Analysis of productivity performance of real estate and construction firms in Indonesia

Figures & data

Table 1. Summary of key papers on productivity in real estate and construction sectors using data envelopment analysis framework.

Author and year	Type of firms	Firms	Country	Year of observation	Inputs	Outputs	Results
Xue et al. (2008)	Construction	31 Provinces	China	1997–2003	Total assets and the number of total employees	The amount of value-added	The productivity level increased (except from 2001 to 2002)
Qi and Jia (2010)	Real estate	25 Large listed firms	China	2004–2008	Total assets and salaries	income and profit	The productivity level decreased due to decreases in the efficiency levels
Zheng et al. (2013)	Real estate	Listed firms	China	1997–2008	Paid-in capital, total land asset value, the average number of employees	Total operating income	The positive effect of technical efficiency on TFP is apparent while technological progress has a limited effect on TFP growth
Wang et al. (2013)	Construction	31 Provinces	China	2006–2010	Total assets and number of staff and workers	Total power M&E owned, value-added, and total output value	There is steady improvement in TFP
Lee et al. (2016)	Construction	11 Firms	Korea	2006–2012	Total assets and number of employees	total revenue and market share	A decrease in the productivity level is caused by the timing of the construction industry’s business cycle
Kapelko and Abbott (2017)	Construction, civil engineering	36,949 Firms	Spain	2002 and 2011	Material costs, employee costs, and fixed assets	Firms’ revenue	decrease in productivity due to a decrease in the scale efficiency levels
Azman et al. (2019)	Construction	37 Firms	Malaysia	2003–2016	Materials costs, fixed asset and employee costs	Firms’ revenue	Technical and mix efficiency improve TFP, increases in technological change. Inverse relationship: SME and technological level.
Nabavieh et al. (2019)	Construction	Ten largest and most active	Iran	2006–2012	Number of employees, capital, and operational cost	Added value	average productivity level decreases
Wang et al. (2022)	Construction	31 Provinces	China	2000–2017	number of employees, fixed asset	Added value	Technical progress was the main source of total factor productivity growth.
Camino‐Mogro and Bermudez‐Barrezueta (2021)	Construction	5,087 Firms	Ecuador	2007–2018	Number of employees, business age, net fixed assets	Firms’ revenue	Firm age, size, access to debt are positively related with TFP. Family firm and less competitive environment is negatively related with TFP.
Azman et al. (2022)	Construction	48 Firms	Malaysia	2009–2018	Labour, material and capital	Productivity growth	TFP change is affected by technological stagnation and improvements in technical efficiency, a decline in scale-mix efficiency and environmental efficiency.

Figure 1. Different components of TFP in aggregate input-output framework. Different components of TFP for any observed firm staying below the frontier in an aggregate input-output framework.

Figure 2. Temporal changes in TFP. Conceptualizing different components of temporal changes in TFP.

Figure 3. The concepts of the meta-frontier. Conceptualizing two group-frontiers and meta frontier.

Table 2. Descriptive statistics of key input, output and determinant variables.

Large firm group	Mean (in USD)	Standard deviation (SD)
Revenue	21,481	35,205
Material	7426	11,072
Salary	1969	3408
Operational	9680	18,697
Total asset	99,450	186,965
Medium firm group	Mean (in USD)	SD
Revenue	1598	1989
Material	703	875
Salary	208	276
Operational	615	948
Total asset	9017	35,669

Table 3. Descriptive statistics of efficiency and productivity determinants.

Large	Mean	SD
Years of establishment	24.12	14.53
Expert	1.57	3.00
Branch	2.75	4.47
Side income	0.06	0.08
Medium	Mean	SD
Years of establishment	19.87	8.56
Expert	0.78	2.02
Branch	0.82	1.48
Side income	0.04	0.07

Table 4. Average TFP level and its components using group-specific frontiers as in Equation (2)(2) $${\mathrm{TFP}}_{it}={\mathrm{TFPE}}_{it}\times {\mathrm{TSME}}_{it}=\mathrm{ }{\mathrm{TFPE}}_{it}\times {\mathrm{ITE}}_{it}\times {\mathrm{IME}}_{it}\times {\mathrm{RISE}}_{it}$$(2) .

Period	TFP^g	TFP^g-max	TFPE^g	ITE^g	IME^g	RISE^g
Large firms group
Entire period	0.3954	1.3875	0.2850	0.6373	0.9139	0.4892
1(2012–2013)	0.2781	1.0081	0.2759	0.5540	1.0000	0.4980
2(2013–2014)	0.3429	1.5211	0.2255	0.6150	0.8504	0.4311
3(2014–2015)	0.4836	1.5237	0.3174	0.7177	0.9181	0.4816
4(2015–2016)	0.5299	1.5864	0.3340	0.6748	0.8936	0.5540
Medium firms group
Entire period	0.2289	1.1074	0.2068	0.4620	0.8952	0.5000
1(2012–2013)	0.2502	1.0135	0.2469	0.4746	1.0000	0.5201
2(2013–2014)	0.2256	1.4873	0.1517	0.4325	0.8716	0.4025
3(2014–2015)	0.2145	1.0252	0.2093	0.4702	0.8522	0.5224
4(2015–2016)	0.2269	0.9731	0.2332	0.4719	0.8645	0.5715

Figure 4. Total factor productivity level with respect to group frontiers. Displaying average TFP levels with respect to group frontiers in each period of analysis.

Table 5. Average TFP change and its components using group-specific frontiers as in Equation (6)(6) $${\mathrm{TFPE}}_{it}=\mathrm{ }\frac{{\mathrm{TFP}}_{it}}{{\mathrm{TFP}}_t^{\mathrm{*}}}\mathrm{ }=\left(\frac{{\mathrm{TFP}}_{it}}{{\mathrm{TFP}}_t^{\mathrm{*}g}}\right)\mathrm{\hspace{0.17em}\times \hspace{0.17em}}\left(\frac{{\mathrm{TFP}}_t^{\mathrm{*}g}}{{\mathrm{TFP}}_t^{\mathrm{*}}}\right)=\mathrm{ }{\mathrm{TFPE}}_{it}^g\mathrm{ }\mathrm{\hspace{0.17em}\times \hspace{0.17em}}\mathrm{ }{\mathrm{IMTR}}_t^g$$(6) .

Period	TFPI^g	ETI^g	TFPEC^g	ITEC^g	IMEC^g	RISEC^g
Large group
Entire period	0.9890	0.8413	1.1755	1.0680	0.9872	1.1149
Periods 2 over 1	0.9621	0.6856	1.4031	1.1102	0.9987	1.2655
Periods 3 over 2	0.9983	0.7649	1.3051	1.1670	1.0015	1.1168
Periods 4 over 3	1.0071	1.1355	0.8869	0.9402	0.9620	0.9806
Medium group
Entire period	0.9819	0.9142	1.0741	0.9981	0.9950	1.0816
Periods 2 over 1	0.9969	1.1198	0.8903	0.9113	1.0044	0.9727
Periods 3 over 2	0.9714	0.7574	1.2825	1.0871	0.9678	1.2191
Periods 4 over 3	0.9776	0.9009	1.0851	1.0035	1.0133	1.0671

Table 6. TFP level and its components under the meta-frontier specification as in Equation (2)(2) $${\mathrm{TFP}}_{it}={\mathrm{TFPE}}_{it}\times {\mathrm{TSME}}_{it}=\mathrm{ }{\mathrm{TFPE}}_{it}\times {\mathrm{ITE}}_{it}\times {\mathrm{IME}}_{it}\times {\mathrm{RISE}}_{it}$$(2) .

Period	TFP	TFP-max	TFPE	ITE	IME	RISE
Entire period	0.1870	0.9891	0.1891	0.4195	0.8858	0.5088
1 (2012–2013)	0.1968	0.9611	0.2048	0.4296	1.0000	0.4767
2 (2013–2014)	0.1801	1.2912	0.1395	0.3887	0.8333	0.4306
3 (2014–2015)	0.1779	0.8922	0.1995	0.4329	0.8498	0.5422
4 (2015–2016)	0.1940	0.8644	0.2244	0.4287	0.8693	0.6023

Figure 5. Meta-technology ratios over time.

Table 7. Analysis of determinants of productivity variation.

Determinants	Large (t score)	Medium (t score)	Entire sample (t score)
Year of establishment	0.0446** (3.15)	−0.00338 (−1.69)	0.00288* (2.24)
Number of experts	0.0822*** (12.57)	0.0209*** (4.31)	0.0463*** (10.97)
Number of branches	0.0154*** (4.52)	0.0160** (2.62)	0.0168*** (5.91)
Income from other activities	0.331 (1.86)	0.303** (2.87)	0.337*** (3.55)
Dummy_non-Java	−0.107 (−1.82)	0.0312 (0.78)	−0.0234 (-0.72)
Dummy subsidies		0.369*** (6.56)	0.299*** (5.76)
Unlimited liability	−0.229* (−2.35)	−0.257*** (−4.12)	−0.297*** (−5.64)
Constant	0.287*** (6.63)	0.3715*** (8.05)	0.287*** (8.71)

t statistics in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001.

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Data availability statement

Several elements of data and codes can be made available from the second author upon reasonable request.