Discussion of Consumption and Debt Responses to Unanticipated Income Shocks: Evidence from a Natural Experiment in Singapore By Sumit Agarwal and Wenlan Qian AEA Annual Meetings Philadelphia PA, January 2013 Jonathan A. Parker MIT Sloan Outline A. Summary B. Compare to Broda Parker (2012) C. Comments/concerns A. Summary o Growth dividend program in 2010 • • • • Payment to Singapore citizens not foreigners (40% of pop) Amount based on 2010 income and Dec. 2010 home value Payments avg. 12% of avg. monthly income Other programs also; more to low income & age>35, medical account grants to older than 45, income tax rebate o Bank data: sample of 100,000+ individuals • (Unnamed:) bank has 80% of population as customers • Monthly spending credit and debit cards: 30% of PCE • Income, nationality, ethnicity, occupation, zip o Infer amount of payment from income o Using propensity score matching, compare spending of recipients to non-recipients by month . . . Average log spending (matched sample) Main findings Cumulative change from announcement, disbursement in month 2 1. 2. 3. 4. Total cumulative response: 90% of payment On announcement spending more on credit cards On disbursement and shortly thereafter more on debit cards No pre-treatment effects Heterogeneity • Individuals with low account balances spent only after disbursement and mostly by debit • Individuals with high account balances spent only on credit (stat. insig.) • Low income spend more than high income (stat. insig.) • Low credit card limit spend more • Men spend 100%, women 62% (stat. insig.) Compare to Broda Parker (2012) Key difference: BP use random timing not common date of announcement/disbursement 1. Stimulus payment arrival causes jump up in weekly spending, then smaller but persistently higher level 2. No detectable spending when people learn about stimulus payment 3. Concentrated among those with two months income “available in cash, bank accounts, or easily accessible funds” Weekly spending 1.4% of ESP spent on UPC household goods No pre-treatment effects Rapid dropoff in spending rate, but significant cumulative spending over 7 weeks Consistent with lower Parker, Souleles, Johnson, McClelland Spending upon learning about the ESP Weekly spending Treatment effect by enough liquid funds B. Comments/concerns These comments are concerns that are unlikely to change the qualitative results but might change the quantitative findings 1. Identification from nationality and propensity score matching 2. Identification from payment amount 3. Quantitative implications 4. Individuals vs. households 5. Substantive implications 1. Identification from nationality and propensity score matching (PSM) Key identifying assumption: spending by foreigners is like spending by citizens except due to policy • PSM: “conditional on predictors of treatment status” • Can perfectly predict treatment vs. control status – No foreigners in treatment, no citizens in control – Perfect prediction would violate common support assumption Concern: different religion/culture and seasonal expenditures – Chinese new year is February 3 2011 (year of Rabbit) – Singaporeans have British culture -- drive of the correct side of the street Ethnicity control goes a long way towards dealing with these concerns – Plot propensity score densities of treated and untreated – If differences between the maxima and minima of the density distributions, remove cases that lie outside the support of the other distribution. Estimates LATE only within overlap sample. 2. Identification from payment amount Key identifying assumption: spending by individuals with different incomes and home values is alike except for policy • Propensity score matching in the paper does not address this at all Concern: high income households have wealth/income increases relative to low income households • Particularly acute concern for the inferences involving heterogeneity in treatment response Paper has some robustness tests that address some of these concerns one by one Possible identification strategy: compare households with similar incomes and house values but on either side of the policy discontinuity 3. Quantitative implications • Payouts 12% of average (18% of median) monthly income, 1% (1.5%) of annual • Data capture 30% of consumption • Ratio of sample average consumption to average income 0.15 Yet average spending response is 90% of payment? 4. Individuals vs. households Payment depends on income and home value • Income: total household or individual? • Housing total value? Spending on cards presumably for household Concern: spending is joint, payment is individual, • Spouse gets funds from other government stimulus programs that affect only citizens – response biased upwards • Some cards spend spouses payment: “control group” spending rises; some cards do not spend own payment: “treatment group spending lowered” – response biased downwards Estimates similar for sample of single households 5. Substantive implications Policy: stimulus transfer leads to spending within 9 months of 90 cents on the dollar • The opposite from Ricardian equivalence • Given we get a lot of spending either way, should households get to choose how to spend stimulus instead of government? Theory: reject LCPIH • But some forward-looking behavior among people that spend 90% of a transitory income • Can the paper give us more on anticipatory spending? What characteristics/situation predict anticipatory spending?