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Article

Giovanni Federico

The literature on market integration explores the development of the commodity market with data on prices, which is a useful complement to analysis of trade and the only feasible approach when data on trade are not available. Data on prices and quantity can help in understanding when markets developed, why, and the degree to which their development increased welfare and economic growth. Integration progressed slowly throughout the early modern period, with significant acceleration in the first half of the 19th century. Causes of integration include development of transportation infrastructure, changes in barriers to trade, and short-term shocks, such as wars. Literature on the effects of market integration is limited and strategies for estimating the effects of market integration are must be developed.

Article

Leandro Prados de la Escosura and Blanca Sánchez-Alonso

In assessments of modern-day Spain’s economic progress and living standards, inadequate natural resources, inefficient institutions, lack of education and entrepreneurship, and foreign dependency are frequently blamed on poor performance up to the mid-20th century, but no persuasive arguments were provided to explain why such adverse circumstances reversed, giving way to the fast transformation that started in the 1950s. Hence, it is necessary to first inquire how much economic progress has been achieved in Spain and what impact it had on living standards and income distribution since the end of the Peninsular War to the present day, and second to provide an interpretation. Research published in the 2010s supports the view that income per person has improved remarkably, driven by increases in labor productivity, which derived, in turn, from a more intense and efficient use of physical and human capital per worker. Exposure to international competition represented a decisive element behind growth performance. From an European perspective, Spain underperformed until 1950. Thereafter, Spain’s economy managed to catch up with more advanced countries until 2007. Although the distribution of the fruits of growth did not follow a linear trend, but a Kuznetsian inverted U pattern, higher levels of income per capita are matched by lower inequality, suggesting that Spaniards’ material wellbeing improved substantially during the modern era.

Article

Samuel Berlinski and Marcos Vera-Hernández

A set of policies is at the center of the agenda on early childhood development: parenting programs, childcare regulation and subsidies, cash and in-kind transfers, and parental leave policies. Incentives are embedded in these policies, and households react to them differently. They also have varying effects on child development, both in developed and developing countries. We have learned much about the impact of these policies in the past 20 years. We know that parenting programs can enhance child development, that centre based care might increase female labor force participation and child development, that parental leave policies beyond three months don’t cause improvement in children outcomes, and that the effects of transfers depend much on their design. In this review, we focus on the incentives embedded in these policies, and how they interact with the context and decision makers to understand the heterogeneity of effects and the mechanisms through which these policies work. We conclude by identifying areas of future research.

Article

The Indian Union, from the time of independence from British colonial rule, 1947, until now, has undergone shifts in the trajectory of economic change and the political context of economic change. One of these transitions was a ‘green revolution’ in farming that occurred in the 1970s. In the same decade, Indian migration to the Persian Gulf states began to increase. In the 1980s, the government of India seemed to abandon a strategy of economic development that had relied on public investment in heavy industries and encouraged private enterprise in most fields. These shifts did not always follow announced policy, produced deep impact on economic growth and standards of living, and generated new forms of inequality. Therefore, their causes and consequences are matters of discussion and debate. Most discussions and debates form around three larger questions. First, why was there a turnaround in the pace of economic change in the 1980s? The answer lies in a fortuitous rebalancing of the role of openness and private investment in the economy. Second, why did human development lag achievements in income growth after the turnaround? A preoccupation with state-aided industrialization, the essay answers, entailed neglect of infrastructure and human development, and some of that legacy persisted. If the quality of life failed to improve enough, then a third question follows, why did the democratic political system survive at all if it did not equitably distribute the benefits from growth? In answer, the essay discusses studies that question the extent of the failure.

Article

The links of international reserves, exchange rates, and monetary policy can be understood through the lens of a modern incarnation of the “impossible trinity” (aka the “trilemma”), based on Mundell and Fleming’s hypothesis that a country may simultaneously choose any two, but not all, of the following three policy goals: monetary independence, exchange rate stability, and financial integration. The original economic trilemma was framed in the 1960s, during the Bretton Woods regime, as a binary choice of two out of the possible three policy goals. However, in the 1990s and 2000s, emerging markets and developing countries found that deeper financial integration comes with growing exposure to financial instability and the increased risk of “sudden stop” of capital inflows and capital flight crises. These crises have been characterized by exchange rate instability triggered by countries’ balance sheet exposure to external hard currency debt—exposures that have propagated banking instabilities and crises. Such events have frequently morphed into deep internal and external debt crises, ending with bailouts of systemic banks and powerful macro players. The resultant domestic debt overhang led to fiscal dominance and a reduction of the scope of monetary policy. With varying lags, these crises induced economic and political changes, in which a growing share of emerging markets and developing countries converged to “in-between” regimes in the trilemma middle range—that is, managed exchange rate flexibility, controlled financial integration, and limited but viable monetary autonomy. Emerging research has validated a modern version of the trilemma: that is, countries face a continuous trilemma trade-off in which a higher trilemma policy goal is “traded off” with a drop in the weighted average of the other two trilemma policy goals. The concerns associated with exposure to financial instability have been addressed by varying configurations of managing public buffers (international reserves, sovereign wealth funds), as well as growing application of macro-prudential measures aimed at inducing systemic players to internalize the impact of their balance sheet exposure on a country’s financial stability. Consequently, the original trilemma has morphed into a quadrilemma, wherein financial stability has been added to the trilemma’s original policy goals. Size does matter, and there is no way for smaller countries to insulate themselves fully from exposure to global cycles and shocks. Yet successful navigation of the open-economy quadrilemma helps in reducing the transmission of external shock to the domestic economy, as well as the costs of domestic shocks. These observations explain the relative resilience of emerging markets—especially in countries with more mature institutions—as they have been buffered by deeper precautionary management of reserves, and greater fiscal and monetary space. We close the discussion noting that the global financial crisis, and the subsequent Eurozone crisis, have shown that no country is immune from exposure to financial instability and from the modern quadrilemma. However, countries with mature institutions, deeper fiscal capabilities, and more fiscal space may substitute the reliance on costly precautionary buffers with bilateral swap lines coordinated among their central banks. While the benefits of such arrangements are clear, they may hinge on the presence and credibility of their fiscal backstop mechanisms, and on curbing the resultant moral hazard. Time will test this credibility, and the degree to which risk-pooling arrangements can be extended to cover the growing share of emerging markets and developing countries.

Article

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions). This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates. Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.