HLEG thematic workshop on Economic Insecurity, 4 March 2016, New York, United States. More information at: http://oecd/hleg-workshop-on-economic-insecurity-2016

1. Consumption Volatility as a Measure of
Economic Insecurity.
Olga Gorbachev, University of Delaware, USA
Remarks prepaired for the Conference on “Economic Insecurity:
Forging an Agenda for Measurement and Analysis”, New York,
NY, March 4, 2016

2. Goals:
Consumption volatility is a better measure of realized
economic insecurity than income volatility since families
can smooth income shocks (depending on their persistence
and size) via:
i. Savings
ii. Credit markets
iii. Intra-household smoothing
iv. Public transfers/taxes
Moreover, there are other types of shocks, expenditure
shocks, affecting households for which households would
like protection

3. Data:
Panel data (PSID) on consumption to measure economic
insecurity is essential
i. The PSID had no good measure of total consumption
(except for food and housing) until 1999. So we can
construct measures of economic insecurity using food
consumption (underestimate).
ii. Starting in 1999, the PSID included other consumption
categories covering about 70 percent of non-durable
consumption spending.

4. Data:
Panel data (PSID) on consumption to measure economic
insecurity is essential
i. The PSID had no good measure of total consumption
(except for food and housing) until 1999. So we can
construct measures of economic insecurity using food
consumption (underestimate).
ii. Starting in 1999, the PSID included other consumption
categories covering about 70 percent of non-durable
consumption spending.
Panel vs. repeated cross-section: not a relevant distinction
if the populations are not changing over time in siginficant
ways, but they are in the US.

5. Methodology Gorbachev [2016]
To construct volatility of consumption using the PSID:
1. Impute total consumption following Attanasio and
Pistaferri (2014) as a function of food consumption,
consumer price indices, demographics, socioeconomic
variables and location.

6. Methodology Gorbachev [2016]
To construct volatility of consumption using the PSID:
1. Impute total consumption following Attanasio and
Pistaferri (2014) as a function of food consumption,
consumer price indices, demographics, socioeconomic
variables and location.
2. Compute growth rate of consumption (income) as arc
percent change of consumption (income), following Dynan
et al. (2012)
gCit =
Cit − Cit−2
¯Cit
where ¯Cit =
Cit + Cit−2
2

7. Methodology Gorbachev [2016]
3. Remove predictable variation by regressing consumption
(income) growth on demographics and state dummies.

8. Methodology Gorbachev [2016]
3. Remove predictable variation by regressing consumption
(income) growth on demographics and state dummies.
4. Compute volatility as the absolute value of the residuals.

9. Methodology Gorbachev [2016]
3. Remove predictable variation by regressing consumption
(income) growth on demographics and state dummies.
4. Compute volatility as the absolute value of the residuals.
Thus, volatility of consumption (income) can be thought as
family-specific time-varying changes in consumption
(income) that cannot be predicted by age, cohort, gender,
education, size and change in the household composition,
location, and employment status.
The above controls and sample restrictions reduce the
potential concerns expressed by Aguiar and Hurst (2005)
regarding expenditure vs. consumption response to shocks.
Note: sample excludes retired, students and anyone aged below
25 or above 60.

10. Figure : Consumption vs. Income Volatility
.2.22.24.26.28.3
volatility
1970 1980 1990 2000 2010
Year
Family Income Total Consumption
Total Predicted Consumption

11. Why did the volatility of consumption increase?
Since unconstrained households can smooth temporary
income shocks, this suggests that either a significant
fraction of households were liquidity constrained or that
permanent shocks to income became more volatile, or the
combination of the two.

12. Why did the volatility of consumption increase?
Since unconstrained households can smooth temporary
income shocks, this suggests that either a significant
fraction of households were liquidity constrained or that
permanent shocks to income became more volatile, or the
combination of the two.
There is some research supporting the claim that
permanent shocks increased.
In separate research with Dogra, we find that liquidity
constraints played an important role as well.

13. Why did the volatility of consumption increase?
Since unconstrained households can smooth temporary
income shocks, this suggests that either a significant
fraction of households were liquidity constrained or that
permanent shocks to income became more volatile, or the
combination of the two.
There is some research supporting the claim that
permanent shocks increased.
In separate research with Dogra, we find that liquidity
constraints played an important role as well.
Wealth inequality can also explain differences in
consumption volatility between households.

14. Figure : Proportion of Liquidity Constrained Households by
Education and Race, SCF.
.1.15.2.25.3
1980 1985 1990 1995 2000 2005 2010
year
% constrained
% discouraged
% turned down

15. Figure : Percentage with Net Assets less than Two Months’ Income
by Demographic Group, SCF
.1.2.3.4.5
1980 1990 2000 2010
year
all households
.1.2.3.4.5
1980 1990 2000 2010
year
married single parent
.1.2.3.4.5
1980 1990 2000 2010
year
white black/Hispanic
.1.2.3.4.5
1980 1990 2000 2010
year
college no college

16. The Importance of Liquidity Constraints
We find that the probability of being denied credit has an
independent and strongly significant effect on consumption
volatility beyond the effect of volatility of income.

17. The Importance of Liquidity Constraints
We find that the probability of being denied credit has an
independent and strongly significant effect on consumption
volatility beyond the effect of volatility of income.
Consumption volatility was about 50% higher for quarter
of the PSID households that were most likely to be
liquidity constrained.

18. The Importance of Intra-Household Smoothing
Figure : Mean Income Volatility by Marital Status, PSID
.2.25.3.35
volatility
1970 1980 1990 2000 2010
Year
all continuously married
single/divorced/separated/widowed