Francesco Bellanti

Bertil Kågedal

Dr Oscar Della Pasqua

Do pharmacokinetic polymorphisms explain treatment failure in high-risk patients with neuroblastoma?

Keywords: Neuroblastoma, pharmacogenetics, pharmacokinetics, cytotoxic drugs, modelbased approach

Purpose: Neuroblastoma is the most common extra cranial solid tumour in childhood. It accounts for the 15% of all paediatric oncology deaths. In the last few decades improvement in treatment outcome for high-risk patients has not occurred, with an overall survival rate less than 30-40%. Many reasons may account for such a low survival rate. The aim of this review is to evaluate whether pharmacogenetic factors can explain treatment failure in neuroblastoma.

Methods: Literature search based on Pubmed's database Medical Subject Headings (MeSH) has been performed to retrieve all pertinent publications on current treatment options and on new classes of drugs under investigation. 158 articles have been reviewed and relevant data have been extracted and summarised. Results and conclusions: Few of the large number of polymorphisms identified showed an effect on pharmacokinetics which could be considered clinically relevant. Despite clinical relevance, none of the single nucleotide polymorphisms (SNPs) investigated so far can explain treatment failure. These findings seem to reflect the clinical context, in which these drugs are used, i.e., in combination with multimodal therapy. In addition, many of the pharmacogenetic studies do not assess (differences in) drug exposure, which could contribute to explaining pharmacogenetic associations. Regardless of the evidence that new classes of drugs show significant activity on all neuroblastoma cell lines, it remains unclear whether such activity translates into clinical efficacy, irrespective of resistance or MYCN amplification. Elucidation of the clinical value of pharmacogenetic factors in the treatment of neuroblastoma demands an integrated pharmacokinetic-pharmacodynamic approach to treatment response.

INTRODUCTION

Neuroblastoma

Neuroblastoma (NB) is the most common extra cranial solid tumour in childhood, it belongs to the "small blue round cells" neoplasms, and it accounts for the 15% of all paediatric oncology deaths [START_REF] Maris | Neuroblastoma[END_REF][START_REF] Maris | The biologic basis for neuroblastoma heterogeneity and risk stratification[END_REF][START_REF] Park | Neuroblastoma: biology, prognosis, and treatment[END_REF] . Its incidence peaks at age 0 to 4 years, and less than 5% of patients are over the age of 10 years [START_REF] Maris | The biologic basis for neuroblastoma heterogeneity and risk stratification[END_REF] . It is a neuroendocrine tumour tightly connected to the sympathetic nervous system. Given that it originates from the primitive neuroepithelial cells of the neural crest, it can develop anywhere in the sympathetic system. 50% of primary tumours arise in the adrenal medulla, but other common sites of disease are neck, chest, abdomen and pelvis. At the diagnosis, in most cases, neuroblastoma has already metastasised, and usually metastasis affects liver, bones, bone marrow, lymph nodes and skin [START_REF] Maris | Neuroblastoma[END_REF][START_REF] Maris | The biologic basis for neuroblastoma heterogeneity and risk stratification[END_REF][START_REF] Park | Neuroblastoma: biology, prognosis, and treatment[END_REF] .

The aetiology of neuroblastoma is still poorly understood. Few causative factors have been identified for the neoplasm: familiar forms are rare (about 1%); there is not a clear genetic predisposition, and presumably tumorigenesis may require alterations in more than one gene. The hallmark of the tumour is heterogeneity; its behaviour can vary from a localised tumour, easy to resect with surgery, to a metastatic progressive one with high resistance and poor outcome, and strikingly, in a 5% of cases, it manifests a spontaneous regression. Prognosis, in the same way, ranges from overall survival to high risk for fatal demise. Because of the tumour heterogeneity the use of biomarkers has been required to select the appropriate treatment schedule according to a risk-group classification. MYCN amplification, chromosomal loss (1p) or gain (17q), DNA index (near diploid), age older than 18 months, and "International Neuroblastoma Staging System" (INSS) stages III and IV are all predictors of poor prognosis and disease malignancy [START_REF] Maris | Neuroblastoma[END_REF][START_REF] Maris | The biologic basis for neuroblastoma heterogeneity and risk stratification[END_REF][START_REF] Park | Neuroblastoma: biology, prognosis, and treatment[END_REF][START_REF] Schramm | Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling[END_REF][START_REF] Mora | Evolving significance of prognostic markers associated with new treatment strategies in neuroblastoma[END_REF][START_REF] Castel | Molecular biology of neuroblastoma[END_REF][7] . Like MYCN, many other biomarkers have been studied to specify patient stratification: e.g., the Trk family of neurotrophin receptors, an important regulators of survival, growth and differentiation of normal neuronal cells; high expression of TrkB and TrkA III is associated with MYCN amplification and poor outcome, whereas high expression of TrkA is associated with a favourable status. Up to now, only the amplification of the oncogene MYCN can be considered as an independent marker to assess tumour status and treatment outcome 1;2 . In this manner according to tumour behaviour and biomarker prediction there is a risk group classification of the disease, and therefore of treatment protocols.

Although in the last 20 years this approach substantially improved treatment outcome for low-and intermediate-risk patients, up to date there has been little improvement in the high-risk patient group. In spite of intensive poly-therapy, high-risk patients still have an overall survival rate of 30-40% 1;3 .

Several reasons could explain the low overall survival rate. Considering that important associations have been demonstrated between pharmacogenetics and efficacy in different solid tumours (i.e., the use of Herceptin in the treatment of HER2 positive breast cancers with benefits in terms of disease free survival and overall survival [START_REF][END_REF][9][10] ), the aim of this review is to explore the role of pharmacogenetics in the treatment of neuroblastoma. Two main questions to be addressed are whether pharmacogenetic differences (partly) explain treatment failure and whether model-based interventions, such as different dosing algorithms, may improve the outcome of therapy and the safety profiles in highrisk patients.

Pharmacogenetics

In the context of our review the term pharmacogenetics will be considered as defined by the "International Conference of Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use" (ICH), even if other definitions have been reported in the scientific literature. According to the proposed ICH definitions, pharmacogenomics is the investigation of variations of DNA and RNA characteristics as related to drug response, and pharmacogenetics is a subset of pharmacogenomics and is defined as the influence of variations in DNA sequence on drug response [START_REF][END_REF] . Pharmacogenetics is a relatively new field of research, which aims to improve the medical knowledge using DNA sequence information 11;12 . Clinical research on prognostic, risk and covariate factors often assigns a privileged position to pharmacogenetics compared to other determinants of drug response. This approach often disregards the multivariate nature of the interaction between all relevant factors underlying treatment response, which may cause biased analysis and mis-interpretation of the findings. A too narrow vision of the clinical factors contributing to variability reduces the opportunity to understand the true role of pharmacogenetics and to relate it to real-life applications. It is critical to realise that pharmacogenetics is only one of the many factors which can influence pharmacokinetics, pharmacodynamics and clinical response, and that these factors can interact with each other.

This review represents an effort to answer the previous questions by evaluating the role of pharmacogenetics on the pharmacokinetic properties of the cytotoxic drugs used in the current treatment of neuroblastoma, and by highlighting the requirements to evaluate its role in new therapeutic approaches. In addition, based on our findings, suggestions are given for alternative therapeutic approaches, which can potentially reduce treatment failure.

METHODS

Literature search on Pubmed's database Medical Subject Headings (MeSH) has been performed to retrieve relevant published data. The search method involved structured keywords and was divided into two phases.

First, attention has been given to the cytotoxic drugs used for the current treatment of neuroblastoma. Initially, the drug's generic name was associated to four general keywords: pharmacokinetics, pharmacogenetics, neuroblastoma, and leukaemia, whilst during a second step, the drug name was associated to specific elements of drug disposition.

Secondly, new classes of drugs, which are currently in clinical development, were evaluated. The search involved retrieval of all publications related to the treatment of neuroblastoma, which focused on pharmacogenetics.

In addition, pharmacokinetic data on the aforementioned drugs have been retrieved from paediatric studies, and especially from patients affected by neuroblastoma or leukaemia . Differently, pharmacogenetic data have been gathered from any available study . We have decided to exclude references related to genetic variations in pharmacodynamics and disease.

A total of 158 articles and reviews have been used for abstraction and extraction.

Data on exposure, efficacy and toxicity were summarised using MS Excel spreadsheets. An overview of the findings and potential relevance of genetic variation is presented for both cytotoxic drugs (tables 1, 2, and 3) and drugs under clinical investigation (tables 4, 5, and 6).

RESULTS

CYTOTOXIC DRUGS

The use of cytotoxic drugs in neuroblastoma treatment is considered as an adjuvant or add-on therapy, to chemotherapy (CT), radiotherapy, surgical resection, stem cell transplantation, and treatment of minimal residual disease (MRD) [START_REF] Park | Neuroblastoma: biology, prognosis, and treatment[END_REF] . Chemotherapy approaches used in low-, intermediate-, and high-risk patient groups present differences in terms of dosing regimens and drug associations. In high-risk patients, cytotoxic drugs are used in two different phases of treatment: first during induction CT, which is aimed at metastasis control and primary tumour resection; second, during myeloablative chemotherapy (or high-dose CT) in association with stem cell transplantation aim to consolidate induction CT and surgery [START_REF] Park | Neuroblastoma: biology, prognosis, and treatment[END_REF] .

Despite the various approaches to treatment high-risk groups have a very low overall survival rate (30-40%) 1;3 . To clarify whether or not genetic variants could explain the lack of response, the role of pharmacogenetics in influencing pharmacokinetics and pharmacodynamics of six of the main cytotoxic drugs used in neuroblastoma treatment has been evaluated.

Without taking into account the nature of treatment as adjuvant therapy, it was found that only few SNPs show a relevant effect on pharmacokinetics. In agreement with our hypothesis, published results seem to confirm that a SNP must affect a key enzyme or pathway to translate genetic variation into clinically relevant differences. Tables 1, 2 and 3 provide the summary of the findings.

The most relevant polymorphisms evaluated will be briefly highlighted in the following paragraphs.

IRINOTECAN

Up to date UGT1A1*28 is the only SNP introduced in a label (US) among all cytotoxic drugs included in this review. It is noteworthy to underline that the UGT1A is the main enzyme in controlling the deactivation of SN-38, the active metabolite of Irinotecan.

The label of camptosar (irinotecan hydrochloride injection) has been revised with the following pharmacogenetic information: patients homozygous for the UGT1A1*28, who undergo to a single agent treatment with Irinotecan, have a higher exposure to the active metabolite and are at increased risk for neutropenia [START_REF][END_REF] . In fact, various publications seem to confirm higher exposure to SN-38 86;90;91;108-110;116;117;120 and the increased risk for neutropenia 86;101;109;114;116;118 in patients harbouring the UGT1A1*28 allele. On the same enzyme another SNP showed a similar effect on SN-38 exposure. Studies on the UGT1A1*6, SNP expressed in Asian individuals, showed higher exposure to the active metabolite 87;89;90;107;111 and an increased risk for neutropenia 87;89;90 . However, further studies are needed to assess the clinical value of UGT1A1*6.

VINCRISTINE

According to Dennison et al. 61;70 ,and Renbarger et al. 69 , CYP3A4 and CYP3A5 play a key role in metabolising vincristine to the main metabolite (the secondary amine M1), with a 9-to 14-fold higher selectivity for the CYP3A5. As mentioned before, having a key role in the metabolic pathway is an essential requirement in order to influence the pharmacokinetic profile of a drug. Although in the study from Dennison et al., patients with a high expression of CYP3A5 (homozygous: *1/*1; heterozygous: *1/*3, *1/*6) showed lower exposure to vincristine, and patients with a low expression of the enzyme (homozygous: *3/*3; heterozygous: *1/*7) showed increased exposure, further studies are needed to confirm the hypothesis that SNPs in CYP3A5 does contribute to the inter-individual variability in vincristine metabolism.

OTHER CYTOTOXIC DRUGS

None of the SNPs analysed so far seem to affect the pharmacokinetic profile of cisplatin, melphalan, etoposide and doxorubicin in a way that can be considered clinically relevant.

DRUGS IN CLINICAL DEVELOPMENT

To significantly improve the treatment of neuroblastoma several studies have been carried out to evaluate the potential of novel therapeutic alternatives. New approaches to circumvent the high resistance of NB cells to chemotherapy could have a great impact on future treatment options. Seven new classes of drugs have been identified which may contribute to that objective. Mechanisms of action, metabolic pathway, and efficacy on NB cells have been analysed, with particular attention to pharmacogenetic effects. Tables 4,5, and 6 provide a summary of the findings from published pharmacogenetic studies. On the other hand, Jayanthan et al. 126 showed that all NB cell lines under evaluation (SK-N-MC, SK-N-SH, SK-N-BE2, IMR32, SH-Sy5y, LAN1, SHEP, IMR-5 and NUB-7) were sensitive to the 17-AAG with an IC50 value ranging from 0.5 to 5 µM across different cell lines. In the same study 17-AAG also sensitised NB cells to various chemotherapeutic agents. 17-AAG is metabolised by CYP3A4 and CYP3A5 to the active metabolite 17-AG, and by NAD(P)H dehydrogenase quinone 1 (NQO1) to the very active metabolite 17-AAGH2. The SNP NQO1*2, which seems to delete the enzyme activity, induces a 32-fold increase in 17-AAG resistance 97;98 , which suggests that such variation could have a relevant effect on the clinical response. However further studies are needed to characterise the role of NQO1 and of other related polymorphisms in 17-AAG metabolic pathway. However, polymorphisms affecting CYP450 do not influence the pharmacokinetics of 17-AAG because the metabolite has a similar activity to the parent compound.

APREPITANT

The activation of neurokinin 1 (NK1) receptor by substance P induces mitogenesis, and regulates the active migration of tumour cells and angiogenetic process, besides regulating many biological functions implicated in neurogenic inflammation, pain and depression. Aprepitant, a specific inhibitor of NK1, could inhibit both DNA synthesis and cell proliferation through the mitogen-activated protein kinase (MAPK) pathway 127 .

In the study from Muñoz et al. 127 aprepitant showed a cytotoxic activity on all glioma, neuroblastoma, retinoblastoma, pancreatic carcinoma, larynx carcinoma, gastric carcinoma and colon carcinoma cell lines tested, and after its administration a great number of apoptotic cell were found in all tumour cell lines.

Aprepitant is metabolised mainly by CYP3A4, with CYP1A2 and 2C19 as secondary metabolic routes. Thus far, as reported in tables 2A, 2B and 2C, data on pharmacogenetic variation has not been reported.

BMS-536924

Insuline-like growth factor (IGF), through the IGF-1 receptor, regulates many cellular survival mechanisms, such as cellular growth, differentiation, apoptosis, tumour angiogenesis and metastasis, and different tumour types have shown the over-expression of this receptor 128 .

All NB cell lines tested in the study from Huang et al. 128 were sensitive to the treatment with BMS-536924 (IC 50 range: 0.136-0.277 µmol/L), a specific inhibitor of IGF-1R, but the treatment seems to be successful only when the receptor is on the critical path of the pathogenesis and tumour progression. The same study demonstrated an interaction between BMS-536924 and gefitinib (EGFR inhibitor) or BMS-690514 (panHER inhibitor). Such evidence raises an important issue regarding the mechanisms of resistance by single agents, which involves the adaptation to an IGF-independent growth mechanism. No pharmacogenetic data have been published on this drug.

EFLORNITHINE (alpha-difluoromethylornithine)

Polyamines, essential molecules for cellular activity, are undoubtedly involved in tumour cell growth 129 Eflornithine is not metabolised and pharmacogenetic information pertinent to drug disposition is not available so far.

IMATINIB MESYLATE

The 2-phenylaminopyrimidine imatinib is a specific inhibitor of tyrosine kinase enzymes. It binds the TK domain of Abl, c-kit (or CD117), and PDGF-R. C-kit and PDGF-R have been detected in neuroblastoma [133][134][135] , and the cytokine receptor seems to be expressed mainly in the most aggressive forms of the tumour 136 .

PDGF plays an important role in controlling growth, differentiation and survival of glial cells and immature neuroblasts, whilst c-kit is essential for normal haematopoiesis, gametogenesis, and melanogenesis 135 . In vitro studies from Vitali et al.,Beppu et al.,Rossler et al., demonstrated the ability of imatinib to inhibit neuroblastoma proliferation.

Imatinib is metabolised by a large number of enzymes of the CYP450 family, with a major role played by CYP3A4 and CYP3A5. It is also a substrate of the ABC transporters, Pgp and ABCG2. The common polymorphisms associated with these routes only have a limited influence on the imatinib pharmacokinetics, and therefore do not seem to be the underlying cause of the high inter-individual variability observed in clinical data 99;137 .

NUTLIN 3

MDM2 is a negative regulator of p53. It prevents p53-control on cell cycle and apoptosis, inhibiting the transcriptional activation of the tumour suppressor.

However, the effectiveness of an inhibitor of MDM2 is evident only if p53 is functional. Given this prerequisite, it is important to highlight that less than 2% of NB tumours exhibit mutations on TP53 gene 138 . Nutlin 3 is a specific chiral inhibitor of MDM2, which induces G1 cell cycle arrest, apoptosis and neuronal differentiation in neuroblastoma cells [138][139][140][141] , with an IC50 value of 3.25 µmol/L 142 . The 3a enantiomer shows a ~200-fold higher affinity for MDM2 than the enantiomer 3b 142 . Nutlin 3 is also a Pgp substrate and both enantiomers increase the cytotoxic activity of anticancer agents which are also substrate of Pgp (e.g., Vincristine) 141 . No pharmacogenetic information is available for this compound.

ONCOLYTIC VIRUS

Oncolytic viruses represent a new important therapeutic approach in cancer treatment [143][144][145][146] . They can circumvent chemotherapy-induced resistance mechanism through a specific lysis of tumour cells. In addition, evidence exists of their efficacy and safety in clinical trials [147][148][149] . They basically act by inducing cell lysis and genetic mutations applied to their genome restrict the viral replication only to the tumour cells. An interesting feature of these viruses is the opportunity to achieve an additional mechanism of action arming the virus against specific targets (examples are listed below).

NB cells showed evidence of cancer stem cells, as confirmed by the expression of various stem cell markers, such as CD34, CD133, and Nestin. These cells can form tumorspheres extremely resistant to chemotherapy treatment and cause the tumour relapse 147 . Neuroblastoma is also highly susceptible to HSV-mediated oncolysis 148;150;151 , and the HSV virus represents the best solution for a possible application in NB treatment.

Nestin, a protein expressed in nerve cells and involved in the radial growth of the axon, is one of the possible options to arm an oncolytic virus. Thomas et al. 152 and Mahller et al. 147 Other possible options, besides the nestin targeted vector, are HSV viruses armed with immunomodulatory molecules (B7-1, IL12, and IL18), armed against the activated Ras signalling pathway, or with inhibitors of the matrix metalloproteinases (TIMP 3), as demonstrated respectively by Ino et al.,Li et al. and Mahller et al. 148;149;151 . No pharmacogenetic data have been found for this class of drugs.

DISCUSSIONS AND CONCLUSION

To date, limited improvement in survival rates has been achieved for high-risk patients with neuroblatoma. In this review we have explored whether pharmacogenetic variation in pharmacokinetics could explain treatment failure and have attempted to highlight some of the research gaps in the evaluation of novel molecules for the treatment of neuroblastoma. Numerous pharmacogenetic studies have been proposed in the last ten years, but most of them are basically related to drug disposition.

Based on the published literature, pharmacokinetic polymporphisms do not seem to be the cause of the low survival rate in NB. None of the SNPs analysed so far can explain the poor prognosis in high-risk patients following a variety of treatment options. The lack of correlation between response and pharmacogenetic factors may also reflect the context in which drugs are used (i.e., the response is the result of a multimodal approach to NB). Furthermore, it can be derived from the low therapeutic failure in low-and intermediate-risk patients that the presence of pharmacokinetic polymorphisms in those groups does not seem to alter treatment response rate. Assuming that systemic pharmacokinetics is independent of disease severity, it is conceivable that tumour factors associated with tissue kinetics (e.g., changes in Pgp expression could lead to relevant differences in tumour exposure) and pharmacodynamic variants itself may ultimately underlie differences in response rate.

In addition, we have shown that only few out of the large number of polymorphisms have a clinically relevant effect on pharmacokinetics. Among these, SNPs UGT1A1*28 (already part of the label of camptosar in the USA) and From the examples above, it is clear that genetic variation in drug metabolism is not always clinically relevant per se. Its relevance depends on the enzyme affected and especially on the enzyme contribution to the overall metabolic rate of a given drug. The same concept is applicable to the role of pharmacogenetics on active transporters and their implications for drug disposition. Taking these considerations into account, one needs to characterise a drug's overall pharmacokinetic profile to assess the potential consequences of genetic polymorphisms. Given that compensatory pathways are involved in the disposition of the majority of the drugs suitable for clinical use, it can be anticipated that pharmacogenetic variation in ADME will often have limited impact on variability in pharmacodynamics and response. This is one of the reasons why few drug labels yield useful pharmacogenetic information.

In fact, numerous other intrinsic and extrinsic factors can influence pharmacokinetics, including variation in dosing regimen, treatment compliance, drug-drug interactions, demographic covariates, disease and organ function. For instance, taking into account the neuroblastoma, most of the patients are aged between 0 and 4-5 years, and the large variability in exposure could exclusively be assigned to developmental growth (i.e., ontogeny) rather than genetic variation. Furthermore, other important elements such as organ function (i.e., disease severity) and drug-drug interactions have to be taken into account in oncology.

In brief, the scenario arising from this review confirms the need for an integrated approach to the evaluation of genetic variation in ADME processes. Inferences about the clinical implications of a polymorphism depend upon an integrated assessment of the exposure-response relationship.

With regard to those drugs under clinical investigation, our review reveals the identification of compounds with a prominent pharmacological activity on neuroblastoma cells, irrespective of level of resistance and MYCN amplification.

Between them, oncolytic viruses have raised great interest due to the evidence of a cancer stem cell, which may underlie the high resistance to chemotherapy.

Oncolytic treatment, circumventing the traditional mechanism of resistance, seems to be a valuable solution to improve treatment outcome.

At this time, however it is difficult to say whether such activity translates into clinical efficacy and further studies are needed to confirm the clinical value of novel classes of drugs in NB treatment. Moreover, an effective drug combination and dosing algorithm still need to be identified to ensure maximum effectiveness for most compounds.

Going back to the role of pharmacogenetics, the review shows the importance of the context in which the drug is used in the evaluation of polymorphisms. Many intrinsic and extrinsic factors influence pharmacokinetics, pharmacodynamics and overall response to treatment. Therefore, an isolated analysis of the role of pharmacogenetic factors on ADME processes could probably lead to biased results. All relevant covariates should be considered in the implementation of a clinical study and included in the statistical analysis. In this sense, many of the pharmacogenetic studies summarised in this review have not considered such an integrated approach or include details on pharmacokinetic parameters.

In conclusion, pharmacogenetics is only one of many factors associated with pharmacokinetic variability. A model-based approach is required to address questions regarding the impact of polymorphisms clinical response and as such, should become best practice in the analysis of pharmacogenetic data [154][155][156][157][158] .

Model-based drug development comprises the use of mathematical and statistical concepts which describe longitudinal data (i.e., a disease model), exposure-response relationships (i.e., PKPD model) and clinical trial design (implementation model). The main advantage of this approach is that all relevant covariates (such as age, weight, ethnicity, etc.) can be taken into account concurrently. Furthermore, between-and within-subject variability is assessed parametrically in terms of physiological parameters such as clearance and volume of distribution, rather than relying on the observed variable (e.g. Cmax), which are often prone to experimental artefacts. Also, sparse, rather than rich frequent sampling can be used in population PK or PKPD models. In this sense, modelling represents a strategy for translating the clinical implications of pharmacogenetic variation. And most importantly, a model-based approach can support the selection and individualisation of effective dosing regimens, which may be more critical than the presence of ADME polymorphism in high-risk patients with neuroblastoma.

CONFLICT OF INTEREST STATEMENT

There are no arrangements of financial nature, or of any other kind, that could lead to conflict of interests with regard to this manuscript. * " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug. 88,89,90,91,94,103,106,108,110,117,121,124 1A1*28: (TA)7TAA

LEGEND:

* " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug.

Part of TEDDY Supplement 68

LEGEND:

* " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug.

Part of TEDDY Supplement 70

LEGEND:

* " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug.

  Heat shock protein 90 (Hsp90), an essential chaperone involved in the conformational maturation and stability of different proteins, including regulators of cellular proliferation and inhibitors of apoptosis, is constitutively overexpressed in tumour cell lines 125 . The great advantage of Hsp90 inhibitors should be the simultaneous depletion of multiple oncogenic client proteins 126 . Kang et al. 125 demonstrated the inhibition of SK-N-SH and LAN-1 NB cell line growth by 17-AAG, accompanied by reduced levels of Raf-1 and Akt protein kinases.

  . If their synthesis is inhibited, cell growth is stopped or severely retarded. One of the hallmarks of NB MYCN-amplified cell lines is the polyamine expansion 130 . Ornithine decarboxylase (ODC1) is a key enzyme in the biosynthetic pathway of polyamines, and ODC1 high levels correlate with poor outcome in neuroblastoma. Eflornithine (DFMO) has been shown to induce cell cycle arrest (G1) inhibiting ODC1, in NB cell lines. The underlying cause is a polyamine depletion which arrests the cell cycle through the cyclin-dependent kinase inhibitor (p27kip1) pathway. Wallick et al. 129 demonstrated its inhibitory activity on NB cell lines LAN-1 and NMB-7 with a near-total cessation of cellular growth after three days. While Koomoa et al. 131 confirmed the inhibition of LAN-1 proliferation, in the study from Rounbehler et al. 132 DFMO preferentially abolished the growth of MYCN-amplified cell lines. DFMO also increased the effects of chemotherapy without additional toxicity 130 . It is important to point out that the polyamine depletion, besides the cell cycle arrest, also induces through the PI3K/Akt pathway a mechanism of cell survival which could explain a possible moderate efficacy of DFMO alone.

  demonstrated a correlation between nestin expression and MYCN amplification, whereas the same correlation has not been shown in the study from Korja et al. 153 In the study from Mahller et al. rQNestin34.5 oHSV abolished tumour formation for more than 60 days in mice affected by neuroblastoma 147 .

UGT1A1* 6

 6 were shown to affect the pharmacokinetic profile of Irinotecan. Both polymorphisms cause and increase in exposure to the active metabolite SN-38 and consequently the risk for neutropenia. Moreover, according to Renbarger et al. and Dennison et al., polymorphisms affecting the CYP3A5 could significantly alter the pharmacokinetics of vincristine, given that this isozyme plays a key role in the elimination of vincristine. The same can be assumed from the investigation by Kelland et al. and Guo et al. on NQO1. This enzyme metabolises 17AAG to the very active metabolite 17AAGH2; and the polymorphic variant NQO1*2, which cause deletion of enzymatic activity, increases treatment resistance by 32fold.

7 .

 7 Fischer M, Spitz R, Oberthur A, et al.: Risk estimation of neuroblastoma patients using molecular markers. Klin Padiatr 220:137-146, 2008 8. Krekels EH, van den Anker JN, Baiardi P, et al.: Pharmacogenetics and paediatric drug development: issues and consequences to labelling and dosing recommendations. Expert Opin Pharmacother 8:1787-1799, 2007 9. Pruthi S, Boughey JC, Brandt KR, et al.: A multidisciplinary approach to the management of breast cancer, part 2: therapeutic considerations. Mayo Clin Proc 82:1131-1140, 2007 10. Yan L, Hsu K, Beckman RA: Antibody-based therapy for solid tumors. Cancer J 14:178-183, 2008 11. Gardiner SJ, Begg EJ: Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol Rev 58:521-590, 2006 12. Hedgecoe A, Martin P: The drugs don't work: expectations and the shaping of pharmacogenetics. Soc Stud Sci 33:327-364, 2003 13. Groninger E, Meeuwsen-de Boer T, Koopmans P, et al.: Vincristine pharmacokinetics and response to vincristine monotherapy in an up-front window study of the Dutch Childhood Leukaemia Study Group (DCLSG). Eur J Cancer 41:98-103, 2005 14. Gidding CE, Meeuwsen-de Boer GJ, Koopmans P, et al.: Vincristine pharmacokinetics after repetitive dosing in children. Cancer Chemother Pharmacol 44:203-209, 1999 15. Frost BM, Lonnerholm G, Koopmans P, et al.: Vincristine in childhood leukaemia: no pharmacokinetic rationale for dose reduction in adolescents. Acta Paediatr 92:551-557, 2003 16. De Bernardi B, Nicolas B, Boni L, et al.: Disseminated neuroblastoma in children older than one year at diagnosis: comparable results with three consecutive high-dose protocols adopted by the Italian Co-Operative Group for Neuroblastoma. J Clin Oncol 21:1592-1601, 2003 17. Groninger E, Meeuwsen-de Boar T, Koopmans P, et al.: Pharmacokinetics of vincristine monotherapy in childhood acute lymphoblastic leukemia. Pediatr Res 52:113-118, 2002 18. Lonnerholm G, Frost BM, Abrahamsson J, et al.: Vincristine pharmacokinetics is related to clinical outcome in children with standard risk acute lymphoblastic leukemia. Br J Haematol 142:616-621, 2008 19. Kushner BH, Kramer K, LaQuaglia MP, et al.: Reduction from seven to five cycles of intensive induction chemotherapy in children with high-risk neuroblastoma. Journal of Clinical Oncology 22:4888-4892, 2004 20. Coze C, Hartmann O, Michon J, et al.: NB87 induction protocol for stage 4 neuroblastoma in children over 1 year of age: a report from the French Society of Pediatric Oncology. J Clin Oncol 15:3433-3440, 1997 21. Garaventa A, Luksch R, Biasotti S, et al.: A phase II study of topotecan with vincristine and doxorubicin in children with recurrent/refractory neuroblastoma. Cancer 98:2488-2494, 2003 22. Valteau-Couanet D, Michon J, Boneu A, et al.: Results of induction chemotherapy in children older than 1 year with a stage 4 neuroblastoma treated with the NB 97 French Society of Pediatric Oncology (SFOP) protocol. J Clin Oncol 23:532-540, 2005 23. Lanvers-Kaminsky C, Krefeld B, Dinnesen AG, et al.: Continuous or repeated prolonged cisplatin infusions in children: a prospective study on ototoxicity, platinum concentrations, and standard serum parameters. Pediatr Blood Cancer 47:183-193, 2006 24. Mastrangelo R, Tornesello A, Lasorella A, et al.: Optimal use of the 131-Imetaiodobenzylguanidine and cisplatin combination in advanced neuroblastoma. J Neurooncol 31:153-158, 1997 25. Souid AK, Dubowy RL, Blaney SM, et al.: Phase I clinical and pharmacologic study of weekly cisplatin and irinotecan combined with amifostine for refractory solid tumors. Clin Cancer Res 9:703-710, 2003 26. Kushner BH, Budnick A, Kramer K, et al.: Ototoxicity from high-dose use of platinum compounds in patients with neuroblastoma. Cancer 107:417-422, 2006 27. Kaneko M, Tsuchida Y, Mugishima H, et al.: Intensified chemotherapy increases the survival rates in patients with stage 4 neuroblastoma with MYCN amplification. J Pediatr Hematol Oncol 24:613-621, 2002 28. Nath CE, Shaw PJ, Montgomery K, et al.: Melphalan pharmacokinetics in children with malignant disease: influence of body weight, renal function, carboplatin therapy and total body irradiation. Br J Clin Pharmacol 59:314-324, 2005 29. Nath CE, Shaw PJ, Montgomery K, et al.: Population pharmacokinetics of melphalan in paediatric blood or marrow transplant recipients. Br J Clin Pharmacol 64:151-164, 2007 30. Vassal G, Tranchand B, Valteau-Couanet D, et al.: Pharmacodynamics of tandem high-dose melphalan with peripheral blood stem cell transplantation in children with neuroblastoma and medulloblastoma. Bone Marrow Transplant 27:471-477, 2001 31. Nieto Y, Vaughan WP: Pharmacokinetics of high-dose chemotherapy. Bone Marrow Transplant 33:259-269, 2004 32. Canete A, Gerrard M, Rubie H, et al.: Poor survival for infants with MYCNamplified metastatic neuroblastoma despite intensified treatment: the International Society of Paediatric Oncology European Neuroblastoma Experience. J Clin Oncol 27:1014-1019, 2009 33. Diaz MA, Vicent MG, Madero L: High-dose busulfan/melphalan as conditioning for autologous PBPC transplantation in pediatric patients with solid tumors. Bone Marrow Transplant 24:1157-1159, 1999 34. Hartmann O, Valteau-Couanet D, Benhamou E, et al.: Stage IV neuroblastoma in patients over 1 year of age at diagnosis: consolidation of poor responders with combined busulfan, cyclophosphamide and melphalan followed by in vitro mafosfamide-purged autologous bone marrow transplantation. Eur J Cancer 33:2126-2129, 1997 35. Pritchard J, Cotterill SJ, Germond SM, et al.: High dose melphalan in the treatment of advanced neuroblastoma: results of a randomised trial (ENSG-1) by the European Neuroblastoma Study Group. Pediatr Blood Cancer 44:348-357, 2005 36. Eksborg S, Soderhall S, Frostvik-Stolt M, et al.: Plasma pharmacokinetics of etoposide (VP-16) after i.v. administration to children. Anticancer Drugs 11:237-241, 2000 37. Kato Y, Nishimura S, Sakura N, et al.: Pharmacokinetics of etoposide with intravenous drug administration in children and adolescents. Pediatr Int 45:74-79, 2003 neuroblastoma. Results of a phase-II trial. J Cancer Res Clin Oncol 133:653-661, 2007 45. Simon T, Langler A, Berthold F, et al.: Topotecan and etoposide in the treatment of relapsed high-risk neuroblastoma: results of a phase 2 trial. J Pediatr Hematol Oncol 29:101-106, 2007 46. Frost BM, Eksborg S, Bjork O, et al.: Pharmacokinetics of doxorubicin in children with acute lymphoblastic leukemia: multi-institutional collaborative study. Med Pediatr Oncol 38:329-337, 2002 47. Hempel G, Flege S, Wurthwein G, et al.: Peak plasma concentrations of doxorubicin in children with acute lymphoblastic leukemia or non-Hodgkin lymphoma. Cancer Chemother Pharmacol 49:133-141, 2002 48. Palle J, Frost BM, Peterson C, et al.: Doxorubicin pharmacokinetics is correlated to the effect of induction therapy in children with acute myeloid leukemia. Anticancer Drugs 17:385-392, 2006 49. Garaventa A, Luksch R, Biasotti S, et al.: A phase II study of topotecan with vincristine and doxorubicin in children with recurrent/refractory neuroblastoma. Cancer 98:2488-2494, 2003 50. Mugishima H, Matsunaga T, Yagi K, et al.: Phase I study of irinotecan in pediatric patients with malignant solid tumors. J Pediatr Hematol Oncol 24:94-100, 2002 51. Vassal G, Doz F, Frappaz D, et al.: A phase I study of irinotecan as a 3week schedule in children with refractory or recurrent solid tumors. J Clin Oncol 21:3844-3852, 2003 52. Rodriguez-Galindo C, Crews KR, Stewart CF, et al.: Phase I study of the combination of topotecan and irinotecan in children with refractory solid tumors. Cancer Chemother Pharmacol 57:15-24, 2006 53. Bomgaars L, Kerr J, Berg S, et al.: A phase I study of irinotecan administered on a weekly schedule in pediatric patients. Pediatr Blood Cancer 46:50-55, 2006 54. Levy AS, Meyers PA, Wexler LH, et al.: Phase 1 and pharmacokinetic study of concurrent carboplatin and irinotecan in subjects aged 1 to 21 years with refractory solid tumors. Cancer 115:207-216, 2009 55. Gore L, Chawla S, Petrilli A, et al.: Aprepitant in adolescent patients for prevention of chemotherapy-induced nausea and vomiting: a randomized, double-blind, placebo-controlled study of efficacy and tolerability. Pediatr Blood Cancer 52:242-247, 2009 56. Weigel BJ, Blaney SM, Reid JM, et al.: A phase I study of 17allylaminogeldanamycin in relapsed/refractory pediatric patients with solid tumors: a Children's Oncology Group study. Clin Cancer Res 13:1789-1793, 2007 57. Bagatell R, Gore L, Egorin MJ, et al.: Phase I pharmacokinetic and pharmacodynamic study of 17-N-allylamino-17-demethoxygeldanamycin in pediatric patients with recurrent or refractory solid tumors: a pediatric oncology experimental therapeutics investigators consortium study. Clin Cancer Res 13:1783-1788, 2007 58. Bond M, Bernstein ML, Pappo A, et al.: A phase II study of imatinib mesylate in children with refractory or relapsed solid tumors: a Children's Oncology Group study. Pediatr Blood Cancer 50:254-258, 2008 59. Marangon E, Citterio M, Sala F, et al.: Pharmacokinetic profile of imatinib mesylate and N-desmethyl-imatinib (CGP 74588) in children with newly diagnosed Ph+ acute leukemias. Cancer Chemother Pharmacol 63:563-566, 2009 60. Pollack IF, Jakacki RI, Blaney SM, et al.: Phase I trial of imatinib in children with newly diagnosed brainstem and recurrent malignant gliomas: a Pediatric Brain Tumor Consortium report. Neuro Oncol 9:145-160, 2007 61. Dennison JB, Jones DR, Renbarger JL, et al.: Effect of CYP3A5 expression on vincristine metabolism with human liver microsomes. J Pharmacol Exp Ther 321:553-563, 2007 62. Plasschaert SL, Groninger E, Boezen M, et al.: Influence of functional polymorphisms of the MDR1 gene on vincristine pharmacokinetics in childhood acute lymphoblastic leukemia. Clin Pharmacol Ther 76:220-229, 2004 63. Schaefer M, Roots I, Gerloff T: In-vitro transport characteristics discriminate wild-type ABCB1 (MDR1) from ALA893SER and ALA893THR polymorphisms. Pharmacogenet Genomics 16:855-861, 2006 64. Filipski KK, Loos WJ, Verweij J, et al.: Interaction of Cisplatin with the human organic cation transporter 2. Clin Cancer Res 14:3875-3880, 2008 65. Oldenburg J, Fossa SD, Ikdahl T: Genetic variants associated with cisplatin-induced ototoxicity. Pharmacogenomics 9:1521-1530, 2008 66. Rabik CA, Dolan ME: Molecular mechanisms of resistance and toxicity associated with platinating agents. Cancer Treat Rev 33:9-23, 2007 67. Zhang L, Plon SE, Nuchtern JG, et al.: Cyclin D and cisplatin cytotoxicity in primary neuroblastoma cell lines. Anticancer Drugs 15:883-888, 2004 68. de Cremoux P, Jourdan-Da-Silva N, Couturier J, et al.: Role of chemotherapy resistance genes in outcome of neuroblastoma. Pediatr Blood Cancer 48:311-317, 2007 69. Renbarger JL, McCammack KC, Rouse CE, et al.: Effect of race on vincristine-associated neurotoxicity in pediatric acute lymphoblastic leukemia patients. Pediatr Blood Cancer 50:769-771, 2008 70. Dennison JB, Kulanthaivel P, Barbuch RJ, et al.: Selective metabolism of vincristine in vitro by CYP3A5. Drug Metab Dispos 34:1317-1327, 2006 71. Woodahl EL, Crouthamel MH, Bui T, et al.: MDR1 (ABCB1) G1199A (Ser400Asn) polymorphism alters transepithelial permeability and sensitivity to anticancer agents. Cancer Chemother Pharmacol 2009 72. Yang Z, Wu D, Bui T, et al.: A novel human multidrug resistance gene MDR1 variant G571A (G191R) modulates cancer drug resistance and efflux transport. J Pharmacol Exp Ther 327:474-481, 2008 73. Oldenburg J, Kraggerud SM, Cvancarova M, et al.: Cisplatin-induced longterm hearing impairment is associated with specific glutathione stransferase genotypes in testicular cancer survivors. J Clin Oncol 25:708-714, 2007 74. del Amo EM, Urtti A, Yliperttula M: Pharmacokinetic role of L-type amino acid transporters LAT1 and LAT2. Eur J Pharm Sci 35:161-174, 2008 75. Kuhne A, Kaiser R, Schirmer M, et al.: Genetic polymorphisms in the amino acid transporters LAT1 and LAT2 in relation to the pharmacokinetics and side effects of melphalan. Pharmacogenet Genomics 17:505-517, 2007 76. van Schaik RH: CYP450 pharmacogenetics for personalizing cancer therapy. Drug Resist Updat 11:77-98, 2008 77. Kishi S, Yang W, Boureau B, et al.: Effects of prednisone and genetic polymorphisms on etoposide disposition in children with acute lymphoblastic leukemia. Blood 103:67-72, 2004 78. Sohn JW, Lee SY, Lee SJ, et al.: MDR1 polymorphisms predict the response to etoposide-cisplatin combination chemotherapy in small cell lung cancer. Jpn J Clin Oncol 36:137-141, 2006 79. Lal S, Sandanaraj E, Wong ZW, et al.: CBR1 and CBR3 pharmacogenetics and their influence on doxorubicin disposition in Asian breast cancer patients. Cancer Sci 99:2045-2054, 2008 80. Gonzalez-Covarrubias V, Zhang J, Kalabus JL, et al.: Pharmacogenetics of human carbonyl reductase 1 (CBR1) in livers from black and white donors. Drug Metab Dispos 37:400-407, 2009 81. Wojnowski L, Kulle B, Schirmer M, et al.: NAD(P)H oxidase and multidrug resistance protein genetic polymorphisms are associated with doxorubicininduced cardiotoxicity. Circulation 112:3754-3762, 2005 82. Bains OS, Karkling MJ, Grigliatti TA, et al.: Two non-synonymous single nucleotide polymorphisms of human carbonyl reductase 1 demonstrate reduced in vitro metabolism of daunorubicin and doxorubicin. Drug Metab Dispos 2009 83. Crouthamel MH, Wu D, Yang Z, et al.: A novel MDR1 G1199T variant alters drug resistance and efflux transport activity of P-glycoprotein in recombinant Hek cells. J Pharm Sci 95:2767-2777, 2006 84. Lal S, Wong ZW, Jada SR, et al.: Novel SLC22A16 polymorphisms and influence on doxorubicin pharmacokinetics in Asian breast cancer patients. Pharmacogenomics 8:567-575, 2007 85. Mossallam GI, Abdel Hamid TM, Samra MA: Glutathione S-transferase GSTM1 and GSTT1 polymorphisms in adult acute myeloid leukemia; its impact on toxicity and response to chemotherapy. J Egypt Natl Canc Inst 18:264-273, 2006 86. Smith NF, Figg WD, Sparreboom A: Pharmacogenetics of irinotecan metabolism and transport: an update. Toxicol In Vitro 20:163-175, 2006 87. Araki K, Fujita K, Ando Y, et al.: Pharmacogenetic impact of polymorphisms in the coding region of the UGT1A1 gene on SN-38

TABLE 3 :

 3 Influence of pharmacogenetics on the toxicity of cytotoxic drugs;

NA

  1A7*2: 387T > G, 391C > A, 392G > A

TABLE 1 :

 1 Influence of pharmacogenetics on the exposure profile of cytotoxic drugs;

	EXPOSURE

TABLE 4 :

 4 Influence of pharmacogenetics on the exposure profile of drugs in clinical development;

	EXPOSURE

TABLE 5 :

 5 Influence of pharmacogenetics on the efficacy of drugs in clinical development;

	EFFICACY

TABLE 6 :

 6 Influence of pharmacogenetics on the toxicity of drugs in clinical development;

	TOXICITY

Commission's Sixth Framework Program (Contract n. 0005216 LSHBCT-2005-005126).

ACKNOWLEDGEMENTS

This contribution is part of the Task-force in Europe for Drug Development in the Young (TEDDY) Network of Excellence supported by the European

NA 1A1*28: (TA)7TAA 88, 89, 94, 101,103,106,114,118,121,124 (SN-38) 1A1*28: (TA)7TAA 86,90,91,108,109,110,116,117 ( SN-38) * " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug. * " " indicates an increase in drug's exposure, efficacy or toxicity; † " " indicates a decrease in drug's exposure, efficacy or toxicity; ‡ "NA" indicates that there are not pharmacogenetic associations between the gene and the pharmacokinetic profile of the drug; §"NK" (Not Known) indicates that pharmacogenetic data have not been found;

|| "grey cells" indicate that there is not correlation between the gene and the pharmacokinetic profile of the drug.


Selim Sheikh
email: selimst@cardiff.ac.uk.

Quantitative Methods, C51 -Model Construction and Estimation < C5 -Econometric Modeling < C -Mathematical and Quantitative Methods, O13 -Agriculture; Natural Resources; Energy; Environment; Primary Products < O1 -Economic Development < O -Economic Development, Technological Change, and Growth, Q12 -Micro Analysis of Farm Firms, Farm Households, and Farm Input

Keywords: Stochastic frontier, non-neutral frontier, technical efficiency. JEL Classification Code: C33, C51, O13, Q12

In this paper we examine the significance of labour productivity and use of inputs in explaining technical efficiency of rice production in Bangladesh. We find that higher labour productivity can stimulate high efficiency gains, but increased use of inputs (except land) induces negative marginal effect on technical efficiency. While more use of land, improved seeds and fertilizers contributes to the rate of labour-productivity induced marginal efficiency gain, any additional labour depresses this rate. Given the agricultural policy reform history in Bangladesh, our findings imply that rather than providing input subsidy or output price support, future reforms should put more emphasis on providing incentives to enhance labour productivity and encourage formalization of the agricultural labour market.

total agricultural labour force in Bangladesh comprises of self-employed farmers, unpaid family workers and day-laborers 2 . Together they make a large proportion of the agricultural labour market an informal market with little collective bargaining power for wages. Our hypothesis is that the underutilization of productive capacity, or more simply the inefficiency in production that resulted in negative growth rate in production, is mainly due to the interaction of low labour productivity and the use of new technology. We capture this interaction effect by modeling interactions between marginal wage and the factors of production as determinants of technical inefficiency. We find that such interactions significantly affect technical efficiency in hybrid Aus' production. With huge surplus labour in rice production, extended use of scientific inputs such as hybrid seeds and fertilizers depresses the efficiency gains in production. Their use can excel the efficiency gains only if there is an increase in labour productivity.

The Context.

There are three popular rice hybrids in Bangladesh, Aus, Aman and Boro. Both Aman and Boro are cropped round the year, while Aus is cropped during March to September. During the mid eighties, all three hybrids went through modernization with the introduction of high yield seeds, scientific methods of irrigation, fertilization and harvest. This resulted in an initial phase of growth in rice production, which eventually lasted for a brief period. [START_REF] Baffes | Is Growth in Bangladesh's Rice Production Sustainable?[END_REF] argue that the observed growth in aggregate rice production in Bangladesh until the early nineties was mainly because of conversion of rice growing areas from local to modern varieties. We present [START_REF] Baffes | Is Growth in Bangladesh's Rice Production Sustainable?[END_REF] estimates of growth rates of these hybrids in figure 1. From the mid to late nineties, aggregate rice production growth rate declined. According to BBS reports, the rice production growth target in the late nineties was 4.23% and the actual achieved was 0.95%. The projected growth rate of Aman production and Boro production in the late nineties were 3.03% and 6.07% and the actual achieved were 0.61% and 4.41%, respectively. The production growth of Aman actually dropped from its eighties' average 1.64%, while that of Boro dropped from its eighties' average 7.10%. For Aus the statistics are worse. The target growth rate in Aus in late nineties was 3.5% but its production declined by -3.22%, following a trend of 2 All statistics, including the data for our regressions are collected from Yearbook of Agricultural Statistics of Bangladesh, published by Bangladesh Bureau of Statistics (BBS). We verify national level data and the most recent statistics from two other sources, Sustainable Development Network of Bangladesh (SDNBD), and Agricultural Statistical Yearbook of Bangladesh from the Ministry of Agriculture, both of which are available online. The district level data are available from Zila Series Census and Zila statistics of BBS. With the introduction of modern hybrids, more area under cultivation was allocated to Boro production. Boro's cultivation is the least effort-intensive and its growth largely depends on timing of cultivation, land fertility and weather. Given a fixed supply of land, and since there is little choice for diverting Aman's land to other crops because of high soil moisture and poor drainage, this had to be done at the expense of taking away land from Aus production. Some studies find a significant impact of this land reform in the drop in rice production growth rate in Bangladesh. [START_REF] Sharif | An Empirical Study of the Patterns and Sources of Technical Inefficiency in Traditional and HYV Rice Cultivation in Bangladesh[END_REF], for instance, find that there exists low technical efficiency in the production of modern variety of rice in Bangladesh, and one of the key factors affecting technical inefficiency in the production of particular hybrids is the land reform policy. [START_REF] Mahmud | Agricultural Growth Through Crop Diversification in Bangladesh[END_REF] also argue along the same lines. None of these studies, however, examine explicitly if labour productivity had a significant impact on technical efficiency.

We conduct the study only on hybrid Aus. This is mainly because (a) its production growth is negative; (b) its cultivation is effort as well as material input-intensive, which is why we hypothesize labour productivity and scientific means of cultivation and their interaction may have a role to play, and (c) a significant proportion of its area under cultivation was given away to Boro production. We combine these facts, i.e. the persistent negative growth rate in Aus production, high emphasis on scientific inputs and modern varieties, the informal structure of labour market that provides little incentive to enhance productivity, and the land reform policy.

We hypothesize that the decline in Aus production is mainly due to a combination of low marginal wage paid to farmers and depression of efficiency gains due to interaction of low labour productivity and the use of inputs (such as land, improved fertilizers and seeds). We examine the technical inefficiency effects in district level Aus production and attempt to explain the failure to achieve target growth rate in production. In order to identify areas for policy reform, we look at late eighties, the government undertook output price support policy and at tandem withdrew food subsidies in the urban rationing system and agricultural input subsidies. Understandably, the main reason behind this reform was that the rate of increase in prices of imported fertilizer and improved seeds was persistently higher than the rate of increase in rice price. Farmers from smaller districts were subject to high intensity and high price elasticity of demand for fertilizer and improved seeds. This was due to management costs involved in procuring these from divisional headquarters where these inputs were primarily supplied and stocked. The Model.

We follow the standard stochastic frontier production function approach, proposed independently by [START_REF] Aigner | Formulation and Estimation of Stochastic Frontier Production Function Models[END_REF] and [START_REF] Meeusen | Efficiency Estimation from Cobb-Douglas Production Functions with Composite Error[END_REF], which postulates the existence of technical inefficiencies of production of agents involved in producing a particular output. This study therefore complements important works in this area, such as [START_REF] Forsund | A Survey of Frontier Production Functions and Their Relationship to Efficiency Measurement[END_REF], [START_REF] Bauer | Recent Developments in the Econometric Estimation of Frontiers[END_REF], [START_REF] Cornwell | Production Frontiers with Cross-sectional and Time-series Variation in Efficiency[END_REF], [START_REF] Battese | Prediction of Firm-level Technical Efficiencies with a Generalized Frontier Production Functions and Panel Data[END_REF][START_REF]Frontier Production Functions, Technical Efficiency and Panel Data with Application to Paddy Farmers in India[END_REF][START_REF]A Model for Technical inefficiency Effects in a Stochastic Frontier Production Function for Panel Data[END_REF], [START_REF] Sharma | Technical Efficiency of the Longline Fishery in Hawaii: An Application of a Stochastic Production Function[END_REF] and [START_REF] Jha | Some Imperatives of the Green Revolution: Technical Efficiency and Ownership of Inputs in Indian Agriculture[END_REF]. In addition, we consider time variant technical inefficiency and non-neutral efficiency estimation, as in [START_REF] Karagiannis | Explaining Output Growth with a Heteroscedastic Non-neutral Production Frontier: The Case of Sheep Farms in Greece[END_REF]'s study of sheep farming in Greece. In particular, we work with a standard stochastic frontier model: σ . This distribution allows for a wider range of distributional shapes including nonzero modes 3 .

T t N i u v x f q it it it ,.....
Following Battese and Coelli (1995), we define

2 2 2 u v s σ σ σ + ≡ and compute ) ( 2 2 2 V U U σ σ σ γ + ≡ , where ] 1 , 0 [ ∈ γ
. If γ is significantly different from zero, it can be used to determine the source of variation in production and the extent of the impact of technical inefficiency effects as compared to random shocks or stochastic effects 4 . We use a translog production frontier. This specification does not impose assumptions about constant elasticity of production nor elasticity of substitution between inputs, allowing the data to indicate the actual curvature of the function. We use the following specification:

∑ ∑∑ ∑ - + + + + = j j k it it kit jit jk jit jj j jit j it u v x x b x b x b b q ln . ln ) (ln ln ln 2 0
(2) Following [START_REF] Huang | Estimation of a Non-neutral Stochastic Frontier Production Function[END_REF], we further assume that the technical inefficiency is a function of district specific characteristics, use of inputs, and interactions between characteristics and inputs. In particular, we assume that inefficiency depends on two sets of variables, it z and

* it z .
The first represents some district-specific characteristics which may influence the district's 3 There are other possible distributions of it u and there are no a priori reasons for choosing one distributional form over the others. For instance, modelling with a half-normal distribution that has a mode at zero in this case would impose an assumption that a high proportion of the districts being examined are perfectly efficient. This is the same imposition if one considers an exponential distribution. 4 A low value of γ would indicate that 2 u σ is small and 2 v σ is large, so stochastic or random effects such as weather variations cause less than frontier level of output, and variations from frontier level of output (given the set of inputs) are not due to any technical inefficiency effects. A high value of γ would thus indicate that most of the variation from frontier level of output is due to technical inefficiency effects rather than random variations. . The technical efficiency measure is thus constrained to be between zero and one.

District i is operating at the technically efficient level at any time t if its output in on the frontier, since technical efficiency of the i th district at any time t is a relative measure of its output as a proportion of the corresponding frontier output 5 . We use Maximum Likelihood estimation. Thus, our pooled estimation is based on a joint density function for the split error term it it u v -. With district level data we model the frontier with four inputs. The elasticity of output at any time t with respect to the th jinput, jt µ for j = 1, 2, 3, 4; evaluated at the mean values of relevant data points can be derived as:

∑ ≠ + + = j k kt jk jt jj j jt x b x b b ln ln 2 µ (4)
Given our estimation of a panel data, neglecting heteroscedasticity would result in biased estimates of both the b s and technical efficiency, especially when factors affecting technical inefficiency and the inputs in production are highly correlated. Notice that we have assumed a constant variance of it u . If the variance depends on district specific characteristics and this intuition is ignored in the estimation process, the resulting estimation would lead to downward (upward) biased estimates of technical efficiency for relatively small (large) districts. While choosing the district, we make an attempt to get around this problem. We choose the 23 rice producing districts that have nearly similar cropping intensity, where cropping intensity is defined as (total cropped area/net sown area)*100. We also conduct a formal heteroscedasticity test. We assume that since the districts have similar cropping intensity, the heteroscedasticity may be mainly due to other district specific characteristics, namely, meteorological condition (e.g. rain intensity), labour productivity and use of bullock days. We assume, following [START_REF] Karagiannis | Explaining Output Growth with a Heteroscedastic Non-neutral Production Frontier: The Case of Sheep Farms in Greece[END_REF], that the variance function is exponential, which takes the form:

it it it uit z z z 13 3 8 2 6 1 0 2 ξ ξ ξ ξ σ + + + = ln (5)
This extends our set of robustness tests. In summary, we perform the following tests. We test that the technical inefficiency effects are absent, i.e. for hybrid Aus. The districts are selected with similar cropping intensity, from all 6 divisions of Bangladesh (and from all zones) including the divisional headquarters. Districts were divided into four cropping zones (south, north, east and west) and two cropping zones (south and north) and efficiency model was estimated for both using 3 zone dummy variables and 1 zone dummy variable, respectively. The latter was selected on the basis of likelihood ratio test 6 . Six divisional headquarters, namely, Dhaka, Sylhet, Chittagong, Barisal, Rajshahi and Khulna were assigned a value 1 for the head dummy. These headquarters are demand centers and the main divisional market.

The output data of hybrid Aus is taken as aggregate district production of Aus rice in tonnes (or metric ton). The inputs are land area (in hectares) under cultivation of Aus, improved Aus seeds (in tonne), chemical fertilizer used in Aus production (in tonne), and agricultural labour measured in full-time annual working days of total labour force during cropping season of Aus. Since bullocks and tractors are used in cultivation in the event of low rainfall during cropping season, bullock days is used in the technical efficiency model to act as an inverse proxy for rainfall 7 . As determinants of technical inefficiency, some district specific characteristics are considered. The rain intensity variable is created taking a ratio of rainfall in millimeters to humidity in percentage 6 On an average, south zone districts with a land altitude of less than or equal to 10 meters from the sea level are expected to have more fertility of land. This is because weather conditions in these regions allow for more tropical monsoon rain that drenches the land and rivers creating huge deposits of silt in the land. The zone difference of districts is due to different altitudes of land from sea level (and not only on geographical positioning). Since the Bay of Bengal is to the south of Bangladesh, most districts to the south are approximately within 10 meters of land altitude from the Bay of Bengal. Districts with cropping land within 10 meters above the sea level are designated south zone districts (having low land and very low land), and have been assigned a value 1 for the zone dummy variable. Districts with medium highlands, hills and highlands (land altitude of over 10 meters from sea level) are designated north zone districts. 7 We do not find any district level data on tractors, irrigation machinery and pesticides for Bangladesh. We also find very less variation in cropping season average rainfall across districts and over time (although there is reasonable variation in the rain intensity). We therefore include bullock days as an inverse proxy for rainfall. to account for cropping season rain intensity in millimeters for 1% humidity. We follow [START_REF] Nwaokoro | Real Wage Rate And Productivity Relationship In The Declining U.S. Steel Industry[END_REF] and hypothesize that farmers' real (and not nominal) marginal wage has a direct correspondence with their productivity. In addition, keeping in mind the informal structure of the labour market in Bangladesh agriculture, we assume that the wage function is linear such that average real wage is equal to their marginal real wage. We therefore use Aus farmers' average daily real wage as a proxy for labour productivity. The wage data we use is an average wage rate (in real BD taka) for cropping seasons of Aus rice, averaging over per day without meal male and female wage rates.

Inefficiency is modeled to depend on inputs, and four more arguments which are interactions between wage and inputs. . The inputs, and these four in the inefficiency estimation represent the non-neutrality, and enables us to examine the cross effects of labour productivity and input use. The data and variable tables are in appendix, Table 1. The Maximum Likelihood estimation results are presented in appendix table 2. We use likelihood ratio test to choose the translog production frontier over a Cobb Douglas specification. Also, the non-neutral frontier is accepted over the neutral one on the basis of likelihood ratio test.

Tests of Hypotheses.

Most of the estimated parameters are statistically significant (at 5% and 10% levels). Generalized likelihood-ratio tests of various null hypotheses involving restrictions on the variance parameter, γ , b s in the stochastic production frontier, δ s in the technical inefficiency model, choice of model specification, aggregate returns to scale, and heteroscedasticity, are presented in appendix table 3. Critical value for 5% level of significance for the first and second null hypotheses involving 0 = γ , are collected from table 1 of [START_REF] Kodde | Wald Criteria for Jointly Testing Equality and Inequality Restrictions[END_REF], p.1246). This is because the estimated test statistic for these restrictions, if the restriction holds true, follows a mixed chisquare distribution. The critical values for the other tests are taken from standard chi-square tables.

From table 3, the first null hypothesis, which specifies that the inefficiency effects are absent from the model, is strongly rejected at 5% level. The second null hypothesis, that the inefficiency effects are not stochastic, is also strongly rejected at 5% level. The third null hypothesis, which states that inefficiency effects are not a linear function of the determinants considered, is also strongly rejected at 5% level. The null hypothesis of constant returns to scale for the production technology is accepted by the data. The fifth null hypothesis, stating that the model for hybrid Aus production can be estimated using a Cobb-Douglas neutral frontier specification, is strongly rejected, which justifies the choice of translog non-neutral frontier. The null hypothesis that the interaction effects of district specific characteristics and use of inputs on technical inefficiency are insignificant is also rejected at 5% level. This test confirms our choice of the specification.

We accept homoscedasticity over heteroscedasticitiy as we fail to reject the null hypothesis of constant variance of it u .

Computations and Interpretations.

We present a frequency distribution of estimated technical efficiency ratings in our sample in table 4. The estimates of technical efficiency indicate the consistency and reliability of our model specification. Technical efficiency estimate range from 0.29 to 0.99. Estimated mean technical efficiency for 1994-1999 is 79.7%, implying that output could have increased substantially if technical inefficiency was eliminated.

We compute the marginal effect of inputs and wage on technical inefficiency, and the cross effects of inputs on the marginal effect of wage (at mean value), and report these in table 5. The important thing to consider from this table is the sign of these effects. Notice that given our specification of the technical inefficiency, the marginal effect of wage increase (i.e. labour productivity) on technical inefficiency is:

+ + + + = ∂ ∂ (7)
The cross effects are derived by differentiating [START_REF] Meeusen | Efficiency Estimation from Cobb-Douglas Production Functions with Composite Error[END_REF] with respect to individual inputs. The signs of the estimates of 12 11 10 9 δ δ δ δ , , , therefore represent the directions of cross effects. Notice from table 5 that increasing land area for cultivation or wages induce efficiency gain (negative marginal effect on technical inefficiency), a finding which is consistent with both [START_REF] Baffes | Is Growth in Bangladesh's Rice Production Sustainable?[END_REF] and [START_REF] Sharif | An Empirical Study of the Patterns and Sources of Technical Inefficiency in Traditional and HYV Rice Cultivation in Bangladesh[END_REF]. For other inputs, higher use induces efficiency loss. This result is consistent with the intuition that with low labour productivity and existence of surplus labour, introducing more seeds, fertilizer or labour will create a congestion effect that will reduce efficiency of the industry. Untrained workforce will not cope with the scientific materials, and more working days will reduce labour productivity further, both of which are attributable to further inefficiency in production. The cross effects are representative of the marginal change in wage effect (the labour-productivity induced efficiency gain) for additional use of inputs. This effect is positive for land, seed and fertilizer but negative for labour. The positive cross effects suggest that if labour productivity is improved, increased use of inputs such as land, seed and fertilizer will contribute to the rate of labour-productivity induced marginal efficiency gain. Thus any further land reform (which allocates more land to Aus production) or input subsidies will achieve their goals only if labour productivity is improved.

The estimated coefficient for zone dummy variable is -0.02 and it is statistically significant at 5% level, implying that south zone districts with more fertile land tend to be more (and significantly) technically efficient in producing Aus. Head quarter dummy variable is statistically significant and divisional headquarters possess a higher mean inefficiency, implying that favored demand centers did not experience higher efficiency than relatively remote areas. A high and statistically significant value of gamma for the model indicates that most of the deviation of output from frontier level is due to technical inefficiency rather than from "random shocks" like weather variations.

The estimates of output elasticity evaluated at means of relevant data points and returns to scale are presented in appendix table 6. The output elasticity of labour is negative (and low) for all years and insignificant for two years. This suggests that the production technology is a wellbehaved one for inputs area, seeds and fertilizer. This also confirms our key hypothesis of low labour productivity which is consistent with the surplus labour evidence. Among the estimated elasticity, elasticity of output with respect to area is near one and is statistically significant for all years. Elasticity of output with respect to fertilizer and elasticity of output with respect to seeds are also significantly positive. Production tends to vary very less for variation in seeds or labor.

The returns to scale is near one for all years of our sample. These results strongly suggest that agricultural inputs have substitutability in production, and therefore possess significant interaction effects. We estimate a model for technical inefficiency effects in a stochastic production frontier for panel data of Aus rice production across 23 major districts of Bangladesh over 6 years. We assume a translog production function specification and non-neutrality in efficiency distribution. The results indicate that our specification for the technical inefficiency effects is a significant component in explaining the decline in Aus productivity growth in Bangladesh, and that the key reform agenda is one that provides incentives for increasing labour productivity. We also examine output elasticity with respect to different inputs, and degree of homogeneity of production technology.

Concluding Remarks.

We find that due to low marginal productivity of labour, extended use of new technology depresses the efficiency gains in production. The low marginal productivity of labour is primarily due to predominantly informal labour market and the unwillingness to learn new technology. We argue that agricultural reforms towards higher productivity growth should address the incentive schemes for labour. Rather than subsidizing input prices or supporting output prices which has inherent disincentives to learn new technology, reforms should provide incentives for training and formalizing the rural labour market. This phenomenon, i.e. the conflict between traditional practices and new technology practice is not new in developing countries. We show the correspondence between this conflict and utilization of existing capacity, and highlight the importance of resolving this conflict. We conclude that future agricultural policy reforms in Bangladesh should therefore put more emphasis on enhancing labour productivity and formalizing the agricultural labour market.

The issue of high labour productivity induced efficiency gains and productivity growth is one of a vibrant debate in development theory. There exists a strong view, led mainly by Adam Smith, focusing on market induced regionally concentrated scale economies and gains from specialization. The other view, known as the Boserupian view, connects decreasing labour productivity with long lasting output growth induced primarily by demographic pressures. This view fits the current agricultural context of Bangladesh. Without investing in skills of agricultural labour, or without formalizing the labour market, the use of scientific inputs results in a slow technical change. Since land fertility (and area) is not constant, scientific inputs are subject to underutilization, or misuse. On the other hand, low marginal wage paid to farmers and familyheads who work with unpaid members provides little incentives to induce more effort. Without government's direct intervention, wage income thus can only be increased by registering income as farm income rather than farmer income. A wage subsidy fails to serve the purpose of

Period and variety

Growth Rates

Aus Aman Boro

Source: [START_REF] Baffes | Is Growth in Bangladesh's Rice Production Sustainable?[END_REF].

Source: Agricultural Statistical Yearbook of Bangladesh. 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

  3.43% and -2.89% in the eighties and early nineties. In figure2we present the trend in production of the three rice hybrids during 1980-2002, according to BBS reports.

  the history of agricultural policy reform in Bangladesh and shortlist what has been done and what has not been done. We have collected this important information on policy reforms combining various Five-Year Plan Documents of the Ministry of Finance and Planning of the Government of Bangladesh. We present a summary of the key reforms in three main phases in chart 1. During the first phase of reforms, mainly due to quantity rationing, demographic pressure failed to induce agricultural growth. This encouraged the government to adopt a new input technology package including scientific means of irrigation, fertilizers, pest control and high yield seeds. During the

  second represents the inputs in production, and interactions between it z and the input variables in the stochastic frontier. Simultaneously with the stochastic frontier we estimate: are unobservable random variables assumed to be independently distributed, obtained by truncation of the normal distribution with mean zero and variance, 2 w σ , such that it u is non-

..

  The constant returns to scale assumption in the translog stochastic production frontier (2) imposes a number of linear restrictions on the parameters, which are: which says that the translog non-neutral frontier can be rejected in favor of a neutral Cobb-Douglas production frontier. We also test the assumption of interactions of district specific characteristics and use of inputs, i.e. we test whether these interactions are jointly significant in determining technical inefficiency. Finally, we test for heteroscedasticity, i.e. we perform a test on the null hypothesis of homoscedasticity, We test these null hypotheses using the generalized likelihood-ratio statistic, where the statistic follows approximately a chi-square distribution with degrees of freedom equal to the number of restrictions in the null hypothesis, provided the null hypothesis is true, and a mixed chi-square distribution when the null hypothesis involves 0 All data are from Yearbook of Agricultural Statistics of Bangladesh, a publication of Bangladesh Bureau of Statistics (BBS) that presents primary information on district and national level agricultural production. The other two secondary sources of agricultural data are the Sustainable Development Network of Bangladesh (SDNBD) and Agricultural Statistical Yearbook of Bangladesh from the Ministry of Agriculture, both of which are available online, and use our original data source. We collect data for 23 major rice-producing districts over1994-1999 period

  schemes to all farmers including family members. In Bangladesh, approximately 16% of the family unpaid workers are school dropout children of farmers. A labour productivity enhancing policy thus should be supported by awareness programs and incentives for arranging alternative arrangements for these children. Such arrangement may include schooling, but more importantly, they may include formal agricultural training leading to accreditations.

Fig

  Fig 1: Growth Rates in Production of three hybrids of rice

Fig

  Fig 2: Total production of rice hybrids during 1980-2002 (in million metric tons)

Chart 1: Key agricultural policy reforms in Bangladesh 1977-1994.

	providing direct subsidies or output price support to farmers, since these are essentially associated
	with disincentive effects of different magnitudes. There were no reforms particularly targeted at
	formalizing the agricultural labour market (e.g. farm income registration, or introducing farm-
	investment subsidy). Since surplus labour earns zero marginal wage and thus adds little to the
	marginal product, without proper training and awareness, providing this labour force with new
	technology, hybrid seeds and new capital will in general depress agricultural productivity, or
	efficiency in production. To see this more formally, consider a simple correspondence between
	technical efficiency and labour productivity. If all workers are paid their marginal product (and
	no worker is paid zero wage), they essentially induce more effort in order to increase their
	Period 1977-1983 • Input subsidy, (no wage subsidy); Policy marginal product. Higher labour productivity reduces technical inefficiency since it adds value to Purpose Observed consequence Centralized support to Agricultural growth, but low the marginal productivity of other inputs. In the current context it is interesting to examine the F o variation in this effect due to interactions of productivity with the inputs. If labour productivity is
	• Market quantity rationing. r low, and if the labour market is predominantly informal, adding more fertilizer or seeds (or agriculture and self technology growth.
	sufficiency in food. High growth in production, and price support to meet new higher input costs. working days) to cultivation will depress the marginal effect of labour productivity on efficiency, 1983-1988 • Input technology package; • Withdrawal of direct input subsidy; • Output price support. Low output growth, slow rate of technology adoption. P r e e since a predominantly informal labour force is less likely to exert more effort in order to use
	1989-1994 • Deregulation of input supply; • Land reforms; • Multi cropping. It was recognized that in a state of overwhelming dependence on weather, when prices fluctuate High growth in production, high competition and higher efficiency in land use. Decline in Aus production, less than projected growth in production of other hybrids. with output, only price support policy to stimulate output is often ineffective. The concern of overwhelming dependence on land fertility propelled land reform policies. In addition, in order to R e v i e w
	improve the distributional channels, the government decided to move towards deregulation. The
	agricultural reforms in the nineties phase can be characterized as ones of regulatory reforms of
	input supply side towards deregulation and liberalization of input supply chains, crop
	diversification, and extended rice research and widening genetic base of rice.
	The phases of reforms suggest that the government moved gradually towards a more scientific
	package of material inputs, towards allocating more land to weather dependent hybrids, and
	towards a more liberalized agricultural input market; but during the process the government did
	very little in providing incentives to farmers to adopt the new technology. The issue is not one of
	6

scientific inputs efficiently. We test this hypothesis in this paper. If our hypothesis is supported by data, it would imply that policy reforms in future should put emphasis on labour market reforms rather than flat subsidies.

  denotes output of district i in year t , and x represents an input vector. The term it v is a symmetric and normally distributed random error, which represents those factors that cannot be controlled by farmers, measurement errors in the dependent variable, and omitted explanatory variables. We assume, as is standard, that it

	where		it q v have normal distribution with zero mean and
	standard deviation v σ . it v are independent of it u , which are non-negative random variables that
	account for technical inefficiency in production. These are assumed to be independently and
	identically distributed and truncations (at zero) of the distribution	N	( u	it	,	2 u	)
					F o
							r
							P
								e e r
											R e v i e w
	ln	=	(ln	)	+	-	;	=	,......, 1	;	=	1	.,		(1)
											7

Table 2 : Translog non-neutral stochastic frontier estimation.

 2

		Variable/Intercept	Parameter	Estimate	t-ratio
	Production Frontier	Intercept ln(area)		b 0 1 b	0.269 2.071	0.79 1.97*
		ln(seed)			2 b	0.304	2.17**
		ln(fertilizer)		3 b	0.062	2.47**
		ln(labour)		4 b	0.042	1.36
		ln(area)*ln(area)	11 b	-0.081	-2.23**
		ln(seed)*ln(seed)	22 b	-0.0062	-1.95*
		ln(fertilizer)*ln(fertilizer)	33 b	0.0054	2.04*
		ln(labour)*ln(labour)	44 b	-0.0092	-3.27**
		ln(area)*ln(seed)	12 b	0.127	2.21**
		ln(area)*ln(fertilizer)	13 b	-0.051	-3.23**
		ln(area)*ln(labour)	14 b	0.149	3.06**
		ln(seed)*ln(fertilizer)	23 b	0.039	4.69**
		ln(seed)*ln(labour)	24 b	-0.027	-0.61
		ln(fertilizer)*ln(labour)	34 b	-0.0071	-2.42**
	Technical Inefficiency Estimation	Intercept ln(area) ln(seed)		δ δ δ	0 1 2	0.029 -0.922 -0.0007	0.14 -1.41 -2.62**
		ln(fertilizer)		δ	3	-0.0023	-2.33**
		ln(labour)		δ	4	0.403	2.02*
		Head Quarter Dummy	δ	5	0.211	2.19**
		Rain Intensity		δ	6	0.061	0.42
		Zone Dummy		δ	7	-0.020	-2.91**
		Wage				δ	8	0.172	1.98*
		Wage*ln(area)		δ	9	0.015	2.47**
		Wage*ln(seed)		δ	10	0.013	3.42**
		Wage*ln(fertilizer)	δ	11	0.91	2.03*
		Wage*ln(labour)	δ	12	-0.002	2.01*
		Bullock			δ	13	0.0002	0.35
		Time Dummies		δ	14	-0.284	-0.81
						δ	15	0.901	0.25
						δ	16	-0.466	-2.54**
						δ	17	0.914	1.02
						δ	18	0.122	1.55
		γ					0.91	18.26**
		Sigma	sqaured		0.422	8.99**
		log	likelihood	function		32.12
	Notes: *Statistically Significant at 10% level.
	**Statistically Significant at 5% level.

Table 3 : Likelihood ratio test summary.

 3

	Null hypothesis				ln(likelihood)	Critical value for 5% significance	Test Statistic	Decision
	γ	=	δ	0	=	δ	1	=	.......	=	δ	18	=	0	-16.21	19.045	96.66	Reject Null
										γ	=	0			-15.08	5.138	94.4	Reject Null
				1 δ ....... =		=	δ	18	=	0	-4.836	28.869	73.91	Reject Null
									∑ j µ	=	1	34.77	11.071	5.3	Accept Null
	b	jj	=	b	jk	=	δ	*	=	; 0	j	≠	k	12.413	30.144	52.44	Reject Null
		δ	9	=	δ	10	=	δ	11	=	δ	12	=	0	11.26	9.488	41.88	Reject Null
					1 ξ		=	ξ	2	=	ξ	3	=	0	34.11	7.815	3.82	Accept Null

Table 4 : Frequency distribution of technical efficiency ratings.

 4

	Efficiency (%)	1994	1995	1996	1997	1998	1999
	< 40	1	0	0	1	0	0
	40-50	0	0	0	0	0	0
	50-60	2	4	2	2	3	2
	60-70	5	4	4	4	4	5
	70-80	3	3	2	3	3	2
	80-90	5	4	2	3	4	4
	90-100	7	8	13	10	9	10
	Mean (SD)	76.8 (0.20) 78.1 (0.15) 84.2 (0.14) 79.1 (0.16)	80 (0.14)	80.2 (0.15)
	Max	99.4	99.1	99.2	97.5	98.0	98.1
	Min	29.2	53.2	54.2	35.9	55.1	53.2

Table 5 : Technical inefficiency effects of wage and inputs.

 5

								1994	1995	1996	1997	1998	1999
	Marginal Inefficiency Effects (MIE)	Area Seed Fertlizer MIE MIE MIE Labour MIE Wage MIE			-0.000054 (-2.71)** 0.021 (0.101) 0.0015 (2.82)** 0.0024 (1.99)* -15.09 (-3.03)**	-0.000046 (-0.155) 0.023 (2.87)** 0.0027 (2.57)** 0.0022 (2.05)* -14.65 (-4.77)**	-0.000037 (-1.99)* 0.031 (3.33)** 0.0031 (2.91)** 0.0022 (0.98) -14.60 (-2.08)*	-0.000032 (-2.03)* 0.031 (3.12)** 0.0021 (1.91)* 0.0022 (2.99)** -15.01 (-1.52)	-0.000027 (-2.05)* 0.037 (1.09) 0.0026 (2.02)* 0.0021 (1.94)* -14.85 (-2.09)*	-0.000023 (-0.022) 0.033 (3.01)** 0.0024 (1.03) 0.0020 (1.02) -14.94 (-3.001)**
	Second order Cross	Productivity Effects	) ) Wage Wage MIE Wage MIE ( MIE Fertilizer ( Area ∂ ∂ ( Seed ∂ ∂ ∂ ∂	)	0.000002 0.00061 0.000042	0.000002 0.00060 0.000069	0.000002 0.00071 0.000070	0.000003 0.00066 0.000045	0.000002 0.00075 0.000052	0.000002 0.00064 0.000047
			Labour ∂ ∂	(	Wage MIE	)		-0.000014 -0.000014 -0.000015 -0.000015 -0.000013 -0.000013
	Technical Inefficiency			23.2%	21.9%	15.8%	20.9%	20.0%	19.8%
	t-stats in parentheses.

** Statistically significant at 5% level. *Statistically significant at 10% level.

Table 6 : Production elasticity (at means) and returns to scale estimates.

 6

		1994	1995	1996	1997	1998	1999
	Output
	elasticity
	Land	0.874 (3.72)**	0.901 (2.02)*	0.892 (2.01)*	0.875 (4.21)**	0.876 (4.79)**	0.898 (1.99)*
	Seed	0.025 (2.05)*	0.010 (1.79)	0.033 (2.54)**	0.041 (1.71)	0.055 (1.98)*	0.031 (2.02)*
	Fertilizer 0.134 (2.78)**	0.129 (2.59)**	0.129 (2.64)**	0.134 (3.03)**	0.132 (2.03)*	0.132 (2.01)*
	Labour	-0.048 (2.05)*	-0.049 (2.02)*	-0.048 (2.09)*	-0.048 (2.07)*	-0.049 (1.81)	-0.050 (1.77)
	Returns to Scale	0.985	0.991	1.006	1.002	1.015	1.013
	t-stats in parentheses.

** Statistically significant at 5% level. *Statistically significant at 10% level.

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

	Submitted Manuscript
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	e e r
	R e v i e w

2003 Year Production Aus Aman Boro Page 22 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

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

Given the current context, district level technical efficiency may be viewed as the utilization of capacity in an industry.

Acknowledgements.

The author acknowledges Cardiff Business School's seed-corn fund for this particular project.

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