G Ingrid

MD PhD J G Rours
email: rours@mac.com

Ronald R Krijger

MD PhD Alewijn Ott

MSc Hendrina F M Willemse

Ronald Groot

MD PhD Luc J I Zimmermann

MD PhD Renee F Kornelisse

MD PhD Henri A Verbrugh

PhD Roel P Verkooijen

MD PhD Ronald R De Krijger

MD PhD Ronald De Groot

and placental inflammation in early preterm delivery

Keywords: Chlamydia trachomatis, placental inflammation, pregnancy, prematurity

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Preterm birth is the main cause of perinatal morbidity and mortality worldwide accounting for 60%-80% of deaths of infants born without congenital abnormalities [START_REF] Mccormick | The contribution of low birth weight to infant mortality and childhood morbidity[END_REF]. Preterm labor is associated with placental inflammation, especially with acute chorioamnionitis [START_REF] Hillier | A case-control study of chorioamnionic infection and histologic chorioamnionitis in prematurity[END_REF][START_REF] Lettieri | Does "idiopathic" preterm labor resulting in preterm birth exist?[END_REF]. Many studies associated clinical and histological chorioamnionitis with acute neonatal morbidity and mortality and, at least to some degree, also with neurological impairments and chronic lung disease [START_REF] Salafia | Placental pathologic findings in preterm birth[END_REF][START_REF] Watterberg | Chorioamnionitis and early lung inflammation in infants in whom bronchopulmonary dysplasia develops[END_REF][START_REF] De Felice | Early neonatal brain injury in histologic chorioamnionitis[END_REF][START_REF] Mehta | Neonatal morbidity and placental pathology[END_REF][START_REF] Hagberg | Sequelae of chorioamnionitis[END_REF].

Chlamydia trachomatis is the most common bacterial sexually transmitted infection worldwide, including in the Netherlands [START_REF] Laar | Sexually transmitted diseases in the Netherlands[END_REF]. Starting as cervicitis, chlamydial infection may ascend and infect the placenta or amniotic fluid, which may subsequently lead to preterm delivery. The literature regarding the association of maternal C. trachomatis infection with preterm delivery, however, is conflicting due to differences in study design, population and microbiological test methods [START_REF] Martin | Prematurity and perinatal mortality in pregnancies complicated by maternal Chlamydia trachomatis infections[END_REF][START_REF] Harrison | Cervical Chlamydia trachomatis and mycoplasmal infections in pregnancy. Epidemiology and outcomes[END_REF][START_REF] Gravett | Independent associations of bacterial vaginosis and Chlamydia trachomatis infection with adverse pregnancy outcome[END_REF][START_REF] Sweet | Chlamydia trachomatis infection and pregnancy outcome[END_REF][START_REF] Martius | Relationships of vaginal Lactobacillus species, cervical Chlamydia trachomatis, and bacterial vaginosis to preterm birth[END_REF][START_REF]Association of Chlamydia trachomatis and Mycoplasma hominis with intrauterine growth retardation and preterm delivery. The John Hopkins Study of Cervicitis and Adverse Pregnancy Outcome[END_REF][START_REF] Ngassa | Maternal genital Chlamydia trachomatis infection and the risk of preterm labor[END_REF][START_REF] Claman | Serologic evidence of Chlamydia trachomatis infection and risk of preterm birth[END_REF][START_REF] Gencay | Chlamydia trachomatis seropositivity is associated both with stillbirth and preterm delivery[END_REF][START_REF] Andrews | The Preterm Prediction Study: association of second-trimester genitourinary chlamydia infection with subsequent spontaneous preterm birth[END_REF][START_REF] Karinen | Serum C-reactive protein and Chlamydia trachomatis antibodies in preterm delivery[END_REF][START_REF] Odendaal Hj Schoeman | The association between Chlamydia trachomatis genital infection and spontaneous preterm labour[END_REF][START_REF] Blas | Pregnancy outcomes in women infected with Chlamydia trachomatis: a population-based cohort study in Washington State[END_REF][START_REF] Kataoka | Association between preterm birth and vaginal colonization by mycoplasmas in early pregnancy[END_REF][24].

The aims of this study were to detect C. trachomatis infection in placentas of women with early preterm delivery and to associate C. trachomatis infection with histological signs of placental inflammation. The literature is very limited regarding this association, but may be relevant to explain a component causal effect of C. trachomatis infection on preterm delivery. In addition, we studied the association of C. trachomatis infection with delivery characteristics and neonatal outcome in this selected cohort of women.

4

Methods

Patients and Design

The chorioamnionitis study, during which the placentas were collected that we used in the current Chlamydia trachomatis study, was prospective, observational, and non-interventional. Pregnant women, who delivered live newborns between May 2001 and February 2003 at the Erasmus MC-Sophia, Rotterdam, the Netherlands, at a gestational age of 32 weeks or less, were eligible for the study. Regular health care workers (residents, research-nursing staff, neonatologists) informed the women about the study. Enrolment took place immediately after delivery when the newborns were admitted to the neonatal intensive care unit. Women were excluded from the study if the placenta was not available for histological examination and, at a later stage, if no more placental tissue was available to test for C. trachomatis.

Antenatal, perinatal and neonatal data were obtained from maternal and neonatal medical records and prospectively stored in a database. Follow up of neonates was until a postnatal age of 28 days or postmenstrual age of 36 weeks. Neonates, who were transferred to another hospital, were followed in order to complete the data. Placentas were examined for evidence of infection or other pathology.

Retrospectively, the placentas were examined for the presence of C. trachomatis.

Maternal ethnicity was self-classified in this study. In total 32 different nationalities were registered. Most women were Dutch (70%), Turkish (7%), Surinamese (4%) or Moroccan (3%). Nationalities were clustered in six ethnic groups: Central European, Eastern European, Mediterranean, Caribbean/South-American, Azian and African. We used the following definitions. Clinical signs of chorioamnionitis were recorded and defined by the presence of two or more of the following signs: abdominal pain, cervical discharge, leucocytosis (white blood cell count ≥ 15 x 10 9 /l), fever (maternal temperature > 38 ºC), and raised CRP (C-reactive protein ≥ 15µ/L). Pre-eclampsia was defined as new-onset hypertension (blood pressure > 140/90 mmHg) with proteinuria [START_REF] Brown | The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the International Society for the Study of Hypertension in Pregnancy (ISSHP)[END_REF]. Fetal distress was considered present when indicated by the obstetrician based on cardiotocographic criteria. Gestational age was estimated by ultrasonography or by using the last menstrual period when reliable. Small for gestational age (SGA): birth weight less than two standard deviations of the mean for gestational age. RDS: was defined according to Giedeon [START_REF] Giedion | Acute pulmonary X-ray changes in hyaline membrane disease treated with artificial ventilation and positive end-expiratory pressure (PEP)[END_REF].

Broncho-pulmonary dysplasia (BPD): dependency on oxygen supplementation at a postmenstrual age of 36 weeks. Intraventricular haemorrhage (IVH) and periventricular leucomalacia (PVL) were defined 5 according to Volpe [START_REF] Volpe | Hypoxic-ischemic encephalopathy: clinical aspects[END_REF]. Necrotising enterocolitis (NEC) was defined as stage two or higher according to Bell [START_REF] Bell | Neonatal necrotizing enterocolitis. Therapeutic decisions based upon clinical staging[END_REF].

Histopathology

Immediately after delivery placentas and membranes were fixed in formalin for at least 16 hours. Sampling was performed according to a standard protocol with two membrane rolls, two cross sections of the cord and three representative blocks of the placental disk as a minimum. The tissues were thereafter, according to routine standard methodology for pathological specimens, embedded in paraffin until histopathological examination. To prevent inter-examiner variation, the same pathologist who was specialized in perinatal pathology examined all placentas for histological evidence of inflammation. The pathologist was blinded to clinical information. Placental inflammation was categorized according to a maternal inflammatory response (MIR) and fetal inflammatory response (FIR) as suggested by the Amniotic Fluid Infection Nosology Committee [START_REF] Redline | Amniotic infection syndrome: nosology and reproducibility of placental reaction patterns[END_REF], and was considered positive if there was any evidence of a MIR and/or FIR.

The MIR was divided into three stages (acute subchorionitis or chorionitis, acute chorioamnionitis, necrotizing chorioamnionitis) representing progression of disease, and two grades representing intensity of disease; the FIR was similarly divided into three stages (chorionic vasculitis or umbilical phlebitis, umbilical vasculitis or umbilical panvasculitis, (subacute) necrotizing funisitis or concentric umbilical perivasculitis) and two grades [START_REF] Redline | Amniotic infection syndrome: nosology and reproducibility of placental reaction patterns[END_REF].

After histopathological examination, the placentas were stored in paraffin blocks at room temperature.

Before testing for C. trachomatis, the paraffin blocks were cut on different days using the same microtome with single use disposable blades. Care was taken to clean materials between cases to avoid contamination. The paraffin blocks were cut until the surface was straight after which five samples per paraffin block each of 10 µm thickness were cut for further microbiological analysis.

Microbiology

For DNA Isolation, tissue samples were deparaffinized with xylene at room temperature and washed in ethanol. Bacterial DNA was isolated using the QIA Amp Tissue Kit (QIAgen, Hilden, Germany). After a first lysis with proteinase K, DNA was bound to a QIAamp spin column, the column washed twice, the purified DNA eluded from the column in an elution buffer AE, and preheated at 70ºC.

6

For DNA amplification and detection, a Real-time PCR was carried out using the Lightcycler 2.0 system and FastStart DNA Master SYBR Green I kit (Roche Diagnostics, Almere, The Netherlands). Each PCR reaction contained 3 mM MgCl 2 and 0.5 µM of each of the two primers NLO 5'-ATGAAAAAACTCTIGAAATCG-3' (position 1-21) and NRO 5'-CTAACTGTAACTGCGTATTT-3' (position 1128-1108) to amplify C. trachomatis plasmid DNA [START_REF] Lan | Improved PCR sensitivity for direct genotyping of Chlamydia trachomatis serovars by using a nested PCR[END_REF], was denatured for 10 minutes at 95 ºC, subjected to 40 cycles of 15 sec at 95 ºC, 10 sec at 60 ºC and 20 sec at 72 ºC. To check the specificity of amplification products, a melting curve analysis was performed consisting of heating from 65 ºC to 95 ºC at a rate of 0.1 sec per step and holding for 20 sec at each step for data acquisition. The melting temperature for the specific amplicons was 82 ºC. For each sample, the PCR was done using a separate internal validation (β-globine PCR). For each run a negative control (water) and three positive controls (dilutions of purified C. trachomatis) were included.

Ethical aspects

The study was approved by the Medical Ethics Committee for Research on Human Subjects of the Erasmus MC, Rotterdam, the Netherlands.

Statistical analysis

Statistical analysis was performed using the Statistical Package of Social Sciences version 11.0 for Windows (SPSS Inc. Chicago, Illinois, USA). Since pre-eclampsia and chorioamnionitis are two distinct potential pathways to preterm delivery, stratified analysis was done regarding the differences in maternal morbidity. For comparison of categorical variables between groups the Pearson's Chi-square test or Fisher's Exact test was used. For comparison of continuous variables the two-tailed Student's T-test was used. The Mantel-Haenszel test was used to asses trends. Statistical significance was considered P<0.05.

Results

Three hundred and twenty three pregnant women were eligible for the study. Placental tissue and clinical data were available for 304 (94%) women and their newborns.

C. trachomatis in relation to clinical characteristics

C. trachomatis was detected by PCR in placental tissue of 76/304 (25%) women. One woman had a record of C. trachomatis infection prior to the current pregnancy. Three women were tested and found to be positive during the current pregnancy. None of these women had C. trachomatis detected in their placenta. Eighty-four women received antibiotics for PPROM or signs of infection. Chlamydia-positive women received more often antibiotics than chlamydia-negative women: 32/76 (42%) versus 52/228 (23%); (OR 2.5, 95% CI 1.4-4.4), and received more often erythromycin: 14 (18%) versus 20 (9%); (OR 2.4, 95% CI 1.1-5.2). Amoxicillin-clavulanic acid and amoxicillin were prescribed more often to chlamydiapositive women, but did not differ significantly between the two groups: 19 (25%) versus 36 (16%); (OR 1.8; 95% CI 0.9-3.3).

Antenatal, clinical and delivery characteristics of women with and without C. trachomatis infection are compared in tabel 1. Maternal age, parity, and gravidity were similar for women in both groups and the distribution of chlamydial detection was homogenous among ethnic groups (P=0.8). Presence of two or more clinical signs of chorioamnionitis was not significantly more often observed among chlamydiapositive women than among chlamydia-negative women. PROM did not differ between the two groups.

Chlamydia-positive women had significantly less often pre-eclampsia (P=0.002) and delivered significantly less often after fetal distress (P=0.03) and by caesarean section (P=0.002) than chlamydia-negative women, and their newborns were significantly less often small for their gestational age (P=0.02). Neonatal outcomes such as RDS, BPD, IVH, PVL, and neonatal mortality rate were not significantly different between neonates born to chlamydia-positive and chlamydia-negative women (data not shown).

Since the above-mentioned differences could be due to the differences in maternal morbidity (women without and with pre-eclampsia), a stratified analysis was performed as shown in table 2. After stratification, the differences between chlamydia-positive and chlamydia-negative women became nonsignificant. Among the women with pre-eclampsia, neonates born to chlamydia-positive women were less often small for gestational age than those born to chlamydia-negative women (P=0.02). Neonatal outcomes as BPD, IVH, PVL and neonatal mortality rate were not significantly different between strata (data not shown), but RDS grade I-II was observed more often in neonates born to 9/17 (53%) chlamydiapositive women with pre-eclampsia than in 25/96 (26%) chlamydia-negative women with pre-eclampsia (OR 3.2; 95% CI 1.1-9.2; P=0.03).

C. trachomatis in relation to histological signs of placental inflammation

Histological evidence of placental inflammation was present in 123/304 (40%) women of whom 64 (52%) had inflammation diagnosed in both maternal and fetal tissue, 50 (41%) in maternal tissue only, and four (3%) in fetal tissue only. Five (4%) placentas showed other specific features including peripheral funisitis, acute villitis, acute intervillositis with intervillous abscesses, or the presence of decidual plasma cells, but had no signs of a maternal or fetal inflammatory response. Chlamydia-positive women had significantly more often histopathological signs of placental inflammation than chlamydia-negative women: 41/76 (54%) versus 82/228 (36%); (OR 2.1, 95% CI 1.2-3.5).

The progression and intensity of inflammation of maternal tissue only and the combination of maternal and fetal tissues were assessed in relation to C. trachomatis infection (Table 3). For the latter combination, the highest stage or grade of inflammation in either maternal or neonatal tissue was assessed in trend analysis. Chlamydia-positive women had significantly more often signs of inflammation in both maternal and fetal tissue for progression stage 3 and intensity grade 2 in their placentas than chlamydia-negative women. In addition, a significantly increasing trend towards more frequent detection of C. trachomatis was observed with increasingly higher scores for intensity (P=0.04) involving maternal inflammation only, and for progression (P=0.009) and intensity (P=0.007) of inflammation involving both maternal and fetal tissues.

9

Discussion

Chlamydia trachomatis DNA was detected in a high proportion (25%) of placentas from women who had early preterm delivery ( 32 weeks) and was associated with histopathological signs of placental inflammation.

The strength of this study is the prospective, observational design during which the placentas and clinical data were obtained, the large number of well-described participants, a high follow-up rate, and the use of nucleic acid amplification technique (NAAT) for the detection of C. trachomatis. In order to prevent contamination of samples during the process of cutting, during DNA isolation or during the PCR test itself, precautions were taken. Paraffin blocks were cut in badges on different days by two different people using a similar microtome with single-use disposable blades, and care was taken to clean materials between cases. In addition, the order of cutting was recorded and no clustering was observed in the detection of C. trachomatis in the cuts. In addition, DNA isolation was done on different days by different laboratory technicians in different locations, and pipetting was performed using aerosol-resistant tips. Furthermore, the PCR was done using an internal validation (β-globine PCR) for each sample separately, and for each run using a negative control (water) and three positive controls (dilutions of purified Chlamydia). None of the negative controls became positive. Therefore, contamination seems unlikely to have influenced the results of the study.

A limitation of the study is that we did not examine placentas from full term pregnancies.

Inclusion of term specimens would have enabled additional assessment of the contribution of C. trachomatis infection to placental inflammation and its relation to prematurity. Unfortunately, the chorioamnionitis study was initially not designed as such and retrospectively we were not able to trace term specimens from women in the same region and time period for additional comparisons. Another limitation is that although a large number of women participated in this study, the numbers in each category after stratification are small. Furthermore, residual confounding as a result of co-infection by other pathogens was not tested as part of the study. Indeed, the finding of many cases of placental inflammation in the absence of C. trachomatis infection would support the presence of other inflammation-inducing pathogens in this cohort of women. Another limitation was that C. trachomatis testing was done retrospectively and that prospectively no specimens were collected from infants, which made it impossible to study vertical transmission.

10

The PCR technique has been used previously to detect C. trachomatis in placentas [START_REF] Gencay | Chlamydia trachomatis detected in human placenta[END_REF][START_REF] Dong | Detection of Chlamydia trachomatis intrauterine infection using polymerase chain reaction on chorionic villi[END_REF][START_REF] Matovina | Possible role of bacterial and viral infections in miscarriages[END_REF], but reports focusing on the use of PCR to detect Chlamydia in placentas of early preterm deliveries are very limited and from much smaller cohorts. One case report described a stillborn at 36 weeks of gestation in whose placenta C. trachomatis was identified [START_REF] Gencay | Chlamydia trachomatis detected in human placenta[END_REF]. In a Chinese study 59 specimens of chorionic villi were examined that had been collected from women attending an antenatal clinic for artificial abortion within the first trimester of pregnancy, of which three cases (5%) were found to be C. trachomatis positive [START_REF] Dong | Detection of Chlamydia trachomatis intrauterine infection using polymerase chain reaction on chorionic villi[END_REF]. In a Croatian study of women with a miscarriage between four and 19 weeks gestation, C. trachomatis was detected in only one of 108 placental tissues examined [START_REF] Matovina | Possible role of bacterial and viral infections in miscarriages[END_REF].

A 25% prevalence of C. trachomatis in pregnant women is high. It is much higher than the C. trachomatis prevalence (4%) found in one of our previous population-based studies among 4,055 pregnant women in the same region and time period, using the same test method on urines instead of placentas, and for whom the referal centre is the hospital in which women and neonates were enrolled for the present study. However, our finding is similar to the prevalence (18%) found in the women of the latter study who delivered at 32 weeks gestation or less [START_REF] Rours | Chlamydia trachomatis infections during pregnancy: consequences for pregnancy outcome and infants[END_REF]. The difference between these studies may be due to the fact that the current study is hospital based instead of population based, and because not all women who delivered prematurely participated in the other study. Although we were not able to directly assess an association between C. trachomatis infection and preterm delivery from the current study, our findings support those found in the previous study.

We found no significant association of C. trachomatis infection with clinical signs in women, neither in the total group nor after stratification. This is not surprising since up to 80% of chlamydial infections in women are known to be asymptomatic. In addition, urogenital symptoms are often nonspecific and likely to be regarded as part of the pregnancy instead of disease and not to be reported. However, we did find an association between C. trachomatis infection and histopathological signs of placental inflammation. Histologically proven chorioamnionitis is considered the gold standard against which other clinical predictors of inflammation should be measured [START_REF] Hillier | A case-control study of chorioamnionic infection and histologic chorioamnionitis in prematurity[END_REF][START_REF] Pankuch | Placental microbiology and histology and the pathogenesis of chorioamnionitis[END_REF]36]. Histological evidence of placental inflammation was present in 40% of the women and significantly more in chlamydia-positive women than in chlamydia-negative women: 54% versus 36%. Virtually all placentas had signs of maternal inflammation whereas only half the time fetal inflammation was present. Only maternal inflammation would be concordant with infection originating from the maternal side. Our findings, therefore, suggest that ascending C. trachomatis infection during pregnancy may extend into the 11 placental tissues. Such invasion of placental tissue is likely to produce an inflammatory response that may trigger preterm labor and delivery [START_REF] Goldenberg | Intrauterine infection and preterm delivery[END_REF]. Hence, in case of unexplained preterm delivery additional information can be obtained from histopathological examination of placental tissue and from testing for C. trachomatis using highly sensitive nucleic acid amplification techniques.

C. trachomatis is transmitted from mother to newborn during passage through an infected birth canal. However, there are several, mostly anecdotal, reports of newborns delivered by caesarean section that were infected with C. trachomatis [START_REF] Shariat | An interesting case presentation: a possible new route for perinatal acquisition of Chlamydia[END_REF][START_REF] Bell | Chlamydia trachomatis infection in dizygotic twins delivered by caesarean section[END_REF][START_REF] Scolea | Chlamydia trachomatis infection in infants delivered by cesarean section[END_REF][START_REF] Givner | Chlamydia trachomatis infection in infant delivered by cesarean section[END_REF]. Ascending chlamydial infection in women undergoing caesarean section may be due to PROM, but the possibility of a transmembrane or transplacental route has also been suggested in the pathogenesis of neonatal chlamydial infection [START_REF] Shariat | An interesting case presentation: a possible new route for perinatal acquisition of Chlamydia[END_REF][START_REF] Scolea | Chlamydia trachomatis infection in infants delivered by cesarean section[END_REF]. In our study 43% of chlamydia-positive women gave birth by caesarean section of whom 27% had PROM. Since collection and testing of specimens from the newborns was not included in the desing of the study, we are unable to confirm the latter hypothesis. Likewise, we cannot ascribe any neonatal respiratory disease, RDS stage I-II, to C. trachomatis infection.

The data presented are highly suggestive of C. trachomatis being a component cause of preterm delivery through a pathway that involves a transmembrane or transplacental route of transmission for chlamydial infection leading to chorioamnionitis. Further studies regarding the role of C. trachomatis in either pathway leading to preterm delivery are needed.

Conclusion

C. trachomatis DNA was frequently detected in the placenta of women with early preterm delivery and was associated with signs of inflammation in the placenta.

Acknowledgement

The chorioamnionitis study was funded by the Revolving Fund, Erasmus University MC, Rotterdam, the Netherlands. We thank T.J. Schneider, E. Oschatz, F. Jonkers, N. de Graauw and C.J.A.M. Willemsen for their assistance with the data collection and database.

Table 1

 1 Clinical characteristics of pregnancies according to Chlamydia trachomatis status

		C. trachomatis negative	C. trachomatis positive
		n=228	n=76	OR (95% CI)
	-antenatal characteristics
	maternal age	30.9 ± 5.3	30.1 ± 5.0	NA
	para	1.8 ± 1.3	1.9 ± 1.1	NA
	gravida	2.2 ± 1.7	2.0 ± 1.2	NA
	-clinical characteristics
	≥ 2 clinical signs CA	48 (21)	24 (32)	1.7 (0.97 -3.1)
	fetal distress	114 (50)	27 (36)	0.6 (0.3 -1.0)
	PROM	63 (28)	24 (32)	1.3 (0.7-2.2)
	pre-eclampsia	96 (42)	17 (22)	0.4 (0.2 -0.7)
	-delivery characteristics
	caesarean section	146 (64)	33 (43)	0.4 (0.3 -0.8)
	gestational age	29.1 ± 1.9	29.1 ± 1.9	NA

Table 2

 2 Clinical characteristics according to Chlamydia trachomatis status by strata of pre-Numbers are mean ± SD or number (%), n: number, OR: odds ratio, CI: confidence interval, NA: not applicable, CA: chorioamnionitis, PROM: premature rupture of membranes, SGA: small for gestational age. Analysis with Pearson's Chi-square test or Fisher's Exact test; *P=0.02

	eclampsia
		C. trachomatis negative C. trachomatis positive	OR (95% CI)
	No pre-eclampsia	n=132 (69)	n=59 (31)
	-antenatal characteristics
	maternal age	30.8 ± 5.1	30.1 ± 4.8	NA
	para	1.9 ± 1.3	1.9 ± 1.1	NA
	gravida	2.3 ± 1.7	2.0 ± 1.2	NA
	-clinical characteristics
	≥ 2 clinical signs CA	47 (36)	24 (41)	1.2 (0.7-2.3)
	fetal distress	41 (31)	16 (27)	0.8 (0.4 -1.6)
	PROM	60 (45)	24 (41)	0.8 (0.4-1.5)
	-delivery characteristics
	caesarean section	59 (45)	20 (34)	0.6 (0.3 -1.2)
	gestational age	28.8 ± 2.0	29.1 ± 2.0	NA
	birth weight (gram)	1194 ± 354	1223 ± 359	NA
	SGA	18 (14)	9 (15)	1.1 (0.5 -2.7)
	Pre-eclampsia	n=96 (85)	n=17 (15)
	-antenatal characteristics
	maternal age	31.1 ± 5.6	30.0 ± 5.7	NA
	para	1.7 ± 1.2	1.8 ± 1.1	NA
	gravida	2.0 ± 1.7	1.9 ± 1.2	NA
	-clinical characteristics

Table 3

 3 Risk of Chlamydia trachomatis infection according to progression and intensity of placental #OR compared to placenta with no inflammation, ##P=0.01 Mantel-Haenszel trend analysis: *P=0.09, ** P=0.04, ***P=0.01

	tissue inflammation
	Placenta tissue	Total, n	C. trachomatis positive, n (%)	OR (95% CI)#
	no placenta inflammation	181	35 (19)	1.0 (reference)
	any placenta inflammation##	123	41 (33)	2.1 (1.2 -3.5)
	Maternal inflammation only	50	16 (32)	2.0 (0.98 -4.0)
	Progression*
	stage 1	24	8 (33)	2.1 (0.8 -5.3)
	stage 2	20	6 (30)	1.8 (0.6 -5.0)
	stage 3	6	2 (33)	2.1 (0.4 -11.8)
	Intensity**
	grade 1	43	13 (30)	1.8 (0.9 -3.8)
	grade 2	7	3 (43)	3.1 (0.7 -14.6)
	Maternal & fetal inflammation	64	22 (34)	2.2 (1.2 -4.1)
	Progression***
	stage 1	4	1 (25)	1.4 (0.1 -13.7)


Concetta Castiglione
email: castiglc@tcd.ie

Microeconomics, L63 -Microelectronics|Computers|Communications Equipment < L6 -Industry Studies: Manufacturing < L -Industrial Organization, O33 -Technological Change: Choices and Consequences|Diffusion Processes < O3

Keywords: ICT investment, Productivity, Stochastic frontier, Italian manufacturing firms

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I. Introduction

The influence of information and communication technology (ICT) is a topic that has received increased attention from economists over the past two decades, its impact on economic growth being the subject of numerous studies at both aggregate and firm levels [START_REF] Brynjolfsson | Paradox lost? Firm-level evidence on the returns to information systems spending[END_REF][START_REF] Gordon | Does the New Economy Measure Up to the Great Inventions of the Past?[END_REF][START_REF] Oliner | The Resurgence of Growth in the Late 1990s: Is Information Technology the Story?[END_REF].

During the 1980s and early 1990s many researchers asserted that the contribution of ICT to productivity and economic growth was either very small or non-existent [START_REF] Gilchrist | Productivity and the PC Revolution, CRITO[END_REF]. These findings are often associated with the Solow paradox, which states that: "You can see the computer age everywhere but in the productivity statistics". Nevertheless, the latest studies increasingly assert the importance of new technologies.

The empirical literature focuses on the relationship between ICT investments and labour productivity or ICT investments and Total Factor Productivity (TFP). Some attempts have been made to study the relationship between ICT investments and technical efficiency (TE) at firm level [START_REF] Assefa | Technical Efficiency of Small and Medium-Scale Enterprises[END_REF][START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF][START_REF] Gholami | ICT and Productivity of the Manufacturing Industries in Iran[END_REF]Shao and[START_REF] Shao | Measuring the value of information technology in technical efficiency with stochastic production frontiers[END_REF][START_REF] Gordon | Technology and Economic Performance in the American Economy[END_REF]. This work starts by over-viewing the previous literature and moves in two directions. Firstly, two different production functions, Cobb-Douglas and Translog, are used to explore investments and the average distance of producers from the ''best practice'' by using a stochastic frontier approach. Both production functions are used since the Cobb-Douglas requires the elasticity of substitution between factors to be unity while the translog production function is a generalization of the Cobb-Douglas which relaxes this restriction. Secondly, ICT In this work, the impact of ICT technologies on technical efficiency is analysed under the hypothesis that a greater use of ICT at firm and economy level may help firms to increase their efficiency. The purpose of this work is to investigate whether ICT investments significantly affect a firm's distance from the optimal production frontier. In order to test this hypothesis, the stochastic frontier production function is adopted utilising an unbalanced panel data of Italian manufacturing firms elaborated from the results of the VII, VIII and IX surveys carried out by Mediocredito Centrale-Capitalia (MCC). Other works use the same surveys (VII or VIII) to study the relationship between ICT investments and productivity growth [START_REF] Atzeni | The Economic Effects of Information Technology: Firm Level Evidence from the Italian Case, CRENoS[END_REF][START_REF] Bugamelli | Barriers to Investment in ICT[END_REF], total factor productivity growth [START_REF] Gambardella | Nuova industria o nuova economia? L'impatto dell'informatica sulla produttività dei settori manifatturieri in Italia[END_REF] or technical efficiency [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF].

Our results show that ICT investments have a positive effect on the technical efficiency of Italian manufacturing firms when ICT is considered to be a firm specific factor.

The remainder of this paper is structured as follows: the first section focuses on the productivity paradox from a historical context and on the ICT contribution to economic growth. The second section presents the methodology, which encompasses the economic model and the empirical approach in order to evaluate the relationship between ICT and distance from the "efficient frontier", and describes the data used. To conclude, results and comments are presented. 5 investments of the 1990s has led to significant changes in the absolute and relative productivity performance of firms, sectors and countries. For example, at the microeconomic level, [START_REF] Brynjolfsson | Beyond computation: Information Technology, Organization Transformation and Business Performance[END_REF] and [START_REF] Gilchrist | Productivity and the PC Revolution, CRITO[END_REF] show that the payoffs to ICT investments occur not just in labour productivity but also in total factor productivity.

ICT contribution to economic growth

Empirical analysis of economic growth and productivity typically distinguishes three effects of ICT [START_REF] Kenneth | Payoffs from Investment in Information Technology: Lessons from the Asia-Pacific Region[END_REF][START_REF] Pilat | The ICT Productivity Paradox: Insights from Micro Data[END_REF]. First, the "production effect": the firms producing these technologies can help economic growth at an aggregate level, either through a rapid increase in demand for these products compared with other sectors or through a higher level of productivity in the same sector. In fact, technological progress in the production of ICT goods directly raises total factor productivity in ICT-producing industries.

The second is the "using effect": firms in traditional sectors increase capital stock per worker (capital deepening) in order to acquire new technologies, this implies an increase in output per worker. Engelbrecht and Xayavong (2005) assert that the fall in ICT capital prices with respect to those of other types of capital encourages firms to substitute the former for the latter. In this way, an increase in investment in ICT capital contributes to labour productivity growth in ICT-using industries through capital deepening effects. [START_REF] Schreyer | The contribution of information and communication technology to output growth: a study of the G7 countries[END_REF] points out that substitution processes are an important element in assessing the role of ICT in production. This approach treats ICT capital goods like all other types of capital goods. In particular, it is assumed that firms which own ICT assets are able to Moreover, a greater use of ICT throughout the economy may help firms to increase their overall efficiency.

Furthermore, greater use of ICT may contribute to network effects, such as lower transaction costs and more rapid innovation, which should also improve TFP. In fact, the third effect of ICT is "total factor productivity": the adopting of new technology improves the performance of all the factors used. Consequently, output increases without further input investment. An increase in total factor productivity means that, at a given input level and a fixed quality, an economy always obtains higher output levels [START_REF] Castiglione | The impact of Information and Communication Technology on Italian manufacturing firms[END_REF].

Finally, many other authors suggest that ICT generates a fourth effect in the form of spillovers through efficiency gains in the production process, and through the accumulation of intangible organizational capital accompanying investment in ICT capital [START_REF] Jorgenson | U.S. Economic Growth at the Industry Level[END_REF][START_REF] Basu | The case of the missing productivity growth: or, does information technology explain why productivity accelerated in the United States but not the United Kingdom?[END_REF]. Such positive externalities, or spillover effects, may accelerate TFP growth in ICT-using industries.

In this work ICTs are considered as General Purpose Technologies (GPTs) i.e. fundamental technological innovations that are characterised by pervasiveness, technological dynamism and innovative complementarities. The technological dynamism characteristic indicates continuous efforts at innovation, as well as learning, increasing over time the efficiency with which the generic function is performed [START_REF] Bresnahan | General Purpose Technologies: 'Engines of Growth'?[END_REF]. This should show up as a reduction in prices, an increase in quality, or both. The improvement a GPT brings about can therefore be measured in terms of how much cheaper a unit of quality becomes over time. If technology is embodied in capital, then presumably capital During the GPT phase, technological progress could be separated into two dimensions. One would be a new technological paradigm (i.e. steam engine or electricity) and the other would be an increasing number of innovations within the paradigm. With the two different types of technological progress the production function is shifted up. [START_REF] Helpman | Diffusion of General Purpose Technologies[END_REF] assert that each time a new GPT appears, it generates a cycle consisting of two distinct phases. During the first phase resources are diverted to the development of complementary inputs permitting exploitation of the new GPT. Output and productivity experience negative growth and the real wage stagnates. In the second phase, after enough complementary inputs have been developed, old inputs are replaced by the more productive GPT.

During this phase output, real wages and profits increase.

In this way it is possible also to explain the productivity paradox. In fact, from the study by [START_REF] Jovanovic | General Purpose Technologies[END_REF], it is possible to assert that the slowing down of productivity during the ICT period could be explained as a learning phase, but when investments in new technologies increase productivity should rapidly increase [START_REF] Castiglione | Nuove tecnologie e crescita nell'Unione Europea[END_REF].

III. Stochastic frontier approach

We assume that ICT are GPT because, according to [START_REF] Bresnahan | General Purpose Technologies: 'Engines of Growth'?[END_REF], they open the road to new opportunities for existing technologies too, improving the performances of all the factors used. To verify this hypothesis the contribution of ICT investment to firm productivity is explored empirically using a stochastic production frontier approach. The production frontier, which characterises the relationship between inputs and output, specifies the maximum output achievable by employing a combination of inputs. The distance between the production frontier and the actual output is regarded as the firm's technical inefficiency. Thus, a firm either operates below the frontier when it is technically inefficient or it operates on the production frontier when it is technically efficient.

The concept of TE was elaborated by [START_REF] Farrell | The measurement of productive efficiency[END_REF] who stated that the efficiency of a firm consists of two components: technical efficiency and allocative efficiency. TE is concerned with the maximisation of output for a given set of resource inputs and indicates the ability of a firm to obtain maximum output from a given set of inputs. Allocative efficiency reflects the firm's ability to use inputs in optimal proportions, given their respective price and the production technology. [START_REF] Jha | Productivity, technical and allocative efficiency and farm size in wheat farming in India: a DEA approach[END_REF] use Data Envelopment Analysis (DEA) in order to measure both technical and allocative efficiency in Indian firms. Together technical and allocative efficiency provide a measure of total economic efficiency.

According to [START_REF] Battese | A model for technical efficiency effect in a stochastic frontier production functions for panel data[END_REF] productivity growth from one year to the next can be the result either of efficiency improvements or to technical change. Fig. 1 shows a typical production frontier, SF, with one input x and one output y. Technical change may be represented by an upward shift in the production frontier. This is depicted in Fig. 1 i.e. changing the units of measurements does not change the scores of efficiency measurement. This ratio of technical efficiency will take on a value between zero and one, with a higher score implying higher technical efficiency" (Shao and Lin, 2001: 448). A technically inefficient firm could produce the same The measurement of technical efficiency has widely been associated with the use of production frontier functions. As [START_REF] Arestis | Technical Efficiency and Financial Deepening in the non-OECD Economies[END_REF] pointed out traditional econometric estimation techniques (such as ordinary least squares) fail to measure a production frontier, because they allow some of the observed output bundles produced by a given set of inputs to be greater than the estimated maximal producible output. [START_REF] Albert | Regional technical efficiency: a stochastic frontier approach[END_REF] suggests to estimate an idealized production frontier (best-practice frontier) in which "efficient agents can be localized and the deviations of all other firms from this frontier (inefficiencies) can be calculated." (Albert, 1998: 723).

Several techniques to determine these frontier functions have been used: parametric and non-parametric. The choice of estimation method has been a subject of debate [START_REF] Seiford | Data envelopment analysis: the evolution of the state of the art (1978-1995)[END_REF] since every method has its advantages and disadvantages. The principal advantage of the estimation of a non-parametric production frontier, using for example the data envelopment analysis (DEA)

technique, is that it does not require any assumptions regarding the functional form. Also DEA does not require an explicit assumption about the inefficiency term [START_REF] Odeck | Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data[END_REF][START_REF] Shao | Technical efficiency analysis of information technology investments: a two-stage empirical investigation[END_REF]. However, using this kind of technique, it is not possible to distinguish whether the lack of efficiency is due to technical inefficiency or to statistical noise [START_REF] Odeck | Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data[END_REF](Odeck, : 2618)).

The parametric approach requires the assumption of a specific functional form (e.g. Cobb-Douglas, translog, constant elasticity of substitution -CES) for the technology (constant or variable returns to scale) and an explicit distributional A number of studies [START_REF] Carroll | Understanding the Factors that Influence Dairy Farm Efficiency in the Republic of Ireland[END_REF][START_REF] Gholami | ICT and Productivity of the Manufacturing Industries in Iran[END_REF]Shao and[START_REF] Shao | Measuring the value of information technology in technical efficiency with stochastic production frontiers[END_REF] have estimated both the Cobb-Douglas and the translog functional form and some of them [START_REF] Carroll | Understanding the Factors that Influence Dairy Farm Efficiency in the Republic of Ireland[END_REF] have tested the null hypothesis that the Cobb-Douglas form is an adequate representation of the data, given the specification of the translog model.

Cobb-Douglas and translog production frontier

The Cobb-Douglas production frontier has been one of the most frequently used functional specifications in research on production economics. It satisfies the basic requirements for production frontiers, such as quasi-concavity and monotonicity. It imposes properties upon the production structure such as fixed returns to scale and an elasticity of substitution which is equal to unity.

The Cobb-Douglas stochastic production frontier with two inputs, capital (K)

and labour (L), and one output (Y) can be specified as:

i i L k u v i i i e L K Y - = β β α
where i is the index which considers the number of firms. After taking the natural logarithm, the production function can be rewritten in the following way:

F o r P e e r R e v i e w i i i L i K i u v L K Y - + + + = ln ln ) ln( 0 β β β
The random error v i is assumed to be independent and identically distributed (i.i.d.) with zero mean and constant variance

) , 0 ( 2 v N σ .
On the other hand, the residual component u i of technical inefficiency represents the effects of events incurred by the firm. These technical inefficiencies are assumed to be non-negative random variables of independently, but not identically distributed, truncated normal distributions. The underlying normal distribution is assumed to be ) , (

2 µ σ µ i N
. TE is predicted using the conditional expectation of ) exp( i U -, given the composed error term of the stochastic frontier.

Technical efficiency equals one only when a firm has an inefficiency effect equal to zero; otherwise it is less than one. If U i is equal to zero, this means that there is no inefficiency in production, the firm is technically efficient and produces its maximum potential output. Conversely, when U i has values lower than zero this implies that there is inefficiency in the firm's production and it produces less than its maximum possible output given the technology. The magnitude of U i measures the "efficiency gap", that is how far a given firm's output is from its potential output. In order to compute TE it is, therefore, necessary to estimate across potential output which can be achieved by the econometric estimation of the stochastic frontier production function.

A number of alternative functional forms have also been used in production frontier literature. The most popular is the translog function.

The two input, translog, stochastic production frontier can be specified in the following way:

i i i i KL i LL i KK i L i K i u v L K L K L K Y - + + + + + + = ln ln ) (ln ) (ln 2 1 ln ln ) ln( 2 2 0 β β β β β β
The assumptions about the random error v i and the technical efficiency u i remain the same as in the Cobb-Douglas stochastic production frontier.

The translog function does not impose the same restrictions upon the production structure as the Cobb-Douglas production function, but it may suffer from degrees of freedom and multicollinearity problems. However, the Cobb-Douglas stochastic production frontier is a special case of the translog stochastic production frontier under the following restrictions:

β KK = β KK = β KL = 0
The translog function is non-homogeneous and belongs to the class of flexible functional forms, which provides a second-order local approximation to any functional form [START_REF] Coelli | An Introduction to Efficiency and Productivity Analysis[END_REF].

Stochastic frontier for panel data

Panel data models have some advantages over cross-sectional data in the estimation of stochastic frontier models. [START_REF] Schmidt | Production Frontiers and Panel Data[END_REF] assert that the first advantage is that, while cross-section models assume that the inefficiency term and the input levels are independent, for panel data estimations this hypothesis is not needed. This is useful in order to introduce time-invariant regressors into the specification of the model. Moreover, by adding a temporal dimension to the observed unit data, panel data stochastic frontier models yield consistent estimates of the inefficiency term. Furthermore, by exploiting the link between the "one-sided inefficiency term" and the "firm effect" concepts, The panel data stochastic frontier models can be written in the following way:

T t N i u v X Y N n it it nit n it ,..., 2 , 1 ; ,..., 2 , 1 , 1 0 = = - + + = ∑ = β β
where Y it denotes the output for the i th firm at the t th time period, X it denotes a (1xk) vector of inputs associated with the suitable functional form, β is a (kx1) vector, to be estimated, of unknown scalar parameters, u it are the inefficiency effects in the model and v it are random errors, assumed to be i.i.d. and have

N(0,σ v 2 ) distribution, independent of u it .
Sometimes it is assumed that technical inefficiency effects are time invariant:

T t N i u u i it ,..., 2 , 1 ; ,..., 2 , 1 = = = .
"

The assumption that technical inefficiency effects are time-invariant is more difficult to justify as T becomes larger. One would expect that managers learn from their previous experience in the production process and so their technical inefficiency effects would change in some persistent pattern over time" (Battese and Coelli, 1995: 203).

The model proposed by [START_REF] Battese | A model for technical efficiency effect in a stochastic frontier production functions for panel data[END_REF] specifies technical inefficiency effects in the stochastic frontier model that are assumed to be independently (but not identically) distributed non-negative random variables. For the i th firm in the t th period, the technical inefficiency effect, u it , is obtained by a truncation of the N(µ it ,σ 2 ) distribution, where u it = z it δ . In this case z it is a (1xM) vector of observable explanatory variables, whose values are fixed constants; and δ is a (Mx1) vector, to be estimated, of unknown scalar parameters.

The log-likelihood function of this model is described in Battese and Coelli (1992) where σ s 2 = σ v 2 + σ 2 and γ = σ 2 /σ s 2 , with γ-parameter values between zero and one.

IV. Economic model and empirical approach

The main purpose of this work is to investigate whether ICT investments significantly affect a firm's distance from the optimal production frontier. Using the stochastic frontier approach, the inefficiency effects are expressed as an explicit function of a vector of firm-specific variables and a random error. This approach has been widely recognised to be better than the two-stage estimation procedure which assumes independence of the inefficiency effects. The two-stage estimation procedure is unlikely to provide estimates which are as efficient as those that could be obtained using a single-stage estimation procedure [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF].

The empirical analysis is based on the following hypothesis: ICT investment factors may give a relatively higher efficiency to a firm which is using a greater quantity of these inputs. If this happens, two firms which, ceteris paribus, produce the same output may be located at the same point (e.g.: point B in fig. 1) on the production frontier, while, in reality, the one that uses a greater quantity of the non included input, lies at a lower point (e.g.: point A in fig. 1), because it is less efficient [START_REF] Infante | Produzione, fattori e progresso tecnico nell'industria manifatturiera italiana (1973-1984[END_REF].

Following [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF] and [START_REF] Assefa | Technical Efficiency of Small and Medium-Scale Enterprises[END_REF], raw materials are considered along with labour and capital as inputs in the production function. So, the Cobb-Douglas production model takes the following form:

it it u v it it it it e RM L K Y - = 3 2 1 β β β α .
After taking the natural logarithm and adding a set of dummy variables (i.e. three for the four Pavitt sectors 2 , and two for the three time periods 3 ) the equation becomes:

ln(Y it ) = β 0 + β 1 lnK it + β 2 ln L it + β 3 ln RM it + α j * Pav ijt + α i D it t=1 T ∑ + j=1 m-1 ∑ v it -u it (1)
where Y it is the real output of the i th firm at time t (i=1,2,…,N and t=1,2,…T); K is the capital, L the labour, RM the raw materials and Pav and D are, respectively, the dummy variables for Pavitt sector and time period.

As indicated, the Cobb-Douglas production frontier imposes some restrictions, such as fixed returns to scale and unitary elasticity of substitution, on production technology. Hence, it is tested against the translog functional form. The translog 2 In the Pavitt taxonomy the sectors are classified in the following way: supplier dominated (Pavitt 1), scale intensive (Pavitt 2), specialised supplier (Pavitt 3) and science based (Pavitt 4). 3 The three periods are: 1995-1997, 1998-2000 and 2001-2003.

(Y i ) = β 0 + β 1 lnK it + β 2 ln L it + β 3 ln RM it + β 4 (lnK it ) 2 2 + β 5 (ln L it ) 2 2 + + β 6 (ln RM it ) 2 2 + β 7 lnK it ln L it + β 8 lnK it ln RM it + β 9 ln L it ln RM it + + α j * Pav ijt + α t D it t=1 T ∑ + j=1 m-1 ∑ v it -u it (2)
To estimate the model a second set of independent variables is required and they are assumed to affect the level of efficiency at which manufacturing firms convert factors of production into output. The first variable is ICT investments since it is assumed that they are able to influence technical (in)efficiency. For the other variables, the theory does not point to any specific factor that should be included "it is more of an empirical question. As such, variables are selected on the basis of economic intuition" (Carroll et al., 2007: 6).

In this work ICT investment, age, firms affiliated to corporate firm, size of firm, geographic macroarea, Pavitt sectors and time period are considered as explicative firm efficiency variables. So, the inefficiency equation, in both cases (Cobb-Douglas and translog production frontier), is:

(3) * * * The empirical evidence of the impact of ICT on firm performance is mixed. In fact, in developed countries, the growth of total factor productivity associated with technical change has even declined with the increased use of ICT of the past 10 to 20 years (Jorgenson and Stiroh, 1999). It was only in the 1990s that empirical evidence showed that ICT has a substantial effect on firm productivity levels. Therefore, in this model, ICT investments have been included in order to understand whether there is a positive relationship between technical efficiency and ICT investments. Consequently, if the coefficient estimates for δ 1 is significantly negative, there is empirical evidence to confirm that ICT has a favourable effect on technical efficiency.

The expected signs for all estimated beta parameters in the Cobb-Douglas production frontier and the elasticities in the translog production frontier are positive since each factor contributes to production positively. For delta parameters, the economic literature is used to hypothesise about the expected sign.

A positive relationship between age and technical efficiency can be expected due to "learning by doing" which occurs through production experience. Over time, firms become more efficient as a result of a growing stock of experience in the production process. However, other economists argue that when an innovation is introduced, newer firms generally adopt it easily, while older firms may have to delay adoption as it may be too costly to substitute old methods, thus implying that efficiency may decrease with age [START_REF] Assefa | Technical Efficiency of Small and Medium-Scale Enterprises[END_REF]. Empirical studies also report mixed results regarding the relationship between a firm's age [START_REF] Assefa | Technical Efficiency of Small and Medium-Scale Enterprises[END_REF] assert that some studies have found a positive relationship between firm age and efficiency, other studies have reported a negative relationship between firm efficiency and age; while, some other studies have indicated that the effect of age could be neutral (Cheng and Tang, 1987).

The relationship between firms affiliated to corporate firms and TE should be positive as indicated in the literature which affirms that groups have relatively higher productivity and superior competitiveness with respect to individual firms [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF] (i.e. the expected sign is negative).

The effect of firm size on efficiency is ambiguous since empirical evidence does not suggest a strong link between efficiency and firm size in either direction.

"While a positive effect may be expected on the grounds of scale of economies, firm size may be negatively linked to efficiency if large firms experience management and supervision problems" (Assefa and Matambalya, 2002: 20). On the other side, [START_REF] Jha | Productivity, technical and allocative efficiency and farm size in wheat farming in India: a DEA approach[END_REF] find that large firm size is associated with higher technical as well as allocative efficiency.

Finally, dummy variables for the Italian macro area are also included to control for regional differences due to different level of development; Pavitt dummies are included because an industrial sector may have, in principle, a different production function from others; and temporal dummies are included to take into account technological progress. The expected sign for firms located in the centre or the north Italy is negative since those firms should be more efficient than firms located in the south. For the time dummy, the expected sign for the parameter is negative because, if there is technological progress, inefficiency should decrease.

V. Variables and descriptive statistics

For this analysis, the VII (1995VII ( -1997)), VIII (1998VIII ( -2000) ) and IX (2001IX ( -2003) ) surveys of manufacturing firms by the MCC were used. The database has been published every three years since 1968.

The survey provides a great deal of information about the production and financial indicators of Italian manufacturing firms. In the last survey, the database considers a stratified sample of 3,452 Italian manufacturing firms. The sample is stratified according to industry, geographical and dimensional distribution for firms from 11 to 500 employees. It is by census for firms with more than 500 employees.

The database contains questionnaire information regarding the individual firms' structure and behaviour, three years of balance sheet data, additional data on employees, employees' education, age of the firm, turnover, etc. Information relating to ICT expenditure has only been present since 1995, is displayed on a three-year level (1995-1997, 1998-2000 and 2001-2003) and total annual investment is provided. However, data on the stock of ICT capital are not provided. Also the variable for employees' education is displayed as one value for three years.

Table 1 analyses the ICT variable in the last three surveys. For example, in the IX survey (2001)(2002)(2003), of over 3,452 firms, 591 did not invest in ICT, 253 firms said that they had invested in ICT in those three years but did not reply to the question "How much money did you invest on average in the last three years?" and 497 firms did not answer either question. Following the method of [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF], who used the same data source (seventh survey), both models are estimated with the variables expressed as three year averages. Due to the fact that the ICT investment variable is expressed as a three year value. Turnover was deflated by an implicit price production deflator (2000=100) and capital, raw materials and ICT investments are deflated by an implicit investment deflator (2000=100).

The dependent variable is the firms' turnover, the proxy used for labour is the number of employees and the proxy for capital is the sum of fixed assets and immaterial assets. To choose these variables as proxies of output, capital and labour is quite common in previous works using the same survey (see for example: [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF][START_REF] Gambardella | Nuova industria o nuova economia? L'impatto dell'informatica sulla produttività dei settori manifatturieri in Italia[END_REF][START_REF] Bugamelli | Barriers to Investment in ICT[END_REF].

VI. Results and discussions

ICT investments and technical efficiency

The parameters of the stochastic frontier production function are estimated using the asymptotically efficient maximum likelihood method by FRONTIER 4.1 4 .

The results regarding the impact of ICT investments on technical efficiency specified in equations 1-3 are presented in table 2. Both models (Cobb-Douglas 4 The FRONTIER 4.1 package uses the three steps estimation method procedure. These three steps provide a maximum likelihood estimate of the parameters of the stochastic frontier production function. The first step is an Ordinary Least Squares estimate of the function. Here all the estimators β, with the exception of the intercept β 0 , will be unbiased. In the second step, a grid search on γ is conducted. The value for the parameters β (excepting β 0 ) are set to the OLS value, β 0 and σ 2 parameters are adjusted and all other parameters (µ,η and δ) are set to zero. In the final step, the values in the grid search are used as starting values in an iterative procedure to obtain the maximum likelihood estimates. present in the VII, VIII and IX surveys provided by the MCC. This is done in order to compare the results and to check for sample selections issues. The sign and the significance of the variables between the two models, panel frontier and stochastic frontier, are not different.

[Insert Table 2 about here]

To test whether the Cobb-Douglas production function is an adequate representation of the data, given the specification of the translog model, the likelihood ratio test was used. The purpose of this is to test the null hypothesis that the second order coefficients of the translog frontier are simultaneously zero:

0 0 = β = H ij for all 1,2,3 = j i ≤ .
The value of the generalised likelihood-ratio statistics for testing null hypothesis for the panel frontier in the case of the complete translog production function is computed in the following way:

.464 1563 20038.2) -19256.468

( 2 = + = LR
Thus the null hypothesis that the Cobb-Douglas frontier is an adequate representation of the data is rejected, given the specification of the stochastic frontier. In other words, using a likelihood ratio test, the translog functional form is found to be a more appropriate fit for the data 5 .

All beta coefficients in the Cobb-Douglas production function are significantly positive, confirming that each factor contributes positively to production. The 5 The likelihood ratio test is equal to: (2*(Unrestricted-Restricted)) and follows a chi-squared distribution. The coefficient estimates for the ICT investment is always significantly negative with a 1% significance level, which indicates that more ICT investments have a negative effect on inefficiency (i.e., a positive effect on efficiency).

Therefore, the null hypothesis is never rejected. This implies that ICT investments have a positive effect on the technical efficiency of the Italian manufacturing industry. This finding is consistent with the previous literature [START_REF] Gholami | ICT and Productivity of the Manufacturing Industries in Iran[END_REF][START_REF] Shao | Technical efficiency analysis of information technology investments: a two-stage empirical investigation[END_REF].

Other control variables give the expected results. Firms located in the North (east and west) and in the Centre and firms affiliated to corporate firms are significantly more efficient than the average. This is consistent with the results of [START_REF] Atzeni | The Economic Effects of Information Technology: Firm Level Evidence from the Italian Case, CRENoS[END_REF] and [START_REF] Becchetti | ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach[END_REF]. In other words, firms situated in the north or centre of Italy, which are more industrialised areas, are, on average, more efficient than firms situated in South of Italy.

Despite [START_REF] Jha | Productivity, technical and allocative efficiency and farm size in wheat farming in India: a DEA approach[END_REF] results asserting that large firm in agriculture sector in India are with higher technical and allocative efficiency, in this paper small and medium size firms and firms operating in the first three Pavitt sectors are significantly more efficient than the average. This may be attributed to the specific characteristics of the Italian manufacturing sector. In fact, almost all firms are of small-medium dimensions and tend to be concentrated in the first Pavitt sector.

Results show, moreover, that older firms are significantly more efficient than the average. This supports the theory [START_REF] Assefa | Technical Efficiency of Small and Medium-Scale Enterprises[END_REF]) that firms Mean efficiency is 0.49 which implies that output could, theoretically, be increased. This could be ascribed to the fact that ICT investments are still a small proportion of total investments (22%). This partially confirms David's (1990) hypothesis which states that new technologies have to reach a spread rate of 50% for their beneficial effects to show.

The individual coefficients for the Cobb-Douglas model are elasticities and, thus, can be directly interpreted. In the case of the translog model, the elasticities at the mean levels of output are functions of parameters and level of explanatory variables, and, thus, individual coefficients cannot be directly interpreted as elasticities. The translog elasticities are calculated in the following way, respectively, for capital, labour and raw materials:

= = + + + = i x x x dx dY it it it it β β β β
Where it

x 1 represents the capital of firm i at time t; it x 2 the labour of firm i at time t and it x 3 is the raw materials of firm i at time t.

The calculated elasticities and returns to scale for the translog panel production frontier are displayed in table 3.

[Insert works which show that, in the period considered, the Italian manufacturing sector presented decreasing returns to scale [START_REF] Medda | R&S e Spillover industriali: un'analisi sulle imprese italiane[END_REF][START_REF] Bonaccorsi | L'intelligenza s'industria. Creatività e innovazione per un nuovo modello di sviluppo[END_REF]. In order to check whether selection is an issue in this paper the balanced panel data is estimated and a selection indicator is added to the unbalanced panel data.

The results for the balanced panel data estimations are presented in table 5.

[Insert Table 5 about here]

The sign and the significance of variables are not different from the unbalanced panel data. The coefficient estimates for ICT investments is always significantly negative with a 1% significance level, which indicates that more ICT investments have a negative effect on inefficiency. Therefore, in this case too, the hypothesis that ICT investments are able to increase technical efficiency is not rejected. All the other variables are of the expected sign and the interpretation is the same as before. The Cobb-Douglas panel frontier is rejected in favour of the translog panel frontier.

Table 6 presents the results for elasticities, returns to scale and efficiency by year for the translog complete model.

[Insert Table 6 about here]

The results for the calculated elasticities and returns to scale are similar to the previous estimation. In fact, the elasticities are all positive and the returns to scale are equal to 0.81, which confirms the previous finding that decreasing returns to scale were present in the Italian manufacturing sector over the period 1995-2003.

The only difference with the unbalanced panel is that, in this case, the efficiency scores by year were also increasing over the 2 time periods 1995-1997 The second step of the attrition analysis is to construct the selection indicator.

The selection indicator assumes a value of 0 for firms that are always present in the panel and for attriters the selection indicator is equal to 1 in the period just before attrition (Wooldridge, 2002).

The selection indicator is included in the production function and in the efficiency equation (and given separately to make sure identification is not an issue). The results are displayed in Table 7. In this case the null hypothesis is that: it u is uncorrelated with it s for all periods, where it s represents the selection indicators. In no cases can the null hypothesis be rejected, so it is possible to conclude that selection is not a problem in this sample.

[Insert Table 7 about here]

VII. Conclusion

The impact of ICT investments on firm performance was a highly debated topic after Solow's assertion that "You can see the computer age everywhere but in the productivity statistics". A lot of economists referred to this assertion as "the productivity paradox".

The productivity paradox seemed to be solved after [START_REF] Brynjolfsson | Paradox lost? Firm-level evidence on the returns to information systems spending[END_REF] presented their significant firm-level empirical evidence. In fact, recent Firms located in the North and in the Centre and firms affiliated to corporate groups are significantly more efficient than the average. Moreover older firms are significantly more efficient than the average.

Mean efficiency is 0.49 which implies that output could theoretically be increased. This may be ascribed to the fact that ICT investments are still a small proportion of total investments (22%). This partially confirms David's hypothesis (1990), which states that new technologies have to reach a spread rate of 50% for their positive effects to show.

However, it should be noted that investments in technological capital are not the only way to achieve higher growth; other factors might be the positive externalities due to the ICT investment growth in some sectors, human capital and/or structural changes in different sectors.

  as a factor which is able to influence technical efficiency.

  all of the benefits that accrue from using new technologies.

  should become cheaper and faster during a GPT period, especially capital which is tied to the new technology.

  least one input fewer or could use the same input to produce at least one output more.

  inefficiency term. It uses econometric techniques to estimate the coefficients of the production function as well as the technical efficiency. The main strengths are that the parametric approach deals with stochastic noise and also allows statistical tests of hypotheses concerning production structure and the degree of inefficiency. The first step in parametric stochastic frontier estimation is, then, to select an appropriate functional form for the production function.

  (1984) observed that, when panel data are available, there is no need for any distribution assumption for the inefficiency effect and all the relevant parameters of the frontier technology can be obtained by simply using the traditional estimation procedures for panel data; i.e. fixed-effects model and random-effects model approaches. Finally, panel data permit the simultaneous investigation of both technical change and technical efficiency change over time.

  has a positive effect on technical efficiency in the production process. In order to test this hypothesis the stochastic frontier production function (Cobb-Douglas and translog) is used. To estimate firm efficiency, it is important to include all possible inputs in the production function, because omission of one of the input

  with three inputs (capital, labour and raw materials) can be specified as: ln

  represents investments in information and communication technology, group indicates whether a firm is affiliated to a corporate firm, size is the size of the firm (small if the firm has 11-50 employees, medium if the firm has 51-250 employees; large if the firm has more than 250 employees) and Area, Pav, and D , the dummy variables for the Italian macro-territorial area, Pavitt sectors and time.

  presented in the previous section are estimated as a cross-section of the period 2001-2003 and as an unbalanced panel of 12,629 firms (observations)

  the inefficiency variables is confirmed by again using a likelihood ratio test.

  over time as a result of a growing stock of experience in the production process.

  studies have been able to show the positive relation between ICT investments and productivity, and consequently, end the controversy over the ICT productivity results confirm that the paradox no longer stands for Italian manufacturing firms either.In this work, the impact of ICT technologies on technical efficiency is analysed using an unbalanced panel of data(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) on Italian manufacturing firms.The data utilised were the VII, VIII and IX surveys by the MCC.Compared to the existing empirical literature on the role of ICT investments at firm level, this work deals with the functional form to be used in modelling the impact of ICT on technical efficiency. Not many studies have considered economic performance measures like the technical efficiency of the production process in the area of ICT. However, this methodology may be interpreted as another way to explain the productivity paradox given the close relationship between productivity and technical efficiency.As far as functional form is concerned, both the Cobb-Douglas and the translog production function frontier were used because the translog is more flexible than the Cobb-Douglas. The results support this choice, since the assumption inherent in the technology of a Cobb Douglas was rejected in all models. Moreover, the literature on ICT investments to which this work refers generally omits testing of the suitability of the Cobb-Douglas specification.Our results indicate that information and communication technology investments have a positive and significant effect on technical efficiency in the production process of Italian manufacturing firms. In fact, the coefficient of ICT investments is significantly negative, which indicates that, if ICT investments increase, Italian manufacturing firms tend to have lower values of inefficiency effects i.e. higher efficiency.

  used in the inefficiency equation give expected results.

  Fig. 1: The Production Frontier

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Table 3

 3

	about here]

Table 4

 4 displays mean efficiency by year. It is evident that efficiency declined in three year period 1998-2000 and increased in the next period. However, if the period 2001-2003 is compared with 1995-1997, average efficiency decreased.This result confirms the previous findings of decreasing returns to scale. In fact, if a firm has experienced a decrease in inefficiency, it may use the same input to produce more output or produce the same amount of output with less input.

	[Insert Table 4 about here]
	Unbalanced panel and attrition
	With a balanced panel, the same units appear in each time period. Conversely,
	with an unbalanced panel some units do not appear in every time period. If the
	reason a firm leaves the sample (attrition) is correlated with the idiosyncratic
	error, then the resulting sample section problem may cause biased estimators
	(Wooldridge, 2002).
	In other words, unbalanced panel data may arise for several reasons (i.e.
	rotating panel, incidental truncation). A "problem arises when attrition from a
	panel is due to units electing to drop out. If this decision is based on factors that
	are systematically related to the response variable, even after we condition on

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	and 1998-2000. However, in this case too, mean efficiency is 0.51 which implies
	that output could theoretically have increased.
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Table 1 -

 1 Number of firms surveyed

		1995-1997 survey 1998-2000 survey	2001-2003 survey	All periods
	Observations	4497	4680	3452	514
	Firms that invested in ICT	2984	3480	2111	491
	Firms that invested but did not indicate the amount	128	156	253	..
	Firms that did not invest in ICT	975	851	591	22
	Firms that did not answer the questions about ICT	410	193	497	..
	investments

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

Before taking the logs, 1 was summed to raw materials since there were some firms with 0 values for these variables. 8 Since there were some firms that did not invest in ICT before taking the logs 1 was summed to the investment in ICT.

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Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK 10 Before taking the logs 1 was summed to raw materials since there were some firms with 0 values for these variables.

Submitted Manuscript

11 Since there were some firms that did not invest in ICT before taking the logs 1 was summed to the investment in ICT. 13 Before taking the logs 1 was summed to raw materials since there were some firms with 0 values for these variables. 14 Since there were some firms that did not invest in ICT before taking the logs 1 was summed to the investment in ICT.