# Success or Waste of Taxpayer Money? Impact Assessment of Rural Development Programs in Hungary

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

^{2}with an approximate population of 9.8 million. At NUTS (Nomenclature des Unités Territoriales Statistiques—nomenclature of territorial units for statistics) 1 level, there are three, at NUTS 2 six, and at NUTS 3 twenty regions (nineteen counties plus the capital city, Budapest). At the Local Administrative Unit level (LAU1, formerly NUTS4), there are 174 small regions composed of 3164 administratively independent settlements. We employed a highly disaggregated dataset of yearly data with respect to these administratively independent settlements, which we believe contributes to the unique nature of this research. The T-STAR database of the Hungarian Central Statistical Office was obtained from the CERS-HAS databank (http://adatbank.krtk.mta.hu/adatbazisok___tstar). The related data are designed for use in spatial studies and consist of several hundred variables relating to demographics, public health, education, pollution, unemployment, social care, economic entities, infrastructure, commerce and hospitality, tourism, culture, housing stock, municipal aid, municipal budgets, agriculture and personal income tax. These variables are available for the 2007–2013 period for all 3164 administratively independent Hungarian settlements. An internal migration database was provided by the Hungarian Central Statistical Office. Data about development funds for the period 2008–2013 were taken from the Information Systems of National Regional Development. Using total payments per locality, we created three support indicators: total subsidy, subsidy per km

^{2}, and subsidy per capita in LAU1 regions. The descriptive statistics for the development subsidies (years 2008–2013, total per region, per capita and per square km) presented in Table 1 emphasize the uneven distribution of funds.

^{2}. Figure A1 in Appendix A depicts the box plot graphs of total, per capita and per km

^{2}subsidies; here, we focus on the yearly average and median values of all subsidy variables (Table 2).

_{it}= α

_{0}+ β

_{k}F

_{ikt}+ v

_{i}+ ε

_{it}

_{it}is net migration into region I normalized by the total population of the region i, α

_{0}is a constant, and F

_{ikt}is the value of factor k in region i, at time t. Thus, β

_{k}accounts for the impact of factor k (F

_{k}) upon net migration, and was used as a weight in the construction of RDI. Finally, v

_{i}is the region-specific residual and ε

_{it}is the residual with the usual white noise properties. Given the panel structure of data and the strict underlying assumptions of panel models, various models were estimated using specification and diagnostic tests to facilitate selection of the best one (see, for example, a handbook by Baltagi [43]). The RDI index takes the following form:

_{it}= h(β

_{kt},F

_{ikt}) = ∑

_{k}β

_{k}× F

_{ikt}

_{it}is the Rural Development Index in region i and year t, F

_{ikt}is the factors as defined under Equation (2), and β

_{kt}is the weights for each factor specific to region i and time t resulting from the estimation of the migration function (2). That is, Equation (3) calculates the RDI as the proportion of migration flows explained by local characteristics represented by the factors.

_{1}− Y

_{0}|D = 1) = E[Y

_{1}|D = 1) − (Y

_{0}|D = 1)]

_{0}and Y

_{1}are the outcomes in the non-treated and treated states, respectively. Estimating the treatment effects based on Propensity Score Matching (PSM) requires making two assumptions. First, the Conditional Independence Assumption (CIA) states that for a given set of covariates participation is independent of potential outcomes. The second condition is that the ATT is only defined within the region of common support. For a more comprehensive discussion of the econometric theory behind this methodology, we refer the reader to the works of Imbens and Wooldridge [46] and Guo and Fraser [47].

_{i}is the treatment variable. Following the estimation by maximum likelihood of the treatment conditional distribution parameters γ and ${\mathsf{\sigma}}^{2}$, the GPS is estimated:

## 3. Results

#### 3.1. Estimation of the Rural Development Index

^{2}(29) = 114.6; p = 0.000). Further, the homoscedasticity assumption in the fixed-effects model was rejected by the modified Wald test for group heteroscedasticity (see [53], p. 58) at chi

^{2}(174) = 3730.3; (p = 0.000). In addition, the Wooldridge ([54,55]) test for first-order autocorrelation in panel data also rejected the null assumption (F(1173) = 34.96; p = 0.000). Considering the results of these statistical tests, linear regression methods with panel-corrected standard errors that allow for heteroscedastic and contemporaneously correlated disturbances across panels were employed (we used the xtpsce routine available in Stata). The estimation results of Equation (2) using panel-corrected standard errors model are presented in the first two columns of Table 3.

^{2}subsidies.

#### 3.2. Difference in Differences Estimation Results

^{2}, baseline 2008–2010, end 2011–2013) and 68% (quantile approach, total subsidy, baseline 2008–2010, end period 2011–2013). However, even if we only considered best matching results (for example, where more than half of covariates were matched between treated and non-treated groups), all results point in the same direction: there were mostly insignificant treatment effects, regardless of outcome or support definition. Even more surprisingly is that where effects were significant (30% of all cases), they were small and negative.

#### 3.3. Generalized Propensity Score Matching Results

^{2}subsidy variables were divided by 1000, and the total subsidy by 1,000,000 for the empirical analysis. GPS estimations required us to define treatment intervals. For semiparametric estimations, the dose–response and average treatment effect functions were evaluated at each level of the vectors: Total subsidy (million HUF): 200, 300, 400, 500, 600, 800, 1000, 1400, 2000, and 3000; Subsidy per capita (thousand HUF): 3, 5, 8, 10, 13, 16, 20, 25, 35, and 50; Subsidy per km

^{2}(thousand HUF): 4, 6, 8, 10, 12, 15, 20, 25, 35, and 50. While these levels are arbitrary, but based on the distribution of treatment variables (see also Figure A3 in Appendix A), it should be noted that using 10 evenly spaced values to cover the range of the treatment or using 10 percentiles to cover the empirical distribution of the treatment variables leads to qualitatively the same results. Table 7 presents the balancing tests of semiparametric models.

^{2}models, respectively. Turning our attention to the parametric estimations, four treatment intervals were defined for each subsidy variable: Total subsidy (million HUF): [0, 250), [250, 500), [500, 1000) and [1000, 7112); Subsidy per capita (thousand HUF): [0, 10), [10, 20), [20, 40) and [40, 127); and Subsidy per km

^{2}(thousand HUF): [0, 8), [8, 12), [12, 20) and [20, 132). As described in the Methodology Section, balancing tests were conducted for each treatment interval. Table 8 includes a summary of these tests (complete results are available upon request).

^{2}variable was the most balanced after accounting for GPS scores. With the wealth of covariates that was available (a total of 29), we may cautiously consider the balancing requirement satisfied. Note that this partial result is in line with the results of likelihood ratio tests for the semi-parametric approach. The most important results are depicted in the figures below. Figure 7, Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12 depict dose–response functions and average treatment effects (derivatives) using the Inverted Weighted Kernel, second order Penalized Spline, and Radial Penalized Spline methods, respectively, for the three treatment variables and two outcome variables.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

Year | NMR | |
---|---|---|

2008 | RDI | 0.882 |

2009 | RDI | 0.907 |

2010 | RDI | 0.825 |

2011 | RDI | 0.744 |

2012 | RDI | 0.696 |

2013 | RDI | 0.803 |

**Figure A1.**Boxplot of yearly subsidies received (total, per capita and per km

^{2}) (Hungarian Forints). Source: Own calculations.

**Figure A3.**Histogram of total, per capita and per km

^{2}subsidies, along with normal density (dashed line). Source: Own calculations.

## References

- Bourdin, S. Does the Cohesion Policy Have the Same Influence on Growth Everywhere? A Geographically Weighted Regression Approach in Central and Eastern Europe. Econ. Geogr.
**2018**, 1–32. [Google Scholar] [CrossRef] - Bouayad-Agha, S.; Turpin, N.; Védrine, L. Fostering the development of European regions: A spatial dynamic panel data analysis of the impact of cohesion policy. Reg. Stud.
**2013**, 47, 1573–1593. [Google Scholar] [CrossRef] - RodrÍguez-Pose, A.; Fratesi, U. Between development and social policies: the impact of European Structural Funds in Objective 1 regions. Reg. Stud.
**2004**, 38, 97–113. [Google Scholar] [CrossRef] - Mohl, P.; Hagen, T. Do EU structural funds promote regional growth? New evidence from various panel data approaches. Reg. Sci. Econ.
**2010**, 40, 353–365. [Google Scholar] [CrossRef] - Becker, S.O.; Egger, P.H.; Von Ehrlich, M. Going NUTS: The effect of EU Structural Funds on regional performance. J. Public Econ.
**2010**, 94, 578–590. [Google Scholar] [CrossRef][Green Version] - Becker, S.O.; Egger, P.H.; Von Ehrlich, M. Too much of a good thing? On the growth effects of the EU's regional policy. Eur. Econ. Rev.
**2012**, 56, 648–668. [Google Scholar] [CrossRef][Green Version] - Becker, S.O.; Egger, P.H.; Von Ehrlich, M. Absorptive capacity and the growth and investment effects of regional transfers: A regression discontinuity design with heterogeneous treatment effects. Am. Econ. J. Econ. Policy
**2013**, 5, 29–77. [Google Scholar] [CrossRef] - Cerqua, A.; Pellegrini, G. Are we spending too much to grow? The case of Structural Funds. J. Reg. Sci.
**2018**, 58, 535–563. [Google Scholar] [CrossRef] - Becker, S.O.; Egger, P.H.; von Ehrlich, M. Effects of EU Regional Policy: 1989-2013. Reg. Sci. Urban Econ.
**2018**, 69, 143–152. [Google Scholar] [CrossRef] - Giua, M. Spatial discontinuity for the impact assessment of the EU regional policy: The case of Italian objective 1 regions. J. Reg. Sci.
**2017**, 57, 109–131. [Google Scholar] [CrossRef] - Gagliardi, L.; Percoco, M. The impact of European Cohesion Policy in urban and rural regions. Reg. Stud.
**2017**, 51, 857–868. [Google Scholar] [CrossRef] - Caldas, P.; Dollery, B.; Marques, R.C. European Cohesion Policy impact on development and convergence: A local empirical analysis in Portugal between 2000 and 2014. Eur. Plan. Stud.
**2018**, 26, 1081–1098. [Google Scholar] [CrossRef] - Fiaschi, D.; Lavezzi, A.M.; Parenti, A. Does EU cohesion policy work? Theory and evidence. J. Reg. Sci.
**2018**, 58, 386–423. [Google Scholar] [CrossRef] - Medeiros, E. Assessing territorial impacts of the EU Cohesion Policy: The Portuguese case. Eur. Plan. Stud.
**2014**, 22, 1960–1988. [Google Scholar] [CrossRef] - Michalek, J.; Zarnekow, N. Application of the rural development index to analysis of rural regions in Poland and Slovakia. Soc. Indic. Res.
**2012**, 105, 1–37. [Google Scholar] [CrossRef] - Spicka, J.; Naglova, Z.; Gurtler, M. Effects of the investment support in the Czech meat processing industry. Agric. Econ. Zemed. Ekon.
**2017**, 63, 356–369. [Google Scholar] [CrossRef] - Gustafsson, A.; Stephan, A.; Hallman, A.; Karlsson, N. The “sugar rush” from innovation subsidies: a robust political economy perspective. Empirica
**2016**, 43, 729–756. [Google Scholar] [CrossRef] - Brachert, M.; Dettmann, E.; Titze, M. Public Investment Subsidies and Firm Performance–Evidence from Germany. Jahrbücher Für Natl. Stat.
**2018**, 238, 103–124. [Google Scholar] [CrossRef] - Bachtrögler, J.; Hammer, C.; Reuter, W.H.; Schwendinger, F. Guide to the galaxy of EU regional funds recipients: Evidence from new data. Empirica
**2019**, 46, 103–150. [Google Scholar] [CrossRef] - Dall'Erba, S.; Fang, F. Meta-analysis of the impact of European Union Structural Funds on regional growth. Reg. Stud.
**2017**, 51, 822–832. [Google Scholar] [CrossRef] - Castaño, J.; Blanco, M.; Martinez, P. Reviewing Counterfactual Analyses to Assess Impacts of EU Rural Development Programmes: What Lessons Can Be Learned from the 2007–2013 Ex-Post Evaluations? Sustainability
**2019**, 11, 1105. [Google Scholar] [CrossRef] - Terluin, I.J.; Roza, P. Evaluation Methods for Rural Development Policy; LEI Wageningen UR: Wageningen, Netherlands, 2010. [Google Scholar]
- Bakucs, Z.; Fertő, I.; Varga, Á.; Benedek, Z. Impact of European Union development subsidies on Hungarian regions. Eur. Plan. Stud.
**2018**, 26, 1121–1136. [Google Scholar] [CrossRef] - Michalek, J. Counterfactual Impact Evaluation of EU Rural Development Programmes-Propensity Score Matching Methodology Applied to Selected EU Member States. Volume 2: A Regional Approach; Joint Research Centre (Seville site): Seville, Spain, 2012. [Google Scholar]
- Bia, M.; Mattei, A. Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score. Stat. Methods Appl.
**2012**, 21, 485–516. [Google Scholar] [CrossRef] - D'Attoma, I.; Pacei, S. Evaluating the Effects of Product Innovation on the Performance of European Firms by Using the Generalised Propensity Score. Ger. Econ. Rev.
**2018**, 19, 94–112. [Google Scholar] [CrossRef] - Michalek, J.; Ciaian, P. Capitalization of the single payment scheme into land value: generalized propensity score evidence from the European Union. Land Econ.
**2014**, 90, 260–289. [Google Scholar] [CrossRef] - Esposti, R. The empirics of decoupling: Alternative estimation approaches of the farm-level production response. Eur. Rev. Agric. Econ.
**2017**, 44, 499–537. [Google Scholar] [CrossRef] - Batory, A. 10. Corruption in East Central Europe: has EU membership helped? Handb. Geogr. Corrupt.
**2018**, 169. [Google Scholar] [CrossRef] - Fazekas, M.; Tóth, I.J. From corruption to state capture: A new analytical framework with empirical applications from Hungary. Political Res. Q.
**2016**, 69, 320–334. [Google Scholar] [CrossRef] - Tóth, I.J.; Hajdu, M. Competitive Intensity and Corruption Risks in the Hungarian Public Procurement 2009–2015; The Corruption Research Center: Budapest, Hungary, 2016. [Google Scholar]
- Tiebout, C.M. A pure theory of local expenditures. J. Econ.
**1956**, 64, 416–424. [Google Scholar] [CrossRef] - Liu, B.-C. Differential net migration rates and the quality of life. Rev. Econ. Stat.
**1975**, 57, 329. [Google Scholar] [CrossRef] - Michalos, A.C. Migration and the quality of life: A review essay. Soc. Indic. Res.
**1996**, 39, 121–166. [Google Scholar] [CrossRef] - Nakajima, K.; Tabuchi, T. Estimating interregional utility differentials. J. Reg. Sci.
**2011**, 51, 31–46. [Google Scholar] [CrossRef] - Faggian, A.; Corcoran, J.; Partridge, M. 22 interregional migration analysis. Handb. Res. Methods Appl. Econ. Geogr.
**2015**. [Google Scholar] [CrossRef] - Foley, M.; Angjellari-Dajci, F. Net migration determinants. J. Reg. Anal. Policy
**2015**, 45, 30. [Google Scholar] - Lukovics, M.; Kovács, P. Eljárás a területi versenyképesség mérésére. Területi Stat.
**2008**, 3, 245–263. [Google Scholar] - Lukovics, M. Measuring regional disparities on competitiveness basis. In Regional Competitiveness, Innovation and Environment; JATE Press: Szeged, Hungary, 2009; pp. 39–53. [Google Scholar]
- Douglas, S.; Wall, H.J. ‘Voting with your feet’and the quality of life index: A simple non-parametric approach applied to Canada. Econ. Lett.
**1993**, 42, 229–236. [Google Scholar] [CrossRef] - Douglas, S.; Wall, H.J. Measuring Relative Quality of Life from a Cross-Migration Regression, with an Application to Canadian Provinces; Emerald Group Publishing Limited: Bingley, UK, 2000; Voloume 19, pp. 191–214. [Google Scholar]
- Wirth, B. Ranking German regions using interregional migration. In School of Business and Economics at FAU Erlangen-Nuremberg Working Paper; Erlangen-Nuerenberg: Erlangen, Germany, 2013. [Google Scholar]
- Baltagi, B. Econometric Analysis of Panel Data; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
- Cerulli, G. Econometric Evaluation of Socio-Economic Programs. Theory and Applications; Springer: Berlin/Heidelberg, Germany, 2015; Voloume 49, p. 308. [Google Scholar]
- Rosenbaum, P.R.; Rubin, D.B. The central role of the propensity score in observational studies for causal effects. Biometrika
**1983**, 70, 41–55. [Google Scholar] [CrossRef][Green Version] - Imbens, G.W.; Wooldridge, J.M. Recent developments in the econometrics of program evaluation. J. Econ. Lit.
**2009**, 47, 5–86. [Google Scholar] [CrossRef] - Guo, S.; Fraser, M.W. Propensity Score Analysis: Statistical Methods and Applications; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2010; Voloume 11. [Google Scholar]
- Smith, J.A.; Todd, P.E. Does matching overcome LaLonde’s critique of nonexperimental estimators? J. Econ.
**2005**, 125, 305–353. [Google Scholar] [CrossRef] - Gelman, A.; Meng, X.-L. Applied BAYESIAN Modeling and Causal Inference from Incomplete-Data Perspectives; John Wiley & Sons: Hoboken, NJ, USA, 2004. [Google Scholar]
- Bia, M.; Flores, C.A.; Flores-Lagunes, A.; Mattei, A. A Stata package for the application of semiparametric estimators of dose–response functions. Stata J. Promot. Commun. Stat. Stata
**2014**, 14, 580–604. [Google Scholar] [CrossRef] - Flores, C.A.; Flores-Lagunes, A.; Gonzalez, A.; Neumann, T.C. Estimating the effects of length of exposure to instruction in a training program: the case of job corps. Rev. Econ. Stat.
**2012**, 94, 153–171. [Google Scholar] [CrossRef] - Guardabascio, B.; Ventura, M. Estimating the dose–response function through a generalized linear model approach. Stata J. Promot. Commun. Stat. Stata
**2014**, 14, 141–158. [Google Scholar] [CrossRef] - Greene, W.H. Econometric Analysis, 5th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2000; p. 802. [Google Scholar]
- Wooldridge, J.M. Econometric Analysis of Cross Section and Panel Data; MIT Press: Cambridge, MA, USA, 2010. [Google Scholar]
- Drukker, D.M. Testing for serial correlation in linear panel-data models. Stata J.
**2003**, 3, 168–177. [Google Scholar] [CrossRef] - Elhorst, J.P. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels; Springer: Berlin/Heidelberg, Germany, 2014; Volume 479. [Google Scholar]
- Abadie, A.; Drukker, D.; Herr, J.L.; Imbens, G.W. Implementing matching estimators for average treatment effects in Stata. Stata J.
**2004**, 4, 290–311. [Google Scholar] [CrossRef] - Leuven, E.; Sianesi, B. Psmatch2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing, and Covariate Imbalance Testing; Statistical Software Components: Boston, MA, USA, 2018. [Google Scholar]
- Muraközy, B.; Telegdy, Á. Political incentives and state subsidy allocation: Evidence from Hungarian municipalities. Eur. Econ. Rev.
**2016**, 89, 324–344. [Google Scholar] [CrossRef][Green Version]

**Figure 2.**Levels of rural development in 2008 and 2013 (RDI). Source: Own calculations, using spmap (Stata) (

**a**). Levels of rural development in 2008 and 2013 (NMR). Source: Own calculations, using spmap (Stata) (

**b**).

**Figure 3.**(

**a**) Change in regional development between 2008 and 2013 (RDI). Source: Own calculations, using spmap (Stata); (

**b**) Change in regional development between 2008 and 2013 (NMR). Source: Own calculations, using spmap (Stata).

**Figure 7.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Total subsidy). Source: Own calculations using drf (Stata).

**Figure 8.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Total subsidy). Source: Own calculations using drf (Stata).

**Figure 9.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Subsidy per capita). Source: Own calculations using drf (Stata).

**Figure 10.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Subsidy per capita). Source: Own calculations using drf (Stata).

**Figure 11.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Subsidy per km

^{2}). Source: Own calculations using drf (Stata).

**Figure 12.**Semiparametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Subsidy per km

^{2}). Source: Own calculations using drf (Stata).

**Figure 13.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Total subsidy). Source: Own calculations using glmdose (Stata).

**Figure 14.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Total subsidy). Source: Own calculations using glmdose (Stata).

**Figure 15.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Subsidy per capita). Source: Own calculations using glmdose (Stata).

**Figure 16.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Subsidy per capita). Source: Own calculations using glmdose (Stata).

**Figure 17.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is RDI, treatment is Subsidy per km

^{2}). Source: Own calculations using glmdose (Stata).

**Figure 18.**Parametric dose–response functions and average treatment effects with 95% confidence intervals (outcome is NMR, treatment is Subsidy per km

^{2}). Source: Own calculations using glmdose (Stata).

Variable | Obs. | Mean | Std. Dev. | Min. | Max. |
---|---|---|---|---|---|

T. subsidy (k. HUF *) | 1044 | 780,185.1 | 814,366.4 | −36,435 | 7,111,930 |

Subsidy/cap (k. HUF) | 1044 | 19.707 | 17.077 | −2.106 | 126.25 |

Subsidy/km^{2} (k. HUF) | 1044 | 1386.61 | 1209.213 | −95.581 | 13,203.6 |

Year | T. Subsidy (k. HUF *) | Subsidy/cap (k. HUF) | Subsidy/km^{2} (k. HUF) | |||
---|---|---|---|---|---|---|

Mean | Median | Mean | Median | Mean | Median | |

2008 | 415,932.6 | 329,751.3 | 10.42 | 8.95 | 7.41 | 6.64 |

2009 | 896,959.9 | 675,489.2 | 21.80 | 19.17 | 15.82 | 14.40 |

2010 | 344,438 | 278,852.4 | 8.80 | 7.10 | 6.11 | 5.43 |

2011 | 916,278.1 | 649,107.1 | 23.28 | 18.96 | 16.32 | 13.79 |

2012 | 1,010,492 | 768,804.1 | 25.75 | 22.18 | 18.05 | 16.68 |

2013 | 1,097,327 | 843,807.7 | 28.20 | 22.90 | 19.47 | 16.59 |

**Table 3.**Migration function models (dependent variable net migration into a region, normalized by population).

Non-Spatial Model | Spatial Durbin Model | ||||
---|---|---|---|---|---|

Var. | Coeff. | Var. | Coeff. | Var. | Coeff. |

f1 | 0.00376 *** | f1 | 0.00363 *** | w_f1 | −0.00038 |

f2 | 0.00273 *** | f2 | 0.00390 *** | w_f2 | 0.00003 |

f3 | 0.00101 *** | f3 | 0.00061 *** | w_f3 | 0.00241 |

f4 | 0.00010 | f4 | 0.00020 | w_f4 | −0.00103 |

f5 | 0.00063 *** | f5 | 0.00048 *** | w_f5 | −0.00030 |

f6 | −0.00036 ** | f6 | −0.00072 *** | w_f6 | 0.00046 |

f7 | −0.00013 | f7 | 0.00022 | w_f7 | 0.00088 |

f8 | 0.00055 *** | f8 | 0.00040 *** | w_f8 | 0.00220 * |

f9 | 0.00004 | f9 | 0.00004 | w_f9 | −0.00173 |

f10 | −0.00054 *** | f10 | −0.00035 *** | w_f10 | −0.00109 |

f11 | 0.00000 | f11 | 0.00002 | w_f11 | 0.00006 |

f12 | −0.00000 | f12 | 0.00006 | w_f12 | −0.0020 ** |

f13 | 0.00093 *** | f13 | 0.00070 *** | w_f13 | 0.00126 |

f14 | −0.00031 ** | f14 | −0.00021 | w_f14 | 0.00221 * |

f15 | 0.00016 ** | f15 | 0.00032 *** | w_f15 | −0.00107 |

f16 | −0.00094 *** | f16 | −0.00031 * | w_f16 | 0.00005 |

f17 | 0.00004 | f17 | −0.00006 | w_f17 | 0.0036 ** |

f18 | 0.00021 | f18 | −0.00052 *** | w_f18 | −0.00114 |

f19 | 0.00143 *** | f19 | 0.00054 *** | w_f19 | 0.0032 *** |

f20 | −0.00006 | f20 | −0.00003 | w_f20 | 0.00414*** |

f21 | −0.00002 | f21 | −0.00005 | w_f21 | 0.00151 |

f22 | 0.00019** | f22 | 0.00030 *** | w_f22 | 0.00115 |

f23 | 0.00053 *** | f23 | 0.00030 *** | w_f23 | −0.00073 |

f24 | 0.00008 | f24 | 0.00032** | w_f24 | 0.00319 *** |

f25 | 0.00003 | f25 | −0.00013 | w_f25 | −0.0029*** |

f26 | 0.00039 ** | f26 | 0.00031 * | w_f26 | −0.00201 |

f27 | −0.00015 | f27 | 0.00056 *** | w_f27 | −0.0026 *** |

f28 | −0.00020 | f28 | 0.00004 | w_f28 | 0.00083 |

f29 | −0.00036 * | f29 | −0.00009 | w_f29 | −0.00115 ** |

cons | −0.00275 *** | cons | −0.00282 *** | ||

N | 1044 | 1044 | |||

Cross-sections | 174 | 174 | |||

R^{2}a | 0.5662 | 0.6162 | |||

ll | 4308.04 | 4390.3 | |||

Wald (p) | 0.0000 | 0.0000 |

^{2}a the percentage of total variance explained by the model, ll the loglikelihood, and (p) the Wald regression significance.

Non-Spatial Model | Spatial Durbin Model | |
---|---|---|

H_{0}: Error has no Spatial Autocorrelation | ||

Global Moran MI | 0.0000 | 0.2697 |

Global Geary GC | 0.0000 | 0.0616 |

Global Getis-Ords GO | 0.0000 | 0.2697 |

Moran MI Error Test | 0.0000 | 0.2562 |

LM Error (Burridge) | 0.0000 | 0.2505 |

LM Error (Robust) | 0.0000 | 0.3083 |

H_{0}: Spatial lagged Dependent Variable has no Spatial Autocorrelation | ||

LM Lag (Anselin) | 0.0000 | 0.2453 |

LM Lag (Robust) | 0.0000 | 0.3017 |

H_{0}: No General Spatial Autocorrelation | ||

LM SAC (LMErr+LMLag_R) | 0.0000 | 0.3031 |

**Table 5.**Diff-in-diff treatment effect (PSM-DID) results for total subsidy, subsidy per cap. and subsidy per km

^{2}—threshold approach.

Total Subsidy | Subsidy/Cap | Subsidy/km^{2} | |||||||
---|---|---|---|---|---|---|---|---|---|

Coeff. | Prob. | No ^{#}. | Coeff. | Prob. | No ^{#}. | Coeff. | Prob. | No ^{#}. | |

Baseline period: 2008, end period: 2013 | |||||||||

RDI | −0.004 | 0.084 | 8 | 0.000 | 0.981 | 17 | −0.004 | 0.046 | 12 |

NMR | −0.001 | 0.733 | 8 | 0.003 | 0.253 | 17 | −0.002 | 0.739 | 11 |

baseline period: 2008–2009, end period: 2012–2013 | |||||||||

RDI | −0.004 | 0.012 | 17 | −0.004 | 0.048 | 16 | −0.003 | 0.037 | 7 |

NMR | −0.003 | 0.122 | 18 | −0.002 | 0.272 | 14 | −0.000 | 0.904 | 5 |

baseline period: 2008–2010, end period: 2011–2013 | |||||||||

RDI | −0.003 | 0.045 | 18 | −0.003 | 0.006 | 12 | −0.002 | 0.137 | 7 |

NMR | −0.002 | 0.456 | 17 | 0.000 | 0.890 | 12 | −0.002 | 0.209 | 3 |

^{#}indicates the number of unmatched covariates (total number of covariates is 29). p-values were calculated using bootstrapped standard errors. Coefficients in bold denote a 10% significance level. Common support was imposed.

**Table 6.**Diff-in-diff treatment effect (PSM-DID) results for total subsidy, subsidy per cap. And subsidy per km

^{2}—quantile approach.

Total Subsidy | Subsidy/cap | Subsidy/km^{2} | |||||||
---|---|---|---|---|---|---|---|---|---|

Coeff. | Prob. | No ^{#}. | Coeff. | Prob. | No ^{#}. | Coeff. | Prob. | No ^{#}. | |

baseline period: 2008, end period: 2013 | |||||||||

RDI | 0.001 | 0.634 | 9 | 0.001 | 0.592 | 4 | 0.003 | 0.290 | 12 |

NMR | −0.001 | 0.869 | 9 | 0.002 | 0.456 | 3 | 0.004 | 0.287 | 12 |

baseline period: 2008–2009, end period: 2012-2013 | |||||||||

RDI | −0.003 | 0.089 | 17 | −0.004 | 0.037 | 18 | −0.002 | 0.185 | 6 |

NMR | −0.002 | 0.358 | 17 | −0.006 | 0.027 | 18 | −0.003 | 0.100 | 5 |

baseline period: 2008–2010, end period: 2011–2013 | |||||||||

RDI | −0.003 | 0.550 | 19 | −0.001 | 0.360 | 14 | −0.003 | 0.137 | 11 |

NMR | −0.001 | 0.686 | 20 | −0.005 | 0.088 | 16 | 0.001 | 0.721 | 12 |

^{#}indicates the number of unmatched covariates (total number of covariates is 29). p-values were calculated using bootstrapped standard errors. Coefficients in bold denote a 10% significance level. Common support was imposed.

Total Subsidy | Subsidy/Cap | Subsidy/km^{2} | |
---|---|---|---|

Restricted LL (covariates X) | −6825.18 | −3435.99 | −3475.80 |

Unrestricted LL | −6815.67 | −3424.69 | −3463.51 |

Test statistic | 19.02 | 22.59 | 24.57 |

p-value | 0.920 | 0.794 | 0.700 |

N. of restrictions | 29 | 29 | 29 |

Restricted LL (GPS terms) | −6929.93 | −3498.55 | −3534.69 |

Unrestricted LL | −6815.18 | −3424.69 | −3463.51 |

Test statistic | 228.53 | 147.72 | 142.34 |

p-value | 0.000 | 0.000 | 0.000 |

N. of restrictions | 3 | 3 | 3 |

N. obs. dropped ^{#} | 133 | 167 | 56 |

^{#}number of observations dropped after imposing common support.

Total Subsidy | Subsidy/Cap | Subsidy/km^{2} | |
---|---|---|---|

number of significant test statistics (out of 29) | |||

Treatment interval 1 | 5 | 7 | 3 |

Treatment interval 2 | 1 | 2 | 2 |

Treatment interval 3 | 5 | 4 | 3 |

Treatment interval 4 | 5 | 4 | 1 |

^{#}“Test that the conditional mean of the pre-treatment variables given the generalized propensity score is not different between units who belong to a particular treatment interval and units who belong to all other treatment intervals” [52].

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bakucs, Z.; Fertő, I.; Benedek, Z. Success or Waste of Taxpayer Money? Impact Assessment of Rural Development Programs in Hungary. *Sustainability* **2019**, *11*, 2158.
https://doi.org/10.3390/su11072158

**AMA Style**

Bakucs Z, Fertő I, Benedek Z. Success or Waste of Taxpayer Money? Impact Assessment of Rural Development Programs in Hungary. *Sustainability*. 2019; 11(7):2158.
https://doi.org/10.3390/su11072158

**Chicago/Turabian Style**

Bakucs, Zoltán, Imre Fertő, and Zsófia Benedek. 2019. "Success or Waste of Taxpayer Money? Impact Assessment of Rural Development Programs in Hungary" *Sustainability* 11, no. 7: 2158.
https://doi.org/10.3390/su11072158