Consumer Loan Response to Permanent Labor Income Shocks: Evidence from a Major Minimum Wage Increase

We investigate the impact of a substantial minimum wage increase, which became effective in January 2016, on consumer loans in Turkey. Using bank-level data and designing an original identification strategy, we ask whether the loans provided by banks with a historically high share of low-wage loan customers have increased relative to those provided by banks with a historically low share of low-wage loan customers after January 2016. Our results suggest that consumer loan flows have displayed a limited but statistically and economically meaningful increase following the minimum wage hike. This increase mostly comes from the increase in long-term general-purpose loans. Vehicle loans have also increased, while there is no change in housing loans. In the overall, the minimum wage hike has generated a moderate and transitory increase in the flow of consumer loans extended to low-wage earners in Turkey perhaps due to delayed consumption effect. Consumption of durables, which can further increase household borrowing capacity through collateralized debt channel, has only slightly and temporarily increased. The underlying long-term trends in the stock of consumer loans have hardly changed

Parameter-Efficient Detoxification with Contrastive Decoding

The field of natural language generation has witnessed significant advancements in recent years, including the development of controllable text generation techniques. However, controlling the attributes of the generated text remains a challenge, especially when aiming to avoid undesirable behavior such as toxicity. In this work, we introduce Detoxification Generator (DETOXIGEN), an inference-time algorithm that steers the generation away from unwanted styles. DETOXIGEN is an ensemble of a pre-trained language model (generator) and a detoxifier. The detoxifier is trained intentionally on the toxic data representative of the undesirable attribute, encouraging it to generate text in that style exclusively. During the actual generation, we use the trained detoxifier to produce undesirable tokens for the generator to contrast against at each decoding step. This approach directly informs the generator to avoid generating tokens that the detoxifier considers highly likely. We evaluate DETOXIGEN on the commonly used REALTOXICITYPROMPTS benchmark (Gehman et al., 2020) with various language models as generators. We find that it significantly outperforms previous approaches in detoxification metrics while not compromising on the generation quality. Moreover, the detoxifier is obtained by soft prompt-tuning using the same backbone language model as the generator. Hence, DETOXIGEN requires only a tiny amount of extra weights from the virtual tokens of the detoxifier to be loaded into GPU memory while decoding, making it a promising lightweight, practical, and parameter-efficient detoxification strategy

Consumer Loan Response to Permanent Labor Income Shocks: Evidence from a Major Minimum Wage Increase

We investigate the impact of a substantial minimum wage increase, which became effective in January 2016, on consumer loans in Turkey. Using bank-level data and designing an original identification strategy, we ask whether the loans provided by banks with a historically high share of low-wage loan customers have increased relative to those provided by banks with a historically low share of low-wage loan customers after January 2016. Our results suggest that consumer loan flows have displayed a limited but statistically and economically meaningful increase following the minimum wage hike. This increase mostly comes from the increase in long-term general-purpose loans. Vehicle loans have also increased, while there is no change in housing loans. In the overall, the minimum wage hike has generated a moderate and transitory increase in the flow of consumer loans extended to low-wage earners in Turkey|perhaps due to delayed consumption effect. Consumption of durables, which can further increase household borrowing capacity through collateralized debt channel, has only slightly and temporarily increased. The underlying long-term trends in the stock of consumer loans have hardly changed

Multi-Resolution Video Streaming in Peer-to-peer Networks

We consider multi-resolution streaming in fully-connected peer-to-peer networks, where transmission rates are constrained by arbitrarily specified upload capacities of the source and peers. We fully characterize the capacity region of rate vectors achievable with arbitrary coding, where an achievable rate vector describes a vector of throughputs of the different resolutions that can be supported by the network. We then prove that all rate vectors in the capacity region can be achieved using pure routing strategies. This shows that coding has no capacity advantage over routing in this scenario

Yüksek öğretimde öğrencilerin kopya çekme motivasyonu ile ilgili tutum ve davranışları

In higher education, cheating has appeared as an important problem. It has increasingly led to the inaccurate results obtained from the measurements related to the students’ knowledge and skill gains and to the significant deviations to reach to the objectives of the education strategies to be implicated. The aim of the study is to determine the students’ attitude and behaviors related to their cheating motivations at Faculty of Agriculture, Ataturk University. The results of the study showed that positivist approach, unfair competition effects on the students’ success and the external negative motivation sources were the most important factors preventing their cheating motivation. On the other hand, the results also showed that the most factors stimulating cheating motivation were the external and internal positive motivation, not suitable of exam type, appropriateness of the physical facilities and the exam organizers’ illegal attitude and behaviors. Positive motivation sources could be drawn up by regulations and statutes to create a negative impact by eliminating the most appropriate factors motivating the students to cheat at the exam, and thus it could be made more affective evaluations for their knowledge and skills gains and selected the most appropriate education strategiesYüksek öğretimde, kopya çekme önemli bir problem olarak karşımıza çıkmaktadır. Öğrencilerin bilgi ve beceri kazanımlarına yönelik yapılan ölçümlerin hatalı sonuçlar vermesine ve uygulanacak stratejilerin hedefine ulaşmasında önemli sapmalara neden olmaktadır. Bu araştırmanın amacı, Atatürk Üniversitesi Ziraat Fakültesi’nde eğitim gören öğrencilerin kopya çekme motivasyonu ile ilgili tutum ve davranışları belirlemektedir. Araştırma sonuçları, öğrencilerin kopya çekme motivasyonunu engelleyen en önemli faktörlerin pozitivist yaklaşım, başarı üzerinde haksız rekabet etkisi ve dışsal negatif motivasyon kaynakları olduğunu göstermiştir. Diğer taraftan, araştırma sonuçları kopya çekmeyi teşvik eden faktörlerin en önemlileri dışsal ve içsel pozitif motivasyon kaynakları, fiziki imkanların kolaylaştırıcı etkisi, sınav tipinin uygun olmaması ve sınav organizatörlerinin etik dışı davranış sergilemeleri olduğunu göstermiştir. Öğrencileri sınavlarda kopya çekmeye teşvik eden en uygun faktörler elemine edilerek, pozitif motivasyon unsurları yönetmelik ve tüzüklerle negatif etki yaratacak şekilde düzenlenmeli ve böylece öğrencilerin bilgi ve beceri kazanımlarına yönelik daha etkin değerlendirmeler yapılabilir ve en uygun eğitim stratejileri seçilebilir

Short- and long-term results of harmonic scalpel hemorrhoidectomy versus stapler hemorrhoidopexy in treatment of hemorrhoidal disease

SummaryPurposeIn this prospective randomized study, our aim is to compare the short- and long-term results of harmonic scalpel hemorrhoidectomy (HSH) and stapler hemorrhoidopexy (SH) methods in the surgical treatment of Grade III and Grade IV hemorrhoidal disease.MethodsNinety-nine consecutive patients diagnosed with Grade III or Grade IV internal hemorrhoidal disease were included in the study. Patients were randomized to HSH (n = 48) or SH (n = 51) treatments. Data on patient demographic and clinical characteristics, operative details, postoperative pain score on a visual analog scale, additional analgesic requirement, postoperative short- and long-term complications, and recurrence of hemorrhoidal disease were also recorded. Patients were regularly followed for a total period of 24 (6–36) months.ResultsThe patient demographic and clinical characteristics were similar in the two groups. The operative time was significantly shorter in the HSH group compared with the SH group. Overall pain scores were not significantly different between the groups, although severe pain was significantly more common in the HSH group. Recurrence was significantly lower in the HSH group compared with the SH group.ConclusionHSH and SH are both safe and effective methods for surgical treatment of Grade III and Grade IV hemorrhoidal disease. In our study, the HSH method was determined to be safer, easier, and faster to perform, and associated with fewer long-term recurrences than the SH method

Exploring the Integration Strategies of Retriever and Large Language Models

The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating "unknown" outputs, even when the correct document is among the top-k retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs