This section reports falsification tests, shows that the results are robust to the exclusion of states with ambiguous payday loan laws, and discusses potential biases.
5.1 Falsification Tests
By construction, the difference-in-difference regressions we estimate are intended to identify causal connections between outcomes and payday loan supply. As further evidence, Table 3.7 reports falsification tests that indicate whether Ban and/or Enabled are correlated with outcomes that one would not expect to vary with payday loan supply.54 To save space, we report only δ and δ ́ even though the regressions include the full set of controls. The falsification results indicate that Ban and Enabled are uncorrelated with home prices, the unemployment rate, and
TABLE 3.7: Falsification Tests
Reported are coefficients on Banned and Enabled in regression of outcome indicated in column heading. First regression for each outcome includes state and date fixed effect. Second includes state specific trend and fixed effects. All regressions include controls reported in main regressions excluding the one used as the outcome variable. Row heading indicates sample and/or re-coding.
Dependent Variable
Home prices Unemployment Log (income) Black share Hispanic share Asian share fe fe + trend fe fe + trend fe fe + trend fe fe + trend fe fe + trend fe fe + trend Banned -12.90 -4.58 -0.38 -0.05 -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 [0.63] [0.47] [1.64] [0.23] [1.30] [0.14] [0.44 ] [0.90] [0.22] [1.23] [0.93] [0.43] Enabled -11.67 -11.29 0.12 -0.25 0.01 -0.01 0.00 0.00 0.00 0.00 -0.01 -0.01 [0.58] [1.43] [0.82] [1.25] [0.97] [0.65] [0.51 ] [0.75] [0.23] [0.60] [1.11] [1.23] * significant at 10%; ** significant at 5%, *** significant at 1%
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demographic shares. These falsification tests provide further evidence that the relationships between payday loan supply and outcomes identified above are not merely coincidental.
5.2 Robustness to excluding ambiguous states
There is, or was at times, some ambiguity in the status of payday lending in Alabama (AL), Arkansas (AR), and Oklahoma (OK).55 Table 3.8 shows that the main results are mostly unchanged when those states are excluded. We still observe the weak negative link between Ch. 13 and Banned as in the main results, but with the three ambiguous states excluded, this link is not quite significant at the ten percent level. In the model of Ch. 13 with the state specific trend, we obtain a new result suggesting that Ch. 13 rates fall significantly when payday lending is enabled. While that new result may go against the informal-formal bankruptcy substitution hypothesis, it does not suggest that payday credit access is tipping households into bankruptcy. More generally, the somewhat unstable Ch. 13 results are consistent with the mixed findings in the literature.
The results on the number of returned checks with AL, AR, and OK excluded change slightly, but not in a uniform direction. The absolute size and significance of δ ́ falls in the model with fixed effects model, but estimate of δ in the model with state- specific trends becomes larger and more significant.
Excluding the ambiguous states strengthens the results on fee income per capita. In the main results, δ and δ ́ were only significant at the ten percent level. With AL, AR, and OK excluded, δ remains positive at the ten percent level while δ ́ is significant at the five percent level.
55 See Fox and Mierzwinski (2001) for a discussion of ambiguity about payday loan status in Oklahoma
and Carrell and Zinman (2009) for a discussion of ambiguity in Alabama and Arkansas. We thank a referee for bringing these ambiguous states to our attention.
TABLE 3.8: Robustness Checks
Reported are coefficients on Banned and Enabled in regression of outcome indicated in column heading [robust t-statistics]. First regression for each outcome includes state and date fixed effect. Second includes state specific trend and fixed effects. All regressions include controls reported in main regressions. Row heading indicates sample and/or re-coding.
Ch. 13 per 1000
Complaints v lenders
Number of returned checks
log (fee income)
log (fee income per capita)
residents
and debt
collectors at unit banks at unit banks
fe fe + trend fe fe + trend fe fe + trend fe fe + trend fe fe + trend
Main Results Banned -0.93 -0.48 0.26 0.03 272.4 360.3 0.30 0.18 0.30 0.17
[1.71]* [1.31] [2.36]** [0.58] [1.63] [3.49]*** [1.77]* [1.44] [1.75]* [1.39] Enabled 0.01 -0.07 0.04 0.04 -188.0 -150.1 -0.34 0.18 -0.33 0.18 [0.05] [0.22] [0.68] [0.94] [2.84]*** [1.06] [1.75]* [1.05] [1.70]* [1.04] Excluding AL, AR, OK Banned -0.93 -0.39 0.28 0.00 271.4 356.9 0.32 0.19 0.31 0.18 [1.62] [0.97] [2.49]** [0.04] [1.59] [3.50]*** [1.79]* [1.43] [1.76]* [1.38] Enabled 0.10 -0.47 0.01 0.03 -121.9 -34.8 -0.50 0.19 -0.50 0.19 [0.72] [2.25]** [0.13] [0.78] [1.68]* [0.60] [2.21]** [0.79] [2.15]** [0.79] MD Recoded June 2002 Banned -0.98 -0.59 0.25 0.02 275.7 305.0 0.24 0.12 0.23 0.11 [1.92]* [1.71]* [2.34]** [0.29] [1.59] [2.23]** [1.18] [0.84] [1.16] [0.80] Enabled 0.00 -0.07 0.05 0.00 -188.2 -146.6 -0.34 0.17 -0.34 0.17 [0.02] [0.25] [0.79] [0.12] [2.85]*** [1.04] [1.77]* [1.02] [1.72]* [1.01]
Excluding Indiana Banned -0.91 -0.48 0.27 0.03 274.3 363.2 0.29 0.18 0.28 0.17 [1.71]* [1.33] [2.42]** [0.59] [1.62] [3.54]*** [1.69]* [1.44] [1.67] [1.38]
Enabled 0.03 -0.07 0.05 0.04 -192.0 -148.1 -0.35 0.18 -0.35 0.18
[0.17] [0.22] [0.72] [0.93] [2.86]*** [1.05] [1.79]* [1.05] [1.74]* [1.04] * significant at 10%; ** significant at 5%, *** significant at 1%
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5.3 Potential Biases
As already noted, we follow the literature in taking the changes in payday loan laws as exogenous. If that assumption is violated, δ and δ ́ will be biased. For
example, if payday lenders see an exogenous increase in household debt problems in a state, say increased overdrafts, as an opportunity to enter a state, they seem likely to lobby for enabling legislation. That would impart an upward bias on δ ́. Note
however, that their competitors in the overdraft credit market, depository institutions, would tend to lobby against enabling legislation (or for bans), so the two forces would tend to offset. A priori, the net bias seems ambiguous.56
Our estimates of δ and δ ́ will also be biased if we have excluded any state- specific, time-varying variables that are correlated with both the outcomes and the legal changes. We have included the most likely covariates, namely the economic and demographic variables, and we have confirmed our results when we control for the share of population that are female or in the military.57 However, there may be other covariates that we have overlooked.