BNDES’ impact on steel industry efficiency: a two-stage Malmquist model usage

Moraes, Ricardo Kalil; Wanke, Peter Fernandes

doi:10.1590/1679-395172140

Ricardo Kalil Moraes

Universidade Federal do Rio de Janeiro (COPPEAD UFRJ) / Instituto de Pós-Graduação e Pesquisa, Rio de Janeiro - RJ, Brazil

https://orcid.org/0000-0002-2234-6622

Peter Fernandes Wanke

Universidade Federal do Rio de Janeiro (COPPEAD UFRJ) / Instituto de Pós-Graduação e Pesquisa, Rio de Janeiro - RJ, Brazil

https://orcid.org/0000-0003-1395-8907

Ricardo Kalil Moraes - Master’s Degree in Economic Development from the Federal University of Paraná (UFPR); Doctoral student in Business Administration at UFRJ COPPEAD, Rio de Janeiro - RJ, Brazil. E-mail: ricardo.kalil@coppead.ufrj.br

Peter Fernandes Wanke - Doctorate Degree in Production Engineering from the Federal University of Rio de Janeiro (UFRJ); Co-Professor at UFRJ COPPEAD, Rio de Janeiro - RJ, Brazil. E-mail: peter@coppead.ufrj.br

Author/Year	Research Proposal	DMU	Method	Country	Inputs	Products	Context Variables
Sarkis (2000SARKIS, J. An analysis of the operational efficiency of major airports in the United States. Journal of Operations Management, n. 18, p. 335-351, 2000.)	Evaluation of the operational efficiency of 44 airports	220	DEA	USA	OPEX, employment, gates, departures	Operating revenues, movement of aircraft, general aviation, passengers, freight	-
Ma, Evans, Fuller et al. (2002MA, J. et al. Technical efficiency and productivity change of China’s iron and steel industry. International Journal of Production Economics, n. 76, p. 293-312, 2002.)	Operational efficiency of the steel sector	711	MPI	China	Employment, fixed capital, energy, age, working capital	Production	-
Estache, De La Fé and Trujillo (2004ESTACHE, A.; DE LA FÉ, B.; TRUJILLO, L. Sources of efficiency gains in port reform: a DEA decomposition of Malmquist TFP index for Mexico. Utilities Policy, n. 12, p. 221-230, 2004.)	Breaking down of the sources of efficiency gains in the port sector	44	MPI	Mexico	Dock area, employment	Volume of merchandise traded	-
Kim, Lee, Kim et al. (2006)FARAWAY, J. Extending the linear model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor and Francis, 2006.	Sources of productive efficiency	22	SFA	Several countries	Employment, productive capacity, raw material	Production of crude steel	Ownership (private or public), plant age, scale
Rezvanian, Rao and Mehdian (2008REZVANIAN, R.; RAO, N.; MEHDIAN, S. Efficiency change, technological progress and productivity growth of private, public and foreign banks in India: evidence from the pos-liberalization era. Applied Financial Economics, n. 18, p. 701-713, 2008.)	Evaluation of the effect of the ownership on the efficiency of the banking sector	540	MPI	India	Funds borrowed, employment, fixed assets	Loans, securities, other revenues	-
Feng He et al. (2013HE, F. et al. Energy efficiency and productivity change of China’s iron and steel industry: accounting for undesirable outputs. Energy Policy, n. 54, p. 204-213, 2013.)	Energy efficiency and productivity variation in the steel sector	400	MPI and MPLI	China	Net fixed assets, employment, energy	Added value, gaseous waste, liquid waste, solid waste	-
Lee and Kim (2013LEE, J. K.; KIM, D. Bank performance and its determinance in Korea. Japan and the World Economy, n. 27, p. 83-94, 2013.)	Determinants of bank performance. Comparison between ROE and ROA.	136	MPI Panel data analysis - fixed effects	South Korea Extended to China and Japan	Interest expenses, costs of fees, operating expenses, provision for losses	Interest income, revenue from fees	Assets, assets^2, risk, GDP growth, and dummy (public, foreign, merger)
Barros and Wanke (2014BARROS, C.; WANKE, P. Banking efficiency in Brazil. Journal of International Financial Markets, Institutions & Money, n. 28, p. 54-65, 2014.)	Analysis of banking efficiency	-	Dynamic Bayesian Frontier	Brazil	-	-	Price of labor, cost of capital, price of deposits, total loans, total securities, dummy variables (public, foreign, merger, large, deregulation, stressed banks)
Barros, Dumbo and Wanke (2014BARROS, C.; WANKE, P. Banking efficiency in Brazil. Journal of International Financial Markets, Institutions & Money, n. 28, p. 54-65, 2014.)	Efficiency analysis of insurance companies	70	Main component analysis, Bootstrapping DEA, and neural networks	Angola	OPEX, employment, salaries, capital	Insurance paid, profits paid, premiums paid, reinsurance ceded	-
Debnath and Sebastian (2014DEBNATH, R. M.; SEBASTIAN, V. J. Efficiency in the Indian iron and steel industry: an application of data envelopment analysis. Journal of Advances in Management Research, v. 11, n. 1, p. 4-19, 2014.)	Efficiency in the steel sector	22	BCC Model	India	Employment, fixed assets, current assets, and energy	Income, profits before and after taxes, sales	-
Bahrini (2015BAHRINI, R. Productivity of MENA Islamic banks: a bootstrapped Malmquist index approach. International Journal of Islamic and Middle Eastern Finance and Management, v. 8, n. 4, p. 508-528, 2015.)	Analysis of the Total Factor Productivity (TFP) in the Islamic banking sector	198	Bootstrapped MPI	MENA (Middle East & North Africa)	Employment, fixed assets, deposits	Total loans, portfolio investment, non-operating revenues	Banking, macroeconomics, temporal dummies, and dummy for the global financial crisis
Li and Lin (2015LI, K.; LIN, B. Measuring green productivity growth of Chinese industrial sectors during 1998-2011. China Economic Review, n. 36, p. 279-295, 2015.)	Method for measuring growth of green productivity	504	MLPI	China	Capital stock, use, energy	Gross industrial product, CO2 emissions	-
Azad, Musimamy, Masum et al. (2016AZAD, A. K. et al. Bank efficiency in Malaysia: a use of malmquist meta-frontier analysis. Eurasian Business Review, v. 7, n. 2, p. 287-311, 2016.)	Banking efficiency	215	Two-stage DEA. Simar model of dual regression bootstrap	Malaysia	Expenses with interest rates, salaries, OPEX, capital, deposits	Earnings from interest, net income, deposits, loans	Banking, macroeconomics, ownership, and the bank’s nature
Da Silva, Martins-Filho and Ribeiro (2016)	Banking efficiency	1840	DEA, DEAC, FDH, FDHC, conditional quartile models of order m and α	Brazil	Employment, fixed capital, other capital assets, resources from third parties	Net credit lines, financial investments, other credit lines	Dummies (property - public, private, foreign; size - large, medium, small, micro)
Marchetti and Wanke (2017MARCHETTI, D.; WANKE, P. Brazil’s rail freight transport: efficiency analysis using two-stage DEA and cluster-driven public policies. Socio-Economic Planning Sciences, n. 59, p. 26-42, 2017.)	Efficiency of the railway sector	60	DEA. Bootstrap truncated regression.	Brazil	Cars, employment	Volume transported (TKU)	Dummies (mineral commodities, agricultural commodities, connection, type of regulation)
Nielsen (2017NIELSEN, H. Productive efficiency in the iron and steel sector under State planning: the case of China and former Czechoslovakia in a comparative perspective. Applied Energy, n. 85, p. 1732-1743, 2017.)	Efficiency of the steel sector	21	DEA CRS and VRS	Market and planned economies	Energy and raw materials (coal, iron, and scrap)	Production of crude steel and pig iron

	OLS	GLM	GAM	GAMLSS	MCMC GLMM	MCMC GL
Technical Change	0.1791	0.1675	0.1773	0.1863	0.1780	0.1696
Frontier Shift	0.0472	0.0469	0.0471	0.1110	0.0449	0.0430
Malmquist Index	0.1325	0.1325	0.1301	0.2694	0.1321	0.1241

Variable	Technical Change		Frontier Shift		Malmquist Index
Variable	Sign	Significant	Sign	Significant	Sign	Significant
Price of labor (LN)	-		+	*	+	*
Cost of capital (LN)	+		-	*	-	*
EBITDA/assets	+	*	-	*	-	*
CAPEX/assets	+	*	-		+
Leverage	+		-	*	-
Trend	+	*	-	*	-
Trend^2	-	*	-		-
BNDES	-	*	-		-	*
Brazil	-		+	*	+	*
China	+		+	*	+	*
Korea	-		+	*	+	*
Inflation (%)	-	*	+	*	+	*
Gini Index	+	*	+		+
HDI	-		+		+
GDP Growth (%)	-	*	+	*	+
GDP PPP ($)	-		+	*	+	*
FDI ($)	-	*	-		-	*
Energy Use (kg eq. oil per capita)	+		-	*	-
Infant mortality (1,000 births)	+		-	*	-	*
Life expectancy (years)	-		+	*	+	*
Global innovation index	+		+	*	+	*
Logistics performance index	+		-	*	-	*

P R O D c h = {(\frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t} (x^{t}, y^{t})} \frac{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t}, y^{t})})}^{1 / 2}

T E C H c h = {(\frac{D_{0}^{t} (x^{t + 1}, y^{t + 1})}{D_{0}^{t + 1} (x^{t + 1}, y^{t + 1})} \frac{D_{0}^{t} (x^{t}, y^{t})}{D_{0}^{t + 1} (x^{t}, y^{t})})}^{1 / 2}

\begin{matrix} m i n V a r (w_{1} R 1 + w_{2} R 2 + w_{3} R 3 + w_{4} R 4 + w_{5} R 5 + w_{5} R 6) \\ S . T . \\ \sum_{i = 1}^{6} w_{i} = 1 \\ 0 \leq w_{1} \leq 1 \\ 0 \leq w_{2} \leq 1 \\ 0 \leq w_{3} \leq 1 \\ {0 \leq w}_{4} \leq 1 \\ 0 \leq w_{5} \leq 1 \\ 0 \leq w_{6} \leq 1 \end{matrix}

[1] Ricardo Kalil Moraes - Master’s Degree in Economic Development from the Federal University of Paraná (UFPR); Doctoral student in Business Administration at UFRJ COPPEAD, Rio de Janeiro - RJ, Brazil. E-mail: ricardo.kalil@coppead.ufrj.br

Brasil

Brasil

BNDES’ impact on steel industry efficiency: a two-stage Malmquist model usage

Abstract