Information Frictions

FIGURE 4.9 Varying Reduction of Fixed Entry Costs of Investing Based on Level of Information Frictions

Learning from unrelated (competitor) pioneer experience. High information frictions

fEX fFDI_EX/NEQ fFDI_Related_Follower fFDI_Unrelated_Follower fFDI Rising fixed costs of entry

Biggest drop in fixed costs: Learning from own export experience. Least information frictions.

Learning from experience of related (conglomerate) pioneer. Intermediate information frictions

Source: World Bank. Note: The main arrow marks fixed entry costs in increasing order from lowest (fEX) to highest (fFDI) fEX = sunk entry costs of exporting fFDI_EX/NEQ = sunk entry costs of investing, given own prior exporting or other nonequity engagement fFDI_Related_Follower = sunk entry costs of investing, given prior entry of a related firm in the firm’s business group or conglomerate fFDI_Unrelated_Follower = sunk entry costs of investing, given prior entry of an unrelated pioneer in the same industry fFDI = sunk entry costs of investing This figure illustrates that a follower firm faces high information frictions when learning from a competitor firm’s experience of entry in the previous period, as reflected by a small drop in fixed entry costs (fFDI − fFDI_Unrelated_Follower). The largest drop in fixed entry costs of investing is achieved from the firm’s own learning experience in exporting

(fFDI − fFDI_EX/NEQ).

ENTREPRENEURS WITH HIGH RISK APPETITE ARE MORE LIKELY TO INVEST ABROAD

It is important to take into consideration the characteristics of the entrepreneur in understanding firm behavior (Cusolito and Maloney 2018), as has been backed up by research on management quality and culture and the psychological traits of successful entrepreneurs (Bloom et al. 2013). The characteristics of the entrepreneur are also related to behavioral economics and issues such as why people may not use the information they have (Handel and Schwartzstein 2018; Kremer, Villamor, and Aguinis 2019). Entrepreneurship in the region before the 1990s was subdued, which sociologists have attributed to a British education system geared toward producing graduates with a high preference for service and the episodes of socialism that involved large public sectors and tight control of the private sector. Analysts have also discussed the “attitude” of some state bureaucracies toward entrepreneurs and the role of this attitude in reform (see Panagariya [2005] for a discussion of different views). In addition, society was risk averse and individuals placed a high value on employment stability (Jagannathan et al. 2017).

Thus, certain personal characteristics are needed to overcome obstacles, pursue innovative paths, or even use available information. Risk appetite can be proxied by whether the founder or chief operating officer is a member of a business community or a traditionally well-known business family. Most South Asian countries have important family-owned business groups (or collections of subgroups that have developed after the founder’s passing). The results indicate a positive relationship between investment and being part of a business community or family (table B.13 in appendix B). A person raised in a household in which family businesses routinely succeed and fail is likely to be more open to taking risks. This variable may also be related to ethnic networks, in the case of cross-border business communities working together, and so was not initially introduced in the model. In the lead-up to partition and before, many families in business communities migrated.

The results presented here do not capture the fact that recent technology-driven entrepreneurial success in India suggests a break from the past when family business background mattered or the family business acted as an incubator. For example, entrepreneurship cells in engineering colleges have encouraged entrepreneurship among students from nonbusiness communities, and partnerships are forged through university experiences as opposed to community allegiances.

In India, business communities are identified through a mix of religion, caste, culture, and region. The main business communities include the Marwari, Parsi (of Persian origin), and Gujarati communities (Gujarat-Kathiawar-Kutch area), as well as the Punjabis, Chettiars (of southern India), and Maharashtrians (Patankar and Mehta 2018). In colonial times, the business communities were not identical to the trading communities because for some orthodox Hindu communities, foreign travel was taboo (Gupta et al. 2020). The Marwaris, which originate from Rajasthan state, are characterized by wealth and risk appetite. The top 10 Marwari-owned companies at one point accounted for 6 percent of the Bombay Stock Exchange’s market capitalization (Khaitan 2014). India is home to the largest family businesses in the Asia Pacific region, including the Tata Group (Tata family), Reliance Industries (Ambani family), and Aditya Birla Group (Birla family).

In Pakistan, prominent business communities include Gujarat Muslim Khojas, Memons, and Bohras; the Punjabi commercial communities of the Khatris, Pirachas, Shamsis, and Chinioti Sheikhs; and the Ismailis–Aga Khanis. More specifically, 22 families were identified as dominating business during the 1950s and 1960s, until a wave of nationalizations in the early 1970s (Ghani, Haroon, and Ashraf 2011; Javaid, Shamsi, and Hyder, forthcoming ). In Sri Lanka, the Bohras, Sindhis, and Memons have a strong business presence, the first in diverse sectors and the latter two primarily in the apparel industry. Moors also have a strong presence in business activities.

In Nepal, the Newars are the traditional business community, whereas the Marwaris are more dominant in large-scale enterprises. The Thakalis and Sherpas are more recent entrepreneurs in trading, carpet manufacturing, and the tourism industry. Communities living in the Terai of Nepal along the border with India are naturally involved in cross-border trading, often with connections to India. They are diverse

communities of different religions, though sometimes they are collectively grouped as Madheshis. Family businesses are also prevalent in Bangladesh. Many of the families that owned large businesses in East Pakistan (but had their main assets in West Pakistan) left after Bangladesh became independent in 1971 and businesses were immediately nationalized. However, by 1982 the government had divested many of its acquisitions, and Bengali Muslim family businesses dominated the private sector landscape (Kochanek 1996).

All the regional pioneers in the case studies come from business families. Three of the regional pioneers (Chaudhary of Nepal, and Singhania and Goenka of India) are Marwari. The three Sri Lankan apparel pioneers all have parents born in Gujarat, and two of them are from the Memon community. The Burman family leading CEAT is Punjabi, and the founder of Taj Hotels is Jamsetji Tata, the Parsi founder of the Tata Group.

OTHER FACTORS

Other firm characteristics tested for their influence on OFDI decisions were related to their being family firms, state-owned firms, and foreign-owned firms. There were no consistent results for these explanatory variables. In some cases, the state-owned dummy variable was negative, implying that, unlike Chinese state-owned enterprises, South Asian ones were less likely to invest abroad. An exception is the Indian public sector investment in Bhutan’s hydropower sector, which has led to new private sector investment by Tata Power Ltd. on a public-private partnership basis with the Bhutangovernment-owned Druk Green Power Corporation.

A positive coefficient, especially for goods production, was expected for family firms because they can take a longer-term perspective on investment. The argument is that family firms are more inclined to take risks when the return could be backloaded because they are not motivated by shareholders to maximize share value on an annual basis. The impact of family firms was small but negative, and only for services operations investments. However, the coefficient loses statistical significance when only South Asian destinations are considered. The impact of foreign ownership on investment was statistically not different from zero, suggesting that South Asian–based affiliates were not a channel through which foreign parent companies invested in other economies. In contrast, Singapore has become a platform from which foreign firms enter other Association of Southeast Asian Nations countries and the broader East Asia and Pacific region.

ROBUSTNESS CHECKS

The results of the estimation stood up to robustness checks of the analysis and the estimation techniques. First, investors that were not investing in the South Asia Region were removed from the data set, but variation across the noninvestors was sufficient to generate similar results. The relative importance of networks compared with productivity