Javier Alonso

Ernst F Schumacher Small Is Beautiful

Health, Education, and Welfare, O47 -Measurement of Economic Growth|Aggregate Productivity < O4 -Economic Growth and Aggregate Productivity

Keywords: O4, I2 Human capital indicator, number of years studied

In this paper an alternative methodology is proposed for obtaining long time-series data for a human capital indicator based on the average number of years of education of the working-age population. In contrast to previous studies, we use Labour Force Survey microdata relating to the level of education actually completed, in order to construct temporal profiles of educational attainment and thus avoid the need to interpolate from censuses. To illustrate the method proposed, we evaluate the number of average years of education of the Spanish working-age population for the period 1910-2000.

Introduction

Human capital can be defined as the set of knowledge and qualifications possessed by workers in an economy. The accumulation of human capital through education increases labour productivity and is an important factor in economic growth capacity.

Economic growth is a complex, continuous process and sufficiently long timeseries of potentially relevant variables are therefore needed for proper comparison of the relationships between them and to detect possible structural changes in the relationships. Various works conducted in this field provide researchers with the required data [see, for example, [START_REF] Summers | The Penn World Table (Mark 5): An expanded set of international comparisons, 1950-1988[END_REF] and [START_REF] Maddison | Monitoring the World Economy, 1820-1992[END_REF]], although these often present shortcomings due to the quality of the information offered and the limited duration of the period covered. As De la [START_REF] De La Fuente | Human capital in growth regressions: How much difference does data quality make?[END_REF] have shown, gradual improvements in inference technology have produced increasingly satisfactory indicators of human capital.

Measuring human capital is an extremely complex task. The most widely used method for estimating an economy's human capital centres on formal education, considered the main provider of training. [START_REF] Wöamann | Specifying Human Capital[END_REF] gives an interesting review of the different methods of measuring human capital used in the literature, which will be described briefly here.

One widely-used indicator is literacy rate [see, among others, [START_REF] Azariadis | Threshold externalities un economic development[END_REF] or [START_REF] Romer | Human capital and growth: Theory and evidence[END_REF]]. However, it reflects only one part of human capital investment and does not include another very important part, such as technical and scientific knowledge. Another indicator is the enrolment rate for the different levels of education [see [START_REF] Barro | Economic growth in a cross section of countries[END_REF] or [START_REF] Mankiw | A contribution to the empirics of economic growth[END_REF]] 1 . This measure too is unsatisfactory given that students' qualifications do not play a role in the economy until 1 León- Gonzalez and Montolio (2004) examine the role of this human capital measure as determinant of economic growth for the Spanish provinces, using data elaborated by the IVIE. The most commonly accepted measure is the number of years of education completed by the working-age population. Notably, the World Bank's International Economics Department firmly supports the use of this indicator to measure human capital stock [see, e. g., [START_REF] Nehru | A new database on human capital stocks in developing and industrial countries: Sources, methodology and results[END_REF] and [START_REF] Teles | The role of human capital in economic growth[END_REF]]. In line with this recommendation, authors such as [START_REF] Benhabib | The role of human capital in economic development: Evidence from aggregate cross-country and regional U. S. data[END_REF]Spiegel (1994), Gundlanch (1995) and [START_REF] Barro | Determinants of Economic Growth: A cross-country Empirical Study[END_REF][START_REF] Barro | Human capital and growth American Economic Review[END_REF] have used the measure, obtaining varying results.

Different methods have been employed to construct the indicator: [START_REF] Lau | Education and productivity in developing countries: An aggregate production function approach[END_REF] and [START_REF] Nehru | A new database on human capital stocks in developing and industrial countries: Sources, methodology and results[END_REF], for example, use the perpetual inventory method, which in broad terms seeks to recreate the results of the education system using information on enrolments, school drop-out rate, etc. Such information is difficult to obtain and is often incomplete, and the assumptions that often need to be made can negatively affect accuracy in calculating human capital stock. An alternative method is to incorporate population census information. Proposed originally by [START_REF] Psacharopoulos | The educational composition of the labour force: An international comparison[END_REF], this method has some disadvantages, among them the fact that a census usually covers relatively long time periods (usually 10 years) and the number of observations is therefore considerably lower. A further problem is that in many countries the information has only recently become available or fails to include data on educational attainment. A number of solutions have been put forward in an attempt to remedy these problems. [START_REF] Kyriacou | Level and growth effects of human capital: A cross-country study of the convergence hypothesis[END_REF], for example, estimates econometrically the level of attainment using delayed enrolment variables. Using the results, the author projects the level of attainment by the population of subsequent years. A drawback to the method is that the parameters are presumed to be constant over time. As WöAmann shows (2003), however, the relationship varies over time and between countries, and hence this assumption is inadequate. [START_REF] Barro | International comparisons of educational attainment[END_REF], meanwhile, use census data as stock and carry out a perpetual inventory exercise to complete the information for the years and countries for which no direct census is available. The main problem here stems from the use of the perpetual inventory, given that in many countries the census data Given the current limitations of the usual indicators, and as stated by De la [START_REF] De La Fuente | Human capital in growth regressions: How much difference does data quality make?[END_REF][START_REF] De La Fuente | Human capital in growth regressions: How much difference does data quality make?[END_REF][START_REF] Wöamann | Specifying Human Capital[END_REF], there is a genuine need for improved ways of calculating human capital stock 3 .

Our aim with this paper is to provide a methodology that seeks to address the need for sufficiently long time-series and at the same time to improve the human capital indicator considered as the average number of years of education of the working-age population. In contrast to previous studies, we propose to use the information contained in national Labour Force Surveys (called, in Spain, the 'Working Population Survey' and known by its Spanish acronym 'EPA') rather than use the population census. Such Statistics usually cover a broad sample spectrum and are commonplace in most countries. Another advantage is that surveys of this type are conducted annually, which eliminates the need for interpolation of any kind between census years. Moreover, they include information on the population with studies actually completed, again avoiding the need for interpolation. A final advantage is that the population series drawn up using these surveys cover groups that are directly related to the labour market.

The paper is organised as follows: Section 2 briefly outlines the methodology used. By way of illustration of the proposed procedure, Section 3 presents the results obtained for the period 1910-2000. Finally, Section 4 provides some concluding remarks. 2 See, for example, [START_REF] Gisbert | Further convergence accounting[END_REF] for an empirical use of the [START_REF] Barro | International measures of schooling years and schooling quality[END_REF] data set on educational attainment. 3 See [START_REF] Serrano | Measurement error in schooling data: The OECD case[END_REF] for an empirical assessment on the effect of human capital using both the [START_REF] Barro | International measures of schooling years and schooling quality[END_REF] and De la Fuente-Domenech (2001) data sets.

Methodology

In this paper a methodology is proposed to help improve the estimation of the average number of years in education of the population (I ANYE ), expressed as follows: García's series of population by specific ages was interlinked with series from Spain's National Institute for Statistics (1997,1999,2001) These can then be used to obtain the percentage occupied by each level of education within each cohort.

The problem arises when trying to determine the level of attainment of generations born between 1846 and 1893 (aged between 17 and 64 in 1910). For this purpose we estimated the trends observed for each level of education in the composition by ages, using the EPA77 data, as shown in Figure 1. The regression adjustments are reasonably good, as reflected by the high correlation coefficients obtained. A highly differentiated trend is seen according to age and level of studies, including for a relatively early year such as 1977.

F
To estimate the population cohorts born between 1846 and 1893 (aged between 17 and 64 in 1910), i.e. cohorts who were between 84 and 131 years old in 1977, the estimates shown in Figure 1 were projected to age 131. The results are illustrated in Figure 2. To complete the composition of the 1910 working-age population, we used the percentage composition of the projections given in Figure 2. In order to ensure that the sum of all the percentages of the levels of education total 100% of the population, one of the percentages had be left as a residue for adjustment purposes. We chose the 'lower than compulsory education' level for this since it was the only estimate for which projection served no useful purpose. It should be noted that when the 'lower than compulsory education' group estimate in Figure 1 is taken and the trend projected, negative values are reached long before the age of 131, which we need for our purposes here. The choice of the 'lower than compulsory education' group for the adjustment makes sense, given that -as Figure 2 shows-the projection closely matches the trend seen in EPA77 and adjusting the percentage represented by this group with the other levels of education gives 100%. We thus obtain, for each cohort of the working-age population, the probability of attainment of a given level of education.

F

The number of years actually studied for each level of education , , a e t Ne in equation ( 1) is hard to ascertain, given the rather turbulent history of the Spanish education system4 . Following the Moyano Law of 1856 the system was organised into the current levels of primary, secondary and tertiary education. However, education was a constant political battleground between the Church-backed conservatives and the liberals, and the political instability that characterised the second half of the 19th century and first half of the 20th meant that a new programme was put in place virtually every 4 years between 1836 and 1936. In an attempt to homogenise matters, we have estimated the average duration of studies per level of education as follows: 'illiterate'5 = 1 year, 'lower than compulsory education' = 4 years, 'primary or compulsory education' = 8 years, 'secondary education' = 12 years and 'tertiary education' = 17 years.

Empirical results

The aggregated results given in Figure 3 show the evolution of the population of working age according to the level of academic attainment. The corresponding annual data can be consulted in the Appendix. The period up to 1976 saw a parallel process featuring a dramatic increase in the size of the working-age population with lower than compulsory education and a gradual fall in the number of illiterate people. The passing of the General Education Law, which introduced compulsory primary education, marked the beginning of sustained growth in the working-age population with schooling -initially primary, then secondary and, lastly, tertiary education. Source: Authors´ calculations At this point it would be appropriate to compare our indicator with other historical data on the educational attainment of the Spanish population. However, the fact that this information is virtually nonexistent makes it difficult for us to compare the results obtained using our methodology and those from other independent measurements. The only information available is the degree of illiteracy given for every census year. Table 1 shows the net illiteracy according to the census and the results of our estimates. Although the results are very similar, the figure estimated using our method is somewhat higher than in the census, due to the different population groups studied. The 10-15 age-group included in the net illiteracy is very numerous but presents a lower illiteracy rate. Conversely, the over-64 group is small (particularly at the beginning of the century) but has a higher rate of illiteracy. Together with the fact that the data have been taken from the EPA -a survey -, this may explain the slight discrepancies. Since the methodology has been reasonably successful in calculating the degree of illiteracy, one may assume the other levels of education to be correct also.

Final considerations

This paper puts forward an alternative methodology for obtaining long timeseries of a human capital indicator based on the average number of years' schooling of the population of working age. The procedure is based on the use of national Labour Force Surveys instead of the customary population census. Specifically, we use microdata relating to the level of studies actually completed in order to build temporal profiles of educational attainment, thus avoiding the need for interpolation of census data.

To illustrate the method, we have estimated the average number of years' schooling of the Spanish population of working age over a long historical period that covers most of the twentieth century.

In our opinion, the methodology proposed here could be applicable to other countries also, to give a broad cross-section of countries covering long time-periods, which could then be used for appropriate empirical evaluation of the different economic growth models found in the literature.

F

  -enrolment. Moreover, the rate depends on certain factors (failure at school or the desire to carry on to tertiary education, for instance), which are not taken into account. Lastly, this indicator takes no account of the level of education attained by those who exit the labour market.

  or two years and the bulk of the human capital data is therefore based on the imperfect calculation of the perpetual inventory method 2 .

P

  we have usedHoyo and García's proposals (1988) for the period 1910-1980. In their publication, these two authors aggregate into one group the 'aged 60 and above' population cohorts, corresponding to the period 1910-1930. To estimate the population cohorts aged 60-64 we projected linearly back to 1846 (64 years old in 1910) the generation tables for Spain covering the years 1885 to 2000. These were obtained from the government's Directorate General for Insurance and the National Association of Insurers and Reinsurers (UNESPA). The survival probabilities obtained were applied to the different population cohorts according to year of birth until the generations not included in the aforementioned work by[START_REF] Hoyo | Análisis y predicción de la población española[END_REF]) were completed. For the period 1980-2000, Hoyo and

Figure 4

 4 Figure 3: Evolution of working-age population by level of academic attainment.

  Figure 4:

  Nerepresents the number of years studied by the population cohort aged a, according to the level of educational attainment e.In order to infer the attainment of the working-age population (16 to 64 years) for the period 1910-2000, the level attained by all generations born between 1846 (64 years of age in 1910) and 1984 (16 years of age in 2000) must be determined.

	I	ANYE	=	a	64 16 =	e	5 = 1	, a t P Pe 16 a = a t ,, , P 64 a e t ×	×	,, a e t Ne		(1)
	where , a t P is the population of age a during period t,	, , a e t Pe	is the percentage of the
	population of age a that has attained education level e during period t, and	, , a e t
	Obviously, this information is not systematised and certain assumptions are therefore
	required to estimate it..
	To calculate the working-age population ,

  Peis that someone who has reached the typical age for each stage of schooling and has attained a given level of attainment will maintain that level until the end of his/her life. We will assume also that the level attained is that proposed in the EPA classification. The first available EPA with microdata corresponds to 1977. Using this survey we can obtain the level of education attained by each cohort of the population, which has been grouped into 5 categories (illiterate, lower than compulsory education, primary or compulsory education, secondary education and tertiary education). Sampling problems with the EPA survey made it difficult to obtain data for elderly persons. If we discount those aged over 83, we have the levels of educational attainment for all generations from 1894(people aged 16 in 1910 and 83 in 1977) to the present day (using successive EPAs).

	The main assumption we will adopt to calculate	, , a e t
	F o r
	P
	e
	e r
	R e v
	i e
	w
	for 1977, and no further adjustments
	were required.

Table 1 :

 1 Evolution of illiteracy

		Net illiteracy (age 10	(Result of estimation:
		and above)	population 16-64)
	1910	50.6	55.7
	1920	43.3	44.0
	1930	32.4	34.0
	1940	23.1	25.3
	1950	14.2	18.2
	1960	11.2	13.3
	1970	8.5	8.5
	1981	6.3	4.8
	Sources: Delgado (1994) and own figures.

  Appendix: Working-age population by level of academic attainment

		Submitted Manuscript
	1
	2
	3
	4
	5
	6
	7
	8
	9
	10
	11
	12
	13
	14
	15
	16
	17
	18
	19
	20 21 22 23 24 25	o r F o r F o r F o r
	26 27 28 29 30	P e P e P e P e
	31 32 33 34	e r e r e r e r
	35 36 37 38 39 40 41 42 43	R e v i e R e v i e R e v i e R e v i e
	44 45 46	w w w w
	47
	48
	49
	50
	51
	52
	53
	54
	55
	56
	57
	58
	59
	60

Page 18 of 18 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

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

A comprehensive review of the history of Spain's education system can be found in Delgado(1994).

In line withDelgado (1994), the reason for including 1 year for illiteracy is that there is broad evidence suggesting that a substantial number of people stated they were illiterate when entering military service or in the population census, despite the fact that they had attended school, albeit not regularly, and had learned to read and write, skills they had then forgotten through lack of daily use.

< O -Economic Development, Technological Change, Ministry of Science and Technology (SEC2002-01892) is also gratefully acknowledged.


Ana Rute Cardoso
email: car-doso@iza.org

Rute

Priscila Ferreira

The dynamics of job creation and destruction for University graduates: why a rising unemployment rate can be misleading *

Keywords: gross job flows, aggregate, sectoral, idiosyncratic shocks gross job flows; aggregate, sectoral, idiosyncratic shocks JEL: J21, J60, J63

come

The dynamics of job creation and destruction for

University graduates: why a rising unemployment rate can be misleading

Introduction

Higher education has been expanding in most countries, with enrollment rates and the number of students rising sharply. This trend was particularly pronounced during the first half of the 1990s. For example, between 1990 and 1996 total enrollment in tertiary education more than doubled in Poland and Portugal, having increased by over 80 percent in the UK and Hungary (OECD 1999). Several reasons have combined for this outcome. Changes in the funding system of higher education, steered to rely increasingly on market mechanisms, have led institutions into intense competition to attract students. Rising private returns to higher education, pointed out in studies of wage dispersion for several countries, may have played a role motivating the continuation of studies beyond high school.

Also, the widespread perception of the impact of higher education on development has pushed some countries, in particular developing ones, into expanding their higher education systems.

However, as higher education degrees became more widespread and less exclusive, fears started spreading as to the capacity of the labour market to absorb the newly-graduates. Throughout, employers have been claiming that the higher education system is not providing the necessary skills and it is often advocated that the external efficiency of universities should be evaluated, monitoring the jobs and earnings of graduates. Most often, the unemployment rate has been implicitly set as the criterion to assess employment prospects for graduate workers. As the unemployment rate for this group of workers increased in several countries, the high expectations of the 80s gave place to concern in the 90s. An extreme view seems to be popular nowadays, stating that the expansion of higher education may have gone too far and that investment in a higher education degree has become too risky, possibly not worthwhile, as employers are no longer keen on recruiting newly graduate workers [START_REF] Teichler | Higher education policy and the world of work: conditions and challenges[END_REF] provides a clear analysis of these trends). Such statements are usually not backed up by empirical analysis of sound data, and in this paper we argue that a more balanced view is called for. Relying on F o r P e e r R e v i e w systematic information on job creation and job destruction for higher education graduates compared to other groups of workers, we find that the unemployment rate can provide a misleading idea of the dynamics in labour demand and of the employment prospects for university graduates. What has indeed been the pace of job creation and job destruction for university graduates hiding behind their unemployment rate? Which have been the most dynamic sectors? What is the role of aggregate and sectoral shocks, as opposed to idiosyncratic shocks, driving gross job flows for university graduates?

The study uses a very rich longitudinal data set matching workers and employers in the Portuguese economy, with information on over two million workers and approximately 200 thousand firms each year.

Section 2 describes the evolution of employment prospects for university graduates when compared to other groups of workers in Portugal, as traditionally captured by their unemployment rates and by the job offers advertised nationally over two decades. Section 3 describes the data set and concepts used. In section 4, the results on job creation and job destruction are presented. Section 5 checks the role of aggregate and sectoral shocks versus firm-level shocks driving gross job flows, and concluding comments are presented in the last section.

Higher education and the labour market in Portugal

The schooling achievement of the working population is remarkably low in Portugal when compared to its OECD partners. Indeed, only 11% of the labour force holds a tertiary education diploma, the lowest value reported by the OECD (2002); on the other hand, 45% of the Canadian labour force holds one such degree (42% in Ireland and 36% in Japan).

Nevertheless, the possibility of integrating university graduates into the labour market started to be an issue of concern in Portugal in the early 90s and the claim that employers were no longer interested in hiring university graduates, in particular young ones, and were demanding instead experienced workers, was widely believed upon. Such claim was backed up by the evolution of the unemployment A certain convergence of unemployment rates across schooling levels can be identified in figure 1. Whereas the unemployment rate of university graduates increased from around 1% in 1981 to 3% two decades later, for lower schooling levels it showed signs of declining. This convergence of unemployment rates by schooling level led to the idea that a university diploma was no longer a safe passport out of unemployment and raised doubts about the capacity of the labour market to absorb the newly-graduates. Slack labour demand for university graduates was often identified as the source of the problem.

-------figure 1 about here-------Analysis of the requirements imposed by job offers would apparently lend support to this claim. Between 1989 and 1995, the share of job announcements requiring a university diploma declined sharply, from around 50% to 5%. On the other hand, the share of ads requiring previous labour market experience remained high, though declining from 50% to 20%. By mid 90s, a higher education diploma was back in high demand, and the number of employers advertising jobs for graduates was close to the number of those requiring previous experience. However, the trend in late 90s was not enough to overcome the concerns that meanwhile had grown, especially as the graduates unemployment rate failed to decline.

-------figure 2 This study relies on Quadros de Pessoal, a longitudinal data set matching workers and their employers in the Portuguese private sector, from 1985 to 1999. The data are gathered annually by the Ministry of Employment, based on an inquiry that every establishment with wage-earners is legally obliged to fill in. Reported data cover all the personnel working for the establishment in a reference week (in March until 1993, andin October from 1994 onwards). Public administration and domestic service are not covered, and the coverage of agriculture is low given its low share of wage-earners. For manufacturing and the services, given the mandatory nature of the survey, the population of firms with wage-earners is in fact covered.

Reported data include the firm's location, industry, employment, sales, ownership, legal setting, and the worker's gender, age, skill, occupation, schooling, admission date, earnings, and duration of work. Jobs filled by full-time wage earners were considered in the analysis.

The Ministry assigns a unique identification number to each company when it first reports to the database, and it is thus possible to track firms. Extensive control procedures are implemented to guarantee that a firm is not assigned a different number later on. Such procedures are based in particular on the location of the firm and its official identification codes.

In 1990 no worker data were reported and therefore it is not possible to compute the flows for the periods 1989-90 and 1990-91. Since the date of inquiry changed from March to October in 1994, a wider time frame is covered that year, rendering the flows not comparable to the other years, and therefore that period will not be used in the computations. Tables 3 and4 in appendix describe the sample sizes and the structure of employment by industry, respectively.

Job creation and job destruction

Aggregate employment figures provide no information concerning the expansion or contraction of employment taking place at the firm level. The study of gross job flows at the firm level has therefore deserved increasing attention in the literature, as it provides a more accurate picture of the dynamics in the labour market (see for example [START_REF] Davis | Job Creation and Destruction[END_REF] or [START_REF] Garibaldi | Job creation: why some countries do better[END_REF] for a highlight of its advantages).

The methodology and the concepts used follow [START_REF] Davis | Job Creation and Destruction[END_REF]. Gross job creation and destruction are computed at the firm level as the change in employment from period t -1 to period t.

∆X f,t = X f,t -X f,t-1
where X stands for the employment level, f refers to the firm and t to the moment in time (year). If employment increases, job creation is said to have taken place, while job destruction occurs when employment in the firm decreases.

Aggregating from the firm to the level of the sector or the industry s, gross job creation (pos) equals the sum of employment changes over all firms that expanded its employment or were set up during the period, and similarly, job destruction (neg) is the sum of employment changes over all firms that contracted or shut down.

pos s,t =

X + ∆X f,t and neg s,t = X - |∆X f,t |
Net job creation (net) is the difference between gross job creation and gross job destruction and gross job reallocation is the sum of job creation and job destruction over a certain period for sector s:

net s,t = pos s,t -neg s,t sum s,t = pos s,t + neg s,t
Job reallocation is the maximum amount of worker reallocation required to accommodate the change in employment opportunities across firms. Note that, if no

5 F o r P e e r R e v i e w
worker switched from a contracting to an expanding company, then the amount of worker reallocation would correspond to the job reallocation, as pos workers would move from out of employment into employment, while neg workers would move in the opposite direction. This measure therefore provides an indication of the overall degree of rotation in the labour force resulting from changing job opportunities across firms.

However, certain workers may be counted twice in the gross reallocation measure, if they switch from a contracting to an expanding company. The measure of minimum worker reallocation (m) aims precisely at eliminating the problem of double counting involved in summing gross job creation and gross job destruction.

It is computed as the larger of gross job creation and gross job destruction:

m s,t = max(pos s,t , neg s,t )
It reports the minimum worker reallocation required to account for the changes in job opportunities across firms.

Finally, excess job reallocation (exc) is computed as the difference between gross reallocation and the absolute value of net employment change.

exc s,t = sum s,t -|pos s,t -neg s,t |
It evaluates the amount of job reallocation that took place beyond what would be strictly necessary to accommodate the net job change. This is considered the best indicator of simultaneous job creation and job destruction, capturing the heterogeneity among firms -whereas some are expanding, others are contracting.

Job flows can be expressed as rates, dividing through by a measure of firm size, z, which is the average employment in periods t and t -1.

Z f,t = X f,t + X f,t-1 2
The rate of employment growth in the firm is thus computed as:

g f,t = ∆X f,t Z f,t F o r P e e r R e v i e

w

The use of this size measure is preferred over the traditional size measure (employment in the base period), because the rate of employment change now ranges over a bounded and symmetrical interval, -2 to +2. 1

4 Coexistence of high rates of job creation and job destruction

High rates of job creation coexisted with high rates of job destruction for university graduates, with a net positive impact on overall employment. Indeed, out of 100 jobs existing in the economy performed by university graduates, 23 new ones were created on average each year between 1986 and 1999, whereas 15 were destroyed (table 1). These values suggest that a high job rotation prevails for university graduates in the Portuguese economy, strictly due to heterogeneity in the firms' recruitment behavior. According to [START_REF] Caballero | On the timing and efficiency of creative destruction[END_REF], a positive correlation between job creation and destruction would be indicative of a market operating efficiently, with matches being destroyed to yield better matches. Several authors have reported the coexistence of high rates of job creation and job destruction for different countries (see for example Davis and Haltiwanger (1992) for the USA, [START_REF] Baldwin | A comparison of job creation and job destruction in Canada and United States[END_REF] for Canada and the USA, [START_REF] Blanchflower | Job creation and job destruction in Great Britain in the 1980's[END_REF] and [START_REF] Konings | Job creation and job destruction in the UK manufacturing sector[END_REF] for the UK, [START_REF] Tsou | Worker turnover and job reallocation in Taiwanese manufacturing[END_REF] for Taiwan, or [START_REF] Vainiomaki | Technology, job creation and job destruction in Finnish manufacturing[END_REF] for Finland).

High job creation and simultaneous job destruction is also a characteristic of the labour market for workers with lower levels of schooling, but the rates are lower. Out of 100 existing jobs performed by undergraduates, 13 were created and 12 were destroyed on average each year during the period under analysis.

-------table 1 about here-------

The trends in net job creation rates for graduate workers and for those not holding a university diploma diverged after 1988 (figure 3), as the variable tended

1 It is therefore possible to compute the measure for new firms and for those going out of business (achieving the values +2 and -2, respectively). Values between -1 and 1 correspond approximately to the traditional percent increase in employment. to move in the same direction, but at different paces, for the two groups of workers.

Net job creation for workers not holding a university diploma follows more closely the business cycle. During the second half of the 80s, the Portuguese economy has grown sharply and employment expanded at a fast pace, with the rate of net job creation being similar for the two groups of workers. By the end of the 1980s, however, net job creation for workers holding a university diploma was already taking place at a faster pace than for workers with lower schooling levels. The contrast became more pronounced in the 90s, when employment opportunities for graduates were expanding faster than for non-graduates. In 1993, negative rates of job creation for undergraduates contrasted with a positive 4% net job creation for graduates. Since 1993, employment for undergraduates has grown very modestly, whereas for university graduates it has been expanding strongly, reaching 13% a year in 1997 and 1998.

-------figure 3 about here-------Concerns about the employment prospects for university graduates therefore begun spreading in Portugal precisely when their employment opportunities were expanding the most and in sharpest contrast with the employment prospects for undergraduates. Thus, the rising unemployment rate did not result from a slack labour demand by employers no longer trusting the higher education system and the skills it provides. Instead, demand kept expanding. The employment prospects of university graduates should be compared to those of undergraduates in the same period, and not to those of graduates who left the educational system a decade before. In each of those graphs, industries located on the diagonal did not change their overall employment level for that category of workers, since the rate of job creation was offset by an equal rate of job destruction. Industries located above the diagonal were net job creators and, symmetrically, industries below the diagonal were net job destructors. Industries closer to the origin present lower rates of job reallocation, as both job creation and job destruction were low. The farther away from the origin, the higher the rate of gross job reallocation in the industry.

-------figure 5 about here-------Employment expansion for university graduates took place across industries, with the exception of base metals, where employment declined. The services have shown the largest expansion of graduate jobs. Note however that, whereas in banking and insurance the expansion of employment took place with low reallocation of jobs across firms, in the remaining services high job creation coexisted with high job destruction. The most stable industries, apart from banking and insurance, were electricity, gas and water, chemicals and transportation and communications.

2 Part of the detected homogeneity in firm behaviour when it comes to job changes for the undergraduates may result from the fact that we are dealing with a broader group of workers, and therefore mechanisms of compensation within the firm may operate. Note the example of a firm that may contract its employment level for workers holding 4 years of education, while expanding it for workers with 9 years of education. In such a case, overall employment for undergraduates could remain stable, and neither job creation nor destruction would be captured. The contrast with workers holding lower schooling levels stands out. Note first of all the higher concentration of industries close to the diagonal, indicating low rates of net job creation for undergraduate workers. In electricity, gas, water, base metals, and chemicals, job destruction has surpassed job creation. On the contrary, in services to companies and other services, job creation took place at a fast pace, just like for graduate workers. Job reallocation was considerably lower for workers with lower levels of schooling, indicating that net job expansion or contraction resulted from a more uniform trend across firms than it has been the case for university graduates.

Decomposition of employment changes into idiosyncratic, sectoral and aggregate shocks

This section analyses the time variation in job creation and job destruction. It checks the role of idiosyncratic shocks against sectoral and aggregate shocks as forces driving job flows, using variance decomposition analysis. Following Davis and Haltiwanger (1992), the firm growth rate of employment is decomposed into a firm-specific part, a sector-specific part and an aggregate part, by estimating:

g ft = e g f t + g st + g t , (1)
where g f t is the firm employment growth rate between years t -1 and t, e g ft is the residual or idiosyncratic component of firm-level employment growth, g st is the sector-time specific component and g t is the aggregate-time component. The job creation associated with idiosyncratic shocks is then computed as:

g P OS t = X e g ft >0 e g f t z ft z t (2)
where z ft stands for the firm size and z t for overall employment in the economy, both defined, as previously described, as the average of the current and previous periods. The following identity holds: (4)

P
The first term on the right-hand side is the effect of idiosyncratic shocks on job creation, the second term gives the impact of sectoral and aggregate shocks, whereas the third term provides the interaction between those two types of shocks, revealing whether the two effects reinforced each other or operated in different directions.

Dividing each of these terms by the left-hand side, yields their relative importance in accounting for time variation in job creation. Similar computations were made for job destruction and job reallocation, with equation 2 adapted to become:

g NEG t = X e g ft <0 |e g f t | z ft z t (5) g SUM t = X |e g f t | z ft z t (6)
-------table 2 about here-------From table 2 it is noticeable, first of all, that aggregate and sectoral shocks are the main force driving changes over time in job flows. Using the same methodology on different samples, Davis andHaltiwanger (1990, 1992) found slightly higher contributions of the idiosyncratic component to job creation and destruction in the USA; [START_REF] Gautier | The timing of labour reallocation and the business cycle: evidence for the Netherlands[END_REF] found much higher contributions in

The Netherlands; in the UK, the contribution reported by [START_REF] Konings | Job creation and job destruction in the UK manufacturing sector[END_REF] for job creation is higher than in Portugal, whereas that for job destruction is similar to the Portuguese. A regulated labour market and stiff employment protection legislation could in Portugal leave little scope for firms to adjust their employment level when faced with firm-specific shocks. It has been documented that higher employment protection has an impact, not just on job destruction flows, but as well on job creation (see [START_REF] Blanchard | What hides behind an unemployment rate: comparing Portuguese and U.S. labor markets[END_REF]; similarly, [START_REF] Broersma | The effect of wage restrain on labour market flows[END_REF] The composition of employment changed, with the relevance of manufacturing declining (specially in the case of textiles, clothing, and heavy manufacturing), in favour of the services (table 4 in appendix). Under this framework, demand for a qualified labour force increased, reflected in rising wage premia [START_REF] Cardoso | Firm's wage policies and the rise in labor market inequality: the case of Portugal[END_REF].

Though demand for workers holding a university degree kept expanding, in certain scientific areas it did not match the rise in supply resulting from the expansion of higher education. Table 6 in appendix reports by age group the share of population attaining tertiary education. Whereas just 3 percent of the cohort aged 55-64 in 2001 held a university diploma, 11 percent of the cohort aged 25-34 held one. The latter cohort of university graduates entered the labour market during the 1990s, bringing about the sharpest increase in schooling achievement ever registered in the country's labour market.

Table 2 indicates moreover that the impact of the different shocks is similar for university graduates and non-graduates, when considering just continuing firms.

However, when progressing to analyse all firms in the economy, different patterns can be detected for university graduates and non-graduates. The different action taking place in terms of job creation or job destruction for university graduates and non-graduates results from the behaviour of new firms and those going out of business. Idiosyncratic shocks play a larger role for university graduates than Results highlight a much more flexible and flowing labour market for higher education graduates than for non-graduates, and a more relevant impact of idiosyncratic shocks driving job flows for the most schooled labour force.

Conclusion

Analysis of gross job creation and destruction at the firm level provides systematic information on job dynamics that cannot be captured by the evolution of aggregate employment or unemployment rates.

It is interesting to note that the rising unemployment rate for university graduates and its decline for undergraduates over the 80s and 90s provide a misleading idea of the dynamics of job creation in the Portuguese economy, as net job creation rates point precisely in the opposite direction -net job creation took place at a much faster pace for higher education graduates than for the rest of the labour force. The rise in the unemployment rate for graduate workers did not result from declining demand for that group of workers, and support is thus not found for the simplistic view that seems to be widespread nowadays, according to which employers would no longer be willing to recruit university graduates, given the inability of the system to provide workers with adequate skills.

The labour market for graduate workers is more flexible and flowing than that for workers with lower schooling levels. On average, one in four graduate workers will have to switch employer or employment status each year, just to respond to the reallocation of job opportunities across firms. That ratio is one in seven for the undergraduate labour force. Irrespective of the type of contract the worker holds, individuals holding a higher education diploma are asked to switch jobs more frequently, simply as a result of the reshuffling of employment opportunities across firms, and will thus have to adapt more frequently to changing work environment.

Using variance decomposition to check whether idiosyncratic or sectoral and aggregate shocks drive the time variation in job flows further highlights that the graduate labour market is more volatile. The impact of idiosyncratic shocks is more (1985-1999).

year net: graduates net: non-graduates growth GDP 1986GDP 1987GDP 1988GDP 1989GDP 1990GDP 1991GDP 1992GDP 1993GDP 1994GDP 1995GDP 1996GDP 1997GDP 1998GDP 1999 - - 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 (1985-1999). (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999). Codes: A: Food, beverages, tobacco; B: Textiles, clothing, leather; C: Wood, cork; D: Paper; E: Chemicals; F: Non-metallic mineral prod; G: Base metals; H: Metal prod; I: Elect., gas, water; J: Building; K: Trade; L: Restaur., hotels; M: Transport, communic; N: Banking, insurance; O: Serv. to companies; P: Social, cultural and personal serv. (1985-1999).

18

F

GDP per capita GDP per person employed year 1981 1985 1990 1995 2000 1981 1985 1990 1995 2000 thousand 1995

  levels and by the requirements advertised in job openings.

  about here-------Relevant questions are therefore: To what extent did the economy indeed reduce its pace of job creation for graduate workers? Which were the most and the least dynamic sectors? To what extent does the evolution of the unemployment rate provide relevant clues for a decision on whether to invest in university education?

Figure 4

 4 Figure4presents other measures of rotation of job opportunities among companies (gross job reallocation and excess job reallocation). It reveals that the pace of change in the labour market for university graduates is faster than for other workers. In fact, approximately 40% of the existing jobs for higher education graduates are either created or destroyed each year, whereas for undergraduates that value is around 20-25%. The reallocation beyond what would be strictly necessary to ac-

Figure 3 :

 3 Figure 3: Rate of net job creation by schooling level, 1986-1999. Sources: Computations based on Portugal, MTS(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999).

Figure 4 :

 4 Figure 4: Rate of gross job reallocation and excess job reallocation by schooling level, 1986-1999. Sources: Computations based on Portugal, MTS(1985-1999).

Figure 5 :

 5 Figure 5: Gross job flows by industry and schooling level, 1986-1999. Sources: Computations based on Portugal, MTS(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999). Codes: A: Food, beverages, tobacco; B: Textiles, clothing, leather; C: Wood, cork; D: Paper; E: Chemicals; F: Non-metallic mineral prod; G: Base metals; H: Metal prod; I: Elect., gas, water; J: Building; K: Trade; L: Restaur., hotels; M: Transport, communic; N: Banking, insurance; O: Serv. to companies; P: Social, cultural and personal serv.

  OS t ) = var( g P OS t ) + V ar(P OS t -g P OS t ) + 2Cov( g P OS t , (P OS t -g P OS t )).

	and therefore
	V ar(P
	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	OS t = g P OS t + (P OS t -g P OS t )	( 3 )

  system had an impact on flows in the labour market. It should however be noted that idiosyncratic shocks reinforce in Portugal the impact of sectoral and aggregate shocks, for most of the cells reported in table 2. Job destruction increases during recessions and this effect is amplified by firm-level effects; similarly, job creation increases during expansions, an effect reinforced by firm-level shocks.The relevance of aggregate and sectoral shocks may also have resulted from the pace of change taking place in the economy. During the period under analysis, the Portuguese economy underwent structural adaptation, partly driven by the accession to the European Union that had taken place in 1986. The growth in GDP per capita (see table 5 in appendix) illustrates the speed of change, as it increased from 40 percent to 50 percent of the USA level between 1985 and 2000.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	found
	that regulations leading to wage restrain or the generosity of the social security
	11

Table 1 :

 1 relevant driving job flows for higher education graduates than for non-graduates.The contrasts between the two labour markets results mostly from the behaviour of new firms and those going out of business, most likely because continuing firms Requirements imposed by job offers advertised at the national level, 1989-2000. Source: Newspaper Expresso. Notes: This newspaper is the major means for advertising job offers at the national level for qualified workers. The graph reports moving averages, with a smoothing window of 13 weeks (approximately one trimester). Rates of gross job creation and destruction by schooling level, 1986-1999. Source: Computations based on Portugal, MTS

	13

16 Page 17 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 :

 2 Relative impact of idiosyncratic shocks and sectoral plus aggregate shocks on gross job flows. Source: Computations based on Portugal,MTS (1985MTS ( -1999)). Note:All firms includes new and exiting firms; continuing firms are the ones remaining in business between two periods.

			Continuing firms	All firms
			uni grads non-grads uni grads non-grads
	Job creation
	idiosyncratic component	0.01	0.03	0.13	0.08
	sector and aggregate component	0.89	0.81	0.75	1.19
	interaction		0.10	0.16	0.12	-0.27
	Job destruction
	idiosyncratic component	0.01	0.02	0.25	0.03
	sector and aggregate component	1.05	0.91	0.65	0.79
	interaction		-0.06	0.07	0.10	0.17
	Job reallocation
	idiosyncratic component	0.02	0.07	0.32	0.21
	sector and aggregate component	0.81	0.65	0.47	0.57
	interaction	r o	0.17	0.27	0.21	0.22
		P
		e
		e r
		year	R firms workers
		1985 1986	82 957 1 431 112 e 87 339 1 436 487
		1987 1988 100 543 1 500 771 91 777 1 484 690 v 1989 112 841 1 613 063 i 1991 123 534 1 675 050 e 1992 132 398 1 704 686
		1993 137 963 1 670 933 1995 160 065 1 721 608 1996 163 875 1 712 677	w
		1997 180 040 1 823 672
		1998 186 555 1 854 468
		1999 202 522 1 959 354

Table 3 :

 3 Size of sample under analysis. Source: Computations based on Portugal, MTS (1985-

	1999).

19 Page

20 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	1985 1991 1995 1999
	food, beverages, tobacco	5.5	4.5	4.5	3.8
	textiles, clothing, leather	16.6	18.1	15.1	12.4
	wood, cork	3.9	3.6	4.1	3.7
	paper	2.7	2.4	2.2	2.0
	chemicals	4.4	3.1	2.5	2.1
	non-metalic mineral prod	3.6	3.3	3.1	2.8
	base metals	1.4	0.9	0.5	0.5
	metal prod	10.5	9.3	9.0	8.6
	elect., gas, water	1.6	1.3	1.0	0.8
	building	8.4	9.2	9.4	10.1
	trade	15.9	16.7	19.9	20.4
	rest., hotels transp, communic banking, insurance r F o social, cultural and personal serv.	4.5 9.3 4.6 7.3	5.2 7.8 4.7 9.9	6.2 6.9 4.8 10.9	6.4 6.7 4.2 15.3
	P
	e
	e r
	R
	e
		v i e
				w

Table 4 :

 4 Structure of employment by industry (manufacturing and services), Portugal. Source: Computations based on Portugal, MTS

Table 5 :

 5 GDP per capita and per person employed, Portugal. Source: OECD, 2002a.

	USD	9.3	9.5	12.7	13.8	16.4	23.8	23.6	28.2	30.4	33.6
	as % of USA		40			50		45			50
			age 25-34 35-44 45-54 55-64
			Portugal	11		7	5	3
		OECD mean	18		16	14	10

Table 6 :

 6 Percentage of the population that has attained tertiary education, by age group, 2001. Source:OECD, 2002.

20 Page 21 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

* This study was prepared under project PRAXIS/P/ECO/13014/1998 funded by Fundação para a Ciência e a Tecnologia. We are grateful to the Ministry of Employment, Department of Statistics, for access to the dataset Quadros de Pessoal and to Vítor Andrade from newspaper Expresso for the data on announced job offers.


Vani K Borooah
email: vk.borooah@ulst.ac.uk

Bridging the Gap Between the Measurement of Poverty and of Deprivation

Keywords: Poverty, Deprivation, Possessions, Economising, Northern Ireland Poverty, Deprivation, Possessions, Economising, Northern Ireland. JEL classification: I31, I32

Bridging the Gap Between the Measurement of Poverty and of Deprivation

Introduction

A definition of deprivation or poverty that has gained increasing currency, and one which has been incorporated into a variety of official formulations, including that of the Council of the European Union, is "exclusion from the life of society owing to a lack of resources" [START_REF] Nolan | Resources Deprivation and Poverty[END_REF]. The question of how to measure deprivation or poverty remains, however, vexed. One way of measuring the deprivation/poverty of persons1 is to use money based measures: a person is regarded as "poor" if his/her income (or expenditure) falls below a poverty line value. Such an approach -usually termed poverty analysis -has spawned a large literature embodying several sophisticated measures of poverty2 . The downside to this is that low income or expenditure may not be very good indicators of deprivation, as defined above [START_REF] Townsend | Poverty in the United Kingdom[END_REF][START_REF] Ringen | Direct and Indirect Measures of Poverty[END_REF].

An alternative to the money-metric approach, embodied in "poverty analysis" is an "item-based" approach which underpins deprivation analysis. Broadly speaking, this approach (as epitomised by the work of [START_REF] Townsend | Poverty in the United Kingdom[END_REF][START_REF] Mack | Poor Britain[END_REF][START_REF] Mack | Poor Britain[END_REF] for Britain, and of [START_REF] Nolan | Resources Deprivation and Poverty[END_REF] for Ireland) constructs a "possessions index". The value of this index is defined, for each person, as the number (or proportion) of items, from a prescribed list, that he/she possesses: persons are then regarded as "deprived" if their index value is below some threshold value3 . The value of this approach is that it offers a direct method of identifying deprived persons (by whether their lack of possessions falls below a critical threshold) as an alternative to the more indirect -and, arguably, less reliable -method of identifying poor persons by whether their income or expenditure is below a "poverty line" value. The downside of the "item-based" approach is that -in contrast to the range of measures employed by the low income/expenditure approach -it measures deprivation exclusively in terms of the proportion of deprived persons in the total number of persons.

Against this background, this paper has two purposes. The first, is to extend the range of deprivation measures associated with a lack of possessions (so far

Deprivation Measures

Suppose that a person ( i ) may be said to be "deprived" if his/her "possessions"as measured by his/her value for an indicator variable, i y -is lower than some threshold represented by z, the "deprivation line" . If the indicator variable represents a binary phenomenon -possesses a particular item ( 1 i y = ) or not possessing it ( 0 i y = ) -then z may be taken as the higher of the two values; for continuous variables (for example, i y is the proportion of a certain number of items which a person possesses), z may be some proportion of the mean or median value of i y . We refer to i y as the possessions outcome for person i, hereafter referred to simply as "outcome" .

Then, an aggregate measure of deprivation is provided by any of the following deprivation indices:

1.

The Headcount Ratio (H), which is the proportion of persons who are

deprived: / H M N = ,
where N and M are, respectively, the total number of persons and the total number of deprived persons.

2.

The Deprivation Gap Ratio (I) is the mean distance of the outcomes of deprived persons from the deprivation line, expressed as a proportion of the deprivation line:

1 ( ) 1 M P i i z y I Mz z µ = = =
, where: µ P is the mean outcome of the deprived. The Achievement Gap Ratio ( R) is the mean distance of the outcomes of the entire population from the deprivation line -the non-deprived being assigned a distance of zero -expressed as a proportion of the deprivation line:

1 1 ( ) ( ) M M i i i i z y z y M R IH Nz Mz N = = = = = × 4.
The [START_REF] Sen | Poverty: An Ordinal Approach to Measurement[END_REF] Measure:

(1 )

P S H I I G = × + ×
, where: G P is the Gini coefficient computed over the incomes of the poor5 . If there was no inequality between deprived persons in respect of their outcomes (i.e.

Decomposition by subgroup

An important aspect of poverty analysis is to identify groups which make a particularly large contribution to poverty and whose members are especially at risk of being poor. In order to do this, we need a poverty index which decomposes aggregate poverty as the sum of subgroup poverty. This is provided by the poverty index due to Foster, Greer, and Thorbecke (1984), hereafter referred to as the FGT index.

In order to analyse the decomposition of deprivation, as defined above, suppose that there are K (k=1 An attractive feature of the FGT index is that it is decomposable in the sense that the value of the overall index can be expressed as the weighted average of the subgroup values:

1 ( ; ) ( ; ) K k k FGT v FGT = = × k y y (2)
where:

/ k k v N N =
is the population share of group k. The proportionate contribution made by group k to overall deprivation is then:

( ; ) ( ; ) k k v FGT C FGT × = k y y (3)
and the 'deprivation risk' of a group is:

( ; ) ( ; ) ( ; ) ( ; ) k k k k k FGT C C FGT FGT v FGT v = = = k k k y y y y (4)
The deprivation risk thus is the ratio of a group's contribution to deprivation to its contribution to the population: 1 ( 1)

k k
> < means that it contributes more (less) to deprivation than its population share warrants. If the norm for deprivation risk is taken to be unity, then, say, 1.3 k = means that the deprivation risk for members of group k is 30% above the norm; similarly, 0.82 k = means that the deprivation risk for members of group k is 18% below the norm.

Constructing the Indices

In this section we use the PSENI data to construct two indices of outcomes. The first index is based on the possession of items. The second index is based on economising on, or postponing, the purchase of items. Deprivation in Northern

Ireland is then measured by applying the measures, discussed in the preceding section, to both indices.

deprivation through I, the deprivation gap ratio; it takes account of relative deprivation through G P , the Gini coefficient calculated on the achievement.

6 2 2 ( ;2) [ (1 ) ] FGT H R R = + y
, where is the (square of) the coefficient of variation, computed over the outcomes of deprived households.

Possession of Items

The PSENI Survey asked respondents whether they owned a variety of items and, if they did not, whether it was because they did not want that item or because they could not afford it. These answers were scored, for the purposes of this study, as: 1, if the respondent owned the item; 0, if the respondent did not own the item because he/she could not afford it; as a missing value if the person did not want the item. The aggregate score for each respondent was obtained as the sum of the item scores.

The problem with this approach is that, if there were N items, the aggregate score would only be defined for those respondents who reported a non-missing value for each of the N items (i.e. owned the items or did not own them because of not being able to afford them). In other words, an aggregate score would not be returned for a person who, say, did not have a car because he/she did not want one. Since the number of persons who reported a non-missing value for each of the 38 "possession items" distinguished in the PSENI Survey was very small -approximately 300 out of a total of over 3,000, respondents -we dropped the items for which the largest number of missing values (i.e. items which the largest number respondents neither had nor desired ) were recorded 7 . This left us with the following items all of which were either owned, or not owned but desired, by 1,542 respondents: a television; a telephone; a refrigerator; a dry, damp-free home; a washing machine; a video recorder; home contents insurance; a microwave oven; fresh fruit and vegetables; deep freeze; central heating; a vacuum cleaner; new, not second hand, clothes; a warm water proof coat; two pairs of strong shoes; a good outfit to wear on special occasions; a meal with meat, chicken or fish every other day; enough money for keeping the home in a decent state of decoration, for replacing worn out furniture, for replacing or repairing non-functioning electrical goods, and for paying heating, electricity and telephone bills on time; a small amount of money for the respondent to spend on himself/herself; regular savings of £10 a month.

The scores over these 23 items were scaled so that the maximum score was 100 (a respondent possessed all these items) and the minimum was 0 (a respondent desired all, but possessed none of, these items); a score of x meant that a respondent owned x% of the 23 items. The mean and median "possession" scores, over the 1,542 7 For example, items which the largest number of respondents neither had nor desired were: second homes; boats; cars; dishwashers; home computers; cable television; a roast dinner every week; a decent pension; a dictionary; a daily newspaper; good clothes for an interview; a pet.

Economising on Items

The PSENI Survey asked over 3,000 respondents whether they had to economise on a variety of items. Questions under this head were prefaced as: "In the last twelve month, to help you keep your living costs down, have you..?". The items to which this question referred were: bought cheaper cuts of meat or less than you would have liked to buy; gone without fresh fruit and vegetables; bought second hand, instead of new, clothing; continued wearing worn out clothing because you couldn't afford replacements; put off buying clothing for as long as possible; relied on gifts of clothing; continued wearing worn out shoes because you couldn't afford replacements; put up with feeling cold because you couldn't afford heating costs; stayed in bed longer to save heating costs; skimped on food so that others in the household would have enough to eat; postponed visits to the dentist; not picked up a prescription; gone without, or cut back on, visits to family and friends; gone without, or cut back on, telephone calls to family and friends; gone without, or cut back on, trips to the shops or local places; spent less on hobbies than you would like; not gone to a funeral you would have liked to attend because of the costs; cut back on visits to the local pub; ever used less than you needed of gas, electricity, and the telephone because you couldn't afford it We scored the responses to these questions as: 2, if the answer was "No"; 1, if the answer was "Sometimes"; 0, if the answer was "Often". Summing over the individual item scores, the maximum possible score for a person was 38 (no economising on any of the above 19 items) and the minimum was 0. These aggregate scores were then scaled upwards so that the maximum possible (aggregate) score for a person was 100, the minimum remaining at 0. The mean and median "economising" scores, over the 2,407 responses, were, respectively, 85.53 and 94.74. This points to a heavily skewed distribution in which the observations are bunched to the right of the distribution and, indeed, the skewness value for the distribution was -1.67.

Deprivation Rates in Northern Ireland

Table 1 shows deprivation rates, as they related to "economising" and "possession". The deprivation line adopted was a score of 80% of the relevant overall mean score. This turned out to be a score of 68 for "economising" and a score of 75 for "possession": any person with an "economising score" less than 68 was regarded as deprived in terms of "economising" and any person with a "possession score" less than 75 was regarded as deprived in terms of "possessions".

<Table1>

Table 1 shows that nearly 16% of the 2,407 respondents to the economising questions were deprived on the above definition; the mean normalised gap computed over deprived persons (the Deprivation Gap Ratio, I, defined earlier) was 29% and the mean normalised gap computed over all persons (the Achievement Gap Ratio, R, defined earlier) was 5%. Similarly, nearly 9% of the 1,524 respondents to the possessions questions were deprived on the above definition; the mean normalised gap computed over deprived persons (the Deprivation Gap Ratio, I, defined earlier) was 17% and the mean normalised gap computed over all persons (the Achievement Gap Ratio, R, defined earlier) was 1.5%. The Sen index, as discussed earlier, is an equity sensitive deprivation index (in the sense that it takes account of inequality in scores between deprived households) and its values are shown in Table 1. The difference between the Achievement Gap Ratio and the Sen Ratio is, as shown earlier, due to the fact that the latter is equity sensitive while the former is not. This difference was greatest for single parents (a rise from 13.98 to 19.68 on the economising indicator and a rise from 9.43 to 13.43 on the possessions indicator) suggesting that relative deprivation (as defined earlier) was greatest for this group.

In terms of both economising and possession, single parents were, by far, the most deprived group: of the 140 single parent respondents to the economising questions, 44% were deprived and their deprivation and achievement gaps were, respectively, 31% and 14%; of the 148 single parent respondents to the possession questions, 40% were deprived and their deprivation and achievement gaps were, respectively, 23% and 9%.

The next most deprived group was the jobless: of the 918 jobless respondents to the economising questions, 28% were deprived and their deprivation and achievement gaps were, respectively, 32% and 9%; of the 918 jobless respondents to the The third most deprived group consisted of the separated/divorced/widowed: 25% of this group who responded to the economising questions were deprived and their deprivation and achievement gaps were, respectively, 29% and 7% while 18% of this group who responded to the possession questions were deprived and their deprivation and achievement gaps were, respectively, 22% and 4%. So, from the group-specific results shown in Table 1, being a single parent, jobless, and separated/divorced/widowed were the three main factors influencing the risk of deprivation.

Overarching these group specific results is, of course, the issue of gender equality.

The proportion of women who were deprived with respect to economising (23%) and with respect to possession (14%) was nearly twice that of the corresponding proportions for men (13% and 7%, respectively). The gender gap in deprivation remained unchanged when the definition of deprivation was broadened: both the achievement gap ratio and the value of the Sen index, for economising and for possession, were twice as high for women as they were for men.

Prosperity in Northern Ireland

Although deprivation is intensively studied in the social policy literature its obverse, prosperity, is relatively neglected. This subsection complements the study of deprivation in Northern Ireland with that of prosperity, but this time focusing only on the proportion of persons in Northern Ireland who could be regarded as "prosperous".

The aggregate score for 818 of the 2,407 respondents to the economising questions (34%), and for 1,059 of the 1,542 respondents analysed (69%), was 100.

The former set of respondents did not have to economise on their expenditure on any of the above items and the latter set of respondents possessed all the above items. We define the proportion of such respondents as the prosperity rate. 1 that deprivation rates were lowest for these groups. In contrast, prosperity rates on both indicators were lowest for single parents, for separated/divorced/widowed persons, and for the jobless: only 6% of single parents were prosperous on the economising indicator and only 26% were prosperous on the possessions indicator; fewer than one in four of jobless, or separated/divorced/widowed, persons were prosperous on the economising indicator and less than half were prosperous on the possessions indicator.

The Decomposition of Deprivation in Northern Ireland

The risk of deprivation, defined by k in equation ( 4), is the ratio of a group's contribution to deprivation to its contribution to the population: 1 ( 1)

k k > < means
that it contributes more (less) to deprivation than its population share warrants. Table 3 shows the risk of deprivation in Northern Ireland by various subgroups of its population. If the norm for deprivation risk is taken to be unity, then the values in Table 3 show the percentage amounts by which the risk of deprivation for a group is above or below the norm.

When the sample was divided by religion, Catholics, on the ownership indicator of deprivation, had values 1.10, 1.22, and 1.33 for, respectively, =0, =1, and =2. In other words, when deprivation was measured using the headcount count ratio ( =0), the risk of Catholics being deprived was 10 percent above the norm;

when the Achievement Gap Ratio was used to measure deprivation ( =1), this risk was 22 percent above the norm; and when 'relative deprivation' was included in the measure ( =2), the risk was 33 percent above the norm. By contrast, the risk of Protestants being deprived, on the ownership indicator of deprivation, was 7, 15 and 23 per cent below the norm for, respectively, =0, =1, and =2.

Women had a much a higher risk of being deprived than men. On the ownership indicator, the risk of men being deprived rose from 52 percent above the norm ( =0) to twice the norm ( =2); by contrast, the risk of being deprived for men fell from 18 percent below the norm ( =0) to 33 percent below the norm ( =2).

The two groups in Northern Ireland most at risk of being deprived were the unemployed and single parents. With =0 (i.e. on a Headcount measure of deprivation) the unemployed had a risk of being deprived, on the ownership indicator, times the norm; with =2, the risk for the unemployed rose to three times the norm while for lone parents it rose to seven times the norm. These risks were lower on the economising indicator: with =2, the unemployed had a risk of being deprived which was twice the norm while for lone parents the corresponding risk was three times the norm.

Conclusions

This paper made two contributions. First, using data for Northern Ireland it constructed deprivation indices of the genre pioneered by [START_REF] Townsend | Poverty in the United Kingdom[END_REF]. Second, in using these indices to measure deprivation in Northern Ireland, the paper constructed a range of measures routinely used in the analysis of poverty, but never in the analysis of deprivation. This meant that the measurement of deprivation, through "economising" and "possession" outcomes, took account not just of the number of deprived persons but also -as poverty analysis has long done -of the depth of their deprivation and of inequality of outcomes between them.

  proportion of deprived persons in the total sample) to parallel the range of measures routinely used in the analysis of poverty based on income or expenditure based poverty lines. The second, is to apply these extended measures to data to assess the value added (if any) from these extensions. The data to which these measures are applied come from a recent survey of Northern Ireland residents, conducted between May and June 2002, which focused on their poverty and social exclusion (hereafter, PSENI Survey) 4 .

0 PG

 0 = ) then S H I R = × = . But, if there was inequality between deprived persons (i.e. 0 P G > ), then the Sen index would exceed the Achievement Gap Ratio by the amount: Sen index, therefore, represents an equity sensitive deprivation index in the sense that, in addition to taking account of the proportion of deprived households and the depth of their deprivation, it also takes account of inequality in outcomes between deprived households or, as Sen(1976) termed it, relative deprivation.

  94.2 and 100, respectively, implying that at least half the respondents possessed all the 23 items listed above. The distribution of the possession indicator was heavily skewed (with a skewness value of -2.63) with the observations bunched at the right of the distribution.

  were deprived and their deprivation and achievement gaps were, respectively, 19% and 5%.

  the norm while for lone parents the corresponding risk was 4.5

Table 2

 2 the possessions indicator. This reinforces the point made earlier in Table

	shows

Table 1 Deprivation Rates for Economising on and Possession of Items

 1 The deprivation score was set at 68, i.e. 80% of the overall mean score of 85.5. Non-pensioners who are not in paid employment.

	Economising

*

Table 2 Prosperity Rates for Economising on and Possession of Items

 2 * The prosperity rate is the proportion of persons who possessed every item, i.e. persons whose aggregate score was 100.Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Page 15 of 16	Submitted Manuscript
			Economising Possession
			Prosperity Rate * (%)	Prosperity Rate ** (%)
	Religion:
	Catholics	32	65
	Protestants	36	71
	Family Type:
	Pensioners	55	73
	Couples with children	34	71
	Childless couples	38	74
	Single parents	6	26
	Age:
	<35 years	24	63
	36 years & 65 years	34	70
	>65 years	51	73
	Marital status:
	Single (never married)	22	57
	Married (living with spouse)	37	75
	Separated/divorced/widowed	24	47
	Employment Status:
	Employed	38	79
	Jobless		21	44
	Sex:
	Men		36	72
	Women		28	59
	Overall		34	69

* The prosperity rate is the proportion of persons who did not economise on any item, i.e. persons whose aggregate score was 100. *

Table 3 The Risk of Deprivation in Northern Ireland, By Subgroup of Population

 3 Figures in parentheses represent population shares.The risk of deprivation is the ratio of a group's contribution to deprivation to its contribution to the population:

	Ownership:

Page 16 of 16 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

The term "person" is used to indicate the unit being analysed: this could be a person, a family, or a household.

[START_REF] Zheng | Aggregate Poverty Measures[END_REF] a good review of this literature.

In order to account for differences in tastes, households which do not possess an item are distinguished by whether they did not want it or they wanted, but could not afford it. This meetsVeit- Wilson's (1987) criticism of[START_REF] Townsend | Poverty in the United Kingdom[END_REF] work.

The Survey was based on a sample of 2,000 addresses, with people living in institutions being excluded. The number of households resident at each address were then identified and one, or more, households from that address were selected. The interviewers then listed all the members of a household who were eligible for inclusion in the sample: currently aged 16 years or over and living at that address. From this listing of eligible persons the interviewer's computer randomly selected a person to complete the interview. For further details see[START_REF] Hillyard | Bare Necessities: Poverty and Social Exclusion in Northern Ireland, Belfast: Democratic Dialogue[END_REF].

It is important to emphasise three aspects of Sen's measure: it takes account of the number of deprived persons, relative to the population, through H, the headcount ratio; it takes account of the depth of their

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