Browsing by Subject "Transparency"

How do political leaders strategically manage fiscal policy formation to enhance their political survival? What are the implications of the fiscal mechanics of survival for theories of redistribution and democratic transition? This dissertation examines the complex relationship between political regime types and fiscal information asymmetries. I focus on budgetary policies (taxation and public spending) as major strategic tools available to the executive for co-optation and punishment of opponents. I argue that allowing some degree of contestation and transparency on the fiscal contract in electoral authoritarian regimes helps the executive identify distributive claims and co-opt the opposition. Paradoxically, in new democracies, political survival depends more on lower levels of budget transparency than existent theories would have us expect. Chapters 1 and 2 present a general formal model from which I derive the major hypotheses of the study. Second, Chapters 3, 4 and 5 use new cross-national measures of fiscal transparency and test empirically the theoretical implications. The statistical models confirm the main theoretical intuitions. Finally, Chapter 6 compares in greater detail the evolution of fiscal transparency in Morocco, Turkey and Romania between 1950 and 2000. I argue that fiscal taboos closely followed the shifting political alliance and their distributional consequences for leader's survival.

This dissertation consists of two chapters studying monetary and fiscal policy. In the first chapter, I study the welfare benefits and costs of increased central bank transparency in a dynamic model of costly information acquisition where agents can either choose to gather new costly information or remember information from the past for free. Information is costly to acquire due to an agent's limited attention. Agents face an intratemporal decision on how to allocate attention across public and private signals within the period and an intertemporal decision on how to allocate attention over time. The model embeds a coordination externality into the dynamic framework which motivates agents to be overly attentive to public information and creates the possibility of costly transparency. Interestingly, allowing for intratemporal and intertempral tradeoffs for attention amplifies (attenuates) the benefits (costs) of earlier transparency whereas it attenuates (amplifies) the benefits (costs) of delayed transparency.

The second chapter, co-authored with Barbara Rossi and Atsushi Inoue, studies the empirical effects of unexpected changes in government spending and tax policy on heterogeneous agents. We use data from the Consumption Expenditure Survey (CEX) to estimate individual-level impulse responses as well as multipliers for government spending and tax policy shocks. The main empirical finding of this paper is that unexpected fiscal shocks have substantially different effects on consumers depending on their age, income levels, and education. In particular, the wealthiest individuals tend to behave according to the predictions of standard RBC models, whereas the poorest individuals tend to behave according to standard IS-LM (non-Ricardian) models, due to credit constraints. Furthermore, government spending policy shocks tend to decrease consumption inequality, whereas tax policy shocks most negatively affect the lives of the poor, more so than the rich, thus increasing consumption inequality.

A central function of commodity futures markets is to help firms in the real sector insure against commodity price fluctuations. I examine how greater availability of information about commodity fundamentals (henceforth, information transparency) affects the capacity of these markets to accommodate firms’ hedging needs. Theory suggests that while greater availability of information can reduce adverse selection and increase traders’ willingness to absorb risk, it can also accelerate the realization of risk and hinder the transfer of risk via the markets. Using both cross-sectional variation in information transparency across 26 commodity markets over 20 years and information shocks induced by the launch of the Agricultural Market Information System (AMIS) and the SEC’s revision of firms’ 10-K oil and gas reserve disclosures, I document that information transparency (i) makes it costlier to use commodity futures to hedge commodity price risk and (ii) reduces traders’ propensity to trade futures. Evidence suggests that these findings are consistent with theories positing that information disclosure impairs risk-sharing opportunities. This study contributes new evidence on how information influences the efficient allocation of commodity price risk across the real and financial sectors.

As machine learning models are playing increasingly important roles in many real-life scenarios, interpretability has become a key issue for whether we can trust the predictions made by these models, especially when we are making some high-stakes decisions. Lack of transparency has long been a concern for predictive models in criminal justice and in healthcare. There have been growing calls for building interpretable, human understandable machine learning models, and "opening the black box" has become a debated issue in the media. My dissertation research addresses precisely the demand for interpretability and transparency in machine learning models. The key problem of this dissertation is: "Can we build machine learning models that are both accurate and interpretable?"

To address this problem, I will discuss the notion of interpretability as it relates to machine learning, and present several new interpretable machine learning models and methods I developed during my dissertation research. In Chapter 1, I will discuss two types of model interpretability -- predicate-based and case-based interpretability. In Chapters 2 and 3, I will present novel predicate-based interpretable models and methods, and their applications to understanding low-dimensional structured data. In particular, Chapter 2 presents falling rule lists, which extend regular decision lists by requiring the probabilities of the desired outcome to be monotonically decreasing down the list; Chapter 3 presents two-layer additive models, which are hybrids of predicate-based additive scoring models and small neural networks. In Chapter 4, I will present case-based interpretable deep models, and their applications to computer vision. Given the empirical evidence, I conclude in Chapter 5 that, by designing novel model architectures or regularization techniques, we can build machine learning models that are both accurate and interpretable.

Political factors play a big role in influencing the ebbs and flows of corruption. The literature seems unanimously in agreement that, even in places where corruption is entrenched and systemic, the political calculations of individual politicians can still have an impact on corruption outcomes. On the other hand, while canonical research has delved into the divergent inner workings of different types of corruption for decades, studies on the link between politics and corruption have not paid sufficient attention to these distinctions. This dissertation speaks to the idea that politics influences different types of corruption differently in different settings. I seek to shed some light on how types of corruption and political contexts matter by studying wrongdoing at local governments in Vietnam. A contribution of the dissertation is the data collection efforts to acquire novel datasets. I got access to fine-grained data on bribery behavior at Vietnamese firms by working on the survey team for the Vietnam Provincial Competitiveness Index (PCI) for four years. On the politics side, I constructed a dataset on the career paths of Vietnamese provincial leaders since late 1990s, using information from newspapers, administrative almanacs, and various Internet sources.