The fiscal cost of weak governance: Evidence from teacher absence in India.

The relative return to strategies that augment inputs versus those that reduce inefficiencies remains a key open question for education policy in low-income countries. Using a new nationally-representative panel dataset of schools across 1297 villages in India, we show that the large public investments in education over the past decade have led to substantial improvements in input-based measures of school quality, but only a modest reduction in inefficiency as measured by teacher absence. In our data, 23.6% of teachers were absent during unannounced school visits, and we estimate that the salary cost of unauthorized teacher absence is $1.5 billion/year. We find two robust correlations in the nationally-representative panel data that corroborate findings from smaller-scale experiments. First, reductions in student-teacher ratios are correlated with increased teacher absence. Second, increases in the frequency of school monitoring are strongly correlated with lower teacher absence. Using these results, we show that reducing inefficiencies by increasing the frequency of monitoring could be over ten times more cost effective at increasing the effective student-teacher ratio than hiring more teachers. Thus, policies that decrease the inefficiency of public education spending are likely to yield substantially higher marginal returns than those that augment inputs

Teacher Performance Pay: Experimental Evidence from India

Performance pay for teachers is frequently suggested as a way of improving education outcomes in schools, but the theoretical predictions regarding its effectiveness are ambiguous and the empirical evidence to date is limited and mixed. We present results from a randomized evaluation of a teacher incentive program implemented across a large representative sample of government-run rural primary schools in the Indian state of Andhra Pradesh. The program provided bonus payments to teachers based on the average improvement of their students' test scores in independently administered learning assessments (with a mean bonus of 3% of annual pay). At the end of two years of the program, students in incentive schools performed significantly better than those in control schools by 0.28 and 0.16 standard deviations in math and language tests respectively. They scored significantly higher on "conceptual" as well as "mechanical" components of the tests, suggesting that the gains in test scores represented an actual increase in learning outcomes. Incentive schools also performed better on subjects for which there were no incentives, suggesting positive spillovers. Group and individual incentive schools performed equally well in the first year of the program, but the individual incentive schools outperformed in the second year. Incentive schools performed significantly better than other randomly-chosen schools that received additional schooling inputs of a similar value.

Biometric payment systems and welfare benefits

Biometric payment systems are claimed to reduce leakages in public welfare programmes. Indeed, 230 programmes in more than 80 countries are currently deploying such systems to improve security and reduce corruption and fraud. Yet, there is little evidence for their effectivenes

Biological and therapeutic aspects of breast cancer progression

Breast cancer is the second leading cause of cancer related death in women worldwide1. Although majority of primary breast cancers are curable with current treatment strategies, treatment outcome of metastatic breast cancer is dismal. The main focus of my doctoral studies is to investigate the causes of breast cancer recurrences and to eventually improve the survival outcome of metastatic breast cancer. Several factors has been attributed for the recurrence of breast cancer such as presence of cancer stem cells (CSCs) and the ongoing genomic evolution of cancer cells leading to intra tumor heterogeneity, which can in turn give rise to therapy resistant subclones. In this thesis we sought to investigate these two factors using breast cancer specimens. Firstly, in paper I, we optimized the method called “superficial scraping from tumor”. Using this method we were able to isolate epithelial breast cancer cells from which we can generate CSCs with ultra-low attachment and serum free conditions. Mammospheres generated from scraping material phenotypically resemble CSCs with ALDH1+, CD44+, and CD24- expression. Apart from CSC generation, scraping method can be used to biobank small tumors for future research purposes, without compromising routine histopathological analysis of patient samples. Next, we evaluated the expression of second estrogen receptor ERβ and its role in patient derived CSCs (Paper II), using the method optimized from paper I. We found that ERβ was predominantly expressed in both normal mammary stem cells (MSC) and CSCs. ERβ was found to be crucial for cancer stem cell phenotype and stimulation of ERβ using specific agonist increased mammosphere formation. Microarray analysis on ERβ stimulated MCF7 derived mammospheres, identified enhanced glycolysis metabolism pathway. Antagonizing ERβ in cell lines and in patient derived xenografts (PDX) demonstrated that ERβ is a therapeutical target in breast cancer and can be utilized to specifically target the CSC population. Tamoxifen is an important therapy for ERα positive breast cancers, however around 30-40% of patients relapse during endocrine therapy2. To investigate the endocrine resistance from a cancer stem cell perspective (Paper III), we treated adherent breast cancer cells (ER+) and CSCs with tamoxifen. Interestingly, CSCs where found to be resistant to tamoxifen treatment, while tamoxifen inhibited the adherent cancer cell population. To understand the mechanism behind the CSC induced endocrine resistance, we performed microarray analysis on patient derived CSCs treated with tamoxifen. Interestingly, mTOR signaling related pathways were found to be induced by tamoxifen in CSCs. This induction of mTOR effector downstream targets were observed only in CSCs but not in adherent cancer cells. Further, mTOR signaling was also found to be elevated in CSCs compared to the adherent cancer cell population. mTOR inhibitors such as rapamycin and everolimus were found to be effective in reducing the mammosphere formation. Therefore, combined tamoxifen and mTOR inhibitors can effectively target both differentiated cancer cells and the CSC population. Next, we explored the genomic landscape of metastases, patterns of metastatic spread and the role of axillary lymph node metastasis in seeding distant metastasis (Paper IV). We performed whole exome sequencing on 99 tumor samples from 20 breast cancer patients with matched primary and metastatic lesions. We observed both linear progression (i.e. metastasis seeding successive distant metastasis) and parallel progression (i.e. different distant metastasis were seeded from primary tumor directly rather than seeded by other distant metastasis) model during breast cancer progression. Majority of the distant metastasis where polyclonally seeded. We observed lack of axillary lymph node involvement in seeding distant metastasis. This indicates that, the majority of cancer cells are seeded hematogenously rather than utilizing the lymphatic system for cancer spreading. On average, only half of primary mutations were retained in the distant metastatic lesions with considerable disparity between individual patients ranging from 9 to 88%. Several putative driver alterations occurred late, privately in distant metastasis, highlighting the need to characterize the genomic alterations of metastatic lesions for making better informed clinical decision at metastatic setting. Further, we also observed specific mutational signatures such as APOBEC-associated signature, were significantly higher in distant metastasis compared to their respective primary tumors. Finally, in paper V, we profiled (RNA sequencing) multiple regions of the same tumor from 12 breast cancers. Molecular subtypes and transcriptomic grades for each tumor piece was determined. Primary breast cancers exhibited substantial intra-tumor genomic heterogeneity, but limited transcriptomic heterogeneity at macroscopic level. Our data suggested that, intra-tumoural heterogeneity is unlikely to have an impact on transcription based molecular diagnostics for most patients. In conclusion, we have identified potential therapeutic targets such as ERβ and mTOR pathway for inhibiting CSCs. Drugs targeting both CSCs and differentiated cancer cells are promising strategies to eradicate cancer recurrences. More clinical trials involving cancer stem cell targeting agents along with traditional therapies are required to investigate their clinical efficacy. Further, genomic characterization of both primary tumors and metastatic lesions are crucial for improving the treatment outcome for advanced breast cancer patients

School inputs, household substitution, and test scores

Empirical studies of the relationship between school inputs and test scores typically do not account for the fact that households will respond to changes in school inputs. This paper presents a dynamic household optimization model relating test scores to school and household inputs, and tests its predictions in two very different low-income country settings -- Zambia and India. The authors measure household spending changes and student test score gains in response to unanticipated as well as anticipated changes in school funding. Consistent with the optimization model, they find in both settings that households offset anticipated grants more than unanticipated grants. They also find that unanticipated school grants lead to significant improvements in student test scores but anticipated grants have no impact on test scores. The results suggest that naïve estimates of public education spending on learning outcomes that do not account for optimal household responses are likely to be considerably biased if used to estimate parameters of an education production function.Tertiary Education,Education For All,Access to Finance,Teaching and Learning,Disability

Double for nothing? Experimental evidence on an unconditional teacher salary increase in Indonesia

How does a large unconditional increase in salary affect the performance of incumbent employees in the public sector?We present experimental evidence on this question in the context of a policy change in Indonesia that led to a permanent doubling of teacher base salaries. Using a large-scale randomized experiment across a representative sample of Indonesian schools that accelerated this pay increase for teachers in treated schools, we find that the large pay increase significantly improved teachers' satisfaction with their income, reduced the incidence of teachers holding outside jobs, and reduced self-reported financial stress. Nevertheless, after two and three years, the increase in pay led to no improvement in student learning outcomes. The effects are precisely estimated, and we can rule out even modest positive impacts on test scores. Our results suggest that unconditional pay increases are unlikely to be an effective policy option for improving the effort and productivity of incumbent employees in public-sector settings

Performance Assessment of Fuel and Core Structural Materials Irradiated in FBTR

AbstractPost-irradiation examination (PIE) is a vital link in the nuclear fuel cycle for providing valuable feedback on the performance and residual life of the fuel and structural materials to designers, fabricators, and reactor operating personnel. The challenging task of setting up of α,β,γ inert atmosphere hot cell facility for PIE of Fast Breeder Test Reactor (FBTR) was accomplished successfully and irradiation performance of the FBTR mixed carbide fuel was assessed stage wise at various burnups starting from 25 GWd/t upto 155 GWd/t. With FBTR being used as a test bed for irradiation experiments on various FBR fuels and structural materials, PIE of various materials subjected to experimental irradiation like the PFBR MOX fuel, FBTR grid plate material have also been carried out to provide valuable feedback to the designers. This paper highlights the (i) results of comprehensive PIE carried on mixed carbide fuel & structural material (ii) control rod performance and (iii) outcome of the examinations on the experimental irradiated sub assemblies.

An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. Autoencoders are a special type of feed forward networks that have been used for construction of large-scale deep networks. Although autoencoders based on rate-coded and spiking neuron networks have been proposed, there are no autoencoders based on oscillators. We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals

Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India

The authors present experimental evidence on the impact of a personalized technology-aided after-school instruction program on learning outcomes. The setting is middle-school grades in urban India, where a lottery provided winning students with a voucher to cover program costs. They find that lottery winners scored 0.36σ higher in math and 0.22σ higher in Hindi relative to lottery losers after just 4.5-months of access to the program. IV estimates suggest that attending the program for 90 days would increase math and Hindi test scores by 0.59σ and 0.36σ respectively. The researchers find similar absolute test score gains for all students, but the relative gain was much greater for academically-weaker students because their rate of learning in the control group was close to zero. They show that the program was able to effectively cater to the very wide variation in student learning levels within a single grade by precisely targeting instruction to the level of student preparation. The program was cost effective, both in terms of productivity per dollar and unit of time. The results suggest that well-designed technology-aided instruction programs can sharply improve productivity in delivering education

School Inputs, Household Substitution, and Test Scores

Empirical studies of the relationship between school inputs and test scores typically do not account for the fact that households will respond to changes in school inputs. We present a dynamic household optimization model relating test scores to school and household inputs, and test its predictions in two very different low-income country settings – Zambia and India. We measure household spending changes and student test score gains in response to unanticipated as well as anticipated changes in school funding. Consistent with the optimization model, we find in both settings that households offset anticipated grants more than unanticipated grants. We also find that unanticipated school grants lead to significant improvements in student test scores but anticipated grants have no impact on test scores. Our results suggest that naïve estimates of public education spending on learning outcomes that do not account for optimal household responses are likely to be considerably biased if used to estimate parameters of an education production function.