lets go global, but where?

8/14/2019 Lets Go Global, but Where?

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Lets Go Global!But Where?

Selecting the Best OffshoringDestination in Asia withData Envelopment Analysis

Authored by Andry Haryantofor Prof. Lawrence M. Seifordas IOE 551 Winter 2008 Final Assignmentsubmitted on April 21, 2008

University of MichiganAnn Arbor

THE TOP 10ChinaIndia

JapanHong Kong

SingaporeIsrael

Pakistan

YemenQatar

Myanmar


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1. Problem DefinitionOffshoring has been a controversial emerging trend in international business. Both local andmultinational companies compete in the pursuit of global resources to gain competitive

advantage. Offshoring refers to the transfer of manufacturing or service operations

(outsourcing) to foreign countries.

Offshoring activities traditionally intended to take advantage of a supply of skilled but relativelycheap labor, both in services and manufacturing. As customer demand extends beyond cost,companies are increasingly offshoring more knowledge-intensive non-manufacturing activities,such as software programming, market analytics, financial analysis, and product development.

At the beginning of the 21st century, advances in technology for remote delivery have rapidlyexpanded the offshoring market. By 2005, the potential market of global outsourcing has grown

to $300 billion, with only ten percent being realized.1 This leaves an ample opportunity forcompanies to grow and leverage global resources.

As companies are increasingly catching up with this emerging trend, they are constantly tryingto define a solid global strategy. Part of the global offshoring strategy is the selecting the mostsuitable the destination country. This project will try to answer a piece of the complex puzzle byhelping companies choose the most appropriate offshore location in Asia. We will adopt theview of a home company in the United States looking for a potential offshore destinationcountry in Asia.

The potentials of countries would be judged based on their abilities to convert a set of socio-economic indicators to financial output that measure their capabilities to add value to foreigninvestors. Data Envelopment Analysis was used as an aid to analyze the relative efficiencies ofthese countries based on multiple inputs and outputs.

2. DataWe considered 42 Asian countries in this analysis. Due to the high-volatility nature of the region,a variable for political risk was included. Afghanistan, Iraq, and North Korea were excludedbecause their current political situations were considered too risky to justify an investment.Most recently available data were derived from year 2006. In any case of unavailability, the mostrecent set or point from previous year was used as the best available proxy.2.1 Input measures2.1.1 Wage ($/hour)Wage was included as one of the inputs because cost reduction has been traditionally a primary

driver for business offshoring. Data from Legislative Reference Bureau (2008) were utilized tocompare the labor costs among countries. The wage input used is the hourly non-poverty wagefor persons outside the United States with no benefit provided. 2

Data from US Import Administration were used for Kyrgyzstan and Myanmar because theybetter reflected the current situation based on periodical research.3 Every year, ImportAdministration evaluates labor cost of select foreign countries as a basis of analysis to preventdumping from foreign countries.


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2.1.2 Political StabilityLearning from the 1998 financial crash in Asia, it is important that we incorporate a measure ofpolitical risk into the model. Every year, World Bank publishes its benchmark of countrygovernance, Worldwide Governance Index.4 In this analysis, we utilized a subset of the index:Political Stability from year 2006. The score ranges from 0 to 5, with higher values indicatingbetter governance.

2.1.3. Education (% secondary school enrollment)Education has been becoming increasingly important as a selection criterion due to theincreasing number of knowledge-intensive activities being offshored. As an indicator of generalworkforce education level, we utilized gross secondary school enrollment as a percentage ofpopulation in 2006. Data were obtained from World Banks World Development Indicators.

Values from 2005 and 2004 were used for some countries as proxy values. The percentage ofpeople in secondary education was chosen because a country with higher enrollment ratiowould have a more educated workforce, thus increasing its capability in global competition.

Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of

the age group that officially corresponds to the level of education shown. Secondary educationcompletes the provision of basic education that began at the primary level, and aims at layingthe foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Percent enrollment above 100% means that there enrolled students outside the secondary-school age. These values would be limited to 100% to keep the transformed variableconsistently positive.

2.1.4 Infrastructure

Infrastructure development is essential for both manufacturing and service providers.Transportation infrastructure is especially important for manufacturing supply chain and

logistics. Information, technology, and communications infrastructure to support remotedelivery and knowledge-intensive activities. Both transportation and IT & communication aremeasured from 2007-2008 Global Competitiveness Index Second Pillar: Infrastructure.5 TheGCI Infrastructure score gives an estimate of the infrastructure development in each country. Itranges from 0 to 7, with 7 being the best.

Due to limited country coverage, some data points have to be obtained from a proxy. Theproxy analysis was accomplished by looking for another country with similar ITC and TRANSindicators. A country not covered in GCI will be assigned the score of another country withsimilar ITC and TRANS scores.

ITC: Information, Technology, and Communications Infrastructure (unit/100 people)There are three data sets obtained from World Banks World Development Indicators in 2006(or 2005 proxy): number of broadband subscribers per 100 people, mobile and landline phonesubscribers per 100 people, and number of personal computer per 100 people. The three

variables are combined into ITC = (Broadband subscriber + Mobile and landline phone + PC)per 100 people.


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TRANS: Transportation Infrastructure (million ton-km/sq km)The variable TRANS was defined as the total goods transported per area of the country. TRANS= (Railway freight + Air freight + Road freight) / Area. The freight measures were obtained fromWorld Banks 2005-2006 World Development Indicators.

2.2 Output measures 2.2.1 Foreign Direct Investment Inflow ($ million)FDI, Foreign Direct Investment, is investment of foreign assets into domestic structures,equipment, and organizations. It does not include foreign investment into the stock markets.Foreign direct investment is thought to be more useful to a country than investments in theequity of its companies because equity investments are potentially "hot money" which canleave at the first sign of trouble, whereas FDI is durable and generally useful whether things gowell or badly.6

The FDI inflows to and outflows from a country may be useful indicators of economic health andinvestment appeal. Countries with major inflows probably represent growth opportunities.

Countries with major outflows probably represent created wealth, or strategic positioning, orless appealing home country investments. Inflows are probably clearer indications of what isgoing on than outflows.7

Data of FDI inflows were primarily obtained from World Banks World Development Indicators.Information from other government agencies and independent organizations were used tocomplete the data set.8

2.2.2 Export ($ million)Since we are interested in the value-added manufacturing and service activities, data ofcommercial services export and manufactured goods export (as opposed to merchandise

export) were obtained from World Banks World Development Indicators (2006 or latest). Thereare two variables utilized in the model: commercial services export (SEX) and manufacturedgoods export (MEX).

3. Variable Introduction and TransformationFour input variables and three output variables fed into DEA model are summarized in Exhibit 1.The values of input and output variables are available in Appendix A.

Exhibit 1. Description of DEA input and output variablesVariable Unit Description Transformed?INPUTSNONWAGE ($/hr) Transformation of hourly wage YesSTAB _ Political stability

EDU (%) Secondary school enrollment

INFRA _ Infrastructure development

OUTPUTSFDI ($ million) Foreign Direct Investment Inflows Yes

MEX ($ million) Manufactured-goods export

SEX ($ million) Commercial services export


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Wage was transformed because it was negatively correlated with output: an increase in wagewould result in decrease of foreign investment output. This orientation was against DEAconvention that input and output be positively correlated. Variable WAGE was converted toNONWAGE using the max-minus method, in which NONWAGE = max[WAGE{i}] WAGE[i]+0.1 * max[WAGE{i]], variable i = pointer of countries. The maximum wage for thistransformation is $ 13.96/hr in Qatar.

FDI had to be transformed to ensure non-negative values. Japan has a negative FDI of$6,783.58 million. Therefore, a positive value of $6,783.58 million was added to every single FDIdata point to maintain non-negativity.

4. Model Selection4.1 OrientationThe problem would be defined as an output oriented problem because each country would aimto increase the amount of FDI inflow and export to make their countries more attractive to

foreign investment. The rest of the inputs are measures that generally should not be decreased,if not improved: education level, infrastructure development, or political stability.

4.2 Returns to scale (RTS)Macroeconomic variables generally follow the principle of diminishing return: in a productionsystem with fixed and variable inputs, beyond some point, each additional unit of variable inputyields less and less additional output. Therefore, variable returns to scale BCC model ispreferred to constant returns to scale CCR model.

The hypothesis was confirmed after running the BCC-O model. The data set indeed showed avariable returns to scale behavior because 8 out of the 12 efficient countries showed increasingRTS behavior. See Appendix B for the RTS analysis result of BCC-O model.

4.3 Non-controllable variables (NCN)This analysis was set up as an evaluation aid for a company, not an improvement tool for localregulators. Therefore, efforts to improve the attractiveness of a country are outside the scope ofthe project. Please note that non-controllable variables (i.e. inputs) were not used because theywould reduce the objectivity of the model by failing to include the non-controllable variables.All the inputs given here are not to be reduced, but they can be managed/controlled. Instead ofbeing reduced, these inputs could be better leveraged/managed. Please jump to Section 6.2 for more explanation.


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5. Model ProgressBCC-O models were run in three distinct configurations shown in Exhibit 2. MEXSEXconfiguration was used to determine the overall-efficient countries, while MEX and SEX wereused to determine efficient countries in manufacturing and commercial services.

Exhibit 2. BCC-O configurations to analyze different output prioritiesConfig. INPUTS OUTPUTS MODEL

MEXSEX NONWAGE, STAB, EDU, INFRA FDI, MEX, SEX BCC-O

MEX NONWAGE, STAB, EDU, INFRA FDI, MEX BCC-O

SEX NONWAGE, STAB, EDU, INFRA FDI, SEX BCC-O

Super BCC-O was used to rank the efficient producers based on how much they affect theshape of the efficient frontier. The efficient countries with Super BCC-O scores above 1.0 wereranked based on the highest score. Those with scores of 1.0 were ranked based on FDI becauseit measures country attractiveness and willingness of foreign investors to permanently reside in

the country. Appendix C summarizes the prioritization process of MEXSEX BCC-O analysis.Additional analyses were also performed to determine relative efficiencies of each country inresponse to a single input. These configurations were also run in BCC-O model andsummarized in Exhibit 3.

Exhibit 3. BCC-O configurations to analyze various input prioritiesConfig. INPUTS OUTPUTS MODEL

NONWAGE NONWAGE FDI, MEX, SEX BCC-O

STAB STAB FDI, MEX, SEX BCC-O

EDU EDU FDI, MEX, SEX BCC-O

INFRA INFRA FDI, MEX, SEX BCC-O

The efficiency score results of both simulation sets are summarized in Appendix D.


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6. DiscussionCountries with 1.0 score are those most efficient in leveraging their resources to satisfy demand

in global competition / global offshoring market, making them the most attractive destinationoffshore countries for investment.

6.1 Companies choosing most attractive destinationA company might be considering different parts of its operations. Generally, these activities canbe divided into two major categories: manufacturing and services.

The MEXSEX configuration determines best countries in overall categories, both manufacturingand services. To choose the best offshoring destination for a specific type of operation, we usedMEX for manufacturing and SEX for commercial services. The country selection in Exhibit 4 wasderived from the simulation result summarized in Appendix D. The top 3 destinations weredetermined by ranking the Super BCC-O scores of the efficient countries, shown in Appendix E.

Exhibit 4. Given the type of operations, which country should we go to?Config. What type ofoperationoutsourced?Best Destinations(unordered) Top 3 Destinations(best to worst)

MEXSEX General / overall China, Hong Kong, India, Israel, Japan,Laos, Myanmar, Nepal, Pakistan, Qatar,Singapore, Yemen

China, India, Japan

MEX Manufacturing China, Hong Kong, Israel, Japan, Laos,Myanmar, Nepal, Pakistan, Qatar,Singapore, Yemen

China, Hong Kong,Japan

SEX Commercialservices

China, Hong Kong, India, Israel, Japan,Laos, Myanmar, Nepal, Qatar,Singapore, Yemen

China, India, Japan

China was efficient in all three configurations because it has the highest FDI, highestmanufacturing export, and second highest services export. The country also has one of thelowest labor costs, moderately stable government, moderate infrastructure, and education level(around the 75% median). Given what it has, China is extremely efficient at being the leaders inall three output dimensions. Hong Kong and Japan made it to the second and third spot inmanufacturing despite their relatively high cost. India is the number two country for commercialservices. Indias FDI is only number six among the pack, but it is extremely efficient in

capitalizing its 54% education level to produce commercial services export at the number 3spot.

In another case, a company might have a specific concern about the condition at thedestination country. In the early 2000s, a lot of manufacturing companies offshored theiroperations to India just to be disheartened when the total cost ownership skyrocketed due tounderdeveloped land infrastructure. Derived from results in Appendix D, information in Exhibit5 provides insights to address such specific concerns.


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Exhibit 5. If wage, politics, education, or infrastructure is important, where should we go to?Config. What input factor most important? Best DestinationsNONWAGE Labor cost China, Hong Kong, Japan,

Qatar

STAB Political stability and low-volatility China, India, Japan, Nepal

EDU Education of workforce China, India, Japan, PakistanINFRA IT, communications, transportationinfrastructure

China, Japan, Yemen

6.2 DEA Projections as a guide to achieve efficiencyThis analysis originally was only intended for a company to evaluate a countrys potential.Therefore, efforts to improve the attractiveness of a country are not the main goal of the

simulation model. Nevertheless, the Projections of the MEXSEX overall analysis providedinteresting insights for regulators in each country.

The projections summarized in Exhibit 6 on the following page could guide a country how to

become efficient either by reducing inputs or increasing outputs. Let us explore a fewexamples.

Example 1: YemenTo become efficient, increase output

Yemen is on the verge of being efficient because almost all of its projections are virtually zero. It

has all the resources required to become an efficient country. The only homework left forregulators in Yemen is increasing manufacturing exports by 0.05 or 5%. This could beaccomplished by providing economic incentives such as tax break or investment aid.

Example 2: Bhutan

To become efficient, increase output and better leverage the input (cannot reduce input)

In order to become an efficient producer, Bhutan has to increase its wage (reduce nonwage) by1%, reduce stability by 65%, reduce education by 7%, increase FDI by 14%, increasemanufacturing export by 14%, and increase service export by 1000%. Increasing FDI andmanufacturing export by 14% are practically attainable, while increasing service export by1000% is numerically attainable but realistically requires a superhuman effort.


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Exhibit 6. Projections from MEXSEX overall analysis as a pathway to reach efficient frontier


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A negative projection in wage might be interpreted in two ways. First, in DEA language, anonwage decrease (wage increase) would incur a handicap advantage that would allowBhutan to attain a higher efficiency score with a lower output. Second, in macroeconomics,increasing wage may seem counterintuitive, but might conform to the principle of efficiencywage, in which the productivity increases as a result of wage increase.

Why, then, would we possibly want to reduce education level or political stability to becomeefficient? We do not. Negative projections of NONWAGE, STAB, EDU, and INFRA should beinterpreted as underleveraged assets of a country. Instead of reducing the inputs, therebyreducing advancement of a country, the countrys regulators should put an effort to leveragethe underutilized resources to produce more output. In shot, a negative EDU projection meansa country is not capitalizing enough on its intellectual capital.

For Bhutan, 65% reduction projection in STAB implied that it has very good governance. Thedata confirmed this observation: Bhutan has the highest score of 3.80/5 in a dataset withmedian of 1.91/5 and minimum of 0.24/5. Bhutan could be more efficient by capitalizing moreon its political stability by, for example, educating foreign investors about its superior

governance, giving them incentive to increase FDI inflow to Bhutan.

The other variables could also be interpreted in a similar way. A country can translatereducewage as create a culture with great work ethic so that more value is created with the sameamount of pay or reduce infrastructure as reform school curriculum so that students couldbetter perform in the global workplace.

While the data could not provide immediate improvement suggestions, one insight can bederived from Exhibit 6: a country with negative input projection should capitalize more on thecorresponding input to increase the output indicators. This is an inverse interpretation of a

typical DEA projection: instead of reducing the input, increase the productivity of each input toimprove the output.


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7. Conclusions, l imitations, and recommendationsDEA analysis with four inputs and three outputs was utilized to determine the most lucrativeoffshoring destination in Asia for a US company. Super BCC and FDI inflow were used to rankthe efficient units. The top ten destination countries are:

Rank Country

1 China

2 India

3 Japan

4 Hong Kong

5 Singapore

6 Israel

7 Pakistan

8 Yemen

9 Qatar

10 Myanmar

We were able to choose the best destination countries based on type of offshored operations(manufacturing, operations, or both) and type of macroeconomic priority (labor cost,governance, education, and infrastructure).

Biased on its original purpose as an evaluation rather than improvement device, the modeldescribed here is not smart enough yet to determine feasible specific improvement steps forinefficient countries. The useful interpretations of the projections are guides to identifyabundant and underutilized resources in those countries. These resources should be betterutilized but not reduced due to the nature of the input. Some of the improvement suggestions,such as 1000% increase in export, were also realistically unattainable. Looking forward, a moreadvanced method is expected to be able to take these effects into account to transform theprojection result into a quantifiable objective and impose limits to the variables to produce arealistic game plan.


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References1http://www.mckinsey.com/locations/india/mckinseyonindia/pdf/NASSCOM_McKinsey_Report_2005.pdf

2http://www.ci.mil.wi.us/ImageLibrary/Groups/doaPurchasing/forms/nonpovertywage.pdf

3http://ia.ita.doc.gov/wages/04wages/04wages010907.html

4http://info.worldbank.org/governance/wgi2007/home.htm

5http://www.gcr.weforum.org/

6http://econterms.com/glossary.cgi?action=++Search++&query=fdi

7http://seekingalpha.com/article/40674foreigndirectinvestmentflowsindicativeofgrowthopportunities

8http://www.adb.org/Documents/Books/Key_Indicators/2006/pdf/rt24.pdf


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Appendix A. Data feed for DEA Software


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Appendix B. RTS analysis of BCC-O model


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Appendix C. Prioritization process of MEXSEX BCC-O analysis.


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Appendix D. Efficiency scores of various inputs or various outputs DEA runs


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Appendix E. Super BCC-O prioritization of MEX and SEX models