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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.

This paper addresses three questions: 1) what would have been the growth and income trajectory of Syria in the absence of war; 2) given the war, what explains the reduction in economic growth in terms physical capital, labor force, human capital, and productivity; and 3) what potential growth scenarios for Syria there could be in the aftermath of war. Estimates of the impact of conflict point to negative gross domestic product (GDP) growth of -12 percent on average over 2011-18, resulting in a GDP contraction to about one-third of the 2010 level. In post-conflict simulation scenarios, the growth drivers are affected by the assumed levels of reconstruction assistance, repatriation of refugees, and productivity improvements associated with three plausible political settlement outcomes: a baseline (Sochi-plus) moderate scenario, an optimistic (robust political settlement) scenario, and a pessimistic (de facto balance of power) scenario. Respectively for these scenarios, GDP per capita average growth in the next two decades is projected to be 6.1, 8.2, or 3.1 percent, assuming that a final and stable resolution of the conflict is achieved.

This is the background paper for the productivity extension of the World Bank’s Long-Term Growth Model (LTGM). Based on an extensive literature review, the paper identifies the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions. Based on underlying proxies, the paper constructs indexes representing each of the main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. This is done for every year in the three decades spanning 1985–2015 and for more than 100 countries. In parallel, the paper presents a measure of total factor productivity (TFP), largely obtained from the Penn World Table, and assesses the pattern of productivity growth across regions and income groups over the same sample. The paper then examines the relationship between the measures of TFP and its determinants. The variance of productivity growth is decomposed into the share explained by each of its main determinants, and the relationship between productivity growth and the overall determinant index is identified. The variance decomposition results show that the highest contributor among the determinants to the variance in TFP growth is market efficiency for Organisation for Economic Co-operation and Development countries and education for developing countries in the most recent decade. The regression results indicate that, controlling for country- and time-specific effects, TFP growth has a positive and significant relationship with the proposed TFP determinant index and a negative relationship with initial TFP. This relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved. The paper presents and discusses some of these simulations for groups of countries by geographic region and income level. An accompanying Excel-based toolkit, linked to the LTGM, provides a larger set of simulations and scenario analysis at the country level for the next few decades.

The reallocation of resources from low- to high-productivity firms can generate large aggregate productivity gains. The paper uses data from the Malaysian manufacturing census to measure the country's hypothetical productivity gains when moving toward the level of within-sector allocative efficiency in the United States to be between 13 and 36 percent. Across three census periods in 2000, 2005, and 2010 (the most recent available), the productivity gaps appear to have somewhat widened. This suggests that the "catching-up" process remains a challenge and a potential opportunity, particularly if total factor productivity is expected to be the dominant source of future economic growth. The simulations, based on different magnitudes of the realization of hypothetical productivity gains, show that Malaysia's gross domestic product growth can potentially increase by 0.4 to 1.3 percentage points per year over five years. The analysis accounts only for resource misallocation within sectors. There may be other, possibly large, resource misallocation across sectors. If so, closing those gaps could boost total factor productivity and gross domestic product growth even further.

"Informality" is a term used to describe the collection of firms, workers, and activities that operate outside the legal and regulatory systems. It is widespread in the majority of developing countries--in a typical developing economy, the informal sector produces about 35 percent of gross domestic product and employs 70 percent of the labor force. This paper studies informality in the context of economic development by presenting a model and projections that link informality, regulations, migration, and economic growth. This analytical framework highlights the trade-offs between formality and informality, the relationship between the different types of informality, and the connection between them and the forces of labor, capital, and productivity growth. The paper models the behavior of the informal sector based on the following fundamental asymmetry: formal firms confront higher labor costs while informal firms face higher capital costs and lower productivity. Using mandated minimum wages as the policy-induced distortion, the model first studies the static allocation of formal and informal capital and labor in a modern economy. Second, it opens the possibility of labor migration from a rudimentary economy with an ample supply of labor (rural areas or less advanced neighboring countries). Third, the model analyzes the dynamic behavior of the formal and informal sectors, considering how they affect and are affected by economic growth and labor migration. Then, the paper presents projections for the size of labor informality, in the modern and rudimentary economies, in the next two decades for a large group of countries representing all regions of the world. The projections are based on the calibration and simulation of the model and serve to discuss its usefulness and limitations.

This paper contributes to the economic analysis of illicit activities and money laundering. First, it presents a theoretical model of long-run growth that explicitly considers illicit workers, activities, and income, alongside a licit private sector and a functioning government. Second, it generates estimates of the size of illicit income and provides simulated and econometric estimates of the volume of laundered assets in the Colombian economy. In the model, the licit sector operates in a perfectly competitive environment and produces a licit good through a standard neoclassical production function. The illicit sector operates in an imperfectly competitive environment and is composed of two different activities: The first activity produces an illicit good that nonetheless is valuable in the market (for example illicit drugs); the second does not add value to the economy but only redistributes wealth (for example robbery, kidnapping, and fraud). The paper provides a series of comparative statics exercises to assess the effects of changes in government efficiency, licit sector productivity, and illicit drug prices. From the model, the analysis derives a set of estimable macroeconometric equations to measure the size of laundered assets in the Colombian economy in the period 1985 to 2013. The paper assembles a data set whose key components are estimates of illicit income from drug trafficking and common crime. Illicit incomes increased drastically until 2001, reaching a peak of nearly 12 percent of gross domestic product and then decreasing to less than 2 percent by 2013. The decline overlaps not only in a period of high economic growth, but also after the implementation of Plan Colombia. The data set is used to estimate the volume of laundered assets in the economy by applying the Kalman filter for the estimation of unobserved dynamic variables onto the derived macroeconometric equations from the model. The findings show that the volume of laundered assets increased from about 8 percent of gross domestic product in the mid-1980s to a peak of 14 percent by 2002, and declined to 8 percent in 2013.