Grant Schemes and Pork-Barrel Politics in Local Government Funding in Hungary

Figures & data

FIGURE 1. Local Income of Municipalities (HUF Billion) and VFI (%)

Source: Hungarian Central Statistics Office (2022b).

FIGURE 2. Settlements in SMDs, 2014

Source: National Election Office (2022).

TABLE 1 Local Government Characteristics According to Political Alignment

All settlements
	Mayor			Member of Parliament
	Aligned	Neutral	Opposition	Aligned	Opposition
Number of LGs (N = 3,155)	620	2,485	50	3,131	24
Total population (millions)	5.5	3.5	1.0	8.8	1.2
Average population	8,794	1,420	19,304	2,800	49,060
Population density	275	58	772	86	3,545
Unemployment, %	7	7	8	7	4
Elderly population, %	25	25	24	25	25
Tax income, %	20	17	26	18	39
Operation, %	77	78	83	78	78
Expenditures per capita	0.250	0.222	0.231	0.228	0.216
Settlements with aligned mayor candidate
	Mayor			Member of Parliament
	Aligned	Neutral	Opposition	Aligned	Opposition
Number of LGs (N = 918)	620	271	27	903	15
Total population (millions)	5.5	1.1	0.9	6.3	1.2
Average population	8,794	4,126	33,671	6,992	77,697
Population density	275	131	1,315	174	5,629
Unemployment, %	7	8	6	7	3
Elderly population, %	25	24	27	25	27
Tax income, %	20	21	36	20	55
Operation, %	77	80	81	78	80
Expenditures per capita	0.250	0.220	0.217	0.241	0.221

Notes: Population density is measured by inhabitants per km². Real values are expressed in HUF million.

Source: Based on Hungarian State Treasury (2019), Hungarian Central Statistics Office (2022a), National Election Office (2022).

TABLE 2 OLS Estimations for 2015–2018

	All settlements
	Application-based (N = 3,155)				Targetable (N = 3,155)				EU funds (N = 2,463)
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
Mayor aligned	0.165*** (0.051)	0.147*** (0.051)		0.149*** (0.051)	0.232*** (0.044)	0.229*** (0.044)		0.229*** (0.044)	0.469*** (0.053)	0.460*** (0.053)		0.456*** (0.053)
Mayor opposition		−0.478*** (0.161)		−0.496*** (0.162)		−0.081 (0.138)		−0.077 (0.139)		−0.222 (0.160)		−0.187 (0.161)
MP aligned			−0.123 (0.245)	−0.246 (0.246)			0.111 (0.211)	0.044 (0.212)			0.614** (0.243)	0.476** (0.241)
R square	0.238	0.241	0.243	0.243	0.203	0.210	0.211	0.211	0.221	0.244	0.244	0.245
	Settlements with aligned mayor candidate
	Application-based (N = 918)				Targetable (N = 918)				EU funds (N = 856)
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
Mayor aligned	0.250*** (0.077)	0.199** (0.079)		0.199** (0.079)	0.328*** (0.071)	0.324*** (0.074)		0.324*** (0.074)	0.376*** (0.071)	0.332*** (0.074)		0.333*** (0.073)
Mayor opposition		−0.581*** (0.222)		−0.560** (0.224)		−0.051 (0.207)		−0.067 (0.210)		−0.474** (0.200)		−0.399** (0.202)
MP aligned			0.365 (0.323)	0.202 (0.325)			−0.080 (0.303)	−0.149 (0.304)			0.826*** (0.296)	0.721** (0.295)
R square	0.377	0.383	0.388	0.388	0.155	0.174	0.174	0.174	0.413	0.426	0.430	0.434

Notes: See Table A2 in the online Appendix for the complete table of results. Standard errors in parentheses. *** Significant at 1% level. ** Significant at 5% level.

FIGURE 3. RD Plots Applying Parametric and Non-parametric Estimations

Notes: Margin is measured on the x-axis, while log grant value per capita is measured on the y-axis. Red lines represent the predicted values of best fit global polynomial estimations (left hand side), and local linear estimation with optimal bandwidth (right hand side) based on Imbens and Kalyanaraman (2012), applying triangular kernel and 20 intervals on both sides.

TABLE 3 RDD Estimations for 2015–2018

	Settlements with aligned mayor candidate
	Application-based (N = 918)				Targetable (N = 918)				EU funds (N = 856)
	(0)	(1)	(2)	(4)	(0)	(1)	(2)	(4)	(0)	(1)	(2)	(4)
Mayor aligned	0.088 (0.140)	0.110 (0.114)	0.043 (0.116)	0.043 (0.116)	0.153 (0.112)	0.191 (0.106)	0.183 (0.108)	0.183 (0.108)	0.136 (0.134)	0.278** (0.105)	0.220** (0.108)	0.220** (0.107)
Mayor opposition			−0.605*** (0.222)	−0.584*** (0.224)			−0.074 (0.207)	−0.074 (0.207)			−0.492** (0.201)	−0.417** (0.202)
MP aligned				0.203 (0.324)				0.183 (0.108)				0.723** (0.295)
Margin significant?	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	No
Controls?	No	Yes	Yes	Yes	No	Yes	Yes	Yes	No	Yes	Yes	Yes
R square	0.034	0.385	0.390	0.391	0.046	0.177	0.177	0.177	0.049	0.427	0.431	0.435

Notes: Linear function applied. Table A4 in the online Appendix presents estimations for further functions. Standard errors in parentheses. *** Significant at 1% level. ** Significant at 5% level.

TABLE 4 Impact of Grants on Government Supporting Votes in the National Election of 2018

	Δ share of government votes 2014–2018
	All settlements		Settlements with aligned mayor candidate
Δ Intergovernmental grants (log)	−0.004 (0.002)		−0.001 (0.003)
Δ EU funds (log)		0.001 (0.001)		0.005** (0.002)
N	2,820	1,511	825	650
R Square	0.051	0.142	0.082	0.162

Notes: See Table A6 in the online Appendix for a complete table of results. Standard errors in parentheses. *** Significant at 1% level. ** Significant at 5% level.

TABLE 5 Mapping of Grant Schemes to Channels of Pork-Barrelling

Design	Grant type	Rewarding core supporters / Increase vote share of the government	Credit sharing with aligned mayors	Disfavouring opposition mayors	Credit sharing with aligned MP
‘Simply given’	Targetable discretionary transfers	X	(X)
Subject to submission	Application-based discretionary grants	X		X
	EU funds		X	X	X

Notes: Regarding targetable discretionary transfers, credit sharing with aligned mayor is identified only in the case of larger settlements with population over 10,000.

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