Impactos da política fiscal de inovação brasileira na composição de investimentos privados e no tipo de inovação

Colombo, Daniel Gama e; Cruz, Helio Nogueira da

Acessibilidade / Reportar erro

Brasil

Revista Brasileira de Inovação

Español English

Brasil

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Sumário

Artigo • Rev. Bras. Inov. 17 (02) • Jul-Dec 2018 • https://doi.org/10.20396/rbi.v17i2.8651500 copiar

Impactos da política fiscal de inovação brasileira na composição de investimentos privados e no tipo de inovação

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

O objetivo deste artigo é investigar se os incentivos fiscais para inovação da Lei 11.196/05 (BRASIL, 2005) alteraram a composição dos investimentos em inovação das empresas e seus resultados. Tomando por base o conceito de adicionalidade comportamental e o desenho da política fiscal, são apresentadas três proposições sobre como esses incentivos afetam a cesta de gastos com inovação, o nível educacional dos pesquisadores e o tipo de inovação desenvolvido pelas firmas. A partir de dados desagregados da Pesquisa de Inovação (PINTEC) de 2008 e 2011 e de outras fontes, o efeito médio do tratamento nas unidades tratadas (ATT) é estimado utilizando-se um pareamento por escore de propensão (PSM) com diferença-em-diferenças. As principais conclusões do estudo são: os incentivos impactaram positivamente a intensidade de pesquisa e desenvolvimento (P&D) dos investimentos em inovação das firmas, elevandoos gastos com P&D em cerca de R$ 1,1 milhão na média, assim como reduzindo os gastos médios com aquisição de outros conhecimentos externos e com introdução de inovações no mercado; embora a política tenha fomentado a contratação adicional de pesquisadores com diploma de graduação, não foi encontrado impacto significativo para pessoal de pesquisa com titulação de mestrado ou doutorado; e não há evidência de impacto no balanço entre inovações de produto e processo.

Palavras-chave:
Adicionalidade comportamental; Composição de Investimentos; Incentivos Fiscais; Inovação privada

Broad classification	Incentivized activity or category	Applicable tax	Incentive	Incentive rate or ceiling
R&D and other innovative activities	R&D expenditures	Income tax; CSLL^a	Tax deduction	Up to 160% of expenditures
Educational Level of R&D personnel	Hiring new research personnel (up to 5%)	Income tax; CSLL^a	Tax deduction	Up to 20% additional deduction of R&D expendituresb
Type of innovation	R&D expenditures related to valid patent	IIncome tax; CSLL^a	Additional tax deduction	Additional 20% deduction of R&D expenditures
Type of innovation	Filing of patents and trademarks abroad	Withholding income tax	Tax reduction	Total exemption
Other incentives	New equipment	Income tax; CSLL^a	Accelerated depreciation	Full depreciation in the first year
	New equipment	IPI^b	Tax reduction	50% of the tax
	Other capital goods	Income tax	Tax deduction	Deduction of non-depreciated capital
	Intangible goods	Income tax	Accelerated amortization	Full amortization in the first year
	Innovation outsourcing to non-profit research institutes	Income tax; CSLL^a	Tax deduction	50% to 250% of expenditures

Broad classification	Outcome variable	Unit	Descriptive statistics
Broad classification	Outcome variable	Unit	Treatment	N	Mean	Std. err.
Innovative activities	rd_tot	Brazilian R$ 1,000	0	6,412	290.46	15,562.81
	rd_tot		1	260	2675.66	20,468.64
	knowledge		0	6,412	12.82	1,673.42
	knowledge		1	260	-103.33	733.51
	software		0	6,412	2.37	4,898.28
	software		1	260	-166.27	1,380.23
	machinery		0	6,412	48.14	39,610.41
	machinery		1	260	-1515.65	18,866.47
	training		0	6,412	-14.55	579.13
	training		1	260	-3.63	876.80
	intro		0	6,412	-0.72	4,281.54
	intro		1	260	-637.62	5,240.06
	project		0	6,412	5.40	6,736.65
	project		1	260	-1758.34	19,849.95
Educational level of R&D personnel	phd	Number of researchers	0	6,412	0.18	13.58
	phd		1	260	0.11	2.42
	master		0	6,412	0.09	5.92
	master		1	260	0.20	2.80
	undergrad		0	6,412	1.13	45.55
	undergrad		1	260	6.70	25.30
	high		0	6,412	0.20	8.46
	high		1	260	2.53	14.11
	others		0	6,412	-0.11	9.30
	others		1	260	0.38	4.29
Type of innovation	product	Binary	0	5,901	0.13	0.33
	product		1	151	0.14	0.35
	process		0	5,901	0.42	0.49
	process		1	151	0.19	0.39
	both		0	5,901	0.45	0.50
	both		1	151	0.68	0.47

Covariate	Broad Classification
Covariate	Innovative activities	Educational level	Type of innovation
personnel	-0.076 (0.15)	0.004 (0.148)	0.43 (0.193)**
age	-0.001 (0.008)	0.004 (0.008)	0.005 (0.008)
nac_control	-0.643 (0.513)	-0.785 (0.511)	-0.605 (0.492)
for_control	-0.562 (0.497)	-0.506 (0.496)	-0.117 (0.492)
rd_cont	1.465 (0.209)***	0.908 (0.385)**	1.46 (0.256)***
group	0.212 (0.205)	0.277 (0.207)	0.412 (0.232)*
revenue	0.714 (0.137)***	0.509 (0.133)***	0.344 (0.162)**
imp	0.85 (0.388)**	0.808 (0.425)*	1.168 (0.624)*
exp	0.432 (0.268)	0.271 (0.29)	0.553 (0.409)
for_market	-0.888 (0.422)**	-0.73 (0.371)**	-0.4 (0.376)
dummyN	-0.11 (0.712)	-1.216 (0.765)	-0.348 (0.606)
dummyNE	-0.44 (0.443)	-0.501 (0.458)	-0.254 (0.574)
dummySE _b	-0.025 (0.272)	0.127 (0.268)	-0.371 (0.391)
dummyS	0.404 (0.21)*	0.13 (0.229)	1.022 (0.255)***
dummyCO	-0.985 (0.785)	-0.6 (0.707)	n/a^a
constant (β₀ )	-9.745 (1.755)***	-6.116 (2.013)***	-8.263 (1.868)***

N	2,311	1,144	2,683
Log-likelihood	-468.367	-406.315	-336.785
PseudoR2	0.268	0.170	0.356
Prob>chi2	0.000	0.000	0.000

Broad Classification	Unit	Variable	Mean		ATT
Broad Classification	Unit	Variable	Treated	Control	ATT
Different innovative activities	Brazilian R$ 1,000	rd_tot	1,036.61	-55.82	1,092.43***
					(377.9)
		knowledge	-73.06	127.90	-200.96**
					(95.31)
		software	-47.62	-98.68	51.06
					(122.54)
		machinery	-308.36	197.02	-505.38
					(397.51)
		training	18.42	-7.78	26.21
					(78.87)
		intro	-109.07	271.34	-380.41**
					(187.72)
		project	-160.99	-155.74	-5.25
					(179.5)
Educational Level	Number of researchers	phd	0.00	0.04	-0.04
					(0.13)
		master	0.31	0.29	0.01
					(0.24)
		undergrad			3.35**
					(1.61)
		high	3.44	2.39	1.05
					(1.46)
		other	0.96	-0.43	1.39
					(1.08)
Type of innovation	Binary	product	0.15	0.15	0
					(omitted)
		process	0.15	0.18	-.031
					(0.55)
		both	0.70	0.67	0.31
					(0.69)

Broad classification	Unit	Outcome variable	Definition
Innovative activities	Brazilian R$ 1,000	rd_tot	Total R&D
		knowledge	Acquisition of knowledge from third parties
		software	Software license or acquisition
		machinery	Acquisition of machinery and equipment
		training	Training
		intro	Introduction of innovations in the market
		project	Industrial design and other measures
Educational level of R&D personnel	Number of researchers	phd	PhD degree
		master	Master’s degree
		undergrad	Undergraduate level⁽¹⁾
		high	High school level
		others	Others
Type of innovation	Binary	product	Product innovation only
		process	Process innovation only
		both	Both product and process innovation

Covariate	Definition
personnel	Firm size (number of employees-log-linearized)
age	Firm age
nac_control	Dummy for national controlling capital
for_control	Dummy for foreign controlling capital
rd_cont	Dummy for continuous R&D activity in the last three years
group	Dummy for firms belonging to a corporate group
revenue	Net revenue (log-linearized)
imp	Dummy for importing firms in (t-1)
exp	Dummy for exporting firms in (t-1)
for_market	Dummy for main firm market being international
dummyN	Dummies for the North region
dummyNE	Dummies for the Northeast region
dummyCO	Dummies for the Midwest region
dummySE	Dummies for the Southeast region (excluding firms from the state of São Paulo to avoid perfect collinearity)
dummyS	Dummies for the South region
cnae_ (dummies)	Industrial sector dummies, using the National Classification of Economic Activities

Covariate
	personnel	age	nac_control	for_control	rd_cont	group	revenue	imp	exp	for_market	dummy N	dummy NE	dummmy SE	dummy S	dummy CO
Control group (treatment = 0)
N	13,403	6,410	13,403	13,403	13,266	13,403	13,401	13,403	13,403	13,403	13,403	13,403	13,403	13,403	13,403
mean	4.55	26.15	0.94	0.08	0.11	0.13	9.14	0.38	0.31	0.04	0.04	0.10	0.18	0.28	0.05
std. Dev.	1.41	12.87	0.24	0.27	0.31	0.34	2.27	0.49	0.46	0.20	0.20	0.30	0.38	0.45	0.22
Treated group (treatment = 1)
N	303	260	303	303	298	303	303	303	303	303	303	303	303	303	303
mean	6.23	28.18	0.73	0.34	0.71	0.39	12.00	0.85	0.76	0.09	0.02	0.03	0.16	0.32	0.02
std. dev.	1.28	13.38	0.45	0.47	0.46	0.49	1.68	0.36	0.43	0.28	0.13	0.18	0.37	0.47	0.13

Economic sector dummy variables (cnae_ dummies)																											Total
	10	11	13	15	17	18	20	21	22	23	24	25	26	27	28	29	30	31	32	33	35	50	62	71	72	81
Control group (treatment = 0)
N	1,405	179	538	525	327	269	578	154	803	726	325	1,011	400	430	908	441	122	483	380	239	52	9	458	379	43	176	11,360
%	12.37	1.58	4.74	4.62	2.88	2.37	5.09	1.36	7.07	6.39	2.86	8.90	3.52	3.79	7.99	3.88	1.07	4.25	3.35	2.10	0.46	0.08	4.03	3.34	0.38	1.55	100
Treated group (treatment = 1)
N	23	5	4	4	10	0	36	14	10	10	9	11	14	18	29	32	5	10	4	0	6	0	30	4	0	0	288
%	7.99	1.74	1.39	1.39	3.47	0.00	12.50	4.86	3.47	3.47	3.13	3.82	4.86	6.25	10.07	11.11	1.74	3.47	1.39	0.00	2.08	0.00	10.42	1.39	0.00	0.00	100

Covariate	Mean		%bias	% reduct \|bias\|	t-test		V(T)/ V(C)^b
Covariate	Treated	Control	%bias	% reduct \|bias\|	t	p>\|t \|	V(T)/ V(C)^b
revenue	3.00E+05	4.30E+05	-9.6	71.8	-1.42	0.158	0.31
personnel	6.1522	6.1492	0.3	99.7	0.03	0.978	0.86
age	28	29.815	-13.8	15.5	-1.29	0.199	1.03
nac_control	0.73034	0.70787	5.9	87.7	0.47	0.638	.
for_control	0.30337	0.33708	-8.4	82	-0.68	0.497	.
rd_cont	0.78652	0.7809	1.3	98.7	0.13	0.898	.
group	0.3427	0.32022	5.2	86.9	0.45	0.654	.
imp	0.86517	0.8764	-2.7	95.4	-0.32	0.753	.
exp	0.77528	0.80337	-6.1	89.7	-0.65	0.517	.
for_market	0.05056	0.07303	-10.2	-410.4	-0.88	0.38	.
dummyN	0.01685	0.00562	7.9	-28.2	1	0.316	.
dummyNE	0.03933	0.04494	-2.3	88.5	-0.26	0.793	.
dummySE	0.14607	0.17416	-7.8	-78	-0.72	0.471	.
dummySa	0.33146	0.29213	8.4	-18	0.8	0.425	.
dummyCO	0.01124	0	7.5	54.8	1.42	0.157	.
cnae_10	0.10112	0.08989	3.6	-22	0.36	0.719	.
cnae_11	0.01124	0.02247	-10.8	-2666.7	-0.82	0.412	.
cnae_12	0	0	0	100	.	.	.
cnae_13	0.01685	0.01124	3.2	82.2	0.45	0.654	.
cnae_14	0.00562	0	3.9	82.7	1	0.318	.
cnae_15	0.01124	0	8	40.4	1.42	0.157	.
cnae_16	0	0	0	100	.	.	.
cnae_17	0.04494	0.05056	-2.8	64.4	-0.25	0.804	.
cnae_18	0	0	0	100	.	.	.
cnae_19	0	0	0	100	.	.	.
cnae_20	0.1236	0.1236	0	100	0	1	.
cnae_21	0.04494	0.0618	-10.1	39.8	-0.71	0.481	.
cnae_22	0.02247	0.00562	8	67.8	1.35	0.178	.
cnae_23	0.03371	0.05056	-9.6	-3281.6	-0.79	0.43	.
cnae_24	0.01124	0.02809	-13.2	-45.7	-1.14	0.253	.
cnae_25	0.04494	0.0618	-7.3	-5.4	-0.71	0.481	.
cnae_26	0.03371	0.03933	-2.8	71.4	-0.28	0.778	.
cnae_27	0.08427	0.07865	2.3	84.9	0.19	0.847	.
cnae_28	0.08989	0.06742	7.9	-447.1	0.79	0.432	.
cnae_29	0.10674	0.1236	-6.5	72	-0.5	0.62	.
cnae_30	0.02247	0.01124	9.2	13	0.82	0.412	.
cnae_31	0.03933	0.03933	0	100	0	1	.
cnae_32	0.02247	0.01124	7.5	-279.8	0.82	0.412	.
cnae_33	0	0	0	100	-	-	-
cnae_35	0	0	0	100	.	.	.
cnae_38	0	0	.	.	.	.	.
cnae_50	0	0	0	100	.	.	.
cnae_58	0	0	0	100	.	.	.
cnae_59	0	0	.	.	.	.	.
cnae_60	0	0	.	.	.	.	.
cnae_61	0.01124	0.00562	6.2	-8.6	0.58	0.563	.
cnae_62	0.10112	0.11236	-4.1	66.6	-0.34	0.732	.
cnae_63	0.00562	0.00562	0	100	0	1	.
cnae_71	0	0	0	100	.	.	.
cnae_72	0	0	0	100	.	.	.
cnae_81	0	0	0	100	.	.	.
cnae_89	0.00562	0	8.3	-271.1	1	0.318	.
cnae_91	0.00562	0	9.4	-53.5	1	0.318	.
cnae_99	0	0	.	.	.	.	.
Sample	Ps R2	LR chi2	p>chi2	MeanBias	MedBias
Unmatched	0.247	315.49	0	17.9	11.5
Matched	0.038	18.59	0.979	4.8	4.7

Covariate	Mean		%bias	% reduct \|bias\|	t-test		V(T)/ V(C)^b
Covariate	Treated	Control	%bias	% reduct \|bias\|	t	p>\|t \|	V(T)/ V(C)^b
revenue	6.50E+05	1.20E+06	-38	-36.2	-2.01	0.045	0.30
personnel	6.4075	6.4722	-5.4	92.7	-0.48	0.635	0.55
age	28.638	29.431	-6	65.1	-0.53	0.594	0.9
nac_control	0.6954	0.74138	-11.4	74.4	-0.95	0.342	.
for_control	0.34483	0.3046	9.3	77.1	0.8	0.424	.
rd_cont	0.94828	0.97701	-9.8	68.1	-1.41	0.158	.
group	0.41379	0.4023	2.5	93.7	0.22	0.828	.
imp	0.89655	0.91379	-4.5	90.3	-0.55	0.584	.
exp	0.8046	0.84483	-9	78.3	-0.99	0.325	.
for_market	0.07471	0.06322	4.5	-39.3	0.42	0.673	.
dummyN	0.01724	0.01724	0	100	0	1	.
dummyNE	0.04023	0.02874	5	67.6	0.59	0.558	.
dummySE	0.17241	0.12644	12.5	-220.6	1.2	0.23	.
dummySa	0.26437	0.32184	-12.7	-51.4	-1.18	0.24	.
dummyCO	0.01724	0.01724	0	100	0	1	.
cnae_10	0.10345	0.10345	0	100	0	1	.
cnae_11	0.01724	0	14.2	-322.6	1.74	0.082	.
cnae_12	0	0	0	100	.	.	.
cnae_13	0.01724	0.01724	0	100	0	1	.
cnae_14	0.00575	0	5.9	28.8	1	0.318	.
cnae_15	0.01724	0.03448	-11.5	-39.7	-1.01	0.312	.
cnae_16	0	0	0	100	.	.	.
cnae_17	0.03448	0.08046	-25.2	-353.9	-1.85	0.066	.
cnae_18	0	0	0	100	.	.	.
cnae_19	0	0	0	100	.	.	.
cnae_20	0.13218	0.10345	9.1	18.4	0.83	0.407	.
cnae_21	0.06897	0.04023	13.4	24.8	1.18	0.239	.
cnae_22	0.01724	0.02299	-3	86.5	-0.38	0.704	.
cnae_23	0.02874	0.04598	-10.2	-488	-0.85	0.398	.
cnae_24	0.02874	0.02299	3.7	7.2	0.34	0.736	.
cnae_25	0.04598	0.04023	2.6	-297.6	0.26	0.793	.
cnae_26	0.04598	0.05172	-2.3	85.6	-0.25	0.804	.
cnae_27	0.08621	0.1092	-8.8	0.6	-0.72	0.472	.
cnae_28	0.06897	0.04598	8.7	-47.2	0.92	0.358	.
cnae_29	0.0977	0.12069	-9	62.3	-0.69	0.493	.
cnae_30	0.02299	0.01149	9.5	24.2	0.82	0.412	.
cnae_31	0.02874	0.03448	-3.6	-31.6	-0.31	0.76	.
cnae_32	0.01724	0.02874	-7.9	-19.7	-0.71	0.476	.
cnae_33	0	0	0	100	.	.	.
cnae_35	0	0	.	.	.	.	.
cnae_38	0	0	.	.	.	.	.
cnae_50	0	0	0	100	.	.	.
cnae_58	0	0	0	100	.	.	.
cnae_59	0	0	.	.	.	.	.
cnae_60	0	0	.	.	.	.	.
cnae_61	0.01149	0.02299	-11.2	-858.3	-0.82	0.412	.
cnae_62	0.08621	0.05747	10.2	-578.7	1.04	0.301	.
cnae_63	0	0	0	100	.	.	.
cnae_71	0.00575	0	9.4	-51.5	1	0.318	.
cnae_72	0	0	0	100	.	.	.
cnae_81	0	0	0	100	.	.	.
cnae_89	0.00575	0.00575	0	100	0	1	.
cnae_91	0.00575	0	10.1	-22.1	1	0.318	.
cnae_99	0	0	.	.	.	.	.
Sample	Ps R2	LR chi2	p>chi2	MeanBias	MedBias
Unmatched	0.158	155.1	0	14.4	8.9
Matched	0.05	23.79	0.904	6.5	5.4

Covariate	Mean		%bias	% reduct \|bias\|	t-test		V(T)/ V(C)^b
Covariate	Treated	Control	%bias	% reduct \|bias\|	t	p>\|t \|	V(T)/ V(C)^b
revenue	9.90E+05	1.10E+06	-3.7	93.6	-0.3	0.763	0.56
personnel	6.8242	6.8727	-3.3	97.8	-0.27	0.787	0.82
age	29.456	28.669	5.9	74.9	0.46	0.649	1.15
nac_control	0.632	0.70824	-20.1	74.7	-1.28	0.201	.
for_control	0.424	0.33931	21.2	73.9	1.38	0.169	.
rd_cont	0.808	0.81137	-0.9	99.4	-0.07	0.946	.
group	0.424	0.4555	-7.4	89.2	-0.5	0.618	.
imp	0.96	0.94596	3.6	97.5	0.52	0.602	.
exp	0.912	0.90369	2	98.5	0.23	0.821	.
for_market	0.112	0.10407	3	87.7	0.2	0.841	.
dummyN	0.04	0.0391	0.5	-1190	0.04	0.971	.
dummyNE	0.032	0.02683	1.9	88.7	0.24	0.81	.
dummySE	0.104	0.08586	5.2	73.5	0.49	0.626	.
dummySa	0.4	0.45979	-12.8	38.8	-0.95	0.342	.
dummyCO	0	0	0	100	.	.	.
cnae_10	0.12	0.17326	-17.1	-552.1	-1.19	0.236	.
cnae_11	0.008	0.0097	-1.4	52.8	-0.14	0.887	.
cnae_12	0.008	0.00294	7.9	-62.5	0.54	0.59	.
cnae_13	0.016	0.01823	-1.4	92.3	-0.14	0.892	.
cnae_14	0	0	0	100	.	.	.
cnae_15	0.008	0.00645	1	94.4	0.14	0.885	.
cnae_16	0	0	0	100	.	.	.
cnae_17	0.008	0.00995	-1.5	85.1	-0.16	0.871	.
cnae_18	0	0	0	100	.	.	.
cnae_19	0	0	0	100	.	.	.
cnae_20	0.064	0.06038	1.5	89.9	0.12	0.906	.
cnae_21	0.048	0.03805	6.3	64.5	0.39	0.7	.
cnae_22	0.04	0.04707	-3.3	72.8	-0.27	0.785	.
cnae_23	0.024	0.0248	-0.4	97.5	-0.04	0.968	.
cnae_24	0.04	0.03176	4.7	46.1	0.35	0.727	.
cnae_25	0.04	0.048	-3.6	83	-0.31	0.759	.
cnae_26	0.128	0.1089	7.8	73.4	0.47	0.642	.
cnae_27	0.048	0.04508	1.5	84.4	0.11	0.913	.
cnae_28	0.144	0.15515	-3.7	84.8	-0.25	0.806	.
cnae_29	0.168	0.14836	6.8	84.3	0.42	0.672	.
cnae_30	0.016	0.00771	8.2	-277.5	0.6	0.547	.
cnae_31	0.016	0.01454	0.9	92.4	0.09	0.926	.
cnae_32	0.024	0.01948	2.9	21.4	0.24	0.807	.
cnae_33	0	0	0	100	.	.	.
cnae_35	0	0	0	100	.	.	.
cnae_38	0	0	.	.	.	.	.
cnae_50	0	0	0	100	.	.	.
cnae_58	0	0	0	100	.	.	.
cnae_59	0	0	0	100	.	.	.
cnae_60	0	0	0	100	.	.	.
cnae_61	0.008	0.00941	-1.4	49.6	-0.12	0.905	.
cnae_62	0.016	0.0175	-1	91.3	-0.09	0.927	.
cnae_63	0	0	0	100	.	.	.
cnae_71	0.008	0.0033	3.7	79.2	0.49	0.621	.
cnae_72	0	0	0	100	.	.	.
cnae_81	0	0	0	100	.	.	.
cnae_89	0	0	0	100	.	.	.
cnae_91	0	0	0	100	.	.	.
cnae_99	0	0	0	100	.	.	.
Sample	Ps R2	LR chi2	p>chi2	MeanBias	MedBias
Unmatched	0.369	516.5	0	28.5	16.5
Matched	0.019	6.74	1	3.4	1.5

Broad classification	Outcome variable	Estimated ATT - Robustness checks
Broad classification	Outcome variable	kernel matchinga	radius matchingb	Log-linearizedc	SUR and lineard
Innovative activities expenditures	rd_tot	835.45*** (220.77)	543.37** (223.69)	0.0014*** (0.0005)	1186.5*** (342.61)
	knowledge	-154.05*** (59.35)	-138.07** (60.94)	-0.0027** (0.0013)	-180.23** (80.01)
	software	-80.28 (58.18)	-45.51 (60.4)	0.0003 (0.0006)	51.58 (142.1)
	machinery	-19.69 (222.79)	-41.36 (230.18)	-0.0007 (0.0006)	-501.3 (303.35)
	training	66.27 (45.33)	89.11* (46.58)	0.0002 (0.0008)	29.36 (42.23)
	intro	-198.03 (123.74)	-146 (127.48)	-0.0011** (0.0005)	-320.13* (177.24)
	project	149.01* (84.67)	85.58 (86.68)	0 (0.0006)	-33.42 (116.69)
Educational level	phd	-0.11 (0.12)	-0.12 (0.12)	-0.0015 (0.0031)	-0.06 (0.13)
	master	-0.06 (0.18)	-0.04 (0.18)	-0.0002 (0.0053)	-0.05 (0.23)
	undergrad	1.78 (1.31)	1.87 (1.33)	0.0134** (0.0065)	3.38** (1.4)
	high	0.98 (1.08)	1.13 (1.1)	0.0019 (0.0024)	1.51 (1.38)
	other	0.9* (0.48)	0.9* (0.5)	0.0025 (0.0019)	1.47 (1.18)
Type of innovation	product	-0.02 (0.04)	0.02 (0.04)		0 (0.05)
	project	-0.04 (0.04)	-0.03 (0.04)		-0.03 (0.05)
	both	0.06 (0.05)	0.003 (0.05)		0.03 (0.07)

Outcome variable		sig+	sig-	t-hat+	t-hat-	CI+	CI-
rd_tot	1	0.0011	0.0011	451.0500	451.0500	154.1740	842.2120
	1.1	0.0057	0.0001	359.0780	552.5000	67.5165	959.5570
	1.2	0.0208	0.0000	287.1750	656.8300	1.9385	1078.6900
	1.3	0.0562	0.0000	218.0360	744.9500	-42.0000	1187.2800
	1.4	0.1205	0.0000	158.5370	835.5930	-110.6250	1306.3300
	1.5	0.2152	0.0000	100.0000	907.1880	-190.3430	1403.1600
	1.6	0.3337	0.0000	44.2325	999.7590	-300.0000	1503.0400
	1.7	0.4629	0.0000	1.9385	1077.0000	-375.6720	1619.3800
	1.8	0.5886	0.0000	-22.0615	1138.4200	-441.3260	1698.1000
	1.9	0.6999	0.0000	-57.5000	1236.2300	-512.4920	1823.1700
	2	0.7908	0.0000	-109.5000	1303.8300	-571.3720	1922.6800
knowledge	1	0.0011	0.0011	0.0006	0.0006	0.0002	0.0011
	1.1	0.0058	0.0001	0.0004	0.0007	0.0001	0.0012
	1.2	0.0210	0.0000	0.0004	0.0008	0.0000	0.0013
	1.3	0.0567	0.0000	0.0003	0.0009	-0.0001	0.0015
	1.4	0.1212	0.0000	0.0002	0.0010	-0.0001	0.0016
	1.5	0.2163	0.0000	0.0001	0.0011	-0.0002	0.0018
	1.6	0.3351	0.0000	0.0001	0.0013	-0.0004	0.0019
	1.7	0.4644	0.0000	0.0000	0.0013	-0.0005	0.0020
	1.8	0.5901	0.0000	0.0000	0.0014	-0.0006	0.0021
	1.9	0.7012	0.0000	-0.0001	0.0015	-0.0006	0.0023
	2	0.7919	0.0000	-0.0001	0.0016	-0.0007	0.0024
intro	1	0.0793	0.0793	-2.3267	-2.3267	-6.2035	0.7197
	1.1	0.0289	0.1771	-3.1240	-1.4738	-7.4549	1.4015
	1.2	0.0095	0.3131	-3.9962	-0.6686	-8.4748	2.1775
	1.3	0.0029	0.4666	-4.7354	-0.0634	-9.5999	2.9312
	1.4	0.0008	0.6139	-5.6807	0.2720	-10.6116	3.7912
	1.5	0.0002	0.7381	-6.3769	0.7588	-11.6235	4.4780
	1.6	0.0001	0.8324	-7.2433	1.3005	-12.5934	5.1309
	1.7	0.0000	0.8981	-7.8894	1.6522	-13.3530	5.8128
	1.8	0.0000	0.9408	-8.5494	2.2328	-14.1341	6.6436
	1.9	0.0000	0.9670	-9.2593	2.7492	-14.7766	7.3947
	2	0.0000	0.9822	-9.9878	3.1584	-15.5141	8.3672
undergrad	1	0.0124	0.0124	2.1250	2.1250	0.2500	4.1000
	1.1	0.0445	0.0026	1.6000	2.6500	-0.3000	4.6750
	1.2	0.1135	0.0005	1.1000	3.1500	-0.7500	5.2500
	1.3	0.2244	0.0001	0.7300	3.6000	-1.2000	5.7500
	1.4	0.3665	0.0000	0.3000	4.0500	-1.6500	6.2250
	1.5	0.5183	0.0000	-0.0175	4.4250	-2.0000	6.6500
	1.6	0.6583	0.0000	-0.4500	4.8350	-2.4500	7.0800
	1.7	0.7729	0.0000	-0.7500	5.2250	-2.8000	7.5500
	1.8	0.8577	0.0000	-1.0250	5.5250	-3.0750	8.0000
	1.9	0.9155	0.0000	-1.3500	5.8600	-3.4000	8.3000
	2	0.9521	0.0000	-1.6000	6.1750	-3.7000	8.7500

Universidade Estadual de Campinas Rua: Carlos Gomes, 250. Bairro Cidade Universitária, Cep: 13083-855 , Campinas - SP / Brasil , Tel: +55 (19) 3521-5176 - Campinas - SP - Brazil
E-mail: rbi@unicamp.br

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[TFN1] Source: 11,196/05 (BRASIL, 2005).

[TFN2] ^a
CSLL - Social Contribution to Net Profit.

[TFN3] ^b
Tax on industrialized products.