-essential products, by sub-group

Non-essential Alcohol Apparel Entertainment Books AppliancesHousehold expenses

Food away from home Miscelleaneous

Income 0.00906*** 0.000377*** 0.000788*** 0.00193*** 0.0000865*** 0.000146*** 0.00148*** 0.00170*** 0.00248***

[76.07] [63.36] [17.30] [40.91] [35.40] [12.74] [40.22] [82.54] [36.35]

After July 2014 15.48 2.658*** 4.684*** 10.61*** 0.672** 4.892*** 0.118 4.921** -8.330

[1.51] [4.71] [4.34] [3.85] [2.88] [5.03] [0.03] [2.66] [-1.02]

High gasoline 198.3*** 4.568*** 2.114 39.45*** 3.304*** 6.753*** 22.14*** 49.71*** 71.52***

[19.07] [8.37] [1.13] [11.89] [14.60] [7.29] [6.03] [26.82] [9.26]

Combined effect 63.06*** 2.613*** 8.890*** 9.712* -1.625*** -2.267 11.43* 9.335*** 21.70*

[4.97] [3.72] [3.90] [2.22] [-5.44] [-1.77] [2.56] [3.96] [2.36]

Constant Y Y Y Y Y Y Y Y Y

Controls* X X X X X X X X X

Observations190852 190852 190852 190852 190852 190852 190852 190852 190852

Adjusted R-squared0.149 0.100 0.024 0.043 0.027 0.006 0.035 0.159 0.036


6. Other specifications:Mortgage, urban, oil states, etc.

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Boost to consumption from lower oil prices is seen more in CUs : Median income Without mortgage Living in non-oil states

Conclusion

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Recap of Key Findings

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Clear evidence of differential response for those highly exposed to the shock.– Benchmark case suggests a consumption boost of +\- 100 $ per

CU– In line with other studies

Proves the existence of the positive windfall on consumption from lower gas prices– Maybe did not get capture properly by aggregate macro data…

Questions

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Thank you !

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Back-up slides

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1.1. Results for car owners with core spending

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Table 2.Regression on core spending ex gas for car owners as treatedModel A Model B Model C Model D Model E

Income 0.0252*** 0.0251*** 0.0243*** 0.0243*** 0.0222***(124.74) (124.74) (117.82) (117.82) (97.26)

After July 2014 111.0*** 113.1*** 35.33* 48.43**(8.13) (8.31) (2.23) (3.08)

Car owners 813.6*** 770.4*** 586.4***(68.05) (48.51) (36.02)

Combined effect 85.63*** 92.24***(3.94) (4.27)

Constant Y Y Y Y YControls* X

Observations 190852 190852 190852 190852 190852

Adjusted R-squared 0.302 0.303 0.308 0.308 0.32


More spending on gas ?

Question: How large was the stimulus for non-gasoline spending?

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Number of gallons

Number of gallons consumed per household (LHS) Dollars per gallon including taxes (RHS)

Chart 1: The number of gallons consumed is inversely proportionate to gasoline prices

Dollars per gallon

Existing literature

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Previous findings on 2015-2015 oil shock stimulus. Farrell and Grieg (2016):

– Consumers spent 80% of savings on non-gas items.– Weakness: Non-representative

Gelman et al. (2016):– Consumers spent 65-75% of savings on non-gas items.– Weakness: Non-representative

Baumeister and Killian (2017):– Consumption stimulus was roughly 1.2% of real consumption.– Weakness: Uses aggregate data, no micro evidence– Criticized as “not theoretically sound” (Ramey, 2016)

Cleaning the data

1. Steps to transform the “quarterly data” in monthly space:2013Q3 2013Q4

xyz_pq = 500$ xyz_cq =1000$

(1) (2) (3)Jul Aug Sep Oct Nov Dec

if qintrvmo =

Results:Sep_xyz = 500$Oct_xyz = 500$Nov_xyz=500$

Data by sub-groupsAverage monthly consumption excluding gasoline

All sample With a car Without a car Urban Rural Oil Non-oil

Before shock (Jan2013-Jun2014) $3,948.45 $4,168.57 $1,820.03 $4,010.80 $2,917.92 $4,245.78 $4,069.19

After shock (Jul2014-Dec2015) $4,214.31 $4,454.26 $1,837.03 $4,295.63 $3,146.98 $4,564.08 $4,334.11

Difference (%) 6.7% 6.9% 0.9% 7.1% 7.8% 7.5% 6.5%Average monthly gasoline consumption

All sample With a car Without a car Urban Rural Oil Non-oil

Before shock (Jan2013-Jun2014) $218.75 $241.14 $ - $218.46 $229.12 $236.60 $215.22

After shock (Jul2014-Dec2015) $184.75 $203.36 $ - $184.84 $229.12 $206.63 $181.18

Difference (%) -15.5% -15.7% - -15.4% -19.2% -12.7% -15.8%Number of observations 191,002 173,484 17,518 179,238 11,764 21,241 134,250

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The evolution of core spending before and after the shock

between the 2 groups respect the diff in diff principles

Normal gas spenders (LHS)

Slope for normal gas spenders (LHS)

Low gas spenders (RHS)

Slope for low gas spenders (RHS)

Empirical strategy

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Health insurance spending differs

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Non-car owners (RHS)

Changes in Medicare in 2014 ?

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Retirement savings is differnt

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This is not consumption.

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Education spending exhibits weird patterns

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Weird spike in January 2013 for non-car owners

Let’s remove these sub-categories to create a core measure of spending without gas.

Documents

How Consumers Responded to the 2014-2015 Oil Price Shock ... · the shock from lower gas prices. Jan13. Jul13. Jan14. Jul14. Jan15. Jul15. Treatment group. Control group. Assumption