Grants and Marginal Cost of Public Funding: Empirical Evidence for Local Governments in Brazil

Mattos, Enlinson; Cardim, Rafael; Politi, Ricardo

doi:10.5935/0034-7140.20180023

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Articles • Rev. Bras. Econ. 72 (4) • Oct-Dec 2018 • https://doi.org/10.5935/0034-7140.20180023 copy

Grants and Marginal Cost of Public Funding: Empirical Evidence for Local Governments in Brazil

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

This paper documents empirical evidence on price-effect caused by lumpsum grants for local governments in Brazil between 2006 to 2010. Dahlby (2011)Dahlby, B. (2011). The marginal cost of public funds and flypaper effect. International Tax and Public Finance, 18(3), 304-321. http://dx.doi.org/10.1007/s10797-010-9160-x
http://dx.doi.org/10.1007/s10797-010-916... demonstrates theoretically that lump-sum grants can reduce the cost of public goods provision (price-effect), in addition to the traditional income effect. Our contributions are threefold. First we estimated semielasticity of the effects of tax rate changes on tax base (−0.016). Second, we calculate the MCF of the local tax imposed on the supply of services (ISS) for Brazilian municipalities (average of 0.04). Finally, we estimate the priceeffect estimation for ISS tax. Our results suggests that for the entire sample, that an increase in R$ 1.00 in per capita unconditional transfers reduces the local price effect (MCF) around 0.07%, but this result is not consistently estimated across all subsamples.

Keywords
Price effect; income effect; transfers; taxes; fiscal federalism

		Tax Revenue
Population Range	Unconditional Grant (FPM)	ISS	IPTU	State Grant	GDP	Nº Munic.
up to 5 k (thous.)	1,367.5	44.1	10.4	567.4	11,918	1,178
>5 to 10 k	630.8	42.8	14.1	404.6	10,873	1,172
> 10 to 25 k	473.0	41.0	16.9	319.4	10,299	1,612
> 25 to 50 k	343.8	59.0	30.1	322.1	12,305	650
> 50 to 250 k	245.2	89.7	48.8	359.8	15,483	466
> 250 k	132.8	156.7	97.6	410.2	20,873	95

Variable	Definition	Source	Mean	Std. Dev.	Min	Max
FPM	Total FPM transfers revenues	Finbra dataset	9,182,481	17,600,000	3,293	802,000,000
ISS	Total service tax revenue	Finbra dataset	4,966,098	106,000,000	11	11,200,000,000
Payroll	Total payroll costs for firms in service sector	Cadastro Central de Empresas	139,940	2,199,693	2	206,000,000
HDI	Human Development Index	FIRJAN	0.616	0.119	0.237	0.916
Firms	Number of firms in service sector	Cadastro Central de Empresas	934	8,555	4	599,434
Ln Employees	Natural logarithm of employees in the service sector	Cadastro Central de Empresas	7.21	1.46	1.61	15.62
Tax capacity	State grant from VAT (ICMS) normalized	Authors	0.00	1.00	-1.02	43.86
AETR	Average effective tax rate (ISS revenue/ payroll costs)	Authors	0.040	0.225	0.000	30.076
MCF	Marginal costs of public fund	Authors	0.001	0.006	1.000	1.946
Metropolitan region	Binary variable (=1) if municipality is in metropolitan area	IBGE	0.138	0.345	0	1
Ln Population	Natural logarithm of local population	IBGE	9.42	1.14	6.69	16.25
Density	Populational density	IBGE	103.8	546.0	0.1	13,347
Share of young	Individuals from 5 to 15 years old	DATASUS	0.266	0.052	0.074	0.513
Share of elderly	Individuals above 60 years old	DATASUS	0.116	0.031	0.021	0.294

Bandwidth size	500	1,000	1,500	2,000	3,000	4,000	Total
Cut-off 1	-0.200 ^* ***** p < 0.01,	-0.107 ^ p < 0.05,	-0.052
	(-5.310)	(-2.530)	(-1.321)
Number of cases	1,355	2,687	3,904

Cut-off 2		-0.010	0.002	-0.002
		(-1.448)	(0.374)	(-0.175)
Number of cases		2,003	2,947	3,914

Cut-off 3			0.175 ^* ***** p < 0.01,	0.110	-0.007
			(3.222)	(0.980)	(-0.150)
Number of cases			2,214	2,476	4,298

Cut-off 4				-0.250	0.002	0.194
				(-1.191)	(0.011)	(0.971)
Number of cases				1,769	2,596	3,520

Cut-off 5				-0.630 ^ p < 0.05,	-0.570 ^ p < 0.05,	-0.476 ^ p < 0.05,
				(-2.359)	(-2.465)	(-2.437)
Number of cases				997	1,519	2,023

Total							-0.016 ^ p < 0.05,
							(-2.463)
Number of cases							36,211

Bandwidth size	500	1,000	1,500	2,000	3,000	Total
Cut-off 1	0.020	0.002	0.002
	(0.963)	(0.864)	(1.166)
F-test	1.01	31.73	65.16

Cut-off 2		0.003	-0.002	-0.002
		(0.941)	(-0.434)	(-0.585)
F-test		8.87	25.80	56.80

Cut-off 3			-0.000	0.000	0.001
			(-0.069)	(0.006)	(0.877)
F-test			4.60	12.27	30.56

Cut-off 4				-0.001	-0.001	-0.001
				(-0.641)	(-1.074)	(-0.955)
F-test				7.24	23.77	38.82

Cut-off 5				-0.007	-0.005	-0.004 ^* * p < 0.1.
				(-0.946)	(-1.347)	(-1.779)
F-test				1.18	4.25	10.16

Total						-0.003 ^ p < 0.05,
						(-2.276)
F-test						48.37

Population Range	Factor
Up to 10,188	0.6
From 10,189 to 13,584	0.8
From 13,585 to 16,980	1.0
From 16,981 to 23,772	1.2
From 23,773 to 30,564	1.4
From 30,565 to 37,356	1.6
From 37,357 to 44,148	1.8
From 44,149 to 50,940	2.0
From 50,941 to 61,128	2.2
From 61,129 to 71,316	2.4
From 71,317 to 81,504	2.6
From 81,505 to 91,692	2.8
From 91,693 to 101,880	3.0
From 101,881 to 115,464	3.2
From 115,465 to 129,048	3.4
From 129,049 to 142,632	3.6
From 142,633 to 156,216	3.8
> 156,216	4.0

Bandwidth size	500	1,000	1,500	2,000	3,000	4,000	Total
Cut-off 1	-0.140 ^* ***** p < 0.01,	-0.094 ^ p < 0.05,	-0.050
	(-3.129)	(-2.453)	(-1.430)
Number of cases	1,355	2,687	3,904

Cut-off 2		-0.023	-0.009	-0.013
		(-1.114)	(-0.507)	(-0.640)
Number of cases		2,003	2,947	3,914

Cut-off 3			0.177 ^* ***** p < 0.01,	0.111	-0.041
			(3.253)	(0.993)	(-0.934)
Number of cases			2,214	2,476	4,298

Cut-off 4				-0.249	-0.004		0.191
				(-1.182)	(-0.021)		(0.957)
Number of cases				1,769	2,596		3,520

Cut-off 5				-0.629 ^ p < 0.05,	-0.570 ^ p < 0.05,		-0.476 ^ p < 0.05,
				(-2.357)	(-2.467)		(-2.437)
Number of cases				997	1,519	2,023

Total							-0.016 ^ p < 0.05,
							(-2.463)
Number of cases							37,708

Population cutoffs (binary variables)
dfxpop1	-0.446 ^* ***** p < 0.01,	(-19.360)
dfxpop2	-0.310 ^* ***** p < 0.01,	(-15.240)
dfxpop3	-0.211 ^* ***** p < 0.01,	(-11.530)
dfxpop4	-0.147 ^* ***** p < 0.01,	(-9.220)
dfxpop5	-0.102 ^* ***** p < 0.01,	(-7.590)
dfxpop6	-0.064 ^* ***** p < 0.01,	(-6.300)
dfxpop7	-0.029 ^* ***** p < 0.01,	(-4.100)
dfxpop9	0.020 ^ p < 0.05,	(2.040)
dfxpop10	0.007	(0.820)
dfxpop12	0.008	(0.710)
dfxpop14	-0.010	(-0.440)
dfxpop16	-0.006	(-0.360)

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