Does a Guaranteed Basic Income Encourage Entrepreneurship? Evidence from Robert M. Feinberg, American University and Daniel Kuehn, Urban Institute

American University Working Paper Series

March 2019

ABSTRACT

While the concept has been around for years, recently the policy notion of a “guaranteed basic income” (GBI) – or -- has had a resurgence of interest. In addition to rationales relating to fairness and response to structural employment shifts due to automation and globalization, another motivation sometimes put forward for these plans is to encourage risk- taking by providing a safety net. One would think this would imply greater entrepreneurial activity if an unsuccessful entrepreneur had the GBI to fall back on. In this paper we investigate a rare long-standing example similar to a GBI in the US, the Alaska Permanent Fund Dividend program. This was not put forth as a GBI and is frankly too small an annual amount to fully allow an individual to rely on these funds, but for a moderate-to-large family the APF can replace a large share of a poverty-level of income. Receipt of the APF also does not preclude a family from receiving other safety net benefits (e.g., food stamps, unemployment compensation), suggesting that the downside risk for a potential entrepreneur may be lower than in other US states. We initially examine trends in small-firm births in Alaska over time from the Census Bureau’s Business Dynamics Statistics 1977-2014 – before and after the institution of the APF program (the first payment was in 1982) – relative to other US states to investigate a possible impact on entrepreneurship, with results suggestive of a positive effect (perhaps wearing off over time). We then turn to micro data to look at changes in self-employment behavior in Alaska, with somewhat similar findings.

1 I. Introduction

While the concept has been around for years, recently the policy notion of a “guaranteed basic income” (GBI) has had a resurgence of interest.1 In addition to rationales relating to fairness and response to structural employment shifts due to automation and globalization, another motivation sometimes put forward for these plans is to encourage risk-taking by providing a safety net. One would think this could imply greater entrepreneurial activity if an unsuccessful entrepreneur had the GBI to fall back on. In this paper we investigate a rare long- standing example of a GBI in the US, the Alaska Permanent Fund (APF) Dividend program.2

The APF program is not generally thought of as a GBI and is frankly too small an annual amount to fully allow an individual to rely on these funds, but for a moderate-to-large family the

APF can replace a large share of a poverty-level of income. For example, in 2000 a family of 6 would have received more than 40% of the federal poverty threshold calculated for Alaska by the

US Department of Health and Human Services. Receipt of the APF does not preclude a family from receiving other safety-net benefits (e.g., food stamps, housing vouchers, unemployment compensation), suggesting that the downside risk for a potential entrepreneur may be lower than in other US states.

In this paper we examine trends in small-firm births in Alaska over time from the Census

Bureau’s Business Dynamics Statistics 1977-2014 – before and after the institution of the APF program (the first payment was in 1982)– relative to other US states to investigate a possible impact on entrepreneurship. We then turn to utilizing individual data for Alaskans from the

1 See Forget (2011) for a discussion of a past Canadian experiment, and a recent New York Times article discussing a Finnish experiment planned for this year as well as other proposals under consideration around the world (https://www.nytimes.com/2016/12/17/business/economy/universal-basic-income-finland.html). Other terms are used to describe this, e.g., Basic Income Guarantee, Universal Basic Income. 2 For a detailed description of the history and implications of the APF program, see Widerquist and Howard (2012).

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Current Population Survey’s Annual Social and Economic Supplement (ASEC) to examine more disaggregate motivations behind movements towards self-employment.

II. Previous Literature on GBI and Entrepreneurship

A considerable amount of research has examined labor market incentive effects of non-wage income through the lens of estimating income effects on labor supply – a natural extension is to examine whether self-employment is affected. A recent study by Cesarini et al. (2015) utilized

Swedish lottery winnings and found modest labor-market disincentive effects.3 Henley (2004) finds labor hours reductions in response to wealth effects in the form of British housing capital gains. A recent broad survey by McClelland and Mok (2012) of labor supply studies suggests that head of household and single workers have small negative income elasticities (while married women have some higher labor supply elasticities of all types).

In terms of previous work analyzing the Alaska PFD program, little analysis of its economic impacts has been conducted. Hsieh (2003) examined consumption impacts, finding limited effects. Feinberg and Kuehn (2018) look into the labor supply impacts and identify significant effects, especially on secondary household members; this leads to the question of whether reduced labor supply efforts are being translated into home care and/or leisure hours, or whether some of this may be reflected in greater entrepreneurship activities.

As for the entrepreneurship literature, financial constraints have often been raised as a factor in limiting small firm entry, starting with Holtz-Eakin et al. (1994) – focusing on inheritances,

Lindh and Ohlsson (1996) – examining Swedish lottery winners, and Johansson (2000) – and

3 Imbens et al. (2001) found similar effects on Massachusetts lottery players.

3 personal wealth. Hurst and Lusardi (2004) have questioned this result.4 Lofstrom et al. (2014) find wealth holdings to promote small firm entry, but only in “high-barrier” industries. In general, then, considering the risk involved with new firm startups, one would expect a “safety net” in the form of a GBI would promote such activity. Acknowledging the limited extent to which the Alaskan PFD program resembles the “ideal” GBI (below-poverty-line, and variable year-to-year), it is worth exploring its impact on small-business entry.

III. The Alaska Permanent Fund5

Since 1976, the state of Alaska has managed a , the Alaska Permanent

Fund, which is funded by license and fee revenue generated from the oil and gas extraction industry in the state. In 1982, the state began making dividend payments to residents out of the

APF known as “permanent fund dividends,” or PFDs. The Alaska Permanent Fund Corporation announces each year’s payment amount in mid-September and payments are generally made by

October to individuals that file for the dividend by the end of March. The payments vary from year to year according to the following formula:

푇 [0.105 × ∑푡=푇−4 퐹푆푁퐼푡] − 퐸푋푃푡−1 푃퐹퐷푡 = [ ] 퐴푃푃퐿퐼퐶퐴푁푇푆푡 where PFDt is the PFD payment in year t, FSNI is the Fund Statutory Net Income, EXP is the

APF’s expenses, obligations, and PFD program operations costs, and APPLICANTS is the number of applicants to receive a PFD.6 Notice that since the PFD depends most directly on income (rather than the total investment based on prior oil royalties) from the APF, there is very little correlation with short-term movements in oil prices.

4 Recent work by Schmalz et al. (2017) supports the view that credit constraints can limit entrepreneurial entry. 5 Much of the following discussion is based on Goldsmith (2010) and Widerquist and Howard (2012). 6 A detailed discussion of dividend calculation is available on the Permanent Fund Dividend website: The Alaska Department of Revenue, Permanent Fund Dividend Division – About Us Page

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Nominal and real PFD amounts from 2000 to 2016 are presented in Table 1. Real PFD payments are in 2016 dollars and are calculated using the Bureau of Labor Statistics’ (BLS)

Anchorage consumer price index. The nominal payments determined by the formula varied between $846 and over $2000 per household member during this period. For a low-income family of reasonable size these payments can be quite large relative to income over the period.

Recently there have been two notable departures from the formula initiated by the legislature or the governor. In 2008, under Governor Palin, Alaskans received a $1,200 “Alaska resources rebate” in addition to the $2,069 specified by the formula. Again, in 2016 (outside our sample period), Governor Walker ensured the PFD payment diverged from the formula by vetoing the estimated $2,052 payment amount, resulting in a $1,022 payment instead. High payments have occurred during periods of both growth and recession.

The PFD amount is formulaic (with the two exceptions noted above), so it is somewhat predictable prior to the September announcement of the current year PFD payment.

Nevertheless, the PFD payments are clearly exogenous to Alaskans’ individual and aggregate labor supply decisions.

Since the PFD is available to every Alaskan, a family’s total PFD payment is equal to the

PFD amount in Table 1 times the number of eligible family members who claim the payment.

The only eligibility restrictions on the PFD are that claimants must reside in Alaska for a full calendar year, intend to remain in Alaska indefinitely, and not have been sentenced or incarcerated for a crime during the next year.

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IV. Data and Descriptive Statistics

Table 2 presents descriptive statistics on small-firm entry, population growth, and real-GDP for Alaska and two other states – Hawaii and Montana -- as well as for the US in aggregate. To proxy new entrepreneurial activity, small-firm entry is defined as births (gross entry) of firms with between one and four employees. This measure corresponds to the type of firm (at least initially) a formerly salaried employee would be likely to create; it should be noted, however, that the correlation (across time and states) between this measure and gross entry defined as firms under 10 employees is 0.9992 (and between gross entry under 5 employees and gross entry under 50 employees, the correlation is 0.9981). These data are available annually from 1977 through 2014 at the state level from the Census Bureau’s Business Dynamics Statistics.

Data on the Alaskan PFD are obtained from the website of the Alaska Permanent Fund

Corporation (at The Alaska Department of Revenue, Permanent Fund Dividend Division,

SUMMARY OF DIVIDEND APPLICATIONS & PAYMENTS); the first year of payouts was

1982, and – to illustrate its purchasing power -- the PFD has averaged 3.7 percent of Alaskan per-capita personal income, ranging from 1.7 to 6.8 percent (and if three especially low and three especially high years are excluded, the range of the payments for the remaining 27 years is from

2.8 to 5.6 percent of per-capita income). Keep in mind that family payouts are linear with size, so that a family of six (e.g.) would have received as much as forty percent of Alaskan per-capita income in a year during this period. Other time-varying controls include state real GDP (as an index, with 1997=100) and population growth.

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Below we present some results comparing the Alaska experience with two somewhat similar states, Hawaii and Montana. Small firm entry in Alaska averaged 955 firms per year, compared to an average of 1362 in Hawaii and 1745 in Montana. Population growth averaged 1.7% per year in Alaska, 1.2% in Hawaii, and 0.8% in Montana (for the US in total population growth was just at 1.0% annually). Total population in Alaska averaged 585,000 compared to 1.17 million in

Hawaii, and 876,000 in Montana. Real GDP grew on average 2.3% in Alaska, 2.2% in Hawaii, and 2.0% in Montana (this compares to a growth rate of 2.8% for the US in total).

V. State-Level Econometric Specification and Results

Initially we utilize a very simple specification on 38 time-series observations (1977-2014), explaining the natural log of Alaskan small-firm entry by an index of the state’s real GDP, growth in population (both proxies for level and change in state economic activity), as a trend variable the log of US small-firm entry, and by a dummy variable equal to one for years 1983 and later, indicating an effect of the PFD program (allowing a one-year lag from the first PFD payment in 1982). We also examine whether there was an initial boost to entrepreneurship in

Alaska, which may have worn off over time (perhaps as the actual PFD amounts were seen as insufficient to fully replace salaried work in the state).

Results from these specifications are presented in Table 3. We see that, after controlling for state-level GDP, population growth, and nation-wide trends, the presence of the PFD program increased small-firm entry by 16% over the subsequent 32 years. However, when we allow for the effect of the PFD to decline over time, the PFD effect is to increase small-firm entry by 28%

7 immediately but rapidly decline, with an effect of under 4 percent 20 years later, approaching zero by the end of the sample period.

In results not reported here, we also consider whether the amount of the PFD matters, adding the PFD as a percentage of per-capita income, finding no significant impact of the latter. This is not surprising, as one would expect that decisions on new entrepreneurial activity would be based on the longer-term implications of the program. The implication is that potential entrepreneurs look to the existence of the program as a financial backstop rather than worrying about year-to-year variations in the dividend amount.

While sticking with state-level data, we now compare Alaska’s small-firm entry performance post-PFD with that of two somewhat similar states, in a difference-in-difference approach.

Admittedly somewhat ad hoc, the states chosen for this exercise are Montana and Hawaii.7

Hawaii and Alaska are both isolated from the continental United States, raising possibly similar barriers to entry. Like Alaska, neither Hawaii nor Montana has a large metropolitan area --

Alaska’s largest city, Anchorage, has a population of approximately 300,000, which is comparable to the size of Honolulu. Montana’s largest city (Billings) is even smaller, with a population just over 100,000; its Western location, mountainous terrain, and substantial mining and extraction activities also make it a second plausible comparison state for Alaska.

Table 4 presents results explaining small-firm entry in the three states by the same variables included previously, plus dummy variables for Hawaii and Montana and the log of each state’s

7 These were the states considered in the analysis of Feinberg and Kuehn (2018).

8 real GDP index.8 We see a similar pattern in regards to the effect of the PFD on Alaskan entrepreneurial effort – though somewhat smaller in magnitude after controlling for the Hawaii and Montana trends. The PFD increases Alaskan entrepreneurial activity by 15%. As in the

Table 3 results, there is a clear pattern of a reduced effect over time, starting at a 26% bump but hitting zero after 28 years (in 2010).

For robustness, we also include a full panel analysis of all US states (and the District of

Columbia) across the sample period; while this approach requires assuming the underlying model motivating small-firm entry across all states is the same, it provides another look at the question of the effects of the PFD on entrepreneurship.9 The results are presented in Table 5. We no longer find a significant effect when the impact of the PFD is assumed to be unchanged over time; however, when we allow for a declining effect, we see an immediate 30.5% increase in the number of small firms in Alaska, but this effect declines to zero within 7 years.

VI. Micro-data Specification and Results

Gross small firm entry is one metric of entrepreneurial activity, but an alternative is individually reported self-employment. In this section, we use Current Population Survey data on self-reported self-employment (recorded in the “class of worker” variable) to understand the effect of the PFD on individual decisions. At the micro level, we need to consider individual determinants of the self-employment decision. A recent survey (Simoes et al. 2016) discusses

8 This specification assumes that the marginal effect of population growth on small-firm entry is constant across the three states; results of interest are not affected by this restriction. 9 One specification change here involves replacing state-level indices of real GDP with population as measures of the absorptive capacity of the local economy.

9 prior work pointing to the role of demographic factors, family background and personality characteristics. In addition, of course, human capital likely needs to be accounted for. As noted earlier, while there are some contrary results, the bulk of the empirical literature supports a role for access to finance and liquidity constraints, suggesting that the PFD should have a positive impact on entrepreneurship in Alaska.

For the microdata analysis, we utilize Current Population Survey (CPS) data starting in 1977, before the initial PFD payout in 1982, to address the issue of the creation of the policy in addition to the variation in amounts. We use a probit model predicting self-employment as a function of the presence of the PFD policy, a set of individual characteristics, and state and year fixed effects. The sample is restricted to workers’ decisions to enter self-employment so it does not conflate the effect of the PFD on exiting or entering the workforce (an issue discussed in

Feinberg and Kuehn, 2018). We use two versions of the PFD policy variable: a dummy variable indicating whether the policy was in place, and a continuous measure of the PFD’s real value

(per person), divided by $1,000. Descriptive statistics for this sample are presented in Table 6.

Alaskan workers are somewhat more likely to report being self-employed than non-Alaskan workers (11 percent compared to 9.8 percent), although they are comparable on most other characteristics. One important demographic distinction of the Alaskan workforce is that there are considerably more Native Americans and Alaska Natives than there are in the general population

(7.9 percent, compared to 0.5 percent).

Results of the individual-level regressions are presented in Table 7. The first column of Table

7 reports marginal effects from the probit using the dummy policy variable, while the second column reports marginal effects from the probit that uses the scaled PFD value as the policy

10 variable. Non-Alaskans and ineligible Alaskans in the sample are assigned a zero for both variables. Both regressions indicate the presence of the PFD very modestly increases self- employment. The introduction of the policy is associated with a 1.3 percentage point increase in the self-employment rate; this is a substantial effect compared to the average Alaskan self- employment rate over the sample period of 11 percent. When the value of the PFD is included instead, a $1,000 increase in the PFD payment is associated with a modest (and not statistically significant) 0.2 percentage point increase in the self-employment rate. These results are consistent with the conclusion that the introduction of the PFD program increased entrepreneurship, while year-to-year variation in the payment amounts have little impact on entrepreneurship.

Although the PFD significantly increases entrepreneurship, it is possible that this effect is not consistent over time. To explore this possibility, we interact the PFD policy dummy variable with the number of years since the implementation of the program. These results are reported in

Table 8. All other elements of the regression specification remain the same. When the interaction term is included we find an even higher base effect of the PFD on entrepreneurship of 4.0 percentage points. This is about three times as large as the result reported in Table 8. However, we also find that the effect of the PFD on entrepreneurship declined by 0.1 percentage points for every year since the institution of the policy. While this annual decline in the effect of the PFD is small, it implies that the impact on entrepreneurship has almost completely dissipated since the beginning of the program.

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VII. Micro-data Specification and Results (Continued)

One of the advantages of using microdata is that the self-employment regressions can also control for individual characteristics that influence self-employment. The marginal effects on these individual characteristics in Table 7 are unsurprising. Women and people of color are less likely to be self-employed, while self-employment increases with age (at a decreasing rate).

Education is associated with higher rates of self-employment. Marriage and the presence of small children is also associated with a higher rate of self-employment.

Variation in the value of the PFD and the introduction of the policy should be exogenous to self-employment behavior. Nevertheless, in addition to the regressions reported in Tables 7 and

8, we also estimate difference-in-differences (DID) analyses that follow the estimation strategy used in Feinberg and Kuehn (2018). These DID analyses exploit the fact that the PFD is not available to Alaskans who have resided in the state for less than a year and non-Alaskans. One weakness of the CPS data before 1982 is that there is no information on a respondent’s state of residence in the prior year. Since the PFD is only available to Alaskans who have resided in the state for at least a year, this introduces some measurement error in accounting for the PFD payments received by survey respondents. It also restricts the DID to 1982 and later years.10

Since survey respondents in 1982 are reacting to payments made in 1981 (i.e., to a payment of zero because the PFD was not paid until 1982), the DID analyses do include one year of pre- program data.

10 The CPS did not collect interstate migration data in 1985 either, so that year is also excluded from the DID regressions.

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Differencing out the changes in the self-employment of PFD ineligibles guarantees that the

PFD estimates are independent of any common time-varying shocks to self-employment. Two groups of PFD ineligibles are available in the CPS from 1982 forward:

1. Non-Alaskans from other states, and

2. Alaskans who have resided in the state for less than one year

Including these comparison cases transforms the basic estimating equation into a difference in differences model with continuous treatment. The difference-in-differences estimator requires the inclusion of state dummy variables (in the case of the non-Alaskan comparison group) or residency dummy variables (in the case of the non-eligible Alaskan comparison group).

푆퐸퐿퐹퐸푀푃푡 = 훷(훽0 + 훽1푙푛푃퐹퐷푑푢푚푚푦푡−1 + 훽2퐬 + 훽3퐲 + 훃퐗 + 휀) where s is a set of state fixed effects, and,

푆퐸퐿퐹퐸푀푃푡 = 훷(훽0 + 훽1푙푛푃퐹퐷푑푢푚푚푦푡−1 + 훽2퐫 + 훽3퐲 + 훃퐗 + 휀) where r is a one year or more residency eligibility dummy variable. Since the PDF policy dummy is a binary treatment, the coefficient on an interaction between the state fixed effects and the year fixed effects or residency eligibility dummy and the year fixed effects would provide the treatment effect for a DID estimator, and this is equivalent to the policy variable (PFDdummy).

Both DID models are also estimated with the same interaction between the PFD dummy and the years since the implementation of the PFD to understand any time trends in the effect of the

PFD.

Following Bertrand, Duflo, and Mullainathan (2004) and Cameron and Miller (2015), the difference-in-differences models present cluster-robust standard errors, clustered at the state and

13 year level. Since each of these comparison groups offers a very different source of identification, robustness of the results across specifications would strongly indicate a reliable finding.

The marginal effects for the PFD policy dummy variable for the two DID models are presented in Table 9. Unlike the models in Table 7, the DID analyses showed no statistically significant relationship between the Alaska PFD and self-employment in the baseline case with no interacted time trend. As noted only one year of pre-PFD status for Alaskans is included, which may limit the observed impact. The regression with a time trend does provide statistically significant results for the first DID specification, which relies on the differences between

Alaskans and non-Alaskans (Column 2). This regression finds that the baseline effect of the PFD was to increase self-employment by 2.8 percentage points, but that this effect declined by 0.1 percentage points for every year of the program. The DID analyses therefore show smaller effects that decline at about the same rate, so that the effect of the PFD on self-employment was zero in about 2010.

VIII. Exploratory Synthetic Control Analysis

Finally, we can re-aggregate the CPS data on self-employment back up to the state level to conduct a synthetic control analysis of the effect of the introduction of the PFD on self- employment. Synthetic control models are used to compare single treated units (in this case,

Alaska) with a pool of potential comparison units (the other states) by reweighting the comparison states to track Alaska’s pre-treatment self-employment rates (see Abadie, Diamond, and Hainmueller, 2010; 2015). Weights are selected to minimize the distance between Alaska and the “synthetic Alaska,” which is the reweighted pool of comparison states. States which

14 together have a very similar pre-treatment self-employment series will receive higher weights than states that do not. Once the “synthetic Alaska” has been estimated, the difference between the actual Alaskan results and the synthetic Alaska’s result represent the impact of the PFD.

We consider the synthetic control analyses exploratory because of an important risk associated with these methods in the Alaskan case. Jones and Marinescu (2018) use a synthetic control analysis of CPS data to study the effect of the PFD on employment, an analysis that is discussed at length in Feinberg and Kuehn’s (2018) paper on the same subject. Feinberg and

Kuehn (2018) use a DID approach instead of synthetic control approach because of the risks associated with misattributing the employment effects of the oil boom – which happened at the same time – to the PFD. Indeed, the PFD began as a policy in response to the strong demand for

Prudhoe Bay oil, which resulted in large increase in license and fee revenue. The two events are inextricably linked. As Feinberg and Kuehn (2018) point out, synthetic control analysis has no way of separating the effect of the PFD from the general increase in aggregate demand associated with the growing oil industry. These same cautions attach to our synthetic control analysis.

Figure 1 presents the results of the synthetic control analysis for the self-employment rate variable used in the prior CPS regressions. As the regressions suggest, the effect of the PFD on self-employment is positive. Pre-treatment self-employment rates, though, are only somewhat well matched between Alaska and synthetic Alaska. Figure 2 uses an alternative measure of self- employment – the total number of self-employed Alaskans. This version of the variable ensures

15 that Alaska is matched to similarly sized self-employed populations. The pre-treatment match is stronger, and there is also a clear positive effect of the PFD on self-employment.

IX. Conclusion

Using both state-level and individual-level data, the results presented here suggest that the creation of the Alaska Permanent Fund Dividend program and increases in the amounts paid our had significant positive impacts on small-firm entry in that state, likely tapering off over time given the relatively small magnitudes of the PFD payouts. Whether these effects would generalize to Guaranteed Basic Income (or Universal Basic Income) programs is unclear, though one would think those which provided for closer replacement of minimum-wage salaried income should have even stronger incentive effects.

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Table 1. Permanent Fund Dividend Payments, 2000-2015

Year Dividend Payments (per household member) Nominal Real (2016 dollars) 2000 $1,964 $2,835 2001 $1,850 $2,597 2002 $1,541 $2,122 2003 $1,108 $1,485 2004 $920 $1,202 2005 $846 $1,073 2006 $1,107 $1,360 2007 $1,654 $1,988 2008 $2,069 + $1,2001 $3,758 2009 $1,305 $1,483 2010 $1,281 $1,430 2011 $1,174 $1,270 2012 $878 $929 2013 $900 $923 2014 $1,884 $1,902 2015 $2,072 $2,081 2016 $1,022 $1,022 1A $1,200 Alaska Resources Rebate was added to the PFD in 2008

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Table 2. Descriptive Statistics, Means and Standard Deviations

Alaska Hawaii Montana US

Small-firm Entry 955.2 1362.3 1745.1 349,281.1 (births under 5 employees) (155.0) (200.4) (284.4) (32,103.4)

Population Growth (%) 1.69 1.23 0.79 0.99 (1.89) (0.58) (0.75) (1.07)

Real-GDP index (1997=100) 103.2 99.4 103.2 97.8 (18.3) (22.0) (25.4) (29.5)

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Table 3. Regression Results, Estimated Coefficients – Alaska-only Time Series

Dependent Variable = ln (Small-Firm Entry), N = 38

Variable (1) (2) ln Real-GDP-index -0.254** -0.034 (0.121) (0.130)

ln Population Growth 0.455*** 0.322*** (0.070) (0.077)

PFD 0.151*** 0.246*** (0.055) (0.058)

ln US-small-firm entry 1.071*** 1.239*** (0.188) (0.158)

ln (Years since program start) --- -0.070*** (0.023)

R2 0.756 0.811

Standard errors in parentheses below estimated coefficients. *= Significant at 10%, **= Significant at 5%, ***= Significant at 1%

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Table 4. Regression Results, Estimated Coefficients – Alaska plus Hawaii, Montana controls

Dependent Variable = ln (Small-Firm Entry), N = 114

Variable (1) (2)

Hawaii 0.998* 0.612 (0.529) (0.488)

Montana -0.849 -1.167** (0.528) (0.485)

Alaska*ln Real-GDP-index -0.237** 0.005 (0.106) (0.109)

Hawaii*ln Real-GDP-index -0.350*** -0.006 (0.060) (0.090)

Montana*ln Real-GDP-index 0.113* 0.438*** (0.059) (0.086)

ln Population Growth 0.430*** 0.307*** (0.057) (0.058)

PFD 0.143*** 0.233*** (0.051) (0.050)

ln US-small-firm entry 1.114*** 1.221*** (0.093) (0.158)

ln (Years since program start) --- -0.070*** (0.015)

R2 0.924 0.937

Standard errors in parentheses below estimated coefficients.

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*= Significant at 10%, **= Significant at 5%, ***= Significant at 1%Table 5. Regression Results, Estimated Coefficients – All-States Panel Estimation, Fixed Effects Dependent Variable = ln (Small-Firm Entry), N = 1938 (51 states by 38 years)

Variable (1) (2) ln Population 0.294** 0.303*** (0.115) (0.113)

ln Population Growth 0.134** 0.128** (0.066) (0.062)

PFD -0.077 0.266*** (0.047) (0.021)

ln US-small-firm entry 1.198*** 1.198*** (0.043) (0.043)

ln (Years since program start) --- -0.137*** (0.016)

R2 0.900 0.904

Robust standard errors (clustered by state) in parentheses below estimated coefficients. *= Significant at 10%, **= Significant at 5%, ***= Significant at 1%

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Table 6. Descriptive Statistics

Descriptive Statistics for Current Population Survey Workers Sample, 1977-2017 (Standard Deviations in Parentheses)

Alaska All Other States Self-employed 11.0% 9.8% (32.3%) (29.7%) Female 44.9% 45.9% 49.7% (49.8%) Age 38.4 39.4 (13.2) (13.9) White 74.5% 72.6% (43.6%) (44.6%) Black 3.5% 10.8% (18.3%) (31.0%) Latino 4.3% 11.8% (20.2%) (32.2%) American Indian 7.9% 0.5% (26.9%) (6.8%) Asian and Pacific Islander 4.9% 3.4% (21.7%) (18.2%) Other race/ethnicity 5.0% 1.0% (21.7%) (10.0%) Married 57.9% 56.2% (49.4%) (49.6%) Less than high school 19.7% 25.3% (39.8%) (43.5%) High school diploma 54.3% 47.5% (49.8%) (49.9%) Associate's degree 6.2% 6.4% (24.2%) (24.5%) Bachelor's degree 13.0% 13.7% (33.7%) (34.4%) Graduate degree 6.7% 7.1% (24.9%) (25.7%) Number of children less than five years old 0.22 0.18 (0.53) (0.48) State unemployment rate 9.0 6.7 (2.0) (2.2) Observations 49,874 3,470,158 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 State and year fixed effects are included but not displayed Sample is all workers in the ASEC, 1977-2017

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Table 7. Determinants of Self-Employment, Current Population Survey, 1977-2017

(1) (2)

PFD Policy Dummy 0.013*** (0.004) PFD Level (2016 dollars) 0.002 (0.001) Female -0.044*** -0.044*** (0.000) (0.000) Age 0.005*** 0.005*** (0.000) (0.000) Age, squared -0.000*** -0.000*** (0.000) (0.000) Black (White reference) -0.047*** -0.047*** (0.000) (0.000) Latino (White reference) -0.030*** -0.030*** (0.000) (0.000) American Indian (White reference -0.026*** -0.026*** (0.002) (0.002) Asian and Pacific Islander (White reference) -0.012*** -0.012*** (0.001) (0.001) Other race (White reference) -0.010*** -0.010*** (0.002) (0.002) Married 0.022*** 0.022*** (0.000) (0.000) High school diploma 0.002*** 0.002*** (0.000) (0.000) Associate's degree -0.005*** -0.005*** (0.001) (0.001) Bachelor's degree 0.010*** 0.010*** (0.001) (0.001) Graduate degree 0.013*** 0.013*** (0.001) (0.001) Number of children less than five years old 0.014*** 0.014*** (0.000) (0.000) State unemployment rate 0.029** 0.029** (0.014) (0.014) Observations 3,520,032 3,520,032 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 State and year fixed effects are included but not displayed Sample is all workers in the ASEC, 1977-2017

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Table 8. Determinants of Self-Employment with Time Since Implementation Interaction, Current Population Survey, 1977-2017 (1)

PFD Policy Dummy 0.040*** (0.000) PFD Policy Dummy Interacted with Years Since -0.001*** Implementation (0.000) Female -0.044*** (0.000) Age 0.005*** (0.000) Age, squared -0.000*** (0.000) Black (White reference) -0.047*** (0.000) Latino (White reference) -0.030*** (0.000) American Indian (White reference -0.026*** (0.002) Asian and Pacific Islander (White reference) -0.012*** (0.001) Other race (White reference) -0.010*** (0.002) Married 0.022*** (0.000) High school diploma 0.002*** (0.000) Associate's degree -0.005*** (0.001) Bachelor's degree 0.010*** (0.001) Graduate degree 0.013*** (0.001) Number of children less than five years old 0.014*** (0.000) State unemployment rate 0.027** (0.014) Observations 3,520,032 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 State and year fixed effects are included but not displayed Sample is all workers in the ASEC, 1977-2017

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Table 9. Determinants of Self-Employment, DID models, Current Population Survey, 1982- 2017

DID, Alaskan one year DID, Alaskan one year residency vs. non-Alaskan residency vs. Alaskan less one year residency than one year residency (1) (2) (3) (4) PFD policy dummy 0.005 0.028*** 0.0046 -0.051

(0.647) (0.008) (0.552) (0.241)

PFD policy dummy interacted with time -0.001*** 0.002* since implementation (0.000) (0.061)

Observations 3,017,070 3,017,070 43,754 43,754

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Workers in the ASEC, 1982-2017, excluding 1985.

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Figure 1.

Figure 2.

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