metadata: authors: 0: author: Amin,Mohammad
count: India
docna: 0: docna: Are labor regulations driving computer usage
            in India's retail stores ?
docty: Policy Research Working Paper
subtopic: Social Policy,Legal Products,Judicial System Reform,Urban Housing and Land Settlements,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Wholesale & Retail Trade Industry,Agricultural Economics,Common Property Resource Development,Urban Governance and Management,Urban Housing,Legal Reform,Employment and Unemployment,Legislation,Municipal Management and Reform,Regulatory Regimes,Business Cycles and Stabilization Policies
teratopic: Public Sector Development,Social Protections and Labor,Urban Development,Finance and Financial Sector Development
abstracts: cdata!: A recent survey of 1,948 retail stores
            in India conducted by the World Bank's Enterprise
            Surveys shows that 19 percent of the stores use computers
            for their business. In some states like Kerala, computer use
            is as high as 40 percent. Using this data the author finds
            labor regulation as an important determinant of computer
            use. His estimates suggest that when faced with burdensome
            labor regulations, the probability of using a computer rises
            by over 36 percentage points for an average store. These
            findings formally confirm a commonly held but untested view
            that labor regulation may be responsible for the spread of
            labor saving modern technology.
geo_reg_key: 517198,517212,517191
repnme: repnme: Are labor regulations driving computer
            usage in India's retail stores ?
display_title: Are labor regulations driving
            computer usage in India's retail stores
listing_relative_url: /research/2007/07/7848011/labor-regulations-driving-computer-usage-indias-retail-stores
topicv3: Manufacturing Sectors m1326521 1281,Land Tenure m1326631 888,Property and Land Law m1326679 964,Enterprise Surveys m1504072 2762,Retail Industry m1504598 3778
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Public Sector Development,Social Protections and Labor,Urban Development,Finance and Financial Sector Development
no_of_pages: 9
author_keys: 0: author_key: 717499
model_extractions: WPS4274

# **Are Labor Regulations Driving Computer Usage in India’s Retail Stores?**

Mohammad Amin* World Bank

**Abstract** A recent survey of 1,948 retail stores in India conducted by the World Bank’s Enterprise Surveys shows that 19 percent of the stores use computers for their business. In some states like Kerala, computer usage is as high as 40 percent. Using this data we find labor regulation as an important determinant of computer usage. The estimates suggest that when faced with burdensome labor regulations, the probability of using a computer rises by over 36 percentage points for an average store. These findings formally confirm a commonly held but untested view that labor regulation may be responsible for the spread of labor saving modern technology.

Keywords: Labor regulations, Technology, India, Retail JEL: J080, K31, L81, O33, O53 World Bank Policy Research Working Paper 4274, July 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ________________________

*Enterprise Analysis Unit, World Bank, Washington DC, 20433. Email: <u>mamin@worldbank.org. Phone: (202) 473-1915.</u>
# **1. Introduction**

A new survey of 1,948 retail stores in India compiled by the World Bank’s Enterprise Surveys shows that 19% of the stores use computers for their business. Computer usage is higher among stores that find labor regulations to be more burdensome. Specifically, 16% of the stores who report labor regulations as “no obstacle” for their business use computers. Corresponding figures for stores reporting labor regulations as “minor”, “moderate”, “major or very severe” obstacles are 24%, 30% and 34%, respectively. We test if computer usage in India’s retail sector is driven by the severity of labor regulations.

Existing empirical work has paid little attention to the technology-labor regulations nexus.<sup>1</sup> Few studies which do exist treat the choice of technology as exogenous to labor regulations. For example, Card et al (1999) take computer usage in the Canada, France and the US as an exogenous technological shock. They find that the effect of this shock on wages and employment depends on labor regulations in the three countries. In recent theoretical contributions, Alesina and Zeira (2006) and Blanchard and Philippon (2006) have argued that the choice of technology should be treated as endogenous to labor regulations. Our findings confirm this argument.

# **2.  Data and Main Variables**

We use store level data collected by the World Bank in 2006.<sup>2</sup> The data are a pure cross section of 1,948 stores spread over 16 states and 41 cities of India. Our dependent variable, _Computers_ , is a dummy which equals 1 if a store uses a computer for running its

> 1 See, for example, Botero, Djankov, La Porta, Silanes and Shleifer (2004).

> 2 The survey and methodology for data collection are available at <u>www.enterprisesurveys.org.</u>

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business and 0 otherwise.<sup>3</sup> The mean value of _Computers_ is .19 and standard deviation is .39. Our main explanatory variable is an index of labor regulation defined at the state level.<sup>4</sup> In one survey question, stores were asked if labor regulations were an obstacle for their business. Responses were recorded on a 0-4 scale defined as no obstacle (0), minor obstacle (1), moderate obstacle (2), major obstacle (3), and very severe obstacle (4). Our main index of labor regulation, _Perception_ index, equals the proportion of stores in a state who report labor regulations as a problem (minor obstacle or higher).<sup>5</sup> Being a group (state) average, the _Perception_ index suffers less from measurement errors and endogeneity problems associated with firm perceptions (Kreuger and Angrist, 2001). As a robustness check, we also use an index of labor regulation for the manufacturing sectors in India ( _Law Book_ index) due to Besley and Burgess (2004). For the _Law Book_ index, we use the latest year (2000) values for which the index is available. The motivation is that pro-labor governments are likely to implement labor-friendly laws in both manufacturing and services. Correlation between _Perception_ and _Law Book_ index is .564.

One concern could be that labor regulation is likely to be correlated with other regulations such as tax rates, land laws, corruption, etc. However, this is not a problem for us because we expect these (other) regulations to be positively correlated with labor regulation but have a negative effect on computer usage. Thus, failure to control for tax rates, etc., will only weaken the positive association we intend to show between computer usage and labor regulations (downward bias in the estimated coefficient of labor regulation). To confirm this, we use store response (on the same 0-4 scale as above) to

> 3 Data on the number of workers using computers or hours of computer usage is not available.

> 4 In India, labor regulations are made and implemented by the state governments.

> 5 Our results are virtually unchanged if we use state level averages of the actual scores here. The _Perception_ index varies between .09 (Gujarat) and .528 (West Bengal) with a mean of .249.

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questions on tax rates, tax administration, land laws (access to land), business licensing and permits, corruption and restrictions on store hour operations as obstacles to growth. Indices similar to the _Perception_ index were first computed for each of these (non-labor) regulations and then a simple average was taken over them to arrive at _Regulation_ – an overall index of non-labor regulations. The sub indices of _Regulation_ are all highly (positively) correlated and our results are unaffected whether we use _Regulation_ or its sub indices. We expect a negative effect of _Regulation_ and a positive effect of the labor regulation index on computer usage. We single out the sub-index on land laws ( _Land_ index) for special treatment because it shows little correlation with the _Perception_ index. The correlation coefficient between the _Perception_ index and the _Land_ index is .39 and it drops to .06 if we exclude the states of West Bengal and Gujarat (16% of the sample) which are outliers (Figure 1). What this suggests is that at least in the bulk (84%) of the sample we can easily contrast the effects of labor and land regulations on computer use.

A second concern is that labor regulations could be correlated with overall development, biasing our results. The most plausible scenario is that labor regulations are stricter in less developed states. This negative relationship between labor regulation and overall development again implies a downward bias in the estimated labor regulationcomputer usage relationship because the direct effect of less development is likely to be less computer usage. For example, the index of overall development due to Banerjee and Iyer (2005), _BI_ index, shows a negative correlation with the _Perception_ index (correlation of -.290) but is positively correlated with the percentage of stores in a state who use computers (correlation of .320). Below we control for a number of proxy measures of overall development and the results confirm our claim of the downward bias.

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The last concern relates to a possible correlation between labor regulation and store characteristics. We looked at a number of store characteristics and found these to be only weakly correlated with the _Perception_ index. For example, averaged at the state level, current employment, current sales and floor area of the shop show a correlation of -.125, .088 and -.109, respectively, with the _Perception_ index. Our results for labor regulation hardly change due to store level controls (discussed below).

# **3.  Estimation**

We use a probit specification clustering the standard errors on the state. Estimated marginal effects (at the mean value of the explanatory variables) are reported in Table 1. Our main control is the _Land_ index for reasons discussed above. Using the _Perception_ index, we find no significant effect of labor regulation on computer usage without any controls. In column 1 of Table 1 we control for the _Land_ index. Labor regulation now shows a significant effect (at less than 5%). Specifically, a 1 percentage point increase in the number of stores who find labor regulation to be a problem raises computer usage by .179 percentage points. In contrast, stricter land laws have a negative effect on computer usage (significant at less than 1% level). For overall development we use ratio of females to males ( _Sex ratio_ ) at the city level.<sup>6</sup> Other proxy measures like literacy rates, etc. are used in the robustness checks below. Column 2 of Table 1 shows that controlling for _Sex ratio_ increases the estimated coefficient of labor regulations from .179 to .189 (p-value of .004) confirming the downward bias discussed above. _Sex ratio_ has a positive effect on

> 6 The correlation between _Sex ratio_ and _Perception_ index is -.237 in the full sample and -.077 in the restricted sample (excluding West Bengal and Gujarat). We use (lagged) 1991 values of _Sex ratio_ taken from Census of India (1991). Our results do not change if we use 2001 values of sex ratio.

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computer usage (significant at less than 1% level). Estimation results for the restricted sample (West Bengal and Gujarat dropped) are almost similar to the ones above.

# **4.  Robustness**

In column 3 we add the control for _Regulation_ . As expected, _Regulation_ has a negative effect on computer usage (significant at less than 1% level) and controlling for it increases the coefficient of the labor regulation index from .189 to .346. In column 4 we add controls for overall development: _BI_ index, literacy rate, labor force participation rate, total adult population (size of the city) and share of services in total employment.<sup>7</sup> Population and participation rates also control for differences in labor supply across cities in which our stores are located.<sup>8</sup> The impact of these controls is an increase in the estimated coefficient of labor regulation (column 3 vs. column 4) which supports our claim of the downward bias. In column 5 we control for a host of store level characteristics: floor area of the shop (thousand square feet), age of the store, duration of power outages per day ( _Outage_ ) and five dummy variables capturing (equal to 1) if a store owns a generator ( _Generator_ ), has a checking/savings account ( _Checking_ ), a line of credit, overdraft facility ( _Overdraft_ ) and felt no need to borrow from external sources in the last fiscal year ( _Liquid_ ). With these controls added the coefficient of the labor regulation index remains positive and significant at less than 5% level.<sup>9</sup> It drops in magnitude from .412 to .369 which is partly due to the difference in sample size between

> 7 Apart from the _BI_ index, all other development proxies mentioned here are at the city level, for the year 1991 (lagged) and taken from Census of India (1991). Our results are same if we use 2001 values instead. 8 Population of the city is expressed in thousands.

> 9 This result holds with a host of other controls too. Examples include: a dummy for the metropolitan cities of Mumbai, Kolkatta, New Delhi, Chennai, Bangalore and Hyderabad, per capita income of the state (2001, 1991 values), number of telephone lines per capita at the state level (city level data is not available) and store level variables for: current sales, days of inventory, store is audited or not.

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the two columns (due to missing observations) and partly due to the control for age of the store. Our main result of a positive and significant effect of labor regulation on computer usage holds for the _Law Book_ index.<sup>10</sup>

# **5. Conclusion**

The paper shows that rising computer usage in India’s retail sector is in part driven by burdensome labor regulations. The findings suggest a labor saving motive which we confirm in a companion paper. We have also shown that existing work on the labor regulation-technology-employment/wages nexus may suffer from specification problems in that it treats the choice of technology by firms as exogenous to labor regulations. Our results contradict the exogeneity assumption.

# **References**

Alesina, A. and J. Zeira (2006), “Technology and Labor Regulations,” NBER, WP 12581.

Banerjee, A and L. Iyer (2005), “History, Institutions and Economic Performance: The Legacy of Colonial Land Tenure Systems in India,” _American Economic Review_ , 95(4), 1190-1213.

Besley, Timothy and Robin Buregess (2004), “Can Labor Regulation Hinder Economic Performance? Evidence from India,” _Quarterly Journal of Economics_ , February 2004, 91-134.

Blanchard, Olivier and T. Philippon (2006), “The Quality of Labor Relations and Unemployment,” mimeograph.

Botero, Juan, Simeon Djankov, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2004), “The Regulation of Labor,” _Quarterly Journal of Economics_ , November, 118: 1339-82.

Card, David, Francis Kramarz and Thomas Lemieux (1999), “Changes in Relative Structure of Wages and Employment: A Comparison of the United States, Canada and France,” _Canadian Journal of Economics_ , 32(4), 843-877.

Krueger, A.B and J. Angrist (2001), “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments? _Journal of Economic Perspectives_ , 15, pp. 69-85.

> 10 For the specification in column 5 of Table 1, the coefficient of the _Law Book_ index equals .020 which is significant at less than 5% level. The coefficient remains significant at less than 5% level with just a few basic controls such as _Sex ratio_ and _Regulation_ , irrespective of the remaining controls in column 5.

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# **Figure 1**

## **Labor and Land regulation in Indian states**

<!-- Start of picture text --> 0.600<br>West Bengal<br>0.500<br>Rajasthan<br>0.400<br>Maharashtra<br>Delhi Punjab<br>0.300<br>Karnataka<br>MP<br>UP<br>0.200 AP Jharkhand<br>Kerela Tamil Nadu<br>Bihar<br>0.100<br>Orissa Haryana<br>Gujarat<br>0.000<br>0.000 0.200 0.400 0.600 0.800 1.000<br>Land index<br>Perception index<br><!-- End of picture text --> AP: Andhra Pradesh UP: Uttar Pradesh MP: Madhya Pradesh 7
|**Table 1:**<br>Dependent variable|**Marginal**<br>:_Computer_|**effects from**<br>_s_|**Probit re**|**gressions**||
|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|
|_Perception_index|.179<sup>**</sup>|.189<sup>***</sup>|.346<sup>***</sup>|.412<sup>***</sup>|.369<sup>**</sup>|
||(.021)|(.004)|(.001)|(.000)|(.045)|
|_Land_index|-.304<sup>***</sup>|-.208<sup>***</sup>||||
||(.000)|(.000)||||
|_Sex ratio_||<br>.709<sup>***</sup>|.487<sup>***</sup>|.272<sup>**</sup>|.217|
|||(.000)|(.001)|(.031)|(.128)|
|_Regulation_|||-.408<sup>***</sup>|-.411<sup>***</sup>|-.335<sup>***</sup>|
||||(.000)|(.000)|(.008)|
|_BI_index||||<br>.042<sup>**</sup>|<br>.037|
|||||(.035)|(.267)|
|_Literacy_||||.002|.0001|
|||||(.106)|(.997)|
|Labor force||||-.001|.0004|
|Participation rate||||(.556)|(.887)|
|<br>Population of the||||-.005|-.004|
|<br>city||||(.223)|(.552)|
|Services share in||||.001|.001|
|total employment||||(.589)|(.400)|
|<br>Floor area of the|||||<br>.062<sup>*</sup>|
|shop|||||(.087)|
|Age of the store|||||-.002<sup>***</sup><br>(.001)|
|_Outage_|||||-.003|
||||||(.212)|
|_Generator_|||||.199<sup>***</sup>|
||||||(.000)|
|_Checking_|||||.097<sup>***</sup>|
||||||(.000)|
|_Liquid_|||||<br>.015|
||||||(.426)|
|_Overdraft_|||||<br>.133<sup>***</sup>|
||||||(.000)|
|Line of Credit|||||<br>.040<br>(.137)|
|Pseudo R<sup>2</sup>|.015|.029|.030|.032|<br>.323|
|Sample Size|1948|1948|1948|1948|1893|

p-values in brackets; significance level is denoted by ***(1% or less), ** (5% or less) and * (10% or less). All standard errors are clustered on the state. Sample size smaller in column 5 due to missing observations and one outlier dropped.

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