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A land value tax (LVT) is different from a property tax in that it only taxes the underlying land, not the structure built on top of it. LVT is recognized by economists across the political spectrum, as the only tax raising significant revenue which theoretically does not diminish economic activity (and even increases it). This is in part because land is fixed and supply, and cannot be moved when taxed. An LVT instead encourages development of the land. This paper maps the ratio of LVT to other taxes in Denmark over time, and assesses its effect on economic growth. We can conclude that just as in theory, empirical evidence in the case of Denmark also shows positive results with regards to economic growth.

The New Physiocratic League advocates for a platform to shift taxation away from personal & corporate income taxation, and primarily towards a particular form of land value tax (LVT). It also advocates for anti-corruption measures and monetary reforms among other proposals. This study focuses specifically on the results of a shift to an LVT.

Empirical research on true Georgist policy, namely a tax system where the tax burden falls mainly on land, is limited due to a lack of real-world examples. The examples that have existed, mainly lack data. However, Denmark has an explicitly Georgist party which had success in parliament in the 1950s and helped implement a high national rate of land value tax where much of the tax burden existed at the time. Denmark provides a rare example where one can assess economic performance from this Georgist period, gradually transitioning to today where the tax burden falls mainly on incomes. While ample theoretical research exists on the implementation of an LVT, this study is the first of its kind to look at empirical data.

The remainder of this paper is organized as follows: In Section 2 the data and methods used in this study are presented. Section 3 includes stationary testing of time series. The estimated model and its checking are presented in Section 4 and Section 5, respectively. Concluding remarks are offered in the final section.

All data for this study is taken from Statistics Denmark (the central authority on Danish statistics), and spans from 1966 to 2018. This brings to light another advantage of using LVT data from Denmark versus other countries using LVT, in that they have a fully complete set of tax and economic performance data.

The yearly data on gross domestic product (gdp), household consumption expenditure (housc), government consumption expenditure (gov), total taxes and duties (total_tax), total income taxes (income_tax), and land value taxes (lvt) in Denmark from 1966-2018 have been obtained^{1}.

For analysis purposes, the following variables have been derived from the data: gdp growth rate (gdp_gr), household consumption expenditure growth rate (c_gr), government consumption expenditure growth rate (gov_gr), the proportion of income tax in total taxes (inc_div_total), and the proportion of LVT in total taxes (lvt_div_total)^{2}. Below are the formulas:

g d p _ g r t = g d p t − g d p t − 1 g d p t − 1 (1)

c _ g r t = h o u s c t − h o u s c t − 1 h o u s c t − 1 (2)

g o v _ g r t = g o v t − g o v t − 1 g o v t − 1 (3)

i n c _ d i v _ t o t a l t = i n c o m e _ t a x t t o t a l _ t a x t (4)

l v t _ d i v _ t o t a l t = l v t t t o t a l _ t a x t (5)

where t stands for time period.

The summary statistics of the variables are presented in

Furthermore, the time series under study have been tested for stationarity

Variables | Minimum | Maximum | Mean | Std. Dev. | Obs. | Units |
---|---|---|---|---|---|---|

GDP | 82,414 | 2,218,323 | 996,672.2 | 672,478.5 | 53 | DKK mln. |

HOUSC | 47,300 | 1,016,800 | 472,290.6 | 301,185.2 | 53 | DKK mln. |

GOV | 14.800 | 546,500 | 249,154.7 | 172,575.8 | 53 | DKK mln. |

LVT | 886.300 | 26,393,829 | 984,9915 | 7,589,106 | 53 | DKK 1000 |

INCOME_TAX | 11.342.022 | 6.44E+08 | 2.79E+08 | 2.02E+08 | 53 | DKK 1000 |

TOTAL_TAX | 2.445.4941 | 1.01E+09 | 4.55E+08 | 3.20E+08 | 53 | DKK 1000 |

GDP_GR | −0.044035 | 0.158603 | 0.066248 | 0.043826 | 52 | - |

GOV_GR | −0.010089 | 0.205882 | 0.073296 | 0.056574 | 52 | - |

C_GR | 0.024393 | 0.183076 | 0.061662 | 0.044283 | 52 | - |

INC_DIV_TOTAL | 0.459383 | 0.653027 | 0.588312 | 0.046240 | 53 | - |

LVT_DIV_TOTAL | 0.016014 | 0.036242 | 0.023366 | 0.005699 | 53 | - |

applying Augmented Dickey-Fuller (ADF) test, and an autoregressive distributed lag (ARDL) model has been estimated, with Eviews as the tool of choice.

To test the time series for stationarity ADF test has been applied. Otherwise, a time series that is nonstationary can only be studied on its behaviour only for a specific episode. Consequently, it will not be possible to generalize it for other time periods or making predictions^{3}. The results are shown in

The null hypothesis states that the variable under study has a unit root. As we can see, even at 10% significance level neither time series is stationary, except for gdp_gr and gov_gr. Therefore, the first order differences of nonstationary variables (d(c_gr), d(inc_div_total), d(lvt_div_total)) have been tested for unit roots. According to the results, the first order differences of all the nonstationary time series under study are stationary even at 1% significance level.

To begin with, the best ARDL model based on Akaike information criterion (AIC) has been selected using automatic selection option in Eviews. ARDL (1, 0, 0, 4, 0) model fits the data best according to AIC. The results are shown in

According to the estimation results, some parameters are not statistically significant even at 10% level. Furthermore, the non-significant variables have been eliminated from the model. The estimation results of the new model are shown in

As shown, now all parameters are statistically significant even at 1% significance level (except for the constant). Besides, Akaike information criterion has

Null Hypothesis: Variable has a unit root Exogenous: None Lag Length: 1 (Automatic-based on AIC, maxlag = 4) | ||
---|---|---|

Variable | ADF test statistic | Prob. |

GDP_GR | −1.684576* | 0.0868 |

C_GR | −1.297795 | 0.1769 |

GOV_GR | −2.089660** | 0.0364 |

INC_DIV_TOTAL | 1.219252 | 0.9412 |

LVT_DIV_TOTAL | −0.749168 | 0.3869 |

D(C_GR) | −8.183553*** | 0.0000 |

D(INC_DIV_TOTAL) | −6.118797*** | 0.0000 |

D(LVT_DIV_TOTAL) | −6.702513*** | 0.0000 |

Dependent Variable: GDP_GR Sample (adjusted): 1971 2018 Model selection method: Akaike info criterion Number of models evaluated: 2500 Selected Model: ARDL(1, 0, 0, 4, 0) | ||||
---|---|---|---|---|

Variable | Coefficient | Std. Error | t-Statistic | Prob. |

GDP_GR(-1) | 0.351745*** | 0.123164 | 2.855897 | 0.0069 |

D(INC_DIV_TOTAL) | −4.742598*** | 1.710415 | −2.772776 | 0.0086 |

D(LVT_DIV_TOTAL) | −0.121240 | 0.164615 | −0.736510 | 0.4659 |

GOV_GR | 0.261008** | 0.097249 | 2.683908 | 0.0107 |

GOV_GR(-1) | −0.077562 | 0.095713 | −0.810360 | 0.4228 |

GOV_GR(-2) | 0.081657 | 0.096937 | 0.842368 | 0.4049 |

GOV_GR(-3) | −0.028300 | 0.095203 | −0.297256 | 0.7679 |

GOV_GR(-4) | 0.218668** | 0.083147 | 2.629906 | 0.0123 |

D(C_GR) | 0.424335*** | 0.082776 | 5.126289 | 0.0000 |

Constant | 0.007162* | 0.004047 | 1.769503 | 0.0848 |

R-squared (adj.) | 0.885600 | |||

S.E. of regression | 0.014474 | |||

F-statistic | 41.42654 | |||

Prob(F-statistic) | 0.000000 | |||

AIC | −5.449863 |

been improved ( A I C = − 5.58 ). For comparison purposes, the AIC for the previous model was −5.45

Dependent Variable: GDP_GR Sample (adjusted): 1971 2018 | ||||
---|---|---|---|---|

Variable | Coefficient | Std. Error | t-Statistic | Prob. |

GDP_GR(-1) | 0.356995*** | 0.107863 | 3.309695 | 0.0019 |

D(LVT_DIV_TOTAL) | −4.803129*** | 1.457926 | −3.294495 | 0.0020 |

GOV_GR | 0.230744*** | 0.066375 | 3.476384 | 0.0012 |

GOV_GR(-4) | 0.216575*** | 0.072719 | 2.978236 | 0.0048 |

D(C_GR) | 0.420686*** | 0.077568 | 5.423434 | 0.0000 |

Constant | 0.007096* | 0.003902 | 1.818386 | 0.0761 |

R-squared (adj.) | 0.892742 | |||

S.E. of regression | 0.014015 | |||

F-statistic | 79.23903 | |||

Prob(F-statistic) | 0.000000 | |||

AIC | −5.580910 |

Thus, the estimated equation is:

G D P _ G R = 0.36 ∗ G D P _ G R ( − 1 ) + 0.23 ∗ G O V _ G R + 0.22 ∗ G O V _ G R ( − 4 ) − 4.8 ∗ D ( L V T _ D I V _ T O T A L ) + 0.42 ∗ D ( C _ G R ) + 0.007

That is, the GDP growth rate in Denmark at time t , GDP_GR, is explained by its value at time t − 1 , by the rate of change of government expenditure at time and t − 4 , by the difference of LVT ratio at time t , and by the difference of household consumption expenditure change rate at time t .

It is worth noticing that the difference of LVT ratio at time t has a negative impact on GDP growth rate for the same period of time.

The estimated residuals from the regression have been tested for autocorrelation. As we can see from

Furthermore, the errors normality and homoscedasticity assumptions have been checked.

The results are as follows:

As shown above in

The output above (

Heteroskedasticity Test: White Null hypothesis: Homoskedasticity | |||
---|---|---|---|

F-statistic | 1.791272 | Prob. F (20,27) | 0.0788 |

Obs*R-squared | 27.37141 | Prob. Chi-Square (20) | 0.1251 |

Scaled explained SS | 53.31813 | Prob. Chi-Square (20) | 0.0001 |

Heteroskedasticity Test: Breusch-Pagan-Godfrey Null hypothesis: Homoskedasticity | |||

F-statistic | 1.698014 | Prob. F (5,42) | 0.1562 |

Obs*R-squared | 8.071355 | Prob. Chi-Square (20) | 0.1523 |

Scaled explained SS | 15.72259 | Prob. Chi-Square (20) | 0.0077 |

Breusch-Godfrey Serial Correlation LM Test Null hypothesis: No serial correlation at up to 4 lags | |||
---|---|---|---|

F-statistic | 1.279533 | Prob. F (4,38) | 0.2951 |

Obs*R-squared | 5.697611 | Prob. Chi-Square (4) | 0.2229 |

^{5} fails to reject the null at 5% significance level.

Furthermore, the cumulative sum of recursive residuals (CUSUM) and the CUSUM of squared residuals (CUSUMSQ) tests have been applied in order to test for the stability of the estimated parameters.

A shift towards a land value tax is the only solution to many of the growth and taxation issues governments are facing. However, it is important that tax reform is delivered in as few packages as possible, as the economy appears to respond best when tax rates are predictable, as in Denmark. The LVT-based platform offered by the New Physiocratic League, if implemented, would best ensure real progress if put into place quickly, and then allowed the economy time to reorient itself towards its new trajectory of sustainable yet stronger growth.

The authors declare no conflicts of interest regarding the publication of this paper.

Allan, P. and Hovsepyan, G. (2019) The Effect on Growth of a Tax Shift between Land Value Taxes and Income Taxes in Denmark. Modern Economy, 10, 1843-1850. https://doi.org/10.4236/me.2019.108119