Did the Rise of Service O -shoring and IT-Boom Change ... · Devaki Ghose UVA O↵-shoring and Changing Education April 2018 3/29. Types of Skills Di↵erent sectors require di↵erent

Did the Rise of Service O↵-shoring and IT-BoomChange India’s Economic Landscape?

Devaki Ghose

University of Virginia and Dartmouth College, Visiting

Presented at CEP-IMF-WB-WTO Conference, Geneva

April 2018

Motivation

Rising high-skill service exports from developing countries

Specific types of service exports, examples:China 3rd largest exporter of services, India 8th but 2nd largestexporter of ICT services

Devaki Ghose UVA O↵-shoring and Changing Education April 2018 2 / 29

Motivation

Rising high-skill service exports from developing countries

Specific types of service exports, examples:China 3rd largest exporter of services, India 8th but 2nd largestexporter of ICT services


Types of Skills

Di↵erent sectors require di↵erent types of skills: Hotel management intourism, CS graduates in IT


Types of Skills

Di↵erent sectors require di↵erent types of skills: Hotel management intourism, CS graduates in IT

“With the downfall in IT sector, is engineering the right career choice toopt for” ? (India today, 2017)


Types of Skills

“With the downfall in IT sector, is engineering the right career choice toopt for” ? (India today, 2017)


Research Question

1. How does a sector-specific trade shock a↵ect the supply of di↵erenttypes of skilled labor across regions?


Research Question


Ignoring this margin can lead to over-prediction of wage response, noprediction about the economy’s skill composition


Research Question



Not in today’s presentation:

2. What are the e↵ects for the distribution of worker welfare across regionsand types of skill?

Skill-intensity of the sectorSector specific comparative advantage of di↵erent regions


Research Question



Not in today’s presentation:

2. What are the e↵ects for the distribution of worker welfare across regionsand types of skill?

Skill-intensity of the sectorSector specific comparative advantage of di↵erent regions

Context: IT industry in IndiaDevaki Ghose UVA O↵-shoring and Changing Education April 2018 4 / 29

Contribution of IT to India’s GDP

Export’s Share of Total Output


Growth of IT Firms and Engineering Colleges in India





Survey Evidence: About 2/3rd of all job o↵ers to CS graduates camefrom software companiesShare of Workers in IT-CS Occupations with an Engineering Degree


Spatial Distribution of no. of IT Firms and TechnicalColleges

NonCS Colleges and Other Maps


This Paper

New instrument to identify how colleges respond to IT employmentIdeal Experiment: Exogenously increase IT employment in a locationand see how many colleges enter

Challenge: Unobservables such as location fundamentals, changinginfrastructure could be driving both

New Instrument: Use an o↵-shoring shock from abroad combined withpre-existing regional di↵erences to identify e↵ect of firm location oncolleges

Key Finding:Supply response: If a district’s IT sector employs 1,070 more workers,then this results in the creation of 4 new colleges, 1 of whichspecializes in providing an engineering education


This Paper

New instrument to identify how colleges respond to IT employmentIdeal Experiment: Exogenously increase IT employment in a locationand see how many colleges enter

Challenge: Unobservables such as location fundamentals, changinginfrastructure could be driving both

New Instrument: Use an o↵-shoring shock from abroad combined withpre-existing regional di↵erences to identify e↵ect of firm location oncolleges

Key Finding:Supply response: If a district’s IT sector employs 1,070 more workers,then this results in the creation of 4 new colleges, 1 of whichspecializes in providing an engineering education


Contribution

Trade Liberalization and Educational attainment: Non-tertiaryenrollment in developing countries

Edmonds et al (2010), Shastry (2012), Jensen (2012), Oster et al (2013),

Atkin (2016), Blanchard et al (2017) (multi-country study),Greenland et al

(2016) (USA)

Contribution: Response of tertiary education to an export demandshock in a developing country

Quantitative Economic Geography Models:(see Redding andRossi-Hansberg 2016 for a review)

Multiple sectors: Cosar et al (2016) Kucheryavyy (2016), Fajgelbaum et al

(2014) Artuc et al (2010)

Migration Costs: Allen et al (2016) Bryen at al (2015)

Contribution: Worker field of education and location choice, takinginto account long-term occupation choice


Contribution

Trade Liberalization and Educational attainment: Non-tertiaryenrollment in developing countries

Edmonds et al (2010), Shastry (2012), Jensen (2012), Oster et al (2013),

Atkin (2016), Blanchard et al (2017) (multi-country study),Greenland et al

(2016) (USA)

Contribution: Response of tertiary education to an export demandshock in a developing country

Quantitative Economic Geography Models:(see Redding andRossi-Hansberg 2016 for a review)

Multiple sectors: Cosar et al (2016) Kucheryavyy (2016), Fajgelbaum et al

(2014) Artuc et al (2010)

Migration Costs: Allen et al (2016) Bryen at al (2015)

Contribution: Worker field of education and location choice, takinginto account long-term occupation choice


Roadmap

1 Background

2 Empirical Strategy and Results

3 Mechanism

4 Conclusion


The Growth of Exports


The Y2K Problem

Wide-spread panic that computer systems will fail

Total world-wide y2k expenditure 300- 500 billion


The Y2K Problem

Wide-spread panic that computer systems will fail

Total world-wide y2k expenditure 300- 500 billion


The Y2K Shock and India

Rusi Brij, vice-president, Satyam Computer: “Y2K has been a godsend”

Thomas Friedman, “World is Flat”: August 15 commemorates freedom atmidnight, Y2k made possible employment at midnight...Y2k should becalled Indian Interdependence Day” (Interdependence with Westerncompanies)


The Y2K Shock and India

Rusi Brij, vice-president, Satyam Computer: “Y2K has been a godsend”

Thomas Friedman, “World is Flat”: August 15 commemorates freedom atmidnight, Y2k made possible employment at midnight...Y2k should becalled Indian Interdependence Day” (Interdependence with Westerncompanies)

NASSCOM (2000) estimated India’s software exports at 2.65 billionin 1998 - 1999, with Y2K-related projects accounting for 20% ofTotal Revenue, (about 560 million)

Instances of y2k related o↵-shoring to India


Cross-sectional variation in IT demand shock

Huge Variation in number of firms that receive y2k projects: 52% ofdistricts did not receive a y2k project

10 firms on average received y2k projects in the remaining 48%, witha standard deviation of 29 firms

Proportion of historical software exports from that district

Linguistic distance of that district





What generates this cross-sectional variation?







What generates this cross-sectional variation?




Linguistic Distance

1652 di↵erent languages spoked in India (1991 Census)

Vehicular mode of communication: Hindi and English

Example of linguistic distance: distance between languages calculatedon the basis of similarity between grammar and cognates

Shastry (2012): IT firms locate more in districts that are linguisticallycloser to English


Linguistic Distance

1652 di↵erent languages spoked in India (1991 Census)

Vehicular mode of communication: Hindi and English

Example of linguistic distance: distance between languages calculatedon the basis of similarity between grammar and cognates

Shastry (2012): IT firms locate more in districts that are linguisticallycloser to English


Data Sources

Linguistic Distance: Census of India

Data on district-wise number of colleges, year of establishment ofeach college, state-wise intake: O�cial Government of India data oneducation (AICTE and AISHE)

Data on IT industry: (NASSCOM)

More Data Details


Stylized Fact 1

IT employment increases more in relatively linguistically closer toEnglish district following the Y2K shock


Stylized Fact 2

Number of engineering colleges increases more in relativelylinguistically closer to English district following the Y2K shock

Not-normalized graph


Roadmap

1 Background


3 Mechanism

4 Conclusion


E↵ect of Demand Shock

Long di↵erence:

�ydt = ↵+ �frac of y2k projects+ ✏dt

ydt: IT employment, number of firms, number of colleges

pre-period: 1995 post-period: 2002-2003

Instrument frac of y2k projects

frac of y2k projects = �+�frac of software exports from d at t-k+ ✏0dt


E↵ect on IT Employment: OLS Estimation

Table: OLS: Change in IT Employment following IT Demand Shock

(1) (2) (3) (4)Employment Employment No. of firms No. of firms

fracy2k 0.459** 0.432** 0.270*** 0.231**(2.08) (1.99) (3.33) (2.53)

District-specific trends No Yes No YesN 343 343 343 343

Finding: A 1% " in number of firms receiving y2k projects in a districtleads to a .46 standard deviation (around 3485) " in IT employment and a.27 standard deviation. (around 17) " in new firms


E↵ect on IT Employment: IV Estimation

Results using historical software exports as an instrument

Table: IV: Change in IT Employment following IT Demand Shock

(1) (2) (3) (4)Employment Employment No. of firms No. of firms

fracy2k 0.523*** 0.523*** 0.590*** 0.590***(6.96) (7.45) (14.65) (15.42)


Finding: A 1% " in number of firms receiving y2k projects in a districtleads to a .52 standard deviation (around 3500) " in IT employment and a.59 standard deviation. (around 36) " in new firms


First Stage Results

Table: First stage results using historical software exports as an instrument

(1)frac of y2k

Historical Software Exports 0.90***(5.39)

SW Chi-sq/F/Kp Wald F 29.25(0.0000)

sheapr2 .77

Robust Standard errors are used. t statistics reported in parenthesis

A 1% " in historical software exports is associated with a .9% " in numberof firms receiving y2k projects. The instrument is statistically significant-the F-statistic is 29.25.


E↵ect on Engineering Colleges: IV Estimation

Table: Change in Number of CS Colleges following IT Demand Shock

(1) (2) (3) (4)CS Colleges CS Colleges State CS Colleges State CS Colleges

fracy2k 0.583** 0.566** 0.309*** 0.262***(2.07) (2.22) (3.37) (2.94)


list

Finding: For every 1% " in number of firms receiving y2k projects in adistrict, the total number of colleges o↵ering CS, IT and engineeringcourses increases by .58 standard deviation (around 2 in numbers) and 90% of this is driven by private colleges!


E↵ect on Total Colleges: IV Estimation

Table: Change in Number of Colleges following IT Demand Shock

(1) (2) (3) (4) (5) (6)Total Colleges Total Colleges Agricultural Colleges Agricultural Colleges State Colleges State Colleges

fracy2k 0.478* 0.468** 0.057 0.043 0.257*** 0.169***(1.87) (2.08) (0.57) (0.44) (3.45) (4.07)

District-specific trends No Yes No Yes No YesN 340 340 340 340 340 340

Finding: A 1% " in number of firms receiving y2k projects creates 8 newcolleges in a district. Thus, 1/4 th of the increase in colleges that aredriven by the IT demand shock came from growth in engineering colleges !


Roadmap

1 Background


3 Mechanism

4 Conclusion


Conceptual Framework

For now: A framework to understand a possible mechanism

Future goals of the model:

Qualitatively match stylized facts: predictions for the spatialdistribution of IT firms and colleges

Quantify the general equilibrium e↵ects of a sector-specific tradeshock (IT sector): changes in spatial distribution of enrollment,employment and wages in all sectors

New Approach: Endogenize supply of labor to each sector by incorporatingworker field of education choice in a workhorse quantitative economicgeography model


Conceptual Framework

For now: A framework to understand a possible mechanism

Future goals of the model:

Qualitatively match stylized facts: predictions for the spatialdistribution of IT firms and colleges

Quantify the general equilibrium e↵ects of a sector-specific tradeshock (IT sector): changes in spatial distribution of enrollment,employment and wages in all sectors

New Approach: Endogenize supply of labor to each sector by incorporatingworker field of education choice in a workhorse quantitative economicgeography model


An O↵-shoring Shock and the Domestic Economy

Let’s Outsource to India!

Firms (IT, Others) Skilled Workers

Wages

Travel to Work

Colleges (Engineering, Others)Students

Pay tuition and travel to study

Training

Back





Wages

Travel to Work



Training

Back




Firms (IT, Others)

Skilled Workers

Wages

Travel to Work



Training

Back





Wages

Travel to Work



Training

Back





Wages

Travel to Work

Colleges (Engineering, Others)

Students


Training

Back





Wages

Travel to Work



Training

Back





Wages

Travel to Work



Training

Back


Empirical Evidence: E↵ect of IT Employment on No. ofColleges

�Ydt = ↵0 + �0�IT Employmentdt + ✏0dt

�Ydt: change in the number of CS colleges/ total colleges

Instrument �IT Employmentdt with historical software exports (andhistorical linguistic variation in the other specification).

First Stage:

�IT Employmentdt = a+ bHistorical software exportsdt�k + ✏dt (1)


Result: E↵ect of IT Employment in No. of Colleges

Table: Change in Number of CS Colleges following IT Demand Shock

(1) (2) (3) (4)CS Colleges CS Colleges State CS Colleges State CS Colleges

IT Employment 1.255*** 1.223*** 0.665*** 0.567***(3.93) (4.21) (3.34) (3.18)


For every one standard deviation increase in IT employment (about 3747employees), number of engineering colleges increases by 1.25 standarddeviation (about 5 colleges)


Mechanism

26% of increase in total number of colleges driven by increasing ITemployment opportunities came from engineering colleges.

No long-run e↵ect on any other sectors of the economy: eg, Health,finance, manufacturing, agriculture

Evidence that growth in IT sector sustained by increasing supply ofskilled labor, rather than drawing labor away from other sectors

First direct evidence of how a sector-specific trade shock a↵ects theeducation sector, and hence, skill formation in a country by studying how

di↵erent types of colleges respond


Roadmap

1 Background


3 Mechanism

4 Conclusion


Conclusion

Policy Implications

Investment in IT software parks: E↵ect on employment andeducational attainment

How does an external demand shock from abroad, for example, a banon IT o↵-shoring, a↵ect the Indian economy?

Future Work

Model Estimation. Counter-factual questions: What are theimplications for employment, college enrollment, wage inequality andworker welfare if labor markets across states are more integrated?

E↵ect on wages: Short-run spike, in long-run supply responseattenuates wage response


Thank You


Roadmap

5 AppendixAppendix: BackgroundAppendix: Model Derivations


Growth in Undergraduate Number of Institutions

Figure: Growth in Undergraduate Number of Institutions

Back


Growth in Number of Diploma-Granting Institutions

Figure: Growth in Number of Institutions granting Diploma

Back


Growth in Intake of Diploma-Granting Institutions

Figure: Growth in Intake of Diploma Granting Institutions

Back


Growth in Institutions O↵ering Graduate Degrees

Figure: Growth in Graduate Number of Institutions

Back


Growth in Intake of Institutions O↵ering Graduate Degrees

Figure: Growth in Intake in Graduate Number of Institutions

Back


Spatial Distribution in the no. of Colleges O↵ering Non-CSDegree Programs

Back


Aggregate Statistics: India’s Software Exports


Aggregate Statistics: Export’s Share of Total Output

Back


Aggregate Statistics: Export Domestic Growth


Aggregate Statistics: India’s Software Exports


Aggregate Statistics: IT output Level Growth


The Y2K and India

Parent Company O↵shoring CompanyTufts Health Plan WiproFlorida Power and Light Mastech CorpBank of America HCL and HexawareGE India’s Mascot SystemAetna Health Insurance A no. of companies in BangaloreOrion Auto Cognizant Technology SolutionsUS Cold Storage Inc. CognizantMartin & Co. HCL

Back


IT and IT-Enabled Service Exports India


Define IT and IT-Enabled Services

In my study I consider both IT and IT-Enabled services from India. Theseare the definitions as per -


Industry-wise Share of India’s BPO/ITES Services

Back Next

Exports of ITES/BPO services are categorised as per the industrialclassification of the Department of Information Technology (DIT-2003),Ministry of Electronics and Information Technology, Government of India.


Types of IT Service Exports

Back


General Education of IT Vis-vis Other Workers

Back



Back



Back


Average Wage of IT Vis-vis Other Workers

Back


Average Wage of IT Vis-vis Other Workers

Back


Engineering Versus Other Technical Disciplines

Figure: Growth in Student Intake in Undergraduate Programs

Other Levels Back


Field of Training of Workers in CS and RelatedOccupations

Back

0Source: Calculated using NSSO 2009


Model Intuition: A simple 2 Region Example

IT

A

non-IT

IT

B

non-IT

Eng

nonEng

Eng

nonEng

Back



IT

A

non-IT

IT

B

non-IT

Eng

nonEng

Eng

nonEng

ROW

Back



IT

A

non-IT

IT

B

non-IT

Eng

nonEng

Eng

NonEng

ROW

Back



LNT: eg: medical

non-IT

LT: eg: agri, manu

No College

nonEng

NonEng degree

Back


The Market

Market Access Measure: Donaldson (2017), Jedwab (2017) Back


Field of Training of Workers in CS and RelatedOccupations

Back

0Source: Calculated using NSSO 2009


Additional Instruments

Instrument �PD

o=1Not

⌧��odPD

d0=1 ⌧��od0

!with

�PD

d=1nidnd

*(y2k spending by region i)*(Export of IT-services fromIndia to i)

where nid: number of serving country i in district d

n: total number of firms in district d

Traditional Bartic: �PD

d=1xdt�k

Xt�k⇤(Aggregate exports in year t)

Problem: many zeros, di↵erential trends in college growth driven byunobservables that are correlated with intial export exposure of thatregion

Instrument relying on variation in exposure according to countries:�PD

d=1nidt�k

ndt�k*(exports from India to i in year t)

Back


Other Events 1996-1998 A↵ecting Regional IT Growth

State IT Policy by Karnataka in 1997: very low tax policy, opentraining centers for IT firms, encourage private initiative in theeducation sector, promotion of venture capital.


History of Internet in IndiaEarly 1990s: Internet for restricted group ofusers-ERNET,NICNET,STPI through satellite connectivity1993: STPI Bangalore started Internet Services through VSATsatellite link The network consists of a Hub located at Bangalorewhere the VSAT communicates to the HUB through Express AM1Satellite.1995: VSNL provides first public internet- connection between VSNLand MCI in the US starts with ”multiple 64kbps” links. Usersanywhere can connect through the Department ofTelecommunications’ I-NET, an X.25 network accessed throughleased linesTill 2002: STPIs were providing high-speed data communicationservices through satellites. However, VSNL’s control of InternationalFiber connections in India prevented STPIs from using these till theywere liberalized in 2002.

Public internet in US: 1991Devaki Ghose UVA O↵-shoring and Changing Education April 2018 26 / 54

History of Regional Distribution of IT Industry India

Initial Concentration in Bombay: TCS opened in Bombay.SEEPZopened in Mumbai in partnership with American hardware companiesBurroughs.Of the top 8 exporters 7 in Mumbai with 90% export share.

Back


Satellite vs Fiber Optics

Satellite transmission slow, but much less a↵ected by distance thanterrestrial fiber optic connection. Though weather plays a role. Satelliteinternet was the only one in the beginning


Map of Sub-marine Cables India

Back


Composition of India’s Software Exports

IT services + software product development= computer services BPOservices + engineering services=ITES/BPO Back Next


IT Exports

Back


AICTE Norms

Back


Derivation of Worker Indirect Utility

Problem of worker i educated in k in field s who goes to work in k0in sector S is given

by: Max ⇧SC↵SS where CS = (

Pd c

�S�1�S

dk0S )�S

�S�1

s.tP

d

PS0 pdk0S0cdk0S0=Wk0Ss⌘ikk0Ssµkk0

This yields :

cidk0S0 = p��Sdk0S0P

�S�1k0S (↵SWk0Ss⌘ikk0Ssµkk0)

Assuming ice-berg transportation cost:

pdk0S0 = ⌧dk0S0pdS0

cidk0S0 = (⌧dk0S0pdS0)��SP�S�1k0S (↵SWk0Ss⌘ikk0Ssµkk0)

Using the above quantities, worker indirect utility is derived as:

Viksk0S =Wk0Sst⌘ikk0Ssµkk0

⇧SP↵Sk0S

where Pk0S =P

d ⌧dk0SpdS Back


Derivation of Expected Income

Expected income before realizing the Frechet draws: ExpectedIncome=yikst = E(Vikst) = E(max(Wk0Sst

Pk0tµkk0⌘kk0Ss))

Note the similarity with EK :Pn(k) = minPni(k), i = 1, , , , N = max Zi(k)

dniwi

Exactly same derivation as EK Back


Identification

�Ydt = �1�DX

o=1

Not

⌧��odPD

d0=1 ⌧��od0

!+ ↵1T + ↵2Xdt + ✏dt

Challenge: Same unobservables drive firm and college location

Instrument: 1. Demand Shock: y2k

Identifying Assumption: A↵ects colleges only through firms

2. Cross-Sectional Variation: Linguistic Distance

Details linguistic distance

Identifying Assumption: Absent y2k, the rate of growth of engineering colleges is

not correlated with historical linguistic di↵erences.

Threat to Identification: Di↵erential trends in engineering college growths across

districts driven by un-observables correlated with historical linguistic di↵erences


Identification

�Ydt = �1�DX

o=1

Not

⌧��odPD

d0=1 ⌧��od0

!+ ↵1T + ↵2Xdt + ✏dt











Identification

�Ydt = �1�DX

o=1

Not

⌧��odPD

d0=1 ⌧��od0

!+ ↵1T + ↵2Xdt + ✏dt











Robustness & Additional Results

Robustness

Di↵erential time trends of observables

Falsification: Colleges do not respond to non-technical employment,other types of colleges do not respond to opening of IT firms

Additional Instrument: Details

Additional Results

Enrollment Back


General EquilibriumEquilibrium Wages, Prices and Tuitions:

Wages of workers trained in field s working in industry S in district k0

in period t

wek0Sst =

@Qk0Stpk0S@L̃k0sS

= pk0SAk0sSQ1⇢Sk0SQ

( 1⇢eS

� 1⇢S

)

k0eS L̃� 1

⇢eSk0sS

where e= college education (high-skilled), no college education(low-skilled), L̃k0sS =

Pk L̃kk0sS

Prices of goods in industry S in district k0 in period t:

p1�⇢k0St = [(

X

s2hA⇢h

k0sSw1�⇢hk0sS )

11�⇢h ]1�⇢ + [(

X

s2lA⇢l

k0sSw1�⇢lk0sS )

11�⇢l ]1�⇢

Tuition in region k0 in field s in period t

⇢k0st = ck0st

Back




in period t

wek0Sst =

@Qk0Stpk0S@L̃k0sS


( 1⇢eS

� 1⇢S

)

k0eS L̃� 1

⇢eSk0sS


Pk L̃kk0sS


p1�⇢k0St = [(

X

s2hA⇢h

k0sSw1�⇢hk0sS )

11�⇢h ]1�⇢ + [(

X

s2lA⇢l

k0sSw1�⇢lk0sS )

11�⇢l ]1�⇢


⇢k0st = ck0st

Back




in period t

wek0Sst =

@Qk0Stpk0S@L̃k0sS


( 1⇢eS

� 1⇢S

)

k0eS L̃� 1

⇢eSk0sS


Pk L̃kk0sS


p1�⇢k0St = [(

X

s2hA⇢h

k0sSw1�⇢hk0sS )

11�⇢h ]1�⇢ + [(

X

s2lA⇢l

k0sSw1�⇢lk0sS )

11�⇢l ]1�⇢


⇢k0st = ck0st

Back


General Equilibrium

Equilibrium Aggregate Prices and Quantities in Each Location andIndustry:

Market Clearing: total income of sector S in district k0=total sales ofsector S from district k0

Pk0SQk0S = Yk0S =X

d

Xk0d,S =X

d

⌧1��Sk0dS p1��S

k0S P �S�1dS EdS

Trade Balance: Total expenditure in district k0 on sector S goodsmust equal export flows from all other districts

Ek0S =X

d

Xdk0S =X

d

⌧1��Sdk0S p1��S

dS P �S�1k0S Ek0S

Back


General Equilibrium

Equilibrium Employment in each Location and Industry:

E↵ective number of people working in district k0 in sector S with a degreein field s:

L̃k0Sst =X

k

mkk0SstLkst✏kk0Sst

Back


General Equilibrium



L̃k0Sst =X

k


mkk0Ss =(Wk0Sst

Pk0tµkk0)(✓)

�kswhere �ks =

X

k00S00

(Wk00S00st

Pk00tµkk00)

(✓)

Back


General Equilibrium



L̃k0Sst =X

k


Equilibrium Enrollment in each Location and Field of Education:

Lkst =X

o

lokst�1Lot�1

Back


General Equilibrium



L̃k0Sst =X

k



Lkst =X

o

lokst�1Lot�1

lokst�1 =exp(Wkst�1

Pkt�1µok � ⇢ks + �Tks�

1✓ks

Pk00s00(exp(

Wk00s00t�1

Pk00t�1µok � ⇢k00s00 + �Tk00s00�

1✓k00s00))

Back


General EquilibriumEquilibrium Employment in each Location and Industry:


L̃k0Sst =X

k



Lkst =X

o

lokst�1Lot�1

Equilibrium Number of Colleges in each Field and Location:

CkS =Lks

eks

where eks is average mandated intake per field per district Back


Calibrating Consumption Expenditure Shares

Table: Calibrating ↵sK0

Country Sector Calibration Data

India IT 0India trade-able Import/GDP World BankIndia non-tradeable Non-tradeable/GDP NSS Consumer ExpIndia agri Agri/GDP NSS Consumer ExpUSA IT 1USA trade-able 0USA non-tradeable 0


Stylized FactsPercentage increase in employment in IT sector following y2k shockis positively correlated with a district’s linguistic distance fromHindi/ negatively correlated with linguistic distance from English

Back


Stylized Facts

Back


Stylized Facts

Back


Stylized Facts

Back


Below Primary

Back


Graduates

Back


Graduates

Back Next


Graduates

Back


Estimation Step 3: Recovering Trade Elasticities

Regress p�S�1k0St :

ln(p�S�1k0St ) = (�S � 1)ln(((p̃hk0St)

1�⇢ + x�⇢k0St(p̃

lk0St)

1�⇢)1

1�⇢ )

� (�S � 1)(⇢h

⇢h � 1)lnAk0s0S

where p̃ek0St = (P

s2e(Ak0Ss,e

Ak0Ss0,e)⇢e(Wk0Ss)1�⇢e + (W 1�⇢e

k0Ss0 ))1

1�⇢e

and, Q̃ek0St = (

Ps2e

Ak0Ss,e

Ak0Ss0,e(Lk0Ss)

⇢e�1⇢e + (Lk0Ss0)

⇢e�1⇢e )

⇢e1�⇢e

and, xk0St =p̃hk0St

(Q̃hk0St

)1⇢

p̃lk0St

(Q̃lk0St

)1⇢

and productivity:Ak0Ss,e

Ak0Ss0,e= ( Wk0Ss

Wk0Ss0)( Lk0Ss

Lk0Ss0)

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Determinants of Gravity Equation for IT

What goes into the distance measure?

Linguistic distance

Reputation/ prior links (Banerjee et. al., 2001)

Distance to Satellite/ fiber optic cables (Actual physical speed of datatransmission)

Back


Non-normalized

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Percentage of direct and indirect IT value added in totalservices trade

OECD computer service definition: Computer services are defined ascomputer programming, consultancy and related activities and informationservice activities (ISIC 62 and 63). Back


Definition: In the International Standard Industrial Classification (ISIC)Rev. 2 services are defined as all activities in major Divisions 6 through 9described as follows:6 Wholesale and retail trade and restaurants and hotels 7- Transport,storage and communication 8 Financial, insurance, real estate andbusiness services 9 Community, social and personal services.


Summary Statistics

pre-period: 87 % of districts 0 IT presence (out of 343), mean and SD oftotal emp 226 and 1753 resp

post-period: mean and Sd of Total num employees 2132 and 13368 resp.75 % districts had no IT presence

historical software exports: 92 % of districts did not export in 1995 Forthose who exported in pre-period: mean 3.69 , sd 4.86 (in million USD)


Documents

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