This study presents an analysis of the intra-regional performance of South Asian economies in terms of their productive efficiency during the period 1985–2019. A window data envelopment analysis (dea) based technique with bootstrapping is used to model the production frontier for seven South Asian economies to estimate their efficiency levels. The results show substantial differences in technical efficiency levels across South Asian economies and thus, the existence of vast potential to catch up with the best practices is revealed among these economies. On an average, the extent of technical inefficiency in south asia is about 46.92% per annum after adjusting for statistical bias. This is indicative of a huge gap in laggard and best practices within region. Both managerial and scale components of efficiency performance are equally responsible for observed production inefficiency in south asia. The existence of managerial inefficiency reflects failure of policy planners among affiliate countries to catch-up the best practices within South Asian region. However, the observed scale inefficiency (SIE) is because of the dominance of scale economies over diseconomies that urges need of enlarging cooperation among member nations.
BankerR. D., CharnesA., & CooperW. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.
3.
CharnesA., ClarkC. T., CooperW. W., & GolanyB. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US Air Forces. Annals of Operations Research, 2(1), 95–112.
4.
CharnesA., CooperW. W., & RhodesE. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 3(4), 339–338.
5.
CharnesA., CooperW., LewinA. Y., & SeifordL. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the Operational Research Society, 48(3), 332–333.
6.
CoelliT. J., RaoD. S. P., O’DonnellC. J. & BatteseG. E. (2005).An introduction to efficiency and productivity analysis. NY:Springer.
7.
DevarajanS., & NabiI. (2006). Economic growth in South Asia: Promising, unequalising, sustainable?. Economic and Political Weekly, 41(33), 3573–3580.
8.
DimelisS. P., & PapaioannouS. K. (2011). ICT growth effects at the industry level: A comparison between the US and the EU. Information Economics and Policy, 23(1), 37–50.
9.
EmrouznejadA., & AminG. R. (2009). DEA models for ratio data: Convexity consideration. Applied Mathematical Modelling, 33(1), 486–498.
10.
FäreR., GrosskopfS., & LovellC. A. K. (1985). The measurement of efficiency of production. Springer Dordrecht.
11.
FarrellM. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–281.
12.
FeenstraR. C., InklaarR., & TimmerM. P. (2015). The next generation of the penn world table. American Economic Review, 105(10), 3150–3182.
13.
FerrierG. D. & HirschbergJ. G. (1995). Bootstrapping confidence intervals for linear programming efficiency scores: With an illustration using Italian banking data. Journal of Productivity Analysis, 8(1), 19–33.
14.
FriedH. O., LovellC. A. K., & SchmidtS. S.(Eds.). (2008).The measurement of productive efficiency and productivity growth. Oxford Academic.
15.
GaganisC., & PasiourasF. (2009). Efficiency in the Greek banking industry: A comparison of foreign and domestic banks. International Journal of the Economics of Business, 16(2), 221–237.
16.
GkypaliA., KounetasK., & TsekourasK. (2019). European countries’ competitiveness and productive performance evolution: Unraveling the complexity in a heterogeneity context. Journal of Evolutionary Economics, 29(2), 665–695.
17.
GustC., & MarquezJ. (2004). International comparisons of productivity growth: The role of information technology and regulatory practices. Labour Economics, 11(1), 33–58.
18.
HenryM., KnellerR., & MilnerC. (2009). Trade, technology transfer and national efficiency in developing countries. European Economic Review, 53(2), 237–254.
19.
JarrayaB. (2014). Parametric meta-technology frameworks to study technical efficiency and macro-economic effects in the European banking system. Contemporary Economics, 8(1), 73–88.
20.
JayasuriyaR., & WodonQ. (2005). Measuring and explaining the impact of productive efficiency on economic development. The World Bank Economic Review, 19(1), 121–140.
21.
KneipA., SimarL., & WilsonP. W. (2003). Asymptotic for DEA estimators in non parametric frontier models(Discussion Paper No 0317). Belgium:Institute de Statistique, Universitè Catholique de Louvain, Louvain-la-Neuve.
22.
KnellerR., & StevensP. A. (2006). Frontier technology and absorptive capacity: Evidence from OECD manufacturing industries. Oxford Bulletin of Economics and Statistics, 68(1), 1–21.
23.
LemonakisC., VassakisK., GarefalakisA., & PartalidouX. (2016). Manufacturing firms’ performance and productivity: Evidence from North and South European, Scandinavian and Balkan countries. Theoretical Economics Letters, 6(4), 789–797.
24.
MilnerC., & Weyman-JonesT. (2003). Relative national efficiency and country size: Evidence for developing countries. Review of Development Economics, 7(1), 1–14.
25.
OzdincerB., & OzyildirimC. (2008). Determining the factors of bank performance with a focus on risk and technical efficiency. In Proceedings of the 2nd WSEAS international conference on Management, Marketing and Finances (MMF’08) (pp.31–39). Harvard.
26.
RahmanM. M., RanaR. H., & BaruaS. (2019). The drivers of economic growth in South Asia: Evidence from a dynamic system GMM approach. Journal of Economic Studies, 46(3), 564–577.
27.
RepkineA. (2008). ICT penetration and aggregate production efficiency: Empirical evidence for a cross-section of fifty countries. Journal of Applied Economic Sciences (JAES), 3(05), 137–144.
28.
RosegrantM. W., & EvensonR. E. (1992). Agricultural productivity and sources of growth in South Asia. American Journal of Agricultural Economics, 74(3), 757–761.
29.
SahooP., & DashR. K. (2012). Economic growth in South Asia: Role of infrastructure. The Journal of International Trade & Economic Development, 21(2), 217–252.
30.
SimarL. (1992). Estimating efficiencies from frontier models with panel data: A comparison of parametric, non-parametric and semi-parametric methods with bootstrapping. Journal of Productivity Analysis, 3(1), 171–203.
31.
SimarL., & WilsonP. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.
32.
SimarL., & WilsonP. W. (2000a). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13(1), 49–78.
33.
SimarL. & WilsonP. W. (2000b). A general methodology for bootstrapping nonparametric frontier models. Journal of Applied Statistics, 27(6), 779–802.
34.
ThompsonH. G.Jr, & GarbaczC. (2007). Mobile, fixed line and Internet service effects on global productive efficiency. Information Economics and Policy, 19(2), 189–214.
35.
TyboutJ., de MeloJ., & CorboV. (1991). The effects of trade reforms on scale and technical efficiency: New evidence from Chile. Journal of International Economics, 31(3-4), 231–250.
36.
WilsonP. W. (2008). FEAR 1.0: A software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42(4), 247–254.
