08.15.2013 - robert jensen

Market Size and the Growth of Firms: Evidence from a

Natural Experiment in India

Robert Jensen

Wharton School

University of Pennsylvania

Nolan Miller

University of Illinois at Urbana-Champaign

NBER

Firms in Developing Countries

1. On average very small (Tybout 1998, Hsieh-Klenow 2012)

e.g., Hsieh and Klenow (2012)

Avg. manufacturing firm in U.S.: 42 employees

Avg. manufacturing firm in India: 2.6 employees

2. Often do not grow as they age Compared to firm < 5 years old, avg. 40 year old firm has:

U.S.: 8X employees Mexico: 2X employees India: < 1X employees

3. Much less productive than in rich countries (Tybout 1998, Bloom et. al 2010).

4. Productivity varies widely across firms (a puzzle, as in rich countries)

Firm size & growth important for economic growth & development Returns to scale, productivity, competitiveness, etc.

Many Possible Explanations for Small Firm Size

Limited access to credit

Regulations, taxes, permits/licensing may apply if grow more

Institutions (legal, etc.)

Labor markets (esp. as try to expand beyond family labor)

Infrastructure (power, water, transportation, communication)

Organizational factors/management

In this paper...

Test whether limited demand/market size for any given firm’s output,

arising from information problems, limits growth (and productivity).

(highly localized demand)

Take advantage of a natural experiment created by spread of mobile

phones in Kerala, India

Related to previous work, Jensen 2007

Focus on boat manufacturing, using 6 yrs of firm-level census data

Setting: Mobile Phones & Arbitrage

In previous work (Jensen 2007), show that fishermen use mobiles to check price in different markets. Welfare-improving arbitrage. Similar results in Aker 2010 and others.

Figure 1. Region of Study

Source: Reproduced from SIFFS (1999).

Table 1. Prices and Excess Supply and Demand in 15 Beach Sardine Markets

Tuesday, January 14, 1997 Tuesday, January 21, 1997

Price

Excess

Buyers

Excess

Sellers

Price

Excess

Buyers

Excess

Sellers

Kasaragod District

Hosabethe 6.2 0 0 4.3 0 0

Aarikkadi 4.0 0 0 5.9 0 0

Kasaba 0.0 0 4 5.9 0 0

Kanhangad 9.9 15 0 0.0 0 9

Thaikadappuram 0.0 0 11 6.1 0 0

Kannur District

Puthiangadi 9.8 12 0 5.0 0 0

Neerkadavu 6.9 0 0 7.7 0 0

Ayikkara 8.4 1 0 0.0 0 13

Thalassery 4.3 0 0 5.7 0 0

New Mahe 6.2 0 0 0.0 0 5

Kozhikode District

Chombala 8.7 2 0 1.9 0 0

Badagara 9.7 11 0 5.2 0 0

Quilandi 7.2 0 0 0.0 0 8

Puthyiyangadi 0.0 0 5 6.2 0 0

Chaliyam 6.4 0 0 9.7 8 0

Large Changes in Fish Marketing

2001 1997

Large Changes in Fish Marketing

Phones, Effective Market Size and Firm Growth

Now, fishermen visiting different markets learn about different boat builders and their prices and quality Experience good, quality difficult to observe. Particularly, life expectancy.

Reputation, develop ties to others, trust.

Phones may also reduce transactions costs (updates on schedule, payments, modifications/adjustments to costs or design, etc.)

We argue that mobile phones increased the effective market size or demand for any given builder: Prior to phones, builders sold almost all of their boats locally. Firm demand

limited by local market.

more learning leads to shifts in demand towards better builders.

Phones, Effective Market Size and Firm Growth

Provided there are no barriers to trade or bigger limitations on firm growth/output, we expect increased competition, D demand

Increased market share for more productive, highest quality manufacturers.

Growth & increased output of high quality manufacturers.

Possibly exit of least productive or lowest quality manufacturers

Theoretical Motivation—Basic Setup

Producers differ in their location and in the cost of producing a boat (-year).

There is also some fixed cost of production.

Low-cost producers are higher quality (i.e., their boats last longer), Qf. Life years of the boat.

Consumers can perfectly assess the price and quality of their local producer.

u = v QA – pA – x ; u = v QB – pB – (1 – x)

Before cell phones, consumers have no information on non-local producers. Experience good. No direct opportunities for learning.

Theoretical Motivation--outcomes

In this environment, all producers charge monopoly prices for their boats.

Low-cost producers charge lower prices (per boat-year) than high-cost producers (with same local demand curve).

Due to lack of demand, high-quality producers remain small.

Due to lack of competition, low-quality producers can survive.

Quality/productivity/cost dispersion can persist in equilibrium.

Theoretical Motivation—Introduce search/learning

Now introduce cell phones—suppose makes it possible for consumers to

learn prices (per boat-year) or quality of other nearby producers. Thinking of it here more as phonesarbitrageinformation (info networks, direct observation)

Akin to monopolistic competition. Cell phones introduce new substitutes, shift

the demand curve facing any particular firm in.

This will tend to lower all prices.

High-cost producers can no longer earn a profit and exit.

As high-cost producers exit, low-cost producers face increased demand,

increase price and quantity.

Theoretical Motivation—Predictions

Overall impact on price depends:

Competitive pressure initially decreases prices of low-cost producers.

But, as high cost firms exit, competitive pressure decreases and firms

can increase prices. This works in the opposite direction.

Market share and quantity of low-cost producers should increase.

v QB

v QB - pBS

v QA

v QA – pAS

1/2 m*

v QA – pA*

v QB – pB*

v QB – pBm

Welfare Effects

Net gains...

especially if there are returns to scale

...unless market power becomes too great

Will be winners and losers

builders who shut down

builders who lose effective market power

some consumers previously had highest quality guy at low price, will lose

Relation to the Melitz (2003) Trade Model

Melitz (EMA 2003) considers the impact of trade on intra-industry

reallocations and productivity.

Opening up to trade can be thought of as analogous to an increase in

information about non-local producers.

Before trade, can only buy from local producer.

After trade, can buy from anyone.

Melitz model:

Firms differ in production cost, must pay fixed cost to enter.

In closed economy, more productive firms are larger, charge a lower

price, earn higher profits.

Relation to the Melitz (2003) Trade Model

Opening the domestic market to trade is like reducing search costs to zero.

Low-cost firms export (i.e., sell to non-local consumers).

Low-cost firms that export increase profit and market share.

High-cost firms exit.

Setting: Boat Manufacturing in South India

In Kerala, fishing is a big industry: millions employed, 70+% consume daily.

Lagoons, rivers, inlets, streams, lakes, “backwaters” (vs. sea fishing) Prawns, crabs, karimeen/pearl spot (green chromide)

Nets and traps

Water is brackish/briny

Boats Wooden “rope boats.” Planks of anjili or Jack-Wood, fastened together with coir

(fiber from the husk of coconuts) knots. Treated with ghee, fish oil or resin. More recently, fiber glass boats have become available

Non-motorized (while fishing; small motors for traveling)

3-10 meters

1-5 people

Data

No official data on builders available--firms small, unregistered.

Builder census: with local experts and NGO’s (e.g., SIFFS), visited every fishing village, landing spot and fish market in 2 districts

Kerala

Study Region

Data

No official data on builders available--firms small, unregistered.

Builder census: with local experts and NGO’s (e.g., SIFFS), visited every fishing village, landing spot and fish market in 2 districts

Generated a list of name, address of every boat builder

Approximately 143 firms (1997). Nearly every fishing cluster had a person who built boats (home-based). Nearly 1:1 mapping of villages and builders. Cannot rule out we missed some very small-scale builders (subsistence fishermen in non-

fishing villages, maybe building own boats).

In our fishermen survey, no builders reported that were not in our census

Census Labor, capital, output, sales, price (past month, past 6 months)

Also, an accounting of all boats sold (stock at start, flow after)

Conducted census every 6 months from Jan. 1998 to Jan. 2004

Data

Fishermen survey every 6 months from Jan. 1998 to Jan. 2004. 20 randomly selected fishermen/landing Brief Survey (emphasis was on builders)

Landing Canvas—all new boats at every landing spot, every 6 months. Very brief survey.

TABLE 1: Firm Attributes at Baseline

Mean

Standard Deviation

Min

Max

FIRM ATTRIBUTES

Number of employees 2.2 0.72 1 4

Production at dwelling 0.99 0.08 0 1

Boats produced per year 13.6 4.05 4 27

Market share (total market) 0.007 0.002 0.002 0.013

Estimated life expectancy 4.76 0.89 3.56 8.1

Price (5 meter boat) 3,930 270 3,550 4,150

Price/year (5 meter boat) 841 122 583 1,104

Big Difference in Boats Across Builders

Skill matters a lot. Better builders have longer lived boats

How well wood is shaped and fastened affects durability

Big threat from biofouling organisms (e.g., byrozoans & barnacles). Better treatment of wood affects vulnerability to these organisms.

Measuring Boat Life Expectancy I

When we interviewed fishermen for builder census, asked about age of current boat and age of last boat when they replaced it.

Can use this to construct measures of life expectancy of boats for each builder

Limitations: Only available with a lag. Quality of builder may vary over time.

Problem of new entrants (though are not that many)

Local fishing environment (and use) may affect life expectancy—organisms nearby, rocks, water calmness, etc.

Measuring Boat Life Expectancy II

Hired auditor from short-lived government boat insurance program to assess quality (1:5) and life expectancy. Assess quality of wood treatment, craftsmanship (fastenings, etc.)

Life expectancy from boats more objective. But this can factor in: Diff builders may have diff life expectancy if local fishermen use boats

differently (fishing envi (rocky, still, salinity, etc.), biofouling organisms, etc.).

Life expectancy only observed with some lag, and may have changed recently

Auditor assessment allows more contemporaneous measure, not need wait out life expectancy.

Also, we can assess how good the auditor is by comparing (with some error) their estimates to the objective measures (again, still a lag, and still can’t handle newer entrants)

Measuring Boat Life Expectancy (an aside)

Boats are largely homogeneous, but length varies

Demand for boats of different length varies (by fishermen & place)--might be easier to make smaller or larger boats last longer.

5 meters is the most popular size produced for all but 3 builders.

Life Expectancy III

Is boat quality just skill (& experience), or also a choice variable? (imagine poverty & credit constraints). Some of quality may be endogenous—choice of wood (though not much), labor & quality of construction, etc.

Low quality producers may be intentionally making worse boats b/c, e.g., they sell in areas where fishermen are poorer & credit constrained. Can’t afford upfront costs of a better boat, even if they know it would be a lower price per year over the long run.

3rd measure: Like TFP, take residuals from a regression of life expectancy on total labor hours input (separately for adults & kids) and materials input

Life Expectancy--final points

If we are still capturing something that is a choice variable and not underlying ability/skill/productivity, then we would not expect empirical patterns observe.

The “bad” builders will be able to produce just as good boats as the “good” builders and not be driven out.

Except, buyers may be imperfectly informed of role of choice in quality. So,

you see another builder’s boat is better (worse) than your own, and think he’s better (worse), even if it’s choice.

Also, possible state dependence. Low demand for quality means you don’t get experience building good boats. So it’s not underlying skill (bad guys could have been as good) but more historical. Just a different source of skill difference, though.


Mean

Standard

Deviation

Min

Max

FIRM ATTRIBUTES






Price (5 meter boat) 3,930 270 3,550 4,150


Price Variation

No single price (even aside from bargaining/price discrimination)--non-linear in size. Focus again on 5m boat.

Raw price variation is not very large

But very big differences in quality (life expectancy).

So effectively, big differences in price/boat-year.


Mean

Standard

Deviation

Min

Max

FIRM ATTRIBUTES






Price (5 meter boat) 3,930 270 3,550 4,150


Empirical Analysis: Mobile Phones in Kerala

First introduced in 1997

Staggered introduction throughout the state

Introduction centered on largest cities.

Did not penetrate further inland as of 2004

Empirical Strategy

Compare D in firms relative to staggered introduction of mobile phones.

Outcomes Market (firm-level regressions): exit, market share

Production (firm-level regressions): output, employees, productivity.

Consumers (fishermen-level regressions): Price, life expectancy

Z (e.g., education, experience, whether father was a builder)

Controls for fixed differences across regions, time effects common to all regions, differential trends or changes common to all regions.

Identifying Assumption

In the absence of mobile phones, there would have been no

differential changes in these outcomes across the regions.

Certainly, phone placement/timing is non-random.

Spread to most populous, wealthiest areas first.

We’ll consider several key challenges…

Empirical Strategy II


Define 3 regions

I

II

III

Empirical Strategy II


Define 3 regions

Region I: Southern Coastal

Kannur District: Kannur (June 6, 1998) + Thalassery (July 31, 1998)

Region II: Northern Coastal

Kasaragod District: Kasaragod + Khanhangad (May 21, 2000)

Region III: Inland Regions

Did not get phones during my survey period

Earlier regression is just a pooled-treatment version of this

Figure III: Percent

Fishermen Selling in

Local Market

Figure II: Number of

Firms, by Region

TABLE 4: Main Regressions--Market

Constructed Life Expectancy Auditor's Assessment

(1)

Exit

(2)

Market Share

(3)

Exit

(4)

Market Share

Phone 2.49*** -0.096*** 1.61*** -0.049***

(0.22) (0.039) (0.42) (0.018)

Life Span 0.0002 -0.002** -0.0001 -0.008

(0.002) (0.0001) (0.0011) (0.0005)

Phone*Life Span -0.37*** 0.020*** -0.23*** 0.013***

(0.14) (0.007) (0.096) (0.0055)

Number of Obs. 1,606 1,606 1,606 1,606

75th percentile, ~7 years life span

TABLE 6: Builders

REGION I REGION II REGION III

January

1998

January

2004

January

1998

January

2004

January

1998

January

2004

Number of employees 2.3 5.1 2.4 4.1 2.0 1.9

Boats produced/year 14.7 38.2 14.0 36.4 12.8 12.4

Wood Price (Rs.) 217 276 231 286 227 275

Productivity

# boats built largely un-D’d (boat*years/quality ↑↑), yet total labor ↓

# workers: Region I: 12196 (21%), Region II: 11886 (27%)

Hours decrease even more

--Material inputs largely unchanged

--But...power tools. Experimentally, labor hours decline by about 4-6% with full

array of power tools.

increased productivity of ~20% (labor hours) in the two regions

TABLE 7: Consumers

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

Bought Locally?

Price

Assessed Life

Expectancy

Price/boat*year

Region Has Phone -0.73*** 414** 1.33*** -117***

(0.21) (182) (0.54) (45.0)

Time FE YES YES YES YES

Village FE YES YES YES YES

Time*Year FE YES YES YES YES

Identifying Assumption

In the absence of mobile phones, there would have been no

differential change in these outcomes across the regions.

We’ll consider several key challenges…

Alternative Explanations

1. Differential trends by region

Timing of introduction clearly not random. Biggest/wealthiest first.

Control for fixed differences and linear trends over time. But maybe timing

matched other changes going on—e.g., more rapidly growing areas, added

first—maybe affected consolidation in market, demand for quality.

Timing of changes looks good: In Figures, no D for firms in regions that did

not get phones, and no Ds in Region II until they got phones. And no

differential change for more vs. less productive firms.

Can construct (noisy) pre-trends using recall data—at each canvas, asked

fishermen about current & previous boat. Can look at # firms for many past

years. Possible recall error--but use later rounds to investigate extent.

Figure II: Number of

Firms, by Region


2. What if fishermen migrated when phones came in, so demand

shifted with them. Or phones led to differential fishermen entry/exit

or change in demand for fish.

Jensen (2007) shows no such changes in mobility or quantity caught or

location-quantity caught

3. Other changes around same time as phones added?

Largely based on licensing and capacity to roll out infrastructure

Hard to rule out all, but Jensen (2007) shows no obvious changes (roads, etc.)


4. A change in the demand for quality?

More travel now to sell fish greater demand for quality?

Wouldn’t you always want the least expensive (per life year) boat?

Increased P of fishermen (Jensen 2007) greater demand for

quality, more fishing or consolidation among firms for some

other reason.

Income elasticity demand for quality (endogeneity concerns) close to zero

(increased profitability was about 6%)

At baseline, fishermen don’t know quality of non-local firms.


5. Advertising. Still a demand channel. Seems unlikely--phones not effective advertising tool, esp. when there is no “fishermen phone book.” And little to no internet access at this time

6. Technical Knowledge. Anecdotally, not really going on. There is no store of knowledge or technical assistance.

7. Input Markets. Purchase inputs more easily or at lower prices. No evidence any effect on input prices. Wood & rope easily storable, so less P variation.

TABLE 6: Builders

Wood is >90% of material input costs


January

1998

January

2004

January

1998

January

2004

January

1998

January

2004

Number of employees 2.3 5.1 2.4 4.1 2.0 1.9

Production at dwelling 0.98 0.56 1.00 0.62 1.00 1.00

Boats produced/year 14.7 38.2 14.0 36.4 12.8 12.4

Wood Price (Rs.) 217 276 231 286 227 275


5. Advertising. Still a demand channel. Seems unlikely--phones not effective advertising tool, esp. when there is no “fishermen phone book.” And little to no internet access at this time

6. Technical Knowledge. Anecdotally, not really going on. There is no store of knowledge or technical assistance.

7. Input Markets. Purchase inputs more easily or at lower prices. No evidence any effect on input prices. Wood & rope easily storable, so less P variation.

8. D’s in collusion? Phones make it more/less possible for builders to collude. No anecdotal evidence. Also, prices/boat-year declined.


9. D Credit Markets?

If builders were able to get credit they could not get before, maybe that is what allowed them to expand. Phones may have lowered search & transactions costs. Maybe credit markets became

more integrated.

No anecdotal evidence of this.

More importantly, no builders reported receiving loans, even at endline.

Separately, interesting that they were able to expand without credit.

Underlying Mechanisms

We argue phones led to increased demand/market size for

individual firms, exit, market share, productivity, etc.

Separately, which aspect of phones mattered, which made it

possible for effective market size or customer base to expand?

Search costs (just learning about builders, existence, p & Q)

Reputation & trust (fishermen pay ½ upfront)

Transactions costs (changes, updates, delays & status, etc)


Jan

2000

Jan

2001

Jan

2002

Jan

2000

Jan

2001

Jan

2002

Jan

2000

Jan

2001

Jan

2002

# boat builders know of 5.2 4.9 4.4 1.3 3.6 3.7 1.3 1.1 1.1

Best Known Alternate Builder

Life Expectancy Error (s) 0.43

0.41 0.39 1.08 0.41 0.45 1.03 1.11 1.21

Table 9. Underlying Mechanisms

Child Labor

A lot of focus on poverty/credit constraints. Policy focus on bans.

But labor demand side is likely to be very important as well.

Production function with limited (one-way) substitution of skilled &

unskilled labor.

Expansion path more rapid in skilled labor.

e.g., all master builders have one “gopher,” typically a child (&

often underemployed). As scale up, typically still just need one

gopher, so industry shifts to larger firms results in reduced

child labor

52

17

44

17

Children’s Outcomes

Confident there are fewer kids working in this sector. But, what

were the displaced children doing?

Tried to find out what those kids were doing

Many of the reports come from ex-builders we could track

About ¼ were working in some other sector

Overall, slightly more likely to be enrolled than in control area

Many were already “enrolled”

For the rest, just less work.

Lower exposure to hazards, more leisure/study time

Loss of training/apprenticeships?

Conclusions

Using a natural experiment & detailed micro data, we find that increased effective market size led to consolidation & firm growth in boat building sector

Competition exit by worst builders.

Better firms grew. And invested more in capital

All without changes in: access to capital, regulations, labor markets, etc

Big changes since then Fiber glass boats became available

More profitable furniture manufacturing drew many builders out of the sector

Caveats & limitations

Generalizability from one small, narrow sector may be limited High quality data on a narrow product

Concrete studies of Syverson

There may be limitations to further growth management, access to capital, etc.

This may not work as well in other settings non-tradeables

Trust, etc.

Unique aspect here is that phones allowed buyers to learn about quality. In other settings, this may not happen But objective here is not to talk about phones, or even search per se.

Objective is to test whether potential customer base is a factor in firm growth

Next Steps & Extensions

Welfare effects

Decompose D in aggregate productivity in the sector into Ds due to exit, reallocation of market share towards more productive firms and improvements in productivity for survivors (scale, capital, etc.)

Increased specialization of labor within firms

Branding

Expansion and shadow costs of labor (family vs. non-family)

08.15.2013 - robert jensen

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