Measuring outcomes 102

5.3.1 Various outcome indications: Investment, enterprise profit, GDP and PIO

As shown in the model in Chapter 4, the outcomes are supposed to be the target markets’ satisfaction and preference and financial revenues for the locality. It is assumed that an increase or decrease in investment expresses the level of the target market’s preference for the locality. Similarly, the level of enterprise profit would measure the level of satisfaction of investors. GDP per capita is a reasonable measure for possible financial revenues for both, the investor and the locality. The PCI team studied the relationship between the PCI and these three indications with controlling the effect of other factors on them. The PCI research results will be used in Part II. Hence, firstly, the methods used by the PCI team will be reviewed to see the reliability of the PCI research on these issues. Next, the research will take an examination with the outcome indication: private industrial output (PIO) by both domestic (DPIO) and foreign firms (FIO). Particularly, the research uses PIO (sometimes DPIO and FIO separately) per capita to measure the gains which are supposed to be brought when investors consume place products. DPIO and FIO might not measure the entire benefits and revenues created by enterprises operating in localities. However, in terms of financial revenues, it is the major source of the tax base for local governments and of incomes to residents. Another reason for using DPIO and FIO reflects the availability of data over the relevant time-frame. Thus, the level and growth of DPIO and FIO are selected as the criteria representing ‘financial revenues’ in the model.

5.3.2 The issue of initial advantages

A challenge emerging along with the use of these variables to measure the outcomes of place marketing is that these criteria are the end result of a combination of different factors of which the PCI is only one component. Among these the critical factors are:

i) natural position and distance to the attractive and high profitable markets (domestic and international);

ii) initial infrastructure; iii) labour quality;

iv) subsidies from the central government to enterprises;

v) business capacity of enterprises themselves (building strategy, capacity for innovation or so); and

vi) other factors such as industry or business life cycle or international and regional factors.

To be able to analyse the PCI’s effects on these outcomes, it is necessary to separate or control for the influences of these factors. The three latter factors (iv-vi) have little impact when DPOI and FOI are measured and compared between the provinces. Most of the subsidies from the central government (iv) have been poured into state-run enterprises, not into the private sector. If anything, these subsidies tend equally to private enterprises of the provinces because the central government applies common policies. The capacity of enterprises for managing operations, for building strategies or for innovation (vi) is obviously vital for the growth of investment and especially for DPIO. Malesky acknowledges that “some Vietnamese firms in Ha Noi and Ho Chi Minh City have improved the sophistication of their business operations substantially”, but when conducting a large scale survey he remarked that:

… most surveyed entrepreneurs have little knowledge or understanding of these processes. Variance on these factors would be minimal across Vietnamese provinces … for most of the firms in the survey, assessing these factors is rather premature. (Malesky 2006a, p. 2)

If there is a considerable gap, it should be between provinces that have big differences in development level. It is likely that the business level of enterprises in Ho Chi Minh City or Ha Noi is better than those in remote areas such as the Ha Giang or Tuyen Quang provinces. To deal with this matter, the analysis of DPIO and FIO in chapters 7 and 8 uses the method of grouping provinces of similar level together for comparison. Comparison between different levels might be conducted when necessary to highlight a consequence that is opposite to what might be expected. For instance, a province such as Ha Noi or Ho Chi Minh City is expected to get better results than a province with a lower level of development but is, in fact, getting worse results. In particular, there are two groups for which this method is used: the Red River Delta provinces (Chapter 7) and Ha Noi - HCM City (Chapter 8).

The effects of the other factors under (vi) are also likely to be small. The Vietnamese private enterprises have been at an immature level, with small size and low degrees of specialization. This can be seen in all the provinces with private enterprises covering largely diversified kinds of products. In these circumstances, the issues of industry life cycles or business cycles are likely to have, in practice, slight effects. Furthermore, not many Vietnamese private

enterprises have direct relationships with international or regional markets, so that the impact of changes in the world or regional economies on them, through the common macro business environment, can be assumed to be reasonably uniform across provinces.

In contrast to the three latter factors, the first three can be expected to have significant and unequal impacts on business growth across provinces. In this research, they are analysed as initial advantages because they change little over time or are very difficult for provincial governments to change. Proximity to major markets or dynamic centres (i) is a natural advantage and not a consequence of governance activities, although disadvantages of these conditions can be changed in the long term by some activities such as improving infrastructure. Infrastructure (ii) and labour quality (iii) also require a long time to change, and that change is partly as a result of development. Moreover, in the Vietnamese system, these factors are largely under control of the central government.

Controlling for the influence of initial advantages is a prerequisite for evaluating the effect of PCI on the outcome variables. Because the appropriate methods are dependent on the aim and the level of exactness required by the evaluation method in each chapter, this task will be performed in the following chapters.