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Terrorism and Firm Performance: Empirical Evidence from Pakistan

  • Ummad Mazhar ORCID logo EMAIL logo
Veröffentlicht/Copyright: 15. Dezember 2018
The B.E. Journal of Economic Analysis & Policy
Aus der Zeitschrift The B.E. Journal of Economic Analysis & Policy Band 19 Heft 1

Abstract

A secure business environment for private enterprises is desired by all states and is endorsed as a part of United Nation’s sustainable development goals. However, the risk exposure of private enterprises against terrorism, particularly in developing countries, is not adequately studied. Using Enterprise Surveys (ES) data for more than 2000 firms located across the four provinces of Pakistan, this paper studies the link between the risk of terrorism and firm performance. It finds, after controlling for various firm specific performance determinants as well as provincial and sector specific heterogeneities, that terrorism has a significant negative effect on firms’ performance which is independent of firm size. This effect is robust against different specifications and estimation methods including instrumental variables strategy. Beyond much explored aggregate consequences, terrorism has direct consequences for production processes at micro level.

Keywords: terrorism; business enterprises; firms; employment growth; Pakistan
JEL Classification: L10; L20; O10; O40; D74

Funding statement: This work was supported by Lahore University of Management Sciences, Funder Id: 10.13039/100010139, Grant Number: Faculty initiative fund

A Appendix I Tables

Table 1:

Variable definition and sources.

Terror(log)Log of successful terrorist incidents in a given year in a given province. In GTD, a successful terrorist incidents is defined in terms of its tangible effects. (See page 24, GTD Codebook, 2016).GTD (2016)
TerrorIndex(log)Log of terrorism index. The index constructed as a weighted average of number of terrorist attacks in a year, number of people killed and injured in an attack. The weights assigned are those used by Global Terrorism Index (2016) i. e. a weight of 1 is given to number of terrorist incidents, a weight of 3 to number of people killed in an incident, and a weight of 0.5 to number wounded.GTD (2016)
PViolence(log)Log of the number of violent incidents in a given year. This variable considers only successful incidents and ignores all others. The incident is defined as successful if the attacker(s) hit their intended target.BFRS data. Bueno de Mesquita et al. (2015).
Growth(%)Annual percentage change in Gross Provincial Product.State Bank of Pakistan Hand Book of Statistics on Pakistan’s Economy2
CurrentExp(%)Annual percentage share of current expenditures in Gross Provincial Product.State Bank of Pakistan Hand Book of Statistics on Pakistan’s Economy
Age(years)Age of a firm in years.ES data
StateOwnership(%)Proportion of state/government ownership in a firm (measured in percentage).ES data
ExporterDBinary variable assumes a value of 1 if a firm directly exports at least 10 % of its salesES data
IntQaulityCertBinary variable, assumes a value of 1 if a firm has an internationally recognized quality certification e. g. ISO9000, 9002, or 14,000ES data
InternalFin(%)Proportion of working capital financed by internal funds (measured in percent).ES data
LaborRegConstBinary variable, assumes a value of 1 if a firm identifies labor regulations as a major constraintES data
ElectricLoss(%Sales)Loss due to electrical outages as a percentage of annual sales.ES data
AnnualSalesGr(%)Real annual sales growth (in percent). The annualized sales in a given fiscal year t for a firm i are calculated using the following formula: Salesit−Salesi(t−3)(Salesit+Salesi(t−3))/2, where Sales denote nominal sales. To get real sales, “all values for sales are converted to USD using exchange rate in corresponding fiscal year of the survey. Sales are then deflated to 2009 using the USD deflator”.ES data
CorruptionDBinary variable, assumes a value of 1 if a firm identifies corruption as a major constraint.ES data
SecurityCost(%Sales)Security costs (percentage of annual sales).ES data
FirmSizeDBinary variable assuming a value of 1 if the number of workers is more than 1001.ES data
SMEDBinary variable assuming a value of 1 if the firm is identified as either small or medium sized.ES data

  1. Notes: (1) Enterprise Surveys Inidcator Descriptions says “Firm size is a composite measure of permanent and temporary workers. The number of temporary workers is adjusted by the average number of months worked in a year.” P.10. The description file is available at: http://www.enterprisesurveys.org/data access July 5, 2017.

  2. (2) Available at: http://www.sbp.org.pk/departments/stats/PakEconomy_HandBook/access August 11 2017.

Table 2:

Summary statistics.

VariableObs.MeanStd.Dev.MinMax
Terror(log)2,101.003.271.281.765.70
TerrorIndex(log)2,101.006.150.964.307.89
PViolence(log)2,101.005.340.463.676.02
Growth(%)2,101.004.762.371.368.02
CurrentExp(%)2,101.0080.347.2471.6793.94
Age(years)1,957.0022.5213.602.00127.00
StateOwnership(%)2,063.000.828.240.00100.00
ExporterD2,101.000.120.330.001.00
IntQaulityCert2,008.0025.7543.730.00100.00
InternalFin(%)1,930.0086.7022.030.00100.00
LaborRegConst1,995.0010.9831.270.00100.00
ElectricLoss(%Sales)1,703.0019.9718.650.0085.00
AnnualSalesGr(%)1,346.002.4228.24−99.9798.87
CorruptionD2,046.0054.4549.810.00100.00
SecurityCost(%Sales)1,890.001.774.710.0099.10
FirmSizeD2,101.000.190.390.001.00
SMED2,101.000.810.390.001.00
Table 3:

Firms size and performance (EmpGr%).

Obs.MeanSt.Dev
Large (workers>100)3494.0912.66
SME (workers<100)15223.1412.89
Total18712.4228.24

  1. Note: SME stands for Small and Medium enterprises.

  2. The total number of firms in the sample is 2101 while in this table it is 1871. It is because not all firms respond to the performance question.

Table 4:

Terrorism, provincial performance and firm performance.

ProvinceYearTerrorist IncidentsGrowth(%)EmpGr (%)
Baluchistan20071554.051.34
Baluchistan20139911.462.64
Khaiber Pakhtunkhawa2007345.793.24
Khaiber Pakhtunkhawa201314883.133.40
Punjab2007298.021.69
Pumjab20131573.344.29
Sindh2007626.733.66
Sindh20135771.365.08

  1. Note: Number of terrorist incidents for each province are counted over two 5-yearly time periods 2002–2006 and 2008–2012 as explained in the text. The information is extracted from Global Terrorism Database (GTD).

Table 5:

Terrorism and firm performance. (OLS estimates).

Dep.Var: Annual employment growth (EmpGr(%))
(1)(2)(3)(4)(5)
Terror(log)−3.028**−3.003**−3.060**−3.425***−2.828**
(1.246)(1.253)(1.249)(1.265)(1.250)
Growth(%)0.6690.6540.6650.8280.582
(1.038)(1.039)(1.038)(1.052)(1.036)
CurrentExp(%)0.0650.0560.0630.1450.011
(0.246)(0.250)(0.246)(0.250)(0.236)
Age(years)−0.008−0.009−0.007−0.012−0.003
(0.031)(0.031)(0.031)(0.031)(0.031)
ExporterD−0.238−0.272−0.243−0.108−0.289
(1.375)(1.395)(1.375)(1.385)(1.386)
InterQualityCert(%Firms)0.021*0.0200.021*0.022*0.024**
(0.012)(0.012)(0.012)(0.012)(0.012)
InternalFin(%)0.032*0.033*0.032*0.037**0.029*
(0.017)(0.017)(0.017)(0.018)(0.017)
LaborRegConst−0.051**−0.051**−0.051**−0.047**−0.050**
(0.021)(0.021)(0.021)(0.021)(0.021)
LossElectric(%Sales)−0.064−0.063−0.064−0.065−0.040
(0.041)(0.041)(0.041)(0.042)(0.036)
RealSalesGr(%)0.060***0.060***0.060***0.062***0.061***
(0.017)(0.017)(0.017)(0.016)(0.017)
KPK−2.217−2.259−2.191−2.853−2.494
(2.979)(2.988)(2.978)(2.929)(2.968)
Punjab−5.679−5.675−5.715−6.922−5.807
(4.868)(4.867)(4.867)(4.877)(4.882)
Sindh−0.391−0.382−0.394−1.381−0.264
(3.133)(3.132)(3.133)(3.186)(3.122)
Year = 201314.355*14.126*14.383*16.307*12.375
(8.157)(8.237)(8.157)(8.312)(7.878)
FirmSizeD0.399
(1.197)
StateOwnership(%)−0.021
(0.029)
CorruptionD−0.023***
(0.008)
SecurityCosts(%Sales)−0.009
(0.059)
Observations1,0921,0921,0921,0701,080
R-squared0.0730.0740.0740.0820.073
RESET test(p-value)0.5620.6180.5330.5970.852
Probability(F-test)0.0000.0000.0000.0000.000
Probab F-test (provincial dummies)0.19120.19740.18280.18070.1874

  1. Notes: Robust standard errors in parentheses;*** p<0.01, ** p<0.05, * p<0.1; constant is included but not reported; in binary variables for provinces, Baluchistan is the reference category.

Table 6:

Terrorism index and firm performance. (OLS estimates).

Dep. Var: Annual employment growth (EmpGr(%))
(1)(2)(3)(4)(5)
TerrIndex(log)−4.339**−4.302**−4.385**−4.907***−4.051**
(1.785)(1.795)(1.790)(1.812)(1.791)
Growth(%)2.3402.3102.3542.718*2.141
(1.574)(1.578)(1.574)(1.594)(1.569)
CurrentExp(%)0.4120.4010.4140.538*0.336
(0.302)(0.307)(0.302)(0.312)(0.288)
Age(years)−0.008−0.009−0.007−0.012−0.003
(0.031)(0.031)(0.031)(0.031)(0.031)
ExporterD−0.238−0.272−0.243−0.108−0.289
(1.375)(1.395)(1.375)(1.385)(1.386)
InterQualityCert(%Firms)0.021*0.0200.021*0.022*0.024**
(0.012)(0.012)(0.012)(0.012)(0.012)
InternalFin(%)0.032*0.033*0.032*0.037**0.029*
(0.017)(0.017)(0.017)(0.018)(0.017)
LaborRegConst−0.051**−0.051**−0.051**−0.047**−0.050**
(0.021)(0.021)(0.021)(0.021)(0.021)
LossElectric(%Sales)−0.064−0.063−0.064−0.065−0.040
(0.041)(0.041)(0.041)(0.042)(0.036)
RAnnualSalesGr(%)0.060***0.060***0.060***0.062***0.061***
(0.017)(0.017)(0.017)(0.016)(0.017)
Year = 201326.836**26.502**26.998**30.423**24.030**
(12.458)(12.570)(12.467)(12.691)(12.191)
FirmSizeD0.399
(1.197)
StateOwnership(%)−0.021
(0.029)
CorruptionD−0.023***
(0.008)
SecurityCosts(%Sales)−0.009
(0.059)
Observations1,0921,0921,0921,0701,080
R-squared0.0730.0740.0740.0820.073
RESET test0.5620.6180.5330.5970.852
Probability F-test0.0000.0000.0000.0000.000

  1. Robust standard errors in parentheses;*** p<0.01, ** p<0.05, * p<0.1; constant is included but not reported. Provincial fixed effects are included but are not reported because they are mostly insignificant.

Table 7:

Further checks. (Alternative measure of terrorism, outliers, sectoral effects).

Dep. Var: Annual employment growth (EmpGr(%))
(1)(2)(3)(4)
PViolence(log)−4.154**
(1.709)
Terror(log)−1.927**−2.390**−2.616**
(0.764)(0.946)(1.255)
Growth(%)0.715−0.1100.3170.346
(1.051)(0.571)(0.708)(1.050)
CurrentExp(%)0.1090.0870.1010.054
(0.250)(0.132)(0.164)(0.246)
Age(years)−0.0080.0110.005−0.012
(0.031)(0.015)(0.018)(0.031)
ExporterD−0.2380.2170.273−0.491
(1.375)(0.620)(0.768)(1.365)
InterQualityCert(%Firms)0.021*0.0040.0070.021*
(0.012)(0.005)(0.007)(0.012)
InternalFin(%)0.032*−0.0080.0010.036**
(0.017)(0.008)(0.010)(0.017)
LabRegConst(%Firms)−0.051**−0.012*−0.025***−0.049**
(0.021)(0.007)(0.008)(0.021)
LossElectric(%Sales)−0.064−0.011−0.022−0.060
(0.041)(0.016)(0.020)(0.042)
RealSalesGr(%)0.060***0.042***0.044***0.059***
(0.017)(0.006)(0.008)(0.017)
Year = 201311.8546.904*10.234**11.959
(7.423)(4.052)(5.018)(8.213)
Observations1,0921,0921,0921,092
R-squared0.0730.1070.0830.079
Provincial EffectsYesYesYesYes
Sectoral EffectsNoNoNoYes

  1. Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Columns (2) and (3) report results, respectively, of robust regression and Huber regression M-estimator to take care of the possible outlier effect. In column (4) the sectorial effects are controlled for following major sectors in the sample of firms used in this study: Chemicals; machinery and electronics; motor vehicles and transport; non-metallic mineral products; services; garments and textiles. The original ES survey data provide 11 sector categories. The classification here merge categories related to garments, textiles, and services to avoid multicollinearity and also to make distinctions sharper across sectors.

Table 8:

Instrumental variable regressions and interaction models.

Dep Var: Annual employment growth (EmpGr(%))
(1)(2)(3)(4)
IVIVInteractionInteraction
Terror(log)−2.924***−3.001***−3.110**−2.931**
(0.899)(0.929)(1.262)(1.253)
Growth(%)0.4980.5170.7890.631
(0.555)(0.557)(1.041)(1.047)
CurrentExp(%)0.196*0.195*0.0510.067
(0.104)(0.104)(0.250)(0.245)
Age(years)−0.006−0.007−0.009−0.008
(0.031)(0.031)(0.031)(0.031)
ExporterD−0.299−0.263−0.295−0.281
(1.362)(1.365)(1.401)(1.384)
InterQualityCert(%Firms)0.021*0.020*0.021*0.021*
(0.012)(0.012)(0.012)(0.012)
InternalFin(%)0.033**0.033**0.032*0.032*
(0.017)(0.017)(0.017)(0.017)
LaborRegConst−0.051**−0.051**−0.051**−0.051**
(0.021)(0.021)(0.021)(0.021)
LossElectric(%Sales)−0.068*−0.068*−0.063−0.065
(0.040)(0.040)(0.041)(0.041)
RealSalesGr(%)0.060***0.060***0.060***0.060***
(0.016)(0.016)(0.017)(0.017)
Year = 201314.798***15.009***14.960*13.982*
(4.029)(4.106)(8.231)(8.331)
SMED−0.193
(1.190)
Manufacture0.458
(1.293)
Observations1,0921,0921,0921,092
R-squared0.0730.073
Provincial EffectsYesYesYesYes
(a) First-stage F-stat168.4241.7
(b) Underidentification test (p-value)0.0000.000
(c) Hansen J-statistic0.6550.835

  1. *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parenthesis for columns (1) and (2) and Delta method standard errors in parenthesis for columns (3) and (4). Columns (1) and (2) report the results of two stage GMM instrumental variable regression. In model (1) Terror(log) is instrumented using (i) number of perpetrators, (ii) and the number of terrorists killed in an incident. In model (2) the instruments are (i) number of terrorists killed, (ii) and number of terrorists wounded in an incident. Information on instruments is extracted from Global Terrorism Database (GTD 2016).

  2. Interaction models in columns (3) and (4) report marginal effects. Terrorism is interacted with SMED and Manufacturing, respectively, in columns (3) and (4). Dependent variable in all models is EmpGr%.

  3. (a) The first stage regression uses heteroskedastic robust standard errors;

  4. (b) H0: with given instruments the equation is underidentified;

  5. (c) H0: Overidentified restrictions are valid.

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Published Online: 2018-12-15

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Research Articles
  2. Public Health Insurance and Prescription Medications for Mental Illness
  3. Inter-Ethnic Friendship and Hostility between Roma and non-Roma Students in Hungary: The Role of Exposure and Academic Achievement
  4. The Costs of Firm Exit and Labour Market Policies: New Evidence from Europe
  5. Parental Transfers, Intra-household Bargaining and Fertility Decision
  6. Do Firms Supported by Credit Guarantee Schemes Report Better Financial Results 2 Years After the End of Intervention?
  7. Estimating the Impact of Ride-Hailing App Company Entry on Public Transportation Use in Major US Urban Areas
  8. Announced or Surprise Inspections and Oligopoly Competition
  9. Terrorism and Firm Performance: Empirical Evidence from Pakistan
  10. The Curious Case of Farmer Credit Cards: Evidence from an Indian Policy Reform
  11. Population Policy, Demographic Change, and Firm Returns: Evidence from China
  12. Sorting into Contests: Evidence from Production Contracts
  13. She-E-Os and the Cost of Debt: Do Female CEOs Pay Less for Credit?
https://doi.org/10.1515/bejeap-2018-0041
Schlagwörter für diesen Artikel
terrorism; business enterprises; firms; employment growth; Pakistan

Artikel in diesem Heft

  1. Research Articles
  2. Public Health Insurance and Prescription Medications for Mental Illness
  3. Inter-Ethnic Friendship and Hostility between Roma and non-Roma Students in Hungary: The Role of Exposure and Academic Achievement
  4. The Costs of Firm Exit and Labour Market Policies: New Evidence from Europe
  5. Parental Transfers, Intra-household Bargaining and Fertility Decision
  6. Do Firms Supported by Credit Guarantee Schemes Report Better Financial Results 2 Years After the End of Intervention?
  7. Estimating the Impact of Ride-Hailing App Company Entry on Public Transportation Use in Major US Urban Areas
  8. Announced or Surprise Inspections and Oligopoly Competition
  9. Terrorism and Firm Performance: Empirical Evidence from Pakistan
  10. The Curious Case of Farmer Credit Cards: Evidence from an Indian Policy Reform
  11. Population Policy, Demographic Change, and Firm Returns: Evidence from China
  12. Sorting into Contests: Evidence from Production Contracts
  13. She-E-Os and the Cost of Debt: Do Female CEOs Pay Less for Credit?
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