Introduction

What explains the patterns of economic development across countries and over time? What policies can help foster economic development and improve the life of the least well off in poor economies? These are central questions in Macro-Development, a growing field that integrates modern quantitative macroeconomics methods with the wealth of micro-evidence provided by Micro-Development research. Macro-Development is a close cousin of Economic Growth: it shares its interest in medium-term dynamics, it draws from its rich methods to study dynamic systems, but it pays a closer attention to the frictions and heterogeneity that characterizes developing economies. In this regard, macro-development is part of a growing trend in quantitative macro research, analyzing models where heterogeneity and frictions are at the core of the analysis.

In terms of questions and methods, macro-development follows the call by Robert Lucas to build “pen and pencil (and computer equipped) laboratories” to evaluate explanations and policies (Lucas, 1988, 1992). It is also inspired by the pioneering work by Robert Townsend building general equilibrium model motivated and informed by micro data from development countries (Lim and Townsend, 1998, Gine and Townsend, 2004, Townsend, 2010). It is an exciting research area with gains from trade between micro and macro development, as recently reviewed in Buera, Kaboski and Townsend (2023).

In what follows I summarize some of my work studying particular aspects of the broader macro-development question: (i) the role of financial markets; (ii) the interaction between policies, distortions, and the adoption of complementary technologies; (iii) the process of structural transformation; (iv) and the diffusion of technologies, economic policies and institutions across countries.

A lot of prior work exists on these questions, both in terms of theoretical contributions and empirical evidence, within different sub-fields of economics. The work discussed in the review that follows, aims at filling the gap between the empirical work and the theoretical work, and lies at the intersection of macro and development. It builds aggregate models with empirically motivated micro-economic considerations, such as heterogeneity across sectors and individuals, and market failures such as financial frictions. The goal is to evaluate the quantitative performance of different theories in explaining the data and use them to evaluate development policies.

1. The Role of Financial Markets

Developing countries are characterized by poor contract enforcement and weak creditor protection (La Porta et al., 1998), and low level of financial development (King and Levine, 1993, Beck et al., 2000). A related perspective is provided by Banarjee and Duflo (2005), who review micro-level evidence of financial frictions and misallocation of capital in developing countries, and by Townsend (2010) in a detailed study of the Thai experience prior and following a financial liberalization.

A parallel theoretical literature showed that in economies with financial frictions and non-convexities, multiple equilibria and aggregate poverty traps are possible (Banerjee and Newman, 1993, Galor and Zeira, 1993, Aghion and Bolton, 1997, Piketty, 1997). These results suggested a potentially large role for institutions and policies that promote financial development. These theoretical developments opened many possibilities, but left unanswered the question of which is the quantitatively relevant answer.

Motivated by these questions, in collaboration with Joe Kaboski, Jeremy Majerovitz, Ben Moll, Yongs Shin, and Kuldeep Singh we developed quantitative frameworks to evaluate the role of credit frictions, and policies that try to ameliorate these frictions, on economic development. This work builds upon developments of quantitative heterogeneous agents’ models of entrepreneurs/firms used to study the role of labor and financial frictions in the US context (Hopenhayn and Rogerson, 1993, Quadrini, 2000, Cagetti and DeNardi, 2006), and idiosyncratic distortions more generally (Restuccia and Rogerson, 2008, Hsieh and Klenow, 2009).

1.1 Explaining Income Per-Capita Differences

In “Finance and Development: A Tale of Two Sectors (Buera, Kaboski and Shin, 2011) we evaluate the long-run effects of financial frictions. We build a quantitative model featuring sector-specific non-convexities and forward-looking savings behavior. Financial frictions limit the ability of entrepreneurs to invest, and they may deter entrepreneurship altogether. These effects are particularly large in the manufacturing sector, where technologies feature larger fixed costs. The model is calibrated to reproduced various micro-moments of the US economy, a relatively undistorted benchmark, e.g., establishment size distribution across and within sectors, and the dynamics of establishments. The model gives us a laboratory to measure the effect of changes to the availability credit, as observed in the cross-country data.

We find that the observed changes in credit availability can account for a substantial part of the observed cross-country differences in output per worker, aggregate TFP, sector-level relative productivity, and capital to output ratios. While these results do not establish that the observed variation in credit availability cause development, they show that institutional and policy driven changes in credit availability would have a large impact in economic development.

1.2 Accounting for Growth Miracles

An important objective of macro-development is to explain (and eventually engineer!) growth miracles. A natural conjecture is that the availability of credit to finance the growth of firms can impact the dynamics of these episodes.

In Buera and Shin (2013) we explore the interaction between credit markets and economic reforms that eliminates micro distortions. Our theory of reforms builds on the work by Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), who show that frictions that distort the allocation of resources across firms account for a substantial part of the low productivity in developing countries.

Our operational notion of a miracle economy are the sustained growth accelerations measured by Haussmann et al. (2005), whose onset tends to be associated with economic reforms. In our work, we show that these episodes are characterized by sustained TFP growth, protracted growth in investment, and reallocation of resources.

We engineer a growth miracle in our model laboratory by a reform that suddenly lowers micro-distortions. Consistent with the data, we keep financial frictions into place. We find that financial frictions are important to account for the observed dynamics of miracle economies. Following a sudden reform, the model economy features a protracted transition, converging at a speed that is half that of the conventional neoclassical model. Consistent with the data, investment rates and total factor productivity are initially low and increase over time. Key to these dynamics, the initial condition in the post reform economy features a weak correlation between the net-worth of entrepreneurs and their productivity.

This analysis is extended to an open economy in “Productivity Growth and Capital Flows: The Dynamics of Reforms” (Buera and Shin, 2017). There we show that the model dynamics are also instrumental in accounting for the Lucas puzzle (squared!): Why doesn’t capital flow into fast-growing countries?

A limitation of the aforementioned theory of reforms is that micro-distortions, and their changes, are not directly measured, but rather chosen to engineer the observed TFP differences across steady states. In addition, in those models the dynamics of firm productivity is exogenous. In work with Roberto Fattal-Jaef “The Dynamics of Development: Innovation and Reallocation” (Buera and Fattal-Jaef, 2023), we directly measured the evolution of distortions in China since 1998. We use a model of endogenous productivity à la Atkeson and Burstein (2010), enriched to incorporate micro-distortions, to quantify the role of measured reforms in accounting for China’s fast growth.

We show that the evolution of measure micro-distortions accounts for a third of the observed growth in TFP, while matching the dynamics of average firm size and income inequality.

1.3 Policy Implications

If financial frictions have a substantial impact in economic (un)development, then policies aimed at ameliorating them are natural candidates to foster development. The analysis of these policies is an active area of micro and macro-development research.

Microcredit is one of the most popular and fastest growing candidates. In “The Macroeconomics of Microfinance” (Buera, Kaboski, and Shin, 2021) we apply and extend the laboratory economy developed in our earlier work to analysis the macro and distribution impact of an economy wide microcredit intervention. The full macro impact consists of the general equilibrium and dynamic effects, together with the distributional welfare implications (which are a function of the dynamic general equilibrium effects!).

We model microcredit as a guaranteed small-size loan, available to all the population. The quantitative model is calibrated to match micro and macro moments of the Indian economy, and it is validated using evidence from recent randomized and quasi-randomized micro evaluations of small-scale microcredit programs. We find that the long-run general equilibrium impact is very different from its short-run experimental effect. In the long-run, scaling micro-credit programs lead to small effect on per-capital income, because the increases in productivity are offset by lower capital accumulation, as income is redistributed from high to low saving individuals. Notwithstanding this, the vast majority of the population benefits, with the gains being particularly large for the very poor, the marginal entrepreneurs/self-employed, and the very rich, who benefit from the higher interest rate.

In related work, we extend the analysis to study asset grants program targeting the ultra-poor, which is another growing micro-development interventions (Buera, Kaboski, and Shin, 2014, 2019). A similar conclusion arises: These interventions cannot lead to wide-scale, transformative aggregate impacts.

One conclusion from this research is that credit policies promoting economic development should target a broad set of firms, including large firms. The work by Itskhoki and Moll (2019), which builds on the tractable framework developed by Moll (2014), is a great example of this line of research. They show that the optimal policy intervention involves pro-business policies like suppressed wages and credit subsidies in early stages of the transition, resulting in higher entrepreneurial profits and faster wealth accumulation. One caveat to these conclusions is that where there is policy inertia and capture, the short-term benefits can result in long-term micro-distortions (Buera, Moll, and Shin, 2013).

1.4 Open Questions and Avenues for Future Research

The papers discussed above are a few examples of a large macro-development literature studying the role of financial frictions. Important contributions include work by Midrigan and Xu (2014), Moll (2014), and Cole, Greenwood, and Sanchez (2016). Buera, Kaboski, and Shin (2015) and Kaboski (2021) provide an earlier and a more recent review, respectively.

In many of the model discussed above, and in the literature more generally, the analysis tends to abstract from investment risk (Angeletos, 2006). Arguably, risk considerations are important determinants of entrepreneurial investment decisions and their ability self-finance to overcome financial constraints. Chris Udry’s SED Plenary lecture and his empirical work provides a persuasive call for more research in this direction (Udry, 2012, Karlan, Osei, Osei-Akoto, and Udry, 2014). In ongoing work, we are extending the model of entrepreneurship and financial frictions by considering irreversible investment. Our preliminary findings show that the ability of self-finance to overcome constraints is substantially suppressed, as entrepreneurs prefer a more balance portfolio of productive and financial assets (Buera, Majerovitz, Shin, and Singh, 2023).

Exploring a richer modelling of credit frictions and the direction of financial development (equity vs. debt, short term vs long term borrowing) are other important avenues for future research. A recent example in this direction is the work by Calvalcanti, Kaboski, Martins, and Santos (2023), who develop a quantitative dynamic general equilibrium model in which spreads arise from intermediation costs and market power.

2. Complementary Investments and Development Policy

Economic development follows the adoption of modern, complementary investments across firms and sectors. In its extreme version, underdevelopment can be understood as a coordination problem, where poor economies are in a vicious cycle of poverty. These perspectives go back to the debate about the (re)development of Europe in the aftermath of the Second World War, especially that of the underdevelopment economies in Southern and Eastern Europe (Rosentein-Rodan, 1943; Hischman, 1958).

In the 80s and 90s, a theoretical literature formalized and clarified these arguments, highlighting the role of imperfect competition and increasing returns (Murphy, Shleifer and Vishny, 1989; Matsuyama, 1995; Ciccone, 2002). Krugman (1992) provides a colorful history of these developments: the fall and rise of “high development theory”.[1] But the rebirth lacked a quantitative perspective, resulting in fading interests for these arguments, at least in academic circles. Again, theoretical developments opened new possibilities, but left unanswered the question of which is the quantitatively relevant answer.

A recent literature is moving beyond theoretical possibilities, enriching the earlier stylized theoretical frameworks to connect with micro and historical evidence. In collaboration with Hugo Hopenhayn, Yongs Shin, and Nico Trachter we contribute to this literature by undertaking a quantitative exploration of these theoretical models.

In “Big Push in Distorted Economies” (Buera, Hopenhayn, Shin, and Trachter, 2023) we build a quantitative model of technology adoption with heterogeneous firms, input-output linkages, and idiosyncratic distortions, in which the gains from technology adoption become larger when more firms adopt. The model connects two seemingly distinct views of development: distortions and coordination failures perspectives. It provides a version of a “Big Push” model enriched to better connect with data, and a quantitative theory of heterogeneous firms, distortions, and technology adoption enhanced with rich complementarities.

We use the quantitative model to explore two questions: (i) Can there be large effects of distortions and policies? (ii) Can development be a story of coordination failures, i.e., multiple equilibria? We show that calibrated complementarities drastically amplify the effect of distortions. The impact of distortions is four times as large as in models without such complementarity. Notwithstanding this, the benchmark calibrations do not feature multiplicity. While multiplicity is possible in a very distorted economies, amplification of distortion occurs in a broader, empirically-relevant range of parameter values.

How should industrial policies be directed to reduce distortions and foster economic development? Should industrial policy favor particular sectors? In particular, which is the best direction to move policy if only a partial reform can be implemented?

We tackle this question in “Sectoral Development Multipliers” (Buera and Trachter, 2024). We extend the previous analysis by considering a multisector economy with rich production and investment interactions across sectors. We also adopt a more general structure of firm heterogeneity that is more amenable to infer the distribution of technologies at the sector level. Following Baqaae and Farhi (2019) and Liu (2019), we consider a local analysis which allows us to provide simple formulas for the sectoral policy multipliers, i.e., the welfare impact per unit of fiscal cost. We also provide insights regarding the power of alternative policy instruments.

We apply the model to Indian data on the size distribution of establishments by sector, and the production and investment networks to estimate the model parameters and the distribution of technologies across sectors. We find that technology adoption greatly amplifies the multipliers’ magnitudes, relative to the implications from an analysis that focuses solely on production efficiency. In particular, the ranking of priority sectors for industrial policy changes. Further, we find that adoption subsidies are the most cost-effective instrument for promoting economic development.

2.1 Open Questions and Avenues for Future Research

As alluded earlier, there is a growing literature empirically evaluating coordination theories of development. These include evaluations of the impact of sectoral industrial policy using historical and recent natural experiments: Juhasz (2018) in the Napoleonic period, Klein and Moretti (2014) for the US in 30s, Lane (2021) for South Korea in the 70s, Manelici and Pantea (2021) for Romania in the 2000s. This empirical literature tends to lack a structural framework to more leverage the evidence. More work along the lines of Demir et al. (2023) would be very welcomed.

The theories described earlier abstracted from dynamics. Therefore, the analysis should be understood as describing the effect of policies across steady-states. Dynamic extension, using the methods in Alvarez et al. (2023), are natural steps forward. More generally, there is ample room to explore how specific distortions get amplified in models with rich complementarities, e.g., labor and financial frictions, informality.

3. Structural Transformation and Development

The economic development involves a process of structural transformation: the decline and rise of sectors, changes in the scale of productive units, and occupations (Kuznets, 1973). The secular decline of agriculture, the rise and fall of manufacturing, and the late rise of services are prominent examples of this process. Naturally, a large literature in macro-development and economic growth studies the causes and consequences of these phenomena.

On the macro-development side, the emphasis is on non-stationarities and the interactions between structural transformation and development (Hansen and Prescott, 2002, Gollin, Parente, and Rogerson, 2004, Restuccia, Yang, and Zhu, 2008). From the economic growth perspective, the focus is on balance growth, which imply that structural transformation is orthogonal to growth (Kognsamut, Rebelo and Xie, 2001, Ngai and Pissarides, 2007). Together with Joe Kaboski, Marti Mestieri, Danny O’Connors, Richard Rogerson, and Nacho Viscaino, we have contributed to both sides of this debate.

In “The Rise of the Service Economy” (Buera and Kaboski, 2012a) we study the rise of the service economy, arguably, one of the most radical changes in the structure of consumption and production of the past century. This process has taken place in conjunction with a large increase in inequality. We show that this transformation is explained by the surge of skill-intensive services, and it is contemporaneous with the increases in the relative quantity of skilled labor and the skill premium.

We develop a theory that explains how demand shifts toward ever more skill-intensive output as income rises, and, because skills are highly specialized, this lowers the importance of home-produced services relative to market services. The theory is also consistent with the rise in the supply skill labor and the skill premium, the rise in the relative price of services that is linked to this skill premium, and rich product cycles between home and market, all of which are observed in the data. Using a related model of product cycles, in “Scale and the Origin of Structural Change” (Buera and Kaboski, 2012b) we study the rise of large-scale technologies, and how these determine the shape of consumption and production patterns in the early phases of development.

The quantitative importance of skill biased structural change in accounting for the rise in the skill premium is explored in “Skilled-Biased Structural Change” (Buera, Kaboski, Rogerson, and Vizcaino, 2022). There we also show that skill biased structural change is a phenomenon that is ubiquitous across developed economies. For the US, we find that skill-biased structural change accounts for 18-24% of the overall increase of the skill premium due to technical change.

These papers show that there are important interactions between growth and structural change. This perspective is reinformed by recent development in the field, which stress the need for sectoral specific factor proportions (Acemoglu and Guerrieri, 2008), generalized non-homothetic preferences (Buera and Kaboski, 2009, Comin, Lashkari, and Mestieri, 2021), and structural change in investment (Garcia-Santana, Pijoan-Mas, and Villacorta, 2019, Herrendorf, Rogerson, and Valentinyi, 2021).

A challenge posed by these (more interesting!) models is that they do not have balanced growth paths (BGPs) in the medium term, i.e., while structural transformation is occurring. For this class of non-stationary models, how can we then describe their medium-term dynamics, i.e., the dynamics that are independent of the initial level of capital?

We propose a generalization of a BGP in “The Stable Transformation Path” (Buera, Kaboski, Mestieri, O’Connors, 2023), which applies to non-stationary models of structural transformation with asymptotic BGPs as time goes arbitrary far into the past and the future. These models include the modern benchmark theories of structural change in Comin, Lashkari, and Mestieri (2021) and Herrendorf, Rogerson, and Valentinyi (2021), but also theories of the emergence of long-run growth (Hansen and Prescott, 2002). We define the Stable Transformation Path (STraP) to be the unique transitional path connecting the asymptotic BGPs. This path is the attractor of all transitional paths, in a generalized turnpike sense (McKenzie, 1986).

We apply the STraP to evaluate the implication of benchmark models of structural transformation. We show that secular structural change can account for a quarter of growth in miracle economies, but it dramatically fails to explain the growth experience in the early industrial period. Counterfactually, the benchmark model predicts that growth should have been largest in the early industrial period.

4. The Diffusion of Technologies and Economic Policies

Ultimately, the development question is why best practices, both technologies and good policies, are not diffused to all countries. Related, external effects are at the center of theories of innovation and growth. Again, borrowing Lucas’ words, “what we know we learn from other people [teachers, students, co-authors, …] the benefits of colleagues from whom we hope to learn are tangible enough to lead us to spend a considerable fraction of our time fighting over who they shall be, and another fraction travelling to talk with those we wish we could have as colleagues but cannot.” (Lucas, 1988)

Following Lucas (2009) modelling of idea flows, a large recent literature has provided richer description of the process of knowledge diffusion (Buera and Lucas, 2018). Together with Fernando Alvarez, Robert E. Lucas, and Ezra Oberfield, we have contributed to this literature by exploring the role of international trade in the diffusion of ideas.

In “Idea Flows, Economic Growth, and Trade,” (Alvarez, Buera, and Lucas, 2017) we develop a model where the distribution of technologies within a country is the outcome of individual producer’s learning by interacting with other producers. In an open economy, international trade influences the diffusion of ideas by affecting the distribution of sellers and domestic producers in a market, and therefore, their learning outcomes. While small trade cost has only second order effects, prohibitively high trade cost has arbitrarily large long-run effects on productivity and development.

The theory provides an interesting departure from the Frechet benchmark that is common in extreme value theory and quantitative Ricardian trade models. This departure implies that the model features an endogenous trade elasticity, which is lower near autarky. Similar results are obtained by Hsieh, Klenow and Nath (2023), who extend a model of creative destruction to an open economy. They show that learning from sellers is a natural assumption in this class of models.

Following Oberfield (2018), in “The Global Diffusion of Ideas,” (Buera and Oberfield, 2020) we provide a tractable theory of innovation and technology diffusion to quantify the role of trade in the process of development. The key innovation is to introduce random noise and a limit to the learning from others. This limit to knowledge diffusion moves away the theory from the “random walk” case, and restore the tractable Frechet limiting case. Importantly, the model remains tractable with many asymmetric countries. We use the model to quantify the contribution trade to long-run changes in TFP. We find that both gains from trade and the fraction of variation of TFP growth accounted for by changes in trade more than double relative to a model without diffusion.

Moving from technologies to policies, a related hypothesis is that the adoption of different policies largely reflects divergent beliefs about their benefits. It can also be argued that it takes time for countries to learn the best policy, as it takes time for social scientist to figure it out.  We explore this hypothesis formally in “Learning the Wealth of Nations” (Buera, Monge, and Primiceri, 2011). In particular, we construct a quantitative model that captures this intuition and can rationalize a large fraction of the policy choices of the postwar period, including the slow adoption of market-oriented policies. We also use the model to predict the magnitude of the reversal to state intervention if nowadays the world was hit by a shock of the size of the Great Depression (like the Great Recession!).

Recent interesting applications of related frameworks include: the diffusion of democracy (Abramson and Montero, 2020), financial liberalization (Li, Papageorgiou, Xu, and Zha, 2021), and the learning of ineffective COVID treatments (Calonico, Di Tella, and Lopez del Valle, 2023).

5. Taking Stock

These papers represent explorations of some corners and edges of the development puzzle. There are many pieces to the puzzle, which include some of the most pressing open questions in economics. There is an obvious conclusion: we need many more researchers working on macro-development.

For those interesting in macro-development, one of the best resources is the Structural Transformation and Economic Growth (STEG) research initiative lead by Doug Gollin and Joe Kaboski. There one can find conferences, lectures, workshops, working papers, research grants, and (free!) online courses by the leaders in the field. In the more immediate future, the macro-development sessions at the SED in Barcelona!

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[1] An even more colorful exposition can be found in the following link: https://web.mit.edu/krugman/www/dishpan.html.