Search

This paper extends the World Bank's Long-Term Growth Model (LTGM) with the addition of a natural resource sector to analyze how long-run growth evolves in resource-rich countries and the growth impacts of price shocks and resource discoveries. In the LTGM-Natural Resource Extension (LTGM-NR), commodity price shocks affect long-term economic growth through physical investment rates. As a large share of resource income typically accrues to the government, the size of the boost to investment in a price boom depends on the government’s fiscal rule. Fiscal rules that prioritize public investment, like a Hartwick Rule, generally lead to the largest increases in long-term growth. However, structural surplus rules, which save commodity revenues, can also boost growth if they free up savings for private investment. The response of growth to discoveries of natural resources is similar to the response to price shocks, although discoveries also produce a direct effect on real GDP, in addition to an indirect effect through investment. The LTGM¬-NR also captures the effect of other (non-resource) growth fundamentals in resource-rich economies, and it is better suited to general growth analysis in these countries than the standard LTGM. However, the LTGM-NR is a supply-side model, and so does not capture the short-run effects of price and discovery shocks that operate through aggregate demand.

The Long-Term Growth Model (LTGM) and its extensions are a suite of models and spreadsheet-based toolkits for analyzing future growth paths in developing countries, based on the Solow-Swan growth model. This volume, The Long-Term Growth Model: Fundamentals, Extensions, and Applications, is a collection of ten chapters that summarize the development of the LTGM over the last decade, and how it has been applied in practice. The volume starts with a description of the Standard LTGM, the simplest and easiest-to-use component of the LTGM suite. It then outlines several extensions to the basic model in important areas such as the implications of growth for poverty (built into the Standard LTGM), the effects of public capital, the determinants of total factor productivity (TFP) growth, and how growth drivers differ in natural resource rich economies. The final part of the book covers six case studies which apply the LTGM in a diverse range of countries: Malaysia, South Korea, Bangladesh, Syria, Egypt, and Sri Lanka. Although growth performances, constraints, and opportunities vary across country contexts, the LTGM framework outlined in this volume, analyzing future growth in terms of TFP, human capital, physical capital, and labor, is universally relevant. The chapters in the volume typically find that popular investment-led growth strategies are unsustainable in the long-run (including those growth strategies relying solely on high rates of public investment). This is mostly due to a declining marginal productivity of capital, though financing high investment rates without high savings rates is also a practical concern. Instead, they find that the most important driver of sustainable rapid long-run growth is productivity (TFP) growth.

Is there a trade-off between raising growth and reducing inequality and poverty? This paper reviews the theoretical and empirical literature on the complex links between growth, inequality, and poverty, with causation going in both directions. The evidence suggests that growth can be effective in reducing poverty, but its impact on inequality is ambiguous and depends on the underlying sources of growth. The impact of poverty and inequality on growth is likewise ambiguous, as several channels mediate the relationship. But most plausible mechanisms suggest that poverty and inequality reduce growth, at least in the long run. Policies play a role in shaping these relationships and those designed to improve equality of opportunity can simultaneously improve inclusiveness and growth.

The COVID-19 (coronavirus) pandemic crisis combines the worst characteristics of previous crises. It features a simultaneous supply and demand shock; domestic, regional, and global scope; a projected long duration; and a high degree of uncertainty. What can be expected for recovery from the pandemic crisis across the world? This brief first assesses the projections of economic activity in 2020 and 2021 and the domestic and international conditions that will constrain and drive a possible recovery. It then discusses the potential shapes of the recovery (or lack thereof) for specific country conditions. Finally, it explores the need to balance short-term and long-term concerns, arguing in favor of policies that focus on sustained recovery, rather than quick but debt-fueled and short-lived gains. Drawing on the lessons from past crises, the brief concludes that sustained economic recovery is possible only when the underlying causes are addressed and the foundations of growth are protected. For the pandemic crisis, this implies mitigating the spread of the disease to manageable levels while keeping the economy sufficiently active. In the short term, economic policy should focus on preventing further poverty, averting unnecessary business closures, and avoiding lasting damage to human capital and productivity. In the long term, policy reform should address the structural vulnerabilities that the pandemic crisis has exposed. This includes reforms to expand labor and business formalization; to improve the coverage and adequacy of social protection; to extend financial inclusion to elderly, rural, and poor people; to promote digital transformation across society; and, most basically, to improve access to and quality of public health care.

This paper discusses and illustrates the analytical foundations of international comparisons (or benchmarking) for assessing a country's potential for improvement along various dimensions of social and economic development. By providing a methodology for international benchmarking, discussing various alternatives and choices, and presenting a cross-country illustration, the paper can help practitioners be less arbitrary and more systematic in their approach to international comparisons, as well as more realistic in their expectations for a country's improvement. The paper presents the stochastic frontier approach and applies it to estimate feasible frontiers or benchmarks for each variable, country, and year. It then interprets a country's (one-sided) departure from the benchmark as inefficiency or potential for improvement. This contrasts with the literature that compares countries by looking at raw variables or indicators, without considering that countries differ in structural endowments that constrain the maximum performance that a country could achieve in a policy-relevant horizon. The Stochastic Frontier approach also improves upon the literature that uses regression residuals to measure performance. Regression residuals are hard to interpret as inefficiency, because they are mixed with noise and take positive and negative values. As an illustration, the paper uses a panel of 142 countries with yearly data for 2005-14 and considers a set of 10 development indicators. It finds that the potential for improvement does not follow a simple relationship with economic development, with some lower-income countries being closer to their own feasible frontier than more advanced countries are.