computer usage and labour regulation in india's retail sector

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Computer Usage and LabourRegulation in India's RetailSectorMohammad Amin aa World Bank , Washington, DC, USAPublished online: 27 Oct 2010.

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Computer Usage and Labour Regulationin India’s Retail Sector

MOHAMMAD AMINWorld Bank, Washington DC, USA

Final version received January 2010

ABSTRACT A recent survey of 1,948 retail stores in India conducted by the World Bank’sEnterprise surveys shows that 19 per cent of all stores use computers. In the state of Kerala, thefigure is as high as 40 per cent. Using this survey, we estimate the effect of computer usage onlabour employment. Our findings show that this effect depends on the stringency of the underlyinglabour laws. Stricter labour laws magnify the labour displacing effect of computers significantly.

I. Introduction

The World Bank conducted a survey of 1,948 retail stores in 16 states (provinces) and41 cities in India in 2006. The survey shows that 19 per cent of all stores use acomputer for running their business, with significant variation in computer usageacross states and cities. Using this survey, we analyse the effect of computer usage onlabour employment at the store level. We find that the computers–employment rela-tionship depends on underlying labour laws, with stricter (pro-worker) labour lawsmagnifying the labour displacing effect of greater computer usage. The findingscontribute to the rich literature on how modern technology affects labour employmentand provide a better understanding of employment generation in Indian retailing.The relationship between computer usage (more broadly, modern technology) and

labour employment is hotly debated in the literature. Most of the studies in thisliterature focus on how the relationship varies across the skilled and the unskilledwith no consideration given to labour regulations. It is argued that dramaticreductions in computer prices and improvements in computing technology over thelast few decades generated a strong substitution towards computers and away fromlabour in those jobs which could be easily automated (Bresnahan, 1997). These werethe jobs that were performed by the unskilled workers who, as a consequence, exp-erienced substantial reductions in wage rates and employment opportunities. Theeffect on the skilled workers was the opposite. Computers vastly improved their

Correspondence Address: Mohammad Amin, World Bank, Washington DC, 20433, USA.

Email: [email protected]

Journal of Development Studies,Vol. 46, No. 9, 1572–1592, October 2010

ISSN 0022-0388 Print/1743-9140 Online/10/091572-21 ª 2010 Taylor & Francis

DOI: 10.1080/00220388.2010.492868

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capability to perform more complex and idiosyncratic tasks implying more jobs andhigher wages for them (Bresnahan, 1997; Autor et al., 1998; Autor et al., 2006). Thedifferential effect across the skilled and unskilled is revealing in that it suggests thatcomputers may substitute or complement labour, depending on how they are used orthe kinds of tasks they are put to.

Another strand of the literature focuses on how labour regulations affect emp-loyment and output. Botero et al. (2004), Nickell (1997), Besley and Burgess (2004),Holmes (1988) and Bertrand and Kramarz (2002), among others, find strong negativeeffects of stricter labour laws on employment and output. However, the interactionbetween technology and labour regulation is not discussed in any of these studies. Theonly exception is a study by Card et al. (1999) which we discuss in detail below.

There are a number of reasons why the computers–employment relationship maydepend on the severity of labour regulations. One reason is explained in Card et al.(1999). That study treats advances in computing technology and the reduction incomputer prices which led to greater computer usage over the 1979–1989 period asan exogenous technological shock. The main hypothesis of the study is that heavyregulation of the labour market creates wage rigidities so that the main effect ofgreater computer usage is to lower employment. In free and flexible labour markets,the wage rate also adjusts (declines due to greater computer usage), stopping some ofthe negative effect of greater computer usage on employment. Using data for theUnited States, Canada and France, the study finds some evidence in support of thestated hypothesis. Another possibility is that cumbersome labour laws, unionactivity and minimum wage restrictions make labour more expensive. Employersmay then respond by using computers for the relatively more labour intensive tasks,implying a greater displacement of labour by computers under stricter labour laws.

The present paper contributes to the literature discussed above. Particularlyimportant is our focus on a service sector in a developing country (retailing in India)for which there is no previous work.1 This is surprising because service sectors arehighly labour intensive, accounting for a majority of jobs across countries. The retailand wholesale sector is the second largest employer (after agriculture) in Indiaaccounting for 9.4 per cent of all jobs. Its contribution to the national gross domesticproduct (GDP) equals 14 per cent. Anecdotal evidence suggests an ongoing moder-nisation of the sector with a rapid expansion of large-sized retailers, modern retailingmethods and computer usage. What these developments entail for employmentgeneration in the sector is a matter of much concern but there is no formal work onthis issue.

The rest of the paper is as follows. In Section II we describe our data and providedescriptive statistics. In Section III we outline the estimation strategy and report ourmain results using the ordinary least squares (OLS) estimation method. Robustnessof the OLS results is discussed in Section IV. Regression results using theinstrumental variable (IV) estimation method are provided in Section V. A summaryof the main findings is provided in the concluding section.

II. Data and Main Variables

Our main data source is (retail) store level data collected by the World Bank in 2006(Enterprise Survey).2 These data are a cross-section of 1,948 registered retail stores

Computer Usage and Labour Regulation 1573

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spread over 16 major states and 41 cities in India. Stores in our sample are a mix ofsmall and large ones. The survey provides information on a variety of storecharacteristics such as annual sales, total employment, availability of infrastructure,access to finance, etc. It also reports on store’s experience with various aspects of theinvestment climate, such as the severity of labour regulations, tax rates, corruption,restrictions on store-hour operation, etc. We use this rich information to establishthe robustness of our main results.

Dependent Variable

A formal definition of all the variables used in the paper is provided in Table 1. Ourmain dependent variable is the total number of employees (temporary pluspermanent) working in a store during the fiscal year 2005–2006 (Employment).The mean value of Employment is 4.7 and the standard deviation equals 24.6. Acrossstates, Employment is highest in the state of Andhra Pradesh (14.1) and lowest inHaryana (0.9). In separate regressions, we also use the number of permanent andtemporary employees at the store level as dependent variables. Permanent employeesare defined as all paid employees that are contracted for a term of one or more fiscalyear and/or have a guaranteed renewal of their employment contract and that workeight or more hours per day. Temporary workers are defined as all paid short-term(less than a fiscal year) employees with no guarantee of renewal of employmentcontract and that work eight or more hours per day. We expect the effect of labourregulations to be much stronger (more negative) on permanent than temporaryemployment (confirmed below) because labour regulations are directed mostlytowards permanent employees.

Explanatory Variables

Our main explanatory variables are measures of labour regulation the spread ofcomputer usage in India’s retail sector, and most importantly, the interactionbetween the two.

Computer Usage

In the Enterprise survey, stores were asked if they use a computer for running theirbusiness. We define our measure of computer usage as the proportion of stores in acity that use computers (Computers).3 About 19 per cent of all stores in the sampleuse computers with a high of 49 per cent in the city of Kozhikode and 0 per cent inthe city of Gurgaon. Figure 1 shows the distribution of computer usage across cities.Kozhikode is an outlier in the figure and we pay due attention to this potentialproblem.We note that our measure of computer usage is an average measure defined at the

city level. We prefer the average measure over the store-level measure because directreverse causality from Employment (which varies at the store level) to Computers(which varies at the city level) is unlikely. However, it is possible that the averagemeasure could be correlated with other city or state characteristics causing anomitted variable bias problem. We discuss this issue in detail below.

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Table 1. Description of main variables

Variable Description

Employment (Store Total number of workers working in the store in the last fiscal year.level variable) Source: World Bank Enterprise Surveys

(www.enterprisesurveys.org)Permanent employment(Store level variable)

Number of permanent employees at the end of last fiscal year.Permanent employees are defined as all paid employees that arecontracted for a term of one or more fiscal years and/or have aguaranteed renewal of their employment contract and thatwork 8 or more hours per day.

Source: World Bank Enterprise Surveys(www.enterprisesurveys.org)

Temporary employment(Store level variable)

Number of temporary employees in the last fiscal year.Temporary workers are defined as all paid short-term (i.e. forless than a fiscal year) employees with no guarantee of renewalof contract employment and that work eight or more hoursper day.


Computers (City levelvariable)

Proportion of stores in a city that use a computer for runningtheir business.


Law Book index (Statelevel variable)

An index of labour laws in formal manufacturing in Indiacompiled by Besley and Burgess (2004). We use year 2000values of the index which is the latest available. The index is notavailable for the state of Delhi.

Population (City levelvariable)

Total adult population of the city in 2001, log values. Adults areall individuals above the age of seven.

Source: Census of India (2001)Income (State levelvariable)

Per capita income of the states (Indian Rupees) in 2003–2004 atconstant 1993–1994 prices.

Source: Reserve Bank of India (based on CSO data)Wage (state levelvariable)

Wage rate at the state level in the unorganized servicessectors (Indian Rupees). The data are for the year2001–2002.

Source: National Sample Survey Organization (NSSO), Governmentof India; 57th Round, Sch. 2.345

Literacy (State level Percentage of Adults in the state that are literate in 2001.variable) Source: Census of India (2001)

Business Regulations(City level variable)

Average at the city level of the scores reported by stores on thefollowing question asked in the survey: Are tax rates, taxadministration, obtaining permits and licenses, corruption,restrictions on store-hour operations and restrictions on pricingand mark-ups an obstacle for the current operations of thestores? Responses were recorded on a 0–4 scale defined as: Noobstacle (0), minor obstacle (1), moderate obstacle (2), majorobstacle (3) and very severe obstacle (4).


Age (Store level 2006 minus the year shop was established.variable) Source: World Bank Enterprise Surveys

(www.enterprisesurveys.org)

(continued)


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Labour Regulation

Not much is known about labour regulations in India’s retail sector. Theseregulations fall under the jurisdiction of the state governments and are contained inthe Shops and Establishments Act (SEA). The SEA is a state legislation and containsvarious laws relating to working conditions of the employees. The main provisions ofthe Act are as follows:

. compulsory registration of shop/establishment within 30 days of commencementof work;

. minimum wage restrictions;

Table 1. (Continued)

Variable Description

Outage (Store levelvariable)

Total number of hours of power failure faced by a store per day ina typical month.


Generator (Store levelvariable)

A dummy variable equal to 1 if a store owns or shares agenerator/inverter and 0 otherwise.


Line of Credit (Storelevel variable)

A dummy variable which equals 1 if a store has a line ofcredit and 0 otherwise.


Traditional (Storelevel variable)

Dummy variable equal to 1 if a store is a traditional store asdefined in the survey and 0 otherwise.


Consumer Durable(Store level variable)

Dummy variable equal to 1 if a store is a consumer durable store(selling consumer durables) as defined in the survey and 0otherwise.


Crime (City levelvariable)

City-level average of the reported scores on the following questionasked in the survey: Are crime, theft and disorder obstacle tothe current operations of the store? Responses were recorded ona 0–4 scale defined as: No obstacle (0), minor obstacle (1),moderate obstacle (2), major obstacle (3) and very severeobstacle (4).


Size (Store level Selling area of the store (in million square foot)variable) Source: World Bank Enterprise Surveys

(www.enterprisesurveys.org)Inventory (City levelvariable)

City-level average of the number of days of inventory maintainedby the stores.


Note: ‘Last fiscal year’ means fiscal year 2005–2006.

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. regulation of hours of work per day and week;

. guidelines for spread-over, rest interval, opening and closing hours, closed days,national and religious holidays, overtime work;

. rules for employment of children, young persons and women;

. rules for annual leave, maternity leave, sickness and casual leave, etc.;

. rules for employment and termination of service;

. other obligations of employers;

. other obligations of employees; and

. communications of closure of the establishment within 15 days from the closingof the establishment.

Detailed information on various aspects of labour laws in the SEA is not readilyavailable. However, information on the level of minimum wages is readily availablefrom various reports of the Labor Bureau of the Government of India. All states inour sample have implemented minimum wage levels for the retailing sector. Theselevels are reported in Table 2 (column 1), along with the ones for some of themanufacturing sectors (columns 2–4) for a comparison. We note that minimumwages reported in Table 2 are for the year 2001, the latest year for whichinformation is available for the states in our sample, while the data on retail storesthat we use is for year 2005–2006. Minimum wage restrictions and other labourregulations in the manufacturing sectors of India are known to be a serious cause oflabour market rigidities. We could expect similar results for the retail sector as wellbecause retailing and manufacturing show roughly similar levels of minimum wages(see Table 2).

One concern could be whether labour laws in the SEA are actually enforced. Inone survey question, stores were asked the following: Are labour regulations noobstacle, minor obstacle, moderate obstacle, major obstacle or very severe obstacleto the current operations of the store? Labour regulations are unlikely to be aproblem (obstacle) for the employers if they are not enforced. Hence, the percentageof stores reporting labour regulations as a problem provides a lower bound for thelevel of enforcement.

Figure 1. Percentage of stores using computers.


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For the full sample, 27 per cent of the stores reported labour regulations to be aproblem (minor or bigger) for their business. For these 27 per cent of the stores,roughly one third find labour regulations as more than a minor problem. At the highend, 53 per cent of the stores in West Bengal, 44 per cent in Rajasthan, 39 per cent inMaharashtra and 33 per cent in Delhi report labour regulations as a problem.Corresponding figures for labour regulations as more than a minor problem are 25per cent, 12 per cent, 10 per cent and 17 per cent, respectively. Figure 2 shows thepercentage of all stores who find labour regulations as obstacles for all the states inour sample. These numbers suggest a substantial enforcement of labour laws andthat a sufficiently large number of retail stores suffer from burdensome labour laws, afinding which is confirmed in other surveys too.4

There is no readily available index of labour regulations for the retail sector inIndia. Perceptions of stores about the severity of labour laws discussed above cannotbe used directly in the regressions because they could be endogenous to storecharacteristics, such as age, size, etc. Hence, we take an alternative approach byusing an index of labour laws for the manufacturing sectors in India, the Law Bookindex, attributable to Besley and Burgess (2004).5 The motivation for using the LawBook index is that pro-labour governments (due to political or ideological reasons)are likely to implement labour-friendly laws in both manufacturing and servicesectors, such as retailing.6 The correlation coefficient between the percentage ofstores in a state who report labour regulations as a problem and the Law Book index

Table 2. Minimum wages as of 31 December 2001 in Indian Rupees (Rs)

(1) (2) (3) (4)

StateShops and

Establishments ActOil millsindustry

Powerloomindustry

Glassindustry

Andhra Pradesh 58.7 78.77 61.73 57.50Bihar 61.98 61.98 61.98 61.98Delhi 99.70 99.70 N.A. 99.70Gujarat 83.6 80.00 79.20 75.40Haryana 75.84 75.84 N.A. 75.84Karnataka 70.38 69.13 70.54 70.53Kerala 84.46 114.73 65.78 N.A.Madhya Pradesh 74.73 74.73 74.73 74.73Maharashtra 62.60 75.86 71.82 51.25Orissa 42.50 42.50 42.50 42.50Punjab 78.28 78.28 78.28 N.A.Rajasthan 60.00 60.00 60.00 60.00Tamil Nadu 68.60 62.63 52.87 N.A.Uttar Pradesh 77.97 77.97 71.73 79.35West Bengal 79.43 78.75 67.81 80.08Jharkhand N.A. N.A. N.A. N.A.

Notes: N.A.: data not reported or not available. 2001 is the latest year for which data is easilyavailable for most of the states in our sample.Source: ‘Report on the Working of the Minimum Wages Act., 1948 for the year 2001’, LaborBureau, Government of India. Accessed at http://labourbureau.nic.in/MW2K1%20Main%20Page.htm.

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http://labourbureau.nic.in/MW2K1%20Main%20Page.htm

http://labourbureau.nic.in/MW2K1%20Main%20Page.htm

is 0.425. We note that the Law Book index varies between -2 and 4 with a highervalue on the index implying more strict (pro-worker) labour laws.

Our variable of interest is the interaction between computer usage and labourregulation index (Computers*Law Book). In the remainder of the paper, we will referto this variable is the ‘main interaction term’.

Other Controls

We control for a number of variables at the state, city and store level to raise ourconfidence against the omitted variable bias problem mentioned above. We discuss afew examples to motivate these variables.

First, it is possible that differences in overall development of cities may cause aspurious correlation between computer usage and employment. For example,frequent power outages (less development) in a city may reduce the marginalproductivity of labour and computers implying (spuriously correlated) lower levelsof both these factors of production at the store level. The possibility of a similaromitted variable bias problem with the differential effect of computers onemployment across pro-worker and pro-employer labour regulation states is lessclear, although it cannot be ruled out completely. For example, computers mayincrease the marginal productivity of labour, and therefore employment, but thiseffect may be weakened if stores face frequent power outages, poor quality of supplychains, etc.

Second, it is possible that city-level determinants of the marginal productivity oflabour and therefore employment (power outages, quality of roads, etc.) may becorrelated with labour regulations. The correlation may arise because richer or moredeveloped cities are likely to be located in the higher income states and labour

Figure 2. Percentage of stores that find labor regulations as obstacles to business


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regulations may depend on income levels or overall development of the states. Ifthis is indeed true then the differential effect of computers on employment acrosspro-workers and pro-employer labour regulation states may be spuriously drivenby the differential effect of computers, if any, across more and less developed statesor cities.Third, it is conceivable that states with heavier regulation of labour may also have

heavier regulation of the market through, for example, higher taxes, restrictions onstore-hours of operation and a more complex web of the required licenses andpermits. So, stricter labour laws could spuriously pick up the effect of more stringentother business regulations, and more generally, heavier involvement of thegovernment in the economic activity. However, what is not obvious is why theeffect of computer usage on employment should depend on business regulations(other than labour regulations) or the quality of the investment climate. Onepossibility could be that while computers increase labour productivity and thereforeemployment, higher taxes, for example, could siphon off part of the increase inproductivity, implying less employment generation.Fourth, for reasons other than labour regulations, the opportunity cost of labour

could be higher in states that have heavier regulation of the labour market. Similarto stricter labour laws, higher opportunity cost of labour is likely to induce labour-saving use of computers. This implies a potential identification problem with ourmain interaction term.Fifth, computer usage may be correlated with store characteristics. For example,

we find that computer usage is higher in stores that get audited, are part of a largerchain and are younger. Although there is no obvious reason to believe why thesecharacteristics should have a direct effect on employment, the possibility cannot beruled out completely. A similar argument can be made for the differential effect ofthese store characteristics on employment across pro-labour and pro-employerlabour regulation states, although this is an even more remote possibility.We address the problems discussed above in two ways. First, we directly control

for a number of store, city and state characteristics. Second, we confirm our mainresults using the instrumental variables estimation strategy.Table 3 provides the correlation coefficients between our main variables and the

various controls used for robustness checks. In our main specification, we control fora measure of the overall development of the cities and its interaction with computerusage. Data on income levels or other direct measures of development are notavailable at the city level. Hence, we use a proxy measure instead which is the total(adult) population of the city (Population).7 The interaction term that we control foris Computers*Population. It is well known that the richer and more developed citiesin India are also bigger in terms of population. For example, in our sample,Population is highest in the cities of Mumbai followed by Kolkatta, Bangalore,Chennai and Hyderabad.8 These cities are known to be the richest and the mostdeveloped cities in the country. As discussed above, we expect a positive effect ofPopulation on employment. We also expect the effect of computer usage onemployment to be higher in cities with higher values of Population.In the robustness section we show that our main results hold when we control for a

number of additional variables, including per capita income of the states, literacyrates, the level of wages, age of the store, access to finance, quality of physical

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infrastructure, business regulations such as tax rates, corruption, etc., andinteraction of these variables with Computers and the Law Book index.

III. Estimation

The base specification is as follows:

Employmentics ¼ b0 þ b1Computersc þ b2LawBooks þ b3Computersc � LawBooksþ b4Populationc þ b5Computersc � Populationc þ uics

where subscript i denotes a store, c the city and s the state in which the store islocated. uics is the error term. The variables in the equation are as defined above andthe subscripts have been added for expositional convenience. All regressionsreported below use Huber-White correction for heteroskedasticity with standarderrors clustered on the state.9

The coefficient of interest in the equation is b3, which we expect to be negative. Itcaptures how the effect of a unit increase in computer usage on employment dependson the stringency of labour laws. The overall effect of greater computer usage onemployment equals b1þ b3LawBooksþ b5Populationc. The overall impact of a unitincrease in the Law Book index on employment is not discussed in the present paperas it is dealt with in detail in a companion paper.10

Base Regression Results

Regression results for the base specification are provided in Table 4. Without anyadditional controls, computer usage has a positive effect on employment significantat less than 10 per cent level (column 1, Table 4). In column 2 of Table 4 we control

Table 3. Correlations between the main variables

Employment Computers Law Book

Population 0.290 0.086 0.291Income 0.089 0.146 0.121Literacy 0.055 0.484 0.128Wage 70.100 0.068 0.302Business Regulations 0.019 70.145 0.289Age 70.135 70.050 0.037Outage 70.297 70.464 0.111Generator 70.142 70.066 70.127Line of Credit 70.031 0.319 70.100Traditional 70.332 70.543 0.132Consumer Durable 70.024 0.046 70.141Crime 70.174 70.166 0.182Size 0.834 0.361 70.128

Note: All correlations are computed taking averages of the variables at the city level except forstate level variables which are defined at the state level. The state level variables other thanLaw Book include Income, Literacy and Wage.


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for the Law Book index and our main interaction term (Computers*Law Book). Theestimated coefficient of the interaction term is negative (76.7) but it is notstatistically significant at the 10 per cent level or less (p-value of 0.240). Further, eventhe overall effect of computers on employment (bþ b3LawBooks) is not significantfor any value of the Law Book index. Later we will show that the results here areweak due to one outlier city (Kozhikode). Controlling for Population, we find thatPopulation has a positive effect on employment significant at less than the 5 per centlevel (not shown). However, our main results for the effect of computers onemployment do not change much from above. Lastly, we control for the interactionbetween Computers and Population (column 3, Table 4). The estimated coefficient ofour main interaction term rises from76.45 (not shown) to717.7 and it is nowsignificant at less than the 5 per cent level (p-value of 0.014). As expected, theestimated coefficient of Computers*Population is positive and significant at less thanthe 5 per cent level (p-value of 0.012).We treat the specification in column 3 of Table 4 as our main specification. In

short, regression results in this column show that stricter labour laws magnify thelabour displacing effect of computers on employment significantly.One concern with our main specification could be that it does not allow for the

effect of labour regulation on employment to vary between small versus large cities.This may create an estimation bias if labour regulation and overall development areeither substitutes or complements for employment creation. We checked for thispossibility by adding Law Book*Population to the list of controls above. The control(Law Book*Population) showed no significant effect on employment and it did nothave any effect on the results reported above (or elsewhere in the paper).

Table 4. Main specification

Dependent variable:Full sample Kozhikode excluded

Employment (1) (2) (3) (4) (5) (6)

Computers*Law Book 76.7 717.7** 711.2* 717.7**[0.240] [0.014] [0.057] [0.015]

Computers 22.3* 17.5 7444.4** 31.6** 29.6*** 7448.5**[0.072] [0.105] [0.013] [0.024] [0.001] [0.023]

Law Book 0.412 1.91 1.21 1.91[0.605] [0.101] [0.131] [0.102]

Population 73.03 73.07[0.126] [0.145]

Computers*Population 33.7** 34.0**[0.012] [0.020]

Constant 1.8 2.51 42.3 0.417 0.530 42.9[0.263] [0.139] [0.112] [0.797] [0.554] [0.132]

Observations 1836 1836 1836 1801 1801 1801R-squared 0.007 0.01 0.026 0.01 0.015 0.026

Notes: p-values in brackets. All regressions use Huber-White correction for heteroskedasticitywith standard errors clustered on the state. Significance levels are denoted by *** (1% or less),** (5% or less) and * (10% or less).Columns (1) to (3) contain results for the full sample while columns (4)–(6) contain results withthe city of Kozhikode excluded from the sample.

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Another concern could be that our main result above depends on the control forPopulation which, at best, is a proxy measure of overall development of the cities. Oncloser inspection, we found that this is largely due to the city of Kozhikode, whichappears to be an outlier (Figure 1). For the specifications discussed above, regressionresults with Kozhikode excluded from the sample are provided in columns (4)–(6) ofTable 4. The estimated coefficient of the main interaction term is larger (morenegative) here than what we found above. It is significant at close to the 5 per centlevel without the controls for population (column 5, Table 4) and at less than the 5per cent level with the controls for Population (column 6, Table 4).

IV. Robustness of OLS Results

State, City and Firm Characteristics

Robustness checks for the OLS specification are provided in Table 5. The sequencein which the robustness variables are included in the specification does not mattermuch for our main results.

We begin by controlling for state level variables for the level of overall developmentand opportunity cost of labour for reasons discussed above. For overall development,we use per capita income of states for the year 2003–2004 (Income) and its interactionwithComputers. For the opportunity cost of labour, we use the literacy rate (Literacy)and the wage rate (Wage) of the states as well as their interaction with Computers.Data for the wage rate are taken from the survey of unorganised services sectorconducted by the National Sample Survey Organization of India (NSSO, 57th Round)in 2001–2002.11 Data source for literacy rates is Census of India (2001).

Regression results provided in column (1) of Table 5 show that our maininteraction term continues to be negative and significant (at less than the 1% level)with the additional controls listed above. Even the magnitude of the main interactionterm does not change much equalling718.5 (column 1, Table 5) comparedwith717.7 (column 3, Table 4) above. As expected, the labour displacing effect ofcomputers is significantly magnified in states with higher wages. However, theopposite seems to be the case for per capita income. One reason for this could be thatricher states are likely to be better placed than the poorer states in using computersto increase labour productivity. Higher labour productivity in turn implies greaterdemand for labour and more employment.

Direct measures of business regulations for the retail sector in India are notavailable. However, in one question, the Enterprise survey asked stores if tax rates,tax administration, corruption, restrictions on store-hour operations, obtainingbusiness licenses and permits and restrictions on pricing and mark-ups were anobstacle for their business. Responses on each of these obstacles were recorded on a0–4 scale with a higher score implying a bigger obstacle. We use city-level averages ofthe reported scores here as our measure of the overall business climate in the variouscities (Business Regulations).12

Regression results in column 2 of Table 5 show that controlling for BusinessRegulations and Computers*Business Regulations has very little effect on our maininteraction term. Further, irrespective of the level of computer usage, there is nosignificant relationship between employment and business regulations.13


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Table

5.Robustnessresults

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Dependentvariables:

Employment

Permanentem

ployment

Tem

porary

employment

Computers*Law

Book

718.5***

719.0***

719.1***

714.0**

713.7**

73.73

73.17

[0.001]

[0.001]

[0.001]

[0.034]

[0.017]

[0.263]

[0.224]

Computers

7409.7***

7398.3***

7343.3***

7374.1**

7332.7**

770.4

13.4

[0.003]

[0.004]

[0.007]

[0.026]

[0.016]

[0.212]

[0.701]

LawBook

2.99***

3.00***

2.90***

1.39*

2.13**

0.52

0.476

[0.007]

[0.004]

[0.006]

[0.082]

[0.015]

[0.386]

[0.286]

Population

72.54**

72.25**

72.60***

72.35

71.85*

70.685

70.399

[0.039]

[0.045]

[0.007]

[0.143]

[0.064]

[0.363]

[0.388]

Computers*Population

26.28***

24.49***

22.45***

28.28**

19.35**

5.46

1.29

[0.001]

[0.002]

[0.003]

[0.023]

[0.014]

[0.199]

[0.597]

Income

71.40***

71.37***

71.18***

70.935***

70.187**

[0.009]

[0.008]

[0.004]

[0.008]

[0.043]

Computers*Income

10.75***

10.46***

9.04***

7.76***

0.942*

[0.002]

[0.002]

[0.000]

[0.000]

[0.065]

Literacy

70.032

70.051

70.003

70.147

0.130**

[0.846]

[0.732]

[0.985]

[0.376]

[0.013]

Computers*Literacy

70.451

70.274

70.793

70.082

70.624**

[0.355]

[0.576]

[0.138]

[0.896]

[0.015]

Wage

0.004***

0.004***

0.003***

0.003***

0.0003

[0.007]

[0.008]

[0.008]

[0.007]

[0.266]

Computers*Wage

70.026***

70.026***

70.022***

70.020***

70.001

[0.003]

[0.004]

[0.003]

[0.002]

[0.578]

BusinessRegulations

70.75

72.00*

72.63*

0.49

[0.598]

[0.090]

[0.064]

[0.441]

Computers*Business

7.73

15.59

15.31

71.02

Regulations

[0.253]

[0.120]

[0.141]

[0.717]

Age

0.197

0.152

0.024*

[0.140]

[0.224]

[0.067]

(continued)

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Table

5.(C

ontinued)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Dependentvariables:

Employment

Permanentem

ployment

Tem

porary

employment

Outage

70.104

70.145*

0.037

[0.238]

[0.094]

[0.324]

Generator

4.49**

3.69**

0.699*

[0.034]

[0.049]

[0.092]

LineofCredit

3.60

3.54

0.176

[0.121]

[0.147]

[0.636]

Traditional

721.72***

718.51***

72.40***

[0.003]

[0.006]

[0.000]

Consumer

Durable

721.37***

718.41**

72.12***

[0.007]

[0.011]

[0.000]

Crime

70.789

70.342

70.279

[0.282]

[0.615]

[0.210]

Observations

1836

1836

1815

1836

1815

1836

1813

Rsquared

0.035

0.035

0.164

0.024

0.151

0.018

0.087

Notes:p-values

inbrackets.Allregressionsrunwithaconstantterm

(notshown)andhaveHuber-W

hiterobust

standard

errors

clustered

onthe

state.Significance

levels:

***(1%

orless),

**(5%

orless)and*(10%

orless).

Dependentvariable

incolumns(1)–(3)is

(total)

Employment,

permanentem

ploymentin

columns(4)–(5)andtemporary

employmentin

columns(6)–(7).


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We complement the city level measure of investment climate mentioned abovewith the extent to which crime is an obstacle to doing business as reported by thestores (Crime) and a number of store characteristics. About 51 per cent of thesampled stores reported crime as a problem for their business with a high of 100 percent in the cities of Chandigarh and Kota followed by 95 per cent in Bhopal.Controls for store characteristics include age of the store (Age), hours of poweroutage faced by a store per day on average during the last fiscal year (Outage), adummy variable equal to 1 if a store owns a generator and 0 otherwise (Generator)and a dummy variable equal to 1 if a store has a line of credit account and 0otherwise (Line of Credit). Last, employment intensity may depend on the kind ofproducts (product-lines) carried by stores. It is not clear whether computer usagevaries systematically by product-lines but the possibility cannot be ruled out.Unfortunately, information on the specific products carried by the stores is notavailable. However, the survey does classify stores broadly into traditional (grocery)stores, consumer durable stores and the modern format stores. These three store-types account for 64 per cent, 26 per cent and 10 per cent of the sample, respectively.We controlled for store-type fixed effects using two dummy variables: Traditionalwhich equals 1 if a store is a traditional store and 0 otherwise, and Consumer Durablewhich is a dummy variable equal to 1 if a store sells consumer durables and 0otherwise. The omitted category is Modern Format stores that consist of stores thatare large and part of a shopping complex.Regression results controlling for all these variables are provided in column 3 of

Table 5. The estimated coefficient of our main interaction term remains virtuallyunchanged equalling719.1 compared with719 (column 2, Table 5) above. Also,the coefficient remains significant at less than the 1 per cent level. There is not muchchange in the results for the remaining variables from above.For additional robustness, we interacted all the variables mentioned in the

previous two paragraphs with the Law Book index and also with Computers. Theseinteraction terms were added to the previous specification (column 3, Table 5)individually and together to check if computer usage were spuriously picking up thedifferential effect of some of other variable (like power outages) across states withvarying severity of labour regulations. However, our main result remained robust tothese controls. For instance, with all the interaction terms mentioned in thisparagraph added to the previous specification (column 3, Table 5), the estimatedcoefficient of the main interaction term remained roughly unchanged equal-ling719.6 (not shown), significant at less than the 1 per cent level, comparedwith719.1 above (column 3, Table 5).

Permanent vs. Temporary Employment

Earlier, we had argued that the effect of labour regulations is likely to be bigger inmagnitude on permanent relative to temporary workers since most labour laws aredirected towards permanent workers. Our results tend to confirm this view.Regression results reported in columns (4)–(7) of Table 5 show that while computerusage has a significantly larger negative effect on permanent employment in stateswith stricter labour laws, there is no such effect on temporary employment. In fact,the overall effect of labour laws and computer usage on temporary employment is

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insignificant. The contrast between temporary and permanent employment providesanother layer of defence against omitted variable bias problem. That is, if our maininteraction term were spuriously picking up the effect of some other variable thenthere is no reason to believe why this other (than labour regulation) variable shouldaffect temporary and permanent employment differently.

Additional Robustness Checks

We have not reported on a number of additional controls that we added to theprevious specification. Briefly, these controls include the following: a dummyvariable equal to 1 if a store has overdraft facility and 0 otherwise; a dummy variableequal to 1 if a store has a checking/savings account and 0 otherwise; a dummyvariable equal to 1 if a store reported ‘no need to borrow from external sources’during the last fiscal year and 0 otherwise; the percentage of a store’s sales during thelast fiscal year that were never paid for; a measure of the availability of transportservices equal to stores’ perception about transportation as an obstacle for theirbusiness (averaged at the city-level);14 a dummy variable equal to 1 if a store has afemale principal owner and 0 otherwise; a dummy variable equal to 1 if a store is partof a larger chain and 0 otherwise; store manager’s years of experience in retailing;number of days of inventory maintained by the store; percentage of stores in the citywho reported an incidence of theft during the last fiscal year; and measures of formaland informal competition faced by the stores in the city.15 The main results of thepaper continued to hold with all these additional controls. Specifically, the estimatedcoefficient of our main interaction term remained significant at less than the 1 percent level and roughly unchanged in magnitude equalling720.1 comparedwith719.1 above (column 3, Table 5).

Floor Area of the Shop

In retailing, floor area of the shop (Size) is often considered to be a proxy for anumber of firm characteristics. Much like employment, Size is also a measure of theoverall scale of operation of a store. We did experiment by controlling for Size andits interaction with computer usage and the labour regulation index in the variousspecifications discussed above. We found that these controls did not change the signor the significance level of our main interaction term, but it did cause its magnitudeto decline. For example, for the specification in column 3 of Table 5, controlling forSize and Law Book*Size caused the estimated coefficient of our main interactionterm to decline (in absolute value) from719.1 (column 3, Table 5) to79.2,significant at less than the 5 per cent level (p-value of 0.022, not shown).16 The sharpdrop in the magnitude could be due to three plausible reasons. First, our maininteraction term is spuriously picking up the effect of some store characteristics thatare correlated with the floor area of the shop. While this possibility cannot be ruledout, it appears unlikely to be too important for the simple reason that controlling forother important firm characteristics above, such as age, power outages and access tofinance related variables, caused virtually no change in the coefficient of the maininteraction term. We note that power outages and access to finance are the two mostimportant obstacles to doing business as reported by the stores. Second, both, floor


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area of the shop and our dependent variable, employment, capture one samephenomenon: the scale of a store’s operation. Hence, controlling for the floor area ina regression with employment as the dependent variable leads to a severesimultaneity problem, causing large changes in the regression results. Third, it isentirely plausible that part of the computers–employment relationship is picked upby the floor area of the shop. For example, stores with larger floor area are moreeasily visible and therefore more likely to be targeted by labour inspectors. This isespecially likely in states with stricter labour laws. So, large stores in the moreheavily labour regulated states are more likely to use computers to economise on theuse of labour. We note that the last two reasons suggest that floor area of the shopshould not be used as an independent control. Either way, the qualitative nature ofour main result holds with and without controlling for the floor area of the shop,although quantitatively, the results do show variation.

V. IV Regressions

In this section we report regression results using the instrumental variablesestimation strategy. Due to data limitations, our aim here is admittedly narrowand modest. We do not instrument for labour regulations but only for computerusage. External data on possible determinants of computer spread at the city levelare not available and we use the survey data instead. Hence, some caution isnecessary in interpreting the results. Our motivation is that, at least to some extent,the IV results provide an additional layer or defence against potential endogeneityproblems with our estimation.In the survey, we find that computer usage is higher for stores that get audited,

maintain a larger stock of inventory and rely more on credit-transactions. Onereason for this could be that all these factors (auditing, larger inventory, and credit-transactions) require better account-keeping for which computers can be especiallyuseful. For the IV regressions, we use the city-level average of the number of days ofinventory maintained by the stores (Inventory) to instrument for Computers. Thecorrelation coefficient between Inventory and Computers equals 0.26.The optimal size of inventory depends on a number of factors, such as fluctuations

in demand, the quality of supply chains, type of product (perishables vs. non-perishables), etc. The key point here is whether the size of inventory has a directeffect on employment and whether any such effect varies in magnitude across pro-worker and pro-employer labour regulation states. To check for this possibility, weran a number of regressions but we did not find any significant (at 10% or less) directeffects of Inventory (with our without interacting with the Law Book index) onemployment.

IV Regression Results

For the IV regression results, we first regressed Computers on Inventory. From thisregression, we took the predicted values of the former (ComputersIV). The estimatedcoefficient of Inventory here equalled 0.005 (significant at less than 1% level) and the R2

of the regression equalled 0.103. These predicted values were then interacted with theLaw Book index to get the instrumented values of our main interaction term.

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Results from the second stage IV regressions are provided in Table 6. Much likethe OLS regression results, without any additional controls, the IV regression resultsshow only a weak differential effect of computers on employment across pro-workerand pro-employer labour regulation states (column 1, Table 6). That is, the estimated

Table 6. IV Regression results

(1) (2) (3)

ComputersIV*Law Book 721.7 749.7*** 741.5***[0.121] [0.005] [0.000]

ComputersIV 32.5 71745** 71029*[0.158] [0.016] [0.054]

Law Book 2.83 6.60*** 5.99***[0.119] [0.005] [0.000]

Population 717.6** 715.4***[0.023] [0.010]

ComputersIV*Population 126.5** 106***[0.015] [0.008]

Income 72.04**[0.029]

ComputersIV*Income 14.0**[0.020]

Literacy 1.54**[0.040]

ComputersIV*Literacy 710.34**[0.033]

Wage 0.002[0.218]

ComputersIV*Wage 70.01[0.196]

Business Regulations 77.63[0.446]

ComputersIV*Business Regulations 47.2[0.470]

Age 0.073***[0.007]

Outage 70.07[0.354]

Generator 4.85**[0.024]

Line of Credit 3.73[0.116]

Traditional 719.1***[0.005]

Consumer Durable 719.0***[0.009]

Crime 71.19[0.226]

Observations 1835 1835 1814R-squared 0.009 0.028 0.162

Notes: p-values in brackets. All regressions run with a constant term (not shown) and haveHuber-White robust standard errors clustered on the state. Significance levels: *** (1% orless), ** (5% or less) and * (10% or less).


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coefficient of our main interaction term is negative equalling721.7 but it isstatistically insignificant at the 10 per cent level (p-value of 0.121). Similarly, as wefound for the OLS regressions above, controlling for Population and ComputersIV*-Population, the estimated coefficient value of the main interaction term increasedfrom721.7 above to749.7, significant at less than the 1 per cent level (column 2,Table 6). The sharp increase in estimated coefficient value here is not surprising sincewe have not instrumented for the Law Book index and Population serves to controlfor a possible correlation between the Law Book index and the overall developmentof cities and sates. Adding the remaining controls to the specification does notchange the results in a qualitative sense, although there is some decline in theestimated coefficient value of the main interaction term (column 3, Table 6).17 TheIV regression results confirm our findings in the previous sections and help raise ourconfidence against possible endogeneity problems.18

VI. Conclusion

The retail sector in India is witnessing a rapid transformation with the entry of large-scaled retailers, modern retailing methods and the spread of computer usage. Whatthese developments entail for job creation in the sector is a crucial question giventhat the sector is the second largest employer in the country. The present papershows that the effect of computer usage on employment depends on the severity oflabour laws. Stricter labour laws magnify the labour-displacing effect of computerusage. Given that the use of computers and other modern technology is likely toincrease in Indian retailing in the near future, reforming labour laws is all the moreimperative to maximise employment generation in the sector.

Notes

1. Regulation of the labour market is quite rigid in India. See, for example, Besley and Burgess (2004)

and World Bank (2007).

2. The survey and methodology for data collection are available at: http://www.enterprisesurveys.org.

3. Data on the number of workers who use computers or hours of computer usage are not available.

4. For example, KPMG recently conducted a survey of retail firms in India (KPMG, 2005). This report

shows that in the ‘Fast moving consumer goods’ (FMCG) section of retailing, about 35 per cent of the

firms reported labour regulations as a significant problem (p. 19). The FMCG section accounts for 80

per cent of consumer spending in the country.

5. We use year 2000 values of the Law Book index, which is the latest year for which the index is available.

6. Besley and Burgess (2004) note that political factors explain much of the variation in their index.

7. We use 2001 values of adult population taken from Census of India (2001), which is the latest year for

which data are available. Our main results do not change if we use total population (adults plus

children) in the city but total population shows a slightly weaker effect on employment than adult

population.

8. Delhi is the second largest city in terms of population in our sample. However, the Law Book index is

not defined for Delhi.

9. Our main results do not change much if we cluster on the city instead of the state. Clustering lowers

the t-statistics of our main variables making it harder for us to establish the central results of the

paper. The regression discussed below exclude three observations (out of 1,948) that have unduly large

effects on some of the results.

10. Amin (2007) uses the same data set and a similar set of controls as in the present paper but without the

interaction terms in the equation above. The study finds a strong negative effect of the Law Book index

on employment which is significant at less than 5 per cent level.

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http://www.enterprisesurveys.org.

11. We also experimented with using the wage rate of the sates for the registered manufacturing sector.

These wage rates were obtained from the Annual Survey of Industry (ASI) and defined as the ratio of

total cost of labour divided by total employment. However, using these wage rates did not change our

main results much from above.

12. Responses of stores here cannot be used directly in the regressions since these are likely to be

endogenous to store characteristics. Being a group average, Business Regulations suffers less from

measurement errors and endogeneity problems associated with firm-level perceptions (Krueger and

Angrist, 2001).

13. Our results do not change much if we control for various aspects of the investment climate (tax rates,

etc.) individually. We also experimented with a state-level measure of business regulations which is the

same as Business Regulations with the average (of reported scores) taken at the state level but this did

not change any of the results above.

14. The measure is equal to the city-level average score reported by the stores on a question whether

transportation was an obstacle for their business or not. Scores were reported on a 0–4 scale defined

as: No obstacle (0), minor obstacle (1), moderate obstacle (2), major obstacle (3) and a very severe

obstacle (4).

15. In one survey question, stores were asked how important was the influence of other domestic

competitors over prices of the main products carried by the stores. Responses of stores were recorded

on a 1–4 scale, defined as not at all important (1), slightly important (2), fairly important (3) and very

important (4). Our measure of formal competition is the average score on this question where the

average is taken at the city level. The same question was asked about the influence of competitors

operating in the informal sector (traders selling for pavements, etc.). Our measure of informal

competition is the average score at the city level reported by the stores on this question. Our results do

not change if we use store-level response on these questions instead of the city level averages. We

prefer to use the latter because, being averages, they are less endogenous to store characteristics.

16. Controlling for Computers*Size does not make any difference to the results here or elsewhere in the

paper. The estimated coefficient of the main interaction term remained virtually unchanged

equalling79.4 (p-value of 0.022) when Computers*Size is also included in the specification compared

with79.2 reported above without controlling for Computers*Size.

17. For the specifications in Table 6, the overall effect of stricter labour laws is positive for some values of

instrumented computer usage values, but these positive effects are never statistically significant at the

10 per cent level or less.

18. The estimated coefficient of our main interaction term in Table 6 continues to be negative and

significant at the 5 per cent level or less even if we drop the city of Kozhikode from the sample and/or

control for the floor area of the shop.

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