1-10 of 361 Results

Article

The Hong Kong Housing Market  

Lok Sang Ho and Yonglin Wang

The Hong Kong Special Administrative Region (SAR) government made a tremendous effort to raise the homeownership rate, starting with Mr. Tung Chee-hwa, the first Chief Executive, who, in his Policy Address in 1997, vowed to raise the homeownership rate from below 50% to 70% in 10 years. The figures showed, however, that while the total homeownership rate had gone up by 18 percentage points in 15 years prior to 1997, from 1997 to Q3 2022, in the space of 25 years, the total homeownership rose from 46.7% to 52.6%, or a mere 5.9 percentage points in 25 years. The nonsubsidized private homeownership rate rose from 30% in 1985 to 36.2% in 1997 (6.2 percentage points in 12 years), yet it rose only from 36.2% in 1997 to 37.2% in 2022Q3, amounting to only 1 percentage point in 25 years. The puzzle may lie in the SAR government’s mistaken strategy of providing increasing opportunities for public housing tenants and subsidized flat owners to profit from the housing benefits. These benefits turn out to be at the expense of the middle-class people who provide much impetus to economic growth and who bear the brunt of the tax burden. The various transaction taxes meant to curb speculation turn out to impede the trading-up process. By significantly reducing the supply of existing homes, and especially that of starter homes, an unintended consequence is much sharper price increases for starter homes than for larger homes. This leads to the proliferation of so-called nanoflats that are tiny yet very expensive.

Article

Labor Market Returns to Higher Education  

Ghazala Azmat and Jack Britton

The persistent high wage premium associated with college education, despite increasing participation rates, continues to generate a great deal of academic and policy interest. While it is widely agreed that the financial benefits associated with college completion outweigh the costs, modeling and empirically estimating the returns are complicated. A simple theoretical framework on educational investment illustrates the decision-making processes and key factors, such as expected returns, that guide the choice of an individual to engage in higher education and to achieve an optimal level of educational investment. Broadening the investment model, however, is instrumental to account for potential heterogeneous returns to higher education—the variation in returns by institution, field of study, and students’ background characteristics, among others—and to recognize the wider societal benefits of higher education, beyond private returns. The challenges involved in estimating the returns to higher education and the heterogeneity in returns are central in the discussion. Interpreting a naive correlation between education and wages is complicated by the nonrandom selection of individuals into higher education, such that individuals who are most likely to benefit from higher education are also those most likely to attend. Advancements in data collection, the ability to track individuals from compulsory education to the labor market, and improvements in econometric methodologies have enabled researchers to causally estimate the impact of higher education on earnings and allow for an improved insight into the disparities in returns to higher education. Recognizing the links between students’ characteristics (or backgrounds) and associated constraints helps to understand differences in higher education choices. Similarly, identifying differences in labor market returns associated with attending certain colleges or pursuing particular academic disciplines is as important in shedding light on the complex nature of human capital disparities and the signaling effect of higher education. As the costs of higher education provision constitute an increasingly large share of government spending all over the world, the high returns to college raise questions associated with who should pay for attending college and the role of the state. Internalizing the social returns to education and their broader implications on the growth and the persistence of inequality complicates this discussion. Higher education funding is one potential policy instrument to influence college attendance and returns. It is not, however, the only one. Better information on returns to education or access to policies that target members of certain social groups might be other potential tools to overcome constraints.

Article

Leverage Cycle Theory of Economic Crises and Booms  

John Geanakoplos

Traditionally, booms and busts have been attributed to investors’ excessive or insufficient demand, irrational exuberance and panics, or fraud. The leverage cycle begins with the observation that much of demand is facilitated by borrowing and that crashes often occur simultaneously with the withdrawal of lending. Uncertainty scares lenders before investors. Lenders are worried about default and therefore attach credit terms like collateral or minimum credit ratings to their contracts. The credit surface, depicting interest rates as a function of the credit terms, emerges in leverage cycle equilibrium. The leverage cycle is about booms when credit terms, especially collateral, are chosen to be loose, and busts when they suddenly become tight, in contrast to the traditional fixation on the (riskless) interest rate. Leverage cycle crashes are triggered at the top of the cycle by scary bad news, which has three effects. The bad news reduces every agent’s valuation of the asset. The increased uncertainty steepens the credit surface, causing leverage to plummet on new loans, explaining the withdrawal of credit. The high valuation leveraged investors holding the asset lose wealth when the price falls; if their debts are due, they lose liquid wealth and face margin calls. Each effect feeds back and exacerbates the others and increases the uncertainty. The credit surface is steeper for long loans than short loans because uncertainty is higher. Investors respond by borrowing short, creating a maturity mismatch and voluntarily exposing themselves to margin calls. When uncertainty rises, the credit surface steepens more for low credit rating agents than for high rated agents, leading to more inequality.. The leverage cycle also applies to banks, leading to a theory of insolvency runs rather than panic runs. The leverage cycle policy implication for banks is that there should be transparency, which will induce depositors or regulators to hold down bank leverage before insolvency is reached. This is contrary to the view that opaqueness is a virtue of banks because it lessens panic.

Article

Unintended Fertility: Trends, Causes, Consequences  

Christine Piette Durrance and Melanie Guldi

Unintended fertility occurs when an individual, who did not intend to, becomes pregnant or gives birth. Most measures of unintended fertility account for whether the pregnancy (birth) was wanted and whether it occurred at a desired time. Economic models of fertility provide a framework for understanding an individual’s desire to have children (or not), the number of children to have alongside the quality of each child, and the timing of childbirth. To study fertility intendedness, researchers often classify pregnancies or births as unintended using self-reported retrospective (or prospective) survey responses. However, since survey information on the intendedness of pregnancies and births is not always available, the research on unintended fertility using survey data is necessarily limited to the population surveyed. Consequently, to broaden the population studied, researchers also often rely on reported births, abortions, and miscarriages (fetal deaths) to estimate intendedness. However, other factors (such as laws restricting access or financial hurdles to overcome) may restrict access to the methods used to control reproduction, and these restrictions in turn may influence realized (observed) pregnancies, births, and abortions. Furthermore, abortion and miscarriages are not consistently reported and, when reported, they exhibit more measurement error than births. Despite these research challenges, the available data have allowed researchers to glean information on trends in unintendedness and to study the relationship between fertility-related policies and unintendedness. Over the last 2 decades, unintended fertility has declined in many countries and fewer births are happening “too soon.” There are multiple factors underlying these changes, but changes in access to and quality of reproductive technologies, changes in macroeconomic conditions, and socioeconomic characteristics of fertility-aged individuals appear to be crucial drivers of these changes.

Article

Asset Pricing: Cross-Section Predictability  

Paolo Zaffaroni and Guofu Zhou

A fundamental question in finance is the study of why different assets have different expected returns, which is intricately linked to the issue of cross-section prediction in the sense of addressing the question “What explains the cross section of expected returns?” There is vast literature on this topic. There are state-of-the-art methods used to forecast the cross section of stock returns with firm characteristics predictors, and the same methods can be applied to other asset classes, such as corporate bonds and foreign exchange rates, and to managed portfolios such mutual and hedge funds. First, there are the traditional ordinary least squares and weighted least squares methods, as well as the recently developed various machine learning approaches such as neutral networks and genetic programming. These are the main methods used today in applications. There are three measures that assess how the various methods perform. The first is the Sharpe ratio of a long–short portfolio that longs the assets with the highest predicted return and shorts those with the lowest. This measure provides the economic value for one method versus another. The second measure is an out-of-sample R 2 that evaluates how the forecasts perform relative to a natural benchmark that is the cross-section mean. This is important as any method that fails to outperform the benchmark is questionable. The third measure is how well the predicted returns explain the realized ones. This provides an overall error assessment cross all the stocks. Factor models are another tool used to understand cross-section predictability. This sheds light on whether the predictability is due to mispricing or risk exposure. There are three ways to consider these models: First, we can consider how to test traditional factor models and estimate the associated risk premia, where the factors are specified ex ante. Second, we can analyze similar problems for latent factor models. Finally, going beyond the traditional setup, we can consider recent studies on asset-specific risks. This analysis provides the framework to understand the economic driving forces of predictability.

Article

COVID-19 and Mental Health: Natural Experiments of the Costs of Lockdowns  

Climent Quintana-Domeque and Jingya Zeng

The global impact of the COVID-19 pandemic has been profound, leaving a significant imprint on physical health, the economy, and mental well-being. Researchers have undertaken empirical investigations across different countries, with a primary focus on understanding the association between lockdown measures—an essential public health intervention—and mental health. These studies aim to discern the causal effect of lockdowns on mental well-being. Three notable studies have adopted natural experiments to explore the causal effect of lockdowns on mental health in diverse countries. Despite variations in their research methodologies, these studies collectively support the conclusion that lockdowns have had detrimental consequences on mental health. Furthermore, they reveal that the intensity of these negative effects varies among distinct population groups. Certain segments of the population, such as women, have borne a more profound burden of the mental health costs associated with lockdown measures. In light of these findings, it becomes imperative to consider the implications for mental health when implementing public health interventions, especially during crises like the COVID-19 pandemic. While rigorous measures like lockdowns are essential for safeguarding public health, striking a balance with robust mental health support policies becomes crucial to mitigating the adverse impacts on mental well-being.

Article

Persistence Change and Segmented Cointegration Testing  

Paulo M. M. Rodrigues

The change in persistence of a time series refers to a shift in the order of integration. Rather than displaying stationary or nonstationary behavior throughout the whole sample period, as is frequently considered in empirical work, many time series display changes in persistence over time. The analysis and impact of possible changes in persistence has been an important topic of research and has led to a large literature devoted to the development of procedures to detect such behavior. This review explores different tests designed to detect changes in the persistence and in the long-run equilibrium of time series.

Article

A Review of the Effects of Pay Transparency  

Emma Duchini, Stefania Simion, and Arthur Turrell

An increasing number of countries have introduced pay transparency policies with the aim of reducing gender inequality in the labor market. Firms subject to transparency requirements must disclose publicly or to employees’ representatives information on their employees’ pay broken down by gender, or indicators of gender gaps in pay and career outcomes. The argument at the base of these policies is that gender inequality may in part persist because it is hidden. On the one hand, employers rarely keep track of employees’ pay and career progression by gender, and, on the other hand, employees rarely engage in conversations with their colleagues about pay. The lack of information on within-firm disparities by gender may therefore hamper progress toward a more egalitarian labor market. Transparency policies have the potential to improve women’s relative pay and career outcomes for two reasons. First, by increasing the salience of gender gaps in the labor market, they can alter the relative bargaining power of male and female employees vis-à-vis the firm and lead lower-paid individuals to demand higher pay from their employer. Second, together with pressures from employees, the public availability of information on firms’ gender-equality performance may also increase public pressure for firms’ action in this domain. A clear message emerges from the literature analyzing the impact of pay transparency policies on gender inequality: these policies are effective at pushing firms to reduce their gender pay gaps, although this is achieved via a slowdown of men’s wage growth. Related results point to a reduction in labor productivity following the introduction of transparency mandates but no detrimental effect on firms’ profits because this effect is compensated by the reduction in labor costs. Overall, the findings in this literature suggest that transparency policies can reduce the gender pay gap with limited costs for firms but may not be suited to achieve the objective of improving outcomes for lower-paid employees.

Article

Earnings Inequality in Latin America: A Three-Decade Retrospective  

Manuel Fernández and Gabriela Serrano

Latin American countries have some of the highest levels of income inequality in the world. However, earnings inequality have significantly changed over time, increasing during the 1980s and 1990s, declining sharply in the 2000s, and stagnating or even increasing in some countries since 2015. Macroeconomic instability in the region in the 1980s and early 1990s, as well as the introduction of structural reforms like trade, capital, and financial liberalization, affected the patterns of relative demand and relative earnings across skill-demographic groups in the 1990s, increasing inequality. Significant gains in educational attainment, the demographic transition, and rising female labor force participation changed the skill-demographic composition of labor supply, pushing the education and experience premiums downward, but this was not enough to counteract demand-side trends. At the turn of the 21st century, improved external conditions, driven by China’s massive increase in demand for commodities, boosted economies across Latin America, which began to grow rapidly. Growth was accompanied by a positive shift in the relative demand for less-educated workers, stronger labor institutions, rising minimum wages, and declining labor informality, a confluence of factors that reduced earnings inequality. In the aftermath of the global financial crisis, particularly after the end of the commodities price boom in 2014, economic growth decelerated, and the pace of inequality decline stagnated. There is extensive literature documenting and trying to explain the causes of recent earnings inequality dynamics in Latin America. This literature is examined in terms of themes, methodological approaches, and key findings. The focus is on earnings inequality and how developments in labor markets have shaped it.

Article

The Empirics of Network Models  

Pedro CL Souza

The literature documenting a wide range of network or peer effects has blossomed in the past decade and have appeared in most major economics journals, using a variety of methods and identification strategies. Reviewing the empirics of those papers suggests a few broad classes of econometric models. The first and canonical model is the “linear-in-means,” which grows from Manski’s seminal work. More recently, it has shown that network asymmetry conditions (known as “peers-of-peers” instrument approach) can be used to instrument the endogeneity inherently present in the linear-in-means model. Moving to more recent empirical practice reveals novel and creative instrumentation strategies exploring particular empirical settings. The network identification strategies can also be combined with traditional differences-and-differences, event-study, and regression discontinuity designs. For example, under certain conditions, one can explore the variation that stems from the differential comparison of the evolution over time of well-connected versus less-well-connected individuals; or explore the variation in the network structure induced by a discontinuous change in the network. Randomized, controlled studies had substantial importance in revealing network effects in the past literature using standard methods; and, more recently, in understanding the extent to which networks can themselves be endogenous to the provision of the treatment itself. This, in turn, will present future challenges for the econometrics of networks and the identification or evaluation of treatment effects under a causal framework with endogenous interference.