WITH RISING FOREIGN DIRECT INVESTMENT? AN EMPIRICAL EXAMINATION OF THE CASE OF CHINA

Keywords: Foreign direct investment, Spillover effects, LOEs, Performance, China

HAL is

I. INTRODUCTION

The relationship between inward foreign direct investment (FDI) and the performance of host country locally-owned enterprises (LOEs) has been studied both intensively and extensively. Although the arguments for a positive relationship seem compelling, the results of empirical research are decidedly mixed. Prior studies reported either a positive, indeterminate, or negative relationship [START_REF] Buckley | The impact of inward FDI on the performance of Chinese manufacturing firms[END_REF](Buckley et al. , 2004;;[START_REF] Liu | Productivity spillovers from foreign direct investment: evidence from UK industry level panel data[END_REF]. [START_REF] Görg | Multinational companies and productivity spillovers: a Meta-Analysis[END_REF] sought an explanation for the lack of congruent findings, believing that inconsistent results might be associated with underlying differences between the data sets employed. We argue in this paper that the disarray may be easily attributed to a misspecification of the nature of the relationship.

Theoretical shortcomings have been a severe handicap to investigating the subject. As a result, methodological approaches employed to date have been flawed.

A drawback of the existing literature, with a few notable exceptions, is that it has largely been confined to examining linear forms of relationship. What the inconsistent results suggest, however, is that the relationship is far from a simple linear one. There is considerable evidence to suggest that the form of the relationship might be curvilinear [START_REF] Aitken | Does domestic firms benefit from direct foreign investment? evidence from Venezula[END_REF]. Thus, further evidence on the impact, and the form that this takes, of inward FDI on LOE's performance is still needed to advance theories and to promote the research stream's capability to effectively inform government policy formation. The paper presents results from a study that attempts to remedy previous deficiencies in theory and methodology. We present an approach that addresses explicitly the possibility of both positive and negative spillovers associated with the operations of multinational enterprises (MNEs), which leads to a non-monotonic relationship between the level of foreign presence and LOEs' performance. The resulting empirical model is tested using the latest data for Chinese industry.

The paper finds a considerable support for a curvilinear and inverted U-shaped relationship between foreign presence and spillover benefits. This finding indicates that the currently prevailing euphoria about FDI rests on weak empirical foundations.

It challenges the laissez-faire view that advocates the unfettered inflow of FDI as an optimal policy prescription for the development of Chinese industry. It suggests that in certain circumstances inward FDI can entail winners and losers, corresponding with foreign and indigenous firms, respectively. The presence of foreign firms beyond some level impedes the performance of LOEs in the same industry. The challenge for governments is to use their policy making powers to maximize the economic and social gains from foreign capital and to mitigate the losses for indigenous industry.

Our analysis yields a hitherto unseen pattern of spillover effects.

In section 2, we present a theoretical framework by reviewing the literature. Then a description of the model and data follows. The penultimate section discusses the econometric results. The last section concludes. Spillovers arise from non-market transactions when resources, notably knowledge, are spread without a contructual relationship, so-called externalities [START_REF] Meyer | Perspectives on multinational enterprises in emerging economies[END_REF]. In the empirical literature on FDI, intra-industry spillovers have been defined as the performance-related benefits accruing to domestic firms from foreign firms operating within the same sector. [START_REF] Görg | Much ado about nothing? do domestic firms really benefit from foreign direct investment?[END_REF] mention imitation, the acquisition of skills, competition and exports as channels through which developing host countries may achieve productivity gains via intra-industry spillovers.

A limited number of studies have considered how the relationship between foreign presence and spillover benefits might change as inward FDI rises. Some argue that a moderate foreign presence is all that is required to generate positive spillovers, even when there is a relatively wide technological gap [START_REF] Perez | Multinational enterprises and technological spillovers: an evolutionary model[END_REF]. Evidence that the mere presence of only a few competitors is sufficient to sharpen incentives for allocative efficiency gains is provided in an empirical study of entry thresholds. [START_REF] Bresnahan | Entry and competition in concentrated markets[END_REF] find that most of the competitive impact from entry comes from the first two entrants to challenge a monopolist, with the effect levelling out once market participants number around five. Likewise, demonstration effects that benefit local technical efficiency are available even when foreign presence is moderate [START_REF] Haddad | Are there dynamic externalities from direct foreign investment? evidence for Morocco[END_REF]. The employment of advanced foreign technology in just a few foreign affiliates is all that is needed to prove to local firms the benefits of acquiring this technology. According to this argument, small foreign capital shares are sufficient to generate spillovers, since foreign skills and managerial [START_REF] Hsu | Labor productivity of small and large manufacturing firms: the case of Taiwan[END_REF].

More recently a number of theoretical reasons for negative spillover effects have been put forward. The key argument is that at greater levels of foreign presence, negative effects start to become apparent, and may begin to counteract positive effects. First, the productivity of LOEs might fall owing to a 'market stealing' effect. This is based on the argument that foreign affiliates (FAs) are able to compete at low marginal cost through access to the parent's ownership advantages [START_REF] Aitken | Does domestic firms benefit from direct foreign investment? evidence from Venezula[END_REF]. In final and intermediate markets, incoming FAs are able to draw demand away from LOEs or confine competing LOEs to less profitable segments of industry through the introduction of new differentiated products and of process innovation with improved quality control. Thus, domestic firms that compete directly with the foreign investor may lose market share and, left strand with excess capacity, experience a decline in the productive use of their resources [START_REF] Mody | Is FDI integrating the world economy[END_REF]. There is evidence that the FDI reduces productivity of local firms through market-stealing in the UK manufacturing sectors [START_REF] Mcvicar | Spillovers and foreign direct investment in UK manufacturing[END_REF]. Negative externalities may also arise in factor market where foreign investors increase demand for scarce resources such as skilled labour and domestic credit, and hence raise production costs. Feestra and Hanson (1997) argue that foreign investors will use a more skill-intensive technology than the typical domestic investors and hence raise the wages of skilled workers, a proposition for which they find support in Mexican data [START_REF] Mody | Is FDI integrating the world economy[END_REF] 1 . The labourstealing by foreign firms would deprive local firms of their most productive labour and make the process of assimilating foreign technology harder. Second, senior management in FAs often uses aggressive strategies to secure market share [START_REF] Zhou | The impact of FDI on the productivity of domestic firms: the case of China[END_REF]. This leads to fierce competition, which may drive the LOEs to reduce production and therefore to incur higher average costs [START_REF] Konings | The effects of foreign direct investment on domestic firms: evidence from firm level panel data in emerging economies[END_REF]. This effect will be most pronounced in instances where local firms are subject to large fixed costs, which cannot be easily shed. Inflexible organizational structures in established domestic firms, especially state-owned enterprises (SOEs), will exacerbate this impact and the process of adjustment will be slow and inefficient.

The above reasoning suggests that at higher levels of foreign presence the market share of local firms will be cannibalized, so raising their costs of production and resulting in a 'crowding out' effect. In this situation negative spillover effects can even come to dominate positive effects. This clearly points to the possibility of curvilinearities in the effect of foreign presence on spillovers to locally owned industry. The above discussion implies that spillover benefits will increase with foreign presence up to a point. Beyond this cross-over point increased foreign presence will act at least in part as an impediment to the rate of growth of LOEs' performance, and spillover benefits may start to decline.

The form and the strength of the relationship between foreign presence and spillover benefits, however, may be associated with the characteristics of the industries under consideration [START_REF] Kathuria | Foreign firms, technology transfer and knowledge spillovers to Indian manufacturing firms: a stochastic frontier analysis[END_REF]. Conventional theory on MNEs and FDI suggests that knowledge-based assets are the key firm-specific advantages held by MNEs, which motivate them to invest across borders. Thus, mainstream theoretical perspectives such as the OLI paradigm [START_REF] Dunning | Globalization of Business[END_REF] [START_REF] Caves | Multinational firms, competition and productivity in host country markets[END_REF] and reiterated by [START_REF] Feinberg | Technology spillovers and foreign direct investment in the Indian pharmaceutical industry[END_REF]. These positive effects arise from technology transfer on the basis of demonstration effects and the movement of labour from foreign to locally-owned firms. In addition, foreign firms which produce differentiated products are more likely to compete with LOEs in different market segments of the technology-intensive sectors. This would mean that the scope for negative spillovers is limited since the two types of firms are less likely to be involved in head-to-head competition. It is thus expected that in technologyintensive industries the more FDI there is, the more technologies LOEs can acquire from FAs, and the higher of the performance of LOEs. Indeed, the expectation that inward FDI brings more technology spillovers has motivated many governments to offer attractive incentive packages to entice investors to technology-intensive industries.

In labour-intensive industries, however, more inward FDI may not lead to proportionately more spillover benefits to LOEs. First, the scope for technological spillovers in labour-intensive sectors is limited. This is because MNEs in these industries are not very technologically advanced. Foreign MNEs operating in labourintensive industries may base their competitiveness more on organizational skills and marketing skills, such as experiences in organizing labour-intensive production and the ability to specialize across international borders [START_REF] Shi | Technological assets and the strategy of foreign firms to fnter the China market[END_REF]. It follows that LOEs in labour-intensive industries would not reap much technology spillovers from FAs. unprotected by brand loyalty and, as a result negative primary and secondary effects interact in a reinforcing manner.

The above discussion leads to the following hypotheses:

Hypothesis 1: The relationship between foreign presence and spillover benefits is curvilinear, i.e., past some level of foreign presence, spillover benefits begin to fall.

Hypothesis 2: The relationship between foreign presence and spillover benefits is curvilinear in labour-intensive industries.

Hypothesis 3: The relationship between foreign presence and spillover benefits is linear in technology-intensive industries. In common with standard approach prevailing in the study of FDI spillovers (e.g., [START_REF] Aitken | Does domestic firms benefit from direct foreign investment? evidence from Venezula[END_REF], an augmented Cobb-Douglas production function is implemented in this study. It is assumed that the output in LOEs is a function of inputs, measures of foreign presence in these firms' industry and other control regressors. We interpret coefficient estimates on foreign presence regressor as evidence consistent with spillovers from inward FDI to domestic firms' output.

III. METHODOLOGY

i i i i i i i i FP FP SIZE MGT L K C Y + + + + + + + = 2 ) 1 , ( 6 ) 1 , ( 5 4 3 2 1 (1)
Where Y is sales by LOEs and FP represents the level of presence of FAs, and it is measured in two dimensions in this study: the capital share accounted for by all FAs in each industry ( k FP ) and the employment share accounted by all FAs in each industry ( l FP ). By using two measures of foreign presence, we can get a sense of the robustness of our results. For all FP variables, one-year lag is adopted to allow time for spillovers to grow to be observable. The adoption of a lag structure for the FDI variable (

) 1 , ( i

FP

) also helps to address the question of causality with respect to spillovers 2 [START_REF] Haskel | Does foreign direct investment boost the productivity of domestic firms?[END_REF]. We expect that FP exerts a positive and significant impact onY . preferred because spillovers are commonly hypothesized to fall along industry or regional lines [START_REF] Haskel | Does foreign direct investment boost the productivity of domestic firms?[END_REF]. In addition, there is more variation in the foreign presence variable in industry level data.

There are 196 sectors (at three-digit level) all together which, according to Chinese convention, range from mining and manufacturing to public utilities. Our sample is reduced to 166 industries for two reasons. First, on theoretical grounds we include only those industries which have been largely liberalised, with prices determined in for which data are imperfect are also excluded 6 . Since many of the excluded industries are mining and utilities, most industries left in our sample are pure manufacturing.

Nevertheless, the data that remain provide a reliable and very rich source of information for the investigation of the relationship between foreign presence and spillover benefits.

IV. EMPIRICAL RESULTS

Table 1 provides descriptive statistics and correlation matrix for independent variables to facilitate the interpretation of regression results. As can be seen, most of the correlations are small, accounting for little common variance, and therefore are not of present concern. The high correlation between K and L demonstrates the high capital-labour substitution effect within Chinese industry. The FP variables show high correlations in the expected direction.

(Insert Table 1 here)

Tables 2 and3 report the estimated results. Suffice to say here that in terms of diagnostics, all the models estimated were satisfactory 7 . The first set of results estimated from model (1) of both linear and curvilinear specifications is reported in ) is entered. More importantly, the squared term is statistically significant. We can conclude that the curvilinear specification fits the data better than the linear specification. Columns (3) and ( 4) show the estimates using the measurement of foreign employment share ( l FP ). We find that it makes no material difference to the results. Therefore, we focus only on the estimates using the capital share measure in this study.

(Insert Table 2 here)

The results show that there is a statistically significant, positive relationship between foreign presence and LOEs' performance. Furthermore, there is a negative relationship between foreign presence squared and LOEs' sales. The latter relationship suggests a curvilinear relationship and combined, these two relationships denote a potential inverted-U shaped relationship between foreign presence and LOEs' performance, thereby supporting Hypothesis 1. Thus, in our sample, low and moderate levels of foreign presence are positively related to LOEs' performance, but further rising level of foreign presence is likely to lead to declining spillover benefits to LOEs. The relationships depicted provide some support for the theoretical arguments presented earlier. This finding is also consistent with that of [START_REF] Buckley | The impact of inward FDI on the performance of Chinese manufacturing firms[END_REF], which find some evidence that inward FDI negatively impacts on the performance of Chinese domestic industry. Our review has suggested that the literature offers some reasons for negative competitive impacts in the form of market stealing effects [START_REF] Aitken | Does domestic firms benefit from direct foreign investment? evidence from Venezula[END_REF]. . This partial derivative represents the slope of the spillover benefit curve with respect to k FP . It implies that positive spillover effects reach a maximum at the cross-over point, and subsequently decline as negative effects come to dominate with rising levels of foreign presence.

Hence, by substituting the coefficients, the point of inflection can be obtained.

Based on this procedure, the turning point was in our case 0.619 (61.9 percent). In our sample we identify twenty-three industries as beyond the turning point in the FDIspillover relationship, and find that the majority of industries (143 or 86.2%) lie to the left of this maximum. According to our results, for the most part, domestic Chinese industry is not colonized to an extent that should cause concern.

The discussion in the literature review suggested that technology intensity is a potentially important means of discriminating between those host industries likely to experience differential level of spillover benefits. Accordingly, we test for structural difference in the relationship between foreign capital share and LOEs' performance, on the basis of those industries with high and low capital-labour ratio share. This is done by breaking the sample into two equal groups of 83 industries according to the level of capital-labour ratio.

= F

). Through this method of classification, in addition to alleviating the problem of heterogeneity, the difference in foreign presence effect on LOEs' performance between the labour-and technologyintensive sectors can be explored.

We now re-estimate equation ( 1) for these two groups of Chinese industries separately. The results are presented in Table 3. The variable MGT is dropped since it is insignificant in all regressions throughout in the full sample. In the labourintensive group,

2 k
FP is significant in the curvilinear regression (column ( 2)) and this regression again has higher explanatory power than the linear specification (column (1)). Therefore, the curvilinear specification is still preferable to the linear specification in this group.

(Insert Table 3 FP records larger magnitude in both coefficient and significance in the labour-intensive group than in the full sample.

These results suggest that the proposed curvilinear relationship between foreign presence and the performance of LOEs is most prominent among labour-intensive sectors. This finding corroborates Hypothesis 2. The finding illustrates that it is the labour-intensive industries that dominate the relationship between foreign presence and spillover benefits in the full sample. It also suggests that market stealing effects are even stronger in labour-intensive sectors, in which competition is most intense. We again calculate the point of inflection with respect to the foreign presence variable in column (2). In this case it is 0.542 (54.2%). In the labour-intensive group we find twenty-three sectors as beyond the turning point (quite a coincidence with the case of full sample). Though this result means that the majority of the labourindustries (60 or 72.3%) still lie to the left of this maximum, the lower level of the threshold implies that spillover benefits reach the maximum earlier in labour-intensive industries than in the whole manufacturing. Furthermore, we note that the percentage of sectors which are beyond the cross-over point in the labour-intensive group (27.7%) is twice that in the full-sample (13.8%). This result reinforces our above conclusion with respect to the particularly strong market-stealing effect in the labour-intensive industries.

In technology-intensive group, however, the linear regression (column (3)) is found to be superior to the curvilinear one (column 4)) with the explanatory power being higher in the former. More importantly, the coefficient on 2 k FP is not statistically significant in the curvilinear model, indicating that a curvilinear relationship can not be established in the technology-intensive group.

The positive and significant k FP in column (3) suggests that the performance of LOEs is linearly correlated with rising foreign presence in technology-intensive sectors and in stark contrast to the case of labour-intensive sectors. This result is consistent with previous studies such as [START_REF] Kathuria | Foreign firms, technology transfer and knowledge spillovers to Indian manufacturing firms: a stochastic frontier analysis[END_REF] LOEs, therefore dramatically reducing the scope for market stealing effects. This finding is also consistent with the view that LOEs in technology-intensive industries are more prone to benefit from FDI technological spillovers than their counterparts in labour-intensive industries.

The credibility of our findings is enhanced through the use of other theoretically relevant variables as controls. Labour variable ( L ) is positive and statistically significant at the 1 per cent level in all the regressions throughout, while capital variable ( K ), though significant in the Table 2, records smaller magnitude in both coefficient and significance, and even becomes marginally significant and insignificant in the Table 3. This finding confirms the generally labour intensive nature of the Chinese industry. The management variable ( MGT ) is positive but insignificant, highlighting the weak role of management in the Chinese industry though China has for many years been in its transition to a market-oriented economy. SIZE reaches significance in all instances with an exception of technologyintensive group. This indicates that size of firm (in terms of net fixed assets) is an important strategic variable for business performance in Chinese domestic industry, and suggests the importance of exploiting scale economies. We interpret the lack of significance of SIZE variable in the technology-intensive sectors as an indication that these industries are dominated by small-and medium-sized high technology enterprises to which size of operation is not crucial for business success, compared with firms operating in traditional sectors.

V. CONCLUSIONS

The results provide support for all three hypotheses. The paper finds clear evidence of a curvilinear relationship between foreign presence and spillover benefits to domestically owned industry in China. This curvilinear relationship, compared with linear relationship found in previous studies, defines better the nature of the FDIinduced spillover benefits. The finding suggests that 'the more FDI, the better' thesis in the sense of spillover benefits is only verified in the range defined by the threshold of just about two-third of foreign capital share in the Chinese industries concerned.

Indeed, along with our finding of declining spillovers, another key conclusion of this paper is that moderate levels of foreign presence are beneficial to the performance of Chinese locally owned firms.

Furthermore, we find that the form and the strength of the relationship between foreign presence and LOEs' performance depend on the characteristics of the industry involved. The curvilinear relationship is particularly stronger in labour-intensive industries, where foreign and locally-owned firms compete head-to-head in the saturated market for standardized goods, leading to 'crowding out' effect on domestic firms. This finding is in marked contrast with that for technology-intensive sectors where spillover benefits grow linearly with rising foreign presence. The approach that this study employs is a methodological improvement on existing studies, and our finding of a curvilinear relationship has implications for research on spillovers. We conclude that the mixed, contradictory and weak results found in many studies have been caused, at least in part, by a failure to accurately capture the nonlinear nature of the relationship between foreign presence and spillover benefits.

Methodological improvement along the lines that we advocate is perhaps most critical where the host is a developing rather than a developed country. In developed countries competitive locally-owned firms are present. But in developing countries we should expect the productivity gap between foreign and domestically-owned firms to be large. In these conditions many local firms may be unable to improve their performance quickly. Fast growing and high foreign penetration rates are more likely the less competitive is domestic industry, providing a source of negative productivity spillovers, and an early diminution of spillover benefits.

Policy implications arise from our findings. This study indicates some avenues for future research. First, further investigation at the industrial level will be beneficial. This will help to more accurately determine not simply the point at which the presence of foreign firms ceases to add to spillover benefits, but also what industrial characteristics are involved and what precise policy measures may be used to extend the growth of positive spillovers. Second, if future research can investigate the apparent co-existence in the data of negative and positive spillovers from foreign presence, it may be of considerable value for the fine tuning of policy to limit the negative and encourage the positive effects.

ENDNOTES 1 Chakraborty and Basu (2002) however found that inward FDI in the host country produces a labourdisplacing effect. This arises because the technology transfer brought in by FDI causes an excess supply of labour creating downward pressure on unit labour costs. It seems that further evidence on the impact of FDI on labour costs is still needed. The association between FDI and LOEs' performance may be a result of MNEs entering industries with higher performance, rather than of performance being raised by FDI [START_REF] Meyer | Perspectives on multinational enterprises in emerging economies[END_REF]. While we make optimal use of the data that are available by adopting a lag structure, panel data would be better able to address the issue of causality.

3 This measure of managerial input is recorded in the Chinese statistical source, and refers to the salaries of managers plus other fees that facilitate transactions. 4 The Report is not publicly published and is produced primarily for internal use within the SSB. It bears similarities with the widely-cited Third Industrial Census of China published by the SSB (1997) in contents, and indeed it is produced especially for the years for which the industrial census is not conducted. 5 According to this principle, we excluded industries in which the government prohibits the operation of non state-owned enterprises, and this naturally includes foreign firms. For ease of expression, the estimates for the Constant term are suppressed.

3. The approach for 'normality of distributed errors' also serves to test for outliers and, is sometimes used as a general test for misspecification [START_REF] Kennedy | A Guide to Econometrics[END_REF].

  then transferred via original equipment manufacturing and other mechanisms to local firms

  host country local firms typically have a larger presence in labour-intensive industries, such as 'textile' and 'food', than in technology-intensive industries. These light industries are often characterized by mature saturated markets, in which circumstances the growth of FAs is likely to lead to more fierce head-tohead competition and results in the rapid cannibalization of markets serviced by traditional Chinese domestic firms. The negative effects may arise because of the price competitive nature of these markets. This would mean that local firms are

K

  and L are routine capital and labour, proxied by total received capital and total number of employees in each industry; MGT and SIZE are management input and scale of economy proxied by, respectively, management cost per employee 3 and net fixed assets per firm in an industry. Along with Y , all these control variables are defined for LOEs only. The employment of these control variables increases our robustness of the findings through controlling for influences other than foreign presence.Nonlinearity can be captured by a variety of functional forms. The quadratic form of nonlinearity is selected for Equation (1) because it is a general form capable of identifying both accelerating and decelerating relationships. We are in particular investigating the co-existence of negative and positive spillovers from foreign presence. The quadratic form is appropriate to capture this nonlinear decline. In Equation (1) the data are transformed into natural logarithms (except for FP variables whose values are less than one). The equation is estimated using OLS cross-section regression analysis with[START_REF] White | A Heteroscedasticity consistent covariance matrix estimator and a direct test for Heteroscedasticity[END_REF] heteroscedasticity-consistent covariance matrix correction for unknown form of heteroscedasticity. The examination of related issues is mainly based on the Industrial Annual Report of China for 2001, compiled by the State Statistical Bureau (SSB) of the People's Republic of China. The SSB kindly provided the author with the data on input and output of Chinese industries at different levels of aggregation 4 . Industry data is

  competitive markets and which enjoy largely free entry and exit 5 . FDI in these industries has been encouraged, with almost no restriction at all. This decision allows us to avoid biased results arising from industry selection. Second, industries

  inflection, where spillovers begin to decline, can be computed by taking the partial derivative of regression equation (1) with respect to foreign presence variable (

  are amplified by barriers to exit by Chinese SOEs or by impediments to LOEs' exploitation of scale economies arising from foreign presence.

  a curvilinear effect that begins positive and eventually turns negative fits the theoretical arguments presented. At this point, the complexity of spillovers overwhelms the positive benefits of foreign presence, and LOEs' performance begins to suffer. The results of the study clear up of the mixed findings from previous empirical studies and add insights to otherwise unexpected questions. More empirical validation is necessary for the construction of a more complete theory of the relationship between inward FDI and spillover benefits.

Table 2

 2 so reproducing the standard result. What merits our attention, however, is the results in the curvilinear specification (column (2)). We note that the explanatory power of the models increases when the nonlinear foreign presence term

	statistically significant, ( 2 k FP
	F o
	r
	P e
	e
	r
	R e
	v i
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	12

. The linear specifications of the model (column (1)) show that the effect of foreign presence ( k FP ) on the sales of local Chinese firms ( Y ) is positive and

  firms belonging to the 'scientific' sectors. Our finding of linear profile might be generated by a situation in technology-intensive industries in which negative spillovers are either absent or are more or less invariant with the level of technology while positive spillovers are rising. One interpretation is that in technology-intensive sectors foreign firms tend not to compete head-to-head with

	effects to domestic
	F o
	r
	P e
	e
	r
	R e
	v i
	e w
	who find greater spillover
	16

  The limiting of further growth in foreign presence in liberalized industries is circumscribed by China's accession to the However, it is quite permissible for policy to encourage the upgrading of Chinese domestic industry to keep pace with the growth of foreign presence. The strengthening of local competitiveness might involve the stimulation of new privately owned Chinese firms that are best able to benefit from the presence of FAs. To date the main instrument for building up Chinese firms has been inward FDI itself, and our research suggests that this has been successful. But as foreign penetration shares rise, it becomes imperative to ensure that domestically-owned firms are capable of appropriating the beneficial aspect of spillovers. This study also suggests that China should accelerate the removal of remaining foreign ownership restrictions in Chinese industry. Our results demonstrate that foreign presence is of general benefit across industrial sectors. Above all our findings suggest that a policy mix to promote a moderate rather than a dominant foreign presence is best.

	WTO.
	F o
	r
	P e
	e
	r
	R e
	v i
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	19

Table 1 .

 1 These state monopoly industries include 'Oil extraction', 'Gas extraction', 'Petrolo-shale mining', 'Other black metal ore mining', 'Precious metal ore mining', 'Salt production', 'Tobacco production', 'Other Tobacco processing', and other industries relating to the production and supply of electricity, gas and water. Also excluded are industries where the government is the monopoly or major purchaser of products, and where non-state firms are largely denied from entering. These industries include 'Locomotive manufacturing', 'Far ocean ships', 'Aerospace crafts', 'Telecommunication transmission equipment', 'TV and broadcasting equipment', 'Radar'. The results of the normality test are not highly satisfactory for some models and caution needs to be exerted when interpreting results. It is generally accepted that this test has no properly defined alternatives and so has limited power.8 The literature often links the magnitude of FDI spillovers with technology intensity of the industries involved (e.g.,[START_REF] Liu | Productivity spillovers from foreign direct investment: evidence from UK industry level panel data[END_REF] DescriptiveStatistics and Correlation Matrix (N=166)

	F o
	r
	P e
	e
	r
	R e
	v i
	e w

6

These include 'Other non-metal ore mining', 'Other ore mining', 'Logging of timber', 'Logging of bamboo', 'Salt processing', 'Tramcar manufacturing', 'Instrument repairing', 'Arts and crafts', 'Daily groceries', 'Other groceries'. 7

Table 2 .

 2 Regression Results(Full sample, N=166)

	Dep. Var.: Y	Foreign Capital Share	Foreign Employment Share
				Linear	Curvilinear	Linear	Curvilinear
				(1)	(2)	(3)	(4)
	K			0.343	0.324	0.275	0.261
				(3.12)***	(2.97)***	(2.37)**	(2.29)**
	L			0.577	0.585	0.650	0.659
				(5.01)***	(5.13)***	(5.42)***	(5.57)***
	MGT	0.072	0.069	0.118	0.121
				(0.82)	(0.81)	(1.30)	(1.35)
	SIZE	0.312	0.339	0.296	0.328
				(5.09)***	(5.58)***	(4.71)***	(5.10)***
	k FP	0.625	1.639
				(4.43)***	(3.03)***
	2 k FP		-1.327
					(-2.15)**
	l FP				0.471	1.930
						(2.85)***	(3.86)***
	l FP	2				-2.409
							(-3.25)***
	R	2		0.960	0.962	0.956	0.959
	F-statistics	798.07***	689.07***	727.50***	649.70***
	Functional Form	0.07	0.06	0.38	0.00
	(Ramsey's RESET, one term)
	Normality of Distributed Errors	3.85	1.59	5.21*	2.20
	(Jarque-Bera)
	Notes:

1. Figures in parentheses are

t statistics (two-tailed tests); *, **, and *** denote significance at the 10%, 5% and 1% levels respectively.

Table 3 .

 3 Regression Results (sub-samples)

	Dep. Var.: Y	Labour-intensive sectors	Technology-intensive sectors
			ዊpN=83ዊp		(N=83)
			Linear	Curvilinear	Linear	Curvilinear
			(1)	(2)	(3)	(4)
	K		0.250	0.233	0.246	0.236
			(1.83)*	(1.64)*	(1.35)	(1.29)
	L		0.684	0.696	0.714	0.723
			(5.34)***	(5.21)***	(3.74)***	(3.79)***
	SIZE	0.390	0.420	0.153	0.154
			(4.35)***	(4.90)**	(1.10)	(1.09)
	k FP	0.729	2.634	0.556	0.767
			(3.48)***	(4.21)***	(2.67)***	(0.99)
	2 k FP		-2.432		-0.279
				(-3.17)***		(-0.33)
	R	2	0.942	0.948	0.972	0.971
	F-statistics	317.01***	288.34***	673.54***	532.75***
	Functional Form	0.01	0.03	0.51	0.38
	(Ramsey's RESET, one term)
	Normality of Distributed Errors	3.63	0.89	5.42*	6.18**
	(Jarque-Bera)
	Notes:
		See Table 2.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UKSubmitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


C41 -Duration Analysis < , I18 -Government Policy|Regulation|Public Health < , D12 -Consumer Economics: Empirical Analysis <

Keywords: Duration, Smoking, Split Population I18, D12, C41 Duration, Split Population, Smoking

This paper uses duration analysis to investigate the role of tobacco taxes in starting and quitting smoking. Applying a variety of parametric duration models to a sample of Irish women, it finds that in general tobacco taxes do influence starting and quitting smoking in the expected direction. It also finds that the effect for starting differs by education but in a non-monotonic way, with the greatest effect for women with intermediate levels of education. The results for quitting suggest the greatest effect for women with the lowest level of education. These results are unchanged when account is taken of unobserved heterogeneity.

Introduction

There is ample medical evidence indicating the adverse effects of tobacco consumption upon health (see Mooney, 2000, for a summary). Recent Government publications in Ireland have suggested the long-term goal of a "tobacco-free society".

As the accompanying letter to a Government report stated: "…there is a common objective of seeking the most effective measures possible to dramatically reduce the level of smoking in our society and above all to prevent our children from starting to smoke" (Mooney, 2000). It follows that identifying the factors behind the decision to start smoking and the decision to quit is crucial in terms of formulating policy.

Given this background, this paper has two principal aims. First, it examines the effect of tobacco taxes on starting and quitting smoking, applying duration analysis to a sample of Irish women. Secondly, it explores the extent to which such a tax effect differs by educational background. 1For duration modelling in the case of starting we use the split-population duration model of Schmidt and Witte (1989), Douglas and Harihan (1994) and Foster and Jones (2000) while for quitting we follow the approach of Tauras and Chaloupka (1999) and Foster and Jones (2000) in applying standard parametric models.

The remainder of the paper is as follows: in section 2 we briefly discuss some of the existing literature. In section 3 we discuss our data and describe and present results from our empirical model while section 4 provides concluding comments. It was believed at one time that cigarette smoking and other addictive behaviour was not rational and so not suitable for conventional economic analysis (e.g. Schelling, 1984). There is now however a substantial body of literature to testify that the demand for cigarettes clearly responds to changes in prices and other factors.

Early studies of cigarette demand employed aggregate time-series data and produced estimates of the price elasticity of demand in the region of -0.4. One disadvantage of these studies was that they were unable to distinguish between the elasticity of cigarette demand conditional upon smoking and the elasticity of participation. Later studies used the type of individual level data employed in this study. These studies are able to consider separately the effect of price on the probability of smoking and on average consumption of smokers. Furthermore, studies on the probability of smoking can be divided into those which view starting and quitting as binary events within a discrete choice framework and those which use duration analysis.

Studies which have examined smoking initiation in a discrete choice framework have typically estimated elasticities of participation with respect to tax in the region of -0.5 to -1.0 with an apparent inverse relationship between age and smoking elasticity (for a summary see [START_REF] Chaloupka | The Economics of Smoking[END_REF]. There are fewer applications of duration analysis. Douglas and Harihan (1994) use a split population model to analyse starting smoking. They find no evidence of a statistically significant price effect. [START_REF] Douglas | The Duration of the Smoking Habit[END_REF] analysed the hazards of starting and quitting, once again using a split population model, but this time with an ordered probit, which distinguishes between those who never start smoking, those who start and quit and those who start but do not quit. The "delay" before starting and quitting are modelled using a log- 2 The first part of this section draws upon the excellent survey by [START_REF] Chaloupka | The Economics of Smoking[END_REF]. logistic and Weibull specification respectively. The price of cigarettes is included as a time-varying covariate. The price of cigarettes has no significant effect upon the hazard of starting smoking but the number of years an individual smokes has an approximately unitary elasticity with respect to price. [START_REF] Forster | The Role of Tobacco Taxes in Starting and Quitting Smoking: Duration Analysis of British Data[END_REF] analyse retrospective UK data with a split population model for starting smoking and a variety of parametric duration models for quitting. They find a tax elasticity of the age of starting smoking of +0.16 for men and +0.08 for women. The estimates of the tax elasticity of quitting are -0.6 for men and -0.46 for women. They also include a variety of specification tests and find their estimates to be quite robust. Finally, Lopez-Nicolas (2002) analyses Spanish data, once again using a split-population model, and finds elasticities of delay with respect to starting of 0.07 and elasticities of duration before quitting of -1.3 to -1.5.

One feature of the above duration studies is that, while they include measures of education or income as covariates, they do not interact such variables with the tax, thus constraining the tax/price response to be identical across the distribution of education/income. This is despite evidence from cross-section studies that such a response does differ according to socio-economic characteristics such as income and education. For example, [START_REF] Evans | Tobacco Taxes and Public Policy to Discourage Smoking[END_REF] note that in the US higher-income individuals are less responsive to tax changes than others. Townsend et al (1994), using UK data, found that men and women in lower socio-economic groups are more responsive than are those in higher socio-economic groups to changes in the price of cigarettes and less to publicity concerning the adverse health effects of smoking (this is also implied in the celebrated model of [START_REF] Becker | A Theory of Rational Addiction[END_REF]. [START_REF] Borren | Are Increases in Cigarette Taxation Regressive?[END_REF] find evidence of an "inverse-U" relationship in terms of price responsiveness, with a higher elasticity for middle-income men compared to lower F o r P e e r R e v i e w and higher-income men. Their evidence for women, while less clearcut, appears to indicate that elasticity declines as income increases. We explicitly investigate this issue in this paper by including interaction terms which permit the effect of taxes to differ across the education spectrum.

We complete this section by briefly reviewing the existing Irish studies on tobacco consumption. A variety of models of tobacco consumption have been estimated mostly using aggregate time-series data for Ireland dating from O'Riordan (1969) to Madden (1993). These studies have produced broadly comparable results with a median estimate for the price elasticity of tobacco in the region of -0.5, which is in line with results from elsewhere in the world. The use of aggregate time-series data precludes distinguishing between the effect of price on the probability of smoking and on the demand for cigarettes conditional on smoking. [START_REF] Conniffe | Models of Irish Tobacco Consumption[END_REF] remedies this to some extent by combining analysis of aggregate time-series data with data on the proportion of the total population who are smokers. He found that the proportion of the population smoking is unaffected by price (or income) but exhibits a downward trend related to health concerns. Consumption by smokers does not exhibit such a downward trend but appears to have a significant price elasticity of around -0.3. We now discuss our data and the empirical approach we adopt.

Data and Empirical Model

In this section we discuss our data and the empirical model adopted. Our data comes from a survey known as the Saffron Survey which was carried out in 1998 by the Centre for Health Economics at University College Dublin. The Saffron Survey's aim was to survey women's knowledge, understanding and awareness of their lifetime health needs. Much of the focus of the survey was on the issue of hormone replacement therapy 3 but other information regarding health, lifestyle choices and demographics was also collected. For our purposes in this paper the relevant questions regarding smoking were as follows: " Do you currently smoke?". People who answered "yes" to this question were then asked "For approximately how many years have you smoked?". People who replied that they did not currently smoke were asked had they ever smoked and if they answered yes to this question they too were asked for approximately how many years they had smoked, and in what year they had stopped smoking. From the answers to these questions it is possible to calculate the years people started (and stopped if applicable) smoking. The great advantage of this type of information is that it is possible to examine the effect of the tax rate in each given year on the probability of starting/quitting smoking.

Before the formal analysis it is useful to look at some summary information on our sample. The original sample size for the Saffron survey was 1260. However, we only have tax and price data going back as far as 1960 and so we drop all women who were aged 10 or more in 1960. This is because we are effectively assuming that subjects were at risk of starting smoking from the age of ten, and for women aged ten or over in 1960 we do not have the relevant tax and price data for part of the time when they were at risk of starting smoking (i.e. the period before 1960). Our data suggests that a starting age of ten or over is a reasonable assumption since the number of subjects who reported starting smoking before ten was miniscule. Thus we only have women who were born after 1950, leaving us with a total sample of just over 700. Of these, about half have smoked at some stage of their lives and about 35 per cent were smoking at time of interview. In table 1 we give summary statistics (with 3 See Thompson (2000). There is relatively little difference across the groups by age except for ex-smokers who tend to be older. As might be expected this group also tends to have worse health (which perhaps prompted them to quit smoking). They also show a higher proportion of married, which may reflect people giving up smoking on getting married. Probably the biggest difference across the columns is to be observed in educational attainment. 4 Of the total population (including smokers) over 60 per cent have obtained Leaving Cert or higher, but of those who have ever smoked only about 47 per cent have. This drops to about 44 per cent when we examine those people still smoking in 1998. Thus getting beyond Junior Cert appears to not only lower the chances of starting smoking, but also increases the chances of quitting if you do start to smoke.

As explained above, the Saffron survey was a cross-section survey carried out in 1998. However, we are exploiting the retrospective information which enables us to examine the impact of a time-varying covariate such as tax or price on the decision to start/quit smoking. One issue which must first be discussed in the choice of tax/price.

The choice of such a variable is motivated by the theory of consumer demand which suggests that the quantity consumed (or in this case the decision to consume) will be influenced by a number of factors, including the consumer price (which in turn is influenced by the tax on tobacco).

The tax element in the retail price of a packet of cigarettes has two components, excise duty and value-added tax (VAT). In Ireland there is a specific and an ad valorem excise duty as well as VAT. Thus the specific excise duty is added to the producer price and the ad valorem excise duty and VAT is then applied at the appropriate rate to obtain the retail price. While the retail price is thus influenced by two tax instruments (the rate of excise duty and VAT) it is arguable that only excise duty can be regarded as a specific tax instrument to address smoking, since any increase in the rate of VAT will also cause the prices of many other goods to rise. To engineer a rise in the relative price of tobacco, a rise in excise duty is appropriate.

Unfortunately the data supplied to us by the Revenue Commissioners does not break down the tax component into excise and VAT for the period up to 1973. Thus we have taken the total tax component of the retail price and deflated it by the personal consumption deflator to arrive at a real tax on tobacco. This sidesteps the need for such a breakdown since any excise tax increase in excess of overall inflation will appear as an increase in the real tax whereas increases in VAT will also be reflected in increases in the overall price level and thus contribute less to any increase in the real tax.

We thus have a choice between using the real tax content or the consumer price as the relevant time-varying covariate. It can be argued that from the point of view of the decision which the consumer makes re starting or quitting it is the consumer price which is relevant. On the other hand, from the point of view of government it is the tax content which is the policy variable. However, from a practical point of view, the choice between them is largely irrelevant. As figure 1 shows, the two series move pretty much in tandem and the correlation coefficient between them is 0.97.

We now turn to discuss the more formal analysis of starting and quitting, dealing with starting first. To analyse the decisions to start/quit smoking, we employ duration analysis, with the extra proviso that when examining the decision to start smoking we employ a split population model. When modelling the decision to smoke, we include as one of our covariates the tax rate for the year in question. Thus say we observe woman A, aged 40 in 1998 (the year of the survey) who commenced smoking aged 20 (i.e. in 1978).

Starting Smoking

We assume people are at risk of starting smoking from the age of ten (1968 in the case of woman A). Thus we will have ten observations on woman A where she does not smoke, followed by the transition in year 11 where she does smoke. Each observation for each year for this woman (up to when she starts smoking) is regarded as a separate observation.5 Thus the observation for 1968 for woman A has a duration of one, with the tax rate for 1968 as one of the covariates and is regarded as rightcensored. The observation for 1969 for woman A has a duration of 2, is still rightcensored and has the tax rate for 1969 as a covariate. This continues up to the observation for 1978 where there is a "failure" or transition to smoking.

Consider now the case of woman B with the same age as woman A but who has not started smoking by 1998. In this case there are thirty observations for woman B, all of them right-censored. No "failure" or transition to smoking is observed.

Standard duration models assume that failure will eventually take place. Thus even if the last observation for an individual is right-censored (i.e. in our case they have not started smoking by 1998) it is assumed that at some stage they will start smoking. If, say, we were trying to model the duration of a light bulb then this assumption is realistic. At some stage the light bulb will fail. But the assumption is not realistic for the case of starting smoking, since a substantial proportion of the population never smoke at any stage of their lives. In this case a split population model is appropriate

F o r P e e r R e v i e w
where the likelihood of each observation is weighted by the probability that the individual will ever start to smoke and so the duration analysis is applied only to those individuals predicted to start smoking.

We also estimate log-logistic duration models where the population is not split.

We estimate then for the population as a whole and also for the population of smokers only.

Before discussing the appropriate parametric duration model we first present in figure 2 the plot of the empirical hazard function. This is particularly useful when choosing a parametric hazard function. We use the lifetable estimate of the hazard function (with confidence intervals as shown) and this is qualitatively very similar to the Kaplan-Meier estimate of the empirical hazard.

This figure shows that the hazard increases at first and then decreases. It reaches its peak when the time period equals seven, which corresponds to age seventeen since we assume subjects are at risk of smoking from age ten. There is another local peak at age twenty-one and then a fairly sharp decrease. What this suggests is that a monotonic hazard function is not appropriate for this dataset. A hazard function which at first increases and then decreases seems most appropriate, suggesting the log-logistic model is worth trying.

For the smokers in our sample we infer the age of starting as outlined above and the duration data can be viewed as a complete spell. The sample, of course, also contains individuals who are not observed to have started smoking. A parametric duration model would interpret these individuals as incomplete spells and assume that they will eventually fail and start smoking. They are viewed as "right-censored" at the time of the survey. As explained above this does not appear reasonable when dealing with smoking and consequently Douglas and Harihan (1994) in their analysis data and Foster and Jones (2000) in their analysis of UK data have argued that a split population model be used. In this model duration analysis is applied only to those individuals who are predicted to eventually start smoking.

F o r P e e r R e v i e w of US
Following Foster and Jones (2000) suppose we define 1 = s for an individual who will eventually start smoking and modelling eventual failure (i.e. starting smoking) using a probit specification we have

) ( ) 1 Pr( i z s α ′ Φ = = and ) ( 1 ) 0 Pr( i z s α ′ Φ - = =
where i z is a vector of time invariant covariates, Φ is the cumulative density function for the standard normal distribution and α is a parameter vector. Thus the probability of starting smoking at a given time t is defined conditional upon eventually starting.

Given the plot of the empirical hazard function above the most appropriate parametric duration model is the log-logistic. The probability density function (.) f and the survival function (.) S of the log-logistic distribution for those individuals who eventually start smoking are

2 1 1 1 1 ] ) ( 1 [ )) ( ; 1 | ( γ γ γ λ γ λ t t t x s t f i + ≡ = - γ λ 1 ) ( 1 1 )) ( ; 1 | ( t t x s t S i + ≡ = where )) ( exp( t x i β λ ′ - = , ) (t x i
is a vector of time variant and time-invariant covariates and γ is a scale parameter.

Then the contribution to the log-likelihood function for the split population model becomes, for individual i:

))] ( ; 1 | ( ) ( ) ( 1 ln[ ) 1 ( ))] ( ; 1 | ( ) ( ln[ t x s t S z z c t x s t f z c i i i i i i i = ′ Φ + ′ Φ - - + = ′ Φ α α α F o r P e e r R e v i e

w

For those who are observed as smokers in the sample,

1 = i c
and their contribution to the likelihood function is simply the log of the probability of being a smoker

) ( i z α ′ Φ
times the probability density function of starting at the observed starting age, (.) f .

For those who are observed as not starting (

0 = i c
), the contribution is the log of the probability of never starting

) ( 1 i z α ′ Φ -
plus the probability of starting after the age observed at the time of the survey, (.)

) ( S z i α ′ Φ .
In table 2 to distinguish between these different mechanisms but it is likely that all three (and perhaps others) are at work. 6However, in an attempt to distinguish between these influences to some degree, we include a variable which we label health knowledge. As mentioned above, the Saffron survey collected a variety of information regarding the health habits and needs of women. Owing to its concentration on hormone replacement therapy, a number of questions were asked regarding health knowledge in this area. As our measure of health knowledge we include a dummy variable which measures the response to the question "Have you ever heard of osteoporosis?". 7 Clearly this question refers to a dimension of health which differs from smoking, but we do not believe it is unreasonable to expect that knowledge regarding osteoporosis may be correlated with other aspects of health knowledge, including the health effects of smoking.

There is one further point which must be borne in mind in terms of interpreting the effect of education on the hazard of starting smoking. Some people will have started smoking before completing the higher levels of education, such as Leaving Cert or Third Level. In that respect education is endogenous and could not be regarded as a pre-determined variable. In fact, if we allow for an effect of health upon education as well as an effect of education upon health, it is possible that the very act of starting to smoke will affect future educational achievement. Ideally we would like to instrument for education to remove this endogeneity. However, our

F o r P e e r R e v i e w
data does not include any obvious instrumental variable. Thus for these people education may be capturing the effect of other unobserved factors, which influence not just the probability of starting to smoke, but also the degree of education obtained (e.g. such as the rate of time-preference referred to above). Such an effect is less relevant for the analysis of quitting smoking, since in this case, the vast majority of smokers quit after they have achieved their highest level of education. 8

Ideally we would like to include income as a covariate also. However, the income variable for the data in question is not very satisfactory for a couple of reasons. Firstly, data on this variable is missing for over 20% of the observations whereas data for education is available for all observations. Secondly the income data which is available is presented in the form of income ranges. Thus it is not possible to construct a variable for equivalised income (unless we assume income is at the midpoint of the range), since we do not know the precise value of income which must be equivalised. On balance, it was felt best not to include income, though this runs the risk that the estimated effect of education may be confounded with that of income, presuming the two to be highly correlated. On the other hand, it could be argued that the specification we adopt here be regarded as a reduced form, since it is likely that income itself will be a function of education.

To allow for the possibility of a secular drift in smoking habits over time, perhaps related to increased health awareness or general public intolerance towards smoking we also include a time trend. Given that all the variation in tax rates is attributable to variation across calendar years there may be an identification problem in separating the time trend and tax effects. Following Foster and Jones (2000) we include a higher-order polynomial in time, which allows for a smooth but flexible 8 I am grateful for the comments of an anonymous referee with regard to this point. Before discussing the results it is important to point out that while educational achievement and marital status are factors which clearly will vary over a woman's lifetime, we are not treating them as time-varying covariates in this analysis. We do not have information on age of marriage nor on age of completion of education. Thus even though a women may be classified as "married" she will not have been married

for at least part of the time during which her smoking behaviour is analysed (e.g.

during her early teens). However, if education and/or marital status is correlated with some more fundamental attribute such as an individual's underlying rate of time preference then the use of "ultimate" educational/marital status is justified, to a degree at least.

Bearing in mind the evidence cited above that tax responsiveness may differ by education or socio-economic characteristics we also include specifications with interaction terms which attempt to capture such an effect.

Unobserved Heterogeneity or "Frailty"

The specifications we have outlined above, plus the included covariates may still not explain all the variability in observed time to failure. Frailty may also be "shared" in the sense that the subjects in a given group may experience the same unobserved heterogeneity e.g. the group may represent a family, or, as is the case here, a single subject for which multiple episodes are observed.

Recall that in the case of frailty the hazard function becomes ) (t h α . For purposes of identifiability it is usually assumed that α is distributed with mean one and variance θ . The issue then becomes the estimation of the additional frailty variance, θ . Probably the two most common parametric choices for ) (α g , the probability density function for α , are the gamma and the inverse gaussian. While the associated hazard function for the two distributions are quite alike there is one important distinction. Suppose we have two individuals with common frailty. Conditional on the given frailty their respective hazards are proportional with, say,

c t h t h = ) 1 ( ) 2 ( / ) ( .

Marginally however, for gamma frailties the hazard ratio

c t h t h = ) ( / ) ( 1 ) 2 ( θ θ at 0 = t ,
but diminishes with time so that in the limit the ratio becomes unity.

For the inverse-Gaussian once again suppose that

c t h t h = ) ( / ) ( 1 ) 2 ( θ θ at 0 = t .
However, in this case the limit of this ratio is not unity but 2 / 1 c so that the frailty effect does not diminish completely over time.

Starting Smoking: Results and Discussion

We first of all discuss the results in tables 2, the split-population model and table 3, the log-logistic model applied to smokers only (thus there are no rightcensored observations). The results from the probit for becoming a smoker show the expected negative effect for Leaving Cert (and to a lesser extent for third level Turning now to the duration part of the split population model, when no interaction terms between tax and education are used, the coefficient on tax is in the expected direction (i.e. positive, indicating that higher taxes delay the period before starting), but is not significant. When the interaction terms are used, the coefficient on tax alone is negative but even less well-determined that when no interactions terms are used. The interaction term with Junior Cert is statistically significant however, albeit at only the 10% level. The interaction term with Leaving Cert is similar in magnitude and very near conventional significance levels, while the interaction term with third level is smaller and insignificant. Thus tax has no significant effect on duration for the default group, followed by a positive effect on duration for those with Junior Certificate education. When we test the null hypothesis of equality of any two of the tax interaction coefficients with Junior Cert, Leaving Cert and Third Level with each other, we find we cannot reject the null.

Thus the tax effect for Junior Cert is higher (and statistically significant) than for those with minimal education but not statistically significantly different than the tax effect for Leaving Cert and Third Level. In turn the tax effect for Leaving Cert is almost significantly different than for those with minimal education but different than the tax effect for Third Level, while the tax effect for Third Level is not significantly different than that for minimal education. These results are partly in line with those of Townsend and Borren and Sutton in that the tax effect for those with least education appears smallest. However, our evidence does not clearly support the second part of the "inverse U" type argument, in that we don't find significantly different tax effects between Junior Cert and higher education groups. 9

Turning now to the duration model for smokers only in table 3, we see that in general, the coefficients are better determined. In the model without the interaction terms, the coefficient on tax is similar to that in the split population model, and this time it is statistically significant. Perhaps surprisingly, education has little or no effect. This broadly carries through to the model with interaction terms. Even though the coefficient on third level education is positive and significant, the overall effect of third level education is given by this coefficient plus the product of the coefficient on the interaction term between tax and third level education and the average value of tax. Since it is the log of tax which is used, this second term will be negative, thus offsetting to some degree the positive effect of third level education on duration.

Similarly, the effect of tax for each educational group is given by the sum of the overall coefficient for tax and the interaction coefficient. In this respect we see tentative evidence once again of the first part of an "inverse-U" effect with the greatest impact of tax upon those with Junior Cert education. Evidence of the second part of the inverse U is much less clearcut since once again when we test the null hypothesis of equality of any two of the tax interaction coefficients with Junior Cert, Leaving Cert and Third Level with each other, we find we cannot reject the null.

In table 4 we present evidence for the log-logistic model, this time applied to the population as a whole i.e. there are some right-censored observations. We also 9 I am grateful for the comments of an anonymous referee with regard to this point. include the frailty models here, given that the p-values for the LR test for frailty indicates its presence. 10 For the models without interaction terms, the coefficient on tax is in the expected direction and for the model without frailty and the model with inverse gauss frailty the coefficients are significant (and similar in magnitude). The results when interaction terms are used are similar across models and once again indicate non-monotonicity but interestingly the greatest effect of tax is now seen for those with Leaving Cert rather than Junior Cert. What is also of interest is that here we see much clearer evidence of the full "inverse U "effect in that not only is the coefficient on the tax/Leaving Cert variable significantly different from zero, it is also significantly different from that on tax/Third Level (p=0.0815) and very near to being significantly different from the tax/Junior Cert variable (p=0.1227). Results for the models with frailty are qualitatively similar and available on request.

Summarising the results from tables 2-4 it seems fair to say that tax does appear to have an effect on duration before starting and there is evidence that the effect for Junior Cert is greater than the effect for minimal levels of education.

However the evidence that this effect diminishes again as we move to higher levels of education is much less clearcut.

When using a parametric duration model it is important to determine whether the data support the particular parametric form of the hazard function. Probably the most frequently employed method is to use the model based estimate of the cumulative hazard function to form what is known as the [START_REF] Cox | A General Definition of Residuals (with discussion)[END_REF] Figures 3 and4 show such plots for the log-logistic model for starting smoking for smokers only and then for the total population (for brevity we include only the specifications with the interaction terms with education. The plots for the other specifications are available on request).

In evaluating these plots care must be taken regarding the scale of the axes. For the case of smokers only, the residuals deviate from the 45 degree line after the estimated cumulative hazard takes a value of around 1.3 indicating a limited degree of misspecification. For the whole population this deviation is observed when the cumulative hazard is around 0.9, suggesting a greater degree of misspecification for this model.

Quitting Smoking: Results and Discussion

For the case of quitting smoking the analysis is in many ways the mirror image of the analysis reported above. In this case the transition, or "failure", is the act of observations. We do not employ the split-population model for quitting since it seems more reasonable to assume that from a population of smokers, all, or at least a majority of them, will quit or would eventually quit if they could be observed for long enough, than to assume that from a population of non-smokers, all will eventually start smoking. Also as [START_REF] Forster | The Role of Tobacco Taxes in Starting and Quitting Smoking: Duration Analysis of British Data[END_REF] point out, it may be extremely difficult to identify the separation between those who will eventually quit and those who will never quit from data of this sort. 11 Figure 5 shows the plot of the empirical hazard function for quitting, once again using the lifetable estimate with confidence intervals.

Apart from the spikes at ten and twenty years there is relatively little evidence of an increasing or decreasing hazard. 12 This suggests that amongst parametric duration models, the exponential or Weibull might be an appropriate choice. The survival function for the exponential model (in accelerated failure time format) is: . We also include estimates from the generalised gamma model. This is an extremely flexible model which nests both the exponential and Weibull model. Its

survival function is )) exp( ( 1 ) ( κ κ z I t S - =
where I is the incomplete gamma 11 We estimated a split population model for quitting and obtained results qualitatively similar to those in tables 5 and 6. Results are available on request. 12 These spikes suggest that "heaping" may be present i.e. when asked when they quit smoking respondents round off their answers to "ten years ago" or "twenty years ago". Following one of the strategies proposed in [START_REF] Forster | The Role of Tobacco Taxes in Starting and Quitting Smoking: Duration Analysis of British Data[END_REF] we include dummy variables for 10, 20 and 30 years in the models estimated in tables 5 and 6. The results are virtually identical to those presented here and are available on request. Table 5 gives the results for the exponential and gamma models while table 6 gives results for the Weibull model where we have also included frailty models (with the results presented in accelerated time format). 13 Note that in terms of intuition, we expect coefficients to be of opposite sign to the starting models, since here we are estimating the effect of variables on the delay before quitting. Looking at the models first of all without interaction effects, we note that in all cases the coefficient on tax has the expected negative sign i.e. higher tax reduces the delay before quitting. The magnitude of the coefficient is also very similar across models but in no case is it statistically significant.

When the interaction terms are included the coefficient on tax increases in absolute size and becomes statistically significant for the non-frailty specifications.

The coefficients on the interaction terms are only significant for Junior Cert for the gamma and Weibull models. For all models it is also the case that when we test the null hypothesis of equality of any two of the tax interaction coefficients with Junior Cert, Leaving Cert and Third Level with each other we do not reject the null. Thus there is reasonable evidence that the tax effect for Junior Cert is less than that for those with minimal education, but no evidence that the tax effect for Junior differs significantly from that for Leaving and Third Level. This is somewhat at odds with the evidence for starting. However, given that relatively few of the coefficients are well determined such statements must be made with caution.

Since the parametric models are nested it is possible to check their validity against each other, which is carried out in table 7. In all cases of the test for σ=1 i.e. 13 The null hypothesis of no frailty was not rejected for the gamma and exponential models between Weibull and exponential, the null is rejected, indicating the Weibull is to be preferred over the exponential. For the test κ=1. i.e. between gamma and Weibull, the null is rejected for the case with interaction terms, indicating a choice of gamma over Weibull, while it is not rejected when interaction terms are not included. We also present values of the Aikake information criterion, whereby AIC=-2 log likelihood +2(c+p+1) where c is the number of model covariates and p the number of model specific ancillary parameters. The model with the lowest AIC is to be preferred. We see that in terms of AIC the exponential model has values well in excess of the gamma or Weibull, but there is very little to choose between the Weibull and gamma specifications. The estimated coefficients for these models are also quite similar in sign and magnitude. We also note that all models have a Ramsey RESET p-value in excess of 0.8. What about the Cox-Snell residuals? Figures 6 to 8 present Cox-Snell residuals and once again for the sake of brevity we present only the residuals where the interaction terms are included (the residuals for the models without interaction terms are available on request).

Casual eyeballing of these plots suggests the greatest degree of misspecification for the exponential model with relatively little to choose between the gamma and Weibull models, though perhaps the gamma model is to be marginally preferred. This is entirely consistent with the results from table 7.

Discussion and Conclusion

This paper has examined the factors influencing starting and quitting smoking for a sample of Irish women using duration analysis. The innovation in the paper is that retrospective data in the sample allows for the inclusion of the real tax on tobacco as a time-varying covariate, thus permitting analysis of the effectiveness of a major policy variable in terms of combating smoking. The inclusion of interaction terms with education also permits analysis of how the effectiveness of taxation differs according to education. The evidence presented here is mixed. In terms of starting smoking, there is limited evidence of at least the first part of an "inverse-U" effect, with the strongest effect of taxation on those with intermediate levels of education and weaker effects for those with least education. Evidence on the second part of the inverse U effect i.e. a stronger effect for intermediate education than for higher levels of education is less convincing. It has to be stressed however, that these results are relatively tentative given the calculated significance levels. There is also some evidence of misspecification in the diagnostics.

The evidence for quitting suggests an even more complex relationship between education and the effectiveness of taxation. Tax seems to be most effective in terms of encouraging quitting for those with the least education. However, there seems to be little evidence of any significant difference in the effectiveness of taxation between Junior, Leaving and Third Level.

Overall, the results in this paper are probably more suggestive than definitive in terms of the role of tobacco taxes in influencing starting and quitting. It should also be borne in mind that the results here apply only to a sample of women aged 48 or under. However, given the wealth of results from other countries and time periods regarding the effect of taxes and prices on tobacco consumption, it is clear that tobacco taxation is likely to remain a major instrument in public policy to discourage smoking. 1 9 6 1 1 9 6 3 1 9 6 5 1 9 6 7 1 9 6 9 1 9 7 1 1 9 7 3 1 9 7 5 1 9 7 7 1 9 7 9 1 9 8 1 1 9 8 3 1 9 8 5 1 9 8 7 1 9 8 9 1 9 9 1 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9

F

  brackets) for a number of key variables for the various subgroups in our sample.

  Finally, we also include a cohort dummy, which takes on a value of one if the individual is aged 33 or less and a dummy variable for marital status.

  to the default category of primary education only. What is also of interest is the positive coefficient on Junior Cert indicating a non-monotonic effect of education on the probability of becoming a smoker. The results also show that marriage has a negative effect while younger cohorts are more likely to become smokers.

  quitting smoking. Thus a person who has smoked for say 10 years and then quits represents ten observations, where the duration variable increases by one each year.Each year up to the point of quitting is regarded as right-censored and then the quitting year is the transition year. A person who say starts smoking in 1988 and has not quit by 1998 (the year of the survey) is simply treated as having ten right-censored

  are as before. Clearly the exponential model is a special case of the Weibull model, where 1 = p

Figure

  Figure 1: Tobacco Taxes and Prices 1960-1998

  we present estimates of the above split population model and in tables 3 and 4 we present estimates of the log-logistic model where the population is not split. In tables 3 and 4 we apply the log-logistic model to the whole sample and

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also to the sample of smokers only. We include both time-varying and non-timevarying covariates in these models. Education is frequently regarded as an important determinant of health and smoking habits (see

Meara, 2000)

and we include categorical variables for Junior Certificate, Leaving Certificate and third level. The default category is Primary Education whereby formal schooling ends at about twelve years of age.

The mechanism whereby education affects smoking behaviour is unclear. It may indicate that more educated people simply have more information regarding the effects of smoking upon health. It may also indicate that more educated people are better able to process or act upon information on regarding the health effects of smoking. Finally it may reflect the presence of a "third" variable whereby which simultaneously influences attitudes towards both education and smoking/health. Thus individuals with a low discount rate (i.e. they are more "future-oriented") will invest in both their health capital (by refraining from activities such as smoking) and their human capital. In the absence of reliable measures of such discount rates it is difficult

  The excess unexplained variability, or overdispersion, may be caused by misspecification or omitted covariates. In survival analysis this is known as "frailty" since the model is unable to explain fully why subjects with shorter time to failure are more frail than others. A frailty model attempts to measure this overdispersion by modelling it as resulting

	from a latent multiplicative effect on the hazard function. Thus given a hazard
	function	) h the hazard becomes (t	h α	(t	)	.
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  residuals. Cox and Snell argued that if we have n subjects then if the correct model has been fitted to the data, these residuals are n observations from an exponential distribution with unit mean. Thus a plot of the model-based cumulative hazard against the cumulative hazard function obtained from a nonparametric or empirical estimator should yield a straight line with slope equal to one if the parametric model is correct.

	( ˆj j t S	)	is the estimated survival function.
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	The Cox-Snell residual for subject j at time j t is defined as	( ˆj j ln ) ( j j t S t H -=	)	, the
	estimated cumulative hazard function obtained from the fitted model, given that

10

The presence of frailty was decisively rejected for the models run on smokers only. Hence we do not include them. They are available on request.

., P. Roderick and J. Cooper (1994): "

  "How Important are Tobacco Prices in the Propensity to Start and Quit Smoking? An Analysis of Smoking Histories from the Spanish Cigarette Smoking by Socioeconomic Group, Sex and Age: Effects of Price, Income and Health Publicity",British Medical Journal, Vol. 309,

	o F o López Nicolás, A., (2002): F o Townsend, JF o
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Table 1 : Summary Statistics for Sample

 1

	Variable	Total	Ever Smoked	Smokers	Ex-Smokers
		(N=703)	(N=348)	(N=246)	(N=102)
	Age	34.38265	34.70977	33.02846	38.76471
		(8.697518)	(8.701864)	(8.511409)	(7.806035)
	Age Started Smoking		19.35777	19.37295	19.31959
			(5.294379)	(5.169976)	(5.622771)
	Health Problem	.1038407	.1264368	.1178862	.1470588
		(.305271)	(.3328195)	(.3231307)	(.3559135)
	Single	.3499289	.3390805	.4065041	.1764706
		(.4772868)	(.4740788)	(.4921821)	(.3831026)
	Married	.5860597	.591954	.5284553	.745098
		(.4928887)	(.4921794)	(.5002074)	(.4379582)
	Widowed	.0085349	.0057471		.0196078
		(.0920547)	(.0757005)		(.1393331)
	Separated	.0554765	.0632184	.0650407	.0588235
		(.229071)	(.2437058)	(.2471003)	(.2364561)
	Primary Education	.1052632	.1293103	.1463415	.0882353
		(.3071107)	(.3360263)	(.3541688)	(.2850375)
	Junior Cert	.2745377	.3821839	.4146341	.3039216
		(.4465988)	(.4866208)	(.4936632)	(.4622205)
	Leaving Cert	.4054054	.3189655	.2723577	.4313725
		(.4913199)	(.4667468)	(.4460806)	(.4977137)
	Third Level	.2147937	.1695402	.1666667	.1764706
		(.4109716)	(.3757687)	(.3734378)	(.3831026)
	Working	.5504979	.4971264	.4756098	.5490196
		(.4977976)	(.5007117)	(.5004229)	(.5000485)
	Cigarettes per Day		15.47977	15.0449	16.53465
			(8.827224)	(8.063403)	(10.42071)

Table 2 : Split Population Duration Model for Starting Smoking (N=11733)

 2

		Duration	Probit	Duration	Probit
	Junior Cert	0.019	0.212	0.482	0.206
		(0.168)	(0.090)**	(0.330)	(0.092)**
	Leaving Cert	-0.097	-0.231	0.372	-0.234
		(0.178)	(0.089)***	(0.346)	(0.090)***
	Third Level	0.291	-0.158	0.516	-0.177
		(0.207)	(0.103)	(0.383)	(0.105)*
	Ln (Tax)	0.451		-0.268
		(0.391)		(0.728)
	Cohort	0.258	0.205	0.232	0.206
		(0.267)	(0.065)***	(0.296)	(0.067)***
	Health Knowledge	0.211	-0.077	0.206	-0.080
		(0.144)	(0.079)	(0.155)	(0.080)
	Married	0.054	-0.115	-0.034	-0.122
		(0.123)	(0.063)*	(0.131)	(0.065)*
	Tax*Junior Cert			1.214
				(0.728)*
	Tax*Leaving Cert			1.123
				(0.763)
	Tax*Third Level			0.681
				(0.793)
	Time	0.074		0.064
		(0.077)		(0.083)
	Time^2/100	-0.310		-0.216
		(0.470)		(0.499)
	Time^3/1000	0.012		-0.013
		(0.085)		(0.090)
	Constant	1.618	-1.553	1.494	-1.528
		(0.386)***	(0.100)***	(0.448)***	(0.102)***
	Gamma	0.412		0.434
		(0.032)***		(0.035)***
	Standard errors in
	parentheses
	* significant at 10%; ** significant at
	5%; *** significant at 1%

Table 3 : Log-Logistic Duration Model for Starting Smoking, Smokers Only (N=3202)

 3

	Junior Cert	-0.094	0.312
		(0.114)	(0.200)
	Leaving Cert	-0.104	0.273
		(0.120)	(0.213)
	Third Level	0.084	0.454
		(0.122)	(0.217)**
	Ln (Tax)	0.523	-0.321
		(0.293)*	(0.472)
	Cohort	-0.143	-0.154
		(0.138)	(0.140)
	Health Know.	0.205	0.203
		(0.092)**	(0.096)**
	Married	0.038	0.031
		(0.071)	(0.072)
	Tax*Junior		0.994
			(0.441)**
	Tax*Leaving		0.932
			(0.491)*
	Tax*3 rd Level		0.907
			(0.467)*
	Time	0.074	0.066
		(0.062)	(0.063)
	Time^2/100	-0.150	-0.106
		(0.329)	(0.335)
	Time^3/1000	-0.023	-0.030
		(0.053)	(0.054)
	Constant	1.730	1.441
		(0.324)***	(0.336)***
	Gamma	0.349	0.348
		(0.022)***	(0.022)***
	Observations	3202	3202
	Standard
	errors in
	parentheses
	* significant at 10%; ** significant at 5%; ***
		significant at 1%

Table 4 : Log-Logistic Duration Model for Starting Smoking, Total Population (N=11733), S.E. in parentheses, * significant at 10%; ** significant at 5%;*** significant at 1%

 4

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Table 5 : Gamma and Exponential Models for Quitting (AFT format, N=8625), S.E. in parentheses, * significant at 10%; ** significant at 5%;*** significant at 1%

 5

		Gamma	Gamma	Exponential	Exponential
	Junior Cert	-0.133	0.205	-0.113	0.217
		(0.165)	(0.253)	(0.200)	(0.311)
	Leaving Cert	-0.498	-0.262	-0.548	-0.359
		(0.163)***	(0.240)	(0.193)***	(0.290)
	Third Level	-0.799	-0.486	-0.804	-0.527
		(0.319)**	(0.276)*	(0.254)***	(0.334)
	Ln (Tax)	-0.380	-1.170	-0.614	-1.379
		(0.453)	(0.639)*	(0.588)	(0.814)*
	Cohort	0.067	0.018	0.594	0.560
		(0.267)	(0.238)	(0.275)**	(0.276)**
	Health Know	-0.546	-0.541	-0.791	-0.791
		(0.242)**	(0.216)**	(0.298)***	(0.298)***
	Married	-0.099	-0.097	-0.044	-0.041
		(0.129)	(0.128)	(0.163)	(0.163)
	Tax*Junior		1.140		1.209
			(0.644)*		(0.851)
	Tax*Leaving		0.798		0.694
			(0.639)		(0.817)
	Tax*3 rd Level		1.136		1.060
			(0.781)		(0.980)
	Time	0.006	-0.001	0.019	0.015
		(0.070)	(0.069)	(0.090)	(0.091)
	Time^2/100	0.040	0.080	-0.060	-0.038
		(0.386)	(0.377)	(0.486)	(0.489)
	Time^3/1000	-0.014	-0.020	-0.002	-0.005
		(0.063)	(0.060)	(0.076)	(0.076)
	Constant	4.125	3.902	4.560	4.363
		(0.364)***	(0.369)***	(0.470)***	(0.498)***
	Ln(σ)	-0.152	-0.141
		(0.312)	(0.136)
	κ=1 (Wald p)	0.534	0.064
		(0.608)	(0.226)***
	Log Likelihood	-424.44	-422.769	-435.573	-434.554
	AIC	861.9	861.5	882.1	883.1
	RESET (Wald p)	0.961	0.963	0.958	0.823

Table 6 : Weibull Models for Quitting (AFT format, N=8625), S.E. in parentheses, * significant at 10%; ** significant at 5%;*** significant at 1%

 6

	Gamma	Gamma	Inv Gauss	Inv Gauss
	Frailty	Frailty	Frailty	Frailty

Table 7 : Specification Tests for Parametric Models for Duration up to Quitting

 7

		Gamma	Exponential	Weibull
			Interaction		Interaction		Interaction
	Log L	-424.44	-422.769	-435.573	-434.554	-425.248	-423.78
	Reset (p)	0.961	0.963	0.958	0.823	0.997	0.921
	AIC	861.9	861.5	882.1	883.1	862.5	862.6
	Test G-W	0.534	0.064
		(0.608)	(0.226)***
	Test W-E					0.345 (0.066)***	0.351 (0.066)***

Empirical Hazard, Starting Smoking Figure 3: Cox-Snell Residuals, Log-logistic model, smokers only

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		Hazard		.05
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Cox-Snell Residuals, Log-logistic model, smokers and non-smokers

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Cox-Snell Residuals for Gamma Model

	H	cum. Cox-Snell residual
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	.000887
	.000887	1.87806
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	Figure 7:

Cox-Snell Residuals for Weibull Model

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Cox-Snell Residuals for Exponential Model

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Submitted Manuscript

Note that we are not trying to estimate what the optimal tax on tobacco should be. For a discussion in the Irish case, seeMadden (2002).

The default category for education is "Primary Cert" indicating that formal schooling ended at approximately the age of 12. "Junior Cert" indicates formal schooling ceased at approximately 16, while "Leaving Cert" indicates schooling ended at approximately 18.

This expansion of the data has the effect of increasing the sample size. Hence the sample sizes in tables 2-6 differ from that in table 1. Standard errors are adjusted to take account of such clustering.

For a discussion on the relative importance of these mechanisms for the link between smoking, health and socio-economic status, seeMeara (2001).

While knowledge about osteoporosis may appear to be a narrow definition of health knowledge, it may be a suitable measure for our sample. Typically osteoporosis is more common amongst older women. Given that our sample are all aged 48 or less, this reduces the chances that knowledge regarding it comes from direct experience but instead comes from being generally well-informed on health issues. Also since smoking increases the risk factor for osteoporosis it may be a good proxy for health knowledge specifically related to smoking.

National Health Survey", Health Economics, Vol. 11, No. 6, pp. 521-535. Madden, D., (1993): "A New Set of Consumer Demand Estimates for Ireland", Economic and Social Review, Vol. 24,.

- ---------------.,(2002): "Setting the Appropriate Tax on Cigarettes in Ireland", in Budget Perspectives 2003 (eds. T. Callan, D. Madden and D. McCoy). Economic and Social Research Institute, Dublin, 2002. Meara, E (2001): "Why is Health Related to Socioeconomic Status?", NBER Working Paper, W8231. Mooney T. (2000): Towards a Tobacco Free Society : Report of the Tobacco Free Policy Review Group. Dublin : Department of Health and Children. O'Riordan, W. (1969): "Price Elasticity of Demand for Tobacco in Ireland", Economic and Social Review, Vol. 1, pp. 109-115. Schelling, T., (1984): "Self-command in practice, in policy and in a theory of rational choice", American Economic Review, Vol. 74, pp. 1-11. Schmidt, P and A. Witte (1989): "Predicting Criminal Recidivism using 'Split Population' Survival Times", Journal of Econometrics, Vol. 40, pp. 141-159. Tauras, J. and F. Chaloupka (1999): "Determinants of Smoking Cessation: an Analysis of Young Adult men and Women", NBER Working Paper, W7262. Thompson, J., (2000): Economic Aspects of Women's Health with regard to Hormone Replacement Therapy in Ireland, unpublished MA thesis, Economics Department, University College Dublin.