Date: June 4, 2010

Contracting and Efficiency in the Surgical Goods

Cluster of Sialkot, Pakistan

Theresa Thompson Chaudhry

Associate Professor of Economics

Lahore School of Economics

Intersection Main Blvd Phase VI DHA and Burki

Road

Burki (Lahore) 53200 Pakistan

Phone (US): 1-240-486-0936, 1-301-656-8040

Phone (Pakistan): 92-301-842-2700, 92-42-666-

3244

Fax (Pakistan): 92-42-571-4936

Email: theresa@lahoreschool.edu.pk

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Acknowledgements: I would like to gratefully acknowledge the

assistance of Ms. Shamyla Chaudry at the Lahore School of

Economics in collecting the data for this research.

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Contracting and Efficiency in the Surgical Goods

Cluster of Sialkot, Pakistan

Abstract:

This paper provides empirical evidence of the inefficiency

of contracting institutions (measured by high switching costs)

among surgical instrument producers in Sialkot, Pakistan, even

though it is an industrial cluster where manufacturers have

access to a multiplicity of suppliers. Following the methodology

of Johnson, McMillan, and Woodruff (2002), we found that nearly

50% of firms in the sample would reject an untried supplier

offering a lower price. The decision to reject a prospective new

supplier offering a 10% discount was positively related to the

complexity of the input and measures of relational contracting,

and negatively related to a belief in informal contract

enforcement mechanisms. Firms were more likely to switch to the

prospective discount supplier when they were introduced through a

business network. Belief in formal contract enforcement was not

significant in any of the regressions.

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Keywords: Relational contracting, switching costs, law and

economics, agglomeration, Pakistan

JEL Codes: K0, L14, O13, O17

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I. Introduction and Literature

The importance of institutions, especially contract

enforcement, has been well established in both theoretical and

empirical economic literature. The absence of strong institutions

has been recognized as a major constraint to economic growth in

developing countries (North, 1990). In the absence of an

effective legal system or formal system of contract enforcement,

individuals and firms must rely on informal means to enforce

agreements. In many cases, long term bilateral relationships or

third party social pressure may either substitute for, or

complement, a legal system in the enforcement of contracts. Firms

may ‘hedge their bets’ by staying in current trading

relationships and avoid entering into agreements with new or

unknown trading partners, thus circumventing defective contract

enforcement system. In this way, firms may choose to stay in

inefficient relationships to avoid switching costs.

More formally, switching costs refer to situations where

trading with a known partner can yield a surplus relative to

trading with a new, unknown partner.1 In other words, a buyer may1 Switching costs may occur within any type of seller/buyer relationship; switching costs may come into play between a retailer (seller) and a consumer (buyer) or between an input supplier (seller) and a manufacturer (buyer). Important contributions in the theoretical literature on switching costs have

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continue to purchase from a current supplier simply because it

may be costly to switch to another supplier (Farrell and

Klemperer, 2007). In this way, suppliers can exercise monopoly

power over customers facing switching costs by charging prices in

excess of marginal cost, thus creating deadweight loss and

reducing economic efficiency. According to Klemperer (1995),

switching costs can create additional inefficiencies by creating

barriers to entry for new firms and reducing the product variety

offered by firms.

Weak contract enforcement can be a source of switching

costs. Switching costs may arise due to, for example, specific

investments in relationships binding an agent to current trading

partners, transactional costs related to changing partners,

uncertainty regarding alternate partners, and weak contract

enforcement. Trading partner uncertainty as a cause of switching

costs refers to a situation where agents tend to have better

information about their current supplier (including quality,

been made by Klemperer (1987a, b, 1995). The theoretical model of Nilssen (1992) distinguished between two types of switching costs: the costs associated with the act of changing suppliers and the costs associated with learning about a new supplier’s goods. The analysis concluded that switching costs reduce welfare. For additional discussion of the theoretical literature on switching costs, see Farrell and Klemperer (2007).

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specifications, and reliability), and may have imperfect

information about prospective new partners. Contract enforcement

can become an important issue when an agent is uncertain whether

a new trading partner will uphold agreements.2

There are other reasons why firms stay in relationships, in

particular when the goods provided by the supplier are complex

and/or manufactured to the customer’s specifications, and in

these situations, the decision ‘not to switch’ is not necessarily

inefficient. However, the inputs provided to the firms in our

sample do not appear to be exceedingly complex. In 61 of the 139

supply relationships studied here, the input provided was steel

and/or forging, whereas only 27 suppliers were providing semi-

finished goods.3

As was already mentioned, imperfect contract enforcement can

contribute toward increasing switching costs when a buyer is

uncertain whether a new trading partner will uphold contracts. In

the absence of an effective legal system or formal system of

2 These are not the only sources of switching costs. Klemperer (1995) identified a variety of sources of switching costs, including i) investment incurrent equipment (that an alternate supplier’s goods may be incompatible with) and relationships (for example the cost of closing existing accounts), ii) information problems (learning to use new brands and uncertainty about a new supplier’s quality), iii) artificial transaction costs (such as discounts for repeat transactions), iv) psychological costs (brand loyalty).3 There was no information on the input supplied for 44 of the 139 suppliers.

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enforcement, individuals and firms in developing countries rely

on informal means to enforce agreements, called relational

contracting. The empirical analysis carried out in our paper

provides evidence on the inefficiency of contracting institutions

(through the existence of switching costs) among Sialkot’s

surgical instrument producers.

The methods of informal enforcement, laid out in the New

Institutional Economics literature (Macaulay, 1963; North, 1990;

Greif, 1994; Kranton, 1996;), consist of agents’ ability to

sanction individuals who have reneged on their agreements. The

three major mechanisms to deal with the lack of formal contract

enforcement through the judicial system are relying on trusted

partners, repeated interaction, and community enforcement. The

first approach is for an agent to only deal with those who are

known to them and can be trusted, in many instances friends and

family members. Secondly, one can deal with the same agent

repeatedly over an extended period of time, using the threat of

breaking off the profitable trading relationship as a means to

prevent the other party from cheating. Finally, informal

enforcement can also be carried out through community

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enforcement. In this case, if an agent reneges on an agreement,

all members of the community sanction this individual by refusing

to trade with them. For community enforcement to be effective, it

is necessary that knowledge about cheaters be diffused through

the community, and that other members of the community are

willing to refuse to trade with a known cheater; therefore it

often limited to a specific geographic area and/or to agents of a

common cultural/social background. In Sialkot, since all of the

manufacturers are producing similar goods, the threat of an

individual manufacturer (customer) breaking off a trading

relationship with a problematic supplier is unlikely to prevent

cheating or the delivery of shoddy goods by the supplier unless

there is community enforcement.

There have been many contributions, both theoretical and

empirical, on the role of social networks in contracting. Kranton

(1996) developed a theoretical model of the institutions of

reciprocal exchange (also called relational contracting) and

market exchange when agents face search costs (similar to

switching costs) and found that the prevailing system of exchange

to be path dependent and that an economy may not necessarily

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converge to the most efficient mode of exchange.4 Mookherjee

(1999), in his review of the literature, noted that firms in

developing countries tend to have difficulties in transacting,

and community contractual networks have a special role as an

alternative to vertically integrated enterprises. Ilias (2001)

focused on the role of family labor in Sialkot’s surgical

instrument cluster and the distortionary effects of the decision

to use family versus non-family labor.5 Banerjee and Munshi

(2000) found credit market distortions due to social network

based lending in the Tiruppur knitwear cluster in India.6 In this

paper, we focus on the role of community networks and their

effect on contracting in Sialkot’s surgical instrument

manufacturing cluster.

Johnson et al. (2002) examined the role of formal legal

enforcement in reducing switching costs in Eastern Europe and

found that greater belief in the (formal) judicial system reduced

4 In Kranton’s model, when goods are more (less) substitutable, reciprocal (market) exchange is socially efficient.5 Ilias (2001) concluded that there existed a labor market distortion such that family managers were preferred to non-family and therefore firm output was correlated with family size. 6 Banerjee and Munshi’s (2000) analysis found that the established producers (a caste called the Gounders), with access to cheaper informal credit through a social lending network, had lower output growth but invested more at all levels of experience as compared to migrants.

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the probability that a firm would reject an untried supplier

offering a lower price.7 In their sample, half of the firms

stated that they would abandon their current supplier for the

discount supplier (while only 15.5 per cent would reject the

discount supplier’s offer), and 76.5 per cent of firms believed

that courts could enforce contracts. However, a much different

picture emerges in Sialkot; only 16 per cent would be willing to

switch exclusively to a prospective discount supplier (whereas

nearly 50 per cent of firms in the sample would reject the

offer), and fewer than 12 per cent of firms believe that the

judicial system can resolve disputes. On the other hand, 37 per

cent of firms believe that other businesses will refuse to deal

with supplier who has been unfair with them, and 44 per cent of

the sample firms believe that the only reliable suppliers are

those owned by relatives. We posit that different set of

contracting institutions must be at work here.

We use a methodology similar to Johnson et al. (2002).

However, our research makes a unique contribution to the

literature since switching costs have not yet been studied

7 Our paper draws heavily on the research conducted by Johnson et al. (2002), and their research will be discussed in more detail in the following sections.

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empirically in the context of a cluster. We found that the

decision to reject a prospective new supplier offering a 10 per

cent discount was positively related to the complexity of the

input and measures of relational contracting, and negatively

related to a belief in informal contract enforcement mechanisms.

Firms were more likely to switch to the prospective discount

supplier when they were introduced through a business network.

Belief in formal contract enforcement was not significant in any

of the regressions.

II. Description of the Surgical Instrument Cluster in Sialkot,

Pakistan

A cluster of firms consisting of approximately 220 producers

and 1500 subcontracting firms are located in Sialkot, a city in

the Punjab province of Pakistan (see Table 1), which produces

surgical instruments mainly for foreign markets including the

United States and Western Europe, with 75 per cent of instruments

being exported to these two regions (SMEDA, 2001). The cluster’s

output is significant, as verified by the $124 million worth of

goods exported in 2000-2001 (SMEDA, 2001). The firms of the

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cluster manufacture approximately 10,000 different types of

disposable and re-useable surgical instruments (SMEDA, 2001).

In the cluster, production of the surgical instruments takes

place in stages, including input production, manufacturing, and

complementary services. The large vendor segment consists of

small firms that specialize in one or more stages of the

production process. Except for the largest manufacturers,

production of a final good is not generally carried out in a

single, vertically integrated firm. Nadvi (1999a, b; 2002) has

written extensively about the cluster in Sialkot, in particular

on issues of ‘collective efficiency.’

The cluster also has local business associations, including

the Metal Industries Development Centre, the Sialkot Dry Port

Trust, the Sialkot Chamber of Commerce and Industry (SCCI) and

the Surgical Instrument Manufacturer’s Association (SIMA).

The cluster has a long and interesting history. Local

blacksmiths began producing surgical instruments around the start

of the twentieth century at the request of the American Mission

Hospital in Sialkot. In the 1930s, the cluster began exporting

regionally to countries such as Egypt and Afghanistan, and it was

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a vital supplier to both Indian and Allied forces during World

War II. The industry continued to expand in the decades after the

Second World War. Strong pro-labor legislation passed in 1973 led

the industry to shift to extensive subcontracting, referred to as

‘vendorization’ (SMEDA, 2001). At times, the cluster has

experienced problems with quality, which reached a crisis point

in 1994 when the US Food and Drug Administration (FDA) halted

imports from Pakistan until the firms adopted Good Manufacturing

Practice (GMP) standards.

III. Description of the Survey

For purposes of this study, we designed and commissioned a

survey of the surgical instrument cluster in Sialkot, Pakistan,

based in large part on the survey questionnaire developed by

Johnson et al. (2002) for their study in Eastern Europe and

Russia. Faculty from a local university conducted the survey in

2002. A breakdown of the entire survey sample (before data

cleaning) is provided in Table 2.8 After the sample was cleaned 8 When the interviewer went to the cluster to begin the survey, she found thatonly about 180 of the 220 exporting firms that were listed by SIMA (the local business association) were actually in operation at that time. Of these, 76 firms at least partially answered the survey, leading to a response rate of 43percent. The interviewer then met with 47 vendor firms in the villages surrounding Sialkot, where the cottage industry is located.

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to ensure a balanced sample for the relevant variables, 139

observations remained representing 76 distinct firms. In the

switching cost section of the survey, firms were asked a series

of questions about their oldest and newest supplier.

In order to assess the existence of switching costs, the survey

asked the following question, in reference to both the firm’s oldest

and newest (current) suppliers:

If another firm you have never purchased from offered to

supply this input for a price 10 per cent less than this

supplier, would you purchase from the new firm instead of

this supplier?

Firms were offered the option to reject the (potential) new

firm offering the discount, buy only from the new firm, and buy

from both the new firm and the current supplier. In addition to

questions about buying from a new supplier, firms were asked

several other questions such as trade credit received from

suppliers, the length of relationships with their oldest and

newest suppliers, and the nature of the relationships with

suppliers. Characteristics of the relationship with suppliers

included questions such as how often they visited (and were

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visited by) suppliers, how they were introduced to their

suppliers, how difficult it would be to find alternate suppliers,

and contract enforcement. The contract enforcement section

included questions about their belief in the effectiveness of

local courts, and whether informal sanctions existed for reneging

on contracts.9

Johnson et al. (2002) studied relational contracting and

switching costs through a survey conducted in Poland, Slovakia,

Romania, Russia, and Ukraine. When posed the switching cost

question above in regards to buying from a new supplier offering

a 10 per cent discount, half of the firms (from the pooled

sample) stated that they would abandon their current supplier for

the discount supplier, a third responded that they would buy from

both their current and the discount supplier, and only 15.5 per

cent would reject the discount supplier’s offer.10 In the sample 9 We recognize that hypothetical questions, such as those about switching to adiscount supplier and beliefs in courts and informal enforcement mechanisms pose problems; beliefs may be endogenous, correlated with other independent variables, or include measurement error (leading to attenuation bias). However, as Johnson, McMillan and Woodruff (2002) point out, the use of these (courts, or switching suppliers) would be more problematic as regression variables. If the court system is efficient, there is little need to exercise the option; the threat point will be used for an out of court settlement. Likewise for efficient supply relationships, there is no need to switch. In this way, confidence in the ability to use courts, or the ability to switch suppliers become the relevant dimensions.10 However, there were significant differences across countries. Firms in Slovakia were the most likely to reject the discount supplier (21.3%) while

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of firms in Sialkot, 49 per cent of firms in the cleaned sample

responded that they would reject a new supplier that offered a 10

per cent discount (see Table 3), a much higher rejection rate

than the Eastern European sample. Of the 51 per cent who would be

interested in the new discount supplier, only 16 per cent would

abandon their current supplier, and 35 per cent said that they

would buy from both the current and discount suppliers.

The Eastern European sample had a larger proportion of new

supply relationships that were one year or younger (35%) as

compared to the sample from Sialkot (16%). Likewise, Sialkot had

a larger proportion of long term supply relationships of nine

years or longer (37%) as compared to the sample from Eastern

Europe (2.8%). The relative newness of the supply relationships

in Eastern Europe had mostly to do with the short history of

private enterprise in the region since the fall of Communism.

The complexity of the inputs (as measured in the survey) in

the Sialkot sample is not very different from the Eastern

European sample. In Eastern Europe, 11 per cent of suppliers

Romanian firms were the most likely to leave their current supplier and switchcompletely to the new discount seller (64.5%). In both Russia and Ukraine, firms had a high likelihood of wanting to buy from the discount supplier whilestill maintaining a relationship with their current suppliers (95% and 87.5% respectively).

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provided a unique input, while in Sialkot, this was the case for

16 per cent of supply relationships studied. Quality

specifications were written 77 per cent of the time in Eastern

Europe, and 60 per cent in Sialkot.11

There were major differences in the belief in formal

contract enforcement between the two samples. An average of 76.5

per cent of firms believed that courts could enforce contracts,

with a low of Russia (42.5%) and a high of Romania (88.5%).

However, among the Sialkot sample, only 11.5 per cent said that

they believed that the judicial system could help in resolving

disputes with suppliers. Given the low trust in formal

enforcement mechanisms, we hypothesize that informal mechanisms,

especially community enforcement, will have a greater impact on

the efficiency of contracts (by way of switching costs) in

Sialkot than the local courts.

11 There are some important differences, however. Of the sampled firms, 32% had no alternate supplier in Eastern Europe, whereas 78% of the Sialkot samplerelied on a single supplier for a particular input. While 20% of suppliers produce goods to order in Eastern Europe, this figure was almost 90% in Sialkot. Given that only 16% of suppliers were providing a unique input, thesetwo measures may be more related to credit constraints or scale of production than complexity.

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IV. A Simple Framework for Determining the Existence of SwitchingCosts12

The benefit a manufacturer receives from buying an input

from a supplier depends positively on the value of the input and

negatively on the price. The utility from buying from a current

supplier and a prospective new supplier can be denoted as

(respectively):

where:

V=average value of input

= noise parameter of the value of input with mean=0 and

variance σi

Pi = price of input

e refers to an existing supplier

n refers to the untried supplier.

The firm faces some uncertainty about the quality of the

inputs it purchases from any supplier, but has better information

about the quality of the inputs produced by a current supplier

than produced by an unknown supplier, so that the variance of the12 This section is based heavily on Johnson et al. (2002).

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input’s value will be lower for current suppliers (that is, σe 2<

σn 2). Therefore a manufacturer will likely have a greater

expected utility from buying from a known current supplier than

an unknown supplier if both are offering the input at the same

price. Therefore, a manufacturer would be willing to switch to a

new supplier only if a prospective new supplier offers a

discount. A firm will switch to a new supplier if:

where D is the discount offered by the new potential supplier.

Based on the theoretical discussion above, a firm will

reject the offer from new potential supplier if the switching

costs are greater than the 10 per cent discount:

Therefore, the following regression equation can be

estimated as a limited dependent variable model:

where:

Di= 0,1

0 = Accept the new supplier

1 = Reject the new supplier20

and switching costs are proxied with the following variables:

C is the complexity of the input,

S describes the relationship with existing supplier

E is enforcement of contracts

M represents firm (buyer) characteristics

The complexity of the input should increase the probability

that a new, untried supplier is rejected. The variables that

proxy for the complexity of the input include the following: that

the supplier produces a unique good to the surveyed firm, that

the firm has no alternative supplier for a particular input, and

that quality specifications are written. Each of these variables

taking a value of one represent that the surveyed firm is ‘locked

in’ to the supplier, would have difficulty locating alternate

supply if there are problems with a supplier, and should be

therefore more likely to reject a prospective discount supplier.

The duration of the relationship with current supplier may

proxy the average value of an input from a current supplier. A

relationship with a supplier will tend to last when the supplier

is providing high quality inputs. Therefore, a longer duration of

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relationship with current supplier thus would increase the

probability that a new, untried supplier is rejected.

Firms will also engage in attempts to gather information

about suppliers. Efforts to gather information should be

associated with higher switching costs. These variables include:

introduction to a supplier through business or social networks,

information gathering through the trade association (SIMA),

visits at the start of the trading relationship, and frequency of

delivery (proxied here as visits from the supplier during the

trading relationship).

Belief in the enforcement of contracts should reduce

switching costs, and therefore reduce the likelihood that the

surveyed firm rejects the prospective discount supplier. The

variable representing formal contract enforcement is the belief

that courts can enforce contracts. While a variable based on

beliefs may be criticized for appearing subjective, it is likely

a better measure than experience with courts (as pointed out by

Johnson et al. (2002)) since people are less likely to use the

court system the more effective it is. This is because if the

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likely outcome can be predicted by both parties, a dispute will

be adjudicated privately rather than incurring legal costs.

As discussed earlier, informal relationships can substitute

for third party enforcement through relational contracting.

Relational contracting and informal enforcement mechanisms

(representing the ability to sanction) should be strong in the

cluster environment, and reduce switching costs. These variables

taking a value of one should be associated with reducing the

likelihood of rejecting a prospective discount supplier. Informal

enforcement is measured as the belief that other firms will

refuse to deal with a supplier who has been unfair. Again, such a

belief is more valid than experience with informal sanctions,

since such measures will only be resorted to when private

adjudication fails. Other relational contracting variables

include: speaking frequently with other manufacturers and the

belief that only relatives can be trusted as reliable suppliers.

Speaking frequently with other manufacturers (which can be a

source of information about other suppliers) should be associated

with a lower probability of rejecting the untried discount

supplier, and belief that only relatives should be trusted as

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suppliers should decrease the likelihood of trying a new

supplier.

V. Discussion of the Results

The Decision to Switch Completely to the Discount Supplier

For firms that were interested in purchasing from the

hypothetical supplier offering a 10 per cent discount, firms

could choose between buying from the discount supplier (breaking

off their trading relationship with the current supplier) and

buying from both. In Table 4, we present results for the

probability that firms would be willing to abandon their current

supplier and switch completely to purchasing an input from the

prospective supplier offering a 10 per cent discount.

Given the limited belief in the effectiveness of the formal

legal system among the firms in Sialkot, and our hypothesis

(based on the development literature) that informal contract

enforcement mechanisms and relational contracting are likely to

be a significant factors among clustered firms, our main

specifications include variables for these effects. In addition,

we control for the complexity of inputs, duration of the trading 24

relationships with existing suppliers, information gathering

efforts, formal contract enforcement, and firm level controls.

In these regressions, all three complexity variables are

significant and reduce the likelihood of switching to the new

discount supplier, which is consistent with the theoretical

framework. Firms whose trading relationship with a current

supplier was 6-12 months were about 31 per cent more likely to

switch than firms whose relationship with their current supplier

was less than six months. A possible explanation for this result

may be a fear of negative reputation effects from switching

suppliers too often.13 Interestingly, firms with supply

relationships of more than nine years are also more likely to

switch to the new supplier (25%). It may be that firms who expect

to have a longstanding relationship with a supplier are more

willing to invest in a new supplier if the cost savings on inputs

can be maintained over many years. Not surprisingly, firms that

receive trade credit from their current supplier were less likely

to abandon their current suppliers (9-10%).

Another strong effect was being introduced through a

13 This is also the interpretation given by Johnson et al. (2002).25

business network, which positively relates to the probability of

switching suppliers. This effect is large and significant. Firms

that are connected, speaking daily to other producers, are in

some specifications significantly more likely to switch. However,

if a firm was introduced to a current supplier through a social

network, this had a negative effect on the likelihood of

abandoning their current supplier. The magnitude of the social

network’s effect was much smaller than the business network, and

the statistical significance varied by specification.

The two main relational contracting variables, including the

beliefs that ‘family members are the most reliable suppliers’ and

‘businesses will refuse to deal with supplier who has been

unfair’ to the surveyed firm, are insignificant in the regression

for the decision to switch completely to the discount supplier.

Only those firms that fear being cheated, who ‘would never buy

from a supplier heard to have cheated’ are less likely to switch

with statistical significance.

The Decision to Reject the Prospective Discount Supplier

In this section, we study the decision of a firm to reject

the prospective new supplier offering a 10 per cent discount. The26

control variables differ slightly from Table 4 but cover all of

the same categories (complexity of inputs, duration of the

trading relationships with existing suppliers, information

gathering efforts, formal contract enforcement, informal

enforcement, and firm level controls).

Among the complexity variables, not having an alternate

supplier was highly significant and, consistent with the theory,

increased the probability of rejecting the prospective discount

supplier by 24-26 per cent. A relationship of longer duration

between a firm and a supplier increased the probability of

rejection. For relationships of two or more years, the rejection

rate was about 18 per cent higher (Table 5, Column 1). Dropping

the duration variable (Table 5, Column 2) has very little effect

on the size or significance of the estimated marginal effects.

Including the dummy variable for the newest current supplier

causes the duration of relationship variable to lose significance

but has little effect on the rest of the results.

Regarding information gathering efforts, the results were

mixed. On the one hand, an introduction to a supplier through a

social network increased the likelihood of rejecting the discount

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supplier; this is consistent with the switching cost theory. In

fact, Burnham, Frels and Mahajan (2003), describe ‘relational

switching costs’ as those associated with the loss of personal

relationships and brand relationships when changing suppliers. In

a closely knit community, breaking off business relationships

with suppliers managed by members of one’s social network may be

very difficult. Also, the dummy for firms that speak at least

weekly with other firms had the expected negative sign, although

the estimated marginal effect coefficient just missed

significance at the 10 per cent level. On the other hand,

increased visits to the supplier before the start of the trading

relationship (increasing the cost of information gathering

efforts, which should be associated with a higher rejection

rates) actually decreased the probability of rejecting the

hypothetical 10 per cent discount supplier by 30-31 per cent.

The results with regards to relational contracting and

informal enforcement were statistically significant and consistent

with the theory. The variable for the belief that ‘businesses will

refuse to deal with supplier who has been unfair’ to the surveyed

firm was 17-20 per cent less likely to reject the prospective new

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supplier, consistent with the theory that informal enforcement

mechanisms can help to reduce transaction costs and therefore

switching costs. Finally, coinciding with the relational

contracting literature, firms that believed that the only reliable

suppliers were family members were 21-22 per cent more likely to

reject the prospective discount supplier.

Comparing the results for the decision to switch completely

to the new discount supplier (Table 4) and the results for the

decision to reject the new supplier (Table 5), it appears to be

the case that firms that feel constrained by social ties to

suppliers are less likely to change suppliers, and firms that do

not feel these constraints (and rely on professional ties through

business networks) are more likely to switch. Being introduced

through a business network has a much stronger effect than a

social network introduction in the regression for the decision to

switch suppliers, but the reverse is the case in the regressions

on the decision to reject the prospective discount supplier. In

addition, the two most important relational contracting

variables, including the beliefs that ‘family members are the

most reliable suppliers’ and ‘businesses will refuse to deal with

29

supplier who has been unfair’ to the surveyed firm, are only

significant in the regression for the decision to reject the

discount supplier.

Additional Robustness Specifications

Additional robustness checks are included in Appendix

Table 1 for the decision to reject the 10 per cent discount

supplier. When different variables for informal enforcement were

included in place of ‘the belief that businesses will refuse to

deal with supplier who has been unfair’, the informal enforcement

variables maintain the correct negative sign, but lose

significance (Appendix Table 1, Columns 1 and 2). In Column 3 of

Appendix Table 1, we take the main specification from Table 5 and

add interaction terms between the relational contracting

variables and the dummy variable for the vendor firms, to see if

these effects differ between the subcontractors and the exporting

firms. None of the interaction effects were significant.

Replicating the Regressions of Johnson et al. (2002): The

Decision to Reject the Prospective Discount Supplier

30

In addition, we replicated (as closely as possible) the

regressions included in Johnson et al. (2002).14 In Eastern

Europe, they found several results supporting the existence of

switching costs, particularly that the complexity of inputs and

information gathering efforts increase switching costs, and

evidence that the court system may help to reduce them. Johnson

et al. (2002) found that the probability of rejecting a new

supplier in Eastern Europe was significant and positively related

to the complexity of the input (supporting the theory) as

measured by the following dummy variables: that the supplier

provided a unique input, the surveyed firm had no alternate

supplier, and that quality specifications were written. In our

replication of their regressions, only the dummy for the surveyed

firm having no alternate supplier was close to significance, and

it was positively related to the probability rejecting a new

discount supplier. 14 These results are given in Appendix Table 2. There were some minor differences in the specifications. For instance, we did not have measures for frequency of delivery, number of visits from supplier before first transaction, and use of courts in recent dispute used in Johnson et al. (2002). For “frequency of delivery” we used frequent visits from the supplier (daily, weekly) as a proxy. For number of visits, we used “four or more visitsto supplier” before first transaction. Instead of first information from business and social networks, we used “introduction” through business and social networks. Instead of trade association services, we used “SIMA providesinformation about supplier reliability.”

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Johnson et al. (2002) also found that the probability of

rejecting a new supplier was positively related to information

gathering efforts, also supporting the theory of switching costs.

In our replication of their specification, the probability of

rejection was positively related to ‘introduction through social

network’ but negatively related to visits prior to first

transaction.

The study in Eastern Europe showed that switching costs were

negatively related to a belief in the effectiveness of the legal

system in enforcing contracts, reducing the probability that the

surveyed firm would reject the prospective discount supplier.

This concurs with the idea from switching costs theory that

better contract enforcement can help to mitigate transaction

costs, including switching costs. In Sialkot’s surgical goods

cluster, the variable for belief in the courts was insignificant

in all regressions.

The results that Johnson et al. (2002) obtained in Eastern

Europe for the effect of relationship duration did not coincide

with the expected results. According to the theory, a longer

duration of manufacturer/supplier relationship should increase

32

the likelihood that a firm would reject the prospective discount

supplier. This is because the firm has better information about

its current supplier the longer the relationship, and information

about a new supplier is costly to obtain. The results from

Eastern Europe were the opposite; longer duration relationships

were associated with a decreased probability of rejecting the

discount supplier. In Sialkot, the results corresponded better

with the theory, and a longer duration (of two years or more) was

positively related to the likelihood of rejecting the prospective

supplier offering a 10 per cent discount.15

VI. Conclusions

Switching costs are damaging to economic efficiency because

they allow suppliers to charge prices greater than marginal cost

and they discourage firms from starting new trading relationships

with cheaper and possibly better suppliers. Efforts to

standardize inputs should help to reduce switching costs and

improve economic efficiency. 15 There was some concern that the sample selection procedure would bias the results since the surveyed firms were asked questions about their newest and oldest suppliers, particularly if the duration of the relationship is associated with the likelihood of switching to a new supplier. Most of the equations were estimated both with and without duration of the relationship asan explanatory variable, and the impact of removing duration was minimal.

33

The empirical analysis supports the existence of contracting

inefficiencies (through switching costs), even in the environment

of an industrial cluster where there is a multiplicity of

suppliers. Nearly 50 per cent of firms in the sample would reject

an untried supplier offering a lower price, and only 15 per cent

would be willing to abandon their current supplier for a

prospective discount supplier.

Probit regressions for the probability that firms would be

willing to abandon their current input supplier (and switch

completely to a prospective supplier offering a 10 per cent

discount) was negatively related to the complexity of the input

and the receipt of trade credit from suppliers. The decision to

switch suppliers was positively related to information gathering

efforts and introductions through business networks. Following

the methodology of Johnson, McMillan, and Woodruff (2002), we

found that the decision to reject a prospective new supplier

offering a 10 per cent discount was positively related to the

complexity of the input and measures of relational contracting.

The decision to reject the discount supplier was inversely

related to a belief in informal contract enforcement mechanisms,

34

although this result was not robust to the substitution of

alternate measures of community enforcement. Trust in formal

contract enforcement mechanisms (local courts) did not have a

significant impact in any of the specifications.

As mentioned earlier, it is the option to switch, which is

most important for efficiency. When firms are able to switch

easily, then this helps to discipline the firm’s current

suppliers. If firms are satisfied in their current supply

relationships, then infrequent switching will be observed. On the

other hand, lack of desire to switch suppliers might rather be

due to a fear of changing suppliers. Ex ante competition is

especially important when there are high sunk costs needed to

establish working business relationships. In our analysis, those

firms who invested heavily in information gathering at the start

of the relationship, visiting the supplier four or more times

were (unexpectedly) more likely to be willing to try the

prospective discount supplier. This result statistically

significant in both specifications (Table 4 and 5). By indicating

their willingness to switch, they appear to be willing to make

that investment again.

35

Social networks and some relational contracting measures had

stronger and more significant impacts in the decision to reject

the new discount supplier (than the regression for the decision

to switch suppliers), pointing to an interpretation of the

results that firms that feel constrained by social ties to

suppliers are less likely to switch, and firms that do not

experience these constraints (and rely on professional ties

through business networks) are more free to leave their current

suppliers. This lends further supports the conclusions of

Banerjee and Munshi (2000), Ilias (2001), Mookherjee (1999) that

difficulties in transacting often lead firms in developing

countries tend to rely on family members and close knit networks

in their business dealings.

The relative unwillingness of firms in Sialkot to change

suppliers (as compared to Johnson, McMillan, and Woodruff’s

sample in Eastern Europe) remains somewhat of a mystery. There

are two possible interpretations: One, that the information dense

cluster allowed firms to screen suppliers and enforce contracts

with them, so that firms were generally satisfied with their

suppliers; second, that the lack of a reliable court system led

36

firms to be locked in to relationships with suppliers, because

they were forced to rely on an inferior system of informal

enforcement. The first story presents a more optimistic view of

relational contracting. Nonetheless, even if relational

contracting leads to greater satisfaction in supply

relationships, there is a trade off; informal enforcement tends

to be limited to geographic areas or among relatively small

networks, limiting the scope of trade, as in Greif’s model of

Maghribi traders.

As we have seen, relational contracting and informal

contract enforcement mechanisms have positive effects in that

they help to deal with the uncertain contracting environment

faced by many firms in developing countries. However enthusiasm

for these mechanisms should be tempered; while they reduce one

source of inefficiency (imperfect contract enforcement), they

often create others in their place (such as switching costs,

contracting inefficiencies, and labor and credit market

distortions).

37

References

Banerjee, Abhijit, and Kaivan Munshi. 2000. “Networks, Migration,

and Investment: Insiders and Outsiders in Tirupur’s Production

Cluster.” Working paper #313, William Davidson Institute.

Burnham, Thomas; Judy Frels; and Vijay Mahajan. 2003. “Consumer

Switching Costs: A Typology, Antecedents, and Consequences.”

Journal of the Academy of Marketing Science, 31, no. 2: 109-126.

Farrell, Joseph, and Paul Klemperer. 2007. “Coordination and

Lock-In: Competition with Switching Costs and Network Effects.”

In Handbook of Industrial Organization, Vol. 3. ed. Mark Armstrong and

Robert Porter, Amsterdam: North-Holland.

Government of Pakistan Board of Investment. “Light Engineering.”

http://www.pakboi.gov.pk/pdf/Light%20Engineering.pdf (Accessed

February 6, 2010).

Greif, Avner. 1994. “Cultural Beliefs and the Organization of

Society: A Historical and Theoretical Reflection on Collectivist

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and Individualist Societies.” Journal of Political Economy, 102, no. 5:

912 – 950.

Ilias, Nauman. 2001. “Families and Firms: Labor Market Distortion

in Sialkot’s Surgical Industry.” PhD diss., University of

Pennsylvania.

Johnson, Simon, McMillan, John, and Christopher Woodruff. 2002.

"Courts and Relational Contracts." Journal of Law, Economics and

Organization, 18, no. 1: 221-77.

Klemperer, Paul. 1987a. “The Competitiveness of Markets with

Switching Costs.” The Rand Journal of Economics, 18, no. 1: 138-150.

Klemperer, Paul. 1987b. “Markets with Consumer Switching Costs.”

The Quarterly Journal of Economics, 102, no. 2: 375-394.

Klemperer, Paul. 1995. “Competition when Consumers have

Switching Costs: An Overview with Applications to Industrial

39

Organization, Macroeconomics, and International Trade.” The Review

of Economic Studies, 62, no. 4: 515-539.

Kranton, Rachel. 1996. "Reciprocal Exchange: A Self-Sustaining

System." American Economic Review, 86, no. 4: 830-51.

Macauley, Stewart. 1963. “Non-Contractual Relations in Business:

A Preliminary Study.” American Sociological Review, 28, no. 1: 55-69.

Mookherjee Dilip. 1999. “Contractual Constraints on Firm

Performance in Developing Countries.” Working paper # 98,

Institute for Economic Development, Boston University.

Nadvi, Khalid. 1999a. “Collective Efficiency and Collective

Failure: The Response of the Sialkot Surgical Instrument Cluster

to Global Quality Pressures,” World Development, 27, no. 9: 1605-

1626.

40

Nadvi, Khalid. 1999b. “The Cutting Edge: Collective Efficiency

and International Competitiveness in Pakistan.” Oxford Development

Studies, 27(1): 81-107.

Nadvi, Khalid. 2002. “Shifting Ties: Social Networks in the

Surgical Instrument Cluster of Sialkot, Pakistan,” Development and

Change, 30, no. 1: 141- 175.

Nilssen, Tore. 1992. “Two Kinds of Consumer Switching Costs.” The

RAND Journal of Economics, 23, no. 4: 579-589.

North, Douglass. 1990. Institutions, Institutional Change, and Economic

Performance. Cambridge, MA: Cambridge University Press.

Small and Medium Enterprise Development Authority (SMEDA). 2001.

“Surgical Instrument Industry of Pakistan: Issues in Export

Growth and Development Draft Report.” Government of Pakistan.

41

Table 1: Surgical Instrument Firms in PakistanSize of Firm

Number of Firms

Number of Employees

Revenues(Pakistan Rupees)

Capital(Pakistan Rupees)

Large 30 250-400 Rs 60-100 million Rs 50-100 million

Medium

50 100-250 Rs 10-60 million Rs 10-25 million

Small 150 30-50 Rs 1-10 million Rs 1-5 millionVendors

2000 5-20 Rs 1-1.5 million Rs 50,000-1 million

Traders

800-1000 na na na

Source: Board of Investment, Government of Pakistan

42

Table 2: Survey Sample (All firms surveyed)Number of firms

% of sample

Average employment (# of workers)

Average age of firms (years)

Exporters 76 62% 91.8 19.9Vendors 47 38% 15.4 11.7All Firms 123 61.9 16.7

43

Table 3: Sample Statistics

  Mean Median Var. Std.Dev.

Min Max NOB

Would reject newsupplier offering 10% discount (0,1)

0.49 0 0.25 0.50 0 1 139

Would switch entirely to new supplier offering 10% discount (0,1)

0.16 0 0.13 0.37 0 1 139

Supplier makes unique product for me (0,1)

0.16 0 0.13 0.37 0 1 139

Have no other supplier of input (0,1)

0.22 0 0.17 0.42 0 1 139

Input quality specifications written (0,1)

0.60 1 0.24 0.49 0 1 139

Duration of relationship with supplier (in years)

7.66 5 62.88

7.93 0.003

40 139

Supplier introduced through businessnetwork (0,1)

0.17 0 0.14 0.38 0 1 139

Supplier introduced through social network (0,1)

0.35 0 0.23 0.48 0 1 139

Visit supplier four or more times before purchase (0,1)

0.30 0 0.21 0.46 0 1 139

SIMA good source 0.21 0 0.17 0.41 0 1 139

Note: (0,1) means that the variable is a dummy variable.44

info. about reliability of potential suppliers (0,1)Supplier visits customer's factory daily (0,1)

0.13 0 0.11 0.34 0 1 139

Supplier visits customer's factory weekly (0,1)

0.58 1 0.24 0.49 0 1 139

Businesses will refuse to deal with supplier unfair with me (0,1)

0.37 0 0.23 0.48 0 1 139

Speak at least weekly with other producers (0,1)

0.55 1 0.25 0.50 0 1 139

Only reliable suppliers are firms owned/managed byrelatives (0,1)

0.44 0 0.25 0.50 0 1 139

Courts importantfor resolving disputes with suppliers (0,1)

0.12 0 0.10 0.32 0 1 139

Receive trade credit from supplier (0,1)

0.70 1 0.21 0.46 0 1 139

Exporter (0,1) 0.55 1 0.25 0.50 0 1 139Ln(Employment) 3.33 3.00 1.34 1.16 1.61 6.37 139Ln(1+Firm Age inyears)

2.61 2.56 0.45 0.67 1.39 3.97 139

45

Table 4: Probit for Rejecting Current Supplier, Switching Exclusively to 10% Discount Supplier Variable (1) (2) (3) (4) (5) ComplexitySupplier makes unique product for me

-0.17***(-4.73)

-0.17***(-4.83)

-0.12**(-2.54)

-0.18***(-5.16)

-0.18***(-5.16)

Have no other suppliers of input

-0.1*(-1.68)

-0.11**(-2)

-0.07(-1.1)

-0.13**(-2.46)

-0.13***(-2.58)

Input quality specifications written

-0.11*(-1.74)

-0.13**(-2.23)

-0.003(-0.04)

-0.12**(-2.13)

-0.14**(-2.38)

Duration (duration<6 monthsexcluded)Relationship with supplier 6-12 months

0.31*(1.91)

0.31**(2.02)

0.29(1.36)

0.31*(1.69)

0.31*(1.75)

Relationship with supplier 13-24 months

0.18(1.13)

0.19(1.2)

0.13(0.71)

0.18(1.07)

0.18(1.05)

Relationship with supplier 2-9 years

0.08(0.7)

0.16(1.16)

0.16(0.91)

0.11(0.83)

0.15(1.08)

Relationship with supplier >9years

0.24*(1.83)

0.39***(2.7)

0.33(1.56)

0.26*(1.86)

0.35**(2.14)

Information GatheringSupplier introduced through businessnetwork

0.42***(2.79)

0.44***(3.18)

0.44***(3.57)

0.45***(3.79)

Supplier -0.08 -0.09* - -0.08 -0.08

Note: Robust z statistics in parentheses, standard errors clustered by firm;***significant at 1%, **significant at 5%, *significant at 10%; y = Pr(Switch to new supplier offering 10% discount); coefficients are average partial effects.

46

introduced through social network

(-1.43) (1.67) 0.15***(-3.2)

(-1.32) (-1.39)

Supplier visits customer's factory daily

-0.13***(-3.2)

-0.13***(3.36)

-0.16***(-4.27)

-0.14***(-3.75)

-0.14**(-3.67)

Speak daily withother producers

0.19(1.63)

0.21*(1.82)

0.27**(1.98)

0.17(1.48)

0.17(1.53)

Visited supplierfour or more times before first purchase

0.19**(2.19)

0.22**(2.35)

0.12(1.51)

0.18*(1.92)

0.19*(1.94)

Formal EnforcementCourts importantfor resolving disputes with suppliers

0.12(1.1)

0.12(1.08)

-0.01(-0.11)

Relational Contracting and Informal EnforcementBusinesses will refuse to deal with supplier unfair with me

0.12(1.18)

0.11(1.11)

Would never purchase from a supplier heard to have cheated

-0.14**(-2.24)

-0.16***(-2.62)

-0.17***(-2.6)

Only reliable suppliers are firms owned/managed byrelatives

0.004(0.06)

0.003(0.04)

0.001(0.01)

-0.04(-0.56)

-0.04(-0.68)

Firm Level Controls (Buyer)Receive trade credit from supplier

-0.1**(-2.03)

-0.09*(-1.77)

-0.09(-1.49)

-0.17***(-3.22)

-0.17***(-3.17)

Exporter (dummy) 0.02 0.04 0.02 0.1 0.147

(0.27) (0.37) (0.15) (0.96) (0.97)Ln(Employment) 0.001

(0.02)0.01

(0.09)0.001(0.02)

-0.01(-0.29)

-0.004(-0.1)

Ln(1+Age in years)

-0.13*(-1.94)

-0.17**(-2.32)

-0.14*(-1.71)

-0.12*(-1.95)

-0.13*(-1.93)

Newest supplier (dummy)

0.13*(1.73)

0.07(0.92)

0.08(1.08)

Pseudo R-squared 0.37 0.38 0.25 0.35 0.35

48

Table 5: Determinants of Decision to Reject 10% Discount Supplier (Probit)

 Variable (1) (2) (3)ComplexityHave no other suppliers of input

0.26***(2.57)

0.28***(2.59)

0.24**(2.43)

Input quality specifications written

-0.09(-0.91)

-0.08(-0.76)

-0.07(-0.70)

Duration (Excluded category: <2 years)Relationship with supplier2-9 years

0.18***(2.51)

0.02(0.19)

Relationship with supplier>9 years

0.18**(2.31)

-0.09(-0.81)

Information GatheringSupplier introduced through business network

-0.11(-1.03)

-0.09(-0.74)

-0.1(-0.95)

Supplier introduced through social network

0.29***(3.37)

0.33***(3.64)

0.27***(3.35)

Supplier visits customer's factory daily

-0.08(-0.60)

-0.03(-0.26)

-0.1(-0.81)

Supplier visits customer's factory weekly

0.1(1.09)

0.12(1.31)

0.08(0.93)

Speak at least weekly with other producers

-0.13(-1.49)

-0.14(-1.58)

-0.1(-1.16)

Visited supplier four or more times before first purchase

-0.3***(-4.33)

-0.31***(-4.46)

-0.31***(-4.64)

Formal Contract EnforcementCourts important for resolving disputes with suppliers

-0.03(-0.24)

-0.02(-0.16)

-0.02(-0.18)

Relational Contracting andInformal Enforcement

Note: Robust z statistics in parentheses, ***significant at 1%, **significant at 5%, *significant at 10%; y = Pr(Reject new supplier offering 10% discount); coefficients are average partial effects.

49

Businesses will refuse todeal with supplier who has been unfair with me

-0.2**(-2.46)

-0.19**(-2.33)

-0.17**(-2.15)

Only reliable suppliers are firms owned/managed by relatives

0.21***(2.57)

0.21***(2.58)

0.22***(2.84)

Firm Level Controls (Buyer)Receive trade credit fromsupplier

0.12(1.48)

0.12(1.43)

0.1(1.31)

Exporter 0.05(0.43)

0.04(0.44)

0.04(0.4)

Ln(Employment) 0.08*(1.82)

0.08*(1.88)

0.08*(1.89)

Ln(1+Age in years) -0.08(-1.09)

-0.04(-0.55)

-0.02(-0.31)

Newest supplier -0.28***(-2.96)

Pseudo R-squared 0.37 0.34 0.40Number of Observations 139 139 139

50

Appendix Table 1: Additional Robustness  Variable (1) (2) (3)ComplexityHave no other suppliers of input

0.26**(2.36)

0.25**(2.16)

0.22**(2.0)

Input quality specifications written

-0.1(-0.93)

-0.01(-0.96)

-0.103(-0.93)

DurationRelationship with supplier 2-9 years

0.17**(2.27)

0.18***(2.58)

0.17**(2.5)

Relationship with supplier >9 years

0.16**(2.11)

0.16**(2.07)

0.18(2.43)

Information GatheringSupplier introduced throughbusiness network

-0.17(-1.48)

-0.15(-1.29)

-0.09(-0.78)

Supplier introduced throughsocial network

0.28***(3.28)

0.3***(3.33)

0.37***(2.76)

Vender*Social Intro interaction

-0.12(-0.73)

Supplier visits customer's factory daily

-0.07(-0.49)

-0.06(-0.49)

-0.08(-0.61)

Supplier visits customer's factory weekly

0.12(1.33)

0.08(0.84)

0.11(1.33)

Visited supplier four or more times before first purchase

-0.35***(-5.26)

-0.32***(-4.43)

-0.31***(-3.96)

Formal Contract EnforcementCourts important for resolving disputes with suppliers

-0.1(-0.92)

-0.09(-0.93)

-0.05(-0.46)

Relational Contracting and Informal EnforcementWould never purchase from asupplier that heard to havecheated

-0.09(-1.1)

Note: Robust z statistics in parentheses, standard errors clustered by firm;***significant at 1%, **significant at 5%, *significant at 10%; y = Pr(Reject new supplier offering 10% discount), coefficients are average partial effects.

51

If I have dispute with supplier, others would demand larger advanced payment

-0.15(-1.44)

Speak at least weekly with other producers (0,1)

-0.14*(-1.65)

-0.13(-1.44)

-0.12(-1.34)

Businesses will refuse to deal with supplier who has been unfair with me

-0.29**(-2.18)

Vendor*Business refuse interaction

0.1(0.59)

Only reliable suppliers arefirms owned/managed by relatives

0.219***(2.63)

0.191**(2.04)

0.29***

Vendor*Family managed firmsinteraction

-0.17(-1.27)

Firm Level Controls (Buyer)Receive trade credit from supplier

0.13*(1.65)

0.13(1.59)

0.12(1.55)

Exporter (dummy) 0.102(1.03)

0.142(1.39)

-0.074(-0.45)

Ln(Employment) 0.03(0.75)

0.02(0.47)

0.11**(2.21)

Ln(1+Age in years) -0.011(-0.15)

-0.011(-0.15)

-0.112(-1.5)

Pseudo R-squared 0.35 0.34 0.38Number of Observations 139 136 139

52

Appendix Table 2: Replication of (similar to) Johnson et al. (2002)

Variable (1) (2) ComplexitySupplier makes unique product for me

-0.06(-0.53)

-0.05(-0.42)

Have no other suppliers of input

0.17(1.56)

0.19*(1.65)

Input quality specifications written

-0.11(-0.98)

-0.11(-0.9)

DurationRelationship with supplier 13-24 months

0.05(0.38)

Relationship with supplier 2-9 years

0.2**(2.15)

Relationship with supplier >9 years

0.19*(1.86)

Information GatheringSupplier introduced through business network

-0.13(-1.13)

-0.09(-0.78)

Supplier introduced through social network

0.31***(3.11)

0.34***(3.48)

SIMA good source info. about reliability of potential suppliers

-0.04(-0.37)

-0.06(-0.51)

Supplier visits customer's factory daily

-0.08(-0.54)

-0.04(-0.26)

Supplier visits customer's factory weekly

0.12(1.27)

0.15(1.45)

Visited supplier four or more times before first purchase

-0.35***(-4.62)

-0.35***(-4.62)

Formal Contract EnforcementCourts important for resolving disputes with suppliers

-0.07(-0.58)

-0.05(-0.45)

Firm Level Controls (Buyer)Exporter (dummy) 0.13 0.13

Note: Robust z statistics in parentheses, standard errors clustered by firm;***significant at 1%, **significant at 5%, *significant at 10%; y = Pr(Reject new supplier offering 10% discount); coefficients are average partial effects.

53

(1.29) (1.28)Ln(Employment) 0.04

(0.74)0.04

(0.69)Ln(1+Age in years) -0.04

(-0.54)-0.04(-0.49)

Pseudo R-squared 0.30 0.27Number of Observations 139 139

54