The Empirical Nexus between Data-Driven Decision-Making and Productivity: Evidence from Pakistan’s Banking Sector

Figures & data

Table 1. Construction of Variables

Variables	Notations	Definitions	References
Output	Yit	Earning Assets (non-bank loans and investment in securities)	Koeter and Noth (2013); Martin-Oliver and Salas-Fumas (2008)
Data-driven Decision-making	DDDMit	DDDM is a composite index derived from five items in the survey through PCA	Brynjolfsson and McElheran (2019)
Capital	Kit	Fixed Assets less depreciation	Octrina et al. (2019); Koeter and Noth (2013)
Employees	Lit	Number of full-time employees in banks	Brynjolfsson and McElheran (2019); Koeter and Noth (2013);
Information Technology expense	ITEit	The log of the sum of IT expense under the administrative expenses.	Koeter and Noth (2013)
Type (Type)	Xit	Dummy variable, I for the commercial banks and “0” for microfinance banks	Gul and Ellahi (2021)
Listing (LIST)		Dummy variable, “1” for banks listed on Pakistan Stock exchange and “0” otherwise.	Gul and Ellahi (2021) Ehsan and Javid (2018)
Non-Performing Loans (NPL)		Non-performing loans taken as a ratio to total loans	Octrina et al. (2019)
Deposits to Assets Ratio		The sum of all types of deposits such as current deposits, savings deposits, and time deposits as a ratio to total assets	Coulibaly (2020); Onay and Ozsoz (2011)
Age (Age)	Instrument 1	Log of the age of a bank as a proxy for the size of banks	Ehsan and Javid (2018)
Adjustment Cost (AdjC)	Instrument 2	AdjC is a composite index derived from six items in the survey through PCA	Brynjolfsson et al. (2011)

Table 2. Diagnostic test for PCA

	Bartlett test of sphericity	Kaiser-Meyer-Olkin
DDDM	Chi-square = 25.746 (0.004)	0.751
Adjustment Cost (AdjC)	Chi-square = 180.555 (0.000)	0.784

Note. Bartlett test of sphericity H0: variables are not intercorrelated. The p-values are in parentheses.

Table 3. Principal Component Analysis

	Eigenvalue (Component 1)	Variable (%age)
DDDM	1.822	0.7178
Adjustment Cost (AdjC)	2.984	0.7896

Table 4. Descriptive Statistics of Survey Items

	Range of scale	Mean	S.D.	Cronbach’s Alpha
Measure 1: Data Driven Decision Making (DDDM)				0.6536
We rely on data for the creation of a new product or service	1-5	4.1388	0.6393
We depend on data to support our decision making	1-5	3.9998	0.5855
We have the data we need to make decisions	1-5	3.8888	0.7082
Feedback based on data is integrated into existing system	1-5	3.9166	0.6491
data is preferred over management’s ‘experience and intuition.	1-5	3.7777	0.7601
Measure 2: Adjustment Cost (AdjC)				0.9363
Skill mix of existing staff	1-5	4.2501	0.8409
Employment contracts	1-5	3.9166	0.9373
Work Rules	1-5	4.2222	0.9594
Organizational culture	1-5	4.3333	0.8618
customer relationships	1-5	4.5001	0.7745
senior management involvement	1-5	4.3055	0.9507

Table 5. Descriptive Statistics of Production function variable

Variable	Obs	Mean	Std. Dev.	Min	Max
Log (Y)	178	8.019	0.967	4.841	9.413
Log(K)	178	6.500	0.841	3.869	7.878
Log(L)	178	3.463	0.509	1.973	4.324
Log(ITE)	167	5.034	0.915	2.496	7.357
TYPE	178	0.697	0.461	0.000	1.000
Listing	178	0.797	0.651	0.000	1.000
NPL ratio	178	0.478	0.301	0.157	0.710
Deposits ratio	178	0.078	0.071	0.123	0.442

Table 6. Correlation between DDDM and IT expense and employees

	Y	DDDM	K	L	ITE	TYPE	Listing	NPL	Deposits
Y	1.0000
DDDM	0.2046*	1
K	0.9459*	0.2520	1
L	0.8980*	0.2557	0.9170	1
ITE	0.7698*	0.1527	0.6164	0.5329	1
TYPE	0.8027*	−0.0333	0.5031	0.5286	0.5820	1
Listing	0.6311*	0.0589	0.6867	0.6224	0.5251	0.5086	1
NPL	0.1630*	0.1087	0.1835	0.059*	0.0098	0.2715	0.1659	1
Deposits	0.6049*	0.4511	0.6093	0.6105	0.4817	0.3750	0.3130	0.1664	1

*p< 0.05

Table 7. OLS Regressions of DDDM on Productivity Measures

Column 1	Column 2	Column 3	Column 4
	2016-2020 (without DDDM)	2016-2020 (with DDDM)	2020 (with DDDM)
DDDM		0.0345* (0.0123)	0.0364* (0.0182)
Log (K)	0.307*** (0.074)	0.2928*** (0.0641)	0.4858** (0.1431)
Log (L)	0.866*** (0.081)	0.8766*** (0.0798)	0.7543*** (0.1946)
IT expense (ITE)	0.02847 0.0306	0.03056 0.03032	0.0873 (0.0887)
Type	0.788*** (0.052)	0.8225*** 0.0525)	0.7511*** (0.0989)
Listing	0.074* (0.040)	−0.0625 (0.0393)	−00567 (0.0809)
NPL	−0.172 (0.215)	−0.2286 (0.2121)	−0.3161 (0.3352)
Deposits	0.219** (0.101)	0.1461 (0.1030)	0.2529 (0.2395)
Observations	167	167	36

Note: Standard errors are in parentheses. *** p<0.001 ** p<0.05 * p<0.01

Table 8. Panel Regression analysis when the sample period was divided into two periods

Column 1	Column 2	Column 3	Column 4
DV = Log(output)	2016-2020	2016-2017	2018-2019
DDDM	0.0391* (0.0241)	0.0401* (0.0269)	0.0408** (0.0287)
Log (K)	0.3116*** (0.0765)	0.2188* (0.0545)	0.4714*** (0.0936)
Log (L)	0.8460*** (0.0955)	0.8958*** (0.1103)	0.7884*** (0.1203)
ITE	0.0162 (0.0242)	0.0355 (0.0575)	0.0026 (0.0495)
Type	0.7683*** (0.0826)	0.8947*** (0.0974)	0.8473*** (0.1170)
Listing	−0.0834 (0.0734)	0.01484 (0.0922)	0.0474 (0.0974)
NPL	−0.2751** (0.1813)	−0.5102 (0.2007)	−0.3220 (0.2799)
Deposits	0.1348 (0.1064)	−0.1219 (0.1844)	−0.2110 (0.1748)
Observations	167	72	72
R-squared	0.3501	0.406	0.3545

Note: Standard errors are in parentheses. *** p<0.001 ** p<0.05 * p<0.01

Table 9. IV-Regressions of DDDM on Productivity Measures

DV = Log(output)	OLS (2016-2020)	2SLS/IV (2016-2020)
DDDM	0.0410*** (0.0123)	0.0701*** (0.0330)
Log (K)	0.2928*** (0.0641)	0.2138*** (0.0759)
Log (L)	0.8766*** (0.0798)	0.9375*** (0.0917)
ITE	0.0318 (0.0340)	0.0341 (0.0420)
Type	0.8225*** (0.0525)	0.9137*** (0.0638)
List	−0.0626 (0.0393)	−0.0303 (0.0436)
NPL	−0.2284 (0.2121)	−0.4119* (0.2319)
Deposits	0.1462* (0.1030)	0.0609* (0.1271)
Observations	167	167
Adj. R-squared	0.6485	0.6369
OverID test (Sargan Test) Chi-sq (p-value)		2.33 (0.124)
Under identification Chi-sq (P-value)		27.85 (0.000)
Weak instrument: Cragg-Donald Wald F statistic		19.208 (0.0000)
Endogeneity Chi-sq (P-value)		8.989 (0.0023)

Notes: Sargan Test- Ho: over identifying restrictions are valid. The weak instrument hypothesis is rejected for all specifications. Endogeneity of IV- H0: instruments are endogenous. The given values are test statistics and standard errors are in parenthesis.

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