MCC’s evidence-based approach to its investments begins with a mutual understanding of a country’s main growth challenges. During the first phase of the compact or threshold program development process, MCC and the selected partner country, jointly conduct a constraints-to-growth analysis (CA). This analysis identifies the binding constraints to private investment and entrepreneurship that hold back economic growth in the country the most. The results of this analysis enable the country, together with MCC, to select activities most likely to contribute to sustainable, poverty-reducing growth.
Why Use Constraints Analysis?
MCC’s CA approach builds on the pioneering work of economists Ricardo Hausmann, Dani Rodrik and Andrès Velasco (HRV). As HRV point out, all developing countries face significant economic and development challenges, but these challenges do not all equally restrict growth. Prioritizing constraints is important since a country’s implementation capacity, political space, and financial resources to address these challenges are scarce and valuable. A particular strength of HRV‘s “growth diagnostic” methodology, as compared with other tools, is its recognition that every country is different. The tool, which MCC and other practitioners have refined through experience, is designed to sift through available evidence to identify country-specific binding constraints.
What Constraints Analysis Seeks to Do
All countries face many challenges that restrict growth, but not all of them are severe or “binding” on growth. Some have a minimal effect on the aggregate economy, and thus relaxing them would have little impact. Organizing the search for those that are binding is key, as without addressing those, other attempts to foster growth may be misdirected and fail. The CA starts from the premise that sustained, broad-based economic growth requires private investment and entrepreneurship, which depend upon: (1) Overall potential returns on investment in the country, (2) The share of the return an entrepreneur can expect to keep, and (3) The costs of financing investments. The CA investigates the influence of each of these broad factors in each country’s context and diagnoses which specific impediments are binding. The diagnostic tree in Figure 1 illustrates this process, with factors affecting investor returns on the left branch and factors affecting financing costs on the right.
Figure 1: The HRV Growth Diagnostic Tree
Source: HRV (2005)
How Constraints Analysis Works
A lacking factor (such as skilled labor or roads) or condition (such as a stable macro-economy or reliable contract enforcement) can only be a binding constraint to growth where the supply of it is low and demand for it is strong. To assess whether the relative lack of a factor within the diagnostic tree is a binding constraint, the CA looks for signals that these two conditions are met. For example, the quantity of credit in a country can be low, but this alone does not indicate a constrained supply of finance. The low quantity of credit may result from low demand because potential borrowers are constrained by other factors, such as a lack of infrastructure or an unsupportive business environment. Misdiagnosing low credit transactions in such a situation could lead to interventions (for example, subsidized credit) that would not address the underlying causes of weak economic growth.
Supply and demand dynamics can be difficult to disentangle. To help identify when the supply of a factor is so low relative to demand as to be binding on growth, MCC answers four key questions, using tests proposed by The Mindbook (Hausmann, Klinger, and Wagner 2008):
- Is the price or cost to economic agents of the lacking factor or condition high?
- Do changes in its availability tend to move aggregate investment or growth?
- Do economic agents (manufacturers or farmers, for example) incur significant costs or risks to circumvent the constraint? And;
- Are individuals or businesses that rely heavily on the constraining factor unable to thrive? (In the same way that camels, and not hippos, thrive in an environment without water, do activities that do not depend on the constraining factor thrive while activities that do depend on that factor stagnate or are missing altogether?)
The ability to fully apply these tests depends on the nature of the factor. Some indicators, like returns on educational investments or natural capital, may change slowly over time and make it difficult to find a correlation with changes in investment or growth. The availability of data is often an issue as well. Analysts may lack access to comprehensive data on the rate of firm entry, level of competition in key sectors, or maritime shipping charges, for example. More indirect evidence, including perception-based survey data, can complement the direct analysis of supply-demand dynamics, taking potential sources of bias into account. MCC applies the HRV methodology flexibly, taking into account historical events and political context, to inform its interpretation of findings. It is rigorous in that the evidence from the tests should be clear and consistent before a constraint is considered binding, and peer review is used to test the evidentiary basis of findings. Typically only a small set of constraints are deemed to be binding (usually two or three),
A successful CA constitutes a solid foundation for the development of an MCC compact or threshold program that addresses country priorities consistent with MCC’s evidence-based approach. Although the results of the CA do not dictate project areas or specific activities to be funded by MCC, nor that MCC programs address all binding constraints identified, MCC’s investment criteria ask that all its programs address binding constraints identified through the CA.
Binding Constraints by Country
|Finance/Access to Credit||Transport||Energy||Water and Sanitation||Corruption||Governance (Regulatory Quality, Rule of Law, Stability)||Land/Property Rights||Crime||Health||Irrigation/Water for Production Purposes||Geography||Innovation||Education||Other|
|Cabo Verde II||✓||✓||✓||✓||✓|
|El Salvador II||✓||Low productivity in the tradable sector|
|Mongolia II||✓||✓||✓||Weak and unstable macroeconomic environment|
|Nepal||✓||✓||✓||Challenging industrial relations and rigid labor regulations|
|Philippines2||✓||Reduced fiscal space|
|Philippines II||✓||✓||✓||Market failures and the rural economy|
|Togo||✓||High costs, low quality, and limited availability of information and communications technology (ICT) services|
|Tunisia||✓||High fiscal and regulatory cost of employing workers|