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Article

Alessandra Bonfiglioli, Rosario Crinò, and Gino Gancia

International trade is dominated by a small number of very large firms. Models of trade with heterogeneous firms have been developed to study the causes and consequences of this observation. The canonical model of trade with heterogeneous firms shows that trade leads to between-firm reallocations and selection: It shifts employment toward firms with the best attributes and forces marginal firms to exit. The model also illustrates the role of heterogeneity, and its various sources, in explaining the volume of trade and the firm-level margins of adjustment. Consistent with the model, the empirical literature has documented that exporting is a rare activity, that exporting firms are larger and more productive than other firms, and that trade liberalization reallocates market shares toward the best-performing firms in various countries. Studies using transaction-level data have unveiled additional salient features of trade flows. First, sales by foreign firms are very heterogeneous and highly concentrated. Second, both the extensive margin (number of exporting firms) and the intensive margin (average export per firm) are important in explaining the level of exports and its changes over time. More heterogeneity in sales across firms is associated with a higher volume of trade along both margins. Third, increased foreign competition reallocates market shares toward top firms and hence can increase concentration from any country of origin. Numerous extensions of the benchmark model have been proposed to study other important aspects, such as the relevance of multi-product and multinational firms, the import behavior of firms, and the extent to which heterogeneity is endogenous to firms’ choices, but some open challenges still remain.

Article

Moussa P. Blimpo, Admasu Asfaw Maruta, and Josephine Ofori Adofo

Well-functioning institutions are essential for stable and prosperous societies. Despite significant improvement during the past three decades, the consolidation of coherent and stable institutions remains a challenge in many African countries. There is a persistent wedge between the de jure rules, the observance of the rules, and practices at many levels. The wedge largely stems from the fact that the analysis and design of institutions have focused mainly on a top-down approach, which gives more prominence to written laws. During the past two decades, however, a new strand of literature has emerged, focusing on accountability from the bottom up and making institutions more responsive to citizens’ needs. It designs and evaluates a mix of interventions, including information provision to local communities, training, or outright decentralization of decision-making at the local level. In theory, accountability from the bottom up may pave the way in shaping the institutions’ nature at the top—driven by superior localized knowledge. The empirical findings, however, have yielded a limited positive impact or remained mixed at best. Some of the early emerging regularities showed that information and transparency alone are not enough to generate accountability. The reasons include the lack of local ownership and the power asymmetry between the local elites and the people. Some of the studies have addressed many of these constraints at varying degrees without much improvement in the outcomes. A simple theoretical framework with multiple equilibria helps better understand this literature. In this framework, the literature consists of attempts to mobilize, gradually or at once, a critical mass to shift from existing norms and practices (inferior equilibrium) into another set of norms and practices (superior equilibrium). Shifting an equilibrium requires large and/or sustained shocks, whereas most interventions tend to be smaller in scope and short-lived. In addition, accountability at the bottom is often neglected relative to rights. If norms and practices within families and communities carry similar features as those observed at the top (e.g., abuse of one’s power), then the core of the problem is beyond just a wedge between the ruling elite and the citizens.

Article

Gianluca Cubadda and Alain Hecq

Reduced rank regression (RRR) has been extensively employed for modelling economic and financial time series. The main goals of RRR are to specify and estimate models that are capable of reproducing the presence of common dynamics among variables such as the serial correlation common feature and the multivariate autoregressive index models. Although cointegration analysis is likely the most prominent example of the use of RRR in econometrics, a large body of research is aimed at detecting and modelling co-movements in time series that are stationary or that have been stationarized after proper transformations. The motivations for the use of RRR in time series econometrics include dimension reductions, which simplify complex dynamics and thus make interpretations easier, as well as the pursuit of efficiency gains in both estimation and prediction. Via the final equation representation, RRR also makes the nexus between multivariate time series and parsimonious marginal ARIMA (autoregressive integrated moving average) models. RRR’s drawback, which is common to all of the dimension reduction techniques, is that the underlying restrictions may or may not be present in the data.

Article

Stratification economics, which has emerged as a new subfield of research on inequality, is distinguished by a system-level analysis. It explores the role of power in influencing the processes and institutions that produce hierarchical economic and social orderings based on ascriptive characteristics. Macroeconomic factors play a role in buttressing stratification, especially by race and gender. Among the macroeconomic policy levers that produce and perpetuate intergroup inequality are monetary policy, fiscal expenditures, exchange rate policy, industrial policy, and trade, investment, and financial policies. These policies interact with a stratification “infrastructure,” comprised of racial and gender ideologies, norms, and stereotypes that are internalized at the individual level and act as a “stealth” factor in reproducing hierarchies. In stratified societies, racial and gender norms and stereotypes act to justify various forms of exclusion from prized economic assets such as good jobs. For example, gendered and racial stereotypes contribute to job segregation, with subordinated groups largely sequestered in the secondary labor market where wages are low and jobs are insecure. The net effect is that subordinated groups serve as shock absorbers that insulate members of the dominant group from the impact of negative macroeconomic phenomena such as unemployment and economic volatility. Further, racial and gender inequality have economy-wide effects, and play a role in determining the rate of economic growth and overall performance of an economy. The impact of intergroup inequality on macro-level outcomes depends on a country’s economic structure. While under some conditions, intergroup inequality acts as a stimulus to economic growth, under other conditions, it undermines societal well-being. Countries are not locked into a path whereby inequality has a positive or negative effect on growth. Rather, through their policy decisions, countries can choose the low road (stratification) or the high road (intergroup inequality). Thus, even if intergroup inequality has been a stimulus to growth in the past, it is possible to choose an equity-led growth path.

Article

The specification of model equations for nominal wage setting has important implications for the properties of macroeconometric models and requires system thinking and multiple equation modeling. The main models classes are the Phillips curve model (PCM), the wage–price equilibrium correction model (WP-ECM), and the New Keynesian Phillips curve (NKPCM). The PCM was included in the macroeconometric models of the 1960s. The WP‑ECM arrived in the late 1980s. The NKPCM is central in dynamic stochastic general equilibrium models (DSGEs). The three model classes can be interpreted as different specifications of the system of stochastic difference equations that define the supply side of a medium-term macroeconometric model. This calls for an appraisal of the different wage models, in particular in relation to the concept of the non-accelerating inflation rate of unemployment (NAIRU, or natural rate of unemployment), and of the methods and research strategies used. The construction of macroeconomic model used to be based on the combination of theoretical and practical skills in economic modeling. Wage formation was viewed as being forged between the forces of markets and national institutions. In the age of DSGE models, macroeconomics has become more of a theoretical discipline. Nevertheless, producers of DSGE models make use of hybrid forms if an initial theoretical specification fails to meet a benchmark for acceptable data fit. A common ground therefore exists between the NKPC, WP‑ECM, and PCM, and it is feasible to compare the model types empirically.

Article

Eric A. Hanushek and Ludger Woessmann

Economic growth determines the future well-being of society, but finding ways to influence it has eluded many nations. Empirical analysis of differences in growth rates reaches a simple conclusion: long-run growth in gross domestic product (GDP) is largely determined by the skills of a nation’s population. Moreover, the relevant skills can be readily gauged by standardized tests of cognitive achievement. Over the period 1960–2000, three-quarters of the variation in growth of GDP per capita across countries can be accounted for by international measures of math and science skills. The relationship between aggregate cognitive skills, called the knowledge capital of a nation, and the long-run growth rate is extraordinarily strong. There are natural questions about whether the knowledge capital–growth relationship is causal. While it is impossible to provide conclusive proof of causality, the existing evidence makes a strong prima facie case that changing the skills of the population will lead to higher growth rates. If future GDP is projected based on the historical growth relationship, the results indicate that modest efforts to bring all students to minimal levels will produce huge economic gains. Improvements in the quality of schools have strong long-term benefits. The best way to improve the quality of schools is unclear from existing research. On the other hand, a number of developed and developing countries have shown that improvement is possible.

Article

Jeanet Sinding Bentzen

Economics of religion is the application of economic methods to the study of causes and consequences of religion. Ever since Max Weber set forth his theory of the Protestant ethic, social scientists have compared socioeconomic differences across Protestants and Catholics, Muslims, and Christians, and more recently across different intensities of religiosity. Religiosity refers to an individual’s degree of religious attendance and strength of beliefs. Religiosity rises with a growing demand for religion resulting from adversity and insecurity or a surging supply of religion stemming from increasing numbers of religious organizations, for instance. Religiosity has fallen in some Western countries since the mid-20th century, but has strengthened in several other societies around the world. Religion is a multidimensional concept, and religiosity has multiple impacts on socioeconomic outcomes, depending on the dimension observed. Religion covers public religious activities such as church attendance, which involves exposure to religious doctrines and to fellow believers, potentially strengthening social capital and trust among believers. Religious doctrines teach belief in supernatural beings, but also social views on hard work, refraining from deviant activities, and adherence to traditional norms. These norms and social views are sometimes orthogonal to the general tendency of modernization, and religion may contribute to the rising polarization on social issues regarding abortion, LGBT rights, women, and immigration. These norms and social views are again potentially in conflict with science and innovation, incentivizing some religious authorities to curb scientific progress. Further, religion encompasses private religious activities such as prayer and the particular religious beliefs, which may provide comfort and buffering against stressful events. At the same time, rulers may exploit the existence of belief in higher powers for political purposes. Empirical research supports these predictions. Consequences of higher religiosity include more emphasis on traditional values such as traditional gender norms and attitudes against homosexuality, lower rates of technical education, restrictions on science and democracy, rising polarization and conflict, and lower average incomes. Positive consequences of religiosity include improved health and depression rates, crime reduction, increased happiness, higher prosociality among believers, and consumption and well-being levels that are less sensitive to shocks.

Article

Structural Vector Autoregressions (SVARs) have become one of the most popular tools to measure the effects of structural economic shocks. Several new techniques to “identify” economic shocks have been proposed in the literature in the last decades. Identification hinges on the implicit assumption that economic shocks are retrievable from the data. In other words, the data contain enough information to correctly estimate the shocks. SVAR models, however, are small-scale models, only a small number of variables can be handled, and this feature can forcefully limit the amount of information that variables can convey. Narrow information sets present problems for identification, but some theoretical results and empirical procedures can test whether such information is sufficient to estimate economic shocks. Additionally, there are possible solutions to the problem of limited information, such as Factor Augmented VAR or dynamic rotations.

Article

George W. Evans and Bruce McGough

Adaptive learning is a boundedly rational alternative to rational expectations that is increasingly used in macroeconomics, monetary economics, and financial economics. The agent-level approach can be used to provide microfoundations for adaptive learning in macroeconomics. Two central issues of bounded rationality are simultaneously addressed at the agent level: replacing fully rational expectations of key variables with econometric forecasts and boundedly optimal decisions-making based on those forecasts. The real business cycle (RBC) model provides a useful laboratory for exhibiting alternative implementations of the agent-level approach. Specific implementations include shadow-price learning (and its anticipated-utility counterpart, iterated shadow-price learning), Euler-equation learning, and long-horizon learning. For each implementation the path of the economy is obtained by aggregating the boundedly rational agent-level decisions. A linearized RBC can be used to illustrate the effects of fiscal policy. For example, simulations can be used to illustrate the impact of a permanent increase in government spending and highlight the similarities and differences among the various implements of agent-level learning. These results also can be used to expose the differences among agent-level learning, reduced-form learning, and rational expectations. The different implementations of agent-level adaptive learning have differing advantages. A major advantage of shadow-price learning is its ease of implementation within the nonlinear RBC model. Compared to reduced-form learning, which is widely use because of its ease of application, agent-level learning both provides microfoundations, which ensure robustness to the Lucas critique, and provides the natural framework for applications of adaptive learning in heterogeneous-agent models.

Article

The current discontent with the dominant macroeconomic theory paradigm, known as Dynamic Stochastic General Equilibrium (DSGE) models, calls for an appraisal of the methods and strategies employed in studying and modeling macroeconomic phenomena using aggregate time series data. The appraisal pertains to the effectiveness of these methods and strategies in accomplishing the primary objective of empirical modeling: to learn from data about phenomena of interest. The co-occurring developments in macroeconomics and econometrics since the 1930s provides the backdrop for the appraisal with the Keynes vs. Tinbergen controversy at center stage. The overall appraisal is that the DSGE paradigm gives rise to estimated structural models that are both statistically and substantively misspecified, yielding untrustworthy evidence that contribute very little, if anything, to real learning from data about macroeconomic phenomena. A primary contributor to the untrustworthiness of evidence is the traditional econometric perspective of viewing empirical modeling as curve-fitting (structural models), guided by impromptu error term assumptions, and evaluated on goodness-of-fit grounds. Regrettably, excellent fit is neither necessary nor sufficient for the reliability of inference and the trustworthiness of the ensuing evidence. Recommendations on how to improve the trustworthiness of empirical evidence revolve around a broader model-based (non-curve-fitting) modeling framework, that attributes cardinal roles to both theory and data without undermining the credibleness of either source of information. Two crucial distinctions hold the key to securing the trusworthiness of evidence. The first distinguishes between modeling (specification, misspeification testing, respecification, and inference), and the second between a substantive (structural) and a statistical model (the probabilistic assumptions imposed on the particular data). This enables one to establish statistical adequacy (the validity of these assumptions) before relating it to the structural model and posing questions of interest to the data. The greatest enemy of learning from data about macroeconomic phenomena is not the absence of an alternative and more coherent empirical modeling framework, but the illusion that foisting highly formal structural models on the data can give rise to such learning just because their construction and curve-fitting rely on seemingly sophisticated tools. Regrettably, applying sophisticated tools to a statistically and substantively misspecified DSGE model does nothing to restore the trustworthiness of the evidence stemming from it.