“Southeast Asia” is not one market

Chinese companies talking about going abroad often say they are “doing Southeast Asia”, as if it were one market. In fact, from Singapore to Indonesia and from Thailand to Vietnam, the countries differ greatly in legal tradition, religious make-up, language, regulatory style and business networks. This chapter uses the tools of institutional economics to compare these differences within one framework, and shows the limits of such comparison.

Institutional distance

Kostova proposed institutional distance to describe the differences between a home and a host country, adopting Scott’s division of institutions into three pillars: regulatory (law, regulation and enforcement), normative (what society regards as legitimate and worth doing) and cognitive (the frames of thought and knowledge people take for granted) (Kostova 1999; Scott 1995). Xu and Shenkar argued further that institutional distance affects how hard it is for a multinational to gain legitimacy in the host country, whether the parent’s practices can be transferred, and what mode of entry to choose (Xu and Shenkar 2002). Ghemawat offered a framework aimed more at managers, dividing the distance between countries into cultural, administrative and political, geographic and economic components (CAGE), and stressing that many firms overestimate the size of foreign markets and underestimate the costs of distance (Ghemawat 2001).

Institutional outlines of five Southeast Asian countries

What follows is background only; each country’s rules change, and the official text in force should be checked for any real project.

Legal tradition. The legal systems of Singapore and Malaysia belong to the English common-law tradition; Malaysia also has Syariah courts with jurisdiction over matters such as the personal law of Muslims. Indonesia’s civil-law tradition dates from the Dutch colonial period. Thailand, in its legal reforms of the late nineteenth and early twentieth centuries, drew on continental European codes in compiling its Civil and Commercial Code. Vietnam’s legal system belongs to the civil-law tradition, with features of a socialist legal system.

Religion and business norms. Indonesia has the largest Muslim population of any country; Article 3 of Malaysia’s Federal Constitution makes Islam the religion of the Federation, and Muslims were somewhat over 60 % of the population in the 2020 census (Department of Statistics Malaysia 2022). Thailand is predominantly Theravada Buddhist. Singapore and Vietnam are religiously more diverse. For firms entering these markets, religious norms appear both in regulations and in consumers’ expectations.

Islamic commercial law also has two concepts directly relevant to contract design: the prohibition of interest (riba) and of excessive uncertainty (gharar). The latter requires the object, price and terms of a transaction to be sufficiently clear; agreements that are excessively vague or gambling-like are regarded as void or improper. There is an interesting echo here of this book’s repeated advice to write down the conditions of change in advance: in this tradition, clarity in a contract is itself an ethical requirement.

Buddhist ethics includes right livelihood (sammā-ājīva, one of the factors of the Noble Eightfold Path), the requirement to earn a living in a proper way. The Sufficiency Economy philosophy promoted officially in Thailand stresses moderation, reasonableness and resilience against shocks. The practical influence of such ideas on business conduct varies from person to person, and they should not be reduced to claims that the business people of one country “value relationships more” or “think longer-term”, but they remind outsiders that in many Southeast Asian societies the legitimacy of a business relationship is not measured by contract and profit alone.

Chinese business networks. Many Southeast Asian countries have sizeable ethnic Chinese communities: about three-quarters of Singapore’s residents and somewhat over a fifth of Malaysia’s citizens are ethnically Chinese (Department of Statistics Singapore 2021; Department of Statistics Malaysia 2022). Weidenbaum and Hughes called the cross-border business links among overseas Chinese firms, built on kinship, native place and trust, the “bamboo network” (Weidenbaum and Hughes 1996); Redding described, from the perspective of organisational sociology, the “spirit of Chinese capitalism” centred on family firms and personal trust (Redding 1990).

Measuring distance: the Kogut–Singh index

The most common quantitative measure of cultural distance is the Kogut–Singh index: divide the squared gap between two countries on each Hofstede dimension by that dimension’s variance across countries, and average (Kogut and Singh 1988).

# Cultural distance from China to five Southeast Asian markets (and the United States, for contrast),
# with the Kogut & Singh (1988) index on the six Hofstede dimensions as published in Hofstede,
# Hofstede & Minkov (2010): power distance, individualism, masculinity, uncertainty avoidance,
# long-term orientation, indulgence.
#   KS_j = (1/n) * sum_i (I_ij - I_i,China)^2 / Var_i
# Var_i should be the variance across all countries in the database; here it is the variance across
# these seven countries only, so the index is for illustration. (Later online versions of the
# scores show Malaysia's power distance capped at 100 instead of 104.) An entry-friction score then adds
# two non-cultural layers whose weights are the author's assumptions, to show how rankings move.
from statistics import pvariance

DIMS = ("PDI", "IDV", "MAS", "UAI", "LTO", "IVR")
H = {"China":     (80, 20, 66, 30, 87, 24),
     "Malaysia":  (104, 26, 50, 36, 41, 57),
     "Indonesia": (78, 14, 46, 48, 62, 38),
     "Thailand":  (64, 20, 34, 64, 32, 45),
     "Vietnam":   (70, 20, 40, 30, 57, 35),
     "Singapore": (74, 20, 48, 8, 72, 46),
     "USA":       (40, 91, 62, 46, 26, 68)}
VAR = [pvariance([v[i] for v in H.values()]) for i in range(6)]

def ks(c, dims=range(6)):
    return sum((H[c][i] - H["China"][i]) ** 2 / VAR[i] for i in dims) / len(dims)

# Non-cultural layers, 0 (none) to 1 (high), as entered by an analyst for a consumer-goods entrant:
#   halal: share of the mass market where halal certification is expected or required
#   rules: foreign-ownership limits and licensing friction for the planned business (assumed)
LAYERS = {"Malaysia": (0.7, 0.4), "Indonesia": (0.9, 0.7), "Thailand": (0.1, 0.6),
          "Vietnam": (0.0, 0.5), "Singapore": (0.2, 0.1), "USA": (0.0, 0.2)}

print("market     | KS index, 6 dims | KS, original 4 dims | friction (weights 1/1/1) | friction (culture x3)")
rows = []
for c in LAYERS:
    k6, k4 = ks(c), ks(c, range(4))
    f1 = k6 + LAYERS[c][0] + LAYERS[c][1]
    f3 = 3 * k6 + LAYERS[c][0] + LAYERS[c][1]
    rows.append((c, k6, k4, f1, f3))
    print(f"{c:10s} | {k6:16.2f} | {k4:19.2f} | {f1:24.2f} | {f3:21.2f}")
for col, name in ((1, "KS 6-dim"), (3, "friction 1/1/1"), (4, "friction culture x3")):
    print(f"ranking by {name:19s}: " + " < ".join(r[0] for r in sorted(rows, key=lambda r: r[col])))
# market     | KS index, 6 dims | KS, original 4 dims | friction (weights 1/1/1) | friction (culture x3)
# Malaysia   |             2.57 |                1.08 |                     3.67 |                  8.81
# Indonesia  |             1.25 |                1.23 |                     2.85 |                  5.34
# Thailand   |             4.01 |                3.60 |                     4.71 |                 12.74
# Vietnam    |             1.54 |                1.61 |                     2.04 |                  5.13
# Singapore  |             1.35 |                1.21 |                     1.65 |                  4.34
# USA        |             5.64 |                3.55 |                     5.84 |                 17.12
# ranking by KS 6-dim           : Indonesia < Singapore < Vietnam < Malaysia < Thailand < USA
# ranking by friction 1/1/1     : Singapore < Vietnam < Indonesia < Malaysia < Thailand < USA
# ranking by friction culture x3: Singapore < Vietnam < Indonesia < Malaysia < Thailand < USA

Some observations. First, by cultural distance, Indonesia, Singapore and Vietnam are closest to China and the United States farthest, which agrees with the intuition that Southeast Asia is culturally close for Chinese firms. Second, Malaysia’s rank depends on which dimensions are used: with only the original four, its distance from China (1.08) is the smallest of the five; with six, it ranks fourth, mainly because of the gaps in long-term orientation (41 against 87) and indulgence (57 against 24). Third, adding halal expectations and ownership friction moves Indonesia from the culturally closest market to third, and makes Singapore the market with the least friction. Which market is “near” depends on what you measure and how you weight it.

Networks as substitutes for institutions

When formal institutions cannot fully protect transactions, people protect themselves through networks. Granovetter called this the embeddedness of economic action: transactions are embedded in personal relationships, and trust and sanctions travel through them (Granovetter 1985). Uzzi’s fieldwork in 23 New York garment firms led him to propose a paradox of embeddedness: its benefits rise up to a threshold, beyond which over-embeddedness (dealing only within a few close ties) makes firms slow to respond to outside information and opportunities (Uzzi 1997).

# Finding a local partner: the open market versus a referral network (a 'bamboo network' of
# co-ethnic business ties; Weidenbaum & Hughes 1996). Stylised; numbers are assumptions.
#  Open market: 200 candidates; 70 % are reliable. A due-diligence check flags an unreliable firm
#    with probability 0.6 (and wrongly flags a reliable one with probability 0.1). Partner quality
#    (the value created if the partner is reliable) varies across candidates, ~ Normal(10, 3); the
#    firm picks the best-looking unflagged candidate it can find among those it screens.
#  Network: only 20 candidates are reachable, but each comes vouched for by a contact who loses
#    standing in the network if the referral cheats, so the share of reliable candidates is 90 %.
#  An unreliable partner destroys 25 (the firm's specific investments, plus lost time).
import random

def search(pool, reliable_share, screened, rng, flag_bad=0.6, flag_good=0.1, loss=25.0):
    best = None
    for _ in range(min(screened, pool)):
        good = rng.random() < reliable_share
        quality = rng.gauss(10, 3)
        flagged = rng.random() < (flag_good if good else flag_bad)
        if flagged: continue
        if best is None or quality > best[0]: best = (quality, good)
    if best is None: return 0.0, None
    return (best[0] if best[1] else -loss), best[1]

def mean_outcome(pool, share, screened, runs=20000):
    rng = random.Random(7); tot = bad = 0
    for _ in range(runs):
        v, good = search(pool, share, screened, rng)
        tot += v; bad += good is False
    return tot / runs, bad / runs

print("route                                   | candidates screened | mean value | P(unreliable partner)")
for label, pool, share, screened in (("open market", 200, 0.70, 5), ("open market", 200, 0.70, 30),
                                      ("referral network", 20, 0.90, 5), ("referral network", 20, 0.90, 20)):
    v, b = mean_outcome(pool, share, screened)
    print(f"{label:39s} | {screened:19d} | {v:10.2f} | {b:21.1%}")
# The network's closure is also its limit (Uzzi 1997): once the reliable share in the open market
# rises - say, with better courts, credit bureaus and platform ratings - its advantage shrinks.
for share in (0.70, 0.80, 0.90):
    v, b = mean_outcome(200, share, 30)
    print(f"open market with reliable share {share:.0%}, 30 screened: mean value {v:.2f}, P(unreliable) {b:.1%}")
# route                                   | candidates screened | mean value | P(unreliable partner)
# open market                             |                   5 |       6.93 |                 15.7%
# open market                             |                  30 |       9.31 |                 15.9%
# referral network                        |                   5 |      11.44 |                  4.5%
# referral network                        |                  20 |      13.46 |                  4.8%
# open market with reliable share 70%, 30 screened: mean value 9.31, P(unreliable) 15.9%
# open market with reliable share 80%, 30 screened: mean value 11.68, P(unreliable) 10.2%
# open market with reliable share 90%, 30 screened: mean value 13.95, P(unreliable) 4.9%

Under these assumptions, the referral network, even when it can reach only 20 candidates, gives a better expected result than screening 30 on the open market, and cuts the chance of an unreliable partner from about 16 % to about 5 %. But the last lines of the program show where the network’s advantage comes from and where it ends: its value comes from the low reliable share on the open market. Once better courts, credit bureaus and platform ratings raise that share to 90 %, screening 30 candidates on the open market is already slightly better than the network. As business registries, electronic payments and platform ratings improve across Southeast Asia, the network’s relative advantage may be shrinking, but that needs to be tested with data.

Summary

Southeast Asia is many markets, each with its own legal tradition, religious norms and business networks. Cultural distance indices help to ask questions, but their rankings change with dimensions and weights, and they have been criticised for assuming symmetry, stability and linearity. Halal certification and the prohibition of excessive uncertainty are compliance requirements and also a reminder that in some societies clarity in a contract is itself an ethic. Referral networks lower the risk of an unreliable partner, but their advantage shrinks as formal institutions improve. The next chapter turns inside the venture: when headquarters and the local team have different goals, how should incentives be designed?