Boosting Singapore’s Productivity: New Evidence, Innovative Approaches
Catherine Jessica Yihui Lai
Summary
Singapore’s productivity does not make us proud. Why is it difficult to boost
productivity? I did eight field interviews to find out. Some of the causes identified
were novel, some even amusingly perverse. First, nationwide productivity may have
been dragged down by a shift to low-productivity industries, like construction.
Second, subscale businesses won’t or can’t afford to invest in productivity
enhancements. Third, companies may be hoarding or churning labour, either of which
lowers productivity. Finally, some companies play clever games to employ cheap
unskilled foreigners, which again hampers productivity.
To fine-tune the field results, I gathered hard evidence. Using data on all
public (and some private) companies from OneSource, I undertook econometric
analysis at the company-level. This is a first because previous research focused on
industry-level analyses, which relied on industry averages. As it turned out, there is
vast variation within industries. I report evidence that discredits some oft-held
beliefs such as the idea of subpar industries (productivity differences are actually due
less to industry and more to company-specific factors) and fine-tunes others (such as
what a subscale business really is).
I then suggest policies that follow from the diagnosis. For example, the
evidence argues that we should abandon industry-specific programs in favor of
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firm-specific ones. We should focus finite resources to “help the strong, not the
weak.” Some recommendations sound like political paths to damnation, but with
careful thought, they could be crafted to be inclusive and sustainable. I also suggest a
fund to incentivize low-productivity companies to sell out to consolidating
high-productivity ones, and a feasible way to revamp the foreign labour market to
eliminate market failures.
This essay is a modest beginning. With better first-hand and data-driven
research, and a dose of imagination and courage, we can find our way to productivity
pride.
Word count: 296.
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Boosting Singapore’s Productivity: New Evidence, Innovative Approaches
Catherine Jessica Yihui Lai
Singapore’s productivity does not make us proud. Professors Tan Khee Giap and
Tan Kong Yam observed that in construction and hotels, labour productivity is less than
50% of that in the U.S. (Tan and Tan, 2012; Figures 1 and 2 show details).
My grandfather’s contractor took three years to renovate his Eunos house. Just
outside my apartment window in New York City where I have lived the last few years,
the contractor built a 7-storey townhouse in under twelve months, despite frightful
city regulations and materials trucked in through narrow streets. Why is productivity
such a stubborn beast in Singapore?
To find out, I interviewed eight friends and relatives who work or run
businesses. They offered many reasons, some novel, and some even amusingly
perverse. These are corroborated by research and media reports, so they provide a
rich first-hand account of possible causes.
To fine-tune the field results, I gathered hard evidence. Using data on all
public (and some private) companies from OneSource, I undertook econometric
analysis at the company-level. This is a first because previous research focused on
industry-level analyses, which relied on industry averages. As it turned out, there is
vast variation within industries. I report evidence that speaks to my field results.
Productivity is important because it drives wages and incomes (Tan and Guo,
2011). It can support an aging demographic. If low productivity is due to cheap
3
foreign labour, a day of reckoning will come when that labour supply becomes too
costly. Besides, companies that rely on such labour jeopardize their export to
markets that forbid the import of cheap-labor products. The rest of this paper offers
recommendations to boost productivity. These, even if unorthodox, follow from the
diagnosis based on my interviews and analyses. They are also inclusive and
sustainable.
To begin, it is helpful to first define productivity.
What is Productivity?
A company’s productivity is the ratio of its output to inputs (Neo and Chung,
2008). Its output is its dollar value-added (Leong, 2014). There are three alternatives
to define inputs (Chen, 2004; Jayaram and Lee, 2010). First, inputs could be capital
and labour, in dollar terms. Second, input could be only labour, in dollars. Third,
input could be labour, measured by the number of employees.
In our Singapore context, one way to boost productivity under the first or
second definitions is to import as much cheap labour as possible, thus lowering the
dollar value of inputs. But this answer is neither sustainable nor politically viable.
Therefore, I use the third definition.
Many Causes
My interviewees offer four main causes of slow productivity growth.
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Subpar Industries Several interviewees say that booming or less intense
industries have less incentive to boost productivity. The problem lies in these subpar
sectors, such as construction and hotels, dragging down nation-wide productivity. But 1
not all agree. Some say productivity is driven more by firm than industry, akin to
students’ performance being driven more by students than schools. Government
policies reflect this equivocation, with some industry-specific and others proclaiming 2
that one size cannot fit all (Goh, 2014). Nonetheless, with finite resources, policy
focus on by industry or firm. Unfortunately, on industry-versus-firm, there are many
theories, not enough hard evidence.
Subscale Businesses Another interview theme is that small companies are
structurally unable to improve productivity. A metal workshop with three workers
cannot invest in systems that need only one. The workshop would be vulnerable to
that single worker leaving. Furthermore, investments could be lumpy. A small
business cannot implement a half-machine, but a whole machine exceeds its needs . 3
“If you innovate but can’t get jobs, it would take a long time to recover your costs,”
said Mr. Daniel Or of OKP Holdings (Ong, 2014). No assistance short of an outsized
grant could make such an investment viable.
Why are there so many subscale establishments? Mr. Elvin Koh of Samwoh
Corporation said: “there are too many companies…the entry barrier is really very
low...people who have probably made their money from property...and they say 'I
1 This is corroborated by Goh (2013) and Heng (2014). But Wan (2013) offers a contrarian view. 2 An example is the Job Flexibility for Productivity (JFP) initiative, which is specific to the hospitality industry (MOM, 2012). 3 This is corroborated by construction director Jeffrey Teo of Lian Beng Construction, in Ong (2014).
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think F&B [food and beverage] is very easy, let's try'” (NPCEC CWG, 2013). My distant
aunt opened a nail salon after making money on property. When that failed, she
opened a French-style bakery . Her day job: a practising lawyer. Like too many 4
Singaporeans, she is betting on her shop’s valuation to rise. Productivity is not
uppermost in her mind.
Hoarding, Churning, Marginal Labour One interviewee hoards labour even when
times are slow, because it is difficult to import foreign labour . Despite this 5
difficulty, another says that he churns foreign labour, because new blood cost less in
wages. Also, government permits for foreigners are time-restricted, so if the permits
are not renewed, the staff leaves . Churning works in another way: at nearly full 6
employment, it is employees who job-hop. Whatever the cause, churning dampens
employees’ opportunity to learn. Furthermore, as in the UK, the fuller the
employment, the more marginal the labour that enters the workforce (Pessoa, 2013).
Some companies may hoard, others churn, and still others get only marginal labour.
But all three could explain stubbornly low productivity.
Gaming Some interviewees admitted that past policy provided the opium of
cheap foreign labour (Lee, 1985; Krugman, 1994). Half in jest, they suggest that
instead of weaning off cheap labour, the path of lesser resistance is to warn
policy-makers that if foreign labour were curtailed, Singaporeans would be laid off.
4 In the same vein, Mr. Chan Chong Beng, president of the Association of Small and Medium Enterprises said: “MOM (Ministry of Manpower) was telling us that two new restaurants open every day” (NPCEC CWG, 2013). 5 This possibility is corroborated by Yahya (2014). 6 This is corroborated by the SBF (2012).
6
Better, keep job scopes fiendishly demanding so that no Singaporeans want them . 7
Employers are clever in gaming government policies. When the government tightened
the “S Pass” quota, some employers import low-skilled foreigners on “employment
passes.” But lowering labour quality hurts productivity.
Although the above are anecdotal, they are corroborated by research and
media reports . To fine-tune these field results, I gathered hard evidence. 8
New Evidence
My dataset comprises all 601 public companies for 2013 with detailed 9
information such as employee numbers and year founded. Tables 1, 2, 3, and 4
summarize this dataset.
I compute labour productivity by dividing value-added by the number of
employees. OneSource does not have value-added, but a reasonable proxy is pre-tax
profits . Table 1 shows that the average productivity is US$10,000 per employee, but 10
the range is from -US$120,000 to US$440,000 per employee.
7 A sushi restaurant claimed that no locals wanted its $3,000 9-hour dishwasher job. But it back-pedaled to say that was really a 12-hour dishwashing-plus-others job that pays only $1,715, with overtime (Chok, 2012). Cynics alleged that this is an example of a job designed for hiring cheaper foreigners. 8 Some theories of low productivity have been discarded, so I do not discuss these. For example, “Baumol’s disease” argues that some sectors are inherently labour intensive. A surgery needs at least one surgeon, even with robotic surgery. But Baumol’s disease misses the extent to which the surgeon could multi-task, or that she could be replaced by a lesser-trained medical professional. 9 This effort complements the very important survey-based data at the Asia Competitiveness Institute (Tan and Tan, 2013). 10 Value-added is pre-tax profit plus labour cost, interest, and depreciation. Because labour productivity uses employees as a denominator, missing labour cost is mitigated somewhat, even if imperfectly. This is because both numerator and denominator account for labour in some form. The omission on interest and depreciation is also mitigated to the extent that interest and depreciation rates are similar across the data (which is reasonable, since the data consists of a static snapshot) and if debt and capital levels are either similar or small enough. The conclusions are subject to this last qualification.
7
What does the data say about low productivity due to subpar industries such as
construction? Table 5 suggests that this hypothesis is problematic. The range of
productivities within industries is enormous. Indeed, Bukit Sembawang Estates, a
high-productivity construction firm, beat the highs in other industries. An alternative
hypothesis is that productivity is due to more to firm-specific factors such as
management quality and employee training. But no dataset can possibly collect all
firm-specific factors, so how does one link productivity to these factors?
One approach is to use random-effects variance components analysis (VCA) on
crossed-factor data . If the data has different companies in the same industry and the 11
same firm in different industries, VCA can tease apart productivity variance due to
industry or firm. 12
The result surprises me (Table 6). Industry explains only 10.1% of productivity
variance, while firm-specific factors explain 86.4%. This is the first hard evidence
that industry-specific incentive programs are likely to be less effective, maybe eight
times less effective, than firm-specific ones.
What about subscale businesses causing low productivity? One narrative is that
having too few employees diminish the substitutability of machines for employees.
Another theory is that low-margin businesses either don’t care or can’t afford
11 This is a standard methodology in strategy and econometric analyses--e.g., Hawawini, et al. (2003). Please see appendix for details. 12 Unfortunately, OneSource indicates only one primary industry for each company. But there is a way out. I define “firm” as a group of companies with the same management or ownership, and therefore likely to have the same policies regarding productivity. I manually encoded such “firms.” For example, “Far East Group Ltd” and “Far East Orchard Ltd” could be considered the same firm, even though they are listed as different public companies. This is also why I cannot use regression analysis here, since companies are hierarchical within firms and industries, but firms and industries are not hierarchical with each other.
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productivity investments. Regression analysis debunks the first narrative. Table 7
shows that 0.8% reduction in employee count is correlated with a 1% increase in
productivity. One interpretation is that the average small company has found ways to
mitigate the risks of substituting capital for employees. Table 7 also shows that a 0.6%
margin decrease is associated with a 1% productivity decrease. This supports the
second story on low-margin companies. They need help.
Innovative Approaches
What are the implications of our field interviews and analyses for policy?
Abandon Industry-Specific Programs. This sounds radical, but not if
industry-specific programs are merely replaced by company-specific ones. The
Chinese say that 行行出状元 , so we see stars like Hotel Royal in seemingly 13
low-productivity industries like hospitality (Table 5). It will be helpful to find ways to
diffuse productivity know-how from the stars to the rest. There must, however, be
something in it for the stars. Hence my next few suggestions.
Help the Strong, Not the Weak. Politically, “help the strong, not the weak” is
a path to damnation. Before we toss the idea out, we should consider its merits.
There are creative ways to implement this suggestion that is inclusive and sustainable.
My father used to be with Bain, the management consultants, and he tells of
how in its earliest days, it was dedicated to bringing its weakest consultants up to
standard. But in the real world, resources are finite. Every effort to help the weak
takes away that much to help the strong. When the firm shifted to investing in the
13 háng háng chū zhuàng yuán, or every profession has its superstar.
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strong (e.g., assign them the best partners as mentors), it strengthened its ranks of the
strong, who could then mentored the weak.
Similarly, policy should focus on high-productivity companies, whether in
awarding contracts or grants. But politically, how to be inclusive when we only help
the strong? That is my next idea.
Fund Consolidation. We have seen evidence that low-productivity companies
are subscale not by employee count but by low margins. Whether because of ego,
absentee ownership, or family owners making a subsistence living, they may be
reluctant to sell out to a larger, consolidating company.
An inclusive policy could offer a lump-sum payment akin to the Pioneer’s
package (Chua, 2014) to subscale businesses when they sell to more productive
companies. This save the jobs in the weak and enlarge the business of the strong . 14
Importantly, productivity know-how in the strong would trickle down to the weak.
Undoubtedly, we must think through unintended consequences. For example,
there should be ways to prevent speculators from starting businesses just to get this
payment--e.g., tie the sellout incentive amount to a company’s age.
Eliminate Market Failure in the Foreign Labour Supply. Levies on employers,
work permits that restrict foreigners to working at only the sponsoring company , and 15
quotas all create a thicket of ulcers in the employment of foreigners. These market
failures contribute to the hoarding, churning, and gaming described by my
interviewees.
14 There could be creative variations too: different lump sums coupled with a share of post-sellout profits, franchising (which is a looser form of consolidation), or outsourcing to central services. 15 The government is considering eliminating this restriction (Tan and Toh, 2014).
10
Imagine redesigning these regulations by returning to the objective: how can
Singapore companies utilize cheap foreign labor while ensuring that: (1) Singaporeans
get a fair shot at employment, in the spirit of “Singaporeans first”, (2) foreigners are
paid fair wages, in the meritocratic spirit of “equal pay for equal work”, and (3) the
externalities of a foreign workforce (crowded trains and housing) are paid for?
I propose a sustainable solution: foreigners pay levies , after which they enter 16
the Singapore labour market without restriction. A quota, if at all needed, should be
at the national level and not the company level. This proposal satisfies criterion (1),
since Singaporeans are exempt from paying such levies. It satisfies (2), as foreigners
will demand and get the same wage as Singaporeans. It satisfies (3) because the levies
pay for the externalities. Implementation is straightforward as the government
already collects levies today. This proposal also removes today’s alleged ugliness such
as human trafficking, repatriation agencies, and unpaid wages. No matter how paltry,
they pose an unnecessary distraction for manpower policy. Finally, the proposal
attracts more talented foreigners, even among the unskilled, because only they would
be confident and successful enough in their home countries to afford the levies.
Conclusion
No physical law dictates that Singapore’s productivity cannot improve. My
analyses and suggestions are only a modest beginning. Using better first-hand and
16 The levy could be charged daily but is collected as lump-sums on a regular basis and refunded pro-rata on departure. It may depend on qualifications and skills, and demand in Singapore.
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data-driven research, and a good dose of imagination and courage, we can propose,
experiment, and implement our way to productivity pride.
Word count: 1,978.
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References Chen, Liang-Hsuan, Yum-Teo, Tien Hua, Pokharel, Shaligram, “Investigating the Productivity of Singapore,” Asia Pacific Management Review, 2004, 9(2):323-333. Chok, Stephanie, “72-hour work week too long, even for $3,000 a month,” Straits Times, September 21, 2012.
Chua, Mui Hoong, “SMEs need a pioneers-like package too,” Straits Times, March 5, 2014. Ghosh, Abhijit, “Need to tweak policies on productivity and workforce,” Business Times, February 8, 2013. Goh, Kia Hong, “The view from NTU professors: PIC scheme not a one-size-fits-all solution,” Business Times, April 22, 2014. Goh, Tee Wei, “A shift-share analysis of Singapore’s labour productivity growth, 1988-2013,” Economic Survey of Singapore, 2013, pp. 70-77. Hawawini, G., Subramanian, V. and Verdin, P. (2003), “Is performance driven by industry-or firm-specific factors? A new look at the evidence.” Strategic Management Journal, 24:1–16. Heng, Janice, “Call for greater urgency to lift productivity,” Straits Times, March 5, 2014. Jayaram, Shruthi and Lee, Titus, “Singapore’s Productivity Puzzle: Estimating Singapore’s Total Factor Productivity Growth Using the Dual Method,” Economic Survey of Singapore, 2010, pp. 14-26. Krugman, Paul, “The Myth of Asia’s Miracle,” Foreign Affairs, November/December 1994, 73(6):62-78. Lee, Tsao Yuan, “Growth without productivity: Singapore manufacturing in the 1970s”, Journal of Development Economics 1985 19:25-38. Leong, Chee Tung, “Singapore's productivity continues to lag that of other developed economies. Behavioral economics may help close the gap,” Gallup Business Journal, February 11, 2014. MOM (Ministry of Manpower), “Manpower realities: beyond the numbers,” The Manpower Blog, September 30, 2012.
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www.momsingapore.blogspot.sg/2012/09/manpower-realities-beyond-numbers.html Accessed May 8, 2014. Neo, Boon Siong and Chung, Susan, “Singapore’s Declining Productivity Growth: An Exploratory Paper,” Asia Competitiveness Institute Working Paper, October 2008. NPCEC CWG (National Productivity and Continuing Education Council, Communications Work Group, Roundtable discussion at SPH Center, March 28, 2013. http://www.towkayzone.com.sg/entries/23-SMEs-ponder-beyond-carrots-sticks#.U3Aehvn-K-1 Accessed May 8, 2014. Ong, Cheryl, “Productivity challenge stumps builders,” Straits Times, March 3, 2014. Parsons, Talcott. The Social System. 1951. New York:Free Press. Pessoa, Joao Paulo and Reenen, John van, “The UK productivity and jobs puzzle: Does the answer lie in labour market flexibility?” Centre for Economic Performance, June 2013, Special Paper No. 31. SBF (Singapore Business Federation), SME Committee Recommendations for Budget 2013, December 2012. Tan, Aelia and Toh, Yong Chuan, “Govt may let foreign workers switch jobs,” Straits Times, March 10, 2014. Tan, Di Song and Guo, Jiajing, “Productivity and wage growth in Singapore,” Economic Survey of Singapore, 2011, pp. 64-73. Tan, Khee Giap and Tan, Kong Yam, “Assessing Competitiveness of ASEAN-10 Economies,” International Journal of Economics and Business Research, 2013. Tan, Khee Giap and Tan, Kong Yam, “More productive economy still needed,” Straits Times, June 13, 2013. Wan, Michael, “Singapore economics: Labour pains,” Business Times, April 18, 2013. Yahya, Yasmine, “Manufacturers 'need a giant leap forward to thrive,” Straits Times, February 5, 2014.
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Figure 1 - Singapore’s Productivity Compared with Other Countries.
Source: Straits Times, March 6, 2014, pg. A8
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Figure 2 - Decomposition of Labour Productivity Growth, 1970–2011.
Source: APO Productivity Database, 2013.01.
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Table 1 - Summary of Dataset.
Variable N Median 5th %tile 95th %tile
Year founded 599 1996 1963 2010
Sales USD Mil 619 75.2 3.6 2,186.8
Sales 1 year growth 519 3.1% -38.7% 89.3%
Pretax profit USD Mil 619 4.9 -16.1 357.5
Assets USD Mil 580 134.0 13.9 6,850.3
Employees 619 416 21 10,908
Market value USD Mil 568 78.2 5.7 3,468.7
Labour productivity (USD Mil/employee)
619 0.01 -0.12 0.44
Sales/employees USD Mil 619 0.2 0.01 2.89
Pretax profit /sales 614 5.4% -53.0% 63.0%
Source: OneSource, 2013.
Table 2 - Companies by Ownership Type.
Ownership type N %
Private Branch 1 0.16
Private Independent 2 0.32
Private Parent 1 0.16
Private Subsidiary 14 2.26
Public Independent 80 12.92
Public Parent 399 64.46
Public Subsidiary 122 19.71
Total 619 100
Source: OneSource, 2013.
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Table 3 - Companies by Industry.
NAICS Description N %
11 Agriculture, Forestry, Fishing and Hunting 7 1.13
21 Mining, Quarrying, and Oil and Gas Extraction 27 4.36
22 Utilities 8 1.29
23 Construction 59 9.53
31 Manufacturing: food 23 3.72
32 Manufacturing: primary products 52 8.4
33 Manufacturing: metal, others 151 24.39
42 Wholesale Trade 48 7.75
44 Retail Trade: vehicles, clothing 18 2.91
45 Retail Trade: department stores, others 8 1.29
48 Transportation, Warehousing: air, rail, sea, pipeline 30 4.85
49 Transportation, Warehousing: postal, couriers, warehousing
2 0.32
51 Information 16 2.58
52 Finance and Insurance 32 5.17
53 Real Estate and Rental and Leasing 27 4.36
54 Professional, Scientific, and Technical Services 37 5.98
55 Management of Companies and Enterprises 19 3.07
56 Admin, Support, Waste Management, Remediation Svc 9 1.45
61 Educational Services 5 0.81
62 Health Care and Social Assistance 10 1.62
71 Arts, Entertainment, and Recreation 5 0.81
72 Accommodation and Food Services 21 3.39
81 Other Services (except Public Administration) 5 0.81
Total 619 100
Source: OneSource, 2013.
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Table 4 - Companies by Ultimate Parent’s Country.
Ultimate parent’s country N %
Australia 2 0.32
China 33 5.33
Germany 2 0.32
Hong Kong 9 1.45
India 4 0.65
Indonesia 4 0.65
Japan 4 0.65
Malaysia 10 1.62
Norway 1 0.16
Poland 1 0.16
Singapore 536 86.59
South Africa 1 0.16
Taiwan 1 0.16
Thailand 1 0.16
United Arab Emirates 1 0.16
United Kingdom 7 1.13
United States 2 0.32
Total 619 100
Source: OneSource, 2013.
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Table 5 - Labour Productivity Examples at the 5th and 95th Percentiles.
NAICS Short Description “Low example” 5th %tile Prod.* “High example” 95th %tile Prod.*
11 Agriculture... Global Palm Resources Hldg -.01 First Resources .03
21 Extraction CH Offshore -.13 Ezion Holdings 1.79
22 Utilities Asia Power -.0008 SIIC Environment .243
23 Construction Swee Hong -.035 Bukit Sembawang Estates 5.47
31 Mfg 1 Oceanus Group -.59 Super Group .074
32 Mfg 2 Transcu Group -.12 Memstar Technology .27
33 Mfg 3 REC Solar -.53 Ipco International .49
42 Wholesale Federal International -1.96 Cargill International .30
44 Retail 1 Intraco -.055 Second Chance Properties 1.29
45 Retail 2 Hanwell Holdings -.023 Metro Holdings .085
48 Transportation 1 Mercator Lines -.80 Swissco Holdings .19
49 Transportation 2 Keppel Tel. & Transport .07 Singapore Post .071
51 Information S i2i -1.04 Singapore Telecom .15
52 Finance Rowsley -13.8 ARA Asset Management 1.12
53 Real Estate Cedar Strategic Holdings -.013 CapitaCommercial Trust 3.99
54 Services Phorm Corporation -.89 Global Logistic Properties 2.60
55 Management E3 Holdings -.37 Haw Par Healthcare .049
56 Admin Artivision Technologies -.14 Blumont Group .38
61 Education TMC Education .0004 Overseas Education .04
62 Health Pacific Healthcare -.026 Cordlife Group .105
71 Arts St. James Holdings -.038 Straco Corporation .08
72 Hospitality Tung Lok Restaurants -.005 Hotel Royal .08
81 Other Asia Pacific Strategic Inv -.06 Mary Chia Holdings .13
* Prod. = Labour productivity in USD million/employee.
Source: OneSource, 2013.
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Table 6 - Variance Decomposition Analysis (VCA).
Log restricted-likelihood = -1267.1 LR test vs. linear regression: chi-squared(2) = 16.30, probability > chi2 = 0.0003
Driver Num. of groups
Estimate Std. err. % variance
Industry 23 0.22 0.11 10.1%
Firm 616 1.89 0.05 86.4%
Residual error 0.08 0.03 3.5%
Table 7 - Regression analysis of labour productivity on firm-specific factors.
Dependent variable is log labour productivity. N = 240, F(36,203) = 49.6, p = 0.000. R-squared = 89.8%, Adj. R-squared = 88.0% I use a log specification to reduce mis-specification problems. A log specification also facilitates the interpretation of the estimates as percentages (please see text).
Covariate Coeff. Std. err. t or F p-value
Log employees -0.836 0.034 -24.8** 0.000
Log margin 0.593 0.039 15.1** 0.000
Log assets 0.764 0.036 21.3** 0.000
Log sales growth 0.071 0.029 2.5** 0.014
Log year founded 0.004 0.003 1.33 0.185
Ownership status# F = 3.0* 0.033
Industry# F = 2.4** 0.001
Ultimate parent country# F = 2.0 0.05
# Included as indicator variables ** Significant at 1% level, * at 5% level.
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Appendix - Notes on VCA Empirics
How can we test for firm-specific effects? It is impossible, not only in practice but also in theory, to obtain data on every possible type of firm-specific effects. A regression approach would not work, but a variance components analysis does. Such an analysis appeals to the discriminating character of the dataset. For example, if we can compare two companies on a hypothesized driver (e.g., industry) while controlling for everything else (firm, age, parent’s country, etc.), we can identify the significance of the driver. On firm-specific effects as a driver: It might seem implausible for two companies to be in different industries but are in the “same firm,” because one way to get at the firm effect is to consider companies as being the “same firm” if they belong to the same group. I encoded such groups: for example, “Far East Group Ltd” and “Far East Orchard Ltd” could be considered the same firm, even though they are officially listed as different public companies. Another tricky econometric problem is that some of the drivers are hierarchical in nature. For example, the companies are clustered around industry sectors, but of course, not the other way around. More complicated, some of the drivers are, in econometric jargon, “crossed-factors”. For example, two companies can be in different industries but belong to the same firm, but another two could be in the same industry but have different firms. Given the nature of these drivers, I cannot use straightforward regressions, but a random effects variance components analysis using Markov chain Monte Carlo (MCMC) estimation is suitable.
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