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Thai Binh is the province for which the predicted level of the PIO variable according to equation (8) exceeds the actual level by the greatest amount. According to the preferred relationship, PIO in 2008 should be 6.8 million VND but it was only 2.6 million VND. The high predicted level of PIO reflects its high PCI score in 2005 (8th in the country) and the high level of initial advantages assessed by the PCI team, and is in spite of the fact that its PCI score has fallen significantly over 2005-08. But, as has been argued above, the PCI estimates seriously overestimate the initial advantages of Thai Binh, both because of poor

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transport and location conditions and because Thai Binh’s very high ranking in terms of Development Level (Human Capital) overstates the importance of Thai Binh’s high schooling level in terms of economic development. The over-prediction by equation (8) of PIO in Thai Binh can be explained by these over-estimations of Thai Binh’s initial advantages – in terms of the coefficients of the equation, a reduction of 32% in terms of Thai Binh’s initial advantage score would be sufficient to eliminate the over-prediction.

7.4.3 Ha Tay

Ha Tay provides a case of great interest. It has strong initial conditions, even on the PCI team’s estimates, but had low PIO per capita in 2008 (only 2.85 VND million per capita, less than half the unweighted national average) and the lowest PCI in 2005 in all of the 42 provinces assessed in that year, although it increased significantly over 2005-08. As pointed out above, Ha Tay has very strong initial advantages, the strongest among the eight provinces being studied here, and probably stronger than assessed by the PCI team.

While equation (8) again performs relatively well in the case of Ha Tay, the actual level of PIO is still about 10% below the predicted level, and the extent of over-prediction would be increased if a more realistic assessment of Ha Tay’s initial advantages was included. It is clear that the dominant factor in Ha Tay’s poor performance is the very low level of the PCI, and indeed the Ha Tay case (as also that of Nam Dinh and Vinh Phuc below) suggests that the relationship between the PCI and PIO may be non-linear around the mean – that is that large variations above or below the mean generate more than proportional variations in PIO.

7.4.4 Nam Dinh

A similar point applies to Nam Dinh, which has initial advantages, by both measures, in the middle range, a low PCI in 2008 (45.97, 38th out of 42 provinces) and only a modest improvement over 2005-08, and a low PIO in 2008 (2.52 VND per capita, only about 40% of the national average). Equation (8) again over-predicts PIO in 2008, in the case of Nam Dinh by nearly 60%. This again suggests non-linearity in the PCI coefficient, with a very low level of place marketing having a more than proportional effect on PIO, perhaps in the context of only average initial conditions.

7.4.5 Hai Duong

Hai Duong has reasonably strong initial advantages as assessed by the PCI team, and these do not include the high quality transport links that it possesses, with good links to Ha Noi and to the international port at Hai Phong, discussed above. Its PCI score in 2005 was relatively low (45.79, 39th out of 42) but it has increased strongly (8.28 points) over 2005-08. Taking account of these factors equation (8) over-predicts PIO output in Hai Duong in 2008 (4.33 VND million per capita) by about 14%. This deviation is interpreted as being largely due to the high quality transport links not included in the PCI measure, especially in the light of more than half of Hai Duong’s PIO in 2008 being produced by foreign enterprises.

7.4.6 Vinh Phuc

Two things stand out in the case of Vinh Phuc: its high PCI scores and its very high level of PIO per capita. The province’s PCI score in 2005 was 65.09, fifth among the 42 provinces, and the score increase to 69.47 by 2008, placing Vinh Phuc third among all Vietnamese provinces in that year. By 2008 Vinh Phuc’s PIO level was 20.7 VND million per capita, placing it fourth among all Vietnamese provinces. Vinh Phuc’s initial advantages are modest at best, as it is the second lowest of the eight Red River Delta provinces on the PCI team’s assessment and it is also in the lower group of the eight in terms of national transport infrastructure and other locational issues discussed above (Table 7.3)

Equation (8) predicts a high level of PIO in Vinh Phuc in 2008, 8.5 VND million per capita, but this is well below the actual figure of 20.7. Several points seem relevant here. It is likely that the simple equation underestimates the impact of sustained high quality governance and place market, in part because the effects of success are likely to be cumulative. If firms invest in a province and have a successful and profitable experience, this will not only induce them to invest further but also provide a demonstration effect to other firms. It is also notable that in 2008 nearly 90% of Vinh Phuc’s PIO was produced by foreign firms, and it may well be the case that the impact of sustained, high quality place marketing is particularly effective with foreign firms.

The case study of Vinh Phuc in Chapter 9 indicates that the key problems of infrastructure and the establishment of dynamic and cooperative relationships with investors were addressed early by the Vinh Phuc government, and that their programs were developed and

enhanced over the years. The analysis of successful factors in place in Vinh Phuc, and consideration of how compatible these factors are with the model of place marketing developed in Chapter 4, will be undertaken in detail in Chapter 9.

7.5 Conclusion

In examining the experiences of these provinces in the Red River Delta, the relationship developed in Chapter 6 is broadly confirmed. According to this relationship PIO per capita in 2008 is determined by initial conditions, by the opening level of the PCI (in 2005) and by the change in PCI over the period 2005-08. Excluding two provinces where special factors (Vinh Phuc and Thai Binh) appear to be at work, the estimated relationship explains 76.2% of the variation between actual PIO per capita in 2008 and the average of all provinces in that year. In other words, for these six provinces initial conditions and the PCI explain over three quarters of the difference in output per capita in these provinces relative to the all-province average. In terms of the two excluded provinces, Vinh Phuc has a much higher PIO than predicted, while that of Thai Binh is much lower than predicted.

However, a theme of this chapter has been that the measures of initial conditions prepared by the PCI team are inadequate, in that they do not take sufficient account of the key role of transport and related infrastructure conditions in facilitating economic development within provinces. A brief review of these conditions for the eight provinces was undertaken, and assessments were developed by the author for the relative position of the eight provinces in terms of transport and related conditions. These assessments, and the supporting analysis, show that the provinces differ substantially in this regard, from Ha Tay with the strongest transport facilities and Thai Binh with the poorest in terms of accessibility to the major markets and development centres, and do suggest that their exclusion from the PCI team measures is a significant omission.

There are a number of specific conclusions from the consideration of the transport and location assessments in the context of the previous relationship between output, initial conditions and the PCI. First, the lower than predicted level of output per capita for Thai Binh may be explained in good part by its very low level of transport infrastructure. Secondly, the above average (and above predicted) level of output per capita in Bac Ninh has been assisted by a high level of transport infrastructure, and this is the case to a lesser degree also for Hung

Yen. Thirdly, however, the transport scores accentuate the puzzles about Ha Tay and Vinh Phuc. Ha Tay has the best transport facilities of any of the eight provinces reviewed, and is quite high in terms of other initial conditions but has a very low level of PIO per capita, both in actual terms (less than half the national average in 2008) and relative to predicted levels. It did, however, have a very low PCI value in 2005, although this has increased somewhat over time. By contrast, Vinh Phuc has a very high level of PIO per capita, both in actual and predicted terms, but is one of the poorer of the eight provinces in terms of both transport and other initial conditions. However it has a consistently high PCI score, being one of the leaders in the country.

These findings both reinforce the need to consider transport infrastructure and location seriously as part of initial conditions and the role of place marketing/PCI in influencing PIO. They also suggest that there might be lags and/or non-linearities in the output/initial conditions/PCI relationship not captured in the analyses reported in Chapter 6. That is, provinces with low initial conditions and PCI values may find it difficult to grow rapidly even with good policy, while the impact of either very good PCI scores (Vinh Phuc) or of very low scores (Ha Tay) may be larger and more enduring than suggested by the simple linear relationships discussed in Chapter 6. It is clear that a fuller understanding of the cases of Vinh Phuc and Ha Tay may throw further light on the role of place marketing implementation models in Vietnam, and this issue is taken up in Chapter 9.